Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Secure Collaboration Platform for Sharing and Accessing Scientific Research Data

Research teams or institutions in different countries need an effective and secure online platform for collaboration and data sharing. It is essential to build such a collaboration platform with strong data security and privacy. In this paper, we propose a platform for researchers to collaborate and share their data by leveraging attribute-based access control (ABAC) and blockchain technologies. ABAC provides an access control paradigm whereby access rights are granted to users through attribute-based policies, instead of user identities and roles. Hyperledger fabric permission blockchain is used to enable a decentralized secure data sharing environment and preserves user's privacy. The proposed platform allows researchers to fully control their data, manage access to the data at a fine-grained level, keep file updates with proof of authorship, and ensure data integrity and privacy.
Hierarchical graph attention network for relation extraction

Abstract Previous research on relation extraction has proved the effectiveness of using dependency trees, which build non-local connections between tokens. However, the existing dependency-based models treat the dependency trees as an inherently flat graph, which causes a loss of dependency information for representing sentences. Besides, they fail to consider the fact that the importance of tokens on the dependency tree varies with different relations to be extracted. In this paper, we propose a novel hierarchical graph attention network HierGAT , which can generate multi-level dependency trees and extract key information from them to improve relation extraction. Specifically, it contains multiple dependency-based graph attention layers, each of which takes a different dependency tree generated by an adaptive subtree pruning strategy as input, and distinguishes the importance of different tokens in a dependency tree. Finally, HierGAT integrates the output token representations of each layer with a multi-head attention mechanism, and learns sentence representation for relation extraction through a pooling layer. The experimental results demonstrate that our method outperforms state-of-the-art baselines on the benchmark datasets.
Policy Advisory Module for Exploration Hindrance Problem in Multi-agent Deep Reinforcement Learning

This paper proposes a method to improve the policies trained with multi-agent deep learning by adding a policy advisory module (PAM) in the testing phase to relax the exploration hindrance problem. Cooperation and coordination are central issues in the study of multi-agent systems, but agents’ policies learned in slightly different contexts may lead to ineffective behavior that reduces the quality of cooperation. For example, in a disaster rescue scenario, agents with different functions must work cooperatively as well as avoid collision. In the early stages, all agents work effectively, but when only a few tasks remain with the passage of time, agents are likely to focus more on avoiding negative rewards brought about by collision, but this avoidance behavior may hinder cooperative actions. For this problem, we propose a PAM that navigates agents in the testing phase to improve performance. Using an example problem of disaster rescue, we investigated whether the PAM could improve the entire performance by comparing cases with and without it. Our experimental results show that the PAM could break the exploration hindrance problem and improve the entire performance by navigating the trained agents.
Simplification Algorithm and Analysis of Random Sequence Test Packets based on Filtering Ability

Abstract A method was presented for simplifying the random sequences by means of introducing the concept of “the ability of filtering”. On the basis of explication of the relationship between each test and In-depth analysis of the role and contribution of individual tests to the overall test, the algorithm and implementation plan of simplified test package were proposed. Three procedures for filtering descending permutation tables for simplifying links and the algorithm for simulation results were discussed.
Research on seismic exploration techniques and methods in urban environment of loess plateau

Abstract Seismic wave energy being absorbed strongly in thick loose loess stratum is a major problem that seismic exploration has to face. In the urban environment, this problem is particularly prominent due to the inability to use explosives, borehole explosion, and large-scale 3D observation methods to increase fold number. To solve this difficult problem, a series of seismic exploration effect tests were carried out in Shaoling Plateau, a southern suburb of Xi’an City. A large-tonnage vibroseis, 28-ton vibrator vehicle, was selected as seismic source. By comparing and analyzing the effects of various vibrating parameter of vibrator vehicle, cooperating with different model geophones acquisition, and using appropriate observation system, we had figured out the feasible parameters for the source excitation and data acquisition in the field working area. Based on the results of above test, a nearly 7 km long seismic survey line was completed with pretty good data acquisition, and the expected exploration aims were well achieved.
Batch Mode Active Learning for Individual Treatment Effect Estimation

Field experimentation has become a well-established practice to estimate individual treatment effects. In recent years, the Active Learning (AL) literature has developed methods to optimize the design of field experiments and reduce their cost. In this paper, we propose a novel AL algorithm for individual treatment effect estimation that works in batch mode for cases where the outcomes of an intervention are not immediate. It uniquely combines Expected Model Change Maximization and Bayesian Additive Regression Trees. Our approach (B-EMCMITE) uses the predictive uncertainty around the individual treatment effects to actively sample new units for experimentation and decide which treatment they will receive. We perform extensive simulations and test our approach on semi-synthetic, real-life data. B-EMCMITE outperforms alternative approaches and substantially reduces the number of observations needed to estimate individual treatment effects compared to A/B tests.
WITHDRAWN: Mining of negative association rules using correlate frequent pattern tree

