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• Error in running script [closed]

  - by SWEngineer

  I'm trying to run heathusf_v1.1.0.tar.gz found here I installed tcsh to make build_heathusf work. But, when I run ./build_heathusf, I get the following (I'm running that on a Fedora Linux system from Terminal): $ ./build_heathusf Compiling programs to build a library of image processing functions. convexpolyscan.c: In function ‘cdelete’: convexpolyscan.c:346:5: warning: incompatible implicit declaration of built-in function ‘bcopy’ [enabled by default] myalloc.c: In function ‘mycalloc’: myalloc.c:68:16: error: invalid storage class for function ‘store_link’ myalloc.c: In function ‘mymalloc’: myalloc.c:101:16: error: invalid storage class for function ‘store_link’ myalloc.c: In function ‘myfree’: myalloc.c:129:27: error: invalid storage class for function ‘find_link’ myalloc.c:131:12: warning: assignment makes pointer from integer without a cast [enabled by default] myalloc.c: At top level: myalloc.c:150:13: warning: conflicting types for ‘store_link’ [enabled by default] myalloc.c:150:13: error: static declaration of ‘store_link’ follows non-static declaration myalloc.c:91:4: note: previous implicit declaration of ‘store_link’ was here myalloc.c:164:24: error: conflicting types for ‘find_link’ myalloc.c:131:14: note: previous implicit declaration of ‘find_link’ was here Building the mammogram resizing program. gcc -O2 -I. -I../common mkimage.o -o mkimage -L../common -lmammo -lm ../common/libmammo.a(aggregate.o): In function `aggregate': aggregate.c:(.text+0x7fa): undefined reference to `mycalloc' aggregate.c:(.text+0x81c): undefined reference to `mycalloc' aggregate.c:(.text+0x868): undefined reference to `mycalloc' ../common/libmammo.a(aggregate.o): In function `aggregate_median': aggregate.c:(.text+0xbc5): undefined reference to `mymalloc' aggregate.c:(.text+0xbfb): undefined reference to `mycalloc' aggregate.c:(.text+0xc3c): undefined reference to `mycalloc' ../common/libmammo.a(aggregate.o): In function `aggregate': aggregate.c:(.text+0x9b5): undefined reference to `myfree' ../common/libmammo.a(aggregate.o): In function `aggregate_median': aggregate.c:(.text+0xd85): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `linear_optical_density': optical_density.c:(.text+0x29e): undefined reference to `mymalloc' optical_density.c:(.text+0x342): undefined reference to `mycalloc' optical_density.c:(.text+0x383): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `log10_optical_density': optical_density.c:(.text+0x693): undefined reference to `mymalloc' optical_density.c:(.text+0x74f): undefined reference to `mycalloc' optical_density.c:(.text+0x790): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `map_with_ushort_lut': optical_density.c:(.text+0xb2e): undefined reference to `mymalloc' optical_density.c:(.text+0xb87): undefined reference to `mycalloc' optical_density.c:(.text+0xbc6): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `linear_optical_density': optical_density.c:(.text+0x4d9): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `log10_optical_density': optical_density.c:(.text+0x8f1): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `map_with_ushort_lut': optical_density.c:(.text+0xd0d): undefined reference to `myfree' ../common/libmammo.a(virtual_image.o): In function `deallocate_cached_image': virtual_image.c:(.text+0x3dc6): undefined reference to `myfree' virtual_image.c:(.text+0x3dd7): undefined reference to `myfree' ../common/libmammo.a(virtual_image.o):virtual_image.c:(.text+0x3de5): more undefined references to `myfree' follow ../common/libmammo.a(virtual_image.o): In function `allocate_cached_image': virtual_image.c:(.text+0x4233): undefined reference to `mycalloc' virtual_image.c:(.text+0x4253): undefined reference to `mymalloc' virtual_image.c:(.text+0x4275): undefined reference to `mycalloc' virtual_image.c:(.text+0x42e7): undefined reference to `mycalloc' virtual_image.c:(.text+0x44f9): undefined reference to `mycalloc' virtual_image.c:(.text+0x47a9): undefined reference to `mycalloc' virtual_image.c:(.text+0x4a45): undefined reference to `mycalloc' virtual_image.c:(.text+0x4af4): undefined reference to `myfree' collect2: error: ld returned 1 exit status make: *** [mkimage] Error 1 Building the breast segmentation program. gcc -O2 -I. -I../common breastsegment.o segment.o -o breastsegment -L../common -lmammo -lm breastsegment.o: In function `render_segmentation_sketch': breastsegment.c:(.text+0x43): undefined reference to `mycalloc' breastsegment.c:(.text+0x58): undefined reference to `mycalloc' breastsegment.c:(.text+0x12f): undefined reference to `mycalloc' breastsegment.c:(.text+0x1b9): undefined reference to `myfree' breastsegment.c:(.text+0x1c6): undefined reference to `myfree' breastsegment.c:(.text+0x1e1): undefined reference to `myfree' segment.o: In function `find_center': segment.c:(.text+0x53): undefined reference to `mycalloc' segment.c:(.text+0x71): undefined reference to `mycalloc' segment.c:(.text+0x387): undefined reference to `myfree' segment.o: In function `bordercode': segment.c:(.text+0x4ac): undefined reference to `mycalloc' segment.c:(.text+0x546): undefined reference to `mycalloc' segment.c:(.text+0x651): undefined reference to `mycalloc' segment.c:(.text+0x691): undefined reference to `myfree' segment.o: In function `estimate_tissue_image': segment.c:(.text+0x10d4): undefined reference to `mycalloc' segment.c:(.text+0x14da): undefined reference to `mycalloc' segment.c:(.text+0x1698): undefined reference to `mycalloc' segment.c:(.text+0x1834): undefined reference to `mycalloc' segment.c:(.text+0x1850): undefined reference to `mycalloc' segment.o:segment.c:(.text+0x186a): more undefined references to `mycalloc' follow segment.o: In function `estimate_tissue_image': segment.c:(.text+0x1bbc): undefined reference to `myfree' segment.c:(.text+0x1c4a): undefined reference to `mycalloc' segment.c:(.text+0x1c7c): undefined reference to `mycalloc' segment.c:(.text+0x1d8e): undefined reference to `myfree' segment.c:(.text+0x1d9b): undefined reference to `myfree' segment.c:(.text+0x1da8): undefined reference to `myfree' segment.c:(.text+0x1dba): undefined reference to `myfree' segment.c:(.text+0x1dc9): undefined reference to `myfree' segment.o:segment.c:(.text+0x1dd8): more undefined references to `myfree' follow segment.o: In function `estimate_tissue_image': segment.c:(.text+0x20bf): undefined reference to `mycalloc' segment.o: In function `segment_breast': segment.c:(.text+0x24cd): undefined reference to `mycalloc' segment.o: In function `find_center': segment.c:(.text+0x3a4): undefined reference to `myfree' segment.o: In function `bordercode': segment.c:(.text+0x6ac): undefined reference to `myfree' ../common/libmammo.a(aggregate.o): In function `aggregate': aggregate.c:(.text+0x7fa): undefined reference to `mycalloc' aggregate.c:(.text+0x81c): undefined reference to `mycalloc' aggregate.c:(.text+0x868): undefined reference to `mycalloc' ../common/libmammo.a(aggregate.o): In function `aggregate_median': aggregate.c:(.text+0xbc5): undefined reference to `mymalloc' aggregate.c:(.text+0xbfb): undefined reference to `mycalloc' aggregate.c:(.text+0xc3c): undefined reference to `mycalloc' ../common/libmammo.a(aggregate.o): In function `aggregate': aggregate.c:(.text+0x9b5): undefined reference to `myfree' ../common/libmammo.a(aggregate.o): In function `aggregate_median': aggregate.c:(.text+0xd85): undefined reference to `myfree' ../common/libmammo.a(cc_label.o): In function `cc_label': cc_label.c:(.text+0x20c): undefined reference to `mycalloc' cc_label.c:(.text+0x6c2): undefined reference to `mycalloc' cc_label.c:(.text+0xbaa): undefined reference to `myfree' ../common/libmammo.a(cc_label.o): In function `cc_label_0bkgd': cc_label.c:(.text+0xe17): undefined reference to `mycalloc' cc_label.c:(.text+0x12d7): undefined reference to `mycalloc' cc_label.c:(.text+0x17e7): undefined reference to `myfree' ../common/libmammo.a(cc_label.o): In function `cc_relabel_by_intensity': cc_label.c:(.text+0x18c5): undefined reference to `mycalloc' ../common/libmammo.a(cc_label.o): In function `cc_label_4connect': cc_label.c:(.text+0x1cf0): undefined reference to `mycalloc' cc_label.c:(.text+0x2195): undefined reference to `mycalloc' cc_label.c:(.text+0x26a4): undefined reference to `myfree' ../common/libmammo.a(cc_label.o): In function `cc_relabel_by_intensity': cc_label.c:(.text+0x1b06): undefined reference to `myfree' ../common/libmammo.a(convexpolyscan.o): In function `polyscan_coords': convexpolyscan.c:(.text+0x6f0): undefined reference to `mycalloc' convexpolyscan.c:(.text+0x75f): undefined reference to `mycalloc' convexpolyscan.c:(.text+0x7ab): undefined reference to `myfree' convexpolyscan.c:(.text+0x7b8): undefined reference to `myfree' ../common/libmammo.a(convexpolyscan.o): In function `polyscan_poly_cacheim': convexpolyscan.c:(.text+0x805): undefined reference to `mycalloc' convexpolyscan.c:(.text+0x894): undefined reference to `myfree' ../common/libmammo.a(mikesfileio.o): In function `read_segmentation_file': mikesfileio.c:(.text+0x1e9): undefined reference to `mycalloc' mikesfileio.c:(.text+0x205): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `linear_optical_density': optical_density.c:(.text+0x29e): undefined reference to `mymalloc' optical_density.c:(.text+0x342): undefined reference to `mycalloc' optical_density.c:(.text+0x383): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `log10_optical_density': optical_density.c:(.text+0x693): undefined