Reviewerly/exHarmony huggingface dataset:
Situational model of bullying in business communication

The subject of the study is speech activity affecting the participants of bullying situations in the context of business communications; linguistic means of implementing business bullying strategies and tactics when creating a negative media image of companies and persons in the context of information attacks; media linguistic characteristics of texts related to bullying phenomena in business communication (structure, vocabulary, style, etc.); extralinguistic factors influencing the manifestation and perception of bullying in the business environment, including socio-cultural, psychological and economic aspects. The object of the study is destructive verbal content with signs of business bullying placed in open communication channels, including its media and linguistic manifestations. Within the framework of this work, the emphasis is placed on the specifics of the media linguistic analysis of business communication texts in the context of bullying, as well as on external factors that can influence this process. Special attention is paid to the structure of subject-object relations in the practice of information aggression directed against business. The methodology has developed under the influence of modern media linguistics, when an activity-procedural, praxeological approach to the analysis of the media text is implemented. The thematic discourse of business bullying is distinguished by the length of time, complex interactions, and multicomponent structure of the linguistic architecture of media communication, so the basic method was the praxeological transdisciplinary and interdisciplinary integration of research methods, procedures and techniques – formed the basis of the work. The relevance of the research is due to the combination of the rapidly changing digital landscape, the social responsibility of business and the need for effective strategies to prevent or respond to bullying cases in business communication. The scientific novelty lies in the identification of linguistic signs of business bullying and the construction of communication models of business bullying. The results obtained can be used both in academic research and in the practical activities of the business community. The author proposes to build communication situational models of bullying in the context of business communication on the basis of subject-object relations, which allow to determine patterns of information aggression, analyze components, specify the role of participants, isolate manipulative practices, build business risk strategies, formulate strategies to combat and overcome the consequences of business bullying.
Frame Duplication Forgery Detection in Surveillance Video Sequences Using Textural Features

Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and variance). Finally, the combination of GLCM’s parameters and the other statistical features are then used to detect and localize the duplicated frames in the video sequences using the correlation between features. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art (SOTA) methods in terms of Precision, Recall, and F1Score rates. Furthermore, the use of statistical features combined with GLCM features improves the performance of frame duplication forgery detection.
Hubungan Kemampuan Berpikir Kreatif Dan Menulis Deskripsi Pada Siswa Kelas II Sekolah Dasar

Writing activities are creative activities, through learning to write students can develop their creative potential. There are many benefits that can be gained from writing, including increasing intelligence, developing initiative and creativity, growing courage, encouraging the will and ability to gather information. However, the reality in the field shows that students in writing narrative essays have not been able to express their correct ideas as clearly as possible. This shows that students' creative thinking skills in writing narrative essays are still low and causes only a few students to be capable of creative thinking skills in writing narrative essays, especially in the lower classes. The aim to be achieved in this research is to determine the relationship between creative thinking and elementary school students' descriptive writing abilities. This research is correlational research (Correlational Studies) to determine the relationship between the ability to think creatively and the ability to write descriptions. The results of the research concluded that there is a relationship between creative thinking and elementary school students' descriptive writing abilities.
Sector Specific Aid Flow, Official Development Assistance and Income Disparity: An Analysis from Selected Asian Countries

Over the decades, there has been much-observed income disparity in poor and developing countries. For this, donor countries provide aid for disbursement in some specific sectors to alleviate poverty and disparity. Considering the severe issue of income disparity in Asian economies, this research has focused on the role of sector-specific and foreign aid in lessening income disparity in these economies. We have used panel data from 10 selected Asian countries and found that aid for the education and health sectors. This research has utilized the data drawn from 10 selected Asian economies such as Bangladesh, India, Indonesia, Malaysia, Sri Lanka, Pakistan, Philippines, Iran, Jordan and China. The data has been considered from 2003 to 2018 due to data deficiency of important variables. The data has been taken from World Bank Development Indicators. The Gini coefficient income inequality) was considered a dependent variable. Education aid commitments (total number of commitments for education from donor countries), health aid commitments (total number of commitments for aid for the health sector from donor countries), official development assistance ($US million) and total population have been used as independent variables. The study used the fixed effect method to indicate an association of dependent and independent variables. Results of the study revealed that official development assistance has played a key role in lessening income disparity in these economies. Findings suggest its transparent allocation to both sectors for high growth, attracting more aid from the donor countries. There is a serious need for a stable political environment. Finally, measures should be taken to control the population in these countries.
Security optimization of synchronization in DFB lasers under constant-amplitude random-phase drive light by reducing drive-response correlation

