Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic Behavior of Quantum-Controlled Braitenberg Vehicle Agents

The behavior of agents represented by Braitenberg vehicles is investigated in the context of the quantum robot paradigm. The agents are processed through quantum circuits with fuzzy inputs, this permits to enlarge the behavioral possibilities and the associated decisions for these simple vehicles. The logical formulation Eigenlogic, using quantum logical observables as propositions and eigenvalues as truth values is applied in this investigation. Fuzzy logic arises naturally in this formulation when considering input states that are not eigenvectors of the logical observables, the fuzzy membership being the quantum mean value of the logical observable on the input state. Computer simulations permits visualization of complex behaviors resulting from the multiple combination of quantum control gates. This allows the detection of new Braitenberg vehicle behavior patterns related to identified emotions and linked to quantum-like effects.
Preliminary design of crow search metaheuristics algorithm for travelling salesman problem

This paper presents the preliminary design of the crow search algorithm, known as modern or metaheuristics in solving many non-deterministic polynomial hard problems (NP-hard). In this paper, the capability of crow search is shown for solving travelling salesman problem (TSP), a class of NP-hard that received special attention in operations research since last century. Ten instances from TSPLIB dataset were tested and compared with other algorithms such as Ant Colony Optimization and Simulated Annealing. The problems preliminary results show the significant and feasible solution in spite of the algorithm itself that has not yet been tuned to reach its full potential.
Sugar Ka Saathi – A Case Study Designing Digital Self-management Tools for People Living with Diabetes in Pakistan

This paper presents the results of an iterative participatory process to design a smart self-management tool for less-literate people living with diabetes in Pakistan. Initially, interviews and focus groups with sixty-nine people living with diabetes identified issues that they face when self-managing including un-controllable factors, lack of diabetes awareness, low-tech mobile phones, and poor internet availability. We developed personas grounded in the scoping results and adjusted our PD approach to focus on more tangible design artefacts before running narrative scoping PD sessions. Working from older, illiterate persona, we designed a phone-line delivered Interactive Voice Response (IVR) system. We developed a functional IVR Prototype “Sugar ka Saathi” (Diabetes Companion) with input from a group of 4 Pakistan-based healthcare professionals, to act as a design probe in the PD process. We tested the IVR probe with fifty-seven of the original scoping participants which validated the knowledge transferred by the IVR and its acceptability. Invisible design videos were shown to elaborate the IVR and community concept to thirteen participants through two filmed videos using our existing persona characters from the scoping studies, these videos helped to engage older people with diabetes in PD sessions.
Improvement of Data Sparsity and Scalability Problems in Collaborative Filtering Based Recommendation Systems

As the demand for recommendation systems that select and show a lot of information existed on the Internet based on various criteria is growing more and more, the related technologies have also advanced. Among them, the technique, which analyzes customer preference information to measure similarity between customers or between items and recommends items based on it, is called the collaborative filtering. Since it has an advantage that the more amount of data the more exact recommendation result and the better performance, it has been used in various fields. However, there are also some limits. The data sparsity problem, in which the recommendation system’s performance is reduced if there is no sufficient preference information, and the scalability one, in which operation time increases exponentially as the amount of data becomes larger, are considered typical limits. Although studies to improve these limits have been continued, it is needed more practical studies. Therefore, this paper proposed the collaborative filtering technique that improves the limits of the collaborative filtering recommendation technique’s data sparsity and scalability through two-stage clustering. First, it studied how to improve the data sparsity problem. It proposed a method that uses customer’s basic information data to do clustering and then predicts specific customer’s preference scores based on the cluster’s preference information to fill the customer versus item matrix. Second, it proposed a method that reduces data space through clustering to improve the scalability problem. Finally, it verified through experimentation how much the collaborative filtering technique’s performance is improved by the method proposed in this paper. By improving the data sparsity and scalability which is an endemic problem of the collaborative filtering recommendation system through this study, it could recommend more exactly and quickly even if customer preference information is insufficient or there is a large amount of data.
Mixing-RNN: A Recommendation Algorithm Based on Recurrent Neural Network

