Reviewerly/exHarmony huggingface dataset:
A comprehensive survey on the reduction of the semantic gap in content-based image retrieval

In the last few decades, content-based image retrieval is considered as one of the most vivid research topics in the field of information retrieval. The limitation of current content-based image retrieval systems is that low-level features are highly ineffective to represent the semantic contents of the image. Most of the research work in content-based image retrieval is focused on bridging the semantic gap between the low-level features and high-level semantic concepts of image. This paper presents a thorough study of different techniques for the reduction of semantic gap. The existing techniques are broadly categorised as: 1) image annotation techniques to define the high-level concepts in image; 2) relevance feedback techniques to integrate user's perception; 3) machine learning and deep learning techniques to associate low-level features with high-level concepts. In addition, the general architecture of semantic-based image retrieval system has been discussed in this survey. This paper also highlights the current and future applications of content-based image retrieval. The paper concludes with promising future research directions.
Accountability of algorithms in data management of data subjects and business users

This paper intends to investigate the processing and exploitation of data (whether personal and not) through algorithms to understand the profiles of responsibility and the related mechanisms of accountability. In particular, the paper will try to understand how the security of automated and algorithmic decision-making processes can be verified ex post, due to their impact on privacy of both public and private entities.
Advancements and hot research topics of artificial intelligence in mobile learning: a review of journal publications from 1995 to 2019

Based on the Technology-based Learning model, the present study reviewed the Artificial Intelligence-supported Mobile Learning (AImL) studies by searching for journal articles published from 1995 t...
Time-based key management in attribute-based encryption using piecewise key generation

Key management is essential in using attribute-based encryption (ABE) for dynamic access control in the practical world. Although user or key delegation has been widely discussed for ABE, it cannot...
Empirical risk assessment of attack graphs using time to compromise framework

The proliferated complexity of network size together with the expeditious development of software system applications and their large number of vulnerabilities, security hardening is becoming a cha...
Higher-degree supersingular group actions

We investigate the isogeny graphs of supersingular elliptic curves over $\mathbb{F}_{p^2}$ equipped with a $d$-isogeny to their Galois conjugate. These curves are interesting because they are, in a sense, a generalization of curves defined over $\mathbb{F}_p$, and there is an action of the ideal class group of $\mathbb{Q}(\sqrt{-dp})$ on the isogeny graphs. We investigate constructive and destructive aspects of these graphs in isogeny-based cryptography, including generalizations of the CSIDH cryptosystem and the Delfs-Galbraith algorithm.
Incorporating behavioral aspects in Multiple Criteria Decision Making: A framework for trends and future directions

This research intends to shed light on the behavioural multi-criteria decision making through a four-step systematic literature review approach. Hence, based on the research purpose and its theme w...
Fault-based testing for discovering SQL injection vulnerabilities in web applications

In this paper we proposed a model to investigate the behaviour of websites when dealing with invalid inputs. Many vulnerabilities rise from invalid inputs. An invalid input is considered as a form ...
The influence of human rights indicators on foreign direct investment

This study examines the relationship between a country's respect for human rights and the foreign direct investment (FDI) it attracts. It addresses the questions of whether a nation's respect for human development, progress and freedom are a determinant of investment in the global economy. The study uses cross-sectional data from public sources including the World Bank and UNCTAD for 117 countries. Hierarchical regressions were used to analyse the data. Countries with greater institutional advancement (i.e., those that encouraged voice/accountability and those that contributed to political stability and the lack of violence), as well as those with greater economic (lesser corruption and greater labour freedom) and non-economic human development outcomes (such as opportunities for human rights) were shown to attract greater FDI. Furthermore, countries with greater global competitiveness also attract more FDI for those that are innovation and efficiency driven, but not for those that are factor driven. The managerial and public policy implications are also provided.
Considering local context in expanding ICT integration at primary education: lessons learnt from Namibian case study

Many international organisations continuously donate computer equipment to African primary schools with little to no thought of how and what this equipment will achieve in the schools. Moreover, li...
Classifying defective software projects based on machine learning and complexity metrics

Software defects can lead to software failures or errors at any time. Therefore, software developers and engineers spend a lot of time and effort in order to find possible defects. This paper propo...
Classification of P2P file-sharing traffic using heuristic based and statistical based techniques

