Reviewerly/exHarmony huggingface dataset:
Efficient Performative Actions for E-Commerce Agents

Abstract The foundational features of multi-agent systems are communication and interaction with other agents. To achieve these features, agents have to transfer messages in the predefined format and semantics. The communication among these agents takes place with the help of ACL (Agent Communication Language). ACL is a predefined language for communication among agents that has been standardised by the FIPA (Foundation for Intelligent Physical Agent). FIPA-ACL defines different performatives for communication among the agents. These performatives are generic, and it becomes computationally expensive to use them for a specific domain like e-commerce. These performatives do not define the exact meaning of communication for any specific domain like e-commerce. In the present research, we introduced new performatives specifically for e-commerce domain. Our designed performatives are based on FIPA-ACL so that they can still support communication within diverse agent platforms. The proposed performatives are helpful in modelling e-commerce negotiation protocol applications using the paradigm of multi-agent systems for efficient communication. For exact semantic interpretation of the proposed performatives, we also performed formal modelling of these performatives using BNF. The primary objective of our research was to provide the negotiation facility to agents, working in an e-commerce domain, in a succinct way to reduce the number of negotiation messages, time consumption and network overhead on the platform. We used an e-commerce based bidding case study among agents to demonstrate the efficiency of our approach. The results showed that there was a lot of reduction in total time required for the bidding process.
Augmented Reality with HoloLens in Parotid Tumor Surgery: A prospective feasibility study

Objective Augmented Reality (AR) is an innovative way to improve planning and execution of complex surgical procedures. Head-mounted devices (HMD) such as the HoloLens from Microsoft seem to be particularly suitable to achieve these aims. The HoloLens is controlled by hand gestures. This enables contactless handling in a sterile environment. Unlike other surgical fields, so far, these systems have not found their way into head and neck surgery. The goal of this pilot study was to evaluate the feasibility of this HMD in parotid tumor surgery.
Impact of Mobile Assisted Language Learning on the Young ESL Learners’ Vocabulary in Pakistan

Connectedness through mobile phones has revolutionized the modern academic arena, which has presently become a common trend, and termed as Mobile Assisted Language Learning (here on wards MALL). The inclusion of 3G, 4G and Long Term Evolution (LTE) has made mobile phones work like mini computers. The present study explores the impact of Mobile assisted language learning on young ESL learners’ vocabulary. To achieve the objectives of current investigation, quantitative research design was utilized because the data were numeric in nature. The population of the study was taken from Lahore where the sample size was 60.  These 60 students were equally distributed into two different groups (controlled and experimental) of 30 each. All of them were Grade III students, studying in various English medium institutes of Lahore. The samples were selected through random sampling method. Initially a pretest, regarding vocabulary assessment, of both the groups was taken. After that the controlled group learners were taught through conventional method whereas the learners in experimental group learnt vocabulary words though MALL in a learner-centered ambiance for two weeks. After the treatment period was over, a post test was conducted and results were gathered and analyzed. The statistical data reflected marked difference in the standard deviation (2.24137), mean (9.91) and the standard error mean (0.22413) between both the groups. The outcomes revealed that vocabulary teaching could be transformed using MALL in Pakistani ESL learning situations.
Virtual dioramas transform natural history museum exhibit halls &amp; gardens to life with immersive AR

The original motivation and design process of the AR Perpetual Garden App is discussed in detail. Available on Apple iTunes and Google Play Stores, children and parents, teachers and students, may download and learn with it now, thus amplifying the learning impact of immersive experiences even at home. Inspired by the dioramas of the past, both the creation process and their use in museums as interactive, multimodal, knowledge artifacts are discussed and carefully analyzed. This paper may be of interest to researchers and practitioners alike. First, as a way to understand and generalize the critical design factors used and to extend findings into their design research, and second, as an iterative design and development process model, learner-user experience (LUX) design, extensible to other domains.
When Bitcoin Caught COVID-19

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Extracting Message Sequence Charts from Hindi Narrative Text

In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi.An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference.We propose an approach to extract MSC actors and interactions from a Hindi narrative.As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments.We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History.Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English.Moreover, there is relatively less work in the context of event processing in Hindi.Hence, we believe that this work is among the initial works for Hindi event processing.
List Decodable Subspace Recovery

