Reviewerly/exHarmony huggingface dataset:
Variational Inference for Computational Imaging Inverse Problems

Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be trained, which in imaging applications implicates prohibitively expensive collections with specific imaging instruments. This paper introduces a novel framework to train variational inference for inverse problems exploiting in combination few experimentally collected data, domain expertise and existing image data sets. In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts. Extensive simulated experiments show the advantages of the proposed framework. The approach is then applied to two real experimental optics settings: holographic image reconstruction and imaging through highly scattering media. In both settings, state of the art reconstructions are achieved with little collection of training data.
Reconfigurable Hardware Design for Polynomial Galois Field Arithmetic Operations

Galois Field (GF) arithmetic plays an important role in cryptography algorithms and their numerous applications in communications, signal processing, and security. There is scope for optimizing these arithmetic circuits. In this paper, we consider a reconfigurable digital circuit which performs various GF arithmetic operations with the same set of hardware. Using this hardware we can perform a few operations in parallel. The throughput of the proposed design is greater than various existing techniques. Also, our proposed approach reduces the cost of the hardware, where instead of using all the GF arithmetic circuits we can replace with this proposed hardware. The proposed design has the critical path delay of 25.59 ns using Artix 7 FPGA implementation. Also, our proposed design achieves 83% of improvement in the throughput than the direct form based GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> ) multiplier.
Resampling-free Stochastic Integration Filter

The paper deals with the state estimation of nonlinear stochastic systems with additive Gaussian noises by means of the Gaussian filters leveraging numerical integration rules. The filters were derived under the assumption of the joint state and measurement predictive density being Gaussian, which is violated by the system nonlinearity. Such violation can hardly be monitored by the standard Gaussian filters, which re-generate a new set of points for each involved numerical integration to accommodate their variance increase due to the additive noises. The paper proposes a stochastic integration filter algorithm that modifies the points instead of their resampling and thus admits reusing the points in the next time steps. The distribution of the points can thus bear more information than just the first two moments in case of the standard Gaussian filters. The acquired information is then utilized for the Gaussian assumption monitoring purposes. In the event of the assumption violation, the filter may change its behavior. As a by-product of reusing the points, the computational costs of the proposed filter are significantly reduced compared to the standard stochastic integration filter.
Integrating Writing Dynamics in CNN for Online Children Handwriting Recognition

Online handwriting recognition is challenging but an already well-studied topic. However, recent advances in the development of convolutional neural networks (CNN) make us believe that these networks could still improve the state of the art especially in the much more challenging context of online children handwritten letters recognition. This is because, children handwriting is, at an early stage of learning, approximate and includes deformed letters. To evaluate the potential of these networks, we study the early and late fusions of different input channels that can provide a CNN with information about the handwriting dynamics in addition to the static image of the characters. The experiments on a real children handwriting dataset with 27 000 characters acquired in primary schools, show that using multiple channels with CNN, improves the accuracy performance of different CNN architectures and different fusion settings for character recognition.
Haar Wavelet based Block Autoregressive Flows for Trajectories

Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. While previous works have leveraged conditional generative models like GANs and VAEs for learning the likely future trajectories, accurately modeling the dependency structure of these multimodal distributions, particularly over long time horizons remains challenging. Normalizing flow based generative models can model complex distributions admitting exact inference. These include variants with split coupling invertible transformations that are easier to parallelize compared to their autoregressive counterparts. To this end, we introduce a novel Haar wavelet based block autoregressive model leveraging split couplings, conditioned on coarse trajectories obtained from Haar wavelet based transformations at different levels of granularity. This yields an exact inference method that models trajectories at different spatio-temporal resolutions in a hierarchical manner. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets - Stanford Drone and Intersection Drone.
Categorization of the benefits and limitations of immersive environments for education

Virtual and augmented reality have become attractive technologies for application in educational setups. Hence, instructional designers are still struggling with the questions of whether immersive technologies can help to solve their current instructional problems and of how they should be incorporated into the educational program. This paper should bring us a step closer to the solution of this issue by proposing an extensive categorisation of the advantages and disadvantages of VR and AR technology for education. It was iteratively developed based on literature review, expert interviews and user reports. Each benefit category was assigned to the technological features such as multimodality, interactivity and visual-spatial representation that may bring forth the wished benefits. Additionally, some examples serve for a better understanding of how the benefits manifest themselves.
Empowering Communications in Vehicular Networks with an Intelligent Blockchain-Based Solution

