Reviewerly/exHarmony huggingface dataset:
Comparative Study on MATLAB based Joint Photographic Experts Group Image Size Reduction Using Shearlet and Wavelet Packet Transform for X-ray Images with Potential Hospital Data Storage Applications

Aim: Aim of this study is to compress X-ray images using wavelet packet transform and shearlet transform. These two transforms were compared for the better compression ratio to store large hospital data files in small memory space. Materials and methods: Sample X-ray images were, Wavelet packet (30) and Shearlet (30) for compression. Compression ratio was calculated. The significance of the data were calculated using SPSS software. Results: There was statistical significance between the wavelet packet and shearlet transform based compression ratio data (p&lt;0.05, independent sample t- test). Conclusion: wavelet packet transform has a better compression ratio than shearlet transform. Wavelet packet transform appears to produce the most consistent results with higher standard deviation (3.3) when compared with shearlet transform which appears to produce lower results with lower standard deviation (0.8).
Cost-sensitive design of quadratic discriminant analysis for imbalanced data

• Robust design of quadratic discriminant analysis to data imbalance. • The design relies on a multi-model selection strategy to form ROC and PR fronts. • Setting of parameters is less complex and is based only on training data. • Cross-validation procedures are not required in parameter setting. • The design compares favorably with standard QDA and other algorithms. Learning from imbalanced training data represents a major challenge that has triggered recent interest from both academia and industry. As far as classification is concerned, it has been observed that several algorithms provide low accuracy when designed out of imbalanced data sets, among which regularized quadratic discriminant analysis (R-QDA) is the most illustrative example. Based on recent asymptotic findings, the study in [2] has brought a better understanding of the reasons behind the excessive sensitivity of R-QDA to data imbalance, which allowed for the development of a novel quadratic based classifier that presents higher robustness to such scenarios. However, the selection of the parameters for this classifier relied on the minimization of the overall classification error rate, which is not considered as a relevant performance metric in extremely imbalanced training data. In this work, we follow a multi-model selection approach for the selection of the parameters of the classifier proposed in [2] . Such an approach involves solving a multi-objective optimization problem, but, contrary to related works, we do not resort to evolutionary algorithms to solve this problem but rather to a solely training data dependent technique based on asymptotic approximations for the classification performances. This allows us to transform the multi-objective optimization problem into a scalar optimization problem. Our proposed approach presents the main advantages of being more accurate and less complex, avoiding the need for computationally expensive cross-validation procedures. Its interest goes beyond the quadratic discriminant analysis, paving the way towards a principled method for the design of classification algorithms in imbalanced data scenarios.
Asymmetric Anomaly Detection for Human-Robot Interaction

Security in human-robot interaction is the focus of research in this field. Rapid detection of abnormal events that may cause danger in the interaction process can effectively reduce the probability of occurrence of danger. In general anomaly detection methods, 2D or 3D convolutional autoencoders are widely used for anomaly detection. Among them, 2D convolutional autoencoders are with good real-time performance and lower detection accuracy, while 3D convolutional autoencoders are with higher detection accuracy and insufficient real-time performance. In order to ensure realtime performance and obtain higher accuracy, an end-to-end asymmetric convolutional autoencoder network (ACANet) using both 2D and 3D convolutions is designed. Specifically, 3D convolution is used to build the encoder to learn comprehensive information in continuous input frames, and 2D convolution is used to build the decoder to model the information fast, a dimensional alignment module is constructed to connect the encoder and the decoder while avoiding a large number of calculations in the latent space of the 3D features output by the encoder, and the skip connections module is used to obtain accurate predictions. Anomaly detection can then be completed by evaluating the differences between results predicted by the ACANet and real frames. The experimental results show that our method achieves competitive accuracy on mainstream datasets and at the same time obtains the fastest speed. Compared with mainstream methods, this method is more suitable for anomaly detection tasks in human-robot interaction.
Face Detection in Blurred Surveillance Videos for Crime Investigation

