Reviewerly/exHarmony huggingface dataset:
Learning-Based Fast VVC Affine Motion Estimation

This paper presents a fast Affine Motion Estimation (AME) of Versatile Video Coding (VVC) Standard, based on Machine Learning and using Random Forest (RF) classification method. This encoding approach develops an RF model for each block size. The models were trained with information extracted during the VVC encoding process of the current, parent, and neighboring Coding Units (CU). Each model is applied to predict whether the Affine Motion Estimation (AME) will be skipped or not for that CU size. The proposed solution achieves a reduction of 20% on average in AME encoding time, with an insignificant impact of 0.07% on BD-BR.
Fuzzy set-based Bernoulli Random Noise Weighted Loss for unsupervised person re-identification

Clustering-based methods have achieved impressive performance on unsupervised person ReID task. However, most of these models suffer from noisy labels. In this paper, we propose a novel method to evaluate and suppress noisy labels. Since the feature network is barely trained and the clustering results are unreliable consequently, noisy samples can be hardly screened out with a hard threshold in the early stage of training. To address this issue, we construct a Noisy Label Fuzzy Set (NLFS) whose membership function can be used to evaluate the noise level of each sample. Furthermore, we propose a new loss function, referred to as Bernoulli Random Noise Weighted Loss (BRNWL), with which some samples with higher noise membership will be recognized as noisy samples in higher probability and assigned with corresponding weights to limit their contributions to the training. Through above improvements, randomness is introduced into the alternating update between the feature network and the clustering results, which can effectively prevent the model from trapping into local minima or vicious circles. Extensive experiments demonstrate that our method achieves state-of-the-art performance on popular ReID datasets.
Evaluation of the hourly ERA5 radiation product and its relationship with aerosols over China

Surface solar radiation (SSR) and its components are important parameters in studies of land, hydrology and the atmosphere. However, the SSR of ECMWF Reanalysis 5 (ERA5) rather than observations is often used in related research fields due to the sparse and uneven spatial distribution of observations. Therefore, understanding the deviation from observations and the attribution is essential for the appropriate use of reanalysis SSR. Based on station observations in China for 2014–2020, this study evaluates the ERA5 SSR products including global radiation (Rs), direct radiation (Rdir) and diffuse radiation (Rdif) under various PM2.5 concentrations to explore the influence of aerosols on ERA5 radiation. Overall, Rs from ERA5 has higher accuracy than Rdir and Rdif. The ERA5 product generally overestimates (underestimates) Rdir (Rdif), and this overestimate (underestimate) increases with the intensification of particle pollution. Under the influence of biomass burning (BB) events, the Rdir (Rdif) of ERA5 exhibits overestimation (underestimation) similar to that under the heaviest particle pollution. In terms of the time series, the deviations of Rdir and Rdif from ERA5 in 1999–2020 still vary with the local aerosol optical depth (AOD). The diurnal variation in Rdir (Rdif) from ERA5 also shows larger deviations under air pollution conditions than under clean conditions, especially at the hours around noon. Furthermore, the misrepresentation of aerosols also causes large deviations in the Rdif/Rdir of ERA5 from observations, especially at stations in eastern China. Similar changes in the deviation of ERA5 radiation from observations with increasing aerosol loading are found under both all sky and clear sky conditions. The high association with aerosol pollution found here indicates that ERA5 SSR products should be used with caution in the presence of atmospheric particle pollution.
An energy efficient RL based workflow scheduling in cloud computing

In recent times, many modern businesses have been adopting cloud computing platforms to deploy their workflow applications. However, scheduling workflows and allocating resources is a difficult task due to factors such as task dependency, heterogeneity, and computational intensity. Resource allocation can pose significant challenges in terms of execution time and cost. As a result, there is a need to reduce energy consumption, makespan, and cost simultaneously, which is often framed as a multi-objective optimization problem. In this paper, we have proposed an energy efficient RL-based workflow scheduling framework for cloud computing. The framework integrates the X-NOR Whirlpool hashing algorithm to enhance security by allowing legitimate users into the cloud environment. The Linearly Weighted Moving Average - Sea Lion Optimization method is used to compute minimal parameters, the Jordan Normal Form - Deep Kronecker Neural Network is utilized for resource monitoring, and the Fuzzy Self-Defense algorithm is employed for efficient selection of virtual machines. We considered the Heterogeneous Computing Scheduling Problem (HCSP) and the Grid Workload Archive (GWA) - T-12 Bitbrains datasets to compare our proposed framework with existing works. Based on the result analysis, the proposed LJC Framework outperforms the QoS-HEFT, TCCS, and SHEFTEX algorithms, achieving better performance.
Implementation and management of cloud security for industry 4.O - data using hybrid elliptical curve cryptography

