Reviewerly/exHarmony huggingface dataset:
Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer

Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)—AlexNet, VGG, MobileNet, GoogLeNet, and ResNet—using a leave-two patient-out cross validation approach, and we evaluated the networks’ performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient’s response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.
Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users

Transliteration is very important in the Indian language context due to the usage of multiple scripts and the widespread use of romanized inputs. However, few training and evaluation sets are publicly available. We introduce Aksharantar, the largest publicly available transliteration dataset for Indian languages created by mining from monolingual and parallel corpora, as well as collecting data from human annotators. The dataset contains 26 million transliteration pairs for 21 Indic languages from 3 language families using 12 scripts. Aksharantar is 21 times larger than existing datasets and is the first publicly available dataset for 7 languages and 1 language family. We also introduce a test set of 103k word pairs for 19 languages that enables a fine-grained analysis of transliteration models on native origin words, foreign words, frequent words, and rare words. Using the training set, we trained IndicXlit, a multilingual transliteration model that improves accuracy by 15% on the Dakshina test set, and establishes strong baselines on the Aksharantar testset introduced in this work. The models, mining scripts, transliteration guidelines, and datasets are available at https://github.com/AI4Bharat/IndicXlit under open-source licenses.
SCA-LDPC: A Code-Based Framework for Key-Recovery Side-Channel Attacks on Post-quantum Encryption Schemes

Whereas theoretical attacks on standardized cryptographic primitives rarely lead to actual practical attacks, the situation is different for side-channel attacks. Improvements in the performance of side-channel attacks are of utmost importance. In this paper, we propose a framework to be used in key-recovery side-channel attacks on CCA-secure post-quantum encryption schemes. The basic idea is to construct chosen ciphertext queries to a plaintext checking oracle that collects information on a set of secret variables in a single query. Then a large number of such queries is considered, each related to a different set of secret variables, and they are modeled as a low-density parity-check code (LDPC code). Secret variables are finally determined through efficient iterative decoding methods, such as belief propagation, using soft information. The utilization of LDPC codes offers efficient decoding, source coding, and error correction benefits. It has been demonstrated that this approach provides significant improvements compared to previous work by reducing the required number of queries, such as the number of traces in a power attack. The framework is demonstrated and implemented in two different cases. On one hand, we attack implementations of HQC in a timing attack, lowering the number of required traces considerably compared to attacks in previous work. On the other hand, we describe and implement a full attack on a masked implementation of Kyber using power analysis. Using the ChipWhisperer evaluation platform, our real-world attacks recover the long-term secret key of a first-order masked implementation of Kyber-768 with an average of only 12 power traces.
Dual Pseudo-Labels Interactive Self-Training for Semi-Supervised Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) aims to match a specific person from a gallery of images captured from non-overlapping visible and infrared cameras. Most works focus on fully supervised VI-ReID, which requires substantial cross-modality annotation that is more expensive than the annotation in single-modality. To reduce the extensive cost of annotation, we explore two practical semi-supervised settings: uni-semi-supervised (annotating only visible images) and bi-semi-supervised (annotating partially in both modalities). These two semi-supervised settings face two challenges due to the large cross-modality discrepancies and the lack of correspondence supervision between visible and infrared images. Thus, it is diffi-cult to generate reliable pseudo-labels and learn modality-invariant features from noise pseudo-labels. In this paper, we propose a dual pseudo-label interactive self-training (DPIS) for these two semi-supervised VI-ReID. Our DPIS integrates two pseudo-labels generated by distinct models into a hybrid pseudo-label for unlabeled data. However, the hybrid pseudo-label still inevitably contains noise. To eliminate the negative effect of noise pseudo-labels, we introduce three modules: noise label penalty (NLP), noise correspondence calibration (NCC), and unreliable anchor learning (UAL). Specifically, NLP penalizes noise labels, NCC calibrates noisy correspondences, and UAL mines the hard-to-discriminate features. Extensive experimental results on SYSU-MM01 and RegDB demonstrate that our DPIS achieves impressive performance under these two semi-supervised settings.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation

We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions. Our approach consists of a powerful Action-conditioned motion TransFormer (ActFormer) under a GAN training scheme, equipped with a Gaussian Process latent prior. Such a design combines the strong spatio-temporal representation capacity of Transformer, superiority in generative modeling of GAN, and inherent temporal correlations from the latent prior. Furthermore, ActFormer can be naturally extended to multi-person motions by alternately modeling temporal correlations and human interactions with Transformer encoders. To further facilitate research on multi-person motion generation, we introduce a new synthetic dataset of complex multi-person combat behaviors. Extensive experiments on NTU-13, NTU RGB+D 120, BABEL and the proposed combat dataset show that our method can adapt to various human motion representations and achieve superior performance over the state-of-the-art methods on both single-person and multi-person motion generation tasks, demonstrating a promising step towards a general human motion generator. The project website can be found at https://liangxuy.github.io/actformer/.
Analysis of Online Question-Answering Forums as Heterogeneous Networks

