Reviewerly/exHarmony huggingface dataset:
Eigenvalues of Self Complementary and Compound Magic Squares with an Application in Image Cryptography

In this research, we mathematically calculated the eigenvalues of special matrices of orders: 3, 5, 7 and 9 and then verified the results using Maple simulation software. First, we found that for the matrix of order 3, the characteristic polynomial takes the form: (λ - 2s)(λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + 4Θ) with the eigenvalues: 2s, and two conjugates. Second, for the magic matrix of order 5, we calculated the characteristic polynomial to be (λ - α) (λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> + Lλ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + K) with eigenvalues of the form α, ∓√(λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) , ∓√(λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), which are the roots of the quadratic equation: λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + Lλ + K = 0. Third, for the magic matrix of order 7, we computed the characteristic polynomial of the self- complementary magic. In this case, the eigenvalues have the form α, ∓√(λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) , ∓√(λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), ∓√(λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ), where λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> are the roots of a cubic equation. Finally, we utilized magic squares and logic operations to propose a robust reversible image encryption/decryption technique.
Analysis on the Macro Influencing Factors in the Flow of Science and Technology Talent in Chongqing—A Quantitative Empirical Study

In view of the lack of research on the science and technology talent flow in Chongqing, this study takes the flow and aggregation of science and technology talents in Chongqing as the object, aiming to find out the macro influencing factors of talent flow in Chongqing by regression analysis of the data from 2007 to 2017. The conclusion presents that infrastructure investment and the proportion of financial expenditure on science and technology have a significant positive influence on the number of employees in scientific research and technological services as well as the high-quality talents in Chongqing; research on industrial enterprises above designated size, expenditure on research and development (R&D), and financial expenditure on science and technology have a significant positive influence on the full-time equivalent (FTE) of R&D personnel. The proportion of tertiary industry output has a significant negative impact on the FTE of R&D personnel. The conclusion of this study is helpful to enrich the relevant theories on the science and technology talent flow, clarify the macro influencing factors in different types of science and technology talent flow in Chongqing, as well as provide reference for the construction of high-level science and technology talent team in Chongqing.
Improved Voting System Based on Blockchain

In the operation of the voting system, this paper found that the system had the problem of no counting tickets and data redundancy.Although the system has carried on the statistics to the voting information, but has not completed the counting publicity very well.In view of this problem, we have optimized the counting item.In order to solve the problem of data redundancy in the operation of the system, we choose an improved homomorphic encryption algorithm based on ECC, which effectively improves the operation efficiency.After comparing with other models, we find that there are risks in private key storage.We cooperate with the external database and propose a scheme to reduce the risks by associating private key, security problem and password.
IoT Perception Layer Security

The IoT perception layer is where digital data are connected to physical devices. Although many IoT devices have sufficient computation and communication capabilities, the resource-constraint feature of some IoT devices lead to different security requirements and security techniques. Some security threats in IoT perception layer are introduced, including eavesdropping attack which has little cost in wireless communication networks, traffic analysis attack which does not need to recover the content of the communication data, impersonation attack and data modification attack as in traditional network environment, laboratory analysis which is specific to chips, and applies to IoT devices that are easy to capture and disassemble, cloning attack which is mainly on RFID tags, sybil attack which was proposed for wireless sensor networks and applies to some IoT applications, energy exhaustion attack which applies to battery powered devices, replay attack which may cause severe problems when the replayed data is a control command, regardless whether the data has encryption and/or integrity protection, and botnet control attack which makes use of large amount of IoT devices. Some possible countermeasures against those attacks are briefly described, and some advices about IoT perception layer security mechanism are proposed.
Context Matrix Methods for Property and Structure Ontology Completion in Wikidata