Associative rule mining is the discovery of rules from the item set constraint that contain a set of specific items in antecedent rule and a class label. Frequent Pattern (FP-Tree) is associative rule mining method that doesn’t provide large set of candidate sets. Recently, the researchers are focusing on finding the infrequent item set leads to Negative Association Rule (NAR) mining. In this research, (FP-Tree) is added with the linkage table called Correlated Frequent Pattern Tree (CFPT) that allows the method to analyze the patterns with frequent items. The Coverage Co-efficient Measure (CCM) is applied in the FP-Tree with linkage table to identify the correlated item sets. In the mining process, CCM helps to remove irrelevant item set from the transaction. The developed IFP-Tree reduces the memory usage and the execution time in mining the frequent pattern. The four datasets have used to experiment the performance of proposed CFPT technique. The execution time of proposed CFPT method is the 0.043 s in frequent pattern mining and 0.075 s in infrequent pattern mining.
Research on HAND Diagnosis Algorithm Based on Transfer Learning

HIV-associated neurocognitive disorders (HAND) is an HIV infection Early (1 week) manifestations. Effective recognition of HAND will improve the prognosis and the patient quality of life, which has important scientific significance and clinical value. Based on the HAND datasets, this paper proposes an algorithm to build a medical image classification model using transfer learning. The results show that the model improves the performance of the HAND classification. In order to further optimize the classification performance, data augmentation, multi-task learning algorithms are used in this paper. Experimental results show that it can achieve a better classification performance.
Performance of Deep Learning and Traditional Techniques in Single Image Super-Resolution of Noisy Images

The improvement of the spatial resolution of natural images is an important task for many practical applications of current image processing. The endeavor of applying single image super-resolution, which only uses one low-resolution input image, is frequently hampered by the presence of noise or artifacts in the input image, so denoising and super-resolution algorithms are usually applied to obtain a noiseless high-resolution image. In this work, several traditional and deep learning methods for denoising and super-resolution are hybridized to ascertain which combinations of them yield the best possible quality. The experimental design includes the introduction of Gaussian noise, Poisson noise, salt-and-pepper noise, and uniform noise into the low-resolution inputs. It is found that denoising must be carried out before super-resolution for the best results. Moreover, the obtained results indicate that deep learning techniques clearly outperform the traditional ones.
The impact of technological progress on everyday social communications in modern society

In the presented article, the author examines the problems of the impact of technological progress on everyday social communications in society of the XXI century. The author analyzes the positive and negative aspects of the influence of the latest technologies on communication processes in modern society, and also touches on the topic of human interaction with artificial intelligence in the digital era. Particular attention is paid to the characteristic features of variable interactions and communications carried out through the Internet, social networks and instant messengers. As a result, it is concluded that it is necessary to form in modern generations of people a balanced, rational and critical approach to using the achievements of scientific and technological progress, not to the detriment of their intellect, independence, moral merit and the quality of communication with others, but for the good.
Occlusion Area Removal in Binocular 3D Reconstruction of Train Running Parts

The state-of-the-art approaches of stereo matching rely on trained CNNs. However, there are not proposed effective methods in the current end-to-end deep learning stereo matching network to solve the problem that a pair of rectified stereo images are matched in the occlusions and background areas, which leading to erroneous reconstruction of 3D point clouds in these areas. In this paper, a softmax module to the back-end of the stereo matching network is added to calculate the confidence of the disparity map. Moreover, the occlusion part and the background part of the disparity map is removed according to the characteristic that the correct area and the occlusion area have a morphological difference on the waveform of the confidence response. Finally, a train running parts dataset is generated to prove our method. This work realizes the rapid three-dimensional reconstruction and measurement of the bottom part of the motor car under the single image acquisition, and providing a feasible solution for removing the occlusion and background parts in stereo matching.
Searching in CCTV: effects of organisation in the multiplex

