Reviewerly/exHarmony huggingface dataset:
Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: <i>epsilon</i> (fixed neighborhood radius) and <i>MinPts</i> (the lowest number of objects in <i>epsilon</i>). However, it can't handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one. Only user input in the proposed method is the <i>MinPts</i>. <i>Epsilon</i> on the other hand, is computed automatically based on statistical information of the dataset. The proposed method finds the core distance for each object in the dataset, takes the average of these distances as the first value of <i>epsilon</i>, and finds the clusters satisfying this density level. The remaining unclustered objects will be clustered using a new value of <i>epsilon</i> that equals the average core distances of unclustered objects. This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset's size. The proposed method requires <i>MinPts</i> only as an input parameter because <i>epsilon</i> is computed from data. Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results. Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them. The accuracy of the method ranges from 92% to 100% for the experimented datasets.
A Universal Activation Function for Deep Learning

Recently, deep learning has achieved remarkable results in fields that require human cognitive ability, learning ability, and reasoning ability. Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity. Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process. However, it takes a lot of time and effort for researchers to use the existing activation function in their research. Therefore, in this paper, we propose a universal activation function (UA) so that researchers can easily create and apply various activation functions and improve the performance of neural networks. UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters. The famous Convolutional Neural Network (CNN) and benchmark dataset were used to evaluate the experimental performance of the UA proposed in this study. We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which the UA is applied. In addition, we evaluated the performance of the new activation function generated by adjusting the hyperparameters of the UA. The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5% through the UA, although most of them showed similar performance to the traditional activation function.
iTelos—Case Studies in Building Domain-Specific Knowledge Graphs

The construction of a domain-specific knowledge graphs is a complex task requiring the synergistic work of domain experts, knowledge engineers, and data scientists. The goal of the iTelos methodology is to support this task when carried out by working groups with competence in computer science and in the targeted domain, but with little know-how in the software and knowledge engineering development process. The concrete usefulness of iTelos is presented by discussing six concrete case studies where it has been applied and evaluated. The main features of iTelos validated in this paper are (i) how to create an explicit and clear purpose for the construction of the domain-specific Knowledge Graph; (ii) how to enhance of the quality of the Knowledge Graph produced by defining an iterative validation of intermediate results; (iii) how to organize the project activities based on the specific roles involved; and (iv) how to make the methodology as automated and iterative as possible, resulting in a more cost-efficient and time-efficient methodology. The evaluation is both qualitative and quantitative. The results clearly show that iTelos is an important step in the right direction, still highlighting the need for refinement and improvement of the methodology.
The model of informative ordering in the analysis of socio-psychological processes

The article presents the possibilities of informative ordering in the analysis of socio-psychological processes. We carried out a brief review of publications covering the use of mathematical apparatus in sociological and psychological research. The main groups of research strategies that have found application in the study of modern socio-psychological processes are identified. We substantiated the necessity and advantages of using the method of information ordering. In our research we described the stages of application of the proposed method, the mathematical apparatus based on the main components, estimates of structural similarity and the amount of information. The developed model of informative ordering can be used to determine the significance of information obtained in the course of sociological, psychological, social, economic and other types of research, where information about the state of the object and subject of research is organized in the form of variational series.
Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document's visual and structural appearance. Thus, document layout analysis (DLA) is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives. This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis (SDLA) by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts. The proposed SDLA approach enables the derivation of implicit information and semantic characteristics, which can be effectively utilized in dozens of practical applications for various purposes, in a way bridging the semantic gap and providing more understandable high-level document image analysis and more invariant characterization via absolute and relative labeling. This approach is validated and evaluated on a large dataset of Arabic handwritten manuscripts comprising complex layouts. The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts. It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional, real-life tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.
FPG-AI: A Technology-Independent Framework for the Automation of CNN Deployment on FPGAs

