Reviewerly/exHarmony huggingface dataset:
An Image-based Human Physical Activities Recognition in an Indoor Environment

In this paper, we propose real-time image-based recognition of human activities from series of images considering different human actions performed in an indoor environment.The proposed image-based human activity recognition(IHAR)system can be utilized for assisting the life of disabled persons, surveillance and human tracking, human computer interaction,and efficient resource utilization. The proposed IHAR system consists of closed-circuit television (CCTV) camera based image acquisitioning, various filtering based image enhancement, principle component analysis(PCA) based features extraction, and various machine learning algorithms for recognition accuracy performance comparison. We collected dataset of 10 different activities such as walking, sitting down and standing up consists of 35,530 images. The dataset is divided into(90%,10%),(80%,20%), and(70%,30%)training and testing respectively and evaluated three classifier K-nearest neighbors (KNN), Random Forest (RF), and Decision Tree(DT). The experimental results show the accuracy of 95%, 97%, and 90% by KNN, RF, and DT respectively.
Side-Channel Leakage Detection Based on Constant Parameter Channel Model

Side-channel analysis (SCA) becomes a serious realistic threat to crypto devices, it is thus imperative to evaluate the resistance of a device to SCA. Side-channel leakage detection aiming to identify the leakage points potentially revealing secrets in side channel signals, is considered as a preliminary step before further security assessment. This work proposes a novel black-box leakage detection approach, which views the side channel as a constant parameter communication channel when it outputs leakage points. The approach distinguishes leakage points by utilizing the kurtosis-based consistency check for channel parameter estimators. To examine the efficiency of this approach, false negative and false positive rates were first quantitatively analyzed by comprehensive experiments. Considering the fact that side-channel leakage can be from multiple channels in practice, we further investigated the applicability of the proposed approach to multi-channel leakage detection. Interestingly, equipped with the proposed detection approach, we correspondingly devised a novel side-channel attack exploiting a kurtosis-based distinguisher. Overall, extensive experiments have validated the efficiencies of our proposed leakage detection method and the novel SCA attack.
Dual-template Siamese Network with Cross-Correlation Fusion for Tracking

Siamese network based trackers treat tracking as a maximum matching between the detection region and the template, in which the template is either fixed or updated. The template-fixed trackers degrade accuracy since the appearance variations of the object in the subsequent frames are lacked; while the template-updated trackers depress robustness once the tracking drift occurs. In this paper, we apply a dual-template tracking strategy in the Siamese region proposal network to compensate for the separate merits of fixed template and mutative template. As for the dual-template, the one template is fixed and the other template is constantly updated. Meanwhile, we integrate a learnable convolutional neural network to fuse two Cross-Correlation responses generated from two template tracking branches. Based on Siamese network, we try to utilize the complementary fusion of different template responses to promote tracking performances. Extensive experiments on five tracking benchmarks of OTB100, VOT2016, VOT2018, GOT-10k and UAV123 demonstrate that our approach achieves the state-of-the-art tracking performances.
ICITPM: Integrity Validation of Software in Iterative Continuous Integration Through the Use of Trusted Platform Module (TPM)

Software development has passed from being rigid and not very flexible, to be automated with constant changes. This happens due to the creation of continuous integration and delivery environments. Nevertheless, developers often rely on such environments due to the large number of amenities they offer. They focus on authentication only, without taking into consideration other aspects of security such as the integrity of the source code and of the compiled binaries. The source code of a software project must not be maliciously modified. Notwithstanding, there is no safe method to verify that its integrity has not been violated. Trusted computing technology, in particular, the Trusted Platform Module (TPM) can be used to implement that secure method.
Attention-based Convolutional Neural Network for MRI Gibbs-ringing Artifact Suppression

Gibbs-ringing artifact is a common artifact in MRI image processing. As MRI raw data is taken in a frequency domain, 2D in- verse discrete Fourier transform is applied to visualize data. Inability to take inverse Fourier transform of full spectrum (full k-space) leads to the insufficient sampling of the high frequency data and results in a well-known Gibbs phenomenon. It is worth to notice that truncation of high frequency information generates a significant blur, thus some techniques from other image restoration problems (for example, image deblur task) can be successfully used. We propose attention-based convolutional neural network for Gibbs-ringing reduction which is the extension of recently proposed GAS-CNN (Gibbs-ringing Artifact Suppression Convolutional Neural Network). Proposed method includes simplified non-linear mapping, amended by LRNN (Layer Recurrent Neural Network) refinement block with feature attention module, controlling the correlation between input and output tensors of the refinement unit. The research shows that the proposed post-processing refinement construction considerably simplifies the non-linear mapping.
Towards a Decentralized Access Control System for IoT Platforms based on Blockchain Technology

The Internet of Things (IoT) technologies are transforming traditional businesses into digital-based platforms allowing for more service innovation, performance efficiency and customer satisfaction. Novel services enable users to utilize their personal devices (eg. mobile phones or laptops) to access the IoT platform, process data, and control the IoT infrastructure. However, these services impose critical user authentication and access control requirements. In this paper, we propose a decentralized user authentication and access control system for the IoT platforms via a permissioned blockchain network. We define an authorization sensitivity factor to provide clients with specific access control privileges and we consider an ehealth system as a use case example to demonstrate our solution. The proposed system is implemented using Ethereum platform. Besides, we investigate a threat model that considers an insider Distributed Denial of Service (DDoS) attack. The proposed defense mechanism utilizes a modifier function in the smart contract and keeps a real-time record of legitimate users to restrict function calls. The results illustrate the benefits of the defense mechanism in terms of the system response time.
In depth learning based method of denoising joint transform correlator optical image encryption system

There is serious noise interference in the decryption process of the joint transform correlator (JTC) optical encryption system, so the quality of the decrypted image cannot meet the accuracy requirements in most cases. The quality of decrypted image can be improved to a certain extent when the phase key is designed by the Gerchberg-Saxton algorithm and the iterative algorithm fuzzy control algorithm, but the complexity of the design process is inevitable and the quality of the decrypted image still needs improving. Recently, the in depth learning technology has attracted the attention of scholars in the fields of computer vision, natural language processing and optical information processing. In order to deal with the noise interference in the JTC optical encryption system, combining the current deep learning method, in this paper we propose a new denoising method for JTC optical image encryption system based on in depth learning, the dense modules are added into the generated network to enhance the reuse of feature information and improve the performance of the network. The latest self-attention mechanism area is added into the network to distinguish the weights of different channels and learn the relationship between channel and channel, so that the network can selectively strengthen the useful feature information but suppress useless feature information. The density module and the channel attention module are integrated into a DCAB synthesis module, which can effectively extract the image feature information and improve the performance of the network. The receptive field of the convolution kernel is enlarged by two down-sampling and the feature map is restored to its original size by two up-sampling. The VGG-19 is used to extract high-frequency details and texture features of images, meanwhile, the non-adversarial loss and mean-square error (MSE) loss are added into the loss function to reduce the difference among the image samples. The quality of noise-reduced images in this method are obviously better than that of the existing denoising algorithms by evaluating intuitive visual observation or SSIM (structural similarity), PSNR (peak signal to noise ratio) and MSE. The results of numerical calculation and simulation experiments show that this method can greatly eliminate the influence of noise on the JTC optical image encryption system, and effectively improve the effectiveness and feasibility of JTC optical image encryption system for high-quality image encryption.
Strategies to improve engagement of long-lived elderly women (80+) in the interaction co-design process

The growing population ageing highlights the need for strategies that enable older people to remain healthy and active. Digital technologies can contribute to this; however, the interaction of the elderly subject with these technologies is still frustrating, as these technologies are usually not designed with the needs and expectations of the elderly subject in mind. Engaging the elderly person in a process of interaction codesign may be a viable solution. However, conducting them in participatory activities is challenging. In this paper, we discuss our experiences in engaging ten elderly women, mostly long-lived (80+) in a process of interaction codesign, presenting some strategies used to improve engagement throughout the process, reflecting on the challenges and lessons learned. It is hoped that these strategies can help researchers and designers to engage elderly people in design processes with an effective participatory approach.
How to Efficiently Train Your AI Agent? Characterizing and Evaluating Deep Reinforcement Learning on Heterogeneous Platforms

Deep Reinforcement Learning (Deep RL) is a key technology in several domains such as self-driving cars, robotics, surveillance, etc. In Deep RL, using a Deep Neural Network model, an agent learns how to interact with the environment to achieve a certain goal. The efficiency of running a Deep RL algorithm on a hardware architecture is dependent upon several factors including (1) the suitability of the hardware architecture for kernels and computation patterns which are fundamental to Deep RL; (2) the capability of the hardware architecture's memory hierarchy to minimize data-communication latency; and (3) the ability of the hardware architecture to hide overheads introduced by the deeply nested highly irregular computation characteristics in Deep RL algorithms. GPUs have been popular for accelerating RL algorithms, however, they fail to optimally satisfy the above-mentioned requirements. A few recent works have developed highly customized accelerators for specific Deep RL algorithms. However, they cannot be generalized easily to the plethora of Deep RL algorithms and DNN model choices that are available. In this paper, we explore the possibility of developing a unified framework that can accelerate a wide range of Deep RL algorithms including variations in training methods or DNN model structures. We take one step towards this goal by defining a domain-specific high-level abstraction for a widely used broad class of Deep RL algorithms - on-policy Deep RL. Furthermore, we provide a systematic analysis of the performance of state-of-the-art on-policy Deep RL algorithms on CPU-GPU and CPU-FPGA platforms. We target two representative algorithms - PPO and A2C, for application areas - robotics and games. we show that a FPGA-based custom accelerator achieves up to 24× (PPO) and 8× (A2C) speedups on training tasks, and 17× (PPO) and 2.1 × (A2C) improvements on overall throughput, respectively.
The Cross-modal Congruency Effect as an Objective Measure of Embodiment

Remote control of robots generally requires a high level of expertise and may impose a considerable cognitive burden on operators. A sense of embodiment over a remote-controlled robot might enhance operators? task performance and reduce cognitive workload. We want to study the extent to which different factors affect embodiment. As a first step, we aimed to validate the cross-modal congruency effect (CCE) as a potential objective measure of embodiment under four conditions with different, a priori expected levels of embodiment, and by comparing CCE scores with subjective reports. The conditions were (1) a real hand condition (real condition), (2) a real hand seen through a telepresence unit (mediated condition), (3) a robotic hand seen through a telepresence unit (robot condition), and (4) a human-looking virtual hand seen through VR glasses (VR condition). We found no unambiguous evidence that the magnitude of the CCE was affected by the degree of visual realism in each of the four conditions. We neither found evidence to support the hypothesis that the CCE and embodiment score as assessed by the subjective reports are correlated. These findings raise serious concerns about the use of the CCE as an objective measure of embodiment.
Special Session: Quantum Error Correction in Near Term Systems

Large-scale quantum computers mandate error correction and fault tolerance. Due to constraints on the number of qubits, fault tolerance is difficult to achieve in near-term quantum systems. Therefore, error correction should require minimal resources. Gates in the near-term devices are also noisy. Quantum error correction code blocks built with these noisy gates can inject further error in the circuit. The goals for error correction in near-term systems are as follows: (i) using a small number of qubits for encoding, and (ii) keeping cost of circuits for encoding and decoding low. In this paper, we propose two techniques to achieve these mutually orthogonal goals. For a binary quantum system we propose an error estimation method that can aid in reducing the number of error correcting blocks via sparse scheduling. For ternary quantum systems, we propose an approximate code that can correct errors with high probability while significantly reducing the circuit cost. These techniques are expected to be helpful for error mitigation in near-term systems in the absence of fault tolerance.
Multiquadric Radial Basis Function Approximation Scheme for Solution of Total Variation BasedMultiplicative Noise Removal Model

This article introduces a fast meshless algorithm for the numerical solution nonlinear partial differential equations (PDE) by Radial Basis Functions (RBFs) approximation connected with the Total Variation (TV)-based minimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising, edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and structural similarity index (SSIM) corresponded to the current conventional TV-based schemes.
A Method for Extracting Keywords from English Literature Based on Location Feature Weighting

Natural language processing (NLP) is a frontier technology in the field of artificial intelligence. Keywords extraction is a key link in NLP and plays an important role in NLP. TFIDF algorithm is considered as the most important invention in information mining. This paper uses the position characteristics of words in the title and full text in the text, and makes weighted improvement on the basis of TFIDF algorithm to improve the accuracy of keyword extraction. In this paper, 400 articles of ACM were used as the training data set, 40 articles as the test set, and accuracy rate, recall rate and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> value were used as the evaluation criteria. Experimental data show that this method improves the accuracy of keyword extraction and improves the performance of the original algorithm.
Data mining tool to discover DevOps trends from public repositories

Public repositories have been performing an essential role in bringing software and services to technical communities and general users. Most of the cases, public repositories have a DevOps tool, with a live and historical database behind it, to support delivering and all steps this software or service should adopt before going to production. This paper introduces gthbmining, a data mining set of tools to discover DevOps trends from public repositories, and presents the module gthbmining.rc. Considering the premise of a GitHub public repository, the main contribution here is predicting release candidates, an important label a software release has. The methodology, architecture, components and interfaces are explained, as well as potential users. The results show a reliable and flexible tool, as classifiers metrics and graphics are provided, along with the possibility to add new data mining algorithms in the open source module presented. Related works are also supplied, and a conclusion shows the outcomes gthbmining.rc can provide.
High Average Utility Itemset Mining: A Survey

The pastKenny Kumar, Mathe John twoRana, Dipti decades has seen a great amount of research being done in the area of "Data Mining". "Frequent Itemset Mining" is one application of "Data Mining" used to mine "Frequent Patterns" from databases. Itemsets that occur frequently only extract patterns that are frequent. This type of mining has its disadvantage. Items that are frequent may or may not be profitable to an organization. For this type of disadvantage to be overcome "High Utility Itemset Mining" had been introduced. It is used to mine profitable patterns from databases. This can help organizations to market profitable patterns. "High Utility Itemset Mining" also has its own disadvantage, because while mining itemsets, the lengths of the itemsets are not taken into consideration. The bigger the length of the itemset the more the profit. This does not give a real representation of the value or profit of the itemset. To get over this problem "High Average Utility Itemset Mining" had been introduced.
Timely Reliability Analysis of Virtual Machines Considering Migration and Recovery in an Edge Server

For the edge computing network, whether the end-to-end delay satisfies the delay constraint of the task is critical, especially for delay-sensitive tasks. Virtual machine (VM) migration improves the robustness of the network, whereas it also causes service downtime and increases the end-to-end delay. To study the influence of failure, migration, and recovery of VMs, we define three states for the VMs in an edge server and build a continuous-time Markov chain (CTMC). Then, we develop a matrix-geometric method and a first passage time method to obtain the VMs timely reliability (VTR) and the end-to-end timely reliability (ETR). The numerical results are verified by simulation based on OMNeT++. Results show that VTR is a monotonic function of the migration rate and the number of VMs. However, in some cases, the increase in task VMs (TVMs) may conversely decrease VTR, since more TVMs also brings about more failures in a given time. Moreover, we find that there is a trade-off between TVMs and backup VMs (BVMs) when the total number of VMs is limited. Our findings may shed light on understanding the impact of VM migration on end-to-end delay and designing a more reliable edge computing network for delay-sensitive applications.
Generalized zero-shot classification via iteratively generating and selecting unseen samples

Generalized zero-shot classification (GZSC) is a challenging task to recognize seen and unseen samples from target domain by seen samples in source domain. Since the lack of unseen data, many methods train a generative adversarial network (GAN) to generate unseen samples. However, the GAN model trained by seen samples is not suitable for generating unseen samples. For dealing with this problem, we train the GAN model by generating seen and unseen samples, simultaneously. In order to generate high-quality unseen samples, the visual prototypes of the generated unseen samples are made near to the real unseen visual prototypes. We select the confident unseen samples based on the agreement of the current two unseen classifiers and use them to update the unseen visual prototypes. Through the iteratively generating and selecting method (IGS), we can generate high-quality unseen samples and select the most confident unseen samples. Experimental results on the standard benchmarks show the superiority of the proposed model over the state-of-the-art methods for GZSC tasks.
Improving Accuracy of Differentially Private Kronecker Social Networks via Graph Clustering

Using graph clustering, we improve accuracy of Kronecker social networks which are protected by differential privacy. Ensuring the differential privacy implicates addition of marginal changes to the network and publishing the modified network data. In many cases, it induces a large gap between the original network and the modified graph statistics, such that very little useful information can be inferred from the published graph. We use the fact that network structures in all graph clusters are similar, to improve the utility of the publication methods based on Kronecker graphs. Instead of anonymizing the social network as a whole, we anonymize each cluster of the network separately, and combine the sanitized results thereafter. We justify why this idea provides an anonymized social network with high utility and also prove that our output social network ensures rigorous differential privacy guarantees. Our experimental results show that our mechanism exhibits good agreement of the structural properties with the real graphs, and outperforms the existing anonymization techniques for certain utility measures.