Reviewerly/exHarmony huggingface dataset:
Adaptive Industrial Control System Attack Sample Expansion Algorithm Based on Generative Adversarial Network

The scarcity of attack samples is the bottleneck problem of anomaly detection of underlying business data in the industrial control system. Predecessors have done a lot of research on temporal data generation, but most of them are not suitable for industrial control attack sample generation. The change patterns of the characteristics of the underlying business data attack samples can be divided into three types: oscillation type, step type, and pulse type. This paper proposes an adaptive industrial control attack sample expansion algorithm based on GAN, which expands the three types of features in different ways. The basic network structure of data expansion adopts GAN. According to the characteristics of oscillation type changes, momentum is selected as the optimizer. Aiming at the characteristics of step type changes, the Adam optimization method is improved. For pulse type features, attack samples are generated according to the location and length of the pulse. Compared with previous time-series data generation methods, this method is more targeted for each feature and has higher similarities.
SmartTips: Online Products Recommendations System Based on Analyzing Customers Reviews

Online customers’ opinions represent a significant resource for both customers and enterprises to extract much information that helps them make the right decision. Finding relevant data while searching the internet is a big challenge for web users, known as the “Problem of Information Overload”. Recommender systems have been recognized as a promising way of solving such problems. In this paper, a product recommendation system called “SmartTips” is introduced. The proposed model is built based on aspect-based sentiment analysis, which exploits customers’ feedback and applies the aspect term extraction model to rate various products and extract user preferences as well. Several factors were considered, including readers’ votes, aspect term frequency, opinion words’ frequencies, etc. We tested our model on benchmark datasets that are widely used, and the results show that it outperforms the baseline methods regarding the mean squared errors of generated predictions.
THE DESIGN OF EVOLUTIONARY ALGORITHMS: A COMPUTER SCIENCE PERSPECTIVE ON THE COMPATIBILITY OF EVOLUTION AND DESIGN

Abstract The effectiveness of evolutionary algorithms is one of the issues discussed in The Compatibility of Evolution and Design , where it is argued that such algorithms are only effective when stringent preconditions are met. This article considers this issue from the perspective of computer science. It explores the properties of problems that can be effectively solved by evolutionary algorithms, and the extent to which such algorithms need to be carefully adjusted. Although there are important differences between the study of evolutionary algorithms in computer science and the study of biological evolution, it is argued that the experience of computer science can help to strengthen the claim that evolutionary mechanisms generally require a considerable degree of fine‐tuning.
Protecting Electoral Integrity in Emerging Democracies

Abstract A lack of electoral integrity in developing democracies undermines political accountability and the public good by yielding leaders who lack a governing mandate. Despite citizen activism and resources from donors to improve transparency, poor administrative functioning, corruption, and barriers to participation persistently degrade elections. This chapter presents “photo quick count” election technology and an ICT-enabled citizen adaption platform “VIP:Voice.” Photo quick count is a low-cost, ICT-capable, independently managed monitoring system of election results that provides polling station level photographic records of tally sheets to audit alongside certified results. The audit detects procedural failures by election officials and aggregation fraud (rigging that occurs in results transmission), and can deter administrative problems and corruption by announcing the audit to polling officials. First deployed in Afghanistan, iterations in Uganda and Kenya helped develop usage across national coverage and new mobile devices. This pivoted to broadening adoption and functionality using a crowdsourced platform in South Africa, VIP:Voice, that recruited citizen users through ICT channels with no pre-existing infrastructure and incorporates volunteers for photo quick count. This case furthers evidence on instruments for policy guidance on the mechanisms and cost-effective tools to bolster institutional performance and elections at scale.
Customised E-Learning Platforms

Abstract More than 617 million children and adolescents lack the basic reading and mathematics skills required to live healthy and productive lives. Malawi ranks particularly poorly, with an average pupil to teacher ratio of 77:1 and a 50% dropout rate among primary school children. Established in 2013, the Unlocking Talent initiative uses e-Learning technology to help overcome educational challenges. It equips touch-screen tablets with customisable software that delivers lessons through multisensory experiences (e.g. pictures, sound, video and animation). Throughout Malawi, small groups of students in public primary schools have accessed these tablets during weekly sessions on-site. This case study describes a series of evaluations of this e-Learning technology in Malawi, conducted in tandem with experiments in other countries (including the United Kingdom, Brazil, South Africa, Tanzania, Kenya and Ethiopia). Following a pilot evaluation to assess the feasibility of e-Learning in raising learning outcomes, multiple large-scale randomised control trials were conducted. Learning gains hold across multiple cohorts of children and across different countries, generating more than a 3-month advantage in basic mathematics and more than a 4-month advantage in basic reading on average. The intervention also bridges gender gaps in mathematics skills attainment in Malawi.
Residual inpainting using selective free-form attention

