Reviewerly/exHarmony huggingface dataset:
The role of digital literacy capability of Babinsa Kodim 0705/Magelang to improve community preparedness for natural disasters

The intensity of disaster events that occur in the Kodim 0705/Magelang area is high. Therefore, Babinsa, who serves in the Kodim 0705/Magelang area as the spearhead who is in direct contact with the community in villages, has a central role in increasing community preparedness in the village under his guidance. To support the implementation of this task, Babinsa must have digital literacy capabilities in the face of natural disasters that occur. This research aims to analyze the role of the digital literacy capability of Babinsa, who serve in the Kodim 0705/Magelang area, in improving community preparedness in the village under their guidance in the face of natural disasters. The method used in this research is the library research method, where in this method the collection of reference sources related to the problem that is the topic of research is carried out, and then from the reference sources it is carefully understood so that the research results are obtained. The results showed that the Chapter’s digital literacy capability plays a very important role when faced with natural disasters, and advances in digital technology can improve commun ity preparedness to deal with natural disasters.
The concept of interactive learning technologies

The article analyzes the essence and main aspects of the use of interactive technologies in the learning process. The concept of interactive technologies is defined as means aimed at active interaction of students with educational material. The article highlights the key benefits of using interactive technologies, such as increasing the interest and motivation of students, creating a favorable environment for individualized learning and developing critical thinking. Possible challenges and limitations associated with the introduction of interactive technologies into the educational process are also discussed. Interactive technologies make it possible to create a more dynamic and memorization-promoting learning environment. They help students to remember more and better understand the educational material thanks to visualization, animation and interactive tasks. With the help of interactive technologies, the educational material can be adapted to the specific needs and level of knowledge of each student. This allows each student to learn at their own pace and focus on their own needs. Multimedia tools and interactive applications help to visualize abstract or complex material, which facilitates understanding and memorization. Interactive learning technologies are an approach to education that actively uses various technologies to engage students in the learning process. These technologies are aimed at creating interaction between students, teachers and educational material. The main principles of interactive learning technologies include active participation of students, individualization of learning and the use of different learning formats.
Standing up to problematic content on social media: which objection strategies draw the audience’s approval?

Abstract Problematic content on social media can be countered through objections raised by other community members. While intended to deter offenses, objections can influence the surrounding audience observing the interaction, leading to their collective approval or disapproval. The results of an experiment manipulating seven types of objections against common types of offenses indicate audiences’ support for objections that implore via appeals and disapproval of objections that threaten the offender, as they view the former as more moral, appropriate, and effective compared to the latter. Furthermore, audiences tend to prefer more benign and less threatening objections regardless of the offense severity (following the principle of “taking the high road”) instead of objections proportionate to the offense (“an eye for an eye”). Taken together, these results show how objections to offensive behaviors may impact collective perceptions on social media, paving the way for interventions to foster effective objection strategies in social media discussions.
Revocable-Attribute-Based Encryption with En-DKER from Lattices

Cloud computing offers abundant computing resources and scalable storage, but data leakage in the cloud storage environment is a common and critical concern due to inadequate protection measures. Revocable-attribute-based encryption (RABE) is introduced as an advanced form of identity-based encryption (IBE), which encrypts sensitive data while providing fine-grained access control and an effective user revocation mechanism. However, most existing RABE schemes are not resistant to quantum attacks and are limited in their application scenarios due to the revocation model. In this paper, we propose a RABE scheme constructed from lattices. Our scheme has several advantages, including a near-zero periodic workload for the key generation center (KGC), ensuring scalability as the number of users increases. Additionally, the encryptor is relieved from managing a revocation list. Moreover, our scheme guarantees the confidentiality and privacy of other ciphertexts even if the decryption key for a specific period is compromised. We validated the correctness of our scheme and demonstrated its security under the assumption of learning with errors (LWE), which is widely believed to be resistant to quantum attacks. Finally, we provide an application example of our RABE scheme in the electronic healthcare scenario.
The Implementation Mechanism and Effectiveness of a National Plan of a Digital Competence Training Program for Chinese Primary and Secondary School Teachers

