Reviewerly/exHarmony huggingface dataset:
A Case Study of the Liberal Arts Curriculum at a College of Lifelong Education for Adult Learners

This study aimed to explore the operation case of the liberal arts curriculum found at ‘A’ university, a long term participating university of the ‘Lifelong Education at Universities for the Future Education Project.’ For this study, qualitative research methodology was used and the data collection for this study was conducted for five months. As a result of the study, we found that ‘A’ university, like other universities involved in this project, has been trying to establish a flexible academic system, such as a RPL (Recognition of Prior Learning Experience), on-line education, night and weekend classes, and blended education. Moreover, ‘A’ university recently made a progress in preparing a liberal arts curriculum, tailored to adult learners as it took into account the age of adult learners and the level of their basic academic ability. However, several problems were also found in the process of introducing this type of liberal arts curriculum. The first problem was the university’s top down operation with administrative practices. And The second problem involved issues pertaining to quality in the management of the liberal arts curriculum. These findings were consistent with the recent concerns raised in previous studies. The results of this study suggest that universities participating in the ‘Lifelong Education at Universities for the Future Education Project’ need to consider the gap between the ideal goals found in the project and the realistic needs of adult learners enrolled in a liberal arts curriculum.
Motion vectors and deep neural networks for video camera traps

Commercial camera traps are usually triggered by a Passive Infra-Red (PIR) motion sensor necessitating a delay between triggering and the image being captured. This often seriously limits the ability to record images of small and fast moving animals. It also results in many “empty” images, e.g., owing to moving foliage against a background of different temperature. In this paper we detail a new triggering mechanism based solely on the camera sensor. This is intended for use by citizen scientists and for deployment on an affordable, compact, low-power Raspberry Pi computer (RPi). Our system introduces a video frame filtering pipeline consisting of movement and image-based processing. This makes use of Machine Learning (ML) feasible on a live camera stream on an RPi. We describe our free and open-source software implementation of the system; introduce a suitable ecology efficiency measure that mediates between specificity and recall; provide ground-truth for a video clip collection from camera traps; and evaluate the effectiveness of our system thoroughly. Overall, our video camera trap turns out to be robust and effective.
Neural network based fuzzy cognitive map

A Fuzzy Cognitive Map (FCM) is a powerful technique for modeling and analyzing complex systems. In this study, we propose a novel learning algorithm that, unlike existing FCM-based learning algorithms, ensures matching the desired system state by computing the otherwise “unexplained” biases in the model. Our learning algorithm considers both the whole system bias and the individual biases for each system factor (concept). We explore the impact of FCM structure and characteristics for the proposed algorithm and suggest an interpretation of computed biases. Finally, we propose an FCM visualization technique which enables comparison between and a deeper understanding of modeled systems. As FCMs offer a broader, quantifiable view of the causal relationships between factors, the approach used in this study provides insights into FCM modeling and application to real-world complex systems.
Design and Implement an Enhanced Simulator for Autonomous Delivery Robot

As autonomous driving technology is getting more and more mature today, autonomous delivery companies like Starship, Marble, and Nuro has been making progress in the tests of their autonomous delivery robots. While simulations and simulators are very important for the final product landing of the autonomous delivery robots since the autonomous delivery robots need to navigate on the sidewalk, campus, and other urban scenarios, where the simulations can avoid real damage to pedestrians and properties in the real world caused by any algorithm failures and programming errors and thus accelerate the whole developing procedure and cut down the cost. In this case, this study proposes an open-source simulator based on our autonomous delivery robot ZebraT to accelerate the research on autonomous delivery. The simulator developing procedure is illustrated step by step. What is more, the applications on the simulator that we are working on are also introduced, which includes autonomous navigation in the simulated urban environment, cooperation between an autonomous vehicle and an autonomous delivery robot, and reinforcement learning practice on the task training in the simulator. We have published the proposed simulator in Github.
Reviewing the receipts of cartographic publications to the Russian State Library in 2021

The traditional annual meeting of the Russian cartographic community representatives, on reviewing the receipts to the fund of the Russian State Library (RSL), was held in the cartographic publications department of the RSL. The best samples of domestic and foreign cartographic products issued in 2020–2021 were demonstrated. They undoubtedly include the miniature “Historical Geographical Atlas” published for the 75th anniversary of the Victory in 2020, the new edition of JSC Red Star “Atlas of the Officer of Russia” 2021, “Map of Tobolsk Cultural Heritage Sites, Sights and Tourist Infrastructure”, “Atlas of World War II
A Shapley Value Index for Market Basket Analysis: Efficient Computation Using an Harsanyi Dividend Representation

