Reviewerly/exHarmony huggingface dataset:
A Heat-Recirculation-Aware Data Placement Strategy towards Data Centers

The development of cloud computing leads to an exponential growth of data, which requires expanding the storage capacity to meet the storage needs, in exchange the energy consumption of the data center will also increase. Many traditional data placement schemes attempt to achieve energy consumption minimization by optimizing the distribution of data requests, however, ignoring the impact of heat recirculation in data placement. To fill this gap, we propose a heat-recirculation-aware data placement strategy called HRADP to achieve optimized data placement to data centers. Furthermore, our strategy can minimize the overall energy consumption of data centers and improve throughput. Specifically, we consider heat recirculation between data nodes by regulating the energy consumption limit of each data node. We implement this data placement strategy on a data center simulation platform, CloudSim, and compare it with two data placement strategies, TIGER and HDFS. The experimental results unveil that HRADP achieves 2.6x - 25.7x performance improvement in the same energy consumption.
Analyzing the Impact of Outlier Data Points on Multi-Step Internet Traffic Prediction Using Deep Sequence Models

The task of predicting Internet traffic is challenging, particularly in multi-step forecasting due to the volatile and random nature of data. In addition, real-world traffic may contain outlier data points, so developing a prediction model that integrates anomaly detection and mitigation is necessary. This paper compares several deep sequence models, such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM Encoder-Decoder (LSTM_En_De), LSTM Encoder-Decoder with attention layer (LSTM_En_De_Atn), and Gated Recurrent Unit (GRU), with our proposed methodology for single-step prediction. Our proposed LSTM_En_De model, integrated with outlier detection, outperforms traditional deep sequence models in single-step prediction, reducing the deviation between actual and predicted traffic by over 11%. We also apply our methodology to multi-step forecast analysis, using multiple output strategies for forecast horizons of 3, 6, 9, and 12 steps ahead. Experimental results demonstrate the effectiveness of our proposed methodology in improving the accuracy of single-step prediction and multi-step forecasting tasks, especially when dealing with outlier data points that adversely affect model accuracy. In summary, this paper investigates the challenges of real-world Internet traffic prediction, proposes a novel prediction model integrated with anomaly detection and mitigation, and compares different deep sequence models for single-step and multi-step forecasting tasks.
Adaptive Synapse Adjustment and Decoding in Action-Prediction Cortical Learning Algorithm

This paper proposes the adaptive synapse adjustment and the adaptive decoding in the action-prediction cortical learning algorithm (ACLA) for an uncertain environment with probabilistically missing multiple input state values. The increase in the number of missing state values negatively affects the action prediction representation, and it is emphasized as unnecessary synapses increases. The adaptive synapse adjustment works to suppress excessive synapses. Also, the increase in the number of missing state values brings difficulty in outputting the action to be made. The adaptive prediction decoding robustly outputs the prediction action. Experimental results on the uncertain mountain car task showed that the proposed adaptive ACLA achieved a robust action prediction performance even though the number of missing state values and their frequencies increased.
<i>SDR</i>Querier: A Visual Querying Framework for Cross-National Survey Data Recycling

Public opinion surveys constitute a powerful tool to study peoples' attitudes and behaviors in comparative perspectives. However, even worldwide surveys provide only partial geographic and time coverage, which hinders comprehensive knowledge production. To broaden the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or years. The resulting new datasets can be analyzed as a single source, which can be flexibly accessed through many data portals. However, such portals offer little guidance to explore the data in-depth or query data with user-customized needs. As a result, it is still challenging for social scientists to efficiently identify related data for their studies and evaluate their theoretical models based on the sliced data. To overcome them, in the Survey Data Recycling (SDR) international cooperation research project, we propose SDRQuerier and apply it to the harmonized SDR database, which features over two million respondents interviewed in a total of 1,721 national surveys that are part of 22 well-known international projects. We design the SDRQuerier to solve three practical challenges that social scientists routinely face. First, a BERT-based model provides customized data queries through research questions or keywords. Second, we propose a new visual design to showcase the availability of the harmonized data at different levels, thus helping users decide if empirical data exist to address a given research question. Lastly, SDRQuerier discloses the underlying relational patterns among substantive and methodological variables in the database, to help social scientists rigorously evaluate or even improve their regression models. Through case studies with multiple social scientists in solving their daily challenges, we demonstrated the novelty, effectiveness of SDRQuerier.
Feature Selection Algorithms: A Comparative Study

