Reviewerly/exHarmony huggingface dataset:
Automating Big Data Analysis Based on Deep Learning Generation by Automatic Service Composition

Automation of Big Data Analysis (BDA) procedure gives us a great profit in the era of Big Data and Artificial Intelligence. BDA procedure can be efficiently automated by the automatic service composition concept efficiently. Our previous work for Auto-BDA shows a great future prospect in reducing turnaround time for data analysis. Moreover, it requires consideration of the automation with a well-geared combination of the data preparation and the optimal model (deep learning) generation. This paper shows the construction of automating BDA and model generation (here deep learning) together with data preparation and parameter optimization.
Prerequisites and AI challenges for model-based Anti-Money Laundering

Money laundering is a large societal problem. Anti-money laundering is arguably ineffective and knows many challenges. In this position paper we highlight prerequisites for comparable model-based anti-money laundering, indicate whether these are met, and make recommendations on how to further this field in both a fundamental as well as an experimental manner.
Cyber Threat Intelligence for Improving Cybersecurity and Risk Management in Critical Infrastructure

Cyber-attack is one of the significant threats affecting to any organisation specifically to the Critical Infrastructure (CI) organisation. These attacks are nowadays more sophisticated, multi-vectored and less predictable, which make the Cyber Security Risk Management (CSRM) task more challenging. Critical Infrastructure needs a new line of security defence to control these threats and minimise risks. Cyber Threat Intelligence (CTI) provides evidence-based information about the threats aiming to prevent threats. There are existing works and industry practice that emphasise the necessity of CTI and provides methods for threat intelligence and sharing. However, despite these significant efforts, there is a lack of focus on how CTI information can support the CSRM activities so that the organisation can undertake appropriate controls to mitigate the risk proactively. This paper aims to fill this gap by integrating CTI for improving cybersecurity risks management practice specifically focusing on the critical infrastructure. In particular, the proposed approach contributes beyond state of the art practice by incorporating CTI information for the risk management activities. This helps the organisation to provide adequate and appropriate controls from strategic, tactical and operational perspectives. We have integrated concepts relating to CTI and CSRM so that threat actor's profile, attack detailed can support calculating the risk. We consider smart grid system as a Critical Infrastructure to demonstrate the applicability of the work. The result shows that cyber risks in critical infrastructures can be minimised if CTI information is gathered and used as part of CSRM activities. CTI not only supports understanding of threat for accurate risk estimation but also evaluates the effectiveness of existing controls and recommend necessity controls to improve overall cybersecurity. Also, the result shows that our approach provides early warning about issues that need immediate attention.
Learning from Heterogeneous Networks: Methods and Applications

Complex systems in different disciplines are usually modeled as heterogeneous networks. Different from homogeneous networks or attributed networks, heterogeneous networks are associated with complexity in heterogeneous structure or heterogeneous content or both. The abundant information in heterogeneous networks provide opportunities yet pose challenges for researchers and practitioners to develop customized machine learning solutions for solving different problems in complex systems. We are motivated to do significant work for learning from heterogeneous networks. In this paper, we first introduce the motivation and background of this research. Later, we present our current work which include a series of proposed methods and applications. These methods will be introduced in the perspectives of personalization in web-based systems and heterogeneous network embedding. In the end, we raise several research directions as future agenda.
Wireless Capsule Endoscopic Image Enhancement Method Based on Histogram Correction and Unsharp Masking in Wavelet Domain

Due to the limited illumination and complex environment in the gastrointestinal (GI) tract, endoscopic images are usually not clear enough to be interpreted by doctors or used for further processing. In order to solve this problem, a method based on histogram correction and unsharp masking in the wavelet domain is proposed in this paper, to improve the contrast and reveal details of endoscopic images. The HSI color space is adopted for color naturalness, and a logarithm based histogram correction method (LHCM) is proposed to improve the contrast and avoid aritifacts. Details are further enhanced by unsharp masking. Experimental results show that performance of the proposed method is better compared with the state-of-art methods. It can be applied to general images and have good effects as well.
Semi‐supervised uncorrelated dictionary learning for colour face recognition