Market basket analysis (MBA) aims to discover purchasing patterns and item associations from customer transaction data. A major drawback of current techniques for MBA is a lack of quantitative metrics to measure the real value associated with basket items. This paper addresses this gap by deriving a practical game-theoretic measure for MBA based on the Shapley value of cooperative games, which we call Shapley value index for MBA (SIMBA). The SIMBA of an item represents the average revenue it earns, including its influence on the revenue earned from sales of other items. A significant challenge when applying Shapley value-inspired approaches in practical domains is the exponential complexity of Shapley value computation. However, for the MBA domain, we show that SIMBA admits a scalable exact computation method that does not require sampling or other approximations. Specifically, a characteristic function for the MBA game is constructed so that the transaction dataset input corresponds to the game’s Harsanyi dividends. The relationship between Harsanyi dividends and the Shapley value is then exploited to efficiently compute SIMBA. This approach scales linearly in the number of transactions, making SIMBA a feasible approach for quantitative MBA. SIMBA can be used to screen conventional MBA techniques, such as association rules, to identify significant rules based on the items’ cross-selling capacity. This combination of existing MBA methods and SIMBA will generate rules based not only on frequency of co-occurrence, but also on the significance of the items. We demonstrate the working of the algorithm by analyzing openly available transaction data from an online retail store. To the best of our knowledge, this is the first time Shapley value is used in this way to solve market basket analyses of a practical size.
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems

This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem. We propose a variational quantum circuit (VQC) design as a new denoising solution. For a practical grant-free communications system having correlated device activities, variational quantum parameters for Pauli rotation gates in the proposed VQC system are optimized to facilitate to the non-linear estimation. Numerical results show that the VQC method can outperform modern compressed sensing techniques using an element-wise denoiser.
A method of classification-based Spark job performance modeling

Prediction of Apache Spark job execution time is a key technology to guide Spark cluster resource allocation and parameter tuning. In the existing research, a unified modeling method is used for different jobs, and the prediction model considers less factors, resulting in poor prediction effect. In view of the above problems, this paper proposes a classification-based Spark job performance modeling method. The method first selects features that are strongly correlated with job execution time, then classifies jobs according to the selected features, and finally uses GBDT algorithm to build an execution time prediction model for each class of jobs classified. The experimental results show that, compared with the method using unified modeling, the method proposed in this paper can reduce the RMSE and MAPE of the prediction results by an average of 42.5% and 51.1%.
Sustainable Insights for Energy Big Data Governance in China: Full Life Cycle Curation from the Ecosystem Perspective

With the development of the Energy Internet and the Internet of Things, diversified social production activities are making the interactions between energy, business, and information flow among physical, social, and information systems increasingly complex. As the carrier of information and the hub between physical and social systems, the effective management of energy big data has attracted the attention of scholars. This work indicates that China’s energy companies have carried out a series of activities that are centered on energy big data collection, as well as development and exchange, and that the energy big data ecosystem has begun to take shape. However, the research on and the application of energy big data are mainly limited to micro-level fields, and the development of energy big data in China remains disordered because the corresponding macro-level instructive governance frameworks are lacking. In this work, to facilitate the sustainable development of the energy big data ecosystem and to solve existing problems, such as the difficult-to-determine governance boundaries and the difficult-to-coordinate interests, and to analyze the structure and mechanism of the energy big data ecosystem, data curation is introduced into energy big data governance, and a paradigm is constructed for sustainable energy big data curation that encompasses its full life cycle, including the planning, integration, application, and maintenance stages. Key paradigmatic issues are analyzed in-depth, including data rights, fusion, security, and transactions.
Work-in-Progress: Computing Sentence Similarity for Short Texts using Transformer models

The field of natural language processing is being revolutionized with transformers. The latter is based on a novel type of neural network framework that is already pre-trained. Hence, large datasets to train models are no longer required. This framework is suitable for automated assessment systems (AAS), where a large number of labeled data is needed. The larger the dataset, the higher the accuracy of the AAS. In this work-in-progress paper, a prototype for an AAS has been built where two transformer models, namely the Sentence-Transformers from hugging face and the OpenAI GPT-3 models have been used. The transformer models generate the similarity index between students&#x2019; answers and reference answers from the Texas dataset. Then the similarity index is used to compute marks for students. The performance of the prototype is evaluated using the quadratic weighted kappa metric.
Neural Extended/Unscented Kalman Filter for Submarine Passive Target Tracking