Machine Learning models are built on top of features, all the features present in data may not contribute towards building robust model. Further as the number of features becomes large, model becomes complicated resulting in curse of dimensionality. Decrease in the number of features helps in the both time and space complexity towards model development. Here, various feature selection methods like Sequential Forward Selection (SFS), Backward Elimination (BE), Recursive Feature Elimination (RFE), correlation, one-way ANOVA test and hybrid methods are adopted. The present study make use of XGBoost method to build the model. Using feature selection techniques there is significant change in the performance of the model.
DTagRecPLS: Diversification of Tag Recommendation for Videos Using Preferential Learning and Differential Semantics

Video tag recommendation is not just necessary but also mandatory in the present-day scenario where multimedia content specifically videos are trending and becoming viral on the internet. In this paper, a video recommendation framework DTagRecPLS that is semantically driven and knowledge-centric has been proposed. It extracts the categories from the video dataset and enriches them by subjecting them to Latent Semantic Indexing. The proposed framework is ontology centered as ontology alignment to the enriched categories of the videos with that of the standard domain ontologies has been achieved and moreover, ontology-driven knowledge harvesting from differential heterogeneous knowledge stores has been used to enrich the number of instances and format them into an enriched knowledge pool. The model encompasses a deep learning framework namely the Convolutional Neural Network to classify video datasets from the perspective of using the actual video and image features, while the Logistic Regression Classifier classifies the dataset by extracting entities from the enriched feature pool with a perspective of annotations and labels. The common incidences are then used to compute the semantic similarity with that of the enriched knowledge pool and are sent for ranking and review. The DTagRecPLS gives the highest precision of 97.09%, highest average recall of 98.72%, highest overall accuracy and F-Measure of 97.91% and 97.90% respectively, and an overall average lowest False Discovery Rate of 0.03.
Object Detection of Overhead Transmission Lines Based on Improved YOLOv5s

Given that the aerial image to scan overhead transmission line contains complex backgrounds and small objects, it is difficult for traditional algorithms to accurately identify the details of power transmission lines, so this paper develops an object detection method based on improved YOLOv5s. To improve the detecting accuracy of small objects, the network structure is optimized by adding a larger scale detection layer and jump connections. Then, a self-attention mechanism is introduced to merge the feature relationships between spatial and channel dimensions. The self-attention mechanism could suppress the interference of complex backgrounds and boost the salience of objects. In addition, this paper proposes a small object enhanced complete intersection-over-union as the loss function of the bounding box regression. This loss function could adjust the derived loss for objects of different sizes automatically, therefore, improving the detection of small samples. The experimental results demonstrate that compared to classic YOLOv5s, the detection accuracy of our algorithm is improved by 4.2%.
Research on Transmission Line Insulator Defects Detection using YOLOv7

Electrical component inspection has been a significant problem in the power distribution system. If not thoroughly investigated, poorly connected transmission lines could result in catastrophic power outages or blackouts. Much research on transmission line insulators, transformers, cables, twisted conductors, electric poles, and other power grid equipment and parts has been conducted to prevent such failures. In this work, a total of 1975 insulator images are used as the dataset, and an image augmentation technique is used to overcome the data insufficiency problem. This article’s main contribution is applying the recently proposed YOLOv7 single-stage object detector and its variants for accurately and efficiently detecting outdoor insulator defects. The experimental results show that the object detectors successfully identify insulator string as the primary class and three other subclasses, such as flashover damage, good, and broken insulator shells. The detection accuracy of the YOLOv7x object detector, among different YOLOv7 variants, can reach up to 97.5% with proper hyperparameter tuning.
Lightweight sit-ups recognition and counting method based on OpenPose

The study of accurate, fast and automatic sit-ups, mainly depends on human bone point recognition and supine sitting judgment algorithm. In terms of human bone point recognition, the existing models depend on the main network with a large amount of parameter computing, which cannot be adapted to computers with poor performance and some embedded devices. In response to the above issues, on the basis of the OpenPose model, this paper adds a multi -scale fusion module and spatial attention module to the feature extraction network, using the method of structured knowledge transfer (SKT) The identification module proposes a lightweight SKT-OpenPose network. The network parameters are only 6.1% of the original OpenPose model, which greatly shortens the operating time and reduces the requirements for hardware. Regarding the judgment algorithm of sit-ups, the existing method pays attention to whether the angle of the pelvis meets the conditions of sitting up and lying down, and needs to shoot testers from a specific perspective. The angle limit at the knee joint is used to determine the standardability of the current action; add an angle judgment mechanism to judge the motion of sit-ups of different angles. It has been proved by experiments that the lightweight sitting recognition and counting method proposed in this article have a good effect in identifying the sitting action recognition.
A speaker recognition method based on Res-SE-MFA and SpecAugment data enhancement

