Reviewerly/exHarmony huggingface dataset:
Verifiable Multi-use Multi-secret Sharing Scheme on Monotone Span Program

Based on the monotone span program, this paper proposes a verifiable multi-secret sharing scheme on the general access structure. Each participant can choose their own secret share and use the RSA cryptosystem to send the share to the dealer through open channels. In the secret recovery phase, the scheme uses the Hash Function to achieve multi-use property and verifiability. Analysis shows that each participant can recover multiple secrets by keeping only one secret share, and shares do not need to be changed when the multi-secrets are renewed. Furthermore, the distribution of secret shares in the scheme does not require a secure channel, which effectively reduces the computational cost of the system. Compared with the (t, n) threshold secret sharing, the scheme has more vivid access structures.
A new technique for understanding large-scale software systems

Comprehending a huge execution trace is not a straightforward task due to the size of data to be processed. Detecting and removing utilities are useful to facilitate the understanding of software and decrease the complexity and size of the execution trace. The goal of this study is to develop a novel technique to minimize the complexity and the size of traces by detecting and removing utilities from the execution trace of object-oriented software. Two novel utility detection class metrics were suggested to decide the degree that a specific class can be counted as a utility class. Dynamic coupling analysis forms the basis for the proposed technique to address object-oriented features. The technique presented in this study has been tested by two case studies to evaluate the effectiveness of the proposed technique. The results from the case studies show the usefulness and effectiveness of our technique.
The Interactive Dance Club: Avoiding Chaos In A Multi Participant Environment

In 1998 we designed enabling technology and a venue concept that allowed several participants to influence a shared musical and visual experience. Our primary goal was to deliver musically coherent and visually satisfying results from several participants' input. The result, the Interactive Dance Club, ran for four nights at the ACM SIGGRAPH 98 convention in Orlando, Florida. In this paper we will briefly describe the Interactive Dance Club, our "10 Commandments of Interactivity," and what we learned from its premiere at SIGGRAPH 98.
Commodity Classification Based on Multi-modal Jointly Using Image and Text Information

Considering that there exists image and text information almost on every commodity web page, although these two kinds of information belong to different modals, both of them describe the same commodity, so there must be a certain relationship between them. We name this relationship "symbiosis and complementary", and propose a multi-modal based on image and text information for commodity classification algorithm (MMIT). Firstly, we use \(\ell _{2,0}\) mixed norm to optimize sparse representation method for image classification, and then employ Bayesian posterior probability to optimize k-nearest neighbor method for text classification. Secondly, we fuse two modal classification results, and build MMIT mathematical model. Finally, we utilize a dataset to train MMIT model, and then employ trained MMIT classifier to classify different commodities. Experimental results show that our method can achieve better classification performance than other state-of-the-art methods, which only exploit image information.
Did You Get The Gist Of It? Understanding How Visualization Impacts Decision-Making

As visualization researchers evaluate the impact of visualization design on decision-making, they often hold a one-dimensional perspective on the cognitive processes behind making a decision. Several psychological and economical researchers have shown that to make decisions, people rely on quantitative reasoning as well as gist-based intuition -- two systems that operate in parallel. In this position paper, we discuss decision theories and provide suggestions to bridge the gap between the evaluation of decision-making in visualization and psychology research. The goal is to question the limits of our knowledge and to advocate for a more nuanced understanding of decision-making with visualization.
DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue

Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability. This has made it difficult to adopt the multi-turn multi-domain dialogue generation capabilities of streamlined end-to-end open-domain dialogue systems. In this paper, we present a new framework, DLGNet-Task, a unified task-oriented dialogue system which employs autoregressive transformer networks such as DLGNet and GPT-2/3 to complete user tasks in multi-turn multi-domain conversations. Our framework enjoys the controllable, verifiable, and explainable outputs of modular approaches, and the low development, deployment and maintenance cost of end-to-end systems. Treating open-domain system components as additional TOD system modules allows DLGNet-Task to learn the joint distribution of the inputs and outputs of all the functional blocks of existing modular approaches such as, natural language understanding (NLU), state tracking, action policy, as well as natural language generation (NLG). Rather than training the modules individually, as is common in real-world systems, we trained them jointly with appropriate module separations. When evaluated on the MultiWOZ2.1 dataset, DLGNet-Task shows comparable performance to the existing state-of-the-art approaches. Furthermore, using DLGNet-Task in conversational AI systems reduces the level of effort required for developing, deploying, and maintaining intelligent assistants at scale.
Pedagogical Conditions for Developing Students’ Social Interaction Competence in Collaborative Learning Activity

