Reviewerly/exHarmony huggingface dataset:
Multimodal Analysis of Preschool Children’s Embodied Interaction with a Tangible Programming Environment

Direct physical interaction with the world is a key constituting factor of cognitive development during childhood. Computational technologies, such as Tangible User Interfaces (TUI), offer opportunities for physically mediated interaction in learning environments, exploiting more embodied forms of interaction through hands-on physical manipulation and exploration. Many studies have introduced young children to programming by tangibles programming environments, although they do not address the effects of embodied interaction in the learning experience. In this paper, we present a case study conducted to introduce preschool children into programming through TaPrEC+mBot, an environment that allows programming of a robot car by arranging wooden programming blocks. Drawing on the multimodal method of analysis, we investigate the embodied interaction of the children with the TaPrEC+mBot to examine if and how the embodied forms of interaction shape the learning experience. The analysis shows the role of body position, gaze, manipulation, and speech, in terms of how these aspects support the construction of strategies to tangible programs; also the implications of this interaction in the meaning-making process.
Web Archiving and Digital Libraries

This workshop will explore integration of Web archiving and digital libraries and cover all stages of its complete life cycle, including creation/authoring, uploading/publishing, crawling, indexing, exploration, and archiving, etc. It will include particular coverage of current topics of interest, like: big data, social media archiving, and systems.
Fuzzy AHP Based Prioritization and Taxonomy of Information Quality Factors in Accounting Information Systems

Enterprise Resource Planning (ERP) systems undertake the task of producing the data they need on time and reliably. Accounting Information Systems (AIS) is located in the centre of ERP Architecture and AIS, generates data on the financial status of businesses. The information produced by AIS is used for decision making by internal and external stakeholders. In this context, the quality of the information produced by AIS also affects the quality of the decisions made by stakeholders. The main objective of this study is to develop the prioritization-based taxonomy of the AIS’s information quality factors using the fuzzy Analytical Hierarchy Process (AHP) approach. The information production processes of AIS directly affect the quality of information produced. For this reason, critical factors affecting the information quality were determined by focusing on the information production processes of AIS. Nineteen information quality factors were extracted from the literature. Then, Multi-Criteria Decision Making method fuzzy AHP is used to prioritize and develop the taxonomy of the identified factors. The implications of fuzzy AHP approach are novel in this research area as it has been successfully used previously in different other domains e.g. electrical and electronics, supplier selection, agile software development and personnel selection.
Designing Unconscious and Enactive Interaction for Interactive Movie Experience

In a world full of sensors that gather personal data and digital solutions that use these data to provide feedback and personalized experiences, biofeedback is increasingly involved in the definition of new paradigms for tailoring interactions. Companies are collecting and using personal data to propose personalized services. Content providers are pushing users to produce data in order to create personalized storytelling experiences. In this context, the tech market is offering new low-cost solutions able to gather biodata. The paper reports the results of evidence-based explorations aimed at formalizing knowledge regarding the use of passive and unconscious interaction to control the fruition of storytelling artifacts. We investigate a new interaction paradigm that promise to seamlessly enable unconscious and enactive interactions for movie experiences. We propose the use of emotion recognition and eye-tracking as exploratory technologies that promise to be a potential contribution to richer access to the spectators’ emotional involvement. We reflect on disruptive power of non-invasive technologies, given by the possibility to be used for home-cinema experiences. Investigating on emotional states of users in their decision we leverage on the emotive-cognitive data as a matter of creation and enabling of tailored movie experiences. Our research intends to explore the possibility of extracting knowledge from recognition of facial expressions that will contribute to foster its use in real-time passive interaction using emotion recognition as a trigger of enactivity that is not limited to interactive storytelling but opens new scenarios in the design of proactive systems for screens, spaces and environments. Furthermore, we provide suggestions as guidelines for the design of enactive experiences that leverage on emotion recognition and eye-tracking.
Agent-Based Methods in Support of Adaptive Instructional Decisions

This paper examines the functionality of artificially-intelligent agents as a methodology for supporting automated decisions in adaptive instructional systems (AISs). AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, preferences, and interests of each individual learner or team in the context of domain learning objectives. AISs are a class of instructional technologies that include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. This paper explores various agent-based methods to gauge their impact on four automated decisions within the Learning Effect Model (LEM): 1) determining current and predicting future learner states, 2) making recommendations for new experiences (e.g., courses or problem selection), 3) selecting high level instructional strategies to influence long-term learning, and 4) selecting low level instructional tactics to influence near-term learning.
Geometric Shapes-based PSO Approach for Routing in Vehicular Networks

