Reviewerly/exHarmony huggingface dataset:
Maximal Margin Support Vector Machine for Feature Representation and Classification

High-dimensional small sample size data, which may lead to singularity in computation, are becoming increasingly common in the field of pattern recognition. Moreover, it is still an open problem how to extract the most suitable low-dimensional features for the support vector machine (SVM) and simultaneously avoid singularity so as to enhance the SVM’s performance. To address these problems, this article designs a novel framework that integrates the discriminative feature extraction and sparse feature selection into the support vector framework to make full use of the classifiers’ characteristics to find the optimal/maximal classification margin. As such, the extracted low-dimensional features from high-dimensional data are more suitable for SVM to obtain good performance. Thus, a novel algorithm, called the maximal margin SVM (MSVM), is proposed to achieve this goal. An alternatively iterative learning strategy is adopted in MSVM to learn the optimal discriminative sparse subspace and the corresponding support vectors. The mechanism and the essence of the designed MSVM are revealed. The computational complexity and convergence are also analyzed and validated. Experimental results on some well-known databases (including breastmnist, pneumoniamnist, colon-cancer, etc.) show the great potential of MSVM against classical discriminant analysis methods and SVM-related methods, and the codes can be available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.scholat.com/laizhihui</uri> .
Visual Anomaly Detection via Partition Memory Bank Module and Error Estimation

Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous samples. Unfortunately, the existing memory module is not fully applicable to the anomaly detection task, and the reconstruction error of the anomaly samples remains small. Towards this end, this work proposes a new unsupervised visual anomaly detection method to jointly learn effective normal features and eliminate unfavorable reconstruction errors. Specifically, a novel Partition Memory Bank (PMB) module is proposed to effectively learn and store detailed features with semantic integrity of normal samples. It develops a new partition mechanism and a unique query generation method to preserve the context information and then improves the learning ability of the memory module. The proposed PMB and the skip connection are alternatively explored to make the reconstruction of abnormal samples worse. To obtain more precise anomaly localization results and solve the problem of cumulative reconstruction error, a novel Histogram Error Estimation module is proposed to adaptively eliminate the unfavorable errors by the histogram of the difference image. It improves the anomaly localization performance without increasing the cost. To evaluate the effectiveness of the proposed method for anomaly detection and localization, extensive experiments are conducted on three widely-used anomaly detection datasets. The encouraging performance of the proposed method compared to the recent approaches based on the memory module demonstrates its superiority.
An optimal energy storage system sizing determination for improving the utilization and forecasting accuracy of photovoltaic (PV) power stations

As a new type of flexible regulation resource, energy storage systems not only smooth out the fluctuation of new energy generation but also track the generation scheduling combined with new energy power to enhance the reliability of new energy system operations. In recent years, installing energy storage for new on-grid energy power stations has become a basic requirement in China, but there is still a lack of relevant assessment strategies and techno-economic evaluation of the size determination of energy storage systems from the perspective of new energy power stations. Therefore, this paper starts from summarizing the role and configuration method of energy storage in new energy power stations and then proposes multidimensional evaluation indicators, including the solar curtailment rate, forecasting accuracy, and economics, which are taken as the optimization targets for configuring energy storage systems in PV power stations. Lastly, taking the operational data of a 4000 MWPV plant in Belgium, for example, we develop six scenarios with different ratios of energy storage capacity and further explore the impact of energy storage size on the solar curtailment rate, PV curtailment power, and economics. The method proposed in this paper is effective for the performance evaluation of large PV power stations with annual operating data, realizes the automatic analysis on the optimal size determination of energy storage system for PV power stations, and verifies the rationality of the principle for configuring energy storage for PV power stations in some regions of China.
Quantum Phase Transitions for an Integrable Quantum Rabi-like Model with Two Interacting Qubits

A two-interacting-qubit quantum Rabi-like model with vanishing transverse fields on the qubit pair is studied. Independently of the coupling regime, this model can be exactly and unitarily reduced to two independent single-spin quantum Rabi models, where the spin-spin coupling plays the role of the transverse field. This transformation and the analytical treatment of the single-spin quantum Rabi model provide the key to prove the integrability of our model. The existence of different first-order quantum phase transitions, characterized by discontinuous two-spin magnetization, mean photon number, and concurrence, is brought to light.
Short-term probabilistic forecasting models using Beta distributions for photovoltaic plants

This article presents original probabilistic forecasting models for day-ahead hourly energy generation forecasts for a photovoltaic (PV) plant, based on a semi-parametric approach using three deterministic forecasts. Input information of these new models consists of data of hourly weather forecasts obtained from a Numerical Weather Prediction model and variables related to the sun position for future instants. The proposed models were satisfactorily applied to the case study of a real-life PV plant in Portugal. Probabilistic benchmark models were also applied to the same case study and their forecasting results compared with the ones of the proposed models. The computer results obtained with these proposed models achieve better point and probabilistic forecasting evaluation indexes values than the ones obtained with the benchmark models.
Room-Scale Hand Gesture Recognition Using Smart Speakers

