Reviewerly/exHarmony huggingface dataset:
A secure location-based alert system with tunable privacy-performance trade-off

Monitoring location updates from mobile users has important applications in many areas, ranging from public health (e.g., COVID-19 contact tracing) and national security to social networks and advertising. However, sensitive information can be derived from movement patterns, thus protecting the privacy of mobile users is a major concern. Users may only be willing to disclose their locations when some condition is met, for instance in proximity of a disaster area or an event of interest. Currently, such functionality can be achieved using searchable encryption. Such cryptographic primitives provide provable guarantees for privacy, and allow decryption only when the location satisfies some predicate. Nevertheless, they rely on expensive pairing-based cryptography (PBC), of which direct application to the domain of location updates leads to impractical solutions. We propose secure and efficient techniques for private processing of location updates that complement the use of PBC and lead to significant gains in performance by reducing the amount of required pairing operations. We implement two optimizations that further improve performance: materialization of results to expensive mathematical operations, and parallelization. We also propose an heuristic that brings down the computational overhead through enlarging an alert zone by a small factor (given as system parameter), therefore trading off a small and controlled amount of privacy for significant performance gains. Extensive experimental results show that the proposed techniques significantly improve performance compared to the baseline, and reduce the searchable encryption overhead to a level that is practical in a computing environment with reasonable resources, such as the cloud.
Exploring the Construction Task Performance and Cognitive Workload of Augmented Reality-Assisted Rebar Inspection Tasks

Augmented reality (AR) is capturing increasing attention as a way to provide cognitive assistance in many fields. Inspiring from the benefits of AR, architecture, engineering, and construction industries and researchers are interested to apply AR technologies for various construction tasks such as design review, assembly, inspection, or facility maintenance, etc. However, even though AR technologies are designed to support users' cognitive capability by providing superimposed information on real-world scenes, this information from AR may lead to one's cognitive overload, causing an adverse effect on task performance. As a result, it is important to understand how AR would affect one's cognitive load, and in turn, task performance for better use of AR systems in construction. As a preliminary investigation, this research compared task performance and cognitive loads during traditional paper-based and AR-assisted rebar inspection tasks in the laboratory setting. Participants' task performance was measured by task completion time and the number of rebars correctly detected, whereas cognitive load was measured by the NASA task load index (TLX). Based on the result, we discussed the impact of AR application on rebar inspection tasks from a cognitive perspective.
Hardware Implementation of Cancellable Biometric Systems

The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.
Design of an Automotive Radar Sensor Firmware Resilient to Cyberattacks

In this paper, we introduce a novel automotive radar sensor design resilient to cyberattacks. The proposed design can be implemented at the firmware level of the system which provides faster detection of cyberattacks without adding hardware complexity or being computationally expensive. This approach can be combined with any predictive filtering based approach implemented at higher system layers to provide additional security. Frequency modulated continuous wave (FMCW) radar is chosen to demonstrate the efficiency of the design in preventing cyberattacks as will be demonstrated by simulation results.
High speed VLSI architecture for improved region based active contour segmentation technique

Active contour segmentation is an important stage in image analysis applications. In this article, an improved region based active contour segmentation is proposed. The proposed active contour model speeds up the contour convergence by up to 40% while maintaining the advantages of a local region based active contour model by reducing the number of iterations. Moreover, we propose a low-complexity pipelined VLSI architecture for improved region based active contour model targeting FPGA and 90 nm ASIC platforms. The proposed pipelined design offers an increased speed of operation. Its complexity is independent of the size of image.
Learning performance in inverse Ising problems with sparse teacher couplings

Abstract We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher–student scenario under the assumption that the teacher’s couplings are sparse and the student does not know the graphical structure, the learning curve and order parameters are assessed in the typical case using the replica and cavity methods from statistical mechanics. Our formulation is also applicable to a certain class of cost functions having locality; the standard likelihood does not belong to that class. The derived analytical formulas indicate that the perfect inference of the presence/absence of the teacher’s couplings is possible in the thermodynamic limit taking the number of spins N as infinity while keeping the dataset size M proportional to N , as long as α = M / N &gt; 2. Meanwhile, the formulas also show that the estimated coupling values corresponding to the truly existing ones in the teacher tend to be overestimated in the absolute value, manifesting the presence of estimation bias. These results are considered to be exact in the thermodynamic limit on locally tree-like networks, such as the regular random or Erdős–Rényi graphs. Numerical simulation results fully support the theoretical predictions. Additional biases in the estimators on loopy graphs are also discussed.
Managing SLA Violation in the cloud using Fuzzy re-SchdNeg Decision Model

