Reviewerly/exHarmony huggingface dataset:
DA3G: Detecting Adversarial Attacks by Analysing Gradients

Deep learning models are vulnerable to specifically crafted inputs, called adversarial examples. In this paper, we present DA3G, a novel method to reliably detect evasion attacks on neural networks. We analyse the behaviour of the network under test on the given input sample. Compared to the benign training data, adversarial examples cause a discrepancy between visual and causal perception. Although visually close to a benign input class, the output is shifted at the attacker’s will. DA3G detects these changes in the pattern of the gradient using an auxiliary neural network. Our end-to-end approach readily integrates with a variety of existing architectures. DA3G reliably detects known as well as unknown attacks and increases the difficulty of adaptive attacks.
Digitalization of Propositional Algebra and NPC

Recently, digitalization issues have become more and more relevant. Digitalization currently covers not only the scientific sphere, all areas related to the implementation of scientific results and production activities, but also practically all aspects of the life of modern society. In this regard, the role of the mathematical methodology and methods underlying the digitalization of complex smart systems is increasing. In this paper, the digitalization of propositional algebra and NPC (Narrow Predicate Calculus) is considered, that is, various ways of numerical estimating the truth values of propositional algebra and NPC closed formulas are considered. The problem of the information reliability and its decision are given.
Role of Symmetry in Quantum Search via Continuous-Time Quantum Walk

For quantum search via the continuous-time quantum walk, the evolution of the whole system is usually limited in a small subspace. In this paper, we discuss how the symmetries of the graphs are related to the existence of such an invariant subspace, which also suggests a dimensionality reduction method based on group representation theory. We observe that in the one-dimensional subspace spanned by each desired basis state which assembles the identically evolving original basis states, we always get a trivial representation of the symmetry group. So we could find the desired basis by exploiting the projection operator of the trivial representation. Besides being technical guidance in this type of problem, this discussion also suggests that all the symmetries are used up in the invariant subspace and the asymmetric part of the Hamiltonian is very important for the purpose of quantum search.
Exact Training of Restricted Boltzmann Machines on Intrinsically Low Dimensional Data

The restricted Boltzmann machine is a basic machine learning tool able, in principle, to model the distribution of some arbitrary dataset. Its standard training procedure appears, however, delicate and obscure in many respects. We bring some new insights to it by considering the situation where the data have low intrinsic dimension, offering the possibility of an exact treatment and revealing a fundamental failure of the standard training procedure. The reasons for this failure-like the occurrence of first-order phase transitions during training-are clarified thanks to a Coulomb interactions reformulation of the model. In addition, a convex relaxation of the original optimization problem is formulated, thereby resulting in a unique solution, obtained in precise numerical form on d=1, 2 study cases, while a constrained linear regression solution can be conjectured on the basis of an information theory argument.
Learning to Hallucinate Examples from Extrinsic and Intrinsic Supervision

Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks. This work investigates two important yet overlooked natural supervision signals for guiding the hallucination process – (i) extrinsic: classifiers trained on hallucinated examples should be close to strong classifiers that would be learned from a large amount of real examples; and (ii) intrinsic: clusters of hallucinated and real examples belonging to the same class should be pulled together, while simultaneously pushing apart clusters of hallucinated and real examples from different classes. We achieve (i) by introducing an additional mentor model on data-abundant base classes for directing the hallucinator, and achieve (ii) by performing contrastive learning between hallucinated and real examples. As a general, model-agnostic framework, our dual mentor-and self-directed (DMAS) hallucinator significantly improves few-shot learning performance on widely-used benchmarks in various scenarios.
F-AttNet: Towards Multi-scale Feature Fusion for Fashion Attribute Prediction

Large-scale attribute recognition in fashion retail images is a crucial task in image-based recommendation systems. The challenges are due to the visually-similar instances, localized minute information and overlapping features. Moreover, the class imbalance further exacerbates the challenge, needing for a specific solution to alleviate the problem. In this work, F-AttNet architecture is proposed, which is designed by the hierarchical alignment of the novel Attentive Multi-scale Feature (AMF) encoder blocks. AMF encoders extract mid-level multi-scale fine-grained attribute features involving multiple representations of low-level features and finally, the high-level global description is encoded by adaptively calibrating the channel weights. For improving the training performance, a novel gamma-variant focal loss is developed to handle class imbalance by assigning more penalty and assigning relative weights to positive and negative instances shifting the focus of the network to false instances. Experimental results and ablation studies of F-AttNet using a large-scale fashion attribute recognition database iMaterialist-2018 demonstrate significant performance improvement than the state-of-the-art methodologies.
Enhanced Machine Learning using Quantum Computing

In recent times, increasing amount of the data have enriched the decision making using machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. A few research efforts have demonstrated that quantum computers which leverages the properties of quantum mechanics, carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. The paper focuses on the concepts of quantum computing that would be used by any machine learning technique to facilitate quantum machine learning. It also studies different quantum algorithms that could be used as core subroutines in machine learning techniques and highlights the famous Grovers algorithm. These subroutines are used to enhance the efficacy of machine learning tasks. The paper towards the end advocates the use of quantum application software and throws light on the existing challenges faced by quantum computers in the current scenario.
Fuzzy c-Varieties Clustering for Vertically Distributed Datasets

Fuzzy c-varieties (FCV) is a linear fuzzy clustering method, whose clustering criterion is constructed by replacing the fuzzy c-means (FCM) prototypes with linear varieties, and has been utilized for local principal component analysis. In this paper, the FCV iterative algorithm is modified for handling vertically distributed datasets under consideration of privacy preservation. A cryptographic calculation scheme is realized by utilizing the least square criterion without eigen solutions. The characteristics of the proposed algorithm is demonstrated through numerical experiments.
Arabic Text Documents Recommendation Using Joint Deep Representations Learning

Although a huge amount of data has been provided on the web to meet different needs of users, it has been more difficult to quickly make satisfactory choices. Therefore, recommender systems have been introduced for offering relevant data and providing users with personalized recommendations. Traditional collaborative based methods modelled efficiently users and items from their rating patterns. By cons, they suffered from the data sparsity problem. As a solution, text user-generated data (e.g., reviews) were useful to provide rich semantic information of user preferences and item features. Seeing that opinions and ratings were complementary, we proposed to fuse them and learn more accurate representations of users and items for recommendation. Unlike Arabic which has received less attention, the majority of methods offered have been focused on English language. It has been represented a fundamental issue in Natural Language Processing (NLP) and artificial intelligence. In this paper, a neural collaborative embedding- and filtering-based approach was proposed for Arabic textual documents recommendation. Due to the recent advances on neural networks, our idea was to couple deep features learning- and deep interaction modelling- based encoders. Indeed, two branches of recurrent neural networks were combined to extract latent features of users and items. Using the generated representations as inputs, deep user-item interactions were captured by stacking multiple layers. This method has proved its efficiency to simultaneously capture the global context and the word frequency information, learn thereafter the obtained high-level latent features and produce final ratings. Extensive experiments on a standard Arabic dataset showed the superiority of our model than state-of-the art methods.
Conducting a secret ballot elections for virtual meetings

Communication plays a crucial role in business, education, and generally in everyday people's interactions. Face-to-face communication has been banned by the COVID-19 pandemic restrictions and had to be replaced with its electronic remote form. Popular digital applications allowed us to switch to online life quite easily. That conversion wasn't problematic for most (especially young) people. Working online and meeting people virtually became a standard, and people have mostly adapted to the new reality. Moving conventional communication to the Internet wasn't much challenging, because it was only a matter of existing ICT solutions popularization. They have already existed and were functional, but haven't been used much often. COVID-19 pandemic changed it permanently because there was no other way as rapid adoption to this unusual situation. Although most of the actions could have been realized online, some were more problematic to conduct electronically. One of them was secret balloting for virtual meetings. As open voting was not much complicated to arrange using remote communication, conduction the secret type of elections was not so obvious. In open voting electors' data can be revealed and the results may be easily verified when it's finished. Secret voting demands to remain voters' data and their choices confidential. That leads to the question of how to verify the users' identity and voting rights and keep them anonymous at the same time? This paper provides an overview of a person's remote identification and verification methods, also explores the possibilities of using them for secret voting authentication. Results show that conducting a secret ballot with remote voter authentication is possible. The method was widely described and also applied in a authors' digital system. A fully functional ICT solution has been tested in real elections across several organizations in Poland, in which present authorities were elected electronically during the COVID-19 lockdown period.
Dark, clear or brackish? Using Reddit to break down the binary of the dark and clear web

