Reviewerly/exHarmony huggingface dataset:
Improving Water Allocation Using Multi-agent Negotiation Mechanisms

This paper describes a multi-agent irrigation management system that can be used to distribute water efficiently among farmers in a community irrigation scheme during water scarcity. Each farm is represented as an agent that can calculate how much water is needed in the farm and hence estimate the marginal profit for the farm based on how much water is available. During water scarcity such as drought, some farmers would face water shortages and some would have excess water for irrigation. To ensure efficient water distribution, those farmers with excess water should share their water with other farmers needing water. In this study, we used the auction mechanism to distribute water efficiently among the farmers with the objective of maximizing the farmer's expected utility (profit margin). Our preliminary experiments showed that water distribution using an auction mechanism yielded a higher profit margin for all farmers in a community irrigation scheme when compared to direct negotiation strategy with a fixed price.
Improve Basic Sequential Algorithm Scheme using Ant Colony Algorithm

Basic Sequential Algorithm Scheme BSAS is a sequential algorithm for data clustering. It is suitable for unraveling compact dataset. The BSAS algorithm is sensitive to the order of data presentation; different clustering results could be produced if the input data are presented in a different order. Because the number of clusters in the results varies depending on the value of threshold, multiple run is one of the solutions to obtain optimal threshold.In this paper, BSAS is optimized using Ant Colony Optimization ACO Algorithm to solve the order sensitivity problem. The new proposed algorithm obtains the best order from ACO algorithm, which is based on the calculations of minimum distances between points, and passes the optimal order to BSAS algorithm as an input order. Finally, the proposed algorithm is compared and verified using the Sum Square Error SSE. The experimental results show that the proposed algorithm developed the BSAS algorithm.
Resources of the single scientific and educational space of the libraries of academic institutes and universities

The article is devoted to the role of scientific libraries in the process of transition from the traditional model of Russian science to the Western. The traditional model unites all scientific potential in academic research institutes, the western – in universities. The article notes that the interaction of research libraries of the university and research institutions is not counted. A parallel development of libraries without taking into account the accumulated bilateral experience in information and bibliographic service is proposed. The experience of libraries in the period of automation of library processes shows that a foundation has been created for the further development of information and bibliographic service during the period of digitalization – this is integration and corporate. Integration – the establishment of relationships between any objects that were previously separate from each other. Integrated links between academic and university libraries is a way to create a unified scientific and educational space. The new of the presented material consists in systematization of various forms of online services for creating, storing and transmitting the necessary information to the user. The possibilities of providing information on a digital basis when creating personal offices in libraries of research and educational institutions are shown. When the digital platform for full integration is ready, scientific libraries can choose or exclude resources for organizing successful work in information and bibliographic service for research.
Design of an Executable Specification Language Using Eye Tracking

Increasingly complex systems require powerful and easy to understand specification languages. In the course of the design and the extension of an executable specification language based on the Abstract State Machines formalism we recently performed a user survey. The question, how new language features are comprehended, remains unsolved. The main emphasis of this study is on eye gaze behavior of participants learning language features. Eye-tracking experiments were performed in two different countries using a Pupil Labs eye-tracking headset. An example specification and simple comprehension tasks were used as stimuli. The preliminary results of the eye-gaze behaviour analysis reveal that the new language feature was undestood well, but the new abstractions were frequently confused by participants. The foreknowledge of specific programming concepts is crucial how these abstractions are comprehended. More research is needed to infer this knowledge from viewing patterns.
Similarity function for intrusion detection

Similarity measure plays a vital role in classification, clustering and several other techniques related to data mining and machine learning. Detection of intrusions requires comparison of one instance of training input to another instance of testing input. Many times when classifiers are applied on datasets the input instances which occur relatively lesser are not classified by classifier properly. This paper suggests a similarity measure to compare an incoming instance with training instance to identify the relative similarity between them.
Wavelet Convolution Neural Network for Classification of Spiculated Findings in Mammograms

