Reviewerly/exHarmony huggingface dataset:
Africa Under the Impact of the Fourth Industrial Revolution

The chapter stresses that Africa requires a clear position of how “leapfrogging” into the FOIR should be informed by clear and unambiguous policy goals and strategies so that a sustainable development route is created. Political will, global connectivity, and straightforward pathways are fused in the menu. Africa is neither a monolithic nor a homogenous space where events and developments happen in a linear pattern. The traditional perspective about Africa perceives the region as motley of so many variations and controversies – spatial, temporal, gender, racial, and ideological. This is evident from the fact that Africa has portions of its economy that are pre-modern, modern, and post-modern. The “resource curse” phenomenon seems to hold water in both debate and practice in some circles. Countries and states should avoid falling into the proverbial trap of unequal exchange in form of transacting the natural endowments that Africa is known for.
Research on Image Content Authentication Algorithm of Semi-fragile Watermarking for Adaptive Embedding Based on Characteristic of Image

In this paper, we propose a novel quantization-based semi-fragile watermarking algorithm for image authentication and tamper localization based on discrete wavelet transform (DWT). In this algorithm, the watermarking is generated by extracting image feature from the approximation subband in the wavelet domain. The linear congruential pseudo random number generator is used to control the watermark embedding location and realize adaptive embedding. The generation and embedding of watermark are carried out in the original image. The authentication are not require original image and any additional information, which improves the security and confidentiality of watermark. The scheme can resist the mild modification of digital image and able to detect the malicious modifications precisely. Experimental results show that the proposed algorithm can distinguish high quality JPEG compression, small amount of noise from shear attack, classification of intentional and incidental tampering, and has good localization ability.
RIICS: Risk Based IICS Segmentation Method

Nowadays, one of the major challenges in industrial business world is integrating industrial control systems (ICS) with corporate systems (IT) and keeping the integrated system secured. Connecting this two totally different networks has numerous benefits and advantages, but introduces several security problems. Defense-in-depth is one of the most important security measures that should be applied to integrated ICS systems. This security technique consists essentially of “Segmentation” and “Segregation”. Segmentation of an integrated ICS may be based on various types of characteristics such as technical characteristics, business impact, risk levels or other requirements defined by the organization. This paper presents RIICS (Risk based IICS Segmentation) a new segmentation method that aims to simplify security zones identification by focusing on systems characteristics that are really relevant for segmentation especially technical industrial specificities and risk.
On the smallest non-trivial tight sets in Hermitian polar spaces <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e19" altimg="si11.gif"><mml:mi>H</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll…

We show that every x-tight set of a Hermitian polar spaces H(2n,q2), n≥2, is the union of x disjoint generators of the polar space provided that x≤12(q+1). This was known before only when n∈{2,3}. This result is a contribution to the conjecture that the smallest x-tight set of H(2n,q2) that is not a union of disjoint generators occurs for x=q+1 and is for sufficiently large q an embedded subgeometry.
Quantum Key Distribution Protocol Optimization

Abstract Quantum key distribution is a practically implementable cryptographic communication methodology from the hardware and software point of view. It is an information‐theoretic secure method for transmitting keys to remote partners practicing quantum communication. After examining various protocols from the most basic on, BBM92, DPSK, SARG04 and MDI (Measurement Device Independent) protocols are described in view of targeting the longest possible communication distance with the highest secret key bitrate. How any protocol can be optimized with respect to distance is discussed by analyzing the various steps impacting hardware and software that are developed, starting from the underlying assumptions proper to every protocol and ending with the corresponding performance in each case.
The Nordic research infrastructure for syntactic variation: Possibilities, limitations and achievements

The Scandinavian Dialect Syntax project was a collaboration between ten research groups from all of the five Nordic countries lasting for a period of about ten years. Besides resulting in a large number of scientific papers and theses on a range of different topics, a concrete outcome of the collaboration was the establishment of lasting research infrastructures in terms of two databases: the Nordic Dialect Corpus (NDC) and the Nordic Syntax Database (NSD). This paper first describes the two infrastructures and then proceeds to showcase how they may be used for the exploration of two selected dialect syntactic topics: the relative placement of sentence adverbs and infinitive markers (±split infinitives) across varieties of Mainland North Germanic, and the lack of Verb Second in wh-questions across Norwegian dialects.
GBCNN: A Full GPU-Based Batch Multi-Task Cascaded Convolutional Networks

