Reviewerly/exHarmony huggingface dataset:
Design and Application of Virtual Simulation Teaching Function of Chime Performance and Creation Based on VR

In order to solve the difficulty in teaching chime bells, a virtual simulation was developed according to the requirements of course construction. This research reconstructed a set of virtual chimes and performances that were completely consistent with the shape, scale, and proportion of the chimes. Students conducted experiments such as tone recognition, solo, ensemble, solo, and ensemble creation through the way that “the left-hand keyboard controls the performance strength, and the right-hand mouse controls the performance speed and pitch”. The teaching effect showed that the experiment broadened the depth of traditional music teaching, improved students' learning enthusiasm and initiative, extended students' learning time and learning space, and cultivated students' collaborative interaction ability and cooperative innovation spirit.
Intelligent Evaluation Model of English Translation Content Quality Based on Improved Neural Network Algorithm

English translation content estimation is a key work in natural language processing. Unlike the conventional automatic evaluation method of English translation content, the translation quality estimation method does not use manual reference translation to evaluate the ability of English translation. However, according to the content quality estimation of the current sentences in English translation, the feature information extraction method lacks the generalization analysis of linguistic research, which also affects the use of subsequent vector regression methods. Therefore, the feature information of the vocabulary vector is studied to obtain the context vocabulary prediction model and matrix analysis model of deep learning. They are combined with the recursive neural network language modeling to enhance the reliability of the independent estimation and manual evaluation of translation quality. The experimental results using the data set of the sub-task of translation content quality estimation in WMT 15 and WMT 16 show that the method of obtaining the feature of sentence vector through context lexical analysis is consistently more effective than the original QuEst method and the feature acquisition method of sentence vector graph in continuous space language mode. It is also clarified that the newly established feature extraction method does not require linguistic means but significantly enhances the effectiveness of translation quality evaluation.
ActiviSee: Activity Transition Detection for Human Users through Wearable Sensor-augmented Glasses

Falls are the biggest threat among all events to elderly people and patients and pose a serious risk to their well-being, confidence, and mortality. Visual impairments which distort depth perceptions are often identified as major reasons for falls. Single and/or multi-focal length glasses used to correct myopia (blurriness of distance vision) and presbyopia (blurriness of near vision) cause distorted depth perceptions if not used properly. Users, especially older adults, need to change between glasses while transitioning between sedentary to mobile activities and vice versa. Forgetting to change into proper glasses may lead to fall. In this paper, we plan to develop a novel system called Activisee which uses sensor-augmented glasses to automatically detect activity transitions and alert users to change into appropriate glasses whenever necessary. Although, wireless sensor systems have long been used for fall detection and prevention through human activity recognition, using sensor-augmented glasses for activity transition detection has never been studied. Results using data collected from 23 human users performing a set of five distinct activities show that Activisee can successfully detect transitions from sedentary to mobile activities with up to 92% accuracy.
Classification by Estimating the Cumulative Distribution Function for Small Data

In this paper, we study the classification problem by estimating the conditional probability function of the given data. Different from the traditional expected risk estimation theory on empirical data, we calculate the probability via Fredholm equation, this leads to estimate the distribution of the data. Based on the Fredholm equation, a new expected risk estimation theory by estimating the cumulative distribution function is presented. The main characteristic of the new expected risk estimation is to measure the risk on the distribution of the input space. The corresponding empirical risk estimation is also presented, and an ε-insensitive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> cumulative support vector machines (ε- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> VSVM) is proposed by introducing an insensitive loss. It is worth mentioning that the classification model and the classification evaluation indicator based on the new mechanism are different from the traditional one. Experimental results show the effectiveness of the proposed ε- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> VSVM and the corresponding cumulative distribution function indicator on validity and interpretability of small data classification.
Proof of Earnestness- Subjective information’s Trustworthiness in Blockchains as a Service

