Reviewerly/exHarmony huggingface dataset:
Privileged Features Distillation at Taobao Recommendations

Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available. However, the consistency in turn neglects some discriminative features. For example, when estimating the conversion rate (CVR), i.e., the probability that a user would purchase the item if she clicked it, features like dwell time on the item detailed page are informative. However, CVR prediction should be conducted for on-line ranking before the click happens. Thus we cannot get such post-event features during serving.
Delving into Inter-Image Invariance for Unsupervised Visual Representations

Contrastive learning has recently shown immense potential in unsupervised visual representation learning. Existing studies in this track mainly focus on intra-image invariance learning. The learning typically uses rich intra-image transformations to construct positive pairs and then maximizes agreement using a contrastive loss. The merits of inter-image invariance, conversely, remain much less explored. One major obstacle to exploit inter-image invariance is that it is unclear how to reliably construct inter-image positive pairs, and further derive effective supervision from them since no pair annotations are available. In this work, we present a comprehensive empirical study to better understand the role of inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design. To facilitate the study, we introduce a unified and generic framework that supports the integration of unsupervised intra- and inter-image invariance learning. Through carefully-designed comparisons and analysis, multiple valuable observations are revealed: 1) online labels converge faster and perform better than offline labels; 2) semi-hard negative samples are more reliable and unbiased than hard negative samples; 3) a less stringent decision boundary is more favorable for inter-image invariance learning. With all the obtained recipes, our final model, namely InterCLR, shows consistent improvements over state-of-the-art intra-image invariance learning methods on multiple standard benchmarks. We hope this work will provide useful experience for devising effective unsupervised inter-image invariance learning. Code: https://github.com/open-mmlab/mmselfsup .
Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses

<h3>Background and Aims</h3> Detection and characterization of focal liver lesions (FLLs) is key for optimizing treatment for patients who may have a primary hepatic cancer or metastatic disease to the liver. This is the first study to develop an EUS-based convolutional neural network (CNN) model for the purpose of identifying and classifying FLLs. <h3>Methods</h3> A prospective EUS database comprising cases of FLLs visualized and sampled via EUS was reviewed. Relevant still images and videos of liver parenchyma and FLLs were extracted. Patient data were then randomly distributed for the purpose of CNN model training and testing. Once a final model was created, occlusion heatmap analysis was performed to assess the ability of the EUS-CNN model to autonomously identify FLLs. The performance of the EUS-CNN for differentiating benign and malignant FLLs was also analyzed. <h3>Results</h3> A total of 210,685 unique EUS images from 256 patients were used to train, validate, and test the CNN model. Occlusion heatmap analyses demonstrated that the EUS-CNN model was successful in autonomously locating FLLs in 92.0% of EUS video assets. When evaluating any random still image extracted from videos or physician-captured images, the CNN model was 90% sensitive and 71% specific (area under the receiver operating characteristic [AUROC], 0.861) for classifying malignant FLLs. When evaluating full-length video assets, the EUS-CNN model was 100% sensitive and 80% specific (AUROC, 0.904) for classifying malignant FLLs. <h3>Conclusions</h3> This study demonstrated the capability of an EUS-CNN model to autonomously identify FLLs and to accurately classify them as either malignant or benign lesions.
An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin

Teaching-learning-based optimization (TLBO) is a recently proposed meta-heuristic optimization method and demonstrates outstanding performance for solving numerous sciences and engineering problems. However, many studies have shown that TLBO has a strong bias towards converging to origin and poorly performs in the problems with shifted solutions. To conquer this problem, a novel self-adaptive hybrid self-learning based TLBO (SHSLTLBO) is proposed in this paper. By constructing a self-adaptive framework, the original TLBO is fused with the Gaussian distribution, and the novel updating law switches in two modes corresponding to the fitness during the searching process. Additionally, in order to avoid local convergence in the initialization, a self-learning phase is introduced, associated to the original teaching and learning phase. The performance of the proposed SHSLTLBO is tested in the numerical benchmark functions, where the comprehensive comparison carries out with the state-of-the-art TLBO variants and other meta-heuristic approaches. The results demonstrate superior advantages of the proposed algorithm at balancing along the evolutionary stages among the TLBO variants. In the experiments, the two well-performed meta-heuristic optimization methods, namely LSHADE and HCLPSO, are used as the comparative methods to SHSLTLBO, where their superior performances of them are shown on the shifted problems. With increasing dimensionality, the performance of TLBO goes down rapidly while LSHADE's relative ranking improves. Apart from these scenarios, the comparative results of different dimensional problems indicate that the proposed SHSLTLBO has the best convergence and stability in solving all 28 benchmarks with low dimension and shifted solutions.
DS-NLCsiNet: Exploiting Non-Local Neural Networks for Massive MIMO CSI Feedback

