Reviewerly/exHarmony huggingface dataset:
An Effective Data Fusion Model for Detecting the Risk of Transmission Line in Smart Grid

With the rapid prosperity of wireless communication, a new era of smart cities has arisen. During the implementation of smart cities, electricity supply plays a profound role and needs to be guaranteed. To do this, a health model with terminal sensors data is usually adopted to enable the security of the transmission line. However, due to the extreme weather condition and the interference of sensors, some transmitted values are missing, which renders the sensors unreliable. Unfortunately, the existing prediction methods have a lower accuracy and there still exists a gap between the real value and the predicted value. In this article, we propose a novel data fusion model, named as KNN-XGBoost, to predict the missing values. Specifically, in this model, it mainly consists of two parts: 1) KNN stage: a KNN model is used to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> nearest labels as new attributes and 2) XGBoost: an XGBoost model is applied to predict missing attribute values. The experimental results have demonstrated that our proposed model can improve the accuracy from 1.11% to 4.05%, 0.55% to 1.84%, and 1.10% to 4.04% than the other methods under the three grid health models, respectively.
Towards Trained Model Confidentiality and Integrity Using Trusted Execution Environments

In the security of machine learning, it is important to protect the confidentiality and integrity of trained models. The leakage of the data in a trained model leads to not only the infringement of intellectual property but also various attacks such as adversarial example attacks. Recent works have proposed several methods using trusted execution environments (TEEs), which provide an isolated environment that cannot be manipulated by malicious software, to protect the trained models. However, the protection using TEEs generally does not work well by simply porting the program of a trained model to a TEE. There are mainly several reasons: the limitation of the memory size in TEEs prevents loading a large number of parameters into memory, which is particularly a characteristic of deep learning; the increase in runtime due to TEEs; and the threat of parameter manipulation (the integrity of the trained model). This paper proposes a novel method based on TEEs to protect the trained models, mainly for deep learning on embedded devices. The proposed method is characterized by memory saving, low runtime overhead, and detection of parameter manipulation. In the experiments, it is implemented and evaluated using Arm TrustZone and OP-TEE.
$$\overline{\text {pin}}$$-TSVM: A Robust Transductive Support Vector Machine and its Application to the Detection of COVID-19 Infected Patients

Training a machine learning model on the data sets with missing labels is a challenging task. Not all models can handle the problem of missing labels. However, if these data sets are further corrupted with label noise, it becomes even more challenging to train a machine learning model on such data sets. We propose to use a transductive support vector machine (TSVM) for semi-supervised learning in this situation. We make this model robust to label noise by using a truncated pinball loss function with it. We name our approach, pin¯ -TSVM. We provide both the primal and the dual formulations of the obtained robust TSVM for linear and non-linear kernels. We also perform experiments on synthetic and real-world data sets to prove the superior robustness of our model as compared to the existing approaches. To this end, we use small as well as large-scale data sets to perform the experiments. We show that the model is capable of training in the presence of label noise and finding the missing labels of the data samples. We use this property of pin¯ -TSVM to detect the coronavirus patients based on their chest X-ray images.
Learning local descriptors with multi-level feature aggregation and spatial context pyramid

Despite that efforts have shifted to learning local descriptors with convolutional neural network (CNN) from hand-crafted realm, the inherent feature hierarchy within CNN has been rarely explored. To increase both the invariant and discriminative abilities of the CNN-based local descriptors by making use of the complementary representation powers of the feature maps at different levels of CNN, in this paper, we design a multi-level feature aggregation (MLFA) module to communicate information across pyramid levels effectively. Then, each level extracts a feature vector after feature fusion and the final descriptor concatenates these outputs. Moreover, to leverage the spatial structure within a local patch, we propose a novel spatial context pyramid (SCP) module to capture the spatial information. SCP is devised in a residual manner and only several additional parameters are introduced to the model. We implement our algorithm based on the HardNet framework and carry out comprehensive evaluation on the UBC Phototour, HPatches and ETH datasets. The experimental results demonstrate that the proposed method performs favorably against the state-of-the-art ones. Ablation study is also provided to show the effectiveness of each component.
Video Integrity Verification based on Blockchain

