Reviewerly/exHarmony huggingface dataset:
NISQ+: Boosting quantum computing power by approximating quantum error correction

Quantum computers are growing in size, and design decisions are being made now that attempt to squeeze more computation out of these machines. In this spirit, we design a method to boost the computational power of nearterm quantum computers by adapting protocols used in quantum error correction to implement “Approximate Quantum Error Correction (AQEC):” By approximating fully-fledged error correction mechanisms, we can increase the compute volume (qubits $\times$ gates, or “Simple Quantum Volume (SQV)”) of near-term machines. The crux of our design is a fast hardware decoder that can approximately decode detected error syndromes rapidly. Specifically, we demonstrate a proof-of-concept that approximate error decoding can be accomplished online in near-term quantum systems by designing and implementing a novel algorithm in superconducting Single Flux Quantum (SFQ) logic technology. This avoids a critical decoding backlog, hidden in all offline decoding schemes, that leads to idle time exponential in the number of T gates in a program [58]. Our design utilizes one SFQ processing module per physical quantum bit. Employing state-of-the-art SFQ synthesis tools, we show that the circuit area, power, and latency are within the constraints of typical, contemporary quantum system designs. Under a pure dephasing error model, the proposed accelerator and AQEC solution is able to expand SQV by factors between 3,402 and 11,163 on expected near-term machines. The decoder achieves a 5% accuracy threshold as well as pseudo-thresholds of approximately 5%, 4.75%, 4.5%, and 3.5% physical error rates for code distances 3, 5, 7, and 9, respectively. Decoding solutions are achieved in a maximum of $\sim$20 nanoseconds on the largest code distances studied. By avoiding the exponential idle time in offline decoders, we achieve a 10x reduction in required code distances to achieve the same logical performance as alternative designs.
Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease

Single-modal neuroimaging-based diagnosis for brain diseases is a main routine due to the lack of advanced imaging devices, especially in rural hospitals. Transfer learning (TL) has demonstrate edits effectiveness in improving the performance of a computer-aided diagnosis (CAD) system, which transfers knowledge from the model of a related imaging modality (source domain (SD)) to that of the diagnosis modality (target domain (TD)). Multiple empirical kernel learning machine (MEKLM) is a newly proposed classifier with superior performance to the conventional classifiers with implicit kernel mapping. In this work, we propose a novel projective model(PM) based sparse MEKLM(PM-SMEKLM) algorithm to learn a cross-domain transformation by PM in way of the parameter-based TL, and then apply it to the neuroimaging-based CAD for brain diseases. Sparse learning is integrated into MEKLM to further select effective information in SD for knowledge transfer and enhance the generalization ability of the classifier in TD. The projection matrix in PM and sparse representations in MEKLM are jointly learned, which effectively improves the performance of PM-SMEKLM. Three experiments are conducted on two neuroimaging datasets for the diagnosis of Alzheimer’s disease and Parkinson’s disease, respectively. Experimental results show that the proposed PM-SMEKLM algorithm outperforms all the compared algorithms.
Bitcoin and Cryptocurrency: Challenges, Opportunities and Future Works

Bitcoin and other prominent cryptocurrencies have gained much attention since the last several years.Globally known as digital coin and virtual currency, this cryptocurrency is gained and traded within the blockchain system.The blockchain technology adopted in using the cryptocurrency has raised the eyebrows within the banking sector, government, stakeholders and individual investors.The rise of the cryptocurrency within this decade since the inception of Bitcoin in 2009 has taken the market by storm.Cryptocurrency is anticipated as the future currency that might replace the current paper currency worldwide.Even though the interest has caught the attention of users, many are not aware of its opportunities, drawbacks and challenges for the future.Researches on cryptocurrencies are still lacking and still at its infancy stage.In providing substantial guide and view to the academic field and users, this paper will discuss the opportunities in the cryptocurrency such as the security of its technology, low transaction cost and high investment return.The originality of this paper is on the discussion within law and regulation, high energy consumption, possibility of crash and bubble, and attacks on network.The future undertakings of cryptocurrency and its application will be systematically reviewed in this paper.
Bezier Search Differential Evolution Algorithm for numerical function optimization

Differential Evolution Algorithm (DE) is a commonly used stochastic search method for solving real-valued numerical optimization problems. Unfortunately, DE's problem solving success is very sensitive to the internal parameters of the artificial numerical genetic operators (i.e., mutation and crossover operators) used. Although several mutation and crossover methods have been developed for DE, there is not still an analytical method that can be used to select the most efficient mutation and crossover method while solving a problem with DE. Therefore, selection and parameter tuning processes of artificial numerical genetic operators used by DE are based on a trial-and-error process which is time consuming. The development of modern DE versions has been focused on developing fast, structurally simple and efficient genetic operators that are not sensitive to the initial values of their internal parameters. Problem solving successes of the Universal Differential Algorithms (uDE) are not sensitive to the structure and internal parameters of the related artificial numerical genetic operators used, unlike DE. In this paper a new uDE, Bezier Search Differential Evolution Algorithm, BeSD, has been proposed. BeSD’s mutation and crossover operators are structurally simple, fast, unique and produce highly efficient trial patterns. BeSD utilizes a partially elitist unique mutation operator and a unique crossover operator. In this paper, the experiments were performed by using the 30 benchmark problems of CEC2014 with Dim=30, and one 3D viewshed problem as a real world application. The problem solving success of BeSD was statistically compared with five top-methods of CEC2014, i.e., CRMLSP, MVO, WA, SHADE and LSHADE by using Wilcoxon Signed Rank test. Statistical results exposed that BeSD’s problem solving success is better than those of the comparison methods in general.
Attention! A Lightweight 2D Hand Pose Estimation Approach

Vision based human pose estimation is an non-invasive technology for Human-Computer Interaction (HCI). The direct use of the hand as an input device provides an attractive interaction method, with no need for specialized sensing equipment, such as exoskeletons, gloves etc, but a camera. Traditionally, HCI is employed in various applications spreading in areas including manufacturing, surgery, entertainment industry and architecture, to mention a few. Deployment of vision based human pose estimation algorithms can give a breath of innovation to these applications. In this article, we present a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module. Our proposed model can be deployed on an embedded system due to its lightweight nature with just 1.9 Million parameters. The source code and qualitative results are publicly available.
Optimized Single-Axis Schedule Solar Tracker in Different Weather Conditions

Improving the efficiency of solar panels is the main task of solar energy generation. One of the methods is a solar tracking system. One of the most important parameters of tracking systems is a precise orientation to the Sun. In this paper, the performance of single-axis solar trackers based on schedule and light dependent resistor (LDR) photosensors, as well as a stationary photovoltaic installation in various weather conditions, were compared. A comparative analysis of the operation of a manufactured schedule solar tracker and an LDR solar tracker in different weather conditions was performed; in addition, a simple method for determining the rotation angle of a solar tracker based on the encoder was proposed. Finally, the performance of the manufactured solar trackers was calculated, taking into account various weather conditions for one year. The proposed single-axis solar tracker based on schedule showed better results in cloudy and rainy weather conditions. The obtained results can be used for designing solar trackers in areas with a variable climate.
Improving Maritime Transport Sustainability Using Blockchain-Based Information Exchange

In this paper, the authors perform a comprehensive literature review of the positive impacts of blockchain-based information exchange in the maritime transport sector, as well as the challenges and barriers for successful blockchain-based information exchange, considering all three aspects of the sustainability (economic, environmental, and social). The papers from relevant databases (Web of Science and Scopus) and selected studies have been used. The literature coverage was expanded by using backward snowball sampling. In total, 20 positive impacts and 20 challenges/barriers were singled out. Despite the identified barriers and challenges (such as the slow acceptance of blockchain technology in the maritime transport sector or the high implementation cost), blockchain technology possesses a definite potential to improve the information exchange between all involved stakeholders (for example, by improving the visibility across transport routes and by reducing the paper-based processes), positively affecting all three aspects of sustainability. The authors contribute to the existing research of the economic aspect of maritime transport sustainability by blockchain-based information exchange by expanding it and by researching the environmental and social aspects of sustainability.
Cyberbullying severity detection: A machine learning approach

With widespread usage of online social networks and its popularity, social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. In this study, we have proposed a cyberbullying detection framework to generate features from Twitter content by leveraging a pointwise mutual information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation. Extracted features were applied with Naïve Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine algorithms. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa, classifier accuracy and f-measure metrics, as well as in a binary setting. These results indicate that our proposed framework provides a feasible solution to detect cyberbullying behavior and its severity in online social networks. Finally, we compared the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection.
The Topologies of Data Practices: A Methodological Introduction

This paper offers a methodological framework to research data practices in education critically. Data practices are understood in the generic sense of the word here, i.e., as the actions, performances, and the resulting consequences, of introducing data-producing technologies in everyday educational situations. The paper first distinguishes between data infrastructures, datafication and data points as three distinct, yet interrelated, phenomena. In order to investigate their concrete doings and specificities, the paper proposes a topological methodology that allows disentangling the relational nature and interwovenness of data practices. Based on this methodology, the paper proceeds with outlining a methodical toolbox that can be employed in studying data practices. Starting from nascent work on digital education platforms as a worked example, the toolbox allows researchers to investigate data practices as consisting of four unique topological dimensions: the Interface of a data practice, its actual Usage, its concrete Design, and its Ecological embeddedness - IUDE.
COVID-19 and Your Smartphone: BLE-Based Smart Contact Tracing