The study aims to identify and substantiate pedagogical conditions involved in development of students’ social interaction competence in collaborative learning activity. The article provides a comparative analysis and summary of conditions involved in development of social interaction competence and competences of similar essence. The study is novel in that the author, in contrast to the existing scientific concepts, proposes and substantiates pedagogical conditions involved in development of students’ social interaction competence in collaborative learning activity. As a result, psychological and pedagogical studies dealing with conditions contributing to development of social interaction competence were analysed and the existing scientific concepts were summarised. Basing on the theoretical analysis and summary, pedagogical conditions facilitating development of students’ social interaction competence in collaborative learning activity were drafted.
Collective Entity Disambiguation Based on Deep Semantic Neighbors and Heterogeneous Entity Correlation

Entity Disambiguation (ED) aims to associate entity mentions recognized in text corpus with the corresponding unambiguous entry in knowledge base (KB). A large number of models were proposed based on the topical coherence assumption. Recently, several works have proposed a new assumption: topical coherence only needs to hold among neighboring mentions, which proved to be effective. However, due to the complexity of the text, there are still some challenges in how to accurately obtain the local coherence of the mention set. Therefore, we introduce the self-attention mechanism in our work to capture the long-distance dependencies between mentions and quantify the degree of topical coherence. Based on the internal semantic correlation, we find the semantic neighbors for every mention. Besides, we introduce the idea of “simple to complex” to the construction of entity correlation graph, which achieves a self-reinforcing effect of low-ambiguity mention towards high-ambiguity mention during collective disambiguation. Finally, we apply the graph attention network to integrate the local and global features extracted from key information and entity correlation graph. We validate our graph neural collective entity disambiguation (GNCED) method on six public datasets and the results demonstrate a better performance improvement compared with state-of-the-art baselines.
가족주의 가치관, 직장지지가 취업모의 일ㆍ가정 양립 갈등수준에 미치는 영향

Objectives: The purpose of this study is to explore the effect of family values and workplace support on the level of conflict over work-family compatibility in working mothers. Methods: The subjects were 322 employed women working full-time, having more than one child, and living Seoul. Data were analyzed SPSS 21.0 Package. A Hierarchical Multiple Regression Analysis were used to analyze the effect of family values and work place support on the level of conflict over work-family compitability in working mothers. Results: The study results are as follows: First, family values, workplace support, and the level of conflict over work-family compatibility were assessed in the subjects and the score of family values was 3.76(SD=.73). In subcatagorie of family values, child value was 3.42(SD=.97), parenthood value was 3.94(SD=.74) The score of workplace support was 3.88(SD=.51). When balancing their jobs with their family life, the score for the level of conflict was 3.08(SD=.78). Second, household income(β=.15, p<.001), the number of children(β=.21, p<.05) workplace support(β=-.24, p<.001) were found to have a statistically significant impact on the level of conflict over work-family compatibility. Conclusions: Women who have a higher income were likely to have more conflict over work-family balance. Women who have more children were likely to have more conflict over work-family balance. Women who perceived higher emotional support from coworkers were likely to have less conflict over work-family balance. In this study, workplace support was the most influential variable for work-family compatibility in employed mothers.
DXOMARK Objective Video Quality Measurements

Video capture is becoming more and more widespread. The technical advances of consumer devices have led to improved video quality and to a variety of new use cases presented by social media and artificial intelligence applications. Device manufacturers and users alike need to be able to compare different cameras. These devices may be smartphones, automotive components, surveillance equipment, DSLRs, drones, action cameras, etc. While quality standards and measurement protocols exist for still images, there is still a need of measurement protocols for video quality. These need to include parts that are non-trivially adapted from photo protocols, particularly concerning the temporal aspects. This article presents a comprehensive hardware and software measurement protocol for the objective evaluation of the whole video acquisition and encoding pipeline, as well as its experimental validation.
Are Registration Uncertainty and Error Monotonically Associated

In image-guided neurosurgery, current commercial systems usually provide only rigid registration, partly because it is harder to predict, validate and understand non-rigid registration error. For instance, when surgeons see a discrepancy in aligned image features, they may not be able to distinguish between registration error and actual tissue deformation caused by tumor resection. In this case, the spatial distribution of registration error could help them make more informed decisions, e.g., ignoring the registration where the estimated error is high. However, error estimates are difficult to acquire. Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error. It is intuitive and believed by many clinicians that high uncertainty indicates a large error. However, the monotonic association between uncertainty and error has not been examined in image registration literature. In this pilot study, we attempt to address this fundamental problem by looking at one PIR method, the Gaussian process (GP) registration. We systematically investigate the relation between GP uncertainty and error based on clinical data and show empirically that there is a weak-to-moderate positive monotonic correlation between point-wise GP registration uncertainty and non-rigid registration error.
Accuracy Improvement For Remote Sensing Based Lithological Mapping By Using Randomisation And Categorical Coincidence

