Reviewerly/exHarmony huggingface dataset:
Machine learning‐based load distribution and balancing in heterogeneous database management systems

Summary For dynamic and continuous data analysis, conventional OLTP systems are slow in performance. Today's cutting‐edge high‐performance computing hardware, such as GPUs, has been used as accelerators for data analysis tasks, which traditionally leverage CPUs on classical database management systems (DBMS). When CPUs and GPUs are used together, the architectural heterogeneity, that is, leveraging hardware with different performance characteristics jointly, creates complex problems that need careful treatment for performance optimization. Load distribution and balancing are crucial problems for DBMSs working on heterogeneous architectures. In this work, focusing on a hybrid, CPU‐GPU database management system to process users' queries, we propose heuristical and machine‐learning‐based (ML‐based) load distribution and balancing models. In more detail, we employ multiple linear regression (MLR), random forest (RF), and Adaboost (Ada) models to dynamically decide the processing unit for each incoming query based on the response time predictions on both CPU and GPU. The ML‐based models outperformed the other algorithms, as well as the CPU and GPU‐only running modes with up to 27%, 29%, and 40%, respectively, in overall performance (response time) while answering intense real‐life working scenarios. Finally, we propose to use a hybrid load‐balancing model that would be more efficient than the models we tested in this work.
Time-Optimal Trajectory Planning for Industrial Robot based on Improved Hybrid-PSO

To improve the actual performance of the time-optimal trajectory planning of industrial robots, a trajectory planning method based on the improved hybrid particle swarm optimization algorithm (Hybrid-PSO) is proposed in this paper. The strategy is designed as a combination of the planning with NURBS in Cartesian space and cubic B-spline in joint space. Following implementation, on the premise of accurately passing the required data points, NURBS is used for Cartesian space planning, while cubic B-spline is used in joint space to plan the trajectory through the data points (obtained from Cartesian space planning) of joint angle. The method of separately planning the trajectory of each joint angle is used to avoid the disaster of the high dimension. A bionic rollback algorithm inspired by the behavior of natural bird blocks is proposed, which reduces the possibility of the particle swarm optimization processes entering the local optimal state, and at the same time increases the diversity of the population. The simulation results show that this method is an effective trajectory planning method, which can generate a smooth, time-optimal trajectory for industrial robots.
Correction of PZT scanner errors using a phase compensation method in white-light phase-shifting interferometry

White-light phase-shifting interferometry (WLPSI) is widely used for obtaining surface profiles of inspection targets in liquid-crystal displays (LCD) and semiconductor manufacturing processes. Phase errors caused by irregular and nonlinear scanner movement are inevitable in WLPSI. Moreover, these errors affect the measurement stability. It is difficult to eliminate the mechanical and electrical delay of scanner movement entirely. A novel method, to the best of our knowledge, is proposed that consists of a scanner position tracking system and a phase correction algorithm called compensated peak detection and modified bucket algorithm. The proposed method is experimentally verified and outperforms the conventional phase corrections in both the measurement accuracy and the repeatability.
Gradient Matters: Designing Binarized Neural Networks via Enhanced Information-Flow

Binarized neural networks (BNNs) have drawn significant attention in recent years, owing to great potential in reducing computation and storage consumption. While it is attractive, traditional BNNs usually suffer from slow convergence speed and dramatical accuracy-degradation on large-scale classification datasets. To minimize the gap between BNNs and deep neural networks (DNNs), we propose a new framework of designing BNNs, dubbed Hyper-BinaryNet, from the aspect of enhanced information-flow. Our contributions are threefold: 1) Considering the capacity-limitation in the backward pass, we propose an 1-bit convolution module named HyperConv. By exploiting the capacity of auxiliary neural networks, BNNs gain better performance on large-scale image classification task. 2) Considering the slow convergence speed in BNNs, we rethink the gradient accumulation mechanism and propose a hyper accumulation technique. By accumulating gradients in multiple variables rather than one as before, the gradient paths for each weight increase, which escapes BNNs from the gradient bottleneck problem during training. 3) Considering the ill-posed optimization problem, a novel gradient estimation warmup strategy, dubbed STE-Warmup, is developed. This strategy prevents BNNs from the unstable optimization process by progressively transferring neural networks from 32-bit to 1-bit. We conduct evaluations with variant architectures on three public datasets: CIFAR-10/100 and ImageNet. Compared with state-of-the-art BNNs, Hyper-BinaryNet shows faster convergence speed and outperforms existing BNNs by a large margin.
Perceptual Quality Assessment of Chest Radiograph

