Reviewerly/exHarmony huggingface dataset:
Training Specialists for the Digital Economy in Orenburg State University

The article analyzes the experience of Orenburg State University in the field of training specialists for the digital economy. It describes key competences for the digital economy: hybrid interdisciplinary management skills, team work abilities, skills in the spheres of management in cyberphysical systems, management in institutional changes and options for their generation in the training process of a university. It actualizes the task for universities and employers to coordinate their activities in making up the list of requirements to provide successive development of the digital economy competences. Special focus is given to designing and implementation of an electronic information and educational environment at university, and its characteristics are described. The article highlights the use of new format learning materials to meet the challenges of the digital economy in the fields of engineering, construction, computer safety, management.
Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models

We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model $M$ and a sampling oracle for the distribution $\mu_{\hat{M}}$ of an unknown model $\hat{M}$, can we efficiently determine if the two models $M$ and $\hat{M}$ are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalakis et al. (2018) presented a polynomial time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. In particular, for $n$-vertex graphs of maximum degree $d$, we prove that if $|\beta| d = \omega(\log{n})$ (where $\beta$ is the inverse temperature parameter), then there is no polynomial running time identity testing algorithm unless $RP=NP$. We also establish computational lower bounds for a broader set of parameters under the (randomized) exponential time hypothesis. Our proofs utilize insights into the design of gadgets using random graphs in recent works concerning the hardness of approximate counting by Sly (2010). In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs. In particular, we prove that identity testing is hard in the same range of parameters where structure learning is known to be hard.
Image Stitching Based on Improved SURF Algorithm

In order to solve the problem of uneven distribution of picture features and stitching of images, an improved SURF feature extraction method is proposed. Image feature extraction and image registration are the core of image stitching, which is directly related to stitching quality. In this paper, a comprehensive and in-depth study of feature-based image registration is carried out, and an improved algorithm is proposed. Firstly, the Heisen detection operator in the SURF algorithm is introduced to realize feature detection, and the features are extracted as much as possible. Secondly, the characteristics are described by BRIEF operator in the ORB algorithm to realize the invariance of the rotation change. Then, the European pull distance is used to complete the similarity calculation, and the KNN algorithm is used to realize the feature rough matching. Finally, the distance threshold is used to remove the matching pair with larger distance, and then the RANSAC algorithm is used to complete the purification. Experiments show that the proposed algorithm has good real-time performance, strong robustness and high accuracy.
A fuzzy GGA-based approach to speed up the evolutionary process for diverse group stock portfolio optimization1

Investment is always an interesting and important issue for people since the international financial crises are hard to predict and the government’s policy may have an influence on economic activities. In the past, many researches have been proposed on portfolio issues. In some of these studies, th e group stock portfolio (GSP) is utilized to provide various alternative stocks to an investor. The diverse group stock portfolio (DGSP) optimization approach has then been designed because the diversity of industries within a group can affect the performance of a final GSP. However, these approaches still have some problems to be solved. In this paper, we propose an algorithm to improve the efficiency and effectiveness of the previous work. In the proposed approach, a new chromosome representation and an enhanced fitness function are applied to find a better DGSP with lower risk than before. Moreover, we design a fuzzy grouping genetic algorithm (FGGA) based on the concept of collective intelligence which utilizes the fuzzy logic to dynamically tune the parameters in the evolution process for finding an appropriate DGSP. A mechanism is also designed to repair non-feasible chromosomes in the population. Through the above improvements, the proposed approach can not only focus on finding the best solution but also speed up the evolution process. Finally, experiments made on real datasets show the merits of the proposed approach.
Attack Prevention Scheme for Privacy Preservation (APSP) Using K Anonymity in Location Based Services for IoT

Internet of Things (IoT) is tagging our day-to-day usable objects across the internet. To make the life easy one should be more connective and communicative with the objects which are usable in daily life. In IoT, the privacy of users may be sacrificed while enjoying the conveniences and flexibilities provided by it. All the queries generated by the users, contain some relevant information about their location and identity. Location Based Server may uses this information maliciously and also reveal to third parties. In this paper, a new scheme, named APSP, has been proposed to preserve privacy of user’s location and to make it difficult for the attacker to identify the real user’s location. APSP implements an attack prevention algorithm on DLP for a given degree of anonymity. It selects number of dummy locations to ensure the maximum entropy with minimum variance. Simulation results show that, in APSP, there is least probability to reveal the identification of real user’s location under attack and achieves better privacy preservation for selected dummy locations. APSP maximize the complexity for attacker by providing the optimal set of locations. The attacker gets confused with the same historical query probabilities of all selected locations and fails to identify the real user’s location.
A Divide-and-Conquer-based Approach for Diverse Group Stock Portfolio Optimization Using Island-based Genetic Algorithms