Colour images are increasingly used in the fields of computer vision, pattern recognition and machine learning, since they can provide more identifiable information than greyscale images. Thus, colour face recognition has attracted accumulating attention. Its key problem is how to remove the similarity between colour component images and take full advantage of colour difference information. Decision‐level similarity reduction between colour component images directly affects the recognition effect, but it has been found in no work. In this study, the authors propose a novel colour face recognition approach named semi‐supervised uncorrelated dictionary learning (SUDL), which realises decision‐level similarity reduction and fusion of all colour components in face images. SUDL employs the labelled and unlabelled colour face image samples into structured dictionary learning to achieve three uncorrelated discriminating dictionaries corresponding to three colour components of face images, and then uses these dictionaries and the sparse coding technique to make a classification decision. Experimental results in multiple public colour face image databases demonstrate that the dictionary decorrelation, structured dictionary learning and unlabelled samples used in the proposed approach are effective and reasonable, and the proposed approach outperforms several representative colour face recognition methods in recognition rates, despite of its poor time performance.
Quantitative Evaluation to Investigate Further Tendency of Obtaining Potential Appropriate Respondents to Questions at Q&amp;A Sites

In order to eliminate mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for statements have experimentally been obtained. Factor scores have been then estimated by using the feature values of statements. So far the possibility of detecting respondents who can appropriately answer a newly posted question has been established for several categories e.g. Auction, PC, etc. It has been shown that the distance and the number of appearance could lead to selecting users who are able to appropriately answer a question. In order to inspect these tendencies, this paper tries to find the possibility of detecting appropriate respondents by adding respective eight question statements for each of the nine categories. As a result of analysis, the tendencies that particular several users are apt to appear in each category has been verified. Meanwhile, several different outstanding tendencies have been also observed.
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. We train COSNet with pairs of video frames, which naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. We propose a unified and end-to-end trainable framework where different co-attention variants can be derived for mining the rich context within videos. Our extensive experiments over three large benchmarks manifest that COSNet outperforms the current alternatives by a large margin.
Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods

Convolutional neural networks have been achieving the best possible accuracies in many visual pattern classification problems. However, due to the model capacity required to capture such representations, they are often oversensitive to overfitting and therefore require proper regularization to generalize well. In this paper, we present a combination of regularization techniques which work together to get better performance, we built plain CNNs, and then we used data augmentation, dropout and customized early stopping function, we tested and evaluated these techniques by applying models on five famous datasets, MNIST, CIFAR10, CIFAR100, SVHN, STL10, and we achieved three state-of-the-art-of (MNIST, SVHN, STL10) and very high-Accuracy on the other two datasets.
Network Device Monitoring System based on Geographic Information System dan Simple Network Management Protocol

Network infrastructure monitoring is an important part of an institute to maintain the stability of computer network devices. One of the functions of computer network monitoring is to find out the data traffic generated in network application. Simple Network Management Protocol is one of protocols for monitoring the data traffic in network device. However, network device administrators still have problems when they want to monitor their network infrastructure, such as device location and data traffic information of network device that is only temporarily stored in the monitoring system, and physical location of network device is not contained in the monitoring system. In order to make it easier for them how to monitor network devices, the Researcher intends to combine Geographic Information Systems (GIS) and SNMP into a web-based monitoring application. Geographic Information System application can display the physical location of network devices, whilethe SNMP application using for monitoring the data traffic in network device,finding out the data traffic that can be generated in real time, and displaying data traffic of network device that has been monitored in to graphical form based on the time and network device used.
Building a Lightweight Container-Based Experimental Platform for HPC Education

HPC (High Performance Computing) is of great significance due to its excellent performance in computing acceleration. However, unlike other techniques in computer science, learning HPC requires advanced computing resources such as large-scale clusters which directly increase the cost of study for students. To help students to learn HPC programming easily, we design and develop a lightweight container-based experimental platform to provide students with easily accessible and customizable HPC practice environments. In our platform, we integrate multiple practical functional modules for students, teachers, and administrators respectively. It is convenient for a user to access high-performance computing resources via a web portal, use highly customizable basic environments and have nice graphical hands-on interactive HPC learning experiences from our platform.
Why Do Not We Use Password Managers? A Study on the Intention to Use Password Managers

