Reviewerly/exHarmony huggingface dataset:
Variational Inference with Latent Space Quantization for Adversarial Resilience

Despite their tremendous success in modelling high-dimensional data manifolds, deep neural networks suffer from the threat of adversarial attacks - Existence of perceptually valid input-like samples obtained through careful perturbation that lead to degradation in the performance of the underlying model. Major concerns with existing defense mechanisms include non-generalizability across different attacks, models and large inference time. In this paper, we propose a generalized defense mechanism capitalizing on the expressive power of regularized latent space based generative models. We design an adversarial filter, devoid of access to classifier and adversaries, which makes it usable in tandem with any classifier. The basic idea is to learn a Lipschitz constrained mapping from the data manifold, incorporating adversarial perturbations, to a quantized latent space and re-map it to the true data manifold. Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference. We demonstrate the efficacy of the proposed formulation in providing resilience against multiple attack types (black and white box) and methods, while being almost real-time. Our experiments show that the proposed method surpasses the state-of-the-art techniques in several cases. The implementation code is available at - https://github.com/mayank31398/lqvae.
Research on Pedestrian Attitude Detection Algorithm from the Perspective of Machine Learning

In the rapid development of science and technology today, the intelligence of the visual system has been highly valued. The recognition and detection of pedestrian attitudes in a complex environment have become the application trend of intelligent video. The widely used of camera machine does not have such a function. Therefore, this article deeply discusses the relevant algorithms of pedestrian gesture detection and recognition based on machine learning. The traditional HOG feature detection can only achieve the relevant detection of the upright walking crowd target. While when the pedestrian makes different gestures, its detection effect is directly affected and challenging to be recognized. So, this article uses the checking methods of the deformable part model (DPM) to check the target pedestrian gesture and elaborate pedestrian's attitude estimation algorithm for the deformable parts principle. Finally, it combines the algorithm with HOG+SVM principles to simulate with the MATLAB and gets the experimental results to show that this approach can make a pedestrian posture test implemented to achieve high precision accuracy.
Application of an Improved SIFT algorithm in GPR images

Classical scale invariant feature transform (SIFT) and its corresponding improved algorithm are widely used in computer vision and remote sensing images. However, in the detection and matching task of GPR images, the final feature matching quality of traditional SIFT algorithm will be directly affected by the richness of underground information. This paper introduces a more robust method to adapt to GPR images. The method combines the classical SIFT and NCC, and finally selects the best matching pair by stable matching strategy, and extracts more correct matching points and improves the accuracy rate from GPR images with low pixel density and less feature information. The experimental results show that, compared with the classical SIFT algorithm and the conventional combination of RANSAC and sift algorithm, the proposed algorithm can obtain more correct matching point pairs, and can be effectively applied to the detection of underground environment changes by GPR tomography images.
The Negative Consequences of the Infodemic and Technological Support to Deal With Psychological Issues

The chapter deals with a theoretical generalization and new approaches of solving the scientific problem, which is manifested in the disclosure of the peculiarities of the experience of the threat of COVID-19 by residents of Ukraine, due to Infodemic. To prevent the negative consequences of Infodemic influence, psychological technology has been developed and tested, which is aimed at the formation/development of adaptive potential of the individual, which determines his ability to overcome difficult situations. The peculiarities of the organization and implementation of work in the training group on overcoming negative feelings and frustration caused by individual perception of personal information about the coronavirus and the spread of COVID-19 in the world are described. It is valuable to present the methods of training and the most effective exercises that were used in active social and psychological training. The results of implementation showed high efficiency and expediency of using this psychological technology in the work of a psychologist.
Investigating the Interplay between Developers and Automation

