Reviewerly/exHarmony huggingface dataset:
Relation Classification based on Selective Entity-Aware Attention

Relation classification aims to classify the entity pairs into a certain relation, which is an important task of natural language processing. The latest end-to-end models based on attention mechanism still have shortcomings, i.e., the attention will be gradually weakened when processing long sequences, and they cannot make use of the hidden type information of the entities. To solve these problems, we propose a relation classification model based on the selective entity-aware attention mechanism, which consists of context encoder and entity-aware attention network. In the context encoder, contextual word semantics are learned through self-attention. Entity selection is applied to adapt the fact that different words can determine each other’s importance. Latent types of entities are taken as auxiliary information to make full use of the entities’ hidden features. Experiments on the SemEval-2010 Task 8 dataset and TACRED show that our model outperforms the baselines without implementing any external resources or NLP tools, and the entity-aware attention indeed improve the model’s performance.
Integrating Users’ Long- and Short-Term Preferences for Session-based Recommendation

Different from conventional recommendation models which typically capture long-term yet static user preferences, session-based recommendation (SR) models aim to capture short-term but dynamic user preferences to provide more timely recommendations. Recent studies have shown that incorporating users’ long-term preferences into the SR model can help improve recommendation accuracy. However, since information on the time intervals between interactions is not fully exploited and/or long-term and short-term preferences are not effectively distinguished, most existing SR models that explicitly/implicitly capture users’ long-term and short-term preferences have defects in preference mining and integration, which leads to inaccurate recommendations. This paper addresses the problem of recommending the next interaction (item) given a user’s current session and some historical sessions by effectively integrating the user’s long-term and short-term preferences. We propose a novel model named Integration of Long- and Short-Term Preferences (ILSTP) for session-based recommendation. ILSTP employs time interval embedding function, self-attention, and hybrid aggregator combining max pooling and average pooling to capture long-term preference from the historical sessions; it utilizes gated graph neural network to capture short-term preference from the current session; it integrates the captured long-term and short-term preferences through attention aggregator. Experiments show that ILSTP achieves new state-of-the-art performance in next interaction (item) recommendation.
An Attention Mechanism-based Relation Network for Few-Shot Image Classification

The key to solving the few-shot image classification problem is learning image category information from a handful of image samples. Few-shot image classification can easily cause overfitting problems that impact classification performance due to insufficient labeled data. In this paper, an attention mechanism-based relation network model for few-shot image classification is proposed. Inspired by classic methods of relation networks for few-shot learning, the attention mechanism in a convolutional block attention mechanism (CBAM) for metric learning is utilized in the feature extraction network. Then, an update strategy selecting a validation set during the training is adopted to reduce the possibility of overfitting. Through comparison experiments on different datasets, the results demonstrate that our model has better accuracy than traditional methods.
Design and Development of Study Style Evaluation System Based on Data Analysis Algorithm

With the continuous development of education informatization, schools pay more and more attention to the construction of study style and the evaluation of study style, and the construction of study style is also a systematic project. Schools also pay more and more attention to the evaluation of students' performance, and the style of study is an important factor affecting the quality and effectiveness of teaching, teachers' teaching level and school reputation. At the same time, data analysis is a very important technology, which plays an important role in the field of information. In addition, the style of study evaluation system, as an auxiliary tool for the evaluation of style of study, is also a typical dynamic data model, which has an important impact on the evaluation of students' learning behavior and ideology and morality. In this paper, experimental research method and data analysis method are used to better understand the performance of the system through experiments. According to the experimental results, when the number of concurrent requests is 500, the time for the system test page to complete the request is 1291, 1304, 1240, and 1354ms, respectively. From this, it can be seen that the test page of the system takes a short time to complete the request, which can meet the basic usage requirements.
3D Design for Lightweight S-Box

The Internet of Things (IoT) applications are considered as smart device applications because of its remote monitoring/ controlling access. The vast quantity of devices is linked with heterogeneous IoT infrastructures which increases various attacks in IoT applications. Therefore, the security analysis should be evaluated to find out the best secured encryption process before implementing IoT applications. Moreover, lightweight algorithms are suitable for small power and high speed IoT devices. In this paper, lightweight algorithm is chosen because of its faster speed among other algorithms. However, the security of lightweight algorithms is not satisfactory for IoT/mHealth applications. To enhance the security of lightweight algorithms, we have designed a 4x4 composite S-Box rather than Look Up Table (LUT) method. Additionally, the composite S-Box value is again secured using 3D cipher for confusion and diffusion process to improve its security. Thus, we have proposed a lightweight 3D Composite S-Box which will give more security compared to other existing S-Boxes.
A Text Structure-based Extractive And Abstractive Summarization Method