Learning from data in the presence of outliers is a fundamental problem in statistics. In this work, we study robust statistics in the presence of overwhelming outliers for the fundamental problem of subspace recovery. Given a dataset where an $\alpha$ fraction (less than half) of the data is distributed uniformly in an unknown $k$ dimensional subspace in $d$ dimensions, and with no additional assumptions on the remaining data, the goal is to recover a succinct list of $O(\frac{1}{\alpha})$ subspaces one of which is nontrivially correlated with the planted subspace. We provide the first polynomial time algorithm for the 'list decodable subspace recovery' problem, and subsume it under a more general framework of list decoding over distributions that are certifiably resilient capturing state of the art results for list decodable mean estimation and regression.
Rigorous Guarantees for Tyler's M-estimator via quantum expansion

Estimating the shape of an elliptical distribution is a fundamental problem in statistics. One estimator for the shape matrix, Tyler's M-estimator, has been shown to have many appealing asymptotic properties. It performs well in numerical experiments and can be quickly computed in practice by a simple iterative procedure. Despite the many years the estimator has been studied in the statistics community, there was neither a non-asymptotic bound on the rate of the estimator nor a proof that the iterative procedure converges in polynomially many steps. Here we observe a surprising connection between Tyler's M-estimator and operator scaling, which has been intensively studied in recent years in part because of its connections to the Brascamp-Lieb inequality in analysis. We use this connection, together with novel results on quantum expanders, to show that Tyler's M-estimator has the optimal rate up to factors logarithmic in the dimension, and that in the generative model the iterative procedure has a linear convergence rate even without regularization.
PAC learning with stable and private predictions

We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset. Specifically, we consider the notions of uniform stability (Bousquet and Elisseeff, 2001) and prediction privacy (Dwork and Feldman, 2018). Previous work on these notions shows how they can be achieved in the standard PAC model via simple aggregation of models trained on disjoint subsets of data. Unfortunately, this approach leads to a significant overhead in terms of sample complexity. Here we demonstrate several general approaches to stable and private prediction that either eliminate or significantly reduce the overhead. Specifically, we demonstrate that for any class $C$ of VC dimension $d$ there exists a $\gamma$-uniformly stable algorithm for learning $C$ with excess error $\alpha$ using $\tilde O(d/(\alpha\gamma) + d/\alpha^2)$ samples. We also show that this bound is nearly tight. For $\eps$-differentially private prediction we give two new algorithms: one using $\tilde O(d/(\alpha^2\eps))$ samples and another one using $\tilde O(d^2/(\alpha\eps) + d/\alpha^2)$ samples. The best previously known bounds for these problems are $O(d/(\alpha^2\gamma))$ and $O(d/(\alpha^3\eps))$, respectively.
AN IMPROVED AUTONOMOUS EXPLORATION FRAMEWORK FOR INDOOR MOBILE ROBOTICS USING REDUCED APPROXIMATED GENERALIZED VORONOI GRAPHS

Abstract. In the field of autonomous navigation for robotics, one of the most challenging issues is to locate the Next-Best-View and to guide robotics through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes a reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. To be specific, a fast and practical algorithm for constructing RAGVGs from metric maps is presented, and a RAGVG-based autonomous robotic exploration framework is designed and implemented. The proposed method for constructing RAGVGs is validated with two known common maps, while the RAGVG-based autonomous exploration framework is tested on two simulation and one real-world museum. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and indicate that the mobile robot controlled by the RAGVG-based autonomous exploration framework, compared with famous frontiers-based method, reduced the total time by approximately 20% for the given tasks.
When Nominal Analogical Proportions Do Not Fail

