Reviewerly/exHarmony huggingface dataset:
The use of blockchain technology in IoT-based healthcare: A concise guide

Blockchain technology allows for a decentralized network environment without a central authority. Due to the employment of cryptographic concepts, transactions are both safe and reliable. Due to the popularity of cryptocurrencies, blockchain technology has become prominent in diverse areas. Blockchain technology offers significant potential in the healthcare sector with an advanced conservative approach to entire healthcare systems for integrating legacy computations and improving the correctness of electronic medical data—a study of blockchain in the healthcare sector is considered in this comprehensive study. The goal is to showcase the technology’s advantages, issues, and prospects in healthcare. The study begins with a discussion of the background material and a detailed description of the approach employed. The results are then analyzed with a quantitative overview and an evaluation of the acquired information and its attributes, with the outcome of literary quality computations included. Finally, there is a study of the analysis and findings. According to the results, blockchain technology research in healthcare is increasing and it is mainly utilized for data exchange, health record management, and other applications. Other situations are incredibly uncommon. Most research provides distinct morphological designs as foundations, structures, or models. Results also reveal that most of the reviewed papers do not include technical specifications regarding the blockchain parts utilized. Most studies do not disclose any prototype construction or help with the development.
ImprovedSSGAN: Ranking Discriminator With Semi-Supervised GAN for Ordinal Information

We propose Improved SSGAN, a multi-Generator/Discriminator semi-supervised GAN architecture to address the well-known problem of mode collapse in addition to an improved classification for ordinal information. To reduce the vulnerability of the generator to a relatively superior discriminator, semi-supervised GAN was introduced to make the job of the discriminator tough. However, such architecture doesn’t solve the collapse problem where the generator is stuck generating some specific mode of the data. In this work, N-1 rank discriminators with two-dimensional outputs are proposed for ordinal information by applying rank estimation techniques. The first dimension in each discriminator is used to predict binary rank information which is aggregated to make the final prediction. The second dimension in each discriminator is independently used to train one or more generators where a collapse in any of the discriminators is supported by other discriminators. We have also extended the architecture to a conditioned generator where the output of one generator is fed into another, which improves image quality. Weight-sharing techniques among the discriminators have also shown a faster convergence during training. Through extensive experiments on age face data, we have demonstrated that Improved SSGAN outperforms the semi-supervised GAN both in image generation quality and age estimation.
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU

Over the past few years, super-resolution (SR) processing has achieved astonishing progress along with the development of deep learning. Nevertheless, the rigorous requirement for real-time inference, especially for video tasks, leaves a harsh challenge for both the model architecture design and the hardware-level implementation. In this article, we propose a hardware-aware acceleration on embedded GPU devices as a full-stack SR deployment framework. The most critical stage with dictionary learning applied in SR flow was analyzed in details and optimized with a tailored dictionary slimming strategy. Moreover, we also delve into the programming architecture of hardware while analyzing the model structure to optimize the computation kernels to reduce inference latency and maximize the throughput given restricted computing power. In addition, we further accelerate the model with 8-bit integer inference by quantizing the weights in the compressed model. An adaptive 8-bit quantization flow for SR task enables the quantized model to achieve a comparable result with the full-precision baselines. With the help of our approaches, the computation and communication bottlenecks in the deep dictionary learning-based SR models can be overcome effectively. The experiments on both edge embedded device NVIDIA NX and 2080Ti prove that our framework exceeds the performance of state-of-the-art NVIDIA TensorRT significantly and can achieve real-time performance.
DPBA-WGAN: A Vector-Valued Differential Private Bilateral Alternative Scheme on WGAN for Image Generation

