Cleverpath AION Business Rules Expert (formerly Platinum AIONDS, and before that Trinzic AIONDS, and originally Aion) is an expert system and Business rules engine owned by Computer Associates by 2000. == History == The product was created around 1986 as "Aion" by the Aion company. In its initial release Aion was multi-platform and continues to be deliverable to the PC, Unixs, and Mainframe computer's. In addition it ties in seamlessly with a variety of databases including Oracle, Microsoft SQL Server, and ODBC. Aion was founded by Harry Reinstein, Larry Cohn, Garry Hallee, Scott Grinis, and others. From Scott Grinis's bio: Scott founded Aion, a company that developed expert systems and whose advanced inference engine and object technology were used by financial services and insurance firms to develop risk-scoring and underwriting applications. Harry Reinstein was quoted as saying: “Our biggest competitor was not AICorp, it was COBOL” Trinzic owned AION by 1993. A reference in a 1993 announcement indicates that Trinzic's formation was the result of a merger (paraphased): Trinzic set three development initiatives shortly after its formation from the merger of Aion Corp. and AICorp. The other initiatives -- adding SQL extensions to Aion/DS and evaluating the unbundling of some of that product's object-oriented programming capabilities -- are still active. Writing in 1993 Judith Hodges and Deborah Melewski give the date for the merger: Two rival artificial intelligence software vendors -- AICorp, Inc. and Aion Corp. -- merged in September 1992 to form Trinzic Corp. As part of the merger, redundant jobs were eliminated (20% of the combined work force), leaving a total work force of 245 employees worldwide. The new firm also boasted a combined installed base of more than 1,200 sites representing more than 10,000 software licenses. Although in the merger, technically AICorp bought Aion, as AICorp was a public company and Aion was still private, the reality was that Aion's leadership and technology subsumed AICorp's. Jim Gagnard, the CEO of Aion, became CEO of Trinzic and AICorp's flagship product, KBMS, was discontinued, while the Aion Development System continued to be enhanced and KBMS customers were assisted in converting to AIONDS, under the continued technical leadership of Garry Hallee and Scott Grinis. On August 1, 1994 Trinzic released version 6.4 of AIONDS saying, in part: Trinzic Corp., Palo Alto, Calif., has unveiled The Aion Development System (AionDS) Version 6.4, an upgrade to the company's development environment for building business process automation applications. Version 6.4 provides a visual development environment for Microsoft Windows or OS/2 PM applications using business rules. Trinzic was acquired by PLATINUM Technologies in 1995 which retained at least some of Trinzic's acquisitions Platinum Technologies was acquired by Computer Associates in 1999. CA changed the system's name to CA Aion Business Rules Expert" on or before 2009. It is currently (June 2011) at Release 11 on a wide range of supported platforms. == Applications using Aion == Aion has been used in a variety of industries including Energy, Insurance, Military, Aviation, and Banking. At one point an Aion expert system application written by Covia, LLC existed to do airport gate assignment. Colossus, a computer program, developed by Computer Sciences Corporation is the insurance industry’s leading expert system for assisting adjusters in the evaluation of bodily injury claims (aka "pain and suffering"). Colossus helps adjusters reduce variance in payouts on similar bodily injury claims through objective use of industry standard rules.
