Reviewerly/exHarmony huggingface dataset:
Survey: Clustering Techniques of Data Stream

Data stream mining is an emerging specialty in the field of mining huge data that extract useful knowledge of the whole of the data stream. There are several mining processes to handle the data stream. One of the most important and widely used is the clustering data stream. The clustering is either hard (exclusive) or fuzzy (soft) clustering. Recently, significant sources are made available to generate data stream, therefore, the clustering of these data is an important and vital topic for many researchers. Several data stream algorithms have been proposed by researchers during the past years, while some have developed other algorithms.The data stream clustering varies from the traditional clustering in many principles where the main differences between them are explained. Meanwhile, data stream clustering has new challenges such as the single pass on the raw data sets, the unbounded size of this data and the high speed arriving of data samples. But the most prominent one is the dynamic nature of the data.This paper presents a comprehensive study on the hard data stream clustering methods and their algorithms. In addition to the advantages and disadvantages of these methods. Where the paper deals with many aspects that surrounds the data stream, such as stream conditions, challenges, dynamics that it needs, and it changes over time. Then, it presented a transition to the modern trends of clustering algorithms and their utility in online applications. The survey aims to be an auxiliary reference for the researcher in determining the clustering algorithm that compatible with the available data set to achieve the desired goal.
An Analysis on Data Mining for Sentiment Analysis Using Different Classification Algorithms

A rapid rise in the field of data mining has occurred in recent years. Analysis of Sentiment is an idea that includes numerous happenings including sentiment extraction, classification of sentiment, classification of subjectivity, a summary of the opinion, or spam detection amongst others. opinion mining (OM) or Sentiment analysis (SA) is a mathematical study of views, emotions, sentiments, assessments, and behaviors of people, including products, organizations, services, issues, individuals, topics, events also their attributes. The advancement in web technology tools has given rights to their users to share their opinions on things that they interact with within their daily life. They can post it online on different platforms available. This massive amount of data is used to find out major curiousness that concerns people most and is very beneficial to many firms. To get benefited from this unstructured, inconsistent data written in natural language, first, it needs to be clean and transform and then we will be able to perform different operations like classification and aspect-based sentiment analysis. In this paper, we have thoroughly discussed 4 data mining algorithms utilized for prediction & classification.
AI-Fairness Towards Activity Recognition of Older Adults

Wireless wearable sensor networks (WSN)-based activity recognition has implicit impacts on context-aware application and connected health research for older adults. Although, many existing researches focus on different WSN integration, signal processing and intelligent detection to recognize different activities, none of the existing works address the Artificial Intelligence (AI) fairness in activity recognition of older adults domain. We argue that AI-fairness towards detecting activities of older adults is different than to fairness for other protected attributes such as age, gender or race. The primary reason behind this difference is the diversity of same activity among different older adults based on their functional abilities (walking using crutches, walker). However, the diversity also exists among the same older adults based on time and space as well. The above constraints limit the AI capabilities and causes unfair detection of daily activities that has potential impacts on healthcare interventions for older adults. In this paper, we investigate, first of its kind, AI-fairness of activity recognition for older adults using a single wearable WSN sensor in presence of diverse disabilities. In this regard, we (i) employ signal processing and Bi-directional LSTM model to recognize diverse multi-label activities of older adults using single WSN; (ii) identify the existence of biases in activity recognition considering the age and functional ability as protected attributes; (iii) mitigate the biases by applying different bias mitigation techniques in different stages (pre-, in- and post-processing) of machine learning model development; and finally (iv) we experimentally evaluate the proposed AI-fairness framework using older adults data collected from a retirement center.
Student Perceptions and Use of Mobile Devices for LIS Coursework: Implications for Educators

This study offers insights regarding the use of mobile devices by students to help educators in Library and Information Science (LIS) and across disciplines better understand their current and potential use. Using a web survey and descriptive content analysis methods, this article reports student perceptions of the role mobile devices play in LIS coursework, identifying areas where the technology is effective, providing recommendations regarding creative enhancements to course delivery, and offering insights for instructors into mobile-friendly course design and pedagogy. The data in this study indicate that current LIS students take their courses “on the go,” moving easily between a formal learning setting to an informal one, and provide instructors with considerations for the multiple ways in which students may access course content and engage with others in their classes using mobile devices.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and return natural language answers. The aim of the competition was to build systems that can predict correct answers while also satisfying strict on-disk memory budgets. These memory budgets were designed to encourage contestants to explore the trade-off between storing large, redundant, retrieval corpora or the parameters of large learned models. In this report, we describe the motivation and organization of the competition, review the best submissions, and analyze system predictions to inform a discussion of evaluation for open-domain QA.
Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development

