Reviewerly/exHarmony huggingface dataset:
VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems

Federated learning for edge computing is a promising solution in the data booming era, which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server. However, the frequently transmitted local gradients could also leak the participants' private data. To protect the privacy of local training data, lots of cryptographic-based Privacy-Preserving Federated Learning (PPFL) schemes have been proposed. However, due to the constrained resource nature of mobile devices and complex cryptographic operations, traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously. To tackle this problem, we propose a Verifiable Privacy-preserving Federated Learning scheme (VPFL) for edge computing systems to prevent local gradients from leaking over the transmission stage. Firstly, we combine the Distributed Selective Stochastic Gradient Descent (DSSGD) method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality, so as to reduce the computation cost of the complex cryptosystem. Secondly, we further present an online/offline signature method to realize the lightweight gradients integrity verification, where the offline part can be securely outsourced to the edge server. Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality, authentication, and integrity. At last, we evaluate both communication overhead and computation cost of the proposed VPFL scheme, the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.
Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization

In digital pathology, segmentation is a fundamental task for the diagnosis and treatment of diseases. Existing fully supervised methods often require accurate pixel-level annotations that are both time-consuming and laborious to generate. Typical approaches first pre-process histology images into patches to meet memory constraints and later perform stitching for segmentation; at times leading to lower performance given the lack of global context. Since image level labels are cheaper to acquire, weakly supervised learning is a more practical alternative for training segmentation algorithms. In this work, we present a weakly supervised framework for histopathology segmentation using only image-level labels by refining class activation maps (CAM) with self-supervision. First, we compress gigapixel histology images with an unsupervised contrastive learning technique to retain high-level spatial context. Second, a network is trained on the compressed images to jointly predict image-labels and refine the initial CAMs via self-supervised losses. In particular, we achieve refinement via a pixel correlation module (PCM) that leverages self-attention between the initial CAM and the input to encourage fine-grained activations. Also, we introduce a feature masking technique that performs spatial dropout on the compressed input to suppress low confidence predictions. To effectively train our model, we propose a loss function that includes a classification objective with image-labels, self-supervised regularization and entropy minimization between the CAM predictions. Experimental results on two curated datasets show that our approach is comparable to fully-supervised methods and can outperform existing state-of-the-art patch-based methods. https://github.com/PhilipChicco/wsshisto.
Charting Past, Present, and Future Research in the Semantic Web and Interoperability

Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field.
5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation

Encryption of visual data is a requirement of the modern day. This is obvious and greatly required due to widespread use of digital communication mediums, their wide range of applications, and phishing activities. Chaos approaches have been shown to be extremely effective among many encryption methods. However, low-dimensional chaotic schemes are characterized by restricted system components and fundamental structures. As a result, chaotic signal estimation algorithms may be utilized to anticipate system properties and their initial values to breach the security. High-dimensional chaotic maps on the other hand, have exceptional chaotic behavior and complex structure because of increased number of system parameters. Therefore, to overcome the shortcomings of the lower order chaotic map, this paper proposes a 5D Gauss Map for image encryption for the first time. The work presented here is an expansion of the Gauss Map’s current 1D form. The performance of the stated work is evaluated using some of the most important metrics as well as the different attacks in the field. In addition to traditional and well-established metrics such as PSNR, MSE, SSIM, Information Entropy, NPCR, UACI, and Correlation Coefficient that have been used to validate encryption schemes, classification accuracy is also verified using transfer learning. The simulation was done on the MATLAB platform, and the classification accuracy after the encryption-decryption process is compared.
A Blockchain-Based Digital Asset Platform with Multi-Party Certification

Assets such as warehouse receipts are important for enterprises, which can be used to pledge in supply chain finance (SCF). However, traditional pledges are performed manually, which inevitably encounters inefficiency and security problems such as multiple pledges. To improve asset security, we propose a blockchain-based digital asset platform (BDAP) with multi-party certification. BDAP not only has a security protocol based on the threshold ECDSA algorithm to make related participants confirm the authenticity of assets but also embeds a Verifiable Byzantine Fault Tolerant (VBFT) mechanism, randomly selecting the consensus nodes and improving the safety of the nodes. Moreover, data stored on the blockchain makes traceability possible. Through a set of experiments, we have verified the functionality and performance of BDAP. When the pressure test reaches 100 concurrent user volume, BDAP’s average response time is 1.441 s, showing a high ability to process transactions. However, now just a few open-minded banks are willing to access BDAP, it might take a long time to change the traditional perception of the participants in supply chain finance.
Semantic Segmentation in Art Paintings

