Reviewerly/exHarmony huggingface dataset:
Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation

It is very important to classify datasets with high accuracy in order to make meaningful decisions in today's science world. In particular, machine learning-based models are known to effectively classify complex datasets. One of the powerful ways to test the success rate of models used for classification is k-fold cross validation. However, very few studies have investigated how k value affects classification results. In this study, the performance of different kernel functions such as Gaussian, linear, and polynomials in SVM were compared for different k values in three different data sets. The most accurate results were obtained with the Gaussian and linear kernel functions.
Natural evolutionary strategies for variational quantum computation

Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized parametrized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable natural evolutionary strategies, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver (VQE) and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of evolutionary strategies to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.
Economic analysis of residential solar photovoltaic systems in China

Residential solar photovoltaic (PV) installations have boomed in China over recent years. However, knowledge about the economic performance of residential PV investments is still limited. Therefore, this study attempts to make a complete economic assessment of residential PV systems at the county-level. After a brief description of China’s incentive policies, a model is constructed with economic measures of internal rate of return and payback period based on discounted cash flow. Then, a reference PV system of 5 kWp is investigated in 2181 geographical areas using scenario analysis and sensitivity analysis. There is breakeven for residential PV investments all over China even without incentives. The areas with good performance of residential PV investments are located mostly in Northern China (Shandong and Hebei), Southern China (Guangdong and Hainan), northeast China (Liaoning, Jilin, and Heilongjiang), and southwest China (Sichuan and Tibet). The boom of residential PV installations is likely to recede in 2021 as the population with acceptable profitability declines since the central and local subsidies expire. But some excellent residential PV suppliers have the potential to compensate for the performance decrease in many ways such as increasing annual generation and reducing initial cost. This paper contributes by providing some insights into the economic performance and market of residential PV installations in China.
Secure and Efficient Data Aggregation for IoT Monitoring Systems

The proliferation of Internet of Things (IoT) as a promising paradigm has contributed enormously to modern technology design. The wireless body sensor network (WBSN) technology is an application of IoT in healthcare, whereas data security and privacy impediments have raised some concerns. The collected data via IoT wireless body sensors is vulnerable to a variety of internal and external attacks. One solution is to encrypt or sign the collected data to provide confidentiality and integrity, but the computational complexity hinders the application in the real IoT-based healthcare devices. Although there have been some attempts to provide secure and efficient IoT schemes, there is a lack of achieving secure data analysis in modern healthcare. The aggregated data statistics about the patient's medical status is useful to doctors and healthcare providers. However, the dynamic data continually updating over time is challenging. In this article, we present an efficient and provably secure scheme, which is the first step toward secure data analysis for handling the data collection and analysis for IoT wireless body sensors. The main contribution of our work is a novel cryptographic accumulator based on our novel authenticated additive homomorphic encryption which can collect and accumulate data from IoT wireless wearable devices. These encrypted data can be used for analysis in an encrypted form so that the information is not revealed. To validate security and efficiency, we present security analysis and performance evaluations of our proposed scheme for IoT wireless body sensors.
Multi-scale attention network for image inpainting

Recently, deep learning based inpainting methods have shown promising performance, in which some multi-scale networks are introduced to characterize image content in both details and structures. However, few of these networks explore local spatial components under different receptive fields and internal connection between multi-scale feature maps. In this paper, we propose a novel multi-scale attention network (MSA-Net) to fill the irregular missing regions, in which a multi-scale attention group (MSAG) with several multi-scale attention units (MSAUs) is introduced for fully analysing the features from shallow details to high-level semantics. In each MSAU, an attention based spatial pyramid structure is designed to capture the deep features from different receptive fields. In this network, attention mechanisms are explored for boosting the representation power of MSAU, where spatial attention is combined with each scale to highlight the most probably attentive spatial components and the channel attention is used as a globally semantic detector to build the connection between the multiple scales. Furthermore, for better inpainting results, a max pooling based mask update method is utilized to predict the missing parts from the border regions to the inside. Finally, experiments on Places2 dataset and CelebA dataset demonstrate that the proposed method can achieve better results than the previous inpainting methods.
Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux.
Lightweight Deep CNN for Natural Image Matting via Similarity-Preserving Knowledge Distillation

Recently, alpha matting has witnessed remarkable growth by wide and deep convolutional neural networks. However, previous deep learning-based alpha matting methods require a high computational cost to be used in real environments including mobile devices. In this letter, a lightweight natural image matting network with a similarity-preserving knowledge distillation is developed. The similarity-preserving knowledge distillation makes pairwise similarities from a compact student network similar to those from a teacher network. The pairwise similarity measured on spatial, channel, and batch units enables to transfer knowledge of the teacher to the student. Based on the similarity-preserving knowledge distillation, we not only design a lighter and smaller student network than the teacher one but also achieve superior performance compared to that of the student network without the knowledge distillation. In addition, the proposed algorithm can be seamlessly applied to various deep image matting algorithms. Therefore, our algorithm is effective for mobile applications (e.g., human portrait matting), which are in growing demand. The effectiveness of the proposed algorithm is verified on two public benchmark datasets.
A conceptual model for Indian public distribution system using consortium blockchain with on-chain and off-chain trusted data

The public distribution system (PDS) of a country plays a vital role in eradicating hunger and ensuring food security. However, operational inefficiencies like poor beneficiary identification, non-uniform transaction records, and lack of integration among stakeholders hinder the success of such a system. This paper proposes a conceptual model to explore the possibility of using blockchain to eliminate such inefficiencies and ensure trust in the system. It first studies the existing operations of Indian PDS and identifies the transactions requiring information exchanges, storage, and verifications. Then, it proposes a blockchain based solution that leverage smart contracts and consortium-based ecosystem that can bring efficiencies in the PDS system. This paper also proposes a conceptual model for off-chain big data and enlists the possible analytics to support the decision-making of various stakeholders. Finally, it explores the suitability of implementing the proposed model as a consortium blockchain using Hyperledger Fabric and big data analytics.
Vulnerable Code Detection Using Software Metrics and Machine Learning

Software metrics are widely-used indicators of software quality and several studies have shown that such metrics can be used to estimate the presence of vulnerabilities in the code.In this paper, we present a comprehensive experiment to study how effective software metrics can be to distinguish the vulnerable code units from the non-vulnerable ones.To this end, we use several machine learning algorithms (Random Forest, Extreme Boosting, Decision Tree, SVM Linear, and SVM Radial) to extract vulnerability-related knowledge from software metrics collected from the source code of several representative software projects developed in C/C++ (Mozilla Firefox, Linux Kernel, Apache HTTPd, Xen, and Glibc).We consider different combinations of software metrics and diverse application scenarios with different security concerns (e.g., highly critical or non-critical systems).This experiment contributes to understanding whether software metrics can effectively be used to distinguish vulnerable code units in different application scenarios, and how can machine learning algorithms help in this regard.The main observation is that using machine learning algorithms on top of software metrics helps to indicate vulnerable code units with a relatively high level of confidence for security-critical software systems (where the focus is on detecting the maximum number of vulnerabilities, even if false positives are reported), but they are not helpful for low-critical or non-critical systems due to the high number of false positives (that bring an additional development cost frequently not affordable).
TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines

Accurate detection and segmentation of transmission towers (TTs) and power lines (PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety. Meanwhile, aerial images of TTs and PLs pose a number of new challenges to the computer vision researchers who work on object detection and segmentation – PLs are long and thin, and may show similar color as the background; TTs can be of various shapes and most likely made up of line structures of various sparsity; The background scene, lighting, and object sizes can vary significantly from one image to another. In this paper we collect and release a new TT/PL Aerial-image (TTPLA) dataset, consisting of 1,100 images with the resolution of 3,840 \(\times \) 2,160 pixels, as well as manually labeled 8,987 instances of TTs and PLs. We develop novel policies for collecting, annotating, and labeling the images in TTPLA. Different from other relevant datasets, TTPLA supports evaluation of instance segmentation, besides detection and semantic segmentation. To build a baseline for detection and segmentation tasks on TTPLA, we report the performance of several state-of-the-art deep learning models on our dataset. TTPLA dataset is publicly available at https://github.com/r3ab/ttpla_dataset.
Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector

Tenders or technical terms contain a large quantity of both technical, legal, managerial information mixed in a nested and complex net of relationships. Extracting technical and design information from a document whose aim is both legal and technical, and that is written using several specific jargons, is not a trivial task: the purpose of the research is to try to detect, extract, split and assign information from the text of a tender in an automatic way. It means being able to understand technical and legal terms and organize them in multiple ways: according to product structure, internal organisational structure, etc. The focus is in providing a handy tool that could speed up and facilitate human analysis and allow tackling also the process of transforming customer’s requirements into design specifications. The approach chosen to overcome the various issues is to support state-of-the-art Computational Linguistic tools with a wide Knowledge Base. The latter has been constructed both manually and automatically and comprises not only keywords but also concepts, relationships and regular expressions. The implementation of the methodology has been carried out during a project for AnsaldoBreda S.p.A. (now Hitachi Rail Europe). A case study about the tender for a high-speed train has been included to show the functioning and output of the entire software system.
Complicated robot activity recognition by quality-aware deep reinforcement learning

Automatic robot activity understanding plays an important role in human–computer interaction (HCI), especially in smart home service robots. Existing manipulator control methods, such as position control, vision-based control method, fail to meet the requirements of autonomous learning. Reinforcement learning can cope with the interaction of robot control and environment; however, the method should relearn the control method when the position of target object changes. To solve this problem, this paper proposes a quality model to utilize deep reinforcement learning scheme to achieve an end-to-end manipulator control. Specifically, we design a policy search algorithm to achieve automatic learning of manipulator. To avoid relearning of manipulator, we design convolutional neural network control scheme to remain the robustness of manipulator. Extensive experiment has shown the effectiveness of our proposed method.
Fear of panoptic surveillance: using digital technology to control the COVID-19 epidemic

