Reviewerly/exHarmony huggingface dataset:
Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa

Abstract Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.
USING AUGMENTED REALITY IN BIOLOGY TEACHING

In this study, prospective teachers' views on Augmented Reality (AR) in biology teaching were examined. The study was designed as a case study. The study participants consisted of 16 prospective teachers who took Instructional Technologies and Material Design in the third grade of the Biology Teaching Department of the Faculty of Education of a state university in the spring term of the 2018-2019 academic year. During the application process, AR activities were carried out with the students. The data of the study were obtained using the interview form and the "Mobile Augmented Reality Questionnaire" developed by Küçük, Kapakin, and Göktaş (2015). After the activities, prospective teachers were asked for their views about AR in biology teaching. As a result, it was observed that future biology teachers mostly expressed positive thoughts about AR activities. Some advantages of the AR activities were as follows: concretising the subjects; retention; being exciting and entertaining; repetition capability; and multimedia support. Apart from these advantages, some disadvantages were emphasised such as internet connection cut off, the necessity to keep the phone stable, passivation of the students over time, being costly, and not being suitable for every subject. Prospective teachers also suggested extending AR activities in schools, ensuring equality in accessing technological tools, using these activities in different lessons and developing various applications.
Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)

AI-based smart thermal perception systems can cater to the limitations of conventional imaging sensors by providing a more reliable data source in low-lighting conditions and adverse weather conditions. This research evaluates and modifies the state-of-the-art object detection and classifier framework for thermal vision with seven key object classes in order to provide superior thermal sensing and scene understanding input for advanced driver-assistance systems (ADAS). The networks are trained on public datasets and is validated on test data with three different test approaches which include test-time augmentation, test-time with no augmentation, and test-time with model ensembling. Additionally, a new model ensemble-based inference engine is proposed, and its efficacy is tested on locally gathered novel test data comprising of 20K thermal frames captured with an uncooled LWIR prototype thermal camera in challenging weather and environmental scenarios. The performance analysis of trained models is investigated by computing precision, recall, and mean average precision scores (mAP). Furthermore, the smaller network variant of thermal-YOLO architecture is optimized using TensorRT inference accelerator, which is then deployed on GPU and resource-constrained edge hardware Nvidia Jetson Nano. This is implemented to explicitly reduce the inference time on GPU as well as on Nvidia Jetson Nano to evaluate the feasibility for added real-time onboard installations.
Quantum phase transition and quench dynamics in the two-mode Rabi model

The second-order quantum phase transition and quench dynamics in the two-mode Rabi model are investigated. We propose a diagonalization approach of jointly using a beam-splitter operator and a squeezed operator for each effective low-energy Hamiltonian when the ratio of the qubit transition frequency to the oscillator frequency approximates infinity. Eigenenergy and eigenstate of the normal and superradiant phases in the system are analytically derived, demonstrating the second-order quantum phase transition at a critical point. This critical point means that the requirement of coupling strength between the qubit and two oscillators is largely loosened by joint effect of two modes when compared with the standard Rabi model. The universal scaling between residual energy and quench time from ${\ensuremath{\tau}}^{\ensuremath{-}\frac{1}{3}}$ to ${\ensuremath{\tau}}^{\ensuremath{-}2}$ is confirmed.
Neural Network-Based Strong Motion Prediction for On-Site Earthquake Early Warning

Developing on-site earthquake early warning systems has been a challenging problem because of time limitations and the amount of information that can be collected before the warning needs to be issued. A potential solution that could prevent severe disasters is to predict the potential strong motion using the initial P-wave signal and provide warnings before serious ground shaking starts. In practice, the accuracy of prediction is the most critical issue for earthquake early warning systems. Traditional methods use certain criteria, selected through intuition or experience, to make the prediction. However, the criteria thresholds are difficult to select and may significantly affect the prediction accuracy. This paper investigates methods based on artificial intelligence for predicting the greatest earthquake ground motion early, when the P-wave arrives at seismograph stations. A neural network model is built to make the predictions using a small window of the initial P-wave acceleration signal. The model is trained by seismic waves collected from 1991 to 2019 in Taiwan and is evaluated by events in 2020 and 2021. From these evaluations, the proposed scheme significantly outperforms the threshold-based method in terms of its accuracy and average leading time.
Compressed $$\varSigma $$-Protocols for Bilinear Group Arithmetic Circuits and Application to Logarithmic Transparent Threshold Signatures