Abstract Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintings, an even more challenging task because of a much larger diversity in colors, textures, and shapes and because there are no ground truth annotations available for segmentation. We propose an unsupervised method for semantic segmentation of paintings using domain adaptation. Our approach creates a training set of pseudo‐paintings in specific artistic styles by using style‐transfer on the PASCAL VOC 2012 dataset, and then applies domain confusion between PASCAL VOC 2012 and real paintings. These two steps build on a new dataset we gathered called DRAM (Diverse Realism in Art Movements) composed of figurative art paintings from four movements, which are highly diverse in pattern, color, and geometry. To segment new paintings, we present a composite multi‐domain adaptation method that trains on each sub‐domain separately and composes their solutions during inference time. Our method provides better segmentation results not only on the specific artistic movements of DRAM, but also on other, unseen ones. We compare our approach to alternative methods and show applications of semantic segmentation in art paintings. The code and models for our approach are publicly available at: https://github.com/Nadavc220/SemanticSegmentationInArtPaintings .
Blockchain-based Voting System Powered by Post-Quantum Cryptography (BBVSP-PQC)

Voting is a well-proven method of democratizing human societies and achieving consensus to avoid any conflicts. The unbiased trusted third parties are involved to conduct and administer the voting. However, in the past, various incidents of misconduct and biases were reported in the voting process. The blockchain eliminates the need for any trusted third party by using consensus algorithms and smart contracts, hence is the best fit to conduct voting as blockchain provides tamperproof, transparent, secure, and auditable voting solutions. Blockchain uses public-private key cryptography to ensure end-to-end encryption and uses hash functions and hashes to tamperproof voting ledgers. Quantum computing is going to be a major threat to the blockchain as current public-private key cryptography and hash functions are being used in almost all blockchains and Distributed Ledger Technologies (DLTs) belonging to the Pre-quantum cryptography era. This research article critically reviews the advancement and current state of blockchain-based online voting systems, their features, and challenges along with advancements in quantum computing, post-quantum cryptography. This research article proposes a system design of a voting system on the blockchain using post-quantum cryptography along with systematic and critical views and conclusions towards quantum-resistant blockchain for a future online voting system in the post-quantum cryptography era.
Survey on Malicious URL Detection Techniques

Crimes in the cyberspace are increasing day by day. Recent cyber threat defense reports states that 80.7% of the systems are compromised at least once in 2020. Cyber criminals taking the pandemic situation as an opportunity for the mass attack through malicious URL circulated by email or text messages in social media. Performing cyber-attacks through malicious URLs is the handy method for the cyber criminals. Protecting from such attacks requires proper awareness and solid defense system. Some of the common approaches followed by the cybercriminals to deceive the victims are 1. Phishing URLs which is very similar to the legitimate URLs. 2. Redirecting URLs 3. Using JavaScript, redirects to the phishing URL when user interacts with webpage 4. Social engineering etc. As soon as the novice internet users clicks on the malicious URL link, cyber criminals can easily steal personal information or install malware on their device to get additional access. Recently malicious URLs are generated algorithmically and uses URL shortening service to evade the existing security setup such as firewall and web filters. In literature, the researchers have proposed several ways to detect the malicious URLs but, new attack vectors that are introduced by the cyber criminals can easily bypass the security system. The purpose of this paper is to provide an overview of various malicious URL detection techniques which includes blacklist based, rules based, machine learning and deep learning-based techniques. Most importantly, the paper discusses the common features used by the detection system from webpages to classify the URL as malicious or benign and various performance metrics. This will encourage the new researchers to bring out the innovative solutions.
Evaluation of clinical named entity recognition methods for Serbian electronic health records

The importance of clinical natural language processing (NLP) has increased with the adoption of electronic health records (EHRs). One of the critical tasks in clinical NLP is named entity recognition (NER). Clinical NER in the Serbian language is a severely under-researched area. The few approaches that have been proposed so far are based on rules or machine-learning models with hand-crafted features, while current state-of-the-art models have not been explored. The objective of this paper is to assess the performance of state-of-the-art NER methods on clinical narratives in the Serbian language.We designed an experimental setup for a comprehensive evaluation of state-of-the-art NER models. The gold standard corpus we used for the evaluation is comprised of discharge summaries from the Clinic for Nephrology at the University Clinical Center of Serbia. The following models were evaluated: conditional random fields (CRF), multilingual transformers (BERT Multilingual and XLM RoBERTa), and long short-term memory (LSTM) recurrent neural networks, and their ensembles. In addition, we investigated the necessity of the pretraining task of transformer based models and the use of pretrained word embeddings with LSTM model.Our results show that individually CRF had the best precision, the pretrained BERT Multilingual model had the best recall values, and the LSTM model had the best F1 score. The best performance was achieved by combining the existing models in a majority voting ensemble with an F1 score of 0.892. The presented results are similar to the inter annotator agreement on our gold standard corpus and are comparable to existing state-of-the-art results for clinical NER reported in literature.Existing state-of-the-art models can provide viable results for clinical named entity recognition when applied to languages with the complexity of the Serbian language without major modifications.
Shared motivations, goals and values in the practice of personal science: a community perspective on self-tracking for empirical knowledge

