Reviewerly/exHarmony huggingface dataset:
Interpretable Charge Prediction with Multi-Perspective Jointly Learning Model

Interpretable charge prediction refers to making a judgment interpretation with legal effects while predicting the charge according to the case, which can help people intuitively understand reasons of the judgment. Existing works only focus on the performance improvement of either charge prediction or judgment interpretation generation. However, the judgment interpretation that eliminates redundant information is conducive to charge prediction and the charge can guide the generation of the judgment interpretation. In addition, in order to make the charge-discriminative judgment interpretation less stereotyped, details in the case need to be paid attention to. Accordingly, we explore a dual-encoder to model the case from multi-perspective to extract richer information and establish two bridges between these two tasks to improve both of them so as to achieve interpretable charge prediction with a jointly learning model. Experimental results show that our model outperforms all strong baselines, improving the accuracy of charge prediction and generating flexible judgment interpretations.
A Common General Access Structure Construction Approach in Secret Image Sharing

(k, n) threshold is a special case of the general access structure (GAS) in secret image sharing (SIS), therefore GAS is more extensive than (k, n) threshold. Most of conventional SIS, including visual secret sharing (VSS), polynomial-based SIS, linear congruence (LC)-based SIS, etc., were proposed with only (k, k) threshold or (k, n) threshold other than GAS. This article introduces a common GAS construction approach in SIS with on pixel expansion from existing (k, k) threshold or (k, n) threshold SIS. The authors input classic SIS methods to test the efficiency and feasibility of the proposed common GAS construction approach. Experiments are presented to indicate the efficiency of the approach by illustrations and analysis.
Adversarial Attacks on Deep Unfolded Networks for Sparse Coding

Previous works have discovered that general DNNs are vulnerable under some subtle and specific perturbations on classification tasks. In recent years, deep neural networks (DNNs) unfolded through sparse coding algorithms have achieved great success in sparse coding problem. Some applications of sparse coding have important strategic significance in many cases, and the security of learned models is vital. However, it has not achieved enough attentions. Our paper is the first work to study the adversarial performance on unfolded DNNs for sparse coding. We first verify the effectiveness of the existing attack or defense strategies, and surprisingly discover the defense strategies are useless. In addition, we propose a special attack strategy to eliminate the element on certain dimension of the sparse output code of DNNs. Furthermore, we propose a succinct black-box attack strategy, which could generate adversarial perturbations without knowing the parameters of DNNs and data.
Conditional Density Driven Grid Design in Point-Mass Filter

The paper is devoted to the state estimation of nonlinear stochastic dynamic systems. The stress is laid on a grid-based numerical solution to the Bayesian recursive relations using the point-mass filter (PMF). In the paper, a novel conditional density driven grid (CDDG) design is proposed. The CDDG design takes advantage of non-equidistant grid points by combination of two grids; dense and sparse. The dense grid is designed to cover the state space region, where the significant mass of one or both conditional (i.e., predictive and filtering) densities is anticipated. The sparse grid covers the support of the conditional distribution tails only. As a consequence, the CDDG design improves the point-mass approximation of the conditional densities and offers better estimation performance compared to the standard equidistant grid with the same number of points and, thus, with the same computational complexity. Performance of the CDDG-based PMF is illustrated in a terrain-aided navigation scenario.
Non-Experts or Experts? Statistical Analyses of MOS using DSIS method

In image quality assessments, the results of subjective evaluation experiments that use the double-stimulus impairment scale (DSIS) method are often expressed in terms of the mean opinion score (MOS), which is the average score of all subjects for each test condition. Some MOS values are used to derive image quality criteria, and it has been assumed that it is preferable to perform tests with non-expert subjects rather than with experts. In this study, we analyze the results of several subjective evaluation experiments using the DSIS method. Our first contribution is to discuss the statistical meaning of the MOS values, which has not been previously addressed in the literature. Second, our results show that, contrary to the established belief, there are advantages when performing subjective tests with experts, in that they allow experiments to be performed with fewer subjects, and to better determine the lower threshold of image quality.
User Identification by Keystroke Dynamics Based on Feature Correlation Analysis and Feature optimization