The subject of this paper is computer-aided recognition of spiculated findings in low-contrast noisy mammograms, such as architectural distortions and spiculated masses. The issue of computer-aided detection still remains unresolved, especially for architectural distortions. The methodology applied was based on wavelet convolution neural network. The originality of the proposed method lies in the way of input image creation. The input images were created as the maximum value maps based on three wavelet decomposition subbands (HL,LH,HH), each describing local details in the original image. Moreover, two types of convolution neural network architecture were optimized and empirically verified. The experimental study was conducted on the basis of 1585 regions of interest (512 $$\times $$ 512 pixels) taken from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), containing both normal (1191) and abnormal (406) breast tissue images including clinically confirmed architectural distortions (141) and spiculated masses (265). With the use of wavelet convolutional neural network with a reverse bioorthogonal wavelet, the recognition accuracy of both types of pathologies reached over 87%, whereas the recognition accuracy for architectural distortions was 85% and for spiculated masses - 88%.
Rudin-Shapiro-Like Sequences With Maximum Asymptotic Merit Factor

Borwein and Mossinghoff investigated the Rudin-Shapiro-like sequences, which are infinite families of binary sequences, usually represented as polynomials. Each family of Rudin-Shapiro-like sequences is obtained from a starting sequence (which we call the seed) by a recursive construction that doubles the length of the sequence at each step, and many sequences produced in this manner have exceptionally low aperiodic autocorrelation. Borwein and Mossinghoff showed that the asymptotic autocorrelation merit factor for any such family is at most 3, and found the seeds of length 40 or less that produce the maximum asymptotic merit factor of 3. The definition of Rudin-Shapiro-like sequences was generalized by Katz, Lee, and Trunov to include sequences with arbitrary complex coefficients, among which are families of low autocorrelation polyphase sequences. Katz, Lee, and Trunov proved that the maximum asymptotic merit factor is also 3 for this larger class. Here we show that a family of such Rudin-Shapiro-like sequences achieves asymptotic merit factor 3 if and only if the seed is either of length 1 or is the interleaving of a pair of Golay complementary sequences. For small seed lengths where this is not possible, the optimal seeds are interleavings of pairs that are as close as possible to being complementary pairs, and the idea of an almost-complementary pair makes sense of remarkable patterns in previously unexplained data on optimal seeds for binary Rudin-Shapiro-like sequences.
Progressive graph‐based subspace transductive learning for semi‐supervised classification

Graph-based transductive learning (GTL) is the efficient semi-supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature information with label information. To address these issues, we propose an approach called Progressive Graph-based subspace transductive learning (PGSTL) in this paper. PGSTL gradually find the intrinsic relationship between samples that more accurately aligns feature with label. Meanwhile, PGSTL develops a feature affinity matrix in the subspace of original high-dimensional feature space, which effectively reduce the interference of noise points. And then, the representative relation matrix and the feature affinity matrix are optimized by iterative optimization strategy and finally aligned. In this way, PGSTL can not only effectively reduce the interference of noisy points, but also comprehensively consider the information in the feature and label domain of data. Extensive experimental results on various benchmark datasets demonstrate that the PGSTL achieves the best performance compared to some state-of-the-art semi-supervised learning methods.
Intelligent pseudo‐location recommendation for protecting personal location privacy

Summary Individuals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution; the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.
Bayesian Nonparametric Joint Mixture Model for Clustering Spatially Correlated Time Series

Youngmin Leea & Heeyoung Kim*aa Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Multi-heap constraint handling in gray box evolutionary algorithms

Many optimization problems provide access to the partial or total explicit algebraic representation of the problem, including subfunctions and variable interaction graphs. This extra information allows the development of efficient solvers through new appropriate operators. Besides distinctive reproduction operators for a variety of problem categories, Gray Box algorithms have been proposed as a form to explore this additional information during the search. Considering recent evolutionary operators in the literature, we propose adaptations to Gray Box evolutionary reproduction operators and local search algorithms to deal with constrained problems, a field still little explored in gray box evolutionary optimization. The results show that the proposed methods achieve better solutions than traditional algorithms in a set of constrained binary and integer problems and reach optimal solutions in the literature for some instances.
Scenario co-evolution for reinforcement learning on a grid world smart factory domain

