Reviewerly/exHarmony huggingface dataset:
Relational distance and document-level contrastive pre-training based relation extraction model

Document-level relation extraction has multi-entity and multi-mention compared to sentence-level, existing sentence-level relation extraction models cannot meet the requirements of document-level relation extraction. Existing graph-based document-level models usually design points and edges manually, which often introduces man-made noise; while the Transformer-based models cannot deeply solve the difficulties such as coreference resolution by designing pre-training tasks or other methods. In this paper, we propose a new Relational Distance and Document-level Contrastive Pre-training (RDDCP) based relation extraction model, which achieves coreference resolution by simple and effective mention replacement; we also introduce the concept of relational distance to achieve document-level contrastive pre-training, and find the most likely relational mention pairs from the plural mention pairs existing in the document-level dataset for contrastive learning; for the relation information in distant mentions ignored by the relational distance, we quantified the distances as weights and incorporated the information with weights into the embedding representation of entities. Each entity presents different entity embedding representations in different entity pairs. We conducted experiments on three popular datasets and the RDDCP model outperformed GAIN, SSAN and ATLOP as well as other baseline models in terms of performance and time complexity.
Peer-to-Peer User Identity Verification Time Optimization in IoT Blockchain Network

Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms' taxonomy.
The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

The recent pandemic had a major impact on online transactions.With this trend, credit card fraud increased.For the solution to this problem the authors explore existing solutions and propose an optimized solution.The solution is based on an extreme gradient boosting algorithm (XGBoost) and a teaching-learning-based-optimization algorithm.The dataset optimizes the hyperparameters of the XGBoost which is utilized as the main driver for the solution.The evaluation was performed among other similar techniques that have solved this problem successfully in the past.Standard performance metrics were applied which are accuracy, recall, precision, Matthews correlation coefficient, and area under the curve.The result of this research presents a dominant solution that was proposed and successfully outperformed all other compared solutions overall.
Breast Cancer Detection Using Deep Learning

Breast cancer is a very common type of cancer found in women. Approximately, 43,000 deaths are recorded per annum worldwide due to breast cancer. With the advancement in medical technology, computer-aided detection and diagnosis (CAD) system is being used widely for the early detection of cancerous cells. Rapid development in deep learning has made the task of detecting cancerous cells accurate and trivial. In this paper, researcher used convolutional neural network (CNN) for classifying cancerous cells. CNN is a type of neural network which is extensively used for image processing, classification, and segmentation. The proposed system has achieved 82% accuracy by successfully classifying cancerous cells into benign and malignant which are the two common types of cancer cells found.
Automatic Forensic Analysis of Criminal Navigation System using Machine Learning

There are many crime situations witnessed in everyday life nowadays, and they are expanding at an alarming rate. We've been building an APP to assist police investigators and locate those criminals to find those crimes and reduce the crime rate. Its study and analysis are dependent on information provided by users of crime scenes. The given data will next be evaluated to determine whether the user's data is fraudulent or authentic. If the data provided is accurate, the user's location will be pinpointed and shared with the location of a nearby police station or officer, as well as an alert warning to others to be cautious of people who are close to that location. If there are any public casualties, it will notify and contact the nearest hospital and ambulance for assistance to the injured. If the user takes a photo of a criminal, it will be validated or examined by a criminal database to identify the criminal's details and call the police station closest to him. By doing so, the rate of crime can be minimized, and prompt actions can be made to solve crime scenes more effectively and quickly. This allows users and the general public to assist and learn about the nation's laws and offenses. The software is entirely based on user safety and is designed to assist investigators.
Cryptanalysis of Lightweight Block Ciphers using Metaheuristic Algorithms in Cloud of Things (CoT)