Why don't individuals follow the best information security practices? We address an aspect of this question by focusing on one of the most common authentication methods – passwords. To promote better password habits, security experts consistently recommend the use of password managers as a best practice, but recent research shows their usage rate is low. Therefore, understanding the factors that influence the use of a password manager is important. We contribute to this cause by drawing on information security and technology adoption literature. Survey results from 120 participants with varying numbers of internet accounts yield some counterintuitive findings. As proposed, perceived severity and perceived vulnerability of password loss strongly influenced intent to use password managers. However, perceived ease of use diminished the intent to use password managers, and trust is only partially supported. Our results indicate that 'security' aspects of password managers are more important than 'usability' aspects. The implications of these findings for password management are discussed.
COMPETENCIES OF LIBRARY PROFESSIONALS IN THE DIGITAL ECONOMY EPOCH

The issues of library specialists’ competencies in the aspect of Federal State Educational Standards (GEF) are examined, the problems of determining competencies and their composition taking into account the requirements of the digital economy and the professional standard are identified. It is stated, that at present only lists of competencies of the Federal State Educational Standard are to be considered as the basic model of library specialists’ competencies. It is proposed to implement a number of special measures for to form the professional community’ consolidated position concerning the competences and their determination due to the type of the library and workers’ professional status.
Discussion on the Construction and Management of Smart Classrooms in Higher Vocational Colleges

The trend of education informatization continues to spread, and the application of wisdom education has broadened. The establishment of smart classrooms is mainly dependent on information technology. It is a new type of intelligent teaching method, which creates a teaching and learning atmosphere for teachers and students. Teachers can use novel information technology and a variety of teaching resources to teach students knowledge, the use of teaching models should be based on the teaching methods and activities standards. In order to speed up the construction of smart teachers and serve teachers and students more quickly, this paper deeply analyzes the work focus of construction and management of smart classrooms in colleges and universities.
Learning bag of spatio-temporal features for human interaction recognition

Bag of Visual Words Model (BoVW) has achieved impressive performance on human activity recognition. However, it is extremely difficult to capture high-level semantic meanings behind video features with this method as the spatiotemporal distribution of visual words is ignored, preventing localization of the interactions within a video. In this paper, we propose a supervised learning framework that automatically recognizes high-level human interaction based on a bag of spatiotemporal visual features. At first, a representative baseline keyframe that captures the major body parts of the interacting persons is selected and the bounding boxes containing persons are extracted to parse the poses of all persons in the interaction. Based on this keyframe, features are detected by combining edge features and Maximally Stable Extremal Regions (MSER) features for each interacting person and backward-forward tracked over the entire video sequence. Based on feature tracks, 3D XYT spatiotemporal volumes are generated for each interacting target. Then, the K-means algorithm is used to build a codebook of visual features to represent a given interaction. The interaction is then represented by the sum of the frequency occurrence of visual words between persons. Extensive experimental evaluations on the UT-interaction dataset demonstrate the strength of our method to recognize the ongoing interactions from videos with a simple implementation.
PUBLISHING STRATEGIES OF THE ХХI CENT.: UNIVERSAL, SPECIFIC, COMPOUND

The conception of the term «publishing strategy» is given, its main elements are determined. Universal, specific and compound publishing strategies of existing publishing enterprises are considered. Characteristic features and advantages of each of them are defined. Specificity of the regional publishing space is revealed, possible options for the development of future publishing strategies are reflected.
Design of Immersive Walking Interaction Using Deep Learning for Virtual Reality Experience Environment of Visually Impaired People

In this study, a novel virtual reality (VR) experience environment is proposed for enabling walking adaptation of visually impaired people.The core of proposed VR environment is based on immersive walking interactions and deep learning based braille blocks recognition.To provide a realistic walking experience from the perspective of visually impaired people, a tracker-based walking process is designed for determining the walking state by detecting marching in place, and a controller-based VR white cane is developed that serves as the walking assistance tool for visually impaired people.Additionally, a learning model is developed for conducting comprehensive decision-making by recognizing and responding to braille blocks situated on roads that are followed during the course of directions provided by the VR white cane.Based on the same, a VR application comprising an outdoor urban environment is designed for analyzing the VR walking environment experience.An experimental survey and performance analysis were also conducted for the participants.Obtained results corroborate that the proposed VR walking environment provides a presence of high-level walking experience from the perspective of visually impaired people.Furthermore, the results verify that the proposed learning algorithm and process can recognize braille blocks situated on sidewalks and roadways with high accuracy.
Discrete factor analysis using a dependent Poisson model

