Reviewerly/exHarmony huggingface dataset:
AIDPS: Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks (e.g., blackhole, grayhole and flooding attacks). To determine the most effective configuration of the proposed construction, we conduct a number of experiments using several state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest (ARF), light gradient-boosting machine, and K-nearest neighbours) and concept drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our experimental results show that incremental ARF using ADWIN provides optimal performance when implemented with One-class support vector machine (SVM) anomaly-based detectors. Furthermore, our extensive evaluation results also show that the proposed scheme outperforms state-of-the-art bench-marking methods while providing a wider range of desirable features such as scalability and complexity.
Depth Injection Framework for RGBD Salient Object Detection

Depth data with a predominance of discriminative power in location is advantageous for accurate salient object detection (SOD). Existing RGBD SOD methods have focused on how to properly use depth information for complementary fusion with RGB data, having achieved great success. In this work, we attempt a far more ambitious use of the depth information by injecting the depth maps into the encoder in a single-stream model. Specifically, we propose a depth injection framework (DIF) equipped with an Injection Scheme (IS) and a Depth Injection Module (DIM). The proposed IS enhances the semantic representation of the RGB features in the encoder by directly injecting depth maps into the high-level encoder blocks, while helping our model maintain computational convenience. Our proposed DIM acts as a bridge between the depth maps and the hierarchical RGB features of the encoder and helps the information of two modalities complement and guide each other, contributing to a great fusion effect. Experimental results demonstrate that our proposed method can achieve state-of-the-art performance on six RGBD datasets. Moreover, our method can achieve excellent performance on RGBT SOD and our DIM can be easily applied to single-stream SOD models and the transformer architecture, proving a powerful generalization ability.
Automatic Detection of Gaze and Smile in Children’s Video Calls

With the increasing use of video chats by children, the need for tools that facilitate the scientific study of their communicative behavior becomes more pressing. This paper investigates the automatic detection – from video calls – of two major signals in children’s social coordination: smiles and gaze. While there has been significant advancement in the field of computer vision to model such signals, very little work has been done to put these techniques to the test in the noisy, variable context of video calls, and even fewer studies (if any) have investigated children’s video calls specifically. In this paper, we provide a first exploration into this question, testing and comparing two modeling approaches: a) a feature-based approach that relies on state-of-the-art software like OpenFace for feature extraction, and b) an end-to-end approach where models are directly optimized to classify the behavior of interest from raw data. We found that using features generated by OpenFace provides a better solution in the case of smiles, whereas using simple end-to-end architectures proved to be much more helpful in the case of looking behavior. A broader goal of this preliminary work is to provide the basis for a public, comprehensive toolkit for the automatic processing of children’s communicative signals from video chat, facilitating research in children’s online multimodal interaction
Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Tunisian Dialectical Facebook Content During the Spread of the Coronavirus Pandemic

Sentiment analysis (SA) is a multidisciplinary field that aims to predict sentiment tone or attitude expressed in a text, SA using social media data has become a popular topic especially during critical events such as natural disasters, social movements and recently the spread of the Coronavirus Pandemic. Sentiments can be expressed explicitly or implicitly in text and identifying these expressions can be challenging. SA in Tunisian dialect is particularly difficult due to the complexity of the language, its morphological richness and the lack of contextual information. Recently, deep learning (DL) models have been widely adopted in the field of SA, especially in the context of Arabic SA. These models, such as Bi-directional LSTM networks (Bi-LSTM) and LSTM networks, have shown to achieve high accuracy levels in sentiment classification tasks for Arabic and dialectical text. Despite the successes of DL models in Arabic SA, there are still areas for improvement in terms of contextual information and implicit mining expressed in different real-world cases. In this paper, the authors introduce a deep Bi-LSTM network to ameliorate Tunisian SA during the spread of the Coronavirus Pandemic. The experimental results on Tunisian benchmark SA dataset demonstrate that our model achieves significant improvements over the state-of-art DL models and the baseline traditional machine learning (ML) methods. We believe that this contribution will benefit anyone working on Tunisian pandemic management or doing comparative work between Tunisian and other jurisdictions, which can provide valuable insights into how the public is responding to the crisis and help guide pandemic management decisions.
Gesture Recognition for Media Interaction: A Streamlit Implementation with OpenCV and MediaPipe.