The Cloud of Things (CoT) has transformed communication and network technologies by expanding the Iimitation of IoT devices. The Cloud of Things is a fusion of Cloud Computing with IoT that provides endless storage, networking, and compute capabilities to IoT devices. Cloud of Things (CoT) has substantially boosted IoT applications in healthcare, automotive, agriculture, education, welfare, and businesses. Even though Cloud of Things (CoT) is scalable, faster, cost-effective, and remotely accessible,it also faces many challenges like interoperability, compatibility, performance, longevity, and security. Security is the biggest hurdle in adopting Cloud of Things (CoT) because it directly involves users’ private data. Researchers and academicians are equally concerned and proposed various lightweight block ciphers (LWBC) to secure the user’s data. In this paper, the relative cryptographic strength of three lightweight block ciphers (LWBC) is measured by performing Cryptanalysis with three metaheuristic algorithms using the proposed framework. The chosen three lightweight block ciphers (LWBC) are SIMON, SPECK, and PRESENT ciphers, which belong to different categories of Lightweight Block Ciphers (LWBC), and three metaheuristic algorithms are Cuckoo Search (CS), Whale optimization Algorithm (WOA), and Salp Swarm Algorithm (SSA). The result of the experiments shows that SIMON and SPECK are equally strong while PRESENT is a relatively weak cipher. Also, the Cuckoo Search (CS) is found to be better than both WOA and SSA. However, for the weak keys, WOA is able to break all three ciphers completely.X
The Design and Application of C-STEAM Instructional Resources Based on Unity 3D Blending Physical and Virtual Manipulatives

PM (Physical manipulatives) are widely used in modern classrooms. However, in interdisciplinary integration courses, the difficulties of developing, operating and carrying PM often limit the development of courses and the achievement of teaching objectives. In response to the above problems, this study designed and developed C-STEAM instructional resources based on Unity 3D blending PM and VM (Virtual manipulatives), which was applied them to the integrated practical classroom in an elementary school. By analyzing the lag sequence of teacher-student behavior and the interviews with students, it is confirmed that the instructional resources enable students to focus more on the classroom, generate more inquiry and interaction, as well as promote their perception of excellent Chinese traditional culture with the assistance of technology. Furthermore, it can improve students' interdisciplinary literacy and innovative practical skills. This study provides a reference path for the development and application of teaching aids in classroom teaching.
Semantic similarity for mobile application recommendation under scarce user data

The More Like This recommendation approach is ubiquitous in multiple domains and consists in recommending items similar to the one currently selected by the user, being particularly relevant when user data is scarce. We studied the impact of using semantic similarity in the context of the More Like This recommendation for mobile applications, by leveraging dense representations in order to infer the similarity between applications, based on their textual fields. Our approach was validated by comparing it to the solution currently in use by Aptoide, a mobile application store, since no benchmarks are available for this specific task. To further evaluate the proposed model, we asked 1262 users to compare the results achieved by both approaches, also allowing us to build an annotated dataset of similar applications. Results show that the semantic representations are able to capture the context of the applications, with more useful recommendations being presented to users, when compared to Aptoide's current solution. For replication and future research, all the code and data used in this study was made publicly available, including two novel datasets (installed applications for more than one million users, and app user-labeled similarity), the fine-tuned model, and the test platform.
Infrared ship target segmentation based on Adversarial Domain Adaptation

Infrared ship target segmentation is one of the key technologies for automatically detecting ship targets in ocean monitoring. However, it is a challenging work to achieve accurate target segmentation from the infrared ship image. To improve its segmentation performance, we present an Adversarial Domain Adaptation Network (ADANet) for infrared ship target segmentation, where the labeled visible ship images are used as the source domain and the unlabeled infrared ship images are as the target domain. To address the issue of style difference between the two domains, we preprocess the visible images of the source domain in turn with graying and whitening to convert them into the images with the style of the target domain. For the infrared images in the target domain, we optimize them with a denoising network. Furthermore, to solve the matter of limited receptive field of the discriminator, we design a discriminator based on atrous convolution to improve its discriminative ability. Finally, for the issue of low confidence of the target domain predicted images, we add the information entropy of the target domain predicted images to the adversarial loss. Experimental results on the home-made dataset as well as a public dataset show that infrared ship target segmentation achieves higher mean intersection over union than the state-of-the-art methods without significantly increase of parameters, demonstrating its effectiveness.
Why Individuals Do (Not) Use Contact Tracing Apps: A Health Belief Model Perspective on the German Corona-Warn-App