The rapid increase in the number of consumers and a dynamic change like cloud computing causes different challenges for the service providers to fulfil the service level agreement (SLA). When the SLA violation occurs, the SaaS providers not only to pay the agreed penalty in terms of money, but the trusted relationship may affect that impact on lose of current or potential customers. Therefore, the interacting parties need a proactive SLA management system that alerts beforehand the service provider when it detects any expected violation and provides an optimal decision system to regulate and scale available resources to avoid service violation. Most of the existing studies focused on predicting SLA violation to avoid SLA violation, however, they have ignored considering the process of rescheduling and renegotiation while dealing with service violation that plays a vital role to prevent service violation. In this paper, we propose a fuzzy rescheduling/negotiation (re-SchdNeg) decision support system that offers the best alternative solution for SaaS providers to apply rescheduling techniques to allocate potential violated jobs on available resources. When the system finds the available services are insufficient to handle the possible violated job, the system will start a renegotiation session.
Scalable Graph Neural Networks via Bidirectional Propagation

Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use sampling or layer-wise sampling techniques to reduce training time. However, these methods still suffer from degrading performance and scalability problems when applying to graphs with billions of edges. This paper presents GBP, a scalable GNN that utilizes a localized bidirectional propagation process from both the feature vectors and the training/testing nodes. Theoretical analysis shows that GBP is the first method that achieves sub-linear time complexity for both the precomputation and the training phases. An extensive empirical study demonstrates that GBP achieves state-of-the-art performance with significantly less training/testing time. Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than half an hour on a single machine. The codes of GBP can be found at this https URL .
Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a multi-task edge-recalibrated network (MTER-Net), which aims to overcome the challenges, including blurry boundaries, large inter-patient appearance variations, and low soft-tissue contrast. The proposed MTER-Net is equipped with the following novel components. (a) To exploit the saliency and stability of femoral heads, we employed a light-weight localization module to locate the target region and efficiently remove the complex background. (b) We add an edge stream to the regular segmentation stream to focus on processing the edge-related information, distinguish the organs with blurry boundaries, and then boost the overall segmentation performance. Between the regular segmentation stream and edge stream, we introduce an edge recalibration module at each resolution level to connect the intermediate layers and deliver the higher-level activations from the regular stream to the edge stream to denoise the irrelevant activations. (c) Finally, using a 3D Atrous Spatial Pyramid Pooling (ASPP) feature fusion module, we fuse the features at different scales in the regular stream and the predictions from the edge stream to form the final segmentation result. The proposed segmentation network was evaluated on 200 prostate cancer patient CT images with manually delineated contours of bladder, rectum, seminal vesicle, and prostate. The segmentation performance of the proposed method was quantitatively evaluated using three metrics including Dice similarity coefficient (DSC), average surface distance (ASD), and 95% surface distance (95SD). The proposed MTER-Net achieves average DSC of 86.35%, ASD of 1.09 mm, and 95SD of 3.53 mm on the four organs, which outperforms the state-of-the-art segmentation networks by a large margin. Specifically, the quantitative DSC evaluation results of the four organs are 96.49% (bladder), 86.39% (rectum), 76.38% (seminal vesicle), and 86.14% (prostate), respectively. In conclusion, we demonstrate that the proposed MTER-Net efficiently attains superior performance to state-of-the-art pelvic organ segmentation methods.
Distributed quantum information processing via single atom driving

We propose an unconventional scheme for quantum entangled state distribution (QESD) and quantum state transfer~(QST) based on a fiber-cavity-atom system, in which three atoms are confined, respectively, in three bimodal cavities connected with each other by optical fibers. The key feature of the scheme is the virtual excitation of photons, which yields QESD and QST between the two atoms in the edge-cavities conditioned on one-step operation only on the atom in the middle cavity. No actual operation is performed on the two atoms in the edge cavities throughout the scheme. Robustness of the scheme over operational imperfection and dissipation is discussed and the results show that system fidelity is always at a high level. Finally, the experimental feasibility is justified using laboratory available values.
Simulation of sports and health big data system based on FPGA and Internet of Things

The Internet of Things is an emerging information network technology, which can realize automatic identification, monitoring, and management of personnel, medical devices, medicines, etc. via networks. It is an effective means to reduce medical errors and improve work efficiency. This article is the first research theory and collects body temperature and exercise steps, temperature and acceleration sensors, collect health and exercise information, and estimate human health and exercise. Terminal node and customer information management system: Health and sports information gathering system notices that the system is divided into two parts. The Adaptive Data Transfer Technique (ADTT) method for the Internet of things is proposed. This method builds the type of parts based on the various features of the Internet of things devices, uses the model's visual methods, and between the components on the Internet-based on the communication characteristics of the Internet of the tourist network. Design a set of communication mechanisms. The experimental results confirm that a method of analysis is possible by collecting information on human movements using a motion information collection system. Multiple methods need to be combined to obtain as much information as possible to analyze human movements more scientific and rational. In this work, it introduced a cost-effective design of medical surveillance system based on IoT technology. The proposed system is modeled, controlled by a Xilinx FPGA device, and monitored with the IoT.
A survey of facial expression recognition based on deep learning

