Reviewerly/exHarmony huggingface dataset:
Improving Student Intuition Via Rensselaer's New Mobile Studio Pedagogy

Although computer literate, today's engineering students don't enter college with the same level of hands-on experience with hardware that prior generations exhibited.Experimentation provides students with a sense of where things deviate from theory, offering the opportunity to explore non-ideal conditions; while also giving them the chance to play with hardware and gain the experience and expertise that helps them become successful designers. 1,2For example, electronics technicians who had vast hands-on experience were able to reproduce large portions of complex circuit diagrams after only a few seconds of viewing; whereas novices could not. 3 This was due to their ability to chunk the individual circuit elements that functioned together as an amplifier.Expert scientists and engineers are able to quickly recognize patterns of information; for example, physicists recognize problems of river currents and problems of headwinds and tailwinds in airplanes as involving similar mathematical principles, such as relative velocities. 4Gone are the days when students were ham radio operators, played with Erector/LEGO sets, tinkered with electronic kits or simply taken things apart for fun.As a result, students have less "gut intuition" and expert skills than prior generations possessed when entering the job market. 5
Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record

A wealth of important clinical information lies untouched in the Electronic Health Record, often in the form of unstructured textual documents.For patients with Epilepsy, such information includes outcome measures like Seizure Frequency and Dates of Last Seizure, key parameters that guide all therapy for these patients.Transformer models have been able to extract such outcome measures from unstructured clinical note text as sentences with human-like accuracy; however, these sentences are not yet usable in a quantitative analysis for large-scale studies.In this study, we developed a pipeline to quantify these outcome measures.We used text summarization models to convert unstructured sentences into specific formats, and then employed rules-based quantifiers to calculate seizure frequencies and dates of last seizure.We demonstrated that our pipeline of models does not excessively propagate errors and we analyzed its mistakes.We anticipate that our methods can be generalized outside of epilepsy to other disorders to drive large-scale clinical research.
Reducing the Effect of Imbalance in Text Classification Using SVD and GloVe with Ensemble and Deep Learning

Due to the recent escalation in the amount of text data available and used online, text classification has become a staple for data analysts when extracting relevant information. Yet, machine learning algorithms are susceptible to biases when implemented on any large-scale automated task, especially in text analysis. With the popularization of newer branches of study emerging from the field of machine learning – such as ensemble and deep learning – we must analyze the potential pitfalls in the common experimental setup centered around learning algorithms. Imbalance in text data is one such pitfall – when data is not equally distributed across all categories in a dataset, it can influence and undermine the classification of underrepresented categories. In our research, we have proposed several techniques and unique approaches to tackle this obstacle. We prepared four datasets of varying degrees of imbalance to conduct our experimentation. We proved that feature extraction techniques singular value decomposition (SVD) and GloVe are the key to reducing the effect of imbalance in text classification, especially in ensemble and deep learning. Using the result of our research, we have also proposed a modified ensemble classifier that can classify imbalanced and balanced data alike.
SCD: Self-Contrastive Decorrelation of Sentence Embeddings

In this paper, we propose Self-Contrastive Decorrelation (SCD), a self-supervised approach.Given an input sentence, it optimizes a joint self-contrastive and decorrelation objective.Learning a representation is facilitated by leveraging the contrast arising from the instantiation of standard dropout at different rates.The proposed method is conceptually simple yet empirically powerful.It achieves comparable results with state-of-the-art methods on multiple benchmarks without using contrastive pairs.This study opens up avenues for efficient self-supervised learning methods that are more robust than current contrastive methods. 1
Multimodal Conversational AI: A Survey of Datasets and Approaches

As humans, we experience the world with all our senses or modalities (sound, sight, touch, smell, and taste).We use these modalities, particularly sight and touch, to convey and interpret specific meanings.Multimodal expressions are central to conversations; a rich set of modalities amplify and often compensate for each other.A multimodal conversational AI system answers questions, fulfills tasks, and emulates human conversations by understanding and expressing itself via multiple modalities.This paper motivates, defines, and mathematically formulates the multimodal conversational research objective.We provide a taxonomy of research required to solve the objective: multimodal representation, fusion, alignment, translation, and co-learning.We survey state-of-the-art datasets and approaches for each research area and highlight their limiting assumptions.Finally, we identify multimodal co-learning as a promising direction for multimodal conversational AI research.
Neural Pipeline for Zero-Shot Data-to-Text Generation

