Reviewerly/exHarmony huggingface dataset:
Models of errors at the pre-design stage of the development of information and computing systems components

The paper gives an overview of approaches to the study of errors in specifications to requirements, identifies the system-forming factors of errors of the pre-design stage, proposes a cognitive map of the errors of the pre-design stage, and analyzes its adequacy. The importance of the further development of the construction of formal models of processes during the implementation of which errors occur is noted. It was established that the known archetype “growth limits” can be associated with the processes of preventing defects in the specifications of the external appearance, which implies that there is a limit to the ability to prevent defects, due to both the uncertainty of the problem situation and the competence of the developers of the requirements. Using a set of structural models of the pre-design stage of processes of the development of information and computing systems components, it was shown that defects in the specifications of the external appearance are caused by both the features of cognitive models of the subjects involved in the formation of requirements and the organization of the formation of requirements.
The univariate marginal distribution algorithm copes well with deception and epistasis

In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis.
TB-DNN: A Thin Binarized Deep Neural Network with High Accuracy

Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) applications. However, due to the huge demand for computing and storage resources as well as the high power consumption, deploying DNN models on embedded devices is full of challenges. Recent works have shown that DNN models can be compressed by removing their inner redundancy without obviously performance decay. In this work, we propose a two stage pipeline way to compress the ResNet-14 model and test it on CIFAR-10 and SVHN dataset respectively. Firstly, we use a filter level pruning method to remove the less important filters with different compression rate, and a considerable computation costs are reduced. Secondly, we binarize the pruned model to further reduce the model size and computational complexity. The training results show that we achieve 87.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy with only 1.86Mb model size on CIFAR-10 and 96.2% accuracy with 1.34Mb on SVHN. Compared to the original model, we have 57% to 68% FLOPs reduction and 45.6× to 63.1× model size compression at the cost of roughly 4% accuracy drop. Finally, we implement the thin binarized ResNet-14 model on the Xilinx KC705 board with a shared, flexible accumulator, which can save 46.8% logic resources. And the entire network parameters are store into on-chip RAM, which can greatly reduce the energy consumption and memory overhead caused by off-chip accesses. The experimental results show that on CIFAR-10 dataset, we achieve an overall performance of 1200 FPS, energy efficiency of 571 FPS/W, which denote 2.3× and 3.6× improvements over the most recent work.
Real-Time Block Rate Targeting

A proof-of-work blockchain uses a retargeting algorithm, also termed a difficulty adjustment algorithm, to manage the rate of block production in the presence of changing hashrate. To derive the parameters that guide the search for the next block, nearly all such algorithms rely on averages of past inter-block time observations, as measured by on-chain timestamps. We are motivated to seek better responsiveness to changing hashrate, while improving stability of the block production rate and retaining the progress-free property of mining. We describe a class of retargeting algorithms for which the sole inter-block time input is that of the block being searched for, and whose response is nonlinear in that time. We discuss how these algorithms allow the other consensus rules that govern allowable timestamps to be tightened, which may improve the blockchain’s effectiveness as a time-stamping machine.
BERT for Open-Domain Conversation Modeling

The RNN encoder-decoder structures have critical problems in generating meaningful responses. Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling, as the latent variables can encode the high-level information (topics, tones, sentiments, etc.) in conversations. On the other hand, BERT, one of the latest deep pre-trained language representation models, has achieved the remarkable state of the art across a wide range of tasks in natural language processing. However, BERT has not yet been investigated in a conversation generation task. In this paper, we explore different BERT-empowered conversation modeling approaches by incorporating BERT, RNNs, and VAEs. Moreover, BERT can be used either with weights fixed as feature extraction module or with weights updated and optimized for a specific task. In this paper, we demonstrate that simply using fixed pre-trained BERT as part of the model without further finetuning is powerful enough for generating better responses in terms of fluency, grammar, and semantic coherency. Fine-tuning can achieve the comparable results. This paper sets new baselines for conversation generation task and we are the first to demonstrate the success of BERT in conversation modeling.
Sum Epsilon-Tube Error Fitness Function Design for GP Symbolic Regression: Preliminary Study

