Reviewerly/exHarmony huggingface dataset:
Criminological classification of robots: risk-based approach

The subject of the research is key criminal risks in robotics. The purpose of the article is to confirm or disprove the hypothesis that key criminal risks of using robots may be identified and classified. The author dares to describe the key aspects of the application of risk-based approach in the assessment of robotic activities, identify the key risks of using robots, give a criminological classification. The methodology includes a formal logical method, systematic approach, formal legal interpretation of legal acts and academic literature, SWOT analysis. The main results of the study. The author applies the main provisions of criminal riskology when assessing encroachments involving robots. Key risks and challenges when using robots are identified. The severity of the consequences of harm caused by using robots (from minor to critical risk) is assessed and a matrix of the probability of its occurrence is provided. The author's criminological classification of robots is based on the risk-based approach and is substantiated on two grounds. The first one is the category of public danger and the second is the potential severity of the consequences of harm caused by robots. The causal complex that can lead to criminal risks in robotics is identified. The grounds of such risks are divided into those related to the mechanical subsystem of robots, digital subsystem of robots and power supply subsystem of robots. Conclusions. The risk-based approach is the most progressive and effective basis for regulating the criminal relations in robotics. The author demonstrates the existence of real risks to the peace and security of mankind, life and health of people, objects of wildlife, nonliving material objects from the use of robots. It is necessary to recognize robotics as source of increased potential criminal danger and to adopt appropriate regulation as soon as possible. The necessity and expediency of applying a risk-based approach to robotics is theoretically substantiated, and the characteristics of robots that are important in assessing the criminal potential of their exploitation are evaluated. The conclusions and recom mendations of this paper may become a basis for the implementation of the risk-based approach in legal regulation of robotics. The risk matrix presented in the article can be used to establish a framework for regulatory impact on robotics, assess the consequences of potential harm and minimize it.
How FAIR are Security Core Ontologies? A Systematic Mapping Study

Recently, ontology-based approaches to security, in particular to information security, have been recognized as a relevant challenge and as an area of research interest of its own. As the number of ontologies about security grows for supporting different applications, semantic interoperability issues emerge. Relatively little attention has been paid to the ontological analysis of the concept of security understood as a broad application-independent security ontology. Core (or reference) ontologies of security cover this issue to some extent, enabling multiple applications crossing domains of security (information systems, economics, public health, crime etc.). In this paper, we investigate the current state-of-the-art on Security Core Ontologies. We select, analyze, and categorize studies on this topic, supporting a future ontological analysis of security, which could ground a well-founded security core ontology. Notably, we show that: most existing ontologies are not publicly findable/accessible; foundational ontologies are under-explored in this field of research; there seems to be no common ontology of security. From these findings, we make the case for the need of a FAIR Core Security Ontology.
Semi-Supervised Feature Embedding for Data Sanitization in Real-World Events

With the rapid growth of data sharing through social media networks, determining relevant data items concerning a particular subject becomes paramount. We address the issue of establishing which images represent an event of interest through a semi-supervised learning technique. The method learns consistent and shared features related to an event (from a small set of examples) to propagate them to an unlabeled set. We investigate the behavior of five image feature representations considering low- and high-level features and their combinations. We evaluate the effectiveness of the feature embedding approach on five collected datasets from real-world events.
Quantum Speedup for Index Modulation

This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks.The NP-hard index selection problem is first formulated by a quadratic unconstrained binary optimization (QUBO) problem consisting of constraints of feasible solutions.To minimize the number of qubits required for a quantum circuit, this formulation is then simplified by a dictionary-based approach that partially exploits a classical computer.For both formulations, the numbers of required qubits and nonzero elements in QUBO matrices are analyzed algebraically, and found to be in close agreement with the actual measurement.It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem.This promising result implies that the on-off structure of index modulation is suitable for quantum computation, and future fault-tolerant quantum computers may be useful for obtaining high-performance index activation patterns.
A note on cloud computing security

