Reviewerly/exHarmony huggingface dataset:
Cyber Insurance as a Way of Cyber Risks Management

A review of the cyber insurance domain has been carried out with a description of classical terms from the insurance industry. Have been considered two the most comprehensive today definitions of cyber risk in authors’ opinion. A diagram of processes for cyber risk management using insurance has been presented, and the place of cyber-risk among other company’s risks has been demonstrated, i. e. the context of cyber risk among the risks of any commercial organization has been shown. A typical cyber insurance process has been described, and a scheme of cyber insurance processes has been developed. A brief description of problem areas and controversial issues in cyber insurance, with which cyber-risk insurance practices may face, has been presented, as well as a table showing at which stage of cyber-insurance the specific problems may arise. Has been provided the basic economic utility function, which formalizes decision making for agents with a different attitude to risk. Standards in cyber security, and various software products that can be used as a tool for assessing the security level of an enterprise’s IT infrastructure have been presented, and it has been demonstrated how these products can help in cyber risk assessment. Different methods used at each stage of cyber insurance have been shown.
Entropy of tournament digraphs

The Rényi α-entropy Hα of complete antisymmetric directed graphs (i.e., tournaments) is explored. We optimize Hα when α=2 and 3, and find that as α increases Hα's sensitivity to what we refer to as ‘regularity’ increases as well. A regular tournament on n vertices is one with each vertex having out-degree n−12, but there is a lot of diversity in terms of structure among the regular tournaments; for example, a regular tournament may be such that each vertex's out-set induces a regular tournament (a doubly regular tournament) or a transitive tournament (a rotational tournament). As α increases, on the set of regular tournaments, Hα has maximum value on doubly regular tournaments and minimum value on rotational tournaments. The more ‘regular’, the higher the entropy. We show, however, that among all tournaments on a fixed number of vertices H2 and H3 are maximized by any regular tournament on that number of vertices. We also provide a calculation that is equivalent to the von Neumann entropy, but may be applied to any directed or undirected graph and shows that the von Neumann entropy is a measure of how quickly a random walk on the graph or directed graph settles.
Can You Hear the Player Experienceƒ A Pipeline for Automated Sentiment Analysis of Player Speech

This paper presents the ongoing work on a technological pipeline to automate the recording and analysis of player speech data in the evaluation of player experience. The pipeline generates a heatmap that visualizes where the players in the virtual environment have made positive, negative, and neutral statements. Moreover, the heatmap provides spatiotemporal information of in-game events, as well as interactive features like filters and modifiers. We describe how the pipeline is set up for the Unity game engine, how we implemented the sentiment classification module as well as the visualization of the tracked data. Concluding, we define goals for further development.
Possibilities of Wolfram-Technologies in Training in Elements of Probability Theory of Students of an Economic Bachelor Degree

The article is devoted to the problem of the formation of future social lawyers in colleges of a professional information culture. The article contains the results of empirical studies aimed at identifying the relationship between the level of formation of future lawyers of the middle tier of the professional information culture and the degree of expression of their negative communicative attitudes. The study was carried out on the basis of Moscow colleges, implementing programs of secondary vocational education in the direction of training 40.02.01 “The Law and Organization of Social Security” (a program for in-depth training of middle-level specialists of the APSP). In the empirical study was attended by 225 college students enrolled in graduate courses relevant training. In the article, the authors reveal the essence and content of the professional information culture of future lawyers as a psychological and pedagogical phenomenon, determines ways of measuring the level of its formation, conducts an empirical study, as a result of which it comes to a conclusion about the importance and significance of correcting communicative attitudes spheres.
Portability of Serious Game Software Components

In recent studies, a component-based software engineering framework (RCSAA) has been proposed to accommodate the reuse of game software components across diverse game engines, platforms, and programming languages. This study follows up on this by a more detailed investigation of the portability of a RCSAA-compliant game software component across three principal programming languages: C#, JavaScript (TypeScript), and Java, respectively, and their integration in game engines for these languages. One operational RCSAA-compliant component in C# is taken as the starting point for porting to the other languages. For each port, a detailed analysis of language-specific features is carried out to examine and preserve the equivalence of transcompiled code. Also, implementation patterns of required RSCAA constructs are analysed for each programming language and practical workaround solutions are proposed. This study demonstrates that the software patterns and design solutions used in the RCSAA are easily portable across programming languages based on very different programming paradigms. It thereby establishes the practicability of the RSCAA architecture and the associated integration of RCSAA-compliant game components under real-world conditions.
A Dynamic Word Representation Model Based on Deep Context

