Reviewerly/exHarmony huggingface dataset:
A PBFT Consensus Scheme with Reputation Value Voting Based on Dynamic Clustering

At present, the consensus algorithm based on reputation voting generally has the problem of credit value accumulation caused by Matthew effect, which will lead to the risk of system centralization. Therefore, we propose a new blockchain consensus scheme based on PBFT mechanism, which divides the nodes into three categories: production node, upper node and common node, and the first two types are generated by node selection algorithm and replaced regularly. In the node selection algorithm, random parameters are introduced to make the reputation value no longer the only standard. In addition, in order to solve the problems of high message complexity and poor scalability shortcomings in PBFT, we use ISODATA algorithm to segment the nodes in the system, and simplify the consensus process of these existing PBFT algorithm, which greatly reduces the message complexity of the consensus processing without compromising the fault-tolerant performance of the system.
Now You See Me (CME): Concept-based Model Extraction

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech UCSD Birds), we demonstrate how CME can be used to (i) analyse the concept information learned by a DNN model (ii) analyse how a DNN uses this concept information when predicting output labels (iii) identify key concept information that can further improve DNN predictive performance (for one of the case studies, we showed how model accuracy can be improved by over 14%, using only 30% of the available concepts).
On the Practice of College English Reading and Writing Course from the Perspective of POA Theory

Established in the teaching status quo of college English in china, Production-oriented Approach (POA), proposed by Professor Wen Qiufang, advocates the teaching concept of "learning-using integrated", and constructs "motivating-enabling-assessing" classroom teaching process, thus providing new ideas for teachers to enhance teaching efficiency of college English classroom instruction.Through a comparative study of the control group which adopts traditional mode of classroom teaching and the experimental group that implements teaching methods based on POA Theory, the paper aims to explore the differences of teaching efficiency in college English reading and writing course under the two teaching ways mentioned above.By means of qualitative and quantitative studies, it is found that compared with traditional lecture-style classroom teaching, POA Theory applied in English reading and writing class under the framework of output-driven and input-enabled hypothesis can innovate the traditional approach of English teaching and promote students' ability to think and practice.In addition, the interaction between teachers and students and students' autonomous learning capacity are also effectively strengthened.With data collection and analysis, the author also digs up the reasons for the findings in this research from various perspectives.The reflections upon the research, its limitations and the conclusion are stated in the final part of this thesis.
Combination of Active and Random Labeling Strategy in the Non-stationary Data Stream Classification

A significant problem when building classifiers based on data stream is information about the correct label. Most algorithms assume access to this information without any restrictions. Unfortunately, this is not possible in practice because the objects can come very quickly and labeling all of them is impossible, or we have to pay for providing the correct label (e.g., to human expert). Hence, methods based on partially labeled data, including methods based on an active learning approach, are becoming increasingly popular, i.e., when the learning algorithm itself decides which of the objects are interesting to improve the quality of the predictive model effectively. In this paper, we propose a new method of active learning of data stream classifier. Its quality has been compared with benchmark solutions based on a large number of test streams, and the results obtained prove the usefulness of the proposed method, especially in the case of a low budget dedicated to the labeling of incoming objects.
Epistemological Framework for Computer Simulations in Building Science Research: Insights from Theory and Practice

Computer simulations are widely used within the area of building science research. Building science research deals with the physical phenomena that affect buildings, including heat and mass transfer, lighting and acoustic transmission. This wide usage of computer simulations, however, is characterized by a divergence in thought on the composition of an epistemological framework that may provide guidance for their deployment in research. This paper undertakes a fundamental review of the epistemology of computer simulations within the context of the philosophy of science. Thereafter, it reviews the epistemological framework within which computer simulations are used in practice within the area of building science research. A comparison between the insights obtained from the realms of theory and practice is made, which then interrogates the adequacy of the epistemological approaches that have been employed in previously published simulation-based research. These insights may help in informing a normative composition of an adequate epistemological framework within which computer simulation-based building science research may be conducted.
Review on Deep Fake: A looming Technological Threat

The misuse of technologies increases with the increase in technology development. Due to the recent progress that has met the face of social media, tools for editing videos and audios, and Generative Adversarial Networks, manipulated videos has been going around on social media networks and the like, in a rapid manner. Most of these intentionally manipulated videos, created for deliberate misinformation and the like have initiated blockbusting fiascos in communities around the whole world. In fact, there are cases where people body parts are switched from one person to another, using neural networks to form DeepFake videos, as it is called. Some of these editing tools are freely accessible, and that is a threat, a technical one! To deal with this, it is required to develop a platform where such DeepFake contents can be automatically filtered.
Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

