Reviewerly/exHarmony huggingface dataset:
Development of Gamification-Based Wordwall Game Platform on Reaction Rate Materials

The purpose of this study was (1) to determine the development of a wordwall game platform on the reaction rate material, (2) to determine the feasibility of a gameification-based wordwall game platform from the results of validator and respondent data. wordwall is an application that can be used as a learning media, learning resource or online-based assessment tool that is attractive to students. The advantage of this application is that it has many templates that teachers can create to develop in the form of games. Wordwall is rarely used in learning media because there has not been socialization and application in the teaching and learning process for teachers. In this study, media development was carried out using wordwall applications on chemicals, namely the reaction rate that will be studied by students. This material contains the concept of reaction rate, factors that affect reaction rate, collision theory and reaction rate equations. The novelty of this study compared to existing research is that this research develops a wordwall medium that focuses on increasing students' interest in learning materials, especially chemistry in the online-based teaching and learning process.The wordwall media validators were 2 lecturers from Nusa Cendana University and the question validators were validated by 2 teachers at SMAN 3 Sidoarjo. The trial subjects were 37 students of high school class XI at SMAN 3 Sidoarjo. The research method used is R&amp;D from the Alessi and Trollip model. Data collection using a google form-based questionnaire filled out online and test results. The data were analyzed by qualitative descriptive and percentage calculation. The results obtained: (1) the value of the validity of the material questions is 90%, (2) the value of media validity is 94%, and (3) the student implementation test score is 81.75%.
Evaluating Usability and User Experience of AR Applications in VR Simulation

Validating an augmented reality application in a virtual reality simulation can offer many advantages compared to testing in real conditions and can speed up development processes. With such a simulation, developers and designers do not need to have constant physical access to the real place. They can save physical navigation, experiment with different kinds of devices and isolate testing parameters. While the validity of functional testing in virtual reality simulations is not particularly challenged, the validity of such simulations to evaluate user experience and usability, similarly as in real conditions, still needs to be assessed. We then conducted a user study to explore the validity of evaluating these criteria with a virtual reality simulation tool and the importance of simulation fidelity for that purpose. Indeed, we also seek to determine whether it is necessary to simulate the limited field of view of augmented reality glasses and if the simulation can take place in a virtual world that is not a replica of the real targeted environment. To do so, we have developed an augmented reality application for smart-homes where a user can interact with different connected objects. One group of users performed the experiment in the real place with augmented reality glasses and three other groups performed the same experiment in virtual reality with various simulation conditions (field of view and environment). Users’ subjective feedback and quantitative results only highlight very few differences between real-world conditions and simulation in virtual reality, whatever the simulation parameters used. These results suggest the interest in using virtual reality simulation to evaluate an augmented reality application but should be confirmed on other use cases and interaction tasks.
Design of Multimodal Sensor Module for Outdoor Robot Surveillance System

Recent studies on surveillance systems have employed various sensors to recognize and understand outdoor environments. In a complex outdoor environment, useful sensor data obtained under all weather conditions, during the night and day, can be utilized for application to robots in a real environment. Autonomous surveillance systems require a sensor system that can acquire various types of sensor data and can be easily mounted on fixed and mobile agents. In this study, we propose a method for modularizing multiple vision and sound sensors into one system, extracting data synchronized with 3D LiDAR sensors, and matching them to obtain data from various outdoor environments. The proposed multimodal sensor module can acquire six types of images: RGB, thermal, night vision, depth, fast RGB, and IR. Using the proposed module with a 3D LiDAR sensor, multimodal sensor data were obtained from fixed and mobile agents and tested for more than four years. To further prove its usefulness, this module was used as a monitoring system for six months to monitor anomalies occurring at a given site. In the future, we expect that the data obtained from multimodal sensor systems can be used for various applications in outdoor environments.
Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method’s effectiveness is verified on a fully annotated dataset.
TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation

As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions between training data and testing data are inconsistent. In this article, we propose a novel ELM called two-stage transfer extreme learning machine (TSTELM) to solve this problem. At the statistical matching stage, we adopt maximum mean discrepancy (MMD) to narrow the distribution difference of the output layer between domains. In addition, at the subspace alignment stage, we align the source and target model parameters, design target cross-domain mean approximation, and add the output weight approximation to further promote the knowledge transferring across domains. Moreover, the prediction of test sample is jointly determined by the ELM parameters generated at the two stages. Finally, we investigate the proposed approach in classification task and conduct experiments on four public domain adaptation datasets. The result indicates that TSTELM could effectively enhance the knowledge transfer ability of ELM with higher accuracy than other existing transfer and non-transfer classifiers.
Cluster, Cloud, Grid Computing via Network Communication Using Control Communication and Monitoring of Smart Grid

