Reviewerly/exHarmony huggingface dataset:
Voyager: Software Architecture Trade-off Explorer

Software engineers must ensure that systems under development are endowed with software architectures that enable them to meet their requirements. Apart from functionality, systems also have to satisfy extra-functional requirements that may include behavioural constraints that the software must adhere to, as well as qualities to optimise such as performance, availability, and energy efficiency. These qualities are often inter-dependent and heavily influenced by the structure of the system. This results in poorly understood multi-dimensional design spaces, in which trade-offs among qualities are not evident when making architectural decisions. This paper presents Voyager, a tool which allows engineers to visualise architectural configurations and explore the trade-offs among their quality attributes in a multi-dimensional design space. The tool produces contextual visualisations to facilitate trade-off analysis, providing engineers with a streamlined way of understanding architectural design spaces, using an approach that combines architectural structure with multi-dimensional data visualisations. A user study was conducted to evaluate the effectiveness of the tool. Results show that participants achieved a significantly higher accuracy in a shorter time span and had a better user experience when using Voyager, with respect to an existing comparable tool.
No Phishing With the Wrong Bait: Reducing the Phishing Risk by Address Separation

Email-based phishing is still a widespread problem, that affects many users worldwide. Although many aspects of phishing have been extensively studied in the past, they mainly focus on the execution and prevention of different types of phishing and do not consider the process how attackers collect the contact information of potential victims. In this paper, we analyze the collection process of email addresses in more detail. Based on the results of this analysis, we propose email address separation as a way for users to detect phishing emails, and reason about its effectiveness against several typical types of phishing attacks. We find, that email address separation has the potential to greatly reduce the perceived authenticity of general phishing emails, that target a large amount of users, e.g., by impersonating a popular service and spreading malware or links to phishing websites. It is, however, not likely to prevent more sophisticated phishing attacks, that do not depend on the impersonation of a previously known organization or entity. Our results motivate further studies to analyze the usability and applicability of the proposed method, and to determine, whether address separation has additional positive effects on users’ phishing awareness or automated phishing detection.
Dynamic Local Ternary Patterns for Gender Identification Using Facial Components

Automated human face detection is a topic of significant interest in the field of image processing and computer vision. In the last years much emphasis has been on using facial images to extract gender of human which has become much used in many modern programs used in mobile phones. Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Ternary Pattern (LTP) are popular appearance-based methods for extracting the feature from images. This paper proposes an improved Local Ternary Pattern technique called Dynamic Local Ternary Patterns to address the problem of gender classification using frontal facial images. The proposed Dynamic Local Ternary Patterns solve the limitation found in LBP (poorly performed in illumination variation and random noise) and LDP (produces inconsistence pattern), by increasing the number of the face components, applying the four cardinal directions namely North, East, West, South and using a dynamic threshold in LTP instead of a static one.
A novel vision based pointer instrument reading algorithm

A novel pointer instrument reading algorithm is presented to replace human reading. The image sequences of pointer instrument are captured. Firstly, this paper proposes an image sequences analysis method based on unsupervised learning to segment the moving objects and background. This paper proposes a new method for extracting motion features based on the gray change rules in image sequence. Then a novel unsupervised learning method is used to classify the image sequences into the motion objects and backgrounds efficiently according to these features. Secondly the according to the characteristic of pointer instrument, the rotation central of pointer is get by Hough transform. And then Then the angles of the center of scales and pointer regions relatives to the rotation center can be calculated separately. At last the readings of pointer instrument are calculated from these angles. This study verified the effectiveness of the proposed algorithm through experiments.
Spatial Transformer Part-based Siamese Visual Tracking

Local appearance representation of tracking objects plays a key role in robust visual tracking. While, holistic appearance representation mostly dominates the feature extraction model when describing the target appearance in existing DNN-based trackers, leading to high sensitivity to appearance variation, such as rotation, appearance change, and partial occlusion. We introduce a spatial transformer local part detector, which provides more robust local representation for tracking object and combine the existing Siamese networks for visual tracking simultaneously. To this end, we explicit the spatial transformer networks in the local part detector to detect the discriminative region of tracking objects. The local part detector further passes the detection results to the local cropping module, which comes up with the local structure for the region of interest. Differentiable operations can perform end-to-end learning during local structure construction. We implement the tracking procedure by matching the combined local pattern between candidates and templates in Siamese networks. We extensively prove the effectiveness of the proposed method through the ablation studies of the tracking benchmark, including OTB-2015, VOT-2018, and UAV123.
Protest Event Detection: When Task-Specific Models Outperform an Event-Driven Method