reference to `mymalloc' optical_density.c:(.text+0x74f): undefined reference to `mycalloc' optical_density.c:(.text+0x790): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `map_with_ushort_lut': optical_density.c:(.text+0xb2e): undefined reference to `mymalloc' optical_density.c:(.text+0xb87): undefined reference to `mycalloc' optical_density.c:(.text+0xbc6): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `linear_optical_density': optical_density.c:(.text+0x4d9): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `log10_optical_density': optical_density.c:(.text+0x8f1): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `map_with_ushort_lut': optical_density.c:(.text+0xd0d): undefined reference to `myfree' ../common/libmammo.a(virtual_image.o): In function `deallocate_cached_image': virtual_image.c:(.text+0x3dc6): undefined reference to `myfree' virtual_image.c:(.text+0x3dd7): undefined reference to `myfree' ../common/libmammo.a(virtual_image.o):virtual_image.c:(.text+0x3de5): more undefined references to `myfree' follow ../common/libmammo.a(virtual_image.o): In function `allocate_cached_image': virtual_image.c:(.text+0x4233): undefined reference to `mycalloc' virtual_image.c:(.text+0x4253): undefined reference to `mymalloc' virtual_image.c:(.text+0x4275): undefined reference to `mycalloc' virtual_image.c:(.text+0x42e7): undefined reference to `mycalloc' virtual_image.c:(.text+0x44f9): undefined reference to `mycalloc' virtual_image.c:(.text+0x47a9): undefined reference to `mycalloc' virtual_image.c:(.text+0x4a45): undefined reference to `mycalloc' virtual_image.c:(.text+0x4af4): undefined reference to `myfree' collect2: error: ld returned 1 exit status make: *** [breastsegment] Error 1 Building the mass feature generation program. gcc -O2 -I. -I../common afumfeature.o -o afumfeature -L../common -lmammo -lm afumfeature.o: In function `afum_process': afumfeature.c:(.text+0xd80): undefined reference to `mycalloc' afumfeature.c:(.text+0xd9c): undefined reference to `mycalloc' afumfeature.c:(.text+0xe80): undefined reference to `mycalloc' afumfeature.c:(.text+0x11f8): undefined reference to `myfree' afumfeature.c:(.text+0x1207): undefined reference to `myfree' afumfeature.c:(.text+0x1214): undefined reference to `myfree' ../common/libmammo.a(aggregate.o): In function `aggregate': aggregate.c:(.text+0x7fa): undefined reference to `mycalloc' aggregate.c:(.text+0x81c): undefined reference to `mycalloc' aggregate.c:(.text+0x868): undefined reference to `mycalloc' ../common/libmammo.a(aggregate.o): In function `aggregate_median': aggregate.c:(.text+0xbc5): undefined reference to `mymalloc' aggregate.c:(.text+0xbfb): undefined reference to `mycalloc' aggregate.c:(.text+0xc3c): undefined reference to `mycalloc' ../common/libmammo.a(aggregate.o): In function `aggregate': aggregate.c:(.text+0x9b5): undefined reference to `myfree' ../common/libmammo.a(aggregate.o): In function `aggregate_median': aggregate.c:(.text+0xd85): undefined reference to `myfree' ../common/libmammo.a(convexpolyscan.o): In function `polyscan_coords': convexpolyscan.c:(.text+0x6f0): undefined reference to `mycalloc' convexpolyscan.c:(.text+0x75f): undefined reference to `mycalloc' convexpolyscan.c:(.text+0x7ab): undefined reference to `myfree' convexpolyscan.c:(.text+0x7b8): undefined reference to `myfree' ../common/libmammo.a(convexpolyscan.o): In function `polyscan_poly_cacheim': convexpolyscan.c:(.text+0x805): undefined reference to `mycalloc' convexpolyscan.c:(.text+0x894): undefined reference to `myfree' ../common/libmammo.a(mikesfileio.o): In function `read_segmentation_file': mikesfileio.c:(.text+0x1e9): undefined reference to `mycalloc' mikesfileio.c:(.text+0x205): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `linear_optical_density': optical_density.c:(.text+0x29e): undefined reference to `mymalloc' optical_density.c:(.text+0x342): undefined reference to `mycalloc' optical_density.c:(.text+0x383): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `log10_optical_density': optical_density.c:(.text+0x693): undefined reference to `mymalloc' optical_density.c:(.text+0x74f): undefined reference to `mycalloc' optical_density.c:(.text+0x790): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `map_with_ushort_lut': optical_density.c:(.text+0xb2e): undefined reference to `mymalloc' optical_density.c:(.text+0xb87): undefined reference to `mycalloc' optical_density.c:(.text+0xbc6): undefined reference to `mycalloc' ../common/libmammo.a(optical_density.o): In function `linear_optical_density': optical_density.c:(.text+0x4d9): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `log10_optical_density': optical_density.c:(.text+0x8f1): undefined reference to `myfree' ../common/libmammo.a(optical_density.o): In function `map_with_ushort_lut': optical_density.c:(.text+0xd0d): undefined reference to `myfree' ../common/libmammo.a(virtual_image.o): In function `deallocate_cached_image': virtual_image.c:(.text+0x3dc6): undefined reference to `myfree' virtual_image.c:(.text+0x3dd7): undefined reference to `myfree' ../common/libmammo.a(virtual_image.o):virtual_image.c:(.text+0x3de5): more undefined references to `myfree' follow ../common/libmammo.a(virtual_image.o): In function `allocate_cached_image': virtual_image.c:(.text+0x4233): undefined reference to `mycalloc' virtual_image.c:(.text+0x4253): undefined reference to `mymalloc' virtual_image.c:(.text+0x4275): undefined reference to `mycalloc' virtual_image.c:(.text+0x42e7): undefined reference to `mycalloc' virtual_image.c:(.text+0x44f9): undefined reference to `mycalloc' virtual_image.c:(.text+0x47a9): undefined reference to `mycalloc' virtual_image.c:(.text+0x4a45): undefined reference to `mycalloc' virtual_image.c:(.text+0x4af4): undefined reference to `myfree' collect2: error: ld returned 1 exit status make: *** [afumfeature] Error 1 Building the mass detection program. make: Nothing to be done for `all'. Building the performance evaluation program. gcc -O2 -I. -I../common DDSMeval.o polyscan.o -o DDSMeval -L../common -lmammo -lm ../common/libmammo.a(mikesfileio.o): In function `read_segmentation_file': mikesfileio.c:(.text+0x1e9): undefined reference to `mycalloc' mikesfileio.c:(.text+0x205): undefined reference to `mycalloc' collect2: error: ld returned 1 exit status make: *** [DDSMeval] Error 1 Building the template creation program. gcc -O2 -I. -I../common mktemplate.o polyscan.o -o mktemplate -L../common -lmammo -lm Building the drawimage program. gcc -O2 -I. -I../common drawimage.o -o drawimage -L../common -lmammo -lm ../common/libmammo.a(mikesfileio.o): In function `read_segmentation_file': mikesfileio.c:(.text+0x1e9): undefined reference to `mycalloc' mikesfileio.c:(.text+0x205): undefined reference to `mycalloc' collect2: error: ld returned 1 exit status make: *** [drawimage] Error 1 Building the compression/decompression program jpeg. gcc -O2 -DSYSV -DNOTRUNCATE -c lexer.c lexer.c:41:1: error: initializer element is not constant lexer.c:41:1: error: (near initialization for ‘yyin’) lexer.c:41:1: error: initializer element is not constant lexer.c:41:1: error: (near initialization for ‘yyout’) lexer.c: In function ‘initparser’: lexer.c:387:21: warning: incompatible implicit declaration of built-in function ‘strlen’ [enabled by default] lexer.c: In function ‘MakeLink’: lexer.c:443:16: warning: incompatible implicit declaration of built-in function ‘malloc’ [enabled by default] lexer.c:447:7: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:452:7: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:455:34: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:458:7: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:460:3: warning: incompatible implicit declaration of built-in function ‘strcpy’ [enabled by default] lexer.c: In function ‘getstr’: lexer.c:548:26: warning: incompatible implicit declaration of built-in function ‘malloc’ [enabled by default] lexer.c:552:4: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:557:21: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:557:28: warning: incompatible implicit declaration of built-in function ‘strlen’ [enabled by default] lexer.c:561:7: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c: In function ‘parser’: lexer.c:794:21: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:798:8: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:1074:21: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:1078:8: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:1116:21: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:1120:8: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:1154:25: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:1158:5: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:1190:5: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:1247:25: warning: incompatible implicit declaration of built-in function ‘calloc’ [enabled by default] lexer.c:1251:5: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c:1283:5: warning: incompatible implicit declaration of built-in function ‘exit’ [enabled by default] lexer.c: In function ‘yylook’: lexer.c:1867:9: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast] lexer.c:1867:20: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast] lexer.c:1877:12: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast] lexer.c:1877:23: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast] make: *** [lexer.o] Error 1

