Reviewerly/exHarmony huggingface dataset:
A Systematic Review of using Virtual and Augmented Reality for the Diagnosis and Treatment of Psychotic Disorders

Immersive virtual reality (VR) and augmented reality (AR) have the potential to improve the treatment and diagnosis of individuals experiencing psychosis. Although commonly used in creative industries, emerging evidence reveals that VR is a valuable tool to potentially improve clinical outcomes, including medication adherence, motivation, and rehabilitation. However, the efficacy and future directions of this novel intervention require further study. The aim of this review is to search for evidence of efficacy in enhancing existing psychosis treatment and diagnosis with AR/VR.2069 studies involving AR/VR as a diagnostic and treatment option were reviewed via PRISMA guidelines in five databases: PubMed, PsychInfo, Embase, and CINAHL.Of the initial 2069 articles, 23 original articles were eligible for inclusion. One study applied VR to the diagnosis of schizophrenia. Most studies demonstrated that the addition of VR therapies and rehabilitation methods to treatment-as-usual (medication, psychotherapy, social skills training) was more effective than traditional methods alone in treating psychosis disorders. Studies also support the feasibility, safety, and acceptability of VR to patients. No articles using AR as a diagnostic or treatment option were found.VR is efficacious in diagnosing and treating individuals experiencing psychosis and is a valuable augmentation of evidence-based treatments.The online version contains supplementary material available at 10.1007/s40501-023-00287-5.
Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms

The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria for optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation of optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selection-integrated approach for AUV optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) for optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-based optimization algorithm selection. In all three motion types – “depth”, “yaw”, and “theta” – Kp_θ had the highest weight value, with respective weights of 0.143277, 0.149578, and 0.142432. In contrast, Ki_depth consistently received the lowest weight value across all three motion types, with respective weights of 0.057375, 0.0598901, and 0.057085. Regarding the “depth” motion, the Archimedes optimization algorithm (AOA) was the highest-performing alternative with a score of 0.077364, while the eagle strategy–particle swarm optimization algorithm was the worst alternative with a score of 0.022525. The Cuckoo optimization algorithm was identified as the best alternative for the “yaw” motion with a score of 0.072785, whereas black hole optimization had the lowest score of 0.036454. The best alternative for the “theta” motion was AOA with a score of 0.067716, and the worst algorithm was sunflower optimization with a score of 0.028433. This developed approach was evaluated based on systematic ranking and sensitivity analysis, which confirmed the validity of the proposed work.
Real-time anomaly detection on time series of industrial furnaces: A comparison of autoencoder architectures

Anomaly detection in industrial environments aims at detecting anomalies in the monitoring data of industrial machinery or equipment, as soon as possible, preferably presenting real-time alarms, to alert the monitoring staff and start maintenance activities timely. In this paper, the problem of anomaly detection of an industrial furnace is tackled, for the real-time recognition of punctual anomalies on multivariate time series. To this aim, a real-time anomaly detection approach is proposed: first, time series acquired from the real machinery are filtered, to select those of interest for possible anomalies, and pre-processed, to obtain sliding windows for real-time detection, then distinct univariate models are applied, to identify different anomaly types. For the application considered here, data regarding the machinery behaviour were available only for normal functioning, thus an unsupervised approach is chosen. In particular, deep learning models based on autoencoders are used to detect punctual anomalies, by reconstructing each window and evaluating the reconstruction error of its last point. An extensive set of autoencoder models is proposed, with varying architecture in terms of type of model (vanilla/variational autoencoders), type of layers (fully connected/LSTM/BiLSTM), and hyperparameters (number of layers, intermediate sizes, BiLSTM type). Available data are split, and used to train the models, and to test them on the normal signal and on synthetic anomalies injected on it, which are of particular interest and were designed according to domain experts. Performances of the proposed models show differences among them, depending on the model architecture. The most efficient models, in terms of F1 score of detection and number of parameters, are identified by their t-test comparison, and the capability of detecting anomalies online is demonstrated. In particular, the proposed anomaly detection approach, including a selected autoencoder with LSTM layers, is able to correctly recognize normal trends, with very few false positives, and promptly give alarms as different anomalous trends appear.
Robust anomaly detection for multivariate time series through temporal GCNs and attention-based VAE

