Reviewerly/exHarmony huggingface dataset:
Statement Voting

The conventional (election) voting systems, e.g., representative democracy, have many limitations and often fail to serve the best interest of the people in a collective decision-making process. To address this issue, the concept of liquid democracy has been emerging as an alternative decision-making model to make better use of “the wisdom of crowds”. However, there is no known cryptographically secure e-voting implementation that supports liquid democracy. In this work, we propose a new voting concept called statement voting, which can be viewed as a natural extension of the conventional voting approaches. In the statement voting, instead of defining a concrete election candidate, each voter can define a statement in his/her ballot but leave the vote “undefined” during the voting phase. During the tally phase, the (conditional) actions expressed in the statement will be carried out to determine the final vote. We initiate the study of statement voting under the Universal Composability (UC) framework, and propose several construction frameworks together with their instantiations. As an application, we show how statement voting can be used to realize a UC-secure liquid democracy voting system. We remark that our statement voting can be extended to enable more complex voting and generic ledger-based non-interactive multi-party computation. We believe that the statement voting concept opens a door for constructing a new class of e-voting schemes.
Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.
Risk Convergence of Centered Kernel Ridge Regression With Large Dimensional Data

This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset. This modified technique is obtained by accounting for the bias in the regression problem resulting in the old kernel ridge regression but with \emph{centered} kernels. The analysis is carried out under the assumption that the data is drawn from a Gaussian distribution and heavily relies on tools from random matrix theory (RMT). Under the regime in which the data dimension and the training size grow infinitely large with fixed ratio and under some mild assumptions controlling the data statistics, we show that both the empirical and the prediction risks converge to a deterministic quantities that describe in closed form fashion the performance of CKRR in terms of the data statistics and dimensions. Inspired by this theoretical result, we subsequently build a consistent estimator of the prediction risk based on the training data which allows to optimally tune the design parameters. A key insight of the proposed analysis is the fact that asymptotically a large class of kernels achieve the same minimum prediction risk. This insight is validated with both synthetic and real data.
IoT and Big Data Technologies: Opportunities and Challenges for Higher Learning

Internet of Things (IoT) and Big data have been speculated to result in various opportunities and challenges to higher learning. The two mentioned technologies inclusive of cloud computing, pervasive learning and 3D printer can improve teaching and learning beyond classrooms. Furthermore, the above-mentioned technologies have led to enhancements and valuable living in many sectors like education, medicine, agriculture, and even security. Taking into consideration the predictions made for IoT and Big Data, it is beneficial to provision for confidence in this current world. IoT and Big Data have therefore emerged as innovations to change the education sector for quality training, education, and research. However, there have been challenges accruing to the mentioned technologies. This study gave an approach of how both IoT and Big Data could play similar beneficial roles to higher learning. This paper suggests that IoT and Big data can be incorporated in learning as they complement each other in supporting intelligent connections. The outcome is an enriched learner with effective and efficient study surrounding. Further, from the study, both technologies also share similar issues like security, transparency, and huge gathered information. Hence, this paper concentrates on the relationship between IoT and Big Data, examines their opportunities in education and mentions their challenges to improve the education sector.
Robotic manipulator trajectory optimisation using an improved modified bat algorithm

This research article presents a modified bat algorithm (MBA) that provides an improved optimal solution to trajectory planning problem of industrial robot manipulators. For demonstration purpose of the trajectory optimisation problem, two robotic manipulators viz. planar 3R manipulator and 4-DOF SCARA robot manipulator has been chosen. The main objective of the proposed algorithm is to minimise Cartesian trajectory length, joint angle variation and positional error in different directions of motion. In order to achieve the above stated objective, the Cartesian length of trajectory has been selected as fitness function for the MBA and basic bat algorithm (BA) and cuckoo search (CK) algorithm. The validation of the effectiveness of the proposed MBA is performed by conducting Friedman rank test, comparing its result with the optimal solution of BA and CK for the adopted manipulators. The experimental validation of MBA results is verified with real-time simulation of a SCARA robot manipulator.
Phishing and Cybercrime Risks in a University Student Community

In an exploratory quasi-experimental observational study, 138 participants recruited during a university orientation week were exposed to social engineering directives in the form of fake email or phishing attacks over several months in 2017. These email attacks attempted to elicit personal information from participants or entice them into clicking links which may have been compromised in a real-world setting. The study aimed to determine the risks of cybercrime for students by observing their responses to social engineering and exploring attitudes to cybercrime risks before and after the phishing phase. Three types of scam emails were distributed that varied in the degree of individualization: generic, tailored, and targeted or ‘spear.’ To differentiate participants on the basis of cybercrime awareness, participants in a ‘Hunter’ condition were primed throughout the study to remain vigilant to all scams, while participants in a ‘Passive’ condition received no such instruction. The study explored the influence of scam type, cybercrime awareness, gender, IT competence, and perceived Internet safety on susceptibility to email scams. Contrary to the hypotheses, none of these factors were associated with scam susceptibility. Although, tailored and individually crafted email scams were more likely to induce engagement than generic scams. Analysis of all the variables showed that international students and first year students were deceived by significantly more scams than domestic students and later year students. A Generalized Linear Model (GLM) analysis was undertaken to further explore the role of all the variables of interest and the results were consistent with the descriptive findings showing that student status (domestic compared to international) and year of study (first year student compared to students in second, third and later years of study) had a higher association to the risk of scam deception. Implications and future research directions are discussed.
Technology to form Students’ Readiness for Research in Engineering Universities