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• How advanced are author-recognition methods?

  - by Nick Rtz

  From a written text by an author if a computer program analyses the text, how much can a computer program tell today about the author of some (long enough to be statistically significant) texts? Can the computer program even tell with "certainty" whether a man or a woman wrote this text based solely on the contents of the text and not an investigation such as ip numbers etc? I'm interested to know if there are algorithms in use for instance to automatically know whether an author was male or female or similar characteristics of an author that a computer program can decide based on analyses of the written text by an author. It could be useful to know before you read a message what a computer analyses says about the author, do you agree? If I for instance get a longer message from my wife that she has had an accident in Nigeria and the computer program says that with 99 % probability the message was written by a male author in his sixties of non-caucasian origin or likewise, or by somebody who is not my wife, then the computer program could help me investigate why a certain message differs in characteristics. There can also be other uses for instance just detecting outliers in a geographically or demographically bounded larger data set. Scam detection is the obvious use I'm thinking of but there could also be other uses. Are there already such programs that analyse a written text to tell something about the author based on word choice, use of pronouns, unusual language usage, or likewise?

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• Looking for speech-to-text tool (convert .wav to text)

  - by David

  I have the ability to get .wav files of voice mails emailed to me, but sometimes I'll be sitting in a meeting and I need to know the content of a message without playing it out loud. Are there any good (and, preferably, free) tools for converting .wav files to text? I know Google Voice has this capability, but I can't determine if it'll work on a file-by-file basis. I realize that this is a difficult research problem, but even an 80% solution might be workable.

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• How to split a text file into multiple text files