This study investigated the implementation and effectiveness of a national plan for a digital competence training program (DCTP) aimed at Chinese teachers. A relational content analysis was performed using policy documents, observations, and assessments to identify four DCTP implementation stages. Local educational departments and training institutes were afforded a moderate degree of autonomy to organize training activities. A t-test indicated disparities in the effectiveness of training between privately funded and publicly funded institutes, with the latter demonstrating stronger assessment outcomes in certain subject areas. The study examined the advantages and disadvantages of this implementation process, proposing collaborative efforts between privately funded and publicly funded training institutions.
AIOL: An Improved Orthogonal Lattice Algorithm for the General Approximate Common Divisor Problem

The security of several fully homomorphic encryption (FHE) schemes depends on the intractability assumption of the approximate common divisor (ACD) problem over integers. Subsequent efforts to solve the ACD problem as well as its variants were also developed during the past decade. In this paper, an improved orthogonal lattice (OL)-based algorithm, AIOL, is proposed to solve the general approximate common divisor (GACD) problem. The conditions for ensuring the feasibility of AIOL are also presented. Compared to the Ding–Tao OL algorithm, the well-known LLL reduction method is used only once in AIOL, and when the error vector r is recovered in AIOL, the possible difference between the restored and the true value of p is given. Experimental comparisons between the Ding-Tao algorithm and ours are also provided to validate our improvements.
Voice spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients

Nowadays, biometric authentication has gained relevance due to the technological advances that have allowed its inclusion in many daily-use devices. However, this same advantage has also brought dangers, as spoofing attacks are now more common. This work addresses the vulnerabilities of automatic speaker verification authentication systems, which are prone to attacks arising from new techniques for the generation of spoofed audio. In this article, we present a countermeasure for these attacks using an approach that includes easy to implement feature extractors such as spectrograms and mel frequency cepstral coefficients, as well as a modular architecture based on deep neural networks. Finally, we evaluate our proposal using the well-know ASVspoof 2017 V2 database, the experiments show that using the final architecture the best performance is obtained, achieving an equal error rate of 6.66% on the evaluation set.
Project Activity in an Educational Institution of Secondary Vocational Education

The article discusses the possibilities of forming professional competencies when performing a diploma project in educational institutions of secondary vocational education through the involvement of students in project activities. The substantiation of the approach to the design activity of the pedagogical worker is proposed, the procedural scheme of the design of the educational process in the implementation of the diploma project is proposed. The role of project activity in achieving educational goals, as well as the impact on the professional development of the student is shown. The conducted studies allow us to conclude that it is necessary to divide the design process into stages that differ in priority deterministic goals. The structure of the professional module, made on the basis of the federal state standard in the system of secondary vocational education, is given.
How to work together for engineering materials!

Abstract To share and utilize data effectively for collaborative work, a common understanding of the knowledge behind the data, including its context and meaning, is a fundamental requirement. This paper focuses on the gaps that hinder common understanding between the real world and the data space, acting as barriers between systems, organizations, data spaces, and disciplines. To understand the core reasons and devise strategies for bridging the gap, the author has endeavored to synthesize a case study of material data activities from two perspectives: diachronic and synchronic, which is framed into a two‐step process, involving the establishment of intersubjectivity among experts and interobjectivity among materials/substances data. As a result, the author has formulated an action plan for the digitization of engineering materials, encompassing tacit knowledge, know‐how, and intellectual property rights to establish a foundation for their use with traceability, interoperability, and reusability. In order to create a conceptual framework that enhances a productive ecosystem facilitated by networked materials and substance databases, this plan is conclusively based on two key steps: fostering interactions among experts rooted in substances and materials and standardizing digitized data related to substances/materials based on their geospatial structural information.
Restoration and enhancement optimization of blurred images based on SRGAN