The relevance of the problem under consideration is caused by the existing discrepancy between the increased need of society and the state for high quality engineering education, and the actual level of readiness of graduates of engineering universities to perform their professional functions, as well as insufficient development of the theoretical foundations of students training in research in the context of competence-oriented education. The purpose of the article is to solve the problem of training students for the research activity in the course of independent work and research during the educational process at engineering university. Based on the systematization and development of existing approaches to solving the problem of training of students of engineering universities for research, which contributes to the development of a functional model, the authors propose the method of preparing students for research. The article describes the development of practical experience in research activities during the participation of students in scientific seminars; represents the process of using the project method, end-to-end research tasks when preparing the course and qualification works; the issue is considered on consolidating and improving the experience of research activities of future engineers during all kinds of practical training. The pedagogical experiment was aimed at identifying the effectiveness of independent and research work in the course of formation of students' readiness for research activities in the framework of the functional model. The materials of the article can be used by the academic staff of engineering universities, specialists in the field of pedagogy, theory, and methodology of vocational education, as well as in the system of extended education.
Development of QoS Evaluation Algorithm for MQTT Protocol with Reference to Threat Model

MQTT protocol is publishing-subscribing model for IoT communication. In case of Quality of Services analysis, it is important to check the request and responses between publisher and subscriber. Any threat in communication channel is mostly leads to delay in operation. Hence, if we able to identify the delay parameter, we can suggest by means of QoS that there is a immediate need of security check for IoT system. As many IoT devices performed in unchecked, complicated, and often aggressive surroundings, safe-guarding IoT units present many different challenges. The key purpose for support quality degradation of IoT device interaction can be harmful attacks. Plenty of gadgets are often susceptible to port attacks/botnets hits, such as network attack events, which usually assessed by performing QoS Analysis. To start with factors affecting Quality of Services (QoS), in this paper we developed QoS evaluation algorithm “MQoS” for MQTT protocol and considered QoS-0 as an evaluation parameter. This paper refers the threat model which represents the flow of threats for proposed case study and can help to identify QoS by evaluating the possible communication threats. End–to-end device communication requests and responses are needed to be evaluated for large systems to get the actual QoS parameters for that system. For this reason the actual QoS tests will be conducted for third party applications.In this paper we presented results of MQTTv311 simulation for cooling sensor system.
Privacy Preserving Using Extended Euclidean Algorithm Applied To RSA-Homomorphic Encryption Technique

Communication of confidential information over Internet is the key aspect of security applications. Providing protection to sensitive information is of major concern. Many cryptographic algorithms have been in use for providing security of confidential information. Providing security for data has become major challenge in this era. Classical cryptography is playing a major role in providing security for applications. In modern days securing confidential information in the cloud is considered as an important challenge. Homomorphic Encryption technique is one of the best solutions that provide security in the cloud[1]. In this paper, Extended Euclidean Algorithm is used for generating keys. This technique follows RSA Homomorphic encryption technique. .RSA Homomorphic encryption using Extended Euclidean algorithm (RSA-HE-EEA) is secure when compared to RSA as it based on the generation of private key which makes the algorithm complex .This technique of using Extended Euclidean Algorithm(EEA) is fast and secure when compared to RSA homomorphic encryption technique. The encryption process utilizes modulo operator which gives security as well.The beauty of this algorithm is in generation of private key which uses Extended Euclidean Algorithm (EEA) that helps in avoiding brute force attacks. Also, this technique uses Homomorphic operations which gives enhance security to confidential information in the cloud.
Cognitive Factors of "Art+" Cross-Cultural Pedagogical Technology

The authors believe that the strategy of modern education should be aimed at updating the content of education and developing mechanisms for its mastering in accordance with the realities of the modern world and the psychology of present-day childhood. The article reveals the ways of finding answers to the issues, which are topical for modern education, namely, how to convey to pupils the necessary amount of fundamental and most modern knowledge, how to ensure knowledge acquisition, how to spark children's interest in learning the world, and teach them to apply acquired knowledge in practice. Based on the research in the field of cognitive psychology and pedagogy of art, the authors distinguish the factors of creative cognition, describing the "ART+" cross-cultural pedagogical technology and its principles, as well as provide guidelines on the application of technology in the educational process of secondary school to make the learning process fun and creative.
Techniques of Recommender System