Abstract: This project aims to create a media player application that responds to hand gestures, using Python and the OpenCV library. The system taps into computer vision methods, like those used in depth-sensing cameras such as Kinect or Intel RealSense, to track and understand hand movements. It processes the depth data to extract hand features and employs machine learning (like CNNs or decision trees) to classify these into gestures. This lets the application accurately interpret user gestures and apply them to media commands—play, pause, volume, and more. All this works through a user-friendly interface that even lets users customize gestures for specific commands. The combo of OpenCV and Python enables an efficient and adaptable media control system. This fusion of computer vision and machine learning offers a seamless, natural way to navigate media playback, making it an immersive experience without needing physical controllers.
A Family of Pretrained Transformer Language Models for Russian

Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) architectures. We provide a report on the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we aim to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language.
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness

As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern.
AP2FL: Auditable Privacy-Preserving Federated Learning Framework for Electronics in Healthcare

The growing application of machine learning (ML) techniques in healthcare has led to increased interest in federated learning (FL), which enables the secure and private training of robust ML models. However, conventional FL methods often fall short of providing adequate privacy protection and face challenges in handling non-independent and identically distributed (Non-IID) training data. These shortcomings are of significant concern when employing FL in electronic devices in healthcare. To address these issues, we propose an Auditable Privacy-Preserving Federated Learning (AP2FL) model tailored for electronics in healthcare settings. By leveraging Trusted Execution Environments (TEEs), AP2FL ensures secure training and aggregation processes on both client and server sides, effectively mitigating data leakage risks. To manage Non-IID data within the proposed framework, we incorporate the Active Personalized Federated Learning (ActPerFL) model and Batch Normalization (BN) techniques to consolidate user updates and identify data similarities. Additionally, we introduce an auditing mechanism in AP2FL that reveals the contribution of each client to the FL process, facilitating the updating of the global model following diverse data types and distributions. In other words, it ensures the FL process’s integrity, transparency, fairness, and robustness. Our results demonstrate that the proposed AP2FL model outperforms existing methods in accuracy and effectively eliminates privacy leakage.
Deep Learning Multi-Agent Model for Phishing Cyber-attack Detection

Phishing attacks have become one of the most prominent cyber threats in recent times, which poses a significant risk to the security of organizations and individuals. Therefore, detecting such Cyber attacks has become crucial to ensure a secure digital environment. In this regard, deep learning techniques have shown promising results for the detection of phishing attacks due to their ability to learn and extract features from raw data. In this study, we propose a deep learning-based approach to detecting phishing attacks by using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our proposed model extracts features from the URL and email content to detect phishing attempts. We evaluate the proposed approach on a real-world dataset and achieve an accuracy of over 95%. The results indicate that the proposed approach can effectively detect phishing attacks and can be utilized in real-world applications to ensure a secure digital environment.
Developing smart buildings to reduce indoor risks for safety and health of the elderly: A systematic and bibliometric analysis

