Reviewerly/exHarmony huggingface dataset:
UAV Image Stitching With Transformer and Small Grid Reformation

Due to parallax and inadequate key points, it is difficult to stitch unmanned aerial vehicle (UAV) images that are not rich in structure, which is addressed in this letter. Global matching is judged along with local similarity by a trained transformer, which provides the possibility of finding plenty of key points in low-feature regions. A new point matching constraint is designed based on the scores from the transformer. Line protection and distortion resistance are also used in local correction to alleviate global aberrations. The experiment shows that our method outperforms four state-of-the-art algorithms significantly, which reduces the position error by half in stitching inconspicuous features, such as woodland, bare land, and river.
Securing Health Records Using Quantum Convolutional Neural Network

In block chain technology, a distributed architecture, blocks are the data storage and processing unit. Data must be exchanged from one party to another safely and securely, and the block chain must be updated using smart contracts. But still, the problem facing was securing the healthcare data. So, this paper proposed securing health records using the QCNN method, which stands for a Quantum Convolutional Neural Network with Block Chain Technology, to safeguard medical data. Block chain technology's consensus process ensures that all data is correct and all transactions are safe. The datasets are collected from the data world repository. The datasets are pre-processed using label coding with the standard scalar method. The features are analyzed with QCNN and the patient healthcare data to increase the security and interoperability using block chain technology. The research specifically looks at Electronic Health Record (EHR) security issues to be overcome. This proposed quantum conventional neural network (QCNN) deep learning architecture provides higher accuracy and security than the other existing algorithms.
Image Based Lighting - Current Capabilities and Limitations

The widespread adoption of Virtual Production workflows in general and ICVFX (In- Camera Visual Effects) with LED video screens in particular undoubtedly represents some of the biggest technological advancements in film and television production in recent years. With modern color management workflows and frameworks, we can gain full colorimetric control over our entire video/real-time pipeline. However, Virtual Production is not based on LED video screens alone. Image-based lighting (IBL), a computer graphics lighting method well understood in visual effects production, has now evolved into principal photography lighting workflow where the LED video screens are augmented with traditional lighting fixtures, and the video content used to drive the LED video screens also drives the lighting fixtures. While today's lighting fixtures can produce accurate, beautiful, high quality light for magnificent skin tone reproduction, these protocols must evolve to sufficiently communicate all necessarily color and other device parameters to do so. In this paper, we will review and evaluate existing control protocols and data exchange formats and propose a unified, device independent control and metadata infrastructure for both video and lighting systems that will generate a predictable outcome regardless of manufacturer, device type, color space, media source, and control system.
Invited Paper: Challenges and Opportunities in Enabling Secure 5G Positioning

5G positioning will be a natural component in many anticipated 5G industrial use cases and verticals such as logistics, smart factories, autonomous vehicles, localized sensing, digital twins, augmented and virtual reality. Driven by the aim to support such a wide array of location-aware services and applications, the current release of 5G is exploring positioning as an integral part of 5G technology. In recent years, many attacks on positioning systems have been demonstrated, and hence it is important to build 5G systems that are resilient to location manipulation attacks. In this paper, we describe our vision for the secure 5G positioning infrastructure, which requires support from architecture, positioning protocols, and secure physical layer designs. We discuss that the designs should be optimized for the use cases, as a single higher layer protocol and a physical layer are insufficient to fulfill all applications' availability, latency, security, and privacy requirements. We further discuss positioning for autonomous vehicles where secure 5G positioning will help in collision avoidance, phantom vehicle detection, and navigation in the presence of an adversary.
XMI-Based SysML Model Transformation

With the development and progress of Model-Based Systems Engineering (MBSE), it has gradually replaced the traditional text-based methods and has been widely used. Systems Modeling Language (SysML) is the de facto standard for MBSE approaches. However, there are various SysML-based system engineering modeling softwares, transformation of heterogeneous data from various softwares become an urgent need to meet. Here, we base on the XMI standard and the system engineering modeling software M-Design, achieve transformation from the XMI format file to the objects design according to SysML language. Firstly, we analyze a XMI format file structure, and secondly design a transformation solution according to analysis and SysML standard. After transformation, we compare result project and the original one to check consistency. With this method, heterogeneous data from different software can be communicated easily.
A Framework for Neural Network Inference on FPGA-Centric SmartNICs

FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication thereby mitigating the von Neumann bottleneck. Existing work however has generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs with most control tasks left to the CPUs. This leads to frequent waiting reduced performance and scaling challenges. In this work we propose a new distributive data-centric computing framework named FCsN for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture FCsN allows the complete detaching of NN kernel execution control logic system scheduling and network communication to the SmartNICs. This boosts performance by (i) avoiding control dependency with CPUs and (ii) supporting streaming NN kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network kernels and applications including deep neural networks (DNN) and graph neural networks (GNN) as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with FCsN can achieve 10 × speedups over the MPI-based standard CPU baselines
Unsupervised Visual Representation Learning via Multi-Dimensional Relationship Alignment

Recently, contrastive learning based on augmentation invariance and instance discrimination has made great achievements, owing to its excellent ability to learn beneficial representations without any manual annotations. However, the natural similarity among instances conflicts with instance discrimination which treats each instance as a unique individual. In order to explore the natural relationship among instances and integrate it into contrastive learning, we propose a novel approach in this paper, Relationship Alignment (RA for abbreviation), which forces different augmented views of current batch instances to main a consistent relationship with other instances. In order to perform RA effectively in existing contrastive learning framework, we design an alternating optimization algorithm where the relationship exploration step and alignment step are optimized respectively. In addition, we add an equilibrium constraint for RA to avoid the degenerate solution, and introduce the expansion handler to make it approximately satisfied in practice. In order to better capture the complex relationship among instances, we additionally propose Multi-Dimensional Relationship Alignment (MDRA for abbreviation), which aims to explore the relationship from multiple dimensions. In practice, we decompose the final high-dimensional feature space into a cartesian product of several low-dimensional subspaces and perform RA in each subspace respectively. We validate the effectiveness of our approach on multiple self-supervised learning benchmarks and get consistent improvements compared with current popular contrastive learning methods. On the most commonly used ImageNet linear evaluation protocol, our RA obtains significant improvements over other methods, our MDRA gets further improvements based on RA to achieve the best performance. The source code of our approach will be released soon.
Crypto Assets and CBDCs in Latin America and the Caribbean: Opportunities and Risks

After providing a general overview of the nature, pros, and cons of crypto assets and CBDCs, this paper focuses on documenting their recent experience in LAC. The region records a high interest in unbacked crypto assets and stablecoins and its authorities’ policy responses have varied substantially, ranging from the introduction of Bitcoin as legal tender in El Salvador to their prohibition in many other countries worried about their impact on financial stability, currency/asset substitution, tax evasion, corruption, and money laundering. This paper also describes briefly the results of a survey on CBDCs’ introduction plans and crypto assets regulation. Finally, this paper presents some general lessons and policy recommendations for the region on the regulation of cypto assets, digital currencies and cross-border payments, and on the potential introduction of CBDCs.
Looking into Africa's Future: The Fourth Industrial Revolution and the Role of ICT Education

ICT in education has been considered a luxury in developing countries, where, particularly in least developed countries, emphasis was placed more on building schools and classrooms. In 2020, the view of ICT and its role changed radically. Firstly, the COVID-19 pandemic highlighted ICT as a central tool. Secondly, the rise of the Fourth Industrial Revolution and awareness of its enormous potential placed ICT in a new light. Together, these triggers of change can be embraced in order to integrate key learning modules and lessons to form appropriate skills into curricula in developing countries. Frontier fields in science and technology must be promoted and supported especially in the education sector in Africa and the developing world in general.
Pedagogical university students' satisfaction with preparation for interaction in educational activities in a digital society

