Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Human behaviour recognition and monitoring based on deep convolutional neural networks

We, the Editors and Publisher of the journal Behaviour & Information Technology, have retracted the following article which was part of the Special Issue on Behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning:Kechao Wang, Tiantian Wang, Lin Liu & Chengjun Yuan (2019) Human behaviour recognition and monitoring based on deep convolutional neural networks, Behaviour & Information Technology, DOI: 10.1080/0144929X.2019.1702101After publication it came to our attention that the person named as the Guest Editor of the Special Issue was impersonated by a fraudulent entity and the articles were not reviewed fully in line with the journal’s peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract the articles. The authors have been informed of this decision.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
Secure Outsourcing of Lattice Basis Reduction

In the recent twenty years, lattice basis reduction algorithm, such as the celebrated LLL (A.K. Lenstra-H.W. Lenstra Jr.-L. Lovász) algorithm plays an important role in the public-key schemes design and cryptanalysis. To assess the security of the public-key cryptosystems by lattice reduction algorithms, the most time-consuming part is running the lattice reduction algorithm on the corresponding lattice with high dimension or big coefficients. In this paper, we investigate secure outsourcing for lattice basis reduction for the first time. We propose a lattice basis reduction scheme such that the clients can outsource their main computation workloads to the untrusted cloud servers. We employ rounding technique and unimodular transformation matrix for privacy protection before sending the target lattice basis to the cloud. The results returned from the cloud need to be decrypted and verified to satisfy reduction conditions. The experimental results indicate that our scheme is correct, efficient and feasible.
An efficient approach for mining weighted frequent patterns with dynamic weights.

Natural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba
Securing Relational Database Storage with Attribute Association Aware Shuffling

With the fast-increasing prevalence of mobile devices, mobile cloud computing has emerged as an important computing paradigm. Storing databases in the cloud server has been one significant application of mobile cloud computing. However, there is one severe security problem: the protection of most database system relies on the security schemes of the operating system or the hard drive of the server. If an attacker successfully hacks into a cloud server and accesses the database files, then the attacker is able to recover the database and get information about every attribute of a tuple in the database. Encrypting the database does not fully address the problem as seen in the continued incidents of large-scale database breaches. In this paper, we propose a novel and practical database shuffling approach that breaks the original relations between the value of the attributes while keeping the ostensible integrity. It protects the database storage by storing shuffled relations in data files instead of putting shuffled relations in memory only. For semantically or statistically associated attributes, we shuffle them together as a bundle to ensure that the shuffled database appears deceptively genuine to the attacker. We show that our algorithm is compatible with the current relational database design and can be used to provide an extra layer of protection in addition to encryption.
Adaptive Mixed-Initiative Maintains Players’ Willingness to Talk with NPCs

In most role-playing games (RPGs), non-player characters (NPCs) tend to have most initiative in conversation between the players and the NPCs. The NPCs are mainly talking to lead the players along the storylines of games, and sometimes ask the player to answer simple questions. Such a unilateral conversation is unnatural and unrealistic, and consequently, it is concerned that the player’s sense of immersion into the game will decrease. In order to improve the reality of the conversation, this study proposed a conversation method whereby an NPC adaptively switch the initiative according to the players’ preference of which of the player and the NPC has the initiative. To confirm the effect of this method, we conducted an experiment in which the participants talked with an NPC in a game scenario. This experiment compared the proposed method with the existing method that an NPC has the most initiative and the method that an NPC randomly switches the initiative. The result of the experiment suggested that the proposed method can decrease the unilateral impression compared with the existing method. Furthermore, the result also showed a probability that the proposed method motivates the players to talk to the NPC.
ArchiMate based Security Risk Assessment as a service: preventing and responding to the cloud of things' risks

The Cloud Computing and the Internet of Things (IoT) combination “Cloud of Things” renders the devices' generated data more efficient for the development of intelligent ap- plications. However, due to the data outsourcing, it is confronted to a constant risk. The paper introduces an ArchiMate based Security Risk Assessment as a service model (ASRAaaS) in order to strengths the Cloud of Things' security. This service model integrates two frameworks: preventive and reactive risk analysis. Indeed, the combination of preventive and responsive approaches delivered valuable results. In fact, the Responsive Risk Analysis investigates all the relevant risks earlier, so accordingly before their potential occurrence. This investigation is then tailored as a Model-base, which a mobile agent is responsible for its management. Mobile Agents technology is the key combining the preventive and responsive approaches, shifting thus risk countermeasures decisions from the preventive approach to the responsive one. Our contribution grants security for Cloud of things without influencing its performance.
Anonymising personal data: where do we stand now?