Symbolic Regression (SR) is a well-studied method in Genetic Programming (GP) for discovering free-form mathematical models from observed data, which includes not only the model parameters but also its innate structure. Another level of the regression problem is the design of an appropriate fitness function, by which are individual solutions judged. This paper proposes a new fitness function design for symbolic regression problems called a Sum epsilon-Tube Error (STE). The function of this criterion can be visualized as a tube with a small radius that stretches along the entire domain of the approximated function. The middle of the tube is defined by points that match approximated valued (in the so-called control points). The evaluation function then compares, whether each approximated point does or does not belong to the area of the tube and counts the number of points outside of the epsilon-Tube. The proposed method is compared with the standard sum square error in several test cases, where the advantages and disadvantages of the design are discussed. The obtained results show great promise for the further development of the STE design and implementation.
A Revocable Publish-Subscribe Scheme Using CP-ABE with Efficient Attribute and User Revocation Capability for Cloud Systems

With the increasing popularity of publish-subscribe systems in the cloud environment, it is of great value to implement an efficient and secure searchable and encrypted publishing subscription scheme with dynamic attributes in a cloud environment. In a publish-subscribe system, it is very important that the user's attributes can be dynamically changed. Most existing schemes which combine searchable encryption with attribute base encryption do not take real-time changes to user attributes into account or are inefficient. To address these problems, we propose a revocable publish-subscribe scheme (RPS) using CP-ABE with efficient attribute and user revocation capability for cloud systems. RPS supports multiple publishers and subscribers, and protects data from access of unauthorized users and cloud server. In addition, it also protects the privacy of the subscription policy, privacy of the uploaded data and keywords, and is forward and backward secure.
Batik-DG: Improved DeblurGAN for Batik Crack Pattern Generation

Abstract As a traditional handcraft in China, batik is valuable for both decoration and fashion design due to its unique crack pattern. However, previous researches on batik crack pattern generation remain space in promoting verisimilitude. In recent years, Generative Adversarial Networks (GAN) show great ability in realistic image generation, and DeblurGAN performs well in image deblurring task, showing good potentiality in image generation. In this paper, we improve DeblurGAN for batik crack pattern generation by several methods. First, transposed convolution is replaced with resize-convolution. Second, Hybrid Dilated Convolution (HDC) is added to the network. Third, RaLSGAN is utilized instead of WGAN-GP, and several additional loss functions such as L1, L2 and so forth are tested respectively. Our network is trained on BIFT-Batik dataset and performs well to some degrees both in visual verisimilitude and IQA indexes including PSNR and SSIM. We name our network as Batik-DG (Batik-DeblurGAN).
Attention-Guided Deraining Network Via Stage-Wise Learning

Due to diverse rain shapes, directions, densities as well as different distances to cameras, rain streaks in the air are interweaved and overlapped. However, most existing deraining methods are inherently oblivious this phenomenon and tend to learn a single rain streak layer to simulate this complex distribution, consequently failing to restore high-quality rain-free images. To solve this problem, along with the stage-wise learning, we propose a novel attention-guided deraining network (ADN) for rain streak removal. Specially, we decompose the rain streaks into multiple rain streak layers, and individually model them along the stages of the network to match the increasing abstracts. Moreover, the attention mechanism is utilized to guide the fusion of these rain streak layers by handling the overlaps between them. Extensive experiments on several benchmark datasets and real-world scenarios show substantial improvements both on quantitative indicators and visual effects over the current top-performing methods.
Designing for Positive Emotional Responses in Users of Interactive Digital Technologies: A Systematic Literature Review

In this study, 20 papers were reviewed to identify means, methods, techniques, tools or other interventions that practitioners may use to positively influence the affective state of users of digital interactive technologies. A systematic literature review was conducted in order to find such interventions. A literature background covering concepts from the fields of Human-computer interaction, as well as certain concepts from the field of psychology that is relevant to this study is provided. Search criteria were determined and used to identify research papers from various academic resources. Four categories of intervention categories were identified from the papers reviewed, which can be applied to interactive digital technologies by practitioners in order to evoke positive affect on users. These four categories were aesthetics, affective computing, need fulfilment and novel interaction techniques. The identified categories for practitioners were consolidated alongside metadata such as the types of publications of the reviewed papers, the regions the studies were conducted and the growth in the number of studies.
Using Eye Tracking to Gain Insight into TV Customer Experience by Markov Modelling