As aging problem gets severe, more elderly people require better residences to support their later lives. Considering complicated safety and health risks the elderly would encounter indoors, such as falls and sudden diseases, researchers have tried to implement smart buildings to reduce indoor risks for the elderly. Fortunately, residential buildings have benefited from rapid development of smart techniques. It is meaningful to look back on how smart buildings deal with indoor risks of the elderly. This article adopts approaches of systematic literature review to identify 92 eligible articles which proposed smart buildings for the elderly. Then the time trend, publication journals, co-authorship and co-occurrence of these eligible articles are revealed by the bibliometric analysis. Five critical targets of these smart building solutions are summarized, including fall detection, activity recognition, disease prediction, health monitoring, and emotional care. Previous smart buildings mostly adopted Support Vector Machine (SVM), classifier comparison, neural network, Hidden Markov Model (HMM) and robotics as main smart techniques. Furthermore, these different smart building techniques are generally developed for different sub-targets or integrated for one main target, that are regarded as two correlation modes between techniques and targets. Current challenges and future direction of the development of smart buildings are pointed out. This review helps to know what kinds of indoor risks of the elderly were focused by smart buildings and which smart techniques were widely applied to develop smart buildings, then provides suggestions for future research to promote smart buildings to be more safe and healthy for the elderly.
Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering

Despite their competitive performance on knowledge-intensive tasks, large language models (LLMs) still have limitations in memorizing all world knowledge especially long tail knowledge. In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. However, their approaches lack a well-formed verbalization of KG knowledge, i.e., they ignore the gap between KG representations and textual representations. To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA. Based on this approach, we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. Experiments on several KGQA benchmarks show that the proposed KG-to-Text augmented LLMs approach outperforms previous KG-augmented LLMs approaches regarding answer accuracy and usefulness of knowledge statements.
A Principle-Based Analysis of Bipolar Argumentation Semantics

In this paper, we introduce and study seven types of semantics for bipolar argumentation frameworks, each extending Dung’s interpretation of attack with a distinct interpretation of support. First, we introduce three types of defence-based semantics by adapting the notions of defence. Second, we examine two types of selection-based semantics that select extensions by counting the number of supports. Third, we analyse two types of traditional reduction-based semantics under deductive and necessary interpretations of support. We provide full analysis of twenty-eight bipolar argumentation semantics and ten principles.
Classification of nursing students' self-directed learning ability types and verification of influencing factors in online learning environment

Objectives This study was conducted with the purpose of classifying the latent types of self-directed learning ability of nursing students in an online learning environment, and verifying the relationship between the teaching presence, academic achievement, academic resilience and perceived interaction that affect each type classification.&#x0D; Methods The potential profile of self-directed learning ability was analyzed using the data of 250 nursing students from one university located in J province in an online class environment. LPA, ANOVA, and multinomial logistic regression analysis were conducted to verify the difference between the latent profile and the variable and the factors affecting the effect.&#x0D; Results It was classified into 4 groups based on the estimated sub-factors of self-directed learning ability, and the results of verification of general characteristics and differences between variables according to latent profile types of self-directed learning ability, and latent profile types were class participation, major satisfaction , academic achievement, perception of real teaching, academic resilience, and perceived interaction were significantly different. In the ‘self-directed learning maintenance group’, the higher the grade, the higher the probability of being classified in this group by 2.3 times.&#x0D; Conclusions According to the result of analysis, it was suggested that it is necessary to consider academic achievement and academic resilience in order to improve the self-directed learning ability of nursing students, but also consider the learner's grade level and degree of participation.
Video Game Equipment Loss and Durability in a Circulating Academic Collection

Objective – This article reviewed twelve years of circulation data related to loss and damage of video game equipment, specifically consoles, game controllers, and gaming peripherals such as steering wheels, virtual reality headsets, and joysticks in an academic library collection. Methods – The authors analyzed data gathered from game equipment bibliographic and item records. Only data related to the console system, game controllers, and peripherals such as steering wheels, virtual reality headsets, and joysticks were evaluated for rate of circulation, loss, and damage. Cables and bags were not evaluated because the replacement cost for these items is negligible when considering long-term budgeting and maintenance of a game collection. Results – The majority of video game equipment can be circulated without unsustainable loss or damage. The library has been able to continue circulating video game equipment without undue replacement costs or loss of access for its patrons. Conclusion – Although equipment will occasionally break or be lost, libraries should not let this unduly affect consideration when starting a video game collection.
Unsupervised Domain Adaptation with Asymmetrical Margin Disparity loss and Outlier Sample Extraction