In communication between vehicles, many mobile nodes are linked from the source to the final destination for message transfer purpose when the vehicles are constantly moving and the topology is wide. There are scenarios in which a node wants to multicast some type of alert or specific message to nearby neighbor nodes. In this paper, an efficient routing protocol called geometric shapes-based PSO approach for routing in vehicular networks (named GPSV) is proposed, which exploits the particle swarm optimization (PSO) technique and the angle between the nodes to determine a suitable route to transfer the alerts or specific messages from a source node to a group of destinations. In the proposed method, each route request packet is considered as a swarm message which has an inertia, velocity, global best experience, personal best experience, and current position coordinates. By using the PSO technique as backbone of angular routing, the swarm determines the next best hop of a node. Simulations are conducted to evaluate the performance of the proposed GPSV protocol, showing that it can take any possible shape. We have used circular, lane, and sine waves to test the swarm formations. In addition, GPSV outperforms the flooding method in terms of packet delivery ratio, average delay, throughput, and loss packet percentage.
Breast mass classification method based on convolutional neural networks

To classify the X-ray mammograms images as benign or malignant is a long-standing unresolved problem, due to the high similarity of different between the mammograms images. In this study, a novel convolutional neural network based X-ray breast mass classification method is proposed. The method receives original breast mass image and its transformed image simultaneously, and extracts more abundant features from the breast mass images. Though the transformed image is a simple inverse of the original image, it allows another side of the thing to be perceived by the network at a very low cost. Experiment results demonstrate that the proposed method significantly outperforms the compared state-of-the-art classification methods for breast mass.
Health Education VR

We developed a full-body virtual-presence system in which users crawl to travel within the human body, and gain insight into the structures of digestive organs through visual, tactile, and auditory sensations. Digestive organs (esophagus, stomach, duodenum, small intestine, large intestine, rectum) are created in detail with 3-D images that users could look around using a head-mounted display, and an appropriate feeling of touch is presented to the abdomens and thighs of users by vibrotactile devices. Pressure sensors detect the crawling action of users so that they can travel in the digestive organs while hearing realistic 3-D sounds. Participants felt as if their body turned into digested food and was moving inside another person’s body. Participants also felt as if their bodies became smaller. Thus, this system gives users the sensation of a reduced body size and provides a telepresence at improbable locations such as the stomach. Participants enjoyed the experience and were interested in the human body structure and function. This system can serve as an interactive, educational experience by simulating the contents of the human digestive system.
Cognitive Variability Factors and Passphrase Selection

Security policies require a secret code to access electronic information. Challenges exist between the usability and memorability of passwords. This study spotlights individualistic behavioral assimilation of passphrase styles for design insight and recall abilities. Data captured categorical authentication behavior toward enhanced usability outcomes. Validated locus of control personality and memory associative instruments demonstrated the internal and external personality types and cognitive response types that contribute to the systematic quest toward a more memorable passphrase scheme. Personalized criteria contributed to practical evaluation employing a repeated measures structure. This study tested 58 participants who successfully completed a passphrase survey consisting of four rulesets applied to imposed and user created passphrases designed for repeated measures. Although electrophysiological data was collected, it was not analyzed in time for this publication. Results indicate that memory associative factors of cognition represent a significant factor in the recall of 75% of imposed passphrase category types. The locus of control and memory associative variables are significant at the .05 level. Internally controlled participants preferred the created room objects and created no vowel passphrases. Additionally, the created room objects and animal association passphrases ranked the highest among the externally controlled subjects. The imposed passphrases constructed without vowels and associated with animals received the least recall. This descriptive study informs passphrase usability identifying cognitive demands that impact memory.
A Design Kit for Mobile Device-Based Interaction Techniques

Beside designing the graphical interface of mobile applications, mobile phones and their built-in sensors enable various possibilities to engage with digital content in a physical, device-based manner that move beyond the screen content. So-called mobile device-based interactions are characterized by device movements and positions as well as user actions in real space. So far, there is only little guidance available for novice designers and developers to ideate and design new solutions for specific individual or collaborative use cases. Hence, the potential for designing mobile-based interactions is seldom fully exploited. To address this issue, we propose a design kit for mobile device-based interaction techniques following a morphological approach. Overall, the kit comprises seven dimensions with several elements that can be easily combined with each other to form an interaction technique by selecting at least one entry of each dimension. The design kit can be used to support designers in exploring novel mobile interaction techniques to specific interaction problems in the ideation phase of the design process but also in the analysis of existing device-based interaction solutions.
A Formalization of Properties of Continuous Functions on Closed Intervals

