Reviewerly/exHarmony huggingface dataset:
A Study of Gender Bias in Face Presentation Attack and Its Mitigation

In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models’ performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model’s performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Uncertainty assessment has gained rapid interest in medical image analysis. A popular technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique. From a network with MC dropout and a single input, multiple outputs can be sampled. Various methods can be used to obtain epistemic uncertainty maps from those multiple outputs. In the case of multi-class segmentation, the number of methods is even larger as epistemic uncertainty can be computed voxelwise per class or voxelwise per image. This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel). We applied this quantitative analysis to a multi-class segmentation of the carotid artery lumen and vessel wall, on a multi-center, multi-scanner, multi-sequence dataset of Magnetic Resonance (MR) images. We validated our analysis over 144 sets of hyperparameters of a model. Our main analysis considers the relationship between the order of the voxels sorted according to their epistemic uncertainty values and the misclassification of the prediction. Under this consideration, the comparison of combined uncertainty maps reveals that the multi-class entropy and the multi-class mutual information statistically out-perform the other combined uncertainty maps under study (the averaged entropy, the averaged variance, the similarity Bhattacharya coefficient and the similarity Kullback-Leibler divergence). In a class-specific scenario, the one-versus-all entropy statistically out-performs the class-wise entropy, the class-wise variance and the one versus all mutual information. The class-wise entropy statistically out-performs the other class-specific uncertainty maps in term of calibration. We made a python package available to reproduce our analysis on different data and tasks.
The System of Versions of the Library Bibliographic Classification: Evolution and Modernisation

The Library Bibliographic Classification (LBC) is the national classification sistem of the Russian Federation. Its support is one of the priority areas of activity of the Russian State Library. The LBC is used in most libraries of the country, fully covering the most numerous groups — public and school libraries, all central libraries of the entities of the Russian Federation, libraries for children and youth, special libraries for the blind and visually impaired persons, libraries of higher educational institutions, libraries of the armed forces of the Russian Federation as well as part of the libraries of the Russian Academy of Sciences (RAS), other systems and authorities. The work with the LBC involves its development as a system of versions and publications (maintaining the standard of the LBC schedules in machine-readable form; timely updating the content and structure of the LBC; preparation, publishing, and distribution of the Abridged, Medium and Complete LBC schedules in printed and machine-readable forms, immediate changes and corrections to the schedules). There is a need for methods to systematise documents on the basis of the LBC, provision of consulting work to the library network of the country (conducting training seminars, publishing recommendations and practical guide-books, conducting operational consulting, using available communication channels); maintaining the system of training and professional development of personnel, etc. Since the 2000s, all areas of scientific and practical activity on the management of the LBC have focused on the task of modernising the national classification system. The paper analyses the system of versions of the LBC which was implemented in the form of various versions of the classification schedules. The identifying features are the title and the year of publication. The main schedules are Complete, Medium, and Abridged schedules which are supplemented with Special ones. The article presents the historical evolution of the system of BC versions depending on the conditions of its functioning. The theoretical justification is given and the problems of practical implementation of the modernisation of the schedules are considered.
Large-scale Interactive Conversational Recommendation System using Actor-Critic Framework

We propose AC-CRS, a novel conversational recommendation system based on reinforcement learning that better models user interaction compared to prior work. Interactive recommender systems expect an initial request from a user and then iterate by asking questions or recommending potential matching items, continuing until some stopping criterion is achieved. Unlike most existing works that stop as soon as an item is recommended, we model the more realistic expectation that the interaction will continue if the item is not appropriate. Using this process, AC-CRS is able to support a more flexible conversation with users. Unlike existing models, AC-CRS is able to estimate a value for each question in the conversation to make sure that questions asked by the agent are relevant to the target item (i.e., user needs). We also model the possibility that the system could suggest more than one item in a given turn, allowing it to take advantage of screen space if it is present. AC-CRS also better accommodates the massive space of items that a real-world recommender system must handle. Experiments on real-world user purchasing data show the effectiveness of our model in terms of standard evaluation measures such as NDCG.
Trends and Risks of Network Technologies

Several new directions and trends in the development of network technologies are analyzed by a system approach using scientometric methods and comparative analysis. The spectrum of potential negative consequences of applying new information and network technologies in social, economic, and scientific and engineering activities is considered. Topical problems of developing the national information infrastructure are discussed, as well as information and digital inequality issues. Proposals and recommendations are formulated. The most significant tasks of elaborating on the topic of risks in and threats to the development of network and information technologies are highlighted.
Analyzing the Effectiveness of Touch Keystroke Dynamic Authentication for the Arabic Language

