Reviewerly/exHarmony huggingface dataset:
A Novel Transferable Sparse Regression Method for Cross-Database Facial Expression Recognition

In this letter, we propose a novel transferable sparse regression (TSR) method, for cross-database facial expression recognition (FER). In TSR, we firstly present a novel regression function to regress the data into a latent representation space instead of a strict binary label space. To further alleviate the influence of outliers and overfitting, we impose a row sparsity constraint on the regression term. And a pairwise relation term is introduced to guide the feature transfer learning. Secondly, we design a global graph to transfer knowledge, which can well preserve the cross-database manifold structure. Moreover, we introduce a low-rank constraint on the graph regularization term to uncover additional structural information. Finally, several experiments are conducted on three popular facial expression databases, and the results validate that the proposed TSR method is superior to other non-deep and deep transfer learning methods.
Relation between Contact Area and Distribution of an End Effector for Pleasant Feeling using a Touch Care Robot

Affective touch enables to relieve stress and pain. If robots can perform affective touch, they would support the patients who cannot receive enough care from therapists. In developing robots for touch care, it is important to develop an end-effector that can evoke pleasant feelings as much as or more than a human hand. In this study, we hypothesize that the feeling is changed by the variety of the contact area between an end-effector and the human body. Then we develop a simple and reconfigurable end-effector consisting of a plate and flexible or rigid joint parts. Its simpleness contributes to its productivity and we prepared and compared three end-effectors having different contact areas using its reconfigurableness. In the experiments, we evaluated 1) the contact area of the three end-effectors quantitatively, and 2) the difference in a person’s valence and arousal when touched by the end-effectors subjectively. The results suggest that 1) the end-effectors with flexible joints have a larger contact area than the end-effector with rigid joints, and 2) the end-effector with a larger contact area give lower arousal values and higher valence than the end-effector without enough contact area.
A Speech recognition of isolated words in the “Baoulé” language via back-propagation neural networks (BNN)

The main objective of this research is to explore how a back propagation neural network (BNN) can be applied to the speech recognition of isolated words. The simulation results show that a BNN offers an efficient approach for small vocabulary systems. The recognition rate reaches 100% for a 5 word system and 94% for a 10 word system. The general techniques developed in this article can be extended to other applications, such as the recognition of sonar targets and the classification of underwater acoustic signals.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind / Double-blind / Triple-blind / Open / Other In our conference, we do a single-blind peer review where the author does not know who the reviewers are. Submitting papers, editor decisions, review processes were conducted through our system http://ice-elinvo.uny.ac.id/. Reviews were conducted by expert referees to the professional and scientific standards. • Describe criteria used by Reviewers when accepting/declining papers. Was there the opportunity to resubmit articles after revisions? Six criteria must be met by the papers that are included in the ICE-ELINVO 2021 international conference. All reviewers must refer to these six criteria. 1. Is the paper relevant to the conference theme? 2. Does it contribute to the theoretical or practical domains? 3. Does it state the problem statement? 4. Does it comprise an appropriate research methodology or proposed method? 5. Does it describe indicative results? 6. Does it fulfill the requirements of the paper template? Each criterion has a value from the range 1-4. The reviewer will evaluate each criterion, and then the system will add up all the scores for each criterion. The maximum total value is 24. The next stage is that the reviewer will send back the paper reviewed to the author through the system. Finally, authors are given two weeks to correct and resubmit. • Conference submission management system : http://ice-elinvo.uny.ac.id (our University has our own management system for reviewing articles) • Number of submissions received: 84 Papers • Number of submissions sent for review: 68 Papers • Number of submissions accepted: 56 Papers • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 66.67 • Average number of reviews per paper: 2 • Total number of reviewers involved: 48 • Any additional info on review process (ie plagiarism check system) : Each manuscript submitted to the ICE-ELINVO 2021 international conference was checked for plagiarism using Turnitin. • Contact person for queries : Muhammad Luthfi Hakim, Department of Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Yogyakarta, Indonesia. Email: luthfihakim93@uny.ac.id
Best neural approximation for 2-quasi inner product spaces