&lt;p class="0abstract"&gt;Quadriplegia is a paralysis condition in both arms and legs so that the patient is only able to move his neck and head. Manual or electric wheelchairs with joystick or switch control as a tool for people with paralysis certainly cannot be controlled independently by quadriplegia sufferers. This study aimed to help quadriplegia sufferers not to depend on others in carrying out daily activities by developing electric wheelchairs that can be controlled independently. The bioelectric signal which has only been used for diagnostic purposes can be utilized as an electric wheelchair control system for quadriplegia sufferers. In this study, electric wheelchairs were controlled by electromyography (EMG) signals from muscle contractions that can be driven by quadriplegia sufferers, namely the neck and face muscles. The increase in EMG signal amplitude during the muscle contraction is used as a trigger for the electric motor in a wheelchair to move forward, backward, turn right, and turn left. An electronic circuit for signal conditioning was used to amplify the EMG signal leads and filter frequencies that are not needed by the system before being processed by the microcontroller circuit. The use of wireless systems was developed to reduce the use of cables connecting electrodes to patients with electronic devices that will provide comfort to the user. Based on the results of the data collection on the wheelchair system, the detectability and selectivity values were for the 100% and 94% forward commands, 94.33% and 100% reverse commands, 92.31%, and 96% right turn commands and 97.96% and 94.12% left turn commands. The electric wheelchair system with EMG signal control is expected to help the mobility of quadriplegia sufferers.&lt;/p&gt;
Multirotor Configuration Trades Informed by Handling Qualities for Urban Air Mobility Application

Many contemporary Advanced Air Mobility (AAM), and more specifically, urban air mobility (UAM) vehicle designers are attracted to variable rotor speed-controlled designs with multiple rotors because of the great potential for mass savings compared to more traditional, variable blade pitch-controlled vehicles. These designs are based on the assumption that the stability and control of recreation or basic utility-sized drones can be scaled to larger passengersized vehicles. Previous work had shown the challenges in stabilizing passenger-sized quadcopters. In this study, power constraints were made less restrictive and varied, allowing more control power. Motor parameters such as efficiency, nominal voltage and current operating point, and rise time of the rotor speed controller step response were studied. By fixing the efficiency of the motor to 95% and assuming a motor voltage to current ratio of 2.0 (previously, assumed to be 1.0), the authors were able to stabilize the quadcopter in the roll axis because this allowed the vehicle to achieve adequate rise times between 0.4 and 0.8 s. This motor optimization was extended to a hexacopter and octocopter designed to the same payload size and mission as the quadcopter. The three vehicle configurations and their motor speed controllers were compared. It was found that while hexacopter and octocopter required more mass and overall power; all three configurations had similar margins required for control. However, the hexacopter and octocopter were able to use this power margin to achieve lower rise times (i.e. the vehicle responded more quickly to pilot inputs) than the quadcopter, with the octocopter having the lowest rotor response rise time of the three vehicle configurations studied.
Homogenization with Explicit Semantics Preservation for Heterogeneous Information Network

Heterogeneous information network (HIN), especially its embedding task, has drawn much attention recently as its rich latent information brings great benefits to complex classification and clustering. Many prior embedding works focus on designing a specific approach for the HIN while others implicitly homogenize the HIN with losing some semantic information. In this paper, a novel explicit homogenization method is proposed to preserve more semantic information, where the latent information of intermediate nodes among each meta-path instance and that among multiple meta-path instances are incorporated into the conventional adjacent matrix (or weight matrix). Then, the transfer of weight matrix and the fusion of node-level embeddings are considered to obtain graph-level embedding to solve the HIN problem. In such way, much more latent information of meta-path is preserved so that the proposed method exhibits its superiority in comparison to state-of-the-art works in classification and clustering tasks.
Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridization approach

