Reviewerly/exHarmony huggingface dataset:
Improving Classification Performance on Rare Events in Data Starved Medical Applications

Predicting rare events is a critical task in many medical applications. Recent improvements in sensor technologies and superior computing power have enabled machine learning techniques to provide valuable data insights pertaining to patients’ symptoms and risk factors. However, for individual-level analysis, class imbalance and small sizes of training datasets for classification models are the main roadblocks to the full utilization of machine learning for prediction of rare events in data starved medical applications. While several techniques have been proposed in this field, little exists in the literature about their performance in data starved contexts. In this study, we propose a novel extension to synthetic minority oversampling techniques, called "Average Neighbor Vector Oversampling (ANVO)", which maintains the data distribution within the minority class but increases data diversity and keeps similar class boundaries. We systematically compare it with state-of-the-art data augmentation techniques using synthetic and real asthma patient datasets. Further, we present a transfer learning framework that integrates oversampling techniques and focuses on retraining a large data model with a small amount of specialized training data and is therefore well-suited to data starved contexts. Results from our experiments demonstrate that the proposed solutions improved the performance of classifiers with a significant increase, 23.7%-61.6%, in sensitivity compared with baseline models.
Identification of Fraudulent Reviews

This paper introduces a comprehensive system designed to bolster the trustworthiness of product reviews in e-commerce applications. Leveraging logistic regression, the system filters out fake reviews obtained through web scraping, providing users with an authentic product rating. The algorithm analyzes textual features to assign a probability score, effectively distinguishing genuine reviews from deceptive ones. The resultant authentic rating serves as a reliable metric for users navigating the crowded marketplace. In addition to enhancing review authenticity, the system integrates a comparative pricing feature. Multiple e-commerce links are scrutinized to compile and analyze pricing information, enabling users to make well-informed decisions based on both review credibility and cost-effectiveness. The user-friendly interface displays the authentic product rating alongside a graphical representation of the percentage of genuine and fake reviews, empowering consumers to interpret feedback reliability intuitively. The system contributes to e-commerce advancement by addressing the pervasive issue of fake reviews, offering users a sophisticated toolset for assessing product authenticity and making informed purchasing decisions. This research amalgamates machine learning, web scraping, and comparative analysis into a seamless framework, ultimately providing users with a holistic solution for navigating the intricacies of online shopping
CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition

Scene text recognition, as a cross-modal task involving vision and text, is an important research topic in computer vision. Most existing methods use language models to extract semantic information for optimizing visual recognition. However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text. To tackle this issue, we propose a novel cross-modal fusion network (CMFN) for irregular scene text recognition, which incorporates visual cues into the semantic mining process. Specifically, CMFN consists of a position self-enhanced encoder, a visual recognition branch and an iterative semantic recognition branch. The position self-enhanced encoder provides character sequence position encoding for both the visual recognition branch and the iterative semantic recognition branch. The visual recognition branch carries out visual recognition based on the visual features extracted by CNN and the position encoding information provided by the position self-enhanced encoder. The iterative semantic recognition branch, which consists of a language recognition module and a cross-modal fusion gate, simulates the way that human recognizes scene text and integrates cross-modal visual cues for text recognition. The experiments demonstrate that the proposed CMFN algorithm achieves comparable performance to state-of-the-art algorithms, indicating its effectiveness.
A general spammer indicator of rating systems uncovering rating preferences and bias

With the rapid development of e-commerce recently, massive spammers purposely distort the ranking of goods, which affects the market order and the fair competition of businesses seriously. Therefore, identifying such spammers is significant to rational decision making of customers. However, it is difficult to discriminate between normal users and malicious spammers on extremely large rating networks. In this paper, we propose a common indicator based on the historic rating records from spammers and normal users, which is widely applied to many existing methods. It is inspired by the idea that normal users have their preferences and rating bias shown in rating ladder, while spammers do not have such rating ladders in practice. Such an indicator is complement with other existing methods including Deviation-based Ranking (DR), Iterative Group-based Ranking (IGR) and Iterative Balance Ranking (IBR). Experimental study on three real rating networks shows that this indicator can significantly improve the accuracy of DR, IGR and IBR. To deal with malicious spammers, DR, IGR and IBR are improved by at least 9.38%, 2.90% and 2.53%, respectively. To deal with random spammers, DR, IGR and IBR are improved by at least by 5.52%, 17.12% and 32.24%, respectively.
Attention-Guided HDR Reconstruction for Enhancing Smart City Applications

In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address this limitation, this paper introduces HDRFormer, an innovative framework designed to enhance high dynamic range (HDR) image quality in edge–cloud-based video surveillance systems. Leveraging advanced deep learning algorithms and Internet of Things (IoT) technology, HDRFormer employs a unique architecture comprising a feature extraction module (FEM) and a weighted attention module (WAM). The FEM leverages a transformer-based hierarchical structure to adeptly capture multi-scale image information. In addition, the guided filters are utilized to steer the network, thereby enhancing the structural integrity of the images. On the other hand, the WAM focuses on reconstructing saturated areas, improving the perceptual quality of the images, and rendering the reconstructed HDR images with naturalness and color saturation. Extensive experiments on multiple HDR image reconstruction datasets demonstrate HDRFormer’s substantial improvements, achieving up to a 2.7 dB increase in the peak signal-to-noise ratio (PSNR) and an enhancement of 0.09 in the structural similarity (SSIM) compared to existing methods. In addition, the framework exhibits outstanding performance in multi-scale structural similarity (MS-SSIM) and HDR visual difference predictor (HDR-VDP2.2). The proposed method not only outperforms the existing HDR reconstruction techniques but also offers better generalization capabilities, laying a robust foundation for future applications in smart cities.
Insights on Transferring Software Engineering Scientific Knowledge to Practice

CONTEXT. In software engineering (SE), aligning research and practice has long been challenging. GOAL. To assist researchers in extracting practical issues from the practical knowledge repositories of SE and making scientific information about SE accessible to practitioners. METHOD. We conducted several empirical studies to determine the causes of the disconnect between research and practice, which makes it challenging for practitioners to seek out and apply scientific knowledge. RESULTS. We obtained data on practitioners' difficulties with finding, comprehending, and evaluating SE scientific knowledge that supported us in creating a set of eight heuristics for conducting practical research in SE.
How to Use Bibliometric Study for Writing a Paper: A Starter Guide

Bibliometric analysis is an essential statistical tool to map the state of the art in a given area of scientific knowledge. Bibliometric is one family of measures that uses a variety of approaches for counting publication, citation, co-citation, bibliographic coupling, keyword co-occurrence, and co-authorship networks. Bibliometric methods involve the use of several tools that can help researchers to identify a relevant and current research problem. Bibliometric paper can be written before writing a literature review article and at the introduction section of any research papers. Researcher who develops a research project based on bibliometric analysis has the possibility of presenting the objectives and methods of his work clearly and concisely. In this workshop, you will learn “How to Use Bibliometric Study for Writing a Paper”.
A Blockchain-Based Framework Solution to Enhance the Remote Assessment Process in HEIs

Major improvements in eLearning system can result from the powerful benefits of blockchain technology. Much personally identifiable information is passed between the student, the school, and third-party providers as part of the educational process. This requires a system that combines information security and the ability to send data across an extensive network in a purely virtual way. Blockchain appears to be tailor-made to assist in safeguarding and protecting this new education model. The reliability of a distance assessment system deserves the scientific community’s attention, which addresses the definition of assessment strategies and the adoption of technologies that promote a high degree of integrity of the distance assessment system. Enormous challenges are found and include the use of smart contracts, mitigation of plagiarism and fraud, copyright, authenticity, integrity, and immutability of academic assessment documents. The main goal of this research paper is to create a blockchain framework that enables the secure exchange of sensitive information in the remote assessment of a Higher Education Institution environment while maintaining the data's authenticity, immutability, and overall security, to prevent e-cheating.
A multimedia art interaction model for motion recognition based on data-driven model

Ergonomics is a relatively important part of user experience in multimedia art design. The study aims to improve human–computer interaction efficiency using a data-driven neural network model combined with video data and wearable devices to achieve high-precision human motion recognition. Firstly, the human motion skeleton information is extracted based on video information through the OpenPose framework, and the motion characteristics of each joint of the human body are calculated. Then, use the inertia data of the wearable bracelet to calculate the exercise intensity. At last, the two kinds of information are sent to recurrent neural networks (RNN) together to achieve high-precision human motion recognition. The innovative contribution of the article is to establish a multimodal fusion model for human activity recognition. The experimental results show that the recognition precision of the proposed method reaches 97.85%, much higher than the backpropagation neural network (BPNN) and K-nearest neighbor (KNN), whose precision is 94.35% and 90.12%, respectively. The method’s superior performance convinces us that the model can provide strong technical support for the interaction design of multimedia art.
An explorative single-arm clinical study to assess craving in patients with alcohol use disorder using Virtual Reality exposure (CRAVE)—study protocol