In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise.We examine how to avoid finetuning pretrained language models (PLMs) on D2T generation datasets while still taking advantage of surface realization capabilities of PLMs.Inspired by pipeline approaches, we propose to generate text by transforming single-item descriptions with a sequence of modules trained on generaldomain text-based operations: ordering, aggregation, and paragraph compression.We train PLMs for performing these operations on a synthetic corpus WIKIFLUENT which we build from English Wikipedia.Our experiments on two major triple-to-text datasets-WebNLG and E2E-show that our approach enables D2T generation from RDF triples in zero-shot settings. 1
Efficient yet Competitive Speech Translation: FBK@IWSLT2022

The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translation quality.As such, we first question the need of ASR pre-training, showing that it is not essential to achieve competitive results.Second, we focus on data filtering, showing that a simple method that looks at the ratio between source and target characters yields a quality improvement of 1 BLEU.Third, we compare different methods to reduce the detrimental effect of the audio segmentation mismatch between training data manually segmented at sentence level and inference data that is automatically segmented.Towards the same goal of training cost reduction, we participate in the simultaneous task with the same model trained for offline ST.The effectiveness of our lightweight training strategy is shown by the high score obtained on the MuST-C en-de corpus (26.7 BLEU) and is confirmed in high-resource data conditions by a 1.6 BLEU improvement on the IWSLT2020 test set over last year's winning system.
The USTC-NELSLIP Offline Speech Translation Systems for IWSLT 2022

This paper describes USTC-NELSLIP's submissions to the IWSLT 2022 Offline Speech Translation task, including speech translation of talks from English to German, English to Chinese and English to Japanese.We describe both cascaded architectures and end-toend models which can directly translate source speech into target text.In the cascaded condition, we investigate the effectiveness of different model architectures with robust training and achieve 2.72 BLEU improvements over last year's optimal system on MuST-C English-German test set.In the end-to-end condition, we build models based on Transformer and Conformer architectures, achieving 2.26 BLEU improvements over last year's optimal end-toend system.The end-to-end system has obtained promising results, but it is still lagging behind our cascaded models.
Analysis and prediction of second-hand house price based on random forest

Abstract Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, a Random Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the model variables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict house prices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted by the real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value.
Continual Prompt Tuning for Dialog State Tracking

A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle.However, continually training a model often leads to a well-known catastrophic forgetting issue.In this paper, we present Continual Prompt Tuning, a parameterefficient framework that not only avoids forgetting but also enables knowledge transfer between tasks.To avoid forgetting, we only learn and store a few prompt tokens' embeddings for each task while freezing the backbone pre-trained model.To achieve bi-directional knowledge transfer among tasks, we propose several techniques (continual prompt initialization, query fusion, and memory replay) to transfer knowledge from preceding tasks and a memory-guided technique to transfer knowledge from subsequent tasks.Extensive experiments demonstrate the effectiveness and efficiency of our proposed method on continual learning for dialog state tracking, compared with state-of-the-art baselines.
The Implementation of English for Specific Purposes (ESP) in Improving Students Speaking Skill of UKI Paulus Makassar

Communicative speaking skill is considered the most essential skill for everyday situations and for a wide range of professions especially in the economic and business sector. The purpose of this research is to improve students speaking skill of UKI Paulus Makassar through the implementation English for Specific Purposes (ESP). This study used Classroom Action Research (CAR) namely (1) planning, (2) actions, (3) observation, and (4) reflection.&#x0D; For the purpose of the topic, 35 students were selected as the subject. Research method was Classroom Action Research (CAR) with Kemmis and Taggart model. It was conducted for about two cycles namely cycle I and cycle II. According to the results of speaking test, findings showed that the implementation English for Specific Purposes (ESP) in teaching had positive meaningful effect on improving students’ speaking skill after being obtained test results both cycle 1 and cycle II in Economic and Business students of UKI Paulus Makassar
Learning Attention Augmented Spatial-temporal Normality for Video Anomaly Detection

Video anomaly detection is an essential and challenging task in the computer vision community, which aims to automatically detect and localize abnormal events in videos. In this paper, we propose an attention augmented spatial-temporal normality learning framework to explore the unique spatial and temporal patterns of normal events. Specifically, we first slice the videos into local spatial-temporal cubes along the spatial and temporal dimensions to facilitate independent learning of the prototypical spatial and temporal patterns of normal videos. In the training phase, we use parallel deep convolutional neural networks to learn the spatial features of each cube and introduce an attention module to guide the model to focus on the important local cubes. Then, to exploit the complementary information of adjacent video fragments in the temporal dimension, we use a convolutional long-short memory network to model temporal patterns. In the testing phase, we calculate the prediction errors of the salient areas and compute the anomaly score by measuring the difference between the testing samples and the learned spatial-temporal normality. Experimental results on standard benchmarks show that the proposed method achieves a comparable performance to the state-of-the-art methods with frame-level AUCs of 96.6&#x0025;, 85.2&#x0025;, and 68.8&#x0025; on UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively.
CATrans: Context and Affinity Transformer for Few-Shot Segmentation

Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query images for query segmentation while being robust to the large variations in appearance and context. To this end, previous Transformer-based methods explore global consensus either on context similarity or affinity map between support-query pairs. In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture. Specifically, the Relation-guided Context Transformer (RCT) propagates context information from support to query images conditioned on more informative support features. Based on the observation that a huge feature distinction between support and query pairs brings barriers for context knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures attention-aware affinity as auxiliary information for FSS, in which the self-affinity is responsible for more reliable cross-affinity. We conduct experiments to demonstrate the effectiveness of the proposed model, outperforming the state-of-the-art methods.
A Survey on Neural Open Information Extraction: Current Status and Future Directions

Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. The technique well suits many open-world natural language understanding scenarios, such as automatic knowledge base construction, open-domain question answering, and explicit reasoning. Thanks to the rapid development in deep learning technologies, numerous neural OpenIE architectures have been proposed and achieve considerable performance improvement. In this survey, we provide an extensive overview of the state-of-the-art neural OpenIE models, their key design decisions, strengths and weakness. Then, we discuss limitations of current solutions and the open issues in OpenIE problem itself. Finally we list recent trends that could help expand its scope and applicability, setting up promising directions for future research in OpenIE. To our best knowledge, this paper is the first review on neural OpenIE.
Human Activity Recognition From Sensorised Patient's Data in Healthcare: A Streaming Deep Learning-Based Approach

Physical inactivity is one of the main risk factors for mortality, and its relationship with the main chronic diseases has experienced intensive medical research.A well-known method for assessing people's activity is the use of accelerometers implanted in wearables and mobile phones.However, a series of main critical issues arise in the healthcare context related to the limited amount of available labelled data to build a classification model.Moreover, the discrimination ability of activities is often challenging to capture since the variety of movement patterns in a particular group of patients (e.g.obesity or geriatric patients) is limited over time.Consequently, the proposed work presents a novel approach for Human Activity Recognition (HAR) in healthcare to avoid this problem.This proposal is based on semi-supervised classification with Encoder-Decoder Convolutional Neural Networks (CNNs) using a combination strategy of public labelled and private unlabelled raw sensor data.In this sense, the model will be able to take advantage of the large amount of unlabelled data available by extracting relevant characteristics in these data, which will increase the knowledge in the innermost layers.Hence, the trained model can generalize well when used in real-world use cases.Additionally, real-time patient monitoring is provided by Apache Spark streaming processing with sliding windows.For testing purposes, a real-world case study is conducted with a group of overweight patients in the healthcare system of Andalusia (Spain), classifying close to 30 TBs of accelerometer sensor-based data.The proposed HAR streaming deep-learning approach properly classifies movement patterns in real-time conditions, crucial for long-term daily patient monitoring.
Methodical preparation as a means of developing prospective mathematics teachers' ICT competency

