Reviewerly/exHarmony huggingface dataset:
Improved electric bike helmet wearing detection algorithm for YOLOv5s

Aiming at the problems of low detection accuracy and poor robustness of electric vehicle helmets in complex traffic scenarios, an improved YOLOv5-based electric vehicle helmet detection model is proposed. First, a shallow feature detection layer is added to enable the model to better detect small targets. Then, the attention mechanism is introduced in the backbone network, which optimizes the feature extraction ability of the network. Finally, replacing the CIoU loss function with WIoU improves the convergence speed and regression accuracy. The improved YOLOv5s algorithm was trained and tested on a homemade electric vehicle helmet dataset, and the improved YOLOv5s algorithm improved the average accuracy mean, accuracy rate, and recall rate by 6%, 4%, and 6.7%, respectively, over the original algorithm. The experimental results show that the electric vehicle helmet detection model with improved YOLOv5s algorithm has high helmet detection accuracy, excellent detection of small targets, and strong generalization capability.
An efficient and privacy-preserving federal learning scheme

With the rapid development of artificial intelligence technologies, federated learning (FL) is increasingly linked to the Internet of Things (IoT). Recently, researchers have found that the screening and privacy of low-quality users has a significant impact on the training of federation learning. The goal of this work is to ensure that data is trained locally, that data is not out of domain, and that the process of model aggregation is performed in ciphertext, while FL models can be generated quickly and iteratively. However, existing work is still in its infancy, with most research efforts focused on the protection of private data, and implementing their solutions is the main challenge .To address this problem, we propose an efficient and privacy-preserving federal learning scheme with the ability to screen users with low computational power. Specifically, we design a new scheme to reduce the impact of excessive time costs caused by low computational power users by iteratively executing our "Low quality user screening algorithm". Meanwhile, to achieve secure aggregation of the model, we invoke a security framework based on the threshold Palile cryptosystem [8]. Experimentally, we prove that our scheme can greatly improve the training efficiency and save the time overhead of training.
3D Plotting of Gold Solubility and Gold Fineness: Quantitative Analysis of Ore-Forming Conditions in Hydrothermal Gold Deposits

Abstract The 3D plotting of gold solubility and gold fineness aims to illustrate how to quantify their correlations with ore-forming conditions in hydrothermal gold deposits. The thermodynamic calculation of the Au-Ag solid solutions in Mathematica and the 3D plotting in MATLAB are used to build isopleths of gold solubility and gold fineness at different temperatures (200℃, 400℃), pressures (0.1, 5 kbar), salinities (1, 40 wt% NaCl eq.), and sulfur concentrations (0.01, 0.5 mol/kg). The plot indicates that the ore-forming conditions have different correlations with gold solubility and gold fineness. Average rates of change for the correlations are quantified, showing distinct values in the four pH-logfO2 fields of (I) HSO4−, (II) SO42−, (III) H2S, and (IV) HS−, where dominant gold and silver complexes have different dependencies on the conditions. The quantification of the plots illustrates that a decrease in gold solubility by one order of magnitude is possibly caused by a decrease in temperature of ≥40℃, the salinity of ≥9.6 wt% NaCl eq. or sulfur concentration of ≥0.14 mol/kg, or an increase in pressure of ≥3 kbar, while a decrease in gold fineness by 100 units is possibly caused by a decrease in temperature of ≥14 ℃, pressure of ≥1.4 kbar, or salinity of ≥4 wt% NaCl eq., or an increase in sulfur concentration of ≥0.07 mol/kg. Quantification results suggest that a sharp decrease in temperature may result in large-scale gold mineralization and a great variation in gold fineness. In addition, the quantification reveals that the correlation between gold solubility and gold fineness can be expressed by a function, providing a rapid method for 3D plotting.
ML-Based Feature Selection Technique for Imbalanced Data Streams

In the data stream classification domain, efficient feature selection (FS) plays an ornamental role in improving the overall performance of machine learning (ML) frameworks. Also, the imbalanced data significantly affects the classifier's performance. This study proposes an efficient ML framework with a hybrid FS based on the filter and wrapper approach. Another primary goal of this rigorous study is to show the efficacy of ML using the proposed FS in the classification of imbalanced data. First, feature subsets are rapidly selected using statistical (filter) FS methods. Modified correlation-based feature subset selection with greedy stepwise search and chi-square method has been implemented as filter-based methods. Then the selected feature subset is fine-tuned using the wrapper approach recursive feature elimination (RFE) and an ensemble of classifiers. The framework is built and tested on imbalanced datasets from the healthcare domain. The proposed framework has been compared to three datasets used in disease prediction. The framework effectively achieves better diagnostic and prediction performance with the optimal number of features without computational overheads.
Multi-GPU Accelerating Strategies of Ant Colony Optimization Algorithms Using Rank Based and Strong Elitist Versions