Many researchers studied two-normed spaces for objects of function approximation. Also, neural networks are great instruments for function approximation from function spaces, especially, Lebesque spaces (Lp). In this paper, the aim is to approximate Lebesque integrable functions via two-quasi inner product that is defined here in terms of two-norm. Moreover, there is a definition of a special type of neural network with special weights to estimate the approximation error, which is equivalent to the modulus of smoothness of functions of study. Estimates of infimum and supremum bounds so that the modulus of smoothness is involved in each bound. It is interesting to investigate more work related to what we presented in this study, not only for those fields of neural network function approximation, but also to approximate functions by various types of operators.
KM-BERT: A Pre-trained BERT for Korean Medical Natural Language Processing (Preprint)

<sec> <title>BACKGROUND</title> With advances in deep learning and natural language processing, analyzing medical texts is becoming increasingly important. Nonetheless, a study on medical-specific language models has not yet been conducted given the importance of medical texts. </sec> <sec> <title>OBJECTIVE</title> Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train language models. </sec> <sec> <title>METHODS</title> In this paper, we present a Korean medical language model based on deep learning natural language processing. The proposed model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. </sec> <sec> <title>RESULTS</title> After pre-training, the proposed method showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation. </sec> <sec> <title>CONCLUSIONS</title> The results demonstrated the superiority of the proposed model for Korean medical natural language processing. We expect that our proposed model can be extended for application to various languages and domains. </sec>
Computational Overhead of a Novel Scheme for Identity Confidentiality on IoT Devices in 5G Mobile Networks

Mobile networks are becoming a preferred choice for the Internet of Things (IoT), due to its flexibility, broad coverage, increasing bandwidth, low latency and low subscription cost. However, a long-standing security issue in any mobile network across the various generations is identity confidentiality. In a recent technical specification standardised by 3rd Generation Partnership Project (3GPP) for 5G mobile network, a novel scheme called the Elliptic Curve Integrated Encryption Scheme (ECIES) is adopted to tackle the issue of identity confidentiality. While this mechanism seems to provide a reasonable solution for modern resource affluent smart phones, it’s suitability for resource constrained IoT devices needs to be analysed. In this paper, we study the computational overhead of the ECIES on IoT devices.
On the Classification of OFDM Signals Modulation Types Using Neural Networks

Automatic classification of modulation types is an important step in the development of a cognitive transceiver. In this paper, an algorithm was proposed for the automatic classification of six types of OFDM signal modulations: BPSK, QPSK, 16-, 64-, 256-, 1024-QAM. The method is based on the extraction of features, for which the statistics of the deviation of the instantaneous value of the amplitude, cumulants and adaptive wavelet entropy were used. In order to identify the algorithm that gives the optimal result in terms of the classification quality and the work speed, various combinations of used features were investigated. For each combination of features, simulation modeling was carried out, and characteristic curves of the classification accuracy were obtained.
Dropping Fake Favorable Feedback for Better Sentiment Analysis

Fake favorable feedback is a big obstacle for customers to get better experiences on shopping websites. As far as we know, the effects of fake favorable feedback on natural language processing models have not been reported. To investigate the distraction of fake feedback, this paper developed a method to resolute fake feedback (RFF). The proposed RFF first analyzes the tokenizes of feedback, and then several short texts are generated to replace some long texts. Experimental results on the raw data and processed data show the effectiveness of our proposal.
Classifying Suspicious Content on Social Media Networks

With the emergence and expansion of web technologies, the web now contains a huge amount of data and information for Internet users, in addition to a large amount of new data being generated at every moment. The Internet, with its various platforms, has evolved to be a forum for learning through these platforms, exchanging information, and exchanging opinions and ideas. For example, Twitter and other social networks are rapidly gaining popularity because they encourage users to communicate and convey their opinions on a variety of topics, engage in discussions with different cultures, and send messages around the world. In the areas of sentiment about Twitter information, a lot of studies have been done. This study focuses on the use of sentiment analysis technology on tweets generated by Twitter which is useful for analyzing details in tweets where opinions are highly disorganized, heterogeneous, and either negative, positive, or neutral in some cases. In this paper, the mentioned technique (sentiment analysis) was used to elicit opinions on two suspicious or non-suspicious measures as well as categorize these tweets using machine learning and a lexicon-based approach, along with rating scales. Using different machine learning algorithms such as Naive Bayes and Random Forest Classifier (RFC), we achieved a classification accuracy of 88.07 and 92.61 for NB and RFC. The accuracy of NB and RFC is 88.07 and 92.61, respectively, which is very good when compared to the recent research mentioned below.
An Analysis on Multi – Image Classification Techniques

