Reviewerly/exHarmony huggingface dataset:
Ethical and Legal Aspects of Digital Forensics Algorithms: The Case of Digital Evidence Acquisition

The first step that forensic examiners perform is identifying and acquiring data. Both are among the most critical segments in the forensic process since they are sine qua non for completing the examination and analysis phases. The evidence acquisition must be managed in a deliberate, ethical and legal manner. On many occasions, the outcome of the investigation depends mainly on the relevance and precision of the evidence acquired. The goal of this research is to identify both legal and ethical issues that forensic investigators face during evidence acquisition and to design a framework using design science, which recognises and resolves the problems identified. The framework must preserve the forensic soundness of the investigation, overall integrity, effectiveness, and efficiency. The elicitation of the requirements for the framework is based on a literature review and ex-ante expert interviews, while the validation and evaluation of the framework stem from ex-post expert interviews. The designed framework aims to minimise hazardous practices that lead to negative consequences and to effectively align the new technologies in digital forensics with human expertise for improved results during the phase of digital evidence acquisition.
Nonlinear Feature Selection Neural Network via Structured Sparse Regularization

Feature selection is an important and effective data preprocessing method, which can remove the noise and redundant features while retaining the relevant and discriminative features in high-dimensional data. In real-world applications, the relationships between data samples and their labels are usually nonlinear. However, most of the existing feature selection models focus on learning a linear transformation matrix, which cannot capture such a nonlinear structure in practice and will degrade the performance of downstream tasks. To address the issue, we propose a novel nonlinear feature selection method to select those most relevant and discriminative features in high-dimensional dataset. Specifically, our method learns the nonlinear structure of high-dimensional data by a neural network with cross entropy loss function, and then using the structured sparsity norm such as l2,p -norm to regularize the weights matrix connecting the input layer and the first hidden layer of the neural network model to learn weight of each feature. Therefore, a structural sparse weights matrix is obtained by conducting nonlinear learning based on a neural network with structured sparsity regularization. Then, we use the gradient descent method to achieve the optimal solution of the proposed model. Evaluating the experimental results on several synthetic datasets and real-world datasets shows the effectiveness and superiority of the proposed nonlinear feature selection model.
Clothing Retrieval from Vision Transformer and Class Aware Attention to Deep Embedding Distance Learning

Clothing is an indispensable item in human life. Since the types and names of clothing are various, sometimes texts are not enough for searching the desired clothing item. The method of searching pictures through pictures has been widely used in many online shopping platforms. In order to help customers find similar clothes accurately, we crawl images from some online shopping websites to build a new dataset to conduct clothing retrieval task. We also propose a new clothing retrieval neural network model, which integrates the advantages of Triplet Network, Perceptual Loss and Capsule Network, and extends the triplet sample learning mode to add multiple negative samples for training. In our proposed model, Vision Transformer and two newly proposed attention mechanisms are applied to the Capsule Network to strengthen the features in each capsule. The Perceptual concept helps the model to extract color and texture features, and connect them. Our newly modified loss function can help the model retrieve better results. The overall training does not need landmark annotation which makes model increase complexity. In the experiments, our model is compared with other state-of-the-art models and shows better performance.
Research on social recommendation model based on enhanced neighbor perception

Recent years, people have been gradually influenced by online socialization. The emergence and development of Graph Neural Networks (GNN) has shown great advantages in mining implicit data and expressing node relationships, etc. However, due to the arbitrary nature of establishing neighborly relationships, the judging reliability of trusting relationships between neighbors is a difficult issue. Therefore, this paper proposes an innovative approach to obtain the valid neighbor relationships based on real user-item interactions and friend trust relationships in the dataset. The proposed method can address the impact of invalid neighbors on the social model and achieve the effect of enhancing neighbor perception. For neighbor interaction in the graph neural network model, this paper establishes the direct connection between users and items through mapping of multilayer perceptron firstly. Then it integrates neighbor similarity and neighbor sampling to mitigate the interference information of invalid neighbors and achieves the effect of enhancing the perceived information of neighbor interaction. Finally, this paper establishes the item social space and user social space with enhanced neighbor perception according to the dyadic nature and organically integrated them to enrich the social data. Comparison experiments are carried out based on two publicly available datasets Epinions and Ciao, the recommended method performs better than other social recommendation models with the MAE and RMSE values being improved by 0.81%-1.09% and 1.15%-1.41% respectively.
METRIC: Toward a Drone-based Cyber-Physical Traffic Management System

