Reviewerly/exHarmony huggingface dataset:
Development of AI Algorithm for Weight Training Using Inertial Measurement Units

Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure intelligent algorithm for weight training using two inertial measurement units (IMUs) is proposed in this paper. The first procedure is for motion tracking that estimates the arm orientation and calculates the positions of the wrist and elbow. The second procedure is for posture recognition based on deep learning, which identifies the type of exercise posture. The final procedure is for exercise prescription variables, which first infers the user’s exercise state based on the results of the previous two procedures, triggers the corresponding event, and calculates the key indicators of the weight training exercise (exercise prescription variables), including exercise items, repetitions, sets, training capacity, workout capacity, training period, explosive power, etc.). This study integrates the hardware and software as a complete system. The developed smartphone App is able to receive heart rate data, to analyze the user’s exercise state, and to calculate the exercise prescription variables automatically in real-time. The dashboard in the user interface of the smartphone App can display exercise information through Unity’s Animation System (avatar) and graphics, and records are stored by the SQLite database. The designed system was proven by two types of experimental verification tests. The first type is to control a stepper motor to rotate the designed IMU and to compare the rotation angle obtained from the IMU with the rotation angle of the controlled stepper motor. The average mean absolute error of estimation for 31 repeated experiments is 1.485 degrees. The second type is to use Mediapipe Pose to calculate the position of the wrist and the angles of upper arm and forearm between the Z-axis, and these calculated data are compared with the designed system. The root-mean-square (RMS) error of positions of the wrist is 2.43 cm, and the RMS errors of two angles are 5.654 and 4.385 degrees for upper arm and forearm, respectively. For posture recognition, 12 participants were divided into training group and test group. Eighty percent and 20% of 24,963 samples of 10 participants were used for the training and validation of the LSTM model, respectively. Three-thousand-three-hundred-and-fifty-nine samples of two participants were used to evaluate the performance of the trained LSTM model. The accuracy reached 99%, and F1 score was 0.99. When compared with the other LSTM-based variants, the accuracy of one-layer LSTM presented in this paper is still promising. The exercise prescription variables provided by the presented system are helpful for weight trainers/trainees to closely keep an eye on their fitness progress and for improving their health.
Adversarial Machine Learning in Text Processing: A Literature Survey

Machine learning algorithms represent the intelligence that controls many information systems and applications around us.As such, they are targeted by attackers to impact their decisions.Text created by machine learning algorithms has many types of applications, some of which can be considered malicious especially if there is an intention to present machine-generated text as human-generated.In this paper, we surveyed major subjects in adversarial machine learning for text processing applications.Unlike adversarial machine learning in images, text problems and applications are heterogeneous.Thus, each problem can have its own challenges.We focused on some of the evolving research areas such as: malicious versus genuine text generation metrics, defense against adversarial attacks, and text generation models and algorithms.Our study showed that as applications of text generation will continue to grow in the near future, the type and nature of attacks on those applications and their machine learning algorithms will continue to grow as well.Literature survey indicated an increasing trend in using pre-trained models in machine learning.Word/sentence embedding models and transformers are examples of those pre-trained models.Adversarial models may utilize same or similar pre-trained models as well.In another trend related to text generation models, literature showed effort to develop universal text perturbations to be used in both blackand white-box attack settings.Literature showed also using conditional GANs to create latent representation for writing types.This usage will allow for a seamless lexical and grammatical transition between various writing styles.In text generation metrics, research trends showed developing successful automated or semi-automated assessment metrics that may include human judgement.Literature showed also research trends of designing and developing new memory models that increase performance and memory utilization efficiency without validating real-time constraints.Many research efforts evaluate different defense model approaches and algorithms.Researchers evaluated different types of targeted attacks, and methods to distinguish human versus machine generated text.
Intelligent Fault-Tolerant Mechanism for Data Centers of Cloud Infrastructure

Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to several faults; (2) an algorithm to optimize the checkpoint interval time is designed; (3) lastly, the asynchronous checkpoint/restart with log-based recovery mechanism is used to restart the failed tasks. The valuation results obtained using a real-time dataset shows that the proposed model reduces power consumption and improves the performance with a better fault tolerance solution compared to the nonoptimization method.
Secure Real-Time Chaotic Partial Encryption of Entropy-Coded Multimedia Information for Mobile Devices: Smartphones

