Reviewerly/exHarmony huggingface dataset:
Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices

Abstract Gender classification of mobile devices’ users has drawn a great deal of attention for its applications in healthcare, smart spaces, biometric-based access control systems and customization of user interface (UI). Previous works have shown that authentication systems can be more effective when considering soft biometric traits such as the gender, while others highlighted the significance of this trait for enhancing UIs. This paper presents a novel machine learning-based approach to gender classification leveraging the only touch gestures information derived from smartphones’ APIs. To identify the most useful gesture and combination thereof for gender classification, we have considered two strategies: single-view learning, analyzing, one at a time, datasets relating to a single type of gesture, and multi-view learning, analyzing together datasets describing different types of gestures. This is one of the first works to apply such a strategy for gender recognition via gestures analysis on mobile devices. The methods have been evaluated on a large dataset of gestures collected through a mobile application, which includes not only scrolls, swipes, and taps but also pinch-to-zooms and drag-and-drops which are mostly overlooked in the literature. Conversely to the previous literature, we have also provided experiments of the solution in different scenarios, thus proposing a more comprehensive evaluation. The experimental results show that scroll down is the most useful gesture and random forest is the most convenient classifier for gender classification. Based on the (combination of) gestures taken into account, we have obtained F1-score up to 0.89 in validation and 0.85 in testing phase. Furthermore, the multi-view approach is recommended when dealing with unknown devices and combinations of gestures can be effectively adopted, building on the requirements of the system our solution is built-into. Solutions proposed turn out to be both an opportunity for gender-aware technologies and a potential risk deriving from unwanted gender classification.
Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.
Metaverse Native Communication: A Blockchain and Spectrum Prospective

Metaverse depicts a vista of constructing a virtual environment parallel to the real world so people can communicate with others and objects through digital entities. In the real world, communication relies on identities and addresses that are recognized by authorities, no matter the link is established via post, email, mobile phone, or landline. Metaverse, however, is different from the real world, which requires a single identity belongs to the individual. This identity can be an encrypted virtual address in the metaverse but no one can trace or verify it. In order to achieve such addresses to hide individuals in the metaverse, re-mapping the virtual address to the individual's identity and a specific spectrum to support the address-based communication for the metaverse are needed. Therefore, metaverse native or meta-native communications based on blockchain could be a promising solution to directly connect entities with their native encrypted addresses that gets rid of the existing network services based on IP, cellular, HTTP, etc. This paper proposes a vision of blockchain, encrypted address and address-based access model for all users, devices, services, etc. to contribute to the meta-verse. Furthermore, the allocation architecture of a designated spectrum for the metaverse is proposed to remove the barrier to access to the metaverse/blockchain in response to the initiatives of metaverse and decentralized Internet.
Understanding Cybersecurity Frameworks and Information Security Standards—A Review and Comprehensive Overview

Businesses are reliant on data to survive in the competitive market, and data is constantly in danger of loss or theft. Loss of valuable data leads to negative consequences for both individuals and organizations. Cybersecurity is the process of protecting sensitive data from damage or theft. To successfully achieve the objectives of implementing cybersecurity at different levels, a range of procedures and standards should be followed. Cybersecurity standards determine the requirements that an organization should follow to achieve cybersecurity objectives and facilitate against cybercrimes. Cybersecurity standards demonstrate whether an information system can meet security requirements through a range of best practices and procedures. A range of standards has been established by various organizations to be employed in information systems of different sizes and types. However, it is challenging for businesses to adopt the standard that is the most appropriate based on their cybersecurity demands. Reviewing the experiences of other businesses in the industry helps organizations to adopt the most relevant cybersecurity standards and frameworks. This study presents a narrative review of the most frequently used cybersecurity standards and frameworks based on existing papers in the cybersecurity field and applications of these cybersecurity standards and frameworks in various fields to help organizations select the cybersecurity standard or framework that best fits their cybersecurity requirements.
Digital competence of pedagogical university student: definition, structure and didactical conditions of formation

The article defines and substantiates didactic conditions of digital formation competences of students of pedagogical universities: actualization of motivational value training of students of pedagogical universities; organization of interaction between students and teachers of pedagogical universities on the Internet through the creation of digital information educational environment; creation of individual educational trajectories of students.
Model of using cloud-based environment in training databases of future IT specialists

The authors substantiates and develops the model of using cloud-based environment (CBE) in the training of databases of future information technology (IT) specialists, which consists of interrelated units: target (purpose, task of using CBE), conceptual (pedagogical approaches, didactic principles), organizational and semantic (characteristics of CBE, basic requirements for CBE, subjects of training, CBE of the teacher, CBE of the student, curricula of institution of higher education, educational-methodical complex of discipline “Databases”, installation and configuration of database management system, development of educational material from the database in electronic form, selection of cloudbased systems of distance learning, introduction of cloud-based systems of distance learning in the training of students’ databases, selection of CBE in database training (databases, forms, methods, tools), evaluative (criteria, indicators, levels of professional and practical competence of future IT specialists on the use of CBE in database training), effective (increased formation of the information and communication technologies of future IT specialists on the use of CBE in database training).
Balanced MSE for Imbalanced Visual Regression

Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, im-balanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression in modern context. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE. Code and models are available at github.com/jiawei-ren/BalancedMSE.
Cybersecurity in the AI-Based Metaverse: A Survey

The Metaverse is a multi-user virtual world that combines physical reality with digital virtual reality. The three basic technologies for building the Metaverse are immersive technologies, artificial intelligence, and blockchain. Companies are subsequently making significant investments into creating an artificially intelligent Metaverse, with the consequence that cybersecurity has become more crucial. As cybercrime increases exponentially, it is evident that a comprehensive study of Metaverse security based on artificial intelligence is lacking. A growing number of distributed denial-of-service attacks and theft of user identification information makes it necessary to conduct comprehensive and inclusive research in this field in order to identify the Metaverse’s vulnerabilities and weaknesses. This article provides a summary of existing research on AI-based Metaverse cybersecurity and discusses relevant security challenges. Based on the results, the issue of user identification plays a very important role in the presented works, for which biometric methods are the most commonly used. While the use of biometric data is considered the safest method, due to their uniqueness, they are also susceptible to misuse. A cyber-situation management system based on artificial intelligence should be able to analyze data of any volume with the help of algorithms. To prepare researchers who will pursue this topic in the future, this article provides a comprehensive summary of research on cybersecurity in the Metaverse based on artificial intelligence.
A Fuzzy Clustering Validity Index Induced by Triple Center Relation

The existing clustering validity indexes (CVIs) show some difficulties to produce the correct cluster number when some cluster centers are close to each other, and the separation processing mechanism appears simple. The results are imperfect in case of noisy data sets. For this reason, in this study, we come up with a novel CVI for fuzzy clustering, referred to as the triple center relation (TCR) index. The originality of this index is twofold. On the one hand, a new fuzzy cardinality is built on the strength of the maximum membership degree, and a novel compactness formula is constructed by combining it with the within-class weighted squared error sum. On the other hand, starting from the minimum distance between different cluster centers, the mean distance as well as the sample variance of cluster centers in the statistical sense are further integrated. These three factors are combined by means of product to form a triple characterization of the relationship between cluster centers, and hence a 3-D expression pattern of separability is formed. Subsequently, the TCR index is put forward by combining the compactness formula with the separability expression pattern. By virtue of the degenerate structure of hard clustering, we show an important property of the TCR index. Finally, based on the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C $ </tex-math></inline-formula> -means (FCMs) clustering algorithm, experimental studies were conducted on 36 data sets (incorporating artificial and UCI data sets, images, the Olivetti face database). For comparative purposes, 10 CVIs were also considered. It has been found that the proposed TCR index performs best in finding the correct cluster number, and has excellent stability.
Distributed triangle detection via expander decomposition

We present improved distributed algorithms for triangle detection and its variants in the CONGEST model. We show that Triangle Detection, Counting, and Enumeration can be solved in O(n1/2) rounds. In contrast, the previous state-of-the-art bounds for Triangle Detection and Enumeration were O(n2/3) and O(n3/4), respectively, due to Izumi and LeGall (PODC 2017).The main technical novelty in this work is a distributed graph partitioning algorithm. We show that in O(n1 − δ) rounds we can partition the edge set of the network G = (V, E) into three parts E = Em ∩ Es ∩ Er such that• Each connected component induced by Em has minimum degree Ω(nδ) and conductance Ω(1/polylog(n)). As a consequence the mixing time of a random walk within the component is O(polylog(n)).• The subgraph induced by Es has arboricity at most nδ.• |Er| ≤ |E|/6.All of our algorithms are based on the following generic framework, which we believe is of interest beyond this work. Roughly, we deal with the set Es by an algorithm that is efficient for low-arboricity graphs, and deal with the set Er using recursive calls. For each connected component induced by Em, we are able to simulate CONGESTED-CLIQUE algorithms with small overhead by applying a routing algorithm due to Ghaffari, Kuhn, and Su (PODC 2017) for high conductance graphs.
An improved bound for the lengths of matrix algebras

Let $S$ be a set of $n\times n$ matrices over a field $\mathbb{F}$. We show that the $\mathbb{F}$-linear span of the words in $S$ of length at most $$2n\log_2n+4n$$ is the full $\mathbb{F}$-algebra generated by $S$. This improves on the $n^2/3+2/3$ bound by Paz (1984) and an $O\left(n^{1.5}\right)$ bound of Pappacena (1997).
SAFE: A Neural Survival Analysis Model for Fraud Early Detection

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-theart fraud early detection approaches.
Development of a collaborative model of low back pain: report from the 2017 NASS consensus meeting

