Luca Maria Gambardella (born 4 January 1962) is an Italian computer scientist and author. He is the former director of the Dalle Molle Institute for Artificial Intelligence Research in Lugano, in the Ticino canton of Switzerland. He is currently the prorector of Università della Svizzera italiana, where he directs the Master of Science in Artificial Intelligence degree course. Several of his papers have been extensively cited, with his collaborators including Marco Dorigo, with whom he has published papers on the application of ant colony optimization theory to the traveling salesman problem, and Jürgen Schmidhuber with whom he has published research on deep neural networks.. Beside working in research, Gambardella explores the potentials of AI applied for the generation of art. Some of his artistic installations received significant media coverage. As a novelist, the genres he approached broad from Bildungsroman of his first book "Sei vite" ("Six lives"), to romance of his second book "Il suono dell'alba" ("The sound of sunrise").
Data augmentation
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. Data augmentation has important applications in Bayesian analysis, and the technique is widely used in machine learning to reduce overfitting when training machine learning models, achieved by training models on several slightly-modified copies of existing data. == Synthetic oversampling techniques for traditional machine learning == Synthetic Minority Over-sampling Technique (SMOTE) is a method used to address imbalanced datasets in machine learning. In such datasets, the number of samples in different classes varies significantly, leading to biased model performance. For example, in a medical diagnosis dataset with 90 samples representing healthy individuals and only 10 samples representing individuals with a particular disease, traditional algorithms may struggle to accurately classify the minority class. SMOTE rebalances the dataset by generating synthetic samples for the minority class. For instance, if there are 100 samples in the majority class and 10 in the minority class, SMOTE can create synthetic samples by randomly selecting a minority class sample and its nearest neighbors, then generating new samples along the line segments joining these neighbors. This process helps increase the representation of the minority class, improving model performance. == Data augmentation for image classification == When convolutional neural networks grew larger in mid-1990s, there was a lack of data to use, especially considering that some part of the overall dataset should be spared for later testing. It was proposed to perturb existing data with affine transformations to create new examples with the same labels, which were complemented by so-called elastic distortions in 2003, and the technique was widely used as of 2010s. Data augmentation can enhance CNN performance and acts as a countermeasure against CNN profiling attacks. Data augmentation has become fundamental in image classification, enriching training dataset diversity to improve model generalization and performance. The evolution of this practice has introduced a broad spectrum of techniques, including geometric transformations, color space adjustments, and noise injection. === Geometric Transformations === Geometric transformations alter the spatial properties of images to simulate different perspectives, orientations, and scales. Common techniques include: Affine Transformation Rotation: Rotating images by a specified degree to help models recognize objects at various angles. Reflection: Reflecting images horizontally or vertically to introduce variability in orientation. Translation: Shifting images in different directions to teach models positional invariance. Scaling Shear Mapping Cropping: Removing sections of the image to focus on particular features or simulate closer views. Elastic Distortion Morphing within the same class: Generating new samples by applying morphing techniques between two images belonging to the same class, thereby increasing intra-class diversity. === Color Space Transformations === Color space transformations modify the color properties of images, addressing variations in lighting, color saturation, and contrast. Techniques include: Brightness Adjustment: Varying the image's brightness to simulate different lighting conditions. Contrast Adjustment: Changing the contrast to help models recognize objects under various clarity levels. Saturation Adjustment: Altering saturation to prepare models for images with diverse color intensities. Color Jittering: Randomly adjusting brightness, contrast, saturation, and hue to introduce color variability. === Noise Injection === Injecting noise into images simulates real-world imperfections, teaching models to ignore irrelevant variations. Techniques involve: Gaussian Noise: Adding Gaussian noise mimics sensor noise or graininess. Salt and Pepper Noise: Introducing black or white pixels at random simulates sensor dust or dead pixels. == Data augmentation for signal processing == Residual or block bootstrap can be used for time series augmentation. === Biological signals === Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and scarce. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Data scarcity is notable in signal processing problems such as for Parkinson's Disease Electromyography signals, which are difficult to source - Zanini, et al. noted that it is possible to use a generative adversarial network (in particular, a DCGAN) to perform style transfer in order to generate synthetic electromyographic signals that corresponded to those exhibited by sufferers of Parkinson's Disease. The approaches are also important in electroencephalography (brainwaves). Wang, et al. explored the idea of using deep convolutional neural networks for EEG-Based Emotion Recognition, results show that emotion recognition was improved when data augmentation was used. A common approach is to generate synthetic signals by re-arranging components of real data. Lotte proposed a method of "Artificial Trial Generation Based on Analogy" where three data examples x 1 , x 2 , x 3 {\displaystyle x_{1},x_{2},x_{3}} provide examples and an artificial x s y n t h e t i c {\displaystyle x_{synthetic}} is formed which is to x 3 {\displaystyle x_{3}} what x 2 {\displaystyle x_{2}} is to x 1 {\displaystyle x_{1}} . A transformation is applied to x 1 {\displaystyle x_{1}} to make it more similar to x 2 {\displaystyle x_{2}} , the same transformation is then applied to x 3 {\displaystyle x_{3}} which generates x s y n t h e t i c {\displaystyle x_{synthetic}} . This approach was shown to improve performance of a Linear Discriminant Analysis classifier on three different datasets. Current research shows great impact can be derived from relatively simple techniques. For example, Freer observed that introducing noise into gathered data to form additional data points improved the learning ability of several models which otherwise performed relatively poorly. Tsinganos et al. studied the approaches of magnitude warping, wavelet decomposition, and synthetic surface EMG models (generative approaches) for hand gesture recognition, finding classification performance increases of up to +16% when augmented data was introduced during training. More recently, data augmentation studies have begun to focus on the field of deep learning, more specifically on the ability of generative models to create artificial data which is then introduced during the classification model training process. In 2018, Luo et al. observed that useful EEG signal data could be generated by Conditional Wasserstein Generative Adversarial Networks (GANs) which was then introduced to the training set in a classical train-test learning framework. The authors found classification performance was improved when such techniques were introduced. === Mechanical signals === The prediction of mechanical signals based on data augmentation brings a new generation of technological innovations, such as new energy dispatch, 5G communication field, and robotics control engineering. In 2022, Yang et al. integrate constraints, optimization and control into a deep network framework based on data augmentation and data pruning with spatio-temporal data correlation, and improve the interpretability, safety and controllability of deep learning in real industrial projects through explicit mathematical programming equations and analytical solutions.
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Phraselator
The Phraselator is a weatherproof handheld language translation device developed by Applied Data Systems and VoxTec, a former division of the military contractor Marine Acoustics, located in Annapolis, Maryland, USA. It was designed to serve as a handheld computer device that translates English into one of 40 different languages. == The device == The Phraselator is a small speech translation PDA-sized device designed to aid in interpretation. The device does not produce synthesized speech like that utilized by Stephen Hawking; instead, it plays pre-recorded foreign language MP3 files. Users can select the phrase they wish to convey from an English list on the screen or speak into the device. It then uses speech recognition technology called DynaSpeak, developed by SRI International, to play the proper sound file. The accuracy of the speech recognition software is over 70 percent according to software developer Jack Buchanan. The device can also record replies for translation later. Pre-recorded phrases are stored on Secure Digital flash memory cards. A 128 MB card can hold up to 12,000 phrases in four or five languages. Users can download phrase modules from the official website, which contained over 300,000 phrases as of March 2005. Users can also construct their own custom phrase modules. Earlier devices were known to have run on an SA-1110 Strong Arm 206 MHz CPU with 32MB SDRAM and 32MB onboard Flash RAM. A newer model, the P2, was released in 2004 and developed according to feedback from U.S. soldiers. It translates one way from English to approximately 60 other languages. It has a directional microphone, a larger library of phrases and a longer battery life. The 2004 release was created by and utilizes a computer board manufactured by InHand Electronics, Inc. In the future, the device will be able to display pictures so users can ask questions such as "Have you seen this person?" Developer Ace Sarich notes that the device is inferior to human interpreter. Conclusions derived from a Nepal field test conducted by U.S. and Nepal based NGO Himalayan Aid in 2004 seemed to confirm Sarich's comparisons: The very concept of using a machine as a communication point between individuals seemed to actually encourage a more limited form of interaction between tester and respondent. Usually, when limited language skills are present between parties, the genuine struggle and desire to communicate acts as a display of good will – we openly display our weakness in this regard – and the result is a more relaxed and human encounter. This was not necessarily present with the Phraselator as all parties abandoned learning about each other and instead focused on learning how to work with the device. As a tool for bridging any cultural differences or communicating effectively at any length, the