Reviewerly/exHarmony huggingface dataset:
Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.
Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective

As machine learning (ML) technologies and applications are rapidly changing many domains of computing, security issues associated with ML are also emerging. In the domain of systems security, many endeavors have been made to ensure ML model and data confidentiality. ML computations are often inevitably performed in untrusted environments and entail complex multi-party security requirements. Hence, researchers have leveraged the Trusted Execution Environments (TEEs) to build confidential ML computation systems. This paper conducts a systematic and comprehensive survey by classifying attack vectors and mitigation in TEE-protected confidential ML computation in the untrusted environment, analyzes the multi-party ML security requirements, and discusses related engineering challenges.
New Predictor-Based Attacks in Processors

The microarchitectural state held by predictors in modern processors can leak sensitive information. This is the first work to analyze the security of a special type of predictor, the value predictor, and demonstrate new security attacks. The new attacks bypass all the existing predictor defenses which have not yet considered value predictors as sources of vulnerabilities. This work further shows there are many value predictor attack variants, as derived using our new attack model. This paper highlights the importance of security analysis of processor features before they are realized in silicon, so the security is understood at the design time.
MW-GAN+ for Perceptual Quality Enhancement on Compressed Video

The great success of deep learning has boosted the fast development of video quality enhancement. However, existing methods mainly focus on enhancing the objective quality of compressed video, and ignore their perceptual quality that plays a key role in determining quality of experience (QoE) of videos. In this paper, we aim at enhancing the perceptual quality of compressed video. Our main observation is that perceptual quality enhancement mostly relies on recovering the high-frequency details with fine textures. Accordingly, we propose a novel generative adversarial network (GAN) based on multi-level wavelet packet transform (WPT), which is called multi-level wavelet-based GAN+ (MW-GAN+), to exploit high-frequency details for enhancing the perceptual quality of compressed video. In MW-GAN+, we first propose a multi-level wavelet pixel-adaptive (MWP) module to extract temporal information across video frames, such that frame similarity can be utilized in recovering high-frequency details. Then, a wavelet reconstruction network, consisting of wavelet-dense residual blocks (WDRB), is developed to recover high-frequency details in a multi-level manner for enhanced frame reconstruction. Finally, we develop a 3D discriminator to encourage temporal coherence with a 3D-CNN based architecture. Experimental results demonstrate the superiority of our method over state-of-the-art methods in enhancing the perceptual quality of compressed video. Our code is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/IceClear/MW-GAN</uri></i> .
Development of Human Pose Recognition System by Using Raspberry Pi and PoseNet Model

New sports tools, which can be used anywhere, are needed for sports, recreation and/or health. In this paper, we propose a human pose recognition system developed on a single-board computer. The proposed system is constructed by PoseNet model, which is a neural networks-based pose estimation method, implemented by a Raspberry Pi. The proposed system detects 17 key-points of a human body from a camera. Then a set of key-points is matched to that of key-points registered beforehand. The validity and the effectiveness of the proposed system have been verified by some experiments.
Utilizing External Knowledge to Enhance Semantics in Emotion Detection in Conversation

Enabling machines to emotion recognition in conversation is challenging, mainly because information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. We propose KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets.
Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called 'Raptor'. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.
A Novel Tracking Algorithm Using Thermal and Optical Cameras Fused With mmWave Radar Sensor Data

Tracking of moving objects in pan-tilt-zoom (PTZ) cameras is a critical feature for automated surveillance applications. The PTZ auto tracking function enables the camera to control its electric motor mechanisms to adjust the course of the optical sensor field of view (FOV) to track moving objects automatically. In this paper, a proposed novel object tracking method based on the fusion of thermal, optical sensor and millimeter-wave data is implemented. The implementation of the proposed method is performed on a pan-tilt setup, which ultimately tracks the object of interest based on thermal data while keeping the optical image resolution the same as the thermal image resolution and provides valuable distance measurements to the target based on the millimeter-wave radar readings. The proposed method offers improvements over conventional optical sensor tracking by adding an extra layer to object tracking through additional thermal and millimeter-wave sensor data, which in turn provides a surplus of information of the object of interest that is being tracked. The proposed method compares favorably with commercially available surveillance systems.
Multi-Angle Projection Based Blind Omnidirectional Image Quality Assessment