Collaborative filtering algorithms have been used by recommender systems for item (e.g., movie) recommendation. However, traditional collaborative filtering algorithms face challenges to provide accurate recommendation when users’ interest and context suddenly changed. In this paper, we present a new Recurrent Neural Network-based model, namely Mixing-RNN that is able to capture time and context changes for item recommendation. In particular, Mixing-RNN integrates the insight from Rating-RNN and Category-RNN which are developed to predict users’ interest based on rating and category respectively. Different from the traditional RNN, we integrate the forget gate and input gate in the model, where the forget gate decides what information to remain or discard and the input gate inputs rating information to the model. Our experiment evaluation on MovieLens indicates that Mixing-RNN outperforms the state-of-art methods.
Bayesian optimal design using stochastic gradient optimisation and Fisher information gain

Finding high dimensional designs is increasingly important in applications of experimental design, but is computationally demanding under existing methods. We introduce an efficient approach applying recent advances in stochastic gradient optimisation. To allow rapid gradient calculations we work with a computationally convenient utility function, the trace of the Fisher information. We provide a decision theoretic justification for this utility, analogous to work by Bernardo (1979) on the Shannon information gain. Due to this similarity we refer to our utility as the Fisher information gain. We compare our optimisation scheme, SGO-FIG, to existing state-of-the-art methods and show our approach is quicker at finding designs which maximise expected utility, allowing designs with hundreds of choices to be produced in under a minute in one example.
Probabilistic Epistemic Updates on Algebras

The present article contributes to the development of the mathematical theory of epistemic updates using the tools of duality theory. Here, we focus on Probabilistic Dynamic Epistemic Logic (PDEL). We dually characterize the product update construction of PDEL-models as a certain construction transforming the complex algebras associated with the given model into the complex algebra associated with the updated model. Thanks to this construction, an interpretation of the language of PDEL can be defined on algebraic models based on Heyting algebras. This justifies our proposal for the axiomatization of the intuitionistic counterpart of PDEL.
A challenge-response mechanism for securing online social networks against social bots

Social bots is fast becoming a serious security threat to online social networks (OSNs). Social bots are automated software tools able to execute malicious activities in OSNs systems in an automated fashion. It can perform auto-sharing and posting, sending fake friend requests, harvesting private information, etc. There is evidence that social bots play a crucial role in penetrating privacy and security of social networks. Hence, these malicious software tools represent a big security challenge against social network service provider (SNSP). In this paper, we introduce a novel antibot mechanism called Necklace CAPTCHA for securing OSNs platforms against the automated behaviours of social bots. Necklace CAPTCHA is an Image-based CAPTCHA, which utilises the functionality of Necklace Graph approach to generate its challenge-response tests in a novel fashion. The results demonstrated that Necklace CAPTCHA is an effective and secure anti-bot mechanism compared with other CAPTCHAs in the literature with respect to the usability and security metrics.
Knowledge Graph Programming with a Human-in-the-Loop

In this paper we introduce knowledge graph programming, a new method for writing extremely succinct programs. This method allows programmers to save work by writing programs that are brief but also underspecified and underconstrained; a human-in-the-loop "data compiler" then automatically fills in missing values without the programmer's explicit help. It uses modern data quality mechanisms such as information extraction, data integration, and crowdsourcing. The language encourages users to mention knowledge graph entities in their programs, thus enabling the data compiler to exploit the extensive factual and type structure present in modern KGs. We describe the knowledge graph programming user experience, explain its conceptual steps and data model, describe our prototype KGP system, and present some preliminary experimental results.
Am I Moving Along a Curve? A Study on Bicycle Traveling-In-Place Techniques in Virtual Environments

There are many techniques for locomotion and navigation that can support the exploration of large virtual environments in a limited physical area. Previous studies focused on measuring curvature gains and bending gains applied to the walking direction in the real world. However, the effects of different moving techniques and their relationship with shapes and patterns of virtually moving paths have not been studied extensively before. In this study, we present our experimental results on how users perceive two different traveling-in-place techniques with different bending gains of moving paths using a hybrid electric bike simulator. Moreover, the impact of different factors including road textures, road widths, and road curve directions and their relationships with the techniques are investigated. Generally, users could travel along a curve without noticing with a point of subjective equality (PSE) at bending angle $$\beta $$ = 1.42 $$^\circ $$ , and a just-noticeable difference (JND) of 0.75 $$^\circ $$ for a movement at around 20 km/h during 5 s. In addition, movement technique, curve direction, and future travel path significantly affected how they perceived the curvature of their travel path.
Compositional De-Attention Networks