In recent years, Convolutional Neural Networks (CNNs) have demonstrated outstanding results in several emerging classification tasks. The high-quality predictions are often achieved with computationally intensive workloads that hinder the hardware acceleration of these models at the edge. Field Programmable Gate Arrays (FPGAs) have proven to be energy efficient platforms for the execution of these algorithms and works proposing methods for automating the design on these devices have acquired relevance. The common purpose is to enable a wide range of users without specific skills to accelerate CNNs on FPGAs with reduced development times. In this paper, we present FPG-AI, a technology-independent toolflow for automating the deployment of CNNs on FPGA. The framework combines the use of model compression strategies with a fully handcrafted Hardware Description Languages (HDL)-based accelerator that poses no limit on device portability. On top of that, an automation process merges the two design spaces to define an end-to-end and ready-to-use tool. Experimental results are reported for reference models extracted from the literature (LeNet, NiN, VGG16, MobileNet-V1) on multiple classification datasets (MNIST, CIFAR10, ImageNet). To prove the technology independence of FPG-AI, we characterize the toolflow on devices with heterogeneous resource budgets belonging to different vendors (Xilinx, Intel, and Microsemi). Comparison with state-of-the-art work confirms the unmatched device portability of FPG-AI and shows performance metrics in line with the literature.
Dual-Objective Personalized Federated Service System With Partially-Labeled Data Over Wireless Networks

Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users' data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assumptions may not hold in most cases, where data labeling is costly, or most users have no sufficient local data to satisfy their personalized needs. To this end, we first formulate the problem, DoLP, that studies the issue of insufficient and partially-labeled data on FL-based services. DoLP aims to maximize two service objectives: 1) personalized classification objective and 2) the personalized labeling objective for each user within the constraint of training time over wireless networks. Then, we propose a PFL-based service system DoFed-SPP to solve DoLP. The DoFed-SPP's novelty is two-fold. First, we devise an inference-based first-order approximation metric, similarity ratio, to identify the similarity between users' local data. Second, we design an approximation algorithm to determine the appropriate size and set of users for uploading in each round. Extensive experiments show DoFed-SPP outperforms the state-of-the-art in final accuracy and time-to-accuracy performance on CIFAR10/100 and DBPedia.
Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.
Stackelberg Game-Based Resource Allocation with Blockchain for Cold-Chain Logistics System

Cold-chain logistics system (CCLS) plays the role of collecting and managing the logistics data of frozen food. However, there always exist problems of information loss, data tampering, and privacy leakage in traditional centralized systems, which influence frozen food security and people’s health. The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability. This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem. This system aggregates the production base, storage, transport, detection, processing, and consumer to form a cold-chain logistics union. The blockchain ledger guarantees that the logistics data cannot be tampered with and establishes a traceability mechanism for food safety incidents. Meanwhile, to improve the value of logistics data, a Stackelberg game-based resource allocation model has been proposed between the logistics data resource provider and the consumer. The competition between resource price and volume balances the resource supplement and consumption. This model can help to achieve an optimal resource price when the Stackelberg game obtains Nash equilibrium. The two participants also can maximize their revenues with the optimal resource price and volume by utilizing the backward induction method. Then, the performance evaluations of transaction throughput and latency show that the proposed distributed CCLS is more secure and stable. The simulations about the variation trend of data price and amount, optimal benefits, and total benefits comparison of different forms show that the resource allocation model is more efficient and practical. Moreover, the blockchain-based CCLS and Stackelberg game-based resource allocation model also can promote the value of logistic data and improve social benefits.
Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network. The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent. The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol (SAW_WDA). The network has been analyzed by authentication protocol to avoid malware attacks. The data of network users will be collected and classified using multilayer perceptron gradient decision tree (MLPGDT) classifiers. Based on the classification output, the decision for the detection of attackers and authorized users will be identified. The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput, end-end delay, network security, network concept drift, and results based on classification with regard to accuracy, memory, and precision and F-1 score.
Blockchain and Bank Lending Behavior: A Theoretical Analysis