In order to apply undersea target tracking using passive bearings only, this work aims to investigate the performance capabilities of the neural-based extended/unscented Kalman (EKF/UKF) filter. In this paper, the assumption is that a submarine is tracking in underwater another submarine or ship. Application of UKF for target tracking utilizing passive bearings-only measurements is a standard and well-known technique and published in the literature. Here a case study is taken up with the objective of improving results further adding neural network (NN) to EKF/UKF. This technique uses NN nonlinear state-space model approximation and NN's weights are trained on-line by the EKF/UKF. The Monte-Carlo simulations is utilized, and the outcomes are compared to the EKF/UKF procedure, since it is stochastic. It is discovered that the results with EKF/UKF are better than that of the corresponding EKF/UKF with NN. It is understood that NN is to be used when the process uncertainties cannot be modeled exactly or where there is unobservability. In passive target tracking, unobservability exists and this problem is solved by observer maneuver. However, when NEKF/NUKF is used, the observer maneuver is not able to solve the problem. Hence, NN need not be utilized when the analytical solution is feasible, and the solution is tractable.
Adversarially robust sequential hypothesis testing

The problem of sequential hypothesis testing is studied, where samples are taken sequentially, and the goal is to distinguish between the null hypothesis where the samples are generated according to a distribution p and the alternative hypothesis where the samples are generated according to a distribution q. The defender (decision maker) aims to distinguish the two hypotheses using as few samples as possible subject to false alarm constraints. The problem is studied under the adversarial setting, where the data generating distributions under the two hypotheses are manipulated by an adversary, whose goal is to deteriorate the performance of the defender—for example, increasing the probability of error and expected sample sizes—with minimal cost. Specifically, under the null hypothesis, the adversary picks a distribution p∈P with cost c0(p), and under the alternative hypothesis, the adversary picks a distribution q∈Q with cost c1(q). This problem is formulated as a non-zero-sum game between the defender and the adversary. A pair of strategies (the adversary’s strategy and the defender’s strategy) is proposed and proved to be a Nash equilibrium pair for the non-zero-sum game between the adversary and the defender asymptotically. The defender’s strategy is a sequential probability ratio test and thus is computationally efficient for practical implementation.
Educational Reforms Amid COVID-19 in Thailand

Education is essential for any country to secure its future, but COVID-19 has caused considerable difficulty in Thailand. The COVID-19 pandemic harms the education of the students. The online education system is adopted in almost all parts of the globe. However, the pandemic has been impacting the overall quality of the education system. This research explored the experiences of law students in Thailand by analyzing several factors which affect their intellect. The methodology adopted for this study was a multiple method study. The sample size of 330 law students selected from leading Thai universities was analyzed qualitatively through Strength, Weakness, Opportunity, and Threat (SWOT) analysis and regression model for the quantitative analysis. The findings revealed that factors including internet availability, accessibility to own gadgets, online resources, government, social media, and classrooms have a significant impact on the education system of Thai law students. The study paves way for further microanalysis of the educational system in Thailand with the perceptions and experiences of teachers and parents.
Intelligent Campus and English Visual Education System Design Based on Internet of Things

With the continuous development of the Internet of Things technology, learning and acquiring knowledge through Internet will become an important way for human beings to learn, work, and conduct research. In the field of English language teaching, the cutting-edge research on the Internet of Things-based interactive teaching models is important. The intelligent campus is a new type of campus built aided by advanced techniques such as machine learning and the Internet of Things. In the smart campus, the classroom usually consists of a tangible physical space and an invisible digital space, which can be used to support teaching content through various types of intelligent equipment, promote classroom interaction, and achieve more convenient visualization of educational functions. Based on the initial results of the Internet of Things research, this research establishes a visual education system based on the Internet of Things. At the same time, the Internet of Things is used to study the interactive English teaching mode in order to improve the level of English teaching and thus enhance the technology level of the intelligent campus.
Geometric Analysis of Conditional Bias-Informed Kalman Filters