Prof. S. Stalla-Bourdillon explores the continuing confusion around whether personal data may be truly anonymised, and advises on how to do so using a risk-based approach.
ATTACK IDENTIFICATION IN I&amp;C SYSTEMS BASED ON PHYSICAL DATA

In digital instrumentation and control (I&C) systems of nuclear power plants (NPPs), cybersecurity is significant to the normal operation of the systems, because if the I&C system is under attack, physical equipment and processes controlled by the I&C system are all at risk. Different types of attacks may affect different components of I&C system and cause different consequences. Thus, once an attack is detected, an assessment of the range and the extent of the attack is essential to predict the consequences. Attack identification is the first priority step of the attack assessment. Correct determination of the attack type not only helps to assess the attributes and characteristics of the attack, but also is the basis of making appropriate response to avoid the spread of attack effects. Previous work is about attack detection using network data of packets based on replicator neural network method. The packets are recorded from an industry control system (ICS) testbed. The data include normal data and attack data. The attack data are generated on the real ICS testbed attacked by an attack server. Further than attack detection, this paper analyzes network packets deeper and extracts physical data from packets for attack identification. Data parts are separated from packets and physical data are extracted by packet processing according to industrial control protocols. For identification, three typical types of attack, including denial of service (DoS) attack, command injection attack, and data tampering attack, are considered. Features of different types of attack are extracted by analyzing physical data.
An Efficient Phase-Based Binarization Method for Degraded Historical Documents

<p class='IJASEITAbtract'>Document image binarization is the first essential step in digitalizing images and is considered an essential technique in both document image analysis applications and optical character recognition operations, the binarization process is used to obtain a binary image from the original image, binary image is the proper presentation for image segmentation, recognition, and restoration as underlined by several studies which assure that the next step of document image analysis applications depends on the binarization result. However, old and historical document images mainly suffering from several types of degradations, such as bleeding through the blur, uneven illumination and other types of degradations which makes the binarization process a difficult task. Therefore, extracting of foreground from a degraded background relies on the degradation, furthermore it also depends on the type of used paper and document age. Developed binarization methods are necessary to decrease the impact of the degradation in document background. To resolve this difficulty, this paper proposes an effective, enhanced binarization technique for degraded and historical document images. The proposed method is based on enhancing an existing binarization method by modifying parameters and adding a post-processing stage, thus improving the resulting binary images. This proposed technique is also robust, as there is no need for parameter tuning. After using document image binarization Contest (DIBCO) datasets to evaluate this proposed technique, our findings show that the proposed method efficiency is promising, producing better results than those obtained by some of the winners in the DIBCO.
The Relationship between University Autonomy, Lecturer Empowerment, and Organizational Citizenship Behavior in Indonesian Universities

The purpose of this study was to bring forth academics’ opinion or assessment regarding university autonomy, lecturer empowerment, and organization citizenship behavior, and to establish the existing correlations among these three variables. A descriptive and correlational survey method was used, taking in consideration 350 lecturers from five universities, all found in South Kalimantan, Indonesia. Data were collected using a research instrument comprising of 199 items. These items were categorized in three forms, basing on the three variables, that is: University autonomy, which was identified as UA with 77 items; Lecturer empowerment, shortened to LE with 52 items; and Organizational citizenship behavior, coded as OCB with 69 items. A pilot study was conducted to ensure that all items and instrument used were tested for validity and reliability. Data were analyzed using descriptive statistics and Pearson Product-Moment Correlation. The results reveal that university academics sampled from the five premier universities believe that their universities meet the criteria for the established three variables of UA, LE, and OCB. It is therefore, concluded that, there is a moderate understanding of the variables and also a significant relationship existing among them.
Open Research Data Repositories: A Content Analysis to Comprehend Data Equitable Access