Recently, the face detection and alignment is so popular and widely used in many research and application fields. Many superior face detection algorithms such as multi-task cascade convolutional network have been presented. However, it has difficulty in predicting faces among the big scale images in real time due to its three stages cascade architecture with less optimization. In this paper, we propose a full GPU-based batch multi-task cascade convolutional network which is carefully designed and optimized in each step to gain a superior speed performance. In addition, we present a novel parallel memory allocation strategy, which further enables our algorithm to support the batch operation, so that the system throughput increases significantly. In the experiment, our method achieves up to 300fps, over 600% speedup with an equal accuracy over the state-of-the-art methods on the face detection benchmarks.
On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals

Bayesian nonparametric models provide a general framework for flexible statistical modeling of modern complex data sets. We compare a rate-optimal and rate-suboptimal Bayesian nonparametric model for density estimation for data supported on a compact interval, by means of the analyses of simulated and real data. The results show that rate-optimal models are not uniformly better, across sample sizes, with respect to the way in which the posterior mass concentrates around a true model and that suboptimal models can outperform the optimal ones, even for relatively large sample sizes.
Playing With Feelings: Video Games and Affect. AubreyAnable. Minneapolis, MN: University of Minnesota Press, 2018. 200 pp. $25.00 (paperback). (ISBN 978‐1‐5179‐0025‐0)

Journal of the Association for Information Science and TechnologyVolume 70, Issue 10 p. 1165-1166 BOOK REVIEW Playing With Feelings: Video Games and Affect. Aubrey Anable. Minneapolis, MN: University of Minnesota Press, 2018. 200 pp. $25.00 (paperback). (ISBN 978-1-5179-0025-0) Emily Flynn-Jones, Emily Flynn-Jones eflynnjones@wlu.ca Wilfrid Laurier University, Brantford, ON, CanadaSearch for more papers by this author Emily Flynn-Jones, Emily Flynn-Jones eflynnjones@wlu.ca Wilfrid Laurier University, Brantford, ON, CanadaSearch for more papers by this author First published: 20 January 2019 https://doi.org/10.1002/asi.24146Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume70, Issue10October 2019Pages 1165-1166 RelatedInformation
CORRELATIONS BETWEEN ELEMENTS AND SEQUENCES IN A NUMERICAL PRISM

A numerical prism, previously introduced by the author as an ordered set regarding the research of a three-parameter probability distribution of the hyperbolic-cosine type, which is a generalization of the known two-parameter Meixner distribution, is being considered. In geometry-related terminology, elements of a numerical prism are the coefficients of moment-forming polynomials for the specified distribution, which are obtained with the use of both differential and algebraic recurrence correlations. Each one of the infinite number of elements depends on three indices determining its position in a prism. Fixation of one or two indices results in cross-sections of the prism, which are numerical triangles or sequences. Among them, there are such well-known cross-sections as the Stirling number triangle, number triangle of coefficients in the Bessel polynomials, sequences of tangent and secant numbers, and others. However, the majority of numerical sets in the prism’s cross-sections have never been described in literature before. Considering the structure and construction algorithm, cross-sections of the numerical prism turn out to be interconnected not only by the general construction formula but also by certain correlations. As a result, formulas of connection between various groups of elements are presented in the article. In particular, expansion of secant numbers for the sum of products grouped by the number of tangent numbers’ cofactors with specification of corresponding coefficients in the expansion, representation (automatic expression) of elements of a sequence of alternating secant and tangent number through the previous ones, as well as a number of other correlations for the sequences and particular elements is determined.
A Case Study of Library Data Management: A New Method to Analyze Borrowing Behavior

The library data management system of academic institutions generates and stores data with information on lends, returns and queries made by users daily. This research aims to provide a useful method to transform this data to obtain relevant knowledge for the management of the library. The process used consisted of a data pre-processing, data transformation and cleaning, and finally the use of privacy and association algorithms. We clarify the proposed method for a university library in Peru where significant results were obtained in the form of association rules. This shows that it is a efficient alternative to have a more detailed knowledge to make better decisions.
A Systemic Dynamics Model of Text Production