Formal mathematics is getting increasing attention in mathematics and computer science. In particular, the formalization of calculus has important applications in engineering design and analysis. In this paper, we present a formal proof of some fundamental theorems of continuous functions on closed intervals based on the Coq proof assistant. In this formalization, we build a real number system referring to Landau’s Foundations of Analysis. Then we complete the formalization of the basic definitions of interval, function, and limit and formally prove the theorems including completeness theorem, intermediate value theorem, uniform continuity theorem and others in Coq. The proof process is normalized, rigorous and reliable.
The Effects of Preprocessing on Colorectal Polyp Detecting by Fuzzy Algorithm

In the following study the effects of two image preprocessing methods, namely Gaussian filtering and Wiener filtering, is studied on the results of a fuzzy inference method previously developed by the authors, for determining whether a colonoscopy picture segment contains any colorectal polyp. As earlier results show that less blurry, less compressed and less noisy images tend to be better classifiable, the effects of noise suppression with a Gaussian filter, which makes the images also blurrier, was beneficial on noisy, compressed images, and rather maleficent in good quality pictures. The effects of the Wiener filter, which both decreases noise and enhances edges, did not really manifest in classification improvement.
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning

Energy consumption is one of the most critical concerns in designing computing devices, ranging from portable embedded systems to computer cluster systems. Furthermore, in the past decade, cluster systems have increasingly risen as popular platforms to run computing-intensive real-time applications in which the performance is of great importance. However, due to different characteristics of real-time workloads, developing general job scheduling solutions that efficiently address both energy consumption and performance in real-time cluster systems is a challenging problem. In this paper, inspired by recent advances in applying deep reinforcement learning for resource management problems, we present the Deep-EAS scheduler that learns efficient energy-aware scheduling strategies for workloads with different characteristics without initially knowing anything about the scheduling task at hand. Results show that Deep-EAS converges quickly, and performs better compared to standard manually-tuned heuristics, especially in heavy load conditions.
Building referring expression corpora with and without feedback

The design of data collection experiments involving human participants is a common task in Referring Expression Generation (REG) and related fields. Many (or most) REG data collection tasks are implemented by making use of a human–computer (e.g., web-based) communicative setting, in which participants do not have any particular addressee in mind and do not receive any feedback regarding the appropriateness (e.g., uniqueness) of the descriptions that they produce. Others, at a possibly higher cost, make use of participant pairs engaged in some form of dialogue in which hearers may provide feedback allowing speakers to rephrase ambiguous or otherwise ill-formed descriptions. Leaving the issue of cost aside, however, it remains unclear whether the two methods elicit similar referring expressions for the purpose of REG research. To shed light on this issue, this paper presents a REG corpus built under three experimental conditions: a standard human–computer (or web-based) setting in which no feedback is available to the speaker, and two settings in which feedback regarding the appropriateness of the description may be provided either by an automated parsing tool or by a second participant at the receiving end of the communication. The corpus contains fully annotated descriptions in two domains—simple geometric objects and realistic human face images—and it is provided as a resource for the training and testing of REG algorithms in these communicative settings.
Security Model for the Integration of the Ministry of Telecommunications and the Information Society with a Public Organization of Ecuador

Information security is one of the most important aspects in organizations that has been constantly developing, driven by advances in Information and Communication Technologies. It should be noted that such development also entails the constant identification of new threats that put at risk the Information Security within the organization, so it is important the implementation of a security model that would guarantee the security of computer assets in the process of integration of the Ministry of Telecommunications and the information society with a Public organization of Ecuador with the objective of ensuring the basic principles of Information Security: Integrity, availability and confidentiality. Therefore, using the ISO / IEC 27001 standard, the guidelines of the safety model were defined. It should be noted that for the implementation of the ISO / IEC 27001 standard, it was based on the Deming Cycle which based the analysis stages that were implemented in the security model. The result was a security model that allowed an early identification of risk and the establishment of corrective measures to mitigate it, based on the criticality analysis of the assets generated in the integration of organizations, the impact measurement generated from the involvement of an asset due to the materialization of a risk, among others. In addition, the defined security model was based on the implementation of the Deming cycle, resulting in a dynamic model for risk management.
Applying Text Analytics to the Mind-section Literature of the Tibetan Tradition of the Great Perfection

