Reviewerly/exHarmony huggingface dataset:
Frame recovery using multiple images in rolling shutter based systems

Abstract An algorithm for recovering transmitted static identifiers (IDs) in rolling shutter based Optical Camera Communication systems is proposed by the authors, considering a system comprised of a camera and a circular light source. The goal is to allow the correct decoding when the ID frame is only partially detected in the image. A baseline algorithm as reference for the frame recovery success rate (FRSR) and a reconstruction algorithm based on the idea of capturing multiple frame fragments and reassembling them is proposed in order to recover the transmitted ID not entirely seen on a single image. It was proven, by simulation, that the maximum distance at which the IDs recovery can be guaranteed is increased by 2.5 fold with the proposed algorithm, for 6‐bit, 8‐bit and 10‐bit codewords. An experimental validation algorithm was also proposed, using image processing techniques to extract the bitstreams and test the ID recovery process. The proposed algorithm improves the FRSR for a given distance, even in the presence of considerable bit errors in the bitstreams extracted from the images.
A classification scheme for static origin–destination data visualizations

Origin–destination (OD) visualizations can help to understand movement data. Unfortunately, they are often cluttered due to the quadratic growth of the data and complex depictions of the multiple dimensions in the data. Many domain experts have designed visualizations to reduce visual complexity and display multiple data variables. However, OD visualizations have not been well classified, which makes it hard to employ such methods for reducing the visual complexity systematically. In this article, we propose a novel classification scheme for static OD visualizations that considers five aspects: the granularity of flows, the dimensionality in and of the display space, the semantics of the display space, the representation of nodes and flows, and the ways of relating two visualizations. We evaluate the proposed classification scheme using published visualization examples and show that it is effective and expressive.
Versatile Weight Attack via Flipping Limited Bits

Many attack paradigms against deep neural networks have been well studied, such as the backdoor attack in the training stage and the adversarial attack in the inference stage. In this article, we study a novel attack paradigm, the bit-flip based weight attack, which directly modifies weight bits of the attacked model in the deployment stage. To meet various attack scenarios, we propose a general formulation including terms to achieve effectiveness and stealthiness goals and a constraint on the number of bit-flips. Furthermore, benefitting from this extensible and flexible formulation, we present two cases with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA). SSA which aims at misclassifying a specific sample into a target class is a binary optimization with determining the state of the binary bits (0 or 1); TSA which is to misclassify the samples embedded with a specific trigger is a mixed integer programming (MIP) with flipped bits and a learnable trigger. Utilizing the latest technique in integer programming, we equivalently reformulate them as continuous optimization problems, whose approximate solutions can be effectively and efficiently obtained by the alternating direction method of multipliers (ADMM) method. Extensive experiments demonstrate the superiority of our methods.
MarketTrust: blockchain-based trust evaluation model for SIoT-based smart marketplaces

Due to the significance of trust in Social Internet of Things (SIoT)-based smart marketplaces, several research have focused on trust-related challenges. Trust is necessary for a smooth connection, secure systems, and dependable services during trade operations. Recent SIoT-based trust assessment approaches attempt to solve smart marketplace trust evaluation difficulties by using a variety of direct and indirect trust evaluation techniques and other local trust rating procedures. Nevertheless, these methodologies render trust assessment very sensitive to seller dishonesty, and a dishonest seller may influence local trust scores and at the same time pose a significant trust related threats in the system. In this article, a MarketTrust model is introduced, which is a blockchain-based method for assessing trust in an IoT-based smart marketplace. It has three parts: familiarity, personal interactions, and public perception. A conceptual model, assessment technique, and a global trust evaluation system for merging the three components of a trust value are presented and discussed. Several experiments were conducted to assess the model's security, viability, and efficacy. According to results, the MarketTrust model scored a 21.99% higher trust score and a 47.698% lower average latency than both benchmark models. Therefore, this illustrates that using the proposed framework, a potential buyer can efficiently choose a competent and trustworthy resource seller in a smart marketplace and significantly reduce malicious behavior.
Application Level Resource Scheduling for Deep Learning Acceleration on MPSoC