The keystroke dynamic authentication (KDA) technique was proposed in the literature to develop a more effective authentication technique than traditional methods. KDA analyzes the rhythmic typing of the owner on a keypad or keyboard as a source of verification. In this study, we extend the findings of the system by analyzing the existing literature and validating its effectiveness in Arabic. In particular, we examined the effectiveness of the KDA system in Arabic for touchscreen-based digital devices using two KDA classes: fixed and free text. To this end, a KDA system was developed and applied to a selected device operating on the Android platform, and various classification methods were used to assess the similarity between log-in and enrolment sessions. The developed system was experimentally evaluated. The results showed that using Arabic KDA on touchscreen devices is possible and can enhance security. It attains a higher accuracy with average equal error rates of 0.0% and 0.08% by using the free text and fixed text classes, respectively, implying that free text is more secure than fixed text.
exp(ASPc) : Explaining ASP Programs with Choice Atoms and Constraint Rules

We present an enhancement of exp(ASP), a system that generates explanation graphs for a literal -an atom a or its default negation ∼ a-given an answer set A of a normal logic program P, which explain why is true (or false) given A and P. The new system, exp(ASP c ), differs from exp(ASP) in that it supports choice rules and utilizes constraint rules to provide explanation graphs that include information about choices and constraints.
Exploring Convolutional Recurrent architectures for anomaly detection in videos: a comparative study

Abstract Convolutional Recurrent architectures are currently preferred for spatio-temporal learning tasks in videos to the 3D convolutional networks which accompany a huge computational burden and it is imperative to understand the working of different architectural configurations. But most of the current works on visual learning, especially for video anomaly detection, predominantly employ ConvLSTM networks and focus less on other possible variants of Convolutional Recurrent configurations for temporal learning which warrants a need to study the different possible variants to make informed, optimal design choices according to the nature of the application at hand. We explore a variety of Convolutional Recurrent architectures and the influence of hyper-parameters on their performance for the task of anomaly detection. Through this work, we also intend to quantify the efficiency of the architectures based on the trade-off between their performance and computational complexity. With comprehensive quantitative and visual evidence, we establish that the ConvGRU based configurations are the most effective and perform better than the popular ConvLSTM configurations on video anomaly detection tasks, in contrast to what is seen from the literature.
Introduction of Entrepreneurship Development Fund for Startups and Small and Medium-sized Enterprises: Case of Kazakhstan

This chapter addresses the efficiency of financing mechanisms and tools of the state implemented in Kazakhstan through the national small and medium-sized enterprise (SME) development institute Entrepreneurship Development Fund (hereafter Damu). This study considers the data on Damu financing provided to SMEs during 2005–2019 and the relationship of this financing to the overall contribution of SMEs to the GDP of the country during this period. This chapter analyzes whether Damu’s activities promoted the economic development of the country and whether Damu is overall financially efficient as an organization, how Damu has promoted startup businesses, and the factors by which microfinancing affects economic development in Kazakhstan. The results indicate that Damu has been successful, as shown by the number of SMEs financed and the number of jobs created, to reduce regional disparity in financing SMEs. The results also show that many small firms cannot grow into medium-sized enterprises in a competitive market, even with government support. Public financial institutions are recommended to help SMEs improve their management quality and to reduce information asymmetry between loan providers and clients so SMEs can grow with a long-term strategy in a competitive market.
Comparative evaluation of contribution-value plots for machine learning understanding

Abstract The field of explainable artificial intelligence aims to help experts understand complex machine learning models. One key approach is to show the impact of a feature on the model prediction. This helps experts to verify and validate the predictions the model provides. However, many challenges remain open. For example, due to the subjective nature of interpretability, a strict definition of concepts such as the contribution of a feature remains elusive. Different techniques have varying underlying assumptions, which can cause inconsistent and conflicting views. In this work, we introduce local and global contribution-value plots as a novel approach to visualize feature impact on predictions and the relationship with feature value. We discuss design decisions and show an exemplary visual analytics implementation that provides new insights into the model. We conducted a user study and found the visualizations aid model interpretation by increasing correctness and confidence and reducing the time taken to obtain an insight. Graphic Abstract
Highly Secure Residents Life Event Management System Based on Blockchain by Hyperledger Fabric

This article describes the design and implementation of Residents Life Event Management System (hereinafter called as RLEMS) with high level reliability and security by blockchain technology.The data access environment provided by blockchain is highly secure and trustworthy.In Blockchain system, some data fragments are grouped into one piece called as blocks, and all blocks are connected to create a chain of blocks in database.When blocks are connected, hash value is used to connect blocks properly.Blockchain technology enables highly secure and reliable data management system under relatively poor ICT environment.For example, developing countries such as African countries do not have sufficient ICT environment.Therefore adopting blockchain technology is suitable for such countries.Based on this consideration, we have started to build RLEMS on the blockchain system.In previous work, we used the MultiChain as a blockchain platform.However, as MultiChain platform is mainly for private blockchain system, it is not suitable for government-level data management system.Therefore, we tried to use another blockchain framework.We selected Hyperledger Fabric which was developed by Linux Foundation.It enables to implement all styles of blockchain system.This article describes the design and implementation of RLEMS by using Hyperledger Fabric.Furthermore, to provide the best user experience, we also built the web application interface with Java web application framework named PrimeFace.The implementation of a prototype revealed that the Hyperledger Fabric blockchain technology is more suitable than MultiChain.
β-Variational autoencoder as an entanglement classifier

We focus on using an architecture similar to the β-Variational Autoencoder (β-VAE) to discriminate if a quantum state is entangled or separable based on measurements. We split the data into two sets, the set of local and correlated measurements. Using the latent space, which is a low dimensional representation of the data, we show that restricting ourselves to the set of local data it is not possible to distinguish between entangled and separable states. Meanwhile, when considering both correlated and local measurements, an accuracy of over 80% is attained in the structure of the latent space.
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO

To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications. Over the last years,the number of efficient algorithms and tools for HPO grew substantially. At the same time, the community is still lacking realistic, diverse, computationally cheap,and standardized benchmarks. This is especially the case for multi-fidelity HPO methods. To close this gap, we propose HPOBench, which includes 7 existing and 5 new benchmark families, with in total more than 100 multi-fidelity benchmark problems. HPOBench allows to run this extendable set of multi-fidelity HPO benchmarks in a reproducible way by isolating and packaging the individual benchmarks in containers. It also provides surrogate and tabular benchmarks for computationally affordable yet statistically sound evaluations. To demonstrate the broad compatibility of HPOBench and its usefulness, we conduct an exemplary large-scale study evaluating 6 well known multi-fidelity HPO tools.
From Centralized Protection to Distributed Edge Collaboration: A Location Difference-Based Privacy-Preserving Framework for Mobile Crowdsensing

Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.
The Sentimental Analysis for E-Commerce Application

Right now, characterize every one of those viewpoints as a component of item, and present a multidimensional feeling investigation approach for E-business audits. Specifically, we utilize an assessment dictionary growing system to evacuate the word uncertainty among various measurements, and propose a calculation for estimation investigation on Ebusiness audits dependent on rules and a dimensional feeling vocabulary. Make word net lexicon: In this sort of archive, every single positive word are worked out independently and every negative word are worked out at one spot. Extraction of dataset: First dataset of openly accessible item audits were downloaded from the web and afterward the entry extraction structure recognizes significant areas of the content which is generally illustrative of the substance of the record. All the more explicitly, this progression includes distinguishing and extricating those particular item includes and the assessments on them.
Significance Support Vector Regression for Image Denoising

As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual.
Self-orthogonal codes over a non-unital ring and combinatorial matrices

There is a local ring E of order 4, without identity for the multiplication, defined by generators and relations as $$E=\langle a,b \mid 2a=2b=0,\, a^2=a,\, b^2=b,\,ab=a,\, ba=b\rangle .$$ We study a special construction of self-orthogonal codes over E, based on combinatorial matrices related to two-class association schemes, Strongly Regular Graphs (SRG), and Doubly Regular Tournaments (DRT). We construct quasi self-dual codes over E, and Type IV codes, that is, quasi self-dual codes whose all codewords have even Hamming weight. All these codes can be represented as formally self-dual additive codes over $$\mathbb {F}_4.$$ The classical invariant theory bound for the weight enumerators of this class of codes improves the known bound on the minimum distance of Type IV codes over E.
Path Planning Method for Unmanned Surface Vehicle Based on RRT* and DWA

Based on the optimized rapidly-exploring random tree (RRT*) and dynamic window approach (DWA), this paper proposes a method for path planning of unmanned surface vehicle. By inputting the global path generated by RRT* to the local path planner through soft constraints, and combining the obstacle information received by the sensor when the USV is working, the improved DWA is used to obtain a route that can be operated by the USV. The simulation results verify it can effectively avoid dynamic obstacles, ensure the safe navigation of USV in complex environments, and can meet real-time requirements.
Solution Enumeration by Optimality in Answer Set Programming

Given a combinatorial search problem, it may be highly useful to enumerate its (all) solutions besides just finding one solution, or showing that none exists. The same can be stated about optimal solutions if an objective function is provided. This work goes beyond the bare enumeration of optimal solutions and addresses the computational task of solution enumeration by optimality (SEO). This task is studied in the context of Answer Set Programming (ASP) where (optimal) solutions of a problem are captured with the answer sets of a logic program encoding the problem. Existing answer-set solvers already support the enumeration of all (optimal) answer sets. However, in this work, we generalize the enumeration of optimal answer sets beyond strictly optimal ones, giving rise to the idea of answer set enumeration in the order of optimality (ASEO). This approach is applicable up to the best k answer sets or in an unlimited setting, which amounts to a process of sorting answer sets based on the objective function. As the main contribution of this work, we present the first general algorithms for the aforementioned tasks of answer set enumeration. Moreover, we illustrate the potential use cases of ASEO. First, we study how efficiently access to the next-best solutions can be achieved in a number of optimization problems that have been formalized and solved in ASP. Second, we show that ASEO provides us with an effective sampling technique for Bayesian networks.