Abstract In this paper, we present a method for factor analysis of discrete data. This is accomplished by fitting a dependent Poisson model with a factor structure. To be able to analyze ordinal data, we also consider a truncated Poisson distribution. We try to find the model with the lowest AIC by employing a forward selection procedure. The probability to find the correct model is investigated in a simulation study. Moreover, we heuristically derive the corresponding asymptotic probabilities. An empirical study is also included.
Comparative Study of Semantic and Keyword Based Search Engines

Day by day, the data on the web becomes very huge which makes it difficult to find relevant information.Search engines are one of the successful factors that can retrieve information from the Web.The process of seeking information by search engines helps users find information on the internet, however it is not an easy task to find the exact information from this massive data available on the Web.Semantic Web technology has an ability to focus on metadata rather than syntax, which made the semantic search engines to search for the meaning of keywords instead of the keyword syntax.Consequently, an effective role of performance in conventional search engines can be achieved by rising the accuracy of information returned by a search query.In this paper, a survey for syntactic-based search engines and semantic-based search engines are studied, a comprehensive comparison between the two is presented, finally, their technologies are compared and discussed.
Using Age Information as a Soft Biometric Trait for Face Image Analysis

Soft biometrics refers to a group of traits that can provide some information about an individual but are inadequate for identification or recognition purposes. Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related face image analysis (including age estimation, age synthesis and age-invariant face recognition) still unsolved. Specifically, age estimation is concerned with inferring the specific age from human face images. Age synthesis is concerned with the rendering of face images with natural ageing or rejuvenating effects. Age-invariant face recognition involves the recognition of the identity of subjects correctly regardless of their age. Recently, thanks to the rapid development of machine learning, especially deep learning, age-related face image analysis has gained much more attention from the research community than ever before. Deep learning based models that deal with age-related face image analysis have also significantly boosted performance compared to models that only use traditional machine learning methods, such as decision trees or boost algorithms. In this chapter, we first introduce the concepts and theory behind the three main areas of age-related face image analysis and how they can be used in practical biometric applications. Then, we analyse the difficulties involved in these applications and summarise the recent progress by reviewing the state-of-the-art methods involving deep learning. Finally, we discuss the future research trends and the issues that are not addressed by existing works. We also discuss the relationship among these three areas and show how solutions within one area can help to tackle issues in the others.
Bridging Server and Cooling: Toward Effective Energy Management in Data Centers

This paper studies the energy management problem in data centers. As the most energy-consuming sub-systems, server and cooling have been considered as the key of energy saving for data center. However, due to the fact that servers are not directly connected to cooling devices, it is not trivial to optimize them jointly. In this paper, we formulate the joint server and cooling energy management problem and analyze its challenges, and design a Learning-based Energy management Scheme (LEES) to solve the joint energy management problem. Firstly, machine learning methods are investigated in the cooling power modeling and cabinet temperature modeling. Based on the models, a simple yet efficient energy management scheme is proposed to find an optimized configuration of server load distribution, server status and air supply temperature setting for the data center to reduce the total energy consumption. Extensive simulation results demonstrate that our method is able to reduce the total energy consumption of data centers significantly.
An Overview of Blockchain-Based Swarm Robotics System

As a disruptive technology, swarm robotics is developing everyday, and attracts great attention. However, swarm robotics face some challenges which hinder swarm robots from broader application. Blockchain technology can provide a basic credible information environment of swarm robots, expand its application. This paper discusses how blockchain technology can provide benefits to swarm robotics, give a basic structure of swarm robots-oriented blockchain conceptual model. Finally, limitations and possible future problems that arise from the combination of these two technologies are described.