Facial expression recognition is the key research direction in many fields such as machine vision, pattern recognition and artificial intelligence. lt has become a research hotspot of many scholars and experts. This paper gives a comprehensive overview of deep expression recognition from image preprocessing, common expression recognition networks, and common expression databases. Then we review the current design and training strategies of deep neural networks based on static images and dynamic images, and discuss their advantages and limitations. Finally, the future challenges and corresponding opportunities in the field are analyzed, as well as the future direction of deep learning expression recognition systems.
ClsGAN: Selective Attribute Editing Model based on Classification Adversarial Network

Attribution editing has achieved remarkable progress in recent years owing to the encoder–decoder structure and generative adversarial network (GAN). However, it remains challenging to generate high-quality images with accurate attribute transformation. Attacking these problems, the work proposes a novel selective attribute editing model based on classification adversarial network (referred to as ClsGAN) that shows good balance between attribute transfer accuracy and photo-realistic images. Considering that the editing images are prone to be affected by original attribute due to skip-connection in encoder–decoder structure, an upper convolution residual network (referred to as Tr-resnet) is presented to selectively extract information from the source image and target label. In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images. Experimental results on CelebA demonstrate that our ClsGAN performs favorably against state-of-the-art approaches in image quality and transfer accuracy. Moreover, ablation studies are also designed to verify the great performance of Tr-resnet and Atta-cls.
A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears ( n = 125) were collected from patients with microscopy-confirmed P . falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P . falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p &lt; 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p &lt; 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P . falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.
Efficient Cross-Validation of Echo State Networks

Echo State Networks (ESNs) are known for their fast and precise one-shot learning of time series. But they often need good hyper-parameter tuning for best performance. For this good validation is key, but usually, a single validation split is used. In this rather practical contribution we suggest several schemes for cross-validating ESNs and introduce an efficient algorithm for implementing them. The component that dominates the time complexity of the already quite fast ESN training remains constant (does not scale up with k) in our proposed method of doing k-fold cross-validation. The component that does scale linearly with k starts dominating only in some not very common situations. Thus in many situations k-fold cross-validation of ESNs can be done for virtually the same time complexity as a simple single split validation. Space complexity can also remain the same. We also discuss when the proposed validation schemes for ESNs could be beneficial and empirically investigate them on several different real-world datasets.
Exploring the construction and infiltration strategies of social bots in sina microblog

Nowadays, millions of people use Online Social Networks (OSNs) like Twitter, Facebook and Sina Microblog, to express opinions on current events. The widespread use of these OSNs has also led to the emergence of social bots. What is more, the existence of social bots is so powerful that some of them can turn into influential users. In this paper, we studied the automated construction technology and infiltration strategies of social bots in Sina Microblog, aiming at building friendly and influential social bots to resist malicious interpretations. Firstly, we studied the critical technology of Sina Microblog data collection, which indicates that the defense mechanism of that is vulnerable. Then, we constructed 96 social bots in Sina Microblog and researched the influence of different infiltration strategies, like different attribute settings and various types of interactions. Finally, our social bots gained 5546 followers in the 42-day infiltration period with a 100% survival rate. The results show that the infiltration strategies we proposed are effective and can help social bots escape detection of Sina Microblog defense mechanism as well. The study in this paper sounds an alarm for Sina Microblog defense mechanism and provides a valuable reference for social bots detection.
The World of Defacers: Looking Through the Lens of Their Activities on Twitter

Many web-based attacks have been studied to understand how web hackers behave, but web site defacement attacks (malicious content manipulations of victim web sites) and defacers' behaviors have received less attention from researchers. This paper fills this research gap via a computational data-driven analysis of a public database of defacers and defacement attacks and activities of 96 selected defacers who were active on Twitter. We conducted a comprehensive analysis of the data: an analysis of a friendship graph with 10,360 nodes, an analysis on how sentiments of defacers related to attack patterns, and a topical modelling based analysis to study what defacers discussed publicly on Twitter. Our analysis revealed a number of key findings: a modular and hierarchical clustering method can help discover interesting sub-communities of defacers; sentiment analysis can help categorize behaviors of defacers in terms of attack patterns; and topic modelling revealed some focus topics (politics, country-specific topics, and technical discussions) among defacers on Twitter and also geographic links of defacers sharing similar topics. We believe that these findings are useful for a better understanding of defacers' behaviors, which could help design and development of better solutions for detecting defacers and even preventing impeding defacement attacks.
Alpha-divergence minimization with mixed variational posterior for Bayesian neural networks and its robustness against adversarial examples

In the approximate inference of Bayesian neural networks (BNNs), the variational posterior distribution is often taken an exponential family form (such as Gaussian). We propose to make the mixtures of exponential family distributions instead to get a more flexible approximation posterior. A novel reparameterization trick is introduced in this paper, in order to apply the reparameterization trick to mixed density distributions in Alpha divergence minimization. Our method is extendable to various neural architectures such as fully-connected neural networks and convolutional neural networks. The analysis on time complexity demonstrates that our method has less computation-consuming than normalizing flows. It also outperforms some related state-of-the-art techniques in the experiments of uncertainty estimation and robustness against adversarial examples.