Reviewerly/exHarmony huggingface dataset:
Keyword-based technology radar based on IEEE conference papers on human-robot interaction

Knowledge on emerging technologies is essential for a forward-looking development regarding human-centered design and implementation. There are different possibilities of mapping the state of the art and forecasting future developments, for example employer and employees surveys of technology diffusion, expert Delphi surveys, mapping news media or analyzing sales data. In this paper, we test an approach based on the analysis of keywords from IEEE conference papers in the field of human-robot interaction. For this reason, we analyzed all given keywords of IEEE conference papers from 2015 until 2019 in the field of human-robot interaction. We calculated and visualized the frequencies of given keywords per year, identified the most frequent keyword pairs as well as correlations among the most frequent keywords using the software package R. The results show an emerging trend towards artificial intelligence, consistencies in mobile robotics and for example a decline in telerobotics. Additionally, we found strong interconnections of keyword pairs, highlighting subdomains in the research field. Correlation analysis helped further understanding the relationship within keyword clusters. Limitations of this approach are that any kind of knowledge structure is inherently incomplete. We avoided standardization issues presented in prior keyword publications by using controlled keywords.
Digital Governance: The Case of Proofs of Vaccination

The article aims to cast light on how digital governance tools, such as proofs of vaccination, would impact European freedoms during unstable Covid-19 times. Considering the EU approach to proofs of vaccinations, the e-Health network established trust framework and EU proposal for the “Digital Green Certificate” (“DGC”), the article scrutinizes some of already implemented proof of vaccination solutions in the EU. The analysis investigates whether and to what extent this initiative would be adequate to cope with the manifold challenges in managing Covid-19 pandemic. While digital governance tools can significantly improve crisis management, such tools can also affect the rights and freedoms of individuals and communities by exasperating already existing social, political, and economic inequalities. By taking an in-depth look at the existing proof of vaccination solutions in the EU, the article sheds light on how the same digital government proposed solution, the DGC, affect individuals and communities.
Design and Evaluation of Large Sized Floating Point Matrix Inversion Modules for Onboard Computer

Modern satellites and aircrafts employ several algorithms to cater for various requirements onboard including control system, image processing, target recognition, image compression, wireless communication and many more. All these algorithms employ matrix algebra including matrix inversion of a large scale floating point matrix. FPGAs are being employed in almost all onboard computers and systems of aircrafts and satellites. This has spearheaded research in the field of hardware implementation and evaluation of large scale floating point matrix inversion methods. In this paper, FPGA implementation of eight floating-point matrix inversion algorithms for an onboard computer is proposed using a novel combination of model based design with high level language programming for FPGA based designs. The proposed designs were evaluated for matrix size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$120\times 120$</tex> and can be easily scaled to larger matrix sizes.
Intelligent Automatic Modulation Classification for Radar Transmitting Signals

We propose a technique on automatic modulation classification (AMC) of radar emitter in electronic warfare system. The classification method is based on the perception of instantaneous auto-correlation using a type of 3 dimensional (3D) convolutional neural network (CNN). We can accomplish the AMC without using feature extraction process compared to the conventional methods. The proposed approach is appropriate for the discrimination of several types of signal modulation including single frequency, linear frequency modulation, phase shift keying and frequency shift keying, and its performance is validated via numerical simulation.
Multimodal Registration of Fish and Nanosims Images Using Convolutional Neural Network Models

Nanoscale secondary ion mass spectrometry (nanoSIMS) and fluorescence in situ hybridization (FISH) microscopy provide high-resolution, multimodal image representations of the identity and cell activity respectively of targeted microbial communities in microbiological research. Despite its importance to microbiologists, multimodal registration of FISH and nanoSIMS images is challenging given the morphological distortion and background noise in both images. In this study, we use convolutional neural networks (CNNs) for multiscale feature extraction, shape context for computation of the minimum transformation cost feature matching and the thin-plate spline (TPS) model for multimodal registration of the FISH and nanoSIMS images. Registration accuracy was quantitatively assessed against manually registered images, at both, the pixel and structural levels using standard metrics. Although all six tested CNN models performed well, ResNet18 was observed to outperform VGG16 , VGG19 , GoogLeNet and ShuffleNet and ResNet101 based on most metrics . This study demonstrates the utility of CNNs in the registration of multimodal images with significant background noise and morphology distortion. We also show aggregate shape, preserved by binarization, to be a robust feature for registering multimodal microbiology-related images.
Zero-Reference Fractional-Order Low-Light Image Enhancement Based on Retinex Theory