Eye tracking technologies are widely used in studies on Human-Computer Interaction (HCI). To get a better understanding of TV customer experience, a study was conducted at the BT Ireland Innovation Centre in which an eye tracker was used to record user interactions with a video-on-demand application, the BT Player. This paper presents the analysis of eye movement data integrated with the layout information from the BT Player. A Discrete Time-Markov Chain is applied to the dataset as the eye tracker records each gaze movement at a particular frequency, which can be regarded time-discrete. The Markov model is found to fit well to the dataset and reveals new insights, including the most likely gaze trajectory and how some application features attract attention in preference to others. The analysis has also identified several potential areas for further work.
The Use of Digital Guessing Game to Improve Students’ Speaking Ability

This study was investigated the effects of Digital Guessing Games strategy on students? speaking ability of Islamic Senior High School students as well as to find out their perceptions of using it. In doing so, the subjects consisted of 44 students who were in tenth grade. Then they were divided into two equal experimental and control groups randomly (N=22). This study used explanatory sequential mixed-method whereas pre-test and post-test were administrated to collect quantitative and questionnaire was given to collect qualitative data.The experimental group was taught speaking using DGG application, whereas the control group using conventional guessing game. Analyzing the data through the independent sample t-test revealed the effectiveness of DGG application, the experimental group out performed the control group of speaking narrative text. Furthermore, an Islamic Senior High School students in Tasikmalaya had a positive attitude toward utilization of DGG application.
An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone.
Communication effectiveness in social networks of leading universities

The article presents the results of communication effectiveness analysis in social networks of leading universities with strong economic departments. The study was carried out using the textual information analysis i.e. content, links, posts, comments, tags, etc.; multimedia i.e. photos, videos, audio, etc., and the ontology method, which estimated the social networks parameters like the number of subscribers, average path length, etc. The study showed that the main platform for international higher education institutions is Facebook; Instagram is actively developing, and then comes YouTube. The number of publications does not directly affect the number of subscribers, in contrast to the number of students. The high frequency of posts does not lead to subscribers increase. There is no single strategy for universities social network development. The use of informal communication, which encourages action, significantly increases the audience and its involvement; but is not used by all universities, because it might contradict with the university’s image. It should be noted that good design, the use of professional photos and videos, has its impact on potential audience.
Using Convergent Parallel Design Mixed Method to Assess the Usage of Multi-Touch Hand Gestures Towards Fine Motor Skills Among Pre-School Children

The Convergent Parallel Design mixed method is used as the research methodology to assess the usage of multi-touch hand gestures towards fine motor skills among preschool children.This study involved 30 pre-school children aged between 5 to 6 years old from Pusat Asuhan Tunas Islam.The samples are divided into multi-touch gestures group and single touch gestures group.The quantitative data were the test scores of a dot-to-dot activity and the qualitative data were the preschool children's emergent behaviors while engaging with mobile apps and controlling writing instruments.The quantitative data were analyzed by using independent sample T-Test and paired sample T-Test.The findings showed that there is a significant difference between the two groups as the independent sample T-Test indicate the value of sig, p = 0.029.The paired sample T-Test also indicate that the pre-test score and post-test score are significantly positive correlated (r = 0.503) and the value of sig, p = 0.005 showed that there is significance difference between pre-test score and post-test score.The statistical data proved that there is an effect of the use multi-touch hand gesture in mobile application towards pre-school children's fine motor skills which is also supported by qualitative findings.As for conclusion, the use of multi-touch hand gestures can help the pre-school children to implement small movements of their fingers which indirectly develop their fine motor skills thus helping them holding and using pencils and some other instruments appropriately.
THE CORRELATION BETWEEN STUDENTS’ READING HABIT AND THEIR READING COMPREHENSION

