Reviewerly/exHarmony huggingface dataset:
A Generic Multicore CPU Parallel Implementation for Fractional Order Digital Image Moments

The image moments are considered a key technique for image feature extraction. It is utilized in several applications such as watermarking and classification. One of the major shortcomings of the image moment descriptors is the computation time. The image moments algorithm is a compute-intensive task. This can be justified by the fact that computing the image moment of two kernels on a 2D image makes the time complexity of the image moment computation of four orders. This high level of time complexity prohibits the image moment from being widely used as an image descriptor, especially for real-time applications. In this context, several methods are proposed to speed up the image moment computation on parallel architectures such as multicore CPUs and GPUs. To our knowledge, these efforts are focused on non-fractional moments. Due to their recent development, fractional order moment implementation is sequential. This motivates this work to propose a generic parallel implementation of fractional order moments on the multicore CPU architecture. The proposed implementation utilized the well-known OpenMP application programming interfaces (APIs) to evenly distribute the computational load over the CPU cores. The obtained results show that the proposed parallel implementation achieved a close to optimal speed up. Using a 16 core CPU, the obtained speedup was 15.1 $$\times $$ .
Use of Brainstorming Technique in Increasing Student Confidence in English Speaking Skills at SMAN 4 Kota Serang

The objective of the research to describe and analyze the use of Brainstorming technique in increasing students' confidence in English speaking skills in class X IPS 4 SMAN 4 Kota Serang. In this research, focus on the use of Brainstorming Technique and as sub focuses on improving Self-confidence in English speaking skills the writer. This research uses qualitative research with this type of research method is descriptive. The instrument used voice and video recording devices and cameras from cellphones as well as accurate documents, journal, or books that are considered relevant based on records from various sources, observation and inteview guidelines. Technique of collecting data using three ways, namely interviews, observations and documentation. The data analysis technique is to perform data reduction stages, display data, and conclude data. The results of the analysis are conveyed through the delivery of descriptive data. The results of the study, it can be concluded in general that the Brainstorming technique is considered capable of increasing students' confidence in English speaking skills in class X IPS 4 SMAN 4 Kota Serang in the 2021/2022 school year. Although the effect is not significant when used in distance learning. This can be proven based on the results of research conducted in non-test with the informants. Brainstorming can be used both as a teaching technique and as a teaching strategy. It is very useful for students to develop their reasoning skills, creative problem-solving skills, fluency in English language, self- motivation, and self-confidence
Extracting Crime Prosecution Elements based on Neural Machine Reading Comprehension Model

In this paper, we explore the task of extracting prosecution elements (text description about prosecution elements) in an indictment. We approach the prosecution element extraction problem by formulating it as a reading comprehension task. Specifically, our idea is to train a reading comprehension model to extract a text span to indicate the statement of a crime element according to an asked question. By such a reformulation, we leverage the power of neural machine reading models to prosecution element extraction task. Experimental evaluation demonstrates the feasibility of the machine reading reformulation. We also make our code and data available on https://github.com/NCHU-NLP-Lab/Legal-Document-Question-Answering
Implementasi Literasi Digital Melalui Program Sapulidi pada Masa Covid-19: Studi Kasus di Sekolah Menengah Atas

This descriptive qualitative study aims to investigate the implementation of digital literacy during Covid-19 through Sapulidi program at one of high schools in Southeast Sulawesi. Data were garnered through observation, interviews, and documentation from the principal, the head of school literacy team, and members of the Sapulidi team. The findings show that the implementation of digital literacy at the school was carried out in three stages: a) increasing the capacity of facilitators through training for school residents, b) increasing the number and variety of learning resources through the provision of QR code-based digital books on library websites and the use of learning applications, and c) providing computer and internet access in schools and providing digital information through school websites, libraries, and QR codes in the school environment. The forms of QR codes used in the Sapulidi program include the use of in learning, on school plants, on trophy displays, in students' works at school, school literacy galleries, in literacy corners in classroom, and in digital books.KEYWORDS: Covid-19; digital literacy program; high school; QR code
A Target-based Calibration Method for LiDAR-Visual-Thermal Multi-Sensor System