The World Health Organization declared the emergence of the novel coronavirus (SARS-CoV-2) in January 2020. To trace infection chains, Germany launched its smartphone contact tracing app, the "Corona-Warn-App" (CWA), in June 2020. In order to be successful as a tool for fighting the pandemic, a high adoption rate is required in the population. We analyse the respective factors influencing app adoption based on the health belief model (HBM) with a cross-sectional online study including 1752 participants from Germany. The study was conducted with a certified panel provider from the end of December 2020 to January 2021. This model is primarily known from evaluations of medical treatments, such as breast cancer screenings, but it was rarely applied in prior work for a health-related information system such as the CWA. Our results indicate that intrinsic and extrinsic motivation to use the CWA are the strongest drivers of app use. In contrast, technical barriers, privacy concerns and lower income are the main inhibitors. Our findings contribute to the literature on the adoption of contact tracing apps by questioning actual users and non-users of the CWA, and we provide valuable insights for policymakers regarding influences of adoption and potential user groups of disease prevention technologies in times of pandemics.
Secure Global Software Development: A Practitioners’ Perspective

Global software development (GSD) is rapidly becoming standard practice in the software industry due to its many potential benefits. However, one of the biggest challenges in GSD projects is to explicitly include security in the different phases of the global software development life cycle (GSDLC). To make GSD projects secure and successful, it is necessary to identify secure software development (SSD) practices vital to GSD project success. This article aims to identify SSD practices critical for GSD projects. To do this, we selected 36 security practices vital to the security of non-GSD projects from existing scientific and grey literature on software security. From the identified security practices, we shortlisted the security practices which are critical for GSD projects based on practitioners’ opinions using an online survey. Fifty-four GSD practitioners participated in this survey. Participants who evaluated these practices were asked to score each SSD practice on a four-point scale to indicate its relevance to GSD projects. The results obtained from the survey uncovered critical SSD practices that are primarily applicable to GSD projects. Our findings reveal variations of opinion among GSD practitioners with varying experience and company size regarding the importance of selected security practices for GSD. According to study findings, 16/36 practices are critical for GSD projects. These identified security practices belong to various phases of GSDLC.
A visual measurement method of structural body vibration displacement combined with image deblurring

The visual vibration displacement measurement task is limited by the acquisition equipment or the acquisition environment, and there are still defects in the recognition rate. In this paper, starting from the aspect of enhancing the feature information of the input image, a visual measurement method for structural body vibration displacement is constructed combining deblurring and image feature enhancement. Aiming at problems such as blurring that may be encountered during image data acquisition, this paper designs a Multi-Scale Structural Body Image Deblurring Network (MSDNet) based on the Encoder–Decoder architecture to enhance the target feature details in the structural body image. MSDNet obtains more detailed feature information of the structural body by aggregating feature information at different scales in the Encoder–Decoder architecture. Meanwhile, this paper uses the super-resolution image reconstruction method in the Decoder stage instead of the upsampling method to better preserve the feature information of the underlying image. To verify the effectiveness of the method proposed in this paper, a variety of structural body vibration displacement image datasets were produced, and the sensor simultaneously collected structural body displacement data as standard data. The follow-up experiments can also be concluded that the image data after deblurring processing can have a better measurement accuracy of structural body vibration displacement.
Deep Learning for Medication Recommendation: A Systematic Survey

ABSTRACT Making medication prescriptions in response to the patient's diagnosis is a challenging task. The number of pharmaceutical companies, their inventory of medicines, and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload. To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient, researchers have exploited electronic health records (EHRs) in automatically recommending medication. In recent years, medication recommendation using EHRs has been a salient research direction, which has attracted researchers to apply various deep learning (DL) models to the EHRs of patients in recommending prescriptions. Yet, in the absence of a holistic survey article, it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges. To fill this research gap, this survey reports on state-of-the-art DL-based medication recommendation methods. It reviews the classification of DL-based medication recommendation (MR) models, compares their performance, and the unavoidable issues they face. It reports on the most common datasets and metrics used in evaluating MR models. The findings of this study have implications for researchers interested in MR models.
A robust visible and infrared image matching algorithm for power equipment based on phase congruency and scale-invariant feature