Abstract Background In a bid to reduce infection rates by COVID-19 the authorities in some countries, in collaboration with medical regulators and experts, have employed digital technologies to control and regulate citizens’ behavior. Public opinion and the public’s compliance with these technologies come into play here. The objective of the present study was to examine attitudes expressed in the public discourse toward the use of digital technologies to control people’s behavior during the COVID-19 pandemic, as reflected in the media. Methods Qualitative analysis was performed on posts and comments submitted in response to 12 articles that appeared on the four leading Israeli news sites, on three significant occasions: first, upon the announcement of the use of surveillance technologies by the Israeli security agency (ISA); second, upon the announcement of the launch of the Health Ministry’s app that tracks contacts with COVID-19 patients; and third, following reports of petitions lodged with Israel’s supreme court challenging the use of surveillance technologies. The analysis was performed using ATLAS-Ti software for systematic analysis. Results A total of 2551 posts and comments referring to these 12 articles were found, 714 of which were relevant to the purpose of the study. The analysis revealed disagreement between those who supported the measures taken and opponents. Supporters regarded contact tracing by means of digital technologies as essential to the effort to protect people during the pandemic, and believed that employing the ISA’s capabilities was the correct way to combat the epidemic. Opponents of the measures rejected the use of tracking technologies, regarding this step as a move toward dictatorship and a violation of fundamental civil rights. Some proposed alternative measures that would obviate the use of such tracking. Conclusions The primary task of medical practice is to heal illness and alleviate suffering. The debate surrounding the employment of digital technologies during the COVID-19 pandemic highlights the complexity of the close connection between social control and care in times of pandemic. The context of this pandemic has highlighted the interrelatedness of advanced digital surveillance technologies, medical care, and social control exercised by authorities and medical regulators and experts, which raises issues of transparency, trust and mistrust among the public. These issues become all the more relevant when the number of patients grows rapidly, the authorities need to deal with the extended ongoing COVID-19 pandemic, the country has entered a second lockdown, and the public must be persuaded to comply with highly restrictive regulations. Recommendations to policy makers, practical implications, and suggestions for future research are discussed.
Continual Learning Strategy in One-Stage Object Detection Framework Based on Experience Replay for Autonomous Driving Vehicle

Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.
Physical Zero-Knowledge Proof for Suguru Puzzle

Suguru is a paper and pencil puzzle invented by Naoki Inaba. The goal of the game is to fulfil a grid with numbers between 1 and 5 and to respect three simple constraints. In this paper we design a physical Zero-Knowledge Proof (ZKP) protocol for Suguru. A ZKP protocol allows a prover (P) to prove that he knows a solution of a Suguru grid to a verifier (V) without leaking any information on the solution. For constructing such a physical ZKP protocol, we only rely on a small number of physical cards and an adapted encoding. For a grid of Suguru with n cells, we only use $$5n+5$$ cards. Moreover, we prove the three classical security properties of a ZKP: completeness, extractability, and zero-knowledge.
Condensed Movies: Story Based Retrieval with Contextual Embeddings

Our objective in this work is long range understanding of the narrative structure of movies. Instead of considering the entire movie, we propose to learn from the ‘key scenes’ of the movie, providing a condensed look at the full storyline. To this end, we make the following three contributions: (i) We create the Condensed Movies Dataset (CMD) consisting of the key scenes from over 3 K movies: each key scene is accompanied by a high level semantic description of the scene, character face-tracks, and metadata about the movie. The dataset is scalable, obtained automatically from YouTube, and is freely available for anybody to download and use. It is also an order of magnitude larger than existing movie datasets in the number of movies; (ii) We provide a deep network baseline for text-to-video retrieval on our dataset, combining character, speech and visual cues into a single video embedding; and finally (iii) We demonstrate how the addition of context from other video clips improves retrieval performance.
“Last mile” optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures

In the context of edge computing (EC) paradigm the new type of specific System on a Chip (SoC) devices with tensor processing architectures (TPAs) appeared for running deep learning (DL) models efficiently on edge computing accelerators (ECAs). Despite availability of numerous benchmarks of ECAs on the real world applications the contribution of the overheads under different available configurations and actual EC performance is not covered thoroughly at the moment. The matter is the high performance hardware (ECAs of the latest releases) can be applied for the highly optimized DL models, but all these accelerations can be diminished by the overheads at the "last mile" where the end-users can use inefficient support provided by the default OS, applicability of slow connectivity modes, and ignorance of some specific manufacturer recommendations like overclocking regimes and so on. In this work several different TPA hardware implementations were considered, including Coral Edge TPU by Google, and Movidius Neural Compute Stick by Intel. The comparative analysis of their performance (inference time, iteration time, speedup due to optimization measures) was performed including comparison with the traditional computing architectures on the basis of central processing unit (CPU) and general purpose graphic processing unit (GPU). These results give the opportunity to estimate the performance of ECAs for the specific use cases (the human pose recognition practical use case is considered here) and find the optimal environment configurations for the better speedup, low overhead and high efficiency of ECAs.