Unsupervised domain adaptation (UDA) trains models using labeled data from a specific source domain and then transferring the knowledge to certain target domains that have few or no labels. Many prior measurement-based works achieve lots of progress, but their feature distinguishing abilities to classify target samples with similar features are not enough; they do not adequately consider the confusing samples in the target domain that are similar to the source domain; and they don't consider negative transfer of the outlier sample in source domain. We address these issues in our work and propose an UDA method with asymmetrical margin disparity loss and outlier sample extraction, called AMD-Net with OSE. We propose an Asymmetrical Margin Disparity Discrepancy (AMD) method and a training strategy based on sample selection mechanism to make the network have better feature extraction ability and the network gets rid of local optimal. Firstly, in the AMD method, we design a multi-label entropy metric to evaluate the marginal disparity loss of the confusing samples in the target domain. This asymmetric marginal disparity loss designment uses the different entropy measurement algorithms of the two domains to excavate the differences of the two domains as much as possible, so as to find the common features of the two domains. Secondly, A sample selection mechanism is designed to evaluate which part of the sample in target domain is confusable. We define the certainty of the sample in the target domain, adopt a progressive learning scheme, and adopt one-hot marginal disparity loss for most of the samples in the target domain with low uncertainty and easy to distinguish. The multi-label marginal calculation method is used only for the uncertainty samples in the target domain whose certainty is less than the threshold value, so that the network can get rid of the local optimal as much as possible. At last, we further propose an outlier sample extraction algorithm (OSE) based on weighted cosine similarity distance for source domain to reduce the negative migration effect caused by outlier samples in the source domain. Extensive experiments on four datasets Office-31, Office-Home, VisDA-2017 and DomainNet demonstrate that our method works well in various UDA settings and outperforms the state-of-the-art methods.
Multiscale major factor selections for complex system data with structural dependency and heterogeneity

The unknown multiscale structure hidden in large complex systems is explored bottom-up through discovered heterogeneity under structural dependency embedded within structured data sets. Via two real complex systems, we demonstrate computed hierarchical structures with broken symmetry constituting data's information content. Through graphic displays, such information content indirectly, but efficiently resolves system-related scientific issues that are difficult to resolve directly. All bottom-up explorations and computations are based on conditional entropy and mutual information evaluated upon contingency table platforms after categorizing all quantitative features. Categorical Exploratory Data Analysis (CEDA) first extracts global major factors that share significant mutual information with the targeted response (Re) variable against many covariate (Co) features under the presence of structural dependency. Then each global major factor is taken as one perspective of heterogeneity to subdivide the entire data set according to its categories into sub-collections. This simple "de-associating" protocol significantly reduces structural dependency among the rest of the features such that another run of major factor selection performed on the sub-collection scale can precisely identify which feature sets could provide extra information beyond the global major factor. Finally, informative patterns collected from multiple perspectives of heterogeneity are displayed to explicitly resolve issues of prediction, classification, and detecting minute dynamic changes.
Similarity contrastive estimation for image and video soft contrastive self-supervised learning

Abstract Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that are considered as noise. However, several instances in a dataset are drawn from the same distribution and share underlying semantic information. A good data representation should contain relations between the instances, or semantic similarity and dissimilarity, that contrastive learning harms by considering all negatives as noise. To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE). Our training objective is a soft contrastive one that brings the positives closer and estimates a continuous distribution to push or pull negative instances based on their learned similarities. We validate empirically our approach on both image and video representation learning. We show that SCE performs competitively with the state of the art on the ImageNet linear evaluation protocol for fewer pretraining epochs and that it generalizes to several downstream image tasks. We also show that SCE reaches state-of-the-art results for pretraining video representation and that the learned representation can generalize to video downstream tasks. Source code is available here: https://github.com/juliendenize/eztorch .