Reviewerly/exHarmony huggingface dataset:
Predicting Sensitivity of Local News Articles from Odia Dailies

The news article manifests different issues of public domain. The news article are organized in different sections like local, national, international, sports, politics, editorial, readers view, sports etc. This paper purposes to categorize positive, negative and neutral local news and later on prediction of sensitivity from negative local news articles. The purpose of sensitivity analysis of negative local news articles is to set priority of action to be taken by the local administration. The sensitive news discusses the issues or event of urgent in nature, which need sudden intervention. The experiment is carried out basing on odia Syntactosemantic knowledge for categorization of 1000 local Odia news article and for sensitivity analysis tf and tf-idf score is calculated using unigram and bigram representation of data at document level, tf and tf-idf vector is passed to SVM for result and the results are analyzed by calculating the accuracy and F1 score.
On Asymptotic Analysis of Relative Generalized Hamming Weight

The relative generalized Hamming weight (RGHW) M r (C,C1) of an [n, k] (linear) code C and an [n, k 1 ] subcode C1, a generalization of generalized Hamming weight (GHW), has been applied to wiretap channel, network coding, linear ramp secret sharing, and trellis complexity, etc. Asymptotic analysis of RGHW is useful for investigating the optimal performance of these applications when code length is sufficiently large. For linear ramp secret sharing schemes, the asymptotic metric we previously introduced is inconvenient for characterizing its performance mainly because the rate of information leakage is not considered.In this paper, we improve the previous work by introducing two new asymptotic metrics, respectively, for the cases that r is fixed and r is proportionally increasing with n. For fixed r, we show the asymptotic Singleton, Plotkin and Gilbert-Varshamov bounds on the first metric. For increasing r, we determine the value of the second metric.
Cybersecurity and the Evolution of the Customer-Centric Service Desk

Cybersecurity is now seen as a central function of the modern IT Service Desk. This article examines two case studies of Helpdesk or Service Desk operations in different technology eras, and highlights the recent emergence of Cybersecurity as a critical area of Service Desk responsibilities. The article profiles the Helpdesk operations at Glaxo Pharmaceuticals in the late 1980s and the Service Desk functions at the University of Gloucestershire in 2019. Comparative analysis shows that whilst the range of technologies requiring support has increased markedly, this has been counter-balanced somewhat by the emergence of standards and dominant products in many technology categories. Cybersecurity, however, has emerged as a key concern that permeates all fields of Service Desk support. It also finds that the role of the end-user has evolved significantly in a rapidly changing technology landscape.
A copyright overview

Copyright can apply to a wide range of artefacts, but of particular relevance to scholars are words (as in this article or in a book, and sometimes called "literary works", though there is no requirement for literary merit), numbers (as in research data), images such as photographs, paintings and drawings, (also known as "artistic works", though, again, there is no requirement that they must have artistic merit.)Another category is moving images (as in, for example, a film or video).There is also protection for sound recordings, for dramatic works (such as the text of a play) and for musical works.At the moment, copyright only protects products of the human mind, whether created in a few seconds or the result of years of effort.There is no copyright in things produced by nature, or by other living organisms.There is a question regarding things produced by computers; arguably purely computer-generated outputs where there has been no human input apart from the original programming effort (where there would be copyright in the software) do not enjoy copyright, but this is an area where novel copyright concepts might emerge in the future.I discuss copyright and artificial intelligence later in this article.Countries differ somewhat on what outputs of the human mind might be protected by copyright.Some countries classify copyright works into formal headings, e.g., literary works, artistic works, sound recordings as listed above, whilst others simply demand protection for works of the human mind, without classifying them.All countries require, whether explicitly or implicitly in their laws, that the protectable subject matter must be fixed in some way, though they might not specify a minimum length of time for fixation or the medium on which the fixation occurs.Some countries' laws further require that the creator (or someone authorised by the creator) must carry out the fixation.Indeed, sometimes if an unauthorised person carries out the fixation (say they tape record a live music performance contrary to the performers' wishes), they have indeed created a copyright work, but in doing so they will have infringed the performers' rights.If two people totally independently create something identical (say a poem), then both authors will independently own copyright in the work. CreativityTo enjoy copyright, the item created must involve a minimum of creativity.Thus, a simple photograph of (say) a statue will not enjoy copyright, but if the photographer used skill and judgement on lighting, exposure time, etc., then it will enjoy copyright.As the well-known Infopaq case (decided by the European Court of Justice) confirmed, even a sentence comprising 11 words can enjoy copyright 2 .
Non-Rigid Joint Registration for Multi-Contrast MR of Infant Brain Based on the Unsupervised Deep Regression Network