Semantic compression
In natural language processing, semantic compression is a process of compacting a lexicon used to build a textual document (or a set of documents) by reducing language heterogeneity, while maintaining text semantics. As a result, the same ideas can be represented using a smaller set of words. In most applications, semantic compression is a lossy compression. Increased prolixity does not compensate for the lexical compression and an original document cannot be reconstructed in a reverse process. == By generalization == Semantic compression is basically achieved in two steps, using frequency dictionaries and semantic network: determining cumulated term frequencies to identify target lexicon, replacing less frequent terms with their hypernyms (generalization) from target lexicon. Step 1 requires assembling word frequencies and information on semantic relationships, specifically hyponymy. Moving upwards in word hierarchy, a cumulative concept frequency is calculating by adding a sum of hyponyms' frequencies to frequency of their hypernym: c u m f ( k i ) = f ( k i ) + ∑ j c u m f ( k j ) {\displaystyle cumf(k_{i})=f(k_{i})+\sum _{j}cumf(k_{j})} where k i {\displaystyle k_{i}} is a hypernym of k j {\displaystyle k_{j}} . Then a desired number of words with top cumulated frequencies are chosen to build a target lexicon. In the second step, compression mapping rules are defined for the remaining words in order to handle every occurrence of a less frequent hyponym as its hypernym in output text. Example The below fragment of text has been processed by the semantic compression. Words in bold have been replaced by their hypernyms. They are both nest building social insects, but paper wasps and honey bees organize their colonies in very different ways. In a new study, researchers report that despite their differences, these insects rely on the same network of genes to guide their social behavior.The study appears in the Proceedings of the Royal Society B: Biological Sciences. Honey bees and paper wasps are separated by more than 100 million years of evolution, and there are striking differences in how they divvy up the work of maintaining a colony. The procedure outputs the following text: They are both facility building insect, but insects and honey insects arrange their biological groups in very different structure. In a new study, researchers report that despite their difference of opinions, these insects act the same network of genes to steer their party demeanor. The study appears in the proceeding of the institution bacteria Biological Sciences. Honey insects and insect are separated by more than hundred million years of organic processes, and there are impinging differences of opinions in how they divvy up the work of affirming a biological group. == Implicit semantic compression == A natural tendency to keep natural language expressions concise can be perceived as a form of implicit semantic compression, by omitting unmeaningful words or redundant meaningful words (especially to avoid pleonasms). == Applications and advantages == In the vector space model, compacting a lexicon leads to a reduction of dimensionality, which results in less computational complexity and a positive influence on efficiency. Semantic compression is advantageous in information retrieval tasks, improving their effectiveness (in terms of both precision and recall). This is due to more precise descriptors (reduced effect of language diversity – limited language redundancy, a step towards a controlled dictionary). As in the example above, it is possible to display the output as natural text (re-applying inflexion, adding stop words).
Ogle app
Ogle is a free smartphone based social media application. It is available for iOS and Android. Ogle acts like a school wide forum that lets users and users' classmates share and interact. Users can share photos, videos, questions, even thoughts and watch submissions grow in popularity as other users vote and comment on them. == App Features == Campus Feed: Interact by watching and posting videos or pictures to your campus story. Photos and Videos: share what you want with many different timing options. Interact: Chat with friends and groups, or share a moment for all to see. Real-name system: choose to register an account with username and profile picture. Custom Stickers: Create stickers to add creativity and zest to your pictures. Flash Interaction: All private chat and group chat history will be deleted after 24 hours on Ogle Chat. == Controversies == Users can post anything on Ogle using text, photos, and videos. As a result, some Ogle user's sense of anonymity, posts have targeted specific schools and students with abusive and hurtful content. The Ogle app's user anonymity makes it difficult for school officials to quickly investigate issues that occur within the Ogle app. On March 28, 2016, three people were arrested after violent threats were made against an Anaheim high school. 18-year-old Miguel Meza was arrested Sunday afternoon during a traffic stop, along with his passenger, 23-year-old Johnny Aguilar. Police said both men had loaded handguns. Aguilar was also accused of violating his probation. "It is concerning the fact that they did have firearms, but we don't have a crystal ball. We can't determine if they possessed those firearms to engage in some kind of school violence or if they had it for another reason," Sgt. Daron Wyatt with the Anaheim Police Department said. Officials said Meza and Aguilar have known gang ties and detectives began investigating Meza after threats were made against the school on Ogle. On February 29, 2016, Santa Cruz County sheriff's deputies arrested a 16-year-old Aptos High School student Friday, accused of making an online threat of gun violence at Aptos High and Monte Vista Christian."He basically told detectives that it was all a joke. It's not a joke. You have multiple resources being spent to investigate these cases," said Santa Cruz County Sheriff's Sgt. Roy Morales. The schools remained open throughout the week, with a huge police presence on campus. In an anonymous emailed statement to the Daily Pilot on Thursday, the "Ogle team" said: "We are aware of the concern, and cyberbullying is absolutely NOT our intention for the app. Our goal for this app is to create a free and safe community space for students, for a better communication. We are currently working around the clock to improve the app. As a matter of fact, we are also in contact with local police departments, anti-bullying organizations and local high schools to try to help the students." In response to these incidents, Ogle expressed that they takes the safety of its users seriously and does not condone any type of behavior that is illegal or in violation of its content policies. The company also said it has instituted a content moderation team to increase review and identify and remove inappropriate content, and take action against “those who violate our community guidelines.”