Drone-based system has a big potential to be applied for traffic monitoring and other advanced applications in Intelligent Transport Systems (ITS). This paper introduces our latest efforts of digitalising road traffic by various types of sensing systems, among which visual detection by drones provides a promising technical solution. A platform, called METRIC, is under recent development to carry out real-time traffic measurement and prediction using drone-based data collection. The current system is designed as a cyber-physical system (CPS) with essential functions aiming for visual traffic detection and analysis, real-time traffic estimation and prediction as well as decision supports based on simulation. In addition to the computer vision functions developed in the earlier stage, this paper also presents the CPS system architecture and the current implementation of the drone front-end system and a simulation-based system being used for further drone operations.
Learning Label Independence and Relevance for Multi-Label Biomedical Text Classification

The rapidly growing biomedical literature requires accurate and robust automatic computational methods to quickly select the most relevant labels in biomedical topic candidate label sets to specific biomedical documents. Such methods can facilitate hypothesis generation and knowledge discovery. Many multi-label biomedical text classification methods have been proposed in the last few years, such as DeepMeSH, MeSHProbeNet, and BERTMeSH. However, these methods encode the labels as one-hot vectors, which ignore the semantic relevance between the labels. Moreover, the loss function they employ does not consider the unbalanced label distribution, resulting in overfitting labels with high frequencies. To alleviate the above problems and improve the performance of multi-label biomedical document classification tasks, we propose a model to learn Label Independence And Relevance (LIAR) and integrate them efficiently. LIAR uses BioBERT to fully extract information from biomedical literature to generate textual representations and uses it to construct a one-hot vector and learn label embeddings, respectively. Meanwhile, we construct a new loss function that can adaptively weight and integrates the one-hot vector distribution and label semantic similarity to compute the loss value and Assign Weights (AWLoss) to labels of different frequencies to alleviate the shortcomings of the loss function in the above model. LIAR outperformed the state-of-the-art method by more than 1% on all three benchmark datasets.
BLE RSSI Database for Analyzing Routines of Community-Dwelling Older Adults

The analysis of routines of older adults is a growing field of research, since detecting changes in behaviour patterns can be an indicator of physical or cognitive decline. This requires continuous monitoring of people over long periods. This work introduces a novel dataset, publicly available for the scientific community, which is composed of the BLE RSSI (Received Signal Strength Indicator) measurements recorded by the smartwatches that the elderly volunteers wore, all of them living in a nursing home where a set of BLE beacons have been deployed. The beacons were deployed in different rooms of the building (bedrooms, gym, restaurant, etc.), where the volunteers spend most of the time. This information can be beneficial to obtain, for instance, a symbolic location of the monitored people or to study their routines. The activities that the volunteers perform throughout the day are reported by their caregivers. In addition, the dataset includes ground truth measurement information for the rooms in which the BLE beacons have been installed.
Green Corridor Implementation and Real Time Adaptive Traffic Regulation using Machine Learning and Image Processing

Traditional traffic light control systems rely on traffic light intervals that are set in stone. Because they use technology that operates according to a system that lacks the flexibility of real-time, traditional fixed traffic light controllers have restrictions and are less efficient. The set time intervals between green and red as a result of the fixed time intervals between green and red. There is excessive and unnecessary traffic congestion, and automobiles consume more gasoline. This eventually causes environmental issues as well as a variety of health issues for people. The rise in the number of personal automobiles has a negative impact on the environment. The goal of this project is to develop a traffic system that adapts to current traffic conditions in a lane. In most cases, the average waiting time for all lanes is set. This idea proposes that the number of vehicles in a lane be monitored to reduce the average waiting time. A predictive model will also be implemented, which will make decisions based on prior traffic patterns, mostly at habitually congested crossings. Emergency vehicles will also be identified, and a faster route will be established for them.
A User-Friendly Two-Factor Authentication Method against Real-Time Phishing Attacks

Today, two-factor authentication (2FA) is a widely implemented mechanism to counter phishing attacks. Although much effort has been investigated in 2FA, most 2FA systems are still vulnerable to carefully designed phishing attacks, and some even request special hardware, which limits their wide deployment. Recently, real-time phishing (RTP) has made the situation even worse because an adversary can effortlessly establish a phishing website without any background of the web page design technique. Traditional 2FA can be easily bypassed by such RTP attacks. In this work, we propose a novel 2FA system to counter RTP attacks. The main idea is to request a user to take a photo of the web browser with the domain name in the address bar as the 2nd authentication factor. The web server side extracts the domain name information based on Optical Character Recognition (OCR), and then determines if the user is visiting this website or a fake one, thus defeating the RTP attacks where an adversary must set up a fake website with a different domain. We prototyped our system and evaluated its performance in various environments. The results showed that PhotoAuth is an effective technique with good scalability. We also showed that compared to other 2FA systems, PhotoAuth has several advantages, especially no special hardware or software support is needed on the client side except a phone, making it readily deployable.
Investigation of Adaptive Parameter Strategies for Differential Evolution

