Reviewerly/exHarmony huggingface dataset:
Structure-aware bottle cap art

We propose a new image processing problem that consists in approximating an input image with a set of plastic bottle caps. This problem is motivated by the appreciation caused by low resolution imaging, combined with the goal of finding a new destination for plastic caps that would be discarded and possibly damage the environment. Our solution consists in formulating an energy that measures how well a bottle cap art reproduces structures and features from the reference image and maximizing it with a simulated annealing strategy. Examples in the paper show that our method produces high quality results in this very low spatial and color resolution setting.
An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provides access to commonsense knowledge about household environments and performs semantic matching between entities from the web knowledge graph ConceptNet, using semantic knowledge from DBpedia and WordNet, with the ones existing in the knowledge graph. They offer a set of predefined SPARQL templates that directly address the ontology on which their knowledge retrieval framework is built and querying through SPARQL. The framework also features an argumentation component, where the user can argue against the answers of the knowledge retrieval component of the framework under two different scenarios: the missing knowledge scenario, where an entity should be in the answers, and the wrong knowledge scenario, where an entity should not be in the answers. This argumentation dialogue can end up in learning a new piece of knowledge when the user wins the dialogue.
Privacy and Authentication: An Introduction to Cryptography

chapter Share on Privacy and Authentication: An Introduction to Cryptography Authors: Whitfield Diffie View Profile , Martin E. Hellman View Profile Authors Info & Claims Democratizing Cryptography: The Work of Whitfield Diffie and Martin HellmanAugust 2022 Pages 431–514https://doi.org/10.1145/3549993.3550010Published:26 August 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks2 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
Public Key Cryptography’s Impact on Society: How Diffie and Hellman Changed the World

chapter Share on Public Key Cryptography’s Impact on Society: How Diffie and Hellman Changed the World Author: Paul C. van Oorschot View Profile Authors Info & Claims Democratizing Cryptography: The Work of Whitfield Diffie and Martin HellmanAugust 2022 Pages 19–56https://doi.org/10.1145/3549993.3549997Published:26 August 2022Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 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
Risks and Challenges of Scrum: A Systematic Literature Review

Agile methodologies were increasingly adopted in the last decades. In the Agile methodologies’ universe, Scrum is the most used one and is gaining broader acceptance. In fast changing times, marked by various uncertainties in the organization of workplaces, location of teams and technological challenges, it is necessary to update the risks and challenges of using Scrum. With updated information and awareness of the risks and challenges inherent to the SCRUM methodology, the decision on whether to adopt this methodology will be better addressed. The research is made in the form of a systematic literature review (SLR). This SLR extracted data from 39 papers published from 2017 to 2021, a period that encompasses important impacts of the ongoing transformations with the adoption of cloud computing, the use of big data and the difficulties that the COVID-19 pandemic posed around the world. Several challenges and risks were identified, from team communication to collaboration with stakeholders, from effort estimation to knowledge management, and suggestions are made to improve Scrum adoption success rate.
The two waves of virtual reality in artistic practice

As the title of this article suggests, there appears to be two distinct waves of artistic engagement with virtual reality (VR) in artistic practice: the first during the initial technological development in the 1990s that enabled VR to become accessible to artists such as Char Davies and Toni Dove among others and the current second wave that has been based on a much greater accessibility of immersive headsets for artists and a wider general public, driven by both market forces and technological development. The term ‘virtual reality’ has been interchangeably used to mean virtual environments and virtual worlds, but here the aim is to solely examine the use of VR in artistic practices, where the intention is for the participant to largely experience the virtual space through total immersion, reserving the analysis of the mixed use of VR within mixed-reality environments to another study. The article begins with a brief history of the development of VR and an analysis of the key works created in what can be termed the first wave, before discussing the current use of VR in contemporary practices through the notion of affect in VR.
Quantum state tomography of multi-qubit systems — a comparative study

