Reviewerly/exHarmony huggingface dataset:
Map Sketch Generation as a Service

This paper describes the structure of a web service able to generate simple game levels via constrained evolutionary optimization. The provided web service allows users to generate playable game levels without needing to understand the underlying process and without having to allocate computational resources for doing so; combined with the highly expressive and customizable generator, a broad range of levels for different genres and purposes can meet many user needs.
A Causal Study of Role Play Technique and Drilling Vocabulary to the Students’ Speaking Ability

In this era of Globalization, English Language is important for our lives. In fact, it is the second most spoken language in the world. Globalization made the use of English language more widen. The ability to speak and to write English property has been one essential benchmark skills in the professional world. The teacher can use some methods in teaching and learning process to help the students’ understanding. In this research, the researcher used role play technique and drilling vocabulary to enhance speaking skill. This research problems were; (1) Is there any influence of role play technique to the students’ speaking ability at SMA MUHAMMADIYAH TANJUNG REDEB?, (2) Is there any influence of drilling vocabulary to students’ speaking ability at SMA MUHAMMADIYAH TANJUNG REDEB?, (3) Is there any influence of role play technique and drilling vocabulary to the students’ speaking ability at SMA MUHMAMMADIYAH TANJUNG REDEB?. The method of this research is partial correlation method, The statistic value performed in level good correlation among Role play technique and drilling vocabulary to the students’ speaking ability during conducted for twelve at SMA Muhammadiyah Tanjung Redeb.
BRIDGING STUDENTS' SPEAKING SKILL THROUGH SPINNING WHEEL MEDIA AT SMP NEGERI 1 BONTOMARANNU

This research aimed to verify whether the use of Spinning Wheel Media effective to bridging students’ speaking skill in term of pronunciation and vocabulary after being taught through Spinning Wheel Media at the first grade of SMP Negeri 1 Bontomarannu. The researcher applied pre-experimental method with one group pre-test post-test design and the data were collected by giving pre-test, treatment and post-test. The sample of the research was VII 3 of SMP Negeri 1 Bontomarannu which consisted of 16 students. The population of this study was the first grade students of SMP Negeri 1 Bontomarannu which consisted of 296 students in the academic year of 2021/2022. The sample was taken by cluster random sampling technique to select the sample of the research. The technique used for obtaining data was speaking test. The research findings show that the mean score of the students in pronunciation is proved by post-test 53.12 is higher than the mean score of the students in pre-test 34.37. Mean score of the students vocabulary is proved by pre-test 42.18 and post-test 75 and the hypothesis t-test value were 6.54. It means that Ha is accepted. Based on the findings that there is significance difference between the result of pre-test and post-test. Spinning Wheel Media is very good in enhance the students speaking skill in terms of pronunciation and vocabulary.
Algorithmic Destruction

Contemporary privacy law does not go far enough to protect our privacy interests, particularly where artificial intelligence and machine learning are concerned. While many have written on problems of algorithmic bias and data deletion, this Article introduces the novel concept of the “algorithmic shadow” and explains the new privacy remedy of “algorithmic destruction,” also known as algorithmic disgorgement or machine unlearning. The algorithmic shadow describes the persistent imprint of training data that has been fed into a machine learning model and used to refine that machine learning system. This shadow persists even if data is deleted from the initial training data set, which means privacy rights like data deletion do not solve for the new class of privacy harms that arise from algorithmic shadows. Algorithmic destruction (deletion of models or algorithms trained on misbegotten data) has emerged as an alternative, or perhaps supplementary remedy and regulatory enforcement tool to address these new harms. This Article introduces two concepts to legal scholarship—the algorithmic shadow and algorithmic destruction. First, the Article defines the concept of the algorithmic shadow, a novel concept that has so far evaded significant legal scholarly discussion, despite its importance in changing understandings of privacy risks and harms. Second, the Article argues that data deletion does not solve for algorithmic shadow harms and advocates for the development of new privacy remedies to address these new harms. Finally, the Article introduces algorithmic destruction as a potential right and remedy, explaining its theoretical and practical applications, as well as potential drawbacks and concerns.
Specific Emitter Identification via Spatial Characteristic of Polarization Fingerprint