The large amount of sensitive personal information used in deep learning models has attracted considerable attention for privacy security. Sensitive data may be memorialized or encoded into the parameters or generation of the Wasserstein Generative Adversarial Networks (WGAN), which can be prevented by implementing privacy-preserving algorithms during the parameter training process. Meanwhile, the model is also expected to obtain effective generated results. We propose a vector-valued differential private bilateral alternative (DPBA) algorithm, a novel perturbation method for the training process. The vector-valued Gaussian (VVG) noise involving functional structure information is injected into the WGAN to generate data with privacy protection, and the model is verified to satisfy differential privacy. The bilateral alternative noise can eventually randomly perturb the gradient and generates informative feature-rich samples. The dynamic noise and vector-based perturbation approach ensures privacy strength. After extensive evaluation, our algorithm outperformed state-of-the-art techniques in terms of usability metrics for all validation datasets. The downstream classification accuracy for the generated Modified National Institute of Standards and Technology (Mnist) was 97.04%, whereas that for the Fashion Modified National Institute of Standards and Technology (Fashion-Mnist) dataset was 80.91%. Mnist improved the average accuracy of the neural network classifier by at least 16.81%, and Fashion-Mnist by at least 3.55%. In the multichannel generation tasks, the binary classification accuracy improved by at least 10.4% compared to CelebFaces Attributes (CelebA), and the accuracy of the Street View House Numbers (SVHN) was as high as 86.1%. The perturbation method proved highly resilient to gradient attack recovery under simulated gradient attacks.
Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

Ultra-reliable low-latency communication (URLLC) has been introduced in the 5th Generation (5G) radios for mission-critical applications that demand strict reliability and latency traffic to guarantee the rapid delivery of short packets (up to 1 ms) with a success probability rate of 99.999%.The challenging reliability and latency requirements of URLLC have significant impact on the air-interface design, especially on the Hybrid Automatic Repeat reQuest (HARQ) mechanism.This study focuses on satisfying link latency requirements by reducing the delay that arises in the presence of the HARQ operation.To this end, we propose a Swift HARQ protocol empowered by machine learning techniques to estimate the decodability of a packet early enough within its maximum number of allowable retransmission attempts.This can allow the transmitter to react faster by dropping the non-decodable packets, or activating the repetition mode where parts of the HARQ feedback can be omitted.As shown through system-level simulations, the proposed model achieves a delay reduction of more than 50% compared to the traditional HARQ , and increases the system throughput by up to 40% when multiple HARQ retransmissions are required.
Zone-Enhanced Spatio-Temporal Representation Learning for Urban POI Recommendation

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers’ complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime-z <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> ne-space <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> OI embedding model ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ToP</b> ), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers’ POI check-in actions.
Modifications of bijective S-Boxes with linear structures

Abstract Various systematic modifications of vectorial Boolean functions have been used for finding new previously unknown classes of S-boxes with good or even optimal differential uniformity and nonlinearity. In this paper, a new general modification method is given that preserves the bijectivity property of the function in case the inverse of the function admits a linear structure. A previously known construction of such a modification based on bijective Gold functions in odd dimension is a special case of the new method.
Hybrid Motion Representation Learning for Prediction From Raw Sensor Data

Motion prediction from raw LiDAR sensor data has drawn increasing attention and led to a surge of studies following two main paradigms. One paradigm is global motion paradigm, which simultaneously detects objects from point clouds and predicts the trajectories of each object in the future. The other paradigm is local motion paradigm, which directly performs dense motion prediction pointwisely. We observe that global motion prediction can benefit from local motion representation, since it contains rich local displacement contexts that are not explicitly exploited in global motion prediction. Correspondingly, local motion prediction can benefit from global motion representation, since it provides object contexts to improve prediction consistency inside an object. However, the complement of these two motion representations has not fully explored in the literature. To this end, we propose Hybrid Motion Representation Learning (HyMo), a unified framework to address the problem of motion prediction by making the best of both global and local motion cues. We have conducted extensive experiments on nuScenes dataset. The experimental results demonstrate that the learned hybrid motion representation achieves state-of-the-art performance on both global and local motion prediction tasks.
Phase matching in quantum search algorithm