The most used problem to compare these meta-heuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optimization techniques to solve this problem. Although there is a zoo of possibilities, Ant Colony Optimization (ACO) is still one of the most efficient, parallel, and simple techniques to implement. The pheromone evaporation rate, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha,\beta$</tex> and ant number M are the four parameters fitted for finding the best solutions. Candidate list and the use of a source solution are efficient strategies to optimize large problems, but there are other strategies to improve quality solutions such as local search strategies. Among the local search techniques, k-opt search has proved to be very efficient to deal with path-crossing. The initial route is split into k sub-routes connected in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(k-1)!2^{k-1}$</tex> ways. Thus, 3-opt is an efficient strategy balancing complexity and precision. Moreover, the best <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> are the result of the used strategy. Finally, the solution is improved by accelerating through parallel versions with multi-GPUs. In this article, four ACO algorithms were developed using two variations (Rank Based 3-opt and Strong Elitist 3-opt), each one with two strategies (candidate list and restricted). To test the algorithms, TSPLIB95 was used with test problems between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=51$</tex> and 4, 461 nodes in a server with 4 RTX A4000 GPUs. After improving the algorithms and parallelization, the parameters are tuned to get the highest performance improving the local minimum search. Statistical analysis of repetitions shows good stability of the chosen metaheuristics.
Effect of Input Layer Signal Polarization on the Dynamics of Optical Neural Networks

The polarization encoding-based optical validation and security verification approach is provided in this paper. This technique involves simulating information optically and bonding it to a polarization-encoded mask, such as a biological order or a reaction. The linear polarizers that make up the polarization-encoded mask are positioned at random. The polarization-encoded signal is the name given to this composite signal. In this simulation study, a primary optical neural network adapting a light brain technology is proposed theoretically based on a feed-forward model. Calibration of the nonlinear behavior in such a network is assumed by a semiconductor laser of the Distributed Feedback (DFB) type. Four laser networks are constructed as three influencers, followed by one embedding laser followers. Each of the influencer’s lasers has a different wavelength frequency and polarization (30-60–90) degree, respectively, and then combines the signal with WDM for the last laser. With each value, from the last values, of the polarization effect after this effect, the results indicated that these values would present the greatest weight of spikes and chaotic behavior for the uploaded virtual message.
Comparative Analysis of U-Net and DeepLab for Automatic Polyp Segmentation in Colonoscopic Frames Using CVC-ClinicDB Dataset

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TILDE: Topic-Tracking Infrastructure for Dynamic Message Latency and Deadline Evaluator for ROS 2 Application

Autonomous-driving systems require real-time processing for safety. Detecting the deadline miss (disabling to execute the process until the designated time) of autonomous-driving software is essential for executing real-time processing. We proposed the application of a dynamic message tracking system (Topic-tracking Infrastructure for Dynamic Message Latency and Deadline Evaluator) to software based on Robot Operating System (ROS) 2, e.g., Autoware (autonomous-driving software). TILDE transmits MessageTrackingTag with ROS 2 messages to trace and dynamically specify the data flow of the messages. In particular, MessageTrackingTag contains input and output times at each node with the data flow. The information of the MessageTrackingTag is acquired by a deadline detector to identify the time from the input to output (hereafter latency) of the data flow. The latency is compared to the deadline to detect the occurrence of a deadline miss. In principle, the deadline detector is a package of TILDE for notifying deadline misses. In addition, this paper conducted two evaluations of TILDE. First, we measured the overhead caused by embedding TILDE on ROS 2 nodes through the transmission of a String-type message from one node to another. Second, we measured the deadline miss detection rate using CARET (a latency analysis tool for autonomous-driving systems) to validate the accuracy of the deadline detector in determining deadline misses.
A Literature Survey on Privacy Preserving in Image processing on Cloud