Blockchains have emerged over time as a reliable and secure way to record transactions in an immutable manner in a wide range of application domains. However, current related solutions are not yet capable of appropriately checking the authenticity of data when their volumes are huge. They are not also capable of updating Blockchain data blocks and synchronizing them within reasonable timeframes. This is the case within the specific context of Blockchain vehicular networks, where these solutions are commonly cumbersome when attempting to add new vehicles to the network. In order to address these problems, we propose in this paper a new Blockchain-based solution that intelligently implement selective communication and collaborative endorsement approaches to reduce communications between vehicles. Our solution represents the vehicles of the Blockchain as intelligent software agents with a Belief–Desire–Intention (BDI) architecture. Furthermore, we propose an approach based on multi-endorsement levels to exchange data of varying sensitive categories. This approach, which is based on endorsing scores, is also used to shorten the admission of new vehicles into the Blockchain. We run simulations using the Hyperledger Fabric Blockchain tool. Results show the efficiency of our solution in reducing the processing times of transactions within two different scenarios.
Paillier Homomorphic Encryption with K-Means Clustering Algorithm (PHEKC) for Data Mining Security in Cloud

In the current scenario, data mining is very significant in the IT and business sector, to realize the business goals. Data mining helps to achieve these goals, which converts the data into knowledge and plan for achieving the goals in all sectors. In the IT sector, the current necessity is cloud. For data processing, sharing the resource, transfer the data and storage, the cloud is essential. But the main threats in the cloud are privacy and security. Hence, a system Paillier homomorphic encryption with K-means clustering algorithm (PHEKC) is proposed for data mining security. In this method, the cloud server did not know about the uploaded data. Only the client can get the result. From this proposed method, the data is secure and confidential.
CHVote: Sixteen Best Practices and Lessons Learned

The authors of this paper had the opportunity to closely accompany the CHVote project of the State of Geneva during more than two years and to continue the project after its abrupt stop in 2018. This paper is an experience report from this collaboration and the subsequent project continuation. It describes the lessons learned from this project and proposes some best practices relative to sixteen different topics. The goal of the paper is to share this experience with the community.
End-to-End Multitask Learning for Driver Gaze and Head Pose Estimation

Modern automobiles accidents occur mostly due to inattentive behavior of drivers, which is why driver&#x2019;s gaze estimation is becoming a critical component in automotive industry. Gaze estimation has introduced many challenges due to the nature of the surrounding environment like changes in illumination, or driver&#x2019;s head motion, partial face occlusion, or wearing eye decorations. Previous work conducted in this field includes explicit extraction of hand-crafted features such as eye corners and pupil center to be used to estimate gaze, or appearance-based methods like Convolutional Neural Networks which implicitly extracts features from an image and directly map it to the corresponding gaze angle. In this work, a multitask Convolutional Neural Network architecture is proposed to predict subject&#x2019;s gaze yaw and pitch angles, along with the head pose as an auxiliary task, making the model robust to head pose variations, without needing any complex preprocessing or hand-crafted feature extraction.Then the network&#x2019;s output is clustered into nine gaze classes relevant in the driving scenario. The model achieves 95.8% accuracy on the test set and 78.2% accuracy in cross-subject testing, proving the model&#x2019;s generalization capability and robustness to head pose variation.
Technique for two‐dimensional nearest neighbour realisation of quantum circuits using weighted look‐ahead

Quantum computers that are based on technologies like superconducting and quantum dots impose a physical constraint that requires interacting qubits to be adjacent. The initial placement of qubits and the swap gate insertion techniques affect the circuit cost. The authors proposed a global qubit ordering technique that considers fewer permutations for the number of interactions a qubit does with other qubits of its circuit. They also performed the local re-ordering of qubits by attempting to reduce the cost as much as possible; the cost is estimated by defining a window with weights assigned in such a way that nearby gates to the current gate in question are given higher weightage. Experiments have been conducted on NCV benchmarks, and results have been compared with those of recent state-of-the-art techniques. When compared with the existing works, the proposed method shows improvements of up to 53.3% for smaller benchmarks and up to 51.61% for larger benchmarks.
Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers

Neural encoder-decoder models are usually applied to morphology learning as an end-to-end process without considering the underlying phonological representations that linguists posit as abstract forms before morphophonological rules are applied. Finite State Transducers for morphology, on the other hand, are developed to contain these underlying forms as an intermediate representation. This paper shows that training a bidirectional two-step encoder-decoder model of Arapaho verbs to learn two separate mappings between tags and abstract morphemes and morphemes and surface allomorphs improves results when training data is limited to 10,000 to 30,000 examples of inflected word forms.
Person Voice Recognition Methods

This article is devoted to the selection and evaluation of language features used in the tasks of automatic text-independent speaker verification. The task of automatic speaker identification is one of the most difficult tasks in the field of language processing. The methods used in current speaker identification systems are not ideal, that imposes some restrictions on such systems. Some systems work well in good acoustic conditions, with minimal noise, but noticeably lose recognition accuracy in low signal-to-noise conditions. The requirements for the accuracy of speaker identification for such systems set a certain bar, which increases every year. Improving authentication accuracy extends the scope of such systems, including biometric multi-factor authentication systems, remote banking, access control systems, and more. Thus, to meet the needs of these systems consumers, the high requirements for the speaker recognition accuracy are required.
Challenge Test Sets for MT Evaluation

Most of the test sets used for the evaluation of MT systems reflect the frequency distribution of different phenomena found in naturally occurring data (”standard” or ”natural” test sets). However, to better understand particular strengths and weaknesses of MT systems, especially those based on neural networks, it is necessary to apply more focused evaluation procedures. Therefore, another type of test sets (”challenge” test sets, also called ”test suites”) is being increasingly employed in order to highlight points of difficulty which are relevant to model development, training, or using of the given system. This tutorial will be useful for anyone (researchers, developers, users, translators) interested in detailed evaluation and getting a better understanding of machine translation (MT) systems and models. The attendees will learn about the motivation and linguistic background of challenge test sets and a range of testing possibilities applied to the state-of-the-art MT systems, as well as a number of practical aspects and challenges.
Bounds on Covering Radius of F<sub>2</sub> <i>R</i>-Linear Codes

In this letter, we determine the bounds on covering radius of repetition codes, simplex codes and MacDonald codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> R with respect to the Chinese Euclidean distance.
Research on the Intelligent Identification Method of the Substation Equipment Faults Based on Deep Learning

With the rapid development of the intelligent power grid, the intelligent identification technology of the substation equipment faults also becomes more important. In this paper, the intelligent identification method of the substation equipment faults based on deep learning is designed. Alex Net and Dense Net of the two-channel network's convolutional neural network is used to conduct the intelligent identification for fever faults of power transformation equipment to do intelligent identification, so as to confirm the fever temperature and position of the equipment. This method helps a lot to overhaul fever faults for the substation equipment.
Study to Determine Adverse Diseases Pattern using Rare Association Rule Mining

Data mining is a method for finding patterns from repositories that remain hidden, unknown but fascinating. It has resulted in a number of strategies and emphasizes the detection of patterns to identify patterns that occur frequently, seldom and rarely. With their implementations, the work has improved the efficiency of the techniques. Yet typical methods for data mining are limited to databases with static behavior. The first move was to investigate similarities between the common objects through association rules mining. The original motivation for the search for these guidelines was the consumers ' shopping patterns in transaction data for supermarkets. This attempts to classify combinations of items or items that influence the presence likelihood of other items or items in a transaction. The request for rare association rule mining has improved in current years. The identification of unusual data patterns is critical, including medical, financial, or security applications. This survey seeks to give an analysis of rare pattern mining strategies, which in general, comprehensive and constructed. We discuss the issues in the quest for unusual rules using conventional association principles. Because mining rules for rare associations are not well known, special foundations still need to be set up.
Decentralised Finance: The Case for a Holistic Approach to Regulating the Crypto Industry

Since the launch of bitcoin in 2009, there have been fierce calls for the regulation of cryptocurrencies particularly linked with, amongst other things, their use to facilitate financial crime. One of the mechanisms deemed best to achieve this has been at the point of their intersection with mainstream finance – which is mostly through cryptocurrency exchanges and wallet providers which convert fiat currency to cryptocurrency and vice versa. The Financial Action Task Force (FATF) Recommendation 16 is expected to achieve this, however, this rule is only as strong as its robust implementation globally. The growth of decentralised finance (DeFi) further exacerbates the challenges of regulating the crypto industry, particularly as it is not covered by FATF Recommendation 16.
Reading Between the Lines – Towards an Algorithm Exploiting In-game Behaviors to Learn Preferences in Gameful Systems