  - by Andrew

  I have a text file called entry.txt that contains the following: [ entry1 ] 1239 1240 1242 1391 1392 1394 1486 1487 1489 1600 1601 1603 1657 1658 1660 2075 2076 2078 2322 2323 2325 2740 2741 2743 3082 3083 3085 3291 3292 3294 3481 3482 3484 3633 3634 3636 3690 3691 3693 3766 3767 3769 4526 4527 4529 4583 4584 4586 4773 4774 4776 5153 5154 5156 5628 5629 5631 [ entry2 ] 1239 1240 1242 1391 1392 1394 1486 1487 1489 1600 1601 1603 1657 1658 1660 2075 2076 2078 2322 2323 2325 2740 2741 2743 3082 3083 3085 3291 3292 3294 3481 3482 3484 3690 3691 3693 3766 3767 3769 4526 4527 4529 4583 4584 4586 4773 4774 4776 5153 5154 5156 5628 5629 5631 [ entry3 ] 1239 1240 1242 1391 1392 1394 1486 1487 1489 1600 1601 1603 1657 1658 1660 2075 2076 2078 2322 2323 2325 2740 2741 2743 3082 3083 3085 3291 3292 3294 3481 3482 3484 3690 3691 3693 3766 3767 3769 4241 4242 4244 4526 4527 4529 4583 4584 4586 4773 4774 4776 5153 5154 5156 5495 5496 5498 5628 5629 5631 I would like to split it into three text files: entry1.txt, entry2.txt, entry3.txt. Their contents are as follows. entry1.txt: [ entry1 ] 1239 1240 1242 1391 1392 1394 1486 1487 1489 1600 1601 1603 1657 1658 1660 2075 2076 2078 2322 2323 2325 2740 2741 2743 3082 3083 3085 3291 3292 3294 3481 3482 3484 3633 3634 3636 3690 3691 3693 3766 3767 3769 4526 4527 4529 4583 4584 4586 4773 4774 4776 5153 5154 5156 5628 5629 5631 entry2.txt: [ entry2 ] 1239 1240 1242 1391 1392 1394 1486 1487 1489 1600 1601 1603 1657 1658 1660 2075 2076 2078 2322 2323 2325 2740 2741 2743 3082 3083 3085 3291 3292 3294 3481 3482 3484 3690 3691 3693 3766 3767 3769 4526 4527 4529 4583 4584 4586 4773 4774 4776 5153 5154 5156 5628 5629 5631 entry3.txt: [ entry3 ] 1239 1240 1242 1391 1392 1394 1486 1487 1489 1600 1601 1603 1657 1658 1660 2075 2076 2078 2322 2323 2325 2740 2741 2743 3082 3083 3085 3291 3292 3294 3481 3482 3484 3690 3691 3693 3766 3767 3769 4241 4242 4244 4526 4527 4529 4583 4584 4586 4773 4774 4776 5153 5154 5156 5495 5496 5498 5628 5629 5631 In other words, the [ character indicates a new file should begin. Is there any way I can accomplish automatic text file splitting? My eventual, actual input entry.txt actually contains 200,001 entries. Doing the text split in either Windows or Linux would be great. I do not have access to a Mac machine. Thanks!

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• Text inside <p> shrinks on mobile devices while div does not [migrated]

  - by guisasso

  I asked this question on stack overflow, but didn't get any answers, so I'm trying here. Does anybody know whats happening here? I tested on opera, dolphin and the factory android browser. (although it seems now to be working on opera) The div doesn't change size, but the text somehow is shrunk to fit on part of a div. Anyway to prevent this? Just to be clear, I'm trying to achieve on the mobile browser the same look as the pc version. As the problem seems to be with the browsers, how can I force the text to take the full width of the div? I tried setting the p tag to 100% with no success. The div has to have that width and be aligned to the left of the page. On a Pc, as it should be: I shrunk the code as much as I could: <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" lang="en-us"> <head> <meta content="text/html; charset=utf-8" http-equiv="Content-Type" /> <meta content="" name="keywords" /> <meta content="" name="description" /> <title></title> </head> <body> <div style="width:1000px; margin-left:auto; margin-right:auto;" > <div style="float:left; width:758px; background-color:aqua;"> <p> Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text .<br /> <br /> Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text Random text .<br /> <br /> Random text Random text Random text Random text <a href="http://www.a.com/a.html"> Random text </a> Random text Random text . </p> </div> </div> </body> </html> Thanks.

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• Sublime Text 2 'text bubbling'?

  - by Alex Mcp

  In vim and Notepad++ I have an awesome feature either mapped or built in that I've seen called text bubbling. I know about the Sublime documentation for mapping my own, but wanted to make sure I wasn't duplicating functionality: Basically when I have either block of text selected, or just a cursor on a line, I push (ctrl + up/down) or some other mapping, and the text is moved up or down, in a block, and the rest of the text 'flows' around it. Is this a native feature in Sublime Text or should I script it in?

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• What resources are there for facial recognition

  - by Zintinio

  I'm interested in learning the theory behind facial recognition software so that I can hopefully implement it in the future. Not just face tracking, but being able to recognize individuals. What papers, books, libraries, or source is available so that I can learn more about the subject? I have found libface which seems to use eigenfaces for recognition. If there are any practitioners out there, please share any information that you can.

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• Convert Audio File to text using System.Speech

  - by Kushal Kalambi

  I am looking to convert a .wav file recorded through an android phone at 16000 to text using C#; namely the System.Speech namespace. My code is mentioned below; recognizer.SetInputToWaveFile(Server.MapPath("~/spoken.wav")); recognizer.LoadGrammar(new DictationGrammar()); RecognitionResult result = recognizer.Recognize(); label1.Text = result.Text; The is working perfectly with sample .wav "Hello world" file. However when i record something on teh phone and try to convert to on the pc, the converted text is no where close to what i had recoreded. Is there some way to make sure the audio file is transcribed accurately?

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• how does data clustering help in image or pattern recognition

  - by anon

  I have been playing around with different data clustering algorithms working on finding clusters between random data points represented an nodes, I keep reading that data clustering is used for image recognition. I am failing to make the connection, how does clustering data help in recognizing an image or in facial recognition. can someone explain this?

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• How to get Phonemes on voice recognition?

  - by XBasic3000

  i am working on Voice Recognition to Display the Phonemes and its wave form using the built-in voice recognition on vista and windows 7 using Delphi2009.

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• EXCEL VBA STUDENTS DATABASE [on hold]

