Reviewerly/exHarmony huggingface dataset:
PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation

This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, PA-Tran, that jointly estimates the keypoints status and 3D hand pose from a single RGB image with two dependent branches. The regression branch consists of a Transformer encoder which is trained to predict a set of target keypoints, given an input set of status, position, and visual features embedding from a convolutional neural network (CNN); the classification branch adopts a CNN for estimating the keypoints status. One key idea of PA-Tran is a selective mask training (SMT) objective that uses a binary encoding scheme to represent the status of the keypoints as observed or unobserved during training. In addition, by explicitly encoding the label status (observed/unobserved), the proposed PA-Tran can efficiently handle the condition when only partial annotation is available. Investigating the annotation percentage ranging from 50–100%, we show that training with partial annotation is more efficient (e.g., achieving the best 6.0 PA-MPJPE when using about 85% annotations). Moreover, we provide two new datasets. APDM-Hand, is for synthetic hands with APDM sensor accessories, which is designed for a specific hand task. PD-APDM-Hand, is a real hand dataset collected from Parkinson’s Disease (PD) patients with partial annotation. The proposed PA-Tran can achieve higher estimation accuracy when evaluated on both proposed datasets and a more general hand dataset.
Modification of SqueezeNet for Devices with Limited Computational Resources

In recent years, the computational approach has shifted from a statistical basis to deep neural network architectures which process the input without explicit knowledge that underlies the model. Many models with high accuracy have been proposed by training the datasets using high performance computing devices. However, only a few studies have examined its use on non-high-performance computers. In fact, most users, who are mostly researchers in certain fields (medical, geography, economics, etc.) sometimes need computers with limited computational resources to process datasets, from notebooks, personal computers, to mobile processor-based devices. This study proposes a basic model with good accuracy and can run lightly on the average computer so that it remains lightweight when used as a basis for advanced deep neural networks models, e.g., U-Net, SegNet, PSPNet, DeepLab, etc. Using several well-known basic methods as a baseline (SqueezeNet, ShuffleNet, GoogleNet, MobileNetV2, and ResNet), a model combining SqueezeNet with ResNet, termed Res-SqueezeNet, was formed. Testing results show that the proposed method has accuracy and inference time of 84.59% and 8.46 second, respectively, which has an accuracy of 2% higher than the SqueezeNet (82.53%) and is close to the accuracy of other baseline methods (from 84.93% to 0.88.01%) while still maintaining the inference speed (below nine second). In addition, residual part of the proposed method can be used to avoid vanishing gradient, hence, it can be implemented to solve more advanced problems which need a lot of layers, e.g., semantic segmentation, time-series prediction, etc.&#x0D;
Mobile Eye Tracking during Experimenting with Digital Scaffolding—Gaze Shifts between Augmented Reality and Experiment during Zinc Iodide Electrolysis Set-Up

In this exploratory eye tracking study, gaze behavior during an experiment on electrolysis of a zinc iodide solution has been examined. The experiment is designed according to inquiry-based learning and therefore well suited to students’ self-directed experimenting in science classrooms. Scaffolding by means of stepwise presented digital hints (sequential scaffolds), here by means of augmented reality (AR), supported learners during the experiment. Two succeeding AR-hints were offered. This research focuses on the set-up of the experiment by learners and the change of gaze between the AR-hints and the real experiment. This was investigated by means of mobile eye tracking. For the analysis of the gaze changes, scanpath analyses are used. Areas of Interest-based visualizations such as radial transition graphs and scarf plots are used to compare and detect similar or different gaze behaviors of participants. Results reveal that students first look at the AR-hints, try to memorize the construction and then switch to setting up the experiment. Recurrent changes of gaze between the AR-hints and the experiment hardly, or do not, occur. Furthermore, students who had already set up the experiment correctly with the first hint only use the second hint for evaluating correctness of their own set-up.
iColoriT: Towards Propagating Local Hints to the Right Region in Interactive Colorization by Leveraging Vision Transformer

Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with minimal user effort. However, existing approaches often produce partially colorized results due to the inefficient design of stacking convolutional layers to propagate hints to distant relevant regions. To address this problem, we present iColoriT, a novel point-interactive colorization Vision Transformer capable of propagating user hints to relevant regions, leveraging the global receptive field of Transformers. The self-attention mechanism of Transformers enables iColoriT to selectively colorize relevant regions with only a few local hints. Our approach colorizes images in real-time by utilizing pixel shuffling, an efficient upsampling technique that replaces the decoder architecture. Also, in order to mitigate the artifacts caused by pixel shuffling with large upsampling ratios, we present the local stabilizing layer. Extensive quantitative and qualitative results demonstrate that our approach highly out-performs existing methods for point-interactive colorization, producing accurately colorized images with a user's minimal effort. Official codes are available at https://pmh9960.github.io/research/iColoriT/.
Network Optimization based on Genetic Algorithm for High-Level Data Classification