Current conditions of increasing conflict and uncertainty point out the guidelines for the change of education correlated with the model of “knowledge society” and the goals of sustainable development in a digital society. These guidelines actualize the involvement of all citizens and institutions in the process of lifelong education and upbringing. These two notions as factors of national security require the readiness of future teachers and specialists in the social sphere to build constructive interaction with a wide range of participants in various realms. The transformation of professional training should be carried out considering the attitude to it of students themselves. The article conveys the results of a study of the pedagogical university students' satisfaction with the preparation for educational activities interaction under digitalization conditions. During the survey 446 third- to fifth-year students were interviewed, two groups of students were compared: future teachers (teachers of various subjects), future social sphere specialists (students majoring in social work, psychological and pedagogical education, vocational training, special (defectological) education, and management). The results of the study provided us with the structure of students' satisfaction with educational activities preparation that includes three factors: personal satisfaction, interactive satisfaction and satisfaction with theoretical and practical training. Overall, students have an average level of satisfaction with training in general, while they are relatively less satisfied with the interactive factor. The outcomes of the study established the following difference: future subject teachers are less satisfied with their preparation for interaction in the educational process than future specialists in the social sphere. The use of information technologies by students in educational activities correlates with the level of satisfaction concerning all three factors. Still, the strongest connection is revealed within interaction satisfaction. The results of the research allow us to empirically confirm the structure of the previously developed model of proactive preparation for educational activities and make relevant adjustments: (1) clarify the composition of subsystems (subject-environment, content, process); (2) update networking (expanding the range and productivity of interaction during the training period); (3) enhance the content of subsystems by including up-to-date information and communication technologies to organize students' training in the context of digitalization.
Fine-Grained Feature Generation for Generalized Zero-Shot Video Classification

Generalized zero-shot video classification aims to train a classifier to classify videos including both seen and unseen classes. Since the unseen videos have no visual information during training, most existing methods rely on the generative adversarial networks to synthesize visual features for unseen classes through the class embedding of category names. However, most category names only describe the content of the video, ignoring other relational information. As a rich information carrier, videos include actions, performers, environments, etc., and the semantic description of the videos also express the events from different levels of actions. In order to use fully explore the video information, we propose a fine-grained feature generation model based on video category name and its corresponding description texts for generalized zero-shot video classification. To obtain comprehensive information, we first extract content information from coarse-grained semantic information (category names) and motion information from fine-grained semantic information (description texts) as the base for feature synthesis. Then, we subdivide motion into hierarchical constraints on the fine-grained correlation between event and action from the feature level. In addition, we propose a loss that can avoid the imbalance of positive and negative examples to constrain the consistency of features at each level. In order to prove the validity of our proposed framework, we perform extensive quantitative and qualitative evaluations on two challenging datasets: UCF101 and HMDB51, and obtain a positive gain for the task of generalized zero-shot video classification.
Novel Service Broker and Load Balancing Policies for CloudSim-Based Visual Modeller

Cloud computing enables numerous possibilities to deploy and host applications for application developers without any concern for service demand, installation, and maintenance. The developers can obtain the desired services from the cloud service providers (CSPs) and can pay on a usage basis. However, each CSP offers different pricing schemes, locates in different regions, and provides a different set of services. Therefore, it is challenging for the developers to select the CSPs. As a solution, the Cloud Analyst tool was developed to support large-scale applications for developers. It provides three service broker policies, namely closest data center (CDC), optimize response time (ORT) and reconfigure dynamically with load (RDL), and three load balancing policies, namely round robin (RR), equally spread current execution load (ESCEL) and throttled (TH). These policies map the user bases (UBs) to the data centers (DCs) and further map to the virtual machines (VMs) of the DCs, respectively. The performance of these policies is assessed in terms of four measures, namely load assignment, overall response time, DC request servicing time, and total cost. In this paper, we propose one service broker policy, one VM provisioning policy, and one load balancing policy and compare their performance with existing service broker policies and existing load balancing policies, respectively. Moreover, the proposed policies are incorporated with the existing policies to improve their performance in terms of four performance measures and validated using Cloud Analyst.
Modular Degradation Simulation and Restoration for Under-Display Camera

