Reviewerly/exHarmony huggingface dataset:
Data Breach e-Crime, A Case Study and Legal Analysis

The Bonafede V. EE data breach is a reported data breach e-Crime in the media, also published by the BBC on 8th February 2019 in the United Kingdom which has not yet come to Court. Three laws and regulations of the United Kingdom that have been breached and references to relevant case laws are highlighted in this paper. Further, Facts of the Case; Issues observed; Decision made; Reasonings to the case; Opinions; and Analysis are discussed. The discussions include legal points raised in the case, with the relevant laws to draw attention to the keywords or phrases that are in dispute. There are sufficient details of the organization for the reader to understand both the scale and its activity. Discussions refer to case law in support with the reasonings outlined, point by point in numbered paragraphs. At the analysis stage, the significance of the case, its relationship to other referenced cases, its place in history and the value of the current laws in place are evaluated while making references to current law. KEYWORDS: Data breach, e-Crime, Cyberlaw, Cybercrime, I.T and Internet law
Field Extension in Secret-Shared Form and Its Applications to Efficient Secure Computation.

Secure computation enables participating parties to jointly compute a function over their inputs while keeping them private. Secret sharing plays an important role for maintaining privacy during the computation. In most schemes, secret sharing over the same finite field is normally utilized throughout all the steps in the secure computation. A major drawback of this “uniform” approach is that one has to set the size of the field to be as large as the maximum of all the lower bounds derived from all the steps in the protocol. This easily leads to a requirement for using a large field which, in turn, makes the protocol inefficient. In this paper, we propose a “non-uniform” approach: dynamically changing the fields so that they are suitable for each step of computation. At the core of our approach is a surprisingly simple method to extend the underlying field of a secret sharing scheme, in a non-interactive manner, while maintaining the secret being shared. Using our approach, default computations can hence be done in a small field, which allows better efficiency, while one would extend to a larger field only at the necessary steps. As the main application of our technique, we show an improvement upon the recent actively secure protocol proposed by Chida et al. (Crypto’18). The improved protocol can handle a binary field, which enables XOR-free computation of a boolean circuit. Other applications include efficient (batch) equality check and consistency check protocols, which are useful for, e.g., password-based threshold authentication.
Clustered Gaussian Graphical Model via Symmetric Convex Clustering

Knowledge of functional groupings of neurons can shed light on structures of neural circuits and is valuable in many types of neuroimaging studies. However, accurately determining which neurons carry out similar neurological tasks via controlled experiments is both labor-intensive and prohibitively expensive on a large scale. Thus, it is of great interest to cluster neurons that have similar connectivity profiles into functionally coherent groups in a data-driven manner. In this work, we propose the clustered Gaussian graphical model (GGM) and a novel symmetric convex clustering penalty in an unified convex optimization framework for inferring functional clusters among neurons from neural activity data. A parallelizable multi-block Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the corresponding convex optimization problem. In addition, we establish convergence guarantees for the proposed ADMM algorithm. Experimental results on both synthetic data and real-world neuroscientific data demonstrate the effectiveness of our approach.
Neural Consciousness Flow

The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have been made to model neural-based reasoning as an iterative decision-making process based on recurrent networks and reinforcement learning. Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow). Aiming to bridge the gap between deep learning systems and reasoning, we propose an attentive computation framework with a three-layer architecture, which consists of an unconsciousness flow layer, a consciousness flow layer, and an attention flow layer. We implement the NeuCFlow model with graph neural networks (GNNs) and conditional transition matrices. Our attentive computation greatly reduces the complexity of vanilla GNN-based methods, capable of running on large-scale graphs. We validate our model for knowledge graph reasoning by solving a series of knowledge base completion (KBC) tasks. The experimental results show NeuCFlow significantly outperforms previous state-of-the-art KBC methods, including the embedding-based and the path-based. The reproducible code can be found by the link below.
EFFICIENT HIGH COVERAGE SOCIAL MEDIA OPINION ANALYSIS USING HASH TAGGER APPROACH

This work proposes a novel Sentiment-Based Enhanced Naive Bayes to address the information overload problem through information filtering. The proposed framework first applies a Natural Language Processing (NLP) technique to perform sentiment analysis taking advantage of the huge sums of textual data generated in from the social media are predominantly left untouched. Although some current studies do employ review texts, many of them do not consider how sentiments in reviews influence recommendation algorithm for prediction.General Terms: Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.
Research on the Influence of Sports on College Students’ National Spirit

