Reviewerly/exHarmony huggingface dataset:
Real-Time Data Monitoring of an Industry 4.0 Assembly Line using Pervasive Augmented Reality: First Impressions

Industry 4.0 is the latest industrial revolution, tend to seamlessly integrate digital and physical worlds into industrial procedures in an effort to better support different stakeholders' needs. This work proposes a Pervasive Augmented Reality (AR) first prototype, developed in collaboration with industrial partners, to support real-time data monitoring and problem detection of an industrial assembly line. A Human-Centered Design (HCD) methodology was used to identify stakeholders' difficulties, needs, and challenges, as well as define requirements to guide the development of the prototype. The first impressions of these individuals are described, after an initial user study on the industrial shop floor, conducted to evaluate the proposed prototype, and collect feedback on how it can be improved to better support such contexts.
Concise Memory Organization for a Customizable Hardware Design of a Quantum Coprocessor

This work proposes a concise memory organization for an efficient customizable hardware design of a coprocessor that is able to perform quantum computations, exploring pure parallelism as well as a pipelined execution. The architecture is based on a quantum state memory, an operator memory, and a scratch memory, besides the calculation unit, the measurement unit and the control unit. All functional units communicate with each other via dedicated and shared data buses, depending information exchange frequency. In this paper, we focus on memory organization.
CIDO: Chaotically Initialized Dandelion Optimization for Global Optimization

Metaheuristic algorithms are widely used for problems in many fields such as security, health, engineering. No metaheuristic algorithm can achieve the optimum solution for all optimization problems. For this, new metaheuristic methods are constantly being proposed and existing ones are being developed. Dandelion Optimizer, one of the most recent metaheuristic algorithms, is biology-based. Inspired by the wind-dependent long-distance flight of the ripening seed of the dandelion plant. It consists of three phases: ascending phase, descending phase and landing phase. In this study, the chaos-based version of Chaotically Initialized Dandelion Optimizer is proposed for the first time in order to prevent Dandelion Optimizer from getting stuck in local solutions and to increase its success in global search. In this way, it is aimed to increase global convergence and to prevent sticking to a local solution. While creating the initial population of the algorithm, six different Chaotically Initialized Dandelion Optimizer algorithms were presented using the Circle, Singer, Chebyshev, Gauss/Mouse, Iterative and Logistic chaotic maps. Two unimodal (Sphere and Schwefel 2.22), two multimodal (Schwefel and Rastrigin) and two fixed-dimension multimodal (Foxholes and Kowalik) quality test functions were used to compare the performances of the algorithms. When the experimental results were analyzed, it was seen that the Chaotically Initialized Dandelion Optimizer algorithms gave successful results compared to the classical Dandelion Optimizer.
Increasing Students’ Motivation and Ability to Write Descriptive Text Using Social Media and Photo Comics in PBL Method

Students always have difficulty in writing composition using English. This encourages teachers to conduct research. The purpose of study is to improve students' motivation and ability in writing descriptive text, using social media and photo comics. The social media used in this research are Youtube and Whatsapp which support the learning process. Meanwhile, the photo comic used in this study is expected to be of interest to students, so that it can encourage students to be motivated in learning English, including being eager to write Descriptive Text. Because students interested in photo comics and make them easy to write. Students are helped to make some sentences by images of photo comic. The method used in this research is Project Based Learning (PBL). The subjects are 7th grade students of SMPN 8 Surakarta in second semester 2021/2022. Students had to achieve the Base Competence of English Curriculum 7th grade 4.7.2 that is Compiling oral and written descriptive texts, very short and simple, related to people, animals, and objects that they like by paying attention to social functions, text structure, and linguistic elements, correctly and in context. The results showed that social media and photo comics are very effective in increasing students' motivation and ability to write descriptive text. They are very interesting and effective sources of ideas for students in writing the text. Finally, students increased their motivation and ability in writing Descriptive Text, and also can reach the Base Competence of English curriculum of 7th grade (KD 4.7.2.).
Fair oversampling technique using heterogeneous clusters

Class imbalance and group (e.g., race, gender, and age) imbalance are acknowledged as two reasons in data that hinder the trade-off between fairness and utility of machine learning classifiers. Existing techniques have jointly addressed issues regarding class imbalance and group imbalance by proposing fair oversampling techniques. Unlike the common oversampling techniques, which only address class imbalance, fair oversampling techniques significantly improve the abovementioned trade-off, as they can also address group imbalance. However, if the size of the original clusters is too small, these techniques may cause classifier overfitting. To address this problem, we herein develop a fair oversampling technique using data from heterogeneous clusters. The proposed technique generates synthetic data that have class-mix features or group-mix features to make classifiers robust to overfitting. Moreover, we develop an interpolation method that can enhance the validity of generated synthetic data by considering the original cluster distribution and data noise. Finally, we conduct experiments on five realistic datasets and three classifiers, and the experimental results demonstrate the effectiveness of the proposed technique in terms of fairness and utility.
Proximity-Based Reward System and Reinforcement Learning for Path Planning

Path planning is an important and complex task in the field of robotics and automation. It consists of finding the optimal path given a starting location, obstacles and a final destination. Reinforcement learning is a trial and error approach that has seen success in the field of path planning. Multiple reinforcement learning algorithms such as Q-learning and SARSA exist and have achieved great results. These algorithms typically use a uniform reward system such that every move, collision and goal return a specific reward. We propose a proximity-based reward system for classical reinforcement learning algorithms on path planning scenarios. We compare our reward systems combined with different optimization techniques and algorithms for path planning. These approaches are compared using the total completion rate for the mazes and average training time. We achieved interesting results with our reward systems and optimization techniques allowing us to decrease the training time.
Precise determination of major and trace elements in micrometer-scale ilmenite lamellae in titanomagnetite using LA-ICP-MS technique: application of regression analysis to time-resolved signals

Laser ablation ICP-MS (LA-ICP-MS) is a powerful microbeam technique capable of rapid and precise determination for a large spectrum of trace elements at ppm or sub-ppm levels. Micrometer-scale minerals and inclusions are very common in geological materials, for which direct measurement is restricted by the spot size using LA-ICP-MS (generally 20-50 μm). In this study, ilmenite lamellae intergrown with magnetite were selected as an example to describe a practical algorithm that applies regression analysis to extract the chemical compositions of binary phases from mixed LA-ICP-MS signals. The method accuracy is confirmed by the agreement between the regressed value for various trace elements in ilmenite exsolutions and their reference values (direct analyses using EPMA and LA-ICP-MS). Results were obtained for most detectable components (Mg, Mn, V, Nb, Ta, Sc, Zr, Hf, Sn, et c.) and their relative deviations are within ±10%, even for those <10 ppm (such as Hf and W). Relative standard errors on the regressed value were calculated to evaluate the precision of the method, which is mostly within 10%, and the worst up to 25%. Therefore, the algorithm described in this contribution provides a solution for precise determination of trace element compositions for micrometer-scale ilmenite lamellae in titanomagnetite using LA-ICP-MS, and is potentially practical for other geological materials.
Investigation of You Only Look Once Networks for Vision-based Small Object Detection

Small object detection is a challenging issue in computer vision-based algorithms.Although various methods have been investigated for common objects including person, car and others, small object are not addressed in this issue.Therefore, it is necessary to conduct more researches on them.This paper is focused on small object detection especially jewellery as current object detection methods suffer from low accuracy in this domain.This paper introduces a new dataset whose images were taken by a web camera from a jewellery store and data augmentation procedure.It comprises three classes, namely, ring, earrings, and pendant.In view of the small target of jewellery and the real-time detection, this study adopted the You Only Look Once (Yolo) algorithms.Different Yolo based model including eight versions are implemented and train them using our dataset to address most effective one.Evaluation criteria, including accuracy, F1 score, recall, and mAP, are used to evaluate the performance of the various YOLOv5, YOLOv6, and YOLOv7 versions.According to the experimental findings, utilizing YOLOv6 is significantly superior to YOLOv7 and marginally superior to YOLOv5.
Robust and Tiny Binary Neural Networks using Gradient-based Explainability Methods

Binary neural networks (BNNs) are a highly resource-efficient variant of neural networks. The efficiency of BNNs for tiny machine learning (TinyML) systems can be enhanced by structured pruning and making BNNs robust to faults. When used with approximate memory systems, this fault tolerance can be traded off for energy consumption, latency, or cost. For pruning, magnitude-based heuristics are not useful because the weights in a BNN can either be -1 or +1. Global pruning of BNNs has not been studied well so far. Thus, in this paper, we explore gradient-based ranking criteria for pruning BNNs and use them in combination with a sensitivity analysis. For robustness, the state-of-the-art is to train the BNNs with bit-flips in what is known as fault-aware training. We propose a method to guide fault-aware training using gradient-based explainability methods. This allows us to obtain robust and efficient BNNs for deployment on tiny devices. Experiments on audio and image processing applications show that our proposed approach outperforms the existing approaches, making it useful for obtaining efficient and robust models for a slight degradation in accuracy. This makes our approach valuable for many TinyML use cases.
Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory

Sentiment analysis is one method for evaluating public opinion from the received text. In this study, we evaluate the performance of the LSTM model with Sastrawi in sentiment analysis in Indonesian using a Twitter dataset totaling 2537 data collected regarding the Russo-Ukrainian war. The purpose of this study is to determine the reliability of the LSTM model with Sastrawi in sentiment analysis in Indonesian and to evaluate the performance of the model with the collected Twitter dataset regarding the Russian-Ukrainian war. The method used in this study is data pre-processing, training and validation of the LSTM model with Literature, and model evaluation using the metrics of accuracy, precision, recall, and F1 score. In the dataset collected in this study, positive, neutral and negative sentiments were 54.7%, 35% and 10.2%. The results obtained from this study indicate that the LSTM model with Literature can provide good results in sentiment analysis with a prediction accuracy of 82%. The implication of the results of this study is that the LSTM model with Sastrawi can be used for sentiment analysis on Twitter and further research needs to be carried out with a wider and more diverse dataset, especially to produce even better accuracy.
Graph Contrastive Learning with Learnable Graph Augmentation

Graph contrastive learning has gained popularity due to its success in self-supervised graph representation learning. Augmented views in contrastive learning greatly determine the quality of the learned representations. Handcrafted data augmentations in previous work require tedious trial-and- errors per dataset, which is time-consuming and resource-intensive. Here, we propose a Graph Contrastive learning framework with Learnable graph Augmentation called GraphCLA. Specifically, learnable graph augmentation trains augmented views by minimizing the mutual information (MI) between the input graphs and their augmented graphs. This paper designs a kernel contrastive loss function based on an end-to-end differentiable graph kernel to learn augmented views. In addition, this paper utilizes a min-max optimization strategy to learn challenging augmented graphs and to learn discriminative representations adversarially. Finally, we compared GraphCLA with state-of-the-art self-supervised learning baselines and experimentally validate the effectiveness of GraphCLA.
Unsupervised Action Segmentation of Untrimmed Egocentric Videos

The introduction of affordable wearable cameras and eye trackers have led to a massive amount of egocentric (or first-person view) videos, bringing new challenges to the computer vision community for understanding and leveraging the specificities of the egocentric view. This work proposes a novel approach for unsupervised activity segmentation that detects frames corrupted by ego-motion and estimates action boundaries using kernel change-point detection. The approach leverages the visual characteristics of egocentric videos to improve segments’ temporal accuracy. We report state-of-the-art performances for unsupervised approaches on two challenging large-scale datasets of untrimmed egocentric videos, EGTEA and EPIC-KITCHEN-55, and on the standard third-person view dataset, 50Salads.
Exploiting PRNU and Linear Patterns in Forensic Camera Attribution under Complex Lens Distortion Correction

More complex and ever more common lens distortion correction post-processing is seriously hampering state-of-the-art camera attribution techniques. In this paper, we show that the two main existing techniques, namely PRNU (Photo Response Non Uniformity)-based and linear-pattern-based, can be successfully combined to improve performance. Moreover, we introduce a novel method that is able to correctly invert adaptive distortion correction transformations by successively maximizing the peak-to-correlation energy (PCE) and the linear-pattern energy for much more reliable camera attribution. A novel validation procedure to quickly discard mismatched test images is also proposed. Finally, we show how great reductions in running time can be achieved by using a GPU for interpolation, resampling, and PCE computation. The code is available at https://github.com/AMontiB/PSLR.
Prediksi Pembelian Barang Pada Distributor Lampu Menggunakan Metode Apriori pada PT. XYZ

Apriori is an algorithm that is widely used to determine the pattern of relationships between products that are often bought together in a store. This Apriori algorithm will be suitable to be applied in the field of determining strategy or promotion. PT. XYZ is one of the Lighting Distributor Companies. One of the problems of the company is the imbalance of stock in the warehouse. So, the purpose of this research is to obtain product purchase predictions from suppliers to maintain stock balance with product solds to customers. The object of this research is the application of apriori algorithm. This research data is in the form of sales transaction data at PT. XYZ. Data analysis using RapidMiner Software by determining the valure of support and confidence. The analysis shows that there are 22 Association Rules with a minimum support 20% and a minimum confidence 50%. This information is excpected to help the company in developing a product purchasing strategy from the supplier.
Serious Games for Training in Patient Flow Management in Emergency Departments: State of the Art and Perspectives

Emergency departments (EDs) face significant challenges in providing timely care due to the increase in patient volume and limited resources. To improve patient flow management, new strategies based on artificial intelligence, machine learning, computer modeling, and simulation have been developed, including serious computer games and virtual reality. We performed a systematic review of the use of serious games and virtual reality to train healthcare professionals in the ED.
Wavefront reconstruction of discontinuous phase objects from optical deflectometry

One of the challenges of phase measuring deflectometry is to retrieve the wavefront from objects that present discontinuities or non-differentiable gradient fields. Here, we propose the integration of such gradient fields based on an Lp-norm minimization problem. The solution of this problem results in a nonlinear partial differential equation, which can be solved with a fast and well-known numerical method and does not depend on external parameters. Numerical reconstructions on both synthetic and experimental data are presented that demonstrate the capability of the proposed method.
Improving BERT with local context comprehension for multi-turn response selection in retrieval-based dialogue systems

Multi-turn response selection is an important branch in the field of natural language processing, which aims to select the most appropriate response based on multi-turn dialogue. Most state-of-the-art models adopt pre-trained language models (PrLMs) and multiple auxiliary tasks to enhance their ability to understand the semantics in multi-turn dialogue. However, some critical challenges still remain to be addressed. Optimizing multiple auxiliary tasks simultaneously may significantly increase the training cost. Meanwhile, the semantic gap between the optimization objectives of the main and auxiliary tasks may bring noise to pre-trained language models. To address these challenges, we propose an efficient BERT-based neural network model with local context comprehension (BERT-LCC) for multi-turn response selection. First, we propose a self-supervised learning strategy, which introduces an auxiliary task named Response Prediction in Random Sliding Windows (RPRSW). In a multi-turn dialogue, the RPRSW task takes utterances falling within a random sliding window as input and predicts whether the last utterance within the sliding window is the appropriate response for the local dialogue context. This auxiliary task can enhance BERT's understanding of local semantic information. Second, we propose a local information fusion (LIF) mechanism that collects multi-granularity local features at different dialogue stages and employs a gating function to fuse global features with local features. Third, we introduce a simple but effective domain learning strategy to learn rich semantic information at different dialogue stages during pre-training. Experimental results on two public benchmark datasets show that BERT-LCC outperforms other state-of-the-art models.
The English Course Materials Needs for Sendratasik Study Program

&lt;div class="WordSection1"&gt;&lt;p&gt;The present study aims to conduct an analysis of English course materials that is needed by students of Sendaratasik Study Program at Universitas Jambi. The participants of this study were seventy students of Sendratasik Study program who have taken the English course in the second semester. To collect the data, the researcher used questionnaires and interviews as the instruments. The results of the need analysis show that the English learning goals for the students of Sendratasik is to communicate using English actively. Most of them are interested in learning English because they want to improve their speaking skill in order to prepare themselves to fulfill their professional demand. Besides, there are also some of the students consider that the theoretical material such as grammar is also important to learn but the portion must be reduced. Learning grammar such as tenses, and so forth should be thought in the first two meetings only and then continued with practical activities such as writing, reading, listening, and speaking practice.&lt;/p&gt;&lt;/div&gt;
PERCE: A Permissioned Redactable Credentials Scheme for a Period of Membership

The anonymous credential has broad-ranging applications, for example for the pay-as-you-go strategy in the electronic subscription. However, the ‘plain vanilla’ pay-as-you-go strategy may not be suitable for non-regular users since the latter group is likely to require a tighter identity supervision mechanism. We also note that a key building block in the construction of an anonymous credential system is identity supervision. Since identity supervision is more than revocation, the approach to regulating user behavior needs to be both reasonable and practical. In a situation where the user is allowed to control their own identities, the latter approach could be more flexible compared to the revocation. There are existing works about the limitation on the k-times or epochs. However, due to the weaknesses of these single limitations, the combination of the customized k-times and epochs is necessary and remains to be done. In this paper, we present a permissioned redactable credentials scheme, which allows fine-grained supervision, user control, and user redaction. In our approach, we choose times and epochs as the regulation dimensions, which limits users invoke the credential show method for customized times in each epoch determined by the certificate authority. The users could also redact their credentials to realize selective disclosure. We then evaluate the proposed scheme’s performance and present a comparative summary to demonstrate potential utility.