with the existence of the new revolution of software techniques, tampering with the videos became easier than ever. For retaining the integrity of the captured scenes, novel methods are required. Blockchain is now one of the most interesting integrity verification techniques. Blockchain provides trust among the participant nodes; "users" without the need of a trusted third party. In this paper, we propose a new blockchain-based framework for integrity verification of videos. In the proposed framework, the scene is captured and hashed in its original state and the captured frame; "video frame" is stored as a reference frame. The hash value, frame name, and timestamp of the capturing time are stored in a CSV file. The genesis block of a permissioned blockchain is created with this data. Other frames are captured for the same scene every predefined period and hashed. If the new hash value is identical to the reference hash, then the new frame is deleted to preserve storage space, and the metadata of the frame is stored in the same CSV file. If the hash values are not identical, then the name of the frame is changed to the timestamp of the capturing time, the frame then is propagated to all participant nodes, an email is sent to a predefined mail to take the appropriate action, and finally, a new block will be created with the new metadata. The proposed system ensures the integrity of the captured videos, ease to use and robustness.
Augmented Radiology: Feature Space Transfer Model for Prostate Cancer Stage Prediction

Recent improvements in medical image analysis using deep learning-based neural networks can potentially be exploited to enhance the performance of computer-aided detection/diagnosis systems. In this study, we propose a feature space transfer mode (FSTM) for learning the phenotype relationships between radiological images and pathological images. We hypothesize that high-level features from the same patient can be linked between different modality images with different resolutions. We refer to our method as “augmented radiology” because the inference model only requires radiological images as input while the prediction result can be linked to specific pathological phenotypes. We applied the proposed method to the pathological tumor classification (T0 vs. T2c/T3a and T0 vs. T2c vs. T3a) of prostate cancer and found that it achieved a high classification accuracy (0.880 for T0 vs. T2c/T3a and 0.825 for T0 vs. T2c vs. T3a) given only the radiological images as input. We also analyzed the validity of the proposed method by visualizing the transferred features and found that it can extract useful information for diagnosis embedded in radiological images. We conclude that the proposed method will significantly help improve the diagnostic prediction performance of radiological images.
Applications of Machine Learning in Fintech Credit Card Fraud Detection

Fintech utilizes innovative technology to offer improved monetary administrations and financial solutions. According to data from the prediction of Autonomous Research artificial intelligence (AI) technologies will allow financial institutions to reduce their operational costs by 22% by 2030. Throughout this paper, we study how AI and machine learning algorithms can lead to credit card fraud detection. After making the theoretical approach to the subject, we develop two different methods Autoencoder (semi-supervised learning) and Logistic Regression (supervised learning) for fraud detection with a high level of accuracy. The results obtained with both methods are promising as we were able to predict fraud transactions with 94% certainty.
DataPlanner

Over-the-top video (OTT) streaming accounts for the majority of traffic on cellular networks, and also places a heavy demand on users' limited monthly cellular data budgets. In contrast to much of traditional research that focuses on improving the quality, we explore a different direction---using data budget information to better manage the data usage of mobile video streaming, while minimizing the impact on users' quality of experience (QoE). Specifically, we propose a novel framework for quality-aware Adaptive Bitrate (ABR) streaming involving a per-session data budget constraint. Under the framework, we develop two planning based strategies, one for the case where fine-grained perceptual quality information is known to the planning scheme, and another for the case where such information is not available. Evaluations for a wide range of network conditions, using different videos covering a variety of content types and encodings, demonstrate that both these strategies use much less data compared to state-of-the-art ABR schemes, while still providing comparable QoE. Our proposed approach is designed to work in conjunction with existing ABR streaming workflows, enabling ease of adoption.
Load balancing Based Active Monitoring Load Balancer In Cloud Computing

