Reviewerly/exHarmony huggingface dataset:
Peningkatan Keamanan pada Simple Network Time Protocol (SNTP) untuk Mendeteksi Cybercrime di dalam Aktivitas Jaringan

Today's cybercrime methods are extremely diverse. The methods employed by attackers are becoming more diverse and intricate. Malicious software, commonly referred to as malware, is utilized in these various attacks. Malware threats and their propagation can be accomplished in several different ways. Good governance facilitates prevention via migration, assisting the security team and network infrastructure IT. This paper discusses efforts to enhance the security of the Simple Network Time Protocol (SNTP) to detect cybercrime on the network. Based on the results of the conducted experiments, we determined that by updating the Simple Network Time Protocol, implementing the client-server antivirus, and scanning every client connected to the local network or centrally, we could detect the types of attacks that frequently occur on the network system, thereby protecting all client computers from malware, viruses, worms, and Trojan horses. Based on the antivirus migration data, the employed research method can find information on cybercrimes that involve malware that enters the network and is successfully cleaned or quarantined before being automatically blocked.
Increasing Student Satisfaction Through Improving Service Quality at Tourism Colleges in East Jakarta

At private universities in East Jakarta, this study examines the impact of five service quality factors on student satisfaction. The research instrument consists of 28 questions arranged and structured based on variables, dimensions and indicators using the Likert scale. Data collection using questionnaires distributed via Google form to 30 people for validity and reliability testing and 300 people as research samples. The multiple linear regression method is used for data analysis with the help of SPSS software. The study's results partially show that three service quality dimensions, namely Reliability, Empathy and tangibles, significantly affect student satisfaction. Still, the dimensions of Responsiveness and assurance do not affect student satisfaction. Simultaneously, five dimensions of service quality have a significant effect on student satisfaction.
Evaluasi Program Kepemudaan oleh Pemerintah Provinsi Sumatera Barat dalam Meningkatkan Kualitas Pemuda di Era Disrupsi

The advancement of technology has led to a shift in skill demands, and young people must be able to develop new skills that are relevant to the market needs. Additionally, they also face risks such as job uncertainty, industry disruption, and the digital divide among those with access. This research aims to evaluate youth programs conducted by the West Sumatra Provincial Youth and Sports Office (DISPORA) in improving the quality of the youth. The study employs a qualitative research method using the evaluation analysis of the CIPP (Context, Input, Process, Product) Model and SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. The object of this research is the youth programs implemented by the West Sumatra Provincial Youth and Sports Office, by collecting data and information related to the programs, such as activity reports, budgets, and interview results with stakeholders from the West Sumatra Provincial Youth and Sports Office. The findings indicate that the youth programs implemented have not fully succeeded in enhancing the creativity of the youth, as the focus of the programs mostly centers around character development. Moreover, there are several obstacles, such as the lack of support from local governments, the programs remaining lecture-based (traditional), and the apparent lack of collaboration with the private sector.
Analisis Manajemen Kinerja Badan Penanggulangan Bencana Daerah Kabupaten Musi Rawas Utara dalam Mitigasi Bencana Banjir

Geographical conditions of the lowlands, resulting in North Musi Rawas Regency (Muratara) as an area prone to flooding. Handling this, the Regional Disaster Management Agency (BPBD) of Muratara Regency is authorized to carry out disaster management tasks, including flood disaster mitigation as a prevention effort to reduce potential losses due to future disasters. This study aims to analyze the implementation and constraints of the performance management of BPBD Muratara Regency in flood disaster mitigation in Muratara Regency. The results showed that the performance management of BPBD Muratara in flood disaster mitigation with four stages, namely determining approaches, policies, strategies and flood disaster mitigation activities, has not been well implemented. This can be seen by the fact that there are no programs and activities focused on reducing disaster risk, lack of facilities and infrastructure, not maximizing human resource training activities, lack of expert / civil servants, unavailability of Early Warning System (EWS) and not maximizing coordination and collaboration between stakeholders. In addition, there are also obstacles encountered such as the absence of a Disaster Management Plan (RPB). RPB is very much needed in flood disaster mitigation. Because it contains disaster risk assessments, contingency plans, budgets, programs/activities, coordination and collaboration between stakeholders involved.
Analysis of the Current Situation of Teachers' Innovative Work Behavior in China's Private Universities Under the Background of E-Education

With the rapid development of modern e-Education and information technology, so-ciety has put forward higher requirements for teachers' innovative ability and innova-tive behavior. The innovation level of private universities and the innovation of teachers are relatively weak. Innovation is an impor
Pairwise‐Parallel Entangling Gates on Orthogonal Modes in a Trapped‐Ion Chain