The present study pertains to content analysis of open Research Data Repositories (RDRs) worldwide to comprehend the growth and development in the area. The study is original because no attempt has been made so far to do a content analysis of these open research data repositories. The study used the content analysis method of open RDRs listed on the registry of research data repositories. The dataset was analyzed using Microsoft Excel. Each unique parameter was given a unique variable for the purpose of analysis. Subsequently, the dataset was analyzed using Microsoft Excel to achieve the objectives of the study. A simple percentage method was followed in analyses and is presented through Tables and Figures. The study found that there are 1997 RDRs are open worldwide, which are indexed in the registry of research data repositories. It was also found that out of these data repositories 1509 (75.6 percent) are ‘disciplinary’, ‘institutional’ 398 (19.9 percent) and ‘others’ 90 (4.5 percent). Majority open RDRs worldwide use the World Data System (WDS) certification followed by Core Trust Seal. The study found total 70 countries in the world have 1997 RDRs and the United States is the leading country with 704 (35.3 percent) open RDRs, followed by Germany 201 (10.1 percent), United Kingdom 183 (9.2 percent), Canada 118 (5.9 percent). Scientific and statistical data formats are available in maximum (1238) open research data repositories, followed by standard office documents (1108), images (1047), plain text (935), raw data (881). It is ascertained that 1049 (52.5) RDRs are not following any metadata standard in open RDRs and 948 (47.5 percent) follow metadata standards. It was ascertained that Dublin Core is being used by (237) data repositories, followed by ISO 19115 (128), Data Documentation Initiative (DDI) (125). It was found that 963 (48.2 percent) data repositories are using unknown and in-house developed software worldwide. Majority of research data repositories contains the material relating to ‘Biology’ 715 (23.0 percent). Besides this, the study found that open data repositories have been developed in 60 languages worldwide.
FOMO syndrome and degeneration of written language in elementary school students

The research was aim to determine the degree of relationship between syndrome FOMO (Fear of missing out) and written language students of Secondary Educational Institution “José Antonio Encinas” of Juliaca city, 2017. They used the descriptive-correlational design of such non-experimental, positivist paradigm and approach quantitative. The sample was intentional and not probabilistic, made up of students from the fifth grade of secondary, the which they were evaluated with instruments: the questionnaire Fear of missing out syndrome to measure syndrome Fear of missing out and a second questionnaire to measure written language. At the end of the investigation it was identified that the students present Fear of missing out syndrome, very frequently at a very high level, with a frequency that represents 41.37%. Likewise, it has been identified that 63% of students do not respect the spelling regulations, all evidenced in the level of use of the written language Chat. The results indicate that there is r = 0.89 **, of correlation between the two variables, which implies a high positive relationship between Fear of missing out syndrome and written language.
An Unsupervised Feature Extraction Method based on Multi-granularity Convolution Denoising Autoencoder

In recent years, many cutting-edge research results have emerged in the field of computer vision, especially in the field of image classification. But, researchers are still trying to explore more sophisticated models to further improve the accuracy of image classification. However, many models have failed to achieve satisfactory results due to the complexity of the image and the problems of image noise. Therefore, in order to solve these problems, this paper proposes an unsupervised feature extraction method called multi-granularity convolution denoising autoencoder (MGCDAE). Based on convolutional neural network, the method proposed the concept of multi-granularity convolution kernel to solve the problem of complex image feature extraction. In addition, we introduce denoising autoencoder (DAE) for image noise, which enables our approach to extract more robust features from noise images. The high-level features extracted by the above method are sent to the softmax classifier for classification to evaluate the validity of the feature extraction. Our method has been evaluated on three benchmark data sets, which results show that our approach can extract more discriminative high-level features compared with other existing algorithms.
Avatar Social System Improve Perceptions of Disabled People’s Social Ability

Communication is a keyword of our society. However, disabled and mentally ill people show various levels of difficulties in face-to-face communication with the external environment and strangers. We have designed a virtual avatar social system coined Avatar to Person(ATP), which can transfer a real person’s body into digital being. By using this system, the communication ability of disabled people can be effectively improved and face-to-face communication can be accomplished.
Optimization Model of Land Use Planning to Reduce Land Erosion Level: Case Study in the Upper Ciujung River Basin