Abstract Background Alcohol use disorder (AUD) belongs to the most burdensome clinical disorders worldwide. Current treatment approaches yield unsatisfactory long-term effects with relapse rates up to 85%. Craving for alcohol is a major predictor for relapse and can be intentionally induced via cue exposure in real life as well as in Virtual Reality (VR). The induction and habituation of craving via conditioned cues as well as extinction learning is used in Cue Exposure Therapy (CET), a long-known but rarely used strategy in Cognitive Behavioral Therapy (CBT) of AUD. VR scenarios with alcohol related cues offer several advantages over real life scenarios and are within the focus of current efforts to develop new treatment options. As a first step, we aim to analyze if the VR scenarios elicit a transient change in craving levels and if this is measurable via subjective and psychophysiological parameters. Methods A single-arm clinical study will be conducted including n = 60 patients with AUD. Data on severity of AUD and craving, comorbidities, demographics, side effects and the feeling of presence in VR will be assessed. Patients will use a head-mounted display (HMD) to immerse themselves into three different scenarios (neutral vs. two target situations: a living room and a bar) while heart rate, heart rate variability, pupillometry and electrodermal activity will be measured continuously. Subjective craving levels will be assessed before, during and after the VR session. Discussion Results of this study will yield insight into the induction of alcohol craving in VR cue exposure paradigms and its measurement via subjective and psychophysiological parameters. This might be an important step in the development of innovative therapeutic approaches in the treatment of patients with AUD. Trial registration This study was approved by the Charité—Universitätsmedizin Berlin Institutional Review Board (EA1/190/22, 23.05.2023). It was registered on ClinicalTrials.gov (NCT05861843).
Proving Calculational Proofs Correct

Teaching proofs is a crucial component of any undergraduate-level program that covers formal reasoning.We have developed a calculational reasoning format and refined it over several years of teaching a freshman-level course, "Logic and Computation", to thousands of undergraduate students.In our companion paper [28], we presented our calculational proof format, gave an overview of the calculational proof checker (CPC) tool that we developed to help users write and validate proofs, described some of the technical and implementation details of CPC and provided several publicly available proofs written using our format.In this paper, we dive deeper into the implementation details of CPC, highlighting how proof validation works, which helps us argue that our proof checking process is sound.
Verification of a Rust Implementation of Knuth's Dancing Links using ACL2

Dancing Links" connotes an optimization to a circular doubly-linked list data structure implementation which provides for fast list element removal and restoration.The Dancing Links optimization is used primarily in fast algorithms to find exact covers, and has been popularized by Knuth in Volume 4B of his seminal series The Art of Computer Programming.We describe an implementation of the Dancing Links optimization in the Rust programming language, as well as its formal verification using the ACL2 theorem prover.Rust has garnered significant endorsement in the past few years as a modern, memory-safe successor to C/C++ at companies such as Amazon, Google, and Microsoft, and is being integrated into both the Linux and Windows operating system kernels.Our interest in Rust stems from its potential as a hardware/software co-assurance language, with application to critical systems.We have crafted a Rust subset, inspired by Russinoff's Restricted Algorithmic C (RAC), which we have imaginatively named Restricted Algorithmic Rust, or RAR.In previous work, we described our initial implementation of a RAR toolchain, wherein we simply transpile the RAR source into RAC.By so doing, we leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort.In this paper, we describe the RAR Rust subset, describe our improved prototype RAR toolchain, and detail the design and verification of a circular doublylinked list data structure employing the Dancing Links optimization in RAR, with full proofs of functional correctness accomplished using the ACL2 theorem prover.
Analysis of the amplitude form of the quantum hash function

In this article, the properties of quantum hash functions are further explored. Previous findings show that so-called small-bias sets (special subsets of the set of elements of a cyclic group) generate a “phase” quantum hash function. Here, it was proved that they also generate an “amplitude” quantum hash function. Namely, it turned out that constructing small-bias sets while generating amplitude quantum functions yields a well-balanced combination of the cryptographic properties of unidirectionality and collision resistance. As a corollary of the obtained theorem, a general statement about the generation of new amplitude quantum hash functions based on universal hash families and small-bias sets was proved.
EXPLORING EFL STUDENTS' VIEWS ON USING YOUTUBE VIDEOS FOR IMPROVING LISTENING SKILLS IN ENGLISH

