Reviewerly/exHarmony huggingface dataset:
Information technology for time series forecasting based on the evolutionary method of the forecasting scheme synthesis

The research deals with the development of information technology for time series forecasting. The core principle of the technology is the method of evolutionary synthesis on forecast schemes for fundamental forecasting models. The paper evaluates the process of constructing the objective function and analyzes the critical stages of the genetic algorithm for optimal resolution detection. Authors apply forecast volatility as a quality indicator. Application of the technology results in production rules for forecast future values determination. The authors conducted experimental verification of the research results. The paper justifies the efficiency of the developed technology.
Graph-based adaptive and discriminative subspace learning for face image clustering

Current graph-based subspace clustering methods have achieved some results for the clustering of face images. The core of those methods lies in graph learning. However, they still have the following problems when learning the graph. Firstly, the graph learning processes of those methods do not consider the alignment of the images. It is well known that the obtained images of the same category may not be aligned due to different devices and shooting angles. The unaligned images used for graph learning directly affect the accuracy of the resulting graph. Hence, the graphs obtained by these methods are not accurate. We know that the inaccuracy of the learned graph will directly reduce the clustering performance of those methods. Secondly, they believe that important features, redundant features, and noise play the same contribution in the process of graph construction and feature representation. Redundant features and noise are not beneficial to graph reconstruction and feature representation, and even cause the learned graph to be inaccurate. Thirdly, the intrinsic structural correlation between samples is rarely considered for graph learning, which makes it difficult for the learned graph to reflect the structural correlation, then a good clustering performance cannot be obtained. To address those problems, this paper proposes a graph-based adaptive and discriminative subspace learning method (GADSL). In GADSL, image alignment is introduced and unified with subspace learning under the same graph learning framework which helps reduce the impact of different shooting equipment. Besides, GADSL can adaptively assign large weights to the important features and small weights to the unimportant features by introducing the weighting matrix. Moreover, in order to consider the correlation between samples, the structural consistency constraint is introduced into the subspace learning process so that the intra-class difference decreases and the inter-class difference increases. The experimental results show that GADSL used for face image clustering has achieved better clustering performance than many state-of-the-art methods.
A Comparative Study of Arabic Part of Speech Taggers Using Literary Text Samples from Saudi Novels

Part of Speech (POS) tagging is one of the most common techniques used in natural language processing (NLP) applications and corpus linguistics. Various POS tagging tools have been developed for Arabic. These taggers differ in several aspects, such as in their modeling techniques, tag sets and training and testing data. In this paper we conduct a comparative study of five Arabic POS taggers, namely: Stanford Arabic, CAMeL Tools, Farasa, MADAMIRA and Arabic Linguistic Pipeline (ALP) which examine their performance using text samples from Saudi novels. The testing data has been extracted from different novels that represent different types of narrations. The main result we have obtained indicates that the ALP tagger performs better than others in this particular case, and that Adjective is the most frequent mistagged POS type as compared to Noun and Verb.
Analysis of Fully-Pipelined CNN Implementation on FPGA and HBM2

Contrasting with conventional DRAM memory, High Bandwidth Memory (HBM2) can expand the bandwidth and improve power consumption. In this study, we use HBM2 for implementing an FPGA-based fully-pipelined convolutional neural network inference accelerator. To efficiently utilize high bandwidth, the output feature maps of each layer in the accelerator are computed row-by-row and immediately feed as input to the next layer. The VGG11 model is deployed on the CIFAR100 dataset to evaluate the performance and energy efficiency of the accelerator. The system is implemented on Xilinx Alveo U280 FPGA. Our experimental results show that the system achieves 111× and 1.7× higher than the Intel i9-10940X CPU and GeForce RTX 3090 GPU in energy efficiency, respectively.
Federated Learning Model Training Method Based on Data Features Perception Aggregation

