Reviewerly/exHarmony huggingface dataset:
Monitoring the Learning Progress in Piano Playing with Hidden Markov Models

Monitoring a learner’s performance during practice plays an important role in scaffolding. It helps with scheduling suitable practice exercises and, by doing so, sustains learner motivation and steady learning progress while moving through the curriculum. In this paper we present our approach for monitoring the learning progress of students learning to play piano with Hidden Markov Models. First, we present and implement the so-called practice modes, practice units that are derived from the original task by reducing its complexity and focusing on one or several relevant task dimensions. Second, for each practice mode, a Hidden Markov Model is trained to predict whether the player is in the Mastered or NonMastered latent state regarding the current task and practice mode.
Graph Capsule Network with a Dual Adaptive Mechanism

While Graph Convolutional Networks (GCNs) have been extended to various fields of artificial intelligence with their powerful representation capabilities, recent studies have revealed that their ability to capture the part-whole structure of the graph is limited. Furthermore, though many GCNs variants have been proposed and obtained state-of-the-art results, they face the situation that much early information may be lost during the graph convolution step. To this end, we innovatively present an Graph Capsule Network with a Dual Adaptive Mechanism (DA-GCN) to tackle the above challenges. Specifically, this powerful mechanism is a dual-adaptive mechanism to capture the part-whole structure of the graph. One is an adaptive node interaction module to explore the potential relationship between interactive nodes. The other is an adaptive attention-based graph dynamic routing to select appropriate graph capsules, so that only favorable graph capsules are gathered and redundant graph capsules are restrained for better capturing the whole structure between graphs. Experiments demonstrate that our proposed algorithm has achieved the most advanced or competitive results on all datasets.
Lexicon Enriched Hybrid Hate Speech Detection with Human-Centered Explanations

The phenomenon of hate messages on the web is unfortunately in continuous expansion and evolution. Even if the big companies that offer their users a social network service have expressly included in their terms of services rules against hate messages, they are still produced at a huge rate. Therefore, moderators are often employed to monitor these platforms and use their critical skills to decide if the content is offensive or not. Unfortunately, this censorship process is complex and costly in terms of human resources. The system we propose in this work is a system that supports moderators by providing them a set of candidate elements to censor with annexed explanations in natural language. It will then be a task of the human operator to understand if to proceed with the censorship and eventually supply feedback to the result of the classification algorithm to extend its data set of examples and improve its future performances. The proposed system has been designed to merge information coming from data, syntactic tags and a manually annotated lexicon. The messages are then processed through deep learning approaches based on both transformer and deep neural network architecture. The output is consequently supported by an explanation in a human-like form. The model has been evaluated on three state-of-the-art datasets showing excellent effectiveness and clear and understandable explanations.
A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot

Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e.g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry. In this paper, we propose to represent utterance with a simpler concept named Dialogue Action, upon which we construct a tree-structured TaskFlow and further build task-oriented chatbot with TaskFlow as core component. A framework is presented to automatically construct TaskFlow from large-scale dialogues and deploy online. Our experiments on real-world after-sale customer services show TaskFlow can satisfy the major needs, as well as reduce the developer burden effectively.
Extractive Elementary Discourse Units for Improving Abstractive Summarization

Abstractive summarization focuses on generating concise and fluent text from an original document while maintaining the original intent and containing the new words that do not appear in the original document. Recent studies point out that rewriting extractive summaries help improve the performance with a more concise and comprehensible output summary, which uses a sentence as a textual unit. However, a single document sentence normally cannot supply sufficient information. In this paper, we apply elementary discourse unit (EDU) as textual unit of content selection. In order to utilize EDU for generating a high quality summary, we propose a novel summarization model that first designs an EDU selector to choose salient content. Then, the generator model rewrites the selected EDUs as the final summary. To determine the relevancy of each EDU on the entire document, we choose to apply group tag embedding, which can establish the connection between summary sentences and relevant EDUs, so that our generator does not only focus on selected EDUs, but also ingest the entire original document. Extensive experiments on the CNN/Daily Mail dataset have demonstrated the effectiveness of our model.
Image Recognition Algorithm Based on Artificial Intelligence and Machine Learning

