Reviewerly/exHarmony huggingface dataset:
A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris

Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.
Superradiant Detection of Microscopic Optical Dipolar Interactions

The interaction between light and cold atoms is a complex phenomenon potentially featuring many-body resonant dipole interactions. A major obstacle toward exploring these quantum resources of the system is macroscopic light propagation effects, which not only limit the available time for the microscopic correlations to locally build up, but also create a directional, superradiant emission background whose variations can overwhelm the microscopic effects. In this Letter, we demonstrate a method to perform "background-free" detection of the microscopic optical dynamics in a laser-cooled atomic ensemble. This is made possible by transiently suppressing the macroscopic optical propagation over a substantial time, before a recall of superradiance that imprints the effect of the accumulated microscopic dynamics onto an efficiently detectable outgoing field. We apply this technique to unveil and precisely characterize a density-dependent, microscopic dipolar dephasing effect that generally limits the lifetime of optical spin-wave order in ensemble-based atom-light interfaces.
Deep learning based phishing website detection

Phishing attacks use fraudulent websites that trick people into disclosing sensitive information. More effective and precise methods are required to identify phishing websites so that people and organisations can be protected from the damaging effects of these online threats. The aim of this work is to develop a model that can identify phishing uniform resource locator (URLs) more accurately than current approaches while requiring less training time, testing time, and storage space. This research work proposes a novel method for identifying phishing websites using a long short-term memory (LSTM) gated recurrent unit (GRU) algorithm to detect phishing URLs. The accuracy of the suggested method is 98.89%, which is significantly better than the findings of earlier studies. The model also showed a need for shorter training and testing time, and a reduced amount of storage space.
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction

Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data. Despite the progress in joint learning from pathology and genomics, existing methods still suffer from challenging issues: 1) Due to the large size of pathological images, it is difficult to effectively represent the gigapixel whole slide images (WSIs). 2) Interactions within tumor microenvironment (TME) in histology are essential for survival analysis. Although current approaches attempt to model these interactions via co-attention between histology and genomic data, they focus on only dense local similarity across modalities, which fails to capture global consistency between potential structures, i.e. TME-related interactions of histology and co-expression of genomic data. To address these challenges, we propose a Multimodal Optimal Transport-based Co-Attention Transformer framework with global structure consistency, in which optimal transport (OT) is applied to match patches of a WSI and genes embeddings for selecting informative patches to represent the gigapixel WSI. More importantly, OT-based co-attention provides a global awareness to effectively capture structural interactions within TME for survival prediction. To overcome high computational complexity of OT, we propose a robust and efficient implementation over micro-batch of WSI patches by approximating the original OT with unbalanced mini-batch OT. Extensive experiments show the superiority of our method on five benchmark datasets compared to the state-of-the-art methods. The code is released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation

Semi-supervised learning (SSL) has recently demonstrated great success in medical image segmentation, significantly enhancing data efficiency with limited annotations. However, despite its empirical benefits, there are still concerns in the literature about the theoretical foundation and explanation of semi-supervised segmentation. To explore this problem, this study first proposes a novel causal diagram to provide a theoretical foundation for the mainstream semi-supervised segmentation methods. Our causal diagram takes two additional intermediate variables into account, which are neglected in previous work. Drawing from this proposed causal diagram, we then introduce a causality-inspired SSL approach on top of co-training frameworks called CauSSL, to improve SSL for medical image segmentation. Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature. We then propose a novel statistical quantification of the uncomputable algorithmic independence and further enhance the independence via a min-max optimization process. Our method can be flexibly incorporated into different existing SSL methods to improve their performance. Our method has been evaluated on three challenging medical image segmentation tasks using both 2D and 3D network architectures and has shown consistent improvements over state-of-the-art methods. Our code is publicly available at: https://github.com/JuzhengMiao/CauSSL.
Learning to Upsample by Learning to Sample

We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Code is available at https://github.com/tiny-smart/dysample.
FPR: False Positive Rectification for Weakly Supervised Semantic Segmentation

Many weakly supervised semantic segmentation (WSSS) methods employ the class activation map (CAM) to generate the initial segmentation results. However, CAM often fails to distinguish the foreground from its co-occurred background (e.g., train and railroad), resulting in inaccurate activation from the background. Previous endeavors address this co-occurrence issue by introducing external supervision and human priors. In this paper, we present a False Positive Rectification (FPR) approach to tackle the co-occurrence problem by leveraging the false positives of CAM. Based on the observation that the CAM-activated regions of absent classes contain class-specific co-occurred background cues, we collect these false positives and utilize them to guide the training of CAM network by proposing a region-level contrast loss and a pixel-level rectification loss. Without introducing any external supervision and human priors, the proposed FPR effectively suppresses wrong activations from the background objects. Extensive experiments on the PASCAL VOC 2012 and MS COCO 2014 demonstrate that FPR brings significant improvements for off-the-shelf methods and achieves state-of-the-art performance. Code is available at https://github.com/mt-cly/FPR.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning

Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limited and only constrained to the classification task. In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as Point-CLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection. To better align 3D data with the pre-trained language knowledge, Point-CLIP V2 contains two key designs. For the visual end, we prompt CLIP via a shape projection module to generate more realistic depth maps, narrowing the domain gap between projected point clouds with natural images. For the textual end, we prompt the GPT model to generate 3D-specific text as the input of CLIP’s textual encoder. Without any training in 3D domains, our approach significantly surpasses PointCLIP by +42.90%, +40.44%, and +28.75% accuracy on three datasets for zero-shot 3D classification. On top of that, V2 can be extended to few-shot 3D classification, zero-shot 3D part segmentation, and 3D object detection in a simple manner, demonstrating our generalization ability for unified 3D open-world learning. Code is available at https://github.com/yangyangyang127/PointCLIP_V2.
Fast Generation of Heterogeneous Mental Models from Longitudinal Data by Combining Genetic Algorithms and Fuzzy Cognitive Maps

Models that capture the heterogeneous perspectives of individuals are essential to test tailored interventions, such as behavior change interventions. Although Fuzzy Cognitive Maps (FCMs) have a rich history in depicting systems, they were either developed at an individual level through facilitated sessions, or created for an entire population through machine learning. The need to automatically create individual FCMs from data has started to be addressed, but the proposed solution was computationally prohibitive and thus could not be deployed over a large population. In this work, we use a state-of-the-art evolutionary algorithm (CMA-ES) to create individual FCMs by leveraging the growing availability of longitudinal data. We demonstrate on a real-world case study that our solution is both accurate and fast to compute. Our experiments on synthetic data also show that our approach can scale to a large number of measurements, but it cannot currently be applied to highly noisy datasets.
nuWar: A Prototype Sketch-Based Strategy Game

Today's military strategy games provide unrealistic interfaces for players to interact with their units: Commanders don't use mice and menus, they sketch. Developing strategy games currently involves grafting AI capabilities on top of a separate simulation engine, with new hand-crafted strategies for each game. We are experimenting with a novel approach for solving both problems. We started with nuSketch Battlespace, a knowledge-rich sketch understanding system developed for military users, and built a game engine, nuWar, on top of it. nuWar is a prototype two-player tactical war game which can be played either hot-seat or over a network. nuWar uses sketching as the primary way for players to express their intent to their subordinate commanders. The underlying ontology used by nuSketch Battlespace is used in both the simulation engine and in the bots which serve as subordinate commanders. We describe the architecture of nuWar, focusing on how it uses sketching, how the simulation engine is built upon the rich representational facilities of nuSketch Battlespace, and how the bots work. We discuss the tradeoffs we have found in this approach so far, and describe our plans for future work.
An Automatic Classification of Book Texts to User-Defined Tags

We describe work on automatically assigning labels to books using user-defined tags as the label set. Using supervised learning and exploring both binary and multiclass classification, we train and test classifiers on several sets of features, focusing on the size of the sets, part-of-speech classes and named entities. Results indicate that a binary classifier, trained and tested on a feature space that consists of a limited selection of parts of speech as well as all frequent named entities, achieves a classification precision of 81%, significantly outperforming a baseline which assigns the top-10 most popular tags to each book.
Modeling Unit Classes as Agents in Real-Time Strategy Games

We present CLASSQL, a multi-agent model for playing real-time strategy games, where learning and control of our own team’s units is decentralized; each agent uses its own reinforcement learning process to learn and control units of the same class. Coordination between these agents occurs as a result of a common reward function shared by all agents and synergistic relations in a carefully crafted state and action model for each class. We present results of CLASSQL against the built-in AI in a variety of maps using the Wargus real-time strategy game.
StYLiD: Structure Your Own Linked Data

Information sharing can be effective with structured data. The Semantic Web is mainly aimed at creating standard ontologies. However, users have different preferences and evolving requirements. It is not practical to attempt perfect schema definitions with strict constraints. Creating structured formats should be a collaborative and evolutionary process. Social software motivates wide participation by providing easy interface. We propose a system called StYLiD for sharing wide variety of structured information. Users freely define their own structured concepts. The system consolidates different versions defined by different users. Combined search and browsing can be done. Popular concepts gradually emerge from the concept cloud and stabilize. Concept definitions are flexible. The suggestive range for attribute values does not constrain the contributors. StYLiD generates unique dereferenceable URIs so that data items can form a linked data web. Structured data is embedded using RDFa.
A Course In Computer Security For Criminal Justice Majors