High-level data classification techniques are capable of considering not only physical aspects of the data, such as space, distance, proximity, distribution, but can also consider their functional, topological and structural aspects. High-level techniques are commonly defined in two major steps: the construction of a network from the feature vector data and the uncovering of its underlying patterns using complex networks properties. In the network construction step, heuristics based on k-nearest neighbors strategies have been widely adopted, while several complex network measures (e.g. PageRank) have been modeled to learn high-level patterns of the input data. As both steps are directly related, i.e., the network configuration impacts directly the results obtained by the classifier, in this paper we develop a genetic algorithm (GA) to optimize the network construction step. To be specific, we hypothesize that the salient features of GAs, such as their robust search mechanism and binary representation, may provide a more powerful network representation in the context of the high-level classification based on importance characterization. In summary, extensive experiments with real data sets demonstrate that the networks provided by our GA strategy achieved higher predictive accuracy than those of a widely adopted method based on the nearest neighbors heuristic and competitive results against state-of-the-art ones.
The Effect of Picture Series on Teaching English Writing

Junior high school students are expected to create short essays, and concise texts, according to the junior high school curriculum. The issue is that students are unable to communicate their thoughts. It is proved that when the teacher asks the students to write about recount text or narrative text, they only write a few sentences, and they also make mistakes in using grammar patterns. This research aims to reveal the effect of picture series in teaching writing, and whether students who were taught through a picture series goa t better scores than those who were taught through the conventional method. This research used a quasi-experimental design. The finding showed that picture series could enable students to explore their ideas. The students were able to create and use their imagination to write well after watching the picture series. The result of the T-test analysis showed that the sig (2-tailed) was 0.000, which was lower than 0.05 level significance (0.000 &lt; 0.05). It was assumed that the mean score difference between the experimental and control groups was significant. Thus, it was enough to reject the null hypothesis, which means that picture series-based E-learning significantly affected students’ writing, especially in writing narrative text.
Trust and Distrust in Conversational AI Agents: The Effects of Agent Interaction Style and User Information Need

<p style="margin-bottom: 0px; font-stretch: normal; font-size: 13px; line-height: normal; font-family: &quot;Helvetica Neue&quot;;">Conversational AI agents (CAs) are computer programs with the ability to interpret and respond to users’ natural language and communicate with users as humans would do with another human. CAs are being used more frequently as enterprise assistants, sales and marketing agents, customer care representatives, and data collection tools. The need to comprehend how they affect consumer trust or distrust in CAs has arisen as a result of the expansion of the CA market and the increasing roles that CAs play in consumption decisions. In order to meet this demand, this paper makes propositions regarding how user information requirements and CA interaction style may affect consumer trust and distrust in CAs as well as the mechanisms through which these influences take place based on the&nbsp;<span style="font-family: &quot;Helvetica Neue&quot;; font-size: 13px;">trust-distrust theory, social response theory, construal level theory, and cognitive load theory.</span>
Improvement of Miller Loop for a Pairing on FK12 Curve and its Implementation

Pairing is carried out by two steps, Miller loop and final exponentiation. In this manuscript, the authors propose an efficient Miller loop for a pairing on the FK12 curve. A Hamming weight and bit-length of loop parameter have a great effect on the computational cost of Miller loop. Optimal-ate pairing is used as the most efficient pairing on the FK12 curve currently. The loop parameter of optimal-ate pairing is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6z+2$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$z$</tex> is the integer to make the FK12 curve parameter. Our method uses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$z$</tex> which has a shorter bit-length than the previous optimal-ate pairing as the loop parameter. Usually, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$z$</tex> has a low Hamming weight to make final exponentiation efficient. Therefore, the loop parameter in our method has a lower Hamming weight than the loop parameter of the previous one in many cases. The authors evaluate our method by the number of multiplications and execution time. As a result, the proposed algorithm leads to the 3.71% reduction in the number of multiplications and the 3.38% reduction in the execution time.
AIoT-based smart education and online teaching

