Reviewerly/exHarmony huggingface dataset:
Research on Multi-task Deep Learning Approach Based on Hybrid Sharing and Network Optimization

Multi-task learning which is a branch of deep learning has received extensive attention and in-depth research. However, there are still some difficult problems such as unclear feature sharing, indistinguishable related tasks and overly complex network structure. Therefore, a multi-task learning approach based on hybrid sharing and network optimization is presented. Firstly, training data is fed into the hard parameter sharing network for hybrid training without distinguishing tasks, then the similarity of tasks is measured according to the gradient changes of each task in sharing network layers. Secondly, similar tasks are divided into the same group which is represented by a hard parameter sharing network, while tasks with weak correlation or large differences are divided into different groups which are characterized by soft parameter sharing network. Moreover, it gives a new network training method combining hybrid and alternating, so as to take full advantages of approaches based on the task-level and feature-level. Thirdly, according to the differences of features extracted from the shared layers and the gradient changes in deep layers, the relevant activation value is adjusted and the network is optimized, which not only maintain the conciseness of the network structure, but also help to solve the non-equilibrium problem of data during multi-task learning. Finally, the feasibility and effectiveness of this approach is verified through the applications of MNIST data set and iris and balance data in the UCI data set.
The impact of margin trading and short selling by retail investors on market price efficiency: Empirical evidence from bitcoin exchanges

I exploit the high fraction of retail investors in the early years of Bitcoin and the introduction of margin trading and short selling by the Bitcoin Exchange Kraken to apply a difference-in-differences approach. I document that the potential composite use of margin trading and short selling is associated with lower market price efficiency. Moreover, I find that more likely short selling relative to margin trading is positively related to market efficiency indicating that margin trading causes their negative composite effect. If margin traders are more likely sophisticated retail investors, the negative impact of margin trading is less pronounced.
An Approach For Improving Transparency And Traceability of Industrial Supply Chain With Block chain Technology

The commercial justification for blockchain innovation is based on appropriated data sets and smart contracts. By eliminating the need for delegates, the distributed record innovation disrupts the proprietorship model. When combined with other creative breakthroughs, such as artificial intelligence (AI) and additional material fabrication, it has the potential to have a significant impact on cross-hierarchical cycle computerization. As the blockchain innovation concept has gained traction in recent years, a growing number of companies have jumped on board. To help enable straightforwardness, productive data exchange, and cleanliness, the coordination and inventory network of the board’s company has also realised its latent capacity application potential. Only a small number of companies have identified possible blockchain use cases that would outweigh the benefits of current IT systems. Advanced pioneers and senior leaders are certain about the benefits of blockchain innovation in meetings they lead. The selection is influenced by many variables, such as an immature environment, the lack of a management paradigm, and administrative vulnerability. In order to enable permanent record sharing and observation while still maintaining specific information security, the suggested system incorporates a crossover architecture of private and public blockchains.
Natural Language Processing Environment to Support Greek Language Educational Games

In this paper, we present the so far implemented infrastructure of the “Lexipaignio” project, a research project co-financed by EU and Greek national funds, where an innovative and state-of-the-art NLP (Natural Language Processing) environment is being developed for the creation of digital educational games for Greek students. An initial, brief presentation of the the position of digital games in the today’s educational system is followed by a more detailed presentation of the implemented NLP infrastructure for the Greek language. Examples of the games that have already been implemented are also provided. The paper concludes with the current stage of the project and the pending steps towards its completion.
Task scheduling in geo-distributed big data using ant colony optimization

Our commercial application main design depends on the big data in grid computing, distributed computing, and fog computing. The significant challenges facing big data is higher time consumption and cost consumption. Scheduling algorithm is used to save time and money to introduced in big data analytics. The proposed work deals with a significant challenge in task planning. The formidable Optimization challenge in the meta-heuristic algorithm is a considerable solution for cloud task planning. An effective task Manager includes the changes in the working of the task algorithm's task and schedule. Work consists of the cloud a task-based policy of the Ant Colony Optimization (ACO) listens to the various programming algorithm such as first in first out and Round Robin arbitrary algorithm. The purpose of this algorithm is to increase the efficiency of the scheduled task within cloud computing. ACO is an arbitrary Optimization technique that selects the various functions from the incoming jobs to virtual machines. Cloud-Sim toolkit package is used to simulate the proposed algorithm. Various experimental studies were carried out with cloud activities using first in first out and Round Robin based on the ACO technique.
Review Classification Approach for User Sentiment Analysis