Acoustic signal has been recently adopted for contact-free hand gesture recognition due to its fine-grained sensing granularity and wide availability of microphone and speaker in consumer-grade electronic devices such as smartphones. However, a very limited sensing range constrains acoustic sensing to application scenarios where users interact with devices in close proximity. In this paper, we improve the range of acoustic sensing and demonstrate the feasibility of enabling room-scale hand gesture recognition using commodity smart speakers. We develop a series of novel signal processing techniques and implement our system on two commodity smart speaker prototypes with different numbers of microphones. Extensive evaluations are performed in three different environments with 1440 gestures collected from 16 participants. Experiment results show that our system can significantly increase the sensing range from 1 m to 4--5 m. In the challenging scenario where the user is 4 m away from the smart speaker and there is strong interference, the achieved gesture recognition accuracy is still higher than 90%.
A Survey on Cloud Security: Concepts, Types, Limitations, and Challenges

Given the world's current situation with the COVID-19 pandemic, several businesses have recently encouraged remote working from home. A variety of benefits are provided by cloud computing, including simplified IT and management, secure Internet-based remote access from practically anywhere, and cost savings. As a result, more people use the cloud, but there are also increasing cyber-attacks on cloud networks. However, several companies and organizations, who do not know the security threats that Cloud systems pose, are still worried about using the cloud. Reports previously released by researchers from academia, business, and standard organizations proposed solutions to these problems. This paper examines state-of-the-art papers on topics, challenges to requirements, and identified security system vulnerabilities. In addition, we will review the different components and the security and privacy concerns of current cloud computing systems. Finally, we present a variety of security threats targeting and addressing cloud storage services. In particular, we raise awareness on security issues that cloud organizations including cloud service providers, data owners and cloud users face and address them.&#x0D; &#x0D;
A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network

Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.
RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments

Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this letter, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments.
Joint Detection, Tracking, and Classification of Multiple Extended Objects Based on the JDTC-PMBM-GGIW Filter

This paper focuses on the problem of joint detection, tracking, and classification (JDTC) for multiple extended objects (EOs) within a Poisson multi-Bernoulli (MB) mixture (PMBM) filter, where an EO is described as an ellipse, and the ellipse is modeled by a random matrix. The EOs are classified according to the size information of the ellipse. Usually, detection, tracking, and classification are processed step-by-step. However, step-by-step processing ignores the coupling relationship between detection, tracking, and classification, resulting in information loss. In fact, detection, tracking, and classification affect each other, and JDTC is expected to be beneficial for achieving better overall performance. In the multi-target tracking problem based on RFS, the overall performance of the PMBM filter satisfying the conjugate priors has been verified to be superior to other filters. Specifically, the PMBM filter propagates multiple MB simultaneously during iterative updates and model the distribution of hitherto undetected EOs. At present, the PMBM filter is only applied to multiple extended objects tracking problem. Therefore, we consider using the PMBM filter to solve the JDTC problem of multiple EOs and further improve JDTC performance. Furthermore, the closed-form implementation based on the product of a gamma Gaussian inverse Wishart (GGIW) and class probability mass function (PMF) is proposed. The details of parameters calculation in the implementation process and the derivation of class PMF are presented in this paper. Simulation experiments verify that the proposed algorithm, named the JDTC-PMBM-GGIW filter, performs well in comparison to the existing JDTC strategies for multiple extended objects.
Hybrid quantum-classical multi-cut Benders approach with a power system application

Leveraging the current generation of quantum devices to solve optimization problems of practical interest necessitates the development of hybrid quantum-classical (HQC) solution approaches. In this paper, a multi-cut Benders decomposition (BD) approach that exploits multiple feasible solutions of the master problem (MP) to generate multiple valid cuts is adapted, so as to be used as an HQC solver for general mixed-integer linear programming (MILP) problems. The use of different cut selection criteria and strategies to manage the size of the MP by eliciting a subset of cuts to be added in each iteration of the BD scheme using quantum computing is discussed. The HQC optimization algorithm is applied to the Unit Commitment (UC) problem. UC is a prototypical use case of optimization applied to electrical power systems, a critical sector that may benefit from advances in quantum computing. The proposed approach is demonstrated using the D-Wave Advantage 4.1 quantum annealer.
Low-Resolution Thermal Sensor-Guided Image Synthesis