Wikidata is a crowd-sourced knowledge base built by the creators of Wikipedia that applies the principles of neutrality and verifiability to data. In its more than eight years of existence, it has grown enormously, although disproportionately. Some areas are well curated and maintained, while many parts of the knowledge base are incomplete or use inconsistent classifications. Therefore, tools are needed that can use the instantiated data to infer and report structural gaps and suggest ways to address these gaps. We propose a context matrix to automatically suggest potential values for properties. This method can be extended to evaluating the ontology represented by knowledge base. In particular, it could be used to propose types and classes, supporting the discovery of ontological relationships that lend conceptual identification to the content entities. To work with the large, unlabelled data set, we first employ a pipeline to shrink the data to a minimal representation without information loss. We then process the data to build a recommendation model using property frequencies. We explore the results of these models in the context of suggesting type classifications in Wikidata and discuss potential extended applications. As a result of this work, we demonstrate approaches to contextualizing recently-added content in the knowledge base as well as proposing new connections for existing content. Finally, these methods could be applied to other knowledge graphs to develop similar completions for the entities contained therein.
Flexible Low-Cost Digital Puppet System

Puppet-basedsystems have been developed to help children engage in storytelling and pretend play in much prior literature.Many different approaches have been proposed to implement suchpuppet-based storytelling systems, and new storytelling systems are stillroutinely published, indicating the continued interest in the topic across domains like child-computer interaction, learning technologies, and the broader HCI community. This paper firstpresents a detailed review of the different approaches that have been usedfor puppet-based storytelling system implementations, and then proposesaflexible low-cost approach to puppet-based storytelling system implementation that uses a combination of vision- and sensor-based tracking. We contribute a framework that will help the community to make sense of the myriad of puppet-based storytelling system implementation approaches in the literature, and discuss results from a perceptionstudy that evaluated the performance of the system output using our proposed implementation approach.
A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint

This paper contributes an analysis of how in mental and social processes, humans often apply specific mental models and learn and adapt them in a controlled manner. It is discussed how controlled adaptation relates to the Plasticity Versus Stability Conundrum in neuroscience. From the analysis an informal three-level cognitive architecture for controlled adaptation was obtained. It is discussed here from a self-modeling network viewpoint how this cognitive architecture can be modeled as a self-modeling network. Making use of the specific network characteristics offered by the self-modeling network structure format, a large number of options for different types of adaptation of mental models and different types of control over adaptation of mental models were obtained. Many of these options were illustrated by a several realistic examples that were formalized by self-modeling networks. Other options that were distinguished from the analysis here, are offered as interesting options for future research.
Application Value of Big Data Mining Technology in the Field of Financial Venture Capital

In the context of the rapid development of computer technology, the level of informatization in various industries and fields is also rapidly increasing. In recent years, the scale of big data has continued to expand and has become the backbone of financial venture capital. The market volatility and business complexity brought about by Internet finance have challenged traditional economics and finance research paradigms. This article mainly introduces the application value research of big data mining technology in the field of financial risk investment. This paper uses data mining technology in big data to detect real-time dynamics in the field of financial risk investment and establish an early warning model. The model is solved by the decision tree algorithm, and the data is mined using the typical C4.5 algorithm in the decision tree algorithm to generate a decision tree and transform it into classification rules. Then discover the laws hidden behind financial risk investment to provide a reliable basis for financial investment. The experimental results in this paper show that the decision tree algorithm reduces the occurrence of financial investment risks by 18%, and performs early warning analysis of financial risks. Finally, based on big data, relevant technical analysis is carried out for the financial investment field.
Word Segmentation for Arabic Abstractive Headline Generation

Neural headline generation (NHG) has achieved impressive performance on the abstractive headline generation task in recent years. However, there was a lack of studies conducted to enhance NHGs for agglutinative morphologically rich languages. Part of this is due to the scarcity of resources. This work presents a dataset for the Arabic abstractive headline generation task. The dataset was used in experiments conducted to compare different word segmentation methods like morphological word segmentation and Subword Regularization (SR). Experimental results show that morphological word segmentation and SR outperforms or is at par with the state-of-the-art Byte Pair Encoding (BPE) quantitatively and qualitatively.
Collective Development of Cognitive Abilities Using the “4C” Approach