Load balancing is one of the main issues related to task scheduling in cloud environment. Load balancing aims at avoiding the situation whenever some virtual machines are overloaded and others are under loaded, or idle. It may cause Service Level Agreement (SLA) violation and further may lead to the reduction of customer satisfaction level. In this study, we propose Improved Active Monitoring Load Balancer with Hill Climbing Algorithm (IAMLBHC) to improve the performance metrics such as response time and processing cost. The proposed approach is simulated and implemented in CloudAnalyst simulator. The performance of the proposed approach is compared with the most recent existing load balancing algorithms via the simulation. The experimental results show that the proposed approach IAMLBHC gives better performance in terms of response time, and processing cost compared with the existing similar algorithms.
Measuring and Evaluation of the Results of UI-Re-engineering in the Nursing Field

Since all systems and services are ultimately aimed at human users, it is essential to address the interaction between humans and machines. A disproportionately growing number of care recipients and the associated avalanche of data in all areas of modern care continuously increases the need for more efficient, higher-quality and at the same time more cost saving options for care and its networking of relatives, doctors and nurses. As part of a project to digitize dementia residential communities, an information platform called INFODOQ was developed. The system serves as a transparent information, coordination and communication platform for various dementia residential communities to optimize the daily care and nursing routine. After completion, the system underwent several analytical and empirical evaluations in which nursing teams were involved. The subject of the work presented here is the presentation of the evaluation results, the subsequent reengineering process of nursing documentation and the presentation of a specially developed analysis tool. We discuss the requirements and problems arising from the interaction of nursing staff with digital nursing documentation. Present analysis procedures regarding acceptance and usability for software development. Aiming at improvements in the interaction between caregivers and software through a new guideline for the development of care documentation. The principle of “ambient freedom of objects”, i.e., the proportionality of each individual object in the context of the granularity of the user interface. Characteristics include self-localization, clarity and simplicity.
Facial Expression Animation by Landmark Guided Residual Module

We study the problem of facial expression animation from a still image according to a driving video. This is a challenging task as expression motions are non-rigid and very subtle to be captured. Existing methods mostly fail to model these subtle expression motions, leading to the lack of details in their animation results. In this paper, we propose a novel facial expression animation method based on generative adversarial learning. To capture the subtle expression motions, Landmark guided Residual Module (LRM) is proposed to model detailed facial expression features. Specifically, residual learning is conducted at both coarse and fine levels conditioned on facial landmark heatmaps and landmark points respectively. Furthermore, we employ a consistency discriminator to ensure the temporal consistency of the generated video sequence. In addition, a novel metric named Emotion Consistency Metric is proposed to evaluate the consistency of facial expressions in the generated sequences with those in the driving videos. Experiments on MUG-Face, Oulu-CASIA and CAER datasets show that the proposed method can generate arbitrary expression motions on the source still image effectively, which are more photo-realistic and consistent with the driving video compared with results of state-of-the-art methods.
Efficient User-Generated Video Quality Prediction

Blind video quality assessment of user-generated content (UGC) has become a trending, challenging, unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve intelligent analysis and processing of UGC videos. However, previous video quality models are either incapable or inefficient for predicting the quality of complex, diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art models but with orders-of-magnitude faster runtime. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all datasets at a considerably lower computational expense. An implementation of RAPIQUE is online: https://github.com/vztu/RAPIQUE.
The Improvement of Elliptic Curve Factorization Method to Recover RSA’s Prime Factors