Industry 4.0 places a premium on cloud security since more and more companies are moving their activities to the cloud to reap the benefits of the Fourth Industrial Revolution.The term "cloud computing" refers to a collection of Internet-based hardware and software tools. Providers of cloud services use data centers situated in various physical locations. Cloud computing makes life easier for users by making remote, simulated resources available over the internet. Google Apps and Microsoft SharePoint are two examples of popular cloud applications. In addition to its exciting potential, the "cloud computing" industry's lightning-fast expansion raises serious security concerns. When discussing security, cloud really suffers from Open Systems' and the internet's perennial problem. The only thing stopping the widespread use of cloud computing is the lack of trust in the system. There are a number of security concerns with cloud computing, including protecting user data and vetting cloud service providers' practices. Using encryption, confidential information can be sent over an unsecured channel without fear of data loss or manipulation. Data encryption using various protocols has been used in various settings. Different cryptosystems were developed and used at various times. Additionally, cloud computing enables multiple users to access and retrieve data simultaneously through their own personal Internet connections, which increases the risk of confidential data loss and exposure in a number of different places. Elliptic Curve Cryptography and other cryptographic algorithms have been used to develop numerous methods and protocols that guarantee the security and privacy of transmitted data. In this paper, we suggest a safe and efficient method for sharing information in the cloud without compromising its safety or integrity. The proposed system is able to guarantee authentication and data integrity because it uses a hybrid of the ECC and the Advanced Encryption Standard (AES) technique. The experimental results confirm the proposed approach outperforms the current gold standard.
Optimized super-resolution promote accuracy for projection speckle three-dimensional digital image correlation

Abstract The camera resolution and electronic noise limit the accuracy of the projection speckle three-dimensional digital image correlation (3D-DIC) which is a non-invasive method to detect the reliability of electronic packaging structure. In this study, a measurement method of super-resolution (SR) reconstruction coupled with projection speckle DIC is proposed. The algorithm based on the maximum a posteriori model for DIC measurement systems was also optimized, and a speckle-specific bimodal prior was proposed to adapt to speckle images. By using optimized SR technology as an image pre-processing technique to enhance the resolution of captured images, the accuracy of measurements is improved. Full-field displacement measurement experiments show that, with suitable magnification and speckle size, the use of SR technology reduces the range of displacement errors from 8 μm to 2 μm. Experiments on step block topography measurements show that the use of SR technology reduces the error between DIC measurements and Moiré interferometry from 5 μm to within 2 μm. Therefore, SR technology can be effectively paired with projection speckle DIC measurements to adapt to various measurement scenarios in the field of electronic packaging reliability testing.
An Overview of Energy-Efficient DNN Training Processors

Many edge/mobile devices are now able to utilize deep neural networks (DNNs) thanks to the development of mobile DNN accelerators. Mobile DNN accelerators overcame the problem of limited computing resources and battery capacity by realizing energy-efficient inference. However, it shows passive behavior and it makes AI hard to be customized with individual users or its service environment. The importance of on-chip training becomes rising more and more to provide active interaction. Despite its advantages, DNN training has more constraints than inference and it is impractical in mobile/edge devices. Recently, there are many trials to realize mobile DNN training, and this chapter will summarize a number of prior works. Firstly, it arranges the new challenges of the DNN accelerator induced by training functionality and summarizes new hardware features related to the challenges. Secondly, it discusses algorithm–hardware co-optimization methods and explains why it becomes mainstream in training processor research. Thirdly, it compares the main differences between the conventional inference accelerators and recent training processors. Finally, conclusion is made by proposing the future directions of the DNN training processor at the micro-AI systems.
WITHDRAWN: Chimp optimiation based convolutional neural network for vehicle detection in traffic surveillance system

