Reviewerly/exHarmony huggingface dataset:
Advanced Deep Learning Human Herpes Virus 6 (HHV-6) Molecular Detection in Understanding Human Infertility

To see if HHV-6 may be a cause of infertility, researchers looked at 18 men and 10 women who had unexplained critical fertility and had at least one prior pregnancy. HHV-6 DNA was discovered in both infertile and fertile peripheral blood mononuclear cells (PBMC) (12 and 14%, respectively); endometrial epithelial cells from 4/10 (40%) infertile women were positive for HHV-6 DNA; this viral DNA was not found in the endometrium of fertile women. When endometrial epithelial cells were cultivated, they produced viral early and late proteins, suggesting the existence of an infectious virus. Endometrial HHV-6 infection creates an aberrant NK cell and cytokine profile, resulting in a uterine domain that is not favorable to conception, according to the findings. To corroborate the findings, studies of extra fertile and barren women should be done. Semen samples were taken from 18 guys who visited the Government General Hospital Guntur's infertility department because they were having reproductive issues with their partners. Herpes virus DNA has been discovered in the sperm of symptomatic fertile and infertile male patients on rare instances. Furthermore, researchers must investigate the role of viral diseases in male infertility.
Federated Transfer Learning With Client Selection for Intrusion Detection in Mobile Edge Computing

In this letter, we propose an efficient federated transfer learning (FTL) framework with client selection for intrusion detection (ID) in mobile edge computing (MEC). Specifically, we leverage federated learning (FL) to preserve privacy by training model locally, and utilize transfer learning (TL) to improve training efficiency by knowledge transfer. For FL, unreliable and low-quality clients should not be selected to participate in the training. Therefore, we integrate FTL with a reinforcement learning (RL)-based client selection scheme to achieve the highest ID accuracy within a budget limit on the number of participating clients. Experimental results show that the FTL significantly improves ID accuracy and communication efficiency as compared with the FL. Furthermore, the FTL framework with RL-based client selection can achieve the highest accuracy within budget, which improves performance while saving cost.
Motion Units: Generalized Sequence Modeling of Human Activities for Sensor-Based Activity Recognition

This paper proposes an innovative activity modeling method for human activity recognition, which partitions the human activity into a sequence of shared, meaningful, and activity distinguishing states, called Motion Units, analog to phonemes in speech recognition. The partitions and generalization define a human activity dictionary, which endows this method with operability, universality, and expandability. Our preliminary experiments demonstrate on-par accuracy with other models while requiring fewer parameters and increasing separability between phases. Furthermore, the developed model was easily transferred with minor adjustments to two other datasets, demonstrating the proposed method's scalability. This framework enables expandable, interpretable, and scaleable modeling and recognition of human activities.
Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4

Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this problem, a new vehicle detection and classification model, named YOLOv4_AF, is proposed in this paper, based on an optimization of the YOLOv4 model. In the proposed model, an attention mechanism is utilized to suppress the interference features of images through both channel dimension and spatial dimension. In addition, a modification of the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN), utilized by YOLOv4, is applied in order to enhance further the effective features through down-sampling. This way, the objects can be steadily positioned in the 3D space and the object detection and classification performance of the model can be improved. The results, obtained through experiments conducted on two public data sets, demonstrate that the proposed YOLOv4_AF model outperforms, in this regard, both the original YOLOv4 model and two other state-of-the-art models, Faster R-CNN and EfficientDet, in terms of the mean average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1 score</i> , by achieving respective values of 83.45% and 0.816 on the BIT-Vehicle data set, and 77.08% and 0.808 on the UA-DETRAC data set.
Security-Aware and Privacy-Preserving Personal Health Record Sharing Using Consortium Blockchain