Adversarial learning has been established as a successful paradigm in reinforcement learning. We propose a hybrid adversarial learner where a reinforcement learning agent tries to solve a problem while an evolutionary algorithm tries to find problem instances that are hard to solve for the current expertise of the agent, causing the intelligent agent to co-evolve with a set of test instances or scenarios. We apply this setup, called scenario co-evolution, to a simulated smart factory problem that combines task scheduling with navigation of a grid world. We show that the so trained agent outperforms conventional reinforcement learning. We also show that the scenarios evolved this way can provide useful test cases for the evaluation of any (however trained) agent.
Agent-based modeling of collaborative creative processes with INGENIAS

Many creative methods, such as different types of brainstorming, are based on the collaboration among a set of persons. The collaboration follows some well established workflows, which could be formalized. This would allow the generation of computational models that can be implemented to make some tools that facilitate the enactment of creative processes, or the simulation for the analysis of their characteristics. This work shows how to model this kind of collaborative creative processes as multi-agent systems, by representing the participants as interacting agents in well-defined workflows. This is done with the INGENIAS modeling language and tools, which also support rapid prototyping using the JADE agent platform. A concrete creative method, Symbolic Brainstorming, is used to illustrate and validate the feasibility of the approach.
Splitting APIs: An Exploratory Study of Software Unbundling

Software unbundling consists of dividing an existing software artifact into smaller ones. Unbundling can be useful for removing clutter from the original application or separating different features that may not share the same purpose, or simply for isolating an emergent functionality that merits to be an application on its own. This phenomenon is frequent with mobile apps and it is also propagating to APIs. This paper proposes a first empirical study on unbundling to understand its effects on popular APIs. We explore the possibilities of splitting libraries into 2 or more bundles based on the use that their client projects make of them. We mine over than 71,000 client projects of 10 open source APIs and automatically generate 2,090 sub-APIs to then study their properties. We find that it is possible to have sets of different ways of using a given API and to unbundle it accordingly; the bundles can vary their representativeness and uniqueness, which is analyzed thoroughly in this study.
Econometrics with Privacy Preservation

Many data are sensitive in areas such as finance, economics, and other social sciences. We propose an ER (encryption and recovery) algorithm that allows a central administration to do statistical i...
Because nothing is more normal than diversity

As a community committed to the interaction between humans and computers we are having a responsibility to be aware of marginalization and exclusion processes aroused by technology design. Undoubtedly, designing for diversity comes along with several challenges and universities responsible for educating potential future designers hence have to integrate diversity aspects into HCI curricula. With this workshop we would like to foster the discussion about the inclusion of diversity topics in HCI education and offer participants a platform to share their experiences. One of the main goals of the workshop is to build a community of interested scholars and students by fostering a discourse about the importance of diversity as a theme in HCI lectures and sketch guidelines for practical HCI education.
Managing Exploratory Units in Academic Libraries

This article describes the role of exploratory teams in leading radical innovation within academic libraries, empirically expanding on Jantz’s research on the managerial and environmental conditions necessary for the creation of such units. Using historical and contemporary private-sector examples, as well as an in-depth case study from successful implementation of one such unit, this article documents the components, characteristics, and return on investment associated with academic library R&D. Finally, by tackling the concept of exploratory activities at the organization, team, and individual levels, the article presents actionable techniques for fully supporting, and adequately nurturing creative professionals whose job is to redefine 21st century librarianship.
A Secure and Efficient Code-Based Signature Scheme

The McEliece public key cryptosystem and the Niederreiter public key cryptosystem are the two major branches of the code-based public key cryptosystem. It has been found that the difficult problems relied by these two systems are different from the syndrome decoding problem stated by Berlekamp et al. The Hamming weight of the error vector is fixed and known to the attackers in these two systems, which leading to the disclosure of information, and through which the attacker can implement an effective attack. In this paper, an improved vision of the McEliece public key cryptosystem was given, and a new code-based signature scheme is designed based on the new cryptosystem. As a result, we achieve a more secure variant of McEliece public key cryptosystem and a more secure code-based signature scheme with high security and fast signing speed.
Immersive Virtual Reality in Technical Drawing of Engineering Degrees