Over the past decade, through a mixture of optical character recognition and manual input, there is now a growing corpus of Tibetan literature available as e-texts in Unicode format. With the creation of such a corpus, the techniques of text analytics that have been applied in the analysis of English and other modern languages may now be applied to Tibetan. In this work, we narrow our focus to examine a modest portion of that literature, the Mind-section portion of the literature of the Tibetan tradition of the Great Perfection. Here, we will use the lens of text analytics tools based on machine learning techniques to investigate a number of questions of interest to scholars of this and related traditions of the Great Perfection. It has been necessary for us to participate in all portions of this process: corpora identification and text edition selection, rendering the text as e-texts in Unicode using both Optical Character Recognition and manual entry, data cleaning and transformation, implementation of software for text analysis, and interpretation of results. For this reason, we hope this study can serve as a model for other low-resource languages that are just beginning to approach the problem of providing text analytics for their language.
Swimming Pool Segmentation in Pre-processing for Tracking Water Polo Players

Swimming pool is dynamic environment and it is changeable due splashing, reflections etc., so it is very hard to track people in it. Swimmers in the pool also affects on background. In pre-processing, it is important to find the best way to separate player before tracking. The main research subject in this paper is to find the best method for swimming pool segmentation. In this paper we present new method for swimming pool segmentation. Presented method from YCbCr colour space extract and analyse Cb and Cr components for background extraction. Method is compared with two methods in literature and applied in water polo players tracking system.
Non-Malleable Zero-Knowledge Arguments with Lower Round Complexity

Abstract Round complexity is one of the fundamental problems in zero-knowledge (ZK) proof systems. Non-malleable zero-knowledge (NMZK) protocols are ZK protocols that provide security even when man-in-the-middle adversaries interact with a prover and a verifier simultaneously. It is known that the first constant-round public-coin NMZK arguments for NP can be constructed by assuming the existence of collision-resistant hash functions (Pass, R. and Rosen, A. (2005) New and Improved Constructions of Non-Malleable Cryptographic Protocols. In Gabow, H.N. and Fagin, R. (eds) Proc. 37th Annual ACM Symposium on Theory of Computing, Baltimore, MD, USA, May 2224, 2005, pp. 533542. ACM) and has relatively high round complexity; the first four-round private-coin NMZK arguments for NP can be constructed in the plain model by assuming the existence of one-way functions (Goyal, V., Richelson, S., Rosen, A. and Vald, M. (2014) An Algebraic Approach to Non-Malleability. In 55th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2014, Philadelphia, PA, USA, October 1821, 2014, pp. 4150. IEEE Computer Society and Ciampi, M., Ostrovsky, R., Siniscalchi, L. and Visconti, I. (2017) Delayed-Input Non-Malleable Zero Knowledge and Multi-Party Coin Tossing in Four Rounds. In Kalai, Y. and Reyzin, L. (eds) Theory of Cryptography15th Int. Conf., TCC 2017. Lecture Notes in Computer Science, Baltimore, MD, USA, November 1215, 2017, Part I, Vol. 10677, pp. 711742. Springer). In this paper, we present a six-round public-coin NMZK argument of knowledge system assuming the existence of collision-resistant hash functions and a three-round private-coin NMZK argument system from multi-collision resistance of hash functions assumption in the keyless setting.
Intelligent system for predicting breast tumors using machine learning

The early hidden characteristics of breast tumors make their features difficult to be effectively identified. In order to improve the detection accuracy of breast tumors, this study combined with computer-aided diagnosis techniques such as machine learning and computer vision and used X-ray analysis to study breast tumor diagnosis techniques. Moreover, this study combines breast tumor diagnostic images to determine various parameters of the image. At the same time, through experimental research and analysis of the region segmentation method and preprocessing method of breast detection images, the best diagnostic images are obtained, and the influence of background and other noise on the image diagnosis results is effectively proposed. In addition, this study proposes a method for detecting the distortion of the mammogram image structure, which accurately detects the structural distortion and reduces the interference of various influencing factors. Finally, this paper designs experiments to study the effects of the diagnostic method of this paper. Through comparative analysis, it can be seen that the results of this study have certain advantages in accuracy and image clarity, and have certain clinical significance, and can provide theoretical reference for subsequent related research.