This paper introduces a quantitative model of text as it unfolds in time. The model conceptualizes text as a functional unit of language. This organization can be difficult to identify because it forms complex patterns of linguistic laws, probability and dynamics. These patterns are covert configurations and need complex methods to be investigated. One such method is to draw from qualitative frameworks derived from the quantitative properties of language. Previous studies have shown that covert configurations can be obtained through qualitative frameworks. When dynamics is considered, however, a model of text production including the variable time is needed. This paper therefore aims at addressing this research gap by proposing a dynamics model of text unfolding. It draws from systemic theory and models its categories quantitatively. Time is introduced as variation of choice. The model is applied to a sample of text. Results show how individual choices contribute to text unfolding – describing the amount of meanings at any given moment in text time. In addition, the dynamic accumulation indicates core characteristics of a text, which can be further explored in text behaviour and typology.
Documenting Linguistic Knowledge in an Inuit Language Atlas

The traditional method of orally transmitting language is weakening with the passing of fluent Elders and language erosion in contemporary Inuit society. Language documentation is a vital component of language maintenance and revitalization. In this paper we present a pilot online, multimedia cybercartographic Atlas of the Inuit Language in Canada , the goal of which is to help protect and strengthen the vitality of Inuit dialects through the documentation of their words. The main component of the atlas is a multidialectal database of written and spoken words. We discuss the role of dictionaries in language documentation, introduce the features of the atlas, explore the appeal of the atlas to different types of users (in particular, language learners), and present future directions for the atlas project.
ISACHI: Integrated Segmentation and Alignment Correction for Heart Images

We address the problem of cardiovascular shape representation from misaligned Cardiovascular Magnetic Resonance (CMR) images. An accurate 3D representation of the heart geometry allows for robust metrics to be calculated for multiple applications, from shape analysis in populations to precise description and quantification of individual anatomies including pathology. Clinical CMR relies on the acquisition of heart images at different breath holds potentially resulting in a misaligned stack of slices. Traditional methods for 3D reconstruction of the heart geometry typically rely on alignment, segmentation and reconstruction independently. We propose a novel method that integrates simultaneous alignment and segmentation refinements to realign slices producing a spatially consistent arrangement of the slices together with their segmentations fitted to the image data.
Making Sense of Actor Behaviour

Sense-making with respect to actor-based systems is challenging because of the non-determinism arising from concurrent behaviour. One strategy is to produce a trace of event histories that can be processed post-execution. Given a semantic domain, the histories can be translated into visual representations of the semantics in the form of filmstrips. This paper proposes a general pattern for the production of filmstrips from actor histories that can be implemented in a way that is independent of the particular data types used to represent the events, semantics and graphical displays. We demonstrate the pattern with respect to a simulation involving predators and prey which is a typical agent-based application.
Coordinated Power Management in Data Center Networks

In this paper, we present a coordinated power management technique for data center networks (DCN) which we have implemented in the popular network simulator NS3. The proposed mechanism includes three key entities: (a) a local controller (LC) that manages traffic at each DCN node (switches and routers), (b) a global controller (GC) that has a global view of the DCN and provides hints to the LC's based on this knowledge, and (c) a topology aware user request assignment controller (RAC) that controls placement of the external requests on the endpoint hosts based on hints from the GC. The main goal of the coordination from the energy management perspective is to properly direct and consolidate network traffic to maximize low power (or “sleep”) opportunities for the network interfaces while avoiding link congestion. In the process of testing out our mechanisms, we have enhanced the popular NS3 package for network modeling. The key enhancements are in the accounting of energy consumption at various levels and enhanced traffic routing mechanisms. We show that these mechanisms can reduce the power consumption by up to ∼40% in the common fat-tree based DCNs using the low power idle (LPI) feature of the Ethernet. We also apply our mechanism to the Hypercube network and show that it too can save a significant amount of energy.
Learning Consistent Tree-Augmented Dynamic Bayesian Networks

Dynamic Bayesian networks (DBNs) offer an approach that allows for causal and temporal dependencies between random variables repeatedly measured over time. For this reason, they have been used in several domains such as medical prognostic predictions, meteorology and econometrics. Learning the intra-slice dependencies is, however, most of the times neglected. This is due to the inherent difficulty in dealing with cyclic dependencies. We propose an algorithm for learning optimal DBNs consistent with the tree-augmented network (tDBN). This algorithm uses the topological order induced by the tDBN to increase its search space exponentially while keeping the time complexity polynomial.
Listwise Collaborative Filtering with High-Rating-Based Similarity and Simple Missing Value Estimation