With the fast boom of Internet of Medical Things (IoMT) devices and an increasing focus on personal health, personal health data are extensively collected by IoMT and stored as personal health records (PHRs). PHRs are frequently shared for accurate diagnosis, prognosis prediction, health advice consulting, etc. Since PHRs are highly private, the data-sharing process leads to wide-ranging concerns on privacy leakage and security compromise. Existing research has shown that the centralized systems, as the mainstream mode, are under the great risks. Motivated by this, we propose a consortium blockchain-based PHR management and sharing scheme, which is both security aware and privacy preserving. We adopt the interplanetary file system (IPFS) to store the PHR ciphertext of IoMT. Then, zero-knowledge proof can provide evidence for verifying keyword index authentication on blockchain. Moreover, the scheme jointly leverages modified attribute-based cryptographic primitives and tailor-made smart contracts to achieve secure search, privacy preservation, and personalized access control in IoMT scenarios. Security analysis is conducted to show that the designed protocols attain the expected design goals. This is followed by extensive evaluation results derived from real-world data sets, which demonstrate the superiority of the proposed scheme over current leading ones.
Detection and Mitigation of Label-Flipping Attacks in Federated Learning Systems with KPCA and K-Means

Federated learning is a popular machine-learning technique that is often preferred due to its efficiency and data privacy. However, federated-learning systems face a serious threat of data poisoning that can cause the systems and predictions to fail if not treated in time. This study extends another study of data-poisoning attacks in federated-learning systems conducted by Tolpegin et al. We first investigate the effectiveness of the defense strategy suggested in Tolpegin’s study. Then we propose an improved defense strategy that emphasizes employing KPCA and K-mean clustering. It is demonstrated in this paper that our defense strategy, when combined with improved dimensionality-reduction algorithms, produces better results in defending against data-poisoning attacks in federated-learning systems.
Sentiment analysis on Bangla text using extended lexicon dictionary and deep learning algorithms

Sentiment analysis (SA) is a subset of natural language processing (NLP) research. In the case of categorical weighted based dictionary with rule-based sentiment score generation, no work in SA has been done yet in Bangla language using deep learning (DL) approaches. This paper proposes DL models for SA on Bangla text using an extended lexicon data dictionary (LDD). We implement the rule-based method Bangla text sentiment score (BTSC) algorithm for extracting polarity from large texts. These polarities are then fed into the neural network along with the preprocessed text as training samples. The preprocessed texts are formatted as a vectorization of words of unique numbers of pre-trained word embedding models. Word2Vec matrix with top highest probability word is applied on embedding layer as a weighted matrix to fit the DL models. This paper also presents a remarkably detailed analysis of selective DL models with some fine tuning. The fine-tuning includes the use of drop out, optimizer regularization, learning rate, multiple layers, filters, attention mechanism, capsule layers, transformer with progressive training along with validation and testing accuracy, precision, recall and F1-score. Experimental results indicate that the proposed new long short-term memory (LSTM) models are highly accurate in performing SA tasks. For our proposed hierarchical attention based LSTM (HAN-LSTM), Dynamic routing based capsule neural network with Bi-LSTM (D-CAPSNET-Bi-LSTM) and bidirectional encoder representations from Transformers (BERT) with LSTM (BERT-LSTM) model we achieved accuracy values of 78.52%, 80.82% and 84.18% respectively.
Chaotic Harris hawks optimization algorithm

Abstract Harris hawks optimization (HHO) is a population-based metaheuristic algorithm, inspired by the hunting strategy and cooperative behavior of Harris hawks. In this study, HHO is hybridized with 10 different chaotic maps to adjust its critical parameters. Hybridization is performed using four different methods. First, 15 test functions with unimodal and multimodal features are used for the analysis to determine the most successful chaotic map and the hybridization method. The results obtained reveal that chaotic maps increase the performance of HHO and show that the piecewise map method is the most effective one. Moreover, the proposed chaotic HHO is compared to four metaheuristic algorithms in the literature using the CEC2019 set. Next, the proposed chaotic HHO is applied to three mechanical design problems, including pressure vessel, tension/compression spring, and three-bar truss system as benchmarks. The performances and results are compared with other popular algorithms in the literature. They show that the proposed chaotic HHO algorithm can compete with HHO and other algorithms on solving the given engineering problems very successfully.
Mobile Devices Interface Adaptivity Using Ontologies

