Reviewerly/exHarmony huggingface dataset:
Learners’ View of English Language Learning Through Modular Approach - A Phenomenology

This research aimed to explore the views of English language learners of selected Grade six of Tenorio Elementary School, Maguindanao-II Division, who were undergone the modular approach to learning during the COVID-19 Pandemic. The Collaizi’s (1978) seven steps strategy in phenomenological approach was utilized to provide rigorous analysis of data. The purposive sampling technique was used to select the participants relevant to the research study. The researchers’ made semi-structure guided interview questionnaire with in-depth interview to the participants to explicate the research phenomena. Thus, study revealed that, learners experiences of modular learning affected by personal, teacher, subject matter and technological factor were Acclimatization to Modular Learning, Extrinsic Motivation, Health Issue, Lack of Adult’s Guidance or Teacher Supervision, Complexity of Subject Matter, and Quality Content of Module. Consequently, learners experiences as affected by technological factors were Power Outrage, Accessible to Internet, Lack of Mobile Phone. Moreover, learners observed attitude among in ELL through modular approach were, Knowledge of Content of Module, Development of Macro Skills, Views of Importance of ELL, Preference of Face to Face Learning. Constant Reading and Pre-reading, and Utilization of YouTube Video Lessons. Hence, the effects of learner’s attitude towards modular approach were, Learning Through Social Media Platform, Lack of Engagement of Learning and Guessing Only of Answers. Therefore, various studies have examined the effects of attitudes on ELL; however, few have worked on the influence of attitudes on ELL in the course of the Covid-19 pandemic. Synonymous to popular belief that a proper and positive attitude is imperative for achievement in language learning (Gardner, 1985), the study finds out that positive attitude towards modular learning brings positive actions which resulted to positive effects in ELL performance and that a negative attitude brings unlikely effect in ELL performance.
Solar Electricity Energy: Utilization of Renewable Energy Sources to Realize a Sustainable Campus at UIN Raden Fatah Palembang

Universities play an essential role in achieving Indonesia's 2060 carbon-neutral target. Emissions in the campus are mainly sourced from its electrical energy, transportation, and water use, along with other wastes. Electrical energy from solar is renewable energy, free from pollutants and noise pollution. This paper examines the sustainable energy performance of solar energy at the Universitas Islam Negeri Raden Fatah Palembang (UINRFP) and its development efforts. From August 2022 to March 2023, a solar power plant (SPP) operation with a capacity of 7.74 kWp has generated 6,281.5 kWh of electricity (28.55 kWh/day). Using a radiation factor of 0.8 and an equivalent sun hour of 4.61 (3-5 hours/day), the rooftop capacity of all buildings on Jakabaring Campus is 2,000 kWp, the potential of SPP electricity of UINRFP will exceed 39.3% compared to the 2022 electricity demand. If the capacity is, half the electricity generated from renewable energy meets 70% of the demand. Gradually UINRFP continues to develop its energy sources towards sustainable energy. The development of other research-scale renewable energy with hybrid solar energy and wind power for the aesthetic lighting function on the rectorate building has the potential to generate 290 kWh/year. In addition, micro-hydro energy sources from campus canals (water volume ±9,400 m3) and biomass are potential renewable energy sources at UINRFP for a sustainable campus in the future and contributing to reducing the impact of climate change.
Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things

Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with many sensors that actively collect data from patients and pass it on to cloud based storages using gateway sensors. Securing data have been significant barriers in many applications as false information get injected, or important information are modified or stolen at different phases of health care systems dependent on IoT. The attacks can also result in fatalities making it imperative to secure IoT based health care systems. A Hybrid technique combining MOAES (Modified Optimal Advanced Encryption Standard) with CM (Chaotic Map) Encryptions called HMOAES-CM technique is proposed. This technique can be helpful in securely accessing the patient data over online mode, and in addition, the data sharing can be performed in an encrypted form for the necessary targets of stakeholders. The proposed authentication approach is aimed at IoT, which is resilient to all kinds of network attacks and its implementation is also simpler. Comparing the suggested work to similar works, the level of evaluation is much improved.
Automatic Detection of Vehicular Traffic Elements based on Deep Learning for Advanced Driving Assistance Systems