Multi-qubit state tomography is a key problem in quantum information technology, which has been studied extensively. In this work, we focus on multi-qubit state tomography based on neural network estimators and typical conventional estimation approaches. For multi-qubit pure states, fully connected neural networks and restricted Boltzmann machine networks are designed, respectively, to carry out state tomography of 2-qubit (low-dimensional) systems and 5-qubit (high-dimensional) systems. Comparisons are made with maximum likelihood estimation and least squares estimation, where performance indicators are selected as reconstruction accuracy, time cost and the number of parameters. Simulation results indicate that intelligent approaches have significant advantages over conventional approaches for state tomography of low-dimensional systems; for high-dimensional systems, however, the conventional approach is still dominant when the measurement is complete, while the restricted Boltzmann machine network can achieve higher reconstruction accuracy when the measurement is incomplete.
Consumers’ Perception: Discovering the “Law of Attraction” in Art Using Eye-Tracking

"Today, due to new technologies and seeing the tenacity with which the branch of scientific research, especially neuromarketing, advances, it is easy to understand how they have managed to bring together topics such as fine arts and eye-tracking technology. Not only did they end up with amazing results, but those results were in line with the expectations created from the very beginning by researchers from these industries and beyond. Therefore, using eye-tracking as a market research tool, it is possible to directly track and measure the observable behavior of a visual arts consumer, analyzing both his visual attention and his eye movements. The data collected for this study resulted from the exploratory research, which involves the review of several articles and specialized studies regarding the eye-tracking subject and also from the qualitative research, represented by an in-depth interview with both Romanian art consumers and artists. It has implemented an eye-tracking test, also to gather the primary data for this present study, a tool that was available on an online platform named GazeRecorder. The results illustrate the extent to which attention and the decision-making process (to purchase a painting) affect and influence how the information present in the work of fine art is related to the composition and elements that support the artwork itself. Thus, these results, not only can help analyze a painting in a much more efficient way but can also be incorporated in the future into consultancy services for artists or may represent the basis of a more appropriate and attractive form of education in art. Keywords: eye tracking, observation, art, artwork analysis, paintings JEL classification: L31, C91 "
Teaching Reform of Supply Chain Management Based on the Concept of Autonomous Learning

In the Internet era, with the support of information technology, autonomous learning has become the main learning method of contemporary college students. All teaching activities of supply chain management are based on the basic principles of stimulating students’ active learning interest and awareness and cultivating students’ autonomous learning habits, and implement the educational concept of “learning centered”. This paper discusses the key problems to be solved in curriculum and teaching reform, and constructs a diversified curriculum evaluation model. From the three aspects of usual performance evaluation, formative evaluation and final examination, strengthen the evaluation of the learning process, evaluate the performance structure in a diversified way, pay attention to the accumulation of students’ usual knowledge, and cultivate the adaptability and comprehensive innovation ability to solve problems, so as to improve the effect of classroom teaching. Taking the Department of Business Administration of Shandong women’s University as an example, this paper discusses the construction and application of curriculum content and resources, curriculum teaching content and organization and implementation, curriculum achievement evaluation methods, curriculum evaluation and reform achievements.
A Semi-systematic Literature Review of Holoportation in Education: The Potential of Immersive Technology

The ongoing digital revolution in education and learning is the focus of many research studies due to the significant potential of technology-mediated learning. However, the integration of holoportation as an immersive technology in educational contexts is still somewhat unexplored. In this semi-systematic literature review, we seek to assess the emerging literature on teachers’ and learners’ experiences using Microsoft HoloLens as a teaching and learning medium. The objective is to identify the benefits, challenges, and future development needs related to the use of the HoloLens in education. We limited our review to empirical studies published in international peer-reviewed journals during the last five years. The review was not limited by discipline, educational context, or theoretical perspectives. The findings of this meta-analysis highlight the lack of research on the use of mixed reality (MR) in education. Possible directions for future development of pedagogical approaches that take advantage of innovative immersive learning environments and research in this area are also outlined; these suggestions can benefit researchers and industrial developers.
Automatic Topic-Based Lecture Video Segmentation