Phraselator would not be recommended. This device, at least in the form tested, would best be used in large-scale operations where there is no time for language training and there is a need to communicate fixed ideas, quickly, over the greatest distance by employing large amounts of unskilled users. Large humanitarian or natural disasters in remote areas of third-world countries might be an effective example. == Origin == The original idea for the device came from Lee Morin, a Navy doctor in Operation Desert Storm. To communicate with patients, he played Arabic audio files from his laptop. He informed Ace Sarich, the vice president of VoxTec, about the idea. VoxTec won a DARPA Small Business Innovation Research grant in early 2001 to develop a military-grade handheld phrase translator. During its development, the Phraselator was tested and evaluated by scientists from the Army Research Laboratory. The device was first field tested in Afghanistan in 2001. By 2002, about 500 Phraselators were built for soldiers around the world with another 250 ordered by the U.S. Special Forces. The device cost $2000 to develop and could convert spoken English into one of 200,000 recorded commands and questions in 30 languages. However, the device could only translate one-way. At the time, the only existing two-way voice translator that could convert speech back and forth between languages was the Audio Voice Translation Guide System, or TONGUES, which was developed by Carnegie Mellon University for Lockheed Martin. As part of a DARPA program known as the Spoken Language Communication and Translation System for Tactical Use, SRI International has further developed two-way translation software for use in Iraq called IraqComm in 2006 which contains a vocabulary of 40,000 English words and 50,000 words in Iraqi Arabic. == Notable users == The handheld translator was recently used by U.S. troops while providing relief to tsunami victims in early 2005. About 500 prototypes of the device were provided to U.S. military forces in Operation Enduring Freedom. Units loaded with Haitian dialects have been provided to U.S. troops in Haiti. Army military police have used it in Kandahar to communicate with POWs. In late 2004, the U.S. Navy began to augment some ships with a version of the device attached to large speakers in order to broadcast clear voice instructions up to 400 yards (370 m) away. Corrections officers and law enforcement in Oneida County, New York, have tested the device. Hospital emergency rooms and health departments have also evaluated it. Several Native American tribes such as the Choctaw Nation, the Ponca, and the Comanche Nation have also used the device to preserve their dying languages. Various law enforcement agencies, such as the Los Angeles Police Department, also use the phraselator in their patrol cars. == Awards == In March 2004, DARPA director Dr. Tony Tether presented the Small Business Innovative Research Award to the VoxTec division of Marine Acoustics at DARPATech 2004 in Anaheim, CA. The device was recently listed as one of "Ten Emerging Technologies That Will Change Your World" in MIT's Technology Review. == Pop culture == Software developer Jack Buchanan believes that building a device similar to the fictional universal translator seen in Star Trek would be harder than building the Enterprise. The device was mentioned in a list of "Top 10 Star Trek Tech" on Space.com.
Secure element
A secure element (SE) is a secure operating system (OS) in a tamper-resistant processor chip or secure component. It can protect assets (root of trust, sensitive data, keys, certificates, applications) against high-level software and hardware attacks. Applications that process this sensitive data on an SE are isolated and so operate within a controlled environment not affected by software (including possible malware) found elsewhere on the OS. The hardware and embedded software meet the requirements of the Security IC Platform Protection Profile [PP 0084] including resistance to physical tampering scenarios described within it. More than 96 billion secure elements were produced and shipped between 2010 and 2021. SEs exist in various form factors, as devices such as smart cards, UICCs, or smart microSD cards, or embedded, or integrated, as parts of larger devices. SEs are an evolution of the chips in earlier smart cards, which have been adapted to suit the needs of numerous use cases, such as smartphones, tablets, set-top boxes, wearables, connected cars, and other internet of things (IoT) devices. The technology is widely used by technology firms such as Oracle, Apple and Samsung. SEs provide secure isolation, storage and processing for applications (called applets) they host while being isolated from the external world (e.g. rich OS and application processor when embedded in a smartphone) and from other applications running on the SE. Java Card and MULTOS are the most deployed standardized multi-application operating systems currently used to develop applications running on SEs. Since 1999, GlobalPlatform has been the body responsible for standardizing secure element technologies to support a dynamic model of application management in a multi-actor model. GlobalPlatform also runs Functional and Security Certification programmes for secure elements, and hosts a list of Functional Certified and Security Certified products. GlobalPlatform technology is also embedded in other standards such as ETSI SCP (now SET) since release 7. A Common Criteria Secure Element Protection Profile has been released targeting EAL4+ level with ALC_DVS.2 and AVA_VAN.5 extension to standardize the security features of a secure element across markets.