The quality of a chest X-ray image or radiograph, which is widely used in clinics, is a very important factor affects doctors’ clinical decision making. Since there is no chest X-ray image quality database so far, we conduct the first study of perceptual quality assessment of chest X-ray images by introducing a Chest X-ray Image Quality Database, which contains 2,160 chest X-ray images obtained from 60 reference images. In order to simulate the real noise of X-ray images, we add different levels of Gaussian noise and Poisson noise, which are most commonly found in X-ray images. Mean opinion scores (MOS) have been collected by performing user experiments with 74 subjects (25 professional doctors and 49 non-doctors). The availability of MOS allows us to design more effective image quality metrics. We use the database to train a blind image quality assessment model based on deep neural networks, which attains better performances than conventional approaches in terms of Spearman rank-order correlation coefficient and Pearson linear correlation coefficient. The database and the deep learning models are available at https://github.com/ICT-MIRACLE-lab/CXIQ.
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting

Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a longstanding challenge. Recent advances in using data-driven approaches have achieved significant improvements in terms of prediction accuracy. However, the lack of group-aware analysis has limited the performance of forecasting models. This is especially nonnegligible in highly crowded scenes, where pedestrians are moving in groups and the interactions between groups are extremely complex and dynamic. In this paper, we present Grouptron, a multi-scale dynamic forecasting framework that leverages pedestrian group detection and utilizes individual-level, group-level and scene-level information for better understanding and representation of the scenes. Our approach employs spatio-temporal clustering algorithms to identify pedestrian groups, creates spatio-temporal graphs at the individual, group, and scene levels. It then uses graph neural networks to encode dynamics at different scales and aggregate the embeddings for trajectory prediction. We conducted extensive comparisons and ablation experiments to demonstrate the effectiveness of our approach. Our method achieves 9.3% decrease in final displacement error (FDE) compared with state-of-the-art methods on ETH/UCY benchmark datasets, and 16.1% decrease in FDE in more crowded scenes where extensive human group interactions are more frequently present.
An advanced approach for Smart Parking Solution Based on Ethereum Block chain System

In the past few years, the use of the internet and mobile applications has rapidly increased. People use them for all of their needs, from online shopping to online bank transactions. With such enormous use, there comes a high risk of this data getting hacked and/or misused. Such attacks are quite common with applications that use a centralized server. Such shortcomings of centralized servers can be overcome by using decentralized networks like the block chain. Through this paper, I propose a solution that would help solve the problem of finding a parking lot for your vehicles. This solution has been created with the use of the Ethereum block chain (a distributed computing platform that is based on blockchain), and with the use of smart contracts. [1] This application is designed to provide an efficient way to further utilize the private parking spaces available in urban cities. It provides both the owners of parking lots and those who are in search of parking lots (in urban areas), a platform where they can exchange these resources. In the application, sellers lease out their parking lots while buyers book the parking lots.
Blockchain governance: The missing piece in the competition puzzle

Antitrust law deals with economic matters as if they were all industrial processes, that is, processes through which companies transform inputs into outputs. In this sense, we could say that antitrust law is still a Nineteenth Century law, even when it comes to platforms that transform digital data into services. For this reason, when we look at blockchains through the lenses of antitrust law, they are either framed as the result of an industrial process (the blockchain as an output) or as a means that allows the development of an industrial process (the blockchain as a tool). However, these two pieces do not complete the puzzle: they do not say much about the competitive impact of blockchains. Studying the operating mechanisms of blockchains and, in particular, their governance, one realizes that the puzzle must acquire a new piece: the information on who controls the blockchain. This piece of information reveals that even dominant blockchains, which are still nowhere to be seen, could be harmless if truly devoid of any central authority and that even strategic exchanges of information via permissioned blockchains should not trigger any antitrust liability, if blockchains’ administrators managed to allow the exchange only among non-competing participants.
Universal Programs for Stemming, Segmentation, Morphological Analysis of Turkic Words