While contact tracing is of paramount importance in preventing the spreading of infectious diseases, manual contact tracing is inefficient and time consuming as those in close contact with infected individuals are informed hours, if not days, later. This article proposes a smart contact tracing (SCT) system utilizing the smartphone's Bluetooth low energy signals and machine learning classifiers to automatically detect those possible contacts to infectious individuals. SCT's contribution is two-fold: a) classification of the user's contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communication protocol. To protect the user's privacy, both broadcasted and observed signatures are stored in the user's smartphone locally and only disseminate the stored signatures through a secure database when a user is confirmed by public health authorities to be infected. Using received signal strength each smartphone estimates its distance from other user's phones and issues real-time alerts when social distancing rules are violated. Extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers indicate that a decision tree classifier outperforms other state-of-the-art classification methods with an accuracy of about 90% when two users carry their smartphone in a similar manner. Finally, to facilitate research in this area while contributing to the timely development, the dataset of six experiments with about 123 000 data points is made publicly available.
Decentralized Federated Averaging

Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training with an enormous number of clients. In FedAvg, clients keep their data locally for privacy protection; a central parameter server is used to communicate between clients. This central server distributes the parameters to each client and collects the updated parameters from clients. FedAvg is mostly studied in centralized fashions, requiring massive communications between the central server and clients, which leads to possible channel blocking. Moreover, attacking the central server can break the whole system&#x0027;s privacy. Indeed, decentralization can significantly reduce the communication of the busiest node (the central one) because all nodes only communicate with their neighbors. To this end, in this paper, we study the decentralized FedAvg with momentum (DFedAvgM), implemented on clients that are connected by an undirected graph. In DFedAvgM, all clients perform stochastic gradient descent with momentum and communicate with their neighbors only. To further reduce the communication cost, we also consider the quantized DFedAvgM. The proposed algorithm involves the mixing matrix, momentum, client training with multiple local iterations, and quantization, introducing extra items in the Lyapunov analysis. Thus, the analysis of this paper is much more challenging than previous decentralized (momentum) SGD or FedAvg. We prove convergence of the (quantized) DFedAvgM under trivial assumptions; the convergence rate can be improved to sublinear when the loss function satisfies the P&#x0141; property. Numerically, we find that the proposed algorithm outperforms FedAvg in both convergence speed and communication cost.
Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications

The emergence of deep learning model GAN (Generative Adversarial Networks) is an important turning point in generative modeling. GAN is more powerful in feature and expression learning compared to machine learning-based generative model algorithms. Nowadays, it is also used to generate non-image data, such as voice and natural language. Typical technologies include BERT (Bidirectional Encoder Representations from Transformers), GPT-3 (Generative Pretrained Transformer-3), and MuseNet. GAN differs from the machine learning-based generative model and the objective function. Training is conducted by two networks: generator and discriminator. The generator converts random noise into a true-to-life image, whereas the discriminator distinguishes whether the input image is real or synthetic. As the training continues, the generator learns more sophisticated synthesis techniques, and the discriminator grows into a more accurate differentiator. GAN has problems, such as mode collapse, training instability, and lack of evaluation matrix, and many researchers have tried to solve these problems. For example, solutions such as one-sided label smoothing, instance normalization, and minibatch discrimination have been proposed. The field of application has also expanded. This paper provides an overview of GAN and application solutions for computer vision and artificial intelligence healthcare field researchers. The structure and principle of operation of GAN, the core models of GAN proposed to date, and the theory of GAN were analyzed. Application examples of GAN such as image classification and regression, image synthesis and inpainting, image-to-image translation, super-resolution and point registration were then presented. The discussion tackled GAN’s problems and solutions, and the future research direction was finally proposed.
Photonic extreme learning machine by free-space optical propagation

Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here we present a neuromorphic photonic scheme - photonic extreme learning machines - that can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field that acts as feature mapping space. We experimentally demonstrated learning from data on various classification and regression tasks, achieving accuracies comparable to digital extreme learning machines. Our findings point out an optical machine learning device that is easy-to-train, energetically efficient, scalable and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.
Are NLP Models really able to Solve Simple Math Word Problems?

The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy.Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered "solved" with the bulk of research attention moving to more complex MWPs.In this paper, we restrict our attention to English MWPs taught in grades four and lower.We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets.To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs.Similarly, models that treat MWPs as bag-ofwords can also achieve surprisingly high accuracy.Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets.The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.
The Curious Case of Hallucinations in Neural Machine Translation

In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies.Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman (2020), and present an empirically validated hypothesis that explains hallucinations under source perturbation.Secondly, we consider hallucinations under corpus-level noise (without any source perturbation) and demonstrate that two prominent types of natural hallucinations (detached and oscillatory outputs) could be generated and explained through specific corpus-level noise patterns.Finally, we elucidate the phenomenon of hallucination amplification in popular data-generation processes such as Backtranslation and sequence-level Knowledge Distillation.We have released the datasets and code to replicate our results at https://github.com/vyraun/hallucinations.
Deep Fusion Clustering Network

Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.