Reviewerly/exHarmony huggingface dataset:
Conceptual Graphs Based Approach for Subjective Answers Evaluation

Automated evaluation systems for objective type tests already exist. However, it is challenging to make an automated evaluation system for subjective type tests. Therefore, focus of this paper is on evaluation of simple text based subjective answers using Natural Language Processing techniques. A student's answer is evaluated by comparing it with a model answer of the question. Model answers cannot exactly match with the students' answers due to variability in writing. Therefore, researchers create conceptual graphs for both student as well as model answer and compute similarity between these graphs using techniques of graph similarity measures. Based on the similarity, marks are assigned to an answer. Lastly, in this manuscript authors compare the results obtained by human graders and the proposed system using Pearson correlation coefficient. Also, comparison has been drawn between the results of proposed system with other existing evaluation systems. The experimental evaluation of the proposed system shows promising results.
WITHDRAWN: Trace preserving quantum dynamics using a novel reparametrization-neutral summation-by-parts difference operator

This article has been transferred from JCPX to YJCPH at the request of the author(s) and editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
A hybrid approach towards optimisation of data and knowledge management through cooperation of database and ontology

There are many domains that require managing both data and knowledge together, under a single application. A common issue is database designers would design the knowledge as data. Normal practice is either to use only database or only ontology in managing/retrieving data and knowledge. There is still no proper methodology to differentiate what to be stored as data and/or knowledge in order to obtain benefits from both ontology and database. This paper proposes a methodology to obtain optimum benefits in managing/querying data and knowledge under a single application. Proposed method enhances storing mechanism by defining guidelines as to how to distribute data and knowledge between ontology and database. Further, it improves the retrieval of data and knowledge by allowing some part to be queried through database via SQL and some part to be queried through ontology via SPARQL. Query execution and accuracy were obtained by evaluating a prototype of proposed approach.
Social Spammer Detection Based on Semi-Supervised Learning

With the rapid development of Internet and mobile communication technologies, the social media has been spreading into all aspects of people's life, work and study. People can not only communicate with each other and share news through social media platforms, but also check news and information and learn about popular topics. However, due to the social platform's characteristics, such as rapid information dissemination and interaction mechanism among users, it has also become the main attacking surface for adversaries. Spammers mainly use social media platforms to spread phishing, fraud, publish pornography, malicious content and links to make profits, which seriously disrupts the normal operation of social media platforms and causes adverse effects on society. In this paper, we combine the semi-supervised model with maximum contrastive pessimistic likelihood (MCPL) estimation and the ensemble learning CatBoost algorithm, proposing a semi-supervised ensemble learning classification algorithm model (SSML-CatBoost) for spammer detection in social media, and comparative experimental results show that our model outperforms other models for different amounts of labeled data and different numbers of iterations.
Neural Mechanisms of Imprinting

Filial imprinting is a process by which young animals restrict their social preferences to stimuli through being exposed to these stimuli. The intermediate and medial mesopallium (IMM) of the chick forebrain stores information about a visual imprinting stimulus. Learning-related changes including synaptic modification occur in the IMM, which together with another region (S′) subserves recognition of an imprinting stimulus. Sleep is critical for imprinting and its associated neuronal activity in the IMM. Imprinting occurs during a sensitive period, which can be modified pharmacologically, and acts in conjunction with predispositions that are manifest as predilections for certain animal-like features. The neural mechanisms of filial imprinting and these related processes are discussed.
Listening

Abstract Engaging in music, being affected by it, and experiencing its in/audible effects, is achieved first of all by listening. Responding to music’s call happens in and through several different active–passive regimes of listening. However, this essay will demonstrate, first, that our contact with musical (as well as non-musical) sounds is most often multisensorial. Second, it will stand up for regimes of everyday listening that frequently have been treated with scorn by proponents of structural listening. Listening involves the whole body: visual, tactile, and even olfactory systems contribute in specific ways to auditory experiences. Listening also involves the mind: it triggers our intellect, emotions, imagination, and memories. Listening is influenced by the environment: and immaterial developments determine not only what we are listening to and when we can listen, but also how we listen: attentively, analytically, but also—and perhaps more frequently—distractedly, dipping in and out of concentrated contact with music.
The Impact of Employee Turnover on Organizational Performance: A Case Study of Mada Walabu University, Bale Robe, Ethiopia