At present, most of the existing image recognition methods are not only cumbersome in process but also require manual design of functions, resulting in poor recognition results and time-consuming training. This study explores image recognition algorithms based on artificial intelligence and machine learning, which can simulate the hierarchical structure of the human brain and nervous system, realize automatic extraction of complex features, and have powerful data representation capabilities. In this study, the structure of artificial neuron is introduced first, and the training process of the neural network is introduced in detail. At the same time, this study improves the traditional training algorithm and proposes two new machine learning models for different application scenarios, which effectively improves the performance of the optimal model. In relevant tests, the effect is significant. On the basis of these improved algorithms, an online recognition system is designed and implemented. The recognition accuracy rate of the system for the hidden layer reaches more than 90%, which verifies the effectiveness of the technology. At the same time, after repeated experiments, the results show that the multiview algorithm effectively solves the problem that the recognition results of the traditional multiview algorithm are affected by the size of the target contour.
E-Learning and Understanding of Accounting During Covid-19 Pandemic: Literature Review

E-Learning has been introduced in the economy after the revolution of technologies that increased in many sectors and that has become a necessity in the current period of life specially during the COVID-19 pandemic. E-learning is illustrated in using the electronic communication to deliver education between educators and learners which helped institutions and universities to implement innovative features and techniques in order to enhance interactions between students and educators and make it more flexible in time and place for both. In this research paper, we have examined and investigated the implementation of E-learning during the COVID-19 pandemic and how that has affected the E-learning process, explaining the significance of E-learning and specially in understanding accounting. Furthermore, this research study obtained to one more objective that is identifying some of the major challenges while understanding accounting through E-learning particularly during COVID-19 crisis. This study’s design was to adopt the examination of the implementation of E-learning during COVID-19 on the dependent variable which is understanding and studying accounting. The findings of this research study that the E-learning during COVID-19 has been increased and more consumed by many sectors, resulting from the need of minimizing direct contacts between educators and learners. Besides that, there was found that there might be some various challenges facing E-learning during COVID-19 and specifically in understanding accounting.
Improving Implicit Alternating Least Squares with Ring-based Regularization

Due to the widespread presence of implicit feedback, recommendation based on them has been a long-standing research problem in academia and industry. However, it suffers from the extremely-sparse problem, since each user only interacts with a few items. One well-known and good-performing method is to treat each user's all uninteracted items as negative with low confidence. The method intrinsically imposes an implicit regularization to penalize large deviation of each user's preferences for uninteracted items from a constant. However, these methods have to assume a constant-rating prior to uninteracted items, which may be questionable. In this paper, we propose a novel ring-based regularization to penalize significant differences of each user's preferences between each item and some other items. The ring structure, described by an item graph, determines which other items are selected for each item in the regularization. The regularization not only averts the introduction of the prior ratings but also implicitly penalizes the remarkable preference differences for all items according to theoretical analysis. However, optimizing the recommenders with the regularization still suffers from computational challenges, so we develop a scalable alternating least square algorithm by carefully designing gradient computation. Therefore, as long as connecting each item with a sublinear/constant number of other items in the item graph, the overall learning algorithm could be comparably efficient to the existing algorithms. The proposed regularization is extensively evaluated with several public recommendation datasets, where the results show that the regularization could lead to considerable improvements in recommendation performance.
Communicating Changes Throughout the Electronic Resources Lifecycle to Library Staff and Users

Electronic resources undergo various changes during their lifecycle from evaluation, to acquisitions, to renewal or cancelation. To keep users abreast of these changes, effective communication is necessary between the electronic resources librarian and the library team. Effective communication is equally important between the electronic resources librarian and library users. At Queens College Library, the Library Team consisting of faculty librarians and staff are alerted to electronic resources changes through emails and posts to a Microsoft Teams library channel. Those lifecycle changes communicated to the Library Team are also featured on a public Electronic Resources Status (ERS) Dashboard, accessible to anyone. The ERS Dashboard and related policies and workflows have helped to streamline communication with the Library Team and users. The author details the techniques used in communicating electronic resources lifecycle changes to the Library Team and users.
Analysis of Syntactic Complexity and Semantic Coherence of Academic English Writing Based on Particle Swarm Optimization