Extraction summarization and abstraction summarization have advantages and disadvantages, so how to better combine these two ways has become a difficult problem. To address this challenge, this paper proposed a new fusion method. The major novelty lies in the design of the new method. Wherein our approach, first, use the text structure information and the idea of the K-means algorithm to divide the text into regions, and then the main part and the non-main parts are determined according to the distribution of the subject words. Next, apply information extraction on the main part and text generation on the non-main parts. Finally, the two parts of the summarization are merged according to the sequence of the text. Experimental results show that the quality of the summarization is better than that of extraction summarization and abstraction summarization. In addition, to make the method more targeted in Chinese text processing, the Cw2vec model based on Chinese stroke information is used in the encoding process, and the experiment proves that the quality of summarization can be further improved.
Multi-band discriminant speech synthesis analysis based on Natural Language Processing

With the rapid development of neural networks and deep learning, speech synthesis technology has been significantly improved. The end-to-end speech synthesis systems based on deep learning have been able to synthesize speech with naturalness close to the original human pronunciation. However, the existing end-to-end speech synthesis system model is complex, and it is impossible to achieve real-time speech synthesis on devices with low computing power. In this paper, a multi-band discriminative autoregressive speech synthesis model is proposed based on natural language processing. The model uses an encoder-decoder architecture with attention mechanism, which is mainly composed of DSC-GRN modules. Stacking multiple convolutions with different expansion coefficients by gating the residual structure can increase the receptive field so that the encoder and decoder can pay attention to the context information with a longer time span, which can improve the performance of the model. The whole model uses full convolution architecture and can be trained in parallel. Compared with the existing autoregressive model, the number of parameters of the model is greatly reduced. The synthesis speed is improved, and the quality of synthesized speech is ensured.
Research on characteristics of joint control system of large-load space manipulators

In the work of space manipulators, due to the large load and high flexibility of its joints, the overshoot and stabilization time of the manipulator joint control system are seriously affected. Aiming at the problems of poor stability and overshoot of the space manipulator joint joint control system, this article first establishes the joint dynamics model, and analyzes the control effects of joint motor position closed-loop control and absolute position closed-loop control of space manipulator. Then, the effects of joint stiffness, moment of inertia and friction coefficient on the stability of the control system are studied. Finally, the control method of space manipulator is improved by using full state-feedback method. The results show that the joint stiffness, moment of inertia and friction coefficient of space manipulator will affect the stability of joint control system; The absolute position closed-loop control system with full-state feedback can effectively improve the control effect of space manipulator joints.
Design of Stadium Building Energy Consumption Monitoring System Based on Data Acquisition Technology

With the advancement of China's urbanization process, the amount of building energy consumption is gradually increasing every year, building energy consumption accounts for about one-third of the annual total energy consumption, the total amount is huge and there is a lot of energy waste, so the energy conservation of building energy consumption has become an important part of the "energy conservation and emission reduction" work. Before making plans for building energy conservation, the relevant data of energy consumption within the whole system need to be obtained first, so the building energy consumption monitoring system arises at the historic moment. In this paper, the overall design of building energy consumption monitoring system for stadiums and gymnasiums is based on data acquisition technology. The building energy consumption monitoring system required by this project mainly includes comprehensive collection of electricity and water consumption data of the whole stadium, classified statistics of the collected energy consumption data of each floor, and output various daily, monthly and annual reports. Finally, through the performance test of the system, the feasibility of the system is verified to meet the needs of users on the system.
Stream processing technology based on FPGA network service

The ever-increasing amount of data has brought severe pressure to the data center. Facing the large-scale data processing requirements, the data center not only needs to increase the data bandwidth, but also needs to ensure the timeliness of data processing. It is increasingly unable to meet the processing requirements of high throughput and low latency. This topic adopts the stream processing architecture of heterogeneous collaborative computing based on FPGA and CPU, designs a dual-channel separated stream processing system based on FPGA network offload processing, improves the management and interaction capabilities of FPGA, reduces processing delay, and provides a set of manageable The general data real-time processing platform.
Prediction of the Water Force Field Physical Properties and Build Water Force Field Based on Generate Antagonistic-Recurrent Neural Networks