Since the past decade, peer-to-peer (P2P) applications are popular among the users and they continue to be one of the largest contributors to the Internet traffic. Such applications generate a lot of traffic in the network and pose various issues to the Internet service providers (ISPs) and network administrators such as dealing with network congestion, security, maintaining quality of service for various applications, etc. This raises a need to monitor and classify the Internet traffic generated by P2P applications. Therefore, this field is actively researched as new application protocols keep on emerging. Traditional classification techniques such as port based and payload based are inefficient in classifying such traffic due to their various limitations. File-sharing traffic is the largest contributor of the P2P Internet traffic. Hence, this paper focuses on classifying P2P file-sharing traffic by utilizing a combination of heuristic based and statistical based techniques. We identified a set of heuristics and unique packet size distribution of P2P file-sharing traffic using real offline traffic datasets. The experimental results show that the proposed technique is able to achieve classification accuracy over 98.5%. In addition to that, it works on both TCP and UDP protocols, uses minimum heuristics for classification (and hence has less overhead) and can be used for real-time classification as well.
Abstract Extraction from Audios

Abstract extraction from audios is the method of identifying the beneficial and essential information from a given audio. This process involves speech signals to be converted into a sequence of words, other linguistic units by making use of an algorithm which is implemented as a computer program. An extractive speech abstraction method is devised on the obtained sequence of words. Extractive speech abstraction normally works as a binary classifier determining whether a sentence is a part of the abstract or not. As a result, using rank based classifiers, we represent the importance among sentences. Based on the weights assigned, the sentences are ranked. Consequently, a high-quality abstract is extracted from the input by choosing the sentences which are highly ranked and formed extract is stored as a text.Keywords:abstract, text, document, ranking, sentence
Vulnerability discovery modelling: a general framework

Due to the rising popularity of software-based systems, software engineers are required to continuously monitor the software to have deep insights about the loopholes and keep a close check on the ...
Study on hand gesture recognition with CNN-based deep learning

Natural user interface technology is actively studied to enable a computer to understand a human's natural behaviours including gestures and expressions. To recognise human motions, while most appr...
Effective compression and decompression coding techniques using multilevel DWT decomposition and DCT

Data compression was introduced to utilise the available bandwidth efficiently and also to reduce the storage capacity. This paper presents effective compression and decompression coding techniques...
Analysis of the ARTMAP Neural Network Architecture

In this paper we analyze the ARTMAP architecture for situations requiring learning of many-to-one maps. Our point of focus is the number of list presentations required by ARTMAP to learn an arbitrary many-to-one map. In particular, it is shown that if ARTMAP is repeatedly presented with a list of input/output pairs, it establishes the required mapping in at most Ma − 1 list presentations, where Ma corresponds to the total number of ones in each one of the input patterns. Other useful properties, associated with the learning of the mapping represented by an arbitrary list of input/output pairs, are also examined. These properties reveal some of the characteristics of learning in ARTMAP when it is used as a tool in establishing an arbitrary mapping from a binary input space to a binary output space. The results presented in this paper are valid for the fast learning case, and for small βa values, where βa is a parameter associated with the adaptation of bottom-up weights in one of the ART1 modules of ARTMAP.
AN EFFICEINT HYBRID CLOUD STRUCTURE TO AVOID THE REDUNDENCY ISSUES IN HYBRID CLOUD COMPUTING

Big data has large amount of data representing personal information, the biggest challenge faced by it is from safety opinion that is the defence of privacy of operator. By the growth of data, information in large scale the storage has moved from hard drive to cloud storage. Cloud storage system is basically distributed file system, which solves the problem of storage of large data whereas it faces the challenge of storing duplicate data in the storage. Most of the file systems are created to deal with storing and retrieval of data available on the storage. Among different works done on the cloud file system only little work on the De-duplication techniques at application level, especially for HDFS . In this below work represents the design of file de- duplication framework on HDFS for cloud application developer. Proposed AVOID THE REDUNDENCY ISSUES IN HYBRID CLOUD COMPUTING (ATRIHCC) which de-duplicates the data online that improves storage space usage and reduces the duplicates . At the end of the paper, testing of disk usage and file upload performance on the proposed method and the comparison of HDFS.
An adaptive threshold based data hiding scheme on colour images