Continuous software engineering is a rapidly growing discipline in software engineering.Among its many reported benets is increased development velocity, faster feedback for developers, and better software quality.It also comes with its own share of challenges, most of which are centered on making automated builds more ecient or detecting problems with build conguration.However, the majority of literature in this area does not take into account software developers, which are arguably the cornerstone of software development.Software development is still a human-driven endeavour.It is a developer who writes the code, tests it, makes the nal decision while factoring in the build results, and so on.Furthermore, software development does not happen in a vacuum.Development takes place within the context of practices dictating how it should be done, and perceived benets that drive practice adoption and implementation.Software development, and by extension continuous software development, is a socio-technical endeavour that features interactions between human aspects (developers, testers, etc.), technical aspects (automation), and environmental aspects (process, projectspecic characteristics, infrastructure, etc.).While the software engineering eld has its share of theories, frameworks, and models, or borrows them from other elds, we still do not have a human-centric framework for software engineering that takes into account other socio-technical aspects (technical and environmental).My dissertation addresses this need for a socio-technical framework by illustrating a series of studies that ultimately resulted in the creation of a socio-technical theory of continuous software engineering that focuses on phenomena involving both humans and automation.In particular, I focus on the role of continuous software engineering tools (automation) in the software development process and how they displace existing tools, disrupt existing workows, and feature in software developer decision making.This theory will enable further research in this area as well as allow researchers to make more grounded recommendations for industrial applications.
Social Controls and Bonds of Public Information Consumer on Sustainable Utilization and Provision for Computing

In public areas, employees are both consumers and producers in information. For sustainable usage of information, employees should be aware of information systems security (ISS). Information systems security (ISS) is critical in further developing public sector information systems, such as e-government. Most ISS breaches are committed by insiders rather than outsiders. This study investigates the applicability of adult social bond theory, which proposes social controls in the form of social bonds that provide deterrence based on the potential shame an employee would feel from committing an ISS breach. The proposed research model consists of four antecedents for adult social bonds: commitment, attachment, belief and job stability. Individual ISS compliance is set as the dependent variance and deterrence and shame are set as the mediators between social bonds and compliance. Analysis of 672 data points largely supports the research model, proving the applicability to ISS of social bonds and social control. Belief seems to have the strongest effect on individual compliance. Implications are discussed and further studies are proposed.
Single Image Super-Resolution with Dynamic Residual Connection

Deep convolutional neural networks have shown significant improvement in the single image super-resolution (SISR) field. Recently, there have been attempts to solve the SISR problem using lightweight networks, considering limited computational resources for real-world applications. Especially for lightweight networks, balancing between parameter demand and performance is very difficult to adjust, and most lightweight SISR networks are manually designed based on a huge number of brute-force experiments. Besides, a critical key to the network performance relies on the skip connection of building blocks that are repeatedly in the architecture. Notably, in previous works, these connections are pre-defined and manually determined by human researchers. Hence, they are less flexible to the input image statistics, and there can be a better solution for the given number of parameters. Therefore, we focus on the automated design of networks regarding the connection of basic building blocks (residual networks), and as a result, propose a dynamic residual attention network (DRAN). The proposed method allows the network to dynamically select residual paths depending on the input image, based on the idea of attention mechanism. For this, we design a dynamic residual module that determines the residual paths between the basic building blocks for the given input image. By finding optimal residual paths between the blocks, the network can selectively bypass informative features needed to reconstruct the target high-resolution (HR) image. Experimental results show that our proposed DRAN outperforms most of the existing state-of-the-arts lightweight models in SISR.
A Deep Learning Based Approach to Detect Code Clones

Code clone is a kind of code smells widely exists in practice. Such code smell may lead to serious problems, e.g., code redundancy and code inconsistency. To reduce the negative impact of code clones, researchers have proposed different approaches to detect and remove code clones. However, existing code clone detection approaches mostly rely on manually designed and fine-tuned heuristic rules. Such approaches cannot be exploited in different projects and the precision of them needs to improve further. To this end, this paper proposes a deep learning based approach to detect code clones by statically extracting syntactic features from the ASTs of source files. Evaluation results suggest that the proposed approach is effective in detecting code clones, its precision is around 90%.
DFH-GAN: A Deep Face Hashing with Generative Adversarial Network