Deep learning has dominated the methodology of image inpainting in recent years. However, many of the existing methods still suffer from the defects of unnatural structure and blurry textures when filling large-area holes. To address this issue, we propose a novel coarse-to-fine residual inpainting framework: we first reconstruct the downsampled low-frequency coarse profile at a low computational cost, we then solely focus on generating high-frequency details, which will be added as residuals to the coarse profile, such that structure and texture details in composited result can be better preserved. Meanwhile, we notice that the existing attention mechanism used to build long-distance dependency is unreasonable: either does not adapt to free-form holes, or lacks filtering for invalid information sources, which could compromise feature map reconstruction. To solve this, we propose an improved Selective Free-Form Attention (SFFA) module, selectively allowing the information flow from the valid sources to hole area with free-form shape. We verify the incremental effectiveness of our contributions. We further show that our model outperforms recent state-of-the-art methods both qualitatively and quantitatively on benchmark datasets.
Query answering over inconsistent knowledge bases: A probabilistic approach

Consistent query answering (CQA) is a widely accepted paradigm for querying inconsistent knowledge bases (KBs). A consistent answer to a query is a tuple that is an answer to the query over every repair of the KB, which is in turn a consistent KB whose extensional knowledge “minimally” differs from the original one's. This coarse-grained classification of answers into consistent and non-consistent ones lacks any information about their degree of consistency, i.e., how likely it is that a tuple is an answer to the query, when considering all the repaired KBs. To overcome this limitation, we consider a fine-grained notion of repair for KBs with equality-generating dependencies (EGDs), based on attribute-level updates, and exploit this notion to propose a probabilistic CQA approach, which associates a confidence to each answer, thereby providing more informative query answers. We first show that computing the query answer confidence is # P -hard . Then, in the light of this intractability result, we study the existence of efficient randomized, approximation schemes. In particular, we show that absolute error approximation schemes always exist in the general case, while more refined relative error approximation schemes, i.e., fully polynomial-time, randomized approximation schemes (FPRAS) exist when assuming that the constraints of the knowledge base are primary keys. Finally, we extend our framework to knowledge bases with tuple-generating dependencies (TGDs) and generalize our approximability results to the new setting, and prove additional inapproximability results.
Appraising Virtual Technologies’ Impact on Older Citizens’ Mental Health—A Comparative between 360° Video and Virtual Reality

Aging populations across the world are facing a number of challenges in the context of health and healthcare. These challenges are driven by the aging process and the illnesses associated with aging. Healthcare for older people has become a point of concern with most health organizations, and this is particularly the case with palliative care. In this instance, the movement of the patient may be restricted to a room with no or limited access to the outdoors. This research focuses on the active integration of immersive technologies with healthcare. By addressing the problem of providing patients with the experience of being present in an outdoors space, the associated psychological and physiological benefits can be identified. In this mixed methods research paper, the impact of a crossover study to discern technology preferences in relation to immersive technologies among a sample of older people is reported. In addition, the study highlights factors that contribute to a meaningful immersive experience that can improve psychological and physiological wellbeing. The study identifies that there are two significant categorical aspects that contribute to such immersive experiences, technological aspects (including, for example, the weight of headsets, visual impairment, pixelation, and gamification) and emotive aspects (for example, joy, anger, and fear). The study suggests that older people prefer immersive Virtual Reality (VR) environments rather than 360 video experiences. This can be attributed to the greater flexibility in the provision of interactivity in virtual reality systems.
Feasibility on the Integrated Teaching Method of Machine Learning Algorithm inside and outside Physical Education Class

School education, especially physical education, directly undertakes the tasks of “student physical fitness” and “student health promotion.” In the context of the policy to further strengthen quality training and promote education in all aspects, it is explicitly pointed out that “a sound physical fitness is a primary demand for serving the motherland and the country as a result of youth, and it reflects the vitality of the Chinese nation.” Therefore, the implementation of the National Physical Education Curriculum Guidelines and curriculum criteria for sports for Chinese College Students is the primary task of physical education. This study proposes machine learning-based teaching methods as an integrated teaching method inside and outside physical education. After conducting research and experiments on it, the experimental data show that after machine learning improves the integrated teaching of sports inside and outside, 50.51% of students are satisfied with the teaching, 40.4% are relatively satisfied, 6.06% feel average, and only 6.06% are dissatisfied. Compared with the previous part, the satisfaction rate has increased by 16.51%, the more satisfaction rate has increased by 4.4%, and the dissatisfaction rate has decreased by 16.97%. The practice has proved that the implementation of comprehensive teaching based on machine learning in physical education, integration of a student-led approach with a teacher-led role, promotes students’ interest in sports and improves students’ health concepts, which is in line with the current development trend of physical education.
FGC_SS: Fast Graph Clustering Method by Joint Spectral Embedding and Improved Spectral Rotation

