Reviewerly/exHarmony huggingface dataset:
Video Preview Generation for Interactive Educational Digital Resources based on the GUI Traversal

The interactive educational digital resources (IEDRs) are more and more prevalent in all levels of education. With the proliferation of the Internet, more and more IEDRs are being shared online. How to aid users to explore helpful resources effectively and efficiently has become a challenge. The static preview of resources that current research has mainly focused on is ineffective for the IEDRs, because of the unique feature of the IEDRs that the knowledge is crucial for the users’ comprehension and is hidden in the process of interaction. To unfold the hidden crucial knowledge and ensure a users’ fast acquisition, we proposed a novel approach to generate video previews for the IEDRs, which has achieved a better visualization of the IEDRs. First, the interactive element set and the events set, as well as the dependencies between the interactive elements of the resource are collected. Afterwards, by analyzing the dependencies, the interaction sequence is generated. Consequently, by automatically executing the interaction sequence based on the GUI traversal, the manual interaction is simulated, and the process is recorded into a video. The approach was applied into a dynamic mathematics educational system, SuperSketchpad, and its effectiveness was validated by a user study, in which it was compared with the random event method and event permutation method in correctness, completeness and redundancy.
Application of shared backbone DNNs in ADAS perception systems

Advanced Driver Assistance System (ADAS) is a very important part of an up to date vehicle. For achieving highlevel objectives in such ADAS functionality like LKA (lane keeping assistance), LDW (lane departure warning system), FCW (forward collision warning) the quality of the algorithms under the hood must be extremely high. In the last few years, it is common that these algorithms are based on DNNs (deep neural networks) applied to the tasks of semantic and instance segmentation, 2D/3D object detection and visual object tracking. Recent state-of-the-art DNN models as usual solve only one single task from the listed above and running several neural networks is rather computationally expensive and even impossible due to the lack of the GPU memory. One of the approaches used to overcome such a problem is a shared backbone (also called feature extractor or encoder). The backbone consumes most of the computing resources thus the model with a shared backbone achieves better inference performance. Unfortunately, the training procedure for a shared backbone model has several difficulties. The first one is the lack of datasets with all the required and uniform annotation types. The second problem is a more sophisticated backpropagation procedure. In this paper, we consider several methods for multi-task neural network training and present the results of such training procedures on several public datasets with dissimilar annotation types. The shared backbone is applied to the following three tasks performed simultaneously on the road scene: semantic segmentation, 2D object detection and 3D object detection. While the performance of the DNNs with shared backbone increased significantly, we obtained the quality evaluation results, which are quite close to the original separate state-of-the-art DNNs and even outperforms them in some evaluation indices.
Open Peer Review

In this issue of Serials Review, the Balance Point column delves into issues surrounding peer review, paying particular attention to open peer review. Beginning with some discussion of the history and development of peer review, as well as the advantages and disadvantages of traditional peer review, the column addresses open peer review (OPR) processes and the pros and cons of OPR. Topics such as the mechanisms of open peer review, faculty credit for peer review, as well as open peer review in the Library and Information Science (LIS) field are also touched on.
Game technologies as a means of increasing the educational motivation of university students

The study reflects the impact of gaming technology on changing student motivation. The implementation of gaming technologies in the training of students of a higher educational institution helps to increase the motivation of students to study professional disciplines, the formation of professional competence.
Noising Scheme for Data Augmentation in Automatic Post-Editing.

This paper describes POSTECH’s submission to WMT20 for the shared task on Automatic Post-Editing (APE). Our focus is on increasing the quantity of available APE data to overcome the shortage of human-crafted training data. In our experiment, we implemented a noising module that simulates four types of post-editing errors, and we introduced this module into a Transformer-based multi-source APE model. Our noising module implants errors into texts on the target side of parallel corpora during the training phase to make synthetic MT outputs, increasing the entire number of training samples. We also generated additional training data using the parallel corpora and NMT model that were released for the Quality Estimation task, and we used these data to train our APE model. Experimental results on the WMT20 English-German APE data set show improvements over the baseline in terms of both the TER and BLEU scores: our primary submission achieved an improvement of -3.15 TER and +4.01 BLEU, and our contrastive submission achieved an improvement of -3.34 TER and +4.30 BLEU.
RETRACTED: Independent and innovative learning of gymnastics teaching based on computer interaction