CCTV plays a prominent role in public security, health and safety. Monitoring large arrays of CCTV camera feeds is a visually and cognitively demanding task. Arranging the scenes by geographical proximity in the surveilled environment has been recommended to reduce this demand, but empirical tests of this method have failed to find any benefit. The present study tests an alternative method for arranging scenes, based on psychological principles from literature on visual search and scene perception: grouping scenes by semantic similarity. Searching for a particular scene in the array-a common task in reactive and proactive surveillance-was faster when scenes were arranged by semantic category. This effect was found only when scenes were separated by gaps for participants who were not made aware that scenes in the multiplex were grouped by semantics (Experiment 1), but irrespective of whether scenes were separated by gaps or not for participants who were made aware of this grouping (Experiment 2). When target frequency varied between scene categories-mirroring unequal distributions of crime over space-the benefit of organising scenes by semantic category was enhanced for scenes in the most frequently searched-for category, without any statistical evidence for a cost when searching for rarely searched-for categories (Experiment 3). The findings extend current understanding of the role of within-scene semantics in visual search, to encompass between-scene semantic relationships. Furthermore, the findings suggest that arranging scenes in the CCTV control room by semantic category is likely to assist operators in finding specific scenes during surveillance.
Residual Non-degenerate Temporal Network for Human Action Recognition

Recent research on video human action has progressed with the development of 3-demensional deep convolutional networks (3-D ConNets). In particular, spatiotemporal features exhibited improved performance. However, the temporal information, which commonly exists in video, has not been fully exploited in existing 3-D ConNets. In this paper, we propose a novel Residual Non-degenerate Temporal Network (RNTN) for human action recognition, which can exploit sufficiently temporal information from frames. Specially, RNTN mainly consists of residual nondegenerate temporal blocks (RNTB) and 3-D effective channel attention blocks (3D-ECA). In RNTB, the expression of temporal features is enhanced effectively. In 3D-ECA, the potential connection between features was strengthened by channel feature interactive with the adjacent channel features. Our approach provides the state-of-the-art performance on the datasets of UCF-101(98.33%) and HMDB-51(80.04%).
A Hierarchical Blockchain Framework for Fog Computing-supported IoT Networks

Blockchain has been recently extended into Internet-of-Things (IoT) networks to further improve security and reliability of data communication. Modern IoT networks need to support a wide range of services with various requirements in terms of data delivering security, reliability, and timeliness. In this paper, a fog computing-supported hierarchical blockchain network framework is proposed. In the first layer, the Inter-Planetary File System is used to store the message uploaded by IoT devices. In the second layer, a set of subchains that are referred as channels are designed. Fog nodes subscribed to the same channel maintain the corresponding distributed ledger, and each channel support a specific IoT service. The security, latency, and communication cost of the proposed framework are derived. Besides, tradeoffs among the above three performance metrics are analyzed. Finally, a real deployment scenario based on the actual base station locations in Dublin is considered to evaluate the proposed framework. Numerical results show that it is impossible to optimize all the performance metrics at the same time. By carefully selecting the parameters of different “channels”, the proposed framework can support highly diversified services without sacrificing security.
A Real-time Virtual Reality Adaptive Streaming System

Cloud VR (Virtual Reality) is a VR scheme based on cloud computing, which can reduce the computing burden on terminal equipment. It uses edge computing for adaptive streaming to minimize the response latency and bandwidth consumption. However, traditional adaptive streaming approaches based on preprocessing have some limitations. In this paper, we proposed a novel adaptive Cloud VR system with real-time processing. We design and implement a GPU acceleration scheme to perform efficient projection and coding, which makes the computing latency acceptable. The scheme is further extended to pipeline execution with simple orientation prediction to support higher frame rate. The real-time processing not only reduces the storage size, but also eliminates the drawbacks of pre-generating limited video versions. According to the experimental results, our scheme can provide a VR stream matching user viewport more precisely. Through optimized GPU algorithm, we shorten the processing time to 1/10 of the original. Compared to classic pyramid projection scheme, our system effectively reduces the average orientation deviation by 90.23%, thus provides more robust service.
A Novel Distributed Reinforcement Learning Method for Classical Chinese Poetry Generation

Poetry generation has been a classic natural language generation task recently. But so far the methods for this topic mainly imitate and reproduce the poems on the training data set, which indicates that they either have not much connotation or overfit too much like plagiarism of the existing poems. To solve this problem, unlike previous work, instead of tuning the trade-off between connotation and innovation, we propose a distributed reinforcement learning framework, which consists of two stages of training, to generate creative and meaningful poetry. At the first stage we train a model in parallel on a large poetry corpus at word level to master how poets write poems. At the second stage we train the model with a distributed architecture to learn how connotation is developed in human literary art works at sentence level and force the model to imitate itself when it composes some ‘good poems’ to further improve performance. Experiments on generating classical Chinese poetry demonstrate that the proposed model is able to achieve better performance and the high efficiency of training compared to the state-of-the-art.
Exploring Feature Enhancement in The Modulation Spectrum Domain via Ideal Ratio Mask for Robust Speech Recognition

