Reviewerly/exHarmony huggingface dataset:
Research on the Development Trend and Auditing Mode of High Security Enterprise Intranet Security Audit

Security auditing is an effective means of safety supervision and risk self-identification. It is also a hot issue in the research of major enterprises and institutions. The existing standard norms cannot effectively guide the internal audit of the enterprise intranet, “how to review” and “review” What is not easy to grasp. This paper analyzes the status quo, problems and development trends of security audit, and proposes a behavior-based security audit mode, which can effectively discover the non-compliance behavior of intranet users, and can provide reference for peers through practice.
The Big Deal: Should Libraries Stay or Should Libraries Go?

This article examines the history of the Big Deal more than 23 years after the beginning of large journal packages being offered to libraries. This article also examines solutions and alternatives to large journal packages and looks to the future of the Big Deal.
Lithochemical Stream Sediments of the Dukat Gold–Silver Ore-Forming System (North–East of Russia)

The composition, structure and formation features of the exogenous anomalous geochemical fields (AGFs) identified by lithochemical stream sediments (LSSs) are considered using the examples of the Dukat gold–silver ore-forming system and the deposit with the same name. The research was carried out in the Balygychan–Sugoy trough (Magadan region, north–east of Russia). Areal geochemical surveys on the 1:200,000 and 1:50,000 scales were conducted. Exogenous AGFs of basic element indicators of all known mineralization types were studied. It is shown that the surveys on the 1:200,000 scale are characterized by simplicity, relative depth and the possibility to obtain information operatively about the metallogeny of the area as a whole. At the same time, the anomalies were revealed as a result of surveys often making a relatively poor component composition and low contrast compared with ores. The violation of quantitative and sometimes qualitative relationships can be seen between elements, especially those related to gold–silver mineralization. In this situation, the most informative are surveys of LSSs on the 1:50,000 scale. The AGFs were revealed as a result of their performance to have a richer component composition and high contrast, conforming to different ore types. It is shown that, while prospecting for gold–silver mineralization with LSSs in cryolithogenesis zone conditions, the binding forms study of mineralization element indicators is effective. In watercourse heads, where alluvium is practically absent, mosses are proposed for sampling, as they hold the sandy silt material firmly. The obtained results are recommended for use at all stages of prospecting, not only in the north–east of Russia but also in other similar climatic regions.
i-Siam: Improving Siamese Tracker with Distractors Suppression and Long-Term Strategies

Recently, Siamese Network (SiamFC) has attracted much attention due to its fast tracking capability. Despite many improvements introduced, its accuracy is still far from human performance. In this work, we show that there are several design flaws in SiamFC tracker. In particular, the negative signals produced by SiamFC lead to noisy response map. In addition, background noises prevent SiamFC from extracting clean features from the template. To suppress these distractions, first we propose a negative signal suppression approach such that irrelevant features are deactivated. Secondly, we demonstrate that image-level suppression is also important in maximizing the tracking accuracy in addition to the existing feature-level suppression. With better detection sensitivity, we further propose a Diverse Multi-Template approach for appearance adaptation while reducing the risk of template drifting during long-term tracking. In our experiments, we conduct extensive ablation studies to demonstrate the effectiveness of the proposed components. Our tracker named improved Siamese (i-Siam) Tracker is able to achieve state-of-the-art results on UAV123, OTB-100, OxUvA, and TLP datasets compared to the existing trackers. Nonetheless, our tracker runs in real time, which is around 43 FPS.
BINet: A binary inpainting network for deep patch-based image compression

Recent deep learning models outperform standard lossy image compression codecs. However, applying these models on a patch-by-patch basis requires that each image patch be encoded and decoded independently. The influence from adjacent patches is therefore lost, leading to block artefacts at low bitrates. We propose the Binary Inpainting Network (BINet), an autoencoder framework which incorporates binary inpainting to reinstate interdependencies between adjacent patches, for improved patch-based compression of still images. When decoding a patch, BINet additionally uses the binarised encodings from surrounding patches to guide its reconstruction. In contrast to sequential inpainting methods where patches are decoded based on previous reconstructions, BINet operates directly on the binary codes of surrounding patches without access to the original or reconstructed image data. Encoding and decoding can therefore be performed in parallel. We demonstrate that BINet improves the compression quality of a competitive deep image codec across a range of compression levels.
Learning a representation with the block-diagonal structure for pattern classification