Cloud computing has become an emerging technology which has huge impact. Existing security algorithms cannot meet the needs of cloud computing users as security issues have become increasingly prominent, becoming an important factor restricting the development of cloud computing. Research community has immensely focused on developing and optimising security techniques to overcome this hurdle in cloud. Due to huge literature it becomes difficult to comprehend overall structure and advancements. This domain needs an analytic approach to completely understand the problem domain that is why this article represents same in specific context of security. Further, a notion on security issues in cloud computing with respect to vulnerabilities, threats and attacks is discussed. Paper also proposed two research evaluation parameters that are effective citation and effective impact factor for better appreciation. Article also analyses patterns, trends and other factors for directing research activities. Finally, a global research trends on security domain are evaluated and analysed.
Data set for the comparison of the performance of QuantiFERON-TB Gold Plus with QuantiFERON-T Gold in-tube among highly TB exposed gold miners in South Africa

The HETU dataset was produced as part of an observational, cross-sectional study, conducted in South Africa. The dataset consists of one data table: participant characteristics measured at enrolment; and outcomes data points. These data underpin analysis reported in a Gates Open Research 2020 paper.
Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Abstract With aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks

This paper proposes an ensemble learning model that is resistant to adversarial attacks. To build resilience, we introduced a training process where each member learns a radically distinct latent space. Member models are added one at a time to the ensemble. Simultaneously, the loss function is regulated by a reverse knowledge distillation, forcing the new member to learn different features and map to a latent space safely distanced from those of existing members. We assessed the security and performance of the proposed solution on image classification tasks using CIFAR10 and MNIST datasets and showed security and performance improvement compared to the state of the art defense methods.
Queer in HCI: Strengthening the Community of LGBTQIA+ Researchers and Research

As Queer Human-Computer Interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past two years, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics.
A Light-weight Watermarking-Based Framework on Dataset Using Deep Learning Algorithms

In most decision-based security applications Deep Learning (DL) algorithms have been widely using for improvement. For better performance, a large amount of dataset has been used for training the DL algorithms. As DL has been remained a key element in the performance of the application, hence, several privacy and security issues have reported, which have affected the performance. Such security attacks have also affected the performance by taking the advantage of the huge dataset, because it is easy for an attacker to add executable noise into the dataset to get the information of the dataset and the model used. Most common security attacks like poisoning and evasion attacks have been considered challenging attacks that have caused misclassification and wrong prediction. Hence, a secure metric is needed to mitigate the effects of such attacks from the dataset. Therefore, in this paper, a light-weight watermarking framework has been proposed that provides security to the dataset before training the DL algorithms. We have implemented our proposed framework using the most common Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) against security attacks. The proposed framework has been evaluated based on accuracy, precision, and computational cost, and has maintained the accuracy up to 98.89% and a precision of 0.96, which has maintained the level as in recent literature. We have also reduced the computational cost for the proposed framework. We believed that the proposed framework can be used to mitigate the security issues in DL algorithms and enhanced toward other security applications.
Comparison of Logistic Regression and Decision Tree method for Credit Card Fraud Detection

The use of Credit cards has drastically increased as they become one of the vital and most used modes of payment. Along with this the Credit Card. Fraud has also grown to a greater extent. Thus, it becomes vital for Credit card companies or banks to identify fraudulent transactions. Machine learning algorithms are used to analyze and identify suspicious transactions. Credit Card Fraud detection is a typical case of classification and data analysis, in this, we have focused on analyzing and comparing the data on parameters like accuracy, precision on the basis of two supervised learning classification algorithm Logistics Regression and Decision tree.
Master-Key KDM-Secure ABE via Predicate Encoding