Traditional power consumption management systems are not showing enough reliability and thus, smart grid technology has been introduced to reduce the excess power wastages. In the context of smart grid systems, network communication is another term that is used for developing the network between the users and the load profiles. Cloud computing and clustering are also executed for efficient power management. Based on the facts, this research is going to identify wireless network communication systems to monitor and control smart grid power consumption. Primary survey-based research has been carried out with 62 individuals who worked in the smart grid system, tracked, monitored and controlled the power consumptions using WSN technology. The survey was conducted online where the respondents provided their opinions via a google survey form. The responses were collected and analyzed on Microsoft Excel. Results show that hybrid commuting of cloud and edge computing technology is more advantageous than individual computing. Respondents agreed that deep learning techniques will be more beneficial to analyze load profiles than machine learning techniques. Lastly, the study has explained the advantages and challenges of using smart grid network communication systems. Apart from the findings from primary research, secondary journal articles were also observed to emphasise the research findings.
Vessel identification based on automatic hull inscriptions recognition

The identification of ships plays a crucial role in security and managing vessel traffic for ports and onshore facilities. Existing video monitoring systems help visually identify a vessel where other systems are not present or sufficient. Readable vessel plates and hull inscriptions of detected ships in the video stream allow using text location and recognition methods to obtain ships’ identification names or numbers. The obtained information can be then matched with available ship registers. The automation of the process has met many challenges related to the often-low quality of available video streams, heterogeneous regulations on the marking of ships, and the specifics of natural scene text recognition, such as quickly alternating imaging conditions or the interference of the background. The main contribution of this research is a method that can identify any type of vessel in an image that has visible inscriptions (name, registration number) placed on the hull and must be registered in a public registry. The proposed method works with low-quality images with inscriptions placed under different angles and different, readable sizes. Our method recognised 91% of vessels from our test dataset. Obtained identification times have not exceeded 1s. The quality and efficiency of the proposed solution indicate that it is suitable for practical implementation in onshore monitoring systems.
A Comprehensive Study of Bug Fixes in Quantum Programs

As quantum programming evolves, more and more quantum programming languages are being developed. As a result, debugging and testing quantum programs have become increasingly important. While bug fixing in classical programs has come a long way, there is a lack of research in quantum programs. To this end, this paper presents a comprehensive study on bug fixing in quantum programs. We collect and investigate 96 real-world bugs and their fixes from four popular quantum programming languages (Qiskit, Cirq, Q#, and ProjectQ). Our study shows that a high proportion of bugs in quantum programs are quantum-specific bugs (over 80&#x0025;), which requires further research in the bug fixing domain. We also summarize and extend the bug patterns in quantum programs and subdivide the most critical part, math-related bugs, to make it more applicable to the study of quantum programs. Our findings summarize the characteristics of bugs in quantum programs and provide a basis for studying testing and debugging quantum programs.
RobustFusion: Robust Volumetric Performance Reconstruction Under Human-Object Interactions From Monocular RGBD Stream

High-quality 4D reconstruction of human performance with complex interactions to various objects is essential in real-world scenarios, which enables numerous immersive VR/AR applications. However, recent advances still fail to provide reliable performance reconstruction, suffering from challenging interaction patterns and severe occlusions, especially for the monocular setting. To fill this gap, in this paper, we propose RobustFusion, a robust volumetric performance reconstruction system for human-object interaction scenarios using only a single RGBD sensor, which combines various data-driven visual and interaction cues to handle the complex interaction patterns and severe occlusions. We propose a semantic-aware scene decoupling scheme to model the occlusions explicitly, with a segmentation refinement and robust object tracking to prevent disentanglement uncertainty and maintain temporal consistency. We further introduce a robust performance capture scheme with the aid of various data-driven cues, which not only enables re-initialization ability, but also models the complex human-object interaction patterns in a data-driven manner. To this end, we introduce a spatial relation prior to prevent implausible intersections, as well as data-driven interaction cues to maintain natural motions, especially for those regions under severe human-object occlusions. We also adopt an adaptive fusion scheme for temporally coherent human-object reconstruction with occlusion analysis and human parsing cue. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality 4D human performance reconstruction under complex human-object interactions whilst still maintaining the lightweight monocular setting.
Image Encryption Algorithm Based on a Novel Wide-Range Discrete Hyperchaotic Map

Existing hyperchaotic systems suffer from a small parameter range and small key space. Therefore, we propose herein a novel wide-range discrete hyperchaotic map(3D-SCC) based on the mathematical model of the Sine map. Dynamic numerical analysis shows that this map has a wide-range of parameters, high sensitive, high sensitivity of sequences and good ergodicity, which proves that the system is well suited to the field of communication encryption. Moreover, this paper proposes an image encryption algorithm based on a dynamic cycle shift scramble algorithm and image-sensitive function. First, the image feature is extracted by the image-sensitive function to input into the chaos map. Then, the plaintext image is decomposed by an integer wavelet, and the low-frequency part is scrambled by a dynamic cyclic shifting algorithm. The shuffled low-frequency part and high-frequency parts are reconstructed by wavelet, and the chaotic matrix image is bitwise XOR with it to obtain the final ciphertext. The experimental results show that the average NPCR is 99.6024%, the average UACI is 33.4630%, and the average local Shannon entropy is 7.9029, indicating that the statistical properties of the ciphertext are closer to the ideal value. The anti-attack test shows that the algorithm can effectively resist cutting attacks and noise attacks. Therefore, the algorithm has great application value in the field of image encryption.
What do tokens know about their characters and how do they know it?

