Reviewerly/exHarmony huggingface dataset:
Factors Affecting Teachers' Motivation to Adopt Cloud-based E-learning System in Iraqi Deaf Institutions: A pilot Study

Hard-of-hearing (HoH) and hearing loss or hearing-impaired individuals describe “those who cannot hear or do not hear well” due to being born deaf or who may have lost some or all of their hearing ability at some time in their life. Some of the hearing-impaired people use sign language to communicate with other similarly-affected individuals as well as with those with normal hearing ability. Sign language comprises a range of hand shapes, facial expressions, body movements, and lip patterns. Currently, one of the most urgent requirements of the hearing-impaired, especially the young among them is their education needs. They need to learn how to communicate and convey information as well as effectively share learning materials between them. Towards this end, the potential solution lies in the use of today's advanced modern global technology innovations, such as cloud based on e-learning systems. This system can be made accessible to instructors and students and prove to be a practical and effective pedagogical tool for the hearing-impaired and their instructors. This study aims to investigate the key factors that can positively impact teachers' motivation to adopt cloud based on e-learning systems for deaf students. A survey research design method was chosen to verify the factors and construct the relationship between them. The main factors that may affect teachers' motivation to adopt and use cloud based on the e-learning system and involve the following key factors: hardware availability, software modularity, cost, security and privacy, training, knowledge sharing, top management support, sign language, video and animation, online dictionary, teacher motivation, and system adoption. To verify the reliability of the questionnaire, a pilot study was done among 30 participants. The questionnaire was distributed among teachers of deaf students in Baghdad deaf institutes to examine the hypothesis on each factor. Cronbach's Alpha coefficient reliability and exploratory factor analysis EFA test in SPSS 22 software package was employed to verify the reliability of the questionnaire. The results showed that the value of Cronbach's Alpha coefficient is 0.7 or more and KMO are above the threshold values in the test, which are standard.
Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders

In the loss function of Variational Autoencoders there is a well known tension between two components: the reconstruction loss, improving the quality of the resulting images, and the Kullback-Leibler divergence, acting as a regularizer of the latent space. Correctly balancing these two components is a delicate issue, easily resulting in poor generative behaviours. In a recent work, Dai and Wipf obtained a sensible improvement by allowing the network to learn the balancing factor during training, according to a suitable loss function. In this article, we show that learning can be replaced by a simple deterministic computation, helping to understand the underlying mechanism, and resulting in a faster and more accurate behaviour. On typical datasets such as Cifar and Celeba, our technique sensibly outperforms all previous VAE architectures.
Perceptions of Cybersecurity Readiness among Workgroup IT Managers

Any modern cybersecurity strategy relies upon IT managers who are experts in their field, who are willing to employ best practices, and who educate their users. Large corporations have developed an intrinsic understanding of these factors. However, large corporations are comprised of departmental units that are not always considered in the organization's cybersecurity profile. This is particularly true at colleges and universities where IT managers who are not affiliated with the central IT department sometimes employ their own security strategies. This article describes a study that surveyed 161 IT managers to determine their perceptions of their cybersecurity readiness. The model described in this study is the Practice and Awareness Cybersecurity Readiness Model (PACRM). The survey instrument was developed and validated, then tested using path analysis. Eleven of twenty-six relationships were found to be significant. This model offers a step forward in understanding top-to-bottom cybersecurity readiness in the modern threat environment.
The Multicultural Sensitivity and Cultural Openness of Childhood Teachers Effect on the Multicultural Teaching Efficacy

본 연구는 유아교사의 다문화 감수성과 문화 개방성이 다문화 교수효능감에 미치는 영향요인을 파악하기 위하여 중다회귀분석을 실시하였다. G시에 거주하는 유아교사를 대상으로 2019년 11월 11일부터 11월 22일까지 설문조사가 이루어졌다. 분석결과 첫째, 유아교사의 다문화 감수성, 문화 개방성, 다문화 교수효능감 간에는 정적인 상관관계가 있는 것으로 나타났다. 둘째, 유아교사의 일반생활 교수효능감에는 다문화 감수성의 상호작용 향유도와 상호작용 주의도가 영향요인으로 나타났다. 수업기능 교수효능감에는 상호작용 참여도, 상호작용 자신도, 상호작용 주의도, 문화 개방성이 나타났다. 교수효능감에는 상호작용 향유도, 상호작용 주의도, 문화 개방성이 영향요인으로 제시되었다. 이러한 결과를 바탕으로 다문화 교수법에 대한 연수 등을 통해 유아교사의 다문화 교수효능감을 높이는 방안을 제시하였다.The study conducted a multi-regression analysis to identify how the multicultural sensitivity and cultural openness of early childhood teachers affect multicultural teaching efficacy. A survey was conducted between November 11 and November 22, 2019 for preschool teachers in G City. The analysis result is as follows: First, there is a static correlation between the multicultural sensitivity, cultural openness, and multicultural teaching efficacy of early childhood teachers. Second, enjoyment and awareness of interactions of multicultural sensitivity were the influencing factors for the daily life teaching efficacy of early childhood teachers. Interaction participation, interaction confidence, interaction attention, and cultural openness were shown in the teaching efficacy of the class function. The influencing factors of teaching efficacy were interaction enjoyment, interaction attention, and cultural openness. Based on these results, this study suggested a method for improving the teaching efficacy of multicultural teachers through the training on multicultural teaching methods.
A Comparative Study of Clustering Algorithms