This research focuses on the impact of staff turnover on organizational effectiveness and performance in Mada Walabu University. High staff turnover rates may jeopardize efforts to attain organizational objectives. In addition, when an organization loses a critical employee, there is a negative impact on innovation, consistency in providing service to primary users may be jeopardized and major delays in the delivery of services to customers may occur. The research design used in this study was the descriptive approach, which allowed the researcher to use semi-structured questionnaires when collecting data. The survey method used in this study because the target population only composed of 425 employees. The study employed Purposive, Simple Random sampling, and Convenience sampling techniques. A high response rate of 100% obtained using the personal method of data collection; questionnaire structured in a 5-point Likert scale format. Furthermore, the study interviewed human resource heads and ten voluntary employees with convenient sampling techniques. The study finding suggests that high labor demand and job opportunities in the market, lack of opportunity for career advancement in the organization, Unsatisfied with the working conditions, and no involvement in decision-making, are the foremost causes of employee turnover on organizational performance. The study finding also showed that staff turnover causes loss of some of the very experienced and skilled employees, reduction in work productivity and quality of services rendered as well as it causes too much wastage of resources when new staff settles and loses public confidence in the operation of the organization. In order to return the reduced university’s capacity in terms of national attrition rates, higher education access targets, quality education assurance, significant community, and technology transfer, and standardized research-based problem-solving culture due to staff turnover.
Citizen-to-Citizen Resource Sharing in Emergency Response

During large-scale manmade or natural disasters, such as Superstorm Sandy and Hurricanes Harvey and Irma, collaborations among government agencies, NGOs, and businesses need to be coordinated to provide necessary resources to respond to emergency events. However, resources from citizens themselves are underutilized, such as their equipment or expertise. The citizen participation via social media enhanced the situational awareness, but the response management is still mainly handled by the government or government-sanctioned partners. By harnessing the power of citizen crowdsourcing, government agencies can create enhanced disaster situation awareness and facilitate effective utilization of resources provided by citizen volunteers, resulting in more effective disaster responses. This chapter presents a public engagement in emergency response (PEER) framework that provides an online and mobile crowdsourcing platform for incident reporting and citizens' resource volunteering as well as an intelligent recommender system to match-make citizen resources with emergency tasks.
Teleportation

Teleportation uses entanglement to move a quantum state from one quantum system to another, without having to send the physical system.
Threat Intelligence

This chapter introduces threat intelligence which is defined by McMillan (Definition: threat intelligence, Gartner Research, 2013) as follows: “Threat intelligence is evidence-based knowledge, including context, mechanisms, indicators, implications and actionable advice, about an existing or emerging menace or hazard that can be used to make decisions regarding the subject’s response to that menace or hazard.” In this context threat intelligence is the knowledge that allows preventing or mitigating cyber threat attacks rooted in data. Threat intelligence results in methods that help to make informed decisions about security by answering questions, for instance who is attacking, what is the attacker’s motivation and capability, and others. Therefore, a solid understanding of the impact and potential consequences of cyber threat types is required, designing a mitigation solution to secure the mission critical and crucial systems of the Digital Transformation. This requires a detailed analysis of well-known and documented attacks or incidents, which may reveal identifiable cyber threat interactions or dependency patterns. If such recognizable patterns exist, further studies can be done to highlight their specifications, their severity and their impact and, if possible, to develop a method to reveal them before a cyber threat incident gets executed.
ZeroAUDIT

Consider the problem of auditing an investment fund. This usually involves inspecting each transaction in its trading history, and accumulating its capital gains and losses, so that its net asset value can be computed precisely to avoid financial frauds. We present ZeroAUDIT, a confidential and privacy preserving auditing platform, which accomplishes this goal without having to know any of a transaction’s details. Sitting at the heart of the system is a zero knowledge proof protocol, in the discrete logarithm setting, which allows one to reason about the elements of a Merkle tree. Using it, we can prove that a trading transaction is occurring at a fair market price without disclosing which securities are being traded. We have implemented the system on the Hyperledger Fabric platform and we report the use of batch verification techniques in improving its efficiency.
A Steganalytic Scheme Based on Classifier Selection Using Joint Image Characteristics