Abstract Some individuals do not limit their self-tracking efforts to passively collecting and observing gathered data about themselves, but rather develop it into forms of self-research and self-experimentation, also called “personal science”. This type of N-of-1 research is relevant to the fields of personal informatics, patient-led research and social studies of science, but as a knowledge generation practice is still poorly understood. To fill this gap, we conducted 22 semi-structured interviews to investigate the intrinsic and extrinsic motivations of individuals engaging in personal science activities, as well as shared goals and values present in self-research communities. Our analysis is based on a conceptual framework that integrates previous approaches in self-research, as well as in connection with citizen science, the scientific ethos and cooperation in peer production. We identify how self-researchers seek to go beyond personal metrics about their health and wellbeing regarding data provided by wearables, are engaged over time by individual involvement in technology and scientific-related activity, and collaborate following similar goals and values when learning and sharing empirical knowledge with peers. In this sense, personal science can be understood as a specific type of citizen science and an example of a more participatory and inclusive scientific culture driven by self-reflection, critical thinking and openness.
Face Recognition and Tracking Framework for Human–Robot Interaction

Recently, face recognition became a key element in social cognition which is used in various applications including human–robot interaction (HRI), pedestrian identification, and surveillance systems. Deep convolutional neural networks (CNNs) have achieved notable progress in recognizing faces. However, achieving accurate and real-time face recognition is still a challenging problem, especially in unconstrained environments due to occlusion, lighting conditions, and the diversity in head poses. In this paper, we present a robust face recognition and tracking framework in unconstrained settings. We developed our framework based on lightweight CNNs for all face recognition stages, including face detection, alignment and feature extraction, to achieve higher accuracies in these challenging circumstances while maintaining the real-time capabilities required for HRI systems. To maintain the accuracy, a single-shot multi-level face localization in the wild (RetinaFace) is utilized for face detection, and additive angular margin loss (ArcFace) is employed for recognition. For further enhancement, we introduce a face tracking algorithm that combines the information from tracked faces with the recognized identity to use in the further frames. This tracking algorithm improves the overall processing time and accuracy. The proposed system performance is tested in real-time experiments applied in an HRI study. Our proposed framework achieves real-time capabilities with an average of 99%, 95%, and 97% precision, recall, and F-score respectively. In addition, we implemented our system as a modular ROS package that makes it straightforward for integration in different real-world HRI systems.
Effective route generation framework using quantum mechanism-based multi-directional and parallel ant colony optimization

Path planning in a complex network is a traditional and important research area in the optimization and machine learning fields. Several metaheuristics have been proposed; however, they are limited by the number of iterations, limited resources, and computation times. Therefore, in this study, the above issue is solved by integrating the existing metaheuristic algorithm with a prediction method. As a metaheuristic algorithm, ant colony optimization (ACO) is modified and applied. Instead of modeling ants pursuing higher pheromone densities, the proposed framework predicts pheromone traits using the intermediate pheromone density. As the pheromone volumes in the intermediate stage fluctuate, they are modeled using a quantum mechanism. Thereafter, the following pheromone traits are estimated using the modeled stochastic differential equation and Ito integral. The predicted pheromones are used for the parameters in the subsequent ACO iterations. To demonstrate the effectiveness of the proposed framework, the shortest path generation in several large-scale networks is provided. The proposed framework is considered a highly efficient metaheuristic framework with the integration of quantum mechanism-based prediction and existing metaheuristics.
Evolving software system families in space and time with feature revisions

Abstract Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.
A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing histopathology images. These images, however, are very complex and large. Thus, locating the cancerous nuclei in them is very challenging. Hence, if an expert fails to diagnose their patients via these images, the situation may be exacerbated. Therefore, this study aims to introduce a method to mask as many cancer nuclei on histopathology images as possible with a high visual aesthetic to make them distinguishable by experts easily. A tailored residual fully convolutional encoder-decoder neural network based on end-to-end learning is proposed to issue the matter. The proposed method is evaluated quantitatively and qualitatively on ER + BCa H&E-stained dataset. The average detection accuracy achieved by the method is 98.61%, which is much better than that of competitors.
Identification of opinion trends using sentiment analysis of airlines passengers' reviews

Receiving customer opinions on products and services provided by companies is one of the main needs of company managers to improve products and services. Today, it is common to use social networks, messengers, and review websites to receive data from customer opinions. But analyzing the data received from these sources due to their huge volume requires an efficient tool. In this study, textual data obtained from the reviews of passengers of the top 10 airlines in the Middle East region have been analyzed and evaluated. First, we identified the main topics that are hidden in the customers' reviews by topic modeling method. In the next step, we evaluated the level of passenger satisfaction based on the topics identified in the modeling stage, by using sentiment analysis on passenger reviews to demonstrate the strengths and weaknesses of the airlines in providing various services to customers. In the end, management solutions and recommendations are provided to airline managers. The proposed method is a new approach for evaluating customer satisfaction and can be used as a guide for airlines to develop marketing strategies to attract new customers and increase their market share.