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1661 A Course In Computer Security For Criminal Justice Majors John Sokol General Engineering Penn State Fayette Uniontown, PA 15601 Introduction Computers have grown as a crime factor in the worlds of both public security and private security. In recent remarks, John Ashcroft, Attorney General, stated: “Although there are no exact figures on the costs of cybercrime in America, estimates run into the billions of dollars each year. And unlike more traditional crimes, cybercrime is especially difficult to investigate.1” Computers are used to facilitate traditional crimes, such as child pornography, and are also used to attack networks in a business which, in turn, shuts down the business2. Dr. Richard Ball, a Professor of Administration Of Justice at Penn State Fayette, is also a member of the Advisory Board for the Training and Research Institute of the National White-Collar Crime Conference. Dr. Ball, emerging from the White-Collar Conference meetings, encouraged the development of a computer security course tailored for non- technical students majoring in criminal justice. Since 1992, we have offered a course at Penn State Fayette using the topic of alarm systems to cover topics in analog and digital electronics3. This earlier course includes a short module on computer security. We are not training electronic specialists, but security professionals who are consumers of these electronic services. In the case of computer security, the objective is to give criminal justice students a broad background in computer security and to enhance their understanding of computer crime and computer security. For example, computer forensics is “the process of unearthing data of probative value from computer and information systems4”. A course in computer forensics is suitable for computer science students, but computer forensics in-depth is not suitable for criminal justice students. Instead, this new course will train the criminal justice major to become an intelligent consumer of the computer forensics services provided by technical experts. “Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright © 2003 American Society for Engineering Education”
A Technology Assisted Simulation Of Distributed Team It Solution Development

Using HP mobile technology, students transformed their vision of teamwork then designed and implemented IT solutions addressing challenges posed through lecture and lab.In addition, faculty members not immediately involved in the grant learned how to use technology by seeing its use in the project classroom.The goals of this study were how students' collaborative and dynamic use of technology will transform their vision of teamwork to solve problems and determine the impact of the Tablet PC in improving students' learning.
A Tentative Proposal for Machine Assisted Human Translation (MAHT) – Tool-Specific General Text Typology

The aim of the present article is to contend the widespread but largely unfounded claim that MAHT tools (popularly known as translator ’s work-benches) are best suited for translating so-called technical texts. The article calls for establishing a separate MAHT tool-specific text typology by pre-senting what are – in the author ’s opinion – the most important features characteristic of an MAHT-suitable text. It transpires that it is difficult to simply relate this particular type of text to any of the existing classifications of translation-related or general text typologies (such as those advocated by, for example, Hatim &amp; Mason 1990; Reiß 1983; Snell-Hornby 1988 or Kussmaul 1997) since the existing methodologies do not take MAHT-specif¬ic attributes into account. Therefore, the present article calls for an empi¬rical, corpus-based study that would help establish the relation between the proposed underlying features and actual text types as described elsewhere.
Writing and Editing Online Information

The reasons for moving information from print to online include greater usability, greater economy, and more efficient use of technology. So the first reason for the trend to online information is the possibility of software products that are easier to use and easier to learn. Perhaps because of lack of the necessary computer equipment, or because of inexperience with computers, they attempt to write and edit online information as if they were producing manuals. Then re-edit as often as necessary and be certain to get a final look at the online information before the product goes to manufacturing. Skilled writers and editors are crucial in the development of online information and the user interface of software products. At this time, there are few experts in online information development. An important principle in the planning and design of a software product, and one that writers and editors can assist with, is to base the design on an analysis of user tasks.
Cal Poly's multimedia approach to research

During the past years, libraries such as Ohio State University and Cornell University has siought to develop library instruction programs to teach the elements of a search strategiy and access to local paper and automated sources. The California Polytechnic State University Library has taken this idea one step further by developing a multisensy, multimedia application for teaching the use of print an Internet-accessible sources
What Questions Do Journalists Ask on Twitter?

Social media platforms are a major source of information for both the general public and for journalists. Journalists use Twitter and other social media services to gather story ideas, to find eyewitnesses, and for a wide range of other purposes. One way in which journalists use Twitter is to ask questions. This paper reports on an empirical investigation of questions asked by Arab journalists on Twitter. The analysis begins with the development of an ontology of question types, proceeds to human annotation of training and test data, and concludes by reporting the level of accuracy that can be achieved with automated classification techniques. The results show good classifier effectiveness for high prevalence question types, but that obtaining sufficient training data for lower prevalence question types can be challenging.
Common Interface Extension YANG Data Models

This document defines two modules that augment the Interfacesdata model defined in the YANG Data Model for Interface Managementwith additional configuration and operational data nodes to supportcommon lower layer interface properties, such as interface MTU. TheYANG data model in this document conforms to the Network ManagementDatastore Architecture (NMDA) defined in RFC 8342.
Sensor-Seeded Cryptographically Secure Random Number Generation

Random numbers are essential to generate secret keys, initialization vector, one-time pads, sequence number for packets in network and many other applications. Though there are many Pseudo Random Number Generators available they are not suitable for highly secure applications that require high quality randomness. This paper proposes a cryptographically secure pseudorandom number generator with its entropy source from sensor housed on mobile devices. The sensor data are processed in 3-step approach to generate random sequence which in turn fed to Advanced Encryption Standard algorithm as random key to generate cryptographically secure random numbers.