As a kind of behavioral characteristics, the analysis of keystroke behavior and the selection of keystroke features are crucial operations to improve the accuracy of user identification using shallow machine learning algorithms. In this paper, we discuss the typing behavior phenomena and put forward a targeted feature optimization strategy in order to meet the need of improving the accuracy of user identification. Three types of keystroke features are analyzed including duration time features (Hold Time) and two types of latency time features (UD Time and DD Time) from three aspects of features distribution, features correlation and features contribution. The differential evolution (DE) algorithm is used to optimize keystroke features and the new proposed fitness function of DE algorithm is defined based on the analysis of keystroke features. Finally, random forest (RF) algorithm is devoted to evaluate the performance of feature optimization. Feature analysis results show that latency time features distribution among users is more diverse than duration time; the two types of latency time features have a strong correlation of which is as high as 0.9766; the combination of duration and latency time features have the best classification accuracy. Final experimental results of an open dataset show that DE algorithm based on features correlation analysis selects features which have better contribution to user classification, reduces the correlation between UD Time and DD Time features, and improves the classification accuracy by an average of 2.6206%.
Deadline Constrained Energy-Efficient Workflow Scheduling Heuristic for Cloud

Cloud computing facilitates execution of large-scale scientific workflow applications by providing heterogeneous virtualized resources that can be provisioned dynamically. The proliferation of cloud data centers has introduced a serious challenge of enormous energy consumption. Hence, the key concern for executing performance-driven workflows is to devise a scheduling heuristic which can improve utilization of cloud resources and thus helps to reduce power dissipation while adhering to the user’s Quality of Service (QoS) demands. In this paper, we propose a scheduling approach aiming for optimization of makespan, resource utilization, and energy consumption under given deadlines. The proposed heuristic D-DEWS exploits the novel policy of list-based scheduling incorporated with dynamic voltage and frequency scaling (DVFS). It scales the discrete operating frequencies of resources to minimum possible levels, such that a given workflow gets completed within a user-defined deadline with minimum energy consumption. Simulation conducted with synthetic workflows demonstrates the proficiency of proposed heuristic in achieving a significant trade-off between energy savings and performance with deadline compliance. The results obtained confirm that the proposed heuristic outperforms other state-of-the-art algorithms.
Fine-grained Chinese Named Entity Recognition in Entertainment News Using Adversarial Multi-task Learning

Named Entity Recognition (NER) systems are often used as the first step for most of Natural Language Processing (NLP) tasks like question answering, information retrieval, etc. In our paper, the fine-grained NER for entertainment news is the task of identifying nine named entities like person, TV, music, etc. It is challenging not only because of inherent noisiness but the data contains numerous informal abbreviations. We proposed a novel adversarial multi-task learning framework by employing two auxiliary tasks named Part-of-Speech (POS) and Chinese Word Segmentation (CWS) together with a primary NER task. Our model can use task-shared features better and filter task-specific information of CWS and POS. To the best of our knowledge, our work is the first study that incorporate POS and CWS into fine-grained NER task in entertainment news.
Blind Forensic Method Based on Convolutional Neural Networks for Image Splicing Detection

In order to effectively detect whether digital images are spliced, a blind forensics method of digital image splicing based on deep learning is proposed. The method uses high-pass filter to preprocess the image, weakens the negative influence of image content on tampering forensic analysis, implements feature selection and classification based on convolutional neural networks(CNNs) to realize the classification of real images and spliced images. Experiments on the Columbia image splicing detection evaluation dataset and comparison with traditional forensic methods show that the proposed method can achieve better detection accuracy.
Spatial analysis for calculating closest distance of operators’ location to Base Transceiver Station (BTS) in Banda Aceh city

Abstract Base Transceiver Station (BTS) is used to generate auxiliary signal for cellular uses. BTS will support all the activities regarding data exchange such as messaging and web browsing. The spatial data plays important role to support BTS’s signal coverage area. The spatial data also can be used for designing of new cellular networks and construction new BTS. This research purpose is to help operator manager find the distance of BTS distribution that will be used to make group of BTS position for the operators. In this research Geographic Information Systems (GIS) has been utilized to analyse the nearest distance and to create group of BTS. Spatial network analyst uses network data path to find out the nearest distance from operators’ location to all of BTS in Banda Aceh by using tools closest facility, and to analyse the operators’ location grouping based on the mean value BTS distance by using buffer method. The result of this research has calculated the average distance from the operators’ location to all BTS is 6.2 Kilometres. Based on the average distance the first team will manage 7 BTS and the second team will manage 14 BTS.
Scalability of Container Overlays for Policy Enforcement in Digital Marketplaces