Channel state information (CSI) feedback plays an important part in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. However, it is still facing many challenges, e.g., excessive feedback overhead, low feedback accuracy and a large number of training parameters. In this letter, to address these practical concerns, we propose a deep learning (DL)-based CSI feedback scheme, named DS-NLCsiNet. By taking advantage of non-local blocks, DS-NLCsiNet can capture long-range dependencies efficiently. In addition, dense connectivity is adopted to strengthen the feature refinement module. Simulation results demonstrate that DS-NLCsiNet achieves higher CSI feedback accuracy and better reconstruction quality for the same compression ratio, when compared to state-of-the-art compression schemes.
Quantum Limits of Superresolution in a Noisy Environment

We analyze the ultimate quantum limit of resolving two identical sources in a noisy environment. We prove that in the presence of noise causing false excitation, such as thermal noise, the quantum Fisher information of arbitrary quantum states for the separation of the objects, which quantifies the resolution, always converges to zero as the separation goes to zero. Noisy cases contrast with a noiseless case where it has been shown to be nonzero for a small distance in various circumstances, revealing the superresolution. In addition, we show that false excitation on an arbitrary measurement, such as dark counts, also makes the classical Fisher information of the measurement approach to zero as the separation goes to zero. Finally, a practically relevant situation resolving two identical thermal sources, is quantitatively investigated by using the quantum and classical Fisher information of finite spatial mode multiplexing, showing that the amount of noise poses a limit on the resolution in a noisy system.
Efficient Identity-Based Distributed Decryption Scheme for Electronic Personal Health Record Sharing System

The rapid development of the Internet of Things (IoT) has led to the emergence of more and more novel applications in recent years. One of them is the e-health system, which can provide people with high-quality and convenient health care. Meanwhile, it is a key issue and challenge to protect the privacy and security of the user's personal health record. Some cryptographic methods have been proposed such as encrypt user's data before sharing it. However, it is complicated to share the data with multiple parties (doctors, health departments, etc.), due to the fact that data should be encrypted under each recipient's keys. Although several (t, n) threshold secret sharing schemes can share the data only need one encryption operation, there is a limitation that the decryption private key has to be reconstructed by one party. To offset this shortcoming, in this paper, we propose an efficient identity-based distributed decryption scheme for personal health record sharing system. It is convenient to share their data with multiple parties and does not require to reconstruct the decryption private key. We prove that our scheme is secure under chosen-ciphertext attack (CCA). Moreover, we implement our scheme by using the Java pairing-based cryptography (JPBC) library on a laptop and an Android phone. The experimental results show that our system is practical and effective in the electronic personal health record system.
IOT Based Predictive Maintenance Using LSTM RNN Estimator

Predictive maintenance is a smart solution for many industrial and commercial plants; it enables users to fix their devices before they fail. It is based on a mathematical model that predicts the failure time of the connected devices. In order to optimize the mathematical model large amount of data is required that increases the accuracy of prediction. Internet of Things (IoT) can provide these models with the required large data. Machine learning techniques have been used to produce increasingly effective solutions to predict the remaining useful life (RUL) of devices accurately. In this research a mathematical model is devised for general device that is connected to a central processing unit via IoT. The IoT provides a means of communication for the devices. It collects a huge amount of data from various sites. The large amount of collected data makes the prediction much closer to the true values. Two types of Neural Nets is used, Vanilla-RNN and LSTM-RNN, both showed good prediction results for a simple example of light bulbs. LSTM-RNN demonstrates better prediction results, where we recommend for critical devices. Other devices can use Vanilla-NN for its simplicity.
Health Information Exchange with Blockchain amid Covid-19-like Pandemics