Abstract Face detection itself acts as a significant problem in low-resolution surveillance video, primarily due to out-of-focus blur. Real-time and forensic analysis for Law enforcement desires superior face recognition system for person identification in surveillance applications. However, face detection influences face recognition in critical situations. If faces on the blacklist are not detected, recognition fails, which increases the burden and is a tremendous challenge for police authority. This motivates us to present the deblurring algorithm to improve the face detection rate and reduce the false-positive rate. Hence, this paper focuses on removing blur by applying a blind deconvolution algorithm, suppressing the ringing artifact in surveillance video by adopting Discrete Wavelet Transform (DWT). Finally, after this preliminary work, faces are detected by Yolo v2. The proposed framework works well in the sparsely crowded scenario and improves face detection rate compared to the Lucy-Richardson-Yolo v2 framework, which suffers from the problem mentioned earlier. Experiments and evaluations on the surveillance dataset show that the proposed blind deconvolution with ringing suppression-based solution outperforms the state-of-the-art methods in detecting both frontal and profile faces.
Benchmarking Accuracy and Precision of the Convolutional Neural Networks for Face Recognition on Makeup and Occluded Images

We benchmark established state-of-the-art Convolutional Neural Network (CNN) models for face recognition in the real-life conditions on the novel data set with makeup and occlusions. Strength and weaknesses of different CNN implementation architectures and degree of depth on particular types of makeup and occlusions are identified in the context of the selecting complementary models for the ensemble use. A practical approach of isolating uncertainty of the model verdicts trustworthiness in order to boost precision is investigated.
SLMAD: Statistical Learning-Based Metric Anomaly Detection

Technology companies have become increasingly data-driven, collecting and monitoring a growing list of metrics, such as response time, throughput, page views, and user engagement. With hundreds of metrics in a production environment, an automated approach is needed to detect anomalies and alert potential incidents in real-time. In this paper, we develop a time series anomaly detection framework called Statistical Learning-Based Metric Anomaly Detection (SLMAD) that allows for the detection of anomalies from key performance indicators (KPIs) in streaming time series data. We demonstrate the integrated workflow and algorithms of our anomaly detection framework, which is designed to be accurate, efficient, unsupervised, online, robust, and generalisable. Our approach consists of a three-stage pipeline including analysis of time series, dynamic grouping, and model training and evaluation. The experimental results show that the SLMAD can accurately detect anomalies on a number of benchmark data sets and Huawei production data while maintaining efficient use of resources.
A BGP Hijacking Detection Method based on Multi-dimensional Historical Data Analysis

BGP prefix hijacking is one of the top threats on the Internet. The traditional approaches are mainly to analyze the prefix changes of the control plane, or to use the active measurement detection method to obtain the reachability of the prefix to determine whether there is prefix hijacking. These approaches rely on extensive infrastructures, wide coverage of measurement points, and long-term continuous detection. In this paper, we propose a BGP prefix hijacking detection method based on multi-dimensional historical data analysis, which can avoid the high deployment cost and long detection delay of active measurement methods. We test the proposed method on 1487 prefix hijacking events identified by BGPStream, from which more than 99% (1475/1487) prefix hijacking events are detected. The results show that the proposed method can effectively and accurately detect prefix hijacking.
Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Breast cancer is a leading cause of death in women due to cancer. According to WHO in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25-30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thresholding method and mathematical morphological operations. Wavelet decomposition is performed on the Region of Interest (RoI) and the feature extraction process is performed using a GLCM method with 4 statistical features, namely, contrast, correlation, entropy, and homogeneity. Classification of benign and malignant tumors using the MIAS database provided an accuracy of 95.83 % with a sensitivity of 95.23 % and a specificity of 96.49 %. A comparison with other methods illustrates that the proposed method provides better performance.
OWSNet: Towards Real-time Offensive Words Spotting Network for Consumer IoT Devices

Every modern household owns at least a dozen of IoT devices like smart speakers, video doorbells, smartwatches, where most of them are equipped with a Keyword spotting (KWS) system-based digital voice assistant like Alexa. The state-of-the-art KWS systems require a large number of operations, higher computation, memory resources to show top performance. In this paper, in contrast to existing resource-demanding KWS systems, we propose a light-weight temporal convolution based KWS system named OWSNet, that can comfortably execute on a variety of IoT devices around us and can accurately spot multiple keywords in real-time without disturbing the device’s routine functionalities. When OWSNet is deployed on consumer IoT devices placed in the workplace, home, etc., in addition to spotting wake/trigger words like ‘Hey Siri’, ‘Alexa’, it can also accurately spot offensive words in real-time. If regular wake words are spotted, it activates the voice assistant; else if offensive words are spotted, it starts to capture and stream audio data to speech analytics APIs for autonomous threat and insecurities detection in the scene. The evaluation results show that the OWSNet is faster than state-of-the-art models as it produced $\approx$ 1-74 times faster inference on Raspberry Pi 4 and $\approx$ 1-12 times faster inference on NVIDIA Jetson Nano. In this paper, to optimize IoT use-case models like OWSNet, we present a generic multi-component ML model optimization sequence that can reduce the memory and computation demands of a wide range of ML models thus enabling their execution on low resource, cost, power IoT devices.
Improving Bounce Rate Prediction for Rare Queries by Leveraging Landing Page Signals