In order to improve the independent and innovative learning ability of gymnastics teaching, the independent and innovative learning and resource information interaction model of gymnastics teaching based on computer interaction is proposed, the integrated information statistics and big data analysis model of independent and innovative learning of gymnastics teaching is constructed, the independent and innovative learning and optimal control of gymnastics teaching are carried out by using the methods of big data mining and resource information fusion, and the data clustering analysis model of independent and innovative learning of gymnastics teaching is established. Statistical characteristic analysis method is used to construct independent innovative learning resource set and fuzzy information fusion processing for gymnastics teaching. Statistical evaluation and fuzzy degree optimization detection method are used to design independent innovative learning algorithm for gymnastics teaching. Computer interactive control method is used to realize adaptive optimization in independent innovative learning process for gymnastics teaching and realize independent innovative learning optimization for gymnastics teaching. The simulation results show that the convergence of using the algorithm to carry out independent and innovative learning in gymnastics teaching is good, and the ability of scheduling and clustering of independent and innovative learning resources in gymnastics teaching is good, thus improving the independent and innovative learning ability and the level of resource sharing in gymnastics teaching.
Digital contact tracing and data protection: assessing the French and Portuguese applications

The rapid outbreak of COVID-19 has necessitated a global response to manage the transmission, spread, and impact of the virus. Many countries started developing digital contact tracing applications to contribute to the process of lifting the restrictions imposed on its citizens. Generally, the protection of personal data is a right that appears in constitutional and legal provisions, and its processing can only be performed under very restricted circumstances, the protection of health being a valid justification. This article focusses on the systems developed as digital contact tracing tools in France (StopCovid) and in Portugal (STAYAWAY COVID), presenting their characteristics and the most relevant aspects concerning the processing of personal data. The most relevant positive characteristics of these systems are their voluntary adoption, their developers’ concern with the people’s security and privacy, and the transparency of their functioning. With the negative characteristics, the most relevant are the restricted interoperability with the systems from other EU countries, and the permanent risk to people’s privacy of collecting lists of contacts and the circumstances of their interactions with other users of the systems.
Penarapan Algoritma Prefix Code Dalam Kompresi Data Teks

Data compression technology is the process of converting a set of data into a lower coded representation of data that is not encoded with a certain encoding. Data compression is done by encoding each character into a shorter coded message, in order to save storage space requirements and time for data transmission. Compression is used to speed up the process of sending from uncoded characters to certain encoded characters. By compressing, large data will be reduced in size so as to save storage capacity. In this study the authors use the Prefix Code algorithm, using this method the compression results are very good in transmitting data and moving text data allocations easier and faster. By compressing, large data will be reduced in size so as to save storage space. There are many types of methods or algorithms in text data compression, but in this algorithm the writer only wants to know the compression process using the Prefix Code algorithm using C1 code where the author has succeeded in compressing text data with the prefix code algorithm using C1 code has the ability to compress text data by 58.3% on a text data. Keywords : Compression , Text, Data , Prefix Code, Algorithm .
Recoupling Economic and Social Prosperity

This paper explores a new theoretical and empirical approach to the assessment of human well-being, relevant to current challenges of social fragmentation in the presence of globalization and technological advance. We present two indexes of well-being—solidarity (S) and agency (A)—to be considered alongside the standard indexes of material gain (G) and environmental sustainability (E). The four indexes—SAGE—form a balanced dashboard for evaluating well-being. The solidarity index covers the needs of humans as social creatures, living in societies that generate a sense of social belonging. The agency index involves people's need to influence their fate through their own efforts. While "economic prosperity" (material gain) is conventionally measured through GDP per capita, "social prosperity" can be measured through our solidarity and agency indexes, alongside environmental sustainability that is measured through the Environmental Performance Index. The SAGE dashboard is meant to provide a "sage" approach to assessing well-being, since it aims to denote sagacity in the pursuit and satisfaction of fundamental human needs and purposes. Many of the prominent challenges of the twenty-first century, including the dissatisfaction of population groups who feel left behind by globalization and technological advance, may be viewed in terms of a "decoupling" of economic prosperity from social prosperity. We present a theoretical model that provides a new perspective on the welfare effects of globalization and automation. The dashboard is meant to provide an empirical basis for mobilizing action in government, business and civil society to promote a recoupling of economic and social prosperity.
The influence of preprocessing on text classification using a bag-of-words representation

Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text preprocessing, e.g., conversion of uppercase letters into lowercase letters, HTML tag removal, stopword removal, punctuation mark removal, lemmatization, correction of common misspelled words, and reduction of replicated characters. We hypothesize that the application of different combinations of preprocessing methods can improve TC results. Therefore, we performed an extensive and systematic set of TC experiments (and this is our main research contribution) to explore the impact of all possible combinations of five/six basic preprocessing methods on four benchmark text corpora (and not samples of them) using three ML methods and training and test sets. The general conclusion (at least for the datasets verified) is that it is always advisable to perform an extensive and systematic variety of preprocessing methods combined with TC experiments because it contributes to improve TC accuracy. For all the tested datasets, there was always at least one combination of basic preprocessing methods that could be recommended to significantly improve the TC using a BOW representation. For three datasets, stopword removal was the only single preprocessing method that enabled a significant improvement compared to the baseline result using a bag of 1,000-word unigrams. For some of the datasets, there was minimal improvement when we removed HTML tags, performed spelling correction or removed punctuation marks, and reduced replicated characters. However, for the fourth dataset, the stopword removal was not beneficial. Instead, the conversion of uppercase letters into lowercase letters was the only single preprocessing method that demonstrated a significant improvement compared to the baseline result. The best result for this dataset was obtained when we performed spelling correction and conversion into lowercase letters. In general, for all the datasets processed, there was always at least one combination of basic preprocessing methods that could be recommended to improve the accuracy results when using a bag-of-words representation.
A collaborative healthcare framework for shared healthcare plan with ambient intelligence

Abstract The fast propagation of the Internet of Things (IoT) devices has driven to the development of collaborative healthcare frameworks to support the next generation healthcare industry for quality medical healthcare. This paper presents a generalized collaborative framework named collaborative shared healthcare plan (CSHCP) for cognitive health and fitness assessment of people using ambient intelligent application and machine learning techniques. CSHCP provides support for daily physical activity recognition, monitoring, assessment and generate a shared healthcare plan based on collaboration among different stakeholders: doctors, patient guardians, as well as close community circles. The proposed framework shows promising outcomes compared to the existing studies. Furthermore, the proposed framework enhances team communication, coordination, long-term plan management of healthcare information to provide a more efficient and reliable shared healthcare plans to people.
Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.
Syntactic Data Augmentation Increases Robustness to Inference Heuristics

Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets.We hypothesize that this issue is not primarily caused by the pretrained model's limitations, but rather by the paucity of crowdsourced NLI examples that might convey the importance of syntactic structure at the finetuning stage.We explore several methods to augment standard training sets with syntactically informative examples, generated by applying syntactic transformations to sentences from the MNLI corpus.The best-performing augmentation method, subject/object inversion, improved BERT's accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73, without affecting performance on the MNLI test set.This improvement generalized beyond the particular construction used for data augmentation, suggesting that augmentation causes BERT to recruit abstract syntactic representations.
S-Box Based Image Encryption Application Using a Chaotic System without Equilibrium

Chaotic systems without equilibrium are of interest because they are the systems with hidden attractors. A nonequilibrium system with chaos is introduced in this work. Chaotic behavior of the system is verified by phase portraits, Lyapunov exponents, and entropy. We have implemented a real electronic circuit of the system and reported experimental results. By using this new chaotic system, we have constructed S-boxes which are applied to propose a novel image encryption algorithm. In the designed encryption algorithm, three S-boxes with strong cryptographic properties are used for the sub-byte operation. Particularly, the S-box for the sub-byte process is selected randomly. In addition, performance analyses of S-boxes and security analyses of the encryption processes have been presented.
Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization.

Recent developments on large-scale distributed machine learning applications, e.g., deep neural networks, benefit enormously from the advances in distributed non-convex optimization techniques, e.g., distributed Stochastic Gradient Descent (SGD). A series of recent works study the linear speedup property of distributed SGD variants with reduced communication. The linear speedup property enable us to scale out the computing capability by adding more computing nodes into our system. The reduced communication complexity is desirable since communication overhead is often the performance bottleneck in distributed systems. Recently, momentum methods are more and more widely adopted in training machine learning models and can often converge faster and generalize better. For example, many practitioners use distributed SGD with momentum to train deep neural networks with big data. However, it remains unclear whether any distributed momentum SGD possesses the same linear speedup property as distributed SGD and has reduced communication complexity. This paper fills the gap by considering a distributed communication efficient momentum SGD method and proving its linear speedup property.
Adoption of blockchain in supply chain: an analysis of influencing factors

Purpose Blockchain technology (BT) is setting world-shattering standard in all type of transactions in business. BT has the prospective to drastically transform supply chain (SC). The main challenge is to enhance trust among the SC stakeholders. This paper aims to identify and prioritize the factors and its challenges that influence the adoptability of BT in SC. The prioritization of these factors will be helpful to the practitioners to decide the strategy of implementing the BT in SC. Design/methodology/approach The factors influencing adoption of BT are identified from the review of literature and expert opinion is used to rank the factors influencing the adoptability of BT in SC using grey relational analysis (GRA). Findings We identified and prioritized key factors: inter-organizational trust and relational governance as organizational challenge, data transparency and data immutability as technological challenge, interoperability and product type as operational challenge and social influence and behavioral intention as social challenge that influences adoptability of BT in SC. Originality/value The priority of these factors will guide future researchers and industry practitioners to plan rational and financial strategy for implementing BT in SC.