The unsteady and intermittent feature (mainly due to atmospheric mechanisms and diurnal cycles) of solar energy resource is often a stumbling block, due to its unpredictable nature, to receiving high-intensity levels of solar radiation at ground level. Hence, there has been a growing demand for accurate solar irradiance forecasts that properly explain the mixture of deterministic and stochastic characteristic (which may be linear or nonlinear) in which solar radiation presents itself on the earth’s surface. The seasonal autoregressive integrated moving average (SARIMA) models are popular for accurately modelling linearity, whilst the neural networks effectively capture the aspect of nonlinearity embedded in solar radiation data at ground level. This comparative study couples sinusoidal predictors at specified harmonic frequencies with SARIMA models, neural network autoregression (NNAR) models and the hybrid (SARIMA-NNAR) models to form the respective harmonically coupled models, namely, HCSARIMA models, HCNNAR models and HCSARIMA-NNAR models, with the sinusoidal predictor function, SARIMA, and NNAR parts capturing the deterministic, linear and nonlinear components, respectively. These models are used to forecast 10-minutely and 60-minutely averaged global horizontal irradiance data series obtained from the RVD Richtersveld solar radiometric station in the Northern Cape, South Africa. The forecasting accuracy of the three above-mentioned models is undertaken based on the relative mean square error, mean absolute error and mean absolute percentage error. The HCNNAR model and HCSARIMA-NNAR model gave more accurate forecasting results for 60-minutely and 10-minutely data, respectively.&#x0D; Highlights&#x0D; &#x0D; HCSARIMA models were outperformed by both HCNNAR models and HCSARIMA-NNAR models in the forecasting arena.&#x0D; HCNNAR models were most appropriate for forecasting larger time scales (i.e. 60-minutely).&#x0D; HCSARIMA-NNAR models were most appropriate for forecasting smaller time scales (i.e. 10-minutely).&#x0D; Models fitted on the January data series performed better than those fitted on the June data series.&#x0D;
Perceptual Characterization of 3D Graphical Contents based on Attention Complexity Measures

This paper provides insights on how to perceptually characterize colored 3D Graphical Contents (3DGC). In this study, pre-defined viewpoints were considered to render static graphical objects. For perceptual characterization, we used visual attention complexity (VAC) measures. Considering a view-based approach to exploit the perceived information, an eye-tracking experiment was conducted using colored graphical objects.
Robust Normalized Squares Maximization for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) attempts to transfer specific knowledge from one domain with labeled data to another domain without labels. Recently, maximum squares loss has been proposed to tackle UDA problem but it does not consider the prediction diversity which has proven beneficial to UDA. In this paper, we propose a novel normalized squares maximization (NSM) loss in which the maximum squares is normalized by the sum of squares of class sizes. The normalization term enforces the class sizes of predictions to be balanced to explicitly increase the diversity. Theoretical analysis shows that the optimal solution to NSM is one-hot vectors with balanced class sizes, i.e., NSM encourages both discriminate and diverse predictions. We further propose a robust variant of NSM, RNSM, by replacing the square loss with L2,1-norm to reduce the influence of outliers and noises. Experiments of cross-domain image classification on two benchmark datasets illustrate the effectiveness of both NSM and RNSM. RNSM achieves promising performance compared to state-of-the-art methods. The code is available at https://github.com/wj-zhang/NSM.
Secure Secret Sharing Using Homomorphic Encryption

Secret sharing is an important means to achieve confidentiality and data privacy. Secret sharing deals with splitting a secret information with various players. The goal of the secret sharing is security of secret, privacy and hiding information. There are numerous techniques available for secret sharing e.g. polynomial, Chinese remainder theorem, vector space, matrix projection. Techniques have characteristics like threshold, proactive, verifiable. Proactive secret sharing scheme allow user to change share in case of doubt of theft. In this work we propose the proactive secret sharing scheme based on homomorphic techniques. Our scheme consists of three phases of share construction, share renewal, share reconstruction. Central authority splits an encrypted secret with each parties using homomorphic property of paillier encryption i.e. subtraction. In renewal process two or more parties relate share with each other for to generated renewed share. In reconstruction process all parties share will be add to central authority then encrypted secret will be generated. Central authority will decrypt encrypted secret using secret key then original secret will be generated. Our schemes unique features is share can be renewed any time, Each party can choose secret of their own choice, If any two parties have same content share then also encrypted share will be different due to non-deterministic property of paillier encryption.
Machine versus human translation outputs: Which one results in better reading comprehension among EFL learners?