At present, the non-rigid registration for adult brain Magnetic Resonance (MR) images is widely used, but it still faces challenges in the registration of infant brain MR images because of the low SNR. In this paper, we proposed a multi-input channel unsupervised deep regression network (DRN) to achieve non-rigid joint registration for multi-contrast MR, aiming to improve the registration accuracy of infant brain MR images. We firstly designed a U-Net-like network with multiple input channels, which can separately learn the information of T1- and T2- weighted MR images. The model can estimate one deformation field with information within both images to improve registration accuracy. For the multi-contrast MR, other registration algorithms produce two deformation fields but it is hard to evaluate which one is better. Secondly, the model can be trained in an unsupervised and auto-context manner with a multi-scale loss function. That allows the model to perform coarse registration with the large-scale loss function, and then fine registration with the small-scale loss function to achieve better results. Experimental results show that our algorithm reaches state-of-the-art accuracy and outperforms the others in some aspects for the registration of infant brain MR.
<i>MoMIT</i>: Porting a JavaScript Interpreter on a Quarter Coin

The Internet of Things (IoT) is a network of physical, connected devices providing services through private networks and the Internet. The devices connect through the Internet to Web servers and other devices. One of the popular programming languages for communicating Web pages and Web apps is JavaScript (JS). Hence, the devices would benefit from JS apps. However, porting JS apps to the many IoT devices, e.g., System-on-a-Chip (SoCs) devices (e.g., Arduino Uno), is challenging because of their limited memory, storage, and CPU capabilities. Also, some devices may lack hardware/software capabilities for running JS apps “as is”. Thus, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> , a multiobjective optimization approach to miniaturize JS apps to run on IoT devices. We implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> using three different search algorithms. We miniaturize a JS interpreter and measure the characteristics of 23 apps before/after applying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> . We find reductions of code size, memory usage, and CPU time of 31, 56, and 36 percent, respectively (medians). We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> allows apps to run on up to two additional devices in comparison to the original JS interpreter.
Analysis of the Influence on English Listening Ability based on Computer Aided Technology

As the scale, shape and function of engineering construction projects become more and more complex, the design of building systems has become more and more complex.In order to help students better acquire course resource information, effectively demonstrate the teaching process, and help students understand the curriculum and learn effectively, this paper designs an Auto CAD network architecture design teaching system.We analyze and compare the teaching model based on the network architecture design teaching system, and introduce the cluster analysis to the course resource management.An Auto CAD model was constructed through collaborative design among professionals based on Auto CAD technology.We analyze and compare the characteristics of traditional 2D design and inter-professional collaborative design based on Auto CAD technology, and select appropriate 3D simulation software for inter-professional collaborative design.The teaching of Auto CAD architectural design course proposed in this paper has certain innovation and practicability, and puts forward some constructive suggestions on the solution of the problem.
Face Recognition by Cognitive Discriminant Features

Face recognition is still an active pattern analysis topic. Faces have already been treated as objects or textures, but human face recognition system takes a different approach in face recognition. People refer to faces by their most discriminant features. People usually describe faces in sentences like ``She's snub-nosed'' or ``he's got long nose'' or ``he's got round eyes'' and so like. These most discriminant features have been extracted by comparing a face with average face formed in one's mind. We have mathematically formulated the approach and placed importance upon the most discriminant features. We have explained feature processing and classification parts in details. We also explained the train and test phases of the proposed algorithm. We have compared the proposed classification part with 1-NN classifier to show the strength of the algorithm and reported the results. We have also compared the whole proposed algorithm with a well-known face recognition method, Eigenfaces and achieved promising results in different cases.
Applying a binary artificial bee colony algorithm to the max-cut problem

The max-cut problem (MCP) has many applications in different areas. This paper develops a hybrid binary artificial bee colony algorithm (HBABC) to solve MCP. First, a surjection mapping function is used to map a real vector to a binary vector. Then, the basic artificial bee colony algorithm is modified for solving the max-cut problem. In addition, to improve the performance of ABC, a local search procedure is employed. The HBABC is tested on 24 instances from the literature. Numerical results indicate that the HBABC is very efficient.
STATE-OF-THE-ART REVIEW ON APPLICATIONS OF HARMONY SEARCH META HEURISTIC ALGORITHM 