The purpose of the study is modeling of ICT competence formation of would-be mathematics teachers through the best practice application of effective ICT tools. The objectives of the study are to analyze the possibilities of creating conditions for the formation of competent mathematics teachers using ICT tools. The object of the study is the methodical preparation of students of pedagogical HEIs by means of ICT. The subject of study is the peculiarities of the formation of a competent mathematics teacher in classes on methodological disciplines. The authors of the article analyze, summarize and systematize research on the issue of using ICT tools in the training students’ activities in Universities of Teacher Education. The study determines the role of methodological training in the development of ICT competence of would-be mathematics teachers. The authors of the study developed a model for building competence would-be mathematics teachers, which consists of four components. There is the target, preparatory, procedural, and resulting constituents. The model describes a systematic approach to the organization of students’ training activities in the in teaching the disciplines of professional and practical training. Giving an account of the integration nature of the methodological students’ training, it also considers the organization of distance communication between students and teachers. This paper presents examples of the implementation of the components of the model. The examples describe the implementation by students of course projects and degree projects on teaching mathematics methodologies using ICT tools. A pedagogical experiment was conducted to evaluate the effectiveness of ICT tools in the training of would-be mathematics teachers. The results of the study confirmed the effectiveness of modeling the organization of students’ activities to develop their ICT competency. As the follow-up research directions, we consider summarizing recommendations on the usage of ICT in the preparation of competent socionomic specialists.
Combination of Blockchain and AI for Music Intellectual Property Protection

In the last two years, due to the pandemic and restrictive measures, the dependence of music creators and artists on the Internet, where they could promote their work, organize live streaming concerts, and talk to the public, has increased and expanded even more and seeks higher revenue from digital music platforms. An important issue that arises from the above statement is protecting the authors’ copyright regarding the uses and sharing in the digital services of their works with protected content. Although circulated in digital information, the protected content is not information but a product of ethical and commercial value. While it has an intangible owner and it owes its existence to the creative idea of its creator, it is not an idea. The imposition of legal and commercial conditions on its movement cannot be associated with any restrictions on the free movement of information, as it is not related to them. In general, the unauthorized exchange of digital music files via peer-to-peer violates copyright law. The exchange of files is unauthorized, as it does not have the relevant permission from the creators and beneficiaries and is therefore illegal. With this in mind, this paper proposes a highly effective way of protecting the copyright of music technology, which is based on the widespread use of artificial intelligence, blockchain, and cryptography technologies. Specifically, an advanced blockchain model based on Hyperledger Fabric is introduced, which, however, uses Quantum Homomorphic Encryption and Quantum Zero-Knowledge Arguments. Music files are implemented as Nonfungible Tokens (NFTs), which activate smart contracts. Finally, an advanced collaborative filtering algorithm provides recommendations for effectiveness in securing the copyrights of music industry creators. A specialized scenario was built to model the proposed system to verify the degree of protection on music intellectual property in developing a security simulation with an innovative consensus-based zero knowledge and the quantum fully homomorphic encryption technique. Experiment results show that this technique can aid in implementing a technologically aware system capable of providing a powerful answer to a current real-world problem.
Digital Monitoring of Students’ Soft Skills Development as an Interactive Method of Foreign Language Learning

Nowadays, the curriculum of higher education is mainly based on hard skills development, although soft skills are of critical importance for successful academic and professional performance. Consequently, the purpose of the article is to describe the possible methods of mastering the second-year students’ soft skills within the scope of a foreign language course, using interactive methods. The aim of the research was to elaborate on three types of interactive problem-based tasks and assessment criteria which allow us to monitor the progress of soft skills such as problem solving, teamwork, leadership, time management and technology skills, and analytical and creative thinking. Both tasks and criteria were uploaded onto an online educational platform. The case study method, which was considered to be the main monitoring tool, turned out to be effective in the pedagogical experiment where the students collaborated both online and offline. The experiment demonstrated a positive trend in students’ soft skills improvement alongside their foreign language communication skills. Moreover, statistical analyses showed the effectiveness of the system created to master soft skills. The proposed system for digital monitoring of soft skills development can be useful for teaching personnel who want to make the educational process more interactive and productive for advancing students’ soft skills.