EFL university students in general and Iranian EFL university learners in particular, specifically in the commencement of their study at bachelor degree prefer to have their assigned English texts translated into mother tongue since they do not have a good command of the language. Machine translation (MT) has recently turned into a favorite tool for all students including EFL ones since it is free and readily available. The aim of this study, therefore, was to evaluate the effect of MT output on such students. To achieve this goal, two sample texts in students’ mother tongue were selected and translated into English by a proficient human translator whose native language was English and by an MT system (Google Translate). Next, 167 homogenous EFL freshman students were given a proficiency test and 152 homogenous ones randomly assigned to two groups, namely, control and experimental groups. The control group was given the human translation (HT) and the experimental group was exposed to machine translation. Using an independent t-test, indicated that there was a negligible difference between mentioned groups. That is, MT has improved to such an extent that it can compete with the HT. It can be concluded that due to its great improvements in the last few years and its widespread use among university students, EFL educators should accept the presence of this tool and try to implement it effectively in their teaching instead of banning students from using it.
Interfacing with Sensory Options Using a Virtual Equipment System

We envision the development of a novel Virtual Equipment System to replace existing 2d Interfaces in virtual reality with a set of embedded and distributed input devices. We have built a prototype that takes advantage of the user's spatial awareness, offering them a set of virtual equipment relevant to their sensory organs. It allows the users to quickly access a suite of intuitively designed interfaces using their spatial and body awareness. Our Virtual Equipment System can be standardized and applied to other extended reality devices and frameworks.
Developing Professional Self-Regulation of Students during Pedagogical Practice

The current study aims to understand the development of students’ professional self-regulation during pedagogical practice. Fifty participants of this study were fourth-year students of the Ternopil Volodymyr Hnatiuk National Pedagogical University, Ukraine. The following questions were considered: Has any certain level of professional self-regulation been formed before practice? Will it increase during the pedagogical practice? Is it possible to influence the process of formation of future teachers’ professional self-regulation during the pedagogical practice by offering them specific tasks? To diagnose the formation of self-regulatory mechanisms of students during pedagogical practice, we propose to use the phenomenon of readiness (namely, how to determine, characterize, and measure it). The research was carried out using the methods of questionnaires, conceptual dictionary, and expert evaluation. The level of readiness for professional self-regulation in most respondents has increased from the “lower” to the “upper” limit within the Medium level. The results contribute to the conclusion that professional self-regulation of a teacher as a necessary component of future professional activity is developed effectively during pedagogical practice; moreover, this process is intensified by the implementation of assignments aimed at understanding self-regulatory mechanisms.
The Effects of Predictive Features of Mobile Keyboards on Text Entry Speed and Errors

Mobile users rely on typing assistant mechanisms such as prediction and autocorrect. Previous studies on mobile keyboards showed decreased performance for heavy use of word prediction, which identifies a need for more research to better understand the effectiveness of predictive features for different users. Our work aims at such a better understanding of user interaction with autocorrections and the prediction panel while entering text, in particular when these approaches fail. We present a crowd-sourced mobile text entry study with 170 participants. Our mobile web application simulates autocorrection and word prediction to capture user behaviours around these features. We found that using word prediction saves an average of 3.43 characters per phrase but also adds an average of two seconds compared to actually typing the word, resulting in a negative effect on text entry speed. We also identified that the time to fix wrong autocorrections is on average 5.5 seconds but that autocorrection does not have a significant effect on typing speed.
Explore and Practice of China's Intelligent “New Engineering” — Based on the Grounded Theory

In the era of the Industrial Revolution 4.0, emerging intelligent information technologies represented by the Internet of Things, big data, artificial intelligence, etc. are triggering a new round of educational reforms, driving human education to transition to intelligent education. This article adopts the 612 national-level new engineering research and practice projects released by China in 2018 that involve the integration of new engineering and wisdom education in universities and colleges that also implement the integration of the two in addition to the New Engineering Research and Practice Project，A total of 80 colleges and universities practice samples as research objects，With the help of the grounded theory system, a fusion model consisting of 755 original sentences, 77 concepts, 25categories, 7main categories, and 3 core categories-the TCG model. Its integration path is: Block chain: more open and more credible new ideas; Internet + education: the construction of a curriculum system that combines theory and practice with individuality and multiple coexistence; emotional skills perception + cloud computing: intelligent new teachers Strength training; AI + VR: construction of an open and immersive second learning world; big data + the Internet of Things: the establishment of a precise and intelligent management system; big data + artificial intelligence: the construction of a new mechanism of evaluation and incentives; the Internet of Things + none Seamless Interconnection: Comprehensive Perception of Government-Industry-Research Cooperation System.