Reviewerly/exHarmony huggingface dataset:
A Dynamic Deep Reinforcement Learning-Bayesian Framework for Anomaly Detection

To assure the successful operation of connected and automated vehicles, it is critical to detect and isolate anomalous and/or faulty information in a timely manner. To do so, anomaly detection techniques should be implemented in real-time where if the probability of anomalous information exceeds a certain threshold, the information is dealt with accordingly. Traditionally, the threshold for judging whether the data is anomalous is fixed and determined a priori. However, not only does this approach fail to account for the feedback obtained during a trip on the performance of the algorithms, but it also fails to respond to potential changes in rates of anomalies. Hence, it is important to develop an approach that can dynamically alter this threshold in response to exogenous factors to assure reliable and robust system operation. We develop a mathematical framework which utilizes a dynamic threshold for an anomaly classification algorithm in order to maximize the safety of a trip. Specifically, we develop and pair an anomaly classification algorithm based on convolutional neural networks (CNN), with a partially observable Markov decision process (POMDP) model. We solve the resulting POMDP model using the asynchronous advantage actor critic (A3C) deep reinforcement learning algorithm. The prescribed policy determines the anomaly classification threshold in real-time that maximizes the performance. Our numerical experiments show that the POMDP model outperforms state-of-the-art benchmarks, especially under more difficult to detect anomaly profiles.
Traffic Anomaly Detection and Video Summarization Using Spatio-Temporal Rough Fuzzy Granulation With Z-Numbers

Existing traffic video summarization algorithms are capable of detecting one-class (i.e., collision) anomaly and cannot handle uncertainty issues arising between two-class anomalies, such as collision and near-miss. To address the issues, a new video summarization algorithm, namely Z-number s-based spatio-temporal rough fuzzy granulation (Z-STRFG) is developed. In Z-STRFG, various spatio-temporal features are computed over the video frames and used for obtaining the approximate anomaly-prone regions in terms of granules. In these regions, uncertainty (i.e., fuzziness) may arise among three scenarios, namely collision, near-miss, and normal traffic. Therefore, two types of rough fuzzy granules (RFGs) along with their roughness scores are computed to distinguish the aforesaid three scenarios. For each RFG, Z-number is computed based on the membership value of its roughness score to ensure a higher degree of reliability in the detection of anomaly class. Aforesaid characteristics of Z-STRFG improve its speed and accuracy for traffic anomaly detection. The efficacy of Z-STRFG has been demonstrated over 130 real-time traffic videos containing collisions, near-misses, and normal traffics. The superiority of Z-STRFG over some state-of-the-art is also proved through extensive experiments.
Robust and Explainable Semi-Supervised Deep Learning Model for Anomaly Detection in Aviation

Identifying safety anomalies and vulnerabilities in the aviation domain is a very expensive and time-consuming task. Currently, it is accomplished via manual forensic reviews by subject matter experts (SMEs). However, with the increase in the amount of data produced in airspace operations, relying on such manual reviews is impractical. Automated approaches, such as exceedance detection, have been deployed to flag safety events which surpass a pre-defined safety threshold. These approaches, however, completely rely on domain knowledge and outcome of the SMEs’ reviews and can only identify purely threshold crossings safety vulnerabilities. Unsupervised and supervised machine learning approaches have been developed in the past to automate the process of anomaly detection and vulnerability discovery in the aviation data, with availability of the labeled data being their differentiator. Purely unsupervised approaches can be prone to high false alarm rates, while a completely supervised approach might not reach optimal performance and generalize well when the size of labeled data is small. This is one of the fundamental challenges in the aviation domain, where the process of obtaining safety labels for the data requires significant time and effort from SMEs and cannot be crowd-sourced to citizen scientists. As a result, the size of properly labeled and reviewed data is often very small in aviation safety and supervised approaches fall short of the optimum performance with such data. In this paper, we develop a Robust and Explainable Semi-supervised deep learning model for Anomaly Detection (RESAD) in aviation data. This approach takes advantage of both majority unlabeled and minority labeled data sets. We develop a case study of multi-class anomaly detection in the approach to landing of commercial aircraft in order to benchmark RESAD’s performance to baseline methods. Furthermore, we develop an optimization scheme where the model is optimized to not only reach maximum accuracy, but also a desired interpretability and robustness to adversarial perturbations.
Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning

Sequential recommendation (SR) provides personalized contents based on the user’s historical interactions. Previous SR methods focus on introducing temporal signals of interaction sequence into their sequence encoders without exploring the effect of temporal density information on user preferences. To bridge this gap, we propose the Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning (TDSRec). The specifics of our research mainly consist of two parts. First, we integrate temporal density information into sequential recommendation when capturing user preferences. In detail, through our proposed Temporal KDE Module, we map timestamps into temporal density vectors aiming at improving the recommendation performance. Second, we introduce contrastive learning into TDSRec to alleviate the lack of supervised learning signals as a result of sparse user-item interactions. In detail, instead of directly editing the raw sequence, we leverage two base sequence encoders to derive self-supervision signals. In this way, our approach can naturally eliminate the interference of hand-crafted data augmentation strategies on the raw sequence data. Through extensive experiments on five test datasets, we find that our proposed TDSRec outperforms state-of-the-art baselines. NDCG@10 has been improved by 5.1%, 2.9%, 2.1%, 1.9%, and 1.4% on MovieLens, Beauty, Video Games, CDs&Vinyl, and Movies&TV, respectively.
The Impact of COVID-19 on Teachers’ Integration of Digital Technology

The coronavirus pandemic impacted all aspects of society, causing countries and local communities to close workplaces, move schools to remote instruction, limit in-person contact, cancel public gatherings, and restrict travel. Attempts to mitigate COVID-19 through remote instruction provided unique opportunities for researchers to examine the resources teachers utilize to drive their practices. We examine the impacts of the pandemic on grades 6-12 mathematics teachers and math interventionists, with particular attention to teachers' integration of digital resources. Using purposive sampling, we surveyed 50 participants—across urban, suburban, and rural districts—throughout the United States. The descriptive survey focused on six aspects of teachers' practices with digital resources. Results indicate that challenges encountered and lessons learned included a lack of student engagement and motivation, increased distractions, and varied access to technology. Integration of technology did not positively impact students' mathematical proficiency across all teachers. Common resources used across planning of lessons, implementation of instruction, and assessment included the Google platform, Desmos, and GeoGebra. Where appropriate, we situate our results within the larger context of recent international research. These findings support teacher practices that constantly attempt to optimize students' mathematics and social emotional learning, regardless of the environment or situation.
Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value

On the one hand, grid integration of solar and wind power often requires just point (as opposed to probabilistic) forecasts at the individual plant level to be submitted to grid operators. On the other hand, solar and wind power forecasting can benefit greatly from dynamical ensemble forecasts from numerical weather prediction (NWP) models. Combining these two facts, this study is concerned with drawing out point forecasts from NWP ensembles. The scoring function for penalizing bad forecasts (or equivalently, rewarding good forecasts), in most scenarios, is specified by grid operators ex ante. The optimal point forecast therefore should be an elicitable functional of the predictive distribution, for which the specified scoring function is strictly consistent. Stated differently, the optimal way to summarize a predictive distribution depends on how the point forecast is to be penalized. Using solar irradiance forecasts issued by the ECMWF's Ensemble Prediction System, the statistical theory on consistency and elicitability is validated empirically with extensive data. The results show that the optimal point forecasts elicited from ensembles have constantly higher accuracy than the best-guess forecasts, regardless of the choice of scoring function. Surprisingly, however, the correspondence between the two types of goodness of forecasts, namely, quality and value, is neither linear nor monotone, but depends on the penalty triggers and schemes specified by grid operators. In other words, using the optimally elicited forecasts, in many scenarios, would lead to lower economic values.
Hardware Limitations of Lightweight Cryptographic Designs for IoT in Healthcare

Security is an important aspect of healthcare applications that employ Internet of Things (IoT) technology. More specifically, providing privacy and ensuring the confidentiality, integrity and authenticity of IoT-based designs are crucial in the health domain because the collected data are sensitive, and the continuous availability of the system is critical for the user’s wellbeing. However, the IoT consists of resource-constrained devices that increase the difficulty of implementing high-level-security schemes. Therefore, in the current paper, renowned lightweight cryptographic primitives and their most recent architecture, to the best of the authors’ knowledge, are investigated. Their security, architecture characteristics and overall hardware limitations are analyzed and collected in tables. Finally, all the algorithms are compared based on their effectiveness in securing healthcare applications, the utilized device and the overall implementation efficiency.
3D Reconstruction of Remote Sensing Mountain Areas with TSDF-Based Neural Networks