Writing is an important part of testing language ability, and it is urgent to find some objective indicators to determine and evaluate the surface language structure, which will help language learners’ better master the target language. Complexity and semantic coherence are considered to be an important factor in the teaching of second language writing. In practice, due to the complexity of English writing syntax, such as a large number of high-dimensional nonlinear optimization problems, a new intelligent evaluation method is needed to solve them. At present, particle swarm optimization (PSO) has been widely used in function optimization, neural network training, combinatorial optimization, and other fields. This paper studies the syntactic complexity and semantic coherence of academic English writing based on PSO. The number of phrases is related to writing achievement. When the number of experiments reaches 25, the significant values of syntactic complexity and semantic coherence of data mining algorithm, artificial intelligence algorithm, decision tree algorithm, and PSO algorithm are 0.008, 0.003, 0.002, and 0.013, respectively, which shows that PSO algorithm is the best among them.
Posterior Probability Matters

Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration techniques aims to post-process model predictions to posterior probabilities. Field-level calibration -- which performs calibration w.r.t. to a specific field value -- is fine-grained and more practical. In this paper we propose a doubly-adaptive approach AdaCalib. It learns an isotonic function family to calibrate model predictions with the guidance of posterior statistics, and field-adaptive mechanisms are designed to ensure that the posterior is appropriate for the field value to be calibrated. Experiments verify that AdaCalib achieves significant improvement on calibration performance. It has been deployed online and beats previous approach.
Map and Content-Based Climbing Recommender System

Sport climbing has recently gained large popularity among tourists as a recreational activity. Many people are interested to climb the most beautiful rock climbing places around the world. This has pushed the creation of a large number of climbing routes, to accommodate more and more enthusiasts. However, climbers are not facilitated in their search of routes to climb with any advanced tool, especially in outdoor climbing: they are only provided with either printed or electronic guidebooks, which cannot generate recommendations based on the user’s preferences. Well-tailored climbing routes recommendations have a potential interest for all the involved stakeholders: the users and the companies providing the route information in the form of websites, or guidebooks. To this end, we propose a Content-based Climbing Recommender System prototype. An initial usability study based on the Software Usability Scale (SUS) proves the first version of the prototype to be well-designed (obtained SUS score of 71.6), and the updated version of a system addressing usability problems received an excellent evaluation score (SUS score is 89.3).
Using Machine Learning to Enhance Archival Processing of Social Media Archives

This article reports on a study using machine learning to identify incidences and shifting dynamics of hate speech in social media archives. To better cope with the archival processing need for such large-scale and fast evolving archives, we propose the Data-driven and Circulating Archival Processing (DCAP) method. As a proof-of-concept, our study focuses on an English language Twitter archive relating to COVID-19: Tweets were repeatedly scraped between February and June 2020, ingested and aggregated within the COVID-19 Hate Speech Twitter Archive (CHSTA), and analyzed for hate speech using the Generative Adversarial Network–inspired DCAP method. Outcomes suggest that it is possible to use machine learning and data analytics to surface and substantiate trends from CHSTA and similar social media archives that could provide immediately useful knowledge for crisis response, in controversial situations, or for public policy development, as well as for subsequent historical analysis. The approach shows potential for integrating multiple aspects of the archival workflow and supporting automatic iterative redescription and reappraisal activities in ways that make them more accountable and more rapidly responsive to changing societal interests and unfolding developments.
One-handed Input for Mobile Devices via Motion Matching and Orbits Controls

We introduce a novel one-handed input technique for mobile devices that is not based on pointing, but on motion matching -where users select a target by mimicking its unique animation. Our work is motivated by the findings of a survey (N=201) on current mobile use, from which we identify lingering opportunities for one-handed input techniques. We follow by expanding on current motion matching implementations - previously developed in the context of gaze or mid-air input - so these take advantage of the affordances of touch-input devices. We validate the technique by characterizing user performance via a standard selection task (N=24) where we report success rates (&gt;95%), selection times (~1.6 s), input footprint, grip stability, usability, and subjective workload - in both phone and tablet conditions. Finally, we present a design space that illustrates six ways in which motion matching can be embedded into mobile interfaces via a camera prototype application.
Discursive construction of anti-hijab discourse on Facebook and Twitter: the case of Malaysian former-Muslim women