In the last decade cybercrime shows tremendous growth. There are different platforms available, in which cyber-attacks are performed, data servers one of the most laid-back targets. Due to these attacks users important information leaked such as personal documents and other private documents. Due to this problem researchers are focused on secure and privacy preserved image transmission system, in which images are send to cloud server in encrypted form and avoid the privacy linking problems in cloud services. There are different types of services provide by cloud IaaS, PaaS and,SaaS all required a trustful and secure method for the encryption of users data. In this survey paper discuss the different privacy preserving methods in Image Cloud, also compare these methods. In the last decade different methods was presented, in this survey discuss few of them. Most of the methods are focused on data security; few of them focus on privacy preservation. Also discuss the major issues of privacy preserving in clouds.
Cognitive mapping of innovation components of health-oriented food products: Case study of sweet and chocolate industry

Cognitive mapping of innovation components of health-oriented food products: Case study of sweet and chocolate industry
Enabling On-Demand Crowdsourced Federated Learning Over IoT

Federated learning (FL) is attributed to training a machine learning (ML) model over a large number of distributed devices. Under these settings, the edge devices perform computations on their local data before sending the required updates to the central server to improve the global model. This approach has shown great potential since hundreds of devices can collectively contribute to learning a single task without sharing their local data. However, IoT devices often have only small data samples, and collaboration among a large number of devices is required to collectively provide the extensive training data needed to train a high-performing model. Despite the success in many domains, FL is facing challenges when it comes to audit and scaling the clients to increase the performance of the federated network. In this paper, we argue that there's a need for a dynamic learning platform where IoT devices could volunteer to collaboratively learn a task through FL. Building on that, we propose a dynamic marketplace for on-demand, crowd-sourced FL namely FedOnDemand, to train high-performing ML models over IoT devices. The proposed system coordinates client selection, task assignment, and model training to allow computationally efficient FL while maintaining communication efficiency. To drive FedOnDemand, we use the data valuation mechanism based on the Shapley Value to incentivize participants and ensure a fair reward distribution.
Bridging the Gap Between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations

This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important observations and lessons learned gathered during a large-scale research effort and introduction of ML SDP to the system-level testing quality assurance process of one of the leading telecommunication vendors in the world — Nokia. We adhere to a holistic and logical progression based on the principles of the business analysis body of knowledge: from identifying the need and setting requirements, through designing and implementing the solution, to profitability analysis, stakeholder management, and handover. Conversely, for many years, industry adoption has not kept up the pace of academic achievements in the field, despite promising potential to improve quality and decrease the cost of software products for many companies worldwide. Therefore, discussed considerations hopefully help researchers and practitioners bridge the gaps between academia and industry.
Detecting Memory Errors in Python Native Code by Tracking Object Lifecycle with Reference Count

Third-party Python modules are usually implemented as binary extensions by using native code (C/C++) to provide additional features and runtime acceleration. In native code, the heap-allocated PyObjects are managed by the reference counting mechanism provided in Python/C APIs for automatic reclaiming. Hence, improper refcount manipulations can lead to memory leaks and use-after-free problems, and cannot be detected by simply pairing the occurrence of source and sink points. To detect such problems, state-of-the-art approaches have made groundbreaking contributions to identifying inappropriate final refcount values before returning from native code to Python. However, not all problems can be exposed at the end of a path. To detect those hidden in the middle of a path in native code, it is also crucial to track the lifecycle state of PyObjects through the refcount and lifecycle operations in API calls. To achieve this goal, we propose the PyObject State Transition Model (PSTM) recording the lifecycle states and refcount values of PyObjects to describe the effects of Python/C API calls and pointer operations. We track state transitions of PyObjects with symbolic execution based on the model, and report problems when a statement triggers a transition to buggy states. The program state is also expanded to handle pointer nullity checks and smart pointers of PyObjects. We conduct experiments on 12 open-source projects and detect 259 real problems out of 280 reports, which is twice as many bugs as state-of-the-art approaches. We submit 168 real bugs to those active projects, and 106 issues are either confirmed or resolved.
Modelling And Simulation For Detecting Vulnerabilities And Security Threats Of Smart Contracts Using Machine Learning

Recently, the use and development of a blockchain systems such as Ethereum has increased rapidly, and many systems have relied on a third party as an intermediary between the sender and the receiver. Despite the attempts of developers to protect smart contracts, smart contracts contain many vulner-abilities that hackers resort to exploiting and using due to the attack that caused many financial and economic losses, and with the increase of errors in smart contracts, there are many tools and methods. For the analysis of smart contracts, machine learning models have appeared that facilitate their discovery instead of extracting them manually. In this paper, We have built a model that attempts to cancel the third party and we used machine learning to identify valid and invalid smart contracts. We have used several models and compared them with previous results of previous work in the same field. The result of this research was as expected of height accuracy achieved with approximately.99%.
Improving Design Reviews at Google

