Reviewerly/exHarmony huggingface dataset:
Cyber Security

New attacks and methods seen today indicate an emerging trend and dependency on reverse-engineered technology that was used in the past by espionage and intelligence agencies and their tactics as well as use of modern technology to obtain information and data that is turned into usable intelligence. One of the many disturbing consequences of this is that we are faced with attackers that are versed in stealth, deception, planting false information, and increased training in newer attack technologies that classical tools can no longer reliably find. In addition, advanced attack and deception skills now use OSINT (open source intelligence) data collection tactics that have moved entire attack chains into the espionage and surveillance realm.
The Application of Computer Animation Technology in Information Education in Colleges and Universities

Under the guidance of the action policy of “Internet + education”, the informationization of education and teaching is exerting a profound influence on the educational and teaching concepts of educators. Educational and teaching informatization plays an important role in deepening educational and teaching reform and improving educational and teaching quality. The purpose of this paper is to study the application of computer animation technology in the information education of colleges and universities (CAU), to analyze how to effectively promote the information. Firstly, this paper discusses the characteristics and application of computer and information technology in the teaching of volleyball in CAU. Then, the paper analyzes the importance of computer and information-based teaching animation technology in a certain college by means of literature review, questionnaire survey, interviews with experts and peers. Finally through literature method, experimental method and so on volleyball sports teaching of the vocational colleges animation of the importance of informationization teaching resources construction and its application of related problems are discussed, and the main animation micro teaching space construction, the informatization construction of the network animation teaching space, mixed course teaching in CAU animation teaching the importance of informationization and application problems are discussed.
Experimentation and Result Discussion

This chapter discusses entire approach of proposed framework CSRC, the emphasis is placed on the support vector machine and K-nearest neighbour’s classification approach which depicts how this technique is used for improving classification accuracy. The entire approach with the discussion of the different algorithms used followed by testing the entire system based on parameters such as false positive (FP), false negative (FN), true positive (TP), true negative (TN), recall and precision which are used to evaluate the performance of the proposed work are discussed.
Fuzzy Models: Fundamentals and Design

In this chapter, we introduce a concept of fuzzy models and elaborate on their architectures. We offer a unified structural view at the diversity of the topologies of fuzzy models and outline a general methodological framework within which all the design processes are carried out. The two main directions of the evaluation (numeric and granular) of the performance of the models are outlined. Main classes of representative fuzzy models are outlined and their key characteristics are identified and stressed.
Application of MATLAB in Solving Linear Equations

Abstract In order to cultivate the interest of science and engineering students in learning the direct and iterative methods of solving linear equations, this paper takes the OBE education concept as the guiding ideology and summarizes and discusses how to solve linear equations with MATLAB relevant instructions.
Thinning and Skeletonizing

This chapter provides an introduction to thinning/skeletonizing concepts and to familiarize the reader with the various techniques available and their limitations and capabilities. A general approach in analyzing images is to transform the given image to another where the information represented in the transformed image is more easily understood. For example, investigations of the shape of objects in a binary image often use thinning algorithms. Reduction of an object to a minimal set of pixels representing an invariant of the object’s geometrical shape is called thinning, and we are interested in the minimal set of pixels called the skeleton of the object. The chapter gives the mathematical definitions that lay the foundation for precisely defining a skeleton of an image, including Air Force image algebra as a mode of expressing the image-processing algorithms. It explores three thinning algorithms and their advantages and disadvantages. The chapter briefly reviews other skeletonizing algorithms from the literature.
Developing Requirements for the New Encryption Mechanisms in the Estonian eID Infrastructure

After the Estonian ID-card crisis in 2017, it became apparent that the document encryption-decryption functionality built on top of the ID-card infrastructure requires major rethinking. This paper describes the starting points of this process and reviews some of the requirements that have been identified in the first phase. We study the main usage scenarios and process flows, and discuss the need to distinguish transport encryption from storage encryption.
Academic Library Supporting Research

The purpose of this chapter is to examine how the provision of research support services by the Law Library at Universidade Federal do Paraná (UFPR) in Brazil contributes to achieve the university research goals. The chapter develops a case study taking a qualitative, exploratory, and descriptive approach. The UFPR Law Library provides research support services such as classes on research support, bibliographic research support, orientation on research tools. Those research support services are not part of a formal and strategic program. It is recognized that the existence of a data repository, the UFPR Scientific Database, is an opportunity to provide scientific research data management support services at UFPR libraries. The chapter concludes that the existing research support services have an impact on research at UFPR Law School. However, there is an opportunity to create other services that will meet the user's expectations, considering the new research trends at the university.
Fast Relative Orientation Algorithm of Tilt Photogrammetry