Because portfolio management is a problem of optimization, many approaches have been presented to address it. In this paper, we propose an enhanced approach to obtain a diverse group stock portfolio (DGSP) from a large number of stocks using the island-based grouping genetic algorithm with the divide-and-conquer strategy. The main concept is that the proposed approach divides the given stocks into several subsets, and for every island, it will select a random subset to form its own initial population. During the evolution process, the stock synchronization mechanism is designed to adjust stocks in an island after chromosome migration. At last, experimental results on a real dataset were made to show the effectiveness and efficiency of the proposed approach.
Blockchain innovation and framing in the Netherlands: How a technological object turns into a ‘hyperobject’

Blockchain emerged as a well-defined technological object with limited applicability applications (e.g. Bitcoin). Embraced by more and more ‘stakeholders’, Blockchain has turned into a bounty of possibilities and promises. This raises the question whether Blockchain is turning into an overextending, affective ‘hyperobject’. Adopting a post-ANT topological perspective, and using mixed-methods analysis, this paper traces Blockchain's recent developments in the Netherlands. A media analysis of newspaper items shows a telling divide between stakeholders (including incumbents) stressing Blockchain's radicalising prospects and those (notably involved knowledge and policy workers) warning of its overhyping and lack of governance capacities. A detailed analysis of strategies and operations of the key enabler, the Dutch Blockchain Coalition, reveals how much effort has gone into face-to-face encounters and communication to frame and script the object. Yet, this also causes Blockchain to proliferate in all kinds of directions, turning into a hyperobject beyond the reach of intellectual and practical grasp.
Analysis of Benefits and Barriers as a Critical Success Factor in IT Governance Implementation by Using Interpretive Structural Model

This study aims to identify and analyze the key sub-factors of benefits and barriers as critical success factors for the implementation of IT Governance.The hope of the research is that the results of this identification and analysis can provide a high value of benefits in order to stakeholders in implementing IT Governance.In addition, it is expected to also give a real contribution to the development of theory or concept in the field of IT Governance especially related to critical success factor.The method used in this research is the interpretive structural model, where this method is very suitable to find the key sub-factor with a well-structured hierarchy structure.The first step of this research begins with a literature review, survey and interviews by involving three experts in the field of IT Governance and then the information is processed according to the rules.The final result shows that sub-factors improve customer service and overall responsiveness is a key sub-factor in benefit factors, whereas the lack of financial and human support becomes a key sub-factor in barriers.These results are empirical because depending on the data obtained through expert interviews involved, therefore the quality and capacity of experts greatly affect the results obtained.This study demonstrates how benefits and constraints as a critical success factor in the application of IT Governance are interrelated.The interpretive structural model provides an understanding of how various benefits and barriers interact with each other.This is important because policymakers typically focus on one or two sub-factors only.
New media coming to Kapkoi

This article, by partly adopting a historical perspective, examines the introduction of new media in a Kenyan rural village, Kapkoi, and how it changes the communication ecology in the village. Further it explores how people in Kapkoi use and ascribe meaning to media. The article argues that categories such as gender, age and financial situation influence people’s access to and use of media and demonstrates how media use can simultaneously reinforce and challenge power structures. Moreover, it highlights the clear digital divide between rural areas and urban centres, and yet suggests that the introduction of new media is partly owing to the connection between the urban and the rural and that media and communications technologies sustain and increase the connectedness of the local.
Comparison of acoustical models of GMM-HMM based for speech recognition in Hindi using PocketSphinx

Automatic Speech recognition (ASR) is widely gaining momentum worldwide, to be used as a part of Human Computer Interface and also in a wide variety of commercial applications. In Indian context, commercial applications using automatic speech recognition are still in the evolving process. This paper describes the acoustic models that have been cross language adapted for speech recognition in Hindi using CMU’s PocketSphinx. A database of 177 words in Hindi is prepared, along with transcription and dictionary. Two approaches for developing acoustical model for the speech recognition has been discussed in this paper. In the first approach, English acoustical model has been cross-language adapted to Hindi. In the second approach different acoustical models -continuous, semi continuous and phonetically tied models have been trained. GMM-HMM is used for acoustical modeling and language modeling.
Efficient Constructions for Almost-Everywhere Secure Computation