The currently used word embedding techniques use fixed vectors to represent words without the concept of context and dynamics. This paper proposes a deep neural network CoDyWor to model the context of words so that words in different contexts have different vector representations of words. First of all, each layer of the model captures contextual information for each word of the input statement from different angles, such as grammatical information and semantic information, et al. Afterwards, different weights are assigned to each layer of the model through a multi-layered attention mechanism. At last, the information of each layer is integrated to form a dynamic word with contextual information to represent the vector. By comparing different models on the public dataset, it is found that the model’s accuracy in the task of logical reasoning has increased by 2.0%, F1 value in the task of named entity recognition has increased by 0.47%, and F1 value in the task of reading comprehension has increased by 2.96%. The experimental results demonstrate that this technology of word representation enhances the effect of the existing word representation.
Multiple Instance Classification in the Image Domain

Multiple instance classification (MIC) is a kind of supervised learning, where data are represented as bags and each bag contains many instances. Training bags are given a label and the system tries to learn how to label unknown bags, without necessarily learning how to label individually each of their instances. In particular, we apply concepts drawn from MIC to the realm of content-based image retrieval, where images are described as bags of visual local descriptors. We introduce several classifiers, according to the different MIC paradigms, and evaluate them experimentally on a real-world dataset, comparing their accuracy and efficiency.
A Method of Interest Degree Mining Based on Behavior Data Analysis

Based on big data, this paper starts from the behavior data of users on social media, and studies and explores the core issues of user modeling under personalized services. Focusing on the goal of user interest modeling, this paper proposes corresponding improvement measures for the existing interest model, which has great difference in interest description among different users and it is difficult to find the user interest change in time. For the above problems, this paper takes user-generated content and user behavior information as the analysis object, and uses natural language processing, knowledge warehouse, data fusion and other methods and techniques to numerically analyze user interest mining based on text mining and multi-source data fusion. We propose a user interest label space mapping method to avoid data sparse problem caused by too many dimensions in interest analysis. At the same time, we propose a method to extract and blend the long-term and short-term interests, and realize the comprehensive evaluation of interests. In the analysis of the big data phase, the user preference social property application preference value law, it is expected to achieve user Internet social media application preference data mining from the perspective of big data.
Metrics which turn tilings into binary perfect codes

In this work, we consider tilings of the Hamming cube and look for metrics which turn the tilings into a perfect code. We consider the family of metrics which are determined by a weight and are compatible with the support of vectors (TS-metrics). We determine which of the tilings with small tiles or high rank can be a perfect code for some TS-metric and we characterize all such metrics. Finally, we show some procedures to obtain new perfect codes (relatively to TS-metrics) out of existing ones.
RDF* Graph Database as Interlingua for the TextWorld Challenge

This paper briefly describes the top-scoring submission to the First TextWorld Problems: A Reinforcement and Language Learning Challenge. To alleviate the partial observability problem, characteristic to the TextWorld games, we split the Agent into two independent components: Observer and Actor, communicating only via the Interlingua of the RDF* graph database. The RDF* graph database serves as the “world model” memory incrementally updated by the Observer via FrameNet informed Natural Language Understanding techniques and is used by the Actor for the efficient exploration and planning of the game Action sequences. We find that the deep-learning approach works best for the Observer component while the Actor policy is better served by backtracking over the set of rules.
Construction and Analysis of Chinese Word-Formation Knowledge Base Based on Modern Chinese Dictionary

The construction and application of vocabulary knowledge base has become a common concern in the field of NLP. On the basis of studying the existing vocabulary knowledge base, this paper presents a set of generalized word-formation knowledge annotation scheme based on the Modern Chinese Dictionary, which includes the information of morpheme category and semantics, word-formation structure model and semantic composition. Then, according to the annotation scheme designed, an annotation system is developed, and a word-formation knowledge base containing 63193 records is constructed based on the Modern Chinese Dictionary. Through the annotation in practice, it shows the convenience and good adaptability of this annotation system. Finally, the statistical analysis of the annotated data is completed, and some valuable results are obtained.
Resources for Improving the Cybersecurity Pipeline