The purpose of this study is to conduct a one-year physical education training for public sports students in Jiangxi Normal University of Science and Technology, and to conduct a questionnaire survey on "national spirit" before and after, and to compare two identical questionnaires one year apart, so as to observe the influence of sports on national spirit. In order to coordinate with the national strategic planning, promote the rapid development of campus football. In order to enrich the theoretical achievements of the study of campus football culture, it provides theoretical reference for related research. The results show that physical education has no significant influence on the national spirit of College students, but it has a certain effect on the improvement of patriotism, competition, overall outlook and obedience of College students, especially on the enhancement of collectivism.
XML and JSON

In Chapter 19 we learned how to include external libraries in our Android projects. Kotlin doesn’t have dedicated XML and JSON processing classes included in its standard library, so to achieve XML- and JSON-related tasks we use appropriate external libraries and add some convenience functions in the form of extension functions.
Compression and Enhancement of Medical Images using Opposition Based Harmony Search Algorithm

The growth of telemedicine-based wireless communication for images―magnetic resonance imaging (MRI) and computed tomography (CT)―leads to the necessity of learning the concept of image compression. Over the years, the transform based and spatial based compression techniques have attracted many types of researches and achieve better results at the cost of high computational complexity. In order to overcome this, the optimization techniques are considered with the existing image compression techniques. However, it fails to preserve the original content of the diagnostic information and cause artifacts at high compression ratio. In this paper, the concept of histogram based multilevel thresholding (HMT) using entropy is appended with the optimization algorithm to compress the medical images effectively. However, the method becomes time consuming during the measurement of the randomness from the image pixel group and not suitable for medical applications. Hence, an attempt has been made in this paper to develop an HMT based image compression by utilizing the opposition based improved harmony search algorithm (OIHSA) as an optimization technique along with the entropy. Further, the enhancement of the significant information present in the medical images are improved by the proper selection of entropy and the number of thresholds chosen to reconstruct the compressed image.
CAPTURING MOBILE COLLABORATION IN ACTIVE LEARNING: LESSONS FROM A LONGITUDINAL MIXED METHODOLOGIES STUDY

This paper reflects on an attempt to introduce smartphones into a blended learning context and highlights several methodological considerations relevant to the collection of mobile data.While mixed methods are now common, using this approach for investigating the challenges of mobile data collection is not as common.This study employed a mixed method longitudinal exploratory multiple case study design.The study collected qualitative and quantitative data on student interactions within a yearlong series of collaborative language learning activities.This paper describes the methodological issues that emerged during the planning and implementation of the study.The participants were undergraduate students studying English translation at a four-year private university in Tokyo, Japan.This paper adds to the knowledge of employing mixed methodology design for mobile data collection and analysis.In particular the advantages of incorporating Multidimensional Scaling (MDS) analysis with qualitative data.The results suggest that separate forms of data collected at similar frequencies and times and then triangulated provided an effective methodology for studying collaborative learners in a highly mobile context.This can be seen as evidence for the inclusion of various data collection cycles within a single mobile learning research study.
The Effect of Fix Up Strategy in Enhancing Students� Reading Comprehension in IAIN Curup

This study aimed to find out whether there is any effect of fix-up strategy on students reading comprehension at second semester English Study Program students in IAIN Curup. This study employed a quasi experimental research design. The sample of this study was second semester English Study Program students in IAIN Curup. It consisted of 60 students. The instrument on this research was reading test which was aimed to measure students reading comprehension. The reading test consists of 30 multiple choice questions for pre-and post-test. The finding of this research showed that the t-count value was 3,905 more than t-table value, it means that H0 was rejected and hypothesis H1 that stated there was any effect of applying fix-up strategy on the students reading comprehension at second semester English Study Program students in IAIN Curup was accepted. Fix-up strategy was proved to be effective to be used in teaching reading since there was a significant difference of students meanscore after doing the treatment by using fix-up strategy. Key words: Reading Comprehension, Fix-Up Strategy
Particle Flow Bayes' Rule

We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. Its neural parameterization can be trained in a meta-learning framework, allowing this operator to reason about the effect of an individual observation on the posterior, and thus generalize across different priors, observations and to sequential Bayesian inference. We demonstrated the generalization ability of our particle flow Bayes operator in several canonical and high dimensional examples.
Transformational Leadership Enhances The Action Of Augmentative And Alternative Communication In Stroke Patients Aphasia Motor