Blockchain is being used in the supply chain from a local to a global scale with its ever-increasing maturity and ubiquity. However, in the presence of information flow from a subjective source with no digital footprint, such as a claim, or a promise, there is a gap in the application of the Blockchain framework in supply chain risk management perspective. The existing mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) or Proof of Stake (PoS) do not verify and validate subjective information presented in Service level agreement (SLA). We address this open issue in this paper by proposing the Proof by Earnestness (PoE) consensus mechanism that aims to validate the authenticity and legitimacy of such information. Subjective Information Authenticity Earnestness Framework (SIAEF) seeks to be a decentralised application to enable the trustworthiness of subjective information to become accountable.
Feature Relevance Analysis to Explain Concept Drift - A Case Study in Human Activity Recognition

This article studies how to detect and explain concept drift. Human activity recognition is used as a case study together with a online batch learning situation where the quality of the labels used in the model updating process starts to decrease. Drift detection is based on identifying a set of features having the largest relevance difference between the drifting model and a model that is known to be accurate and monitoring how the relevance of these features changes over time. As a main result of this article, it is shown that feature relevance analysis cannot only be used to detect the concept drift but also to explain the reason for the drift when a limited number of typical reasons for the concept drift are predefined. To explain the reason for the concept drift, it is studied how these predefined reasons effect to feature relevance. In fact, it is shown that each of these has an unique effect to features relevance and these can be used to explain the reason for concept drift.
A Method for Identifying a Person Entering a Bathtub using a Water Pressure Sensor

It has been reported that the number of fatal accidents in bathtubs is particularly high in residential environments, so it is important to obtain information on body posture and other factors associated with individuals while bathing in bathtubs. In this study, we proposed a method to identify a person entering water by obtaining the change of water pressure from a water pressure sensor installed at the bottom of the bathtub in two situations; one is a situation in which a person is entering bathtub while pouring water into the bathtub, the other is a situation in which the bathtub overflows due to entering water. From evaluation experiments using entering water data collected from four subjects in pouring, overflow, and normal situations, an average F-value of 0.552 was obtained for the identification results in the overflow situation, and an average F-value of 0.792 was obtained for the identification results in the pouring situation.
The Nudging Effect on Tracking Activity

Wearables activity trackers are becoming widely adopted to understand individual behavior. Understanding behavior may help in self-regulation such as self-monitoring, goal-setting, self-corrective, etc.; Nevertheless, challenges exist in attaining consistent use and adoption of wearables, which hinders behavior understanding. Research has suggested that nudging strategies may change and sustain human engagement. However, it is still unknown how nudging may affect human wearing behavior on an individual level. We conducted a six-month study in which we tested several nudging techniques on the same participants. The preliminary results of our research show that participants perform better when a nudging strategy is applied. In addition, participants responded differently to different nudging techniques. Future research can focus on developing an individual-based nudging mechanism to encourage users to wear their devices consistently.
A Multi-Dimensional Semantic Pseudo-Relevance Feedback Information Retrieval Model

Recently neural information retrieval systems have spurred many successful applications. Retrieval model to obtain a candidate document collection in the first retrieval stage, then use BERT to sort the candidate documents. Generally, the sentence score or paragraph score obtained using BERT is integrated into the document score to get the final ranking result. Semantic similarity is less often used to select query extensions and integrate semantic information into pseudo-relevance feedback. We propose a new strategy in this paper, selecting query extensions with semantic information using the BERT model. Incorporating semantic information weights into traditional pseudo-relevance feedback can better alleviate problems such as word polysemy and multi-word synonymy. Improve the performance of the retrieval system and return more accurate documents. The experimental results demonstrate that the query extensions selected by incorporating semantic information can help return more accurate results and improve the accuracy of the retrieval system, and the results of MAP and P@10 can prove the validity and feasibility of our proposed model.
Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%.
10th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition.
Design of Augmented Reality based Environment to Promote Spatial Imagination for Mathematics Education in Elementary School