The COVID-19 pandemic is stress-testing existing health information exchange systems.There exists an increasing demand for sharing patient information and efficiently responding to patient medial data requests.Current health information technologies lack data fluidity, especially for remotely sharing medical data beyond their protected, local data storage.This paper presents a blockchain-based data-sharing framework that leverages the properties of immutability and decentralization to ensure a secure, user-centric approach for accessing and controlling access to sensitive medical data.The proposed framework builds its foundations on a peer-to-peer network fueled by the distributed InterPlanetary File System combined with on-chain tagging, and on the use of cryptographic generation techniques for enabling a secure way of sharing medical data.The flow of information is orchestrated by a smart-contract deployed on a blockchain-based protocol to ensure traceability and data integrity.The effectiveness of the framework is demonstrated with the implementation of the framework over a pilot study.
Neural Network-Based Undersampling Techniques

Machine learning models have gained popularity nowadays for their potential to solve real-life issues when trained on pertinent data. In many cases, the real-life data are class imbalanced and hence the corresponding machine learning models trained on the data tend to perform poorly on metrics like precision, recall, AUC, F1, and G-mean score. Since class imbalance issue poses serious challenges to the performance of trained models, a multitude of research works have addressed this issue. Two common data-based sampling techniques have mostly been proposed-undersampling the data of the majority class and oversampling the data of the minority class. In this article, we focus on the former approach. We propose two novel algorithms that employ neural network-based approaches to remove majority samples that are found to reside in the vicinity of the minority samples, thereby undersampling the former to remove (or alleviate) the imbalance issue. We delineate the proposed algorithms and then test the proposed algorithms on some publicly available imbalanced datasets. We then compare the performance of our proposed algorithms to other popular undersampling algorithms. Finally, we conclude that our proposed algorithms outperform most of the existing undersampling approaches on most performance metrics.
Efficient and Scalable FPGA-Oriented Design of QC-LDPC Bit-Flipping Decoders for Post-Quantum Cryptography

Considering code-based cryptography, quasi-cyclic low-density parity-check (QC-LDPC) codes are foreseen as one of the few solutions to design post-quantum cryptosystems. The bit-flipping algorithm is at the core of the decoding procedure of such codes when used to design cryptosystems. An effective design must account for the computational complexity of the decoding and the code size required to ensure the security margin against attacks led by quantum computers. To this end, it is of paramount importance to deliver efficient and flexible hardware implementations to support quantum-resistant public-key cryptosystems, since available software solutions cannot cope with the required performance. This manuscript proposes an efficient and scalable architecture for the implementation of the bit-flipping procedure targeting large QC-LDPC codes for post-quantum cryptography. To demonstrate the effectiveness of our solution, we employed the nine configurations of the LEDAcrypt cryptosystem as representative use cases for QC-LDPC codes suitable for post-quantum cryptography. For each configuration, our template architecture can deliver a performance-optimized decoder implementation for all the FPGAs of the Xilinx Artix-7 mid-range family. The experimental results demonstrate that our optimized architecture allows the implementation of large QC-LDPC codes even on the smallest FPGA of the Xilinx Artix-7 family. Considering the implementation of our decoder on the Xilinx Artix-7 200 FPGA, the experimental results show an average performance speedup of 5 times across all the LEDAcrypt configurations, compared to the official optimized software implementation of the decoder that employs the Intel AVX2 extension.
An Innovative Fuzzy Logic-Based Machine Learning Algorithm for Supporting Predictive Analytics on Big Transportation Data

In the current era of high precision monitoring and big data, many public transit users are still suffering from problems caused by transit delays. To help address this problem, we design and develop an innovative fuzzy logic-based machine learning algorithm for supporting predictive analytics on big transportation data to helps detect and predict the delay of some modes of public transport. To demonstrate the usefulness of our machine learning algorithm as a solution to this problem, we use it on heterogeneous data-namely, transit data and weather data-to predict the expected delay of streetcars (aka trolley cars) in the Canadian city of Toronto. To make accurate prediction, our algorithm takes into account multiple factors such us rain, snow, temperature, time of day, and season. Evaluation results show the effectiveness and usefulness of our fuzzy logic based machine learning algorithm for predictive analytics on big transportation data, which is promising toward development of a predictive intelligent transport system (ITS).
Does code quality affect pull request acceptance? An empirical study