The recent rise in popularity of online learning has completely overhauled the way students consume informative content and how educators provide it. One of the ways teachers can provide more engaging lectures online is by segmenting recorded lectures by topic, which allows students to interact with the video and navigate it. Unfortunately, manually segmenting videos requires time and effort, adding even more to their workload. To address this issue, a system that takes any slide-based lecture videos as input and outputs a list of all the topic transitions contained in the video was developed. Previous research on this topic have placed heavy emphasis on the lecturer's speech to segment the video by topic. However, this research investigates the use of lecture video's presentation slides to determine topics. Topic transitions were determined using a Convolutional Neural Network - based Binary Classification Model trained on an original dataset of lecture videos collected from different educational resources. The video undergoes a series of preprocessing steps that gradually cut down the group of frames to contain only distinct slides before inputting them into the model. The classification model's performance was satisfactory. It obtained 91% accuracy and an 80% F1 score, which is indicative of its reliability in determining whether a slide is a topic transition or not. The system developed in this research provides both lecturers and students with a method to label and segment their videos based on key topics automatically. With further evaluation, this system can potentially be proven to be convenient enough to be introduced as a new tool in the educational industry to supplement online learning.
A Local Self-Attention Sentence Model for Answer Selection Task in CQA Systems

Current evidence indicates that the semantic representation of question and answer sentences is better generated by deep neural network-based sentence models than traditional methods in community answer selection tasks. In particular, as a widely recognized language model, the self-attention model computes the similarity between the specific word and the whole sets of words in the same sentence and generates new semantic representation through the similarity-weighted summation of semantic representations of the whole words. However, the self-attention operation entirely considers all the signals with a weighted sum operation, which disperses the distribution of attention, which may result in overlooking the relation of neighboring signals. This issue becomes serious when applying the self-attention model to online community question answering platforms because of the varied length of the user-generated questions and answers. To address this problem, we introduce an attention mechanism enhanced local self-attention (LSA), which restricts the range of original self-attention by a local window mechanism, thereby scaling linearly when increasing the sequence length. Furthermore, we propose stacking multiple LSA layers to model the relationship of multiscale <inline-formula> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula>-gram features. It captures the word-to-word relationship in the first layer and then captures the chunk-to-chunk (such as lexical <inline-formula> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula>-gram phrases) relationship in its deeper layers. We also test the effectiveness of the proposed model by applying the learned representation through the LSA model to a Siamese and a classification network in community question answer selection tasks. Experiments on the public datasets show that the proposed LSA achieves a good performance.
The perspectives of teaching electroacoustic music in the digital environment in higher music education

Nowadays, modern technologies provide a variety of opportunities in many fields, including education. In this context, it is crucial to comprehend the possibility of developing new ways of teaching electroacoustic music using such technology tools. For this purpose, the researchers of the present study elaborated and implemented a study program that presupposes involving students in the learning process using interactive digital tools. This approach promotes the development of practical musical skills among students. The present study aims to investigate the impact of an OSC-based interactive learning system on the rhythmic abilities of university students enrolled in electroacoustic music education. This research builds on an empirical approach through a quasi-experiment. It involved 160 students in Huaqiao University, China. The experimental work showed better results in the experimental group in both perception (t = 6.931, df = 133, p = 0.000) and performance of musical rhythm (t = 8.387, df = 133, p = 0.000). Consequently, it confirms the positive potential of the digital teaching environment. The present research has scientific and practical value since the results obtained may contribute to developing new music education in other educational institutions.
Canonical Workflow for Experimental Research

Abstract The overall expectation of introducing Canonical Workflow for Experimental Research and FAIR digital objects (FDOs) can be summarised as reducing the gap between workflow technology and research practices to make experimental work more efficient and improve FAIRness without adding administrative load on the researchers. In this document, we will describe, with the help of an example, how CWFR could work in detail and improve research procedures. We have chosen the example of “experiments with human subjects” which stretches from planning an experiment to storing the collected data in a repository. While we focus on experiments with human subjects, we are convinced that CWFR can be applied to many other data generation processes based on experiments. The main challenge is to identify repeating patterns in existing research practices that can be abstracted to create CWFR. In this document, we will include detailed examples from different disciplines to demonstrate that CWFR can be implemented without violating specific disciplinary or methodological requirements. We do not claim to be comprehensive in all aspects, since these examples are meant to prove the concept of CWFR.
Türkçe Otomatik Konuşma Tanıma Sistemi için Dil Modeli Optimizasyon Yöntemi