The use of virtual reality (VR) technology is reaching almost all sectors: medicine, engineering, entertainment, defense, marketing, etc. Especially to those whose main objective is the representation of elements and three-dimensional environments of reality. This is especially relevant within the field of teaching-learning activities of technical drawing. In this case, one of the competences is to represent three-dimensional pieces and objects in two-dimensional drawings. In this process the student must have and develop the spatial skills that depend on the innate abilities of each one. In this communication an experience of the use of the Immersive Virtual Reality is presented to the development of spatial skills in technical drawing. This work describes the design, creation and programming of the computer application, choice of equipment and development of a methodology for use in teaching.
3D image encryption based on computer-generated hologram

We propose a robust 3D image encryption scheme based on computer-generated hologram with pixel superimposed method. The cryptosystem is asymmetric and high resistance against to the chosen-plaintext attack.
Accelerating decentralized reinforcement learning of complex individual behaviors

Many Reinforcement Learning (RL) real-world applications have multi-dimensional action spaces which suffer from the combinatorial explosion of complexity. Then, it may turn infeasible to implement Centralized RL (CRL) systems due to the exponential increasing of dimensionality in both the state space and the action space, and the large number of training trials. In order to address this, this paper proposes to deal with these issues by using Decentralized Reinforcement Learning (DRL) to alleviate the effects of the curse of dimensionality on the action space, and by transferring knowledge to reduce the training episodes so that asymptotic converge can be achieved. Three DRL schemes are compared: DRL with independent learners and no prior-coordination (DRL-Ind); DRL accelerated-coordinated by using the Control Sharing (DRL+CoSh) Knowledge Transfer approach; and a proposed DRL scheme using the CoSh-based variant Nearby Action Sharing to include a measure of the uncertainty into the CoSh procedure (DRL+NeASh). These three schemes are analyzed through an extensive experimental study and validated through two complex real-world problems, namely the inwalk-kicking and the ball-dribbling behaviors, both performed with humanoid biped robots. Obtained results show (empirically): (i) the effectiveness of DRL systems which even without prior-coordination are able to achieve asymptotic convergence throughout indirect coordination; (ii) that by using the proposed knowledge transfer methods, it is possible to reduce the training episodes and to coordinate the DRL process; and (iii) obtained learning times are between 36% and 62% faster than the DRL-Ind schemes in the case studies.
Summarizing Netizens’ Sentiments Towards the 1<sup>st</sup>Indonesian Presidential Debate using Lexicon Sentiment Analysis

Abstract Twitter is one of the popular social media platforms in Indonesia. This platform has been used as a media communication and public engagement tool for many purposes, especially in political and governance domains. During the process of 2019 Indonesian Presidential Election, many people use Twitter to express their opinion/sentiment towards the election process. In this paper, we investigate the nature of people’s opinion towards the Indonesian Presidential Election after the 1st debate. The goal of this study is to perform exploratory sentiment based analysis of Twitter data, and that was gathered after the 1st debate. We used lexicon sentiment analysis to calculate the sentiment of political tweets collected after the 1st debate. The identification of positive and negative opinion was automatically conducted using the available dictionary. Our result shows that sentiment of the netizen towards the 1st Presidential debate was mostly negative. In addition to this result, a predictive model was generated using CART and logistic regression to predict the netizens’ sentiment. This experiment shows that the accuracy of the prediction model reaches 90%. Therefore, our study suggests that Twitter data can be used to analyse citizens’ sentiment toward the Indonesian Presidential Debate and can generate a model to predict citizens’ future sentiment toward the next debate.
User Experience Research on the Human-Computer Interaction System of Connected Car

The study aims to explore how user experience research is used in automotive HMI design planning in order to advance the development of automotive HMI. This study researched the Roewe automotive HMI system based on user experience design methods. Questionnaires and in-depth interviews were used for the purpose of analyzing users’ car using process. The market positioning deviation of ROEWE was explored by using Censydiam model. The HMI framework involved in the use of the automobile is discussed and analyzed, meanwhile the future design concepts of Roewe automotive HMI are proposed in the paper. The results of this study have significant practical benefits for the improvement of user experience as well as a company’s future design strategy.