Abstract Deep Neutral Networks (DNNs) have been widely used in many applications, such as self-driving cars, natural language processing (NLP), image classification, visual object recognition, and so on. Field-programmable gate array (FPGA) based Multiprocessor System on a Chip (MPSoC) is recently considered one of the popular choices for deploying DNN models. However, the limited resource capacity of MPSoC imposes a challenge for such practical implementation. Recent studies revealed the trade-off between the “resources consumed" vs. the “performance achieved". Taking a cue from these findings, we address the problem of efficient implementation of deep learning into the resource-constrained MPSoC in this paper, where each deep learning network is run with different service levels based on resource usage (where a higher service level implies higher performance with increased resource consumption). To this end, we propose a heuristic-based strategy, Application Wise Level Selector (AWLS), for selecting service levels to maximize the overall performance subject to a given resource bound. AWLS can achieve higher performance within a constrained resource budget under various simulation scenarios. Further, we verify the proposed strategy using an AMD-Xilinx Zynq UltraScale+ XCZU9EG SoC. Using a framework designed to deploy multi-DNN on multi-DPUs (Deep Learning Units), it is proved that an optimal solution is achieved from the algorithm, which obtains the highest performance (Frames Per Second) using the same resource budget.
Environmentalism polluted: consumerism and complicity in Studio Ghibli’s media mix

The proliferation of franchise-driven consumption has created a social paradigm where branded symbols are overproduced across an endless array of consumer products. As a paradigm dependent on generating ever-more growth, we argue that transmedia production facilitates social modes which are inherently damaging to the environment. This is accomplished through an examination of the media mix of Japanese animation firm Studio Ghibli, who despite praise from critics and educators for their deeply nuanced environmentalist themes, have heavily commodified their works through plastic merchandise and tourist destinations. While this research demonstrates the studio had already broadly adopted media mix promotion by the early aughts, following the studio's 2014 hiatus in film production, we show a distinct rise in transmedia communication and production of goods made of non-renewable resources. In light of this, we discuss the commodification of Ghibli's themes as a product of recuperation, adding to discussions of capitalism's penchant for co-opting its opposition. Of this instance, we examine the industrial dynamics that led to this specific increase, the cultural-economic paradigms of media mix that frame commodification as a natural extension of texts – despite dissonant values – and consider how contemporary media ecologies may offer any hope of imagining genuinely environmentally conscious societies.
Comparative Analysis of Discrete Subtraction and Cross-Correlation for Subpixel Object Tracking

Many applications in physics and engineering require non-invasive, precise object tracking, which can be achieved with image processing methods at very good cost-efficiency ratios. The traditional method for measuring displacement with subpixel resolution involves cross-correlation between images and interpolation of the correlation peak. While this method enables target tracking with a resolution of thousandths of a pixel, it is computationally intensive and susceptible to peak-locking errors. Recently, a new method based on discrete subtraction between images has been presented as an alternative to cross-correlation to improve computational efficiency, which also results in being free of peak-locking errors. This manuscript presents an experimental evaluation of the performance of the discrete subtraction method (DSM) and compares it with the cross-correlation method in terms of subpixel accuracy and deviation errors. Four different targets were used with apparent displacements as small as 0.002 px, which approaches the theoretical digital resolution limit. The results show that the discrete subtraction method is more sensitive to noise but does not suffer from peak-locking error, thus being a reliable alternative to the correlation method, mainly for calibration processes.
Gaze Estimation Method Combining Facial Feature Extractor with Pyramid Squeeze Attention Mechanism

To address the issue of reduced gaze estimation accuracy caused by individual differences in different environments, this study proposes a novel gaze estimation algorithm based on attention mechanisms. Firstly, by constructing a facial feature extractor (FFE), the method obtains facial feature information about the eyes and locates the feature areas of the left and right eyes. Then, the L2CSNet (l2 loss + cross-entropy loss + softmax layer network), which integrates the PSA (pyramid squeeze attention), is designed to increase the correlation weights related to gaze estimation in the feature areas, suppress other irrelevant weights, and extract more fine-grained feature information to obtain gaze direction features. Finally, by integrating L2CSNet with FFE and PSA, FPSA_L2CSNet was proposed, which is fully tested on four representative publicly available datasets and a real-world dataset comprising individuals of different backgrounds, lighting conditions, nationalities, skin tones, ages, genders, and partial occlusions. The experimental results indicate that the accuracy of the gaze estimation model proposed in this paper has been improved by 13.88%, 11.43%, and 7.34%, compared with L2CSNet, FSE_L2CSNet, and FCBA_L2CSNet, respectively. This model not only improves the robustness of gaze estimation but also provides more accurate estimation results than the original model.
Data Security of Mobile Cloud Computing on Cloud Server