Spectral clustering, one of the most popular clustering methods, has attracted considerable attention in many fields owing to its excellent empirical properties. However, previously developed solutions to spectral clustering problems consist of two successive steps: (1) performing spectral embedding and eigendecomposition to obtain relaxed partition matrix; (2) performing k-means or spectral rotation to obtain the final binary indicator matrix. However, these two steps may miss a co-optimal solution, leading to unreasonable results. Furthermore, the implementation of eigendecomposition is significantly time-consuming and requires a computational complexity of 0(n3). To address these issues, in this study, we propose a new framework for jointly performing spectral embedding and improving spectral rotation using an anchor-based acceleration strategy. In addition, we replace the spectral rotation term with a more mathematically rigorous form. To effectively solve the proposed model, we iteratively use the generalized power iteration method and the improved version of the coordinate descent method to solve our unified framework directly. Extensive experimental results on real benchmark datasets validate the effectiveness of the proposed algorithm compared with other state-of-the-art spectral-based methods. The proposed algorithm achieves the best performance on six-eighths of small-scale datasets, five-sixths of large-scale datasets, and three-fourths of high-dimensional datasets, while maintaining superior implementation efficiency.
Learning from Multiple Annotators: Hierarchical Deep Learning Training Scheme for Prostate Gleason Cancer Grading

The grading of prostate tissue micro-array (TMA) cores is prone to high inter-observer variability. When it comes to determining Gleason scores, there is a lot of disagreement among pathologists. In this context, merging extremely varied decisions made by multiple observers is a problem for developing a machine learning model for automated scoring of prostate TMAs. In the absence of a known ground truth, a machine learning strategy that can reliably mod-el/discover the underlying agreement/true labels from various observers is critical. In this context, this paper proposes a hierarchical training approach for developing a deep learning model that can effectively decode/discover real labels from a multi-observer labeled dataset. Moreover, the proposed method (DL-Hrch) is compared with a number of commonly used approaches for dealing with noisy or multi-observer labeled datasets, such as training based on the confusion matrix (DL-Cmat). The DL-Hrch framework entails training of a deep learning model successively starting from least-agreed to the most-agreed data points. In this scheme, the model would be fine tunned with the consensus of the entire annotators at the latest iterations. For evaluation, 240 prostate TMA cores, graded independently by six observers (from Gleason2019 challenge), were employed. Cohen’s Kappa agreement coefficients among the different observers were between 0.40 and 0.73. The label maps generated from the STAPLE algorithm were regarded as ground truth. The DL-Hrch model exhibited the superior accuracy of 0.94 in terms of Dice index, while the DL-Cmat resulted in the Dice index of 0.92. The other decision fusion algorithms exhibited greatly inferior accuracy compared to the DL-Hrch approach. The proposed hierarchical training scheme yielded the highest accuracy in comparison with the commonly used algorithms to deal with noisy or multi-observer labeled datasets. The proposed training scheme could be utilized for decision fusion from multiple observers and estimation of the true label maps.
Efficient Serial Architecture for PRESENT Block Cipher

In recent years, the use of the Internet of Things (IoT) has increased rapidly in different areas. Due to many IoT applications, many limitations have emerged such as power consumption and limited resources. The security of connected devices is becoming more and more a primary need for the reliability of systems. Among other things, power consumption remains an essential constraint with a major impact on the quality of the encryption system. For these, several lightweight cryptography algorithms were proposed and developed. The PRESENT algorithm is one of the lightweight block cipher algorithms that has been proposed for a highly restrictive application. In this paper, we have proposed an efficient hardware serial architecture that uses 16 bits for data path encryption. It uses fewer FPGA resources and achieves higher throughput compared to other existing hardware applications.
A Lightweight and Efficient Remote Data Authentication Protocol Over Cloud Storage Environment

Cloud storage services need cost-effective, independent, and scalable functionality to manage data. Cloud repositories provide an opportunity to manage and access essential data conveniently. This article proposes a secure authentication protocol to resolve these issues. To add a convenient sharing of data controlled by a data delegator, we use symmetric encryption and decryption algorithms in which cloud servers encrypt and decrypt the data to control the accessibility of the user. Furthermore, informal security analysis demonstrates that our devised protocol is robust enough to resist various attacks. We present the formal analysis of security using the Random Oracle Model (ROM) to evaluate and prove the security strength of the devised protocol. The proposed and related protocols have been implemented on an online cloud server named PythonAnywhere with PyCrypto and Time libraries using Python with the help of a system that has predefined specifications to compare the performance. The results show that our scheme has feasible computation, storage, communication, and energy efficiency compared to various related protocols.
Nature Inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Grey Wolf Algorithm Techniques