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns representation with block-diagonal structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method. The source code of our proposed RBDS is accessible at https://github.com/yinhefeng/RBDS.
Application of numerical techniques to the recognition of structural controls on porphyry Cu mineralization: a case study of Dehaj area, Central Iran

This paper endeavours to evaluate the structural controls of porphyry copper mineralization in Dehaj area, Central Iran, using numerical techniques, an aim for which the various data types including the location of mineral deposits, stream sediment geochemical data and faults were employed. Initially, classical statistics and the concentration-area (C-A) fractal methods were applied to identify mineralization-related geochemical anomalies, revealing the superiority of fractal technique over the traditional methods employed. In addition, a rose diagram portraying the distribution of mineral locations was compared to the distribution and trends of faults using fractal and fry analyses, showing that the spatial pattern of porphyry copper deposits is closely linked to NW- and E-trending faults. In other words, the aforementioned structural trends were plausibly the chief geographical directions controlling the mineralization. The results of this study suggest that NW- and E-trending faults have plausibly operated as corridors for the transportation of ore-bearing fluids.
An Ant Colony Optimization Memorizing Better Solutions (ACO-MBS) for Traveling Salesman Problem

Ant Colony Optimization (ACO) is a population-based meta-heuristic method that mimics the foraging behavior of the ant colony in real life. The pheromone approach as the highlight method of the algorithm is the most effective factor in determining the moving of ants. Therefore, the problem of tuning the pheromone trail is an important topic for ACO that deserves attention. In this paper, a novel method which memorizes the solution costs and updates the pheromone trail according to the memorized costs is introduced for updating the pheromone trail in ACO. The performance of the proposed method was simulated on the Travelling Salesman Problem (TSP) and compared with the versions of ACO algorithm.
A Blockchain based Smart Agent System Architecture

With the popularity of artificial intelligence(AI), intelligent agents are potential to positively change people's lives in every aspect. Meanwhile, blockchain technology opens a new era of value exchange with trustless security among strangers. Its decentralized architecture and cryptography based data storage pattern bring light to vast application areas with high security and scalability requirements. In this work, we provide a general solution for intelligence application development on blockchain. Specifically, we propose a novel design of a smart agent inspired by the smart contract concept in the blockchain. Based on the proposed smart agents, a system architecture composed by smart agents is built supported by blockchain, where the agent network and blockchain network is overlapped. Then, a demonstration of the proposed architecture is designed in a case where private data required to be "properly protected" in a distributed manner. The implementation of the designed system is further conducted in the Ethereum development environment, showing the effectiveness and applicability of the proposed architecture in developing a secure distributed application.
Modeling the redistribution processes of knowledge potential in the formation of the professional competency system

The authors analyze the processes of educational and qualitative personality growth based on diffusion-like models of the process of knowledge potential dissemination and redistribution. In particular, the knowledge potential is given as a characteristic of the certain complex, the amount of knowledge of one or another individual accumulated during the corresponding life period. It is proposed a version taking into account the mutual influence of the relevant components of knowledge potential of individuals (agents) in the formation of the professional competency system.
An Ontology-Based Approach to the Agile Requirements Engineering

The paper presents an approach to the agile requirements engineering based on the OWL ontologies. A brief overview of the benefits of an ontology-based approach to requirements engineering is given. Attention is focused on agile engineering requirements process. The proposed approach uses three ontologies. The first ontology is used to represent knowledge about the agile requirements engineering process. The second ontology is designed to match natural language sentences with the requirements in order to identify conflicts. The third ontology is used to accumulate the knowledge about the domain of the software product. The first ontology is core. This ontology consists of classes corresponding to events, roles and artefacts of agile development. Object properties established between the individuals of class can be used to identify directly or indirectly linked requirements and requirements artefacts. This enables maintaining requirements traceability. Also the ontology takes into account particular qualities of working with the requirements in agile development processes including knowledge about the criteria for assessing the quality of user stories that is the most common form to record the requirements in agile methods. The ontologies are implemented in the Protégé environment.
Aspect Categorization Using Domain-Trained Word Embedding and Topic Modelling

Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.
An approach to improving group recommendation systems based on latent factor matrices

Group activities have been becoming more powerful in various fields today. This results in expanding single user recommendation systems to group recommendation systems. An effective approach to group recommendation systems is to represent a group as a virtual user. Then, the single user recommendations are performed for this virtual user. In this paper, we propose a novel virtual user computation named the observed-filled-rating-based method and its extended version. The aim of our research is to overcome the weaknesses of the previous methods in the virtual user computation to improve group recommendation systems.
Single-Image Facial Expression Recognition Using Deep 3D Re-Centralization

Facial expression recognition (FER) aims to encode expression information from faces. Previous studies often hold the assumption that human subjects should properly face the camera. Such a laboratory-controlled condition, however, is too rigid for in-wide applications. To tackle this issue, we propose a single image facial expression recognition method that is robust to face orientation and light conditions. We achieved this by proposing a novel face re-centralization method by reconstructing a 3D face model from a single image. We then propose a novel end-to-end deep neural network that utilizes both re-centralized 3D model and landmarks for FER task. A comprehensive evaluation on three real-world datasets illustrates that the proposed model outperforms the state-of-the-art techniques in both large-scale and small-scale datasets. The superiority of our model on effectiveness and robustness is also demonstrated in both laboratory conditions and wild images.
Hierarchical Categorization and Review of Recent Techniques on Image Forgery Detection

Abstract Information in the form of the image conveys more details than any other form of information. Several software packages are available to manipulate the images so that the authenticity of the images is being questioned. Several image processing approaches are available to create fake images without leaving any visual clue about the forging operation. So, proper image forgery detection tools are required to detect such forgery images. Over the past few years, several research papers were published in the digital image forensics domain for detecting fake images, thus escalating the legitimacy of the images. This survey paper attempts to review the recent approaches proposed for detecting image forgery. Accordingly, several research papers related to image forgery detection are reviewed and analyzed. The taxonomy of image forgery detection techniques is presented, and the algorithms related to each technique are discussed. The comprehensive analysis is carried out based on the dataset used, software used for the implementation and the performance achievement. Besides, the research issues associated with every approach were scrutinized together with the recommendation for future work.
Emotional Conversation Generation Based on a Bayesian Deep Neural Network

The field of conversation generation using neural networks has attracted increasing attention from researchers for several years. However, traditional neural language models tend to generate a generic reply with poor semantic logic and no emotion. This article proposes an emotional conversation generation model based on a Bayesian deep neural network that can generate replies with rich emotions, clear themes, and diverse sentences. The topic and emotional keywords of the replies are pregenerated by introducing commonsense knowledge in the model. The reply is divided into multiple clauses, and then a multidimensional generator based on the transformer mechanism proposed in this article is used to iteratively generate clauses from two dimensions: sentence granularity and sentence structure. Subjective and objective experiments prove that compared with existing models, the proposed model effectively improves the semantic logic and emotional accuracy of replies. This model also significantly enhances the diversity of replies, largely overcoming the shortcomings of traditional models that generate safe replies.
HEVC Compression Artifact Reduction with Generative Adversarial Networks

Video compression technology is significant to video transmission and storage. However, compression artifacts arise in videos. Specifically, coarse quantization eliminates video details and degrades visual quality. Most of artifact reduction methods use filter processing or Mean Square Error (MSE)loss that leads to over-smoothing results. Moreover, most of the methods target to reduce single image compression artifact instead of video artifact. In this paper, we present an adversarial learning method with recurrent framework called Video Artifact Reduction Generative Adversarial Network (VRGAN). Our network contains a generator with recurrent framework that improves video consistency, a dense block that enhances receptive field for large transform unit, and a relativistic discriminator that evaluates the relationship between the generated frames and the original high-quality frames. Our VRGAN is able to generate more realistic videos. The effectiveness in reducing video compression artifacts of the method has been demonstrated qualitatively and quantitatively. The performance comparison with previous works shows the superiority of the proposed method.
Video Summarization using Convolutional Neural Network and Random Forest Classifier

