Reviewerly/exHarmony huggingface dataset:
Twin minimax probability machine for pattern classification

We propose a new distribution-free Bayes optimal classifier, called the twin minimax probability machine (TWMPM), which combines the benefits of both minimax probability machine(MPM) and twin support vector machine (TWSVM). TWMPM tries to construct two nonparallel hyperplanes such that each hyperplane separates one class samples with maximal probability, and is distant from the other class samples simultaneously. Moreover, the proposed TWMPM can control the misclassification error of samples in a worst-case setting by minimizing the upper bound on misclassification probability. An efficient algorithm for TWMPM is first proposed, which transforms TWMPM into concave fractional programming by applying multivariate Chebyshev inequality. Then the proposed TWMPM is reformulated as a pair of convex quadric programming (QP) by proper mathematical transformations. This guarantees TWMPM to have global optimal solution and be solved simply and effectively. In addition, we develop also an iterative algorithm for the proposed TWMPM. By comparing the two proposed algorithms theoretically, it is easy to know that the convex quadric programming algorithm is with lower computation burden than iterative algorithm for the TWMPM. A linear TWMPM version is first built, and then we show how to exploit mercer kernel to obtain nonlinear TWMPM version. The computation complexity for QP algorithm of TWMPM is in the same order as the traditional twin support vector machine (TWSVM). Experiments are carried out on three databases: UCI benchmark database, a practical application database and an artificial database. With low computation complexity and fewer parameters, experiments show the feasibility and effectiveness of the proposed TWMPM and its QP algorithm.
Serial and parallel memetic algorithms for the bounded diameter minimum spanning tree problem

Abstract Given a connected, weighted, undirected graph G = ( V , E ) and an integer D ≥ 2, the bounded diameter minimum spanning tree (BDMST) problem seeks a spanning tree of minimum cost, whose diameter is no greater than D . The problem is known to be NP‐hard, and finds application in various domains such as information retrieval, wireless sensor networks and distributed mutual exclusion. This article presents a two‐phase memetic algorithm for the BDMST problem that combines a specialized recombination operator (proposed in this work) with good heuristics in order to more effectively direct the exploration of the search space into regions containing better solutions. A parallel meta‐heuristic is also proposed and shown to obtain very good speedups – super‐linear, in several cases – vis‐à‐vis the serial memetic algorithm. To the best of the authors' knowledge, this is the first parallel meta‐heuristic proposed for the BDMST problem, and potentially paves the way for handling much larger problems than reported in the literature. Some observations and theorems are presented in order to provide the underlying framework for the proposed algorithms. Further, the proposed memetic algorithm and parallel meta‐heuristic are shown, in the course of several computational experiments, to in general obtain superior solution quality with much lesser computational effort in comparison to the best known meta‐heuristics in the literature over a wide range of benchmark instances.
Image segmentation based on the minimum spanning tree with a novel weight

Image segmentation is a foundation for the image understanding and advanced application. However, the ill-posedness of image segmentation is a main challenge. To tackle this challenge, this paper proposes a novel minimum spanning tree (MST) based image segmentation algorithm. In the algorithm, we suggest a difference formula of RGB with respect to angular distance color as the weight of judge standard of the segmentation. To improve the stability and applicability of the segmentation, the judge standard not only considers the different sensitivities of the human eyes in regard to the variations of RGB values, but also involves the spatial distance and vector relationship between two pixels. The experiment verified the effectiveness of our proposed MST based image segmentation algorithm, the advantage of the algorithm was also illustrated by comparisons.
Machine-checked natural semantics for Core Erlang: exceptions and side effects

This research is part of a wider project that aims to investigate and reason about the correctness of scheme-based source code transformations of Erlang programs. In order to formally reason about the definition of a programming language and the software built using it, we need a mathematically rigorous description of that language.
Survey and Gap Analysis of Word Sense Disambiguation Approaches on Unstructured Texts