Pull requests are a common practice for making contributions and reviewing them in both open-source and industrial contexts. Our goal is to understand whether quality flaws such as code smells, anti-patterns, security vulnerabilities, and coding style violations in a pull request’s code affect the chance of its acceptance when reviewed by a maintainer of the project. We conducted a case study among 28 Java open-source projects, analyzing the presence of 4.7 M code quality flaws in 36 K pull requests. We analyzed further correlations by applying logistic regression and six machine learning techniques. Moreover, we manually validated 10% of the pull requests to get further qualitative insights on the importance of quality issues in cases of acceptance and rejection. Unexpectedly, quality flaws measured by PMD turned out not to affect the acceptance of a pull request at all. As suggested by other works, other factors such as the reputation of the maintainer and the importance of the delivered feature might be more important than other qualities in terms of pull request acceptance. . Researchers have already investigated the influence of the developers’ reputation and the pull request acceptance. This is the first work investigating code style violations and specifically PMD rules. We recommend that researchers further investigate this topic to understand if different measures or different tools could provide some useful measures.
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach

Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy physics experiments at the Large Hadron Collider (LHC) in Geneva. The center provides resources and services needed for data processing, storage, analysis, and distribution. Log records in the data center is a stochastic and non-stationary phenomenon in nature. We propose a real-time approach to monitor and classify log records based on sliding time windows, and a time-varying evolving fuzzy-rule-based classification model. The most frequent log pattern according to a control chart is taken as the normal system status. We extract attributes from time windows to gradually develop and update an evolving Gaussian Fuzzy Classifier (eGFC) on the fly. The real-time anomaly monitoring system has to provide encouraging results in terms of accuracy, compactness, and real-time operation.
Efficient Monocular Depth Estimation for Edge Devices in Internet of Things

As an essential part of Internet of Things, monocular depth estimation (MDE) predicts dense depth maps from a single red-green-blue (RGB) image captured by monocular cameras. Past MDE methods almost focus on improving accuracy at the cost of increased latency, power consumption, and computational complexity, failing to balance accuracy and efficiency. Additionally, when speeding up depth estimation algorithms, researchers commonly ignore their adaptation to different hardware architectures on edge devices. This article aims to solve these challenges. First, we design an efficient MDE model for precise depth sensing on edge devices. Second, We employ a reinforcement learning algorithm and automatically prune redundant channels of MDE by finding a relatively optimal pruning policy. The pruning approach lowers model runtime and power consumption with little loss of accuracy through achieving a target pruning ratio. Finally, we accelerate the pruned MDE while adapting it to different hardware architectures with a compilation optimization method. The compilation optimization further reduces model runtime by an order of magnitude on hardware architectures. Extensive experiments confirm that our methods are effective for images of different sizes on two public datasets. The pruned and optimized MDE achieves promising depth sensing with a better tradeoff among model runtime, accuracy, computational complexity, and power consumption than the state of the arts on different hardware architectures.
In-Door Assistant Mobile Application Using CNN and TensorFlow

Visually impaired people struggle to live without assistance or face any aspect of life alone especially with people that cannot afford extra assistance equipment. Usually impaired people receive assistance by either human or wearable devices. The first one bears the burden on the human, while the second adds financial burdens nevertheless the hassle of identifying an object is not decreased. Smartphones are almost accessible to everyone and equipped with accessibility features including sensors that can be utilized to help both visually impaired and sighted people. Thus, this paper proposes an approach using Convolutional Neural Network (CNN), speech recognition and smartphone camera calibration aiming at facilitating the process of indoor guidance for visually impaired people. A smartphone's camera acts as the user's eyes. A pre-trained CNN model is used for object detection and the distance to objects is calculated to guide the user toward the right directions and to warn them of obstacles. The speech recognition part is used as a communication channel between visually impaired people and the smartphone. Also, the proposed approach supports object personalising that helps to distinguish user's item from other items found in the room. To evaluate the personalized objected detection, a customized dataset is created for two objects. The experimental results indicate that the accuracy is 92% and 87% for both objects respectively. Also, we experiment the detect distance of two objects against their real distances. The results achieve 0.05 and 0.08 error ratio.
Which predictor is more predictive for Bitcoin volatility? And why?

Abstract Being more and more popular in the past 10 years, Bitcoin has drawn extensive attention from the press, scholars, and practitioners. The aim of this paper is to investigate which predictor is more predictive for Bitcoin volatility from the aspects of in‐sample and out‐of‐sample in a high‐speed changing world. We utilise the GARCH‐MIDAS model to examine the predictive power of five crucial predictors, including VIX, GVZ, Google Trends, GEPU, and GPR. Our findings provide strong evidence that GVZ exhibits strongest predictability for Bitcoin volatility over other competing predictors. Other empirical results based on different out‐of‐sample forecasting periods, alternative loss functions and combination methods further ensure our major conclusions are robust.