Bounce rate prediction for clicked ads in sponsored search advertising is crucial for improving the quality of ads shown to the user. Bounce rate represents the proportion of landing pages for clicked ads on which users spend less than a specified time signifying that the user did not find a possible match of their query intent with the landing page content. In the pay-per-click revenue model for search engines, higher bounce rates mean advertisers get charged without meaningful user engagement, which impacts user and advertiser retention in long term. In real-time search engine settings complex ML models are prohibitive due to stringent latency requirements. Also historical logs are ineffective for rare queries (tail) where the data is sparse, as well as for matching user intent to adcopy when the query and bidded keywords don’t exactly overlap (smart match). In this paper, we propose a real-time bounce rate prediction system that leverages lightweight features like modified tf, positional and proximity features computed from ad landing pages and improves prediction for rare queries. The model preserves privacy and uses no user based feature. The entire ensemble is trained on millions of examples from the offline user log of the Bing commercial search engine and improves the ranking metrics for tail queries and smart match by more than 2x compared to a model that only uses ad-copy-advertiser features.
Magnetic Resonance Image Denoising using Patchwise Convolutional Neural Networks

Magnetic Resonance Image (MRI) signal is usually degraded with additive white Gaussian noise in its real and imaginary components. The spatial 2D and 3D digital images are reconstructed from the complex domain noise corrupted MRI signal. The resulted noisy image follows the Rician probability distribution in which noise become signal-dependent. To provide quality MRI for clinical applications, these images are required to pre-process automatically by computerized algorithms. Thus, it becomes necessary to recover noise-free MRI from its noise degraded counterpart for accurate automatic analysis. In this paper, machine learning convolutional neural network-based (CNN) MRI denoising and artifacts removal technique is presented. The noise filtering networks learn the image details from the image patches pixel-by-pixel from noise residuals to restore the detailed image features in an end-to-end feed-back approach. The proposed technique shows comparable performance with existing other image restoration techniques without losing important image information.
SYSMODIS: A Systematic Model Discovery Approach

In this paper, we present an automated model discovery approach, called SYSMODIS, which uses covering arrays to systematically sample the input spaces. SYSMODIS discovers finite state machine-based models, where states represent distinct screens and the edges between the states represent the transitions between the screens. SYSMODIS also discovers the likely guard conditions for the transitions, i.e., the conditions that must be satisfied before the transitions can be taken. For the first time a previously unseen screen is visited, a covering array-based test suite for the input fields present on the screen as well as the actions that can be taken on the screen, is created. SYSMODIS keeps on crawling until all the test suites for all the screens have been exhaustively tested. Once the crawling is over, the results of the test suites are fed to a machine learning algorithm on a per screen basis to determine the likely guard conditions. In the experiments we carried out to evaluate the proposed approach, we observed that SYSMODIS profoundly improved the state/screen coverage, transition coverage, and/or the accuracy of the predicted guard conditions, compared to the existing approaches studied in the paper.
Anomaly Detection Algorithm Based on Semi-Supervised Collaborative Strategy

Abstract This paper analyzes the existing credit card anomaly detection classification algorithms, summarizes the parts that can be improved, and proposes an outlier detection classification algorithm based on the unsupervised algorithm and active learning decision trees. Since the 1990s, machine learning technology has been widely used in the field of credit card fraud detection. Among them, the supervised learning expert system used for classification tends to perform better than the unsupervised learning model. The good performance of supervised learning methods requires high learning costs. The training process is usually serial or partially parallel. Therefore, the computing power, time, and manual labeling cost for learning need to be considered as algorithm selection factors. However, manual labeling usually involves the counter or ATM sending information to the back-end large-value transaction authorization department for manual review, and then the employee judges whether it is an abnormal transaction based on experience. This rigorous process will produce a very large time delay. Combining this feature, this article selects the appropriate unsupervised outlier detection method, selects part of the training data, and uses a small part of the more valuable data for annotation learning. Experiments show that this method can improve the accuracy while saving the time cost of training. Compared with the training time, the added time of classification is negligible.
Speech Synthesis of Chinese Braille with Limited Training Data