  - by BENTET

  I AM DEVELOPING AN EXCEL DATABASE TO RECORD STUDENTS DETAILS. THE HEADINGS OF THE TABLE ARE DATE,YEAR, PAYMENT SLIP NO.,STUDENT NUMBER,NAME,FEES,AMOUNT PAID, BALANCE AND PREVIOUS BALANCE. I HAVE BEEN ABLE TO PUT UP SOME CODE WHICH IS WORKING, BUT THERE ARE SOME SETBACKS THAT I WANT TO BE ADDRESSED.I ACTUALLY DEVELOPED A USERFORM FOR EACH PROGRAMME OF THE INSTITUTION AND ASSIGNED EACH TO A SPECIFIC SHEET BUT WHENEVER I ADD A RECORD, IT DOES NOT GO TO THE ASSIGNED SHEET BUT GOES TO THE ACTIVE SHEET.ALSO I WANT TO HIDE ALL SHEETS AND BE WORKING ONLY ON THE USERFORMS WHEN THE WORKBOOK IS OPENED.ONE PROBLEM AM ALSO FACING IS THE UPDATE CODE.WHENEVER I UPDATE A RECORD ON A SPECIFIC ROW, IT RATHER EDIT THE RECORD ON THE FIRST ROW NOT THE RECORD EDITED.THIS IS THE CODE I HAVE BUILT SO FAR.I AM VIRTUALLY A NOVICE IN PROGRAMMING. Private Sub cmdAdd_Click() Dim lastrow As Long lastrow = Sheets("Sheet4").Range("A" & Rows.Count).End(xlUp).Row Cells(lastrow + 1, "A").Value = txtDate.Text Cells(lastrow + 1, "B").Value = ComBox1.Text Cells(lastrow + 1, "C").Value = txtSlipNo.Text Cells(lastrow + 1, "D").Value = txtStudentNum.Text Cells(lastrow + 1, "E").Value = txtName.Text Cells(lastrow + 1, "F").Value = txtFees.Text Cells(lastrow + 1, "G").Value = txtAmountPaid.Text txtDate.Text = "" ComBox1.Text = "" txtSlipNo.Text = "" txtStudentNum.Text = "" txtName.Text = "" txtFees.Text = "" txtAmountPaid.Text = "" End Sub Private Sub cmdClear_Click() txtDate.Text = "" ComBox1.Text = "" txtSlipNo.Text = "" txtStudentNum.Text = "" txtName.Text = "" txtFees.Text = "" txtAmountPaid.Text = "" txtBalance.Text = "" End Sub Private Sub cmdClearD_Click() txtDate.Text = "" ComBox1.Text = "" txtSlipNo.Text = "" txtStudentNum.Text = "" txtName.Text = "" txtFees.Text = "" txtAmountPaid.Text = "" txtBalance.Text = "" End Sub Private Sub cmdClose_Click() Unload Me End Sub Private Sub cmdDelete_Click() 'declare the variables Dim findvalue As Range Dim cDelete As VbMsgBoxResult 'check for values If txtStudentNum.Value = "" Or txtName.Value = "" Or txtDate.Text = "" Or ComBox1.Text = "" Or txtSlipNo.Text = "" Or txtFees.Text = "" Or txtAmountPaid.Text = "" Or txtBalance.Text = "" Then MsgBox "There is not data to delete" Exit Sub End If 'give the user a chance to change their mind cDelete = MsgBox("Are you sure that you want to delete this student", vbYesNo + vbDefaultButton2, "Are you sure????") If cDelete = vbYes Then 'delete the row Set findvalue = Sheet4.Range("D:D").Find(What:=txtStudentNum, LookIn:=xlValues) findvalue.EntireRow.Delete End If 'clear the controls txtDate.Text = "" ComBox1.Text = "" txtSlipNo.Text = "" txtStudentNum.Text = "" txtName.Text = "" 'txtFees.Text = "" txtAmountPaid.Text = "" txtBalance.Text = "" End Sub Private Sub cmdSearch_Click() Dim lastrow As Long Dim currentrow As Long Dim studentnum As String lastrow = Sheets("Sheet4").Range("A" & Rows.Count).End(xlUp).Row studentnum = txtStudentNum.Text For currentrow = 2 To lastrow If Cells(currentrow, 4).Text = studentnum Then txtDate.Text = Cells(currentrow, 1) ComBox1.Text = Cells(currentrow, 2) txtSlipNo.Text = Cells(currentrow, 3) txtStudentNum.Text = Cells(currentrow, 4).Text txtName.Text = Cells(currentrow, 5) txtFees.Text = Cells(currentrow, 6) txtAmountPaid.Text = Cells(currentrow, 7) txtBalance.Text = Cells(currentrow, 8) End If Next currentrow txtStudentNum.SetFocus End Sub Private Sub cmdSearchName_Click() Dim lastrow As Long Dim currentrow As Long Dim studentname As String lastrow = Sheets("Sheet4").Range("A" & Rows.Count).End(xlUp).Row studentname = txtName.Text For currentrow = 2 To lastrow If Cells(currentrow, 5).Text = studentname Then txtDate.Text = Cells(currentrow, 1) ComBox1.Text = Cells(currentrow, 2) txtSlipNo.Text = Cells(currentrow, 3) txtStudentNum.Text = Cells(currentrow, 4) txtName.Text = Cells(currentrow, 5).Text txtFees.Text = Cells(currentrow, 6) txtAmountPaid.Text = Cells(currentrow, 7) txtBalance.Text = Cells(currentrow, 8) End If Next currentrow txtName.SetFocus End Sub Private Sub cmdUpdate_Click() Dim tdate As String Dim tlevel As String Dim tslipno As String Dim tstudentnum As String Dim tname As String Dim tfees As String Dim tamountpaid As String Dim currentrow As Long Dim lastrow As Long 'If Cells(currentrow, 5).Text = studentname Then 'txtDate.Text = Cells(currentrow, 1) lastrow = Sheets("Sheet4").Range("A" & Columns.Count).End(xlUp).Offset(0, 1).Column For currentrow = 2 To lastrow tdate = txtDate.Text Cells(currentrow, 1).Value = tdate txtDate.Text = Cells(currentrow, 1) tlevel = ComBox1.Text Cells(currentrow, 2).Value = tlevel ComBox1.Text = Cells(currentrow, 2) tslipno = txtSlipNo.Text Cells(currentrow, 3).Value = tslipno txtSlipNo = Cells(currentrow, 3) tstudentnum = txtStudentNum.Text Cells(currentrow, 4).Value = tstudentnum txtStudentNum.Text = Cells(currentrow, 4) tname = txtName.Text Cells(currentrow, 5).Value = tname txtName.Text = Cells(currentrow, 5) tfees = txtFees.Text Cells(currentrow, 6).Value = tfees txtFees.Text = Cells(currentrow, 6) tamountpaid = txtAmountPaid.Text Cells(currentrow, 7).Value = tamountpaid txtAmountPaid.Text = Cells(currentrow, 7) Next currentrow txtDate.SetFocus ComBox1.SetFocus txtSlipNo.SetFocus txtStudentNum.SetFocus txtName.SetFocus txtFees.SetFocus txtAmountPaid.SetFocus txtBalance.SetFocus End Sub PLEASE I WAS THINKING IF I CAN DEVELOP SOMETHING THAT WILL USE ONLY ONE USERFORM TO SEND DATA TO DIFFERENT SHEETS IN THE WORKBOOK.

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• Image Recognition (Shape recognition)

  - by mqpasta

  I want to recognize the shapes in the picture by template matching.Is the "ExhaustiveTemplateMatching" is the right option given in Aforge.Net for this purpose.Had anyone tried this class and find it working correctly.How accurate and right choice this class is for achieving my purpose.Suggest any other methods or Alogrithms as well for recognizing shapes by matching template.For example Identifying ComboBox in a picture.

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• Speech recognition - MP3 to text software

  - by pako

  I'm looking for a speaker independent program (commercial or free) that would enable me to transcribe MP3 files containing speech recordings to text. I wanted to try Dragon Naturally Speaking, but it seems like it only supports transcribing my own speech recordings. So what are the alternatives?

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• Can I take the voice data (f.e. in mp3 format) from speech recognition? [closed]

  - by Ersin Gulbahar

  Possible Duplicate: Android: Voice Recording and saving audio I mean ; I use voice recognition classes on android and I succeed voice recognition. But I want to real voice data not words instead of it. For example I said 'teacher' and android get you said teacher.Oh ok its good but I want to my voice which include 'teacher'.Where is it ? Can I take it and save another location? I use this class to speech to text : package net.viralpatel.android.speechtotextdemo; import java.util.ArrayList; import android.app.Activity; import android.content.ActivityNotFoundException; import android.content.Intent; import android.os.Bundle; import android.speech.RecognizerIntent; import android.view.Menu; import android.view.View; import android.widget.ImageButton; import android.widget.TextView; import android.widget.Toast; public class MainActivity extends Activity { protected static final int RESULT_SPEECH = 1; private ImageButton btnSpeak; private TextView txtText; @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); txtText = (TextView) findViewById(R.id.txtText); btnSpeak = (ImageButton) findViewById(R.id.btnSpeak); btnSpeak.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { Intent intent = new Intent( RecognizerIntent.ACTION_RECOGNIZE_SPEECH); intent.putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL, "en-US"); try { startActivityForResult(intent, RESULT_SPEECH); txtText.setText(""); } catch (ActivityNotFoundException a) { Toast t = Toast.makeText(getApplicationContext(), "Ops! Your device doesn't support Speech to Text", Toast.LENGTH_SHORT); t.show(); } } }); } @Override public boolean onCreateOptionsMenu(Menu menu) { getMenuInflater().inflate(R.menu.activity_main, menu); return true; } @Override protected void onActivityResult(int requestCode, int resultCode, Intent data) { super.onActivityResult(requestCode, resultCode, data); switch (requestCode) { case RESULT_SPEECH: { if (resultCode == RESULT_OK && null != data) { ArrayList<String> text = data .getStringArrayListExtra(RecognizerIntent.EXTRA_RESULTS); txtText.setText(text.get(0)); } break; } } } } Thanks.