The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.
Investigating effects and learners' perceptions of a student-led, AR-based learning design for developing students' English speaking proficiency

Recent years have witnessed successful application of AR-based, Context-Aware Ubiquitous Learning (CAUL) for students to acquire English as a Foreign Language (EFL). Nevertheless, only a dearth of research is conducted to examine the effectiveness and learners' perceptions of student-led AR-based CAUL for enhancing students' English speaking proficiency. The aim of the research is to gain more understanding on the use of the design in the tertiary education for facilitating EFL students' oral proficiency. Both quantitative (pre- and post-test, and a questionnaire) and qualitative (a semi-structured interview) methodology were employed in the study. The results revealed that students' English speaking abilities were significantly developed after the activity; furthermore, the questionnaire also showed students' positive learning attitudes, with more motivation and satisfaction. In alignment with the quantitative results, benefits of the activity were also elicited in the interview. Finally, some suggestions for practitioners interested in student-led AR CAUL design were also provided.
Sight restoration in congenitally blind humans does not restore visual brain structure

Abstract It is unknown whether impaired brain structure after congenital blindness is reversible if sight is restored later in life. Using structural magnetic resonance imaging, visual cortical surface area and cortical thickness were assessed in a large group of 21 sight-recovery individuals who had been born blind and who months or years later gained sight through cataract removal surgery. As control groups, we included 27 normally sighted individuals, 10 individuals with permanent congenital blindness, and 11 sight-recovery individuals with a late onset of cataracts. Congenital cataract-reversal individuals had a lower visual cortical surface area and a higher visual cortical thickness than normally sighted controls. These results corresponded to those of congenitally permanently blind individuals suggesting that impaired brain structure did not recover. Crucially, structural brain alterations in congenital-cataract reversal individuals were associated with a lower post-surgery visual acuity. No significant changes in visual cortex structure were observed in sight-recovery individuals with late onset cataracts. The results demonstrate that impaired structural brain development due to visual deprivation from birth is not fully reversible and limits functional recovery. Additionally, they highlight the crucial importance of prevention measures in the context of other types of aberrant childhood environments including low socioeconomic status and adversity.
SYSTEMATIC LITERATURE REVIEW: PSYCHOLOGICAL CONCEPTS OF LEARNING IN HANDLING SPEAKING DELAY IN EARLY CHILDREN

Language learning for early childhood is directed at the ability to communicate, both orally and in writing. However, in reality there are still early childhood who have speech delays. The purpose of this review article is to review various journal articles regarding the handling of speech delay in early childhood. The method used is a literature review method with reference sources obtained from the Publish or Perish 7 program and Google Scholar. The criteria for articles used as references are journals published during 2015-2021 and full text can be accessed. The results of the selected article review stated that the teacher's efforts in handling speech delays in early childhood were the teacher doing classroom management, applying modeling methods, correcting word errors, inviting children to tell stories and using body movements, and selecting appropriate and interactive learning methods. Parents' efforts in handling speech delays in children are by increasing parents' knowledge about the concept of speech delay and having good storytelling skills, parents must know 10 signs of a child experiencing speech delay. Handling speech delay in children requires collaboration between schools, parents and the environment to help improve communication between children and their environment.
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning

Understanding causality has vital importance for various Natural Language Processing (NLP) applications.Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal facts to facilitate the causal reasoning process.However, such explanation information still remains absent in existing causal reasoning resources.In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 21K causal reasoning questions, together with natural language formed explanations of the causal questions.Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.
PRISM: Pre-trained Indeterminate Speaker Representation Model for Speaker Diarization and Speaker Verification

Speaker embedding has been a fundamental feature for speaker-related tasks such as verification, clustering, and diarization. Traditionally, speaker embeddings are represented as fixed vectors in high-dimensional space. This could lead to biased estimations, especially when handling shorter utterances. In this paper we propose to represent a speaker utterance as "floating" vector whose state is indeterminate without knowing the context. The state of a speaker representation is jointly determined by itself, other speech from the same speaker, as well as other speakers it is being compared to. The content of the speech also contributes to determining the final state of a speaker representation. We pre-train an indeterminate speaker representation model that estimates the state of an utterance based on the context. The pre-trained model can be fine-tuned for downstream tasks such as speaker verification, speaker clustering, and speaker diarization. Substantial improvements are observed across all downstream tasks.
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text.Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investigated.In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute two new datasets, Ecom-merceMKP and AcademicMKP, covering six languages.Technically, we propose a retrievalaugmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages.The retrievalaugmented model leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages.Given a non-English passage, a cross-lingual dense passage retrieval module finds relevant English passages.Then the associated English keyphrases serve as external knowledge for keyphrase generation in the current language.Moreover, we develop a retriever-generator iterative training algorithm to mine pseudo parallel passage pairs to strengthen the cross-lingual passage retriever.Comprehensive experiments and ablations show that the proposed approach outperforms all baselines.