This research will look at an Artificial Intelligence and Internet of Things (IoT) based model of teaching and learning for online course, as well as teaching-learning methodologies design of the curriculum used in the course. The purpose is to offer an "educational curriculum design model" for engineering students. Students in technical courses can learn about small private online courses (SPOC)-AIoT using these modules, and we demonstrated their usefulness through teaching activities. Using a discover, define, develop, deliver double diamond shape strategy, the course and teaching content were designed in order to evaluate students&apos; self-perception and fear of learning during the experimental teaching of AIoT. Students&apos; happiness and effectiveness were studied using a technological acceptance paradigm. During the paradigmatic phase, routes were calculated and hypotheses were validated by bootstrapping, while SPSS was used to analyse measurement and structural models. Using tiny online learning courses in the flipped teaching method immediately gets students&apos; attention and boosts their learning involvement. It is the findings of the study that "self-perception" has a significant positive effect on a user&apos;s perception of "usefulness" and "ease of use." According to the study, "fear of learning" is not significantly associated with the "ease of use" and "utility" of flipped learning in combination with online e-learning. There is a positive correlation between the ease of use and the usefulness of digital teaching materials used in flipped teaching that can be associated with predictions of student behaviour. "Perceived ease of use" is the most important factor with a high impact on the "usability" of a product. "Engaged learning" results in a substantial improvement in students&apos; actual "behavioral" attitude toward learning. In comparison to other subjects, science and technology are highly relevant to student learning.
Incremental Learning in Time-series Data using Reinforcement Learning

System monitoring has become an area of interest with the increasing growth in wearable sensors and continuous monitoring tools. However, the generalizability of the classification models to unseen incoming data remains challenging. This paper proposes a novel architecture based on reinforcement learning (RL) to incre-mentally learn patterns of time-series data and detect changes in the system state. Our rationale is that RL's ability to learn from past experiences can help increase the performance and generalizability of classification models in time-series monitoring applications. Our novel definition of the environment consists of a set of one-class anomaly detectors to define environment states based on the dynamics of the incoming data and a reward function to reward the RL agent according to its actions. A deep RL agent incrementally learns to perform continuous, binary classification predictions according to the environment states and the received reward. We applied the proposed model for detecting response to medication (ON or OFF) in patients with Parkinson's disease (PD). The PD dataset consisted of 170 minutes of time-series movement signals collected from 12 patients using two wearable sensors. Our proposed model, with a testing accuracy of 77.95%, outperformed Adaptive Boosting, Multi-layer Perceptron, and Support Vector Machines with 53.10%, 44.92%, and 52.70% testing accuracy, respectively. The proposed model had a slight decline in the F-score, decreasing from 88.15% validation score to 78.42% in testing, a significantly slight decline compared to the other three models. These evidence the potential of the proposed RL-based classifier in time-series monitoring applications as a highly generalizable model for unseen incoming data.
OLCBot: Dissemination Of Interactive Information Related To Indonesia’s Omnibus Law With The Implementation of Fuzzy String Matching Algorithm and Sastrawi Stemmer

Omnibus Law is a concept of the primary law that regulates as well as revises several laws. Undang Undang Cipta Kerja (in English: Job Creation Law), which is now ratified as Law of the Republic of Indonesia No. 11 of 2020 on Job Creation, is one of the targets of the omnibus law. However, since it was ratified on Monday, October $5^{\mathrm{th}}$, 2020, the law has continued to cause debate and polemic from various elements of society. The reason is the lack of information media related to the Job Creation Law, especially interactive media for information dissemination. Moreover, the official document has 1187 pages which led the people hard to comprehend the document itself. One of the technologies that can be used as interactive media is a chatbot. A chatbot is a computer program that can simulate a conversation with a human. Chatbot itself is the development of Natural Language Processing. This study aims to build OLCBot, a chatbot that can act as an interactive medium for disseminating information on this law. Using the Fuzzy String Matching approach and Sastrawi stemmer, this chatbot is expected to understand and answer users’ questions related to the law. The chatbot knowledge base was taken from the manuscript of the Law of the Republic of Indonesia No. 11 of 2020 on Job Creation. The results showed that OLCBot had an accuracy rate of 91.30% in answering questions from users regarding the Job Creation Law or the chatbot was able to answer 42 out of 46 questions.
Research Trend Analysis of Empirical Studies of Education Using Metaverse in Korea

In a situation where the metaverse is being actively studied in the field of education due to the development of technology, it is necessary to analyze the current trend of metaverse-related education research and propose guidelines for application in Korea. This study collected 67 empirical studies in Korea using the metaverse for teaching and learning. Each paper was analyzed for the year of publication, participants, learning contents, teaching-learning methods, metaverse scenarios, learning effects, and learning hindrance factors. As a result of the analysis, it was found that there is a lack of empirical studies using mirror worlds and life logging for education compared to AR and virtual worlds. Also, AR was used at earlier school levels, such as kindergartens and elementary schools, unlike the other metaverse scenarios mainly used in universities. The learning contents applied to each metaverse scenario were different, showing each metaverse scenario's unique characteristics. In all metaverse scenarios, complementary teaching methods appeared the most, while learner-centered teaching methods appeared the least. When used in teaching and learning, AR showed high levels of immersion and achievement improvement and strength in ease of use, with overall low learning hindrance factors. It has been confirmed that virtual worlds had strengths in interaction and presence but also had high cognitive load and technical problems, so they required sufficient control by teachers. Mirror worlds provided content that could interact with learners, but they had to be appropriately linked to real-world activities. Life logging showed high interaction and presence, but related studies were limited. Based on the results of this study, future applications and research directions were discussed.
Proficiency Assessment of L2 Spoken English Using Wav2Vec 2.0

The increasing demand for learning English as a second language has led to a growing interest in methods for automatically assessing spoken language proficiency. Most approaches use hand-crafted features, but their efficacy relies on their particular underlying assumptions and they risk discarding potentially salient information about proficiency. Other approaches rely on transcriptions produced by ASR systems which may not provide a faithful rendition of a learner's utterance in specific scenarios (e.g., non-native children's spontaneous speech). Furthermore, transcriptions do not yield any information about relevant aspects such as intonation, rhythm or prosody. In this paper, we investigate the use of wav2vec 2.0 for assessing overall and individual aspects of proficiency on two small datasets, one of which is publicly available. We find that this approach significantly outperforms the BERT-based baseline system trained on ASR and manual transcriptions used for comparison.
Cyber Wars and Cyber Threats Against Mobile Devices

In recent years, the electronic device that has been used and bought the most by the consumers is mobile devices. Recently, there has been a significant increase in cyber crimes committed using mobile devices. It has been seen that this situation has become more critical with the widespread use of mobile devices in society, and it is of great importance to take some precautions. Unlike traditional crimes, detecting and analyzing these types of crimes requires special and technical knowledge. The detection of crimes committed using mobile devices is possible with the evidence obtained in accordance with the procedure. Therefore, in this study, a forensic analysis process in which data in a mobile device has been accessed and extracted using UFED 4PC mobile forensic software, and the acquired data has been analyzed using cellebrite physical analyzer has been carried out. As the analysis shows, these software-based tools can be used to extract mobile device data and analyze mobile devices with different brands, models, and operating systems.
Scene description with context information using dense-LSTM

Generating natural language description for visual content is a technique for describing the content available in the image(s). It requires knowledge of both the domains of computer vision and natural language processing. For this, various models with different approaches are suggested. One of them is encoder-decoder-based description generation. Existing papers used only objects for descriptions, but the relationship between them is equally essential, requiring context information. Which required techniques like Long Short-Term Memory (LSTM). This paper proposes an encoder-decoder-based methodology to generate human-like textual descriptions. Dense-LSTM is presented for better description as a decoder with a modified VGG19 encoder to capture information to describe the scene. Standard datasets Flickr8K and Flickr30k are used for testing and training purposes. BLEU (Bilingual Evaluation Understudy) score is used to evaluate the generated text. For the proposed model, a GUI (Graphical User Interface) is developed, which produces the audio description of the output received and provides an interface for searching the related visual content and query-based search.
Application of particle swarm optimization algorithm in improved association rules data mining method

The traditional particle swarm optimization algorithm is generally only suitable for solving the association rule mining of continuous data, and the improved PSO algorithm needs to be used for the association rule mining of discrete data. Firstly, the FP-growth algorithm is used to construct a fuzzy FP-tree to mine frequent itemsets, and then the PSO algorithm is applied to the mined frequent itemsets. Aiming at the shortcomings of the association rule mining algorithm of PSO, an improved association rule data mining algorithm is proposed. This paper proposes application of particle swarm optimization algorithm in improved association rules data mining method. Experiments show that the particle swarm optimization algorithm proposed in this paper is feasible and efficient in improving association rule data mining.