Steganalysis relies on steganalytic features and classification techniques. Because of the complexity and different characteristics of cover images, to make steganalysis more applicable toward detecting stego images in real applications, we need to train different classifiers so as to match different images according to their characteristics. Selection of classifiers according to characteristics of images is the key point to improve accuracy of steganalysis. In our work, we study the methods of classifier selection based on characteristics of images including image size, quantization factor, or matrix. Besides, we also discuss other characteristics, such as texture, cover source, which makes an appreciable difference to steganalysis.
Subjective Text Mining for Arabic Social Media

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.
Morse Keyboard

This paper talks about the progress on developing a keyboard for people with severe motor paralysis, which will work with a single input on/off sensor. It exposes a brief history of Morse code and its importance as alternative and augmentative system of communication. The reasons why was redesigned the original structure of the Morse code will be explained.
DeepVuler: A Vulnerability Intelligence Mining System for Open-Source Communities

Open-source code repositories play an important role in software development, but they also introduce a slew of security issues. Firstly, everyone can use open-source projects and libraries from the third-party ecosystem, which increases the risk of vulnerabilities attacking. Secondly, it may cause a domino effect and make these products inherit these vulnerabilities when referring to vulnerable repositories. Traditional technical methods were unable to detect these public flaws in a timely manner, leaving these developers in an insecure situation. Although vulnerability management institutes like CVE and NVD provide inadequate coverage, leading to a lack of timely, reliable, and detailed information about open-source projects' vulnerabilities. To better detect and repair vulnerability, we designed a vulnerability intelligence mining system named DeepVuler based on threads analysis and changed code in open-source communities using machine learning. We choose and define a series of effective features extracted from open-source communities to early infer vulnerability intelligence. Our result shows that two proposed models of DeepVuler achieve a detection rate of 0.979 in threads and 0.890 in changed codes. Besides, the detection from DeepVuler is often days or weeks ahead of official vulnerability disclosure.
Introduction to Ambient Intelligence in Internet of Things Environments and Cyber-Physical Systems

This chapter discusses current role and future prospects of ambient intelligence in development of applications for the internet of things environments. The authors provide an introduction to the field of ambient intelligence and specifically discuss what makes it so important to be a core element of cyber-physical systems. The main focus of the chapter is on providing analysis and the reasoning for development of smart spaces and delivering ambient intelligence to internet of things environments. The chapter provides definition and overview of recent trends and advances for service development with all identified key technological enablers of modern info-communication technologies, including data mining, big data analysis, recommendation systems, and so on. The main messages of the chapter are summarized by the conclusion section.
Cognitive Mapping in Support of Intelligent Information Systems

This chapter provides a review of the recent applications and trends on cognitive mapping techniques in support of the design and development of intelligent information systems. Cognitive maps are inference networks, using cyclic directed graphs for knowledge representation and reasoning. Cognitive mapping techniques are widely used to analyze causal systems such as industrial marketing planning, risk management, and product planning. Four knowledge management categories are adopted in this chapter to portray different applications of cognitive mapping techniques in the design and development of intelligent information systems. These four categories are knowledge creation, knowledge storage/retrieval, knowledge transfer, and knowledge application.
Development and Implementation of Interoperable Secure SDI Model Using Open Source GIS

It is observed that none of the academic institutes in India contributes for the development of SDI model at national level. Some of the developed models are not highly encouraging with respect to technical, organisational and institutional aspects. Non-availability of a standard functional SDI model based on SOA, Lack of technical interoperability and security aspects have also been a prime concern in SDI models using Open Source GIS (OSGIS). This article discusses appropriate application areas, i.e. the Education Sector, Geographical Indication and Mineral Resources Information Infrastructures to develop and implement the SDI model by a suitable adoption of OSS. The architecture of SOA-based interoperable and secure SDI models, which links the metadata server to database server, 3-tier interoperability and security framework with the help of OSGIS is developed and implemented.
Pedestrian Re-Identification Algorithm Based on Multivariate Manifold Metric-Anti-Noise Manifold Space Learning