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• Looking for speech-to-text tool (convert .wav to text)

  - by David

  I have the ability to get .wav files of voice mails emailed to me, but sometimes I'll be sitting in a meeting and I need to know the content of a message without playing it out loud. Are there any good (and, preferably, free) tools for converting .wav files to text? I know Google Voice has this capability, but I can't determine if it'll work on a file-by-file basis. I realize that this is a difficult research problem, but even an 80% solution might be workable.

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• Using a "white list" for extracting terms for Text Mining

  - by [email protected]

  In Part 1 of my post on "Generating cluster names from a document clustering model" (part 1, part 2, part 3), I showed how to build a clustering model from text documents using Oracle Data Miner, which automates preparing data for text mining. In this process we specified a custom stoplist and lexer and relied on Oracle Text to identify important terms.  However, there is an alternative approach, the white list, which uses a thesaurus object with the Oracle Text CTXRULE index to allow you to specify the important terms. INTRODUCTIONA stoplist is used to exclude, i.e., black list, specific words in your documents from being indexed. For example, words like a, if, and, or, and but normally add no value when text mining. Other words can also be excluded if they do not help to differentiate documents, e.g., the word Oracle is ubiquitous in the Oracle product literature. One problem with stoplists is determining which words to specify. This usually requires inspecting the terms that are extracted, manually identifying which ones you don't want, and then re-indexing the documents to determine if you missed any. Since a corpus of documents could contain thousands of words, this could be a tedious exercise. Moreover, since every word is considered as an individual token, a term excluded in one context may be needed to help identify a term in another context. For example, in our Oracle product literature example, the words "Oracle Data Mining" taken individually are not particular helpful. The term "Oracle" may be found in nearly all documents, as with the term "Data." The term "Mining" is more unique, but could also refer to the Mining industry. If we exclude "Oracle" and "Data" by specifying them in the stoplist, we lose valuable information. But it we include them, they may introduce too much noise. Still, when you have a broad vocabulary or don't have a list of specific terms of interest, you rely on the text engine to identify important terms, often by computing the term frequency - inverse document frequency metric. (This is effectively a weight associated with each term indicating its relative importance in a document within a collection of documents. We'll revisit this later.) The results using this technique is often quite valuable. As noted above, an alternative to the subtractive nature of the stoplist is to specify a white list, or a list of terms--perhaps multi-word--that we want to extract and use for data mining. The obvious downside to this approach is the need to specify the set of terms of interest. However, this may not be as daunting a task as it seems. For example, in a given domain (Oracle product literature), there is often a recognized glossary, or a list of keywords and phrases (Oracle product names, industry names, product categories, etc.). Being able to identify multi-word terms, e.g., "Oracle Data Mining" or "Customer Relationship Management" as a single token can greatly increase the quality of the data mining results. The remainder of this post and subsequent posts will focus on how to produce a dataset that contains white list terms, suitable for mining. CREATING A WHITE LIST We'll leverage the thesaurus capability of Oracle Text. Using a thesaurus, we create a set of rules that are in effect our mapping from single and multi-word terms to the tokens used to represent those terms. For example, "Oracle Data Mining" becomes "ORACLEDATAMINING." First, we'll create and populate a mapping table called my_term_token_map. All text has been converted to upper case and values in the TERM column are intended to be mapped to the token in the TOKEN column. TERM                                TOKEN DATA MINING                         DATAMINING ORACLE DATA MINING                  ORACLEDATAMINING 11G                                 ORACLE11G JAVA                                JAVA CRM                                 CRM CUSTOMER RELATIONSHIP MANAGEMENT    CRM ... Next, we'll create a thesaurus object my_thesaurus and a rules table my_thesaurus_rules: CTX_THES.CREATE_THESAURUS('my_thesaurus', FALSE); CREATE TABLE my_thesaurus_rules (main_term     VARCHAR2(100),                                  query_string  VARCHAR2(400)); We next populate the thesaurus object and rules table using the term token map. A cursor is defined over my_term_token_map. As we iterate over  the rows, we insert a synonym relationship 'SYN' into the thesaurus. We also insert into the table my_thesaurus_rules the main term, and the corresponding query string, which specifies synonyms for the token in the thesaurus. DECLARE   cursor c2 is     select token, term     from my_term_token_map; BEGIN   for r_c2 in c2 loop     CTX_THES.CREATE_RELATION('my_thesaurus',r_c2.token,'SYN',r_c2.term);     EXECUTE IMMEDIATE 'insert into my_thesaurus_rules values                        (:1,''SYN(' || r_c2.token || ', my_thesaurus)'')'     using r_c2.token;   end loop; END; We are effectively inserting the token to return and the corresponding query that will look up synonyms in our thesaurus into the my_thesaurus_rules table, for example:     'ORACLEDATAMINING'        SYN ('ORACLEDATAMINING', my_thesaurus)At this point, we create a CTXRULE index on the my_thesaurus_rules table: create index my_thesaurus_rules_idx on        my_thesaurus_rules(query_string)        indextype is ctxsys.ctxrule; In my next post, this index will be used to extract the tokens that match each of the rules specified. We'll then compute the tf-idf weights for each of the terms and create a nested table suitable for mining.

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• Coloring even heighten columns

  - by verror

  I try to set different a background colors for left and right columns and to maintain the same height. So I set a background color for outer wrapper ("container" div) so it will set a color to rightBar. But this didn't work. Online Demo I want it to work on all browsers. Markup: <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html lang="en"> <head> <title>Test</title> </head> <body> <div class="contentcontainer"> <div class="container"> <div class="mainBar"> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> <p>Text Text Text Text Text Text Text Text Text Text Text Text Text Text </p> </div> <div class="rightBar"> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> <p>BAR Text BAR Text BAR Text</p> </div> </div> </div> </body> </html> CSS: body { font-family: Verdana,Tahoma,Arial, "Trebuchet MS" ,Sans-Serif,Georgia,Courier, "Times New Roman" ,Serif; margin: 0px; padding: 0px; background: repeat-x scroll center bottom #C4DAE9; text-align:center; } .contentcontainer { } .container { margin-left: auto; margin-right: auto; margin-top:5px; width: 99%; text-align: left; background-color:Gray; clear:both; } .mainBar { width:70%; float:left; background-color:White; } .rightBar { width:30%; float:left; }

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• Using a "white list" for extracting terms for Text Mining, Part 2

  - by [email protected]