Anomaly detection on multivariate time series (MTS) is of great importance in both data mining research and industrial applications. While a handful of anomaly detection models are developed for MTS data, most of them either ignore the potential correlations between different variables or overlook the different importance of variables at each time period in MTS, which leads to poor accuracy in anomaly detection. In this paper, we propose a novel unsupervised MUltivariate Time series ANomaly deTection framework (MUTANT), which simultaneously models the correlations between variables and the importance of variables at each time period. Specifically, we construct a feature graph for variables in each time window and perform graph convolutional network (GCN) to learn embeddings for all variables, which effectively captures the time-varying correlations between variables in MTS. Then, we propose an attention-based reconstruction model to learn robust latent representations to capture normal patterns of MTS by modeling the importance of variables based on time dependencies along with time dimension. Our evaluation experiments are conducted on four real-life datasets from different industrial domains. Experimental results show that MUTANT significantly outperforms state-of-the-art MTS anomaly detection methods, achieving an average anomaly detection F1-score higher than 0.96. The source code is available at https://github.com/Coac-syf/MUTANT.
Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time

Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. This method aims to address the complexities associated with detecting hate speech, resulting in more robust and accurate results. Our findings indicate that the proposed approach significantly enhances hate speech detection performance across various metrics and models in both the Parler and GAB datasets. For reproducibility and further exploration, our code is publicly available at https://github.com/OrKatz7/parler-hate-speech.
A study on the knowledge and perception of artificial intelligence

This study investigated the knowledge and perception of artificial intelligence among library and information science professionals in Zambia by surveying 245 randomly selected participants. The results show that the library and information science professionals had a favourable and encouraging outlook with regard to artificial intelligence but were also concerned about artificial intelligence replacing librarians’ roles and the barriers to adopting artificial intelligence technologies in Zambian libraries. The article suggests that libraries should consider the study’s findings before implementing artificial intelligence, particularly concerning technology and facilities, librarians’ proficiency in artificial intelligence, and leadership positions in artificial intelligence initiatives. The research can be used as a resource by library boards and associations to develop policies for implementing artificial intelligence in academic libraries, and fills a research vacuum in developing nations like Zambia regarding the knowledge of university and college libraries, and their willingness to use artificial intelligence.
Combining Logic Programming and Imperative Programming in LPS

Logic programs and imperative programs employ different notions of computing. Logic programs compute by proving that a goal is a logical consequence of the program, or by showing that the goal is true in a model defined by the program. Imperative programs compute by starting from an initial state, executing actions to transition from one state to the next, and terminating (if at all) in a final state when the goal is solved. In this paper, we present the language LPS (Logic Production Systems), which combines the logic programming and imperative programming notions of computing. Programs in LPS compute by using beliefs, represented by logic programs, to model the changing world, and by executing actions, to change the world, to satisfy goals, represented by reactive rules and constraints.
Leveraging the meta-embedding for text classification in a resource-constrained language

This paper proposes an intelligent text classification framework for a resource-constrained language like Bengali, which is considered a challenging task due to the lack of standard corpora, appropriate hyper-parameter tuning method, and pre-trained language-specific embedding. The proposed framework comprises an average meta-embedding feature fusion module and a convolutions neural network module called AVG-M+CNN. This work also proposes an algorithm, i.e., automatic hyperparameter tuning and selection, for enhancing the performance of the AVG-M+CNN technique. All meta-embedding models are evaluated using the intrinsic, e.g., semantic, syntactic, relatedness word similarity, analogy tasks and extrinsic evaluators. The intrinsic evaluator evaluates 200 Bengali semantic, syntactic and relatedness word pairs. Spearman (ρˆ), Pearson (rˆ) and cosine similarity correlations are used to evaluate 18 individual embedding and 9 meta-embedding models. The 3COSADD and 3COSMUL evaluators evaluate the 300 analogy tasks. The extrinsic evaluator evaluates a total of 156 classification models on four corpora: BARD, IndicNLP, Prothom-Alo and BTCC11 (a newly developed corpus having eleven distinct categories). Among these, the AVG-M+CNN model achieves the highest accuracy regarding four Bengali corpora: 95.92±.001% for BARD, 93.10±.001% for Prothom-Alo, 90.07±.001% for BTCC11 and 87.44±.001% for IndicNLP, respectively.
The role of blockchain technology in the sustainability of supply chain management: Grey based dematel implementation