This paper presents a prototype of an automobile driver assistance system based on YOLOv3. The system detects car types, traffic signs, and traffic lights in real-time and warns the driver accordingly. In the learning phase of the YOLO algorithm, the standard weights are learned first, followed by transfer learning to the objects of interest. The retraining phase uses 2,800 images obtained from the Internet of three countries of the real-life, and the testing phase uses real-time videos of Mexico City roads. In the validation phase, the proposed system achieves 95%, 37%, and 40% performance on the compiled dataset for the detection of road elements. The results obtained are comparable and in some cases better than those reported in previous works. Using a Raspberry Pi 4, the prototype was tested in real-life, generating visual and audible warnings for the driver, with an object recognition rate of 0.4 fps. A mean average precision (mAP) of 53% was reached by the proposed system. The experiments showed that the prototype achieved a poor recognition rate and required high computational processing for object recognition. However, YOLO is a model that can have good performance on low-resource hardware.
MULTI-REPRESENTATION-BASED E-MODULE ANALYSIS OF NEEDS IN THEME 3 SUBTEMA 1 IN 6TH GRADE ELEMENTARY SCHOOL

This research aims to analyze the needs of electronic modules (E-Modules) based on multi-representation using Flipbook PDF Professional. At this stage, researchers made observations and collected data on teaching materials at SDN 146 Palembang. This research uses a questionnaire with qualitative data types as the data collection. The data analysis technique used is descriptive analysis with data reduction, data presentation, conclusion, and verification. The data that needs to be analyzed by researchers are data regarding the use of the types of teaching materials used, the implementation of learning, the evaluation system, and the factors that support it. Based on the questionnaire data, 46.7% of students thought the learning material for theme 3, sub-theme 1, which discussed inventors who changed the world, could have been more interesting. Students stated this because 73.3% answered that no exciting teaching materials could make the material easily understood by students. Furthermore, based on the distribution of needs analysis questionnaires related to the e-module teaching materials to be developed, students with a percentage of 76.7% answered that they needed teaching materials that contained images, text, video, and audio or sound. E-Modules can be an alternative in developing teaching materials to support improving the quality of learning.
Crossseg: Cross-Scene Few-Shot Aerial Segmentation Using Probabilistic Prototypes

In this work, we propose a novel framework called CrossSeg that addresses the task of few-shot semantic segmentation for different aerial imagery. Conventional semantic segmentation approaches struggle to generalize well to unseen object categories, making them a significant limitation for modern intelligent systems, especially those deployed in realistic real-time settings, such as unmanned aerial vehicles (UAVs). CrossSeg overcomes this limitation and generalizes well in a cross-scene setting with only a few labeled samples. Unlike traditional methods that use a set of fixed prototypes for each class, CrossSeg utilizes high-quality probabilistic prototypes that can not only represent different semantic classes but also handle significant variations in different scenes. Experiments show that our approach significantly improves upon conventional few-shot segmentation baselines and does not require extensive tuning.
CapSpeaker: Injecting Commands to Voice Assistants via Capacitors

Recent studies have exposed that voice assistants can be manipulated by various voice commands without being noticed, however, existing attacks require a nearby speaker to play the attack commands. In this paper, we demonstrate that even without a speaker, we can use capacitors inside electronic devices to produce malicious voice commands, i.e., we convert capacitors into speakers and call it <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapSpeaker</monospace> . The underlying principle of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapSpeaker</monospace> is the inverse piezoelectric effect, i.e., varying the voltage across a capacitor to make it vibrate and thus emit acoustic noises. Forcing capacitors to emit target voice commands is challenging because (1) capacitors' response frequency is out of the range of audible voices. (2) We can not directly control the voltage across capacitors to manipulate their emit sounds. To overcome these challenges, we propose a PWM-based modulation scheme to embed the malicious audio onto a high-frequency carrier, e.g., above 20 kHz, and we create malware to induce the designed voltage across the capacitors such that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapSpeaker</monospace> plays the chosen malicious commands. Our evaluation of 7 commercial devices demonstrates that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapSpeaker</monospace> is feasible to inject voice commands, e.g., ”open the door”, at a distance of up to 10.5 cm.
QuDASH: Quantum-Inspired Rate Adaptation Approach for DASH Video Streaming

Internet traffic is dramatically increasing with the development of network technologies and video streaming traffic accounts for large amount within the total traffic, which reveals the importance to guarantee the quality of content delivery service.Based on the network conditions, adaptive bitrate (ABR) control is utilized as a common technique which can choose the proper bitrate to ensure the video streaming quality.In this paper, new bitrate control method, QuDASH is proposed by taking advantage of the emerging quantum technology.In QuDASH, the adaptive control model is developed using the quadratic unconstrained binary optimization (QUBO), which aims at increasing the average bitrate and decreasing the video rebuffering events to maximize the user quality of experience (QoE).In order to formulate the video control model, first the QUBO terms of different factors are defined regarding video quality, bitrate change, and buffer condition.Then, all the individual QUBO terms are merged to generate an objective function.By minimizing the QUBO objective function, the bitrate choice is determined from the solution.The control model is solved by Digital Annealer, which is a quantum-inspired computing technology.The evaluation of the proposed method is carried out by simulation with the throughput traces obtained in real world under different scenarios and the comparison with other methods is conducted.Experiment results demonstrated that the proposed QuDASH method has better performance in terms of QoE compared with other advanced ABR methods.In 68.2% of the examined cases, QuDASH achieves the highest QoE results, which shows the superiority of the QuDASH over conventional methods.
Semantic Face Segmentation Using Convolutional Neural Networks With a Supervised Attention Module