Based on the Generate Antagonistic-Recurrent Neural Network (GAN-RNN), a model for accurately and quickly predicting molecular dynamic potential parameters was established. By training water molecular force field dataset, the mapping relation between its properties and physical properties was established. New water force fields parameters were generated based on GAN through molecular dynamics simulation verification. The simulation results were close to the experimental values This may provide a new procedure for the rapid and accurate construction of water force fields.
Realization of Retrieval Based on Tibetan Proverbs Databas

Proverbs are the artistic summary of people&#x2019;s longterm observation of natural phenomenon, social phenomenon. It is an educational folk language that fully expresses the struggle experience and thoughts and feelings of the working people in rhythmic language. This study included 489 Tibetan proverbs in the Database of Tibetan Proverbs and the query system is analyzed to realize the rapid query and retrieval of Tibetan pro-verbs database and effectively meet the needs of users.
Cursor Control through Eye movement

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Dual-stream infrared object detection network combined with saliency images

Compared with visible spectrum images, thermal imaging has lower requirements for lighting conditions, but it contains many problems (e.g. blurred edges and low contrast). Aims to these defects, a dual-stream infrared object detection network (named DINet) is proposed. In order to bring into play the saliency information of the infrared object, the pseudo-color image is generated from the infrared image. Saliency information is employed as the attention mechanism of our detector. This framework design a dual-stream network with a cross-shaped receptive field aggregation module. It combined with convolutional neural network (CNN) with Transformer to create a local-global complementary dual-stream backbone, which separately extracts the feature of infrared and pseudo-color images. The prediction head fuses the adjacent layers to generate a more refined grid map, which contains high-definition positions and rich semantic information. The experimental results demonstrate that DINet achieves significant performance on infrared object detection benchmark FLIR. The quantitive analysis shows that the mean average precision can reach an excellent level.
VLSI Implementation of crypto coprocessor using AES and LFSR

Data security has been a major concern as that of the faster processing of data. As the capability of data processing is being evolved, the attacks on these devices for the extraction of data also have been increasing day by day. The purpose of this work is to optimise the security of current crypto coprocessors with the help of Linear Feedback Shift Register (LFSR) as key generator. The integration of LFSR with Advanced Encryption Standard (AES) will enhance the security when considering hash algorithms that have hardcoded keys which can be extracted through back tracing. By making the key input of the AES random, the device will be less prone to hardware attacks and back tracing the algorithm to extract the key value and thereby the data will be difficult. Here AES with 128-bit block size and key size is integrated with the 128-bit LFSR. All the simulations and implementations are done on Xilinx-Vivado.
Intelligent Operation and Maintenance Knowledge Graph in Electric-Power Industry: Construction and Applications

As the basic support for the efficient operation and maintenance of the production and management information business in the electric-power industry such as State Grid Corporation of China (short for State Grid), the construction, operation and maintenance of data centers at all levels have always been the key tasks. Recent years have witnessed many approaches to solve the intelligent operation and maintenance problems for complex network systems in all level data centers of electric-power industry. However, most existing methods utilized the operation and maintenance data in a common way, which might ignore the specialization and particularity of data in State Grid. To address this problem, in this paper, firstly, we described the main problems of existing intelligent operation and maintenance systems. Secondly, we proposed the construction method of intelligent operation and maintenance knowledge graph for cloud data center in State Grid. Finally, we proposed decision analysis and information scheduling methods for intelligent operation and maintenance based on knowledge graph in State Grid.
A Systematic Review: Intellectual Detection and Prediction of Cancer using DL Techniques