Analyzing social interactions of CGM (Consumer Generated Media) is important for encouraging commnication among users. In the case of question-answering forums, users and QA boards constitute a heterogeneous network whose nodes are users/boards and edges are authorship. Discovering both user communities and board communities and finding correlations between them will clarify their characteristics. This paper describes an attempt for analyzing heterogeneous social networks obtained from Yahoo! Chiebukuro (Japanese Yahoo! Answers). A new measurement for the correlation between user communities and board communities is defined, and characteristics of discovered communities are analyzed using it.
A tale behind Mum Effect

Mum effect is a situation when one or more project stakeholders decide to withhold critical information for particular reasons. In software project where most of the production is intangible, the seriousness of this challenge increases exponentially. There have been reports indicating that mum effect can surface during any phase of development and ultimately lead to disaster in software projects. Mum effect can be influenced by several factors such as organizational and national cultures. This research investigates potential mum effect scenarios and reveals specific reasons which induce this challenge among information technology practitioners.
Incorporating Advice into Neuroevolution of Adaptive Agents

Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real time, making it possible to e.g. construct video games with adaptive agents. Often some of the desired behaviors for such agents are known, and it would make sense to prescribe them, rather than requiring evolution to discover them. This paper presents a technique for incorporating human-generated advice in real time into neuroevolution. The advice is given in a formal language and converted to a neural network structure through KBANN. The NEAT neuroevolution method then incorporates the structure into existing networks through evolution of network weights and topology. The method is evaluated in the NERO video game, where it makes learning faster even when the tasks change and novel ways of making use of the advice are required. Such ability to incorporate human knowledge into neuroevolution in real time may prove useful in several interactive adaptive domains in the future.
Representing Morals in Terms of Emotion

Morals are an important part of many stories, and central to why storytelling developed in the first place as a means of communication. They have the potential to provide a framework for developing story structure, which could be utilised by modern storytelling systems. To achieve this we need a general representation for morals. We propose patterns of character emotion as a suitable foundation. In this paper, we categorise Aesop’s fables based on the morals they convey, and use them as a source of emotion data corresponding to those morals. We use inductive logic programming to identify relationships between particular patterns of emotion and the morals of the stories in which they arise.
Data In Depth: Web 3 D Technologies Provide New Approaches To The Presentation Of Course Content

For nearly two decades, the Web has provided the classroom with vast, ever-expanding volumes of browser-accessible information.As the web has evolved so too has our desire to become more involved with the process of content-creation and content-sharing.Now new web-based technologies look to provide smarter, more meaningful content and present that content with a new level of depth and interactivity.No longer are faculty and students browsing for information that is largely static; instead, these users are interacting through their threedimensional (3-D) proxies (their avatars) and are querying applications (semantic web agents) soliciting them to collect, filter, verify, correlate, and present answers to their queries.Yet, all of this capability is not without potential challenges.There is an evolving need for faculty and students to find and build out new structure in their 3-D virtual surroundings that visually enables their content, making it more palatable to the user while presenting it in a 3-D format verses the typical 2-D format that has been the mainstay for the past two decades.With the maturation of virtual world (3-D Web) and semantic web technologies, the web-based content available in the classroom increases exponentially and takes on a new look.Following a brief overview of these two technologies and their overall impact in the classroom, this article presents several practical approaches for presenting course content in 3-D Web environments based on recent implementation efforts.In-World lectures and lab assignments, project team briefing sessions, student mentoring activities, and open conference forums are just a few of the areas discussed.Further discussions also focus on setup and future evaluation studies planned in the near-term to further evaluate course content presentation techniques.
Notice of Violation of IEEE Publication Principles: A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images

Notice of Violation of IEEE Publication Principles<br><br> "A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images" <br> by Swagatam Das, Satrajit Mukherjee, Bodhisattwa Prasad Majumder and Aritran Piplai Submitted to IEEE Transactions on Fuzzy System<br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br> This paper duplicates significant amounts of content from the original paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br> "An Optimal Fuzzy System for Edge Detection in Color Images using Bacterial Foraging Algorithm,"<br> by Om Verma<br> Submitted to IEEE Transactions on Evolutionary Computation, 12 April 2012<br><br> <br/> This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.
An Improved Artificial Fish-Swarm Algorithm and Its Applications

The convergence and accuracy of the artificial fish swarm algorithm (AFSA) depend not only on the algorithm parameters, but also on the four different behaviors of the fish swarm algorithm. This paper analyzes the influence mechanism of fish swarm algorithm's four behaviors: preying, swarming, following, and random moving behavior on the convergence speed and performance of the algorithm. Combined with the interval division of the bird swarm algorithm, the number of fish swarm of the four behaviors in the AFSA is modified, and a novel algorithm based on interval division (DAFSA) is proposed. Simulations in 5 test functions show that the DAFSA algorithm can quickly converge to a better global solution, has good convergence accuracy. Meanwhile, DAFSA has good practical value in solving the parameters of neural networks.
A Hardware Approach To Teaching Fsk