Players’ retainment can be fostered by investigating whether the game elements players are interacting with are to their liking and tailoring game dynamics to meet their preferences. Thus, adaptive gameplay is a widely interesting topic in both the Game User Research field and the game industry. Considering that explicit information on players’ preferences often lacks, alternative approaches are needed. This task becomes even more challenging when the gameplay data available is limited due to the simplicity of the system employed, as it occurs in gameful systems in contrast to complex entertainment games or serious games. In this work, we propose an algorithm that exploits user behaviors as an implicit component to compute players’ preferences by measuring their level of activity. The application domain is a persuasive gameful system, and the customizable game elements are single-player challenges. The proposed algorithm uses offline gameplay data to compute a preference score for every viable option. The outcomes are then compared against a ground truth calculated from players’ in-game choices. Our findings suggest that players’ behaviors can be used to inform the generation of tailored game elements.
My Vote, My (Personal) Data: Remote Electronic Voting and the General Data Protection Regulation

On 19 September 2019, the Data Protection Authority of the Åland Islands (in Finland) published its findings on the data processing audit for the autonomous region’s parliamentary election special internet voting procedure. It claimed that there were faults in the documentation provided by the processor, which in turn meant that the election’s integrity could not be guaranteed without further precautions from the government of the Åland Islands. Since the European Union’s General Data Protection Regulation (GDPR) entered into force in May 2018, it has set new critical requirements for remote electronic voting projects. Yet, to date, no specific guidance nor research has been conducted on the impact of GDPR on remote electronic voting. Tacking stock of two recent internet voting experiences in the Åland Islands and France, this paper aims at identifying and understanding these new requirements. More specifically, based on these two case studies it analyses four different challenges on the processing of personal data in remote electronic voting under the GDPR: the definitions and categories of personal data processed in online voting projects; the separation of duties between data controllers and data processors; the secure processing of (sensitive) personal data, including the use of anonymisation and pseudonymisation techniques; as well as post-election processing of personal data, and possible limits to (universal) verifiability and public access to personal data.
A Comprehensive Overview of Sentiment Analysis and Fake Review Detection

Sentiment analysis (SA) is based on natural language processing (NLP) techniques used to extract the user’s feelings and opinions about any manufactured goods or services provided. Opinion mining is the other name for sentiment analysis. Sentiment analysis is very useful in the decision-making process. With greater Internet use, SA is a powerful tool for studying the opinions of customers about any product or services provided by any business organization or a company. Several approaches and techniques have came to existence in past years for sentiment analysis. Sentiment analysis is useful in decision making. In this paper, we offer an exhaustive description about techniques used for SA, approaches used for SA and applications of sentiment analysis.
Transfer Learning in Speaker’s Age and Gender Recognition

In this paper, we study an application of transfer learning approach to speaker’s age and gender recognition task. Recently, speech analysis systems, which take images of log Mel-spectrograms or MFCCs as input for classification, are gaining popularity. Therefore, we used pretrained models that showed good performance on ImageNet task, such as AlexNet, VGG-16, ResNet18, ResNet34, ResNet50, as well as state-of-the-art EfficientNet-B4 from Google. Additionally, we trained 1D CNN and TDNN models for speaker’s age and gender recognition. We compared performance of these models in age (4 classes), gender (3 classes) and joint age and gender (7 classes) recognition. Despite high performance of pretrained models in ImageNet task, our TDNN models showed better UAR results in all tasks presented in this study: age (UAR = 51.719%), gender (UAR = 81.746%) and joint age and gender (UAR = 48.969%) recognition.
X-NET For Single Image Raindrop Removal

Photos taken on rainy days are likely degraded by raindrops adhered to camera lenses. Removing raindrops from images is a tough task. Its difficulties lie in restoring high frequency information from corrupted images while keeping the color of restored images consistent with human perception. To solve these problems, we propose an end-to-end convolutional neural network consisting of X-Net and RAD-Net (Raindrop Automatic Detection Net). X-Net takes advantage of Long Skip Connections and Cross Branch Connections to generate raindrop-free image with enough details. RAD-Net assists X-Net to produce better results by yielding raindrop location. Extensive experiments show our approach outperforms state-of-the-art methods quantitatively and qualitatively.