Analogical proportions are statements of the form “\(\mathbf {a}\) is to \(\mathbf {b}\) as \(\mathbf {c}\) is to \(\mathbf {d}\)”, where \(\mathbf {a}\), \(\mathbf {b}\), \(\mathbf {c}\), \(\mathbf {d}\) are tuples of attribute values describing items. The mechanism of analogical inference, empirically proved to be efficient in classification and reasoning tasks, started to be better understood when the characterization of the class of classification functions with which the analogical inference always agrees was established for Boolean attributes. The purpose of this paper is to study the case of finite attribute domains that are not necessarily two-valued, i.e., when attributes are nominal. In particular, we describe the more stringent class of “hard” analogy preserving (HAP) functions \(f :X_1\,\times \,\dots \,\times \,X_m \rightarrow X\) over finite domains \(X_1, \dots , X_m, X\) for binary classification purposes. This description is obtained in two steps. First we observe that such AP functions are almost affine, that is, their restriction to any \(S_1 \times \dots \times S_m\), where \(S_i \subseteq X_i\) and \(|{S_i}| \le 2\) (\(1 \le i \le m\)), can be turned into an affine function by renaming variable and function values. We then use this result together with some universal algebraic tools to show that they are essentially unary or quasi-linear, which provides a general representation of HAP functions. As a by-product, in the case when \(X_1 = \dots = X_m = X\), it follows that this class of HAP functions constitutes a clone on X, thus generalizing several results by some of the authors in the Boolean case .
Comparative Analysis Between Different Automatic Learning Environments for Sentiment Analysis

Sentiment Analysis is a branch of Natural Language Processing in which an emotion is identified through a sentence, phrase or written expression on the Internet, allowing the monitoring of opinions on different topics discussed on the Web. The study discussed in this paper analyzed phrases or sentences written in Spanish and English expressing opinions about the service of Restaurants and opinions written in the English language about Laptops. Experiments were carried out using 3 automatic classifiers: Support Vector Machine (SVM), Naïve Bayes and Multinomial Naïve Bayes, each one being tested with the three data sets in the Weka automatic learning software and in Python, in order to make a comparison of results between these two tools.
DESIGNING CHATBOT FOR COLLEGE INFORMATION MANAGEMENT

Information plays an important role in human daily life in which it is expected to be more understood, fast, clear and accurate. In line with technology development and the mission of Informatics study program to develop its services for college students, that is, information is derived through information systems and chat. Students in this case is whom study at the university, institution or academy. This study aims to create Chatbot as Virtual Assistant that provides information for college students through data stored on the system which contains Informatics study program and new information if the data is not founded. Chatbot is a computer program designed to socialize interactive conversations or communication to users (humans) through a text, sound and visual. Through this application, they are able to communicate for various pratical purposes such as customer service, information acquisition and a value of digital campus with integrated information system. Thus, Chatbot with artificial intelligence eases users to obtain information quickly and precisely.
A Learning Strategy for Contrast-agnostic MRI Segmentation

We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning for new modalities. Classical Bayesian methods address this segmentation problem with unsupervised intensity models, but require significant computational resources. In contrast, learning-based methods can be fast at test time, but are sensitive to the data available at training. Our proposed learning method, SynthSeg, leverages a set of training segmentations (no intensity images required) to generate synthetic sample images of widely varying contrasts on the fly during training. These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field. Because each mini-batch has a different synthetic contrast, the final network is not biased towards any MRI contrast. We comprehensively evaluate our approach on four datasets comprising over 1,000 subjects and four types of MR contrast. The results show that our approach successfully segments every contrast in the data, performing slightly better than classical Bayesian segmentation, and three orders of magnitude faster. Moreover, even within the same type of MRI contrast, our strategy generalizes significantly better across datasets, compared to training using real images. Finally, we find that synthesizing a broad range of contrasts, even if unrealistic, increases the generalization of the neural network. Our code and model are open source at this https URL.
Can-Do descriptors – Realigning English Language Curriculum at Higher Education Institution to CEFR

The Common European Framework of Reference for Language (CEFR) has become the standard used to describe and evaluate students’ command of a second or foreign language. It is an internationally acknowledged standard language proficiency framework which many countries have adopted such as China, Thailand, Japan and Taiwan. Malaysia Ministry of Education is aware and realise the need for the current English language curriculum to be validated as to reach the international standard as prescribed by the CEFR. The implementation of CEFR has begun at primary and secondary level since 2017 and now higher education institutions are urged to align their English Language Curriculum to CEFR as part of preparation in receiving students who have been taught using CEFR-aligned curriculum at schools by year 2022. This critical reflection article elucidates the meticulous processes that we have embarked on in re-aligning our English Language Curriculum to the standard and requirements of CEFR. The paper concludes with a remark that the alignment of the English curriculum at the university needs full support from the management in ensuring that all the stakeholders are fully prepared, informed and familiar with the framework.
The Taichi programming language

course Share on The Taichi programming language Author: Yuanming Hu MIT CSAIL MIT CSAILView Profile Authors Info & Claims SIGGRAPH '20: ACM SIGGRAPH 2020 CoursesAugust 2020 Article No.: 21Pages 1–50https://doi.org/10.1145/3388769.3407493Published:17 August 2020Publication History 1citation306DownloadsMetricsTotal Citations1Total Downloads306Last 12 Months34Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Media Platforms and Algorithms: content and social implications

