Reviewerly/exHarmony huggingface dataset:
Local-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4063" altimg="si17.svg"><mml:mi>k</mml:mi></mml:math> values for multi-label <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4068" altimg="si17.svg"><mml:mi>k</mml:mi></mml:math>-nearest neighbors rule

Multi-label learning is a growing field in machine learning research. Many applications address instances that simultaneously belong to many categories, which cannot be disregarded if optimal results are desired. Among the many algorithms developed for multi-label learning, the multi-label k-nearest neighbor method is among the most successful. However, in a difficult classification task, such as multi-label learning, a challenge that arises in the k-nearest neighbor approach is the assignment of the appropriate value of k. Although a suitable value might be obtained using cross-validation, it is unlikely that the same value will be optimal for the whole space spanned by the training set. It is evident that different regions of the feature space would have different distributions of instances and labels that would require different values of k. The very complex boundaries among the many present labels make the necessity of local k values even more important than in the case with a single-label k-nearest neighbor. We present a simple yet powerful approach for setting a local value of k. We associate a potentially different k with every prototype and obtain the best value of that k by optimizing the criterion consisting of the local effect of the different k values in the neighborhood of the prototype. The proposed method has a fast training stage, as it only uses the neighborhood of each training instance to set the local k value. The complexity of the proposed method in terms of the testing time is similar to that of the standard multi-label k-nearest neighbor approach. Experiments performed on a set of 20 problems show that not only does our proposed method significantly outperform the standard multi-label k-nearest neighbor rule but also the locally adaptive multi-label k-nearest neighbor method can benefit from a local k value.
Recent trends in mathematical expressions recognition: An LDA-based analysis

Although recognition works on mathematical expressions have been explored for four decades, the current literature and trends are varied and frequently influenced by distinct emerging methods and technology. This situation instigates the necessity of an organized review to provide heedful insight into research trends and patterns currently prevailing in the domain of mathematical expression recognition (MER). To identify and associate (semantic mapping) the leading research zones, core research areas, and research trends steering in the MER domain. Identifying prominent recognition models based on extracted research areas. To develop the development chart from extracted research trends for directing the future works in this direction. A manual and automatic search has been performed across the reputed digital libraries for corpus formation. The formulated corpus is used for topic modeling, and Latent Dirichlet Allocation is deployed for information modeling for achieving defined objectives. The corpus of 325 research papers published from 1967 to 2021 has been processed using LDA. The five major research areas and ten research trends are identified. Leading research area is “Segmentation and Classification Procedures”, and the trend with the highest related publications is “Contextual and Graph-based recognition”. “Attention and Deep Networks” has emerged as the newborn trend, and the identified newborn, young, and matured trends impetrate more exploration from the MER research community.
Cosmo

Human activity recognition (HAR) is a key enabling technology for a wide range of emerging applications. Although multimodal sensing systems are essential for capturing complex and dynamic human activities in real-world settings, they bring several new challenges including limited labeled multimodal data. In this paper, we propose Cosmo, a new system for contrastive fusion learning with small data in multimodal HAR applications. Cosmo features a novel two-stage training strategy that leverages both unlabeled data on the cloud and limited labeled data on the edge. By integrating novel fusion-based contrastive learning and quality-guided attention mechanisms, Cosmo can effectively extract both consistent and complementary information across different modalities for efficient fusion. Our evaluation on a cloud-edge testbed using two public datasets and a new multimodal HAR dataset shows that Cosmo delivers significant improvement over state-of-the-art baselines in both recognition accuracy and convergence delay.
Explanation Guided Contrastive Learning for Sequential Recommendation

Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by some random augmentation (or sequence sampling) on a given anchor user sequence remains to be semantically similar (or different). When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations (EGA) and Explanation Guided Contrastive Learning for Sequential Recommendation (EC4SRec) model framework. The key idea behind EGA is to utilize explanation method(s) to determine items' importance in a user sequence and derive the positive and negative sequences accordingly. EC4SRec then combines both self-supervised and supervised contrastive learning over the positive and negative sequences generated by EGA operations to improve sequence representation learning for more accurate recommendation results. Extensive experiments on four real-world benchmark datasets demonstrate that EC4SRec outperforms the state-of-the-art sequential recommendation methods and two recent contrastive learning-based sequential recommendation methods, CL4SRec and DuoRec. Our experiments also show that EC4SRec can be easily adapted for different sequence encoder backbones (e.g., GRU4Rec and Caser), and improve their recommendation performance.
Study of the Organization and Implementation of E-Learning in Wartime Inside Ukraine