The scaling factor (F) in the mutation operation and the crossover rate (CR) in the crossover operation are considerably critical in assisting differential evolution (DE) to attain good optimization performance. As a result, DE is very sensitive to these two parameters. To address this predicament, many adaptive parameter control methods have been proposed for these two parameters. However, there are no comprehensive comparisons among these adaptive parameter methods. To make up for this defect, this paper mainly investigates the effectiveness of six widely utilized adaptive strategies, namely the ones in JADE, IDE, jDE, SinDE, FDSADE, and RDE. For fairness, this paper selects the binomial crossover and the mutation “DE/current-to-pbest/1” to accompany the six adaptive parameter strategies. Experimental results on the commonly adopted CEC2014 benchmark suite have demonstrated that the adaptive parameter control methods in IDE and JADE help DE achieve the best overall performance. With these investigations, it is envisaged that this paper provides a fundamental guideline for new learners and those looking for an appropriate adaptive parameter technique for their newly created DE algorithms.
Proxy Verification and Validation For Critical Autonomous and AI Systems

A challenging problem for software and systems engineers is to provide assurance of operations for a system that is critical but must operate in situations that cannot be easily created in the testing lab. For example, a space system cannot be fully tested in all operational modes until it is launched and nuclear power plants cannot be tested under real critical temperature overload conditions. This situation is particularly challenging when seeking to provide assurance in critical AI systems (CAIS) where the underlying algorithms may be very difficult to verify under any conditions. In these cases using systems that have a similar underlying application, operational profiles, user characteristics, and underlying AI algorithms may be suitable as testing proxies. For example, a robot vacuum may have significant operational and implementation similarities to act as a testing proxy for some aspects of an autonomous vehicle.In this work we discuss the challenges in assured autonomy for CAIS and suggest a way forward using proxy systems. We describe a methodology for characterizing CAIS and matching them to their non-critical proxy equivalent. Examples are given along with a discussion of the history of other kinds of proxy verification and validation
Keeping the Privacy and the Security of the Knowledge Graph Completion Using Blockchain Technology

Nowadays, Knowledge Graph becomes very important for representing and reasoning about knowledge data. Its name was known from Google knowledge graphs in 2012. Many IA applications have used it for managing the various types of knowledge data and especially the personal data. Therefore, ensuring security, trust, integrity and privacy of its content was the goal of the AI researchers. KG completion is one of the most methods that its aim is completing the KG with missing true triples. In this paper, we propose to use blockchain technology to keep the security and privacy of the KG completion based on KG embedding that embed relations and entities in continues vectors spaces. The completion tasks can benefit from the decentralization feature of BC. Our design proposes to use an offchain storage in order to improve the scalability of BC network that contains only critical KG embedding data. The implementation and the evaluation have used Hyperledger Fabric (HF), MangoDB and several KG completion datasets.
Morphological Operations and Artificial Neural Networks for Multi-scale colored texture classification

Among image descriptors, texture is one of the most used features to represent the visual content of images. According to several studies, texture could be seen differently based on the selected scale of interest (SoI), which raises a serious problem of finding the appropriate SoI. To tackle this issue, a new scheme based on mathematical morphology and artificial neural networks (ANN) has been introduced. Firstly, the image is subjected to a series of morphological operations to extract the different SoIs. From each SoI, a set of color features are extracted and fed to two successive ANNs in order to categorize the textures. The present scheme maintains the extraction of both color and multi-scale texture information. A comprehensive experimental comparison has been conducted where the present scheme has outperformed other widely known texture and CNN-based descriptors.
A deep learning approach for solving linear programming problems

Finding the optimal solution to a linear programming (LP) problem is a long-standing computational problem in Operations Research.This paper proposes a deep learning approach in the form of feed-forward neural networks to solve the LP problem.The latter is first modeled by an ordinary differential equations (ODE) system, the state solution of which globally converges to the optimal solution of the LP problem.A neural network model is constructed as an approximate state solution to the ODE system, such that the neural network model contains the prediction of the LP problem.Furthermore, we extend the capability of the neural network by taking the parameter of LP problems as an input variable so that one neural network can solve multiple LP instances in a one-shot manner.Finally, we validate the proposed method through a collection of specific LP examples and show concretely how the proposed method solves the example.
Semantic network analysis on the geography of food as represented in textbooks: for alternative food education for young people