2019 has been characterized by worldwide waves of protests. Each country’s protests is different but there appear to be common factors. In this paper we present two approaches for identifying protest events in news in English. Our goal is to provide political science and discourse analysis scholars with tools that may facilitate the understanding of this on-going phenomenon. We test our approaches against the ProtestNews Lab 2019 benchmark that challenges systems to perform unsupervised domain adaptation on protest events on three sub-tasks: document classification, sentence classification, and event extraction. Results indicate that developing dedicated architectures and models for each task outperforms simpler solutions based on the propagation of labels from lexical items to documents. Furthermore, we complete the description of our systems with a detailed data analysis to shed light on the limits of the methods.
Enhancement of multiatom non-classical correlations and quantum state transfer in atom-cavity-fiber system*

Taking the advantage of “parity kicks” pulses, we investigate the non-classical correlation dynamics and quantum state transfer in an atom–cavity–fiber system, which consists of two identical subsystems, each subsystem comprising of multiple two-level atoms trapped in two remote single-model optical cavities that are linked by an optical fiber. It is found that the non-classical correlations and the fidelity of quantum state transfer (between the atoms) can be greatly improved by the parity kicks pulses. In particular, with decrease of the time intervals between two consecutive pulses, perfect non-classical correlation transfer and entangled state transfer can be achieved.
Design and Development of Bio-Sensitive Robotic Arm using Gesture Control

Abstract In this paper, a model of a robotic arm is designed and manufactured using a 3D printer, which is to be operated via human gesture by using the Accelerometer and Gyroscope Module. This arm is proposed to help in the medical field, in military operations, in hazardous conditions and in industries to maximize human safety. The robot arm is designed such that, it has 5 degrees of freedom controlled by the 3-axis accelerometer mounted on the IC placed on the glove of the user. The module will replicate the movement of the user’s hand to extend, retract, and rotate accordingly to accurately position as required for the application. The user’s finger action is used to manipulate the working of the gripper. Therefore, gesture control in a broad sense is a computerized interface that allows computers to record and interpret those gestures into commands to adhere to actions.
A Multiple-Model State Estimator based on the Unscented Kalman Filter

We consider the state estimation of nonlinear dynamic systems based on the framework of the Kalman filter. There have been various nonlinear Kalman filters in literatures for dealing with the estimation problem, including the well-known extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we propose a novel algorithm based on the UKF to promote estimation performance. With the application of multiple-model estimation, our proposed filter is formulated by using the fusion of multiple UKFs. Overall estimation rules and corresponding posterior model probabilities are provided as well. Furthermore, we present a convenient method to design the model set, which is based on the unscented transformation (UT). Numerical examples show that the new filter gains competitive performance compared with the EKF and the UKF in both accuracy and robustness.
Bulwark: Holistic and Verified Security Monitoring of Web Protocols

Modern web applications often rely on third-party services to provide their functionality to users. The secure integration of these services is a non-trivial task, as shown by the large number of attacks against Single Sign On and Cashier-as-a-Service protocols. In this paper we present Bulwark, a new automatic tool which generates formally verified security monitors from applied pi-calculus specifications of web protocols. The security monitors generated by Bulwark offer holistic protection, since they can be readily deployed both at the client side and at the server side, thus ensuring full visibility of the attack surface against web protocols. We evaluate the effectiveness of Bulwark by testing it against a pool of vulnerable web applications that use the OAuth 2.0 protocol or integrate the PayPal payment system.
Energy Loss Estimation Based Reference-Free Quality Assessment of DIBR-Synthesized Views