Common-signal-induced laser synchronization promoted a promising paradigm of high-speed physical key distribution. Constant-amplitude and random-phase (CARP) light was proposed as the common drive signal to enhance security by reducing the correlation between the drive and the laser response in intensity. However, the correlation in light phase is not examined. Here, we numerically reveal that the correlation coefficient of the CARP light phase and the response laser intensity (denoted as CC R- φ D ) can reach a value close to 0.6. Effects of parameters including optical frequency detuning, and modulation depth and noise bandwidth and transparency carrier density for CARP light generation are investigated in detail. By optimizing the optical frequency, modulation depth, and noise bandwidth, respectively, CC R-φD can be reduced to 0.32, 0.18, and 0.10. In the meantime, CC R-φD can be further reduced through secondary optimizing of parameters. CC R-φD can be further reduced by increasing transparent carrier density provided response laser synchronization is achieved. This work gives a new insight about the laser synchronization induced by common CARP light, and also contributes a suggestion of security improvement for physical key distribution based on laser synchronization.
Multi-dimensional information sensing of complex surfaces based on fringe projection profilometry

Multi-dimensional and high-resolution information sensing of complex surface profiles is critical for investigating various structures and analyzing their mechanical properties. This information is currently accessed separately through different technologies and devices. Fringe projection profilometry (FPP) has been widely applied in shape measurement of complex surfaces. Since structured light information is projected instead of being attached onto the surface, it holds back accurately tracking corresponding points and fails to further analyze deformation and strain. To address this issue, we propose a multi-dimensional information sensing method based on digital image correction (DIC)-assisted FPP. Firstly, colorful fluorescent markers are introduced to produce modulated information with both high-intensity reflectivity and color difference. And then, the general information separation method is presented to simultaneously acquire speckle-free texture, fringe patterns and high-contrast speckle patterns for multi-dimensional information sensing. To the best of our knowledge, this proposed method, for the first time, simultaneously realizes accurate and high-resolution 2D texture ( T ), 4D shape ( x , y , z , t ) and analytical dimensional mechanical parameters (deformation ( d ), strain ( s )) information sensing based on the FPP system. Experimental results demonstrate the proposed method can measure and analyze 3D geometry and mechanical state of complex surfaces, expanding the measuring dimension of the off-the-shelf FPP system without any extra hardware cost.
A 28-nm Computing-in-Memory-Based Super-Resolution Accelerator Incorporating Macro-Level Pipeline and Texture/Algebraic Sparsity

Super-resolution (SR) task using the convolutional neural network is a crucial task in improving image and video quality. The introduction of the residual block (RB) raises the depth of the algorithm to perform better reconstruction. The processing of the RB leads to a decrease in hardware utilization and frequent off-chip communications. It is hard to apply such algorithms on edge devices with limited performance. Computing-in-memory (CiM) is one promising method to reduce high power caused by massive data movement in multiply-accumulation computation. The algebraic sparsity (AS) is the structured sparsity (SS) optimization for imaging computing. However, it is an unsolved problem to simultaneously realize the texture sparsity (TS) of the image and the SS of the algorithm in the CiM scheme while maintaining high hardware utilization. Thus, we propose a CiM-based SR task accelerator. There are three key contributions: first, a texture-aware workflow and a dynamic grouping CiM engine can concurrently support TS coupling with AS. Second, a macro-level pipeline scheme together with two custom-sized CiM macros and a high reuse-rate Hadamard transformation circuit reaches 91% hardware utilization. Third, a novel weight update strategy is devised to reduce the performance loss induced by the weight updating. The accelerator prototype is fabricated in a 28-nm CMOS. It scores a 22.8-44.3-TOPS/W peak energy efficiency at the voltage supply of 0.54-1.1 V and the operating frequency of 50-200 MHz, indicating 1.8-6.8x higher compared to the state-of-the-art CiM processors.
Blockchain and Sustainability in the Public Sector: A Risk Management Perspective