This study applies relative entropy in naturalistic large-scale corpus to calculate the difference among L2 (second language) learners at different levels. We chose lemma, token, POS-trigram, conjunction to represent lexicon and grammar to detect the patterns of language proficiency development among different L2 groups using relative entropy. The results show that information distribution discrimination regarding lexical and grammatical differences continues to increase from L2 learners at a lower level to those at a higher level. This result is consistent with the assumption that in the course of second language acquisition, L2 learners develop towards a more complex and diverse use of language. Meanwhile, this study uses the statistics method of time series to process the data on L2 differences yielded by traditional frequency-based methods processing the same L2 corpus to compare with the results of relative entropy. However, the results from the traditional methods rarely show regularity. As compared to the algorithms in traditional approaches, relative entropy performs much better in detecting L2 proficiency development. In this sense, we have developed an effective and practical algorithm for stably detecting and predicting the developments in L2 learners’ language proficiency.
Research on Viewpoint Extraction in Microblog

In order to quickly get the viewpoint of key opinion leaders(KOL) on public events, a method of opinion mining in Weibo is put forward. Firstly, according to the characteristics of Weibo language, the non-viewpoint sentence recognition rule is formulated, and some non-viewpoint sentence is eliminated accordingly. Secondly, based on the constructed FastText-XGBoost viewpoint sentence recognition model, the second classification is carried out to identify the opinion sentence according to the dominant and recessive features of Weibo. Finally, the group of evaluation object and evaluation word is extracted from the opinion sentence, according to our proposed multi-task learning BiLSTM-CRFs model. In design, the “BIO” tagging mode is adopted. The sequence tagging of evaluation object and evaluation word based on LSTM-CRFs is conducted as the main task, and the loss function of the main task is optimized by the part of speech tagging based on BiLSTM-CRFs. The experiment result shows that the view recognition model based on FastText-XGBoost has obvious advantages over other recognition models in classification efficiency and accuracy, and the results of the MTL-BiLSTM-CRFs mining is more accurate and the model is more applicable.
Energy-Efficient Digital Beamforming by Means of Linear Computation Coding

We utilize the new concept of linear computation coding to reduce the computational effort in digital beamforming. For that purpose the beamforming matrix is decomposed into the product of two or more matrices which are only composed of zeros and signed powers of two. As shown in [1], this allows for a multifold reduction of the required number of additions (multiplications are treated as multiple additions and bit-shifts) per entry of the beamforming matrix.We also provide an approximate analysis of the performance of linear computation coding for matrices with independent identically distributed matrices.For a typical implementation in beamforming, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 4-bit signed integer arithmetic for 1k-vectors, no multipliers and only half an adder per matrix entry is needed.
Conceptual Challenges for Interpretable Machine Learning

As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A sub-discipline of computer science known as interpretable machine learning (iML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges that are largely overlooked by authors in this area. I argue that the vast majority of iML algorithms are plagued by: (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from iML are considered, demonstrating how failure to acknowledge these problems can result in counter-intuitive and potentially misleading explanations. Without greater care for the conceptual foundations of iML, future work in this area is doomed to repeat the same mistakes.
Some Dynamical Properties of Higher-Order Fuzzy Cognitive Maps

Fuzzy cognitive maps are recurrent neural networks, applied for modelling and simulation of complex dynamic systems. They have been successfully applied to many engineering problems. The conclusion about the system depends on the mathematical behaviour of an iteration, namely, a first-order recursion. The first-order dynamics have some limitations since it takes into account only the previous time step. To overcome these limitations higher-order memory-based fuzzy cognitive maps have been introduced, which use a sequence of preceding states to determine the next one. In this paper, some dynamical properties of higher-order fuzzy cognitive maps are analyzed. Particularly, the existence and uniqueness of equilibrium points and the stability are discussed.
Visualize your data with simplicity, accuracy and beauty