Lai et al. (CCS 2019) have shown how Bulletproof's arithmetic circuit zero-knowledge protocol (Bootle et al., EUROCRYPT 2016 and Bünz et al., S&P 2018) can be generalized to work for bilinear group arithmetic circuits directly, i.e., without requiring these circuits to be translated into arithmetic circuits.
Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification

Deep convolutional networks have been widely used for various medical image processing tasks. However, the performance of existing learning-based networks is still limited due to the lack of large training datasets. When a general deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, and performance degradation problems occur. In this work, by designing the semantic consistency generative adversarial network (SCGAN), we propose a new multimodal domain adaptation method for medical image diagnosis. SCGAN performs cross-domain collaborative alignment of ultrasound images and domain knowledge. Specifically, we utilize a self-attention mechanism for adversarial learning between dual domains to overcome visual differences across modal data and preserve the domain invariance of the extracted semantic features. In particular, we embed nested metric learning in the semantic information space, thus enhancing the semantic consistency of cross-modal features. Furthermore, the adversarial learning of our network is guided by a discrepancy loss for encouraging the learning of semantic-level content and a regularization term for enhancing network generalization. We evaluate our method on a thyroid ultrasound image dataset for benign and malignant diagnosis of nodules. The experimental results of a comprehensive study show that the accuracy of the SCGAN method for the classification of thyroid nodules reaches 94.30%, and the AUC reaches 97.02%. These results are significantly better than the state-of-the-art methods.
Benefits and Challenges of Virtual-Reality-Based Industrial Usability Testing and Design Reviews: A Patents Landscape and Literature Review

With the introduction of new devices, industries are turning to virtual reality to innovate their product development processes. However, before the technology’s possibilities can be fully harnessed, certain constraints must be overcome. This study identifies the benefits and challenges of virtual-reality-based usability testing and design reviews in industry through a patents and articles review. We searched Derwent Innovation, Scopus, and Web of Science and identified 7 patent filings and 20 articles. We discovered an increase in patent filings since 2016 and strong development in the technology space, offering opportunities to enter an area while it is still young. The most frequently researched field is the automotive industry and the most used device is the HTC VIVE head-mounted display, which is frequently paired with motion capture systems and Unity 3D game engines. Virtual reality benefits design reviews and usability testing by providing the visualization of new angles that stimulate novel insights, increasing team engagement, offering more intuitive interactions for non-CAD specialists, saving redesign cost and time, and increasing participants’ safety. The challenges faced by virtual-reality-based prototypes are a lack of realism due to unnatural tactile and visual interactions, latency and registration issues, communication difficulties between teams, and unpleasant symptoms. While these constraints prevent virtual reality from replacing conventional design reviews and usability testing in the near future, it is already a valuable contribution to the industrial product development process.
SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network

Sentiment analysis is one of the efficient models for extracting opinion mining with identification and classification from unstructured text data such as product reviews or microblogs. It is used to gain feedback from political campaigns, brand reviews, marketing analysis, and customers. The sentiment analysis on Twitter data is a recent research field in the natural processing. The dataset is gathered from the “Twitter” package in R along with Twitter API. The main intent of this paper is to understand the public opinion on the recently implemented demonetization policy using the proposed SenDemonNet. Initially, the tweet preprocessing was done, which is intended for cleaning the text data. Then, the feature extraction is performed by Bag of n-grams, TF-IDF, and the word2vec algorithm. The main objective of this work is a weighted feature selection that is developed by the hybrid Forest–Whale Optimization Algorithm (F-WOA) to get the best classification outcome. With these features, the Heuristic Deep Neural Network (HDNN) is adopted for classification, where the proposed FOA and WOA tune the parameter of DNN for reaching the maximum accuracy rate. From the statistical analysis, the performance of the designed F-WOA-DNN is 1.8%, 1.9%, 1.86%, and 2% enhanced than PSO-DNN, GWO-DNN, WOA-DNN, FOA-DNN, SVM, CNN, LSTM, and DNN respectively. Extensive experimental results show that SenDemonNet outperforms its competitors, producing an impressive increase in the classification accuracy on the benchmark dataset.
Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0