Currently, many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces. The context offers the information base for the development of Adaptive user interface (AUI) frameworks to overcome the heterogeneity. For this purpose, the ontological modeling has been made for specific context and environment. This type of philosophy states to the relationship among elements (e.g., classes, relations, or capacities etc.) with understandable satisfied representation. The context mechanisms can be examined and understood by any machine or computational framework with these formal definitions expressed in Web ontology language (WOL)/Resource description frame work (RDF). The Protégé is used to create taxonomy in which system is framed based on four contexts such as user, device, task and environment. Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree. The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases. The semantic context model is focused to bring in the usage of adaptive environment. This exploration has finished up with a versatile, scalable and semantically verified context learning system. This model can be mapped to individual User interface (UI) display through smart calculations for versatile UIs.
A cryptosystem based on a chameleon chaotic system and dynamic DNA coding

In this article, a chameleon cryptosystem, based not only on the properties of a chameleon chaotic system but also on the dynamic DNA coding is proposed. The chaotic chameleon system is studied as a whole to choose the chaotic sequence of each type of system. It emerges from this dynamic that, the chaotic system can produce self-excited attractors and three families of hidden attractors (no equilibrium, stable equilibrium, and line equilibrium). The proposed technique is used for image encryption and applies dynamic DNA coding (i.e. over all stages) to disrupt the pixel image distribution and tie it tightly to the chosen chaotic sequences. To further improve the security of the proposed chameleon cryptosystem, the confusion and diffusion operations are closely related to the chaotic sequences generated by the chaotic chameleon system. The experimental results and the security analysis have shown that the proposed chameleon cryptosystem has good performance and can withstand various typical and classical attacks.
Digital Transformation in Higher Education Institutions as a Driver of Social Oriented Innovations

Abstract Higher education institutions have in the digital transformation an opportunity to facilitate access to education for individuals of different social backgrounds. At the same time, it will provide them with the necessary tools and skills to face current global problems (poverty, health quality, income disparities, environmental crises, among others) from a transdisciplinary perspective. Progress in the digitalization of higher education has been significantly accelerated by the onset of a pandemics in early 2020 (in European conditions). Such a rapid and massive transition to distance (online) education with comprehensive support for digital technologies is unparalleled. The survey conducted reflects the consequences of this change. Results shows that digital transformation improves some of the most demanded skills in the new knowledge society (searching and processing information, digital communication and socialization or working with text). Likewise, it has an important role in reducing costs related to education, but also in degradation of abilities in personal communication.
Deep Learning for Massive MIMO Uplink Detectors

Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers&#x2019; performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector&#x2019;s architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

Aiming to alleviate data sparsity and cold-start problems of tradi- tional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unifying the KG with user-item interactions into a tripartite graph, recent works explore the graph topologies to learn the low-dimensional representations of users and items with rich semantics. These real-world tripartite graphs are usually scale-free, however, the intrinsic hierarchical graph structures of which are underemphasized in existing works, consequently, leading to suboptimal recommendation performance. To address this issue and provide more accurate recommendation, we propose a knowledge-aware recommendation method with Lorentz model of the hyperbolic geometry, namely Lorentzian Knowledge-enhanced Graph convolutional networks for Recommendation (LKGR). LKGR facilitates better modeling of scale-free tripartite graphs after the data unification. Specifically, we employ different information propagation strategies in the hyperbolic space to explicitly encode heterogeneous information from historical interactions and KGs. Additionally, our proposed knowledge-aware attention mechanism enables the model to automatically measure the information contribution, producing the coherent information aggregation in the hyperbolic space. Extensive experiments on three real-world benchmarks demonstrate that LKGR outperforms state-of-the-art methods by 3.6-15.3% of [email protected] on Top-K recommendation.
FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance

The objective of fraud detection is to distinguish fraudsters from normal users. In graph/network environments, both fraudsters and normal users are modeled as nodes, and the connections between those nodes are represented as edges. Fraudsters typically try to camouflage themselves with “normal” behaviors, say, by deliberately establishing many connections to normal users. Such camouflage inherently makes their appearance inconsistent with the essence of what it is to be normal, and gives rise to inconsistencies in the graph. In this paper, we investigate three aspects of these graph inconsistencies: features, topologies, and relations. To date, graph-based fraud detectors have shown a rather limited capability to fuse information about different types of inconsistencies. Apart from that, there is another problem of imbalance to overcome. This is because fraudsters usually only account for a very small percentage of all users. To achieve a promising capability, i.e., dual-resistant to graph inconsistency and imbalance, we present a new fraud detection model FRAUDRE based on Graph Neural Networks. Extensive experiments comparing eight up-to-date baselines on two real-world datasets, Amazon and YelpChi, demonstrate the superiority of FRAUDRE.
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from a heterogeneous source domain. From algorithmic perspectives, several methods have been proposed to solve the SsHeDA problem; yet there is still no theoretical foundation to explain the nature of the SsHeDA problem or to guide new and better solutions. Motivated by compatibility condition in semi-supervised probably approximately correct (PAC) theory, we explain the SsHeDA problem by proving its generalization error - that is, why labeled heterogeneous source data and unlabeled target data help to reduce the target risk. Guided by our theory, we devise two algorithms as proof of concept. One, kernel heterogeneous domain alignment (KHDA), is a kernel-based algorithm; the other, joint mean embedding alignment (JMEA), is a neural network-based algorithm. When a dataset is small, KHDA's training time is less than JMEA's. When a dataset is large, JMEA is more accurate in the target domain. Comprehensive experiments with image/text classification tasks show KHDA to be the most accurate among all non-neural network baselines, and JMEA to be the most accurate among all baselines.
A Survey of Domain-Specific Architectures for Reinforcement Learning

Reinforcement learning algorithms have been very successful at solving sequential decision-making problems in many different problem domains. However, their training is often time-consuming, with training times ranging from multiple hours to weeks. The development of domain-specific architectures for reinforcement learning promises faster computation times, decreased experiment turn-around time, and improved energy efficiency. This paper presents a review of hardware architectures for the acceleration of reinforcement learning algorithms. FPGA-based implementations are the focus of this work, but GPU-based approaches are considered as well. Both tabular and deep reinforcement learning algorithms are included in this survey. The techniques employed in different implementations are highlighted and compared. Finally, possible areas for future work are suggested, based on the preceding discussion of existing architectures.
Domain knowledge-based security bug reports prediction

To eliminate security attack risks of software products, the security bug report (SBR) prediction has been increasingly investigated. However, there is still much room for improving the performance of automatic SBR prediction. This work is inspired by the work of two recent studies proposed by Peters et al. and Wu et al., which focused on SBR prediction and have been published on the top tier journal TSE (IEEE Transactions on Software Engineering). The goal of this work is to improve the effectiveness of supervised machine learning-based SBR prediction with the help of software security domain knowledge. First, we split the words in summary and description fields of the SBRs. Then, we use customized relationships to label entities and build a rule-based entity recognition corpus. After that, we establish relationships between entities and construct knowledge graphs. The information of CWE (Common Weakness Enumeration) is used to expand our corpus and the security-related words and phrases are integrated. Finally, we predict SBRs from target project by calculating the cosine similarity between our integrated corpus and the target bug reports. Our experimental evaluation on 5 open-source SBR datasets shows that our domain knowledge-guided approach could improve the effectiveness of SBRs prediction by 52% in terms of F1-score on average.
Three-learning strategy particle swarm algorithm for global optimization problems

Social Learning Particle Swarm Optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability and low search efficiency. Hence, an improved SL-PSO, namely, Three-Learning Strategy PSO (TLS-PSO) is proposed in this paper. Firstly, a med-point-example learning strategy and a random learning strategy are proposed to replace the imitation component and social influence component of SL-PSO to enhance the exploitation and exploration, respectively. Secondly, the two learning strategies are combined cleverly into an updating equation to balance exploration and exploitation. Finally, a worst-best example learning strategy is merged skillfully to construct TLS-PSO with hybrid learning mechanism and further enhance the search ability. The experimental results on the complex functions from CEC2013 and CEC2017 test sets indicate that TLS-PSO has better performance compared with state-of-the-art PSO variants and other algorithms. For example, TLS-PSO has an advantage over SL-PSO on 50 of the 56 functions from CEC2013, its running time is less than SL-PSO’s and it has higher search efficiency. Simulation results on the 10 engineering problems also show that TLS-PSO outperforms 7 excellent algorithms, such as IUDE and iLSHADE∊. It is expected to solve practical problems better.