The current Automatic Speech Recognition (ASR) modeling strategy still suffers from huge performance degradation when faced with languages with limited resources such as Turkish. Especially when the Language Model (LM) does not support the Acoustic Model (AM) sufficiently, the Word Error Rate (WER) increases. Therefore, a robust LM makes a strong contribution to improving ASR performance by generating word relations from the existing corpus. However, developing a robust language model is a challenging task due to the agglutinative nature of Turkish. Therefore, within the scope of the study, a sentence-level LM optimization method is proposed to improve the WER performance of Turkish ASR. In the proposed method, instead of a fixed word sequence obtained from the Markov assumptions, the probability of the word sequence forming a sentence was calculated. A method with n-gram and skip-gram properties is presented to obtain the word sequence probability. The proposed method has been tested on both statistical and Artificial Neural Network (ANN) based LMs. In the experiments carried out using, not only words but also sub-word level, two Turkish corpora (METU and Bogazici) shared via Linguistic Data Consortium (LDC) and a separate corpus, which we separate corpus that we specially created as HS was used. According to the experimental results obtained from statistical-based LM, 0.5% WER increases for the METU corpus, 1.6% WER decreases for the Bogazici corpus, and a 2.5% WER decrease for the HS corpus were observed. In the Feedforward Neural Networks (FNN) based LM, WER decreases were observed 0.2% for the METU corpus, 0.8% for the Bogazici corpus, and 1.6% for the HS corpus. Also, in the Recurrent Neural Network (RNN)-Long Short Term Memory (LSTM) based LM, WER decreases were observed 0.6% for METU corpus, 1.1% for the Bogazici corpus and 1.5% for the HS corpus. As a result, when the proposed method was applied to the LMs required for ASR, WER decreased, and the total performance of ASR increased.
A Comparative Analysis of the Consensus Algorithms in Blockchain Technology

Blockchain is a decentralized, distributive and peer to peer database that is used for record-keeping. The use of hashing techniques, consensus algorithms, and smart contracts makes it secure. Once the data is stored in a blockchain it is very hard to hack or modify it by an unauthorized party. Being a decentralized technology, there is no central authority that verifies the data before storing it in blockchain and for that reason, consensus algorithms play a vital role. In this paper, we have made a comparative analysis of various consensus mechanisms.
Inference to the Stable Explanations

The process of explaining a piece of evidence by constructing a set of assumptions that are a good explanation for that evidence is ubiquitous in real-life (e.g. in legal systems). In this paper, we introduce, discuss, and formalise the notion of stable explanations in a non-monotonic setting. We show how, while applying it to the process of (1) computing a set of literals able to (2) derive a conclusion (3) from a set of defeasible rules, we obtain a restricted version of the notion of abduction. This is both interesting and useful: when an explanation for a given conclusion is stable, it can, in fact, be used to infer the same conclusion independently of other pieces of evidence that are found afterwards.
Syntactic ASP Forgetting with Forks

In this paper, we present a syntactic transformation, called the unfolding operator, that allows forgetting an atom in a logic program (under ASP semantics). The main advantage of unfolding is that, unlike other syntactic operators, it is always applicable and guarantees strong persistence, that is, the result preserves the same stable models with respect to any context where the forgotten atom does not occur. The price for its completeness is that the result is an expression that may contain the fork operator. Yet, we illustrate how, in some cases, the application of fork properties may allow us to reduce the fork to a logic program, even in conditions that could not be treated before using the syntactic methods in the literature.
Online temporal classification of human action using action inference graph

Nowadays, deep learning methods have achieved state-of-the-art results in human action recognition. These methods process a full video sequence to recognize an action, which is unnecessary because many frames are similar. Recently, keyframe-based methods are proposed to overcome this issue. Though keyframe based methods have shown competitive performance in action recognition, both methods still process all the required frames of a video clip and average the results of individual clips/frames to recognize the action of the video. We argue that by simply using the average of the results of the video clips, deep models are not using the motion information of the video and thus leads to an inaccurate recognition of the action. To cope with the aforementioned issue, we propose a new online temporal classification model (OTCM) that classifies an action from a video in an online fashion and addresses the issue of averaging by making decision of each frame of a video sequence. As well, we propose a new action inference graph (AIG) that enables early recognition. Hence, the proposed model can recognize an action early before using all the keyframes or the whole video sequence and thus, requires less computation for recognizing human actions. Moreover, our OTCM can perform online action detection. To the best of our knowledge, this is the first time that the OTCM model along with the AIG is proposed. The experimental results of the benchmark datasets show that the proposed OTCM model has achieved and set a new record of the SOTA results, in particular, without using full video sequences.