This study compared the structural characteristics of the geography and pedagogy of food for young people, which is represented in geography textbooks used by the IBDP and Korean world geography. By using the KrKwic, which is a semantic network analysis software, the study analyzed the extent to which factors related to the geography and pedagogy of food for young people were reflected in two geography textbooks as well as the semantic network and degree centrality by which these factors formed each other. The results suggest that future world geography textbooks need to consider alternative competencies for young people, such as global citizenship, food citizenship, and environmental sustainability, when dealing with the geography of food.
MFNet: A Novel GNN-Based Multi-Level Feature Network With Superpixel Priors

Since the superpixel segmentation method aggregates pixels based on similarity, the boundaries of some superpixels indicate the outline of the object and the superpixels provide prerequisites for learning structural-aware features. It is worthwhile to research how to utilize these superpixel priors effectively. In this work, by constructing the graph within superpixel and the graph among superpixels, we propose a novel Multi-level Feature Network (MFNet) based on graph neural network with the above superpixel priors. In our MFNet, we learn three-level features in a hierarchical way: from pixel-level feature to superpixel-level feature, and then to image-level feature. To solve the problem that the existing methods cannot represent superpixels well, we propose a superpixel representation method based on graph neural network, which takes the graph constructed by a single superpixel as input to extract the feature of the superpixel. To reflect the versatility of our MFNet, we apply it to an image-level prediction task and a pixel-level prediction task by designing different prediction modules. An attention linear classifier prediction module is proposed for image-level prediction tasks, such as image classification. An FC-based superpixel prediction module and a Decoder-based pixel prediction module are proposed for pixel-level prediction tasks, such as salient object detection. Our MFNet achieves competitive results on a number of datasets when compared with related methods. The visualization shows that the object boundaries and outline of the saliency maps predicted by our proposed MFNet are more refined and pay more attention to details.
Enhanced CRNN-Based Optimal Web Page Classification and Improved Tunicate Swarm Algorithm-Based Re-Ranking

The main intention of this paper is to develop a new intelligent framework for web page classification and re-ranking. The two main phases of the proposed model are (a) classification, and (b) re-ranking-based retrieval. In the classification phase, pre-processing is initially performed, which follows the steps like HTML (Hyper Text Markup Language) tag removal, punctuation marks removal, stop words removal, and stemming. After pre-processing, word to vector formation is done and then, feature extraction is performed by Principle Component Analysis (PCA). From this, optimal feature selection is accomplished, which is the important process for the accurate classification of web pages. Web pages contain several features, which reduces the classification accuracy. Here, the adoption of a new meta-heuristic algorithm termed Opposition based-Tunicate Swarm Algorithm (O-TSA) is employed to perform the optimal feature selection. Finally, the selected features are subjected to the Enhanced Convolutional-Recurrent Neural Network (E-CRNN) for accurate web page classification with enhancement based on O-TSA. The outcome of this phase is the categorization of different web page classes. In the second phase, the re-ranking is involved utilizing the O-TSA, which derives the objective function based on similarity function (correlation) for URL matching, which results in optimal re-ranking of web pages for retrieval. Thus, the proposed method yields better classification and re-ranking performance and reduce space requirements and search time in the web documents compared with the existing methods.
Marathi <scp>SentiWordNet</scp>: A lexical resource for sentiment analysis of Marathi

Summary Sentiment analysis (SA) is information retrieval and computational linguistics that expresses sentiment about a specific document. Recently, research on SA has received greater interests due to the vast expansion of internet data. The comments and reviews posted online creates difficulties to the researchers, because the comments posted are written in unstructured formats with informal expressions, languages and possibly mixed languages. Most of the works on SA have been focusing only on English language but with respect to Indian languages, few works are done, especially on Hindi, Telugu, Bengali, and so forth. SentiWordNet is a useful lexical resource for determining the sentiment of a document or piece of text. There is currently no lexical resource for the Marathi language used for sentiment analysis. Therefore, this proposed research presents SA in one of the languages of Indian origin, that is, Marathi. In this paper, a new method is proposed for the development of a Marathi SentiWordNet (M‐SWN), synset‐based expansion approach using Marathi WordNet, which consists of Positive, Negative, and Neutral Polarity score, mapped from Hindi‐SentiWordNet (H‐SWN). Finally, sentiment analysis of movie reviews is carried out using M‐SWN using Bi‐LSTM based SVM classifier model.