Radio frequency fingerprint (RFF) is a mature physical-layer identification technique for specific emitter identifi-cation (SEI). However, RFF faces the problem of low discrepancy. Therefore we propose polarization fingerprint (PF). PF has frequency characteristics derived from antenna hardware im-perfections and spatial characteristics derived from the vectorial properties of polarization. Based on these two characteristics, the mathematical model of PF is constructed. Because the spatial characteristics are not limited by the improvement of hardware manufacturing process, it still has a significant discrepancy. We also propose a spatial polarization compensation (SPC) for PF changes due to emitter movement. With spatial characteristics and SPC, PF can be effectively applied to SEI. However, the spatial characteristics also bring a trade-off between the identi-fication accuracy and the maximum emitter capacity. Finally, these two characteristics of PF and the proposed SPC are experimentally verified, then the identification accuracy of the proposed method is measured in real scenarios.
COSINER: COntext SImilarity data augmentation for Named Entity Recognition

To alleviate the scarcity of manually annotated data in Named Entity Recognition (NER) tasks, data augmentation methods can be applied to automatically generate labeled data and improve performance of existing methods. However, based on manipulations of the input text, current techniques may generate too many noisy and mislabeled samples. In this paper we propose COntext SImilarity-based data augmentation for NER (COSINER), a method for NER data augmentation based on context similarity, i.e. we replace entity mentions with the most plausible ones based on the available training data and the contexts in which entities usually appear. We conduct experiments on popular benchmark datasets, showing that our method outperforms current baselines in various few-shot scenarios, where training data is assumed to be strongly limited. Experimental results show that not only does COSINER overcome baselines in terms of NER performances in highly-limited scenarios (2%, 5%), but also its computing times are comparable to simplest augmentation methods.
Introduction to Transformers

Around December 2017, a seminal paper titled “Attention Is All You Need” was published. This paper revolutionized the way the NLP domain would look like in the times to come. Transformers and what is known as a sequence-to-sequence architecture are described in this paper.
Bayesian Tracking of Video Graphs Using Joint Kalman Smoothing and Registration

Graph-based representations are becoming increasingly popular for representing and analyzing video data, especially in object tracking and scene understanding applications. Accordingly, an essential tool in this approach is to generate statistical inferences for graphical time series associated with videos. This paper develops a Kalman-smoothing method for estimating graphs from noisy, cluttered, and incomplete data. The main challenge here is to find and preserve the registration of nodes (salient detected objects) across time frames when the data has noise and clutter due to false and missing nodes. First, we introduce a quotient-space representation of graphs that incorporates temporal registration of nodes, and we use that metric structure to impose a dynamical model on graph evolution. Then, we derive a Kalman smoother, adapted to the quotient space geometry, to estimate dense, smooth trajectories of graphs. We demonstrate this framework using simulated data and actual video graphs extracted from the Multiview Extended Video with Activities (MEVA) dataset. This framework successfully estimates graphs despite the noise, clutter, and missed detections.
Innovation of a Sliding Whiteboard for Teaching and Learning process

The innovation was done at Kenya Medical Training College, Nyeri. The main objectives were to come up with a whiteboard with increased writing surface area to avoid frequent &amp; disruptive rubbing of the written part of a whiteboard during a lecture session and to use a sliding whiteboard as a teaching aid in a classroom setup. The design innovation method was used. The idea was to stack two or three whiteboards and then slide the first board to reveal the second board underneath. Followed by sliding, the second board to reveal the third board underneath. A dummy was developed, followed by the development of the final product. A sliding whiteboard (i.e. Kimwetich sliding board) with increased writing/illustration surface area was developed in March 2022 at a cost of Ksh 35,000 (350 USD). After putting up the first whiteboard ever on campus, students and lecturers were very excited to use it. The lecturers reported that the writing surface area on the whiteboard has surprisingly increased and is very convenient to use a sliding whiteboard. The students said that teaching and learning were more exciting than before. The use of a sliding whiteboard (i.e. Kimwetich sliding board) can easily be replicated in other learning facilities and institutions. The same concept can be used in a blackboard, smart board, 3D screens, and computer PowerPoint slides. Since it offers an opportunity for stimulus variation, teaching and learning was made more enjoyable. Value for money was realized, in that, the normal whiteboard with only two (2) writing surfaces was replaced by the sliding whiteboard with seven (7) writing surfaces. The space for putting up a whiteboard in the classroom walls was reduced hence maximizing space. The whiteboard is simple to use and doesn’t require any training.
UX Poker: Estimating the Influence of User Stories on User Experience in Early Stage of Agile Development