Face Image retrieval is one of the key research directions in computer vision field. Thanks to the rapid development of deep neural network in recent years, deep hashing has achieved good performance in the field of image retrieval. But for large-scale face image retrieval, the performance needs to be further improved. In this paper, we propose Deep Face Hashing with GAN (DFH-GAN), a novel deep hashing method for face image retrieval, which mainly consists of three components: a generator network for generating synthesized images, a discriminator network with a shared CNN to learn multi-domain face feature, and a hash encoding network to generate compact binary hash codes. The generator network is used to perform data augmentation so that the model could learn from both real images and diverse synthesized images. We adopt a two-stage training strategy. In the first stage, the GAN is trained to generate fake images, while in the second stage, to make the network convergence faster. The model inherits the trained shared CNN of discriminator to train the DFH model by using many different supervised loss functions not only in the last layer but also in the middle layer of the network. Extensive experiments on two widely used datasets demonstrate that DFH-GAN can generate high-quality binary hash codes and exceed the performance of the state-of-the-art model greatly.
Extending Python for Quantum-classical Computing via Quantum Just-in-time Compilation

Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. The quantum software community has adopted Python on a number of large-scale efforts due to these characteristics, as well as the remote nature of near-term quantum processors. The use of Python has enabled quick prototyping for quantum code that directly benefits pertinent research and development efforts in quantum scientific computing. However, this rapid prototyping ability comes at the cost of future performant integration for tightly coupled CPU-QPU architectures with fast-feedback. Here, we present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++ language extension and compiler infrastructure to enable a single-source, quantum hardware-agnostic approach to quantum-classical computing that retains the performance required for tightly coupled CPU-QPU compute models. We detail this Python extension, its programming model and underlying software architecture, and provide a robust set of examples to demonstrate the utility of our approach.
IoT Data Privacy Protection Scheme based on Blockchain

Abstract This article introduces the blockchain technology and proposes a blockchain-based IoT sensor data privacy protection scheme from the anonymisation of user identity, the protection of private data and the procedure that allows third parties to use encrypted sensor data. The sensor and the gateway establish a connection after authentication and key negotiation, and the sensor periodically uploads the sampled data and hash-based message authentication code (HMAC) to the gateway. After the gateway verifies the HMAC code of the sensor data, it encrypts the sensor data, constructs a blockchain transaction and submits it to the blockchain node. Blockchain nodes cache transactions and periodically generate blocks to confirm that the transactions are on the chain. After the gateway constructs an authorised transaction and submits it to the blockchain node for confirmation, the designated decentralised application (DApp) obtains the permission and related keys to access the designated sensor managed by the gateway. Finally, the DApp queries the local blockchain node for the specified sensor data and decrypts the results. Experiments have proven through in-depth discussions on sensor synthesis data, sensor/gateway verification, transaction data encryption and transaction signatures that the scheme can be used in IoT systems.
A Mobile Application of Face Recognition Based on Android Platform

With the popularization of smart phones, a large number of mobile applications (Apps) have been developed and become more and more important in people's lives. However, most of them need to verify user identify by their username and password to unlock its full functions, which is an inconvenient operation for users. In order to solve this problem, this paper develops an App which employs face recognition technology to check one's identify. Experiments are conducted and experimental results demonstrate that our app has good performance in terms of recognition rate and time efficiency. Meanwhile, we collect a face dataset for checking one's identify.
Dynamic obstacle avoidance of vision sensor mobile robot based on prior knowledge

Aiming at the problem that obstacle avoidance algorithms do not distinguish the types of obstacles in the process of obstacle recognition and avoidance, this paper designs a robot obstacle avoidance system including knowledge base by combining prior knowledge with obstacle avoidance algorithm. The knowledge in the knowledge base can be defined by human as prior knowledge to classify obstacles. This makes it possible for the robot to identify obstacles according to the vision sensor in the obstacle avoidance process, and can choose different obstacle avoidance strategies for different obstacles according to the existing knowledge in the knowledge base. The knowledge base takes knowrob as the platform, and the experimental results show that the obstacle avoidance system can effectively avoid obstacles.
Real-Time Collection of Training Distance of Long-Distance Runners Based on Wireless Sensor Network