Background Context Low back pain (LBP) is a multifactorial problem with complex interactions among many biological, psychological and social factors. It is difficult to fully appreciate this complexity because the knowledge necessary to do so is distributed over many areas of expertise that span the biopsychosocial domains. Purpose This study describes the collaborative modeling process, undertaken among a group of participants with diverse expertise in LBP, to build a model to enhance understanding and communicate the complexity of the LBP problem. Study Design The study involved generating individual models that represented participants’ understanding of the LBP problem using fuzzy cognitive mapping (FCM), and 4 subsequent phases of consultation and consensus with the participants to characterize and refine the interpretation of the FCMs. Methods The phases consisted of: proposal of Categories for clustering of model Components; preliminary evaluation of structure, composition and focal areas of participant's FCMs; refinement of Categories and Components with consensus meeting; generation of final structure and composition of individual participant's FCMs. Descriptive statistics were applied to the structural and composition metrics of individual FCMs to aid interpretation. Results From 38 invited contributors, 29 (76%) agreed to participate. They represented 9 disciplines and 8 countries. Participants’ models included 729 Components, with an average of 25 (SD = 7) per model. After the final FCM refinement process (Components from separate FCMs that used similar terms were combined, and Components from an FCM that included multiple terms were separated), there were 147 Components allocated to ten Categories. Although individual models varied in their structure and composition, a common opinion emerged that psychological factors are particularly important in the presentation of LBP. Collectively, Components allocated to the “Psychology” Category were the most central in almost half (14/29) of the individual models. Conclusions The collaborative modeling process outlined in this paper provides a foundation upon which to build a greater understanding and to communicate the complexity of the LBP problem. The next step is to aggregate individual FCMs into a metamodel and begin disentangling the interactions among its Components. This will lead to an improved understanding of the complexity of LBP, and hopefully to improved outcomes for those suffering from this condition.
Context Aware 3D CNNs for Brain Tumor Segmentation

In this work we propose a novel deep learning based pipeline for the task of brain tumor segmentation. Our pipeline consists of three primary components: (i) a preprocessing stage that exploits histogram standardization to mitigate inaccuracies in measured brain modalities, (ii) a first prediction stage that uses the V-Net deep learning architecture to output dense, per voxel class probabilities, and (iii) a prediction refinement stage that uses a Conditional Random Field (CRF) with a bilateral filtering objective for better context awareness. Additionally, we compare the V-Net architecture with a custom 3D Residual Network architecture, trained on a multi-view strategy, and our ablation experiments indicate that V-Net outperforms the 3D ResNet-18 with all bells and whistles, while fully connected CRFs as post processing, boost the performance of both networks. We report competitive results on the BraTS 2018 validation and test set.
Searchable encryption: A survey on privacy‐preserving search schemes on encrypted outsourced data

Summary Outsourcing confidential data to cloud storage leads to privacy challenges that can be reduced using encryption. However, with encryption in place, the utilization of the data is reduced, which leads to reduced quality of experience of the users. To overcome this, searchable encryption (SE) schemes are utilized, which allow the end users to retrieve the relevant documents from the cloud, for which various researchers have worked utilizing different techniques. Despite the popularity of the searchable encryption schemes, most of the surveys either do not provide or present an incomplete taxonomy of SE schemes. Hence, in this paper, we attempt to present a complete taxonomy/classification of the searchable encryption schemes in terms of the type of search, type of index, results retrieved, implementation type, multiplicity of users, and the technique used. From the literature, it is observed that inner product similarity is widely adopted by researchers to compute the similarity of the query and the document index as it provides both conjunctive and disjunctive searching (ie, have better search capability) but requires high search time (ie, have lower search efficiency). On the other hand, schemes based on binary comparisons exist, which require less search time (ie, have better search efficiency) but support only conjunctive searching (ie, have limited search capability). Thus, a major conclusion drawn from our work is that there is an imbalance between search capability and search efficiency, ie, in the existing schemes, search capability can be improved at the cost of search time only. Therefore, we suggest that one direction where researchers should work on is to provide a balance between search capability and search efficiency.
Bitcoin Awareness and Usage in Canada: <i>An Update</i>

This article provides an update of the results of the 2017 Bitcoin Omnibus Survey (BTCOS) conducted by the Bank of Canada from December 12 to 15, 2017. The BTCOS was previously conducted in November and December 2016 and the results were reported in Henry, Huynh, and Nicholls (2017, forthcoming). The 2017 survey took place in an interesting time, as Bitcoin prices were increasing and reached an all-time high on December 17, 2017. During this period, the level of awareness of Bitcoin increased from 64 percent in the 2016 BTCOS to 85 percent in the 2017 BTCOS, while ownership rose from 2.9 to 5.0 percent respectively. The main reason cited by survey participants for owning Bitcoin changed from transactional purposes in 2016 to investment purposes in 2017. Further, only about half of Bitcoin owners were found to regularly use Bitcoin to buy goods or services or to send money to other people. <b>TOPICS:</b>Currency, portfolio construction, wealth management