Agile methods are used more and more frequently to develop products by reducing development time.Requirements are typically written in user stories or epics.In this paper, a new method called UX Poker is presented.This is a method to estimate the impact of a user story on user experience before development.Thus, there is the opportunity that the product backlog can also be sorted according to the expected UX.To evaluate UX Poker, a case study was conducted with four agile teams.Besides, a workshop followed by a questionnaire was conducted with all four agile teams.The goal of being able to estimate the UX even before development was achieved.Using UX Poker to create another way to sort the product backlog can be considered achieved in this first evaluation.The results show that UX Poker can be implemented in a real-life application.Additionally, during the use of UX Poker, it was found that a shared understanding of UX began.The participants clarified in the team discussion about UX Poker what related to influence the user stories had on UX and what UX meant for their product.
Handheld Augmented Reality: Overcoming Reachability Limitations by Enabling Temporal Switching to Virtual Reality

The paper presents an approach for handheld augmented reality in constrained industrial environments, where it might be hard or even impossible to reach certain poses within a workspace. Therefore, a user might not be able to see or interact with some digital content in applications like visual robot programming, robotic program visualizations, or workspace annotation. To overcome this limitation, we propose a temporal switching to a non-immersive virtual reality, enabling the user to see the workspace from any angle and distance. To explore how people would use it and what the benefits would be over pure augmented reality, we chose a representative task of object alignment and conducted a study. The results revealed that mainly physical demands, which is often a limiting factor for handheld augmented reality, could be reduced and that the usability and utility of the approach are rated as high. In the next iteration, we want to investigate other possibilities of controlling the viewpoint in the virtual environment, as the current approach has potential for improvements.
Density-Convoluted Support Vector Machines for High-Dimensional Classification

The support vector machine (SVM) is a popular classification method which enjoys good performance in many real applications. The SVM can be viewed as a penalized minimization problem in which the objective function is the expectation of hinge loss function with respect to the standard non-smooth empirical measure corresponding to the true underlying measure. We further extend this viewpoint and propose a smoothed SVM by substituting a kernel density estimator for the measure in the expectation calculation. The resulting method is called density convoluted support vector machine (DCSVM). We argue that the DCSVM is particularly more interesting than the standard SVM in the context of high-dimensional classification. We systematically study the rate of convergence of the elastic-net penalized DCSVM under general random design setting. We further develop novel efficient algorithm for computing elastic-net penalized DCSVM. Simulation studies and ten benchmark datasets are used to demonstrate the superior classification performance of elastic-net DCSVM over other competitors, and it is demonstrated in these numerical studies that the computation of DCSVM can be more than 100 times faster than that of the SVM.
Dynamic Hand Gesture Authentication Based on Improved Two-Stream CNN

Recently, dynamic hand gesture (DHG) has been discovered to be a promising biometric trait containing both physiological and behavioral characteristics simultaneously. DHGs are recorded by videos, so the authentication process is a challenging fine-grained video understanding task. Fully exploring physiological and behavioral characteristics to capture the fine-grained spatiotemporal identity features is the key to DHG authentication. Thus, in this paper, we propose to use the classic two-stream CNN for video understanding-based DHG authentication due to their explicit spatial (static) and temporal (dynamic) information modeling ability. Through analyzing prior two-stream CNN-based authentication methods in depth, we find five improvable aspects and propose the corresponding enhancement strategies. Comprehensive experiments on the SCUT-DHGA dataset show that the improved two-stream CNN can significantly outperform existing SOTA DHG authentication methods.
AI AND ML BASED GESTURE CONTROLLED VIRTUAL MOUSEACTIONS SYSTEM - A NOVEL APPROACH

In the current system, a wired/wireless mouse device is used. This needs a battery, a USB cable, and a dongle to connect mouse to a PC. In this work, AI and ML based gesture controlled virtual mouse actions system is proposed and developed. This is a novel approach. In this approach, human and PC interaction is achieved using hand gestures. To detect hand gestures, to control the position of the virtual mouse with the fingertip rather than a physical mouse device, a media pipe model is used. This proposed system is implemented in Python and it will reduce the likelihood of COVID-19 spreading by eliminating human intervention and the dependency on devices to control the PC directly.
PACS: A Dataset for Physical Audiovisual CommonSense Reasoning