This paper presents a data hiding scheme based on difference expansion (DE), including an adaptive threshold for color images. This is seen that the different layers, Red, Green and Blue behaves di...
FPGA based implementation of reversible data hiding scheme for content verification and quality access control of image

Reversible watermarking (RW) is the type of lossless data-hiding technique where the original object can be retrieved pixel by pixel at the receiver. This paper represents the hardware design for t...
The Price of Going out to the Movies — the History and the Contemporaneousness

The price of the theater admission is viewed as one of the factors for the massness of cinema. With the use of statistical materials, the dynamics of the massness is surveyed for the past 50 years. In the USSR the practice of forming the admission prices was determined by the focus of the state on providing for the broadest massness of cinema and its economic accessibility for all of the population. Despite the decline of the attendance of many years, the selected course was staid on. Over the 18 years of waning admissions (1968–1986), the average price of the cinema ticket increased for less than 3 kopeks (27.09 kop. as compared to 24.46 kop.). The societal changes and the steering of the course toward the market in the late 1980s created an impetus for the drastic growth of the price for going out to the movies. In the 1990s this trend surged sharply under the impact of two economic process: the precipitous drop in the incomes of the main populace and the no less dramatic increase in the price of the cinema ticket. The population became divided into two categories — those solvent for cinema attendance (mostly the relatively scarce and precarious middle class) and those insolvent. From the yesteryear factor of cinema’s broadest massness the ticket price was transformed into a factor of the former’s demassization. As a contribution to it, also came the profound restructuring of the theatrical repertory and the radical dismantling of communicative conventions between the cinematography and its spectators the most evident in the escalation of entertainment violence in films. Materials of three sociological surveys of spectators are used as the basis for an analysis of their estimating the price of the cinema ticket in the past three decades, the effects of its increase on cinema attendance. The issue of the «fair» ticket price is put in some perspective. Accentuated is the importance of rational price formation as a factor of reproducing the cinema audience.
Objective and subjective factors of educational risks for students

Analyzed is the concept of risk as an inevitable fact of our time. They researched conceptual approaches to risk of foreign and domestic scientists who substantiate risk as a way of mastering reality, a condition, and stimulus of activity. The article presents an understanding of risk as a new opportunity, which is provided by competent use of information, an ability to make high-quality decisions in society of risk. The article attempts to show some aspects of education not only through its involvement in the risk system, but also as product and risk generator. Attention is focused on the fact that the concept of “educational risk” has not finally formed in science; most scientists research it through factors that affect processes and outcomes of education. Educational risks exist in the sphere of interests and competences of the state, employers, and youth. The authors provide a classification of educational risk and substantiate the relationship of risk groups. The authors propose specific measures for educational risk management, which require systemic multi-level solutions involving participation in risk management of risk groups. It is concluded that students are able to manage educational risk due to a set of knowledge, awareness of the labor market, and understanding of state policies related to education.
Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization

In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the proposed pipeline consisted of three steps. In the beginning, for each magnetic resonance image, a 3D U-net was employed to obtain the automatic hippocampus segmentation at each hemisphere. Secondly, ASM was performed on a group of pre-obtained template surfaces to generate mean shape and shape variation parameters through principal component analysis. Ultimately, hybrid particle swarm optimization was utilized to search for the optimal shape variation parameters that best match the segmentation. The hippocampal surface was then generated from the mean shape and the shape variation parameters. The proposed pipeline was observed to provide hippocampal surfaces at both hemispheres with high accuracy, correct anatomical topology, and sufficient smoothness.Clinical relevance—This work provides a useful tool for generating hippocampal surfaces which are important biomarkers for a variety of brain disorders
New Secondary Constructions of Generalized Bent Functions

Three new secondary constructions of generalized bent functions are presented. We provide a secondary construction of generalized bent functions from indirect sum methods proposed by Carlet et al. A new secondary construction of generalized bent functions from four initial functions is also investigated. We demonstrate that many known constructions can be derived from our proposed construction as special cases by choosing proper initial functions and parameters. By modifying the new construction, a novel secondary construction of generalized bent functions from two initial generalized bent functions is obtained. For the binary case, the dual functions of the bent functions by our method are presented, which share the same formula as the indirect sum.
The school of future full with digital books