We study the problem of almost-everywhere reliable message transmission; a key component in designing efficient and secure Multi-party Computation (MPC) protocols for sparsely connected networks. The goal is to design low-degree networks which allow a large fraction of honest nodes to communicate reliably even when a small constant fraction of nodes experience byzantine corruption and deviate arbitrarily from the assigned protocol. In this paper, we achieve a $$\log $$ -degree network with a polylogarithmic work complexity protocol, thereby improving over the state-of-the-art result of Chandran et al. (ICALP 2010) who required a polylogarithmic-degree network and had a linear work complexity. In addition, we also achieve:
NEEDS ANALYSIS OF ESP FOR TOURISM STUDY PROGRAM AT SMKN 7 (SENIOR VOCATIONAL SCHOOL) KOTA BENGKULU

The design of this research is quantitative-qualitative descriptive research. The purpose of this research are first to analyze the English learning needs of students in Tourism Study Program and second to analyze the suitability of existing English textbook used. The subjects of this research are students of SMKN 7 Bengkulu 12th grade, English teachers in SMKN 7 Bengkulu, graduates of Tourism Study Program, and the English language users in the field of Tourism. In this study, researcher used the needs analysis to find what are the need, lacks, and want of students in learning English. The instruments that used in this study are questionnaires, interview, and existing English textbooks used in English language learning. The result of the research indicate that the needs of the Tourism Study Program student grade XII of SMKN 7 Kota Bengkulu are materials and activities that support their activities to work in the tourism field and the existing English textbook used by Tourism Study Program student grade XII of SMKN 7 Kota Bengkulu that is English textbook from Kementerian Pendidikan dan Kebudayaan Indonesia (2015) do not cover the students’ need.
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

Generative models produce realistic objects in many domains, including text, image, video, and audio synthesis. Most popular models—Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)—usually employ a standard Gaussian distribution as a prior. Previous works show that the richer family of prior distributions may help to avoid the mode collapse problem in GANs and to improve the evidence lower bound in VAEs. We propose a new family of prior distributions—Tensor Ring Induced Prior (TRIP)—that packs an exponential number of Gaussians into a high-dimensional lattice with a relatively small number of parameters. We show that these priors improve Frechet Inception Distance for GANs and Evidence Lower Bound for VAEs. We also study generative models with TRIP in the conditional generation setup with missing conditions. Altogether, we propose a novel plug-and-play framework for generative models that can be utilized in any GAN and VAE-like architectures.
Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing

Are two sets of observations drawn from the same distribution? This problem is a two-sample test. Kernel methods lead to many appealing properties. Indeed state-of-the-art approaches use the $L^2$ distance between kernel-based distribution representatives to derive their test statistics. Here, we show that $L^p$ distances (with $p\geq 1$) between these distribution representatives give metrics on the space of distributions that are well-behaved to detect differences between distributions as they metrize the weak convergence. Moreover, for analytic kernels, we show that the $L^1$ geometry gives improved testing power for scalable computational procedures. Specifically, we derive a finite dimensional approximation of the metric given as the $\ell_1$ norm of a vector which captures differences of expectations of analytic functions evaluated at spatial locations or frequencies (i.e, features). The features can be chosen to maximize the differences of the distributions and give interpretable indications of how they differs. Using an $\ell_1$ norm gives better detection because differences between representatives are dense as we use analytic kernels (non-zero almost everywhere). The tests are consistent, while much faster than state-of-the-art quadratic-time kernel-based tests. Experiments on artificial and real-world problems demonstrate improved power/time tradeoff than the state of the art, based on $\ell_2$ norms, and in some cases, better outright power than even the most expensive quadratic-time tests. This performance gain is retained even in high dimensions.
Development and Preliminary Evaluation of a Visual Annotation Tool to Rapidly Collect Expert-Annotated Weight Errors in Pediatric Growth Charts.