The quality of images taken in an insufficiently lighting environment is degraded. These images limit the presentation of machine vision technology. To address the issue, many researchers have focused on enhancing low-light images. This paper presents a zero-reference learning method to enhance low-light images. A deep network is built for estimating the illumination component of the low-light image. We use the original image and the derivative graph to define a zero-reference loss function based on illumination constraints and priori conditions. Then the deep network is trained by minimizing the loss function. Final image is obtained according to the Retinex theory. In addition, we use fractional-order mask to preserve image details and naturalness. Experiments on several datasets demonstrate that the proposed algorithm can achieve low-light image enhancement. Experimental results indicate that the superiority of our algorithm over state-of-the-arts algorithms.
On The Legislation Prospective For Consumer Protection Of Stablecoin

Stablecoin, as a specific type of cryptocurrency, is undoubtedly one of the underlying infrastructures that underpin the rapidly growing decentralised finance (Defi) ecosystem. It is regarded and used as the on-chain “legal currency”. Questions arise as to is it such a low-volatility cryptocurrency really stable and is it able to always stay stable. The answers are probably no. Whether the underlying code is law is for debate, but it is not yet applicable to Defi and stablecoins. The law would undoubtedly play a positive role in improving the stability of stablecoin. Through this paper, we focus on the consumer protection aspect of stablecoin legislation and regulation. We set out to analyse the nature of stablecoins in a legal sense. We first survey the legislative practice of cryptocurrencies in multiple jurisdictions and extract proposals specifically for stablecoins for comparative analysis propose how to best regulate stablecoins. The definition of “stable” is explored from three critical aspects: price stability, collateral stability and permissionless. Finally, we conclude by proposing a general legislative framework for stablecoins. We aim to contribute to the current state of stablecoin and provide insight for international principles for such regulation in the future.
A Medical Dialogue System Based on DIET and Knowledge Graph

Medical dialogue systems (MDSs) are quite suitable for automated medical consultation. To construct a state-of-the-art MDS, a prototypal system is designed and verified in this paper. The system is implemented based on the dual intent and entity transformer (DIET) and knowledge graph. The test results show that the system owns good application potential.
Smart-Area-Selection Based Location Privacy Enhancement

Protecting location privacy from location-based service (LBS) providers is a challenging problem. Existing mechanisms either use a trusted third party (TTP) or location perturbation techniques. In TTP-based mechanisms, when the security of a TTP is compromised, the user’s privacy is breached. On the other hand, in location perturbation-based mechanisms, privacy is achieved at the expense of query accuracy. To overcome these problems, in this article, a novel smart-area-selection (SAS)-based location privacy protection algorithm is proposed. The proposed approach does not require the user to trust any party including LBS. The proposed efficient mechanism achieves privacy without compromising the accuracy of the query. Furthermore, our SAS-based mechanism uses a user-specific geographical coordinate system and public key encryption for additional security. In the existing location privacy protection mechanisms, when LBSs are accessed continuously, more location information can be extracted by a smart adversary. A specially designed query region spreading algorithm is used in the proposed mechanism to overcome this problem. The validity of the proposed SAS-based location privacy protection mechanism is demonstrated by simulation results.
Privacy-Preserving Classification in Multiple clouds eHealthcare

Internet of Things (IoT) is increasingly being used in real life, especially in the eHealthcare field. Among eHealthcare, the application of predicting patients' health status based on their daily activity data which is collected by IoT equipment has attracted extensive attentions and researches. In this application, patients' data which are treated as time-series data are transmitted to healthcare center (HC), then HC makes predictions based on an established classification model. However, making predictions using classification models requires a lot of computing resources, while HC usually cannot afford such numerous calculations. The use of the cloud solves the problem of insufficient computing resources, but it causes another problem, namely the leakage of user privacy. In particular, not only patients' data leak patients' privacy information, the classification model also causes the privacy disclosure of patients and HC. We design a new system model and propose an algorithm which can protect patients' data and classification model from leakage and offload calculation to multiple clouds. Our algorithm can better protect privacy of patients and HC in more complex classification scene, and can effectively reduce the computational cost of the healthcare center
The Analysis of Human Attitude toward Cybersecurity Information Sharing

Over the years, human errors have been identified as one of the most critical factors impacting cybersecurity in an organization that has had a substantial impact. The research uses recent articles published on human resources and information cybersecurity. This research focuses on the vulnerabilities and the best solution to mitigate these threats based on literature review methodology. The study also focuses on identifying the human attitude and behavior towards cybersecurity and how that would impact the organization's financial impact. With the help of the Two-factor Taxonomy of the security behavior model developed in past research, the research aims to identify the best practices and compare the best practices with that of the attitude-behavior found and matched to the model. Finally, the study would compare the difference between best practices and the current practices from the model. This would help provide the organization with specific recommendations that would help change their attitude and behavior towards cybersecurity and ensure the organization is not fearful of the cyber threat of human error threat.
High Order Bézier Curves Based Path Planning for Autonomous Movement in Single Lane Roundabouts

