Reviewerly/exHarmony huggingface dataset:
Ensemble Markov Chain Monte Carlo with Teleporting Walkers

We introduce an ensemble Markov chain Monte Carlo approach to sampling from a probability density with known likelihood. This method upgrades an underlying Markov chain by allowing an ensemble of such chains to interact via a process in which one chain’s state is cloned as another’s is deleted. This effective teleportation of states can overcome issues of metastability in the underlying chain, as the scheme enjoys rapid mixing once the modes of the target density have been populated. We derive a mean-field limit for the evolution of the ensemble. We analyze the global and local convergence of this mean-field limit, showing asymptotic convergence independent of the spectral gap of the underlying Markov chain, and moreover we interpret the limiting evolution as a gradient flow. We explain how interaction can be applied selectively to a subset of state variables in order to maintain advantage on very high-dimensional problems. Finally, we present the application of our methodology to Bayesian hyperparameter estimation for Gaussian process regression.
DeepRace: A learning-based data race detector

In this paper, we propose DeepRace, a novel approach toward detecting data race bugs in the source code. We build a deep neural network model to find data race bugs instead of creating a data race detector manually. Our model uses a one-layer convolutional neural network (CNN) with different window sizes to find data race. We adopt the class activation map in order to highlight the line of codes with a data race. Thus, the DeepRace model can detect the data race on a file-level and line of code level. We trained and tested the model with OpenMP and POSIX source code datasets consisting of more than 5000 and 8000 source code files respectively. Comparing to other race detectors, we only had a small number of false positives and false negatives up to 3 and 4 for each OpenMP data race.
A Color Histogram Based Large Motion Trend Fusion Algorithm for Vehicle Tracking

Due to the static nature of roadside cameras, targets of images have the characteristics of near big and far small. In a short time, as distances between targets and cameras get more faraway and the pixel ratios of the same target decrease sharply, which leads to a decrease in appearance information. However, the loss of appearance information makes tracking more challenging in vehicle tracking. To solve this problem, we propose a color histogram based large motion trend fusion (LMT-CH) algorithm for vehicle tracking, suitable for the traffic scene taken by the roadside camera. Based on Generalized Intersection-over-Union and Hungarian algorithm, LMT-CH uses targets of the large motion trend combined with the color histogram to modify the associated results. The algorithm includes four key steps: 1) train model based on the EfficientDet framework as an object detector. 2) First, calculate the correlation matrix between two consecutive frames. Second, get the matching results based on the Hungarian algorithm. Then, divide targets into four different sets based on the matching results. 3) Calculate the color histogram and large motion trend of the target in the suspected increase set of current frame's targets. 4) Re-identification trajectories based on large motion trend and color histogram. We evaluate the algorithm for the UA-DETRAC datasets with different levels of congestion. In several experiments, we show that the algorithm can effectively deal with the problem of target occlusion and loss, and it performs well in the ID switch of long-distance targets.
A simplified scheme for secure offline electronic payment systems

• The proposed offline electronic (e-) payment scheme satisfies essential security requirements of e-payment systems. • It requires much more simple computations and becomes more practical than many existing schemes. • Through the commitment, challenge and response of Schnorr’s Blind Signature, the merchant can verify the spent e-coin. • Besides, the trusted authority can identify the dishonest spender if multiple spending occurs. This paper proposes a secure offline electronic (e-) payment scheme by adopting Schnorr's untraceable blind signature (BS). Thereby, to satisfy the essential security requirements of e-payment systems, it requires much more simple computations and becomes more practical than many existing schemes. Other considerations are: to prevent the forgery of e-coin, the Bank is only the lawful entity to produce the valid e-coin; and others can verify its correctness. To confirm no swindling, the e-coin owner also sticks her private signing key with the e-coin before spending it as the payment. Hence, through the commitment with challenge-response of Schnorr's BS, the merchant can verify the spent e-coin, and the trusted authority can identify the dishonest spender if multiple spending occurs. Moreover, it embeds three distinct information of date, namely expiration, deposit, and transaction dates with every e-coin. Thereby, it minimizes the size of the Bank's database, correctly calculates the interest of the e-coin, and helps in arbiter if multiple spending, respectively. Finally, it evaluates the performance and analyzes essential security requirements of the proposed scheme, plus studies a comparison with existing ones.
Video Abnormal Event Detection Via Context Cueing Generative Adversarial Network