Harmony Search (HS) a meta heuristic algorithm inspired by music improvisation process in which the musician searches for the best harmony and continues to polish the harmony in order to improve its aesthetics.The HS algorithm was introduced in the year 2001 and has found applications in diverse fields.This manuscript reviews state-of-the-art applications of Harmony Search algorithm.As evidenced by a number of studies, this algorithm features several innovative aspects in its operational procedure that foster its utilization in diverse fields such as engineering, construction, telecommunications, robotics, health and energy
Span Programs and Quantum Space Complexity

Span programs are an important model of quantum computation due to their correspondence with quantum query and space complexity. While the query complexity of quantum algorithms obtained from span programs is well-understood, it is not generally clear how to implement certain query-independent operations in a time-efficient manner. In this work, we prove an analogous connection for quantum time complexity. In particular, we show how to convert a sufficiently-structured quantum algorithm for f with time complexity T into a span program for f such that it compiles back into a quantum algorithm for f with time complexity O(T). This shows that for span programs derived from algorithms with a time-efficient implementation, we can preserve the time efficiency when implementing the span program, which means that span programs capture time, query and space complexities and are a complete model of quantum algorithms. One practical advantage of being able to convert quantum algorithms to span programs in a way that preserves time complexity is that span programs compose very nicely. We demonstrate this by improving Ambainis's variable-time quantum search result using our construction through a span program composition for the OR function.
Graph Computing based Parallel Power Flow Algorithm and Graph Visualization for Power Distribution Networks

With the emergence of the graph database and the graph computing technologies, the power system is facing a new era of technology development. Meanwhile, the fast-growing power distribution systems with increased size and complexity require more efficient data management systems and faster power flow solving algorithm. These challenges could be well solved by applying the graph data model (GDM) and graph computing technologies as the GDM provides more efficient data management methods and the graph computing is suitable for the node based parallel computing. This paper proposes a GDM based power distribution network modeling approach. Then a graph computing based parallel power flow algorithm and a graph visualization based power flow software have been developed based on it. The proposed parallel power flow algorithm is implemented on a graph database platform and the simulation results show that it is able to effectively reduce the computing time of the power flow with large test systems. Moreover, the power flow software is able to provide vivid data visualization and perform flexible data analysis and data management functions.
Privacy-aware Decentralized and Scalable Access Control Management for IoT Environment

In recent years, the Internet of Things (IoT) plays a vital role in our daily activities .Owing to the increased number of vulnerabilities on the IoT devices, security becomes critical in the untrustworthy IoT environment. Access control is one of the top security concerns, however, implementing the traditional access control mechanisms in the resource-constrained nature of the IoT devices is a challenging task. With the emergence of blockchain technology, several recent research works have focused on the adoption of blockchain in IoT to resolve the security concerns. Despite, integrating the blockchain in the resource-constrained IoT context is difficult. To overcome these obstacles, the proposed work presents a privacy-aware IoT security architecture to ensure the access control based on Smart contract for resource-constrained and distributed IoT devices. The design of the proposed architecture incorporates three main components such as the contextual blockchain gateway, decentralized revocation manager, and non-interactive zero-knowledge proof based validation. By modeling the contextual blockchain gateway, the proposed architecture ensures the dynamic authentication and authorization based on the contextual information and access policies. Instead of integrating the blockchain technology into resource-constrained IoT devices, the smart contract-based distributed access control system with the contextual blockchain gateway provides the scalable solution. With the association of decentralized revocation manager in the smart contract, it prevents the resource access from the unauthorized users by dynamically generating and updating the revoked user list of all the nodes in the smart contract. Moreover, the proposed architecture employs the non-interactive zeroknowledge proof cryptographic protocol to ensure the transaction privacy within the smart contract. Consequently, it maintains the trade-off between the transparency and privacy while ensuring the security for the distributed IoT environment.
Speaker Embedding Extraction with Virtual Phonetic Information