Training supervised machine learning models like deep learning requires high-quality labelled datasets that contain enough samples from various categories and specific cases. The Data as a Service (DaaS) can provide this high-quality data for training efficient machine learning models. However, the issue of privacy can minimize the participation of the data owners in DaaS provision. In this paper, a blockchain-based decentralized federated learning framework for secure, scalable, and privacy-preserving computational intelligence, called Decentralized Computational Intelligence as a Service (DCIaaS), is proposed. The proposed framework is able to improve data quality, computational intelligence quality, data equality, and computational intelligence equality for complex machine learning tasks. The proposed framework uses the blockchain network for secure decentralized transfer and sharing of data and machine learning models on the cloud. As a case study for multimedia applications, the performance of DCIaaS framework for biomedical image classification and hazardous litter management is analysed. Experimental results show an increase in the accuracy of the models trained using the proposed framework compared to decentralized training. The proposed framework addresses the issue of privacy-preserving in DaaS using the distributed ledger technology and acts as a platform for crowdsourcing the training process of machine learning models.
Unsupervised Temporal Video Grounding with Deep Semantic Clustering

Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant video-query paired data, which is expensive to collect in real-world scenarios. In this paper, we explore whether a video grounding model can be learned without any paired annotations. To the best of our knowledge, this paper is the first work trying to address TVG in an unsupervised setting. Considering there is no paired supervision, we propose a novel Deep Semantic Clustering Network (DSCNet) to leverage all semantic information from the whole query set to compose the possible activity in each video for grounding. Specifically, we first develop a language semantic mining module, which extracts implicit semantic features from the whole query set. Then, these language semantic features serve as the guidance to compose the activity in video via a video-based semantic aggregation module. Finally, we utilize a foreground attention branch to filter out the redundant background activities and refine the grounding results. To validate the effectiveness of our DSCNet, we conduct experiments on both ActivityNet Captions and Charades-STA datasets. The results demonstrate that our DSCNet achieves competitive performance, and even outperforms most weakly-supervised approaches.
Machine learning-based book recommender system: a survey and new perspectives

The exponential growth of recommender systems research has drawn the attention of the scientific community recently. These systems are very useful in reducing information overload and providing users with the items of their need. The major areas where recommender systems have contributed significantly include e-commerce, online auction, and books and conference recommendation for academia and industrialists. Book recommender systems suggest books of interest to users according to their preferences and requirements. In this article, we have surveyed machine learning techniques which have been used in book recommender systems. Moreover, evaluation metrics applied to evaluate recommendation techniques is also studied. Six categories for book recommendation techniques have been identified and discussed which would enable the scientific community to lay a foundation of research in the concerned field. We have also proposed future perspectives to improve recommender system. We hope that researchers exploring recommendation technology in general and book recommendation in particular will be finding this work highly beneficial.
A Literature Review of Digital Literacy over Two Decades

The COVID-19 pandemic has forced online learning to be a “new normal” during the past three years, which highly emphasizes students’ improved digital literacy. This study aims to present a literature review of students’ digital literacy. Grounded on about twenty journal articles and other related publications from the Web of Science Core Collection, this paper focused on the definition of digital literacy; the factors affecting students’ digital literacy (age, gender, family socioeconomic status, and parent’s education level); the relationship between students’ digital literacy and their self-control, technostress, and engagement; and the three approaches to gauge the level of students’ digital literacy. The study also provided some advice for educators and policymakers. Finally, the limitations and implications were presented.
LCCI: A framework for least cybersecurity controls to be implemented for small and medium enterprises (SMEs)