  In my last post, we set the groundwork for extracting specific tokens from a white list using a CTXRULE index. In this post, we will populate a table with the extracted tokens and produce a case table suitable for clustering with Oracle Data Mining. Our corpus of documents will be stored in a database table that is defined as create table documents(id NUMBER, text VARCHAR2(4000)); However, any suitable Oracle Text-accepted data type can be used for the text. We then create a table to contain the extracted tokens. The id column contains the unique identifier (or case id) of the document. The token column contains the extracted token. Note that a given document many have many tokens, so there will be one row per token for a given document. create table extracted_tokens (id NUMBER, token VARCHAR2(4000)); The next step is to iterate over the documents and extract the matching tokens using the index and insert them into our token table. We use the MATCHES function for matching the query_string from my_thesaurus_rules with the text. DECLARE     cursor c2 is       select id, text       from documents; BEGIN     for r_c2 in c2 loop        insert into extracted_tokens          select r_c2.id id, main_term token          from my_thesaurus_rules          where matches(query_string,                        r_c2.text)>0;     end loop; END; Now that we have the tokens, we can compute the term frequency - inverse document frequency (TF-IDF) for each token of each document. create table extracted_tokens_tfidf as   with num_docs as (select count(distinct id) doc_cnt                     from extracted_tokens),        tf       as (select a.id, a.token,                            a.token_cnt/b.num_tokens token_freq                     from                        (select id, token, count(*) token_cnt                        from extracted_tokens                        group by id, token) a,                       (select id, count(*) num_tokens                        from extracted_tokens                        group by id) b                     where a.id=b.id),        doc_freq as (select token, count(*) overall_token_cnt                     from extracted_tokens                     group by token)   select tf.id, tf.token,          token_freq * ln(doc_cnt/df.overall_token_cnt) tf_idf   from num_docs,        tf,        doc_freq df   where df.token=tf.token; From the WITH clause, the num_docs query simply counts the number of documents in the corpus. The tf query computes the term (token) frequency by computing the number of times each token appears in a document and divides that by the number of tokens found in the document. The doc_req query counts the number of times the token appears overall in the corpus. In the SELECT clause, we compute the tf_idf. Next, we create the nested table required to produce one record per case, where a case corresponds to an individual document. Here, we COLLECT all the tokens for a given document into the nested column extracted_tokens_tfidf_1. CREATE TABLE extracted_tokens_tfidf_nt              NESTED TABLE extracted_tokens_tfidf_1                  STORE AS extracted_tokens_tfidf_tab AS              select id,                     cast(collect(DM_NESTED_NUMERICAL(token,tf_idf)) as DM_NESTED_NUMERICALS) extracted_tokens_tfidf_1              from extracted_tokens_tfidf              group by id;   To build the clustering model, we create a settings table and then insert the various settings. Most notable are the number of clusters (20), using cosine distance which is better for text, turning off auto data preparation since the values are ready for mining, the number of iterations (20) to get a better model, and the split criterion of size for clusters that are roughly balanced in number of cases assigned. CREATE TABLE km_settings (setting_name  VARCHAR2(30), setting_value VARCHAR2(30)); BEGIN  INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.clus_num_clusters, 20);  INSERT INTO km_settings (setting_name, setting_value)     VALUES (dbms_data_mining.kmns_distance, dbms_data_mining.kmns_cosine);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_off);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.kmns_iterations,20);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.kmns_split_criterion,dbms_data_mining.kmns_size);   COMMIT; END; With this in place, we can now build the clustering model. BEGIN     DBMS_DATA_MINING.CREATE_MODEL(     model_name          => 'TEXT_CLUSTERING_MODEL',     mining_function     => dbms_data_mining.clustering,     data_table_name     => 'extracted_tokens_tfidf_nt',     case_id_column_name => 'id',     settings_table_name => 'km_settings'); END;To generate cluster names from this model, check out my earlier post on that topic.

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• HTML: How to create a DIV with only vertical scroll-bar to show long paragraphs on a webpage?

  - by Awan

  I want to show terms and condition note on my website. I dont want to use text field and also dont want to use my whole page. I just want to display my text in selected area and want to use only vertical scroll-bar to go down and read all text. Currently I am using this code: <div style="width:10;height:10;overflow:scroll" > text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text text </div> It is not fixing the width and height and spread until the all text appears. Second it is showing horizontal scroll-bar and I don't want to show it. Any Idea ? Thanks

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• How can I use the voice recognition used by Android on Ubuntu?

  - by aking1012

  If I'm developing an Android app that uses TTS and Voice recognition, which libraries are used for the same voice recognition and speech on Ubuntu? I'm assuming espeak for text to speech, but I'm unsure which voice recognition library and dictionary/learning/calibration system is used for voice recognition. I'ld like to make the app available on Ubuntu Desktop. as well as test it outside an emulator

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• The Best Text to Speech (TTS) Software Programs and Online Tools

  - by Lori Kaufman

  Text to Speech (TTS) software allows you to have text read aloud to you. This is useful for struggling readers and for writers, when editing and revising their work. You can also convert eBooks to audiobooks so you can listen to them on long drives. We’ve posted some websites here where you can find some good TTS software programs and online tools that are free or at least have free versions available. 8 Deadly Commands You Should Never Run on Linux 14 Special Google Searches That Show Instant Answers How To Create a Customized Windows 7 Installation Disc With Integrated Updates

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• Delphi Speech recognition delphi

  - by XBasic3000

  I need create a programatic equivalent using delphi language... or could someone post a link on how to do grammars in peech recogniton using the delphi. sorry for my english... XML Grammar Sample(s): <GRAMMAR> <!-- Create a simple "hello world" rule --> <RULE NAME="HelloWorld" TOPLEVEL="ACTIVE"> <P>hello world</P> </RULE> <!-- Create a more advanced "hello world" rule that changes the display form. When the user says "hello world" the display text will be "Hiya there!" --> <RULE NAME="HelloWorld_Disp" TOPLEVEL="ACTIVE"> <P DISP="Hiya there!">hello world</P> </RULE> <!-- Create a rule that changes the pronunciation and the display form of the phrase. When the user says "eh" the display text will be "I don't understand?". Note the user didn't say "huh". The pronunciation for "what" is specific to this phrase tag and is not changed for the user or application lexicon, or even other instances of "what" in the grammar --> <RULE NAME="Question_Pron" TOPLEVEL="ACTIVE"> <P DISP="I don't understand" PRON="eh">what</P> </RULE> <!-- Create a rule demonstrating repetition --> <!-- the rule will only be recognized if the user says "hey diddle diddle" --> <RULE NAME="NurseryRhyme" TOPLEVEL="ACTIVE"> <P>hey</P> <P MIN="2" MAX="2">diddle</P> </RULE> <!-- Create a list with variable phrase weights --> <!-- If the user says similar phrases, the recognizer will use the weights to pick a match --> <RULE NAME="UseWeights" TOPLEVEL="ACTIVE"> <LIST> <!-- Note the higher likelihood that the user is expected to say "recognizer speech" --> <P WEIGHT=".95">recognize speech</P> <P WEIGHT=".05">wreck a nice beach</P> </LIST> </RULE> <!-- Create a phrase with an attached semantic property --> <!-- Speaking "one two three" will return three different unique semantic properties, with different names, and different values --> <RULE NAME="UseProps" TOPLEVEL="ACTIVE"> <!-- named property, without value --> <P PROPNAME="NOVALUE">one</P> <!-- named property, with numeric value --> <P PROPNAME="NUMBER" VAL="2">two</P> <!-- named property, with string value --> <P PROPNAME="STRING" VALSTR="three">three</P> </RULE> </GRAMMAR> **Programmatic Equivalent:** To add a phrase to a rule, SAPI provides an API called ISpGrammarBuilder::AddWordTransition. The application developer can add the sentences as follows: SPSTATEHANDLE hsHelloWorld; // Create new top-level rule called "HelloWorld" hr = cpRecoGrammar->GetRule(L"HelloWorld", NULL, SPRAF_TopLevel | SPRAF_Active, TRUE, &hsHelloWorld); // Check hr // Add the command words "hello world" // Note that the lexical delimiter is " ", a space character. // By using a space delimiter, the entire phrase can be added // in one method call hr = cpRecoGrammar->AddWordTransition(hsHelloWorld, NULL, L"hello world", L" ", SPWT_LEXICAL, NULL, NULL); // Check hr // Add the command words "hiya there" // Note that the lexical delimiter is "|", a pipe character. // By using a pipe delimiter, the entire phrase can be added // in one method call hr = cpRecoGrammar->AddWordTransition(hsHelloWorld, NULL, L"hiya|there", L"|", SPWT_LEXICAL, NULL, NULL); // Check hr // save/commit changes hr = cpRecoGrammar->Commit(NULL); // Check hr