The article continues a series of papers on collaborative research and learning. The subject of the study is a system consisting of an educational project group, subjects prescribed and a cognitive learning infrastructure. The latter includes the hardware and software components, an infrastructure service team, and a mentor component. The basic concept of the study is the necessity for mutual adjustment of all components of this system. The main research tool is the “4C” approach, which was the result of the development of cross-technologies of the situational center. The approach is based on the “collaborative”, “cognitive”, “configurable”, and “convergent” concepts. The service team consisted of a logician, a computer assistant and a facilitator. The members of the study groups were diagnosed using various methods, including MBTI. The possibilities of the approach are illustrated by two experimental studies of the authors. The first study involved the implementation of a reflexive version of the well-known Rock-Paper-Scissors game. The peculiarity was that the decision on the choice was made by pairs of players. The task of the study was a preliminary analysis of the algorithms for the solution, which were formed in groups. The second study consisted of an experimental study of the "uncanny valley" phenomenon. The peculiarity of this study was to test the hypothesis about the possibility of explaining this phenomenon using the theory of catastrophes in Whitney assembly format. The experiments were carried out with the participation of schoolchildren and students of Omsk and Vladivostok.
Marker-Based Instrument Tracking System Using Binocular Vision

The optical tracking system has rapidly developed into an essential part of infrared surgical navigation due to its high accuracy and convenience. This paper designs a high-precision and low-cost optical tracking system for preoperative training, which consists of a binocular-vision system, surgical tools, and image processing equipment. Based on the binocular vision principle, markers on the surgical instrument can be acquired by this system. To obtain the sub-pixel coordinates of the center of these markers' projections in real-time, first, a local image distortion correction algorithm was used to correct the region of interest in the captured image, next, the correct contours of the markers' projections can be acquired by filtering the extracted contours with geometric information, and then the accurate coordinates of the center of these markers' projections in pixel coordinate system can be obtained by leastsquares ellipse fitting algorithm. Finally, three different tests were performed on surgical instruments to verify the performance of the system, in which the stability test demonstrated that the system had high stability, as well as the static positioning test and dynamic tracking test proved that the system had high accuracy.
Cross-VM cache attacks on Camellia

Flush+Reload is a powerful cache-based side-channel attack in which the attacker takes advantage of a security weakness in the X86 processor architecture to ascertain whether specific cache lines are accessed by the victim or not. The Flush+Reload attack can be performed in a cross-core setting under the assumption that the last level cache is shared between the cores. In this paper, we demonstrate that Camellia implementations of OpenSSL 1.1.0 running inside the victim VM are vulnerable to the Flush+Reload attacks. Camellia is an ISO/IEC standard and CRYPTREC-portfolio cipher which was jointly designed by NTT and Mitsubishi. Our experimental results demonstrate that the attacker is able to recover the secret keys of Camellia in less than 1 minute in the native setup by utilizing the Flush+Reload technique. Our work demonstrates that common implementations of this standard cipher are vulnerable against Flush+Reload attack in both native and cross-VM setups. To the best of our knowledge, the proposed attacks are the first attack on Camellia that can be applied in the cross-VM setting.
An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks

Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods have been proposed, where random walk strategy is one of the wildly utilized approaches. However, the existing random walk based methods have some challenges, including: 1. The insufficiency of explaining what network knowledge in the walking path-samplings; 2. The adverse effects caused by the mixture of different information in networks; 3. The poor generality of the methods with hyper-parameters on different networks. This paper proposes an information-explainable random walk based unsupervised network representation learning framework named Probabilistic Accepted Walk (PAW) to obtain network representation from the perspective of the stationary distribution of networks. In the framework, we design two stationary distributions based on nodes’ self-information and local-information of networks to guide our proposed random walk strategy to learn representational vectors of networks through sampling paths of nodes. Numerous experimental results demonstrated that the PAW could obtain more expressive representation than the other six widely used unsupervised network representation learning baselines on four real-world networks in single-label and multi-label node classification tasks.
How Do Developers Blog?