The article provides a factual foundation for the possibility of organizing and implementing e-learning in Ukrainian higher educational institutions during the war. The current research topicality is supported by the urgent need for training experience, organization and implementation during wartime because of the fact that both the educational process and the opportunity to obtain an education should not be halted. The study’s goal is to assess the current state of the e-learning organization and implementation, as well as to examine students’ attitude towards the educational process during wartime. Methods such as scientific source analysis, generalization and systematization of the e-learning experience and its practical application were used to achieve the goal. Furthermore, empirical methods such as interviewing and observation were used. Questionnaires have been proposed as important research tools for this purpose. Four structured groups for the use of e-learning have been formed and identified based on the findings. We created an e-learning organization and support model based on them. Furthermore, we identified ten poignant factors as the sources of difficulties for teachers when implementing innovations, with limited resources and a lack of time being among the most significant.
Cybersecurity capabilities and cyber-attacks as drivers of investment in cybersecurity systems: A UK survey for 2018 and 2019

Our study explores how cyber-capabilities and cyber-attacks drive investment in cybersecurity systems. We assume that cybersecurity investment is a strategic decision in the organizations. To analyze this research question, we make use of the Cyber Security Breaches Survey data, with a sample consisting of 4,163 UK organizations in the periods 2018 to 2019, and employ machine learning techniques (ANN and K-mean cluster). The study extends the current literature on cybersecurity systems and improves our understanding of it in several ways. First, it provides evidence for how the cybersecurity systems are developed in organizations. Second, regarding what factors affect investment in cybersecurity systems, it shows that organizations invest in cybersecurity based on their cybersecurity capabilities and the experienced cyber-attacks. Third, from a managerial point of view, the paper contributes to understanding cybersecurity within the management of the organization.
Energy Trading on a Peer-to-Peer Basis between Virtual Power Plants Using Decentralized Finance Instruments

Over time, distribution systems have begun to include increased distributed energy resources (DERs) due to the advancement of auxiliary power electronics, information and communication technologies (ICT), and cost reductions. Electric vehicles (EVs) will undoubtedly join the energy community alongside DERs, and energy transfers from vehicles to grids and vice versa will become more extensive in the future. Virtual power plants (VPPs) will also play a key role in integrating these systems and participating in wholesale markets. Energy trading on a peer-to-peer (P2P) basis is a promising business model for transactive energy that aids in balancing local supply and demand. Moreover, a market scheme between VPPs can help DER owners make more profit while reducing renewable energy waste. For this purpose, an inter-VPP P2P trading scheme is proposed. The scheme utilizes cutting-edge technologies of the Avalanche blockchain platform, developed from scratch with decentralized finance (DeFi), decentralized applications (DApps), and Web3 workflows in mind. Avalanche is more scalable and has faster transaction finality than its layer-1 predecessors. It provides interoperability abilities among other common blockchain networks, facilitating inter-VPP P2P trading between different blockchain-based VPPs. The merits of DeFi contribute significantly to the workflow in this type of energy trading scenario, as the price mechanism can be determined using open market-like instruments. A detailed case study was used to examine the effectiveness of the proposed scheme and flow, and important conclusions were drawn.
Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation

Session-based recommendation is a challenging task, which aims at making recommendation for anonymous users based on in-session data, i.e. short-term interaction data. Most session-based recommendation methods only model user’s preferences with the current session sequence, which ignore rich information from a global perspective. Meanwhile, previous works usually apply GNN to capture the transformation relationship between items, however the graph used in GNN is built through a static mode, which may introduce noise to the graph structure if user’s preferences shift. In this paper, we propose a novel method called Dynamic Global Structure Enhanced Multi-channel Graph Neural Network (DGS-MGNN) to learn accurate representations of items from multiple perspectives. In DGS-MGNN, we propose a novel GNN model named Multi-channel Graph Neural Network to generate the local, global and consensus graphs dynamically and learn more informative representations of items based on the corresponding graph. Meanwhile, in order to reduce the noise information within sessions, we utilize the graph structure to assist the attention mechanism to filter noisy information within each session, so as to generate an accurate intention representation for the user. Finally, combined with a repeat and explore module, a more accurate prediction probability distribution is generated. We conduct extensive experiments on three widely used datasets, and the results demonstrate that DGS-MGNN is consistently superior to the state-of-the-art baseline models.
Video stabilization: A comprehensive survey