Elliptic Curve Factorization Method (ECM) is the general-purpose factoring method used in the digital computer era. It is based on the medium length of the modulus; ECM is an efficient algorithm when the length of modulus is between 40 and 50 digits. In fact, the main costs for each iteration are modular inverse, modular multiplication, modular square and greatest common divisor. However, when compared to modular multiplication and modular square, the costs of modular inverse and greatest common divisor are very high. The aim of this paper is to improve ECM in order to reduce the costs to compute both of modular inverse and greatest common divisor. The proposed method is called Fast Elliptic Curve Factorization Method (F-ECM). For every two adjacent points on the curve, only one modular inverse and one greatest common divisor will be computed. That means it implies that the costs in both of them can be split in half. Furthermore, the length of modulus in the experiment spans from 30 to 65 bits. The experimental results show that F-ECM can finish the task faster than ECM for all cases of the modulus. Furthermore, the computation time is reduced by 30 to 38 percent.
Practical heuristics to improve precision for erroneous function argument swapping detection in C and C++

Argument selection defects, in which the programmer chooses the wrong argument to pass to a parameter from a potential set of arguments in a function call, is a widely investigated problem. The compiler can detect such misuse of arguments only through the argument and parameter type for statically typed programming languages. When adjacent parameters have the same type or can be converted between one another, a swapped or out of order call will not be diagnosed by compilers. Related research is usually confined to exact type equivalence, often ignoring potential implicit or explicit conversions. However, in current mainstream languages, like C++, built-in conversions between numerics and user-defined conversions may significantly increase the number of mistakes to go unnoticed. We investigated the situation for C and C++ languages where developers can define functions with multiple adjacent parameters that allow arguments to pass in the wrong order. When implicit conversions – such as parameter pairs of types – are taken into account, the number of mistake-prone functions markedly increases compared to only strict type equivalence. We analysed a sample of projects and categorised the offending parameter types. The empirical results should further encourage the language and library development community to emphasise the importance of strong typing and to restrict the proliferation of implicit conversions. However, the analysis produces a hard to consume amount of diagnostics for existing projects, and there are always cases that match the analysis rule but cannot be “fixed”. As such, further heuristics are needed to allow developers to refactor effectively based on the analysis results. We devised such heuristics, measured their expressive power, and found that several simple heuristics greatly help highlight the more problematic cases.
A Theoretical Optimum Tilt Angle Model for Solar Collectors from Keplerian Orbit

Solar energy has been extensively used in industry and everyday life. A more suitable solar collector orientation can increase its utilization. Many studies have explored the best orientation of the solar collector installation from the perspective of data analysis and local-area cases. Investigating the optimal tilt angle of a collector from the perspective of data analysis, or guiding the angle of solar collector installation, requires an a priori theoretical tilt angle as a support. However, none of the current theoretical studies have taken the real motion of the Sun into account. Furthermore, a complete set of theoretical optimal tilt angles for solar energy is necessary for worldwide locations. Therefore, from the view of astronomical mechanics, considering the true orbit of the Sun, a mathematical model that is universal across the globe is proposed: the Kepler motion model is constructed from the solar orbit and transformed into the local Earth coordinate system. After that, the calculation of the optimal tilt angle solution is given. Finally, several examples are shown to demonstrate the variation of the optimal solar angle with month and latitude. The results show that for daily fixed solar collectors, the altitude angle of the collector should be about 6° above the noon solar altitude angle in summer and 6° lower in winter. For annual fixed collectors, the tilt angle should be slightly higher than the latitude. In summary, this study demonstrates that when a location is specified, this model can be used to calculate the theoretical optimum tilt angle of solar collectors for that position.
CrowdTC: Crowd-powered Learning for Text Classification

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.
A System to Access Online Services with Minimal Personal Information Disclosure

Abstract The General Data Protection Regulation highlights the principle of data minimization, which means that only data required to successfully accomplish a given task should be processed. In this paper, we propose a Blockchain-based scheme that allows users to have control over the personal data revealed when accessing a service. The proposed solution does not rely on sophisticated cryptographic primitives, provides mechanisms for revoking the authorization to access a service and for guessing the identity of a user only in cases of need, and is compliant with the recent eIDAS Regulation. We prove that the proposed scheme is secure and reaches the expected goal, and we present an Ethereum-based implementation to show the effectiveness of the proposed solution.
An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.