For cancer patients to receive effective treatment, early diagnosis and accurate detection are crucial. Various phenotypes of cancer are associated with genetic and environmental factors. Pathological examinations continue to be the best tools for the diagnosis and surveillance of cancer progression. Cells and tissue samples are used to diagnose and study disease. By staging and grading tumors, the patient plays a critical role in cancer management. Cancer phenotypes are linked to both genetic and environmental variables. Pathological examinations are still the most effective methods for detecting and monitoring cancer progression. It is essential therefore that rapid, non-destructive, non-labeled imaging methods that do not require labels are developed for cancer diagnosis, which can objectively evaluate molecular compositions and sub-cellular morphological features an association between malignancy and these substances. DL gives the medical business the opportunity to observe data at exponential rates while maintaining high precision. It's neither machine learning nor artificial intelligence; rather, it's a sophisticated hybrid of the two that sifts through data at breakneck speed thanks to a multilayered mathematical design.
Multi-object tracking based on spatio-temporal cue fusion and optimized cascade matching

It accurately estimates and updates the scene personnel ID, and uses it to extract object motion trajectory information to realize the prediction of pedestrian motion, which has a wider range of practical value in the fields of automatic driving and traffic video surveillance. To this end, this paper adopts the YOLOV4 pedestrian detector and SiamRPN++ single object tracker, combined with the spatiotemporal cue fusion strategy, and proposes an efficient online multi-object tracking algorithm. First, the overall framework of multi-object tracking based on spatio-temporal cues fusion and optimized cascade matching is designed using the motion model and appearance model. Secondly, in the spatio-temporal cue fusion stage, the expansion of candidate results is achieved through the tracking quality evaluation, and the adaptive SOT stop update conditions are formulated considering the occlusion factor. In the long-term cue extraction stage, a trajectory historical appearance screening strategy and a trajectory scoring mechanism are proposed to improve the quality of long-term clues and optimize the priority order of cascading matching. Finally, motion estimation and motion compensation are used to eliminate the influence of camera shake on the effectiveness of motion information constraints, and the appearance and motion matching and data association algorithms are used to complete the multi-object tracking task. The method is evaluated on the challenging MOT benchmarks and achieves the state-of-the-art results.
Evolutionary Game of Supply Chain Alliance Based on Blockchain

The combination of blockchain and supply chain is conducive to building a trusted and shared alliance environment. This paper discusses the evolution direction of alliance members in a supply chain under the environment of blockchain network. Income matrix is firstly established and then dynamic equation is obtained. Based on dynamic equation, equilibrium points and Jacobian matrix are derived. Through analyzing the sign of determinant and trace of Jacobian matrix, the stability of equilibrium point is studied. Further, the simulation presents the evolution trajectories of alliance members under different situations. The experimental results show that blockchain has a positive effect on the choice of the strategy and on evolution speed of the alliance. The work will help to develop a supply chain alliance relationship that features openness, sharing, credibility and win-win cooperation.
Blockchain Innovation in the Agriculture Aspect

Since its birth, blockchain technology has demonstrated promising growth prospects, and its strong functionalities allow it to be used in a wide range of fields, with the potential to bring about revolutionary changes. Within this paper, we'll start with a quick overview of the blockchain, including its role and value in agriculture. After that, concentrate on the four areas of blockchain&#x2019;s applicability in agriculture. The four aspects are Distributed Agriculture system, Smart Transportation, Blockchain application in product retail and agricultural insurance. Then, we will discuss the Opportunities and Defects of blockchain in agriculture. Finally, we summarize our current work of blockchain technology in the agriculture aspect.
Towards a Utopia of Dataset Sharing

Working with a high-quality (complete and up-to-date) dataset is the key to building a good machine learning model, especially in security research areas. However, it is not easy to collect a good quality dataset for security research communities because of the sensitive property of most security datasets. We believe that having more contributors to share up-to-date samples would increase the quality of datasets. Therefore, this study aims to increase security dataset sharing for research communities by eliminating possible information leakage. We propose a dataset sharing model and the core algorithm, FeatureTransformer, which guarantees no sensitive information leakage from a shared dataset. FeatureTransformer transforms extracted raw features into intermediate features that conceal sensitive information. Meanwhile, models built from transformed features maintain similar performance compared to models built from the original raw features. We show the effectiveness of our model by evaluating FeatureTransformer with typical malware classification problems using (1) traditional machine learning classifiers and (2) neural network-based classifiers. The experiment results show that the models trained with transformed features merely suffer from 2.56% and 1.48% accuracy degradation on the investigated problems. It indicates that models validated by datasets processed by FeatureTransformer work well with the original raw (untransformed) datasets. We believe that our privacy-preserving model can stimulate dataset sharing and advance the development of machine learning approaches in solving security problems.