The aim of this study was to observe on the correlation between students’ reading habit and their reading comprehension of eleventh grade at SMA Negeri 1 Madang Suku 3 OKU Timur. In this quantitative study with correlational study, the writer used questionnaire and test to collect the data. The questionnaire measured students’ reading habit and test measured students’ reading comprehension. Populationof this study was all the eleventh grade students of SMA Negeri 1 Madang Suku 3 OKU Timur in academic year 2018/2019 were 124 student and took sample by using purposive sampling were 64 students. Based on the finding, the mean score of questionnaire was 60,89 and mean score of students’ reading comprehension was 56,94 which was in moderated category. The writer found that there was significantly correlation between students’reading habit and their reading comprehension the correlation coofficient was 0,555. It meant that alternative hypothesis (Ha) was accepted and null hypothesis was rejected in other word there was any significant correlation between students’ reading habit and their reading comprehendion in medium correlation. As the conclusion,reading habit was not only aspect that influenced strudent reading comprehension that many factor also influenced the reading comprehension: internal factor (intellectual, language skills, psychological, environment, experience) and external factor (facilities, time to learning).To have good reading comprehension the student should know aspects reading comprehension were ability of association, conception ability, perception ability, syntactic ability - semantic ability and cognitive understanding.
High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

In order to address the problem that high energy consumption, high memory usage and low clustering effect in traditional data set high-dimensional clustering algorithms, we propose the highdimensional clustering algorithm of incomplete mixed data set based on artificial intelligence.First, we construct the phase space reconstruction to ensure the invariance of features of incomplete mixed data set by analyzing the incomplete mixed data set and introduce the correlation dimension to obtain the feature correlation value.Second, we introduce the standard deviation and realize the extraction of features of incomplete mixed data set through calculating the sparsity of sample features.Third, we conduct repeated clustering for the mixed data set in the subspace according to the degree of correlation between incomplete mixed data sets in the multidimensional subspace.Last, we realize the design of high-dimensional clustering method for incomplete mixed data set in accordance with the stronger relevance in the mixed data sets.Experimental results show that the proposed algorithm has good correlation dimension processing effects, lower memory usage, time-consuming, lower and concentrated ensemble energy consumption (within 300J), good clustering effects, as high as 92%, which has some advantages and practical application value.
Neural Lattice Search for Speech Recognition

To improve the accuracy of automatic speech recognition, a two-pass decoding strategy is widely adopted. The first-pass model generates compact word lattices, which are utilized by the second-pass model to perform rescoring. Currently, the most popular rescoring methods are N-best rescoring and lattice rescoring with long short-term memory language models (LSTMLMs). However, these methods encounter the problem of limited search space or inconsistency between training and evaluation. In this paper, we address these problems with an end-to-end model for accurately extracting the best hypothesis from the word lattice. Our model is composed of a bidirectional LatticeLSTM encoder followed by an attentional LSTM decoder. The model takes word lattice as input and generates the single best hypothesis from the given lattice space. When combined with an LSTMLM, the proposed model yields 9.7% and 7.5% relative WER reduction compared to N-best rescoring methods and lattice rescoring methods within the same amount of decoding time.
Legitimizing Bitcoin as a Currency and Store of Value: Using Discrete Monetary Units to Consolidate Value and Drive Market Growth

Cryptocurrency, and especially Bitcoin, has struggled to gain recognition as a legitimate currency from governments, financial institutions, and consumers. This has occurred because many analysts and consumers believe that Bitcoin is not a stable and consistent store of value, a unit of measurement, or a medium of exchange. One way to overcome this challenge is for Bitcoin to be used as both a currency and store of value by a greater percentage of the world’s population. This paper seeks to identify how a change in Bitcoin’s monetary measurement (or denomination) can more easily facilitate Bitcoin transactions to increase its use. Specifically, we posit that applying whole number bias theory, from the cognitive psychology and mathematics fields, to Bitcoin’s unit of measurement will allow the value of Bitcoin to be referenced in smaller and easier tounderstand units with fewer numbers after the decimal point—such as the “Bit” or the “Satoshi.” In the process, the use of Bitcoin will include more whole numbers and allow the general public to more easily assign value to Bitcoin in day-to-day transactions.&#x0D;