Word Sense Disambiguation (WSD) is considered as one of the pivotal problems of Semantic classification among polysemous words that can be addressed using Natural Language Processing (NLP) for identifying the sense of the ambiguous word in a particular context. The application areas of WSD pertain to machine translation, information extraction and retrieval (IE-IR), dialogue systems, and automatic summarization kind of NLP solutions. This paper presents a survey on WSD approaches in major AI-NLP methods by comparing different approaches for WSD in supervised, unsupervised, and knowledge based algorithms. This paper also aims at providing gap analysis in surveyed WSD systems comparing strengths and weaknesses of various surveyed systems and their accuracy. Based on the findings, a future hybrid approach synergizing rule-based and machine learning based methods are contemplated. The findings of this survey are envisaged through an ongoing research on WSD based Meta-Search algorithm under C-DAC purview for an Intelligent NLP based system to detect the actual sense of search queries and providing semantic classification of news headlines and snippets containing ambiguous words.
Some distributional patterns in the use of typed laughter-derived expressions on Twitter

This paper is a large-scale quantitative examination of the conversational use of nine English Typed Laughter-Derived Expressions (TLDEs) on Twitter: haha, hehe, tehe, lol, lmao, lmfao, (Unicode: 1f602), (1f923), and (1f606). The paper aims to establish a quantitative groundwork capable of supporting future qualitative research attempting to answer questions about how and why TLDEs are used in digital conversation on various platforms and about how our use of TLDEs is related to our use of physical laughter in face-to-face exchanges. The analyses conducted examine various conversation distributional characteristics of TLDEs in Twitter conversations. One analysis examines the placement of TLDEs within tweets (initial, medial, final, alone), concluding that while all TLDEs are preferentially used in tweet-final position, this trend is much stronger for emoji forms than letter-based forms. Another analysis concluded that TLDEs are disproportionately found in tweets with high levels of hearer/reader ratification. Another found that initial-position TLDEs are more likely to be used in contexts which permit reference to a laughable in previous utterances than are medial- or final-position TLDEs. Finally, this study found that the offer/acceptance pattern of spoken laughter is weak to nonexistent in Twitter exchanges.
AiRite: Towards Accurate &amp; Infrastructure-Free 3-D Tracking of Smart Devices

Precise tracking of smart wearables in 3-D space is foundational in domains ranging from Augmented Reality (AR) and gesture recognition to robotics and indoor localization. Usually, a sensor-dependent approximation model, and a complementary infrastructure like acoustic, vision etc. is implemented alongside an onboard IMU (Inertial Measurement Unit) for an effective tracking. In this work, we present a novel 3-D tracking solution, `AiRite' for commercial-grade smart wearables/mobiles using only their onboard IMU. Our tracking method mitigates the manifested inertial errors using a novel progressive zero correction, yielding superior results both for 2-D and 3-D trajectories. AiRite aims for a pervasive usage and is independent of device's form factor, or make of sensor enabling it to be used right out-of-the-box. The solution is shown to be more accurate than prior arts in tracking trajectories of basic shapes by moving the hand both in 2-D and 3-D. We depict trajectories of medium to long duration words written in air in a cursive handwriting using both smartwatch and smartphone. We further demonstrate robustness of AiRite by depicting panoramic 3-D texts written in air in a concave shape and visualizing the same from multiple angles, which are first results of their kind in this domain.
Fair and Protected Profit Sharing for Data Trading in Pervasive Edge Computing Environments

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We formulate a fair revenue sharing problem to maximize the profit of both the data producer and resellers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers. Extensive simulations show that with resellers, our mechanism can achieve higher profit for the data producer and resellers.
EEG Cross Validation of Effective Mobile Technology by Analyzing Attention Level in Classroom

Mobile devices are pervasive technology that has grown ubiquitously in the recent years. It has found a way to our daily life activities including learning. In a large classroom, the difficulties like visibility, audibility, interaction between student and teacher, etc. can be overcome with the use of mobile technology in classroom. This work focus on finding the growth of mobile devices inside the classroom from cognitive point. It considers Electroencephalography (EEG) which calculates the voltage fluctuation along the skull which results from the interaction of neurons in the brain. A single channel Mind Wave EEG headset is used to measure the power spectrum from brain signals that provides with attention level of students inside the class. Later, presented work statistically modeled the data that was collected over survey to find acceptability of mobile devices in the classroom. Bayesian t-test and various other regression analysis were used to validate the presented work strongly.
Computational Thinking in Augmented Reality: An Investigation of Collaborative Debugging Practices