Analysis of Sentiment or opinion mining is one of the fastest growing fields with its demand and benefits that is increasing everyday. With the availability of the internet and modern technology, there has been a tremendous growth in the amount of data. The text that has been posted by people for express their analysis of sentiment. In social media, can be analysed and used in order to draw benefits and quality information. In a proposed paper, the focus is on cyber-hate classification based on public opinion or views, since the spread of hate speech using social media have disruptive impacts on social analysis of sentiment. In particular system, we proposed a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying three type of approach such as positive sentiment, negative sentiment and neutral sentiment, and compare its performance with those popular methods and some existing fuzzy approaches.
Triple-as-Node Knowledge Graph and Its Embeddings

Knowledge Graphs (KGs) aim at semantically representing the world’s knowledge in the form of machine-readable graphs composed of subject-relation-object triples (facts). However, most previous KGs only consider the relationship between individual entities, ignoring connections between facts and entities, which are commonly used to depict useful information about the properties of facts. To this end, we formally introduce FactKG, a new KG form which incorporates fact nodes and extends relations from entity-level to fact-level. This new structure challenges some previous KG techniques. One of the key challenges to FactKG is how to learn compatible representation of entities and facts. In this paper, we mainly focus on the embedding task of FactKG. We contribute a benchmark WD16K with additional fact-relevant relations, and a framework FactE, which can represent facts, entities and relations in the same space via attention. Experiments demonstrate that FactE not only significantly outperforms state-of-the-art models but also brings remarkable benefits for disambiguation of 1-N relations, revealing its potential usefulness.
Deep Spatial and Temporal Information based QoE Evaluation Model for HTTP Adaptive Streaming

The content characteristics of video is one of the important influencing factors affecting the user's Quality of Experience (QoE). In this paper, deep spatial and temporal information are extracted to characterize the content characteristics of video, which are then used to establish a QoE evaluation model for HTTP adaptive streaming. Firstly, a Gabor convolutional layer and Channel Attention (CA) are incorporated into ResNet18 to construct the Gabor-CA-ResNet18 network, which is used to capture the Deep Spatial Information (DSI) of video. To avoid the problem of the "curse of dimensionality", LargeVis is applied to reduce the dimensionality of the DSI features to improve the representative and discriminative ability, obtaining a compact feature representation vector. Secondly, 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Unit (GRU) are used together to capture the Deep Temporal Information (DTI) of video, named 3D CNN-GRU. And finally, the DSI and DTI features are combined with the statistical features of other influencing factors, including video quality level, re-buffering duration, re-buffering frequency, and so on, to form the feature parameter vector. The Gradient Boosting method is adopted to establish the mapping relationship model between the feature parameter vector and Mean Opinion Score (MOS), which can be used to predict the user's QoE. Experimental results on SQoE-III and SQoE-IV datasets demonstrate that the proposed QoE model can achieve the state-of-the-art performance compared with the existing QoE evaluation models.
Fall Detection

Elderly persons often require aid to recover after having fallen, and the consequences of falling may be quite dangerous if such aid is delayed. The development of non-invasive systems for automatic fall detection—both reliable and acceptable for the persons to be monitored—remains challenging despite the considerable amount of research related to this topic, carried out during the last several years. This chapter is devoted to an overview of the trends and problems in the area of fall detection based on depth sensors. The results of a series of experiments, aimed at comparing the performance of selected methods of denoising and numerical differentiation—when applied for the detection of human falls on the basis of data from depth sensors—are also reported.
Anomaly Detection of Sensor Data Based on 1D Depth Separable Dilated Convolution Neural Network

The accuracy sensor data has a strong effect on the service ability of mechanical equipment, so how do you effectively identify the abnormal sensor data is highly important in practical industrial applications. The traditional anomaly detection algorithms have the problem of low accuracy. It is a challenging task for those traditional anomaly detection algorithms to finding anomalous information from massive data. In order to address the trouble, a data outlier detection algorithm based on 1D depth separable dilated convolution neural network is proposed. Firstly, a linear function is used to preprocess the original collection sensor data. Secondly, the proposed method is trained by a normal sensor data to gain the optimal parameters. Lastly, the trained model is utilized to detect abnormal data for testing the detection performance. The results show that the proposed method can improve the outlier identification accuracy and has the state-of-the-art ability, compared with existing methods, including principle component analysis, k-means, isolation forest, local outlier factor, and one-class support vector machines.
Any Feedback is Welcome: Peer Feedback and User Behavior on Digital Platforms