A self-attention module is often used in image segmentation tasks such as facial part segmentation. Because the self-attention module weights the features at each position using the weighted sum of features at all positions obtained by the middle layer of a convolutional neural network (CNN), the target regions for the segmentation might not be weighted sufficiently. The purpose of this study was to develop a semantic segmentation method for facial parts using a CNN with a supervised attention module that focuses on facial part enhancement. To improve the segmentation accuracy of the facial parts, we propose a new supervised attention module that can enhance features corresponding to pixels with the same class labels on input images and then incorporated it into the CNN. In this study, ResNet-FCN with skip connections was used as the baseline CNN model, and the CelebA Mask HQ dataset was used for the network training and evaluation of the network. The mean intersection over union (IoU) and Dice index for the proposed network were greater than those for ResNet-FCN, SegNet, and PSPNet without the self-attention module, non-local neural networks with the traditional self-attention module, Segformer, and U-Segformer-Hyper with a Transformer. The proposed network achieved a high mean IoU and Dice index, and hence will be useful for segmenting facial parts.
The Impact of Personality and Self-Efficacy on Domain Modeling Productivity in Graphical and Textual Notations

CONTEXT: Software development is a complex and human-intensive activity, where human factors can have a significant impact on productivity and quality of results. To address the complexity of software, domain modeling has gained much importance, mainly due to software methodologies such as Model-Driven Engineering and Domain-Driven Design. In particular, domain modeling is an essential task that allows developers to understand and effectively represent the problem domain. However, domain modeling productivity can be affected by several human factors, including developers' personality and self-efficacy.OBJECTIVE: The study aims to explore the influence of human factors, specifically developers' personality and self-efficacy, on domain modeling productivity.METHOD: An empirical controlled study was conducted with 134 third-year computer science students from the University of Alicante, guided by the definition of a theoretical model based on previous studies. Participants were tasked with creating domain models in both graphical and textual notations. The order of notation application was randomized, and participants were given different system specifications to model. Post-modeling, participants completed questionnaires assessing their personality, self-efficacy, and notation satisfaction. For the design and evaluation of the experiment, the Goal, Question, and Metrics framework was employed. Data analysis was conducted using a stepwise selection method to select the most appropriate linear regression model.RESULTS: The study indicate that personality and self-efficacy have a significant impact on the performance of domain model developers. Specifically, developers' belief in ability and use of graphical notation were found to have a positive influence on effectiveness and efficiency in creating domain models, while neuroticism had a negative influence on efficiency in both notations.CONCLUSIONS: These findings highlight the importance of considering human factors and notation choice in software development. Developers' personality and self-efficacy emerge as critical considerations for enhancing both productivity and quality in domain modeling.
Proposing the evaluation criteria for linguistic errors through surveying the Chinese-Vietnamese translation by students at the elementary level: a case study at the Faculty of Chinese Linguistics and Literature, USSH, VNUHCM

In the course of translation-related subject teaching, we have found that the translation teaching at the Faculty of Chinese Linguistics and Literature, USSH, VNUHCM has not had a consensus on the evaluation criteria for the Chinese-Vietnamese translation in accordance with the teaching environment, which makes it difficult for both teachers and learners in the teaching and learning process. Therefore, specifying the error scale is essential for the objective assessment of students' learning outcomes. With the goal of building an error scale as a basis for evaluation, the study uses the actual data on linguistic translation errors collected from learners' Chinese-Vietnamese translation practice, combined with Chinese-Vietnamese translation theories (selecting the criteria that can be applied to the teaching environment) and translation analysis methods, thereby proposing the suitable criteria for the error scale of evaluating the Chinese-Vietnamese translation at the elementary level. The research results suggest the criteria for identifying common linguistic errors in the Chinese-Vietnamese translation at the elementary level. In addition, these results will support teachers better in marking and evaluating elementary-level translation tests as well as helping learners better understand common errors in the process of learning and practicing elementary-level translation.
REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes

Deep Learning models, such as those used in an autonomous vehicle are vulnerable to adversarial attacks where an attacker could place an adversarial object in the environment, leading to mis-classification. Generating these adversarial objects in the digital space has been extensively studied, however successfully transferring these attacks from the digital realm to the physical realm has proven challenging when controlling for real-world environmental factors. In response to these limitations, we introduce REVAMP, an easy-to-use Python library that is the first-of-its-kind tool for creating attack scenarios with arbitrary objects and simulating realistic environmental factors, lighting, reflection, and refraction. REVAMP enables researchers and practitioners to swiftly explore various scenarios within the digital realm by offering a wide range of configurable options for designing experiments and using differentiable rendering to reproduce physically plausible adversarial objects. We will demonstrate and invite the audience to try REVAMP to produce an adversarial texture on a chosen object while having control over various scene parameters. The audience will choose a scene, an object to attack, the desired attack class, and the number of camera positions to use. Then, in real time, we show how this altered texture causes the chosen object to be mis-classified, showcasing the potential of REVAMP in real-world scenarios. REVAMP is open-source and available at https://github.com/poloclub/revamp.
A Principled Decomposition of Pointwise Mutual Information for Intention Template Discovery

With the rise of Artificial Intelligence (AI), question answering systems have become common for users to interact with computers, e.g., ChatGPT and Siri. These systems require a substantial amount of labeled data to train their models. However, the labeled data is scarce and challenging to be constructed. The construction process typically involves two stages: discovering potential sample candidates and manually labeling these candidates. To discover high-quality candidate samples, we study the intention paraphrase template discovery task: Given some seed questions or templates of an intention, discover new paraphrase templates that describe the intention and are diverse to the seeds enough in text. As the first exploration of the task, we identify the new quality requirements, i.e., relevance, divergence and popularity, and identify the new challenges, i.e., the paradox of divergent yet relevant paraphrases, and the conflict of popular yet relevant paraphrases. To untangle the paradox of divergent yet relevant paraphrases, in which the traditional bag of words falls short, we develop usage-centric modeling, which represents a question/template/answer as a bag of usages that users engaged (e.g., up-votes), and uses a usage-flow graph to interrelate templates, questions and answers. To balance the conflict of popular yet relevant paraphrases, we propose a new and principled decomposition for the well-known Pointwise Mutual Information from the usage perspective (usage-PMI), and then develop a Bayesian inference framework over the usage-flow graph to estimate the usage-PMI. Extensive experiments over three large CQA corpora show strong performance advantage over the baselines adopted from paraphrase identification task. We release 885,000 paraphrase templates of high quality discovered by our proposed PMI decomposition model, and the data is available in site https://github.com/Para-Questions/Intention\_template\_discovery.
AmpliBias: Mitigating Dataset Bias through Bias Amplification in Few-shot Learning for Generative Models

Deep learning models exhibit a dependency on peripheral attributes of input data, such as shapes and colors, leading the models to become biased towards these certain attributes that result in subsequent degradation of performance. In this paper, we alleviate this problem by presenting~\sysname, a novel framework that tackles dataset bias by leveraging generative models to amplify bias and facilitate the learning of debiased representations of the classifier. Our method involves three major steps. We initially train a biased classifier, denoted as f_b, on a biased dataset and extract the top-K biased-conflict samples. Next, we train a generator solely on a bias-conflict dataset comprised of these top-K samples, aiming to learn the distribution of bias-conflict samples. Finally, we re-train the classifier on the newly constructed debiased dataset, which combines the original and amplified data. This allows the biased classifier to competently learn debiased representation. Extensive experiments validate that our proposed method effectively debiases the biased classifier.
KONFLIK BATIN TOKOH NOVEL SENJA DAN PAGI KARYA ALFFY REV DAN LINKA ANGELIA: PSIKOLOGI SASTRA

This study aims to describe the characterizations and inner conflicts experienced by the character Alffy. This research method is descriptive qualitative. The research data is in the form of sentences and quotes that contain characterizations and inner conflicts. The data source for this research is the novel Senja Dan Pagi by Alffy Rev and Linka Angelia. Data collection techniques are reading, recording, and analyzing. The analysis techniques of this research are reduction, data presentation, and conclusions. The results of this study are the characterizations of Alffy's character including responsibility, compassionate, kind, and brave. Inner conflict based on Abraham Maslow's theory that found data on the non-fulfillment of five needs, namely physiological, security, belonging and love, appreciation, and self-actualization. Alffy's character experiences an inner conflict due to unfulfilled needs which causes Alffy's character to feel afraid, sad, disappointed, and hopeless.