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• SAPI Speech recognition delphi

  - by XBasic3000

  I need create a programatic equivalent using delphi language... or could someone post a link on how to do grammars in peech recogniton using the delphi. sorry for my english... **Programmatic Equivalent C#:** Ref: http://msdn.microsoft.com/en-us/library/ms723634(v=VS.85).aspx To add a phrase to a rule, SAPI provides an API called ISpGrammarBuilder::AddWordTransition. The application developer can add the sentences as follows: SPSTATEHANDLE hsHelloWorld; // Create new top-level rule called "HelloWorld" hr = cpRecoGrammar->GetRule(L"HelloWorld", NULL, SPRAF_TopLevel | SPRAF_Active, TRUE, &hsHelloWorld); // Check hr // Add the command words "hello world" // Note that the lexical delimiter is " ", a space character. // By using a space delimiter, the entire phrase can be added // in one method call hr = cpRecoGrammar->AddWordTransition(hsHelloWorld, NULL, L"hello world", L" ", SPWT_LEXICAL, NULL, NULL); // Check hr // Add the command words "hiya there" // Note that the lexical delimiter is "|", a pipe character. // By using a pipe delimiter, the entire phrase can be added // in one method call hr = cpRecoGrammar->AddWordTransition(hsHelloWorld, NULL, L"hiya|there", L"|", SPWT_LEXICAL, NULL, NULL); // Check hr // save/commit changes hr = cpRecoGrammar->Commit(NULL); // Check hr XML Grammar Sample(s): <GRAMMAR> <!-- Create a simple "hello world" rule --> <RULE NAME="HelloWorld" TOPLEVEL="ACTIVE"> <P>hello world</P> </RULE> <!-- Create a more advanced "hello world" rule that changes the display form. When the user says "hello world" the display text will be "Hiya there!" --> <RULE NAME="HelloWorld_Disp" TOPLEVEL="ACTIVE"> <P DISP="Hiya there!">hello world</P> </RULE> <!-- Create a rule that changes the pronunciation and the display form of the phrase. When the user says "eh" the display text will be "I don't understand?". Note the user didn't say "huh". The pronunciation for "what" is specific to this phrase tag and is not changed for the user or application lexicon, or even other instances of "what" in the grammar --> <RULE NAME="Question_Pron" TOPLEVEL="ACTIVE"> <P DISP="I don't understand" PRON="eh">what</P> </RULE> <!-- Create a rule demonstrating repetition --> <!-- the rule will only be recognized if the user says "hey diddle diddle" --> <RULE NAME="NurseryRhyme" TOPLEVEL="ACTIVE"> <P>hey</P> <P MIN="2" MAX="2">diddle</P> </RULE> <!-- Create a list with variable phrase weights --> <!-- If the user says similar phrases, the recognizer will use the weights to pick a match --> <RULE NAME="UseWeights" TOPLEVEL="ACTIVE"> <LIST> <!-- Note the higher likelihood that the user is expected to say "recognizer speech" --> <P WEIGHT=".95">recognize speech</P> <P WEIGHT=".05">wreck a nice beach</P> </LIST> </RULE> <!-- Create a phrase with an attached semantic property --> <!-- Speaking "one two three" will return three different unique semantic properties, with different names, and different values --> <RULE NAME="UseProps" TOPLEVEL="ACTIVE"> <!-- named property, without value --> <P PROPNAME="NOVALUE">one</P> <!-- named property, with numeric value --> <P PROPNAME="NUMBER" VAL="2">two</P> <!-- named property, with string value --> <P PROPNAME="STRING" VALSTR="three">three</P> </RULE> </GRAMMAR>

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• Speech.Recognition GrammarBuilder/Choices Tree Structure

  - by user2210179

  In playing around with C#'s Speech Recognition, I've stumbled across a road block in the creation of an effective GrammerBuilder with Choices (more specifically, Choices of Choices). IE considering the following logical commands. One solution would to "hard code" every combination of Speech lines and add them to a GrammarBuilder (ie "SET LEFT COLOR RED" and "SET RIGHT CLEAR", however, this would quickly max out the limit of 1024, especially when dealing with number combinations. Another solution would to Append all 'columns' as "Choices" (and filter out incorrect paths upon 'recognition', however this seems like it's processor heavy and unnecessary. The middle ground, seems like the best path - with Choices of Choices - like a tree structure on a GrammarBuilder - however I'm not sure how to proceed. Any suggestions?

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• Speech Recognition Grammar Rules using delphi code

  - by XBasic3000

  I need help to make ISeechRecoGrammar without using xml format. Like creating it on runtime on delphi. example: procedure TForm1.FormCreate(Sender: TObject); var AfterCmdState: ISpeechGrammarRuleState; temp : OleVariant; Grammar: ISpeechRecoGrammar; PropertiesRule: ISpeechGrammarRule; ItemRule: ISpeechGrammarRule; TopLevelRule: ISpeechGrammarRule; begin SpSharedRecoContext.EventInterests := SREAllEvents; Grammar := SpSharedRecoContext.CreateGrammar(m_GrammarId); TopLevelRule := Grammar.Rules.Add('TopLevelRule', SRATopLevel Or SRADynamic, 1); PropertiesRule := Grammar.Rules.Add('PropertiesRule', SRADynamic, 2); ItemRule := Grammar.Rules.Add('ItemRule', SRADynamic, 3); AfterCmdState := TopLevelRule.AddState; TopLevelRule.InitialState.AddWordTransition(AfterCmdState, 'test', temp, temp, '****', 0, temp, temp); Grammar.Rules.Commit; Grammar.CmdSetRuleState('TopLevelRule', SGDSActive); end; can someone reconstruct or midify this delphi code (above) to be exactly same function below(xml). <GRAMMAR LANGID="409"> <!-- "Constant" definitions --> <DEFINE> <ID NAME="RID_start" VAL="1"/> <ID NAME="PID_action" VAL="2"/> <ID NAME="PID_actionvalue" VAL="3"/> </DEFINE> <!-- Rule definitions --> <RULE NAME="start" ID="RID_start" TOPLEVEL="ACTIVE"> <P>i am</P> <RULEREF NAME="action" PROPNAME="action" PROPID="PID_action" /> <O>OK</O> </RULE> <RULE NAME="action"> <L PROPNAME="actionvalue" PROPID="PID_actionvalue"> <P VAL="1">albert</P> <P VAL="2">francis</P> <P VAL="3">alex</P> </L> </RULE> </GRAMMAR> sorry for my english...

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