Abstract Blurred images pose a significant challenge in many applications, including medical imaging, remote sensing, and surveillance systems. These images suffer from low resolution, noise, and missing data, which can hinder their interpretation and analysis. Traditional methods for image restoration and enhancement have their limitations, such as low quality and slow processing times. To overcome these challenges, this paper proposes an innovative method using Super-resolution Generative Adversarial Networks (SRGANs) to enhance image quality and fidelity. The proposed method employs adversarial training, perceptual loss, residual learning, and feature reconstruction to generate visually realistic and high-quality super-resolution (SR) images from low-resolution (LR) inputs. The SRGANs approach outperforms traditional methods, demonstrating its potential to advance image restoration and enhancement techniques. The paper also discusses possible improvements and future directions for this technique.
Analytic Design Technique for 2D FIR Circular Filter Banks and Their Efficient Implementation Using Polyphase Approach

This paper proposes an analytical design procedure for 2D FIR circular filter banks and also a novel, computationally efficient implementation of the designed filter bank based on a polyphase structure and a block filtering approach. The component filters of the bank are designed in the frequency domain using a specific frequency transformation applied to a low-pass, band-pass and high-pass 1D prototype with a specified Gaussian shape and imposed specifications (peak frequency, bandwidth). The 1D prototype filter frequency response is derived in a closed form as a trigonometric polynomial with a specified order using Fourier series, and then it is factored. Since the design starts from a 1D prototype with a factored transfer function, the frequency response of the designed 2D filter bank components also results directly in a factored form. The designed filters have an accurate shape, with negligible distortions at a relatively low order. We present the design of two types of circular filter banks: uniform and non-uniform (dyadic). An example of image analysis with the uniform filter bank is also provided, showing that the original image can be accurately reconstructed from the sub-band images. The proposed implementation is presented for a simpler case, namely for a smaller size of the filter kernel and of the input image. Using the polyphase and block filtering approach, a convenient implementation at the system level is obtained for the designed 2D FIR filter, with a relatively low computational complexity.
Benchmarking Perception to Streaming Inputs in Vision-Centric Autonomous Driving

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. Although a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming input characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming input characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming input features, we introduce a backbone switcher based on the identified streaming input characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming input features.
Verification and Enforcement of (ϵ, ξ)-Differential Privacy over Finite Steps in Discrete Event Systems

In the realm of data protection strategies, differential privacy ensures that unauthorized entities cannot reconstruct original data from system outputs. This study explores discrete event systems, specifically through probabilistic automata. Central is the protection of state data, particularly the initial state privacy of multiple starting states. We introduce an evaluation criterion to safeguard initial states. Using advanced algorithms, the proposed method counters the probabilistic identification of any state within this collection by adversaries from observed data points. The efficacy is confirmed when the probability distributions of data observations tied to these states converge. If a system’s architecture does not meet state differential privacy demands, we propose an enhanced supervisory control mechanism. This control upholds state differential privacy across all initial states, maintaining operational flexibility within the probabilistic automaton framework. Concluding, a numerical analysis validates the approach’s strength in probabilistic automata and discrete event systems.
Smoclust: synthetic minority oversampling based on stream clustering for evolving data streams

Abstract Many real-world data stream applications not only suffer from concept drift but also class imbalance. Yet, very few existing studies investigated this joint challenge. Data difficulty factors, which have been shown to be key challenges in class imbalanced data streams, are not taken into account by existing approaches when learning class imbalanced data streams. In this work, we propose a drift adaptable oversampling strategy to synthesise minority class examples based on stream clustering. The motivation is that stream clustering methods continuously update themselves to reflect the characteristics of the current underlying concept, including data difficulty factors. This nature can potentially be used to compress past information without caching data in the memory explicitly. Based on the compressed information, synthetic examples can be created within the region that recently generated new minority class examples. Experiments with artificial and real-world data streams show that the proposed approach can handle concept drift involving different minority class decomposition better than existing approaches, especially when the data stream is severely class imbalanced and presenting high proportions of safe and borderline minority class examples.
Imperfect phase randomization and generalized decoy-state quantum key distribution