Most of the existing blind omnidirectional image quality assessment (BOIQA) methods are based on data-driven approach where the end-to-end neural network or deep learning tools are mainly used for feature extraction. However, it usually lacks interpretability and is difficult to discover the perceptual mechanism behind. In this paper, from the perspective of perception modeling, we propose a novel multi-angle projection based BOIQA (MP-BOIQA) method. Considering the omnibearing and near eye display characteristics with head mounted display, multiple color cubemap projection images with respect to different viewpoints are grouped as the color omnidirectional distortion (COD) units so as to simulate the user’s viewing behavior in subjective quality assessment. In the designed multi-angle projection based feature extractor, tensor decomposition is implemented on each COD unit for dimensionality reduction, and piecewise exponential fitting is used to get the distribution of mean subtracted contrast normalized coefficients of the unit’s feature matrices in tensor domain. Finally, the extracted features are pooled with random forest. The experimental results on three omnidirectional image quality datasets show that the MP-BOIQA method can deliver highly competitive performance compared with some representative full-reference quality assessment methods, as well as some state-of-the-art BOIQA methods.
Multilevel Privacy Assurance Evaluation of Healthcare Metadata

Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation comprises the content analysis and process mining discovery and conformance checking techniques using real-world healthcare datasets. For clarity, we illustrate our evaluation findings using conceptual modeling frameworks, namely e3-value modeling and REA ontology. The conceptual modeling frameworks highlight the financial aspect of metadata share with a clear description of vital stakeholders, their mutual interactions, and respective exchange of information resources. The frameworks are further verified using experts’ opinions. Based on our empirical and normative evaluations, we provide the multilevel privacy assurance evaluation with a level of privacy increase and decrease. Furthermore, we verify that the privacy utility trade-off is crucial in shaping privacy increase/decrease because data utility in healthcare is vital for efficient, effective healthcare services and the financial facilitation of healthcare enterprises.
Advanced State Fuzzy Cognitive Maps applied on nearly Zero Energy Building model

Fuzzy Cognitive Maps method combines the advantages of Fuzzy Logic, such as their human reasoning and linguistic features, with the advantages of Neural Networks, such as their low mathematical calculation requirements, in order to model complex dynamic systems on a wide variety of applications. The system variables and their interconnections are described using a graph and a weight matrix. Application of experts' knowledge leads towards more realistic system models. In addition, the implementation of state-space theory in combination with learning algorithms, lead to a new generation of Fuzzy Cognitive Maps, the Advanced State Fuzzy Cognitive Maps. All the above are implemented on a nearly Zero Energy Building model, using real weather data and presenting its annual energy response.
DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets

We present DreamDrug, a crowdsourced dataset for detecting mentions of drugs in noisy user-generated item listings from darknet markets.Our dataset contains nearly 15,000 manually annotated drug entities in over 3,500 item listings scraped from the darknet market platform "DreamMarket" in 2017.We also train and evaluate baseline models for detecting these entities, using contextual language models fine-tuned in a few-shot setting and on the full dataset, and examine the effect of pretraining on in-domain unannotated corpora.
Aerobics Exercise Posture Tracking and Recognition System Based on Wireless Smart Sensors

Wireless sensors are a new, high-end, and popular exploration technology. The positioning and tracking of the human body are two important research issues in sensors. Aerobics is a widely popular sports item that is well-loved by the general public and integrates gymnastics, dance, music, fitness, and entertainment. The application of smart sensors helps to improve the coordination and flexibility of movements. In order to deeply study whether wireless smart sensors can play a role in tracking and recognizing aerobics exercise postures, this article uses sensor design methods, motion analysis, and software and hardware equipment architecture methods to collect samples, analyze smart sensors, and streamline algorithms. And it creates a sensor model and system for posture tracking and recognition. When testing the best position for the placement of the sensor, first fix the sensor at a distance of 2.5 cm from the wrist section and at the middle of the lower arm, keep the upper arm still, and do the stretching and contraction movements of the lower arm. Repeat the exercise 5 times and measure the wrist. The movement curve of the juncture, the result shows that the measured juncture curve at a distance of 2.5 cm is basically the same as the actual winding curve, and the fixed position in the middle deviates more from the normal curve point, and its average error is within 0.9 cm, which is correct. To test the effectiveness of the algorithm again, a total of 8 volunteers’ data were collected during the experiment, and the duration was more than 25 seconds, and each normal posture tried to maintain stability and lasted for about 6 seconds. The statistical accuracy rate is 90.6%. It shows that the algorithm or the system designed in this paper has extremely high accuracy. It is basically realized that from the theory of wireless sensor network, a system model that can be used for posture tracking and recognition of aerobics is designed.
AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing

Emerging edge computing (EC) systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. To this end, existing EC research has proposed several multiple drone-based edge deployments for various purposes, such as data caching, task offloading, real-time video analytics, and computer vision. However, none of them consider the ability of seamlessly integrating edge resources running across multiple drones in a single pool, to holistically manage and control these resources as well as to eliminate vendor lock-in situations. This paper presents an intelligent resource scheduling solution for a federated aerial EC system, called AirEdge, which jointly considers task dependencies, heterogeneous resource demand and drones’ flight time. We propose a multi-task execution time estimation and a dispatching policy, to select the closest drone deployment having congruent flight time and resource availability to execute ready tasks at any given time. For the utilization of the drones’ attached edge resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent tasks that co-locates tasks tightly on nodes to fully utilize available resources. Experiments on real-world data-trace from Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) and resource demands show the effectiveness, fast executions, and resource efficiency of our approach.
Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on direct feedback scenarios, where one has access to exact values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyperparameter configuration problems. However, in domains such as modelling human preferences, A/B tests, or recommender systems, there is a need for methods that can replace direct feedback with preferential feedback, obtained via rankings or pairwise comparisons. In this work, we present preferential batch Bayesian optimization (PBBO), a new framework that allows finding the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets.
Nested aggregation of experts using inducing points for approximated Gaussian process regression

Gaussian process regression is a flexible regression scheme but suffers from its high computational complexity regarding the inversion of a matrix with the same size as the training dataset. Aggregation method is one of the approximation techniques for reducing the complexity. In this paper, we propose a novel aggregation method, Nested Aggregation of Experts using Inducing Points (NAE-IP), which is an extension of a conventional method and enables dimensionality reduction by making use of the idea of linear sketching. There are some options for selecting inducing points in the proposed method. The options can introduce test points of interest as inducing points, albeit at the cost of slightly higher computational complexity. The other options exploiting less informative inducing points can yield a significant reduction of the computational complexity. The proposed NAE-IP is theoretically guaranteed to have consistency under certain conditions. Results of our computational experiments using synthetic and real data show that the proposed method achieves lower prediction error and even lower computing time than conventional methods.
Transfer Learning for Web Services Classification

Web service classification is one of the common approaches to discover and reuse services. Machine learning methods are widely used for web service classification. However, due to the limited high-quality services in the public dataset, the state-of-the-art deep learning methods can not achieve high accuracy. In this paper, we propose a transfer learning approach Tr-ServeNet to reuse the knowledge of the App classification problem for web service classification. We pre-train a deep learning model for the App classification problem, in which the dataset contains high-quality data from Apple Store, and then transfer the embedded and extracted features to assist web service classification. To demonstrate the effectiveness of our approach, we compare the proposed method with other existing machine learning methods on the 50-category benchmark with 10, 000 real-world web services. The experimental results indicate that the proposed transfer learning method can reach the highest Top-1 accuracy in the benchmark of service classification.
Energy-Aware Learning Agent (EALA) for Disaggregated Cloud Scheduling

Cloud data centers require enormous amounts of energy to run their clusters of computers. There are huge financial and environmental incentives for cloud service providers to increase their energy efficiency without causing significant negative impacts on their customers' qualities of experience. Increasing resource utilization reduces energy consumption by consolidating workloads on fewer machines and allows cloud service providers to turn off inactive devices. While traditional architectures only allow virtual machines (VMs) to use the memory and CPU resources of a single device, VMs in a disaggregated cloud can utilize the small residual capacities of multiple separate devices. Separating VM resources across multiple devices leads to severe fragmentation that eventually negates any positive impact disaggregation has on utilization. To address the fragmentation problem, we present a method of ensuring a cloud operates using the minimal number of devices over time. Here we introduce an Energy-Aware Learning Agent (EALA) that uses reinforcement learning to guarantee the system can meet minimal quality of service requirements and provide energy savings without the need for VM migration. We evaluate the use of EALA guiding the decisions of Best-Fit compared to vanilla Best-Fit using the Google cluster trace. We show that EALA improves utilization by 2% and reduces the number of times that compute nodes switch on and off by 11% compared to vanilla Best-Fit.
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark

In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, which is labor-intensive to collect and label. What’s more, the model can be ruined due to the domain shift between training data and testing data. Text recognition is a broadly studied field in computer vision and suffers from the same problems noted above due to the diversity of fonts and complicated backgrounds. In this paper, we focus on the text recognition problem and mainly make three contributions toward these problems. First, we collect a multi-source domain adaptation dataset for text recognition, including five different domains with over five million images, which is the first multi-domain text recognition dataset to our best knowledge. Secondly, we propose a new method called Meta Self-Learning, which combines the self-learning method with the meta-learning paradigm and achieves a better recognition result under the scene of multi-domain adaptation. Thirdly, extensive experiments are conducted on the dataset to provide a benchmark and also show the effectiveness of our method. The code of our work and dataset are available soon at https://bupt-ai-cz.github.io/Meta-SelfLearning/.