In this paper, we propose the first generic framework for attribute-based encryptions (ABE) with master-secret-key-dependent-message security (mKDM security) for affine functions via predicate encodings by Chen, Gay and Wee [Eurocrypt 2015]. The construction is adaptively secure under standard k-Lin assumption in prime-order bilinear groups. By this, we obtain a set of new mKDM-secure ABE schemes with high expressiveness that have never been reached before: we get the first hierarchical IBE (HIBE) scheme and the first ABE scheme for arithmetic branching program (ABP) with mKDM security for affine functions. Thanks to the expressiveness (more concretely, delegability like HIBE), we can obtain mKDM-secure ABE against chosen-ciphertext attack (i.e., CCA security) via a classical CPA-to-CCA transformation that works well in the context of mKDM.
Tourism Destination Recommendation Using Ontology-based Conversational Recommender System

Traveling is one of the human psychological needs.Many choices and lack of information about desired tourist attractions are some of the obstacles in fulfilling the need.One of the technologies to overcome these obstacles is the recommender system which can provide recommendations for the users to choose some interesting tourist attractions from several tourist destinations.A conversational recommender system (CRS) offers a way of recommending tourist destinations in a conversational mechanism.We use ontology as a representation of knowledge to generate conversational interactions, recommendations, and explanation facilities.With this ontology-based CRS, we can overcome cold start problems, and also the system can guide the users to get the desired tourist attractions.In this study, we use a combination of navigation by asking (NBA) and navigation by proposing (NBP) strategy to generate interactions on the CRS.Based on the user study, in general, users find it helpful in finding tourist destinations that suit their needs.It is because the factor of trust and perceived ease of use comes from the interaction and explanation facilities contained in the system.Besides, perceived usefulness directly affects users' interest in utilizing this CRS interaction model in the future.
Slice Profile Estimation From 2D MRI Acquisition Using Generative Adversarial Networks

To save time and maintain an adequate signal-to-noise ratio, magnetic resonance (MR) images are often acquired with better in-plane than through-plane resolutions in 2D acquisition. To improve image quality, recent work has focused on using deep learning to super-resolve the through-plane resolution. To create training data, images can be degraded in an in-plane direction to match the through-plane resolution. To do this correctly, the slice selection profile (SSP) should be known, but this is rarely possible since precise details of signal excitation are usually unknown. Therefore, estimating the SSP of an image volume is desired. In this work, we first show that a relative SSP can be estimated from the difference between in- and through-plane image patches. We further propose an algorithm that uses generative adversarial networks (GAN) to estimate the SSP. In this algorithm, the GAN's generator blurs in-plane patches in one direction using an estimated relative SSP then downsamples them. The GAN's discriminator distinguishes the generator's output from real through-plane patches. The proposed method was validated using numerical simulations and phantom and brain scans. To our knowledge, it is the first work to estimate the SSP from a single MR image. The code is available at https://github.com/shuohan/espreso.
Automatic Unsupervised Texture Recognition Framework Using Anisotropic Diffusion-Based Multi-Scale Analysis and Weight-Connected Graph Clustering

A novel unsupervised texture classification technique is proposed in this research work. The proposed method clusters automatically the textures of an image collection in similarity classes whose number is not a priori known. A nonlinear diffusion-based multi-scale texture analysis approach is introduced first. It creates an effective scale-space by using a well-posed anisotropic diffusion filtering model that is proposed and approximated numerically here. A feature extraction process using a bank of circularly symmetric 2D filters is applied at each scale, then a rotation-invariant texture feature vector is achieved for the current image by combining the feature vectors computed at all these scales. Next, a weighted similarity graph, whose vertices correspond to the texture feature vectors and the weights of its edges are obtained from the distances computed between these vectors, is created. A novel weighted graph clustering technique is then applied to this similarity graph, to determine the texture classes. Numerical simulations and method comparisons illustrating the effectiveness of the described framework are also discussed in this work.
Generalized Proxy Oblivious Signature and Its Mobile Application

