Reviewerly/exHarmony huggingface dataset:
Deep Scattering Network With Fractional Wavelet Transform

Deep convolutional neural networks (DCNNs) have recently emerged as a powerful tool to deliver breakthrough performances in various image analysis and processing applications. However, DCNNs lack a strong theoretical foundation and require massive amounts of training data. More recently, the deep scattering network (DSN), a variant of DCNNs, has been proposed to address these issues. DSNs inherit the hierarchical structure of DCNNs, but replace data-driven linear filters with predefined fixed multi-scale wavelet filters, which facilitate an in-depth understanding of DCNNs and also offer the state-of-the-art performance in image classification. Unfortunately, DSNs suffer from a major drawback: they are suitable for stationary image textures but not non-stationary image textures, since 2D wavelets are intrinsically linear translation-invariant filters in the Fourier transform domain. The objective of this paper is to overcome this drawback using the fractional wavelet transform (FRWT) which can be viewed as a bank of linear translation-variant multi-scale filters and thus may be well suited for non-stationary texture analysis. We first propose the fractional wavelet scattering transform (FRWST) based upon the FRWT. Then, we present a generalized structure for the DSN by cascading fractional wavelet convolutions and modulus operators. Basic properties of this generalized DSN are derived, followed by a fast implementation of the generalized DSN as well as their practical applications. The theoretical derivations are validated via computer simulations.
Gun Tracking System Using Blockchain Technology

One of the major concerns for the government in terms of security is the safety of the citizens. We have come across many cases in which the individuals have access to the ammunition without having the required license. Inadequacy in the management of weapons, especially those which remain reserved is the major contributing factor. Individuals submit counterfeit documents to some third party dealers who help them in getting a fake arms license. That's not all, no one knows whether the individuals who use the ammunition with the fake license have any criminal background at that time. The production of such reserved deteriorating weapons is creating a huge problem leading to an unsafe environment among the citizenry. In this chapter, we lay forward a model that helps to overcome these challenges with the help of blockchain technology. In our model, we provided a simple solution on how the transfer of guns can take place between the dealer and the seller using digital signatures and a digital gun safe just like a bitcoin (BTC) wallet which stores each and every information about an individual securely and it helps us in identifying whether the person who has a weapon holds an original arms license or has a fake one and also checks whether the person has a previous criminal record on his name. Since blockchain is immutable and hackproof, this technology can be used to eliminate the above claims and improve the ownership, traceability of the weapon and keeps the records securely inside the digital safe. Although the use of blockchain technology has some constraints when a large number of records are to be stored inside the safe, it's properties can be used to improve the current scenario.
Experimental demonstration of continuous quantum error correction

The storage and processing of quantum information are susceptible to external noise, resulting in computational errors. A powerful method to suppress these effects is quantum error correction. Typically, quantum error correction is executed in discrete rounds, using entangling gates and projective measurement on ancillary qubits to complete each round of error correction. Here we use direct parity measurements to implement a continuous quantum bit-flip correction code in a resource-efficient manner, eliminating entangling gates, ancillary qubits, and their associated errors. An FPGA controller actively corrects errors as they are detected, achieving an average bit-flip detection efficiency of up to 91%. Furthermore, the protocol increases the relaxation time of the protected logical qubit by a factor of 2.7 over the relaxation times of the bare comprising qubits. Our results showcase resource-efficient stabilizer measurements in a multi-qubit architecture and demonstrate how continuous error correction codes can address challenges in realizing a fault-tolerant system.
Digital Twin for Federated Analytics Using a Bayesian Approach

We are now in an information era and the volume of data is growing explosively. However, due to privacy issues, it is very common that data cannot be freely shared among the data generating. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Federated analytics</i> was recently proposed aiming at deriving analytical insights among data-generating devices without exposing the raw data, but the intermediate analytics results. Note that the computing resources at the data generating devices are limited, thus making on-device execution of computing-intensive tasks challenging. We thus propose to apply the digital twin technique, which emulates the resource-limited physical/end side, while utilizing the rich resource at the virtual/computing side. Nevertheless, how to use the digital twin technique to assist federated analytics while preserving distributed data privacy is challenging. To address such a challenge, this work first formulates a problem on digital twin-assisted federated distribution discovery. Then, we propose a federated Markov chain Monte Carlo with a delayed rejection (FMCMC-DR) method to estimate the representative parameters of the global distribution. We combine a rejection–acceptance sampling technique and a delayed rejection technique, allowing our method to be able to explore the full state space. Finally, we evaluate FMCMC-DR against the Metropolis–Hastings (MH) algorithm and random walk Markov chain Monte Carlo method (RW-MCMC) using numerical experiments. The results show our algorithm outperforms the other two methods by 50% and 95% contour accuracy, respectively, and has a better convergence rate.
Decoupled appearance and motion learning for efficient anomaly detection in surveillance video