In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, it must be able to robustly reason about the physical world. Fundamental to this reasoning is physical common sense: understanding the physical properties and affordances of available objects, how they can be manipulated, and how they interact with other objects. Physical commonsense reasoning is fundamentally a multi-sensory task, since physical properties are manifested through multiple modalities - two of them being vision and acoustics. Our paper takes a step towards real-world physical commonsense reasoning by contributing PACS: the first audiovisual benchmark annotated for physical commonsense attributes. PACS contains 13,400 question-answer pairs, involving 1,377 unique physical commonsense questions and 1,526 videos. Our dataset provides new opportunities to advance the research field of physical reasoning by bringing audio as a core component of this multimodal problem. Using PACS, we evaluate multiple state-of-the-art models on our new challenging task. While some models show promising results ( $$70\%$$ accuracy), they all fall short of human performance ( $$95\%$$ accuracy). We conclude the paper by demonstrating the importance of multimodal reasoning and providing possible avenues for future research.
On Contrasting YAGO with GPT-J: An Experiment for Person-Related Attributes

Language models (LMs) trained or large text corpora have demonstrated their superior performance in different language related tasks in the last years. These models automatically implicitly incorporate factual knowledge that can be used to complement existing Knowledge Graphs (KGs) that in most cases are structured from human curated databases. Here we report an experiment that attempts to gain insights about the extent to which LMs can generate factual information as that present in KGs. Concretely, we have tested such process using the English Wikipedia subset of YAGO and the GPT-J model for attributes related to individuals. Results show that the generation of correct factual information depends on the generation parameters of the model and are unevenly balanced across diverse individuals. Further, the LM can be used to populate further factual information, but it requires intermediate parsing to correctly map to KG attributes.
Witan

Effective supervised training of modern machine learning models often requires large labelled training datasets, which could be prohibitively costly to acquire for many practical applications. Research addressing this problem has sought ways to leverage weak supervision sources, such as the user-defined heuristic labelling functions used in the data programming paradigm, which are cheaper and easier to acquire. Automatic generation of these functions can make data programming even more efficient and effective. However, existing approaches rely on initial supervision in the form of small labelled datasets or interactive user feedback. In this paper, we propose Witan, an algorithm for generating labelling functions without any initial supervision. This flexibility affords many interaction modes, including unsupervised dataset exploration before the user even defines a set of classes. Experiments in binary and multi-class classification demonstrate the efficiency and classification accuracy of Witan compared to alternative labelling approaches.
A summary of personalized learning research

The rapid development of information technology has promoted the reform of both teaching and learning methods. Personalized learning has gradually become a new development trend. At the same time, technologies such as artificial intelligence, learning analysis and big data are also developing in depth, which is of significance to promoting the personalized development of students. To obtain a clearer understanding of the research status of personalized learning in our country. The paper was analyzed with the CiteSpace scientific knowledge graph analysis tool. The sample data of CNKI core journals from 2000 to 2021 were used. The sample data were analyzed by volume analysis, keyword co-occurrence analysis, cluster analysis, emergent analysis and timeline analysis. At the end of the article, the author made his own understanding and thinking about the development of personalized learning in the future.
Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

Industrial machinery is a significant energy consumer, and its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CO<sub>2</sub></i> emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. Estimating the power consumption of industrial machines with greater accuracy assists management and optimizes machine operation parameters. Real-time industrial machine datasets present several challenges, such as changes in the data over time, unknown running conditions, missing data, etc. Most research publications focus on the accuracy of traditional static models of forecasting; however, prediction performance deteriorates over time because data evolves. We implemented deep learning as a prediction model for three distinct real-world industrial datasets. The proposed method, dynamic modeling with memory (DMWM), improved overall prediction performance compared with conventional approaches by identifying concept drifts and optimizing the number of required models in response to industrial datasets’ recurring machine energy consumption patterns.
THE IMPLEMENTATION OF HIGHER-ORDER THINKING SKILL (HOTS) IN LEARNING READING FOR EFL LEARNERS

This research aims at finding out the students’ reading comprehension ability after implementing HOTS in learning process and the differences between those who are taught by implementing HOTS and those who are not, especially in making inference.This quasi-experimental research using two groups, experimental and control group through pre-test and post-test results. The samples are 23 students from class VIIIC and 22 students from class VIIIB of SMP Negeri 1 Cina which are taken by using Purposive Sampling Technique. The data obtained are analyzed using SPSS Application version 25.The finding shows that the mean score of pre-test in experimental class is 48.04 and the post-test score is 71.09. While in control class, pre-test score is 48.42 and in post-test is 50.91. After implementing HOTS in the classroom, reading comprehension ability increases to 32.42% and 4.9% in control class. This finding indicates that HOTS brings good effect in learning reading comprehension process because students can get used to questions related to making inference and their ability to answer reading comprehension questions improves. Students be able to predict questions and provide logical reasons related to texts and learning materials. Then it can be concluded that there are differences between those who are taught by HOTS and those who are not, in terms of learning outcomes and also the way they think.