Using Reddit as a case study, this article blurs the boundary between the ‘dark’ and ‘clear’ web, arguing that in between these spaces there is what I describe as a ‘brackish web’, inhabited by ‘brackish social spaces’. Reddit, I argue, sits in a murky space between the dark and clear, with its technological rationality, politics, and technological affordances exhibiting elements of both the clear and dark infrastructures. I study these similarities through examining Reddit’s relationship to surveillance, arguing that it exhibits similarities to the dark web through a politics of anonymity and its practices of content moderation. Reddit, however, has slowly moved away from some of its ‘dark’ elements, in particular through a growing reliance on advertising revenue. This has pushed the platform to engage in more surveillance of its users, pushing it away from its dark web politics. This article challenges the dark and clear web binary, further nuancing our understandings of the politics, economics and technological rationality that underpins online digital platforms.
Markerless Augmented Reality based application for E-Commerce to Visualise 3D Content

Augmented reality has three principal features: combining the real world environment with the virtual world, real-time interaction for users, and accurate representation of 3D objects. Augmented Reality in E-commerce allows customers to view products or experience services in their physical space before purchasing the required items. Current online shopping services only allow customers to see 2D images of the products they are buying. This type of experience is not personalized and sometimes leads to bad shopping choices choices; the customers find it difficult to shop only with a static image view available. Customers cannot accurately predict whether the product they purchase will fit their home environment. This results in a lot of people returning or exchanging the things their purchases. AR resolves these issues. Thus, a method has been proposed for adding a virtual object in the real world by just using a real-time camera. The main aim of this paper is to provide user visualization of high resolution E-commerce products in a real environment.
Medium-range terrestrial free-space QKD performance modelling and analysis

Medium-range terrestrial free-space quantum key distribution systems enable widespread secure networked communications in dense urban environments, where it would be infeasible to install a large number of short optical fibre links. Such networks need to perform over a wide range of conditions and their design has to balance key rate maximisation versus robust key generation over the greatest range of circumstances. Practicalities, such as manufacturability and deployment, further constrain the design space. Here, we examine challenges in translating experiment into engineering reality and identify efficient BB84 weak coherent pulse-decoy state protocol parameter regimes suitable for medium-range QKD systems considering likely system performance and environmental conditions.
Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions

Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty handling high-dimensional datasets. This paper explores why TSK fuzzy systems with Gaussian MFs may fail on high-dimensional inputs. After transforming defuzzification to an equivalent form of softmax function, we find that the poor performance is due to the saturation of softmax. We show that two defuzzification operations, LogTSK and HTSK, the latter of which is first proposed in this paper, can avoid the saturation. Experimental results on datasets with various dimensionalities validated our analysis and demonstrated the effectiveness of LogTSK and HTSK.
Mask Detection Based On Efficient-YOLO

COVID-19 can be transmitted by respiratory droplets and other means. With the normalization of epidemic prevention and control, mask detection should be performed when entering public places with heavy human flow. A mask detection method for robot was proposed in this paper to prevent cross-infection of the detection personnel. This paper used an improved algorithm of YOLO. First, this article is based on an improved backbone Darknet-53, and add the EfficientNet attention mechanism as the basic network, reduces the consumption of calculation and improve the training speed, secondly the original IoU replaced by DIoU, improves the prediction precision of the border, finally combining with robot platform, according to more than 6000 pieces of VOC data format homemade model training and test data set. The robot detects the target based on the camera it carries, and then guides it to the next step. The experimental results show that the mAP of the algorithm is 86.92% on the self-made verification set, and the preset process can be realized in the actual detection process by using the robot.
State-Of-The-Art And Directions For The Conceptual Design Of Safety-Critical Unmanned And Autonomous Aerial Vehicles

Unmanned and Autonomous Aerial Vehicles (UAV/AAV) must be safe and reliable to prevent catastrophic accidents in population-dense areas. The study reveals the absence of a comprehensive UAV/AAV design for reliability approach in the open literature; in particular, there is no conceptual design methodology including safety and reliability considerations in the sizing. This finding leads to investigating the relevance of pursuing this research direction and identifying the challenges to address. For this matter, a straightforward approach combining sizing, systematic redundancy, controllability, and reliability assessments compares a conventional to a redundant design in a case study. The reliability analysis confirms that the redundant design is fault-tolerant and potentially highly reliable. However, the total mass almost doubles due to the lack of sizing and redundancy optimization. Plus, there is a high risk of under-sizing due to the limitations of a straightforward approach. This result emphasizes the need to develop a new conceptual design methodology based on sizing, including safety and reliability considerations. The paper concludes with research directions towards this goal. Thus, optimized redundant designs will contribute to the emergence of UAV/AAV for safety-critical applications in the near future.
Model-free machine learning of wireless SISO/MIMO communications

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but the training usually requires an explicit model of the signal distortion as it undergoes transmission over a wireless channel. As data rates, number of MIMO streams and carrier frequencies increase to satisfy the demand for wireless capacity, it becomes difficult to design hardware with few imperfections and to model the imperfections that there are. New machine learning schemes for the physical layer do not require an explicit model but can implicitly learn the end-to-end link including channel characteristics and non-linearities of the system directly from the training data. In this paper, we present a novel neural network architecture that provides an explicit stochastic model for both SISO and MIMO channels, by learning the parameters of a Gaussian mixture distribution from real channel samples. We use this channel model in conjunction with an autoencoder to learn a suitable modulation scheme. We experimentally validate our proposed model in an FPGA-based millimeter-wave testbed for both SISO and MIMO channels, showing that it is able to reproduce the channel characteristics with good accuracy.
Quality of Student Support at IT Educational Programmes: Case of Lviv Polytechnic National University

The idea of the article is to analyse the effectiveness of support provided for students of IT educational programs. In order to conduct the latter, the authors conducted the analysis of statistical data on enrolment to IT educational programmes at Lviv Polytechnic National University (LPNU) during the period from 2016 to 2018. The enrolment scores of new coming students were compared with their ratings for the 1 and 2 semesters of study at the university. Strong disproportions between the results of External Independent Testing (EIT, equivalent to maturity test in Western countries) of the applicants and their study ratings for the 1 and 2 semesters were revealed. Students with high EIT results underachieved during the first periods of their studies. Based on these data, conclusions concerning the effectiveness of educational and social support systems for students at the university were made. To improve student support and ensure their full involvement in academic and social life in the university, recommendations were suggested.
Projection-Wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis

Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent to multiple biases. In literature, this is mostly solved by purging the bias information from learned representations. We however expect this strategy to harm the diversity of information in the representation, and thus limiting its prospective usage (e.g., interpretation). Therefore, we propose to mitigate the bias while keeping almost all information in the latent representations, which enables us to observe and interpret them as well. To achieve this, we project latent features onto a learned vector direction, and enforce the independence between biases and projected features rather than all learned features. To interpret the mapping between projected features and input data, we propose projection-wise disentangling: a sampling and reconstruction along the learned vector direction. The proposed method was evaluated on the analysis of 3D facial shape and patient characteristics (N = 5011). Experiments showed that this conceptually simple method achieved state-of-the-art fair prediction performance and interpretability, showing its great potential for clinical applications.
A Novel Speech to Sign Communication Model for Gujarati Language

Deaf Culture is important for deaf community as it is everywhere in the world. Deaf people are using Visual language (Sign language) for communicating. There are around 300 different types of sign languages are available in the globe like British Sign Language, Indonesian Sign Language, American sign language, etc. Each sign language has its own syntax and semantics. Some sign languages are using one hand gesture, some are using two hand gesture as they have their own rules for communication. There is a need of one standard form of sign language so it will be easier to understand. There are so many challenges and problems are facing by deaf community. Different sign languages are provided different solutions for speech to sign language and sign language to speech conversion. As there is no solution is provided by anyone for Gujarati Sign Language, we proposed a one communication model for Speech to Sign language. Speech will be recognized and convert into text, text will give the HamNoSys Notation (Sign language Notation) from a database and then it converts in SiGML format and then it display a sign animation (Avatar). That model will be helpful to Gujarat region deaf and dumb people for communicating with normal people.