Decoy-state methods [1][2][3] are essential to perform quantum key distribution (QKD) at large distances in the absence of single photon sources.However, the standard techniques apply only if laser pulses are used that are independent and identically distributed (iid).Moreover, they require that the laser pulses are fully phase-randomised.However, realistic high-speed QKD setups do not meet these stringent requirements [4].In this work, we generalise decoy-state analysis to accommodate laser sources that emit imperfectly phase-randomised states.We also develop theoretical tools to prove the security of protocols with lasers that emit pulses that are independent, but not identically distributed.These tools can be used with recent work [5] to prove the security of laser sources with correlated phase distributions as well.We quantitatively demonstrate the effect of imperfect phase-randomisation on key rates by computing the key rates for a simple implementation of the three-state protocol.
Underwater Structured Light Stripe Center Extraction with Normalized Grayscale Gravity Method

The non-uniform reflectance characteristics of object surfaces and underwater environment disturbances during underwater laser measurements can have a great impact on laser stripe center extraction. Therefore, we propose a normalized grayscale gravity method to address this problem. First, we build an underwater structured light dataset for different illuminations, turbidity levels, and reflective surfaces of the underwater object and compare several state-of-the-art semantic segmentation models, including Deeplabv3, Deeplabv3plus, MobilenetV3, Pspnet, and FCNnet. Based on our comparison, we recommend PSPnet for the specific task of underwater structured light stripe segmentation. Second, in order to accurately extract the centerline of the extracted light stripe, the gray level values are normalized to eliminate the influence of noise and light stripe edge information on the centroids, and the weights of the cross-sectional extremes are increased to increase the function convergence for better robustness. Finally, the subpixel-structured light center points of the image are obtained by bilinear interpolation to improve the image resolution and extraction accuracy. The experimental results show that the proposed method can effectively eliminate noise interference while exhibiting good robustness and self-adaptability.
Applicability of blockchain technology in improving efficiency in supply chain operations in public procurement in Tanzania

The primary aim of this study was to assess the suitability of blockchain technology for improving the efficiency of supply chain operations in the public sector in Tanzania. Blockchain technology (BT) is a contemporary and rapidly growing technology that has gained widespread popularity globally due to its capacity to connect several stakeholders (blocks) worldwide with the aim of enhancing efficiency in business transactions. With this perspective in mind, the researcher was driven to evaluate the capabilities of blockchain technology in the field of supply chain in Tanzania. This evaluation concentrated on three distinct areas: the degree of public awareness of blockchain technology, the strategies used by public bodies to use blockchain technology, and the significant issues that obstruct the implementation of blockchain technology. The study utilized network theory as a framework to analyze the findings. It employed a descriptive survey approach, with a sample size of 192 respondents who were engaged as registered procurement experts in public institutions. In collecting data from respondents, structured questionnaires were prepared using Google Forms and submitted to respondents through their e-mails. A total of 154 questionnaires were properly filled out and submitted, for a response rate of 80.2%. The data was analyzed using descriptive statistics and correlation analysis. The results show that 74.7% of the respondents are unaware of the application of blockchain technology in supply chain operations. In terms of practice, the majority of public entities (67%) use e-payment as the main application of blockchain technology. Additionally, 64% of the respondents identified technical issues and a lack of understanding as the main challenges affecting the application of blockchain technology. Moreover, the study demonstrates a robust correlation between the utilization of blockchain technology and the enhancement of efficiency in supply chain operations. Ultimately, the study suggests implementing an extensive awareness campaign targeted at practitioners in order to enhance the rate of adoption and fully use the capabilities of blockchain technology in Tanzania.
Digital Embodiment as a Tool for Constructing the Self in Politics

This article offers an exploration of the theoretical and methodological implications related to the concept of digital embodiment in the field of contemporary communication. It seeks to analyze a crucial intersection between the virtual and material dimensions of human experience, enabling a deeper understanding of how bodies are shaped, visualized, and experienced in the digital age. Specifically, through this conceptual lens, we examine how human bodies are engaged in continuous interaction with digital technologies, giving rise to new forms of presence and identity. Therefore, we will seek to analyze how the personalization of the body within political communication has been profoundly affected by the virtualization of human experience. Next, we will introduce a new approach, useful for studying this fusion, that can emphasize the importance of analyzing this issue using ethnography, which is useful for fully understanding the complex dynamics surrounding the personalized digital body.