The uniqueness of Augmented Reality (AR) is its affordance to support learning abstract concepts by rich information, visualization and the integration of user-content interaction. Research has shown that abstract idea and invisible phenomena can be learned better with the support of AR. However, high complexity and conceptual Computational Thinking (CT), such as algorithm design and related programming concepts, are rarely studied empirically in the intersection with immersive technology. This study is aimed at addressing this issue and providing a piece of quantitative and empirical evidence to AR-supported Computer Science discipline-based learning. We designed a mobile AR-enabled application based on a CT framework and related AR affordances in the literature. This app can contextualize a programming debugging task and support program editing & CT learning. A controlled laboratory study was designed and conducted. The result of statistical analyses shows that participants with the AR support made better quality of programs with lower errors and less amount of code edits, compared to those without the AR support.
Work-in-Progress—Conceptual Framework for User Interface in Virtual Reality

Presence in VR is a perceptual sense that leads to an automatic reaction of the brain-body system. An expected interaction in VR can lead to a higher sense of presence. In this work in progress paper we present a conceptual framework for innovative user interface (UI) design in VR with a focus on realistic interactions with the environment. We investigate several successful VR games by focusing on their user interactions and the UI system. The first part of this framework is implemented in a tutorial room where we designed some common UI elements such as the menu, inventory system, and quest manager.
Nontransitive Security Types for Coarse-grained Information Flow Control

Language-based information flow control (IFC) aims to provide guarantees about information propagation in computer systems having multiple security levels. Existing IFC systems extend the lattice model of Denning’s, enforcing transitive security policies by tracking information flows along with a partially ordered set of security levels. They yield a transitive noninterference property of either confidentiality or integrity. In this paper, we explore IFC for security policies that are not necessarily transitive. Such nontransitive security policies avoid unwanted or unexpected information flows implied by transitive policies and naturally accommodate high-level coarse-grained security requirements in modern component-based software. We present a novel security type system for enforcing nontransitive security policies. Unlike traditional security type systems that verify information propagation by subtyping security levels of a transitive policy, our type system relaxes strong transitivity by inferring information flow history through security levels and ensuring that they respect the nontransitive policy in effect. Such a type system yields a new nontransitive noninterference property that offers more flexible information flow relations induced by security policies that do not have to be transitive, therefore generalizing the conventional transitive noninterference. This enables us to directly reason about the extent of information flows in the program and restrict interactions between security-sensitive and untrusted components.
The Improvement of Digital Signature Algorithm Based on Elliptic Curve Cryptography

Electronic digital signature guarantee message integrity. In the present paper we describe existing algorithms for the formation and verification of the electronic digital signatures. Also in the article on the basis of the analysis of modern methods of formation and verification of electronic digital signatures are considered directions of improvement of the electronic digital signatures procedure using a group of points of the elliptic curves with providing the possibility of ensuring the integrity and confidentiality of information. The proposed new method of electronic digital signatures generation and verification is implemented on the Shnorr signature algorithm, that allows to recover data directly from the signature similarly to RSA-like signature systems and the amount of the recovered information will be variable. The main advantages of improvement procedure is the shorter key length with equivalent cryptographic strength, shorter length of the signature itself and reduced the total length of the transmitted data. Thus new secure digital signature scheme minimize time for formation and verification of confidential information witch depending on the used method based on the elliptic curves and adds a privacy service.
Course Recommendation for students using Machine Learning

Choosing an appropriate course is significant for understudies to accomplish their ideal objectives however the wealth obviously related data accessible makes it hard for understudies to choose a course that coordinates their inclinations, objectives and current information. The proposed framework prescribes the discretionary courses to understudies dependent on their inclinations by recommending courses that other comparable understudies have taken. The similitude between understudies is determined by the methods for pearson connection coefficient. It utilizes a memory based community oriented sifting way to deal with anticipate the scores of the understudy for each course and the courses with the most elevated anticipated scores are then suggested.
A Machine Learning-Based Approach for PV Power Forecasting

The contribution of Solar Photovoltaic (PV) power in the energy sector has steadily grown over the past decades. However, the intermittent nature of PV power poses challenges to grid stability. Therefore, it is of significance for electrical utilities to obtain an accurate prediction of PV output power at a small time-scale so as to perform grid scheduling. In this paper, we propose a model based on machine learning to predict hourly-ahead PV output power. The model includes the effect of the sun's position, the tilt angle of the array, PV array aging effect and the cloud cover. Simulations are performed to ascertain the efficacy of the model and to verify the accuracy of the model. Using the proposed model, nth-hour ahead PV output power forecasting is also performed.
A Novel Approach to Interactive Dialogue Generation Based on Natural Language Creation with Context-Free Grammars and Sentiment Analysis