Digital marketplaces (DMPs) are emerging as a framework for organizations to share their data. Security and support for multi-tenancy are the key features of DMPs. DMPs infrastructure can be built upon container-based networks in the cloud environments. However, there is not at the moment an in-depth analysis of the capability of container networks to support this mode of operation. In this paper, we evaluate the capability of Cilium and Calico, the two most popular container network techniques, in providing security (policy scalability) and handling the multi-tenancy requirements (pod scalability) of DMPs. We first measured the policy scalability in the network, and both Calico and Cilium scale well. However, by studying the pod scalability we determine there is around 50% throughput degradation in both technologies by increasing the number of pods from one to forty.
Remote Control System of Spherical Robot based on Silent Speech Recognition

In the past, people control robots by remote-control or keyboard which is quite difficult and not efficient. This method needs to occupy people's hands and is totally unfriendly to the disabled. So we developed a spherical robot remote control system based on silent speech recognition technology. After the surface EMG signals are collected by the equipment, preprocessed and extracted features, the recognition results which are produced by the random forest are sent to the spherical robot to make it move. By this way, the spherical robot can move according to people's thought without hands' movements. In this paper, the reliability of the system is verified by experiments, which prove it has practical application value.
Blockchain based Removable Storage Device Log Management System

Since the use of removable storage devices, security of the storage devices has been required as needed. In response to this demand, a method of managing/controlling removable storage devices through software is being utilized. However, because common management software stores logs about the use of the storage device on a personal PC or on a central server, there may be situations in which insiders tamper with the logs after malicious behavior. This paper proposes a blockchain-based log management system to solve the problem of log tamperability of existing removable storage management software.
A Sentiment-Based Trust and Reputation System in E-Commerce by Extending SentiWordNet

Trust is a decisive factor in e-services and especially in e-commerce. E-customers usually rely on others’ opinions, reviews, recommendations on products, and services to make the right purchase decision. Nevertheless, deceptive reviewers deliberately disseminate fake and dishonest reviews to falsify the products’ reputation. Consequently, there is a need for Trust and Reputation Assessment to aggregate these text reviews and compute their related reputation scores. For this purpose, Natural Language Processing cannot be omitted from the process of generating reputation scores. In this paper, we propose a Trust and Reputation System named SentiTrustCom STC which is composed of two subsystems: (1) A Combined Idiomatic Ontology-based Sentiment Orientation System that employs NLP techniques and extends SentiWordNet to analyze Text reviews and compute their related Sentiment orientation scores; (2) Trust and Reputation Engine that proposes algorithms to generate reliable Trust and Reputation scores using the generated Sentiment Polarities as inputs. STC aims to analyze the users’ behavioral intention in order to detect any ill-intentioned interventions that could falsify the products’ reputation and hence distort the overall trust among reviewers.
Global Structure Graph Guided Fine-Grained Vehicle Recognition

Fine-grained vehicle recognition is a challenging problem due to the subtle intra-category appearance variation, which requires the recognition model can capture discriminative features from distinguishing regions. The structure is an important characteristic of vehicles which can help to find substantial parts and learn distinguishing representations. In this paper, we propose an approach that introduces the structure graph into consideration to learn distinguishing representations for vehicle recognition. Our proposed method first constructs a global structure graph from the features generated by the convolutional network and then it applies the graph as the guidance to produce effective representations of vehicles. The results of extensive experiments demonstrate that our proposed method can produce more promising results than other state-of-the-art methods. The results of the visualization illustrate that our approach can construct a suitable structure graph and the global structure information facilitates learning discriminative representations at crucial parts of vehicles.
Kernel Ridge Regression with Autocorrelation Prior: Optimal Model and Cross-Validation

Kernel regression problem with autocorrelation prior is discussed in this paper. We revealed the optimal model of the kernel ridge regression in terms of the expected generalization error under the assumed autocorrelation prior. This result agrees with the optimal model of the Gaussian process regression, whose optimality is specified by the conditional expectation by a given set of training samples. We also proved that the minimizer of the expected cross-validation criterion is reduced to the optimal model, which gives a novel aspect of nonasymptotic theoretical justification of the cross-validation technique in the kernel regression problem.
Object Detection with Color and Depth Images with Multi-Reduced Region Proposal Network and Multi-Pooling