The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for machine learning purposes. Federated learning is an emerging concept, which allows edge devices to collaboratively learn and share models, while keeping training data on devices. Federated learning decouples &#x201C;model training&#x201D; and &#x201C;direct access to original training data&#x201D;. But, in the IoT where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameter synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy. Moreover, the IoT devices collect data from different users, so the distribution over devices can be highly non-independent identically distributed (non-IID), which results in the variation of feature distribution and label distribution. As a result, the test accuracy of the federated model is reduced, and the communication cost of training the federated model is increased. In this paper, we propose the FedCC algorithm, to improve the accuracy of the federated model in the non-IID scenario. The FedCC method constructs client groups by mining data similarity, and selects one model of every client group to upload to the cloud server for model aggregation. Our experiments show that FedCC not only outperforms popular state-of-the-art federated learning algorithms on CNN and MLP architectures trained on MNIST and CIFAR-10 datasets, but also reduces the overall communication cost.
Vulnerability Analysis and Robust Training with Additive Noise for FGSM Attack on Transfer Learning-Based Brain Tumor Detection from MRI

Deep learning-based high-precision computerized brain tumor diagnosis helps to obtain significant clinical features for proper treatment. Research also revealed that medical deep learning systems are easily compromised by several small imperceptible perturbation strategies and resultant adversarial attacks. Medical deep learning systems for brainGupta, D. Pal, B. MRI-based tumor classification has been unexplored for susceptibility to adversarial attack except some abstract description of the vulnerability. In this research, the vulnerability of a highly accurate pretrained deep learning model has been studied in presence of adversarial samples. The potential risk associated with this model has been illustrated in terms of performance drop for misclassification, correct classification, and visual perceptibility. It is found that a very small perturbation variation of 0.0001–0.0007 can cause the performance to drop from 97 to 82%. Finally, a Gaussian additive noise-based robustness improvement strategy has been presented to overcome the drop of correct classification probability criteria. The results has been validated with publicly available dataset. These findings can be useful to raise safety concerns and design more robust medical deep learning systems.
Failure Detection Of Infrared Thermal Imaging Power Equipment Based On Improved DenseNet

Timely maintenance of the power equipment is the key to ensure the normal operation of the transmission equipment. In the substation scenario, a thermal infrared image detection method is proposed for target detection to detect potential faults in advance. The proposed method replaces the backbone network of Faster RCNN with DenseNet, to extract richer features, and in order to reduce the number of parameters of the backbone network, replaces standard convolution with learnable group convolution. To alleviate the problem of feature loss in packet convolution, the SFR structure is added to activate the features and improve the feature utilization. In order to reduce the complexity of the network and reasonably reduce the number of convolutions, we obtain a better lightweight model, and improve the NMS algorithm for the problem of regional overlap detection omission. Experiments show that the algorithm used has higher accuracy than YOLO and SSD, and the improved model not only reduces the network complexity, but also improves certain performance, and the final detection accuracy is 95.8%, which can be well applied to thermal infrared image detection
A Personalized Learning Framework for Software Vulnerability Detection and Education

The software has become a necessity for many different societal industries including, technology, health care, public safety, education, energy, and transportation. Therefore, training our future software developers to write secure source code is in high demand. With the advent of data-driven techniques, there is now a growing interest in leveraging machine learning and natural language processing (NLP) as a source code assurance method to build trustworthy systems. In this work, we propose a framework including learning modules and hands-on labs to guide future IT professionals towards developing secure programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle following the concept of Secure Software Development Life Cycle (SSDLC). In this research, our goal is to prepare a set of hands-on labs that will introduce students to secure programming habits using source code and log file analysis tools to predict, identify, and mitigate vulnerabilities. In summary, we develop a framework which will (1) improve students' skills and awareness on source code vulnerabilities, detection tools and mitigation techniques (2) integrate concepts of source code vulnerabilities from Function, API and library level to bad programming habits and practices, (3) leverage deep learning, NLP and static analysis tools for log file analysis to introduce the root cause of source code vulnerabilities.
GIS and Remote Sensing for Renewable Energy Assessment and Maps

Geographic Information Systems (GIS) and Remote Sensing (RS) techniques are of great interest for the renewable energy field [...]
Joint face detection and Facial Landmark Localization using graph match and pseudo label