In this paper, we make two proposals. The first aims to accelerate similarity calculations by only using a subset of the rating information (namely the highest ratings), while the second attempts to improve the accuracy of listwise collaborative filtering using a simple missing value estimation process. Experiments using the MovieLens 1M (6,040 users, 3,952 items and 1,000,209 ratings), 10M (71,567 users, 10,681 items and 10,000,054 ratings) and Jester (48,483 users, 100 items and 3,519,448 ratings) datasets demonstrate that these proposals can considerably reduce the computation time (by a factor of up to 50) and improve the normalized discounted cumulative gain value by up to 0.02 compared with ListCF, a well-known listwise collaborative filtering algorithm.
Multinomial Naïve Bayes using similarity based conditional probability

The exponential growth of Internet through sharing text content necessitates the analysis to convert them into useful information. The research areas such as Web mining, Opinion mining and Text mining focus on studies namely content mining, statistical analysis, prediction, and classification. Mult inomial Naïve Bayes (MNB), the state of art of Bayesian classifier is the fastest and simplest text classifier. The objective of the proposed study is to enhance the classification by substituting the conditional probability of existing MNB with probability based frequency computation. A new combination that consists of Pointwise Mutual Information (PMI) and different normalized Term Frequency (TF) is used for computing the conditional probability. The new combinations provide weight to the words based on the information gain carried by the words related to the document that belongs to a class. The robustness of Similarity based Enhanced Conditional Probability MNB (SECP-MNB) is reflected in classification accuracy measurement.
A Research on the Reform of College English Blended Teaching Mode under the Background of Educational Informatization

Nowadays, the rapid development of information technology has increasingly penetrated into all walks of society and even people’s lives. With the development of education informatization, traditional teaching can’t meet the needs of talents in the new era. Developing a new teaching mode by integrating internet, big data and traditional teaching has become the main theme of contemporary education. This paper studies the connotation of educational informatization and Blended Teaching theory and puts forward the Blended Teaching mode of College English by improving teachers’ informationized teaching ability, constructing the diversified teaching platform, and setting up informationized teaching resource base, adopting different teaching methods and strategies, so as to improve students’ enthusiasm and autonomy in College English learning and enhance the effect of College English teaching.
Learning Video Features for Multi-label Classification

This paper studies some approaches to learn representation of videos. This work was done as a part of Youtube-8M Video Understanding Challenge. The main focus is to analyze various approaches used to model temporal data and evaluate the performance of such approaches on this problem. Also, a model is proposed which reduces the size of feature vector by 70% but does not compromise on accuracy. The first approach is to use recurrent neural network architectures to learn a single video level feature from frame level features and then use this aggregated feature to do multi-label classification. The second approach is to use video level features and deep neural networks to assign the labels.
A Model-Driven Method for Fast Building Consistent Web Services from OpenAPI-Compatible Models

Lots of software companies rely on web technologies to test market hypotheses in order to develop viable businesses. They often need to quickly build web services that are at the core of their Minimum Viable Products (MVPs). MVPs must be reliable whereas they are based on specifications and hypotheses that are likely to change. Web services need to be well documented, to make it easy to develop applications that consume them. Model Driven Engineering approaches have been proposed and used to develop and evolve web services on one hand, and document them on the other hand. However, these approaches lack the ability to be suitable for both (i) rapid prototyping, (ii) model verification, (iii) compatibility with common programming languages and (iv) alignment between documentation and implementation. Here we propose a meta-model to express web services, the related tool to verify models consistency and an integration of this approach into the OpenAPI Specification. We adopt a shallow verification process to allow rapid prototyping by developers who are not formal methods experts, while still offering design-time guarantees that improve product quality and development efficiency. Web services are defined using parametric components which enable to express and formally verify web service patterns and to safely reuse them in other contexts. We built a tool to check consistency of extended OpenAPI 3.0 models and associated components implementations in order to generate corresponding web services. This allows us to give flexibility and verification support to developers, even in the context of an incremental development, as illustrated by a case study.
HiddenApp - Securing Linux Applications Using ARM TrustZone

The security of an application depends not only on its design and programming, but also on the platform it runs on: the underlying Operating System and hardware. As today’s systems get more and more complex, the probability of finding vulnerabilities increases and might compromise their security. In order to protect against this scenario, the idea of hardware-assisted trusted execution has appeared: technologies such as Intel SGX and ARM TrustZone promise to solve this by introducing additional checks inside the CPUs for specific resources to be accessible only by trusted programs running in isolated contexts. Our paper proposes a method to run unmodified GNU/Linux programs inside ARM TrustZone’s secure domain, getting the trusted execution benefits while retaining accessibility of the OS’s services (like file and network I/O) by using an automated system call proxying layer. We test that sample applications doing disk/network I/O can run unmodified, having only a small, constant latency overhead.