Video summarization methods aim to generate a shortened representation of the original video. A novel method to extract key-frames based on Convolutional Neural Network and Random Forest Classifier is presented in this paper. The method processes videos on frame by frame basis. The redundant frames are first eliminated based on displacement vectors between the consecutive frames. The high-level feature vectors are extracted using CNN. The feature descriptors corresponding to frames are further classified into key-frames and non-keyframes using the Random Forest Classifier. The method is tested on two benchmark datasets: VSUMM and OVP. The proposed approach attains better results compared to other state-of-the-art video summarization techniques. The results show that the method is able to generate high quality summaries consistently for videos of all categories.
The Architectural Security Tool Suite — ARCHSEC

Architectural risk analysis is a risk management process for identifying security flaws at the level of software architectures and is used by large software vendors, to secure their products. We present our architectural security environ- ment (ARCHSEC) that has been developed at our institute during the past eight years in several research projects. ARCHSEC aims to simplify architectural risk analysis, making it easier for small and mid-sized companies to get started. With ARCHSEC, it is possible to graphically model or to reverse engineer software security architectures. The regained software architectures can then be inspected manually or au- tomatically analyzed w.r.t. security flaws, resulting in a threat model, which serves as a base for discussion between software and security experts to improve the overall security of the software system in question, beyond the level of implementation bugs. In the evaluation part of this paper, we demonstrate how we use ARCHSEC in two of our current research projects to analyze business applications. In the first project we use ARCHSEC to identify security flaws in business process diagrams. In the second project, ARCHSEC is integrated into an audit environment for software security certification. ARCHSEC is used to identify security flaws and to visualize software systems to improve the effectiveness and efficiency of the certification process.
Stolen account credentials: an empirical comparison of online dissemination on different platforms

Account hijacking, i.e. illegitimately accessing someone else's personal online account, is on the rise and affects not only financial accounts, but the full spectrum of online accounts. To gain more insight in the illicit act of online dissemination of stolen account credentials, we systematically examined how such credentials were offered on three different types of online platforms where stolen credentials were disseminated and how offers varied by platform. We used web scrapes of these platforms for our comparative analyses. Our results demonstrate variation by platform in the type of information on accounts and account holders offered, the average asking price for credentials, and rules and services following a transaction. We conclude with policy implications and suggestions for future research based on the criminal event perspective.
Clinic Management System: Business Process Re-engineering based on User Experience (UX)

Abstract Clinic management systems has function to arrange and organize the inherent process in the health facility to be harmonized with the patient’s demand. The purpose of this paper is to analyse the common theme to be used in user interface of the application through identification of user specification based on the context. The user experience has been utilized to consider the critical aspect of successes of implementation related to human factor of mental model, which are utility, ease of use and efficiency. The contribution of this paper is twofold, firstly, the design process should be done through tremendous redesign of primary business process to obtain excessive improvement in the capacity, capability and quality of the service. Secondly, the information presentation should be provided in alternative way involving the effort to have more platform and modes to increase accessibility and availability.
The Development of E-Module English Specific Purpose based on Computer Application for Vocational High School Students

Abstract The development of E-Module English Specific Purpose based on computer application is carried out in the development stage based on the Four-D Model. There are define, design, develop and disseminate. The E-module application developed aims to be accessible to teachers and students. It is designed attractively and innovatively because it is equipped with images, learning animations, audio learning, and exercises that can immediately feedback (answers) instantly; this module provides simulations for students about the use of computer applications. The practical test of e-module application is used to determine the practicality of using e-modules computer application by users. This test was carried out by e-module computer application users, namely students and teachers in three selected schools, which consisted of: 75 students and 3 English language teachers. The practicality test results show that the practicality score for the use of e-module computer application is 76.4%.
Taking Risks with Confidence

Risk-based evaluation is a failure analysis tool that can be combined with traditional effectiveness metrics to ensure that the improvements observed are consistent across topics when comparing systems. Here we explore the stability of confidence intervals in inference-based risk measurement, extending previous work to five different commonly used inference testing techniques. Using the Robust04 and TREC Core 2017 NYT corpora, we show that risk inferences using parametric methods appear to disagree with their non-parametric counterparts, warranting further investigation. Additionally, we explore how the number of topics being evaluated affects confidence interval stability, and find that more than 50 topics appear to be required before risk-sensitive comparison results are consistent across different inference testing frameworks.