In present era, data analysis plays vital role in various domains. Data clustering is a data analysis technique used for grouping of data objects based on unsupervised learning. Many clustering algorithms have been proposed in the literature. Each algorithm possesses some strengths and weaknesses. Therefore, a set of clustering algorithms are appropriate for one set of application area while another set of clustering algorithm are suitable for another set of application areas. In this paper, popular traditional algorithms are discussed. A comprehensive comparative study of different clustering algorithms is presented in this paper. These clustering algorithms are compared in detail based on various parameters used in these methods.
CASCloud: An Open Source Private Cloud for Higher Education

Cloud computing is the delivery of computing as a service rather than product. Cloud has fundamentally changed the way we use software, develop applications, and deliver resources. Studies have shown that cloud computing can play a major role in higher education. Nowadays, higher education institutions are teaching many IT courses. Their curriculum requires multiple servers, operating systems, network devices and many software tools to study and perform lab practical. Cloud computing can fulfil the growing demands of IT lab resources for HEI. Private cloud can provide alternative to hardware resources using virtualization. This paper presents the proof-of-concept private cloud for higher education and discusses the process of setting up a working model of a private cloud using OpenStack. Outcomes show that it can provide a cost effective solution for IT hardware devices and provision multiple operating systems required in higher education.
Fast Traffic Sign Detection Using Color-Specific Quaternion Gabor Filters

A novel and fast traffic sign detection method is proposed based on color-specific quaternion Gabor filtering (CS-QGF). The proposed method is based on the fact that traffic signs are usually specialized in color and shape. Accordingly, we apply a quaternion Gabor transformation to extract the color and shape features of traffic signs simultaneously. Statistical color distribution of traffic sign is analyzed to optimize the construction of quaternion Gabor filters. The feature extracted via CS-QGF is robust to the distortion of color, the change of image resolution, and the change of lighting and shading conditions, which helps the following traffic sign detector reduce the search range of proposal regions. Experiments on GTSDB and TT100K datasets demonstrate that the proposed method helps to localize traffic signs in images with high efficiency, which outperforms state-of-the-art methods on both detection speed and final recognition accuracy.
HushRelay: A Privacy-Preserving, Efficient, and Scalable Routing Algorithm for Off-Chain Payments

Payment channel networks (PCN) are used in cryptocurrencies to enhance the performance and scalability of off-chain transactions. Except for opening and closing of a payment channel, no other transaction requests accepted by a PCN are recorded in the Blockchain. Only the parties which have opened the channel will know the exact amount of fund left at a given instant. In real scenarios, there might not exist a single path which can enable transfer of high value payments. For such cases, splitting up the transaction value across multiple paths is a better approach. While there exists several approaches which route transactions via several paths, such techniques are quite inefficient, as the decision on the number of splits must be taken at the initial phase of the routing algorithm (e.g., SpeedyMurmur [42]). Algorithms which do not consider the residual capacity of each channel in the network are susceptible to failure. Other approaches leak sensitive information, and are quite computationally expensive [28]. To the best of our knowledge, our proposed scheme HushRelay is an efficient privacy preserving routing algorithm, taking into account the funds left in each channel, while splitting the transaction value across several paths. Comparing the performance of our algorithm with existing routing schemes on real instances (e.g., Ripple Network), we observed that HushRelay attains a success ratio of 1, with an execution time of 2.4 sec. However, SpeedyMurmur [42] attains a success ratio of 0.98 and takes 4.74 sec when the number of landmarks is 6. On testing our proposed routing algorithm on the Lightning Network, a success ratio of 0.99 is observed, having an execution time of 0.15 sec, which is 12 times smaller than the time taken by SpeedyMurmur.
Mobile Biometrics, Replay Attacks, and Behavior Profiling: An Empirical Analysis of Impostor Detection

