Reviewerly/exHarmony huggingface dataset:
Trace Element Distribution in Zoned Kyanite of Thassos Island (Greece) Using Combined Spectroscopic Analyses

Comprehensive mineralogical and petrographic studies require analytical methods capable to report the distribution of major to trace elements within crystals in order to unravel their formation conditions and subsequent evolution. Additionally, the investigation of transition elements (e.g., Ti, V, Cr, Mn, Fe, and Zn) is essential for the comprehension of substitution processes within colored minerals. This study is conducted on a zoned kyanite crystal from a deformed quartz vein found within garnet-kyanite-biotite-hematite-plagioclase±staurolite±sillimanite paragneiss of Thassos Island, Greece. Herein, we show the efficiency of combining conventional, for example, cathodoluminescence, electron probe microanalysis (EPMA), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and new methods, for example, micro-laser-induced breakdown spectroscopy (µLIBS), micro-X-ray fluorescence (µXRF), and Raman spectroscopy, to determine the chemical and crystallographic features of minerals. The simple chemistry of this crystal offers an ideal case to compare and valuate the potential of combined spectroscopy techniques to analyze minerals. We demonstrate that µLIBS and µXRF are perfectly adapted to perform multi-element imaging of major to trace elements down to the ppm level within a pluricentimetric crystal (2.3 x 0.5 cm) prior to quantitative analyses. We also highlight the benefit of cathodoluminescence and Raman mapping in the investigation of crystallographic features within minerals. The multispectroscopic approach enabled us to correlate growth stages of kyanite with the polymetamorphic history of the sample. Our results also highlight the spatial dependence of Ti for the generation of blue zonation by Fe2+-Ti4+ substitutions with Al3+.
Improved edge lightweight YOLOv4 and its application in on-site power system work

A “cloud-edge-end” collaborative system architecture is adopted for real-time security management of power system on-site work, and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on- site risk assessment and alert. Owing to its lightweight and fast speed, YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection; however, its accuracy is relatively low. This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods, achieving higher accuracy without compromising the speed. Specifically, a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization. Moreover, three optimized training methods: transfer learning, mosaic data augmentation, and label smoothing are used to improve the training effect of this improved algorithm. Finally, an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it. According to the results, the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS, and the mean average precision (mAP) is increased from 70.89% to 85.03%.
Cyberbullying Detection on Twitter using Support Vector Machine Classification Method

Bullying is when someone or a group of individuals is continuously attacked. Because of the advancement of the internet, it has become very easy for society to engage in harmful acts of bullying by attacking a person or group of people who can hurt the victim, this is known as cyberbullying. Twitter is a social media platform that may be used by the society to share information and can also be used to perpetrate cyberbullying actions by sending messages (tweets) that addressed to the victims. This final project was developing a system to detect cyberbullying on Twitter. The system uses the Support Vector Machine method to classify whether the tweets that are shared include cyberbullying or not. In addition, this research also uses Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram feature extraction for data that has gone through the pre-processing stage. In collecting data, the author crawled tweets based on the keywords 'jelek', 'bodoh', 'goblok', 'brengsek', 'bangsat', 'memalukan', 'laknat', 'bacot' and 'pelacur'. The best performance results of the research is 76.2% accuracy, 73.2% precision, 78.2% recall and 75.6% F1-Score generated by the RBF kernel with a total of n=1
Calibration for light field cameras based on fixed point constraint of spatial plane homography

Light field cameras can record the spatial and angular information of light rays at the same time, which facilitate many vision tasks such as 3D reconstruction and digital refocusing. In this paper, we deduce a projection model from a 3D space point to each sub-aperture image according to the transmission of light rays in the light field camera, and the projection model of traditional cameras can be considered as a special case of it. We propose a camera calibration method using this model. Firstly, the homography from a space plane to each sub-aperture image is estimated, which implies the imaging parameters of the standard light field camera, and then the principal line on each sub-aperture image is derived from the constraint of the fixed point of the plane homography. The principal point of each sub-aperture image is robustly determined from the principal lines by using RANSAC. The other parameters can be determined from the homography constrains on them. Compared with the state of art methods, the proposed method can calibrate the light field images with a varying focal length and has higher accuracy. Moreover, it provides a unified framework for the calibration of standard light field cameras and traditional cameras. Experimental results validate the proposed method.
Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

In wireless body sensor network (WBSN), the set of electrocardiogram (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmitting these collected data some adversaries may capture and misuse it due to the compromise of security.So, the major aim of this work is to enhance secure transmission of ECG signal in WBSN.To attain this goal, we present Pity Beetle Swarm Optimization Algorithm (PBOA) based Elliptic Galois Cryptography (EGC) with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography (ECC) over Galois field or EGC, private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptography algorithm attains better encryption time, decryption time, throughput and SNR than the conventional cryptography algorithms.
Hate Speech and Counter Speech Detection: Conversational Context Does Matter