Creating a compelling visual representation of your data can be challenging, but it is worth it. Infographics and graphical abstracts are common forms of data visualizations within the world of science, but no matter what you call them, these visuals are short stories meant to be shared widely. When done well, a visualization of your data can attract new readers and quickly tell them the main point of your research. Your visual can get more people to read your paper and engage with you about your research. In this article, we’ll talk through the steps of making a simple, accurate and beautiful visual that is sure to catch the eyes of readers everywhere.
A Bilateral Recommendation Strategy for Mobile Event-Based Social Networks

Mobile Event-Based Social Network (EBSN) platforms, such as Meetup and Plancast, have become increasingly popular for online organization of offline (in-person) events. The problem of the existing techniques in ESBN is that they do not reflect the bilateral (two-way) nature of efficient event planning: 1) events enroll more influential participants, and 2) participants are arranged to events they are most interested in. In this paper, we address this weakness by formally defining the bilateral recommendation problem and making two contributions to solving this problem: (a) by analyzing all types of the user's behaviors during the selection session, we can accurately predict which event the users will eventually choose to participate in, and (b) by introducing the concepts of interpersonal similarity and interaction strength in EBSNs, we can calculate the interactive influence of users. We report the results of extensive experiments on real datasets that confirm the improved precision, effectiveness and scalability of our proposed bilateral recommendation strategy as compared to the state of the art.
Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects

Recent developments related to generative models have enabled the generation of diverse and high-fidelity images. In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic and complex images containing distinct objects. These models are generally conditioned on either semantic layouts or textual descriptions. However, unlike natural images, providing auxiliary information can be extremely hard in domains such as biomedical imaging and remote sensing. In this work, we propose a multi-object generation framework <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that can synthesize images with multiple objects without explicitly requiring their contextual information during the generation process. Based on a vector-quantized variational autoencoder (VQ-VAE) backbone, our model learns to preserve spatial coherency within an image as well as semantic coherency through the use of powerful autoregressive priors. An advantage of our approach is that the generated samples are accompanied by object-level annotations. The efficacy of our approach is demonstrated through application on medical imaging datasets, where we show that augmenting the training set with the samples generated by our approach improves the performance of existing models.
Comparison of Hardware and Software Implementations of AES on Shared-Memory Architectures

Nowadays, AES is one of the most widely used encryption algorithms. There are several implementations of AES, both in hardware and software. With the ever-increasing amount of sensitive data that need to be protected, it is natural to turn to parallel AES solutions that exploit the full computational power provided by emerging architectures in order to reduce encryption time. In this paper, we compare the performance of a hardware-based AES solution for multicore CPU (PAES-HW-CPU) with that of two other software-based AES solutions for multicore CPU (PAES-SW-CPU) and GPU (PAES-SW-GPU) respectively. The former is implemented using Intel AES New Instructions and the latter using the OpenSSL library. The results reveal that PAES-HW-CPU achieves higher performance. However, PAES-SW-GPU on 2 GPUs is competitive compared to CPUs with hardware support for AES and few cores and CPUs that do not support AES.
Higher Grassmann codes

We compute the parameters of the linear codes that are associated with all projective embeddings of Grassmann varieties.
The Quality Triage Method: Quickly Identifying User Stories with Quality Risks

Quality requirements often receive insufficient attention, both in agile and in traditional software development. This paper describes the quality triage, a quick, agile method in which user stories or features with quality risks are identified. This paper shows how the four qualities scalability, security, safety, and availability are scored at short expert meetings - triages. In this way, quality risks are made explicit and can be immediately addressed. We illustrate the method with a scenario involving semi-autonomous cars.
Putting virtual theatre models to work: ‘virtual praxis’ for performance research in theatre history