LanguageWare
LanguageWare is a natural language processing (NLP) technology developed by IBM, which allows applications to process natural language text. It comprises a set of Java libraries that provide a range of NLP functions: language identification, text segmentation/tokenization, normalization, entity and relationship extraction, and semantic analysis and disambiguation. The analysis engine uses a finite-state machine approach at multiple levels, which aids its performance characteristics while maintaining a reasonably small footprint. The behaviour of the system is driven by a set of configurable lexico-semantic resources which describe the characteristics and domain of the processed language. A default set of resources comes as part of LanguageWare and these describe the native language characteristics, such as morphology, and the basic vocabulary for the language. Supplemental resources have been created that capture additional vocabularies, terminologies, rules and grammars, which may be generic to the language or specific to one or more domains. A set of Eclipse-based customization tooling, LanguageWare Resource Workbench, is available on IBM's alphaWorks site, and allows domain knowledge to be compiled into these resources and thereby incorporated into the analysis process. LanguageWare can be deployed as a set of UIMA-compliant annotators, Eclipse plug-ins or Web Services.
ISLRN
The ISLRN or International Standard Language Resource Number is Persistent Unique Identifier for Language Resources. == Context == On November 18, 2013, 12 major organisations (see list below) from the fields Language Resources and Technologies, Computational Linguistics, and Digital Humanities held a cooperation meeting in Paris (France) and agreed to announce the establishment of the International Standard Language Resource Number (ISLRN), to be assigned to each Language Resource. Among the 12 organisations, 4 institutions constitute the ISLRN Steering Committee (ST) ADHO ACL Asian Federation of Natural Language Processing ST COCOSDA, International Committee for the Coordination & Standardisation of Speech Databases and Assessment Techniques ICCL (COLING) European Data Forum ELRA ST IAMT, International Association for Machine Translation Archived 2010-06-24 at the Wayback Machine ISCA LDC ST Oriental COCOSDA ST RMA, Language Resource Management Agency == Size and Content == The Joint Research Centre(JRC), the [European Commission]'s in-house science service, was the first organisation to adopt the ISLRN initiative and requested. 2500 resources and tools have already been allocated an ISLRN. These resources include written data (Annotated corpus, Annotated text, List of misspelled word, Terminological database, Treebank, Wordnet, etc.) and speech corpora (Synthesised Speech, Transcripts and Audiovisual Recordings, Conversational Speech, Folk Sayings, etc.) == Objectives == Providing Language Resources with unique names and identifiers using a standardized nomenclature ensures the identification of each Language Resources and streamlines the citation with proper references in activities within Human Language Technology as well as in documents and scientific publications. Such unique identifier also enhances the reproducibility, an essential feature of scientific work.
TimeTiger
TimeTiger is a time and project tracking app developed by Indigo Technologies Ltd. in Toronto, Ontario, Canada. Indigo was founded in 1997 and initially released TimeTiger in 1998. == Company == The company was incorporated in 1997 and began operations as a custom software developer. TimeTiger (internally called TaskMaster) was developed as a tool to help with Indigo's own project planning and estimating. After releasing TimeTiger as a commercial product in 1998, Indigo shifted its focus to time and project management solutions. TimeTiger first introduced support for web-based time logging in 2000, to appeal to workers who were not already tracking their time for billing reasons. Subsequent development emphasized project analysis tools. == Features == Web-based electronic time log "To Do" list to monitor project and non-project activities Pivot table report designer Role-based access control == Software integration == Reports can be exported to Microsoft Excel or saved as Excel-compatible HTML files. Microsoft Project files can be imported and exported. A Software Development Kit is available.