The rise of mobile devices has contributed new biometric modalities which reflect behavioral tendencies as users interact with the device's services. In this paper, we explore replay attacks against such systems and how a remote attack might affect authentication performance. There are few efforts that focus on replay attacks in mobile biometric systems, and none to our knowledge related to user-device interactions, such as the use of mobile apps. Instead, previous efforts have mainly considered spoofing attacks, which implicate that the attacker has learned their target's behavior instead of obtaining a direct copy of logged behavior by theft. Here, we explore temporally-derived replay attacks that assume that application, Bluetooth, and Wi-Fi data has been captured remotely and then intelligently combined with some level of noise to avoid the replay of an exact copy of legitimate data. We study several factors that may affect replay attack detection, including the effects of varying the amount of data available during data collection, the number of samples used for training, and supervised and unsupervised learning on attack detection. In our analysis, false positive rates increased from 2.3% when using zero-effort attacks to over 40% as a result of replay attacks. However, our results also show that by contextualizing behavior in the feature representation, false positive rates decrease by over 25%.
Fall Detection using Accelerometer Calibration

The risk of fall increases in elderly people due to aging factor. In this paper, a new methodology is revised to perform learning and classification of falls using single accelerometer. In learning and classification of falls, the sensor(s) is normally placed at the same location on human body which might not happen practically. In this paper, it is shown that if the sensor is misplaced during classification, the accuracy of fall detection reduces significantly. Furthermore, a calibrated system is designed which detects the falls accurately even if the sensor is misplaced. Classification is performed using Complex Tree, Quadratic Support Vector Machine and Cubic K-Nearest Neighbor, their accuracies are compared in both scenarios; with/without calibration, and the optimum classifier is identified.
The Error Analysis of Learning Mandarin Endocentric Phrases among the Malay Students in Malaysia

The Mandarin language has gained currency in the Malaysian education landscape. Particularly in tertiary educational settings in Malaysia, it is common to find Malay students learning the Mandarin language. Universiti Teknologi MARA (UiTM) is the largest university in Malaysia and it has the most number of Malay students learning Mandarin as a third language in the country. Almost all of the Malay students who study the Mandarin language at the university have no background in Mandarin. They are inevitably influenced by their mother tongue which is the Malay language. This paper aims to analyze Endocentric Mandarin phrasal errors commonly made by Malay students while studying Mandarin in UiTM. In addition, some teaching methods are also recommended to guide those involved in teaching and learning Mandarin. This study is conducted using a mixed method approach. Two research instruments were used in this study, that is question papers and interviews. 40 Malay students who were taking Introductory Mandarin Level II course in UiTM Shah Alam campus were chosen as the survey respondents. The results of the research revealed that there are 4 types of errors commonly made by Malay students in learning the Mandarin Endocentric Phrases, namely: (i) Omission of Measure Words, (ii) Reversed string order of Endocentric Phrases, (iii) Errors in Using Demonstrative Pronouns and (iv) Excess of the particle “de”. The most obvious mistake is the Omission of Measure Words and the Reversed string order of Endocentric Phrases due to the first language disruption. These errors have often resulted in structural and morphological inaccuracies.
An Approach to Find Synonym and Bigram to Improve Query Recommendation Efficiency of Exploratory Search Using Search Goal Shift Graphs

Exploratory search aids in the production of desired results which improve browsing abilities of user in current era. To improve search engine performance, this method inspects search goal shift to obtain pertinent knowledge about query entered by the user. The existing system fails to provide bigram relationship and ignores synonyms of the submitted query. Also there is un-certainty of spam links which may be included in the final result. To overcome these lacunas, the proposed framework will conduct an exploratory search to recommend query by making use of bi-gram approach which will also remove the spam links with the help of trust rank algorithm and finds all possible synonyms of the submitted query in order to obtain a proper result. This phenomenon helps to produce better user recommendations.
Information cartography in association rule mining

Association Rule Mining is a machine learning method for discovering the interesting relations between the attributes in a huge transaction database. Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would be suitable for the user. Recently, an information cartography has been proposed for creating structured summaries of information and visualizing with methodology called maps. This was applied to several problem domains, where pattern mining was necessary. The aim of this study is to develop a method for automatic creation of metro maps of information obtained by Association Rule Mining and, thus, spread its applicability to the other machine learning methods. Although the proposed method consists of multiple steps, its core presents metro map construction that is defined in the study as an optimization problem, which is solved using an evolutionary algorithm. Finally, this was applied to four well-known UCI Machine Learning datasets and one sport dataset. Visualizing the resulted metro maps not only justifies that this is a suitable tool for presenting structured knowledge hidden in data, but also that they can tell stories to users.
CPace, a balanced composable PAKE