About 21% -35% of patients with acute stroke experience aphasia or motor impairment language interpretation. Impact because of the lack of action independence of the nurse in improving Augmentative Alternative Communication (AAC) is not the fulfillment of the needs of the patient. AAC is media in the form of electronic devices, alphabet boards, drawing boards that contain the basic needs which may be designated by the patient that helps in communicating. The research method is a literature research, focused on the search of some previous research. The results of the research show that the role of transformational leadership increase the AAC in stroke patients aphasia motor through insiparasional motivation where the leader becomes an example to be emulated by subordinates, providing a strong motivation for change with guidance and training, individualizad consideration that is hear input to want to change apply the AAC, Idealized influence where the leader as a mentor who has charisma makes followers want to practice the AAC, and intellectual stimulation is to encourage the subordinates to see problems in aphasia motor can be minimized with the AAC. Thus it can be concluded that the Style of transformational leadership has a used of therapeutic AAC to nurses practice.
An adaptive nearest neighbor rule for classification

We introduce a variant of the $k$-nearest neighbor classifier in which $k$ is chosen adaptively for each query, rather than supplied as a parameter. The choice of $k$ depends on properties of each neighborhood, and therefore may significantly vary between different points. (For example, the algorithm will use larger $k$ for predicting the labels of points in noisy regions.) We provide theory and experiments that demonstrate that the algorithm performs comparably to, and sometimes better than, $k$-NN with an optimal choice of $k$. In particular, we derive bounds on the convergence rates of our classifier that depend on a local quantity we call the `advantage' which is significantly weaker than the Lipschitz conditions used in previous convergence rate proofs. These generalization bounds hinge on a variant of the seminal Uniform Convergence Theorem due to Vapnik and Chervonenkis; this variant concerns conditional probabilities and may be of independent interest.
Two classes of linear codes and their generalized Hamming weights

The generalized Hamming weights (GHWs) are fundamental parameters of linear codes. In this paper, we investigate the generalized Hamming weights of two classes of linear codes constructed from defining sets and determine them completely employing a number-theoretic approach.
Relation between scientific activityof academic teachersand quality of tertiary education

The analyses of the quality of tertiaryeducation are frequently in line with scientific activityof academic teachers from the given university. Data regarding applica-tions for research grants within2013—2017 published by the National Center of Science as well as data from the monitoring system of professionalcareers of graduates, allow to verify the above mentioned relationship.The main results of theanalyses coveruniversi-ties with theunits thatapplied for grants in the field of economic science and are showing not strong but statistically significant correlation between the graduates incomes and activites of academic teachers.
Saving Lives Through Shaming

The Occupational Safety and Health Administration (OSHA) routinely employs shaming tactics toward employers, using public denunciations disseminated through social media, press releases, and online databases. These tactics, termed by the agency “regulation by shaming,” aim to name and shame companies into compliance with worker-safety regulations. In the face of heavy criticism of this practice, as well as legislative initiatives that aim to scale back OSHA’s regulation by shaming, this Article argues not only that shaming employers is an important regulatory tool that can help save workers’ lives, but also that OSHA’s “provocative” shaming tactics are in fact soft in comparison to other forms of regulatory shaming, and should be amplified.
How to Create a Facebook Page for a Blog: Easy Fan Page Setup

If you are looking to expand your site readership or help its popularity grow, learning how to create a Facebook page for a blog which is located on your site should be on top of your list and... There is no platform better than Facebook to get you started.
해외교육실습 프로그램 참여가 예비교사의 글로벌 교육역량 함양에 미치는 영향

The purpose of this study was to investigate how an overseas teaching practice program influenced elementary pre-service teachers' global teaching competence and English communication competence. During a prior training program the participants, consisting of 20 pre-service teachers, practiced making teaching-learning lesson plans, and took English communication lessons and special lectures related to the American educational system and cultural communication. The pre-service teachers were tested regarding their pre and post global teaching competence and English communication competence by using survey tools and an interview. Data were collected and analyzed from the survey, interview results, and field notes. The results of this study were as follows. First, for the pre-service teachers' global teaching competence all the components were improved upon and each component was correlated highly comparatively. Second, the pre-service teachers' English communication competence was positively improved, which indicated that the overseas teaching practice and cultural experience influenced their overall competence. Third, after analyzing the subareas of global teaching competence, global teaching comprehension, global teaching skills, and inter-cultural competence, it was found that all four areas were positively improved upon. Several recommendations, based on the research results, for an effective overseas practice program have been suggested.
VisAST: Generic AST Visualiser for Software Language Education