Digital technologies are used in any field of human activities, among them is the informatization of education which has facilitated the use of modern technologies in the learning process and integration in all subject disciplines.
Distributed blockchains for collaborative product designs in smart cities

Personal fabrication tools such as 3D printers encourage designers for rapidly creating innovative prototypes of products. However, an immediate end product realisation is a challenge owing to seve...
Development of a Tracklet Extraction Engine

An efficient algorithm is required to extract moving objects (asteroids, satellites, and space debris) from enormous data with advances in observational instruments. We have developed an algorithm, tracee, to swiftly detect points aligned as a line segment from a three-dimensional space. The algorithm consists of two steps; First, construct a k-nearest neighbor graph of given points, and then extract colinear line segments by grouping. The proposed algorithm is robust against distractors and works properly even when line segments are crossed. While the algorithm is originally developed for moving object detection, it can be used for other purposes.
Research on adaptive prediction of multimedia data transmission path based on machine learning

Aiming at the problems of high probability of data packet loss and long data receiving delay in current methods, an adaptive prediction method of multimedia data transmission path based on machine ...
The information security transmission method for intelligent examination based on ZigBee communication

Aiming at the problems of low network utilisation, poor security and high cost in the traditional intelligent test information security transmission method, a ZigBee communication-based information security intelligent detection and transmission method is proposed. Network key and connection key are realised by using asymmetric key system. Public key encryption and private key decryption are used to verify the trust centre. The sending node uses the trust centre to get the public key of the target node so as to achieve the goal of the intelligent test information security transmission. The simulation results show that the network utilisation rate is above 96%, compared with the traditional method, the index level has been significantly improved. The efficiency of the proposed method is up to 99%. In terms of cost, the proposed method has a lower cost. When the running time reaches 920 min, the cost is RMB 17,000 yuan.
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering

We present RePOSE, a fast iterative refinement method for 6D object pose estimation. Prior methods perform refinement by feeding zoomed-in input and rendered RGB images into a CNN and directly regressing an update of a refined pose. Their runtime is slow due to the computational cost of CNN, which is especially prominent in multiple-object pose refinement. To overcome this problem, RePOSE leverages image rendering for fast feature extraction using a 3D model with a learnable texture. We call this deep texture rendering, which uses a shallow multi-layer perceptron to directly regress a view-invariant image representation of an object. Furthermore, we utilize differentiable Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by minimizing the feature-metric error between the input and rendered image representations without the need of zooming in. These image representations are trained such that differentiable LM optimization converges within few iterations. Consequently, RePOSE runs at 92 FPS and achieves state-of-the-art accuracy of 51.6% on the Occlusion LineMOD dataset - a 4.1% absolute improvement over the prior art, and comparable result on the YCB-Video dataset with a much faster runtime. The code is available at this https URL.
Performance evaluation and reliability analysis of predictive hardware failure models in cloud platform using ReliaCloud-NS

Cloud computing systems at the present time established as a promising trend in providing the platform for coordinating large number of heterogeneous tasks and aims at delivering highly reliable cl...
Artistic Modelling of History in the Literature and Non-Fiction of a Post-Totalitarian Society

An artistic interpretation of historical facts is quite relevant in the literature and non-fiction of a post-totalitarian society. Prose works on historical themes are valuable and interesting in that they create an illusion for readers to be present in a certain period of historical time, and it is the artistic modeling of events that makes priceless facts of history completely disappear. The historical past is an inexhaustible material that word artists have been referring to for centuries, creating the best examples of fiction. Prose texts of historical subjects are perceived by each next generation in a new way, historical events and phenomena are interpreted from different angles, the activities of famous figures of the past are widely covered. Non-fiction literature is a kind of literature that is on the verge of artistry and documentary. The main non-fiction genres are a diary, autobiography, biography, memoirs, confession, letters, etc. On the one hand, non-fiction literature claims to recognize the author’s subjective truth about him in her texts; on the other hand, only the reader can either, demonstrating full confidence in the author, call this text documentary, or admit the presence of poetry and aesthetics in the work and tilt the scales in the direction of artistry. The material for observation and research of the phenomenon of artistic modeling are works that allow us to trace the most common, in our opinion, models of correlation of historical and artistic consciousness in postmodern literature and non-fiction of a post-totalitarian society.
Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTube