Reviewerly/exHarmony huggingface dataset:
A Sport Athlete Object Tracking Based on Deep Sort and Yolo V4 in Case of Camera Movement

Object tracking task has always been a major problem in the CV field. It is different from object detection. Object detection only needs to identify the type of object, while tracking task needs to identify its unique identity when a specific object is detected, such as REID problem. In sports-related fields, object tracking technology also has huge applications. For example, in football matches, camera tracking of footballs and tracking of athletes require this technology. This paper takes NBA and World Cup related scenes as the identification object, and aims to establish a tracking system for all players in the game, complete the realtime tracking of each athlete, so as to obtain relevant track information. In addition, the system can also help teachers review the competition in related education links and find the shortcomings of each student. Unlike most of filtering algorithms in past, this paper used the more cutting-edge deep learning technology in recent years, the YoloV4 and Sort's advanced version Deep Sort.
An IAD type framework for Blockchain enabled smart tourism ecosystem

Abstract The concept of smart tourism remains fuzzy when it comes to practice because of the lack of clarity on the roles and responsibilities of stakeholders in addition to complex institutional ecosystem that exist within the tourism industry. Further, trust and safety of the data generated due to the advancement of smart tourism in varied institutional arrangements pose huge challenges. Extant literature proposes that the application of decentralized ledger promises possible solutions to most of these problems due to the inherent features of the Blockchain technology. This suggestion raises two practical questions. First, does the sophisticated additional layer adds complexity to its practical application? Second, will the suggested solutions really lead to a win-win for all the stakeholders involved? In this study, we use Ostrom's action arena to examine the exchanges (information, contractual and monetary) between various stakeholders in a BCT enabled smart tourism of a Blockchain enabled tourism industry to answer these two questions.
Remote Recognition of In-Bed Postures Using a Thermopile Array Sensor With Machine Learning

Recently, there has been growing attention to sleep quality monitoring. In-bed posture is not only one of the critical factors that evaluates sleep qualities but can also help improve the well-being of bedridden patients. Our previous work has reported the successful recognition of 5 in-bed postures using a wall-mounted thermopile array sensor with the hand-crafted feature extraction-based machine learning. In this article, both ceiling-mounted and wall-mounted thermopile array sensing approaches are devised and compared to identify 9 typical in-bed postures using advanced machine learning models. In particular, a preprocessing method and a synergistic feature extraction approach using integrated histogram of oriented gradient and principal component analysis are proposed. An accuracy of over 99.8% is reached when using the 5-fold cross-validation to classify 9 postures. Besides, the leave-one-subject-out cross-validation (cross-user-validation) is performed to evaluate and compare the robustness of the classifiers using both sensor-mounting approaches. As the thermopile array sensor has the unique property of non-contact, non-privacy invasion, and passive sensing, the proposed system provides a contactless, unobtrusive, low cost and convenient solution for long-term sleep and in-bed patient monitoring.
Job Characteristics on Large-Scale Systems: Long-Term Analysis, Quantification, and Implications

HPC workload analysis and resource consumption characteristics are the key to driving better operation practices, system procurement decisions, and designing effective resource management techniques. Unfortunately, the HPC community does not have easy accessibility to long-term introspective work-load analysis and characterization for production-scale HPC systems. This study bridges this gap by providing detailed long-term quantification, characterization, and analysis of job characteristics on two supercomputers: Intrepid and Mira. This study is one of the largest of its kind - covering trends and characteristics for over three billion compute hours, 750 thousand jobs, and spanning a decade. We confirm several long-held conventional wisdom, and identify many previously undiscovered trends and its implications. We also introduce a learning based technique to predict the resource requirement of future jobs with high accuracy, using features available prior to the job submission and without requiring any application-specific tracing or application-intrusive instrumentation.
Cloud Computing Security Challenges, Threats and Vulnerabilities

Cloud computing has grown to become an integral part of present as well as future information technologies. This technology has been designed to be used with internet by providing features such as information storage, remote access, etc. Cloud computing has been proved as an effective tool for all the provided services but it also comes with various types of threats. Over the years of its development, different fire attacks and data theft has been reported as a crucial factor since the data stored in the cloud by an organization or an individual user is basically confidential and sensitive. These data are illegally accessed by many hackers and further it will be used to fire attack the user. This paper mainly aims to highlight such attacks and provide suggestions for sorting the data breaching issues.
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers

We consider a wireless communication system, where a transmitter sends signals to a receiver with different modulation types while the receiver classifies the modulation types of the received signals using its deep learning-based classifier. Concurrently, an adversary transmits adversarial perturbations using its multiple antennas to fool the classifier into misclassifying the received signals. From the adversarial machine learning perspective, we show how to utilize multiple antennas at the adversary to improve the adversarial (evasion) attack performance. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power. Then, we consider various ways to allocate power among multiple antennas at a single adversary such as allocating power to only one antenna, and proportional or inversely proportional to the channel gain. By utilizing channel diversity, we introduce an attack to transmit the adversarial perturbation through the channel with the largest channel gain at the symbol level. We show that this attack reduces the classifier accuracy significantly compared to other attacks under different channel conditions in terms of channel variance and channel correlation across antennas. Also, we show that the attack success improves significantly as the number of antennas increases at the adversary that can better utilize channel diversity to craft adversarial attacks.
AR3D: Attention Residual 3D Network for Human Action Recognition

At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN's temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer. To solve this problem, this article is based on 3D CNN combined with a residual structure and attention mechanism to improve the existing 3D CNN model, and we propose two types of human action recognition models (the Residual 3D Network (R3D) and Attention Residual 3D Network (AR3D)). Firstly, in this article, we propose a shallow feature extraction module and improve the ordinary 3D residual structure, which reduces the parameters and strengthens the extraction of temporal features. Secondly, we explore the application of the attention mechanism in human action recognition and design a 3D spatio-temporal attention mechanism module to strengthen the extraction of global features of human action. Finally, in order to make full use of the residual structure and attention mechanism, an Attention Residual 3D Network (AR3D) is proposed, and its two fusion strategies and corresponding model structure (AR3D_V1, AR3D_V2) are introduced in detail. Experiments show that the fused structure shows different degrees of performance improvement compared to a single structure.
Multi-directional block based PVD and modulus function image steganography to avoid FOBP and IEP

Since the inception of pixel value differencing (PVD) image steganography, it has drawn considerable interest among the researchers of this field. However, most of the PVD based techniques suffer from either falling-off boundary problem (FOBP) or incorrect extraction problem (IEP). Therefore, to address these two issues, this paper proposes a multi-directional pixel value differencing and modulus function (MDPVDMF) based technique. During the embedding process, the original image (OI) is partitioned into 2 × 2 size pixel blocks. Then, data embedding is performed by exploiting the horizontal, vertical, and diagonal directions for each block. For a 2 × 2 pixel block, two difference values can be obtained in any of the three directions. Next, using the difference values and the remainders of the pixel pairs, the secret bits are embedded. The experiment has been conducted to compute the performance of the proposed technique with regards to the image quality metrics like peak signal-to-noise ratio (PSNR), embedding capacity (EC), and FOBP. Results show that PSNR is optimal for vertical pairs with 39.17 dB whereas the EC is optimal for the diagonal pairs with 3.10 bits per pixel (BPP). Further, the proposed technique has shown exceptional attack resistance ability to regular & singular (RS) attack, salt & pepper (S&P) noise, pixel difference histogram (PDH) analysis, and subtractive pixel adjacency matrix (SPAM) steganalysis.
Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model

Analyzing software maintenance activities is very helpful in ensuring cost-effective evolution and development activities. The categorization of commits into maintenance tasks supports practitioners in making decisions about resource allocation and managing technical debt. In this paper, we propose to use a pre-trained language neural model, namely BERT (Bidirectional Encoder Representations from Transformers) for the classification of commits into three categories of maintenance tasks — corrective, perfective and adaptive. The proposed commit classification approach will help the classifier better understand the context of each word in the commit message. We built a balanced dataset of 1793 labeled commits that we collected from publicly available datasets. We used several popular code change distillers to extract fine-grained code changes that we have incorporated into our dataset as additional features to BERT’s word representation features. In our study, a deep neural network (DNN) classifier has been used as an additional layer to fine-tune the BERT model on the task of commit classification. Several models have been evaluated to come up with a deep analysis of the impact of code changes on the classification performance of each commit category. Experimental results have shown that the DNN model trained on BERT’s word representations and Fixminer code changes ([email protected]+Fix_cc) provided the best performance and achieved 79.66% accuracy and a macro-average f1 score of 0.8. Comparison with the state-of-the-art model that combines keywords and code changes ([email protected]+CD_cc) has shown that our model achieved approximately 8% improvement in accuracy. Results have also shown that a DNN model using only BERT’s word representation features achieved an improvement of 5% in accuracy compared to the [email protected]+CD_cc model.
Sensor Management for Search and Track Using the Poisson Multi-Bernoulli Mixture Filter

A sensor management method for joint multitarget search and track problems is proposed, where a single user-defined parameter allows for a tradeoff between the two objectives. The multitarget density is propagated using the Poisson multi-Bernoulli mixture filter, which eliminates the need for a separate handling of undiscovered targets and provides the theoretical foundation for a unified search and track method. Monte Carlo simulations of two scenarios are used to evaluate the performance of the proposed method.
Stegano-Compression Coding in a Non-Equalible Positional Base

An approach to the construction of a stegano-compression coding in a non-equilible position base presented. The relevance of this approach to the processing of video containers is due to a number of limitations and shortcomings inherent in standardized methods (eg, LSB) based on the JPEG platform. The proposed method allows you to combine the encoding operation with the embedding of a hidden message.
HELP: An LSTM-based approach to hyperparameter exploration in neural network learning

Hyperparameter selection is very important for the success of deep neural network training. Random search of hyperparameters for deep neural networks may take a long time to converge and yield good results because the training of deep neural networks with a huge number of parameters for every selected hyperparameter is very time-consuming. In this work, we propose the Hyperparameter Exploration LSTM-Predictor (HELP) which is an improved random exploring method using a probability-based exploration with an LSTM-based prediction. The HELP has a higher probability to find a better hyperparameter with less time. The HELP uses a series of hyperparameters in a time period as input and predicts the fitness values of these hyperparameters. Then, exploration directions in the hyperparameter space yielding higher fitness values will have higher probabilities to be explored in the next turn. Experimental results for training both the Generative Adversarial Net and the Convolution Neural Network show that the HELP finds hyperparameters yielding better results and converges faster.
Applying Blockchain in the Modern Supply Chain Management: Its Implication on Open Innovation

Over the past decade, the blockchain technology has been actively embraced by an increasing number of companies. Blockchain has proven its effectiveness not only in the financial sector, but also in logistics and supply chain management, and has received extensive coverage in the scientific literature. At the same time, the implementation of blockchain in logistics and supply chain management (SCM) is technically difficult and requires significant financial costs. In this regard, its distribution in countries across the world is uneven. In this study, we compare the implementation of blockchain in countries with developed (Germany) and emerging (Russia) economies. Thus, our study provides new findings and information on the similarities and differences in blockchain implementation strategies in countries with developed and emerging economies. This comparative analysis reveals country peculiarities and different approaches regarding the application of blockchain technologies. The research methodology is based on the case study method. Three economic sectors are selected for the cross-country comparative analysis: the energy, food, and pharmaceutical industries. The analysis is focused on the use of blockchain along all three parts of the supply chain: upstream, production and downstream. Using theory building through case studies, our research results reveal many similarities in blockchain implementation in Germany and Russia. They show that blockchain is actively used in all three analyzed sectors by companies in both countries. Moreover, the technology proves its effectiveness in both upstream and downstream parts of the supply chain. In both Germany and Russia, blockchain is mainly used by large businesses due to its high costs. However, there are some differences regarding the implementation process of the technology in both countries. Firstly, the state support is required for some Russian blockchain projects. Secondly, none of the Russian companies has the necessary "full range" of blockchain competencies, so all Russian projects are carried out in collaboration with other parties, primarily IT partners. Thirdly, most of the Russian blockchain projects are still local in nature and relate to the use of technology in the relationship between the specific supplier and corresponding consumers.
Real-time quantum calculations of phase shifts using wave packet time delays

We present a method to extract the phase shift of a scattering process using the real-time evolution in the early and intermediate stages of the collision in order to estimate the time delay of a wave packet. This procedure is convenient when using noisy quantum computers for which the asymptotic out-state behavior is unreachable. We demonstrate that the challenging Fourier transforms involved in the state preparation and measurements can be implemented in $1+1$ dimensions with current trapped ion devices and IBM quantum computers. We compare quantum computation of the time delays obtained in the one-particle quantum mechanics limit and the scalable quantum field theory formulation with accurate numerical results. We discuss the finite volume effects in the Wigner formula connecting time delays to phase shifts. The results reported involve two- and four-qubit calculations, and we discuss the possibility of larger scale computations in the near future.
Recurrent Neural Network based Text Summarization Techniques by Word Sequence Generation