Social technologies are becoming increasingly central to the day-to-day operations of digital platforms. While an incredible amount of value has been created, social technologies also give rise to complex social feedback effects. A deeper understanding of those social interactions is needed to better facilitate sustainable platform operations. In this study, we investigate how peer feedback shapes user behavior in an online review platform. Leveraging fine-grained digital trace data of users throughout their tenure on the platform, we find asymmetric effects between positive and negative peer feedback. Interestingly, both positive and negative feedback improve user retention on the platform relative to users who receive no feedback at all. Moreover, for users who are retained and write their next reviews, we find that negative feedback improves review frequency and quality relative to the usersâ€™ own prior reviews. These novel findings call for careful design of peer feedback mechanisms. In particular, we note that simply removing negative evaluation options may not be an optimal strategy for platform operation, as our results reveal that no feedback is a worse alternative. Our study suggests that negative peer feedback could be beneficial to platforms if used appropriately
A Novel Method for Estimating the Spatial Frequency Characteristics of Cameras Based on Generative Random Sequences

Cameras have become ubiquitous in our daily lives. The task of measuring and verifying the camera&#x2019;s declared characteristics is of increasing importance. Current measurement methods range from subjective assessments using a large number of experts to more stringent characteristics, such as the determination of the modulation transfer function. Methods used last ten years have proven successful and reliable results. However, the question of how the current computational methods improve the performance of the camera has received much less attention, especially in the conditions of the test. A significant problem is a fact that the measuring instruments used in the test are standardized and do not change between measurements. In this paper, new a generative table-based measurement approach that allows you to create random initial measurement conditions. This new method opens up opportunities for improving the quality assessment of the camera&#x2019;s work and providing real characteristics. The method is based on random sequences and cannot be deceived by processing algorithms inside the current software of the camera. The method is suitable to assess compression implementations in closed systems
Research on Multidimensional Electronic Behavior Portrait Technology of Electronic Archives Based on Blockchain

Blockchain, as the underlying logic of digital technology, has been widely used in many fields of economy and society. The characteristics of blockchain technology, such as tamper-proof, decentralization, privacy protection, multi-node consensus, traceability and verification, deeply fit the needs of credible storage, collaborative sharing, traceability verification, and confidentiality of electronic archive management, and are extremely important for the whole life cycle management of electronic archives. In this paper, by collecting and labeling key electronic behavior information of electronic archives, forming an unstructured labeling system of multidimensional electronic archival behavior information. Using cluster analysis and Hardtop/Spark-based data processing platform, building a multidimensional electronic behavior portrait model based on blockchain. Based on the model, key information of electronic archive collection and arrangement, appraisal and utilization, statistical verification, etc. is stored on the alliance blockchain, so as to build a credible and reliable business application and a process tracking system. The application and system could help to ensure the standardization, intelligence and specialization of archive management under the premise of archive security, and solve the current problems that electronic archives are easily to be tampered with, unstable in storage, high management cost, and difficult to be shared by cross-organizational collaboration.
Safe and Effective Elliptic Curve Cryptography Algorithm against Energy Attack

Elliptic curve cryptosystem (ECC) has high security and calculation efficiency. Energy attack is a powerful crypto attack method, which attacks the chip cryptography algorithm by using the information leaked by the crypto chip during its operation. Aiming at the situation that the side channel attacks of ECC mainly focus on scalar multiplication, The FWNAF is proposed. It uses the fragment window technology to improve the use ratio for storage resources, reduce the &#x201C;jitter&#x201D; caused by rapid change of system resources.
Slardar: Scheduling Information Incomplete Inter-Datacenter Deadline-Aware Coflows with a Decentralized Framework

Nowadays, geographically distributed edge clusters and datacenters generate large volumes of wide-area network (WAN) traffic. Therefore, it is critical to schedule inter-datacenter flows, especially coflows that imply application-level semantics, to improve the performance of geo-distributed applications. However, coflows often have a mixture of soft and hard deadline requirements, which remains unsolved. In this paper, a decentralized deadline-ware coflow scheduling framework, called Slardar, is proposed. It schedules inter-datacenter coflows while considering both hard and soft deadline requirements. However, it is challenging to design such a framework without complete coflow information. Hence, we first model the intra-coflow bandwidth allocation problem as a mean-field game where each datacenter aims to minimize its own cost with only local coflow information. We further prove its mean-field equilibrium is also the optimal bandwidth allocation strategy and propose a deep reinforcement learning based algorithm to find an approximate optimal numerical solution. Then, we investigate the off-line inter-coflow bandwidth allocation problem by modeling it as a linear programming problem, and an online greedy heuristic algorithm is proposed to find an approximate optimal solution. Finally, we compare Balanar with state-of-art methods by large-scale simulation experiments. Experimental results show that our proposed method can improve the transmission revenue and increase the soft deadline guarantee ratio by more than 400% at best.
Gender Classification Hand Recognition System Using Key-Point Detection with Deep Neural Network