A method of relative orientation of tilted aerial photograph image using basic matrix is proposed. First, the basic matrix between stereo images is constructed and each element is solved linearly; then, the relative orientation element is directly derived from the basic matrix; finally, the relative orientation element is regarded as the initial value to be solved strictly and iteratively, so as to obtain the accurate relative square element value. The experimental results show that this method is suitable for both conventional aerial photography and oblique photography, and can meet the application requirements of stereo model reconstruction.
Sparse Multi-task Least-Squares Support Vector Machine

Multi-task learning (MTL) can improve the learning performance by sharing information among different tasks. Multi-task least-squares support vector machine (MTLS-SVM) represents one of most widely used MTL methods to solve convex linear problems by extending the conventional least-squares support vector machine (LS-SVM). Nevertheless, MTLS-SVM suffers from an inherent shortage for being sensitive to noise features. In this paper, we propose a novel algorithm to learn a sparse feature subset for classification based on MTLS-SVM. In particular, a 0–1 multi-task feature vector is introduced into MTLS-SVM to control whether a feature is selected or not. The resultant algorithm namely sparse MTLS-SVM (SMTLS-SVM) iteratively selects the most informative feature through an efficient alternating minimization method. Comprehensive experimental results on both synthetic and real-world datasets show that SMTLS-SVM can obtain better or competitive performance compared with the original MTLS-SVM and other existing MTL methods in terms of classification performance.
College Physical Education Based on Multimedia Network Teaching Platform

With the rapid development of China’s Internet technology, computer multimedia technology has been loved by all sectors of society. Especially in college physical education, computer multimedia technology and network teaching are widely used. Multimedia technology has entered the classroom in an all-round way. It integrates images, sounds, and videos, making the traditional sports teaching model more interesting, three-dimensional, and diversified, which has caused unexpected changes in the effects of sports classroom teaching. It effectively overcomes the phenomenon of teacher demonstration, inadequate skills, incomplete understanding of students, and inaccurate imitation; it can also make teachers’ explanation and analysis more in-depth and accurate through audio-visual combined teaching methods, which greatly activates the classroom atmosphere and enhance Students’ interest in learning. Based on this, this article first elaborated the role of computer multimedia technology in physical education. Second, analyze the current problems in the application of multimedia technology and network teaching in physical education. Finally, according to the existing problems, the corresponding solutions are proposed in a targeted manner. Its purpose is to provide reference opinions for physical education in colleges and universities, so as to continuously improve the quality of physical education in colleges and universities.
Design of Echo State Network with Coordinate Descent Method and $$l_1$$ Regularization

The echo state networks (ESNs) have been applied in many applications. For an ESN, its training error and network size are closely related with output weight matrix. In this paper, the coordinate descent method based ESN (CD-ESN for short) is designed to deal with the relationship between training error and network size. In CD-ESN, the $${{l_1}}$$ regularization is used to penalty the non-important values of output weight into 0. Moreover, the coordinate descent method is used to update the output weights of ESN. Experimental results imply that the proposed CD-ESN has better prediction accuracy and more sparse network topology than original ESN.
A Concurrent Weighted Communication Scheme for Blockchain Transaction

Blockchain technology is a new distributed infrastructure and computing mode in recent years, and it provides a distributed data management solution for storage, authentication, transfer and exchange. In order to shorten the confirmation time of block data transaction and improve the processing capacity of blockchain, it is necessary to solve the problem of data communication performance optimization in blockchain transaction validation. This paper proposes a concurrent weighted communication tree algorithm for blockchain transaction (called WFT). This algorithm comprehensively considers the communication weight and the node communication link number. Several small weight nodes with powerful transmitting capacity are selected to locate in the top or upper part of the communication tree to improve the concurrency and efficiency. The proposed algorithm can provide the routing paths for blockchain transaction, and can improve the communication efficiency on blockchain.
Improving the Integration of Distributed Applications