Dense and accurate matching for visible and infrared images of power equipment is crucial to intelligent diagnosis system of power grid, but existing matching methods usually fail in aligning visible and infrared image pairs because of significant intensity, resolution and viewpoint differences. In this paper, we propose a matching algorithm based on phase congruency and scale-invariant feature to address this problem. The proposed method consists of four steps. First, the maximum moment map of phase congruency of input image is computed based on phase congruency theory, which is then used to enhance the raw image. Second, Canny operator and contour tracking method are employed to detect image contours and scale-invariant feature points are extracted by the curvature scale space (CSS) corner detector. Third, the novel histogram of phase congruency orientation (HPCO) descriptors based on phase congruency information are computed for all feature points. Finally, a set of preliminary matches is obtained by the bidirectional matching, and refinement procedures are implemented to achieve dense and accurate matching results. We conduct the experiments on public available dataset. Experimental results show that the proposed method can robustly match feature points in visible and infrared image pairs of power equipment in spite of intensity, resolution and viewpoint differences, and achieve favorable performance compared to state-of-the-art approaches.
The Future of Hybrid Software Development: Challenging Current Assumptions

This special issue covers a range of exciting topics regarding the future of hybrid software development.
Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning

Federated Learning (FL) is a machine learning technique, where collaborative and distributed learning is performed, while the private data reside locally on the client. Rather than the data, only gradients are shared among all collaborative nodes with the help of a central server. To ensure the data privacy, the gradients are prone to the deformation, or the representation is perturbed before sharing, ultimately reducing the performance of the model. Recent studies show that the original data can still be recovered using latent space (i.e., gradient leakage problem) by Generative Adversarial Network and different optimization algorithms such as Bayesian and Covariance Matrix Adaptation Evolution Strategy. To address the issues of data privacy and gradient leakage, in this paper, we train deep neural networks by exploiting the blockchain-based Swarm Learning (SL) framework. In the SL scheme, instead of sharing perturbed or noisy gradients to the central server, we share the gradients among authenticated (i.e., blockchain-based smart contract) training nodes. To demonstrate the effectiveness of the SL approach, we evaluate the proposed approach using the standard CIFAR10 and MNIST benchmark datasets and compare it with the other existing methods.
MSCAF-Net: A General Framework for Camouflaged Object Detection via Learning Multi-Scale Context-Aware Features

The aim of camouflaged object detection (COD) is to find objects that are hidden in their surrounding environment. Due to the factors like low illumination, occlusion, small size and high similarity to the background, COD is recognized to be a very challenging task. In this paper, we propose a general COD framework, termed as MSCAF-Net, focusing on learning multi-scale context-aware features. To achieve this target, we first adopt the improved Pyramid Vision Transformer (PVTv2) model as the backbone to extract global contextual information at multiple scales. An enhanced receptive field (ERF) module is then designed to refine the features at each scale. Further, a cross-scale feature fusion (CSFF) module is introduced to achieve sufficient interaction of multi-scale information, aiming to enrich the scale diversity of extracted features. In addition, inspired the mechanism of the human visual system, a dense interactive decoder (DID) module is devised to output a rough localization map, which is used to modulate the fused features obtained in the CSFF module for more accurate detection. The effectiveness of our MSCAF-Net is validated on four benchmark datasets. The results show that the proposed method significantly outperforms state-of-the-art (SOTA) COD models by a large margin. Besides, we also investigate the potential of our MSCAF-Net on some other vision tasks that are highly related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, transparent object detection and defect detection. Experimental results demonstrate the high versatility of the proposed MSCAF-Net. The source code and results of our method are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yuliu316316/MSCAF-COD</uri> .
Explainable Emotion Recognition from Tweets using Deep Learning and Word Embedding Models

Social media such as Twitter made it possible for people to express their mood through text, images, audios or videos. Understanding these emotions becomes vital for better human and computer interactions. Natural Language processing play an important role in classifying the textual emotions of people. This research proposes a method to classify emotions from text into six different categories namely anger, fear, joy, love, sadness, and surprise from the Emotion Recognition dataset, using state of the art (SOTA) pre-trained word embeddings and deep learning models. The results of the experiments demonstrate that DistilBert and CNN attained an F-score of 98%. The explainability modules explain the training and prediction of the proposed model by analyzing the contextual contribution of words in classification.