Mobile cloud computing refers to the technology that allows services, such as software, virtual hardware, and bandwidth, to be delivered over the Internet. This technology primarily benefits mobile devices, particularly smartphones. The popularity of mobile cloud computing is rapidly increasing among consumers, and major companies like Apple, Google, Facebook, and Amazon have a large user base in this field. With the help of cloud storage services, users can conveniently access their data anytime, anywhere, and from any device, including mobile devices. While this provides flexibility and scalability in data management, it also introduces new security risks that need to be addressed. However, these security concerns can be mitigated by implementing appropriate data handling practices. Cloud server providers can enhance data security by employing encryption and decryption techniques when storing data in the cloud. In this study, we propose various encryption and decryption methods to safeguard data in the cloud, ensuring that confidential information remains inaccessible to unauthorized individuals or machines due to its encrypted form.
Improving Software Quality through Deep Learning: A Comprehensive Literature Study on Error Prediction in Software Development

The paper explores the significance of error prediction in software development and discusses the use of deep learning approaches to address this task. It emphasizes the need for proactive error prevention and the limitations of reactive bug- fixing strategies. The study examines various deep learning models, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Graph Convolutional Networks (GCNs), and their applicability in error prediction. The conclusions drawn from the study highlight the strengths of each model. RNNs are effective in capturing temporal dependencies and sequential patterns in error data, enabling the analysis of error progression over time. CNNs excel at extracting relevant features and local patterns from software artefacts by treating them as image-like data. GCNs leverage the graph structure of software artefacts to capture structural dependencies and interactions between code elements. To leverage the benefits of both temporal and structural information, the study proposes a hybrid model that combines RNNs with GCNs for error prediction. This hybrid model harnesses the power of deep learning to identify patterns and model relationships, offering promising results in accurate error forecasting and prevention in software development. The adoption of proactive error prediction techniques facilitated by deep learning has the potential to enhance software quality, resource efficiency, and user experience. By proactively identifying and addressing errors, development teams can reduce the impact of issues before they manifest, leading to improved software reliability and customer satisfaction. Overall, the paper highlights the importance of error prediction in software development and demonstrates the potential of deep learning approaches to enhance error prevention strategies.
PENERAPAN PROJECT BASED LEARNING UNTUK MENINGKATKAN KETERAMPILAN MENULIS PADA MATA PELAJARAN BAHASA INDONESIA DI KELAS IV SDN 196 SUKARASA KOTA BANDUNG

The background of this study is the low writing skills of students, especially in writing narrative essay writing skills. This research was conducted in class IV SDN 196 Sukarasa, Bandung. The learning model applied is the Project Based Learning model. The research method used in this research is Classroom Action Research which consists of two cycles, where each cycle consists of two meetings. The purpose of this research is to design a narrative essay writing lesson through the application of a project based learning (PJBL) model for fourth grade students at SDN 196 Sukarasa, Bandung City; To determine the increase in project-based learning outcomes (PJBL) can improve the skills of writing narrative essays in grade 4 students at SDN 196 Sukarasa, Bandung City. Research data obtained through observation and assignment. From the results of this study it can be concluded that the application of the Project Based Learning model can improve the skills of writing narrative essays to increase, it can be seen from the results of student learning completeness in the pre-cycle or initial test obtained 5 students or 23% who achieved 23% mastery in the pre-cycle before implementing the model learning Project Based Learning (PJBL) increased by 54% to 77% in cycle I with the application of the Project Based Learning (PJBL) learning model and experienced an increase of 12%, namely 89% in cycle II. Of the 22 students, there were 5 students who had not achieved completeness in cycle I and in cycle II it decreased so that all students achieved completeness.&#x0D;
Impact of Blockchain Technology on Supply Chain Management

There is a significant research gap in understanding the impact of blockchain technology on supply chain management, particularly in addressing challenges related to transparency, traceability, and trust. This research paper aims to investigate this gap by assessing the benefits, analyzing challenges, exploring real- world case studies, and providing recommendations for organizations. To achieve the research objectives, a comprehensive research methodology is employed. A systematic literature review is conducted to gather relevant studies on blockchain technology in supply chain management published between 2015 and 2021. The selected papers are analyzed to identify key themes, trends, and findings. Additionally, real-world case studies are examined to gain insights into successful implementations and practical implications. The research methodology also includes interviews and surveys with industry experts to gather firsthand insights and perspectives. The findings of this research contribute to the existing body of knowledge by providing a comprehensive understanding of the impact of blockchain technology on supply chain management. The analysis of benefits and challenges helps organizations make informed decisions regarding the adoption of blockchain in their supply chain processes. The exploration of real-world case studies offers practical insights and lessons learned for successful implementation. The recommendations for organizations provide guidance for integrating blockchain technology and mitigating risks. In conclusion, this research paper bridges the research gap by investigating the impact of blockchain technology on supply chain management. The findings and recommendations serve as a valuable resource for practitioners, researchers, and policymakers seeking to leverage blockchain's potential to enhance transparency, traceability, and trust in supply chain operations. The research methodology employed ensures a rigorous and comprehensive analysis, enhancing the credibility and reliability of the research outcomes.
Digital security as an integral component of foreign language teaching in a non-language university