Design review is an important initial phase of the software development life-cycle where stakeholders gain and discuss early insights into the design's viability, discover potentially costly mistakes, and identify inconsistencies and inadequacies. For improved development velocity, it is important that design owners get their designs approved as Quickly as possible. In this paper, we discuss how engineering design reviews are typically conducted at Google, and propose a novel, structured, automated solution to improve design review velocity. Based on data collected on 141,652 approved documents authored by 41,030 users over four years, we show that our proposed solution decreases median time-to-approval by 25%, and provides further gains when used consistently. We also provide qualitative data to demonstrate our solution's success, discuss factors that impact design review latency, propose strategies to tackle them, and share lessons learned from the usage of our solution.
Challenges of Accurate and Efficient AutoML

Embedded Artificial Intelligence (AI) is becoming increasingly important in the field of healthcare where such AI enabled devices are utilized to assist physicians, clinicians, and surgeons in their diagnosis, rehabilitation and therapy planning. However, it is still a challenging task to come up with an accurate and efficient machine learning model for resource-limited devices that work <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$24\times 7$</tex> .. It requires both intuition and experience. This dependence on human expertise and reliance on trial-and-error-based design methods create impediments to the standard processes of effort estimation, design phase planning, and generating service-level agreements for projects that involve AI-enabled MedTech devices. In this paper, we present AutoML search from an algorithmic perspective, instead of a more prevalent optimization or black-box tool view. We briefly present and point to case studies that demonstrate the efficacy of the automation approach in terms of productivity improvements. We believe that our proposed method can make AutoML more amenable to the applications of software engineering principles and also accelerate biomedical device engineering, where there is a high dependence on skilled human resources.
Repeated Builds During Code Review: An Empirical Study of the OpenStack Community

Code review is a popular practice where developers critique each others' changes. Since automated builds can identify low-level issues (e.g., syntactic errors, regression bugs), it is not uncommon for software organizations to incorporate automated builds in the code review process. In such code review deployment scenarios, submitted change sets must be approved for integration by both peer code reviewers and automated build bots. Since automated builds may produce an unreliable signal of the status of a change set (e.g., due to “flaky” or non-deterministic execution behaviour), code review tools, such as Gerrit, allow developers to request a “recheck”, which repeats the build process without updating the change set. We conjecture that an unconstrained recheck command will waste time and resources if it is not applied judiciously. To explore how the recheck command is applied in a practical setting, in this paper, we conduct an empirical study of 66,932 code reviews from the OpenStack community. We quantitatively analyze (i) how often build failures are rechecked; (ii) the extent to which invoking recheck changes build failure outcomes; and (iii) how much waste is generated by invoking recheck. We observe that (i) 55% of code reviews invoke the recheck command after a failing build is reported; (ii) invoking the recheck command only changes the outcome of a failing build in 42% of the cases; and (iii) invoking the recheck command increases review waiting time by an average of 2,200% and equates to 187.4 compute years of waste-enough compute resources to compete with the oldest land living animal on earth. Our observations indicate that the recheck command is frequently used after the builds fail, but does not achieve a high likelihood of build success. Based on a developer survey and our history-based quantitative findings, we encourage reviewer teams to think twice before rechecking and be considerate of waste. While recheck currently generates plenty of wasted computational resources and bloats waiting times, it also presents exciting future opportunities for researchers and tool builders to propose solutions that can reduce waste.
Predicting Compilation Resources for Adaptive Build in an Industrial Setting

Development teams in large companies often maintain a huge codebase whose build time can be painfully long in a single machine. To reduce the build time, tools such as Bazel and distcc are used to build the code base in a distributed way. However, in the process of distributed build, certain remote slave machines can crash due to two types of errors: Out Of Memory (OOM) and Deadline Exceeded (DE) errors. These crashes lead to time-consuming rebuilds, as suffered by the WeiXin Group (WXG) of Tencent Inc. (the vendor of WeChat, a highly popular mobile app in China). Aiming to prevent these two types of errors, in this paper, we propose a new approach named PCRLINEAR, which predicts the memory and time requirements of the given C++ file, allowing the underlying distributed build system to schedule compilation resources adaptively according to the prediction results. Our experiments show that PCRLINEAR reduces the number of OOM and DE errors from 5% to 0.2% and, at the same time, achieves substantial build-performance improvement of 30% on average.