None of the possible advantages of autonomous vehicles are going to become reality if the autonomous vehicles are not going to be able to move properly in various different urban scenarios like lane changing, intersections, roundabouts, etc. One of the first tasks, which must be solved in order to be able to properly autonomously move in different urban scenarios, is the path planning. However, the path planning problems, while taking into consideration different urban scenarios, are analysed not equally and the roundabouts is the scenario, which is still behind. Thus, this work analyses the path planning for roundabouts problem, while applying one of the most popular interpolation based approaches – the Bezier curves. A novel way to select the control points for high order Bezier curves, intended for entering a single lane roundabout, driving within the roundabout and exiting the roundabout, is presented.
A fuzzy multi-criteria decision-making approach for crime linkage utilizing resemblance function under hesitant fuzzy environment

Statistics from the government report shows that the hike of crime rate increases each year from the previous one. Serial crimes bring panic among the people, causes disharmony, and leave psychological scars within the society. Investigating and tracking the crime is very often troublesome due to the lack of valid evidences. Another challenging task is to determine the crimes that were committed by same suspects from a great number of similar crimes. Investigators study a bunch of criminal cases and analyze step-by-step linkage of the crime and reveal the crimes which were committed by common suspects. Here, we will generalize the resemblance function on hesitant fuzzy sets. Moreover, we will analyze with the help of multi-criteria decision-making under hesitant fuzzy environment. With the help of this method, an analyst will be able to find degree of extent to share common suspects. In addition, a numerical case example on credit card fraud detection will be carried out to show the applicability of the methodology in our daily life.
Temporal Early Exits for Efficient Video Object Detection

Efficiently transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts used feature propagation to avoid recomputing unchanged features. However, the overhead is significant when working with very slowly changing scenes, such as in surveillance applications. In this paper, we propose temporal early exits to reduce the computational complexity of video object detection. Multiple temporal early exit modules with low computational overhead are inserted at early layers of the backbone network to identify the semantic differences between consecutive frames. Full computation is only required if the frame is identified as having a semantic change to previous frames; otherwise, detection results from previous frames are reused. Experiments on ImangeNet VID and TVnet show that the approach can accelerate video object detection by 1.7x compared to SOTA, with a reduction of only <1% in mAP.
Particle Swarm Optimized Federated Learning For Securing IoT Devices

Federated learning (FL) focuses on interpreting optimization, privacy, and communication but pays little consideration to enhance training and results on the edge devices. The major challenge on these Internet of Things (IoT) devices is efficient training and inference. Another considerable challenge is securing IoT devices for a long time. This paper resolves it by selecting appropriate parameters for building a local machine learning or deep learning (ML/DL) model. Appropriate parameters will make the model's training less computationally expensive and secure the edge or IoT device. So, we propose a particle swarm optimization (PSO) method to optimize the hyper-parameter environments for the bounded DL model in an FL environment. First, we select the 2-gram represented Application Programming Interface (API) calls of the malicious and benign instances for the dataset's feature. Then, API calls of the sample are represented using 2-gram, and their frequency fills the dataset's rows. Later, we represent the sample's feature in a grayscale image and apply the LeNet-5 model. Our experiment indicates that PSO efficiently tunes the hyperparameters of LeNet-5 compared to the grid search method. The near-optimal parameters for FL do not affect the model's accuracy.
Research on Detection Method of Abnormal Trajectory of Port Operation Vehicles

Port operation vehicles are mainly responsible for the transshipment of goods. If there is inadequate supervision in the process of transshipment it is easy to cause such behaviors as cargo leakage, cargo theft and illegal parking of drivers, which cause economic losses to the port. In order to discover such behaviors in time, the unsupervised anomaly detection algorithm Self-encoder-based Deep Feature Fusion Model(S-DFFM) is proposed to judge whether the trajectory of operation vehicles is abnormal or not. The method comprehensively characterizes the trajectory by fusing the shallow features of low-dimensional trajectory and the deep features of high-dimensional trajectory, which frees the trajectory from the limitation of spatial attributes. The experimental data adopts the real trajectory data of one month (7,547 trips) of operating vehicle trajectories of a port in Chongqing, and the experimental results show that S-DFFM can better represent the trajectory features, and the accuracy of trajectory abnormality detection using S-DFFM is as high as 96%.
Kidney segmentation for quantitative analysis applying MaskRCNN architecture