The remote sensing 3D reconstruction of mountain areas has a wide range of applications in surveying, visualization, and game modeling. Different from indoor objects, outdoor mountain reconstruction faces additional challenges, including illumination changes, diversity of textures, and highly irregular surface geometry. Traditional neural network-based methods that lack discriminative features struggle to handle the above challenges, and thus tend to generate incomplete and inaccurate reconstructions. Truncated signed distance function (TSDF) is a commonly used parameterized representation of 3D structures, which is naturally convenient for neural network computation and computer storage. In this paper, we propose a novel deep learning method with TSDF-based representations for robust 3D reconstruction from images containing mountain terrains. The proposed method takes in a set of images captured around an outdoor mountain and produces high-quality TSDF representations of the mountain areas. To address the aforementioned challenges, such as lighting variations and texture diversity, we propose a view fusion strategy based on reweighted mechanisms (VRM) to better integrate multi-view 2D features of the same voxel. A feature enhancement (FE) module is designed for providing better discriminative geometry prior in the feature decoding process. We also propose a spatial–temporal aggregation (STA) module to reduce the ambiguity between temporal features and improve the accuracy of the reconstruction surfaces. A synthetic dataset for reconstructing images containing mountain terrains is built. Our method outperforms the previous state-of-the-art TSDF-based and depth-based reconstruction methods in terms of both 2D and 3D metrics. Furthermore, we collect real-world multi-view terrain images from Google Map. Qualitative results demonstrate the good generalization ability of the proposed method.
A double-color image watermarking algorithm based on quaternion Schur decomposition

In view of the fact that many watermarking algorithms used for copyright protection have ignored the correlation and synchronization between the color channels, a new double-color image watermarking algorithm based on quaternion Schur (QSchur) decomposition is presented in this paper. Firstly, the color watermark image is encrypted by two-dimensional compound chaotic map; then, the QSchur decomposition is used to associate and transform multiple color channels; finally, the watermark is embedded and extracted blindly by using the high correlation between the coefficient pairs in the block. Due to the high time complexity of the quaternion, a real structure-preserving algorithm is used to shorten the running time of the proposed algorithm by about 20 times compared with the quaternion toolbox of Matlab. Simulation experiments and analysis demonstrate that this algorithm has stronger robustness and higher security when the actual embedding rate and invisibility are the same as those of other algorithms given.
Analysis of Randomization-Based Approaches for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects an individual’s sensory activity, social interaction, and cognitive abilities. In the mental illnesses ASD disorder, the problem starts in infancy and affects more habits as the age progresses, progressing to adolescence and adulthood, also known as a behavioural disorder. Everything nowadays is moving towards automated software, which is relevant not just in terms of time efficiency but also in terms of cost-effectiveness. As a result, there is a pressing need in the healthcare sector to incorporate machine learning to reap the greatest benefits. Over the last two decades, randomization-based approaches such as extreme learning machines (ELM) and random vector functional link (RVFL) have gained popularity amongst researchers due to their better generalization performance. In this work, the classification capability of the ELM and RVFL models with different activation functions is investigated to estimate the autism spectrum disorder in a human population of children, adolescents, and adults on the grounds of publicly accessible UCI datasets. The attainment of the randomization-based approaches is determined using various quality measures such as accuracy, precision, recall, negative predictive value, rate of misclassification, F1-measure, G-mean, and Matthew’s correlation coefficient. From the numerical experiment results, one can show that the generalization capability of RVFL using hardlim, hyperbolic tangent function, and sigmoidal activation function is superior to ELM based on several quality measures.
Cybersecurity Behavior among Government Employees: The Role of Protection Motivation Theory and Responsibility in Mitigating Cyberattacks

This study examines the factors influencing government employees’ cybersecurity behavior in Malaysia. The country is considered the most vulnerable in Southeast Asia. Applying the protection motivation theory, this study addresses the gap by investigating how government employees behave toward corresponding cyberrisks and threats. Using partial least-squares structural equation modeling (PLS-SEM), 446 respondents participated and were analyzed. The findings suggest that highly motivated employees with high severity, vulnerability, response efficacy, and self-efficacy exercise cybersecurity. Incorporating the users’ perceptions of vulnerability and severity facilitates behavioral change and increases the understanding of cybersecurity behavior’s role in addressing cybersecurity threats—particularly the impact of the threat response in predicting the cybersecurity behavior of government employees. The implications include providing robust information security protection to the government information systems.
Bayesian Neural Network Language Modeling for Speech Recognition