This study sheds light on how Malaysian former Muslims construct their anti-hijab discourse in the context of Facebook and Twitter interactions and how a dichotomous meaning is assigned to the hijab. The five participants were identified using the snowball technique, starting from a well-known Malaysian former Muslim. Data was generated by observing the participants’ postings over nine months, from April to December 2019. Three hundred ten postings were collected which challenged Islam and its principles. However, this paper narrowed its scope to focus on 116 anti-hijab postings. The data were analysed using discourse analysis, particularly discursive psychology. The study revealed that participants’ postings had thematic links through which an overall theme emerged; the hijab is a tool of women’s oppression. This theme emerged through the discursive features such as assessment, category entitlement, extreme case formulation, blame, and corroboration with the anti-hijab ideology which exists on Facebook and Twitter, wherein former Muslims contest the wearing of hijab in certain Islamic countries and attempt to enhance the dichotomy of “women oppression versus women rights” to hijab.
Developing a Human Centred AI Masters

The increasing use of AI in industry and society not only expects but demands that we build human-centred competencies into our AI education programmes. The computing education community needs to adapt, and while the adoption of standalone ethics modules into AI programmes or the inclusion of ethical content into traditional applied AI modules is progressing, it is not enough. To foster student competencies to create AI innovations that respect and support the protection of individual rights and society, a novel ground-up approach is needed. This panel presents on one such approach, the development of a Human-Centred AI Masters (HCAIM) as well as the insights and lessons learned from the process. In particular, we discuss the design decisions that have led to the multi-institutional master's programme. Moreover, this panel allows for discussion on pedagogical and methodological approaches, content knowledge areas and the delivery of such a novel programme, along with challenges faced, to inform and learn from other educators that are considering developing such programmes.
Risks in Blockchain – A Survey about Recent Attacks with Mitigation Methods and Solutions for Overall

The purpose of this paper is to analyze the context and attack surface of a variety of security breaches and attacks on cryptocurrency blockchains. For many different types of blockchain attacks, such as distributed denial-of-service (DDoS) attacks and majority (51%) attacks, there is very little literature investigating and comparing particular attack instances as opposed to focusing on general theoretical studies and mitigation techniques. The attacks investigated here include a series of DDoS attacks on the Bitfinex cryptocurrency exchange and a group of instances of the 51% attack on thirteen different cryptocurrency blockchains. The goal of this research is to find traits in the context and attack surface that exacerbate the losses caused by certain attacks as opposed to others. It will then propose solutions and mitigation techniques based on the circumstances of the cases where the impact of the attack was less serious.
HyperNet: A conditional k-anonymous and censorship resistant decentralized hypermedia architecture

Nowadays, the vast majority of Internet services used to distribute hypermedia content follow a centralized model, which is highly dependent on servers and raises several quality and security concerns. Among other issues, this centralized model creates single points of failure, requires trust on providers to avoid censorship and personal data misuse, and results in a scenario where digital content tends to disappear or be inaccessible over time, for example, when a content creator stops maintaining a site or when the content is moved to another location. To improve this, it is necessary to replicate data and follow more distributed models. Nevertheless, current platforms to distribute content in this way, either do not offer an effective mechanism to maintain the privacy of their users or they offer full-anonymity, which contributes to the dissemination of content that goes beyond the law and moral standards of many users. This paper proposes a novel distributed architecture that enables hypermedia resource distribution ensuring censorship resistance and conditional k-anonymity. In the proposed system, users form groups to share hypermedia content where the anonymity of the publisher is preserved only if the publication follows a set of rules defined by the group. To this end, the proposed system uses threshold discernible ring signatures to enable conditional k-anonymity, the Ethereum blockchain platform to manage groups and user identities, and the InterPlanetary File System to store and share hypermedia resources in a distributed way. This document provides the design for the proposed architecture and protocols, it evaluates system risks and its security properties, and it discusses the proposal in general terms.