This paper investigates the use of augmented reality (AR) technology to deliver augmented lectures to support students in acquiring the curricular competency of using spatial imagination in mathematics education. As a very important stage in education to develop spatial abilities this paper focuses on elementary school children. Due to the challenges of working with children in experimental studies, this education level has received comparatively little attention in terms of support through ubiquitous technology. A theory-driven design approach was adopted in the development of an AR-based learning environment to visualize various virtual 3D cube buildings aligned with real blueprints. The designed environment was evaluated by a group of 1st and 2nd grade students in terms of system usability. The results showed that the theory-driven design was successful with a score of 86.56 on the System Usability test. In our future work, we aimed to assess the effectiveness of the proposed AR environment in terms of learning gains by performing more task-focused studies before and after experiment and comparing the results with the traditional teaching methods.
Discovering Causal Rules in Knowledge Graphs using Graph Embeddings

Discovering causal relationships is the goal of many experiments in science. To discover these relationships in observational data, the potential outcome framework is widely used. Within this framework, a recent approach uses Knowledge Graphs (KGs) to discover causal rules embedded within observational data. Such rules were found to express the following relationship: that differences in treatments lead to differences in a studied characteristic. However, this framework relies on matching similar instances which is challenging in knowledge graphs since instance descriptions can be complex, incomplete, or erroneous. To discover differential causal rules in KGs, this paper presents a new approach that uses a matching method based on graph embeddings. Our experiments on KGs from two different domains show that our approach is robust to incomplete KGs. Not only that, but compared to the state of the art, it is able to discover for a studied characteristic meaningful rules that explain many more differences than previously found.
Machine Learning for Load Forecasting in a Green Data Center

Since green cloud computing became a significant advancement in the world of computing in the past few years, its user base has been consistently expanding. Companies are adding more hosts and servers to their data centers to keep up with the ever-growing demand for cloud services. So, these huge servers use a lot of energy, raise costs, and put out a significant amount of greenhouse gasses. Also, even devices that are not being used use a lot of energy. Also, when CPU use reaches its peak, it is hard to keep node clusters running and use less energy. In this paper, we propose machine learning models to determine the minimum execution time. These models reduce the work done by machines and make sure that requests are managed correctly in data centers. Therefore, the purpose of this study is to present an effective strategy for the management of energy with a focus on the sustainability of green cloud data centers.
Excerpt of PPGface: Like What You Are Watching? Earphones Can “Feel” Your Facial Expressions

Facial expression recognition has been widely explored to demonstrate people’s emotional states. However, existing systems primarily rely on external devices which seems less accessible and efficient. To this end, we propose PPGface, a ubiquitous facial expression recognition platform that leverages earable devices with built-in PPG sensor. PPGface understands the facial expressions through the dynamic PPG patterns resulting from facial muscle movements. Through several comprehensive studies, this work validates a great potential to be employed in future commodity earable devices.
Research on Question Answering over Knowledge Graph of Chronic Diseases

The knowledge graph is a kind of semantic knowledge base, which can efficiently manage massive knowledge. Question answering over knowledge graph is one of the promising approaches to obtaining large information from the databases, which is applied to reply to natural language questions using structured relationship information between entities stored in knowledge graphs. Chronic disease care can be lifelong, scientific protection can have a positive effect on the recovery of patients, and lessen the incidence of complications. Therefore, this paper uses the knowledge graph to manage medical information about chronic diseases and provides users with consulting services on health issues to assist in diagnosing and treating diseases. In the paper, data is extracted from the healthcare website and stored in the graph structure database, Neo4j, after a series of data processing. We get upper and lower relationships from the data and use the extraction method, which is proved to be effective, for the domain knowledge graph construction. The platform based on high-quality knowledge, which is provided by the knowledge graph, can effectively identify user intentions and give accurate results. The research is aimed at chronic diseases and can supply references for identification, treatment, and patient self-care.
Web Mining from Interpretable Compressed Representation of Sparse Web