The real-time and accurate collection of long-distance runners’ training distance can provide real-time scientific basis for long-distance runners’ physical training. Therefore, the real-time collection method of long-distance runners’ training distance based on wireless sensor network is studied in this paper. In this model, the position tracking algorithm of long-distance runners based on wireless sensor network is introduced to collect the training distance of long-distance runners. When collecting the training distance, the energy balanced clustering routing algorithm based on energy and location is used to optimize and balance the energy consumption distribution of nodes and prolong the network life cycle, so that the training distance of long-distance runners can be collected in real time. Through the test, in the straight line and curve training path, this method has significant accuracy in collecting the training distance of single and multiple long-distance runners and can collect the training distance of long-distance runners in real time, which has significant application value.
Fusion-Based Digital Image Correlation Framework for Strain Measurement

We address the problem of enabling two-dimensional digital image correlation (DIC) for strain measurement on large three-dimensional objects with curved surfaces. It is challenging to acquire full-field qualified images of the surface required by DIC due to geometric distortion and the narrow visual field of the surface that a single image can cover. To overcome this issue, we propose an end-to-end DIC framework incorporating the image fusion principle to achieve full-field strain measurement over the curved surface. With a sequence of blurry images as inputs, we first recover sharp images using blind deconvolution, then project recovered sharp images to the curved surface using camera poses estimated by our proposed perspective-n-point (PnP) method called RRWLM. Images on the curved surface are stitched and then unfolded for strain analysis using DIC. Numerical experiments are conducted to validate our framework using RRWLM with comparisons to existing methods.
Open-CL based Multi GPU Acceleration for Video Object Detection

Deep-learning technology is widely used in various computer vision tasks. In particular, in a field where high performance is required such as autonomous driving, real-time inference performance of using a highly integrated accelerator is an important. However, frameworks currently provide only vendor-dependent environments, e.g., CUDA, which is problematic in terms of versatility and cost. This paper proposes a general-purpose framework for accelerating object detection performance on real-time video of neural networks based on Open-CL, especially using multi GPUs. Using this approach, the inference speed is faster up to 20 times.
Simple Multi-Resolution Representation Learning for Human Pose Estimation

Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multi-resolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multi-resolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MS-COCO and MPII dataset. The code is made publicly available at https://github.com/tqtrunghnvn/SimMRPose.
Predicting the lifetime of pull requests in open‐source projects

Abstract A recent survey using industrial projects has shown that providing an estimate of the lifetime of pull requests to developers helps to speed up their conclusion. Previous work has explored pull request lifetime prediction in open‐source projects using regression techniques but with a broad margin of error. The first objective of our work was to reduce the average error rate of the prediction obtained by the regression techniques so far. We performed experiments with different regression techniques and achieved a significant decrease in the mean error rate. The second objective of our work was to obtain a more effective and useful predictive model that can classify pull requests according to five discrete time intervals. We proposed new predictive attributes for the estimation of the time intervals and employed attribute selection strategies to identify subsets of attributes that could improve the predictive behavior of the classifiers. Our classification approach achieved the best accuracy in all the 20 projects evaluated in comparison with the literature. The average accuracy was of 45.28% to predict pull request lifetime, with an average normalized improvement of 14.68% in relation to the majority class and 6.49% in relation to the state‐of‐the‐art.
Query Optimization for Cloud Database

The paper work is mainly focussing on query optimization of database on cloud computing environment. We are also taking care of implementation of such cloud databases on public as well as private cloud system. During the research of this topic, after going through various literatures available, we found that resource allocation on private cloud and public cloud is really a challenging one and also working on cloud databases for optimization on NoSQL Databases through various cluster will be a new concepts of implementation and we wish to see their real-time results based on various operation.