In this paper are proposed universal programs for Turkic languages stemming, segmentation, and morphological analysis based on the “complete set of endings” (CSE) model of Turkic morphologies. The CSE-model is based on four types of endings: plural, case, personal, and possessive. For all Turkic languages, these four types of endings are similar. For each of considered NLP tasks is created a special relational data model – decision table. Relational data models – decision tables, algorithms and programs for stemming, segmentation, morphological parsing is shown for the examples of the Kazakh language.
Attention-based network for effective action recognition from multi-view video

A human action recognition system is affected by many challenges such as background clutter, partial occlusion, lighting, viewpoint, execution rate. Using complementary information from different views can improve view changing and occlusion problems. However, how to effectively integrate the information from multi-view images? In this paper, we propose an effective approach for multi-view human action recognition. The proposition is based on attention mechanism to pass discriminate feature between views. It is designed to form a multi-branch network whose each branch takes responsibility for extracting a view-specific feature. Furthermore, we built a cross-view attention module to enhance action recognition by transferring knowledge between views (branches). Experiments on three datasets show that the proposed solution works effectively in different scenarios. Our models have achieved the best results on two datasets (NUMA and MicaHandGesture) for both cross-subject and cross-view evaluations. On the NUMA dataset, the accuracy of our best models reach to 99.56% and 92.74% in cross-subject and cross-view evaluation scenarios respectively. And on the MicaHandGesture dataset, the accuracy are 99.06%, 91.71% in two scenarios respectively. The obtained results surpass other previous works such as Multi-Branch TSN with GRU [5] (93.81% in cross-subject evaluation, 84.4% in cross-view evaluation on the NUMA) and DA-Net [31] (92.1% for cross-subject evaluation (video-level), and 84.2% for cross-view evaluation on the NUMA dataset). We also obtained very promising results on a large-scale NTU RGB+D dataset.
A Character-Level Convolutional Neural Network for Predicting Exploitability of Vulnerability

The continuous discovery of software vulnerabilities have brought great challenges to the cyber security, which will lead to severe systematical or individual losses after being exploited. But the harshly increasing of software vulnerabilities overwhelms the time consuming vulnerability analysis. Security experts must pay more attention to the ones which have the highest priority to be repaired. In general, both severity and exploitability determine the severity of a software vulnerability. Compared with the severity evaluated by the Common Vulnerability Scoring System (CVSS score), the exploitability is still lack of a well-accepted standard. Furthermore, based on the perspective of attack and defense, we found that the exploitability of vulnerabilities is more attractive to hackers so that system or individual is severely affected by the exploitability rather than the severity. In this paper, we propose a deep learning based approach to predict the exploitability of the vulnerability by using the correlated textual description and characteristics. Specifically, our approach takes character-level Convolutional Neural Network (charCNN) to fetch more fine-grained character-level features from the vulnerability description instead of the word-level features considered by the previous literatures. And we highlight the importance of vulnerability characteristics such as Confidentiality Impact, Integrity Impact, Attack Vector etc. during the determination of vulnerability exploitability. Extensive experiments are set to prove the effectiveness of the given charCNN approach through the comparison on both different levels of features and different neural network models. Our approach achieves the best F1 values 93.1% (at least 2.2% more than the baselines). And we also investigate the efficiency of charCNN trained by historical vulnerability when predicting the exploitability of the newly published vulnerabilities. Finally, we further explore the robustness of the proposed model by changing the scale of training sets. For the prediction of vulnerability exploitability, we recommend to adopt 40.0% to 50.0% vulnerabilities to train a robust charCNN model.
Optimizing the Weights and Thresholds in Dendritic Neuron Model Using the Whale Optimization Algorithm

Abstract In recent years, with the great success of dendritic neuron model (DNM) in various fields, the application of intelligent optimization algorithms in dendritic neuron model has attracted increasing attention of researchers. The training process of neural network is regarded as one of the great challenges of machine learning because of its non-linear nature and unknown optimal parameters. The traditional training algorithm of DNM is prone to fall into local optimum and speed of convergence slowly and so on, resulting in the problem of accuracy and low efficiency. In this paper, for solving the classification problem of dendritic neural model, an innovative intelligent optimization algorithm which named whale optimization algorithm (WOA), is applied to the training of DNM for the first time. Compared with six traditional and classic intelligent optimization algorithms in four classic datasets, the results indicate that WOA-DNM has good performance in various aspects, and its advantage is remarkable.
Security Analysis of SFrame