This paper discusses research-based findings of applying metaheuristic optimization techniques and nature-inspired algorithms to detect and mitigate phishing attacks. The focus will be on the Grey Wolf nature-inspired metaheuristic algorithm optimized with Random Forest and Support Vector Machine (SVM) classification. Existing research recommends the development and use of nature-inspired detection techniques to solve complex real-world problems. Existing research using nature-inspired heuristics appears to be promising in solving NP-hard problems such as the traveling salesperson problem. In the same classification of NP-hard, is that of cyber security existing research indicates that the security threats are complex and that providing security is an NP-hard problem. This study is expanding the existing research with a hybrid optimization of nature-inspired metaheuristic with existing classifiers (random forest and SVM) for an improvement in results to include increased true positives and decreased false positives. The proposed study will present the importance of nature and natural processes in developing algorithms and systems with high precision and accuracy.
Addressing cybersecurity threats in prosumer-based nanogrids with MQTT communication

The high price of electricity has given extra impetus to the rapid deployment of solar self-consumption nanogrids, which allow customers to become prosumers. The development of smarter and more efficient nanogrids, “advanced nanogrids”, has been promoted by enabling technologies such as the Internet of things (IoT) and artificial intelligence (AI), along with the bidirectional flow of power between prosumers and the utility. However, advanced nanogrids can be subjected to cyber-attacks which impede the nanogrid operation in terms of economic and physical performance. The aim of this paper is to highlight the vulnerability to attack of the MQTT communication between IoT devices, e.g. a weather station and the advanced nanogrid controller, and to put forward techniques that can be employed to protect it. Data from the weather station is essential to provide predictions which are used, in turn, for making generation/consumption offers. Firstly, a cybersecurity analysis to identify assets, assess threats and quantify risks is performed. Secondly, a penetration test on the MQTT communications is carried out to exemplify the exposed threats. Finally, strategies such a Intrusion Detection, Protection System (IDPS) and communication encryption are implemented.
Industrial control systems cybersecurity: Back to basic cyber hygiene practices

Industrial control systems (ICS)- the backbone of any modern nation’s critical infrastructure (CI) such as the water, energy, transportation and information and communication technology (ICT) sectors-have been subjected to considerable cyberattacks in recent times. This is exacerbated by the gradual convergence of the information technology (IT) and operational technology (OT) domains. This paper sought to establish the basic cyber hygiene practices to stop malicious cyber activities against connected OTs. Cyber hygiene practices refer to knowledge, attitudes and behaviours consistently put into practice to eliminate a few root causes responsible for many cyber incidents. A scoping technique was adopted to review ICS security studies conducted in the past ten years (2012-2022). The key findings indicate that many CI operators have poor password and access control policies for onsite and remote access to networks, do not perform system updates and patching as and when it is necessary to do so, do not actively restrict, segregate and/or separate access to ICS networks and devices, and do not conduct cybersecurity awareness training as frequently as they should. Lastly, due to legacy ICS networks, threat intelligence tools such as extended detection and response and similar others to monitor and detect, in real-time, anomalous behaviour on the networks are rarely utilised for cyber risk assessment on OT networks.
Text-based feature selection using binary particle swarm optimization for sentiment analysis

The upsurge in social media data due to the proliferation of Web 2.0 applications has escalated scholarly studies within the sentiment analysis domain in recent times. Sentiment Analysis usually considered a text classification task in Natural Language Processing (NLP) classifies the views, attitudes, and feelings expressed by people concerning a particular organization or entity. This unstructured textual data can be pre-processed and represented as feature vectors which then serve as input to a machine learning algorithm for sentiment classification. In this process, feature selection which is a binary problem becomes an essential component of the SA exercise. We present a metaheuristic-based approach for optimal selection of features subset via the binary particle swarm optimization (BPSO) metaheuristic algorithm with the view to improve sentiment classification accuracy on the sentiment labelled sentences benchmark dataset. K-Nearest Neighbours, Naïve Bayes, and Support Vector Machine classifiers were employed as baseline classifiers to train the features. Before the sentiment classification process, the BPSO is utilized for selecting the optimal text features subset from the data. We train our sentiment labelled sentences benchmark dataset with SVM, NB, and k-NN using the selected optimal feature subset for sentiment classification. The results of the experiments conducted show impressive performance using our proposed approach for optimal text feature selection and sentiment classification compared to the baseline classifiers.