The paper considers the new features of the digital media environment associated with the widespread introduction of platforms and algorithms in media practices and reveals the technological, business and social background of these innovations. The application of platforms and algorithms is a powerful tool for implementing the commercial imperative in the media. In general, this is a characteristic feature of the development of modern society – a trend towards comprehensive metrization. Along with the advantages, the use of predictive algorithms, personalization of content based on tracking of past communicative behavior has a number of negative social consequences. E.g., ‘filter bubbles’ contribute to the formation of closed information segments. The model of social behaviorism underlying the recommendation services contributes to the modification of people’s informational behavior. Algorithmization of media landscape strengthens the trends of content delivery to individual consumers, and not to citizens inclined to make joint decisions regarding the common interests of social life.
Analisis Performa Mata Uang Virtual (Cryptocurrency) Menggunakan Preference Ranking Organization Method For Enrichment Evaluation (Promethee)

Cryptocurrencies are among the inventions that have caused a stir in the economy of late. Because in its use there are still pros and cons of various countries. Some countries reject the use of cryptocurrencies and others support the use of cryptocurrencies because it is considered a modernization of payment tools. Besides being used for payment instruments, cryptocurrencies can also be one of the options to invest. The number of cryptocurrencies that exist causes investors to be observant in making the right choices. In this study, the Promethee method was used I and II to determine the rank of 7 virtual currencies. Promethee I is a partial assessment method while Promethee II is a complete assessment method. The data used for ranking is obtained from the questionnaire "sentiment on the performance of cryptocurrencies". The results of the cryptocurrency performance analysis showed that the investment commodity of the most recommended in a row is Bitcoin with a net flow value of 0.33267, Cardano 0.14267, Ethereum 0.04800, Ripple 0.04733, Stellar -0.04733, Litecoin -0.04767 and Dogecoin -0.47567.
An Optimized Software Defect Prediction Model Based on PSO - ANFIS

Introduction : The Software defect prediction (SDP) model plays a very important role in today’s software industry. SDP models can provide either only a list of defect-prone classes as output or the number of defects present in each class. This output can then be used by quality assurance teams to effectively allocate limited resources for validating software products by putting more effort into these defect-prone classes.The study proposes an OANFIS-SDP model that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. Method: OANFIS is a novel approach based on the Adaptive neuro-fuzzy inference system (ANFIS), which is optimized using Particle swarm optimization (PSO). OANFIS model combines the flexibility of ANFIS model with the optimization capabilities of PSO for better performance. Results: The proposed model is tested using the dataset from open source java projects of varied sizes (from 176 to 745 classes). Conclusion: The study proposes an SDP model based OANFIS that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. OANFIS is a novel approach that uses the flexibility provided by the ANFIS model and optimizes the same using PSO. The results given by OANFIS are very good and it can also be concluded that the performance of the SDP model based on OANFIS might be influenced by the size of projects. Discussion: The performance of the SDP model based on OANFIS is better than the ANFIS model. It can also be concluded that the performance of the SDP model might be influenced by the size of projects.
An Empirical Study on Emerging Trends in Artificial Intelligence and its Impact on Higher Education

This paper investigates the various emerging trends in Artificial Intelligence (AI), and their impact on teaching and learning practices in Higher Education Institutions (HEIs).Many of the these AI technologies like Hologram technology, the technologies that supports Ubiquitous learning, technologies for Automated evaluations and grading, Green Computing, and Blended learning methodologies, can be used efficiently to shape the future of higher education and can bring innovations in teaching and learning.This study investigates the opportunities and the challenges in each of these areas, which might serve as a guideline for its adoption, and can also be extended further in the direction of research.