The processes of warping and rendering in Depth Image-Based Rendering (DIBR) are usually performed without a reference image. Thus, an effective and fast reference-free (RF) image quality assessment (IQA) model devised for DIBR-synthesized views is more favorable. To this aim, this research propose a novel RF IQA method of DIBR-synthesized views based on Energy Loss Estimation. Firstly, considering that the geometric distortions caused by the processes of warping and rendering might increase the high-frequency (HF) information of a DIBR-synthesized view, the influence of the geometric distortion on the visual quality of a synthesized view can be effectively evaluated by estimating the HF information energies of a given DIBR-synthesized view in the log-gabor domain. Secondly, we estimate the energy loss in the process of JPEG compression as the image complexity, which is used to overcome the problem of different amounts of HF information contained in different content images. Finally, the proposed RF DIBR-synthesized IQA metric is obtained by using the energy loss in JPEG compression to normalize the HF information energy in the log-gabor domain. Experiments conducted on two public datasets demonstrate that the proposed method is advantageous over the relevant state-of-the-art RF IQA metrics.
Exploring the Nexus of Holography and Holophony in Visual Music Composition

In this article the author explores the idea that, owing to their shared three-dimensional nature, holophons and virtual holograms are well suited as mediums for visual music composition. This union is ripe with creative opportunity and fraught with challenges in the areas of aesthetics and technical implementation. Squarely situated upon the bleeding edge of phenomenological research and creative practice, this novel medium is within reach. Here, one methodological pipeline is delineated that employs the convergence of holophony, virtual holography and supercomputing toward the creation of visual music compositions intended for head-mounted displays or large-scale 3D/360-degree projection screens and high-density loudspeaker arrays.
A Keypoint-guided Pipeline for Safety Violation Identification

In this paper, a keypoint-guided pipeline for safety violation identification is proposed. Firstly, a top-down keypoint detector is adopted to detect human areas and 17 keypoints in each human bounding box. Secondly, related regions such as head, trunk, etc. are accurately located. Finally, these regions are cropped from the original image and fed into a unified classifier for feature extraction and violation identification. The experimental results show the superiority of the proposed pipeline than other existing counterparts. An average accuracy of 91.2% is achieved on identifications of not wearing helmet, work clothes and smoking.
On the Legal Revision in PROLEG Program

PROLEG is a legal reasoning system that specializes in representing rules and exceptions separately. This system, as well as most of the computational legal reasoning systems, works based on the literal interpretation of legal rules. However, in some exceptional cases, the literal interpretation of legal rules may create unexpected consequences. In this paper, we introduce the idea of responsible case assignment for tracing back the intended precedents by lawmakers. Based on the responsible case assignment, we propose an algorithm for revising a generated PROLEG rulebase so that the revised rulebase covers the representative precedents intended by lawmakers as well as the new exceptional case.
A Trajectory-Privacy Protection Method Based on Location Similarity of Query Destinations in Continuous LBS Queries

In the continuous location-based service (LBS) queries, the trajectory k-anonymity method that incorporates user’s moving trend is becoming popular because of its high quality of service (QoS). The existing works often apply user’s moving direction to represent the user’s moving trend. However, the query destinations of the users with closer moving directions are not necessarily closer. As a result, the users in a cloaked set may diverge with the proceeding of the continuous queries, leading to large cloaked areas as well as a reduction of QoS. Therefore, in this paper, a new concept named “location similarity of query destination (LS-QD)”, is proposed to reflect user’s global moving trend. By incorporating LS-QD, we develop a novel trajectory k-anonymity method for continuous LBS queries. Our system architecture utilizes a quadtree model with a pyramid structure to construct the user cloaked sets conveniently. Both theoretical analysis and simulation experiments are carried out to evaluate the robustness and security performance of our proposed method. Compared with other existing methods, our LS-QD based k-anonymity method can reduce the cloaking area and cloaking time while increasing the cloaking success ratio by about 40%–70%, which demonstrates its superiority in guaranteeing QoS for continuous LBS queries.
Security Protocol for Cloud Storage Based on Block-Chain

With the development of cloud storage, it is more and more import to ensure the security of clout storage. Many professors and researchers have done a lot of valuable works. Block-chain has the characteristics of “unforgeability”, “whole process trace”, “traceability”, “openness and transparency” and “collective maintenance”. Based on these characteristics, block-chain technology has laid a solid “trust” foundation, created a reliable “cooperation” mechanism, and has broad application prospects. In this paper, we design two protocol based on block-chain to keep the security of cloud storage. One is for enterprises, which generally have large amount of data access, so it is necessary to solve the problem of fast encryption and decryption of large-scale data. The other is for individuals. Generally, the amount of personal data access is relatively small, but the operation is frequent, so the frequent encryption and decryption of small-scale data should be solved. The protocols are designed to make the data can only been attach by the one who hold them.
Scatter Balance based Semi-supervised Dimensional Reduction