Abstract We systematically generalize the Grover algorithm in a density matrix formalism by exploiting the underlying two-dimensional subspace of the problem. Using this, we derive analytic expressions for the success probability after arbitrary iterations of the generalized Grover operator with two generic phase angles <?CDATA $(\alpha, \beta)$ ?> . We show for the phase matching condition <?CDATA $\alpha = -\beta = 0.268\pi$ ?> with three iterations, success probability <?CDATA $\geq0.8$ ?> can be achieved only with knowledge about the lower bound <?CDATA $\lambda = 0.14$ ?> , where λ is the ratio of marked to total number of states in the database. This result will improve the quantum search algorithm when applied to databases with unknown number of marked states in the specified regime of λ , at the cost of decreased efficiency in the smaller λ region.
Robust MelGAN: A robust universal neural vocoder for high-fidelity TTS

In current two-stage neural text-to-speech (TTS) paradigm, it is ideal to have a universal neural vocoder, once trained, which is robust to imperfect mel-spectrogram predicted from the acoustic model. To this end, we propose Robust MelGAN vocoder by solving the original multi-band MelGAN’s metallic sound problem and increasing its generalization ability. Specifically, we introduce a fine-grained network dropout strategy to the generator. With a specifically designed over-smooth handler which separates speech signal intro periodic and aperiodic components, we only perform network dropout to the aperodic components, which alleviates metallic sounding and maintains good speaker similarity. To further improve generalization ability, we introduce several data augmentation methods to augment fake data in the discriminator, including harmonic shift, harmonic noise and phase noise. Experiments show that Robust MelGAN can be used as a universal vocoder, significantly improving sound quality in TTS systems built on various types of data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Audio samples are available at https://RobustMelGAN.github.io/RobustMelGAN/
Using Genetic Programming to Identify Probability Distribution behind Data: A Preliminary Trial

Before conducting any further applications or performing more advanced processing, analyzing and realizing the probability distribution of data is a crucial task. Traditionally, statistical methods are being developed for this procedure. In recent years, researchers in computer science have proposed and applied different machine learning-based techniques to address the abovementioned problem. However, the existing solutions remain problematic and inconvenient, such as the need for human intervention and the complexity of the resulting models. Thus, in this paper, without causing deficiency and inconvenience, a genetic programming-based approach for the identification of probability functions is proposed, implemented, and tested. Based on our empirical trials, in an immense search space of mathematical expressions, the proposed and developed approach can effectively recognize (retrieve) the probability distribution function behind data.
A Study on Mandarin Chinese “Bu” Tone Sandhi Followed by English Words

This paper focuses on tone sandhi of the Chinese word “Bu” followed by English words. In Mandarin Chinese, the tone of the word “Bu” is tone 4 (the high-falling tone), and its tone sandhi rule is that it becomes tone 2 (the mid-rising tone) when followed by another tone 4 syllable. However, few researchers have paid attention to the tone sandhi rules of “Bu” when followed by English words. In this study, the tone sandhi rule of “Bu” when followed by English words is explored by a perceptual experiment and a pitch contour analysis. Results show that the Mandarin Chinese “Bu” tone sandhi also applies when “Bu” is followed by an English word. When followed by a high-falling monosyllabic English word, “Bu” will become tone 2. Furthermore, the pitch contour of English words embedded in Mandarin sentences is predictable according to their syllabic structure and lexical stress. This paper further proves that native Chinese speakers perceive English words inserted into Chinese speech as tonal-like language. The tonal patterns of English words summarized in this study can provide theoretical support for improving the naturalness of mixed-language speech synthesis.
AARS: A novel adaptive archive-based efficient counting method for machine learning applications

For many machine learning methods, while dealing with problems such as classification, clustering, prediction, and association rule mining, counting the occurrences of given queries plays a crucial role. However, these methods, which usually function in two different steps, i.e., learning and sampling, become impractical for large datasets due to computational costs or excessive memory consumption. Therefore, this paper proposes a novel approach to handle the counting queries. The proposed method is an adaptive archive-based method that offers efficient archiving with reduced computational time and moderate mem-ory requirements. We conduct numerous experiments to show the performance and scalability of the proposed approach on random queries, learning probabilistic networks, and association rule mining. From experimental results, we see that our proposed method outperforms the previously proposed ADtree, Bitmap and Radix strategies when applied to the datasets with higher dimensions and a large set of observations.
Conformal transformation twin-hyperspheres for highly imbalanced data to binary classification