This paper presents a comparative geometric analysis of the conditional bias (CB)-informed Kalman filter (KF) with the Kalman filter (KF) in the Euclidean space. The CB-informed KFs considered include the CB-penalized KF (CBPKF) and its ensemble extension, the CB-penalized Ensemble KF (CBEnKF). The geometric illustration for the CBPKF is given for the bi-state model, composed of an observable state and an unobservable state. The CBPKF co-minimizes the error variance and the variance of the Type-II error. As such, CBPKF-updated state error vectors are larger than the KF-updated, the latter of which is based on minimizing the error variance only. Different error vectors in the Euclidean space imply different eigenvectors and covariance ellipses in the state space. To characterize the differences in geometric attributes between the two filters, numerical experiments were carried out using the Lorenz 63 model. The results show that the CBEnKF yields more accurate confidence regions for encompassing the truth, smaller errors in the ensemble mean, and larger norms for Kalman gain and error covariance matrices than the EnKF, particularly when assimilating highly uncertain observations.
Evaluation of Adaptation Methods for Deep Learning-based Wi-Fi Receivers

Machine-learning based transceivers have received increasing attention for next-generation wireless systems. We investigate the application of two meta-learning algorithms &#x2013; Model Agnostic Meta Learning (MAML) and Reptile &#x2013; to a deep learning-based Wi-Fi channel estimation and tracking system, called DeepWiPHY. The meta-learning algorithms were compared against conventional methods such as random initialization, cross-evaluation, and retraining on multiple channel models with varying severity of multipath fading. Comparisons were made fairly with respect to the complexity of the adaptation of the model necessary for a new environment. The results indicate that perhaps surprisingly, conventional training methods are adequate and in fact can outperform meta-learning methods over a wide variety of channels. The key is to train the receiver using the worst-case (most severe) multipath channel model, which then allows strong performance across a wide class of channels without requiring the additional burden of meta-learning.
Beauty of Block Chain Technology in Supply Chain Management

Block chain is an innovative device which plays an important part in Supply chain management (SCM). Most importantly, it solves the discernibility issue in the supply chain management. We in the first place introduce a general block chain-based item detectability structure. This system depends on a cross-fringe web based business flexible chain setting. It is a model which combines multi-chain framework model, information board model and square artefact model. A few other techniques and calculations such as data tying down strategy, key dissemination technique, data encryption calculation and hostile to forging technique are additionally used. The structure, models and techniques assessed by applying to a few regular issues. The adequacy, security, execution and administration problems of applying these arrangements are also important and hence addressed in this article.&#x0D; HIGHLIGHTS&#x0D; &#x0D; It resolves the discernibility issue in the supply chain management&#x0D; This system depends on a cross-fringe web based business flexible chain setting&#x0D; It is a combination of multi-chain framework model, information board model and square artefact model&#x0D;
Automatic Detection Algorithm of Football Events in Videos

The purpose is to effectively solve the problems of high time cost, low detection accuracy, and difficult standard training samples in video processing. Based on previous investigations, football game videos are taken as research objects, and their shots are segmented to extract the keyframes. The football game videos are divided into different semantic shots using the semantic annotation method. The key events and data in the football videos are analyzed and processed using a combination of artificial rules and a genetic algorithm. Finally, the performance of the proposed model is evaluated and analyzed by using concrete example videos as data sets. Results demonstrate that adding simple artificial rules based on the classic semantic annotation algorithms can save a lot of time and costs while ensuring accuracy. The target events can be extracted and located initially using a unique lens. The model constructed by the genetic algorithm can provide higher accuracy when the training samples are insufficient. The recall and precision of events using the text detection method can reach 96.62% and 98.81%, respectively. Therefore, the proposed model has high video recognition accuracy, which can provide certain research ideas and practical experience for extracting and processing affective information in subsequent videos.
Shyper: An embedded hypervisor applying hierarchical resource isolation strategies for mixed-criticality systems

With the development of the IoT, modern embedded systems are evolving to general-purpose and mixed-criticality systems, where virtualization has become the key to guarantee the isolation between tasks with different criticality. Traditional server-based hypervisors (KVM and Xen) are difficult to use in embedded scenarios due to performance and security reasons. As a result, several new hypervisors (Jailhouse and Bao) have been proposed in recent years, which effectively solve the problems above through static partitioning. However, this inflexible resource isolation strategy assumes no resource sharing across guests, which greatly reduces the resource utilization and VM scalability. This prevents themselves from simultaneously fulfilling the differentiated demands from VMs conducting different tasks. This paper proposes an efficient and real-time embedded hypervisor “Shyper”, aiming at providing differentiated services for VMs with different criticality. To achieve that, Shyper supports fine-grained hierarchical resource isolation strategies and introduces several novel “VM-Exit-less” real-time virtualization techniques, which grants users the flexibility to strike a trade-off between VM's resource utilization and real-time performance. In this paper, we also compare Shyper with other mainstream hypervisors (KVM, Jailhouse, etc.) to evaluate its feasibility and effectiveness.