Pre-trained language models (PLMs) that use subword tokenization schemes can succeed at a variety of language tasks that require characterlevel information, despite lacking explicit access to the character composition of tokens.Here, studying a range of models (e.g., GPT-J, BERT, RoBERTa, GloVe), we probe what word pieces encode about character-level information by training classifiers to predict the presence or absence of a particular alphabetical character in a token, based on its embedding (e.g., probing whether the model embedding for "cat" encodes that it contains the character "a").We find that these models robustly encode character-level information and, in general, larger models perform better at the task.We show that these results generalize to characters from non-Latin alphabets (Arabic, Devanagari, and Cyrillic).Then, through a series of experiments and analyses, we investigate the mechanisms through which PLMs acquire English-language character information during training and argue that this knowledge is acquired through multiple phenomena, including a systematic relationship between particular characters and particular parts of speech, as well as natural variability in the tokenization of related strings.
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Motivations for methods in explainable artificial intelligence (XAI) often include detecting, quantifying and mitigating bias, and contributing to making machine learning models fairer.However, exactly how an XAI method can help in combating biases is often left unspecified.In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.
Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data

This paper shows how to use large-scale pretrained language models to extract character roles from narrative texts without domainspecific training data.Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches.
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction

Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage.However, it still faces some challenges since the unseen relations of instances are similar or the input sentences have similar entities, the unseen relation representations from different categories tend to overlap and lead to errors.In this paper, we propose a novel Relation Contrastive Learning framework (RCL) to mitigate above two types of similar problems: Similar Relations and Similar Entities.By jointly optimizing a contrastive instance loss with a relation classification loss on seen relations, RCL can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously.Especially in contrastive instance learning, the dropout noise as data augmentation is adopted to amplify the semantic difference between similar instances without breaking relation representation, so as to promote model to learn more effective representations.Experiments conducted on two well-known datasets show that RCL can significantly outperform previous state-of-the-art methods.Moreover, if the seen relations are insufficient, RCL can also obtain comparable results with the model trained on the full training set, showing the robustness of our approach 1
Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT

The dominating paradigm for content scoring is to learn an instance-based model, i.e. to use lexical features derived from the learner answers themselves.An alternative approach that receives much less attention is however to learn a similarity-based model.We introduce an architecture that efficiently learns a similarity model and find that results on the standard ASAP dataset are on par with a BERT-based classification approach.
ViviPaint: Creating Dynamic Painting with a Thermochromic Toolkit

New materials and technologies facilitate the design of thermochromic dynamic paintings. However, creating a thermochromic painting requires knowledge of electrical engineering and computer science, which is a barrier for artists and enthusiasts with non-technology backgrounds. Existing toolkits only support limited design space and fail to provide usable solutions for independent creation and for meeting the needs of the artists. We present ViviPaint, a toolkit that assists artists and enthusiasts in creating thermochromic paintings easily and conveniently. We summarized the pain points and challenges by observing a professional artist’s entire thermochromic painting creation process. We then designed ViviPaint consisting of a design tool and a set of hardware components. The design tool provides a GUI animation choreography interface, hardware assembly guidance, and assistance in assembly process. The hardware components comprise an augmented picture frame with a detachable structure and 24 temperature-changing units using Peltier elements. The results of our evaluation study (N = 8) indicate that our toolkit is easy to use and effectively assists users in creating thermochromic paintings.
A National Cyberattack Affecting Radiation Therapy: The Irish Experience

On Friday, May 14, 2021, the Health Service Executive, the organization providing public health services in the Republic of Ireland, was the victim of a significant cyberattack on its information technology systems. All systems were subsequently shut down to prevent further damage and to allow cybersecurity experts to investigate the attack. As a result, oncology services were severely disrupted, with the cessation of radiation therapy treatments in all public radiation therapy departments. Ireland has 5 large public and 6 smaller private radiation therapy centers in total. Because of the widespread adoption of electronic medical records in radiation therapy departments, it wasn't possible to retrieve patient details of those who were undergoing radiation therapy at the time of the cyberattack. In total, 513 patients nationally had their radiation therapy interrupted.A national radiation therapy cyberattack response team was formed immediately to oversee the response to the attack. The immediate concerns were radiation therapy emergencies and category 1 patients where gaps in treatment would have an adverse effect on outcome. Communication with patients and the public was also established as a priority and agreements were reached with the private sector for the treatment of patients affected by the cyberattack. The national media was used to alert patients of the need to communicate with their radiation therapy department. Dedicated phone lines were established. Locally, radiation therapy departments held daily crisis meetings with key staff members, including information technology personnel. Individual centers employed different technologies for treatment planning and data storage, so local solutions to the cyberattack to reestablish radiation therapy for patients were developed. In addition, national documentation on prioritization of patients to resume treatment was produced and a national approach was made to compensate for gaps in treatment caused by the attack. All 5 centers had reestablished radiation therapy by May 30, although there has been a long aftermath to the cyberattack. In this article, we provide an overview of the effects of the cyberattack on our national radiation therapy service and our strategy to resume patient treatment in a timely fashion.