So much time, research and effort are expended in accurately developing virtual models of theatres, but what happens when they are completed? It is not automatically clear how one uses a virtual model for research to generate new knowledge about performance. Our research has recreated a series of theatres, which no longer exist, in virtual form in order to visualise elements of performance and the social relations that shape and are shaped by a theatre venue. These international theatres include the Rose in London from the late 1500s, the Bergen Theatre from 1850, the Queen’s Theatre in Adelaide from 1841, an itinerant bamboo theatre for performing Cantonese opera along the Pearl River Delta in China and a tent version used to house the same form in the goldfields of Victoria, and the showroom at the Stardust Hotel in Las Vegas built in the late 1950s. This article has two aspects: the first outlines our work with actors in workshops to explore how the spatial dynamics of the models can inform knowledge of acting traditions and audience response appropriate to each venue. The second explores some of the challenges of installing era-specific lighting and sound technology in several of the venue models.
Unsharp Masking with Local Adaptive Contrast Enhancement of Medical Images

In this paper we present a generalized algorithm for unsharp masking of medical images which takes as one of its inputs a high contrast image underwent local adaptive contrast enhancement. Selection of optimal values of the number of histogram bins, processing window size and intensity lower and upper limits in iterative manner is part of applying Contrast Limited Adaptive Histogram Equalization (CLAHE). Experimental results reveal higher quality of the output images both in terms of root mean square contrast and sharpness. Achieved quality, both visually and quantitatively, is compared to that from the Adaptive Histogram Equalization (AHE) algorithm, limited histogram stretching and ordinary histogram equalization which proves its applicability. The algorithm is considered appropriate for processing a number of types of images, such as CT, X-ray, etc.
A Study on the User Contact of Audiobooks in the Age of Digital Media Based on Computer Technology——A Case Study of the Popular Chinese Application “Himalaya”

Abstract With the development of computer technology, digital media technology has also developed rapidly, the audiobook industry is going from strength to strength in China. Among these audiobook platforms, Himalayan FM undoubtedly stands out, through which people can acquire supplementary knowledge by making the best of their fragmented time. In the meantime, paying for knowledge as a consumption habit is gradually developed in China. The purpose of this research is to find out the motivation behind and the reflection of people’s learning experience with audiobook. Taking Himalayan FM as case study, this study combines communication with psychology theories by online interview as “Cyber-ethnography”.
Development of Interactive Learning Videos to Increase Learning Motivation in Graphic Design Lessons in Vocational Schools

This study aims to find out the increase in learning motivation through the use of Interactive Learning Videos Graphic Design students of class X TKJ in SMK. The research method used is R &amp; D research using the ADDIE development model. The sample is class X TKJ in SMK Muhammadiyah 1 Taman. Developed media declared 79% feasible with high criteria as a medium can be used for research. Based on the results of the use of interactive learning video media makes a difference in student motivation. Before using interactive learning videos, the results of the students with 56-70 grade range (medium) are being categorized C. Using interactive learning videos; students get the results to a range of grades 71-85 with good qualifications and categorized B. They also get the results of the range of grades 86-100 with very good qualifications and categorized A. In conclusion; the interactive learning video graphic design is feasible to be used as a learning medium for students of class X TKJ in SMK Muhammadiyah 1 Taman. With the use of interactive learning videos there is an increase in motivation class X TKJ to learn on graphic design subjects.
Discussion on Ways for Teacher Development Center to Help Teachers Improve Their Teaching Ability under the Background of Informatization

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
The last RTT matters and we can preact

In this poster, we find that the congestion control algorithms which try to acquire close-to-zero queue would encounter bandwidth waste because there would be a RTT gap for other flows to take over after a flow finishes. By distinguishing the packets of the last round in a flow's lifespan, we can improve the bandwidth utilization of datacenter network. We modify the INT process in HPCC and the FCTs of long flows can be reduced by about 20%-30% from our ns3 simulation.
WhatsApp, an Educational Computer System?

Can WhatsApp be used as an educational computer system? This question had not been answered conclusively by current research and was a global imperative for the computers and education research and practice communities given that over a quarter of the entire world's population used WhatsApp. To advance the field, educational theory and practice and to give meaning to WhatsApp in education, empirical quantitative evidence was gathered with a questionnaire to measure mobile collaborative learning on WhatsApp. The results indicated that increased collaboration on WhatsApp improved academic achievement and improving other key aspects such as active learning, trust, support, formality, interaction and interdependence enhanced collaboration and, in turn, improved academic achievement. The study advanced educational computer theory and mobile collaborative learning theory and provided evidence-based learning design guidelines for incorporating WhatsApp into learning programs for improved academic achievement.