This document describes CPace which is a protocol for two parties thatshare a low-entropy secret (password) to derive a strong shared keywithout disclosing the secret to offline dictionary attacks. Thismethod was tailored for constrained devices, is compatible with anygroup of both prime- and non-prime order, and comes with a securityproof providing composability guarantees.
KBR: Knowledge Based Reduction Method for Virtual Machine Migration in Cloud Computing

Through large scale deployment of cloud data centers, burning up of energy by data center is very high and violations of service level agreements (SLA) are increasing. Implementation of Virtual machine consolidation techniques direct to physical host over exploitation which degrades the performance of physical host and Virtual machine. It has been a momentous confronts to get better exploitation of resources and energy effectiveness in cloud data center. This paper proposed knowledge based reduction method for Virtual machine assignment and consolidation. This method chooses the Virtual machines from the overloaded host and migrate the Virtual machines to other physical host to diminish energy burning up and get better quality of service. To provide evidence and effectiveness of proposed method, the experiment is conducted with diverse workload circumstances of real system. The experimental consequences exhibits that the proposed method diminishes energy burning up and optimizes infringement of service level agreements in achieving better performance.
Inference Control in Distributed Environment: A Comparison Study

Traditional access control models aim to prevent data leakage via direct accesses. A direct access occurs when a requester performs his query directly into the desired object, however these models fail to protect sensitive data from being accessed with inference channels. An inference channel is produced by the combination of a legitimate response which the user receives from the system and metadata. Detecting and removing inference in database systems guarantee a high quality design in terms of data secrecy and privacy. Parting from the fact that data distribution exacerbates inference problem, we give in this paper a survey of the current and emerging research on the inference problem in both centralized and distributed database systems and highlighting research directions in this field.
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization

In recent years, Multifactorial Optimization (MFO) has gained a notable momentum in the research community. MFO is known for its inherent capability to efficiently address multiple optimization tasks at the same time, while transferring information among such tasks to improve their convergence speed. On the other hand, the quantum leap made by Deep Q Learning (DQL) in the Machine Learning field has allowed facing Reinforcement Learning (RL) problems of unprecedented complexity. Unfortunately, complex DQL models usually find it difficult to converge to optimal policies due to the lack of exploration or sparse rewards. In order to overcome these drawbacks, pre-trained models are widely harnessed via Transfer Learning, extrapolating knowledge acquired in a source task to the target task. Besides, meta-heuristic optimization has been shown to reduce the lack of exploration of DQL models. This work proposes a MFO framework capable of simultaneously evolving several DQL models towards solving interrelated RL tasks. Specifically, our proposed framework blends together the benefits of meta-heuristic optimization, Transfer Learning and DQL to automate the process of knowledge transfer and policy learning of distributed RL agents. A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality , and the intertask relationships found and exploited over the search process.
Training through the implementation of a web-based laboratory for Artificial Intelligence exercises

Modern ways of learning require the use of innovative educational tools in all disciplines. The development of information and communication technology has provided an innovation known as the virtual laboratory. Such a type of educational tool will help students and their trainees. It will facilitate the experimental activity, which will lead to a better understanding of the subject matter by the students. This study aims to reveal the quality of the virtual laboratory integrated into blended learning the discipline artificial intelligence. It must combine all sub-area presented with their characteristic algorithms. The sub-areas will be divided into groups of tasks. Each group will task have three difficulty levels. One task can be solved with more than one kind of an algorithm area. These solutions can be compared and visualized to see where the differences are. Presented is model of work of the virtual laboratory. The work of the laboratory includes four basic modules: theoretical presentation, graphical presentation, sample tasks and individual tasks. It is divided into these basic modules to give learners a chance to catch up on a lesson if they have missed it. The laboratory should be web-based and therefore the only restriction on its use is the Internet connection. This will make it very practical and convenient for both parties - students and students. The benefits of training with this type of laboratory are also presented. The aim of the article is to showcase the work of this kind of web-based laboratory. How it will be a powerful tool for removing unnecessary assumptions and reducing the time for learning the material.
Inverted LSB Image Steganography

This paper focus on a change in the plain Least Significant Bit based image/picture steganography and implementation of it. The paper asserts the utilization of bit reversal method to enhance the quality of stego-picture. The bit reversal methods that are proposed and implemented based on 2 schemes. In these strategies, least significant bits of a few picture elements of cover picture are rearranged if they happen with a particular pattern of a few bits of the picture elements. Along these lines, less number of picture elements is adjusted in contrast with plain least significant bit strategy. So PSNR of stego-picture is moved forward. In order to obtain correct de-steganography, the bit designs for which least significant bits has modified should be put away inside the stego-picture some place. The proposed bit reversal procedure gives great change to least significant bit steganography. This strategy could be joined along different strategies to enhance effective steganography further.