Attentional models are distinctly characterized by their ability to learn relative importance, i.e., assigning a different weight to input values. This paper proposes a new quasi-attention that is compositional in nature, i.e., learning whether to \textit{add}, \textit{subtract} or \textit{nullify} a certain vector when learning representations. This is strongly contrasted with vanilla attention, which simply re-weights input tokens. Our proposed \textit{Compositional De-Attention} (CoDA) is fundamentally built upon the intuition of both similarity and dissimilarity (negative affinity) when computing affinity scores, benefiting from a greater extent of expressiveness. We evaluate CoDA on six NLP tasks, i.e. open domain question answering, retrieval/ranking, natural language inference, machine translation, sentiment analysis and text2code generation. We obtain promising experimental results, achieving state-of-the-art performance on several tasks/datasets.
A Taxonomy of Secure Two-Party Comparison Protocols and Efficient Constructions

Secure two-party comparison plays a crucial role in many privacy-preserving applications, such as privacy-preserving data mining and machine learning. In particular, the available comparison protocols with the appropriate input/output configuration have a significant impact on the performance of these applications. In this paper, we firstly describe a taxonomy of secure two-party comparison protocols which allows us to describe the different configurations used for these protocols in a systematic manner. This taxonomy leads to a total of 216 types of comparison protocols. We then describe conversions among these types. While these conversions are based on known techniques and have explicitly or implicitly been considered previously, we show that a combination of these conversion techniques can be used to convert a perhaps less-known two-party comparison protocol by Nergiz et al. (IEEE SocialCom 2010) into a very efficient protocol in a configuration where the two parties hold shares of the values being compared, and obtain a share of the comparison result. This setting is often used in multi-party computation protocols, and hence in many privacy-preserving applications as well. We furthermore implement the protocol and measure its performance. Our measurement suggests that the protocol outperforms the previously proposed protocols for this input/output configuration, when off-line pre-computation is not permitted.
A Data Mining Approach Applied to the High School National Examination: Analysis of Aspects of Candidates to Brazilian Universities

In many college courses in several countries are used exams in a national scale, such as Gaokao, in China, Scholastic Aptitude Test - SAT and the American College Testing - ACT in the United States of American, Yüksekögretime Gec̣is Sinavi – YGS in Turkey, among others. This paper examines microdata from the High School National Examination (ENEM) database from Brazil. The database has 8,627,367 records, 166 attributes, and all experiments were performed based on the Spark architecture. The objective of this work is to examine microdata of the ENEM database applying data mining algorithms and creating an approach to handle big data and to predict the profile of those enrolled in ENEM. Through the standards found by the data mining algorithms with classification algorithms, it was possible to observe that family income, access to information, profession, and academic history of the parents were directly related to the performance of the candidates. And with a rules induction algorithm, it was possible to identify the patterns presented in each of the regions of Brazil, such as common characteristics when a candidate was approved and when not, essential factors as disciplines and particular characteristics of each region. This approach also enables the execution of large volumes of data in a simplified computational structure.
Precision-Recall Balanced Topic Modelling

Topic models are becoming increasingly relevant probabilistic models for dimensionality reduction of text data, inferring topics that capture meaningful themes of frequently co-occurring terms. We formulate topic modelling as an information retrieval task, where the goal is, based on the latent topic representation, to capture relevant term co-occurrence patterns. We evaluate performance for this task rigorously with regard to two types of errors, false negatives and positives, based on the well-known precision-recall trade-off and provide a statistical model that allows the user to balance between the contributions of the different error types. When the user focuses solely on the contribution of false negatives ignoring false positives altogether our proposed model reduces to a standard topic model. Extensive experiments demonstrate the proposed approach is effective and infers more coherent topics than existing related approaches.
Implementation of Obfuscated Control Logic Circuit with LegUp and oLLVM