The objective of the research consists in developing and substantiating scientifically a necessity of such disciplines as digital literacy, digital safety, digital standardization, encryption means and Runet peculiarities embedded into the process of foreign language teaching in non-linguistic universities. The results of the conducted research allow to conclude that mastering a competent specialist during digitalization era requires the changes in the foreign language teaching content. It is especially crucial to take this factor into account to teach future journalists a foreign language. The chapter focuses on the method to develop the new competency in the area of digital safety while teaching journalists a foreign language.
MoReLab: A Software for User-Assisted 3D Reconstruction

We present MoReLab, a tool for user-assisted 3D reconstruction. This reconstruction requires an understanding of the shapes of the desired objects. Our experiments demonstrate that existing Structure from Motion (SfM) software packages fail to estimate accurate 3D models in low-quality videos due to several issues such as low resolution, featureless surfaces, low lighting, etc. In such scenarios, which are common for industrial utility companies, user assistance becomes necessary to create reliable 3D models. In our system, the user first needs to add features and correspondences manually on multiple video frames. Then, classic camera calibration and bundle adjustment are applied. At this point, MoReLab provides several primitive shape tools such as rectangles, cylinders, curved cylinders, etc., to model different parts of the scene and export 3D meshes. These shapes are essential for modeling industrial equipment whose videos are typically captured by utility companies with old video cameras (low resolution, compression artifacts, etc.) and in disadvantageous lighting conditions (low lighting, torchlight attached to the video camera, etc.). We evaluate our tool on real industrial case scenarios and compare it against existing approaches. Visual comparisons and quantitative results show that MoReLab achieves superior results with regard to other user-interactive 3D modeling tools.
The role of institutional efficiency in achieving the SDGs: evidence from Africa

Purpose The objective of this study is to analyze the influence of institutional quality on the attainment of the Sustainable Development Goals (SDGs) using a data set comprising 45 African nations during the timeframe 2000 to 2020. Design/methodology/approach The data are divided into two periods, with the Millennium Development Goals (MDGs) data covering the years 2000–2015 and the SDGs data spanning from 2015 to 2020. Controlling for other factors, the researcher employs an index of institutional quality and applies the generalized method of moments (GMM) method to analyze the data. Findings The findings demonstrate a noteworthy inverse relationship between institutional quality and the achievement of both the MDGs and SDGs. The findings reveal a significant and positive link between economic growth and the achievement of the MDGs, while the impact on the SDGs is shown to be insignificant. Population growth significantly drives the SDGs. The results further reveal that trade openness and industrialization contribute positively to the achievement of both the MDGs and SDGs. Practical implications The findings emphasize the importance of improving institutional quality, promoting economic growth and supporting trade openness and industrialization for sustainable development in African countries. Originality/value The contribution of the study is twofold. Firstly and to the best of the author’s understanding, this research marks an initial endeavor to empirically investigate the nexus between institutional quality and the SDGs in the context of Africa. Secondly, it adds novelty to the literature by examining how institutional quality influences both the SDGs and their precursor the MDGs, providing insights into the actual contribution of institutions to development within the framework of these two major global compacts.
Misunderstanding in intercultural electronic written communication in Chinese business field

Abstract The prevalence of intercultural communication in the business domain has led to an increase in the occurrence of misunderstandings. The advancement of telecommunication technology and the implementation of epidemic prevention policies in various countries have resulted in electronic written communication becoming the most widely utilized method for business interactions, thereby giving rise to a significant number of misunderstandings. Hence, it is imperative to investigate the specific reasons for misunderstandings within a particular country. Given China’s prominent global position, it is crucial to conduct a thorough examination of Chinese companies and workers. Thus, this article aims to identify the sources and strategies employed to address misunderstandings in intercultural electronic written communication in China by analyzing naturally occurring electronic written communication documents and conducting interviews with Chinese businesspersons. The findings reveal that language-related and culture-related factors are the primary causes of misunderstandings. Chinese businesspersons have employed strategies such as seeking advice from familiar Chinese coworkers, engaging in further communication with the interlocutors, and modifying their own expression habits to resolve and prevent misunderstandings. Researching the distinctive forms and causes of misunderstandings in electronic written communication is essential for individuals engaged in international business to identify solutions and enhance communication efficiency by minimizing misunderstandings.