Under-display camera (UDC) provides an elegant solution for full-screen smartphones. However, UDC captured images suffer from severe degradation since sensors lie under the display. Although this issue can be tackled by image restoration networks, these networks require large-scale image pairs for training. To this end, we propose a modular network dubbed MPGNet trained using the generative adversarial network (GAN) framework for simulating UDC imaging. Specifically, we note that the UDC imaging degradation process contains brightness attenuation, blurring, and noise corruption. Thus we model each degradation with a characteristic-related modular network, and all modular networks are cascaded to form the generator. Together with a pixel-wise discriminator and supervised loss, we can train the generator to simulate the UDC imaging degradation process. Furthermore, we present a Transformer-style network named DWFormer for UDC image restoration. For practical purposes, we use depth-wise convolution instead of the multi-head self-attention to aggregate local spatial information. Moreover, we propose a novel channel attention module to aggregate global information, which is critical for brightness recovery. We conduct evaluations on the UDC benchmark, and our method surpasses the previous state-of-the-art models by 1.23 dB on the P-OLED track and 0.71 dB on the T-OLED track, respectively. Code is available at Github .
Imbalanced Learning of Regular Grammar for DFA Extraction from LSTM Architecture

In this work, we attempt to extract Deterministic Finite Automata (DFA) for a set of regular grammars from sequential Recurrent Neural Networks (RNNs). We have considered Long Short-Term Memory (LSTM) architecture, which is a variant of RNN. We have classified a set of regular grammars by considering their imbalances in terms of strings they accept and the strings they reject by using an LSTM architecture. We have formulated a set of the extended Tomita Grammar by adding a few more regular grammars. The different imbalance classes we introduce are Nearly Balanced (NB), Mildly Imbalanced (MI), Highly Imbalanced (HI), Extremely Imbalanced (EI). We have used L* algorithm for DFA extraction from LSTM networks. As a result, we have shown the performance of training an LSTM architecture for extraction of DFA in the context of the imbalances for a set of so formed regular grammars. We were able to extract correct minimal DFA for various imbalanced classes of regular grammar, though in some cases, we could not extract minimal DFA from the Network.
Pengaruh Model Problem Based Learning terhadap Hasil Belajar Operasi Pecahan di Kelas V SD

This study aims to determine the effect of the Problem Based Learning model on student learning outcomes on the material of addition and subtraction of two fractions with different denominators in grade V SD. This type of research is an experimental research in the form of a Quasi-Experimental Type Nonequivalent Control Group Design. The sampling technique was cluster random sampling. The data analysis technique in this study used the t-test. Based on the results of data analysis from hypothesis testing using t-test, it was obtained tcount = 2.9, while ttable was at a significant level of 0.05 and was 1.677, then it was obtained tcount&gt; ttable, namely 2.9&gt; 1.677 so that Ha was accepted or Ho was rejected. Thus, there is an effect of the Problem Based Learning model on learning outcomes of addition and subtraction of two fractions with different denominators in grade V elementary schools.
Law Enforcement Against Cybercrime in Online Activities

The development of information and communication technology makes the relationship between individuals and groups with the world not limited to existing norms so that it can cause a change in all fields. The changes also have a major impact on the transformation of values in society. It occurs not only positive but also negative consequences. Information and communication technology used for crime is known as cybercrime. Cybercrime has been confirmed to be adverse to the worldwide community, at the same time as efforts to get rid of cybercrime are nevertheless hampered through numerous factors, therefore, a crook regulation coverage is wanted towards cybercrime crimes. This paper discusses two problems, namely, first, how to enforce the law against cybercrime in the legislation in Indonesia, and second, how the strategy for eradicating cybercrime is formulated in the Law on Information and Electronic Transactions. Law enforcement against cybercrime is performed through criminal and non-criminal policies. Meanwhile, to eradicate cybercrime, it is necessary to prepare appropriate laws and provide legal provisions for law enforcement officers.
Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its rise, with limited exceptions, differential privacy has failed to achieve widespread adoption in the enterprise domain. Our study aims to shed light on the fundamental causes underlying this academic-industrial utilization gap through detailed interviews of 24 privacy practitioners across 9 major companies. We analyze the results of our survey to provide key findings and suggestions for companies striving to improve privacy protection in their data workflows and highlight the necessary and missing requirements of existing differential privacy tools, with the goal of guiding researchers working towards the broader adoption of differential privacy. Our findings indicate that analysts suffer from lengthy bureaucratic processes for requesting access to sensitive data, yet once granted, only scarcely-enforced privacy policies stand between rogue practitioners and misuse of private information. We thus argue that differential privacy can significantly improve the processes of requesting and conducting data exploration across silos, and conclude that with a few of the improvements suggested herein, the practical use of differential privacy across the enterprise is within striking distance.