Reviewerly/exHarmony huggingface dataset:
Parallel Adaptive Stochastic Gradient Descent Algorithms for Latent Factor Analysis of High-Dimensional and Incomplete Industrial Data

Latent factor analysis (LFA) is efficient in knowledge discovery from a high-dimensional and incomplete (HDI) matrix frequently encountered in industrial big data-related applications. A stochastic gradient descent (SGD) algorithm is commonly adopted as a learning algorithm for LFA owing to its high efficiency. However, its sequential nature makes it less scalable when processing large-scale data. Although alternating SGD decouples an LFA process to achieve parallelization, its performance relies on its hyper-parameters that are highly expensive to tune. To address this issue, this paper presents three extended alternating SGD algorithms whose hyper-parameters are made adaptive through particle swarm optimization. Correspondingly, three Parallel Adaptive LFA (PAL) models are proposed and achieve highly efficient latent factor acquisition from an HDI matrix. Experiments have been conducted on four HDI matrices collected from industrial applications, and the benchmark models are LFA models based on state-of-the-art parallel SGD algorithms including the alternative SGD, Hogwild!, distributed gradient descent, and sparse matrix factorization parallelization. The results demonstrate that compared with the benchmarks, with 32 threads, the proposed PAL models achieve much speedup gain. They achieve the highest prediction accuracy for missing data on most cases. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —HDI data are commonly encountered in many industrial big data-related applications, where rich knowledge and patterns can be extracted efficiently. An SGD based-LFA model is popular in addressing HDI data due to its efficiency. Yet when dealing with large-scale HDI data, its serial nature greatly reduces its scalability. Although alternating SGD can decouple an LFA process to implement parallelization, its performance depends on its hyper-parameter whose tuning is tedious. To address this vital issue, this study proposes three extended alternating SGD algorithms whose hyper-parameters are made via through a particle swarm optimizer. Based on them, three models are realized, which are able to efficiently obtain latent factors from HDI matrices. Compared with the existing and state-of-the-art models, they enjoy their hyper-parameter-adaptive learning process, as well as highly competitive computational efficiency and representation learning ability. Hence, they provide practitioners with more scalable solutions when addressing large HDI data from industrial applications.
PEDAL: A Power Efficient GCN Accelerator with Multiple DAtafLows

Graphs are ubiquitous in many application domains due to their ability to describe structural relations. Graph Convolutional Networks (GCNs) have emerged in recent years and are rapidly being adopted due to their capability to perform Machine Learning (ML) tasks on graph-structured data. GCN exhibits irregular memory accesses due to the lack of locality when accessing graph-structured data. This makes it hard for general-purpose architectures like CPUs and GPUs to fully utilize their computing resources. In this paper, we propose PEDAL, a power-efficient accelerator for GCN inference supporting multiple dataflows. PEDAL chooses the best-fit dataflow and phase ordering based on input graph characteristics and GCN algorithm, achieving both efficiency and flexibility. To achieve both high power efficiency and performance, PEDAL features a light-weight processing element design. PEDAL achieves 144.5x, 9.4x, and 2.6x speedup compared to CPU, GPU, and HyGCN, respectively, and 8856x, 1606x, 8.4x, and 1.8x better power efficiency compared to CPU, GPU, HyGCN, and EnGN, respectively.
Multi-Task Momentum Distillation for Multimodal Sentiment Analysis

In the field of Multimodal Sentiment Analysis (MSA), the prevailing methods are devoted to developing intricate network architectures to capture the intra- and inter-modal dynamics, which necessitates numerous parameters and poses more difficulties in terms of interpretability in multimodal modeling. Besides, the heterogeneous nature of multiple modalities (text, audio, and vision) introduces significant modality gaps, thereby making multimodal representation learning an ongoing challenge. To address the aforementioned issues, by considering the learning process of modalities as multiple subtasks, we propose a novel approach named Multi-Task Momentum Distillation (MTMD) which succeeds in reducing the gap among different modalities. Specifically, according to the abundance of semantic information, we treat the subtasks of textual and multimodal representations as the teacher networks while the subtasks of acoustic and visual representations as the student ones to present knowledge distillation, which transfers the sentiment-related knowledge guided by the regression and classification subtasks. Additionally, we adopt unimodal momentum models to explore modality-specific knowledge deeply and employ adaptive momentum fusion factors to learn a robust multimodal representation. Furthermore, we provide a theoretical perspective of mutual information maximization by interpreting MTMD as generating sentiment-related views in various ways. Extensive experiments illustrate the superiority of our approach compared with the state-of-the-art methods in MSA.
Large Language Models