The interaction of distributed applications raises an integration problem that needs to be solved. Current integration technologies, such as Web Services and RESTful APIs, solve the interoperability problem but usually entail more coupling than required by the interacting applications. This is caused by sharing data schemas between applications, even if not all features of those schemas are actually exercised. The fundamental problem of application integration is therefore how to provide at most the minimum coupling possible while ensuring at least the minimum interoperability requirements. This article proposes compliance and conformance as the concepts to achieve this goal, by sharing only the subset of the features of the data schema that are actually used.
Real-World Object Recognition for Robot Based on Lightweight Deep Multimodal Distance Learning

Real-world object recognition is an important and difficult robot vision problem. In this paper, a real-world multi-angle and multi-attitude deformable object recognition method for robot system, named RCOR, is proposed based on lightweight deep multimodal distance learning (DMDL). (1) Deep multimodal convolutional neural network (DMCNN) is proposed to improve the transformation abilities of CNNs and enhance feature maps' resolutions. (2) Deep distance metric learning (DDML) is presented to relieve the problem of lacking adequate labeled data and efficiently reduce redundancy. (3) To apply RCOR into embedded vision applications in real-world environment, a light weight DCNN, Mobile-XB, is proposed. Extensive experiments demonstrate that the proposed approach significantly outperforms state-of-the-arts. And it performs well on computationally limited platforms.
Teaching Logic, from a Conceptual Viewpoint

Logic is not only of foundational importance in mathematics, it is also playing a big role in software engineering and formal verification. Its different roles influence its teaching, which has to take into consideration the recent developments in category theory and proof theory. We show that teaching set theory from a categorical viewpoint, in contrast with Zermelo-Fraenkel axioms, helps develop proper skills that are essential in mathematics and software engineering. The use of a proof assistant provides students with another perspective on both subjects: basic category theory and proof theory.
Methods of Data Encryption for Use in Safe Space Information

The security in the transfer of Raster-type image data using different open computer networks has been extended in recent years, in order to avoid alterations of the information it is necessary to use different encryption methods of the Pixelated data. In order to carry out this purpose, it is required at least to fulfill two objectives in the encryption of Images; the first is to eliminate the correlation between neighboring pixels that have a high similarity and the second is to encrypt the result obtained. The encryption methods of conventional text formats cannot be applied to Raster data, that is why different methods of encryption have been developed, where transformation methods and Chaos theory are used which, due to their high mathematical complexity, allow us to obtain high security encryption. According to the above, the spatial databases require security mechanisms, access control and encryption to guarantee the integrity of the information, therefore there are several types of procedures and techniques oriented to encryption and the security of the databases. Data; some of them will be presented in this article in order to open a new specific field of work in this important area of knowledge.
Ranking with hamiltonian dynamics

In data science, a ranking or linear ordering problem is to place items into a linear order based on comparison data. In this article, we consider a Hamiltonian system, in which particles, representing the items to be ordered, exert forces on each other as determined by the comparison data. An ordering of the items can be derived from the relative positions of the particles in any state of the system. The Hamiltonian is designed so that states with low potential energy yield orderings that agree strongly with the comparison data. Although the dynamics are related to the Toda lattice, no usable ranking information seems to be available from that variation of the Hamiltonian. Instead, a Toda-like Hamiltonian plus a confinement potential yields better results. Several algorithms based on Hamiltonian trajectories, minimization of a ranking potential, and a Hamiltonian Markov chain are compared to the widely used RankBoost algorithm. They all perform relatively well on synthetic test data and on problems from a library of test cases, but none is clearly the best in all circumstances. The trajectory-based methods and Markov chain show interesting dynamics. Furthermore, since these methods attach particle positions to the items as well as an ordering, the distances between the particles indicate how rankable the items are.
Poster Abstract: Model Average-based Distributed Training for Sparse Deep Neural Networks

Distributed training of large-scale deep neural net-works(DNNs) is a challenging work for it's time costing and complicated communication. Existing works have achieved scalable performance on GPU clusters for dense DNNs in the computer vision area. However, little progress has been made on the distributed training of sparse DNNs which is commonly used in the area of natural language processing (NLP). In this poster, we introduce SA-HMA, a sparsity-aware hybrid training method for sparse deep models. SA-HMA combines Model Average (MA) and synchronous optimization methods together, expecting to reduce the communication cost for spare model training. The experimental results show that SA-HMA achieves 1.33× speedup over the state-of-the-art work.
Securing Trustworthy Evidences for Robust Forensic Cloud in Spite of Multi-stakeholder Collusion Problem