András Kornai
András Kornai (born 1957 in Budapest) is a mathematical linguist. == Education == Kornai is the son of economist János Kornai. He earned two PhDs with the first being in mathematics in 1983 from Eötvös Loránd University in Budapest, where his advisor was Miklós Ajtai. His second was in linguistics in 1991 from Stanford University, where his advisor was Paul Kiparsky. == Career == He is a professor in the Department of Algebra at the Budapest Institute of Technology, where he works on an open source Hungarian morphological analyzer. He was Chief Scientist at MetaCarta, where he worked on information extraction before the company was acquired by Nokia. Prior to MetaCarta, he was Chief Scientist at Northern Light. He is on the board of the journal Grammars and YourAmigo PLC. His research interests include all mathematical aspects of natural language processing, speech recognition, and OCR. As area editor he was responsible for the Mathematical Linguistics area of the Oxford International Encyclopedia of Linguistics, and his joint work with Geoffrey Pullum, "The X-bar Theory of Phrase Structure", formally reconstructed that then-popular linguistic theory. == Awards and honors == 2009: ACM Distinguished Member == Monographs == Semantics. Springer Nature, 2020. ISBN 978-3-319-65644-1 Mathematical Linguistics. Springer Verlag, in the series Advanced Information and Knowledge Processing, November 2007. ISBN 978-1-84628-985-9 Hardbound, approximately 300 pages. See description. Formal Phonology. In the series Outstanding Dissertations in Linguistics, Garland Publishing, 1994, ISBN 0-8153-1730-1, hardbound, 240 pages Contents, Preface, Introduction (20 pages) On Hungarian Morphology. In the series Linguistica, Hungarian Academy of Sciences, 1994, ISBN 963-8461-73-X, paperbound, 174 pages Contents, Preface, Introduction (10 pages) == Books edited == Oxford International Encyclopedia of Linguistics (Mathematical Linguistics Area Editor under Editor in Chief William Frawley). 4 volumes, Oxford University Press, 2003, ISBN 978-0-19-513977-8. Proceedings of the HLT-NAACL Workshop on the Analysis of Geographic References. Jointly with Beth Sundheim. Association for Computational Linguistics, 2003, ISBN 1-932432-04-3 (WS9), paperbound, vi+81 pages. See related material. Extended Finite State Models of Language (editor). In the series Studies in Natural Language Processing, Cambridge University Press, 1999, ISBN 0-521-63198-X, hardbound, x+278 pages Contents, Introduction (7 pages). == Selected papers == Digital Language Death. PLoS ONE 8(10): e77056, 2012. [1] Hunmorph: open source word analysis (Jointly with V. Tron, Gy. Gyepesi, P. Halacsy, L. Nemeth, and D. Varga). In Proc. ACL 2005 Software Workshop 77-85 [2] Leveraging the open source ispell codebase for minority language analysis (Jointly with P. Halacsy, L. Nemeth, A. Rung, I. Szakadat, and V. Tron). In J. Carson-Berndsen (ed): Proc. SALTMIL 2004 56-59 [3] Explicit Finitism, International Journal of Theoretical Physics 2003/2 301-307 [4] Mathematical Linguistics (Jointly with G.K. Pullum) In W. Frawley (ed): Oxford International Encyclopedia of Linguistics, Oxford University Press 2003, v3 17-20 [5] Optical Character Recognition, In W. Frawley (ed): Oxford International Encyclopedia of Linguistics, Oxford University Press 2003, v3 33-34 [6] How many words are there? Glottometrics 2002/4 61-86 [7] Zipf's law outside the middle range Proc. Sixth Meeting on Mathematics of Language University of Central Florida, 1999 347-356 [8] A Robust, Language-Independent OCR System. (Jointly with Z. Lu, I. Bazzi, J. Makhoul, P. Natarajan, and R. Schwartz) In: Robert J. Mericsko (ed): Proc. 27th AIPR Workshop: Advances in Computer-Assisted Recognition SPIE Proceedings 3584 1999 [9] Quantitative Comparison of Languages. Grammars 1998/2 155-165 [10] The generative power of feature geometry. Annals of Mathematics and Artificial Intelligence 8 1993 37-46 [11] The X-bar Theory of Phrase Structure. (Jointly with G.K. Pullum) Language 66 1990 24-50 [12]