In December 2015, a group of tech veterans – including Elon Musk, Reid Hoffman, Peter Thiel, and Sam Altman – founded OpenAI. They pledged over $1 billion for the venture.
Transformation through digitalization of higher education - update and need in ensuring the creativity of university teachers and the competitiveness of the university

The topical issue is the awareness of the university environment of the need to adopt a new vision on the digitalization of higher education due to and / or due to the Pandemic, which forced universities to survive many crises. However, thanks to digitalisation, higher education is becoming more accessible. Certainly, digitization generates benefits, but also challenges that the university environment has to face. The purpose of this article is to highlight the role and impact of stakeholders involved in the digital transformation of higher education, thus encouraging the creativity of university teachers and ensuring the competitiveness of the university. A „barometer” of appreciation of the digital performance of the management team, of the university teachers, of the quality of the educational services could be, for the beginning, a Checklist for self-evaluation of the digital potential of the institution and of the university teachers. Stakeholders interested in the digital transformation of higher education should therefore take action if they really want to capitalize on the university environment in order to ensure its sustainability in the following dimensions: education, research and development; internationalization of education; research, consulting and partnerships - all aimed at creative, intelligent, so SMART digital cognitive and behavioral skills.
Meeting Ourselves or Other Sides of Us?—Meta-Analysis of the Metaverse

We were promised that the Metaverse would revolutionize our lives, social interactions, work, and business. However, how and when will this happen? We have seen the growth and development of technology, but there is no agreement or prediction about a specific time, and we can only follow the how question. To investigate more leads about this concept, we considered a main research question: How is the Metaverse actually being perceived? This question is connected with three objectives: to verify how the Metaverse is being represented and characterized, identify the main dimensions that facilitate or influence the acceptance of the Metaverse, and identify the leading technologies that suit the Metaverse concept. This study consisted of a documental analysis—or meta-analysis—of fifty of the most relevant scientific papers (taking into account some inclusion criteria) published in the last three years, using the Leximancer software to create concept maps to illustrate the main concepts and themes extracted from the articles to understand their associations or relations with the Metaverse concept. This study provided us with essential findings about how this concept has been perceived and allowed us to answer our objectives, contributing to a scientific discussion on the topic, and provided some valid suggestions for future research, which is already in progress. It also provided new leads on approaching this concept in development.
BERT for Complex Systematic Review Screening to Support the Future of Medical Research

This work presents a Natural Language Processing approach to screen complex datasets of medical articles to provide timely and efficient response to pressing issues in medicine. The approach is based on the Bidirectional Encoder Representation from Transformers (BERT) to screen the articles using their titles and abstracts. Systematic review screening is a classification task aiming at selecting articles fulfilling the criteria for the next step of the review. A number of BERT models are fine-tuned for this classification task. Two challenging space medicine systematic review datasets that include human, animal, and in-vitro studies are used for the evaluation of the models. Backtranslation is used as a data augmentation technique to handle the class imbalance and a performance comparison of the models on the original and augmented data is presented. The BERT models provide an accessible solution for screening systematic reviews, which are considered complex and time-consuming. The proposed approach can change the workflow of conducting these types of reviews, especially in response to urgent policy and practice questions in medicine. The source code and datasets are available on GitHub: https://github.com/ESA-RadLab/BERTCSRS .
Mobile Robot Navigation Based on Embedded Computer Vision

The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93%. The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements.
A Flexible Architecture for Cryptographic Applications: ECC and PRESENT