Object detection technology has received increasing research attention with recent developments in automation technology. Most studies in this field, however, use RGB images as input to deep-learning classifiers, and they rarely use depth information.So, in this paper, we use images with both RGB and depth information as input to an object detection network. We base our network on the Faster R-CNN proposed by Shih et al., and we develop a fast and accurate object detection architecture. In addition to adding depth as input, we also adjust the type of anchor boxes to improve performance on some objects. We also discuss the impact of pooling training data with multiple region proposal networks (RPN) and regions of interest (ROI).Adding depth information improved the mAP by 8.15%, from 36.86% to 45.01%, when using the SUN RGB-D dataset with 10 classes. Optimizing the anchor boxes improved the mAP from 45.01% to 45.88%. After testing various architectures with different reduced RPNs, we find that the model of 1RRPN-2ROIP performs best. The running time is 0.123 s, which is 1.8 times faster than the 3D-SSD model.
A <i>k</i>‐nearest neighbor query method based on trust and location privacy protection

Abstract Spatial query is an important supporting technology in the Internet of Things (IoT) and location‐based services (LBS). The k ‐nearest neighbor query is widely used for spatial queries. However, user location privacy may be leaked in the query. In addition, some users are malicious or uncooperative. With the objective of overcoming these problems, a k ‐nearest neighbor query method based on trust and location privacy protection is proposed. First, we employ a new K ‐anonymity method based on cooperation to protect a query user's location privacy. In this method, the query user constructs an anonymous group by introducing a trust mechanism to incentivize cooperation among users. Then, according to the different radii of the selection area set by the query user, agent users with higher reputation values who send query requests for the query user are selected. Finally, the agent users obtain the query results from the LBS server and forward them to the query user, and the query user screens the results according to his or her real location. The experiments show that our method can effectively stimulate users to cooperate, better exclude malicious users, and improve the accuracy of the query results while protecting the privacy of the query user.
Deriving compositional random generators

Generating good random values described by algebraic data types is often quite intricate. State-of-the-art tools for synthesizing random generators serve the valuable purpose of helping with this task, while providing different levels of invariants imposed over the generated values. However, they are often not built for composability nor extensibility, a useful feature when the shape of our random data needs to be adapted while testing different properties or sub-systems.
Improving Performance of YOLOv3 for Vehicle Detection

As the need for an intelligent transport system is growing rapidly, vehicle detection has gained a lot of attention recently. This work presents an approach to improve the performance of YOLOv3 model architecture for detecting vehicle objects. The improvement is focused on detection speed and accuracy. The YOLOv3's backbone is changed by adopting MobileNets architecture. Its anchor boxes are also reselected so that they focus on detecting vehicle objects. Additionally, some additional post-processing methods are employed to validate the detected bounding box. Extensive experiments and analysis were carried out and they validated that it is promising to employ the proposed approach in a vehicle detection system.
From Unsupervised Machine Translation to Adversarial Text Generation

We present a self-attention based bilingual adversarial text generator (B-GAN) which can learn to generate text from the encoder representation of an unsupervised neural machine translation system. B-GAN is able to generate a distributed latent space representation which can be paired with an attention based decoder to generate fluent sentences. When trained on an encoder shared between two languages and paired with the appropriate decoder, it can generate sentences in either language. B-GAN is trained using a combination of reconstruction loss for auto-encoder, a cross domain loss for translation and a GAN based adversarial loss for text generation. We demonstrate that B-GAN, trained on monolingual corpora only using multiple losses, generates more fluent sentences compared to monolingual baselines while effectively using half the number of parameters.
Image Denoising Algorithm Based on Local Adaptive Nonlinear Response Diffusion

Abstract We describe an image denoising algorithm to improve the denoising performance and the time efficiency, while keeping the edge texture of the image from being blurred. First, the algorithm discriminates the pixel whether is an edge pixel or a noise pixel according to the similarity region values of the neighborhoods, and calculates the adaptive diffusion coefficients to construct an adaptive influence function, which can control the noise and the edges to have different diffusion speeds. Then, a 5-layer feedback convolution network with 16 small filters in each layer generates the reconstructed image through convolution, diffusion and deconvolution. Last, the algorithm uses multiple noise levels training sets to train and optimize the network parameters, and generates the finial image denoising model. The experimental results show that the proposed algorithm using adaptive diffusion coefficients can maintain the edges and the smaller filter can improve the time efficiency. The algorithm also has higher denoising accuracy for images with unknown noise level by training on multiple-noise-level training sets.