Remote sensing based lithological mapping is commonly applied in the field of earth science as it requires less resource in contrast with real field work. Limitation regarding low accuracy is a challenge that should be tackled in applying remote-sensing based classification. In this paper, an attempt to improve overall accuracy of image classification using randomisation and categorical coincidence analysis was performed. It yielded final majority classification map which has higher overall accuracy compared to the overall accuracy of the population average and the overall accuracy of the map created by including all training data.
Symbolic Verification of Quantum Circuits

This short note proposes a symbolic approach for representing and reasoning about quantum circuits using complex, vector or matrix-valued Boolean expressions. A major benefit of this approach is that it allows us to directly borrow the existing techniques and tools for verification of classical logic circuits in reasoning about quantum circuits.
An innovative ASV for the monitoring of anthropogenic pressure on Wetlands

&amp;lt;p&amp;gt;This paper describes the technological development of an innovative Autonomous Surface Vehicle designed to meet the requirement of accessing the extremely shallow waters peculiar of the Wetlands. Wetlands are those geographic areas where water meets the earth and cover between 5 and 8 % of the Earth's surface. Wetlands include mangrove zones, swamps, bogs and marshes, rivers and lakes, alluvial plains and flooded forests, shallow coasts and coral reefs. In recent years, their importance is becoming more and more recognized and various international conventions, directives and projects work on the protection of these fundamental ecosystems. Wetlands are considered among the world&amp;amp;#8217;s most productive environments for their biological diversity. Nevertheless the number, quality and spacial resolution of surveys in these peculiar environments is reduced due to the absence of suitable tools expressly addressed to work in these areas. This reduces the possibilities of monitoring and protecting the Wetlands. In this paper the first prototype of an innovative class of reliable modular re-configurable lightweight ASVs for extremely shallow water and remote areas applications is presented. SWAMP (Shallow Water Autonomous Multipurpose Platform) is a fully electric, modular, portable, lightweight, and highly-controllable Autonomous Surface Vehicle (ASV). It is a catamaran equipped with azimuth pump-jet actuators, and it is characterized by small draft soft-foam, unsinkable hull structure with high modularity and a flexible hardware/software architecture. The main advantage of pumpjet motors is that they are flush with the hull, thus minimizing the risks of damages due to possible grounding. This system is used to increase the manoeuvrability in narrow spaces and to increase the special resolution also in extremely shallow waters. The introduction of a soft hull structure is suitable for working in coastal areas and in riverine environment where impacts can affect the survival of the robot. The foam of the hulls is drilled in order to make SWAMP a completely modular catamaran that is able to host various types of tools, such as intelligent systems, samplers, and sensors, together with thrusters that are, in this way, protected by the foam. A preliminary study is shown related to the bathymetry carried on with a single beam sonar in a Ligurian river after a series of floods. Liguria is one of the European regions where extreme events related to anthropic changes have had the greatest number of negative effects. In this kind of areas the use of suitable robots can improve the assessment and monitoring the impact of anthropogenic pressure in wetland ecosystems.&amp;lt;/p&amp;gt;
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification

During the last decade, deep neural networks (DNN) have demonstrated impressive performances solving a wide range of problems in various domains such as medicine, finance, law, etc. Despite their great performances, they have long been considered as black-box systems, providing good results without being able to explain them. However, the inability to explain a system decision presents a serious risk in critical domains such as medicine where people's lives are at stake. Several works have been done to uncover the inner reasoning of deep neural networks. Saliency methods explain model decisions by assigning weights to input features that reflect their contribution to the classifier decision. However, not all features are necessary to explain a model decision. In practice, classifiers might strongly rely on a subset of features that might be sufficient to explain a particular decision. The aim of this article is to propose a method to simplify the prediction explanation of One-Dimensional (1D) Convolutional Neural Networks (CNN) by identifying sufficient and necessary features-sets. We also propose an adaptation of Layer-wise Relevance Propagation for 1D-CNN. Experiments carried out on multiple datasets show that the distribution of relevance among features is similar to that obtained with a well known state of the art model. Moreover, the sufficient and necessary features extracted perceptually appear convincing to humans.
Visualization of Educational Information as a Means of Enhancing the Cognitive Activity in Students