Face detection and Facial Landmark Localization (FLL) are not integrated well because training samples with annotations of both bounding box and facial landmarks are costly to get. This paper presents a real-time framework for joint face detection and FLL. We exploit the synergy between the two tasks, then design a fully convolutional network to predict the location of facial landmarks and face regions. Besides, we observe the cluster assumption in FLL and thus propose a progressively pseudo labeling training that not only eliminates the harmful effect caused by inaccurate/noisy annotations, but also makes full use of exact, inexact and coarse-grained labels. To complete the bottom-up face assembly after model inference, we proposed two graph matching algorithms without learnable parameters. The whole framework keeps end-to-end attribute. Extensive experiments show that our approaches achieve state-of-the-art level on face detection and FLL in the different datasets, both in accuracy and runtime.
Super-resolution reconstruction of pneumocystis carinii pneumonia images based on generative confrontation network

Pneumocystis carinii pneumonia, also known as pneumocystis carinii pneumonia (PCP), is an interstitial plasma cell pneumonia caused by pneumocystis spp. It is a conditional lung infectious disease. Because the early and correct diagnosis of PCP has a great influence on the prognosis of patients, the image processing of PCP's high-resolution CT (HRCT) is extremely important. Traditional image super-resolution reconstruction algorithms have difficulties in network training and artifacts in generated images. The super-resolution reconstruction algorithm of generative counter-networks can optimize these two problems well.In this paper, the texture enhanced super-resolution generative adversarial network (TESRGAN) is based on a generative confrontation network, which mainly includes a generative network and a discriminant network. In order to improve the quality of image reconstruction, TESRGAN improved the structure of the Super-Resolution Generative Adversarial Network (SRGAN) generation network, removed all BN layers in SRGAN, and replaced the ReLU function with the LeakyReLU function as the nonlinear activation function of the network to avoid the disappearance of the gradient.The TESRGAN algorithm in this paper is compared with the image reconstruction results of Bicubic, SRGAN, Enhanced Deep Super-Resolution network (EDSR), and ESRGAN. Compared with algorithms such as SRGAN and EDSR, our algorithm has clearer texture details and more accurate brightness information without extending the running time. Our reconstruction algorithm can improve the accuracy of image low-frequency information.The texture details of the reconstruction result are clearer and the brightness information is more accurate, which is more in line with the requirements of visual sensory evaluation.
HDRVideo-GAN

High dynamic range (HDR) videos provide a more visually realistic experience than the standard low dynamic range (LDR) videos. Despite having significant progress in HDR imaging, it is still a challenging task to capture high-quality HDR video with a conventional off-the-shelf camera. Existing approaches rely entirely on using dense optical flow between the neighboring LDR sequences to reconstruct an HDR frame. However, they lead to inconsistencies in color and exposure over time when applied to alternating exposures with noisy frames. In this paper, we propose an end-to-end GAN-based framework for HDR video reconstruction from LDR sequences with alternating exposures. We first extract clean LDR frames from noisy LDR video with alternating exposures with a denoising network trained in a self-supervised setting. Using optical flow, we then align the neighboring alternating-exposure frames to a reference frame and then reconstruct high-quality HDR frames in a complete adversarial setting. To further improve the robustness and quality of generated frames, we incorporate temporal stability-based regularization term along with content and style-based losses in the cost function during the training procedure. Experimental results demonstrate that our framework achieves state-of-the-art performance and generates superior quality HDR frames of a video over the existing methods.
NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems

Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time "NeuroVis," real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.
A paradigm shift to ethical decision-making—incorporating systemic epistemology into complex socio-technical decision support systems research

Current decision support systems (DSS) research is overwhelmingly positivist and limits the adequate representation of relevant stakeholders. This crisis of relevance precludes the systemic inclusion of social and ethical issues in complex socio-technical DSS designs. For their unstable boundary conditions, such systems also require explicit coverage of their dynamics. Lending a novel perspective to DSS research, this paper proposes a framework with the new decision-making paradigm and the systemic inquiry of boundary critique to explore stakeholder perspectives for more ethically-aware DSS development. Using qualitative research, the developed framework is applied to unleash ethical issues for a dynamic and complex socio-technical university timetabling scenario implemented as a web-based group decision support system. Multiple stakeholder perspectives are recognised and analysed for boundary judgements, while a dynamic stakeholder network considers boundary liquidity and emergence. This work aims to couple systemic epistemology with the socio-technical lens to encourage the inclusion of ethical dimensions in dynamic and complex DSS designs.
Systematic Analysis of Artificial Intelligence-Based Platforms for Identifying Governance and Access Control

Artificial intelligence (AI) has become omnipotent with its variety of applications and advantages. Considering the other side of the coin, the eruption of technology has created situations that need more caution about the safety and security of data and systems at all levels. Thus, to hedge against the growing threats of cybersecurity, the need for a robust AI platform supported by machine learning and other supportive technologies is well recognized by organizations. AI is a much sought-after topic, and there is extolling literature available in repositories. Hence, a systematic arrangement of the literature that can help identify the right AI platform that can provide identity governance and access control is the need of the hour. Having this background, the present study is commissioned a Systematic Literature Review (SLR) to accomplish the necessity. Literature related to AI and Identity and Access Management (IAM) is collected from renowned peer-reviewed digital libraries for systematic analysis and assessment purposes using the systematic review guidelines. Thus, the final list of articles relevant to the framed research questions related to the study topic is fetched and is reviewed thoroughly. For the proposed systematic research work, the literature reported during the period ranging from 2016 to 2021 (a portion of 2021 is included) is analyzed and a total of 43 papers were depicted more relevant to the selected research domain. These articles were accumulated from ProQuest, Scopus, Taylor &amp; Franics, Science Direct, and Wiley online repositories. The article's contribution can supplement the AI-based IAM information and steer the entities of diverse sectors concerning seamless implementation. Appropriate suggestions are proposed to encourage research work in the required fields.
Polaron dynamics of Bloch–Zener oscillations in an extended Holstein model

Abstract Recent developments in qubit engineering make circuit quantum electrodynamics devices promising candidates for the study of Bloch oscillations (BOs) and Landau–Zener (LZ) transitions. In this work, a hybrid circuit chain with alternating site energies under external electric fields is employed to study Bloch–Zener oscillations (BZOs), i.e. coherent superpositions of BOs and LZ transitions. We couple each of the tunable qubits in the chain to dispersionless optical phonons and build an extended Holstein polaron model with the purpose of investigating vibronic effects in the BZOs. We employ an extension of the Davydov ansatz in combination with the Dirac–Frenkel time-dependent variational principle to simulate the dynamics of the qubit chain under the influence of high-frequency quantum harmonic oscillators. Band gaps emerge due to energy differences in site energies at alternating qubit sites, and are shown to play key roles in tuning band structures and time periodic reconstructions of the wave patterns. In the absence of qubit–phonon interactions, the qubits undergo either standard BZOs or breathing modes, depending on whether the initial wave packet is formed by a broad or narrow Gaussian wave packet, respectively. The BZOs can get localized in space if the band gaps are sufficiently large. In the presence of qubit–phonon coupling, the periodic behavior of BZOs can be washed out and undergo dynamic localization. The influence of an ohmic bath on the dynamics of BZOs is investigated by means of a Markovian master equation approach. Finally, we calculate the von Neumann entropy as a measure of the entanglement between qubits and phonons.
Dynamically balancing class losses in imbalanced deep learning

Imbalanced datasets commonly exist in real world and greatly challenge the performances of deep neural models. The authors discover that the traditional balance strategies in the existing imbalanced learning methods emphasize/suppress the importance of minority/majority class in a fixed way. Even the minority class is fully represented, the minority/majority class is still emphasized/suppressed, eventually leading to sub-optimal models. The authors argue that the importance of minority/majority class should be emphasized/suppressed in a dynamic manner. They propose to dynamically balance class losses (DBCL), which first suppresses the importance of majority class, and then gradually restores the importance of majority class. By adding the idea of DBCL into three state-of-the-art cost-sensitive loss functions, the authors propose three novel loss functions: dynamic constrained-focal-loss (DCFL), dynamic balance-cross-entropy based on the effective number of samples (DBENS) and dynamic balance-cross-entropy based on class frequencies (DBCEF). They apply DCFL, DBENS and DBCEF to the imbalanced learning in salient-instance segmentation. The results show that their methods significantly improve the classification abilities of models.