Smartphones penetration-rate continues expanding, from 44% in 2017 up to 59% expected increase in 2022 as reported by Strategy Analytics.At present, smartphones dominate the global mobile traffic, which in turn is dominated by video communications.For mobile systems with limited power capabilities, the processing of real-time multimedia information with affixed security represents a real challenge.In this work, we propose a high-performance encryption scheme capable of running on lowpower smartphones without holding back video coding operation.We are aimed at destroying the meaning of the entropy coded bitstream by inserting random bit errors to induce error propagation and impede natural self-resynchronization process.The scheme consists of three main process: 1) A new integer chaos-based Coupled-Map Lattice-CML for creating secure pseudo-random trajectories; 2) Random bit flipping of the bitstream based on a Dynamic Reference Point (DRP) not exposed to attackers; and 3) Random selection of CML byte-trajectories for both DRP and bit flipping processes for increased security.Implementation of the scheme on smartphones with different CPU-power capabilities shows excellent performance to handle high-bandwidth real-time video smoothly (one-to-one and group calls).The scheme provides high scalable security with encrypted data volume fluctuating between 0.7%-3% of the total compressed data (video and still images).
Estimating the Bot Population on Twitter via Random Walk Based Sampling

The rise of social bots, which contribute to marketing, political intervention, and the spread of fake news, has been noted. Analysis methods for the characteristics of Twitter bots have been developed for third-party researchers who have access limitations to Twitter data. Here, we propose a method for estimating the bot population on Twitter based on a random walk. The proposed method addresses two major problems in estimating the bot population on Twitter based on a random walk. First, the maximum number of retrievable friends or followers of a user per query is limited. Second, there is a certain percentage of private users who do not publish personal content, e.g., friends, followers, and tweets. We conduct a simulation analysis using directed social graph datasets to validate whether the proposed estimator is effective on the real Twitter follow graph. Then, we present three different estimates of the bot population on Twitter using the proposed estimator based on the three sample sequences of 25,000 users collected in 2.5 weeks each. The three estimates consistently suggest that 8%–18% of Twitter users during April–June 2021 are bots.
HISTOPATHOLOGICAL IMAGE ANALYSIS FOR ORAL CANCER CLASSIFICATION BY SUPPORT VECTOR MACHINE

Oral cancer is caused by the mutation of the cells in the lips or in the mouth. The incidence rate and prevalence rate of oral cancer are increasing worldwide. Recently, the Machine Learning (ML) approaches play a vital role in medical image diagnosis. They provide accurate and rapid evaluation of the analysis of histopathological images using supervised learning. In this study, three different modules are developed namely preprocessing, feature extraction and classification module. Initially, the raw histopathological image is given to the median filter for the removal of background noise in the preprocessing module. In the next module, the temporal features such as energy, entropy etc., are extracted from the color components of the filtered images. Finally, the classification is done by employing the Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) to classify histopathological images as normal or abnormal. Results show that the SVM classifier is better than KNN for the classification of oral cancer. The classification accuracy on 1224 histopathological images has been improved to 98% by using SVM classifier as compared with the KNN results of 83%.
Preservice EFL Teachers’ Experiences in Their First Teaching Practicum: A Collaborative Autoethnography

This paper reports a collaborative autoethnography on a first teaching practicum at Universidad Surcolombiana. The study aimed at how we, as novice researchers and preservice English as a foreign language teachers, make sense of our teaching experiences in our first teaching practicum using collaborative autoethnography as a research method. The data were collected by reflective journals and ethnographic observations. Results show the meaning that we give to our experiences, before and during the COVID-19 pandemic, by recognizing and analyzing our sociocultural context. Additionally, we were immersed in a virtual learning environment where we had the opportunity to confront unforeseen changes imposed by the pandemic, familiarize ourselves with possible issues that teachers grapple with, and imagine new ways to be ourselves.
Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic <scp>Flow‐Self</scp> Organizing Maps algorithm