As the basis of multi-sensor fusion, accurate extrinsic calibration among multi-sensors is vital for hetero-geneous information fusion. However, most existing methods only focus on the calibration between two specific heteroge-neous sensors, such as camera-LiDAR, LiDAR-thermal, and etc. which may cause inevitable accumulation when applied to a multi-sensor system. To address this problem, a novel calibration target and a high-accuracy method are proposed to simultaneously calibrate the intrinsic and extrinsic of a 3D LiDAR, a thermal camera and a visible camera without any user intervention. The proposed method only relies on one calibration board, to calibrate all the intrinsic and extrinsic parameters in one-step, without any strict pose requirement or long-time optimization. Furthermore, 2D-3D corresponding features can be extracted with higher precision by considering sensor model, comparing to the common feature extraction methods. Experiments with Velodyne VLP-16 LiDAR, ZED camera and D843NT thermal camera demonstrate that the proposed method can complete calibration among three of them automatically. Finally, this paper also presents a target-based method of uniformly expressing the calibration errors among multi-sensor system, with a competitive performance against most state-of-the-art methods.
Kebutuhan Mahasiswa Berbahasa sebagai Bahan Redesain Silabus Mata Kuliah Bahasa Indonesia

There is a need for standards to set linguistic or language standards, or determine language parameter policies required by students. Analysis of language needs is one of the strategies to determine the proposed language policy in the field of education. One of them is by examining student needs for language learning as a material for redesigning the Indonesian Language Course Syllabus. The research focus is oriented to the needs of students reviewed based on an analysis of students' language needs in Indonesian Language Courses, as a material for redesigning the Indonesian Language Course syllabus in Bina Darma University, 2022. The researcher uses a qualitative method with a case study approach. The data collection techniques was a questionnaire. Data analysis used content analysis. For the credibility of the research, the researcher did member checking. The results of the study discovered students' difficulties in language and students' needs for language skills as material for redesigning the syllabus for Indonesian Language Courses in Universities .
Classification of Documents and Images Using an Enhanced Genetic Algorithm

In 1975, John Holland proposed the Genetic Algorithm (GA). The algorithm is widely used to provide superior solutions for optimization and search problems by relying on biologically inspired operators including mutation, crossover, and selection. The fittest individuals are chosen for reproduction in this algorithm to generate the next generation’s offspring. Classification is a technique used in data mining to analyze the collected data and to divide them into different classes. The relationship between a known class assignment and the properties of the entity to be classed may serve as the foundation for the classification procedure. Through this research, it has mainly consider classification for documents and images using GA. In order to enhance the accuracy and to reduce the error rate of traditional models, a new approach is proposed which is based on GA. The primary benefit of using GA in conjunction with classification is the efficiency in which it can address optimization issues. The experiment results are used to verify the suggested algorithm using benchmark data sets gathered from the UCI machine learning repository.
Signal Recovery for Incomplete Ocean Data via Graph Signal Processing

In this paper, the signal recovery problem of incomplete ocean data based on graph signal processing is studied. In order to properly process the sparse and inhomogeneous data, the ocean data set is defined as a graph signal and its spatio-temporal characteristics is analyzed. The data are analyzed using the spatial smoothness of the signal based on topology, the correlation of time-varying signals over time, and the low-rank nature of the signal. Based on that, a signal recovery optimization problem is established and the optimization problem is solved via the alternating optimization and alternating direction method of multipliers (ADMM) framework. Simulations based on real-world dataset are performed to reveal the performance gain of the proposed approach.
Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. To ease the computational complexity of large-scale transport design, we first develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). However, such a distributed algorithm is vulnerable to sensitive information leakage when an attacker intercepts the transport decisions communicated between nodes during the distributed ADMM updates. To this end, we propose a privacy-preserving distributed mechanism based on output variable perturbation by adding appropriate randomness to each node's decision before it is shared with other corresponding nodes at each update instance. We show that the developed scheme is differentially private, which prevents the adversary from inferring the node's confidential information even knowing the transport decisions. Finally, we corroborate the effectiveness of the devised algorithm through case studies.
Construction of a Fully Homomorphic Encryption Scheme with Shorter Ciphertext and Its Implementation on the CUDA Platform