Video stabilization is one of the most fundamental and challenging tasks in video processing, which can be widely applied in many areas, such as video surveillance, robotics, unmanned aerial vehicles and smartphones. It is a video enhancement technology that aims to improve original video quality by removing potentially shaky camera motion. Generally, conventional methods can be divided into three types: 2D, 3D and 2.5D models, which mainly have boundedness. In the past decade, deep learning has emerged as a powerful technique for learning feature representations directly from data, leading to significant progress in video stabilization. However, previous surveys mainly focus on conventional methods and lack performance comparison. In this paper, we present a comprehensive survey of video stabilization. Firstly, we briefly review three stages of video stabilization. Then, we deliver the conventional methods and the deep learning-based methods in detail. Furthermore, we pay special attention to video stabilization quality assessment, benchmark datasets, and state-of-the-art performance. Finally, we provide discussions on current challenges and future directions to overcome the limitations of the existing methods and better meet the needs of researchers in this active area.
Ant colony based optimization model for QoS-based task scheduling in cloud computing environment

Cloud computing has evolved with various techniques for satisfying the needs of users to reduce costs and offer better results. The users' needs may include sharing of resources like memories, processors, apps, information, data, applications, etc., whereas, performance should be in terms of improved DC processing period & response time. Apart from the above-said, there is also a requirement to manage the stability of the system and be flexible to make amendments to the system. Static data is used to optimize the project schedule. However, the traditional does not take into account the personnel allocation matrix when scheduling projects. The ACO model is not a suitable solution to the scheduling problem. The classic ACO methodology operates in two phases: the first phase uses an event-based scheduler to address the complex planning issue. Planning and allocating resources for a project are both constrained by these two approaches. The event-based ACO model was developed to match the resource restrictions and activity schedule to handle dynamic data allocation. When it comes to multi-objective scheduling, EBS with ACO is not an adequate approach. An updated ACO technique to optimum global search employing a neural network approach was presented to schedule many activities to tackle the difficulty of multiple objectives. Using the suggested multi-objective technique, an activity with a defined number of tasks and necessary resources may be optimally organized. The algorithm LBACO is the most effective one to apply for optimizing the objective function, according to the test results, as well as the fastest. An advantage of ACO over other techniques is its ability to provide better plans with higher statistics and mean access times, as well as more consistent job assignments.
Trust-aware location recommendation in location-based social networks: A graph-based approach

With the increase in the use of mobile devices having location-related capabilities, the use of Location-Based Social Networks (LBSN) has also increased, allowing users to share location-embedded information with other users in the social network. By leveraging check-in activities provided by LBSNs, personalized recommendations can be provided. Trust is an important concept in social networks to improve recommendation quality. In this work, we develop a method for predicting the trust scores of LBSN users and propose a trust-aware recommendation technique, TLoRW, to recommend locations to users based on their previous check-ins, the social network, and predicted trust scores of users. In the proposed model, global trust score of user is generated on the basis of check-in history. In addition to trust, spatial context is anther important aspect of TLoRW to generate location recommendations based on the current location of a user. The proposed algorithm runs on a contextual subgraph rather full graph, relaxing the computing resource requirement. We represent a given LBSN with a undirected graph model. Recommendation scores of the locations are generated as the result of the random walk performed on the trust augmented LBSN subgraph. A comprehensive evaluation of TLoRW is conducted by comparing its recommendation performance against baseline techniques, as well as state-of-the-art trust-aware recommendation approaches in the literature, based on benchmark datasets. The experiments reveal that the trust information incorporated into random-walk-based approach improves the accuracy of the recommended locations @5 by minimum 5%.
Poisoning GNN-based Recommender Systems with Generative Surrogate-based Attacks