Image classification is a process where images are classified based on its visual content. It came into existence to reduce the gap between computer vision and human vision. To classify the images, humans involve lot of efforts and it is time consuming, in order to overcome this, technique such as convolutional neural network and random forest is being used. Convolutional neural network is a class of deep neural network and it is most commonly used for analyzing the images. Random forest is classification algorithms which consist of many independent decision trees. Auto encoding technique is being used to denoise the image. Image inpainting technique is adopted to come up with a complete image which contains missing parts. Image inpainting technique is a process to overcome overfitting.
Educational work at the university as an element of the quality management system

The article deals with an important component of the learning process - educational work, in terms of the formation of a moral and patriotic portrait of a graduate, as well as the formation of groups of universal competencies related to intercultural interaction and civic position. The task of improving upbringing work at the university received particular urgency in connection with the implementation of the strategy for the development of education in the Russian Federation. The authors attempt to evaluate and analyze educational work from the point of view of the process approach and system analysis. A hierarchical model of educational work is proposed and the components of the process are highlighted, and a system of indicators for assessing the quality of educational work is developed. The proposed approach can be applied in the formation and evaluation of the strategy for the development of the educational process in higher education and the development of work programs for educational work, as an obligatory element of the main professional educational program.
Addressing Stability in Classifier Explanations

Machine learning based classifiers are often a black box when considering the contribution of inputs to the output probability of a label, especially with complex non-linear models such as neural networks. A popular way to explain machine learning model outputs in a model agnostic manner is through the use of Shapley values. For our use case of abuse fighting in digital advertisements, one primary impediment of using Shapley values in explanations was a problem of instability. Specifically, the instability problem manifests as explanations for the same example varying greatly due to random sampling in the algorithm. We found it useful to view this problem explicitly as Monte Carlo integration in the form of averaging the model output while varying only a subset of features in the example to be explained. In turn, this guides the number of samples needed to achieve a stable estimate of individual Shapley values and unlocked the use of Shapley value based explainers for our models as well as classifiers in general, including neural networks.
Sneak-Attacks in StarCraft using Influence Maps with Heuristic Search

Real-Time Strategy (RTS) games have consistently been popular among AI researchers over the past couple of decades due to their complexity and difficulty to play for both humans and AI. A popular strategy in RTS games is a “Sneak-Attack,” where one player tries to maneuver some of their units into the base of their enemy without being seen for as long as possible to surprise their enemy and deal massive damage to their economy. This paper introduces a novel method for finding sneak-attack paths in StarCraft by combining influence maps with heuristic search. The combined system creates paths that can guide units effectively - and automatically - into the enemy's base by avoiding enemy unit vision and minimizing both travel distance and unit damage. Our results show that our new system performs better than direct paths across a variety of maps in terms of total transport deaths, total damage taken, as well as the total time spent by the transport within enemy vision. We then utilize this new system to demonstrate a proof of concept for calculating building placements to defend against enemy sneak-attacks.
Contact Tracing in Healthcare Settings during the COVID-19 Pandemic (Preprint)

<sec> <title>UNSTRUCTURED</title> The COVID-19 pandemic has caused great damages and it will likely linger for long time. Contact tracing in healthcare settings during the COVID-19 pandemic is of special importance because the population there are the most vulnerable to the pandemic. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. Existing digital technologies can potentially provide elegant solutions for contact tracing in healthcare settings, of which the indoor real-time location system based on Bluetooth Low Energy and artificial intelligence may be a good candidate. </sec>
Educating Voters About Mail Ballot Elections

Even as the effect of the pandemic wanes (at least temporarily), the NLRB continues to varying degrees to conduct elections by mail ballot. This election method is unlikely to retreat completely to pre‐pandemic levels given Chairman McFerran's stated interest in expanding the use of remote ballot processes. With that, it is essential that employers educate employees on the complicated process involved in successfully submitting an election ballot by mail. A recent decision by the NLRB in College Bound Dorchester, Inc ., Case No. 01‐RC‐261667 (June 25, 2021) highlights the importance of mail ballot procedures and, specifically, the signature of employees on the ballot envelope.
Search for the new changes in History textbooks' perspectives and configurations - Examination of accounts regarding the March 1st Movement-