Development of robustness techniques is of paramount importance to the success of automatic speech recognition (ASR) systems. In this paper, we present a novel use of the ideal ratio mask (IRM) method to improve ASR robustness. IRM was originally proposed for time-frequency (T-F) masking-based speech enhancement and has shown considerable promise in preserving the intelligibility of a noisy mixture signal. Further, IRM is alternatively used to normalize the intermediate representations of speech feature vector sequences, in a holistic manner, for both training and test utterances. Finally, we instead treat IRM as a data augmentation method, conducted on speech feature vectors of training utterances or their intermediate representations, to generate additional augmented data for increasing the diversity of training data. A series of experiments carried out on the standard Aurora-4 database and task confirm the effectiveness of our methods.
Wavelet-Based Solutions for Boundary-Value Problems

Nonlinear boundary-value problems (NBVPs) arise from almost every scientific and engineering field.
Comparative Analysis of Different Edge Detection Methodologies in Medical Imaging

Edges of an image are perceived to be a kind of essential information that can be collected by providing different techniques using edge operators. This paper discusses a brief analysis of edge detection process concepts, theories behind various edge detectors, and some massive self-written edge detection operations with simulation tests.
General recommendations for improving efficiency in the development of WEB sites and applications

This article analyzes the various technologies used in WEB-development and provides recommendations and justification for the effective selection of the desired.
Improves Neural Acoustic Word Embeddings Query by Example Spoken Term Detection with Wav2vec Pretraining and Circle Loss

Query by example spoken term detection (QbE-STD) is a popular keyword detection method in the absence of speech resources. It can build a keyword query system with decent performance when there are few labeled speeches and a lack of pronunciation dictionaries. In recent years, neural acoustic word embeddings (NAWEs) has become a commonly used QbE-STD method. To make the embedded features extracted by the neural network contain more accurate context information, we use wav2vec pre-training to improve the performance of the network. Compared with the Mel-frequency cepstral coefficients(MFCC) system, the average precision (AP) is relatively improved by 11.1%. We also find that the AP of the wav2vec and MFCC splicing system is better, demonstrating that wav2vec cannot contain all spectrum information. To accelerate the convergence speed of the splicing system, we use circle loss to replace the triplet loss, making the convergence about 40% epochs earlier on average. The circle loss also relatively increases AP by more than 4.9%. The AP of our best-performing system is 7.7% better than the wav2vec baseline system and 19.7% better than the MFCC baseline system.
Design and Implementation of Marine Multiple Geoscience Sharing WebGIS System Based on OpenLayers

Marine basic geological data play an important role in national defense construction and marine economic development. Based on unified data standards and specifications, building a marine basic geological data resource sharing platform has important practical significance for realizing the sharing of marine geoscience data and improving the utilization of data resources. This paper first analyzes the outstanding problems in the visualization of marine basic geological data, mineral resources and gravity, and magnetic data. For these problems, based on the existing marine geological data as the basic source data, the technical method of visualizing the spatial location of marine basic geological data through map visualization is studied, and the method is applied to the marine geological data service WebGIS system development project. The efficiency of marine geological researchers in querying geological data and the hit rate of screening data have been improved, and it has certain innovation and practicability in the service model of marine geological data.
Persistent Identifiers and the Next Generation of Legal Scholarship

The world of scholarly communications has seen distinct growth regarding the use of persistent identifiers in the effort to preserve, disseminate, analyze, and help locate academic content. A persistent identifier is a unique string of letters, numbers, and/or symbols associated with digital content that will never change over time. Persistent identifiers exist in different forms and for different functions, and this Article will discuss the importance and relevance to legal scholarship of two of the most pervasive persistent identifiers in scholarly communications - the digital object identifier (DOI) and the ORCID identifier (ORCID iD).The use of persistent identifiers in academic publishing has become so pervasive that robust, data-driven services have been developed and integrated into the publication process that rely on and leverage this information standard. Publishers of legal scholarship and other legal materials have not widely adopted persistent identifiers, and, as a result, the legal discipline cannot enjoy the variety of benefits offered by this system. In addition, legal scholarship will be left out of future developments and innovations that rely on persistent identifiers to measure impact and other bibliometrics of scholarship. Obtaining disciplinary adoption of persistent identifiers is necessary, prudent, and feasible. This Article will identify existing barriers regarding the implementation of persistent identifiers among publishers of legal scholarship, as well as provide an anecdotal example of creating a sustainable workflow between the law library and student-run law journals. This Article concludes with a call to action for all stakeholders in legal publishing to adopt persistent identifiers and usher in a new generation for legal scholarship and other legal materials.