This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography (ECC) and PRESENT for cryptographic applications. The features of the proposed work are (i) computation of only the point multiplication operation of ECC over for a 163-bit key generation, (ii) execution of only the variant of an 80-bit PRESENT block cipher for data encryption & decryption and (iii) execution of point multiplication operation (ECC algorithm) along with the data encryption and decryption (PRESENT algorithm). To establish an area overhead for the flexible design, dedicated hardware architectures of ECC and PRESENT are implemented in the first step, and a sum of their hardware area is computed. Then, the implementation of the proposed flexible architecture for ECC and PRESENT algorithms is presented. Implementation results regarding the area, clock cycles, latency, clock frequency, and power after the place-and-route level on Xilinx Virtex-5, Virtex-6, and Virtex-7 FPGA devices are presented. Hence, the implementation results and comparisons show that the proposed architecture suits applications demanding flexible implementation of cryptographic applications.
Recapitulation of empirical research on teachers’ difficulties in applying digital technologies in teaching

This paper provides a concise overview and analysis of research that shows the results of teachers' difficulties in using digital technologies in teaching.The aim of the work is to identify specific research problems, how they have transformed over time, what methodology was applied in those researches and what empirical knowledge was obtained.The analysis of the results showed that different methodological solutions are used in the research, and that there are different problems of teachers, the most dominant of which is information about digital technologies and the function that teachers manifest in the process of their application in teaching.In the concluding discussion of the work, research is listed that can contribute to the understanding of the authentic difficulties of teachers in the application of digital technologies in teaching and the development of a context that would enable teachers to effectively solve these concerns.
Effect of Feedback on Drug Consumption Disclosures on Social Media

Deaths due to drug overdose in the US have doubled in the last decade. Drug-related content on social media has also exploded in the same time frame. The pseudo-anonymous nature of social media platforms enables users to discourse about taboo and sometimes illegal topics like drug consumption. User-generated content (UGC) about drugs on social media can be used as an online proxy to detect offline drug consumption. UGC also gets exposed to the praise and criticism of the community. Law of effect proposes that positive reinforcement on an experience can incentivize the users to engage in the experience repeatedly. Therefore, we hypothesize that positive community feedback on a user's online drug consumption disclosure will increase the probability of the user doing an online drug consumption disclosure post again. To this end, we collect data from 10 drug-related subreddits. First, we build a deep learning model to classify UGC as indicative of drug consumption offline or not, and analyze the extent of such activities. Further, we use matching-based causal inference techniques to unravel community feedback's effect on users' future drug consumption behavior. We discover that 84% of posts and 55% comments on drug-related subreddits indicate real-life drug consumption. Users who get positive feedback generate up to two times more drugs consumption content in the future. Finally, we conducted an anonymous user study on drug-related subreddits to compare members' opinions with our experimental findings and show that user tends to underestimate the effect community peers can have on their decision to interact with drugs.
Reduced benefit of novelty detection on subsequent memory judgments in paranoia.

Novelty detection is critical to the effective employment of memory-guided behavior. While recent work has found impaired novelty detection in subclinical paranoia, other studies show different patterns. Here, we tested the hypothesis that those higher in paranoia receive less benefit from novelty in their immediate environment when making subsequent mnemonic judgments. Using a continuous recognition task (comprising Old, New, and Similar items) in a sample drawn from an online marketplace (N = 450), we found that Similar trial performance was generally enhanced by preceding judgments of "New" versus "Old"-replicating prior work. However, paranoia was associated with a reduction of this novelty-based enhancement-a novel finding. Those experiencing paranoia may thus less readily use novelty to adjudicate between the competing mnemonic processes of encoding and retrieval. We interpret this finding in light of the role of novelty detection in maintaining adaptive predictive models, suggesting that this deficit may reduce coherence between one's active predictive model and one's environment, thereby contributing to perceptions of the world as unduly uncertain and threatening. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Synthetic Smartwatch IMU Data Generation from In-the-wild ASL Videos