Many communication concepts are difficult for undergraduate students to internalize, but demonstrations and laboratory experiences are quite helpful.This paper describes how we teach the concept of frequency shift keying by using a highly successful combination of theory, demonstrations, lab exercises, and real-time DSP experiences that incorporate MATLAB and the Texas Instruments C67x digital signal processing starter kit.
Building Bing Developer Assistant

Software developers heavily rely on code snippets and API usage examples searched on the Internet. This paper presents Bing Code Search, a Visual Studio extension that allows developers to write, within an IDE, free-form natural language questions, and get C# code snippets answering those questions. Bing Code Search automatically adapts the suggested snippets into the user’s programming context via variable renaming, and records users’ interactions to improve its suggestions. Compared to prior related research, Bing Code Search provides a complete automation of the full search-paste-adapt process. Three weeks after we released this free extension, more than 20,000 users downloaded it; and they issue on average 3,000 queries per day. We believe that Bing Code Search is the most widely used tool in its category. In the following, we fully describe our framework, and draw clear empirical evidence of the benefits of Bing Code Search: (1) From our evaluation benchmark, compared with Bing’s result, Bing Code Search delivers more relevant snippet solutions. (2) In a controlled experiment, it was able to save developers 28% of time on completing API related tasks. (3) Telemetries collected from thousands of users show some users already built up the habit of using the tool: they issue multiple queries to solve a complex task, or use it as a fast auto-completion.
MISP taxonomy format

This document describes the MISP taxonomy format which describes asimple JSON format to represent machine tags (also called triple tags)vocabularies. A public directory of common vocabularies called MISPtaxonomies is available and relies on the MISP taxonomy format. MISPtaxonomies are used to classify cyber security events, threats,suspicious events, or indicators.
Engineering Project Team Training System (Eptts) For Effective Engineering Team Management

The BESTEAMS: Building Engineering Student Team Effectiveness and Management Systems Mini-Teaching Center has implemented a pilot, in-class, workshop highlighting the influence of learning style preferences on project team management.
Virtual Reality in Healthcare

The term "virtual reality" describes a computer-generated simulation that enables a person to replicate an interesting environment or experience utilising a VR headset.An simulated three-dimensional environment can be interacted with using a variety of electrical equipment with the aid of VR, such as special eyewear with a screen or gloves with sensors.Healthcare professionals and patients can interact with virtual reality systems that are designed for medical education (including training in simulative surgery), pain management, or rehabilitation.Healthcare is one of the top three areas that continues to lead the adoption of VR technology and will do so until 2025, according to the Goldman Sachs Global Investment Research.
Gathering and Synthesizing Information During the Development of User Requirements and Engineering Specifications

LIGHT). SARL focuses on the design, development, and evaluation of medical devices, especially for balance-impaired populations such as individuals with vestibular loss or advanced age.LIGHT focuses on the co-creative design of frugal
Development of a Virtual Teaching Assistant System Applying Agile Methodology

This research describes a case study of a project to develop a web-based Virtual Teaching Assistant System (ViTAS) for college students and instructors.ViTAS, a digital homework assignments submission and grading system, is an innovative idea to provide homework assignments grading system for the instructors and to provide an interactive learning environment outside the lecture period for the students.Using ViTAS, students will be able to submit the homework assignments online, to engage in anonymous homework review, and to discuss with their peers.The idea of students grading the homework assignments of their peers is very beneficial, because during the process of grading other's homework, the students get the opportunity to learn/review their own work/mistakes.Also, the instructors finalize all grades and post them on the website to confirm the students' learning activities.In particular, this project focuses on the Agile software development process, which is a group of software development methodologies based on iterative, interactive, and incremental development, where requirements and solutions evolve through collaboration between self-organizing and cross-functional teams; i.e., software development and user-experience design of the ViTAS.Also, the research considers that the teams are working cooperatively rather than collaboratively and this cooperation is implemented via articulation work, meaning that the developers had additional tasks to ensure that the user-experience design is accomplished.Overall, the paper provides an insight into a culture where developers and user experience designers work together, adds evidence to support the Agile principle of self-organizing teams and recommends the recognition of interaction tasks as valuable work in Agile software development.
Rapid Relevance Feedback Strategy Based on Distributed CBIR System

This article describes the capability of online data storage which has been enhanced by the emergence of cloud datacenter development. Distributed Hash Table (DHT) based image retrieval system using locality sensitive hash (LSH) has provided an efficient way to set up distributed Content Based Image Retrieval (CBIR) frameworks. However, with the fixed LSH function adopted, LSH and other codebook-based distributed retrieval systems are facing the problem of flexibility, and also are difficult to satisfy the user's demand. In this article, LRFMIR is proposed to introduce semantic search into DHT based CBIR system. LRFMIR is established on a DHT based network, where a flexible result truncating strategy is employed to fuse provided results by using multiple features measurements. Experiments show that LRFMIR provides a higher accuracy and recall rate than single feature employed retrieval systems, and possesses good load balancing and query efficiency performance.