Video abnormal event detection is a significant research subject in intelligent surveillance, referring to identifying events that do not conform to expected behaviors. It is a challenging task due to the diversity of video content and the various definition of anomaly depending on contexts. How to grasp the nuances of anomalies from diverse video content under associated contexts? In this paper, we propose a context cueing generative adversarial network (CC-GAN) to scop attention to specific regions of interest (ROIs). Different from existing work, the designed framework takes advantage of the context cues to preserve the associations between ROIs and contexts. By intrinsic learning of static spatial layouts and apparent velocities, context cues can help to understand ROIs. Optimized in a crop-inpainting fashion, CC-GAN learns to generate ROIs matching given spatio-temporal context and discriminate the distorted or incongruous ones, corresponding to abnormal scenes. Compared with the state-of-the-art method and other abnormal event detection approaches, the proposed framework demonstrates its effectiveness, reliability, and good generalization ability across various scenes and events.
Cognitive Production Systems: A Mapping Study

In order to guarantee the quality and the productivity of a production system in a competitive marketplace, it is important to be able anticipate the changes in specifications of products and systems. The time limits in running productions, the complexity of manufacturing systems, and the diversification of components, are the challenges that human experts cannot handle without cognitive systems. Capabilities of cognitive Systems in observing, learning, and predicting the behavior and the evolution of the manufacturing systems make them special candidates for solving these problems. This mapping study provides an insight into the application of cognitive systems in the domain of production. We categorize different approaches and estimate their progress. We also discuss the optimizations and persisting problems and barriers. These representations can help in recognizing the concrete problems of the field. According to the results of our mapping study, Human-Machine Interaction and Knowledge Gaining/Sharing represents the largest categories of the domain. A gain in efficiency and maximized effectiveness can be achieved as optimization. The most common problem is the missing or only difficult generalization of the presented concepts.
Wavelet-Extreme Learning i-Machine for New Zealand Smart Meter Data

A wavelet decomposition based artificial neural network trained with an extreme learning machine is proposed for long-term load forecasting based on continuous learning. The proposed approach has been undertaken in pursuit of creating a software tool that can predict the impact of increased renewable distributed sources on load profiles. Several concerns and challenges arise as a result of this increase, as large transients are formed in these profiles causing a potential over-generation problem. As the number of smart meters and advanced metering infrastructure also increases, the demand for big data analytics systems becomes increasingly urgent. Pairing an intelligent hybrid neural network solution with an online cloud software makes the management of big data, in the form of smart meter load consumption data, more efficient and effective. The process of the proposed solution is that it first decomposes this data into components that represent more detailed periodic information within the time series. These components are then input into several extreme learning machine algorithms and the outputs are then recombined to make a load forecast and prediction of future load consumption data. The learning behaviour is vital and helps tuning with each new iteration increasing accuracy. A comparison of this paper's approach is benchmarked against the popular Levenberg- Marquardt training algorithm. The results obtained indicate the superiority of the wavelet-extreme learning machine hybrid compared to industry standard statistical methods and other hybrid neural network techniques.
Detection of the autonomous car robot using Yolo

Abstract One of the important object detection applications in smart transportation systems is vehicle detection. Working on self-driving car robots has become an important experiment in recent years to take advantage of innovations and ideas in real self-driving cars, and the detection of robots by multiple algorithms is the most important phase in this work. To solve the problems of self-driving car robot detection. Such as not recognizing shape. In this paper, via the Yolov2 algorithm, we trained a new model for robots. It was proven with the comparison experiments that the proposed method is successful for robot detection. In addition, the proposed model demonstrated excellent feature extraction ability with network visualization.
CHARITY ORGANIZATIONS ADOPTING VIRTUAL REALITY MODALITY: THEORIZATION AND VALIDATION

Communicators of charity organizations have begun to adopt virtual reality modality to draw in more donors in recent years. However, there is still a lack of comprehensive theoretical exploration and empirical verification on the avenues that may optimize the function of virtual reality modality for communication conducted by charity organizations. To fill this gap, the current study proposes and validates a framework based on integrative extension of theories pertaining to enactive cognition, media affordances, and personal norm. As the research result suggests, the five immersive affordance attributes of vividness, authenticity, coherence, interactivity, and controllability are primary facilitators of empathy and personal norm activation in the virtual reality context. The type of virtual reality modality, which provides immersive affordance attributes to exercise the transformative change effect, serves to elicit charitable donation intention through evocation of empathy and personal norm activation as the two key mediators. More noteworthy, the positive impact of empathy on charitable donation intention significantly increases due to the partial mediating effect of personal norm activation. Overall, the research result contributes to enhancing the effectiveness of communication conducted by charity organizations.
Single Image Atmospheric Veil Removal Using New Priors for Better Genericity