Deep image prior
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image statistics are captured by the structure of a convolutional image generator rather than by any previously learned capabilities. == Method == === Background === Inverse problems such as noise reduction, super-resolution, and inpainting can be formulated as the optimization task x ∗ = m i n x E ( x ; x 0 ) + R ( x ) {\displaystyle x^{}=min_{x}E(x;x_{0})+R(x)} , where x {\displaystyle x} is an image, x 0 {\displaystyle x_{0}} a corrupted representation of that image, E ( x ; x 0 ) {\displaystyle E(x;x_{0})} is a task-dependent data term, and R(x) is the regularizer. Deep neural networks learn a generator/decoder x = f θ ( z ) {\displaystyle x=f_{\theta }(z)} which maps a random code vector z {\displaystyle z} to an image x {\displaystyle x} . The image corruption method used to generate x 0 {\displaystyle x_{0}} is selected for the specific application. === Specifics === In this approach, the R ( x ) {\displaystyle R(x)} prior is replaced with the implicit prior captured by the neural network (where R ( x ) = 0 {\displaystyle R(x)=0} for images that can be produced by a deep neural networks and R ( x ) = + ∞ {\displaystyle R(x)=+\infty } otherwise). This yields the equation for the minimizer θ ∗ = a r g m i n θ E ( f θ ( z ) ; x 0 ) {\displaystyle \theta ^{}=argmin_{\theta }E(f_{\theta }(z);x_{0})} and the result of the optimization process x ∗ = f θ ∗ ( z ) {\displaystyle x^{}=f_{\theta ^{}}(z)} . The minimizer θ ∗ {\displaystyle \theta ^{}} (typically a gradient descent) starts from a randomly initialized parameters and descends into a local best result to yield the x ∗ {\displaystyle x^{}} restoration function. ==== Overfitting ==== A parameter θ may be used to recover any image, including its noise. However, the network is reluctant to pick up noise because it contains high impedance while useful signal offers low impedance. This results in the θ parameter approaching a good-looking local optimum so long as the number of iterations in the optimization process remains low enough not to overfit data. === Deep Neural Network Model === Typically, the deep neural network model for deep image prior uses a U-Net like model without the skip connections that connect the encoder blocks with the decoder blocks. The authors in their paper mention that "Our findings here (and in other similar comparisons) seem to suggest that having deeper architecture is beneficial, and that having skip-connections that work so well for recognition tasks (such as semantic segmentation) is highly detrimental." == Applications == === Denoising === The principle of denoising is to recover an image x {\displaystyle x} from a noisy observation x 0 {\displaystyle x_{0}} , where x 0 = x + ϵ {\displaystyle x_{0}=x+\epsilon } . The distribution ϵ {\displaystyle \epsilon } is sometimes known (e.g.: profiling sensor and photon noise) and may optionally be incorporated into the model, though this process works well in blind denoising. The quadratic energy function E ( x , x 0 ) = | | x − x 0 | | 2 {\displaystyle E(x,x_{0})=||x-x_{0}||^{2}} is used as the data term, plugging it into the equation for θ ∗ {\displaystyle \theta ^{}} yields the optimization problem m i n θ | | f θ ( z ) − x 0 | | 2 {\displaystyle min_{\theta }||f_{\theta }(z)-x_{0}||^{2}} . === Super-resolution === Super-resolution is used to generate a higher resolution version of image x. The data term is set to E ( x ; x 0 ) = | | d ( x ) − x 0 | | 2 {\displaystyle E(x;x_{0})=||d(x)-x_{0}||^{2}} where d(·) is a downsampling operator such as Lanczos that decimates the image by a factor t. === Inpainting === Inpainting is used to reconstruct a missing area in an image x 0 {\displaystyle x_{0}} . These missing pixels are defined as the binary mask m ∈ { 0 , 1 } H × V {\displaystyle m\in \{0,1\}^{H\times V}} . The data term is defined as E ( x ; x 0 ) = | | ( x − x 0 ) ⊙ m | | 2 {\displaystyle E(x;x_{0})=||(x-x_{0})\odot m||^{2}} (where ⊙ {\displaystyle \odot } is the Hadamard product). The intuition behind this is that the loss is computed only on the known pixels in the image, and the network is going to learn enough about the image to fill in unknown parts of the image even though the computed loss doesn't include those pixels. This strategy is used to remove image watermarks by treating the watermark as missing pixels in the image. === Flash–no-flash reconstruction === This approach may be extended to multiple images. A straightforward example mentioned by the author is the reconstruction of an image to obtain natural light and clarity from a flash–no-flash pair. Video reconstruction is possible but it requires optimizations to take into account the spatial differences. == Implementations == A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow A Keras-based implementation written in Python 2 and released under the GPLv3: machine_learning_denoising == Example == See Astronomy Picture of the Day (APOD) of 2024-02-18