Abstract Parallel operations are important for both near‐term quantum computers and larger‐scale fault‐tolerant machines because they reduce execution time and qubit idling. This study proposes and implements a pairwise‐parallel gate scheme on a trapped‐ion quantum computer. The gates are driven simultaneously on different sets of orthogonal motional modes of a trapped‐ion chain. This work demonstrates the utility of this scheme by creating a Greenberger‐Horne‐Zeilinger (GHZ) state in one step using parallel gates with one overlapping qubit. It also shows its advantage for circuits by implementing a digital quantum simulation of the dynamics of an interacting spin system, the transverse‐field Ising model. This method effectively extends the available gate depth by up to two times with no overhead when no overlapping qubit is involved, apart from additional initial cooling. This scheme can be easily applied to different trapped‐ion qubits and gate schemes, broadly enhancing the capabilities of trapped‐ion quantum computers.
PENGEMBANGAN INSTRUMEN LITERASI NUMERASI KONTEKS SOSIAL BUDAYA DI SMK PLUS PAKUHAJI

This study aims to produce a Vocational Mathematics numeracy literacy instrument that has been tested for validity and reliability, as well as through a systematic analysis of the instrument using a test of discrimination and index of difficulty. This type of research is development research with the Wilson model and the Oriondo and Antonio models. Methods of data collection using interviews, expert validation sheets, questionnaires and numeracy literacy tests. Data were analyzed descriptively and quantitatively. The research results obtained were: 1) the expert's assessment stated that this numeracy literacy instrument was an appropriate instrument to use; 2) the readability test shows that this numeracy literacy instrument is in the "good" category, 3) the validity test shows that this instrument is valid; 4) this numeracy literacy instrument is a reliable instrument with a reliability value of ????????&gt;????????????????????????; 5) has a good interpretation of discriminating power with conclusions that can be used without revision; 6) Average item difficulty index. So that the resulting instrument is a feasible instrument used to measure students' numeracy literacy
Compressibility Measures for Two-Dimensional Data

In this paper we extend to two-dimensional data two recently introduced one-dimensional compressibility measures: the $$\gamma $$ measure defined in terms of the smallest string attractor, and the $$\delta $$ measure defined in terms of the number of distinct substrings of the input string. Concretely, we introduce the two-dimensional measures $$\gamma _{2D}$$ and $$\delta _{2D}$$ as natural generalizations of $$\gamma $$ and $$\delta $$ and study some of their properties. Among other things, we prove that $$\delta _{2D}$$ is monotone and can be computed in linear time, and we show that although it is still true that $$\delta _{2D}\le \gamma _{2D}$$ the gap between the two measures can be $$\varOmega (\sqrt{n})$$ for families of $$n\times n$$ matrices and therefore asymptotically larger than the gap in one-dimension. Finally, we use the measures $$\gamma _{2D}$$ and $$\delta _{2D}$$ to provide the first analysis of the space usage of the two-dimensional block tree introduced in [Brisaboa et al., Two-dimensional block trees, The computer Journal, 2023].
Electron spin states on a higher-order Bloch sphere

Based on the correspondence between the conventional Poincaré and Bloch spheres, we proposed a higher-order Bloch spheres, which is an extension of the conventional Bloch spheres. By calculating the expectation value of the spin components using the Pauli spin matrices, we found that the new spin state is formed in a ring shape and the orientation of the spin changes depending on the azimuthal angle and topological charge of orbital angular momentum. We also realize the coherent transfer of the azimuth-dependent polarization state of photons to the electron spin state in a GaAs quantum well with a V-shaped three-level system.
ADVANCEMENTS IN OBJECT DETECTION AND TRACKING ALGORITHMS: AN OVERVIEW OF RECENT PROGRESS

In the realm of computer vision, object detection and tracking constitute fundamental tasks, and recent years have borne witness to astounding advancements attributed to the integration of deep learning techniques. This paper aims to provide a comprehensive overview of the remarkable progress achieved in the domain of object detection and tracking algorithms, shedding light on their profound implications for accuracy, speed, and practical applications in the real world. Advances in object detection have been primarily driven by the adoption of Convolutional Neural Networks (CNNs). Prominent models such as Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot Multi-Box Detector) have emerged, significantly enhancing the precision of object identification. Additionally, efficient detectors like Efficient Det and Mobile Net have emerged, striking a balance between accuracy and computational efficiency, thereby enabling real-time applications, even on resource-constrained devices. For tracking, Multi-Object Tracking (MOT) algorithms have undergone improvements, incorporating graph-based approaches such as the Hungarian algorithm and Joint Probabilistic Data Association Filter (JPDAF). These advancements have enabled robust object tracking across video frames. This paper also delves into the synergy between deep learning and real-world applications, emphasizing the impact of these algorithms in domains like autonomous vehicles, surveillance systems, robotics, and augmented reality. KEYWORDS: Object detection, Tracking algorithms, Computer vision, Deep learning, Advancements, Real-world applications, Convolutional Neural Networks, Multi-Object Tracking, Autonomous vehicles, Surveillance, Robotics, Augmented reality.
Research on multiple-image encryption method using modified Gerchberg–Saxton algorithm and chaotic systems