This paper describes to our knowledge the first Chinese Braille speech synthesis system. The system consists of modules of Braille front-end processing, prosody prediction, and speech synthesis. The Braille front-end processing includes conversion from the common Braille to Pinyin, and a high-precision Chinese character prediction model. To achieve high precision prosody prediction under limited corpus conditions, we propose a prosody prediction model based on the RoBERTa pre-trained model, which achieves an accuracy of 94.42%. Finally, a real-time TTS system based on Tacotron2 and LPCNet is proposed. We modify Tacotron2, including introducing a forward attention mechanism and extending the autoregressive correlation step size to obtain more natural speech.
An Online Fault Tolerance Server Consolidation Algorithm

We study server consolidation problem in clouds under simultaneous failures of multiple servers, where consolidation means that cloud providers put tenants on shared servers to improve resource utilization and thus reduce operation and maintenance costs. With replicas of each tenant put on multiple servers, our objective is to minimize the total number of opened servers and ensure that a particular failure will not result in overload on any remaining server. In this paper, we propose Rotation algorithm. It packs comparable sizes replicas into the same type of servers and adopts a cyclic shift method to quickly reuse those already-opened servers without the need of new ones for new tenants. Through experimental evaluations, we show that the proposed algorithms can achieve a better performance than existing works and produce near-optimal replications allocation.
Matching User Accounts across Large-scale Social Networks based on Locality-sensitive Hashing

Cross-site user identification has recently attracted considerable attention from academia. Most existing methods mainly focus on measuring the similarity between two cross-site accounts, and few methods focus on matching the accounts of all users to the greatest extent possible, which may be due to the massive calculation problem of the latter case. As the first work to address this issue, we present a locality-sensitive hashing-based user identification (LoSHui), which mainly consists of four components. 1) We construct locality-sensitive hash function families that are suitable for determining the user trajectory. 2) After that, we present a method for projecting the users into buckets, which guarantees that two users with similar trajectories are placed in the same bucket with high probability. 3) Then, we construct the candidate user pairs in each hash bucket. 4) Finally, we propose a trajectory-based user identification on the chosen candidate pairs. Experiments on three ground-truth datasets show that LoSHui achieves excellent performance with the ratio of reduced running time reaching 82.81%, 70.77%, and 77.44%, which demonstrates that LoSHui can substantially reduce the number of calculations in user identification.
Inverse Reinforcement Learning for Generalized Labeled Multi-Bernoulli Multi-Target Tracking

The work presented here is a preliminary study on the feasibility for using the output of a Generalized Labeled Multi-Bernoulli filter as inputs to online imitation learning via Deep Inverse Reinforcement learning with the end goal of predicting the next states of each trajectory output from the filter. This work samples the labeled state trajectories from the filter and discretizes them to create episodes for learning. It is assumed the multi-target dynamics are unknown, but they are stochastic and act to maximize some unknown reward function. Because the ultimate goal is predicting the multi-target motion using only observations of the targets, Deep Q-learning is used to learn the dynamics. However, as this algorithm depends on the unknown reward function, Deep Inverse Reinforcement learning is used to learn the rewards. Due to the coupled nature of the learning problems, their solutions are iterated in an alternating fashion and upon reaching convergence, future states can be predicted over a given time horizon. The results are preliminary, and many extensions to this work are outlined.
Block-chain based federated learning for knowledge capital

With the rapid development of deep learning and structures of neural networks, parameter settings of trained neural networks become new products from knowledge. Since supervised learning is used in 85% of machine learning to train the neural networks, well-labeled training data becomes new knowledge capital. In order to stimulate the knowledge sharing and protect the Intelligent Property (IP) right and privacy of knowledge capital at the same time, we need a framework to conduct training of neural networks with certain encrypted data and exchange training results with certain rewards. Federated learning provides a framework to train the networks without disclose the privacy and a block-chain based transaction system can organize a group of participants under certain consensus and reward mechanism. Their combination is an effective solution to manage knowledge capital, called knowledge capital bank in this paper. This paper defines the economic meaning of knowledge capital and all behaviors of related participants. Then we propose a block-chain based federated learning algorithm for secured knowledge sharing with IP and privacy protection. Finally, a complete design for a use case of this algorithm called Knowledge capital bank is demonstrated.