An important aspect of traffic monitoring is the traffic surveillance. Vision based traffic surveillance has become the most popular method to perceive the traffic flow. This paper proposed a novel chimp optimization based convolutional neural network (CO–CNN) for the detection of vehicles from video surveillance system. This paper presents an effective way for subtracting moving object from the background using MDCT based Gaussian mixture modeling. The efficient two stage segmentation process is applied to the proposed MDCT domain-based background models. Initially the foreground blocks are easily identified using MDCT coefficient and the spatial domain based GMM background subtraction is done only in the foreground blocks identified from the MDCT domain instead of whole video frame. The chimp optimization based convolutional neural network (CO–CNN) to detect the vehicles from the surveillance system. The proposed model performances are evaluated using various performance metrics thereby obtaining good detection outputs. The proposed model will automatically reduce the computation time and complexity. Also, the proposed method is able to produce good accuracy.
Collaborative filtering hybrid recommendation algorithm incorporating knowledge graph

Nowadays, in the context of the booming digital economy and video becoming the main carrier of data explosion, enhancing the precision of recommendation algorithms in the video industry has emerged as a prominent area of investigation. By using the TransR model to construct the movie knowledge graph into the relationship space to obtain the movie entities and their relationships, the multiple relationships between movies are better reflected, so as to calculate the semantic similarity between movies, and then the collaborative filtering algorithm based on Pearson coefficient calculates the similarity of user behavior, and the two similarities are linearly fused to finally generate the final recommendation list for Top-N recommendation. Comparative experimental results show that the algorithm has improved in the main indexes, such as recall, accuracy, and mean absolute error (MAE).
TVLA*: Test Vector Leakage Assessment on Hardware Implementations of Asymmetric Cryptography Algorithms

Test vector leakage assessment (TVLA*) evaluates the side-channel leakage of sensitive information from the hardware implementation of a design. While TVLA* for symmetric cryptography has been well studied, it is not applicable to asymmetric cryptography algorithms. Asymmetric-key algorithms involve complex computations in multiple stages that can lead to varying trace lengths depending on input parameters and associated constraints. In this article, we design an effective TVLA* technique for asymmetric-key cryptosystems that can compare lengthy trace data with a good statistical resolution and generate valid input (test) patterns to satisfy specific constraints. Specifically, this article makes the following major contributions. The proposed test generation algorithm can produce valid test patterns to maximize the power signature differences. Our proposed partition-based differential power analysis can significantly improve the TVLA* accuracy. Extensive evaluation using elliptic curve cryptography algorithms demonstrates that the proposed TVLA* framework can handle type 1 and type 2 statistical errors and evaluate hardware implementations of asymmetric cryptography algorithms with a statistical confidence of 99.999%.
Fisher pruning for developing real-time UAV trackers

Unmanned aerial vehicle (UAV)-based tracking has shown large potential in various domains such as transportation, logistics, public safety, and more. However, deploying deep learning (DL)-based tracking algorithms on UAVs is challenging because of limitations in computing resources, battery capacity, and maximum load. Discriminative correlation filter (DCF)-based trackers have become a popular choice in the UAV tracking community owing to their ability to provide superior efficiency while consuming fewer resources. However, the limited representation learning ability of DCF-based trackers leads to lower precision in complex scenarios compared to DL-based methods. Filter pruning is a prevalent practice for deploying deep neural networks on edge devices with constrained resources, and it may be an effective way to solve problems encountered when deploying deep learning trackers on UAVs. However, the application of filter pruning to UAV tracking is underexplored, and a straightforward and useful pruning standard is desirable. This paper proposes using Fisher pruning to reduce the SiamFC++ model for UAV tracking, resulting in the F-SiamFC++ tracker. The proposed tracker achieves a remarkable balance between precision and efficiency, as demonstrated through exhaustive experiments on four popular UAV benchmarks: UAVDT, DTB70, UAV123@10fps, and Vistrone2018, showing state-of-the-art performance.
Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives

Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance.To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization.Given a start and an endpoint, we optimize a path regarding the risk in case of a system failure, the radio signal disturbance between the aerial vehicle and a ground station, the energy consumption, and the noise immission on city residents.The optimization includes constraints for static obstacle collision avoidance and compliance with the minimum flight altitude.The result is a set of smooth and three-dimensional paths that realize different trade-offs between the defined objectives.As an example, we consider an urban transportation application for aerial vehicles in San Francisco.For all tests, we use real-world data from OpenStreetMap.In a statistical evaluation, we test the efficiency of our framework against different stateof-the-art optimizers.Moreover, we extend the framework with two features that allow the user to integrate arbitrary objectives and unknown scenarios into the path planning framework.
Vehicle Check-In Data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations.
Fully Flash-Based Reservoir Computing Network With Low Power and Rich States