With the growth of data, imbalanced data emerges in large numbers. When imbalance ratio between classes in the data is high, rare instances represented by minority classes are difficult to be found, moreover, the effect of minority classes is underestimated, which prevents classification models from learning minority class patterns or distributions accurately, thus declining in classification performance. In this paper, we investigated binary classification issue of highly imbalanced data, and proposed a conformal transformation twin-hypersphere method for it. To find hard-to-observe minority classes, twin-hyperspheres introduce conformal transformation to explore those sub-regions containing minority classes. Experiments on highly imbalanced datasets show that the proposed method wins state-of-the-art highly imbalanced classification methods and has better ability of minority class discovery. We indicate that conformal transformation can help non-linear kernels to perceive hard-to-observe sub-regions, and classification models with conformal transformations have better generalization.
Physics-Informed Graph Learning

An expeditious development of graph learning in recent years has found innumerable applications in several di-versified fields. Of the main associated challenges are the volume and complexity of graph data. The graph learning models suffer from the inability to efficiently learn graph information. In order to indemnify this inefficacy, physics-informed graph learning (PIGL) is emerging. PIGL incorporates physics rules while performing graph learning, which has enormous benefits. This paper presents a systematic review of PIGL methods. We begin with introducing a unified framework of graph learning models followed by examining existing PIGL methods in relation to the unified framework. We also discuss several future challenges for PIGL. This survey paper is expected to stimulate innovative research and development activities pertaining to PIGL.
HeteroGuard: Defending Heterogeneous Graph Neural Networks against Adversarial Attacks

Graph neural networks (GNNs) have achieved re-markable success in many application domains including drug discovery, program analysis, social networks, and cyber security. However, it has been shown that they are not robust against adversarial attacks. In the recent past, many adversarial attacks against homogeneous GNNs and defenses have been proposed. However, most of these attacks and defenses are ineffective on heterogeneous graphs as these algorithms optimize under the assumption that all edge and node types are of the same and further they introduce semantically incorrect edges to perturbed graphs. Here, we first develop, HetePR-BCD, a training time (i.e. poisoning) adversarial attack on heterogeneous graphs that outperforms the start of the art attacks proposed in the literature. Our experimental results on three benchmark heterogeneous graphs show that our attack, with a small perturbation budget of 15 %, degrades the performance up to 32 % (Fl score) compared to existing ones. It is concerning to mention that existing defenses are not robust against our attack. These defenses primarily modify the GNN's neural message passing operators assuming that adversarial attacks tend to connect nodes with dissimilar features, but this assumption does not hold in heterogeneous graphs. We construct HeteroGuard, an effective defense against training time attacks including HetePR-BCD on heterogeneous models. HeteroGuard outperforms the existing defenses by 3–8 % on Fl score depending on the benchmark dataset.
Target Tracking Based on Separate Modeling of Trajectory Shape and Dynamic Characteristics

This paper proposes a novel on-road target tracking algorithm based on the separate modeling of target trajectory shape and dynamic characteristics. A description of the target maneuvering behavior in one-dimensional mileage coordinates is given by a mean-adaptive acceleration model. In the absence of prior information about the road, the unknown target trajectory over a sliding window is modeled by third -degree polynomials. The algorithm presented here for on-road maneuvering target tracking based on the separate modeling has been verified through an experiment. Simulation results show that the pro-posed algorithm provides sufficient accuracy compared with existing algorithms with coupled modeling.
Investigation on the Advantages of Quantum Illumination Radar by Using Radar Equation

Quantum radar uses the strong correlations of entangled photons and has a great potential to improve the detection sensitivity. Recently, many research results have been achieved in theoretical analysis and experiments on the performance advantages of quantum illumination radar. However, there are limited researches on its detection range. Based on the analysis of the noise tolerance of entangled state photons, we study the advantages of quantum illumination radar in detection range based on radar equation. It is shown that the detection range of quantum illumination radar is larger than that of classical radar in some regimes due to the strong noise suppression ability of quantum radar.