Hate speech is plaguing the cyberspace along with user-generated content.This paper investigates the role of conversational context in the annotation and detection of online hate and counter speech, where context is defined as the preceding comment in a conversation thread.We created a context-aware dataset for a 3-way classification task on Reddit comments: hate speech, counter speech, or neutral.Our analyses indicate that context is critical to identify hate and counter speech: human judgments change for most comments depending on whether we show annotators the context.A linguistic analysis draws insights into the language people use to express hate and counter speech.Experimental results show that neural networks obtain significantly better results if context is taken into account.We also present qualitative error analyses shedding light into (a) when and why context is beneficial and (b) the remaining errors made by our best model when context is taken into account.
Interactive Landscape Design and Application Effect Evaluation of Community Sports Park by Wireless Communication Technology

To improve the comprehensive quality of urban community sports parks and enhance the modern life quality of residents, first, wireless communication technology is discussed. Second, the construction of community sports parks and the concept of interaction design (IxD) are explored. Finally, a comprehensive assessment of the construction of sports facilities in the park is carried out. In the research method, a field investigation is carried out on the sports park built in Chongqing first, and the research content is designed according to the investigation results. Then, the current sports park construction results are investigated and evaluated by means of a questionnaire survey. The research results show that the five community sports parks in Chongqing still need to be improved in terms of their comprehensive construction. The most obvious in terms of their essential functions is the Yangtianwo community sports park, which has the lowest comprehensive score of 29 (total score is 60). Subsequently, in the comprehensive evaluation of community sports parks, Yangtianwo has the lowest satisfaction evaluation. Finally, in the evaluation of wireless communication technology, residents have the lowest satisfaction with Dashuijing community sports park, and the evaluation score is 20 (total score is 30). It denotes that the comprehensive evaluation of Yangtianwo is the worst among the five community sports parks. The research not only provides a reference for the construction of community sports parks in Chongqing but also contributes to the comprehensive development of community sports parks.
Creative Robot Contests for Decommissioning as Conceived by College of Technology or KOSEN Educators

This paper’s theme “Creative Robot Contest for Decommissioning (hereinafter, this Creative Robot Contest)” may be called a contest that would never have been planned in the absence of the Great East Japan Earthquake on March 11, 2011 and the core meltdown accident at Fukushima Daiichi Nuclear Power Plant of Tokyo Electric Power Company. When it was decided to decommission the nuclear reactors, their decommissioning was immediately put in hand, which, however, met with a high barrier of high radiation doses unconquerable even by the modern science and technologies. In order to advance the stagnating decommissioning work and in hopes of some creative breakthrough ideas from KOSEN students, this Robot Contest, first held in 2016, has been held by KOSEN teams who have shown their willingness to participate in it as a venue useful to address any difficulties they will face in the real world or as teaching material for them to consider social problems. This Robot Contest, sponsored by the Japan Atomic Energy Agency and the Council of Technical Colleges for Human Resource Development for Decommissioning, is planned and managed by the Executive Committee of Creative Robot Contest for Decommissioning (hereinafter, the Executive Committee) with its secretariat at National Institute of Technology, Fukushima College (hereinafter, Fukushima KOSEN) in Fukushima Prefecture where Fukushima Daiichi Nuclear Power Plant is located. In this paper, teachers at Osaka Metropolitan University College of Technology (hereinafter, Osaka Metropolitan University KOSEN) and at Nara National College of Technology, Nara College (hereinafter, Nara KOSEN), whose respective teams have participated in this Robot Contest every year from the first one, and teachers at Fukushima KOSEN who organize and manage the executive committee describe their own views on this Robot Contest based on their viewpoints as KOSEN educators. In the first half, this paper reviews this Robot Contest and outlines the robots of Osaka Metropolitan University KOSEN which participated in it in FY2016 and FY2017 and of Nara KOSEN which did in FY2016. In the latter half, this paper describes the views of the KOSEN teachers who educate engineers in the framework of robot contests from the viewpoints of the teachers who have instructed the teams of Osaka Metropolitan University KOSEN and Nara KOSEN who have participated in this Robot Contest up to the sixth one and who have held this Robot Contest.
Readymades &amp; Repertoires: Artifact-Mediated Improvisation in Tabletop Role-Playing Games

Game masters (GMs) are creative practitioners who plan and orchestrate tabletop role-playing games. Through an interview study, we investigate how eight expert game masters adapt everyday technologies and materials as creativity support tools (CSTs) for improvisational and collaborative play. We integrate theories of improvisational and distributed creativity with the human-artifact model, which provides an activity-theoretical vocabulary for analyzing the mediating relationships between specialist practitioners and their tools. We show how GMs prepare and deploy readymade artifacts: analog and digital CSTs that flexibly mediate recurring creative tasks in their practice, such as improvising narrative elements, facilitating smooth play, and creating aesthetic effects. We find that GMs demonstrate designerly thinking as they create, share, and refine repertoires of readymade artifacts. We argue that our theoretical approach can inform future studies of IT-mediated creativity, and that readymade artifacts can be an analytical and generative concept for the design of novel creativity support tools.
Collective Teacher Self-Efficacy and Burnout: The Mediator Role of Job Satisfaction