From an analysis of the priors used in state-of-the-art algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. Our hypothesis is based on a physical model, considering that the fog appears denser near the horizon rather than close to the camera. It leads to more restoration when the fog depth is more important, for a more natural rendering. For this purpose, the Naka–Rushton function is used to modulate the atmospheric veil according to empirical observations on synthetic foggy images. The parameters of this function are set from features of the input image. This method also prevents over-restoration and thus preserves the sky from artifacts and noises. The algorithm generalizes to different kinds of fog, airborne particles, and illumination conditions. The proposed method is extended to the nighttime and underwater images by computing the atmospheric veil on each color channel. Qualitative and quantitative evaluations show the benefit of the proposed algorithm. The quantitative evaluation shows the efficiency of the algorithm on four databases with different types of fog, which demonstrates the broad generalization allowed by the proposed algorithm, in contrast with most of the currently available deep learning techniques.
Combining Public Machine Learning Models by Using Word Embedding for Human Activity Recognition

Many types of human activity recognition (HAR) techniques have been developed based on machine learning (ML) using diverse sensors in our daily life, such as cameras, microphones, and acceleration sensors on smartphones and wearable devices. For this background, there are many pre-trained ML models of HAR available on the Internet. In addition, there are cases where multiple ML models are utilized in combination for HAR. Although combining multiple ML models can be expected to improve classification accuracy, it requires additional training to combine them, such as stacking. This paper proposes a method to integrate published and trained HAR ML models without additional training by utilizing word embedding, e.g., Glove and Word2vec. Word embedding enables to obtain plausible results by arithmetic operations on the labels output by multiple ML models. Utilizing the proposed integration method, a HAR system is developed, which implements publicly-available ML models. Evaluations using Glove are performed, and their results confirm the proposed method is able to improve classification accuracy compared to using each ML model alone.
The mobile phone in governance for environmental sustainability in Sub-Saharan Africa

Purpose In this study, we assess how the mobile phone can be leveraged upon to improve the role of governance in environmental sustainability in 44 Sub-Saharan African countries. Design/methodology/approach The Generalised Method of Moments is used to establish policy thresholds. A threshold is a critical mass or level of mobile phone penetration at which the net effect of governance on carbon dioxide (CO 2 ) emissions changes from positive to negative. Findings Mobile phone penetration thresholds associated with negative conditional effects are: 36 (per 100 people) for political stability/no violence; 130 (per 100 people) for regulation quality; 146.66 (per 100 people) for government effectiveness; 65 (per 100 people) for corruption-control and 130 (per 100 people) for the rule of law. Practical and theoretical implications are discussed. Originality/value The study provides thresholds of mobile phone penetration that are critical in complementing governance dynamics to reduce CO 2 emissions.
Blockchain Based Monitoring on Trustless Supply Chain Processes

Current trends in global business and enterprise typically force companies that create, distribute and sell products to employ long and diverse supply chain networks. While delivering value, these networks also have several challenges: tracking orders, receipts, and payments while tracking digital assets. This paper presents a decentralized blockchain-based traceability solution that provides secure and transparent transactions between parties in the supply chain. We design smart contracts for authenticating the agreement between multiple parties and traceability of the supply chain. Furthermore, We design a session generation scheme for authentication of the parties. The proposed design achieved user data integrity, secure key agreement, immutability, transaction traceability, and provide secure auditing. Our results show that the proposed smart contract and the session-based scheme have high scalability and performance in latency time and response time.
Multiagent Motion Planning Based on Deep Reinforcement Learning in Complex Environments

When agents in a multiagent system implement motion planning in complex and dynamic environments, model-based planning algorithms have poor adaptability, while intelligent algorithms, such as MADDPG, encounter difficulty in converging when training multiple agents, and the resulting control model has poor stability and robustness. To address the above challenges, this paper proposes a mixed experience multiagent deep deterministic policy gradient algorithm referred to as ME-MADDPG. The algorithm increases the high-quality experience obtained by artificial potential field method and uses dynamic probability to sample from different replay buffers. Simulation experiments have proven that compared to MADDPG, ME-MADDPG greatly improves convergence speed, convergence effect and stability and that ME-MADDPG can efficiently provide shorter and more convenient paths for multiagent systems.
Wireless Federated Learning with Limited Communication and Differential Privacy