The application of blockchain technology in the financial industry has become the focus of global attention; thus, the traditional credit business of commercial banks faces unprecedented opportunities and challenges. Based on the theoretical analysis, this study systematically examines the dynamic impact of blockchain on the lending behavior of commercial banks. The results indicate that, in the case of interest rate marketization, the blockchain expands bank credit through the mechanism of “reducing management costs”; in the case that interest rates have not yet been marketized, the blockchain can also increase bank lending through the channel of “correcting the price of funds” by narrowing the difference between the market equilibrium and the bank deposit interest rate. The results are of great significance for a deeper understanding of the impact and mechanism of blockchain on bank credit. JEL codes: G21; O31; O33
Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the ATE task is the base task for APC, and its result impacts the accuracy of APC. Thus, in this study, we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a single model. This paper integrates the multi-task model, namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification (LCF-ATEPC) and Arabic Bidirectional Encoder Representation from Transformers (AraBERT) as a shred layer for Arabic contextual text representation. The LCF-ATEPC model is based on a multi-head self-attention and local context focus mechanism (LCF) to capture the interactive information between an aspect and its context. Moreover, data augmentation techniques are proposed based on state-of-the-art augmentation techniques (word embedding substitution with constraints and contextual embedding (AraBERT)) to increase the diversity of the training dataset. This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA. Extensive experiments were conducted on the original and combined datasets (merging the original and augmented datasets). Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques. Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer (AR-LCF-ATEPC-Fusion) and the proposed data augmentation word embedding-based method (FastText) on the combined dataset.
Adaptive Regularized Warped Gradient Descent Enhances Model Generalization and Meta-learning for Few-shot Learning

Warped Gradient descent (WarpGrad) is a remarkable meta-learning method for gradient transformation by inserting warp-layers. However, the task-shared initialization provided by WarpGrad is difficult to be adaptive to each task. Moreover, transforming gradients with meta-learned warp-layers ignores the local geometric features or task-specific knowledge, and may lead to a significant risk of overfitting caused by the increase of parameters. In this paper, we propose ARWarpGrad to guarantee better generalization performance with faster convergence speed by modeling both the cross-task and task-specific knowledge. We introduce Initialization Modulation (IM) to meta-learn to initialize the task-learner specifically. Furthermore, the Mixed Gradient Preprocessing (MGP), which includes the Adaptive Learning Rates (ALR) and the Gaussian Momentum Dropout (GMD), is put forward to provide better adaptive optimization direction and length for task adaptation based on the feature of local geometries. In addition, Memory Regularization (MR) is provided to alleviate the overfitting problem effectively with the use of parameter memory. Ultimately, extensive experiments on three settings demonstrate that ARWarpGrad achieves state-of-the-art performance with convergence acceleration and overfitting prevention characteristics.
eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset

Affective state estimation is a research field that has gained increased attention from the research community in the last decade. Two of the main catalysts for this are the advancement in the data analysis using artificial intelligence and the availability of high-quality video. Unfortunately, benchmarks and public datasets are limited, thus making the development of new methodologies and the implementation of comparative studies essential. The current work presents the eSEE-d database, which is a resource to be used for emotional State Estimation based on Eye-tracking data. Eye movements of 48 participants were recorded as they watched 10 emotion-evoking videos, each of them followed by a neutral video. Participants rated four emotions (tenderness, anger, disgust, sadness) on a scale from 0 to 10, which was later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled three self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires' self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low-level eye-recorded metrics, and their correlations with the participants' ratings are investigated. Finally, we take on the challenge to classify arousal and valence levels based solely on eye and gaze features, leading to promising results. In particular, the Deep Multilayer Perceptron (DMLP) network we developed achieved an accuracy of 92% in distinguishing positive valence from non-positive and 81% in distinguishing low arousal from medium arousal. The dataset is made publicly available.