English videos on YouTube can be utilized as an alternative medium for learning listening subjects. This research attempts to explore EFL students’ perspectives on using English videos on YouTube as a learning medium to enhance students’ listening skills. A mix-method research design was used to explore students’ perceptions of using English video to enhance their listening skills. The data in this study were obtained from questionnaires and semi-structured interviews which were administered online to thirty-two students of the English Language Education Study Program (ELESP) at Universitas Teknologi Yogyakarta using a convenience sampling technique. The findings revealed that of 32 respondents, 31 of them viewed the English video platform on YouTube to help them a lot in learning listening subjects. Of 32 respondents, 30 of them utilized the platform for learning vocabulary. Of 32 respondents, 29 of them thought that their listening skills improved a lot after watching and listening to videos on YouTube. It indicates that the majority of respondents have positive perceptions of the utilization of this platform because it helps them in facilitating their learning needs. However, 2 respondents disagree about the statements for some reasons.
A Comprehensive and Credible Assessment Method for Model Adversarial Robustness under Modulated Signal Identification

In complex electromagnetic environments, accurately identifying electromagnetic signals is crucial, but these applications are vulnerable to security threats from adversarial attacks. Therefore, assessing the adversarial robustness of models is imperative to build robust models and enhance the security of intelligent systems. In this paper, we introduce an adversarial robustness evaluation system centered on a modulated signal recognition model. Our proposed method combines data and model layers to provide a comprehensive evaluation, and we introduce an adversarial robustness scoring index for a reliable assessment of the model’s robustness against adversarial attacks. Utilizing this evaluation system, we can effectively appraise the model’s adversarial robustness and bolster the security of electromagnetic signal identification systems.
Enforcement of DNN with LDA-PCA-ELM for PIE Invariant Few-Shot Face Recognition

Face Recognition has been an active area of research but limited work has been done in the domains where the captured face images are of very low resolution, blurred and occluded. The other challenges in the domains like face recognition at a distance or unconstrained face recognition, include pose and expression variations. Additionally, for use cases like person re-identification and surveillance, the availability of limited amount of annotated data for various classes, makes it a highly probable candidate for Few-shot face recognition. In this work, we present a deep learning architecture that uses Deep Neural Networks (DNN) along with Extreme Learning Machines (ELM) to address the problem of generating compact and discriminating face representations, while accounting for the few-shot constraints of the novel dataset. We have used three types of ELMs namely, PCA-ELM, LDA-ELM and LDA-PCA-ELM, to augment the DNN and achieve better accuracy with lower computational cost at the time of inference.
Diagnosing AI Explanation Methods with Folk Concepts of Behavior

We investigate a formalism for the conditions of a successful explanation of AI. We consider “success” to depend not only on what information the explanation contains, but also on what information the human explainee understands from it. Theory of mind literature discusses the folk concepts that humans use to understand and generalize behavior. We posit that folk concepts of behavior provide us with a “language” that humans understand behavior with. We use these folk concepts as a framework of social attribution by the human explainee—the information constructs that humans are likely to comprehend from explanations—by introducing a blueprint for an explanatory narrative (Figure 1) that explains AI behavior with these constructs. We then demonstrate that many XAI methods today can be mapped to folk concepts of behavior in a qualitative evaluation. This allows us to uncover their failure modes that prevent current methods from explaining successfully—i.e., the information constructs that are missing for any given XAI method, and whose inclusion can decrease the likelihood of misunderstanding AI behavior.
Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering

Abstract Transformer-based language models have been shown to be highly effective for several NLP tasks. In this paper, we consider three transformer models, BERT, RoBERTa, and XLNet, in both small and large versions, and investigate how faithful their representations are with respect to the semantic content of texts. We formalize a notion of semantic faithfulness, in which the semantic content of a text should causally figure in a model's inferences in question answering. We then test this notion by observing a model's behavior on answering questions about a story after performing two novel semantic interventions—deletion intervention and negation intervention. While transformer models achieve high performance on standard question answering tasks, we show that they fail to be semantically faithful once we perform these interventions for a significant number of cases (∼ 50% for deletion intervention, and ∼ 20% drop in accuracy for negation intervention). We then propose an intervention-based training regime that can mitigate the undesirable effects for deletion intervention by a significant margin (from ∼ 50% to ∼ 6%). We analyze the inner-workings of the models to better understand the effectiveness of intervention-based training for deletion intervention. But we show that this training does not attenuate other aspects of semantic unfaithfulness such as the models' inability to deal with negation intervention or to capture the predicate-argument structure of texts. We also test InstructGPT, via prompting, for its ability to handle the two interventions and to capture predicate-argument structure. While InstructGPT models do achieve very high performance on predicate-argument structure task, they fail to respond adequately to our deletion and negation interventions.