This paper investigates the role of dimensionality reduction in efficient communication and differential privacy (DP) of the local datasets at the remote users for over-the-air computation (AirComp)-based federated learning (FL) model. More precisely, we consider the FL setting in which clients are prompted to train a machine learning model by simultaneous channel-aware and limited communications with a parameter server (PS) over a Gaussian multiple-access channel (GMAC), so that transmissions sum coherently at the PS globally aware of the channel coefficients. For this setting, an algorithm is proposed based on applying (i) federated stochastic gradient descent (FedSGD) for training the minimum of a given loss function based on the local gradients, (ii) Johnson-Lindenstrauss (JL) random projection for reducing the dimension of the local updates and (iii) artificial noise to further aid user&#x0027;s privacy. For this scheme, our results show that the local DP (LDP) performance is mainly improved due to injecting noise of greater variance on each dimension while keeping the sensitivity of the projected vectors unchanged. This is while the convergence rate is slowed down compared to the case without dimensionality reduction. As the performance outweighs for the slower convergence, the trade-off between privacy and convergence is higher but is shown to lessen in high-dimensional regime yielding almost the same trade-off with much less communication cost.
Detecting Adversarial Images via Texture Analysis

Neural networks have been shown to be vulnerable to carefully crafted adversarial examples. Recently, new adversarial attacks, including dispersion reduction (DR), have been proposed, and shown to be transferable across different computer vision tasks. This means that an ensemble of different defense/detection mechanisms can be evaded all at once. Unlike previous attack methods, the DR attack minimizes the dispersion of an internal feature map providing state-of-the-art results. In this paper, we propose an algorithm to detect the adversarial examples generated by different adversarial attacks, including the dispersion reduction, projected gradient descent, diverse inputs method and momentum iterative fast gradient sign method. Our approach employs 1D Gabor filter responses, and detects adversarial examples generated from different surrogate neural network models and datasets with high accuracy.
Performance Evaluation between RGB and YCrCb in TC-SF-CNNLS for Vehicle Type Recognition System

In this paper, the performance impact of vehicle type recognition (VTR) using Three-Channel of Sparse-Filtered Convolutional Neural Network with Layer-Skipping strategy (TC-SF-CNNLS) technique is observed. Unlike other techniques that lacking in extracting unique features, TC-SF-CNNLS able to extract the unique features from colour image. However, colour image can be in many schemes. Thus, we implemented the technique with two colour schemes which is RGB and YCrCb to analyze which one is able to give better performance in the VTR. We tested the implementation with a benchmark dataset known as BIT and self-obtained dataset known as SPINT. The results are observed based on the accuracy performance that represented in a confusion table. Based on the results, YCrCb outperformed RGB with the highest average accuracy 90.5% and 90.4% for both datasets, respectively. We can conclude that TC-SF-CNNLS is best performed when YCrCb is used as the colour scheme in extracting the vehicle features from the colour image.
Computation of the suffix array, Burrows-Wheeler transform and FM-index in V-order

V-order is a total order on strings that determines an instance of Unique Maximal Factorization Families (UMFFs), a generalization of Lyndon words. The fundamental V-comparison of strings can be done in linear time and constant space. V-order has been proposed as an alternative to lexicographic order (lexorder) in the computation of suffix arrays and in the suffix-sorting induced by the Burrows-Wheeler transform (BWT). In line with the recent interest in the connection between suffix arrays and Lyndon factorization, in this paper we obtain similar results for the V-order factorization. Indeed, we show that the results describing the connection between suffix arrays and Lyndon factorization are matched by analogous V-order processing. We also describe a methodology for efficiently computing the FM-Index in V-order, as well as V-order substring pattern matching using backward search.
From Transformers to Reformers

This paper investigates different deep learning models for various tasks of Natural Language Processing. Recent ongoing research is about the Transformer models and their variations (like the Reformer model). The Recurrent Neural Networks models were efficient up to an only a fixed size of the window. They were unable to capture long-term dependencies for large sequences. To overcome this limitation, the attention mechanism was introduced which is incorporated in the Transformer model. The dot product attention in transformers has a complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) where n is the sequence length. This computation becomes infeasible for large sequences. Also, the residual layers consume a lot of memory because activations need to be stored for back-propagation. To overcome this limitation of memory efficiency and to make transformers learn over larger sequences, the Reformer models were introduced. Our research includes the evaluation of the performance of these two models on various Natural Language Processing tasks.