Large datasets often contain computational constraints when under the non-trivial extraction of implicit, previously unknown, and potentially useful information. These datasets are everywhere, with a popular example being the World Wide Web. It acts as a mass data producer and consumer across multiple devices in a distributed fashion worldwide, containing massive amounts of data. The discovery of knowledge on the Web requires web intelligence solutions, which take advantages of data mining and data science. In the case of web mining, the mining of web structures provides commonly recommended web pages to web surfers by examining incoming and outgoing links on web pages. The overall size of the web is however sparse. Sparsity of the web comes from a high number of vertex nodes (i.e., web pages), with a small number of directed edges (i.e., incoming and outgoing hyperlinks between web pages). In this paper, we present a solution to the mining of frequent patterns from the sparse web. From the sparsity of the web, web pages are captured in compressed bitmaps that are then mined for discovery of these patterns. Our bitmap model ensures readability, flexibility, and allows for the capturing of important information across multiple 31-bit groups. The mining process is demonstrated on real-life web data to present its capacity of mining for interesting patterns from interpretable compressed representation of sparse data.
DFS-NER: Description Enhanced Few-shot NER via Prompt Learning and Meta-Learning

Named Entity Recognition (NER) is a very common task in many social good related domains. Recently, deep learning based NER has gradually matured, but still faces the scarcity problem of labeled data in specific domains. Therefore, researchers focus on few-shot NER to reduce the model’s data dependence and enhance the transferability of the model. However, existing works usually cannot adapt to new entity types and are prone to the so-called negative transfer problem. Therefore, in this paper we propose a type- Description-enhanced Few Shot NER model, called DFS-NER, which effectively integrates the prompt learning paradigm and the meta-learning framework. DFS-NER performs well under frozen pre-training model parameters through continuous templates. It realizes efficient source domain training and target domain parameter fine-tuning through the metalearning framework. We enhance the robustness of entity- type prototype representations by introducing word-word- level and word-type-level contrastive learning objectives and capsule networks as the induction module. Simultaneously, based on discrete prompt learning, a masked-language model learning objective guided by type description is proposed, which can well absorb the semantic information of entity types. Experiments on commonly used datasets, including, SNIPS, Few-NERD, and MIT Movie show that DFS-NER basically surpasses baseline models and achieves the state-of-the-art performance.
Using Multi-task Deep Neural Network to Explore Person Interaction from Social Media

This work sought to identify the interactions between persons mentioned in social media to help readers construct background knowledge of a certain topic. We propose using a rich interactive tree structure to represent syntactic, contextual, and semantic information, and adopt a tree-based convolution kernel to identify segments that carry clues about personal interactions, which are then used to construct person-interaction networks. Empirical evaluations demonstrate that the proposed method is effective in detecting and extracting the interactions between persons in textual data, outperforming other existing extraction approaches. Furthermore, readers will be able to easily navigate through the network of the interactions between persons of interest that is constructed by the proposed method, and efficiently obtain insights from a massive corpus.
Job Recommendation Based on Multiple Behaviors and Explicit Preferences

A lot of job openings have been released online, which makes job recommendation more and more important. Recently, users often enter their preferences into job search websites to receive some job recommendations that they hope to apply for. To achieve this goal, the following two types of data are available: (1) auxiliary behavior data such as viewing job postings, bookmarking them and (2) explicit preference data such as conditions for a job that each user desires. Some researchers propose job recommendation by addressing either of them. However, they have not focused on simultaneously addressing both (1) and (2) so far. Given this point, we propose a method for job recommendation that employs auxiliary behavior data and each user’s explicit preference data simultaneously. Additionally, our proposed method addresses multiple behavior overlaps and refines the latent representations. Experimental results on our dataset constructed from an actual job search website show that our proposed model outperforms several state-of-the-arts as measured by MRR and nDCG. Our source code has been released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .