Reviewerly/exHarmony huggingface dataset:
Face Mask Detection using Convolutional Neural Network

The current development of artificial intelligence (AI) technology makes it possible to detect and classify objects, such as whether a person is wearing a mask or not, using deep learning technology. The system was built using the Python language for modeling and using the Flutter language. Classification of face masks using the Convolutional Neural Network (CNN) method, and the MobileNetV2 architecture. This model use datasets in image format with Mask and Non-Mask classifications. From the test results, the mask detection model gives good results. Testing objects carried out at the furthest distance of one meter gave good results, while system notifications on mobile application also worked well by sending notifications to mobile application when an object was detected by the CCTV tool and displaying it on mobile application.
This issue of <scp>Geophysics</scp>

In this article, the Editor of Geophysics provides an overview of all technical articles in this issue of the journal.
Badminton video action recognition based on time network

With the continuous development of artificial intelligence research, computer vision research has shifted from traditional “feature engineering”-based methods to deep learning-based “network engineering” methods, which automatically extracts and classifies features by using deep neural networks. Traditional methods based on artificial design features are computationally expensive and are usually used to solve simple research problems, which is not conducive for large-scale data feature extraction. Deep learning-based methods greatly reduce the difficulty of artificial features by learning features from large-scale data and are successfully applied in many visual recognition tasks. Video action recognition methods also shift from traditional methods based on artificial design features to deep learning-based methods, which is oriented to building more effective deep neural network models. Through collecting and sorting related research results found that academic for timing segment network of football and basketball video action research is relatively rich, but lack of badminton research given the above research results, this study based on timing segment network of badminton video action identification can enrich the research results, provide reference for follow-up research. This paper introduces the lightweight attention mechanism into the temporal segmentation network, forming the attention mechanism-timing segmentation network, and trains the neural network to get the classifier of badminton stroke action, which can be predicted as four common types: forehand stroke, backhand stroke, overhead stroke and pick ball. The experimental results show that the recognition recall and accuracy of various stroke movements reach more than 86%, and the average size of recall and accuracy is 91.2% and 91.6% respectively, indicating that the method based on timing segmentation network can be close to the human judgment level and can effectively conduct the identification task of badminton video strokes.
Optimal Binary Linear Complementary Pairs From Solomon-Stiffler Codes

Carlet et al. showed that for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\geq 3$ </tex-math></inline-formula> , there exists a linear complementary pair of codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{q}$ </tex-math></inline-formula> whose security parameter is as good as the minimum distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{L}$ </tex-math></inline-formula> of the best linear code with the same length and dimension. For binary codes, they proved that the security parameter of a linear complementary pair of codes is lower bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{L}-1$ </tex-math></inline-formula> . Choi et al. recently presented infinite families of binary linear complementary pairs which are optimal in the sense that their security parameters reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{L}$ </tex-math></inline-formula> . Here, we prove that for every <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\geq 5$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d\geq \lceil (k-1)/2 \rceil 2^{k-1}$ </tex-math></inline-formula> , there exist binary linear complementary pairs of codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g(k,d)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g(k,d)$ </tex-math></inline-formula> denotes the Griesmer bound. This shows the existence of an infinite family of optimal binary LCP of codes for new code parameters, which extensively broaden those obtained by Choi et al. Our construction is explicit and it is based on codes reaching the Griesmer bound, which were constructed by Solomon and Stiffler.
Application of hyperspectral and multispectral datasets for mineral mapping

In this study, hyperspectral datasets are simulated from multispectral data using a spectral reconstruction approach which is a sensor-independent technique. This technique makes use of information from atmospherically corrected multispectral Remote Sensing (MRS) data and normalized ground spectra for the simulation of HRS data. In this study EO-1, the ALI dataset was used for the simulation of hyperspectral Remote Sensing (HRS) data to discover the Udaipur region’s unique minerals. A total of 61 spectral bands with 10 nm bandwidth were simulated. The simulated HRS data were validated using visual interpretation, statistical and classification approaches. Simulated HRS data from EO-1 Advanced Land Imager (ALI) has shown a high correlation with EO-1 Hyperion data. Spectral Angle Mapper (SAM) classification was also performed on simulated hyperspectral data for mineral mapping. It was observed that simulated hyperspectral data have shown comparable results with Hyperion and are better than their corresponding multispectral datasets.
VisionAPI: An API for Offline and Online Segmentation and Identification of Hand-Sketched Graphical User Interfaces

Segmentation and identification of a graphical user interface consist of detecting the location, dimensions, and arrangement of elements of the user interface, such as controls, labels, images, and icons, and recognizing them, respectively. While these problems have been already addressed for a graphical user interface stored in a file and processed offline, it has received less attention for online processing when the interface evolves and is expressed in different formats, such as a whiteboard drawing or a paper sketch. To overcome these limitations, we present VisionAPI, an application programming interface trained for segmenting and identifying elements of a hand-sketched graphical user interface both offline and online using computer vision. For this purpose, we rely on a software architecture based on Resnet101 to extract features and Faster R-CNN to build boundary boxes to obtain an 85% recognition rate for 21 classes of elements found in graphical user interfaces: paragraph, dropdown list, checkbox, radio button, rating, toggle button, text area, date picker, stepper input, slider, video, label, table, list, header, button, image, linebreak, container, link, and text input.
Elliptic-Curve Cryptography

Elliptic-curve cryptography (ECC) represents a public-key cryptography approach based on the algebraic structure of elliptic curves over finite fields. ECC can be used in cryptography applications and primitives, such as key agreements, digital signatures, and pseudorandom generators. They can also be used for operations such as encryption, achieved through a combination of key agreement and a symmetric encryption scheme. Other interesting usages can be seen in several attempts at integer factorization algorithms that are based on elliptic curves (EC), with applications in cryptography, such as Lenstra Elliptic-Curve Factorization (L-ECC) [1]. Elliptic curves appeared for the first time in Diophantus [3], a subject that has remained close to Diophantine geometry [2].
Advanced Bio-Inspired computing paradigm for nonlinear smoking model

Smoking has emerged as one of the leading global factors that is the source of health issues. It damages almost all of the body's organs. It damages various muscles and causes lung cancer. Additionally, it causes ulcers, pulmonary disease, and vascular deterioration. Except for the financial benefit to tobacco companies, manufacturers, and marketing companies, smoking has no advantages. Due to these factors, the present study exploited a feed forward neural networking based global optimization procedure with a local scheme to solve a mathematical model of smoking. A genetic based algorithm and sequential quadratic programming (GA-SQP) are utilized as hybridized global and local strategies. The model is categorized into five classes: potential smokers, occasional smokers, smokers, temporary quit, and permanent quit smokers. An objective optimization function is constructed to minimize the mean square error using the designed smoking model in form of feed forward neural networking. The comparative evaluation of hybrid GA-SQP and Adam numerical scheme is also assessed to authenticate the precision and correctness of the solution of the smoking model. The robustness, perfection, and convergence stability of GA-SQP are verified by establishing various statistical performance indicators. The quantitative analysis provides the minimum, mean, and semi-inter quartile range values for absolute errors up to 6 to 13 decimal places, demonstrating the worthiness and precision of the proposed GA-SQP.
Opportunity Cost and Missed Chances in Optimizing Cybersecurity

The loss of potential gain from other alternatives when one alternative is chosen.
Towards Efficient Incident Detection in Real-time Traffic Management

Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data.
A Research on Various Security Aware Mechanisms in Multi-Cloud Environment for Improving Data Security

The idea of multi-cloud computing has broadened the field of cloud computing and has emerged as a popular term currently. The term "multi-cloud" refers to the use of services from numerous heterogeneous cloud service providers through a common framework at the user’s (e.g., organizations or firms) facilities. Cloud computing is faced with several issues and research gaps, prompting academic and commercial work to pave the way for a multi-cloud system. The concept of multi-cloud is growing, and various research efforts are currently ongoing to create a multi-cloud framework that is supported in all aspects such as easy configuration, security, management, etc. In this study, the main ideas, difficulties, requirements, and future perspectives for multi-cloud environments are discussed briefly. Moreover, a study of conventional methods and solutions given by various multi-cloud frameworks are analyzed, along with their benefits and downsides are also discussed.
Clustering the Distribution of COVID-19 in Aceh Province Using the Fuzzy C-Means Algorithm

COVID-19 is a virus that attacks the respiratory system in humans and spreads rapidly. The government has taken various ways to reduce the rate of transmission of COVID-19, including by providing a COVID-19 information center that can be accessed by anyone, but there is no grouping of regional zones with high to low COVID-19 cases. Therefore, a clustering process system for the spread of COVID-19 is needed so that it is able to provide information on clusters of COVID-19 distribution areas in Aceh with the highest case zone (red zone), medium case zone (yellow zone), and low case zone (green zone). The steps carried out in this study using the Fuzzy C-Means Algorithm are collecting data (input data), conducting the clustering process (determining the number of clusters, weighting rank, maximum iteration and epsilon), displaying clustering results. In this study, the authors collected COVID-19 data from 23 districts/cities in Aceh using 6 variables consisting of confirmed, in care, healed, died, suspected, and probable. The results of the clustering study on the spread of COVID-19 are as follows: One district/city in cluster 1 (red zone), the four districts/cities in cluster 2 (yellow zone), eighteen districts/cities in cluster 3 (green zone). Based on the results of this study, the Fuzzy C-Means Algorithm can be used and applied properly in clustering the spread of COVID-19 in the Province of Aceh.
PISA READING LITERACY: ENCOUNTERING FEMALE AND MALE’ READING LITERACY ABILITY

Much research sounds that male students’ reading competency is lower than females’. This article, then, uncovers the cognitive processes of male and female students seen from the sample questions in the 2018 PISA. This descriptive research involved fifteen male and sixteen female students. They were tested to answer PISA sample test consisting 21 items. It was found that the students score in reading literacy is poor. The highest score was 76; the lowest score was 10; the average score was 31. The result proved that female students are better in reading literacy than males are. Among eight cognitive process, female students are good at accessing and retrieving information, integrating and generating inferences across multiple sources, assessing quality and credibility, reflecting on content and form, and detecting and handling conflict. While, male students are better in integrating and generating inferences. The implication of the research is the teachers should provide HOTS instructional methods.
Transparency Helps Reveal When Language Models Learn Meaning

Abstract Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations (i.e., languages with strong transparency), both autoregressive and masked language models successfully learn to emulate semantic relations between expressions. However, when denotations are changed to be context-dependent with the language otherwise unmodified, this ability degrades. Turning to natural language, our experiments with a specific phenomenon—referential opacity—add to the growing body of evidence that current language models do not represent natural language semantics well. We show this failure relates to the context-dependent nature of natural language form-meaning mappings.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads

With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge. Modern trackers are required to operate on more and more complicated scenes. According to the MOT20 challenge result, the pedestrian is 4 times denser than the MOT17 challenge. Hence, improving the ability to detect and track in extremely crowded scenes is the aim of this work. In light of the occlusion issue with the human body, the heads are usually easier to identify. In this work, we have designed a joint head and body detector in an anchor-free style to boost the detection recall and precision performance of pedestrians in both small and medium sizes. Innovatively, our model does not require information on the statistical head-body ratio for common pedestrians detection for training. Instead, the proposed model learns the ratio dynamically. To verify the effectiveness of the proposed model, we evaluate the model with extensive experiments on different datasets, including MOT20, Crowdhuman, and HT21 datasets. As a result, our proposed method significantly improves both the recall and precision rate on small\&medium sized pedestrians and achieves state-of-the-art results in these challenging datasets.
Efficient semi-quantum secret sharing protocol using single particles

Semi-quantum secret sharing (SQSS) is a branch of quantum cryptography which only requires the dealer to have quantum capabilities, reducing the difficulty of protocol implementation. However, the efficiency of the SQSS protocol still needs to be further studied. In this paper, we propose a semi-quantum secret sharing protocol, whose efficiency can approach 100% as the length of message increases. The protocol is based on single particles to reduce the difficulty of resource preparation. Particle reordering, a simple but effective operation, is used in the protocol to improve efficiency and ensure security. Furthermore, our protocol can share specific secrets while most SQSS protocols could not. We also prove that the protocol is secure against common attacks.
An ultra-lightweight detector with high accuracy and speed for aerial images

&lt;abstract&gt; &lt;p&gt;Aerial remote sensing images have complex backgrounds and numerous small targets compared to natural images, so detecting targets in aerial images is more difficult. Resource exploration and urban construction planning need to detect targets quickly and accurately in aerial images. High accuracy is undoubtedly the advantage for detection models in target detection. However, high accuracy often means more complex models with larger computational and parametric quantities. Lightweight models are fast to detect, but detection accuracy is much lower than conventional models. It is challenging to balance the accuracy and speed of the model in remote sensing image detection. In this paper, we proposed a new YOLO model. We incorporated the structures of YOLOX-Nano and slim-neck, then used the SPPF module and SIoU function. In addition, we designed a new upsampling paradigm that combined linear interpolation and attention mechanism, which can effectively improve the model's accuracy. Compared with the original YOLOX-Nano, our model had better accuracy and speed balance while maintaining the model's lightweight. The experimental results showed that our model achieved high accuracy and speed on NWPU VHR-10, RSOD, TGRS-HRRSD and DOTA datasets.&lt;/p&gt; &lt;/abstract&gt;
Testing the Reliability and Validity of the Solution-Focused vs Problem-Focused Communication Scale in a Workplace Setting

This paper aims to develop a reliable and valid scale for measuring solution-focused and problem-focused communication that can be easily applied in the Japanese workplace. A two-factor scale for measuring solution-focus and problem-focus has already been proposed by Kitai (2020), in which reliability and validity were confirmed. However, this was based on a relatively small sample (N=183) study; the data were collected from a single company and thus had high internal homogeneity. The current study tested the reliability and validity of this scale using data from an online questionnaire with over 500 respondents drawn from many companies. The results of this assessment are used to develop an effective scale for studying solution-focus and problem-focus in the workplace.
A Signal Temporal Logic Planner for Ergonomic Human–Robot Collaboration

This paper proposes a method for designing human-robot collaboration tasks and generating corresponding trajectories. The method uses high-level specifications, expressed as a Signal Temporal Logic (STL) formula, to automatically synthesize task assignments and trajectories. To illustrate the approach, we focus on a specific task: a multi-rotor aerial vehicle performing object handovers in a power line setting. The motion planner considers limitations, such as payload capacity and recharging constraints, while ensuring that the trajectories are feasible. Additionally, the method enables users to specify robot behaviors that take into account human comfort (e.g., ergonomics, preferences) while using high-level goals and constraints. The approach is validated through numerical analyzes in MATLAB and realistic Gazebo simulations using a mock-up scenario.
Decentralized Payment Architecture for E-Commerce and Utility Transactions with Government Verified Identities

In recent years, blockchain technologies have begun to be adopted by a number of industries as a means of improving security and efficiency. One area where blockchain technology is beginning to be used is in the area of payment gateways. Payment gateways are used to process electronic payments between buyers and sellers. By using blockchain, payment gateways can be made more secure and efficient. However, there are concerns that blockchain technologies may be used to launder money or finance terrorism. To address these concerns, some countries have proposed making blockchain technologies only accessible to government-identified individuals. This research paper examines the feasibility of such a proposal and demonstrates how our proposed architecture model can perform in real time. It will discuss the benefits of using blockchain technology in this context and how it can help to improve security and efficiency. The paper focuses on two essential aspects of the design: how to verify the identities of individuals in transactions and how to maximize efficiency of this architecture while promising high security. In addition, the paper will also explore the potential challenges associated with using blockchain technology in payment gateways.