It is an important issue to protect the intellectual property of software. The authors proposed to conceal some part of software by implementing it as logic circuit. Though the security is further improved by obfuscating the logic circuit, it requires much effort to develop the dedicated obfuscation tool. Matsuoka et al. proposed using the software obfuscation tool, Obfuscator-LLVM (oLLVM), with C-backend (CBE) and Xilinx Vivado HLS (high-level synthesis) to generate the obfuscated logic circuit. This study proposes adopting another HLS, LegUp, to obfuscate logic circuit. The feasibility of these two methods are examined with 12 applications of CHStone benchmark, each of which were evaluated with four obfuscation methods (total 48 cases). In our experiments, Matsuoka's method failed to generate the correct hardware in 5 cases out of 48 cases. Meanwhile, the proposed method successfully generated the correct hardware for all 48 cases. The average latency derived by the proposed method was 43% larger than that by Matsuoka's method. The logic scale derived by the proposed method was also 42% larger (LUT) and 112% larger (FF) than that by Matsuoka's method.
Revealing Problems on Teaching English for Young Learners at Al – Azhar 55 Islamic Primary School Yogyakarta and How to Solve Them

The present paper aims to reveal the problems faced by teachers when teaching English for second – grade students at Al – Azhar 55 Islamic Primary Schoolin Yogyakarta. This bilingual school is considered as a pilot project which began theprogram in July 2016. There are only 2 classes which are the first and second grade.However, this study involved 21 second-grade students, 2 homeroom teachers, and 1native speaker. Qualitative method was applied in this study by combining documentanalysis, observation, and interview both students and the teachers. The results showsome problems occurred during teaching and learning process and how the teachersovercome them. The problems were (1) students’ lack of discipline during teaching andlearning process (2) the variety of students’ English ability, (3) difficulty in creating habit to use English as daily language, and (4) less support from parents. While, the solutions were (1) applying Class Dojo application, (2) providing the preparation class, (3) support and assistance from teachers and friends, and (4) hands – on learning.
Multi-Resolution Spectral Graph Matching

In this paper we study the problem of inexact weighted graph matching, where the goal is to find the correspondence between the vertices of two similar graphs. We propose a novel multi-resolution approach that improves the performance of single resolution graph matching which can be jointly combined with state-of-the art graph matching algorithms. Spectral graph matching determines the best correspondence between the graphs at each resolution by identifying vertex permutations that minimize the distance between the spectrum of the two graphs. To obtain graphs at lower resolutions, we propose a graph downsampling method that aims at selecting nodes in each graph so as to guarantee that matching at the lower resolutions will be possible. A key contribution of our work is to estimate the reliability of matching at each resolution and then use this information to obtain a weighted matching reliability across all resolutions. A comparison with other spectral graph matching algorithms demonstrates the benefits of the proposed approach.
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

In regression tasks, aleatoric uncertainty is commonly addressed by considering a parametric distribution of the output variable, which is based on strong assumptions such as symmetry, unimodality or by supposing a restricted shape. These assumptions are too limited in scenarios where complex shapes, strong skews or multiple modes are present. In this paper, we propose a generic deep learning framework that learns an Uncountable Mixture of Asymmetric Laplacians (UMAL), which will allow us to estimate heterogeneous distributions of the output variable and shows its connections to quantile regression. Despite having a fixed number of parameters, the model can be interpreted as an infinite mixture of components, which yields a flexible approximation for heterogeneous distributions. Apart from synthetic cases, we apply this model to room price forecasting and to predict financial operations in personal bank accounts. We demonstrate that UMAL produces proper distributions, which allows us to extract richer insights and to sharpen decision-making.
Building a Chatbot Solution

Chatbots are complete solutions and are created as an independent layer in any solution. The senior management also looks at chatbot functionalities and ROI as of an independent entity. The focus on conversational technologies further demands a holistic view on chatbots from solution and business value perspectives. In previous chapters, we demystified the essentials of developing a chatbot for a closed domain. In this chapter, we will focus on how to build solutions using the best available resources for a closed domain use case. The chapter will also cover a thought process on how to measure success for a chatbot implementation and managing the risks associated with chatbots.
Designing The Code Snippets for Experiments on Code Comprehension of Different Software Constructs

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.