Many organizations are widely using cloud for their day to day business activities. But several attackers and malicious users are targeting cloud for their personal benefits. It is very important to collect and preserve admissible evidences of various activities happened in cloud securely in spite of multi-stakeholder collusion problem. Logs are one of the utmost vital elements to trace the malicious activities happened in cloud computing environment. Thus, forensic investigations involving logs face a grave challenge of making sure that the logs being investigated are consistent and not tampered with. A lot of research has been performed in this field; however with the advent of blockchain and Interplanetary File System (IPFS) new innovative approaches can be applied to secure trustworthy evidences in cloud. In this paper, we used blockchain and IPFS to build a system which stores the logs of cloud users’ activities and assurances the trustworthiness and recovery of such logs to aid in forensic investigation. The integrity of the trustworthy log evidences is assured with the help of blockchain. Using versioning nature of IPFS our system can track the modification of log files. In earlier work, the systems could assure whether a log has been altered with or not, but none provided a mechanism to recover metadata of tampered logs to their original state. With the help of IPFS our proposed technique extend the existing work by providing the original logs for interfered logs.
Local and Deep Features for Robust Visual Indoor Place Recognition

This study focuses on the area of visual indoor place recognition (e.g., in an office setting, automatically recognizing different places, such as offices, corridor, wash room, etc.). The potential applications include robot navigation, augmented reality, and image retrieval. However, the task is extremely demanding because of the variations in appearance in such dynamic setups (e.g., view-point, occlusion, illumination, scale, etc.). Recently, Convolutional Neural Network (CNN) has emerged as a powerful learning mechanism, able to learn deep higher-level features when provided with a comparatively big quantity of labeled training data. Here, we exploit the generic nature of CNN features for robust visual place recognition in the challenging COLD dataset. So, we employ the pre-trained CNNs (on the related tasks of object and scene classification) for deep feature extraction in the COLD images. We demonstrate that these off-the-shelf features, when combined with a simple linear SVM classifier, outperform their bag-of-features counterpart. Moreover, a simple combination scheme, combining the local bag-of-features and higher-level deep CNN features, produce outstanding results on the COLD dataset.
The Origin and History of the Extinct Contact-Induced Language, Matagi

This chapter attempts to find how the Matagi hunters of Tohoku region in Japan contacted the Ainu hunters and how the Matagi language absorbed the Ainu vocabulary. The Ainu hunters contacted the Japanese hunters there on a routine basis because the Ainu people settled down in Tohoku region before the Old Japanese period and have lived there since. The Japanese hunters borrowed some Ainu words necessary for living, hunting, and rituals in the mountains after the Middle Japanese period. The term Matagi, however, might have been employed long before the Middle Japanese period. During the Middle Japanese period the Yamato state came to dominate the entire Tohoku area. Although some Ainu adjusted to the Japanese living environment there, the rest probably escaped up toward Hokkaido. Hence, the Ainu people became less and less in Tohoku region. Eventually the Ainu words remained mainly in the Matagi language, although they are never spoken again.
Electronic System for Protection of People Victims of Domestic Violence in Areas of Interior and Exterior

This paper presents the results of a research project in which a prototype monitoring and control architecture for victims of gender violence, using mobile technology to determine the location of their potential aggressors previously identified by the penal system develops. Hardware, software and configuration needed to implement a viable technological solution is presented, taking into account the legal constraints, to allow victims of abuse or domestic violence to determine the location of his assailant ensuring that restraining orders meet in public places not covered by the prison system.
Becoming Trusted Research Partners Through InfoExpress at The University of Toronto

This case study discusses the importance of building initial trust in the relationship between researcher and academic library. Primary coverage serves the experience of two small humanities-based colleges serving approximately 125 faculty members within a larger university campus by providing the personal document delivery service of InfoExpress. The trust built through this initial research support service creates avenues for further support from the library and the wider university library system. As every relationship has challenges, the ones occurring here are opportunities to improve the relationship in favour of the researcher and library. If the researcher is unaware of what support the library provides, establishing a personal relationship will immediately provide productive research time and create an opportunity for future support through additional personalized services. The researcher, their research, and their library benefit by this trusted partnership.