Oblivious signature ensures users select from the specified candidates. However, users can choose only one candidate. This paper proposes a generalized oblivious signature scheme with proxy function. The scheme can be applied to many applications such as multichoice e-voting or e-lottery. Since there have been many applied research studies on e-voting, in this paper, we decided to apply this scheme to e-lottery, which is fair, secure, efficient, multiselect, and agent-based. In the lottery system, the server cannot cheat after a user makes a choice, and no one even the proxy can get any benefits. The signature scheme along with the lottery system is proved secure in the random oracle model. The lottery system is also implemented on Android smartphones. To the best of our knowledge, this is the first work done on a generalized proxy oblivious signature along with a fair and secure multiple-choice e-lottery system.
Medical Image Annotation Based on Deep Transfer Learning

ABSTRACTThe using of deep learning method belongs to the application and research of artificial intelligence technology in medical field for assisting medical image information processing. Due to the larger difference between the image underlying features and high-level semantic concept described, the existing image annotation algorithms perform not very ideal, therefore, we propose an image annotation framework based on multimodule deep transfer learning method, aiming at the application of deep convolution neural network technology to optimize the neural network parameters, and improve the tagging accuracy in detail. The proposed multimodule deep learning annotation framework uses two stages to optimize neural network parameters: (1) using deep neural network to optimize the single-mode condition parameters; (2) using the phase correlation to realize the multimodule state, the experiment of the optimal combination of public data sets shows that the scheme can effectively improve the performance of image annotation.
Fundamental privacy rights in a pandemic state

Faced with the emergence of the Covid-19 pandemic, and to better understand and contain the disease’s spread, health organisations increased the collaboration with other organisations sharing health data with data scientists and researchers. Data analysis assists such organisations in providing information that could help in decision-making processes. For this purpose, both national and regional health authorities provided health data for further processing and analysis. Shared data must comply with existing data protection and privacy regulations. Therefore, a robust de-identification procedure must be used, and a re-identification risk analysis should also be performed. De-identified data embodies state-of-the-art approaches in Data Protection by Design and Default because it requires the protection of direct and indirect identifiers (not just direct). This article highlights the importance of assessing re-identification risk before data disclosure by analysing a data set of individuals infected by Covid-19 that was made available for research purposes. We stress that it is highly important to make this data available for research purposes and that this process should be based on the state of the art methods in Data Protection by Design and by Default. Our main goal is to consider different re-identification risk analysis scenarios since the information on the intruder side is unknown. Our conclusions show that there is a risk of identity disclosure for all of the studied scenarios. For one, in particular, we proceed to an example of a re-identification attack. The outcome of such an attack reveals that it is possible to identify individuals with no much effort.
A Methodological Comparison of the Most Efficient Load Balancing Algorithms in Cloud Computing

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The effects of age and verbal ability on word predictability in reading.

Increased predictability effects in older compared to younger adults have been mostly observed in late eye-movement measures during reading. However, it is unclear whether and how these effects may be related to verbal ability, which typically improves with age. Past studies have shown that verbal abilities modulate the predictability effect. Here, we aimed to replicate predictability effects in younger and older adults in a sentence reading paradigm and to investigate how verbal ability modulates the predictability effect. We monitored 26 younger and 27 older adults' eye movements as they read sentences with target words varying in predictability and examined the impact of age and verbal ability, as reflected in vocabulary and print exposure measures. Replicating previous studies, we found that older adults relied more heavily on contextual information in their anticipation of upcoming input in one late measure. In one early measure (first-fixation duration), participants with higher scores in verbal ability showed greater predictability effects, whereas the predictability effect was virtually absent in those with low scores. In one late measure (regression-path duration), age interacted with predictability. However, verbal ability, when included as a covariate in this model, could not account for the age-related increases in predictability effects. Collectively, our findings indicate that verbal ability influences predictability effects in early processing stages, suggesting facilitation of initial word processing and that some aspect of aging other than verbal ability influences predictability effects in late measures. The latter finding most likely reflects a shift toward integrative controlled processes with age. (PsycInfo Database Record (c) 2021 APA, all rights reserved).