Decentralized, transparent, immutability, irreversible, autonomy, open source, anonymity, ownership and uniqueness, source, contract automation are at the forefront of the features that allow blockchain technology to be accepted within the sectors. Thanks to these features, it has an important potential in terms of applicability, as well as in supply chain management, as in many other fields. In this study, the compatibility and impact level of the features of blockchain technology with the sustainability of supply chain management are investigated. Within the scope of supply chain management, effective information sharing, integration between processes, trustworthy, systematic data management, auditability, transparency, strong relationship between stakeholders, high efficiency, quick answer, ability to increase sustainability, transition to digitization, uncertain legal status, new technology, integration compatibility with other technologies, optimizing time parameters are evaluated. In the results obtained with the Grey Relational Analysis-based DEMATEL method, effective information sharing, integration between processes, systematic data management, quick answer, uncertain legal status, new technology, and integration compatibility with other technologies are included in the group affecting other criteria. Within the scope of the study, the characteristic features of blockchain technology for supply chain management are interpreted with relationship map and graph diagram. The study provides both theoretical and practical contributions by drawing attention to the compatibility of the blockchain in supply chains, and guides the employees in the supply chain line on how they can best guide their companies and on which parameters they can gain by enabling them to adopt blockchain technology. With blockchain technology, it offers the opportunity to achieve higher efficiency with less time, less cost and faster response, which can realize its sustainability mission, which includes each of the economic, social and environmental dimensions.
A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation

Graph neural networks have been frequently applied in recommender systems due to their powerful representation abilities for irregular data. However, these methods still suffer from the difficulties such as the inflexible graph structure, sparse and highly imbalanced data, and relatively shallow networks, limiting rate prediction ability for recommendations. This paper presents a novel deep dynamic graph attention framework based on influence and preference relationship reconstruction (DGA-IPR) for recommender systems to learn optimal latent representations of users and items. The entire framework involves a user branch and an item branch. An influence-based dynamic graph attention (IDGA) module, a preference-based dynamic graph attention (PDGA) module, and an adaptive fine feature extraction (AFFE) module are respectively constructed for each branch. Concretely, the first two attention modules concentrate on reconstructing influence and preference relationship graphs, breaking imbalanced and fixed constraints of graph structures. Then a deep feature aggregation block and an adaptive feature fusion operation are built, improving the network depth and capturing potential high-order information expressions. Besides, AFFE is designed to acquire finer latent features for users and items. The DGA-IPR architecture is formed by integrating IDGA, PDGA, and AFFE for users and items, respectively. Experiments reveal the superiority of DGA-IPR over existing recommendation models.
A forensics and compliance auditing framework for critical infrastructure protection

Contemporary societies are increasingly dependent on products and services provided by Critical Infrastructure (CI) such as power plants, energy distribution networks, transportation systems and manufacturing facilities. Due to their nature, size and complexity, such CIs are often supported by Industrial Automation and Control Systems (IACS), which are in charge of managing assets and controlling everyday operations. As these IACS become larger and more complex, encompassing a growing number of processes and interconnected monitoring and actuating devices, the attack surface of the underlying CIs increases. This situation calls for new strategies to improve Critical Infrastructure Protection (CIP) frameworks, based on evolved approaches for data analytics, able to gather insights from the CI. In this paper, we propose an Intrusion and Anomaly Detection System (IADS) framework that adopts forensics and compliance auditing capabilities at its core to improve CIP. Adopted forensics techniques help to address, for instance, post-incident analysis and investigation, while the support of continuous auditing processes simplifies compliance management and service quality assessment. More specifically, after discussing the rationale for such a framework, this paper presents a formal description of the proposed components and functions and discusses how the framework can be implemented using a cloud-native approach, to address both functional and non-functional requirements. An experimental analysis of the framework scalability is also provided.
AccessChain: An access control framework to protect data access in blockchain enabled supply chain