Homomorphic encryption supports meaningful operations on ciphertext and is widely used in outsourcing computing, secure multi-party computing and other scenarios. The TFHE-type fully homomorphic encryption scheme has an efficient bootstrapping process, so it supports efficient homomorphic logic gate circuits. However, its single-bit encryption feature results in a large ciphertext expansion (the ciphertext expansion rate of the CGGI16, CGGI17, and ZYL + 18 scheme is 16032). Therefore, how to reduce the scale of ciphertext without affecting the efficiency is a practical problem. TFHE-type fully homomorphic encryption scheme with shorter ciphertexts is constructed. By applying the round function to the encryption process, the ciphertext scale is reduced by 62%; Using the GPU's ability to implement large-scale matrix operations efficiently, a fully homomorphic encryption scheme with shorter ciphertexts is implemented based on the cuFHE. The experimental results show that, compared with the CGGI17 scheme, the running time of a single gate circuit (including the bootstrapping process) in this scheme does not exceed 1 ms on the CUDA platform, and the ciphertext scale of this algorithm is reduced by 62%.
Exploration of Neural Network Imputation Methods for Medical Datasets

Datasets, especially those related to medicine, commonly suffer from missing data. The missing data originates from various sources. Examples include ever-changing medical diagnosis and treatment techniques, the absence of lab results, or even data collection errors. Most machine learning methods are trained on dense datasets. The sparse samples are either discarded or filled in with imputation. Imputation methods generate missing data by examining the variables in the relevant samples. Therefore, the performance of subsequence prediction models might be impacted by these methods. In this study, we explore neural network-based imputation methods to generate the missing data in medical datasets. The experimental results show that compared with traditional imputation methods, neural network imputation can be more effective in the classification and prediction tasks. We discuss some of the method’s differences and assess their suitability for the dataset’s specific characteristics.
Unsupervised Monocular Visual Odometry with Lightweight Depth Architecture

Absolute scale estimation has been a challenging problem of monocular visual odometry using unsupervised technique. In this paper, we propose a novel monocular visual odometry method based on lightweight depth architecture, which can estimate scene depth and geometric poses simultaneously from sequences of images without ground truth. Atrous Spatial Pyramid Pooling (ASPP) is introduced to the depth model to make the network more effective and attention mechanism is applied after the feature extraction part to get better results. We use stereo images to train these two models simultaneously and use monocular images to test them separately or simultaneously. The empirical results shows that the lighter depth architecture provides more accurate estimations than the encoder-decoder architecture. It works well in monocular visual odometry system and the pose model produces better results than many other unsupervised monocular visual odometry under the precondition of this architecture.
Cooperative Operation Path Planning for Multiple Plant Protection UAVs Based on Bi-level ACO

In order to improve the cooperative operation efficiency of multi-plant protection unmanned aerial vehicles (UAVs), the operation field segmentation algorithm is used to divide the complex field into several sub-fields, and then the scanning line method and the step rotation method are used to obtain the endpoint set of operation path for each sub-field. A bi-level ant colony optimization algorithm (bi-level ACO) is proposed, which includes task assignment level ACO and path planning level ACO. The former is used to assign the sub-fields to each UAV, while the latter is used to realize the path planning of sub-fields assigned to each UAV. What’s more, in the task assignment level ACO, the total operation path length index and the task assignment equilibrium index are introduced in the pheromone update. The two level ACOs iterate in loop to realize the cooperative optimization of task assignment and path planning for multiple plant protection UAVs. Finally, the effectiveness of the proposed algorithm is verified by a simulation case.
Dialogue-enriched Knowledge Point Recommendation for Consultation Task

The customer service agent of the government service hotline has been providing great help to people’s daily lives. To facilitate further work analysis, after the customer service agent completes the dialogue consultation each time, it is necessary to select the related knowledge point for summarizing and record the dialogue from the large-scale knowledge point base according to the current consultation dialogue content. However, this work adds a great burden to the customer service staff. In order to reduce the workload of customer service agents in selecting knowledge points, we explore several approaches based on the consultation dialogue in the customer service scenario, e.g., the text classification model based on BERT, the word vector convolutional neural network classification model, and the pseudo-siamese neural network model. Extensive experiments demonstrated that our methods perform well in terms of accuracy and scalability, and further we designed an efficient and well-structured API module which is easily integrated into service application.
Scrum and Remote Work in Video Game Projects: Improving Communication, Trust and Efficiency

In a rapidly expanding video games industry, com-panies have to manage complex development projects with interdisciplinary teams. Nowadays, these teams often produce in remote working (at least partially) with the Scum framework. However, this framework is not adapted to the virtual team context. Moreover, key issues about communication and trust between team members are not well addressed by the original framework tools. In this paper, we show our Com- Scum model extending the original Scum framework to take into account the context of remote work. We also propose to exploit a text mining approaches to automatically detect in real-time the problems of communication and trust between team members. This tool uses data from the team activities in the chat platform to extract the and apply a quantitative analysis of multiple metrics to infer communication health status of team in order to give feedback.
Implementation of Web Scraping on Job Vacancy Sites Using Regular Expression Method