Classification Systems evolve every day with new features, new engines, and updated hardware and software. It indeed depends on what type of system is being assembled and for what purpose. These classification systems will require some biometric-based mechanic to know what reference to identify a particular object or individual. These types of classification systems can be helpful within the public areas, like determining whether that person of interest in the video is a man or a woman. This can help authorities much more outstanding and can assist with their investigations. Our main objective is to classify, label, and compare the recommended biometric used in the gender classification system. This includes knowing the results like the classification type, accuracy, response time, type of biometric, and method used, like in this case, by using the key-point detection and the DNN. Overall, the researchers concluded that the gender classification system using hand recognition has a high accuracy rate with an overall total of 88.2353%. Including a response rate of around 3-4 seconds from a total of 17 test trials.
The Influence of the Application of Agile Practices in Software Quality Based on ISO/IEC 25010 Standard

Agile practices are activities or procedures that are applied during the software development process in order to improve its quality and productivity. The objective of this study is to determine the influence of agile practices on software quality. For this purpose, a model composed of 4 groups of agile practices and 8 quality characteristics according to the ISO/IEC 25010 standard has here been proposed. The results of 146 questionnaires addressed to people involved in the software development process show that the application of agile engineering and project management practices have a significant positive influence on the quality attribute functionality. On the other hand, project management practices have a low impact on the quality characteristics of compatibility, portability, security, and usability.
Using Chaotic Dynamics to Characterize the Complexity of Rough Surfaces and Images

In this work, we use the basic ingredients of chaotic dynamics (stretching and folding of phase space points) for the characterization of the complexity of microscopy images of rough surfaces. The key idea is to use an image as the initial condition of a chaotic discrete dynamical system, such as the Arnold cat map, and track its transformations during the first iterations of the map. Since the basic effects of the Arnold map is the stretching and folding of image texture, the application of the map leads to an enhancement of the high frequency content of images along with an increase of discontinuities in pixel intensities. We exploit these effects to quantify the complexity of S-type (lying between homogeneity and randomness) of the image texture since the first (enhancement of high frequencies) can be used to quantify the distance of texture from randomness and noise and the second (the proliferation of discontinuities) the distance from order and homogeneity. The method is validated in synthetic images which are generated from computer generated surfaces with controlled correlation length and fractal dimension and it is applied in real images of nanostructured surfaces obtained from a Scanning Electron Microscope with very interesting results.
Preface Symposium 9: Recent Advances and Current Research on the Difference Equations and its Applications

First Page
Hierarchical Deep Learning Network Based Framework for Disaster Event Detection

Disaster event detection is the process of identifying events associated with natural disasters, violent events or crisis events from the content of online news articles or social web. This paper proposes a framework for the disaster event detection that consists of a hierarchical ensemble of deep learning model applied at word level encoding and then at sentence level encoding. The framework has been designed in two variants, namely, Hierarchical Deep Learning Networks (HDLN1) that employs Bi-Directional Long Short Term Memory (LSTM) for both word level and sentence level encodings and HDLN2 that comprises of word level encodings with Convolutional Neural Network (CNN) and sentence level encodings with Bi-Directional LSTM. This study analyses the performance of the framework for differentiating disaster events from non-disaster events in a real time dataset of online news articles. The experimental results demonstrate that the HDLN2 model outperforms the traditional deep learning models like Bi-Directional LSTM and CNN in terms of precision, recall and F1-score.
Construction of Continuing Education Teaching Quality Evaluation System Based on BP Neural Network

In the context of advocating lifelong education, continuing education(CE) has attracted more and more attention, and the development of CE has become a trend in the field of education. With the rapid development of modern technology and the rapid expansion of the scope of continuing education, effective evaluation of the quality of CE has become a top priority for the survival of education projects. Therefore, it is very necessary to scientifically and accurately examine the evaluation of continuing education teaching quality(CETQ). This paper establishes a CETQ evaluation system(ES) based on BP neural network(NN). Through the analysis of CETQ evaluation standards, it is concluded that the standards of students' teaching abilities of teachers are in the range of 0.6~1, it shows that students believe that the teaching quality of CE meets the requirements. Such an evaluation system solves the problem of man-made factors affecting the evaluation results and provides a valuable reference for CETQ evaluation research.