Word sequence prediction is the word processing method that can be very useful for generating text summary by reducing the number of keystrokes when in need for typing words. So, mostly typing and summarizing a large text summary document is very complicated and is time consuming process. With a large and huge amount of data circulating in this digital data world, there is necessary to create and develop some machine learning algorithms that can automatically generate accurate summaries. Types of Neural Network and deep learning field techniques are the most popular and efficient method for automatic text summarization. This paper provides various methods and different datasets that may be used to generate text summaries automatically which reduces the time to manually type and summarize large text documents.
Digital Data and Data Literacy in Archaeology Now and in the New Decade

OVERVIEW Digital data play an increasingly important role in how we understand the present and the past. The challenges inherent in understanding and using digital data are as intellectually demanding as any other archaeological research endeavor. For these reasons, data management cannot be regarded as a simple compliance or technical issue. For data to be meaningfully preserved and used in intellectually rigorous ways, they need to be integrated fully into all aspects of archaeological practice, including ethics, teaching, and publishing. In this review, we highlight some of the significant and multifaceted challenges involved in managing data, including documentation, training, methodology, data modeling, trust, and ethical concerns. We then focus on the importance of building data literacy broadly among archaeologists so that we can manage and communicate the data our discipline creates. This involves more than learning to use a new tool or finding a data manager for one's excavation or survey. Long-term, responsible stewardship of data requires understanding the workflows and human roles in data management. Putting effort now into thoughtful data management and broad data-literacy training means we will be able to make the most of the “bigger” data that archaeologists now produce. An important aspect of this reorientation will be to look beyond the boundaries of our own research projects and information systems. Future research, teaching, and public engagement needs will also compel us to explore how our data articulates with wider contexts—within and beyond our discipline.
Interpreting Deep Learning-based Vulnerability Detector Predictions Based on Heuristic Searching

Detecting software vulnerabilities is an important problem and a recent development in tackling the problem is the use of deep learning models to detect software vulnerabilities. While effective, it is hard to explain why a deep learning model predicts a piece of code as vulnerable or not because of the black-box nature of deep learning models. Indeed, the interpretability of deep learning models is a daunting open problem. In this article, we make a significant step toward tackling the interpretability of deep learning model in vulnerability detection. Specifically, we introduce a high-fidelity explanation framework, which aims to identify a small number of tokens that make significant contributions to a detector’s prediction with respect to an example. Systematic experiments show that the framework indeed has a higher fidelity than existing methods, especially when features are not independent of each other (which often occurs in the real world). In particular, the framework can produce some vulnerability rules that can be understood by domain experts for accepting a detector’s outputs (i.e., true positives) or rejecting a detector’s outputs (i.e., false-positives and false-negatives). We also discuss limitations of the present study, which indicate interesting open problems for future research.
Self-Supervised Learning of Person-Specific Facial Dynamics for Automatic Personality Recognition

This article aims to solve two important issues that frequently occur in existing automatic personality analysis systems: 1. Attempting to use very short video segments or even single frames, rather than long-term behaviour, to infer personality traits; 2. Lack of methods to encode person-specific facial dynamics for personality recognition. To deal with these issues, this paper first proposes a novel Rank Loss which utilizes the natural temporal evolution of facial actions, rather than personality labels, for self-supervised learning of facial dynamics. Our approach first trains a generic U-net style model that can infer general facial dynamics learned from a set of unlabelled face videos. Then, the generic model is frozen, and a set of intermediate filters are incorporated into this architecture. The self-supervised learning is then resumed with only person-specific videos. This way, the learned filters’ weights are person-specific, making them a valuable source for modeling person-specific facial dynamics. We then propose to concatenate the weights of the learned filters as a person-specific representation, which can be directly used to predict the personality traits without needing other parts of the network. We evaluate the proposed approach on both self-reported personality and apparent personality datasets. In addition to achieving promising results in the estimation of personality trait scores from videos, we show that the tasks conducted by the subject in the video matters, that fusion of a combination of tasks reaches highest accuracy, and that multi-scale dynamics are more informative than single-scale dynamics.