Automating the analysis of surveillance video footage is of great interest when urban environments or industrial sites are monitored by a large number of cameras. As anomalies are often context-specific, it is hard to predefine events of interest and collect labeled training data. A purely unsupervised approach for automated anomaly detection is much more suitable. For every camera, a separate algorithm could then be deployed that learns over time a baseline model of appearance and motion related features of the objects within the camera viewport. Anything that deviates from this baseline is flagged as an anomaly for further analysis downstream. We propose a new neural network architecture that learns the normal behavior in a purely unsupervised fashion. In contrast to previous work, we use latent code predictions as our anomaly metric. We show that this outperforms frame reconstruction-based and prediction-based methods on different benchmark datasets both in terms of accuracy and robustness against changing lighting and weather conditions. By decoupling an appearance and a motion model, our model can also process 16 to 45 times more frames per second than related approaches which makes our model suitable for deploying on the camera itself or on other edge devices.
Seasonal and Calendar Effects and the Price Efficiency of Cryptocurrencies

We investigate the pricing efficiency of numerous popular cryptocurrencies using a wide range of non-economic events that include calendar anomalies, natural condition-based anomalies, holidays when US exchanges are closed and secular and ethnic holidays when exchanges are open - all documented in the finance literature regarding equities. We document the existence of very few similar effects in the examined cryptocurrencies. Generally, anomalies found in Bitcoin do not hold for other cryptocurrencies and vice versa. The within-the-month effect is the only effect common to all cryptocurrencies. Our results have implications for efficient asset pricing and diversification benefits for these currencies.
Evaluating the Effectiveness of Machine Learning Methods for Spam Detection

Technological advances are accelerating the dissemination of information. Today, millions of devices and their users are connected to the Internet, allowing businesses to interact with consumers regardless of geography. People all over the world send and receive emails every day. Email is an effective, simple, fast, and cheap way to communicate. It can be divided into two types of emails: spam and ham. More than half of the letters received by the user – spam. To use Email efficiently without the threat of losing personal information, you should develop a spam filtering system. The aim of this work is to reduce the amount of spam using a classifier to detect it. The most accurate spam classification can be achieved using machine learning methods. A natural language processing approach was chosen to analyze the text of an email in order to detect spam. For comparison, the following machine learning algorithms were selected: Naive Bayes, K-Nearest Neighbors, SVM, Logistic regression, Decision tree, Random forest. Training took place on a ready-made dataset. Logistic regression and NB give the highest level of accuracy – up to 99%. The results can be used to create a more intelligent spam detection classifier by combining algorithms or filtering methods.
An Evaluation of Multilingual Offensive Language Identification Methods for the Languages of India

The pervasiveness of offensive content in social media has become an important reason for concern for online platforms. With the aim of improving online safety, a large number of studies applying computational models to identify such content have been published in the last few years, with promising results. The majority of these studies, however, deal with high-resource languages such as English due to the availability of datasets in these languages. Recent work has addressed offensive language identification from a low-resource perspective, exploring data augmentation strategies and trying to take advantage of existing multilingual pretrained models to cope with data scarcity in low-resource scenarios. In this work, we revisit the problem of low-resource offensive language identification by evaluating the performance of multilingual transformers in offensive language identification for languages spoken in India. We investigate languages from different families such as Indo-Aryan (e.g., Bengali, Hindi, and Urdu) and Dravidian (e.g., Tamil, Malayalam, and Kannada), creating important new technology for these languages. The results show that multilingual offensive language identification models perform better than monolingual models and that cross-lingual transformers show strong zero-shot and few-shot performance across languages.
Self-admitted technical debt practices: a comparison between industry and open-source

Self-admitted technical debt (SATD) consists of annotations, left by developers as comments in the source code or elsewhere, as a reminder about pieces of software manifesting technical debt (TD), i.e., “not being ready yet”. While previous studies have investigated SATD management and its relationship with software quality, there is little understanding of the extent and circumstances to which developers admit TD. This paper reports the results of a study in which we asked developers from industry and open-source about their practices in annotating source code and other artifacts for self-admitting TD. The study consists of two phases. First, we conducted 10 interviews to gather a first understanding of the phenomenon and to prepare a survey questionnaire. Then, we surveyed 52 industrial developers as well as 49 contributors to open-source projects. Results of the study show how the TD annotation practices, as well as the typical content of SATD comments, are very similar between open-source and industry. At the same time, our results highlight how, while open-source code is spread of comments admitting the need for improvements, SATD in industry may be dictated by organizational guidelines but, at the same time, implicitly discouraged by the fear of admitting responsibilities. Results also highlight the need for tools helping developers to achieve a better TD awareness.
Joint Negative and Positive Learning for Noisy Labels

Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the contaminated labels for the case of noisy data, the indirect learning approach that uses complementary labels (Negative Learning for Noisy Labels; NLNL) has proven to be highly effective in preventing overfitting to noisy data as it reduces the risk of providing faulty target. NLNL further employs a three-stage pipeline to improve convergence. As a result, filtering noisy data through the NLNL pipeline is cumbersome, increasing the training cost. In this study, we propose a novel improvement of NLNL, named Joint Negative and Positive Learning (JNPL), that unifies the filtering pipeline into a single stage. JNPL trains CNN via two losses, NL+ and PL+, which are improved upon NL and PL loss functions, respectively. We analyze the fundamental issue of NL loss function and develop new NL+ loss function producing gradient that enhances the convergence of noisy data. Furthermore, PL+ loss function is designed to enable faster convergence to expected-to-be-clean data. We show that the NL+ and PL+ train CNN simultaneously, significantly simplifying the pipeline, allowing greater ease of practical use compared to NLNL. With a simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification based on the superior filtering ability.
Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

Deep neural networks have significantly improved appearance-based gaze estimation accuracy. However, it still suffers from unsatisfactory performance when generalizing the trained model to new domains, e.g., unseen environments or persons. In this paper, we propose a plug-and-play gaze adaptation framework (PnP-GA), which is an ensemble of networks that learn collaboratively with the guidance of outliers. Since our proposed framework does not require ground-truth labels in the target domain, the existing gaze estimation networks can be directly plugged into PnPGA and generalize the algorithms to new domains. We test PnP-GA on four gaze domain adaptation tasks, ETH-to-MPII, ETH-to-EyeDiap, Gaze360-to-MPII, and Gaze360to-EyeDiap. The experimental results demonstrate that the PnP-GA framework achieves considerable performance improvements of 36.9%, 31.6%, 19.4%, and 11.8% over the baseline system. The proposed framework also outperforms the state-of-the-art domain adaptation approaches on gaze domain adaptation tasks. Code has been released at https://github.com/DreamtaleCore/PnP-GA.
Analysis of digital competence of educators (DigCompEdu) in teacher trainees: the context of Melilla, Spain

Abstract The Spanish autonomous city of Melilla, located in northwest Africa, has one of the highest academic failure and abandonment rates in Europe. An effective way to improve this situation would be to improve students’ digital competence. In order to do so, teachers must have competent digital skills themselves and also be able to teach them. To determine teachers’ level of digital competence, the Spanish adaptation of the European Framework for Digital Competence of Educators was used to analyse the self-assessment responses of teachers in training at the Faculty of Education and Sport Sciences in Melilla, Spain. Several quantitative techniques were used to analyse data collected from a questionnaire based on the items in the framework. Indicators were given to each competence using a factor analysis to contrast differences between undergraduate and postgraduate students. Correlations between some of the students’ characteristics and the competences were estimated using OLS. The results show students’ self-assessment level of digital competence in different areas and differences between the bachelor’s and master’s programmes. Digital competence gaps were also detected in teacher training, especially in security. The conclusions highlight the need to improve digital security and facilitate a higher level of digital skills in line with the framework. Indeed, more hours of training in digital competence are required while taking into account the educational context and the technological, pedagogical and content knowledge needed to teach. Equally, the same skills must be developed by educators in order for them to transmit digital competence to their students and support them in educational centres.
Efficient Blockchain-Enabled Large Scale Parked Vehicular Computing With Green Energy Supply

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced.
SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.
Implementation of a secure and privacy-aware E-Health record and IoT data sharing using blockchain

Medical applications are fast growing in popularity, both for professional use and for patient-centered apps. The current Health record and IoT data sharing systems however have a fair amount of problems associated with privacy and security. The secure and privacy aware E-Health record to propose a mechanism using blockchain and IPFS (InterPlanetary File System) which offers a solution to all these problems. It also includes limitations and safeguards on what can be done and cannot be done with your personal information in some cases. The IPFS data will be dispersed among the nodes. Use of IPFS (Interplanetary File System) to store health records, which has the benefit of being distributed which in turn makes record tamper-free. In addition, the proposed model keeps track of disease statistics without invading any patient's privacy. This is conceivable not only for web pages, but for any type of file that a computer might save, whether it's a document, an email, or even a database record, thanks to IPFS.
Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements

Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.
Evaluation Method of Deep Learning-Based Embedded Systems for Traffic Sign Detection

Traffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous vehicles; it is one of the most critical visual perception problems since failing in this task may cause accidents. This task is fundamental in decision-making and involves different internal conditions such as the internal processing system or external conditions such as weather, illumination, and complex backgrounds. At present, several works are focused on the development of algorithms based on deep learning; however, there is no information on a methodology based on descriptive statistical analysis with results from a solid experimental framework, which helps to make decisions to choose the appropriate algorithms and hardware. This work intends to cover that gap. We have implemented some combinations of deep learning models (MobileNet v1 and ResNet50 v1) in a combination of the Single Shot Multibox Detector (SSD) algorithm and the Feature Pyramid Network (FPN) component for TSD in a standardized dataset (LISA), and we have tested it on different hardware architectures (CPU, GPU, TPU, and Embedded System). We propose a methodology and the evaluation method to measure two types of performance. The results show that the use of TPU allows achieving a processing training time 16.3 times faster than GPU and better results in terms of precision detection for one combination.