Patient weights can be entered incorrectly into electronic health record (EHR) systems. These weight errors can cause significant patient harm especially in pediatrics where weight-based dosing is pervasively used. Determining weight errors through manual chart reviews is impractical in busy clinics, and current EHR alerts are rudimentary. To address these issues, we seek to develop an advanced algorithm to detect weight errors using supervised machine learning techniques. The critical first step is to collect labelled weight errors for algorithm training. In this paper, we designed and preliminarily evaluated a visual annotation tool using Agile software development to achieve the goal of supporting the rapid collection of expert-annotated weight errors. The design was based on the fact that weight errors are infrequent and medical experts can easily spot potential errors. The results show positive user feedback and prepared us for the formal user-centered evaluation as the next step.
Analysis of Encryption and Lossless Compression Techniques for Secure Data Transmission

Security of information is an important concern in the present time. Cryptography consists of techniques to hide information during storage or transit. Compression performs reduction of number of bits in the data. Thus, both compression and encryption are preferably used to ensure maximum security. This paper is an analysis on encryption and lossless compression techniques with added security for secure data transmission over a network. This paper will compare different compression and encryption techniques researched and their respective advantages and disadvantages. The comparative analysis serves to prove that both encryption and compression techniques must be applied for securing data.
A Method for Efficient Argument-Based Inquiry

In this paper we describe a method for efficient argument-based inquiry. In this method, an agent creates arguments for and against a particular topic by matching argumentation rules with observations gathered by querying the environment. To avoid making superfluous queries, the agent needs to determine if the acceptability status of the topic can change given more information. We define a notion of stability, where a structured argumentation setup is stable if no new arguments can be added, or if adding new arguments will not change the status of the topic. Because determining stability requires hypothesizing over all future argumentation setups, which is computationally very expensive, we define a less complex approximation algorithm and show that this is a sound approximation of stability. Finally, we show how stability (or our approximation of it) can be used in determining an optimal inquiry policy, and discuss how this policy can be used to, for example, determine a strategy in an argument-based inquiry dialogue.
Systematic Literature Review of Identifying Issues in Software Cost Estimation Techniques

Software cost estimation plays a vital role in software project management.It is a process of predicting the effort and cost in terms of money and staff required for developing the software system.It is very much clear that software project will be successful if its estimated cost will be near to the real cost.When the project is at the acquisition stage the least details are available about a software project to be developed, due to which Problems arises in cost estimation.As the stages move on details increases for software development of software which is quite fruitful in cost estimating.However, it can be considered that estimating the software cost in the first phases will produce better results.In this research cost estimation techniques are discussed along with the issues in that particular technique and focus will be on understating the points or issues which cause hurdles or issues in estimating the cost of the software project.
Relating Session Types and Behavioural Contracts: The Asynchronous Case

We discuss the relationship between session types and behavioural contracts under the assumption that processes communicate asynchronously. We show the existence of a fully abstract interpretation of session types into a fragment of contracts, that maps session subtyping into binary compliance-preserving contract refinement. In this way, the recent undecidability result for asynchronous session subtyping can be used to obtain an original undecidability result for asynchronous contract refinement.
I3

The scrolling interaction is a pervasive human-computer interaction on smartphones, which can reflect intrinsic characteristics during dynamic browsings. Different from extrinsic statistical measures like frequency of visits and dwell time, intrinsic features underlying scrolling interactions reveal fine-grained implicit feedbacks about user interests. Toward this end, we explore user interest inference by extracting efficient browsing features from scrolling human-computer interactions on smartphones. In this paper, we first analyze browsing traces of 40 volunteers, and find two intrinsic browsing features underlying scrolling interactions, i.e., browsing velocity stability and browsing velocity sequence, which are tightly related to user interests. Inspired by the observation, we propose an Intelligent Interest Inference system, I3, which infers user interests through sensing scrolling interactions during browsings. Specifically, I3 first extracts the two intrinsic browsing features from users' scrolling interactions. Then, I3 applies a Naive Bayesian-based approach to construct an interest discriminator for coarse-grained user interest (i.e., like-preferred or dislike-preferred) inference. Furthermore, we develop a deep learning-based approach for I3 to train rating classifiers for fine-grained rating inference in like-preferred and dislike-preferred browsings respectively. Finally, I3 utilizes the interest discriminator and rating classifiers to infer exact user ratings about browsing contents on smartphones. Experimental results under browsing traces of 46 volunteers present that I3 achieves 92.4% overall accuracy in interest inference.