A flash-based dynamic reservoir computing (RC) system is proposed. It is demonstrated that after applying a high bias voltage, the flash memory shows nonlinear transform and short-term memory properties with low current, which can be utilized as the reservoir node. Meanwhile, the cell’s nonlinear characteristic determined by the stressing time can be controlled to generate rich reservoir states. Also, the low current of flash memory greatly reduces the power consumption to 0.8 pJ per input. When executing time-series prediction tasks, high accuracy is achieved with a low normalized root mean square error (NRMSE) of 0.0096. The RC system based on flash technology exhibits significant potential as a large-scale neural network with exceptionally low power consumption. Our findings suggest that flash-based RC systems could be an attractive option for neuromorphic computing applications, where energy efficiency is a critical consideration.
Exploring Community Smell Co-occurrences in the Context of Bangladesh: An Empirical Study

Software development teams, an essential component of the software ecosystem, frequently face organizational and social anti-patterns known as community smells. The occurrence of these smells leads to technical debt, which affects the entire software ecosystem. Therefore, exploring the nature of these smells and finding ways to refactor them is necessary. Existing studies explored various aspects related to community smells, including their identification, detection, and prediction. However, little is known about how community smells co-occur in development communities. This paper bridges that gap by investigating these issues in the context of software communities in Bangladesh. Using a convenience sample recruitment strategy, 39 local software practitioners were chosen, and an interview-based study was conducted. The interviews were transcribed and analyzed using Straussian Grounded Theory. Data were collected on the twenty-nine community smells defined in the literature. Analyzing the data, we identified the five most prominent community smells in the software industry of Bangladesh, which are: Priggish Members, Informality Excess, Truck Factor, Time Warp, and Cognitive Distance. The co-occurrence pattern between the smells was discovered using association rule mining. Twelve association rules were discovered. Besides, an association graph was developed based on the association rules found, which can assist management to prioritize which smells to refactor first. Furthermore, refactoring strategies adopted in the local industry were identified. Finally, the strategies were ranked using the association graph developed to help practitioners perform efficient community smell refactoring.
Exploring the Use of Natural Language Processing Techniques for Enhancing Genetic Improvement

We explore the potential of using large-scale Natural Language Processing (NLP) models, such as GPT-3, for enhancing genetic improvement in software development. These models have previously been used to automatically find bugs, or improve software. We propose utilizing these models as a novel mutator, as well as for explaining the patches generated by genetic improvement algorithms. Our initial findings indicate promising results, but further research is needed to determine the scalability and applicability of this approach across different programming languages.
Improving Cross-Domain Semi-Supervised Object Detection with Adversarial Domain Adaptation

In autonomous driving, millions of frames with various scenarios for training deep object detectors is required. Labeling such a large number of frames is a costly process, therefore additional data sources support the training task. However, domain gaps from different cameras, weather, or locations typically limit the performance.We apply semi-supervised object detection, which leverages labeled source and pseudo-labeled target domain data in an iterative training paradigm. In addition, we newly include state-of-the-art adversarial style transfer into the semi-supervised training by stylizing images from source and target domains. This reduces the domain gap and improves pseudo-label quality in cross-domain semi-supervised training.In experiments and ablation studies, we show that our novel training framework can improve state-of-the-art detection performance by up to +10.1% on standard domain adaptation benchmarks.
Navigating Complexity in Software Engineering: A Prototype for Comparing GPT-n Solutions

Navigating the diverse solution spaces of non-trivial software engineering tasks requires a combination of technical knowledge, problem-solving skills, and creativity. With multiple possible solutions available, each with its own set of trade-offs, it is essential for programmers to evaluate the various options and select the one that best suits the specific requirements and constraints of a project. Whether it is choosing from a range of libraries, weighing the pros and cons of different architecture and design solutions, or finding unique ways to fulfill user requirements, the ability to think creatively is crucial for making informed decisions that will result in efficient and effective software. However, the interfaces of current chatbot tools for programmers, such as OpenAI’s ChatGPT or GitHub Copilot, are optimized for presenting a single solution, even for complex queries. While other solutions can be requested, they are not displayed by default and are not intuitive to access. In this paper, we present our work-in-progress prototype “GPTCOMPARE”, which allows programmers to visually compare multiple source code solutions generated by GPT-n models for the same programming-related query by highlighting their similarities and differences.