Blockchain (BC) adoption in the public sector can potentially address sustainability issues. Thus, blockchain is seen as an enabler and a booster of sustainability and Sustainable Development Goals accomplishments. However, BC adoption in the public sector brings challenges concerning risk management. This work presents insights from 19 expert interviews organised as a risk management model for blockchain adoption and sustainability in the public sector, following the risk management process from the standard ISO 31000. The model describes nine identified risks, five proposed treatments, and five metrics for monitoring. This will support scholars and practitioners focusing on developing knowledge about adopting BC under the perspective of sustainability and the contribution to the risk management body of knowledge.
A Survey of Decentralized Storage and Decentralized Database in Blockchain-Based Proposed Systems: Potentials and Limitations

Blockchain technology has geared towards many fields and utilities. Blockchain, as an efficient data storage, is one of the use cases that aims to provide a trustable and immutable data storage. This study provides a systematic review and evaluation of peer-reviewed publications aimed at pointing out the potential of blockchain as a data storage and/or queryable distributed database. The study presented herein shows that the undertaking of using blockchain technology as a storage or database technology is increasing exponentially due to the decentralization, immutability, and security that blockchain offers. The methodology used to conduct this study encompassed searching in several scientific databases and selecting related publications indexed in the Journal Citation Reports (JCR) ranking. The outcomes of the selected studies have been extracted, synthesized, and presented. As a conclusion of this analysis, we have detected that blockchain suffers from many challenging limitations, such as the absence of a well-defined data indexing structure, data retrieval methods and queries, significant response times, and storage capacity and performance limitations.
Disparitas Sanksi Administratif Akibat Keterlambatan Pemberitahuan Pengambilalihan Saham Pelaku Usaha Berdasarkan Hukum Persaingan Usaha

In today's growing era of digitalization, data is becoming a very important and valuable resource. Banks around the world generate more than 100 terabytes of data per day. However, only a few banks are able to make good use of the data. The utilization of big data can help banks improve operational efficiency, reduce credit risk, increase customer satisfaction, and increase the profitability of their business. This research is prepared by searching literature and sources of information related to the use of Big Data to improve the business performance of a Bank. The study of 200 people showed that more than 70% already use Big Data. Big Data processing can provide benefits, namely risk mitigation, preventing fraud, building relationships with customers, increasing company profits, increasing company value compared to competitors and increasing company business volume. The benefits of using Big Data generated are improving business performance and assisting the Bank in mitigating the risk of decision-making errors, product cross-selling errors to help identify money laundering practices. Processing Big Data poses challenges, namely the readiness of human resources and large information technology investments. Banks must be careful to anticipate data leaks that cause legal problems.&#x0D; &#x0D; &#x0D;
Bibliometric Analysis of Research Regarding Citarum River in Indonesia and Its Implication for Government Policy

Research mapping related to river revitalization has been carried out on areas experiencing severe pollution to provide a roadmap for knowledge and answers to a number of environmental issues surrounding rivers. Therefore, this study aims to map a number of research publications regarding the Citarum River, which is an important area for people in West Java. This river is currently the most polluted in Indonesia, which led to the implementation of the revitalization program initiated by the government known as "Citarum Harum". The bibliometric method was used in this research to determine annual trends, keywords, field grouping, and location. The results showed a fluctuating increase in the number of research publications on the Citarum River. Furthermore, the mapping results showed that topics, study groups, and locations related to this river were unevenly distributed. These findings indicated the need for coordination from the “Citarum Harum” program coordinator or government to develop a research roadmap and direct the researcher to examine topics and raise issues related to locations that are yet to be studied.
Enhanced Parallel sparse-MLP for Monocular Depth Estimation of Autonomous UAV