A multiple-image encryption method based on a computer-generated phase-only hologram (POH) algorithm and chaotic systems is proposed. In the proposed method, first, a modified Gerchberg–Saxton (GS) algorithm is applied to transform the multiple-image into corresponding sub-sampled POHs. Then, the multiple POHs are combined using spatial division multiplexing (SDM). The combined hologram is then mapped into a digital image, and each pixel of the digital image is transformed by a chaotic system to improve security and form the final ciphertext. The modified GS algorithm is employed to generate a sub-sampled POH, which is a prerequisite for SDM. The adoption of SDM eliminates issues such as information leakage due to inter-image crosstalk and ensures the quality of decrypted images. The transformation based on a chaotic system leads to nonlinearity and unpredictability in the encryption process, which further increases the complexity of the encryption system. Numerical simulation demonstrates the security and feasibility of the proposed multiple-image encryption method.
Automated question answering via natural language sentence similarity: Achievements for Brazilian e-commerce platforms

Chatbots have become indispensable for quickly answering e-commerce customer queries, which is crucial for selling products online. However, in Brazilian e-commerce, finding scalable chatbot solutions can be challenging. This article proposes an automatic question-answering system by replying to incoming questions with Frequently Asked Questions from stores. Our solution builds a store-specific database populated with question-answer pairs by generating the embedding of questions. We define a retrieval process by ranking candidate questions with a neural network to reuse the questions' known answers. Our solution was deployed and evaluated with data in the Portuguese and Spanish languages for several stores in South America's biggest e-commerce platforms. The development approach achieved 97.75% of satisfaction with the given answers.
Annotation of fixed Multiword Expressions (MWEs) in a Portuguese Universal Dependencies (UD) treebank: Gathering candidates from three different sources

Delimiting and correctly annotating multiword expressions (MWEs) is an important task in constructing a gold standard treebank. In this paper, we applied three methods to the PetroGold corpus to identify MWE candidates. The methods include (1) leveraging expressions previously identified by the PALAVRAS annotator, (2) statistical analysis of collocations in Petroles, a larger non-annotated corpus, and (3) a curated list of co-occurring words from the POeTiSA project. Through extensive filtering and alignment with Universal Dependencies (UD) guidelines, we revised the annotations of 2,467 MWEs in the PetroGold corpus, we tested a new annotation for the part-of-speech (POS) of the words that are part of MWEs and we provide two computationally readable resources to assist other annotators.
Label-Description Enhanced Network for Few-Shot Named Entity Recognition

As one of the essential tasks in the context of natural language understanding, Few-shot Named Entity Recognition (NER) aims to identify and classify entities against limited samples. Recently, many works have attempted to enhance semantic representations by constructing prompt templates with text and label names. These methods, however, not only distract attention from the text, but also cause unnecessary enumerations. Furthermore, ambiguous label names always fail in delivering the intended meaning. To address the above issues, we present a Label-Description Enhanced Network (LaDEN) for few-shot named entity recognition, under which we propose a BERT-based Siamese network to incorporate fine-grained label descriptions as knowledge augmentation. The designed semantic attention mechanism captures label-specific textual representations, and the distance function matches similar token and label representations based on the nearest-neighbor criterion. Experimental results demonstrate that our model outperforms previous works in both few-shot and resource-rich settings, achieving state-of-the-art performance on five benchmarks. Our method is particularly efficient in low-resource scenarios, especially for cross-domain applications.
LE-MVSNet: Lightweight Efficient Multi-view Stereo Network

Multi-view Stereo(MVS) has been studied for decades as a critical algorithm for 3D reconstruction. Lately, many learning-based methods have improved the reconstruction performance of traditional algorithms, but they pay limited attention to memory consumption and runtime. To address this issue, we propose a novel and effective learning-based MVS framework(LE-MVSNet), based on our exploration of the depth hypothesis and cost volume in this work. Firstly, to decrease the number of depth hypotheses, we establish a more reasonable depth hypothesis space based on its sparse point cloud corresponding to the image set, replacing the previous method of randomly depth hypothesis in evenly divided depth layers within a predefined depth range. Secondly, to reduce memory consumption, we design a lightweight group-wise correlation by compressing the channel of the aggregated cost volumes to one. In addition, for acceleration, we propose SE-UNet, which executes U-Net regularization in the width and height direction, and SE-Net for self-attention in the depth direction. Finally, our method achieves competitive performance on DTU and BlendedMVS dataset with significantly higher efficiency. Compared to MVSNet, our method reduces memory consumption by 52.78 $$\%$$ and runtime by 88.57 $$\%$$ .
Malicious Domain Detection Based on Self-supervised HGNNs with Contrastive Learning