Thermopile array sensors are cost-effective thermal imaging alternatives and are less vulnerable to privacy intrusion, light conditions, and obtrusiveness. While numerous occupant surveillance systems have been developed based on such sensors, low spatial resolutions prohibit them from deriving more sophisticated applications. To help relieve the limitation, we propose to enrich thermopile array sensors with additional non-thermal features and develop, to the best of our knowledge, the first low-resolution thermal-guided image synthesis model capable of producing realistic and attribute-aligned color images. These thermal heatmaps are regarded as semantic maps, but have very low resolutions. We propose an extension of SPADE (Spatially-Adaptive Denormalization), namely SPADE-SR, to incorporate the spatial property of a thermal heatmap into a conditional GAN through iterative Self-Resampling. Compared to SPADE, SPADE-SR yields better results in terms of image quality and reconstruction error while using significantly fewer model parameters. A new LRT-Human (Low-Resolution Thermal Human) dataset comprised of 22k (thermal heatmap, RGB image) pairs with various thermal and non-thermal coupling is derived to support our claims. Our work explores the cross-thermal-RGB modality paradigm and poses a great opportunity for thermopile array sensors in surveillance usages.
Fluctuation-based outlier detection

Abstract Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation . This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with eight state-of-the-art algorithms on eight real-worlds tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection .
MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles

Trajectory planning is a crucial task in designing the navigation systems of automatic underwater vehicles (AUVs). Due to the complexity of underwater environments, decision makers may hope to obtain multiple alternative trajectories in order to select the best. This paper focuses on the multiple-trajectory planning (MTP) problem, which is a new topic in this field. First, we establish a comprehensive MTP model for AUVs, by taking into account the complex underwater environments, the efficiency of each trajectory, and the diversity among different trajectories, simultaneously. Then, to solve the MTP, we develop an ant colony-based trajectory optimizer, which is characterized by a niching strategy, a decayed alarm pheromone measure, and a diversified heuristic measure. The niching strategy assists in identifying and maintaining a diverse set of high-quality solutions. The use of decayed alarm pheromone and diversified heuristic further improves the search effectiveness and efficiency of the algorithm. Experimental results on practical datasets show that our proposed algorithm not only provides multiple AUV trajectories for a flexible choice, but it also outperforms the state-of-the-art algorithms in terms of the single trajectory efficiency.
Semi-supervised pipeline anomaly detection algorithm based on memory items and metric learning

Traditional detection algorithms of pipeline non-destructive testing extract information from a large number of defect samples to ensure the detection performance, but even if an adequate of defect samples are collected, it is difficult to enumerate the possible defect morphology in nature. In this paper, we proposed a new semi-supervised anomaly detection algorithm to solve the existing problems. We consider using the most representative feature vectors generated by feature extractor as memory items to represent background information. In addition, this paper also imports a few defect samples to form a semi-supervised structure in the training stage and introduces a metric learning module to make the memory items have the ability to fully represent the background and enhance robustness. To prove the effectiveness of our algorithm, this paper has verified its performance in micro-size pipeline defects. In the experiment, the high-definition industrial camera was used to scan and record the image sequence from the inner surface of the pipeline sample. The latest anomaly detection algorithms have been used as a platform for objective performance evaluation. The result shows our algorithm is more effective in pipeline defect detection and has strong robustness for anti-interference.
VLCDoC: Vision-Language contrastive pre-training model for cross-Modal document classification

Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a joint representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the joint representation space. Extensive experiments on public benchmark datasets demonstrate the effectiveness and the generality of our model both on low-scale and large-scale datasets.
Global Trends on Mobile Learning in Higher Education: A Bibliometric Analysis (2002–2022)

This study explores the research landscape of mobile learning in the context of higher education (MLHE) by conducting a comprehensive bibliometric analysis over the years. A total of 2477 papers published in peer-reviewed journals and conferences up to May 2022 were retrieved from the Scopus database. The results revealed an increase in MLHE research over time with a peak in 2021. The first paper was published in 2002, indicating the beginning of the field. The works of J. Gikas and M. M. Grant, L. F. Motiwalla, and J. Cheon et al. stand out as the most cited articles among the analyzed documents. T. Cochrane, F. J. García-Peñalvo, and H. Farley are the most prolific authors. ACM International Conference Proceeding Series, International Journal of Interactive Mobile Technologies, and International Journal of Mobile Learning and Organisation are the most productive sources. University of Salamanca, Science University of Malaysia (Universiti Sains Malaysia), and the University of Southern Queensland are the most active institutions. China, the US, and the UK are the most relevant countries. Keywords such as “mobile learning”, “m-learning”, “higher education”, “e-learning”, and “mobile devices” remain the trending keywords in this area. This review offers a comprehensive overview of scientific production and the future direction of the field.