As people become more and more privacy conscious, the need for end-to-end encryption (E2EE) has become widely recognized. We study the security of SFrame, an E2EE mechanism recently proposed to IETF for video/audio group communications over the Internet. Although a quite recent project, SFrame is going to be adopted by a number of real-world applications. We inspected the original specification of SFrame. We found a critical issue that will lead to an impersonation (forgery) attack by a malicious group member with a practical complexity. We also investigated the several publicly-available SFrame implementations, and confirmed that this issue is present in these implementations.
On Brightness Agnostic Adversarial Examples Against Face Recognition Systems

This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs nonlinear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.
Critic Guided Segmentation of Rewarding Objects in First-Person Views

This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset. For that we train an Hourglass network using only feedback from a critic model. The Hourglass network learns to produce a mask to decrease the critic’s score of a high score image and increase the critic’s score of a low score image by swapping the masked areas between these two images. We trained the model on an imitation learning dataset from the NeurIPS 2020 MineRL Competition Track, where our model learned to mask rewarding objects in a complex interactive 3D environment with a sparse reward signal. This approach was part of the 1st place winning solution in this competition. Video demonstration and code: https://rebrand.ly/critic-guided-segmentation.
Using git metrics to measure students' and teams' code contributions in software development projects

Many software engineering courses are centered around team-based project development. Analyzing the source code contributions during the projects’ development could provide both instructors and students with constant feedback to identify common trends and behaviors that can be improved during the courses. Evaluating course projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions during the development. The adoption of distributed version control sys-tems like git enable the measurement of students’ and teams’ contributions to the project.In this work, we analyze the contributions within eight software development projects,with 150 students in total, from undergraduate courses that used project-based learning.We generate visualizations of aggregated git metrics using inequality measures and the contribution per module, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify inequality among students’ contributions, the modules where students con-tributed, development processes with a non-steady pace, and integration practices render-ing a useful feedback tool for instructors and students during the project’s development.Further studies can be conducted to assess the quality, complexity, and ownership of the contributions by analyzing software artifacts.
PrivaLog: a Privacy-aware Logic Programming Language

Logic Programming (LP) is a subcategory of declarative programming that is considered to be relatively simple for non-programmers. LP developers focus on describing facts and rules of a logical derivation, and do not need to think about the algorithms actually implementing the derivation.
Open Set Recognition for Machinery Fault Diagnosis

AI tasks based on deep neural networks have been widely applied in industrial applications, such as process control, quality inspection or predictive maintenance. Deep neural network classifiers are particularly successful, as they provide powerful and reliable algorithms for many applications such as object recognition and fault diagnosis. However, most deep classifier applications are not able to recognize class samples that are beyond the scope of their training data. Samples of unknown classes (denoted as open set data) lead to significant drops in performance, as the output of deep classifiers is limited to the known classes of the training data (denoted as closed set data). This paper presents a method to recognize open set samples without changing the neural network architecture, the training process, nor the trained models. In our method, we firstly train a neural network for normal closed set fault diagnosis. Then we compare the feature maps of testing samples and known class samples during inference using local outlier factor to recognize open set samples. We evaluate our method with two public datasets and show that our method can increase the overall accuracy by 40% when classifying open set data. Besides, we also compared our method to the state-of-the-art open set recognition approach for fault diagnosis applications and the results show that our method leads to better F1-scores.
Robust Motion Averaging under Maximum Correntropy Criterion

Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem. This method aims to recover global motions from a set of relative motions, where the original method is sensitive to outliers due to using the Frobenius norm error in the optimization. Accordingly, this paper proposes a novel robust motion averaging method based on the maximum correntropy criterion (MCC). Specifically, the correntropy measure is used instead of utilizing Frobenius norm error to improve the robustness of motion averaging against outliers. According to the half-quadratic technique, the correntropy measure based optimization problem can be solved by the alternating minimization procedure, which includes operations of weight assignment and weighted motion averaging. Further, we design a selection strategy of adaptive kernel width to take advantage of correntropy. Experimental results on benchmark data sets illustrate that our method has superior performance on accuracy and robustness for multi-view registration. What’s more, it can be applied to robot mapping.