Results of applying the deep learning Mask Regional Convolutional Neural Network (MaskRCNN) to the problem of segmenting human kidney and delineating the kidney tissue into the 12 classes left/right kidney, cortex, medulla 1, 2, 3, 4 are presented, using a 3T MRI scanner. This problem originates from advances in magnetic resonance imaging MRI techniques to monitor blood perfusion and blood oxygenation in kidneys of persons suffering from reduced kidney function. This segmentation allows the processing of the numerous MRI arterial spin labelling (ASL) images and T2* images with respiration movement to calculate and map renal perfusion and evaluate oxygenation respectively. The quality of the segmentation is assessed visually and by the segment label classifier. In total, a selection of 37 segmentation experiments, combining MRI sequences and kidney tissue types are presented. To the best of the author's knowledge, applying this segmentation method on kidney tissue is new.
Educational Robot Aided Photo Taking and Management

Photo taking and management can make trouble or waste time sometimes. Therefore, this paper develops the educational robot service to provide better photography and management services. People are enthusiasm in photo taking, but they don't have enough time to manage their own photos, because manually selecting and managing personal photos is very laborious and time consuming. In view of these difficulties in taking photos, as well as the low efficiency of users' storage and management of photos in the context of massive photos, this paper adopts artificial intelligence technology to overcome the deficiencies of existing camerawork activities. This paper uses intelligent technology to equip educational robots to provide services. These services can assist in taking photos and post-photo management. In the scenario of photo taking, it helps people to take good photos. During the photo management phase, it uses cloud technology to deliver and process photos. This service saves people's precious time and enhances a better photography experience.
Experimental demonstration of reservoir computing with a silicon resonator and time multiplexing

Reservoir computing (RC) replaces the backbone of deep neural networks with the dynamics of a complex physical system in which only the output synapses are trained. Optical phenomena form a natural substrate for these architectures, while integrated optics can be used to enhance the nonlinear effects. Here, we propose and experimentally validate an all optical RC scheme based on a silicon on insulator microresonator (MR) and time multiplexing. We give proof of concept demonstrations of RC by solving two nontrivial tasks: the delayed XOR and the classification of the Iris flowers dataset. The approach could be scaled up to realize large hybrid spatio-temporal reservoirs of increased computational speed and complexity.
Variation of the Intercession Coefficient Used as a Hyper Parameter in Machine Learning in Regression Models

Within the area of data science, the hyper parameters arguments affect the execution of the algorithms and due to their particularity, must be used separately for each machine learning model in quantitative predictions, known as regression. Two quality metrics from regression models will be used in order to demonstrate the changes: Mean Square Error, (MSE) and the value of the R2 coefficient. In the present work, using simulation of the interception coefficient, it will be demonstrated the existence of machine learning algorithms that are sensitive: Mean Square Error and/or the value of the R2 coefficient. It is important to highlight that the intercept coefficient is considered as a reference argument. The present research is only a very small space of an automated machine learning process (AutoML) with regard to sensitivity analysis.
Tracking Eye Movement for Controlling Real-Time Image-Abstraction Techniques

Acquisition and consumption of visual media such as digital image and videos is becoming one of the most important forms of modern communication. However, since the creation and sharing of images is increasing exponentially, images as a media form suffer from being devalued, as the quality of single images are getting less and less important, and the frequency of the shared content turns to be the focus. In this work, an interactive system which allows users to interact with volatile and diverting artwork based on their eye movement only is presented. The system uses real-time image-abstraction techniques to create an artwork unique to each situation. It supports multiple, distinct interaction modes, which share common design principles, enabling users to experience game-like interactions focusing on eye-movement and the diverting image content itself. This approach hints at possible future research in the field of relaxation exercises and casual art consumption and creation.
Crebot: Exploring Interactive Question Prompts for Critical Paper Reading

Pre-compiled guidelines with a static question list can stimulate critical thinking during scientific paper reading. However, they could be less engaging than taking live question prompts from others. In this paper, we develop CReBot that interactively asks section-level critical thinking questions and customize it for routine paper readers with prior research experience and novices new to research. Our first within-subjects study with 24 routine readers demonstrates CReBot's engagement and usefulness over static guidelines. Then, with more teacher-like question-specific hints prepared for CReBot, we conduct another within-subjects study with 20 novices. The results, however, indicate that CReBot might not be better than static guidelines for beginners. Nevertheless, both user groups favor CReBot's contextualized questions and interaction flexibility. We conclude by design implications for interactive tools to facilitate critical reading.