Abstract Background The Flow‐Self Organizing Maps (FlowSOM) artificial intelligence (AI) program, available within the Bioconductor open‐source R‐project, allows for an unsupervised visualization and interpretation of multiparameter flow cytometry (MFC) data. Methods Applied to a reference merged file from 11 normal bone marrows (BM) analyzed with an MFC panel targeting erythropoiesis, FlowSOM allowed to identify six subpopulations of erythropoietic precursors (EPs). In order to find out how this program would help in the characterization of abnormalities in erythropoiesis, MFC data from list‐mode files of 16 patients (5 with non‐clonal anemia and 11 with myelodysplastic syndrome [MDS] at diagnosis) were analyzed. Results Unsupervised FlowSOM analysis identified 18 additional subsets of EPs not present in the merged normal BM samples. Most of them involved subtle unexpected and previously unreported modifications in CD36 and/or CD71 antigen expression and in side scatter characteristics. Three patterns were observed in MDS patient samples: i) EPs with decreased proliferation and abnormal proliferating precursors, ii) EPs with a normal proliferating fraction and maturation defects in late precursors, and iii) EPs with a reduced erythropoietic fraction but mostly normal patterns suggesting that erythropoiesis was less affected. Additionally, analysis of sequential samples from an MDS patient under treatment showed a decrease of abnormal subsets after azacytidine treatment and near normalization after allogeneic hematopoietic stem‐cell transplantation. Conclusion Unsupervised clustering analysis of MFC data discloses subtle alterations in erythropoiesis not detectable by cytology nor FCM supervised analysis. This novel AI analytical approach sheds some new light on the pathophysiology of these conditions.
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes

Real-time semantic segmentation, which aims to achieve high segmentation accuracy at real-time inference speed, has received substantial attention over the past few years. However, many state-of-the-art real-time semantic segmentation methods tend to sacrifice some spatial details or contextual information for fast inference, thus leading to degradation in segmentation quality. In this paper, we propose a novel Deep Multi-branch Aggregation Network (called DMA-Net) based on the encoder-decoder structure to perform real-time semantic segmentation in street scenes. Specifically, we first adopt ResNet-18 as the encoder to efficiently generate various levels of feature maps from different stages of convolutions. Then, we develop a Multi-branch Aggregation Network (MAN) as the decoder to effectively aggregate different levels of feature maps and capture the multi-scale information. In MAN, a lattice enhanced residual block is designed to enhance feature representations of the network by taking advantage of the lattice structure. Meanwhile, a feature transformation block is introduced to explicitly transform the feature map from the neighboring branch before feature aggregation. Moreover, a global context block is used to exploit the global contextual information. These key components are tightly combined and jointly optimized in a unified network. Extensive experimental results on the challenging Cityscapes and CamVid datasets demonstrate that our proposed DMA-Net respectively obtains 77.0% and 73.6% mean Intersection over Union (mIoU) at the inference speed of 46.7 FPS and 119.8 FPS by only using a single NVIDIA GTX 1080Ti GPU. This shows that DMA-Net provides a good tradeoff between segmentation quality and speed for semantic segmentation in street scenes.
A Deep Learning Accelerator Based on a Streaming Architecture for Binary Neural Networks

Deep neural networks (DNNs) have played an increasingly important role in various areas such as computer vision and voice recognition.While training and validation become gradually feasible with high-end general-purpose processors such as graphical processor units (GPU), high throughput inferences in embedded hardware platforms with low hardware resources and power consumption efficiency are still challenging.Binarized neural networks (BNNs) are emerging as a promising method to overcome these challenges by reducing bit widths of DNN data representations with many optimal prior solutions.However, accuracy degradation is a considerable problem of the BNN, compared to the same architecture with full precision, while the binary neural networks still contain significant redundancy for optimization.In this paper, to address the limitations, we implement a streaming accelerator architecture with three optimization techniques: pipelining-unrolling for streaming each layer, weight reuse for parallel computation, and MAC (multiplication-accumulation) compression.Our method first constructs streaming architecture by pipelining-unrolling method to maximize throughput.Next, the weight reuse method with the K-mean cluster is applied to reduce the complexity of the popcount operation.Finally, MAC compression reduces hardware resources used for remaining computation on MAC operations.The implemented hardware accelerator integrated into a state-of-the-art field programable gate array (FPGA) provides the maximum performance of the classification at 1531k frames per second with 98.4% accuracy for the MNIST dataset and 205K frame per second with 80.2% accuracy for the Cifar-10 dataset.Besides, the proposed design's ratio FPS/LUTs is approximately 57 (MNIST) and 0.707 (Cifar-10), which is much lower than the state-of-the-art design with a comparable throughput and inference accuracy.
Methodology to Assess Quality, Presence, Empathy, Attitude, and Attention in 360-degree Videos for Immersive Communications