Job vacancy information is now not only found in newspapers, brochures, or visiting companies but information can be found on the internet. On the internet many third-party websites that provide job vacancy information that can help job seekers get information, some third party websites sometimes do not have vacancy information on the same company as other websites, because the company that owns the vacancy does not provide their company's job vacancy information to all third-party websites, so job seekers visit many websites to meet each other to get the information they want, so job seekers visit many websites to find each other to get the information they want, which takes a long time. To simplify and shorten the time in searching for information, we can utilize a web that can display more than one third-party web so that job seekers do not need to open many third-party webs. The purpose of this research is to implement Web Scraping to retrieve data about job vacancy information with Regular Expression method and build a web-based information system to display data obtained from Web Scraping. The results of Web Scraping experiments with the Regular expression method on three third-party websites namely Jobstreet, Kalibrr, and Glints were able to retrieve data from the three websites. The resulting accuracy is very good, on Jobstreet and Kalibrr produces 100% accuracy in providing correct results while Glints produces 99.4% accuracy. The data collected from the web scraping process is stored in the database and then displayed on the developed web.
Efficient Online and Privacy-preserving Medical Pre-diagnosis Based on Growing Learning Vector Quantization

With more and more people turning to online medical pre-diagnosis systems, it becomes increasingly important to protect patient privacy and enhance the accuracy and efficiency of diagnosis. That is because the ever rapidly growing medical records not only contain a large amount of private information but are often highly unequally distributed (e.g., the number of cases and the rate of increase of covid-19 can be much higher than that of common diseases). However, existing methods are not capable of simultaneously boosting the intensity of privacy protection, and the accuracy and efficiency of diagnosis. In this paper, we propose an online medical pre-diagnosis scheme based on incremental learning vector quantization (called WL-OMPD) to achieve the two objectives at the same time. Specifically, within WL-OMPD, we design an efficient algorithm, Wasserstein-Learning Vector Quantization (W-LVQ), to smartly compress the original medical records into hypothetic samples. Then, we transmit these compressed data to the cloud instead of the original records to offer a more accurate pre-diagnosis. Extensive evaluations of real medical datasets show that the WL-OMPD scheme can improve the imbalance ratio of the data to a certain extent and then the intensity of privacy protection. These results also demonstrate that WL-OMPD substantially boost the accuracy of the classification model and increase diagnostic efficiency at a lower compression rate.
DeepiForest: A Deep Anomaly Detection Framework with Hashing Based Isolation Forest

With the great success of deep neural networks (DNNs) in a variety of fields, deep learning gains a pioneering development in anomaly detection. Although deep learning achieves good accuracy in anomaly detection, it is troubled with long execution time and high memory consumption. These problems are associated with the inherent drawbacks of deep learning, such as too many parameters and deep training layers. To remedy the above drawbacks, we try to explore an unsupervised non-neural network deep model for anomaly detection based on the experience of the deep forest. In this paper, we propose a deep anomaly detection framework with hashing based isolation forest (DeepiForest) to achieve effective and robust anomaly detection. Specifically, DeepiForest utilizes hashing based isolation forest and tree-embedding scheme to provide enhanced features and apply multi-layer cascaded architecture to establish a deep framework. DeepiForest inherits the advantages of deep forests, i.e., the framework holds fewer hyper-parameters and smaller model complexity than DNNs, simultaneously producing robust accuracy on anomaly detection. Extensive experiments on different-scale datasets illustrate the efficiency of DeepiForest and its comparable effectiveness to the state-of-the-art deep anomaly detection (DAD) methods.
Innovation, Regulation, and Competition in the Electricity Industry: The Challenge of, and to, Blockchain

The blockchain technology has several potential applications in the power industry. Its adoption does not rely on its merits (or lack thereof) only. It also depends on the regulatory barriers to its use in actual markets. In order to assess the relevance of these barriers, blockchain-based innovations are grouped according to their capacity of becoming accustomed in the existing regulatory environment: incremental innovation requires modest changes or improvements in the regulation, whereas disruptive innovation raises more complex challenges. This paper proposes a framework to tell whether a specific blockchain-based innovation is incremental or disruptive, surveys the most recent developments in the adoption of the blockchain in the power industry, and focuses on a specific application, i.e. peer-to-peer trading.