Structural concepts such as abstract syntax trees (ASTs) are often best explained through visual representations. Students may, however, struggle with connecting such visual representations with the corresponding program text. To bridge this gap, we developed visAST, a tool for easily visualising ASTs of small languages written in Haskell. To assess the benets and usability of visAST we conducted a user study in the context of students implementing interpreters. Students reported liking visAST and it being benecial for learning. The experiment's results were not conclusive, but hint at visAST use improving students' performance.
IMPROVING SENIOR HIGH SCHOOL STUDENTS’ SPEAKING ABILITY IN THE PROCESS OF LEARNING ENGLISH DISCUSSION USING TALKING CHIPS TECHNIQUE

This research is a classroom action research. The objectives of this study were: (1) to find out whether there were significant differences in students' speaking abilities after being taught through Talking Chips techniques, and (2) to find out which techniques were better for improving students. 'Speaking ability. This research was conducted on second grade students at YAS Sindangkerta High School. The method used in this study is Class Action Research. The research was on two cycles with total meetings were seven; three meetings were for tests, and four meetings were for treatment. Each cycle covered the step of planning, acting, observing, and reflecting. The data were analyzed by using independent sample t-test in SPSS 16.0. There is a significant difference of the students’ speaking ability achievement between the students who were taught through the Talking Chips technique and taught without Talking Chips technique. In Cycle 1 the increase score 59.62 while in cycle 2 is 72.25. From the result of the data, teaching speaking through Talking Chips technique is more effective to increase students’ speaking ability because Talking Chips technique can give more enjoyable speaking atmosphere for students in the classroom. Keywords: Talking Chips, Speaking ,Car
A General O(n 2 ) Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM.

Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays. The benchmark method using maximum likelihood (ML) estimation and gradient descent (GD) is impractical for processing big data due to its $O(n^3)$ complexity. Many sophisticated global or local approximation models, for instance, sparse GP, distributed GP, have been proposed to address such complexity issue. In this paper, we propose two novel and general-purpose GP hyper-parameter training schemes (GPCV-ADMM) by replacing ML with cross-validation (CV) as the fitting criterion and replacing GD with a non-linearly constrained alternating direction method of multipliers (ADMM) as the optimization method. The proposed schemes are of $O(n^2)$ complexity for any covariance matrix without special structure. We conduct various experiments based on both synthetic and real data sets, wherein the proposed schemes show excellent performance in terms of convergence, hyper-parameter estimation accuracy, and computational time in comparison with the traditional ML based routines given in the GPML toolbox.
Library Guides: Digital Research Notebooks: DRN FAQs

Use Digital Research Notebooks, via the UQ Research Data Manager system, to organise, store, curate and share all your project's research or laboratory data including working documents, images, observations, and data in any format.
PREPARING OF PRESCHOOL ТEACHERS TO WORKING WITH CHILDREN WITH SPECIAL EDUCATIONAL NEEDS

In the article, the authors analyzed the research on the problem of preparing the future preschool teachers for professional activities in an inclusive education. The barriers to the implementation of inclusive education in the practice of general education institutions have been identified. The conditions and ways to overcome obstacles and difficulties in the solving the problems of inclusion are highlighted. Attention is focused on the importance of the forming the professional and personal qualities of the future preschool teachers, required for working with children with special educational needs. It is proved that for effective implementation of the inclusive education in the practice of general education institutions by the future preschool teachers it should make some changes to the methodology of the organization of the educational process of the university in the process of their preparing. A number of tasks aimed at the preparing future preschool teachers for the professional activities in the inclusive education have been identified.
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid

In recent years, transfer learning gained particular interest in the field of vision and natural language processing. In the research field of vision, e.g., deep neural networks and transfer learning techniques achieve almost perfect classification scores within minutes. Nonetheless, these techniques are not yet widely applied in other domains. Therefore, this article identifies critical challenges and shows potential solutions for power forecasts in the field of renewable energies. It proposes a framework utilizing transfer learning techniques in wind power forecasts with limited or no historical data. On the one hand, this allows evaluating the applicability of transfer learning in the field of renewable energy. On the other hand, by developing automatic procedures, we assure that the proposed methods provide a framework that applies to domains in organic computing as well.