Uncontrolled changes in land use located in the upstream river can result in land erosion, which will eventually increase river sedimentation as well as floods, reduce the estimated planned life of the downstream reservoir, and reduce soil productivity due to the loss of nutrients. This study is aimed to find the best land use pattern through optimization, in order to reduce the level of land erosion. The optimization model used is the Linear Programming (LP) method which is combined with the USLE method and Geographic Information System software based on raster grids. The potential sedimentation prediction that is collected from the measurement data in the river is used to calibrate the simulation of the real conditions of land erosion. The optimization results of this study in the Upper Ciujung river basin, Banten, can be obtained by expanding as much as possible the area of corn-fields/moors to become plantations (which are denser and bigger tree fields) or even forests. The results can reduce the level of land erosion by 38.0%, or from 99.03 tons/ha/year to 61.32 tons/ha/year. However, the results above cannot be generally applied in all locations. What can be generally accepted is that the optimization model of this study can be used to reduce the level of land erosion by regulating the size of the area and location of each type of land use, and by considering the factors of soil erodibility, slope, and rain erosivity at the site.AbstrakPerubahan tata guna lahan yang tidak terkendali di daerah hulu aliran sungai dapat mengakibatkan erosi lahan, yang pada akhirnya akan meningkatkan sedimentasi di sungai dan banjir, mengurangi umur rencana waduk di hilirnya, serta menurunkan produktivitas tanah karena lapisan hara yang hilang. Penelitian ini ditujukan untuk mencari pola tata guna lahan yang terbaik dengan cara optimasi guna mengurangi tingkat erosi lahan. Model optimasi yang digunakan adalah metode program linier (LP) yang digabung dengan metode USLE dan software SIG (system informasi geografis) berbasis raster grid. Prediksi potensi sedimentasi dari data pengukuran sedimen di sungai digunakan untuk kalibrasi simulasi kondisi nyata erosi lahan. Hasil optimasi pada penelitian di Sub DAS Ciujung Hulu, Provinsi Banten, bisa didapat dengan merubah seluas mungkin areal ladang/tegalan yang sudah ada untuk menjadi kebun campuran atau hutan. Hasilnya dapat memperkecil tingkat erosi lahan sebesar 38,0%, atau turun dari semula sebesar 99.03 ton/ha/thn menjadi 61,32 ton/ha/thn. Namun hasil tersebut diatas tidak bisa berlaku secara umum di semua lokasi. Yang bisa berlaku umum adalah bahwa model optimasi dari studi ini dapat dipakai untuk mengurangi tingkat erosi lahan dengan mengatur luas areal dan penempatan lokasi jenis-jenis penggunaan lahan, dengan mempertimbangkan faktor erodibilitas tanah, kemiringan lereng, dan erosivitas hujan di lokasi.
Architectural Design for the Thing-user Centric Communication

The thing-user is a thing that uses the service provided by other things. The thing-user centric communication serves a thing-user to convey intended meanings by mutually understood language and to discover a peer thing-user or thing-user group who understands its communicative intention and aids the thing-user to achieve a goal autonomously. In the networks for the thing-user centric communication, the thing-user establishes a social community autonomously to share their experiences and to coordinate their capabilities for achieving a goal. This paper defines the thing-user and the thing-user social community, and proposes an architectural design of the thing-user social community in the future networks for the thing-user centric communication.
Improved ANN Model for Predicting the AC Energy Output of a Realistic Photovoltaic Grid Connected PV System

This paper presents the development of an artificial neural network model for predicting the long-term/monthly mean daily AC energy output generated by a commercialized PV power plant, with enhanced prediction accuracy. The input factors incorporated are system independent. The monitored input data set of a 1 MW <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> realistic PV plant at Erode is used for training and testing the proposed ANN model. The accuracy of the proposed model is documented by the value of mean percentage error, mean absolute percentage error and root mean square error. ANN model with 4 inputs serves to accurately predict with Mean Absolute Percentage Error (MAPE) of 1.68%. Furthermore, the proposed ANN model is also compared with reported ANN and regression based models highlighting its accuracy.
Analysis of Literary Techniques Employed in the Novel The Revelation: Flashback in Focus

This study mainly focused on a critical analysis of flashback employed in the novel The Revelation.Descriptive research design with qualitative data analysis approach was employed.Purposive sampling technique was employed to select the novel under scrutiny.In addition, the researcher used document analysis as data gathering tool.The novelist employed different past events in the novel to reflect different life lessons or experiences.This helped the leading character Blen to learn various life experiences; for instance, she shared the wedding tradition of her grandparents, what traditions they were practicing, the life journey of her parents and this, in turn, helped her to be conscious about her stand, especially on her education and it also helped Blen to know her parents' and grandparents' background very well.Inculcating the past events in the novel had in short motivated Blen to be dynamic in everything.In addition, the author inserted these past events to create tension in the readers mind especially when Hiwot narrated how her husband or father of Blen, Alemayehu escaped from death and left his homeland to a new country.Finally, Conclusions and recommendations have been drawn