The Domain Name System (DNS) facilitates access to Internet devices, but is also widely used for various malicious activities. Existing detection methods are mainly classified into statistical feature-based methods and graph structure-based methods. However, highly hidden malicious domains can bypass statistical feature-based methods, and graph structure-based methods have limited performance in the case of extremely sparse labels. In this paper, we propose a malicious domain detection method based on self-supervised HGNNs with contrastive learning, which can make full use of unlabeled domain data. Specifically, we design a hierarchical attention mechanism and a cross-layer message passing mechanism in the encoder for discovering more hidden malicious domains. Then, we construct a node-level contrastive task and graph-level similarity task to pre-train high-quality domain representations. Finally, we classify domains by fine-tuning the model with a small number of domain labels. Extensive experiments are conducted on the real DNS dataset and the results show that our method outperforms the state-of-the-art methods.
Harnessing Crowdsourcing and Machine Learning for Mobile Services Performance Analysis

Data gathering from user terminals or specialized collection devices is required for crowdsourcing the evaluation of quality of experience (QoE) on mobile networks. With this strategy, mobile operators and academics may more affordably address large-scale issues, enhance resource allocation, and supply networks. Based on this strategy, we present here a taxonomy of dataset generation though mobile signals measurements, and also a comprehensive study utilizing crowdsourced data from user terminals to analyze the QoE of popular Internet services, such as on-demand video streaming, web browsing, and file downloading. The dataset, comprising over 220,000 measurements collected from two different vendor terminals and various mobility test modes, was obtained from a major French mobile operator in the Ile-de-France region over a six-month period in 2021. Various models from the literature are then implemented to estimate the QoE in terms of user Mean Opinion Score (MOS), utilizing features at both the radio and application levels. Furthermore, a detailed analysis of the collected dataset is conducted to identify the root causes of poor performance, revealing that radio provisioning issues are not the sole factor contributing to anomalies. Lastly, the chapter explores the prediction accuracy of HD video streaming services, considering factors such as launch time, bitrate, and MOS, based solely on physical indicators. The analysis encompasses both plain-text and encrypted traffic within different mobility modes, providing valuable insights into QoE optimization in mobile networks.
Students’ Perception of Using Canva Aplication in Writing Short Functional Text

The aims of this study were to investigate the students' perception of ‘Canva for Education’ in writing short functional text (greetings card) at MTSN 2 Kepahiang, in the academic year 2021/2022. The research used descriptive qualitative with a questionnaire using Likert scale. The questionnaire was conducted by students of class VIII as the chosen sample of this study by using purposive sampling. The data instrument of this study was a questionnaire. The research showed on the students’ perception toward the use of ‘Canva for Education’ in writing short functional text (greetings cards) gave several benefits to their experience and for enhance students’ ability and they felt enthusiastic about learning by using ‘Canva for Education’. Using Canva has a good effect on students. combined with greeting cards lessons also make them able to develop creativity and make teachers able to innovate and of course provide benefits in the process of improving student writing.
FORMATION OF THE COMPETENCE OF PEDAGOGICAL PARTNERSHIP BETWEEN TEACHERS AND PARENTS OF STUDENTS ON COURSES OF IMPROVING PEDAGOGICAL QUALIFICATIONS

Currently, the problem of forming the competence of the pedagogical partnership of educators with parents of students is very urgent. At advanced training courses, we explain to educators how to correctly build relationships with parents in order to direc
Time-Series Prediction of Calcium Carbonate Concentration in Flue Gas Desulfurization Equipment by Optimized Echo State Network

As an example of an application of machine learning to industrial problems, there has been active research and development of soft sensors that replace hardware sensors with time-series prediction models, thereby enabling to sensor cost reduction and application to model predictive control. However, the training costs of neural networks (NNs) as a machine learning algorithm are very high due to the gradient descent method. As this paper shows, a prediction model with higher accuracy and about 1600 times higher speed than existing NN-based methods and very low training costs can be implemented for predicting calcium carbonate concentrations in flue gas desulfurization equipment. That performance can be achieved by appropriate setting of the network structure and parameters of the Echo State Network (ESN). Ablation studies of several approaches to improve ESN performance show that it is particularly effective to select an appropriate activation function and to design an appropriate objective function for optimization of the regularization coefficient in ridge regression.