Dimensionality reduction is an important research direction for machine learning. In recent years, semi-supervised learning has become a hot issue. Especially, SDA and SMMC are two representative semi-supervised dimensionality reduction methods, which mainly focus on the combination of local geometry structure and label information. For this reason, SDA and SMMC are deeply analyzed in this paper. However, the subspaces generated by these two methods only focus on within-class scatter or between-class scatter. Here we propose a new semi-supervised dimensionality reduction method that can balance them through the co-angle method. The experiment on real-world datasets can illustrate the efficiency of our method.
Current Status and Prospects of Blockchain Technology

The development of new and increasingly popular digital cryptocurrencies, such as Bitcoin, has led to a decentralized new data structure, the blockchain. The blockchain has the characteristics of decentralization, security and trustworthiness, time series data, and collective maintenance. The emergence of blockchain has been highly valued by government departments, technology companies and financial institutions, while the rational use of blockchain technology has raised the attention of society. This paper introduces the classification of blockchain and the architecture of blockchain. By analyzing the core structure of blockchain, this paper describes in detail the core principles, function in each layer of the blockchain and its relationship with Bitcoin. This paper also approaches to the current level of blockchain technology development and application. This paper as well investigates the problems that need to be faced and solved during the evolution of the blockchain, and foresees the trend of blockchain technology in future application.
An Opinion Spread Prediction Model With Twitter Emotion Analysis During Algeria’s Hirak

Abstract Social media is believed to have played a central role in the mobilization of Algerian citizens to peaceful protest against their country’s corrupt regime. Since no one foresaw these protests (called ‘The Revolution of Smiles’ or ‘The Hirak Movement’), this research conducted social media analysis to elicit vital insights about both the intensity of sentiment and the influence of social media on this unexpected instigation of political protest. This work built a deep learning model and analysed the influence of content, sentiment and user features on information spread. The model used the learning capability of a long short-term memory network to predict ‘retweetability’. Experiments were conducted on two real-world datasets (Hirak and Brexit) collected from Twitter. User features were found to be a key element in the diffusion of information. The strongest feelings about event context actively influenced the spread of tweets. The Twitter emotion corpus was found to improve the predictive ability of the model developed in this study.
A comprehensive test framework for cryptographic accelerators in the cloud

To alleviate the performance degradation introduced by cryptographic operations in web-based services, dedicated hardware accelerators have emerged for calculation offloading. However, accelerators from different vendors can be endowed with variable run-time modes and optimized for different workload types. It is necessary but challenging to fully and fairly quantify the associated acceleration benefits and determine usages for cloud tasks. To this end, we establish a common test framework oriented for cryptographic accelerators. The micro benchmarks of our framework rely on the classic OpenSSL speed tool. The macro benchmarks of our framework present a server–client architecture. The server side modifies the Nginx web server and integrates it with Redis memory storage as well as a MySQL database to mimic practical cloud workloads in a customizable manner. The client side modifies the classic ApacheBench tool to support concurrent server access as well as automatic collection and analysis of test results. Our framework supports diverse running modes and workload types to comprehensively locate and present the capability characteristics of accelerators. Tests are conducted on three accelerators, i.e., Intel QAT, Cavium Nitrox and Exar DX2040a. These accelerators have similar performance as reported by their vendors. The result analyses and product-to-product comparisons show that our framework can benefit cloud providers, application programmers and accelerator vendors simultaneously by providing important conclusions such as whether to offload, how to offload, and potential optimizations for more efficient offloading.
Vision tracking based on adaptive interactive fusion

Under the influence of COVID-19, detection and identification of moving targets are very important for personnel management. A lot of research work has improved the accuracy and robustness of the moving target tracking method, but the recognition accuracy of the traditional target tracking method in complex scenes (lighting changes, background interference, posture changes and other factors) is not satisfactory. In this paper, in view of the limitations of single feature representation of target objects, the method of fusion of HSV color features and edge direction features is used to identify and detect moving targets. In each frame of the tracking process, the weight of each feature is adjusted adaptively according to the proposed fusion strategy, and the position of the target is located by using the method of double template matching. Experiments show that the proposed tracking algorithm based on multi feature fusion can meet the requirements of moving target recognition in complex scenes. The method proposed in this paper has a certain reference value for personnel management under the influence of COVID-19.