The demand for high-quality video games is ever increasing and the ambition to tell truly interactive and dynamic stories is a significant factor contributing to this trend. This paper examines how generated narrative text is perceived by players in terms of meaningfulness, immersion, and flow and furthermore that the presented novel approach can be a valid method to implement computer-generated dialogues. For this approach, generative grammars are used to create written dialogue within a conversation for a learning application. With the support of sentiment analysis, the generated text is analysed with a focus on its semantics. Suitable text lines based on the current game state are provided by a dialogue system. Principles of gamification are used to create a learning application that renders such a generated dialogue scenario playable. To test this hypothesis, a user study examines the capabilities of the process by having players assess factors, which are imperative for a narrative game. The learning application shows strong potential in terms of text variation and dialogue that is easy to follow.
Work-in-Progress–The ARI<sup>2</sup>VE Model for Augmented Reality Books

In this work-in-progress paper, the first attempt of the ARI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> VE model for Augmented Reality books is presented. The components of the model are based on research on instructional design and multimedia learning as well as previous implementations of AR in books. The components interaction, interplay, visualization and engagement are described in detail and illustrated with an example. Due to the early stage of the model no empirical findings can be reported yet, but first investigations are in progress. A call for international cooperation and joint research is made.
Automatic Assessment of Student Homework and Personalized Recommendation

The following topics are dealt with: computer aided instruction; educational courses; teaching; further education; computer science education; natural language processing; Internet; educational institutions; educational administrative data processing; and human computer interaction.
On Reversibility and Self-Duality for Some Classes of Quasi-Cyclic Codes

In this work, we study two classes of quasi-cyclic (QC) codes and examine how several properties can be combined into the codes of these classes. We start with the class of QC codes generated by diagonal generator polynomial matrices; a QC code in this class is a direct sum of cyclic codes. Then we move on to the class of QC codes of index 2; various binary codes with good parameters are found in this class. In each class, we describe the generator polynomial matrices of reversible codes, self-orthogonal codes, and self-dual codes. Hence, we demonstrate how such properties can be merged in codes of these classes. Particularly for QC codes of index 2, we prove a necessary and sufficient condition for the self-orthogonality of reversible codes. Then we show that reversible QC codes of index 2 are self-dual under the same conditions in which self-dual codes are reversible. We clarify that self-orthogonal reversible QC codes of index 2 over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> exist for even and odd <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> , however self-dual reversible codes exist only for even <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> . Theoretical results are reinforced by several numerical examples. Computer search is used to present some self-dual reversible QC codes of index 2 that have the best known parameters as linear codes. Finally, we highlight the class of 1-generator binary QC codes of index 2 by exploring many self-dual reversible codes that achieve the upper bound on the minimum distance for their parameters.
Work-in-Progress—Titration Experiment: Virtual Reality Chemistry Lab with Haptic Burette

With the ever-expanding development of affordable and available virtual reality headsets, there is a great opportunity for improving learning outcomes through virtual learning. Towards a future of online chemistry education, we present a titration experiment with a corresponding physical haptic burette to embed the physical sensations of labwork inside virtual reality learning for broad access. Our work in progress paper investigates how tactile interactions into fully immersive worlds can improve learning outcomes in multi-modal manners and lead to better understanding and retention of key chemistry concepts, e.g., titration principles.
Reconciling progress-insensitive noninterference and declassification

Practitioners of secure information flow often face a design challenge: what is the right semantic treatment of leaks via termination? On the one hand, the potential harm of untrusted code calls for strong progress-sensitive security. On the other hand, when the code is trusted to not aggressively exploit termination channels, practical concerns, such as permissiveness of the enforcement, make a case for settling for weaker, progress-insensitive security. This binary situation, however, provides no suitable middle point for systems that mix trusted and untrusted code. This paper connects the two extremes by reframing progress-insensitivity as a particular form of declassification. Our novel semantic condition reconciles progress-insensitive security as a declassification bound on the so-called progress knowledge in an otherwise progress or timing sensitive setting. We show how the new condition can be soundly enforced using a mostly standard information-flow monitor. We believe that the connection established in this work will enable other applications of ideas from the literature on declassification to progress-insensitivity.