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks , in which the attackers inject fake data to manipulate recommendation results as they desire. This might be due to the fact that existing poison attacks (and countermeasures) are either model-agnostic or specifically designed for traditional recommender algorithms (e.g., neighborhood-based, matrix-factorization-based, or deep-learning-based RSs) that are not gRS. As gRSs are widely adopted in the industry, the problem of how to design poison attacks for gRSs has become a need for robust user experience. Herein, we focus on the use of poison attacks to manipulate item promotion in gRSs. Compared to standard GNNs, attacking gRSs is more challenging due to the heterogeneity of network structure and the entanglement between users and items. To overcome such challenges, we propose GSPAttack —a generative surrogate-based poison attack framework for gRSs. GSPAttack tailors a learning process to surrogate a recommendation model as well as generate fake users and user-item interactions while preserving the data correlation between users and items for recommendation accuracy. Although maintaining high accuracy for other items rather than the target item seems counterintuitive, it is equally crucial to the success of a poison attack. Extensive evaluations on four real-world datasets revealed that GSPAttack outperforms all baselines with competent recommendation performance and is resistant to various countermeasures.
A dual drift compensation framework based on subspace learning and cross-domain adaptive extreme learning machine for gas sensors

Sensor drift has been recognized as the root cause of decreased effectiveness in the gas sensor community. To date, most drift compensation strategies have focused on classification accuracy, while neglecting the labelling cost in the real-world. To simultaneously improve classification accuracy and reduce labelling cost, we propose a novel drift compensation framework for gas sensors, which combines cross-domain subspace learning with balanced distribution and cross-domain adaptive extreme learning machine (CSBD-CAELM). First, in a common subspace, the marginal distribution and conditional distribution between the source domain (clean data) and target domain (drift data) are adaptively aligned, which reduces the distribution divergence between domains. Then, the proposed CSBD-CAELM learns a robust classifier by leveraging the guide samples. Finally, both the subspace and the classifier are optimized continually by iteratively refining the soft labels of the target domain. In this way, a dual drift compensation effect combining the feature level and classifier level is achieved. Compared with the existing methods based on transfer samples, the labelling cost is reduced. The experimental results show that, at the same labelling cost, the CSBD-CAELM can improve the accuracy by 19.5% and 26.4% in the long-term drift scenario and short-term drift scenario, respectively.
Using sentiment analysis to evaluate qualitative students’ responses

Text analytics in education has evolved to form a critical component of the future SMART campus architecture. Sentiment analysis and qualitative feedback from students is now a crucial application domain of text analytics relevant to institutions. The implementation of sentiment analysis helps understand learners' appreciation of lessons, which they prefer to express in long texts with little or no restriction. Such expressions depict the learner's emotions and mood during class engagements. This research deployed four classifiers, including Naïve Bayes (NB), Support Vector Machine (SVM), J48 Decision Tree (DT), and Random Forest (RF), on a qualitative feedback text after a semester-based course session at the University of Education, Winneba. After enough training and testing using the k-fold cross-validation technique, the SVM classification algorithm performed with a superior accuracy of 63.79%.
The unseen Black faces of AI algorithms

An audit of commercial facial-analysis tools found that dark-skinned faces are misclassified at a much higher rate than are faces from any other group. Four years on, the study is shaping research, regulation and commercial practices. Pivotal study of facial recognition algorithms revealed racial bias.
Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning

A commercially viable multi-stakeholder recommendation system maximizes the utility gain by learning the personalized preferences of multiple stakeholders, such as consumers and producers. Existing multi-stakeholder studies rely on a consumer-item interaction matrix to evaluate the producers' preferences and utility gain. However, these methods result in a negligible boost in producers' utility, as consumer-item interaction provides only a limited insight into producers' preferences. Instead, an independent producer-item interaction matrix may better represent the needs and interests of producers. The deep neural networks have recently achieved encouraging results in a recommendation by estimating user preferences and learning user-item non-linear features. The multi-stakeholder recommendation system may employ this strength of the deep neural network to combine consumer-producer preferences and generate the optimal estimate of their common interest. Hence this study proposes a deep neural network-based multi-stakeholder recommendation system model for aggregating consumer and producer preferences. Next, a multi-criteria rating-based interaction matrix is proposed to learn the producers' preference over an item. Further, we perform deep neural network-based model training to generate the cumulative preference matrix by learning and aggregating the preferences of consumer and producer stakeholders. This work performs extensive experiments over Movie Lens-100 K and 1 M datasets with numerous activation functions, hidden layer configuration, and optimizers. The prediction accuracy, ranking, and utility gain-based evaluation results validate the success of the proposed model in developing a multi-criteria matrix for producers' and deep neural network-based multi-stakeholder preference aggregation over the baseline models.