This study examined the predictive relationships between collective teacher efficacy, job satisfaction, and burnout. In addition, the mediating role of job satisfaction in the relationship between collective teacher efficacy and burnout was tested. Three hundred fifty teachers participated in the research in which the correlational research design was used. Collective Teacher Efficacy Scale, Short Form Minnesota Satisfaction Questionnaire, and Maslach Burnout Inventory were used as data collection tools. Pearson Correlation Coefficients were calculated to examine the relationships between collective teacher efficacy, job satisfaction, and burnout. Structural equation model analysis was applied to test the mediating role of job satisfaction in the relationship between collective efficacy and burnout. The results indicated that collective teacher efficacy predicts job satisfaction positively and burnout negatively. Collective teacher efficacy and job satisfaction have a large effect on burnout. Evidence has been obtained that teachers' job satisfaction can increase and, accordingly, burnout can be prevented when collective teacher efficacy is achieved.
Attention-guided sampling for colorectal cancer analysis with digital pathology

Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas. We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging. These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour-stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists' annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing. We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations.
Autonomous Aerial Robot for High-Speed Search and Intercept Applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.
Gauging the acceptance of contact-tracing technology: an empirical study of Singapore residents concerns and trust in information sharing

In response to the COVID-19 pandemic, governments began implementing various forms of contact tracing technology. Singapore's implementation of its contact tracing technology, TraceTogether, however, was met with significant concern by its population, with regard to privacy and data security. This concern did not fit with the general perception that Singaporeans have a high level of trust in its government. We explore this disconnect, using responses to our survey (conducted pre-COVID-19) in which we asked participants about their level of concern with the government and business collecting certain categories of personal data. The results show that respondents had less concern with the government as compared to a business collecting most forms of personal data. Nonetheless, they still had a moderately high level of concern about sharing such data with the government. We further found that income, education and perceived self-exposure to AI are associated with higher levels of concern with the government collecting personal data relevant to contact tracing, namely health history, location and social network friends' information. This has implications for Singapore residents' trust in government collecting data and hence the success of such projects, not just for contact tracing purposes but for other government-related data collection undertakings.
A mission-driven two-step virtual machine commitment for energy saving of modern data centers through UPS and server coordinated optimizations

Recent years witness a rapid increasing of data center energy consumption, among which the power losses of uninterruptible power supply (UPS) and server dominate. This paper proposes a mission-driven two-step virtual machine (VM) commitment by considering the characteristics of UPS power losses in virtual machine allocation. To formulate the optimization problem, a power model of data center is built by combining server power with power losses of rack level UPSs. Further, a two-step virtual machine allocation method is proposed, in which the best fit decreasing algorithm first groups the VMs and then the genetic algorithm searches for the optimal allocation of servers equipped with the grouped VMs. A data center with 100 servers and 100 VMs is simulated using the public data collected from PlanetLab platform. Results have verified that the total power consumption is reduced by up to 2.47%. In parallel, possible combinations need to be searched decrease by 3.35 × 10182 times in the VM allocation problem in the case with 100 servers and 100 VMs.
A Semantic Analysis-Based Method for Smart Contract Vulnerability

Smart contracts are distributed, self-executing programs on blockchain network and they have the potential to impact many industries. A large number of developers are optimistic about the development prospects of smart contracts. However, due to different levels of familiarity of developers with technology, the existence of serious vulnerabilities in code can result in huge property losses. The serious consequences of reentrancy vulnerabilities have attracted widespread attention from researchers, and researchers have proposed many approaches to detect them. Many experiments show that these approaches can only effectively detect certain types of vulnerabilities. Similar vulnerabilities still exist in some smart contracts. In this paper, we propose a semantic analysis-based detection method for reentrancy vulnerabilities. The method utilizes machine learning techniques to efficiently detect the vulnerabilities of the given code. And then, our method provides semantic analysis-based effective correction feedback of the code. Compared with the existing vulnerability detection tools Slither and Mythril, our method improves the work efficiency. Moreover, compared with the current Mythril method with the best vulnerability detection accuracy, our method improves the accuracy by 1.01%.
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis

Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner. It leverages an emotion lexicon and uses contrastive learning and deep clustering. We further integrate the style representation as a conditioned embedding in a multi-style Transformer TTS. Comparing with multi-style TTS by predicting style tags trained on the same dataset but with human annotations, our method achieves improved results according to subjective evaluations on both in-domain and out-of-domain test sets in audiobook speech. Moreover, with implicit context-aware style representation, the emotion transition of synthesized audio in a long paragraph appears more natural. The audio samples are available on the demo web.
12‐1: <i>Invited Paper:</i> Through‐OLED Display Ambient Color Sensing

Integrating an ambient light sensor behind an active OLED display maximizes the useable display area. We discuss our solution that overcomes challenges in through‐display transmission and display‐to‐sensor optical crosstalk and enables ambient color adaptation and autobrightness display features.