In recent years, supply chains have evolved into huge ecosystems, demanding trust, provenance, and data privacy. Since blockchain technology (BCT) allows for the development of a distributed environment, it is ideal for supply chain management (SCM) applications. However, concerns regarding data privacy have impeded the development of blockchains. Despite the fact that some blockchains can restrict participants from reading and/or writing data, blockchain’s transparency makes protecting sensitive data challenging. To solve the data privacy challenge, this paper proposes a framework, AccessChain, that is an SCM access control framework that is based on an attribute-based access control (ABAC) model that restricts access to competing parties while allowing for network scalability. This proposed AccessChain model has two types of ledgers in its system: local and global. Local ledgers are used to store business contracts between stakeholders and the attribute-based access control model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and BCT. This paper’s experimental results illustrate that high throughput can be achieved in a large-scale request environment while maintaining data privacy and sustaining a scalable network.
Session-based cyberbullying detection in social media: A survey

Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modelling and understanding the two defining characteristics of cyberbullying: repetitive behaviour and power imbalance. In this survey paper, we define a framework that encapsulates four different steps session-based cyberbullying detection should go through, and discuss the multiple challenges that differ from single text-based cyberbullying detection. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to proposing evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions.
A Robust Approach to Multimodal Deepfake Detection

The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant situations, it is becoming crucial to distinguish between authentic and fake media. Nonetheless, though deepfake generation systems can create convincing images and audio, they may struggle to maintain consistency across different data modalities, such as producing a realistic video sequence where both visual frames and speech are fake and consistent one with the other. Moreover, these systems may not accurately reproduce semantic and timely accurate aspects. All these elements can be exploited to perform a robust detection of fake content. In this paper, we propose a novel approach for detecting deepfake video sequences by leveraging data multimodality. Our method extracts audio-visual features from the input video over time and analyzes them using time-aware neural networks. We exploit both the video and audio modalities to leverage the inconsistencies between and within them, enhancing the final detection performance. The peculiarity of the proposed method is that we never train on multimodal deepfake data, but on disjoint monomodal datasets which contain visual-only or audio-only deepfakes. This frees us from leveraging multimodal datasets during training, which is desirable given their lack in the literature. Moreover, at test time, it allows to evaluate the robustness of our proposed detector on unseen multimodal deepfakes. We test different fusion techniques between data modalities and investigate which one leads to more robust predictions by the developed detectors. Our results indicate that a multimodal approach is more effective than a monomodal one, even if trained on disjoint monomodal datasets.
Trackez: An IoT-Based 3D-Object Tracking From 2D Pixel Matrix Using Mez and FSL Algorithm

The imaging devices sense light reflected from objects and reconstruct images using the 2D-sensor matrix. It is a 2D Cartesian coordinate system where the depth dimension is absent. The absence of a depth axis on 2D images imposes challenges in locating and tracking objects in a 3D environment. Real-time object tracking faces another challenge imposed by network latency. This paper presents the development and analysis of a real-time, real-world object tracker called Trackez, which is capable of tracking within the top hemisphere. It uses Machine Vision at the IoT Edge (Mez) technology to mitigate latency sensitivity. A novel algorithm, Follow-Satisfy-Loop (FSL), has been developed and implemented in this paper that optimally tracks the target. It does not require the depth-axis. The simple and innovative design and incorporation of Mez technology have made the proposed object tracker a latency-insensitive, Z-axis-independent, and effective system. The Trackez reduces the average latency by 85.08% and improves the average accuracy by 81.71%. The object tracker accurately tracks objects moving in regular and irregular patterns at up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.4ft/s$ </tex-math></inline-formula> speed. This accurate, latency tolerant, and Z-axis independent tracking system contributes to developing a better robotics system that requires object tracking.