The article deals with information visualization techniques aimed at effective development of cognitive processes as parts of students' cognitive activity. The research featured the means of visualization of educational information that encourage the cognitive activity in students. The research objective was to identify, justify, and test the possibilities of using visualization tools for educational information. The cognitive activity is represented as a set of cognitive acts, namely explanation, understanding, and reflection. The authors believe that the process of interaction between participants in educational relations needs to be improved. The article focuses on the organization of the informational approach to training. It describes the stages of work with educational information: search for new material, its perception, processing, and application. The visualization technologies include mind maps and denotational graphs. Visualization technologies provide positive changes in students’ cognitive activity and develop cognitive motivation.
Efficient unstructured search implementation on current ion-trap quantum processors

So far, only the results on 3 qubit spaces (both on superconducting and ion-trap realisations of quantum processors) have beaten the classical unstructured search in the expected number of oracle calls using optimal protocols in both settings. We present experimental results on running unstructured search in spaces defined by 4, 5 and 6 qubits on ion-trapped quantum processor. Our best circuits obtained respectively 66\%, 26\% and 6\% average probability of measuring the marked element. In the case of 4 and 5 qubit spaces we obtained fewer expected number of oracle calls required to find a marked element than any classical approach. Viability of the theoretical result by Grover at these qubit counts is, to authors' knowledge demonstrated experimentally for the first time. Also at 6 qubits, a circuit using a single oracle call returned a measured probability of success exceeding any possible classical approach. These results were achieved using a variety of unstructured search algorithms in conjunction with recent developments in reducing the number of entangling gates. The latter are currently considered to be a dominating source of errors in quantum computations. Some of these improvements have been made possible by using mid-circuit measurements. To our knowledge the latter feature is currently available only on the H0 quantum processor we run on.
Review of the Latest Theories of Education: Pedagogy 2.0, Education 3.0 and Heutagogy

The Internet system has created new qualitative opportunities for the development of education. The creation of a global information infrastructure in the 90s of the XX century transormed the world, making it dynamic, rich in information and compressed. A new generation of pupils and students born in the era of the Internet has grown. Gradually, pedagogy is changing, in other words, expanding its foundations for new teaching strategies for schoolchildren and students, as well as adults. In this article, we will discuss elements of emerging theories – “Pedagogy 2.0” and “Pedagogy 3.0”, “Education 2.0”, “Education 3.0”, Heutagogy, digital pedagogy and their continuity. Are these theories revolutionary in modern education? How do Pedagogy 3.0 and Education 3.0 intersect with digital technology, and in a new way, with digital pedagogy? Is it necessary for teachers to maintain / reorganize the system of training pedagogues and how? The article is of an overview nature. A version for ordering views on modern pedagogy is proposed for discussion. As it is known, in the discussion new ideas and new learning strategies for students can be born. The generalization of experience is used in the course on “Methods of teaching pedagogy”, “Designing in education.” A directed interview with students of pedagogical specialties was carried out and their assessment of the advantages and disadvantages of “pedagogy – androgogy – eutagogy” in modern pedagogical practice is given.
Pengembangan Mobile Learning IPA (MLI) Sasirangan Materi Pencemaran Lingkungan untuk Peserta Didik SMP

This study is aimed at: (1) creating Sasirangan Science Mobile (MLI Sasirangan) Learning product for junior high school students and (2) determining the feasibility level of MLI Sasirangan according to experts, practitioners, academics, and the response of students in limited trial test as users. The type of this research is research and development (R&amp;D). Furthermore, the result of this research shows that: (1) Android-web-application-based MLI Sasirangan was created from Learning Management System (LMS) Moodle. The material was delivered by integrating the waste of typical South Kalimantan fabric making with the environmental pollution material and learned online; (2) based on the assessment result from media and material experts, this product obtained a value of 4.73 and 4.48 with a feasibility percentage of 94.6% and 89.56%. Meanwhile, the evaluation result from practitioners and peers, the values obtained were 4.45 and 4.64. Moreover, from the limited trial test, this product obtained a value of 4.47 with the percentage of feasibility is of 89.00%, 92.80% and 89.40% respectively. Therefore, it can be concluded that MLI Sasirangan which has been developed is considered feasible with very good criteria to be used as science learning media.&#x0D; Keywords: Mobile learning, android, sasirangan, science.