Improving the Pipeline \citeGorka-poster:2017 is a capacity-building NSF funded project to increase the number of Information Assurance and Cyber Defense (IA/CD) professionals. The project developed course materials to allow instruction of college-level cybersecurity concepts to high-school students. The goal was to make the content more accessible, and in turn, to introduce the prospect of a career in security to younger students. Course materials created for this grant are freely available. This paper discusses how to access the materials.
Multilevel Logistic Regression and Neural Network-Genetic Algorithm for Modeling Internet Access

Logistic regression is one of the classical methods for classification. Meanwhile, neural network is the recent method for classification. Both methods are widely used in the supervised learning and competing to be the best methods in many classifications research. This paper aims to study the performance of both methods using data of youth internet access of East Java Province of Indonesia. The first method used is Multilevel Logistic Regression, a hierarchical model which is part of Generalized Linear Mixed Model (GLMM) where the response variable is influenced by fixed and random factors. The second one is Neural Network-Genetic Algorithm in which the weight optimization is performed by selecting the relevant input variables, the optimal number of hidden nodes, and the optimal connection weights. The result shows that Multilevel Logistic Regression produced a slightly better accuracy rate of 0.873 compared to Genetic Neural Network Algorithm with an accuracy rate of 0.871.
Identity-Concealed Authenticated Encryption from Ring Learning with Errors

Authenticated encryption (AE) is very suitable for a resources constrained environment for it needs less computational costs and AE has become one of the important technologies of modern communication security. Identity concealment is one of research focuses in design and analysis of current secure transport protocols (such as TLS1.3 and Google’s QUIC). In this paper, we present a provably secure identity-concealed authenticated encryption in the public-key setting over ideal lattices, referred to as RLWE-ICAE. Our scheme can be regarded as a parallel extension of higncryption scheme proposed by Zhao (CCS 2016), but in the lattice-based setting. RLWE-ICAE can be viewed as a monolithic integration of public-key encryption, key agreement over ideal lattices, identity concealment and digital signature. The security of RLWE-ICAE is directly relied on the Ring Learning with Errors (RLWE) assumption. Two concrete choices of parameters are provided in the end.
Transfer Channel Pruning for Compressing Deep Domain Adaptation Models

Deep unsupervised domain adaptation has recently received increasing attention from researchers. However, existing methods are computationally intensive due to the computational cost of CNN (Convolutional Neural Networks) adopted by most work. There is no effective network compression method for such problem. In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating deep unsupervised domain adaptation (UDA) models. TCP is capable of compressing the deep UDA model by pruning less important channels while simultaneously learning transferable features by reducing the cross-domain distribution divergence. Therefore, it reduces the impact of negative transfer and maintains competitive performance on the target task. To the best of our knowledge, TCP is the first approach that aims at accelerating deep unsupervised domain adaptation models. TCP is validated on two benchmark datasets – Office-31 and ImageCLEF-DA with two common backbone networks – VGG16 and ResNet50. Experimental results demonstrate that TCP achieves comparable or better classification accuracy than other comparison methods while significantly reducing the computational cost. To be more specific, in VGG16, we get even higher accuracy after pruning 26% floating point operations (FLOPs); in ResNet50, we also get higher accuracy on half of the tasks after pruning 12% FLOPs.
Ensemble-based method of answers retrieval for domain specific questions from text-based documentation

Many companies want or prefer to use chatbot systems to provide smart assistants for accompanying human specialists especially newbies with automatic consulting. Implementation of a really useful smart assistant for a specific domain requires a knowledge base for this domain, that often exists only in the form of text documentation and manuals. Lacks of properly built datasets and often expensiveness in resources and time to build one from scratch to apply data-driven methods with high quality. It motivates to seek a solution that can work without such data or require only a small amount of it though having reduced quality. The reformulation of the task into an information retrieval problem where the assistant responds with a piece of documentation instead of generated sentences may make the task easier but doesn’t solve the whole problem. It allows using of metrics-based methods with reduced search quality or data-driven methods which also needs a great amount of data. In this paper, we propose a new ensemble-based data-driven method that tries to learn a scoring function by combining independent functions from a predefined set. The method may substantially improve the quality of the search in comparison with pure metrics-based methods while requiring significantly less data for training than data-driven methods.
Region-based High-resolution Siamese Network for Robust Visual Tracking