A decade ago, the rise of GitHub and StackOverflow as social version control and knowledge sharing environments was about to start. Social media like Twitter were mocked by some software engineering researchers and practitioners as "tools for kids not professionals". At that time, we published one of the first papers [12] on social media in software engineering at MSR 2011, the Mining Software Repositories Conference.
Digital Human in Cybersecurity Risk Assessment

Cybersecurity focuses on technological solutions, skills, and setup. Nevertheless, advanced cyber attacks include aspects of social engineering and exploit the weaknesses of an individual. Advanced persistent threat actors are invisible. Therefore, after harvesting large amounts of publicly available data, they have overabundant time to stage a possible attack and choose a victim as the weakest link. On social media, people tend to disclose personal information in implicit and explicit ways. User profiles and What is on your mind messages on the social network can contain sensitive and private data, e.g. location, address, and birth date. However, user comments, Likes, and shared photos can determine the user’s personality during a personality trait analysis. Habits, interests, locations, and exposed loved ones are vulnerabilities that can be exploited during the cyber attack. Comprehensive image analysis, application of state of the art recognition techniques could be used to analyse shared photos of the individual, assume specific weaknesses, and predict behaviour-related features. The work aims to build a formal ontology-based model for cybersecurity risk assessment that considers digital human characteristics. A multi-layered architecture solution was build as a proof of concept to maintain a set of artificial intelligence algorithms and specially developed questionnaires for data gathering and processing. The prototype enabled us to organise a small scale experiment to validate trait analysis methods. Also, it opened further research directions.
Participation in the Intelligibility Testing of Easy-to-Read Texts: Development of an Interface for People with Learning Difficulties

Standard language texts pose significant challenges for some people because they do not understand the content due to the linguistic complexity. Transforming them into a simplified linguistic form can reduce these communicative hurdles. Easy-to-read texts enable people with learning disabilities to receive texts independently and thus form an important basis for equal participation in social life. In order to ensure a high level of comprehensibility of easy-to-read texts, simplifications are made not only to the language but also to the content. During manual translation, the simplifications made are checked by target group readers. In the case of (semi-)automated intelligibility checking, the check by target group readers is omitted. This paper focuses on the integration of the target audience of easy-to-read texts into the text review process. A web-based interface adapted to the reception needs is created and a rating system is integrated, through which people with learning disabilities can independently give feedback on the text comprehensibility. The interface will be evaluated in the form of a user study with brain-injured people. The results obtained indicate that both the web-based interface and the rating system are suitable for actively involving target group readers in the text review sub-process. The results of the study form the basis for the development of a system that takes into account the real text comprehension of the target group readers in addition to linguistic features when annotating texts in plain language.
Gaze as a Navigation and Control Mechanism in Third-Person Shooter Video Games

Gaze as an input mechanic, to this date, has not been widely adopted as a navigation and control mechanism in video games. However, recent advances in the development of affordable hardware, such as portable eyetrackers, have the potential to drive an increasing interest in the design of gaze-induced interaction metaphors in interactive systems. In order to bridge this gap, we designed a prototype third-person shooter video game controllable through traditional inputs (i.e., a console joystick), gaze-based controls (i.e., mediated through an off-the-shelf commercially available eyetracker), or hybrid mechanics. This paper reports the design rationale of the video game and the results of a proof of concept, aiming to comparatively measure user experience and playability of the video game under the influence of traditional and/or gaze-based navigation and control mechanisms. The obtained results show that users preferred hybrid controls for interacting with the game, as it improves performance and provides assistance and useful affordances for enhancing the overall experience. These results are quite promising and provide insights for designing novel gaming mechanics incorporating gaze as an active navigation and control input.
Let’s Draw

This chapter introduces to the reader a few commonly used freeware software tools—OpenGL, POV-Ray, VRML, and X3D—which will let the readers apply theoretical principles into practice without requesting expensive hardware and software solutions. Also, the readers will learn how immersive visual mathematics can be implemented using the function-based extensions of VRML and X3D, which allow for defining geometric shapes and their appearances with mathematical functions. Finally, the Shape Explorer tool will be presented to the reader as a multi-platform companion viewer for all the examples used in the book.
Superpixel Segmentation via Density Peaks

Superpixel segmentation, a preprocessing component, is widely used in computer vision tasks. Superpixel algorithm is supposed to generate superpixels with required boundary adherence, compactness as well as less computational complexity. However, most of the existing superpixel algorithms do not perform satisfactorily when it comes to compactness and boundary adherence, as they are a paradox in nature. In this paper, we propose a new superpixel segmentation method based on density peak (DP) clustering and modification of the original DP algorithm. The experimental results have been compared with state-of-the-art methods, both quantitatively and qualitatively, revealing the efficient outcomes that adhere to the object boundaries better than others.