In the recent past, deep neural networks have been successfully employed to extract fixed-dimensional speaker embeddings from the speech signal. The commonly used x-vectors are extracted by projecting the magnitude spectral features extracted from the speech signal onto a speaker-discriminative space. As the x-vectors do not explicitly capture the speaker-specific phonological pronunciation variability, phonetic vectors extracted from an automatic speech recognition (ASR) engine were supplied as auxiliary information to improve the performance of the x-vector system. However, the development of ASR engine requires a huge amount of manually transcribed speech data. In this paper, we propose to transcribe the speech signal in an unsupervised manner with the cluster labels obtained from a mixture of autoencoders (MoA) trained on a large amount of speech data. The unsupervised labels, referred to as virtual phonetic transcriptions, are used to extract the phonetic vectors. The virtual phonetic vectors extracted using MoA are supplied as auxiliary information to the x-vector system. The performance of the proposed system is compared with the state-of-the-art x-vector system on NIST SRE-2010 data. The proposed unsupervised auxiliary information provides a relative improvement of 12.08%, 3.61% and 16.66% over the x-vector system on core-core, core-10sec and 10sec-10sec conditions, respectively.
RESEARCH WORK AS AN INDISPENSIBLE PROFESSIONAL ACTIVITY OF ACADEMIC STAFF AT A MODERN UNIVERSITY

Research work of academic staff has become obligatory for several reasons: science index plays a key role when evaluating university activity, academic staff has to supervise students’ research activities, and “research work” has become the main part in a faculty’s individual plan. As research work has become a necessity it requires special knowledge and skills that can be acquired through the system of further education. The article provides (a) the legal basis confirming the legitimacy of university requirements concerning research work in academic staff professional activities, (b) issues raised by academic staff in an empirical study, (с) justification of the need for continuing professional development for academic staff at tertiary education. The author reveals the factors that cause academic staff’ resistance to higher education modernization: teachers’ age, the desire to preserve the traditions of Russian universities, the rapid change from Federal State Educational Standard (FSES) 3 to FSES 3 ++ that results in difficulties in updating programs, the emergence of obligatory research work as a part of professional activities. Based on the questionnaire, the needs of teachers are identified. The author suggests educational modules to provide support to the development of faculty research work.
Scene Text Detection with Feature Pyramid Network and Linking Segments

Scene text detection is one of the most challenging problems in computer vision and has attracted great interest. Different from generic object detection, scene text detection mainly suffers from the large variance of scale, aspect ratio, and orientation in scene text. In this paper, we propose an effective and efficient model (SEG-FPN) for scene text detection, which is based on Feature Pyramid Network (FPN) and Linking Segments (SegLink). We incorporate feature pyramid mechanism with Single Shot Detector (SSD) framework to deal with different scale texts, and link locally detectable elements to detect texts of different orientations and aspect ratios. Moreover, compared with SSD, we enlarge the feature map of deep layers to better localize the large texts and recognize the small texts accurately. Experiments on ICDAR2015 and ICDAR2013 datasets demonstrate that our method can achieve comparable performance in terms of both accuracy and time. Specifically, SEG-FPN achieves an f-measure of 0.820 at 10.3 fps for 1280*768 ICDAR 2015 Incidental text images, and an f-measure of 0.879 at 19.2 fps for 512*512 ICDAR 2013 focused scene text images.
Loan Recommendation in P2P Lending Investment Networks: A Hybrid Graph Convolution Approach

Low successful rate of loan money has become a significant challenge for P2P Lending platforms. In this paper, we propose a hybrid deep learning algorithm combining strengths of both supervised graph convolution network and unsupervised community discovery, to accurately match the loan requirements and investing lenders on P2P Lending platforms. Our hybrid deep architecture learns predictive node embedding from both local neighborhood information and global structural information through complex investment networks with minimal information loss. We evaluate our method on large-scale dataset collected from a real-world P2P platform. Compared with strong baselines, our proposed method provides optimal loan recommendation performance, generates efficient solutions for “Cold-Start” problem and features fast computing speed.
Corruption-Resilient Detection of Event-Induced Outliers in PMU Data: A Kernel PCA Approach

Bad data outliers and malicious corruption in Phasor Measurement Unit (PMU) data having signature similar to that of a highly nonlinear event-induced oulier can challenge reliable event detection when linear principal component analysis (PCA)-based metrics are used. This paper presents a moving window based kernel PCA approach for accurately detecting event-induced outliers in presence of such corruptions in data. It is demonstrated that with appropriate tuning of kernel parameters, the change in the square of the norm of principal component score between successive windows along the direction of maximum variance in feature space can be used as a metric for corruption-resilient detection of event-induced outliers. Analytical justification for the same is provided along with a bound on this change. The performance of the proposed metric is validated on both synthetic data and field measurements.