Visual tracking is an active and challenging research topic in computer vision, as objects often undergo significant appearance variations caused by occlusion, deformation and background clutter. In recent years, many convolutional neural network based trackers have achieved impressive performance by integrating multi-layer features. However, in order to conduct multi-scale feature fusion, most of these trackers recover high-resolution presentations from low-resolution representations produced by a high-to-low resolution network, which tend to result in inaccurate feature maps or lose of details of the target object. In this paper, we propose an end-to-end region-based high-resolution fully convolutional Siamese network for tracking. In the tracker, we propose to extract the spatial information and semantic information of the target object using a high-resolution network that maintains rich high-resolution representations of the target object through the whole process. Furthermore, a set of position-sensitive score maps are obtained for all regions of the target template, and an adaptive weighting method is proposed to fuse score maps of multiple regions. Experimental results on the OTB50 and OTB100 benchmark datasets demonstrate that our tracker performs better than several state-of-the-art trackers while running in real-time.
On the Privacy Enhancement of In-Transit Health Data Inspection: A Preliminary Study

A new healthcare paradigm has emerged lately placing patients at the center of the healthcare scenario, providing more control over their data. Allowing patients to access data from outside healthcare organizations may suppose a security risk and should be done cautiously. Perimeter security elements, such as firewalls, are placed at organizations boundaries to inspect the in-transit data, deciding whether it should be forwarded or not. However, a disjunctive appears, if health data is kept encrypted when traversing the firewall, it could not provide its functionality properly while revealing it could be a violation of the patient privacy. In this work we propose a new method based on multi party computation to perform a secure fine-grained deep packet inspection of health data and results are presented for different number of conditions and packets sizes when using the FHIR standard. Finally, we conclude that although the performance degradation introduced by the proposed method could be too high for a general use, it could be justified by the sensitivity of the involved data in healthcare scenarios.
Case Based Reasoning for Diagnosing Types of Mental Disorders and Their Treatments

Mental health is still a serious problem in Indonesia. Basic health research data in 2013 recorded the prevalence of severe mental disorders in Indonesia reaching 1.7 per mil. On the other hand, the availability of mental health services and experts in the field is not yet adequate and not evenly distributed. Therefore, developing a system to help diagnosing the types of mental disorders and their treatment can be an alternative to overcome these inequalities. Case-Based Reasoning is one of the reasoning methods in expert systems. On Case-Based Reasoning, a base case is required containing cases with sotions that have been achieved. To find a solution to a given new problem, the system will look for cases on the case base that have the highest level of similarity. In this research, a case-based reasoning system has been developed for dagnosing the types of schizophrenic disorders and mood disorders along with their treatment. The case base was constructed based on medical records of mental disorder patients, which were obtained from the collaboration with a Mental Hospital in Yogyakarta. In addition, it also considers Guidelines for Classification and Diagnosis of Mental Disorders in Indonesia III, which contains 10 categories of mental disorders.
Multi-classification of Theses to Disciplines Based on Metadata

Thesis classification is fundamental to a wide range of efficient research management. Current thesis classification is limited to major, research direction and classification number manually labeled by students themselves, which lacks standard and accuracy. Furthermore, previous auto-classification studies do not take account of interdisciplinary. This study intends to make a major contribution to Chinese thesis classification by taking advantage of the metadata such as title, keywords in the thesis. We propose a novel hierarchical classification model based on methods in metadata semantic representation and the corresponding similarity calculation. Experiments on 4K+ Theses show our methods have significant effect.
Objectives and Effectiveness of Brand Protection Systems for Regional Brands

Based on a field survey of 12 products, this study reveals the objectives and effectiveness of the Geographical Indication Protection System (GI) and Regional Collective Trademarks, which promote regional branding of agricultural products. We evaluate the effectiveness of both systems from the viewpoints of “constructing a production management system,” “controlling imitations,” “expanding awareness,” “rising price,” and “expanding production scale.” Moreover, we examine the effectiveness of “rising price” for Tsurajima Gobou and Miyagi Salmon using the Price Premium Method.The main results are as follows. First, the field survey reveals that GI-specific rules are useful. The government uses these rules to regulate imitation, and organizations that do not have corporate status can register their products under the GI system. Second, the prices of many agricultural brands remain unchanged after registration with brand protection systems. After registration, due to their strict production management systems or positive appeal to wholesalers, those prices are raised. Moreover, we confirmed that the price of Tsurajima Gobou and Miyagi Salmon are raised after registration using the Price Premium Method.