Globally Small and Medium Enterprises (SMEs) are the biggest contributors to the economy by providing two-thirds of global employment opportunities and more than half the GDP of the developed economies. Regardless of many existing cybersecurity standards or frameworks, SMEs are most vulnerable to cyber threats. SMEs face serious impacts on their existence if undergo successful cyber-attacks by cybercriminals. There is a need to understand the challenges SMEs are facing especially in the implementation of cybersecurity controls. Through a research survey conducted by authors where one hundred and fifteen SMEs voluntarily participated, this research paper will throw light on the current cybersecurity controls implementation posture for different SMEs, along with the challenges they are facing which are stopping them to decide, plan and implement cybersecurity controls. Further, using an analysis of the inputs and the core concepts of cybersecurity, the authors are also going to propose a recommended solution for SMEs.
A Real Time Arabic Sign Language Alphabets (ArSLA) Recognition Model Using Deep Learning Architecture

Currently, treating sign language issues and producing high quality solutions has attracted researchers and practitioners’ attention due to the considerable prevalence of hearing disabilities around the world. The literature shows that Arabic Sign Language (ArSL) is one of the most popular sign languages due to its rate of use. ArSL is categorized into two groups: The first group is ArSL, where words are represented by signs, i.e., pictures. The second group is ArSl alphabetic (ArSLA), where each Arabic letter is represented by a sign. This paper introduces a real time ArSLA recognition model using deep learning architecture. As a methodology, the proceeding steps were followed. First, a trusted scientific ArSLA dataset was located. Second, the best deep learning architectures were chosen by investigating related works. Third, an experiment was conducted to test the previously selected deep learning architectures. Fourth, the deep learning architecture was selected based on extracted results. Finally, a real time recognition system was developed. The results of the experiment show that the AlexNet architecture is the best due to its high accuracy rate. The model was developed based on AlexNet architecture and successfully tested at real time with a 94.81% accuracy rate.
System-Level Design and Integration of a Prototype AR/VR Hardware Featuring a Custom Low-Power DNN Accelerator Chip in 7nm Technology for Codec Avatars

Augmented Reality / Virtual Reality (AR/VR) devices aim to connect people in the Metaverse with photorealistic virtual avatars, referred to as “Codec Avatars”. Delivering a high visual performance for Codec Avatar workloads, however, is a challenging task for mobile SoCs as AR/VR devices have limited power and form factor constraints. On-device, local, near-sensor processing provides the best system-level energy-efficiency and enables strong security and privacy features in the long run. In this work, we present a custom-built, prototype small-scale mobile SoC that achieves energy-efficient performance for running eye gaze extraction of the Codec Avatar model. The test-chip, fabricated in 7nm technology node, features a Neural Network (NN) accelerator consisting of a 1024 Multiply-Accumulate (MAC) array, 2MB on-chip SRAM, and a 32bit RISC-V CPU. The featured test-chip is integrated on a prototype mobile VR headset to run the Codec Avatar application. This work aims to show the full stack design considerations of system-level integration, hardware-aware model customization, and circuit-level acceleration to meet the challenging mobile AR/VR SoC specifications for a Codec Avatar demonstration. By re-architecting the Convolutional NN (CNN) based eye gaze extraction model and tailoring it for the hardware, the entire model fits on the chip to mitigate system-level energy and latency cost of off-chip memory accesses. By efficiently accelerating the convolution operation at the circuit-level, the presented prototype SoC achieves 30 frames per second performance with low-power consumption at low form factors. With the full-stack design considerations presented in this work, the featured test-chip consumes 22.7mW power to run inference on the entire CNN model in 16.5ms from input to output for a single sensor image. As a result, the test-chip achieves 375 µJ/frame/eye energy-efficiency within a 2.56 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> silicon area.
Embedding metric learning into an extreme learning machine for scene recognition

Metric learning can be very useful to improve the performance of a distance-dependent classifier. However, separating metric learning from the classifier learning possibly degenerates the performance, for instance in scene recognition, especially for some complicated scene images. To address this issue, we propose to embed metric learning into an extreme learning machine (EML-ELM) to tackle scene recognition. Specifically, metric learning is conducted to fully explore discriminative features by taking into account all label information, rendering samples of the same class more compact and those of different classes more separable. An extreme learning machine is employed as a classifier thanks to its effectiveness and fast learning process. By explicitly embedding metric learning into an extreme learning machine, we can jointly learn the discriminative features and an effective classifier in a unified framework, thereby improving the recognition performance for complicated scene images. Extensive experiments on four benchmark scene datasets demonstrate the competitive performance of EML-ELM in comparison with state-of-the-art methods.