The scarcity of training data available for IMUs in wearables poses a serious challenge for IMU-based American Sign Language (ASL) recognition. In this paper, we ask the following question: can we "translate" the large number of publicly available, in-the-wild ASL videos to their corresponding IMU data? We answer this question by presenting a video to IMU translation framework (Vi2IMU) that takes as input user videos and estimates the IMU acceleration and gyro from the perspective of user's wrist. Vi2IMU consists of two modules, a wrist orientation estimation module that accounts for wrist rotations by carefully incorporating hand joint positions, and an acceleration and gyro prediction module, that leverages the orientation for transformation while capturing the contributions of hand movements and shape to produce realistic wrist acceleration and gyro data. We evaluate Vi2IMU by translating publicly available ASL videos to their corresponding wrist IMU data and train a gesture recognition model purely using the translated data. Our results show that the model using translated data performs reasonably well compared to the same model trained using measured IMU data.
CPVD: Cross Project Vulnerability Detection Based On Graph Attention Network And Domain Adaptation

Code vulnerability detection is critical for software security prevention. Vulnerability annotation in large-scale software code is quite tedious and challenging, which requires domain experts to spend a lot of time annotating. This work offers CPVD, a cross-domain vulnerability detection approach based on the challenge of ”learning to predict the vulnerability labels of another item quickly using one item with rich vulnerability labels.” CPVD uses the code property graph to represent the code and uses the Graph Attention Network and Convolution Pooling Network to extract the graph feature vector. It reduces the distribution between the source domain and target domain data in the Domain Adaptation Representation Learning stage for cross-domain vulnerability detection. In this paper, we test each other on different real-world project codes. Compared with methods without domain adaptation and domain adaptation methods based on natural language processing, CPVD is more general and performs better in cross-domain vulnerability detection tasks. Specifically, for the four datasets of chr_deb, qemu, libav, and sard, they achieved the best results of 70.2%, 81.1%, 59.7%, and 78.1% respectively on the F1-Score, and 88.4%,86.3%, 85.2%, and 88.6% on the AUC.
Fusion of deep convolutional and LSTM recurrent neural networks for automated detection of code smells

Code smells is the term used to signal certain patterns or structures in software code that may contain a potential design or architecture problem, leading to maintainability or other software quality issues. Detecting code smells early in the software development process helps prevent these problems and improve the overall software quality. Existing research concentrates on the process of collecting and handling dataset, then exploring the potential of utilizing deep learning models to detect smells, while ignoring extensive feature engineering. Though these approaches obtained promising results, the following issues need to be tackled: (i) extracting both structural and semantic features from the software units; (ii) mitigating the effects of imbalanced data distribution on the performance.In this paper, we propose DeepSmells as a novel approach to code smells detection. To learn the complex hierarchical representations of the code fragment, we apply a deep convolutional neural network (CNN). Then, in order to improve the quality of the context encoding and preserve semantic information, long short-term memory networks (LSTM) is placed immediately after the CNN. The final classification is conducted by deep neural networks with weighted loss function to reduce the impact of skewed data distribution. We performed an empirical study using the existing code smell benchmark datasets to assess the performance of our proposed approach, and compare it with state-of-the-art baselines. The results demonstrate the effectiveness of our proposed method for all kinds of code smells with outperformed evaluation metrics in terms of F1 score and MCC.
A Genetic Causal Explainer for Deep Knowledge Tracing

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based knowledge tracing (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this paper, we propose a causal explanation method based on the genetic algorithm (GA), named Genetic Causal Explainer(GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multi-strategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.
Prolog Meets Biology

This paper provides an overview of the use of Prolog and its derivatives to sustain research and development in the fields of bioinformatics and computational biology. A number of applications in this domain have been enabled by the declarative nature of Prolog and the combinatorial nature of the underlying problems. The paper provides a summary of some relevant applications as well as potential directions that the Prolog community can continue to pursue in this important domain. The presentation is organized in two parts: “small,” which explores studies in biological components and systems, and “large,” that discusses the use of Prolog to handle biomedical knowledge and data. A concrete encoding example is presented and the effective implementation in Prolog of a widely used approximated search technique, large neighborhood search, is presented.