This paper proposes a methodology to assess video quality, spatial and social presence, empathy, attitude, and attention in 360-degree videos for immersive communications. The methodology is validated in an experiment which simulates an immersive communication environment where participants attend three conversations of different genre (everyday conversation, educational, and discussion) and from actor and observer acquisition perspectives. We consider three experimental conditions: (A) visualizing and rating the perceptual quality of contents in a Head-Mounted Display (HMD), (B) visualizing the contents in an HMD, and (C) visualizing the contents in an HMD where participants can see their hands and take notes. In all conditions participants visualize the same 360-degree videos, designed and acquired in the context of international experiences. Fifty-four participants were evenly distributed among A, B, and C conditions taking into account their international experience backgrounds (working or studying in a foreign country), obtaining a balanced and diverse sample of participants. Finally, the annotated dataset, Student Experiences Around the World dataset (SEAW-dataset), obtained from the experiment is made publicly available for the research community.
Smart optimization of PV panel output using Fuzzy Logic Controller based solar tracker

Currently, more people choose renewable energy over conventional energy, such as solar energy, which is the most eligible renewable energy in Palembang and South Sumatra due to its strategic location in the equator. However, solar energy cultivation is experiencing various obstacles resulting in not producing maximum output, which is indicated by low output efficiency. Solar tracker or solar tracking system is one of the best methods to ensure the maximum sunlight received by the PV panels. This paper shows the effectiveness of a solar tracking system controlled by FLC compared to a fixed system. The data is taken for 15 days during the weather changing in Indonesia, from the rainy season to the dry season. The maximum power generated by dual-axis solar tracking and fixed solar panels is 96.4768 W and 63,5106 W, whereas the power generated by dual-axis solar tracking is 32.9662 W higher than fixed solar panels. The highest efficiency obtained by dual-axis solar tracking is 25%, while fixed solar panels are 16%. The application of dual-axis solar tracking can optimize and increase the efficiency of solar panels because the panels are always perpendicular to the position of the highest irradiance. Data results show that FLC-based solar effectively improves power output and efficiency compared to a fixed solar panel.
Looking good but tweeting bad? The social perception of orthodontic-related posts on Twitter and Instagram

Social media plays a major role in the daily life of adolescents and has become highly interesting for healthcare research as well. The aim of this study was to explore the social perception of orthodontic-related posts on Twitter and Instagram by young adults.401 orthodontic-related posts were collected during a 30-day period and categorized with regard to specific characteristics - their content and the social networking site (SNS) being used as well as the presence or absence of a selfie. In order to investigate the social perception of these posts, 42 young adults rated the emotional states of the SNS users using the Self-Assessment Manikin (SAM)-Tool. A total of 4211 poster-rater observations pertaining to the three SAMs dimensions pleasure, arousal and dominance were analyzed by using linear and multinomial logistic regression analyses.The investigated characteristics of the collected posts had significant effects on the perceived emotional state of the SNS users. Besides significant SNS-associated differences, there were also effects that were independent of the SNS being used: Receiving orthodontic appliances was more often associated with rather negative emotions (p < 0.001), while users who posted about the removal of such braces were more often perceived as joyful (p < 0.001). Interestingly, users whose posts contained selfies with visible braces were perceived as significantly more positive and stronger in comparison to users who did not post a picture of themselves (p < 0.05).This research gives insights into the social perception of orthodontic-related posts on SNS. While users' emotional states were perceived highly differential on both SNS, orthodontic-related content also revealed significant effects on social perception. Because selfies with visible braces were associated with positive feelings by young adults, a modern and SNS-related way of coping with a temporary supposed impairment like fixed orthodontic appliances might have been revealed through this research.
Transport-Enabled Entangling Gate for Trapped Ions

We implement a 2-qubit entangling Mølmer-Sørensen interaction by transporting two cotrapped ^{40}Ca^{+} ions through a stationary, bichromatic optical beam within a surface-electrode Paul trap. We describe a procedure for achieving a constant Doppler shift during the transport, which uses fine temporal adjustment of the moving confinement potential. The fixed interaction duration of the ions transported through the laser beam as well as the dynamically changing ac Stark shift require alterations to the calibration procedures used for a stationary gate. We use the interaction to produce Bell states with fidelities commensurate to those of stationary gates performed in the same system. This result establishes the feasibility of actively incorporating ion transport into quantum information entangling operations.