Deep learning is widely used in the field of pedestrian re-identification, but pedestrian images are susceptible to changes in illumination, environmental noise and other factors. These factors have affected the in-depth promotion of this type of method. First, the existing models cannot effectively extract the structural information of low-light pedestrian samples. Second, environmental noise information will cause the model to fail to extract stable and reliable feature information. In this paper, in order to solve the above-mentioned problems, we propose a multi-group manifold metric learning model. Our method can obtain manifold conditions under different lighting conditions. It can retain the inherent structure of low-dimensional spatial data in high-dimensional space, and realize the extraction of features of pedestrians in low-light conditions. In addition, we proposed an anti-noise manifold space learning method and designed a manifold module network structure. The manifold modules are stacked layer by layer to further construct a manifold convolutional neural network. Our method can extract robust pedestrian image features and reduce noise disturbance to the model. We use noise and low-light images as the main experimental objects. The experimental results show that our proposed method can achieve the most advanced performance in multiple ReID data sets.
Cybersecurity Risk

Using textual analysis and comparing cybersecurity-risk disclosures of firms that were hacked to others that were not, we propose a novel firm-level measure of cybersecurity risk for all US-listed firms. We then examine whether cybersecurity risk is priced in the cross-section of stock returns. Portfolios of firms with high exposure to cybersecurity risk outperform other firms, on average, by up to 8.3% per year. At the same time, high-exposure firms perform poorly in periods of high cybersecurity risk. Reassuringly, the measure is higher in information-technology industries, correlates with characteristics linked to firms hit by cyberattacks, and predicts future cyberattacks.
Teaching Experience in Programming through Free Online Resources

In this paper, we present a new methodology for the assessment of Programming and Data Structures courses in conditions of Covid-19. The study is carried out at the Catholic University of Temuco, based on the suggestions received at the beginning of the pandemic, by students of the Computer Engineering career. The proposal consists of redirecting the assessment to freely accessible online programming platforms. As part of the study, a mapping of the contents to be evaluated in both subjects with problems available on platforms such as LeetCode and CodinGame is carried out. We manage to cover all the content to be evaluated using these platforms. An analysis of the results of the course, applying this new methodology, is made. Through a survey, applied at the end of the semester, we collected information about the perception of students, concerning aspects related to the changes in the assessment of the course. The results of the survey are analyzed qualitatively and quantitatively, perceiving improvements in the promotion indicators and the motivation of the students.
Introduction

This chapter introduces the notion of outlier and its essential properties. It presents a characterization of outliers based on their size, diversity and role in a given context. It also provides a theoretical perspective on the significance of outliers along with the practical need for their detection.
Problems of Displaying Transactions with Digital Assets in Accounting

At the present stage of the digital economy, approaches to the use of cash are changing. Electronic non-cash payments are increasingly used to order services and pay for goods online. Therefore, the important value of this process for the accounting system is the reflection of such transactions in accounting. Using e-wallets and e-business environments, displaying cryptocurrency transactions, transferring funds, mining, investing in high-risk assets – all this requires learning how to account for such transactions. The main purpose of the study is to scientifically substantiate the approaches to the reflection in the accounting of transactions with digital assets and to determine the ways of receipt of cryptocurrency in the enterprise. In the course of scientific research such methods of scientific cognition as description, analysis and synthesis were used. It is established that there is no single approach to the recognition and accounting of cryptocurrencies. It is advisable to consider cryptocurrency, which belongs to intangible assets, only in terms of long-term investments. Another vector of development is the identification of cryptocurrency as a resource or stocks and its accounting as stocks. It is determined that, first, before using cryptocurrency, it is necessary to economically justify a certain method of cryptocurrency valuation at the legislative level. In the future, this is necessary for companies that will use cryptocurrency to be able to constantly use the method in their accounting policies. The author analyzed the forms of electronic money and found that they can exist in the form of information in the middle of computer networks (network-based) and may have an additional connection with the payment smart card (card-based). In order to identify the subject of accounting, the author determines that cryptocurrency should be accounted for as an intangible asset, while wallets for storing cryptocurrency should be accounted for as other non-current tangible assets