Speaker recognition is a technology that uses voice signals to extract vocal features to distinguish the identity of different individuals and is widely used in mobile banking, security, and computer access control. In recent years, with the continuous development of deep learning technology, speaker recognition technology based on deep learning has also been developed rapidly and received wide attention. In this paper, we propose a speaker recognition method based on the Residual-Squeeze-Excitation Multilayer feature aggregation (Res-SE-MFA) framework to address the problems of weak generalization and poor robustness of deep learning on small sample datasets and further enhance the robustness of the system by using the SpecAugment data enhancement method on this basis. Experiments show that the comparison with ResNet34 improves by 10.01 % on EER and 2.99% on minDCF.
Mutually Distilled Sparse RCNN for Few-Shot Object Detection

Few-shot object detection(FSOD), which conducted to detect novel object based on massive base class samples and few novel classes samples, has gained extensive research interest from academic and industry. Major existing FSOD approaches basically use Faster RCNN(FRCNN) as basic framework. However, the RPN of the FRCNN architecture generates redundant anchor frames, which leads to slow training and consume massive computing resources. In this paper, we choose Sparse RCNN which has a fixed number of anchor frames and good performances in object detection the basic framework. Traditional FSOD training methods customarily use the method of freezing backbone or deleting the head classification branches, which will lead to overfitting of novel class and perform badly on base classes. To solve this problem, we introduced the mutual distillation layer and IOU Mask module in the head and loss of Sparse RCNN respectively. The mutual distillation layer is a multihead structure, which can distill the base class features when training new class samples, and distill the new class features when training base class samples. Experiments on multiple benchmarks show that our framework is significantly superior to other existing methods, and has faster detection speed.
Research on x-ray contraband detection and overlapping target detection based on convolutional network

The vast majority of target detection methods are based on convolutional networks to extract features. Yolo series is the mainstream algorithm for target detection today. Given the characteristics of a large number of occlusion and overlapping targets detected by X-ray contraband, it is easy to miss detection and erroneous detection. The yolo algorithm is improved to improve the network performance of the algorithm in detecting overlapping targets. To better reflect the improvement effect, the yolov4 algorithm is used as the baseline for improvement. Firstly, the SPPFCSPC module is proposed to replace multi-space pyramid pooling. Secondly, the CBAM mechanism is introduced to resist invalid features and improve accuracy. Finally, the path aggregation network module is improved, and the convolution module is optimized to improve the prediction accuracy of the network for overlapping targets. The improved network increases average accuracy by 4.86 points in the Sixray dataset, and has certain advantages over advanced algorithms such as yolov5 and yolox. Meet real-time detection standards and effectively improve detection accuracy.
Detection of Underwater Plastic Waste Based on Improved YOLOv5n

In order to solve the problems of complex underwater environment and insufficient underwater light in the detection of underwater plastic waste, which leads to limited detection accuracy and large amount of calculation, an underwater plastic waste detection algorithm based on improved YOLOv5n is proposed.Firstly, a CB2D module is designed for the backbone network to reduce the size of model parameters and improve the accuracy of model detection. Then, by fusing the CNeB module in the feature pyramid, the problem of insufficient features of small target detection is solved. Finally, the alpha-IoU loss function is used in the model bounding box regression loss.The experimental results show that under the same training conditions, the AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> value and AP value of the improved YOLOv5n are increased by 12.25% and 13.69% compared with the original model, respectively, and the model size is only 3.2MB. The algorithm has strong practicability and efficiency in the detection of underwater plastic waste.
Research on the Level Test Application System of Computer Artificial Intelligence Technology in English Education

Based on computer artificial intelligence technology, this paper designs an online automatic evaluation and correction system for higher vocational English based on the combination of C/S mode and B/S mode. This paper firstly designs the functional requirements of the system. The design of the system implementation plan adopts the C/S mode to realize the evaluation and reform. The system implements the English online test process according to the scale of the higher vocational English test and the test requirements and through modules such as group test management, score management, and online test. In this paper, the genetic algorithm is used to determine the optimal solution of the test papers. The system has the advantages of high utilization of network resources, flexible deployment and convenient updating. The simulation results show that the online testing system for higher vocational English education can improve students' English level, especially to cultivate students' online reading habits and help students to correct pronunciation and intonation.
Improved Faster R-CNN for Dense Small Objects

Despite the great success of general-purpose object detectors in recent years, the detection performance for small objects is not yet satisfactory. Since small objects and large objects have different sensory field sensitivity and small objects can extract fewer features, there is still great potential for development in the field of small object detection. In this paper, we propose an improved algorithm based on Faster-RCNN for dense small object detection in complex environments. Slicing Aided Hyper Inference (SAHI) is used to improve the small object detection task. To optimize accuracy and computational effort, convolution is replaced by an involution operator and is integrated into the backbone of the network. Then, a novel label assignment strategy based on gaussian receptive field is introduced in the Faster-RCNN algorithm to better assign labels. Experiments were conducted on the VisDrone-DET2019 dataset, and the results show that the proposed algorithm is quite an improvement over the original one. The precision and recall of small objects are improved to 24.6% and 38.3%, respectively.
Optimization of Facial Expression Recgonition on ResNet-18 using Focal Loss and CosFace Loss

Facial expressions are an essential part of interpersonal communication. The ability to automatically recognize and classify different facial expressions has important applications across multiple domains. However, challenges stemming from differences in posture, illumination, and angle persist in Facial Expression Recognition (FER) tasks. Moreover, learning from an imbalanced dataset also impedes the training process and negatively affects the model accuracy. Besides, only making use of cross-entropy to in model training may result in the model's lack in discrimination. In this paper, we incorporated two loss functions, focal loss and CosFace loss, in ResNet-18 and trained it on the FER-2013 dataset, an imbalanced dataset containing images taken from different angles and illuminations. We achieved the validation accuracy of 61.88% using CosFace loss, compared to 60.73% in traditional cross entropy.
English Teaching Method Reform Based on Big Data Analysis Technology

The “Internet +” era with the rapid development of science and technology has provided us with a space of “openness, sharing, interaction and collaboration”, which has provided us with the opportunity to transform the traditional teacher-centered teaching model into a new student-centered teaching model possible. English teaching (ET) is combined with Internet information technology, and the two are deeply integrated to optimize ET resources, cultivate students' innovative thinking, fully enhance the value of ET, and achieve the purpose of efficient English classrooms. The main purpose of this paper is to conduct research on the reform of ET methods based on BD analysis (BDA) technology. Starting from ET, through the analysis of the development of mobile Internet and the current situation of students' English learning, it is believed that mobile Internet has the advantages of rich teaching resources, diversified intelligent interactive methods, and the ability to undertake classrooms without time and space constraints, which can effectively fill traditional teaching. The disadvantages of the method meet the needs of social development. Based on the whole research, this paper finally affirms the application of mobile Internet in English classroom, and gives some suggestions for the application of mobile Internet technology in the classroom, that is, the application of Internet technology should be cautious, the choice of learning software should be less and more precise, and students' psychology should be paid close attention to. Formulate a reasonable teaching plan, improve the comprehensive evaluation mechanism, and grasp the application scale in combination with the actual situation.
Subjective Quality Assessment of HDR Stereoscopic Omnidirectional Videos

High dynamic range stereoscopic omnidirectional video (HSOV) can bring wide field of view and high contrast visual experience to viewers. However, the generation, transmission and display of HSOV will inevitably cause distortions in the video. Therefore, it is important to quantify the impact of the processing of coding and tone mapping on the quality of HSOV. To this end, the subjective quality of HSOV after HEVC coding and tone mapping is evaluated in this paper. Firstly, an HSOV database namely NBU-HSOVD is constructed, which consists of 450 distorted HSOVs. Then, 34 subjects are invited to evaluate the quality of the distorted videos with absolute category rating method so that the MOS value of each video can be provided. Finally, the performance of six existing objective image or video quality assessment methods are tested on NBU-HSOVD. The constructed NBU-HSOVD database can provide reference and basis for the research of objective quality assessment of HSOV in the future.
Efficient Configurable Digit-Serial Multiplier Based on Improved Karatsuba Algorithm over GF(2m)

The core operation of public key cryptosystems such as Elliptic Curve Cryptography (ECC) is modular multiplication. In recent years, designing high-performance multipliers for simplifying circuit structure, reducing hardware area, and improving computational efficiency has become a research hotspot. In this paper, a digit-serial least significant digit (LSD) modular multiplier with pipelined structure is proposed based on the block recombination Karatsuba Algorithm (BRKA). Moreover, by converting the data form of the multiplication operands, we can meet the operational requirements of irreducible polynomials in any field, and achieve the purpose of configurability. From theoretical analysis and synthesis results, compared with the better related work, the proposed multiplier not only meets the operation requirements of various domain-valued ECC algorithms, but also achieves a 20.52 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> reduction in time, 8.82% savings in area consumption, and provides 27.53% saving of area-delay product (ADP).