This article proposes to try new compositions as well as alternative viewpoints in history education. Such proposition is based on the author’s own personal experiences in engaging not only historical studies but various educational activities. And in making this very point, the issue of how the March 1<sup>st</sup> Movement has been depicted and described in History textbooks seems to be particularly useful. The history of certain events have always been depicted in History textbooks through description of certain elements: background, how it unfolded, and what kind of influence or legacy it left. This is a chronological approach to the event in question, based on a perspective or framework considering causes and result to be very important. The March 1<sup>st</sup> Movement is a definitive example of such approach, and as it bear the honor of being a monumental cornerstone in the Korean people’s national efforts for liberation in the 20<sup>th</sup> century’s early half, the event itself was most of the time written with a Nationalistic tone. The Movement’s distinctive image and nature of the narrative in the textbooks’ description of the event has remained relatively the same since the government’s First Education plan. Unfortunately, due to repeated description of the March 1<sup>st</sup> Movement in this fashion, it is becoming increasingly difficult to break free from such mold, and as a result a rather fixed version of the Movement has been established, while recent academic accomplishments on the event have been constantly ignored. Moreover, certain factual errors concerning how the Movement actually unfolded remain virtually uncorrected, while an ever-growing Nationalist viewpoint emphasizing the Movement’s meaning in world history refuses to embrace new facts and interpretations produced by the community of historians. As a result, discrepancies between the image of the March 1<sup>st</sup> Movement in History textbooks, and details of the movement uncovered by historical researches, just continue to build. We should endeavor to escape such Nationalistic point of view, as well as the aforementioned ‘causal’ framework, and indulge ourselves in democratic viewpoints accompanied by thematic approaches, as such effort will lead us to analyze and interpret the March 1<sup>st</sup> Movement from fresh new angles. In that case, a number of new ways to teach this historical event to the students, based on new factual details and proper interpretations, will surface in the future.
Fire music

This chapter explores how to make flame sing. The flame rapidly heats the air in its immediate vicinity, causing it to expand. The rapid expansion creates a pressure wave which travels to the end of the tube and partially reflects back, just as in an open-ended organ pipe. With the flame organ it is the oscillation in flame intensity and the resulting localized expansion and contraction of the air that provides the initial impulse. The sounds of such an arrangement according to people who have worked with flame tones are highly varied. The system can be refined so as to dependably produce clear, steady tones at the frequency of the tube’s fundamental. Or the mechanism can be adjusted to bring out harmonics. References to fire music generally take Kastner’s pyrophone as a starting point. The chapter describes three more fire organs, created by contemporary artists-in-fire.
Relating Timbre and Tuning

The principle of local consonance describes a relationship between the timbre of a sound and a tuning (or scale) in which the timbre will sound most consonant. The principle answers two complementary questions. Given a timbre, what scale should it be played in? Given a scale, how can appropriate timbres be chosen? The ability to answer such questions will likely impact the way we design new musical instruments. This chapter constructs examples of scales appropriate for a variety of timbres and explains various consonance related phenomena in terms of the principle of local consonance. Noninteger ratios of the carrier and modulating frequencies give nonharmonic timbres that are typically relegated to percussive or bell patches because they sound dissonant when played in traditional 12-tone harmonies. The principle of local consonance suggests that such sounds can be played more harmoniously in scales which are determined by the timbres themselves.
Using Speech

The Speech framework lets apps recognize audio commands as a supplement to taps and gestures. In addition, the Speech framework can also transcribe speech into text. By adding speech recognition features, your app can offer more ways for the user to interact in a natural manner that’s easy for everyone to do.
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search

Neural Architecture Search (NAS) has attracted growing interest. To reduce the search cost, recent work has explored weight sharing across models and made major progress in One-Shot NAS. However, it has been observed that a model with higher one-shot model accuracy does not necessarily perform better when stand-alone trained. To address this issue, in this paper, we propose Progressive Automatic Design of search space, named PAD-NAS. Unlike previous approaches where the same operation search space is shared by all the layers in the supernet, we formulate a progressive search strategy based on operation pruning and build a layer-wise operation search space. In this way, PAD-NAS can automatically design the operations for each layer. During the search, we also take the hardware platform constraints into consideration for efficient neural network model deployment. Extensive experiments on ImageNet show that our method can achieve state-of-the-art performance. Take-aways Uses network architecture search methods to find better architectures for lower latencies and higher accuracy Formulates a search strategy to build a layer-wise operation search space through hierarchical operation pruning and mitigates weight coupling issue in One-Shot NAS. Compares the effects of different parameters on memory sizes, latency, and accuracy