Estimating a high-quality depth map from a single RGB image is a challenging task due to its ill-posed nature. Recently, two dominant trends in computer vision have been the subject of extensive research: attention mechanisms and multi-layer perceptron (MLP)-based vision models. Attention mechanisms, especially multi-head attention (MHA), have demonstrated significant improvements in depth estimation. MHA excels in capturing long-distance information and pixel relationships, yet its complexity quadratically increases with spatial resolution. Consequently, applying MHA to unmanned aerial vehicles with limited hardware resources is infeasible. In contrast, MLP-based vision models offer faster inference due to their linear computational complexity concerning spatial resolution. However, the inherent weakness of the MLP’s inductive bias can hinder generalization without a substantial amount of data. Moreover, the absence of location-dependent local dependencies can hinder the precise estimation of locally detailed depth maps. To address these challenges, this study introduces a novel module called EPsMLP (Enhanced Parallel sparse-MLP), which consists of three parallel branches, including sparse-MLP, local sparse attention, and channel attention. This module can capture global and local dependencies while benefiting from the inductive bias on locality. Furthermore, multi-scale convolutions are used to extract context at various scales for diverse objects. The architecture adopts an encoder-decoder-based structure, incorporating a pre-trained DenseNet-121 encoder. Experimental evaluations were conducted using the NYU-Depth-V2 and KITTI datasets, which are commonly used in monocular depth estimation. The extensive results demonstrate that our network is more efficient and effective than previously proposed methods.
Flight Path Planning Method for UAM Considering Urban Airflow Based on A* Algorithm

Urban air mobility (UAM) is widely anticipated as a solution to urban traffic challenges, and numerous companies, including Joby Aviation and Airbus, are actively developing aircraft for this purpose. However, there are several challenges associated with commercializing UAM, such as obtaining airworthiness certification and establishing the necessary systems and policies. Moreover, UAM operates, especially with low-weight electric Vertical Take-off and Landing (eVTOL) aircraft are susceptible to the influence of winds. To address the issue of wind influence, this study presents a method for predicting urban airflow and flight path planning that circumvents wind-hazard areas. Since real-time measurements often fall short in predicting urban airflow accurately, computational fluid dynamics software, OpenFOAM was used to obtain urban airflow data. We have adapted the A* algorithm to create flight paths that avoid wind-hazard areas. The path planning based on A* was conducted according to the defined simulation scenario. Finally, the generated flight paths were compared, and their distinctive characteristics were discussed.
Quantum-secure fault-tolerant distributed cloud storage system

With the increasing prominence of data security in cloud storage, we propose a practical and robust cloud storage scheme, which uses quantum random numbers as encryption keys, disperses the keys using Shamir’s secret sharing scheme, applies erasure coding to the ciphertext, and securely transmits the data through quantum key distribution protected networks to the distributed clouds. This system offers several key advantages, including quantum-level security, fault tolerance, and storage space saving. To validate its feasibility, we conduct comprehensive experimental tests covering essential functionalities such as encryption/decryption, key preservation, and data storage. By successfully demonstrating the effectiveness of our proposal, we aim to accelerate the application of quantum technology in cloud storage.
Learning Primitive-Aware Discriminative Representations for Few-Shot Learning

Few-shot Learning (FSL) aims to learn a classifier that can be easily adapted to recognize novel classes with only a few labeled examples. Recently, some works about FSL have yielded promising classification performance, where the image-level feature is used to calculate the similarity among samples for classification. However, the image-level feature ignores abundant fine-grained and structural information of objects that could be transferable and consistent between seen and unseen classes. How can humans easily identify novel classes with several samples? Some studies from cognitive science argue that humans recognize novel categories based on primitives. Although base and novel categories are non-overlapping, they share some primitives in common. Inspired by above research, we propose a Primitive Mining and Reasoning Network (PMRN) to learn primitive-aware representations based on metric-based FSL model. Concretely, we first add Self-supervision Jigsaw task (SSJ) for feature extractor parallelly, guiding the model encoding visual pattern corresponding to object parts into feature channels. Moreover, to mine discriminative representations, an Adaptive Channel Grouping (ACG) method is applied to cluster and weight spatially and semantically related visual patterns to generate a set of visual primitives. To further enhance the discriminability and transferability of primitives, we propose a visual primitive Correlation Reasoning Network (CRN) based on Graph Convolutional network to learn abundant structural information and internal correlation among primitives. Finally, a primitive-level metric is conducted for classification in a meta-task based on episodic training strategy. Extensive experiments show that our method achieves state-of-the-art results on miniImageNet and Caltech-UCSD Birds.