State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. They are prone to overfitting and poor generalization when given limited training data. To this end, an overarching full Bayesian learning framework encompassing three methods is proposed in this paper to account for the underlying uncertainty in LSTM-RNN and Transformer LMs. The uncertainty over their model parameters, choice of neural activations and hidden output representations are modeled using Bayesian, Gaussian Process and variational LSTM-RNN or Transformer LMs respectively. Efficient inference approaches were used to automatically select the optimal network internal components to be Bayesian learned using neural architecture search. A minimal number of Monte Carlo parameter samples as low as one was also used. These allow the computational costs incurred in Bayesian NNLM training and evaluation to be minimized. Experiments are conducted on two tasks: AMI meeting transcription and Oxford-BBC LipReading Sentences 2 (LRS2) overlapped speech recognition using state-of-the-art LF-MMI trained factored TDNN systems featuring data augmentation, speaker adaptation and audio-visual multi-channel beamforming for overlapped speech. Consistent performance improvements over the baseline LSTM-RNN and Transformer LMs with point estimated model parameters and drop-out regularization were obtained across both tasks in terms of perplexity and word error rate (WER). In particular, on the LRS2 data, statistically significant WER reductions up to 1.3% and 1.2% absolute (12.1% and 11.3% relative) were obtained over the baseline LSTM-RNN and Transformer LMs respectively after model combination between Bayesian NNLMs and their respective baselines.
Representation for action recognition with motion vector termed as: SDQIO

The active human action motion characteristics from moving areas within a particular spectrum are conveyed by the information extraction domain of video data. Meanwhile, most of the video data frequencies are encoded with silent information that has significant redundancy, resulting in low processing efficiency in conventional video models that accept raw frames as input. Human action recognition remains challenging for the computer vision domain in videos. To address this issue, this study introduces SDQIO, a novel video content extractor that focuses on obtaining multiscale and multidimensional motion information for fast action detection. The ultimate goal of our SDQIO is to develop an effective action recognition module (SDQIO) by identifying five distinct types of action capture operators and extensively assessing their effects on movement prediction over short and long periods. Our SDQIO uses a multi-differentiation modeling approach to gather motion-type information over the whole clip. Extensive experiments revealed that our technique outperforms all other action recognition algorithms by a significant margin on standard action datasets, attaining state-of-the-art and equivalent outcomes.
Defining and modeling of students’ professional thinking development dependence on their training process organization

The professional thinking issues are analyzed in the research. The authors pointed that the technical thought concepts, images and practical actions are in a complex and dynamic interaction with each other. The components of professional thinking are considered in detail. The training method based on the implementation of forming influence is proposed. The regression analysis of the students’ academic progress indicators who are trained by the traditional and innovative methodology with forming influence is conducted in the article. Analysis of thinking activity development levels in the process of professional tasks solving performed by the students of the control and experimental groups demonstrated the straight-line correlation dependence of the professional thinking development on the organization of professional activity in general and the training organization in particular.
Multi-instance semantic similarity transferring for knowledge distillation

Knowledge distillation is a popular paradigm for learning portable neural networks by transferring the knowledge from a large model into a smaller one. Most existing approaches enhance the student model by utilizing the similarity information between the categories of instance level provided by the teacher model. However, these works ignore the similarity correlation between different instances that plays an important role in confidence prediction. To tackle this issue, we propose a novel method in this paper, called multi-instance semantic similarity transferring for knowledge distillation (STKD), which aims to fully utilize the similarities between categories of multiple samples. Furthermore, we propose to better capture the similarity correlation between different instances by the mixup technique, which creates virtual samples by a weighted linear interpolation. Note that, our distillation loss can fully utilize the incorrect classes similarities by the mixed labels. The proposed approach promotes the performance of student model as the virtual sample created by multiple images produces a similar probability distribution in the teacher and student networks. Experiments and ablation studies on several public classification datasets including CIFAR-10, CIFAR-100, CINIC-10 and Tiny-ImageNet verify that this light-weight method can effectively boost the performance of the compact student model. It shows that STKD has substantially outperformed the vanilla knowledge distillation and achieved superior accuracy over the state-of-the-art knowledge distillation methods.