Reviewerly/exHarmony huggingface dataset:
Reasons for Nonuse, Discontinuation of Use, and Acceptance of Additional Functionalities of a COVID-19 Contact Tracing App: Cross-sectional Survey Study

In several countries, contact tracing apps (CTAs) have been introduced to warn users if they have had high-risk contacts that could expose them to SARS-CoV-2 and could, therefore, develop COVID-19 or further transmit the virus. For CTAs to be effective, a sufficient critical mass of users is needed. Until now, adoption of these apps in several countries has been limited, resulting in questions on which factors prevent app uptake or stimulate discontinuation of app use.The aim of this study was to investigate individuals' reasons for not using, or stopping use of, a CTA, in particular, the Coronalert app. Users' and nonusers' attitudes toward the app's potential impact was assessed in Belgium. To further stimulate interest and potential use of a CTA, the study also investigated the population's interest in new functionalities.An online survey was administered in Belgium to a sample of 1850 respondents aged 18 to 64 years. Data were collected between October 30 and November 2, 2020. Sociodemographic differences were assessed between users and nonusers. We analyzed both groups' attitudes toward the potential impact of CTAs and their acceptance of new app functionalities.Our data showed that 64.9% (1201/1850) of our respondents were nonusers of the CTA under study; this included individuals who did not install the app, those who downloaded but did not activate the app, and those who uninstalled the app. While we did not find any sociodemographic differences between users and nonusers, attitudes toward the app and its functionalities seemed to differ. The main reasons for not downloading and using the app were a perceived lack of advantages (308/991, 31.1%), worries about privacy (290/991, 29.3%), and, to a lesser extent, not having a smartphone (183/991, 18.5%). Users of the CTA agreed more with the potential of such apps to mitigate the consequences of the pandemic. Overall, nonusers found the possibility of extending the CTA with future functionalities to be less acceptable than users. However, among users, acceptability also tended to differ. Among users, functionalities relating to access and control, such as digital certificates or "green cards" for events, were less accepted (358/649, 55.2%) than functionalities focusing on informing citizens about the spread of the virus (453/649, 69.8%) or making an appointment to get tested (525/649, 80.9%).Our results show that app users were more convinced of the CTA's utility and more inclined to accept new app features than nonusers. Moreover, nonusers had more CTA-related privacy concerns. Therefore, to further stimulate app adoption and use, its potential advantages and privacy-preserving mechanisms need to be stressed. Building further knowledge on the forms of resistance among nonusers is important for responding to these barriers through the app's further development and communication campaigns.
Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment

In the interest of developing an intelligent manufacturing environment with an agile, efficient, and optimally utilized transportation system, mobile robots need to achieve a certain level of autonomy as they play an important role in carrying out transportation tasks. Bearing this in mind, in the paper we propose a novel stereo visual servoing method for nonholonomic mobile robot control based on semantic segmentation. Semantic segmentation provides a rich body of information required for an adequate decision-making process in a clustered, dynamic, and ever-changing manufacturing environment. The innovative idea behind the new visual servoing system is to utilize semantic information of the scene for visual servoing, as well as for other mobile robot tasks, such as obstacle avoidance, scene understanding, and simultaneous localization and mapping. Semantic segmentation is carried out by exploiting fully convolutional neural networks. The new visual servoing algorithm utilizes an intensity-based image registration procedure, which results in the image transformation matrix. The transformation matrix encompasses the relations of images taken at the current and desired pose, and that information is directly used for visual servoing. The developed algorithm is deployed on our own developed wheeled differential drive mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental evaluation is carried out in the 3D simulation environment and in the laboratory model of the real manufacturing environment. The experimental results show that the accuracy of the proposed approach is improved when compared to the state-of-the-art approaches while being robust to the partial occlusions of the scene and illumination changes.
Garnet major-element composition as an indicator of host-rock type: a machine learning approach using the random forest classifier

Abstract The major-element chemical composition of garnet provides valuable petrogenetic information, particularly in metamorphic rocks. When facing detrital garnet, information about the bulk-rock composition and mineral paragenesis of the initial garnet-bearing host-rock is absent. This prevents the application of chemical thermo-barometric techniques and calls for quantitative empirical approaches. Here we present a garnet host-rock discrimination scheme that is based on a random forest machine-learning algorithm trained on a large dataset of 13,615 chemical analyses of garnet that covers a wide variety of garnet-bearing lithologies. Considering the out-of-bag error, the scheme correctly predicts the original garnet host-rock in (i) &gt; 95% concerning the setting, that is either mantle, metamorphic, igneous, or metasomatic; (ii) &gt; 84% concerning the metamorphic facies, that is either blueschist/greenschist, amphibolite, granulite, or eclogite/ultrahigh-pressure; and (iii) &gt; 93% concerning the host-rock bulk composition, that is either intermediate–felsic/metasedimentary, mafic, ultramafic, alkaline, or calc–silicate. The wide coverage of potential host rocks, the detailed prediction classes, the high discrimination rates, and the successfully tested real-case applications demonstrate that the introduced scheme overcomes many issues related to previous schemes. This highlights the potential of transferring the applied discrimination strategy to the broad range of detrital minerals beyond garnet. For easy and quick usage, a freely accessible web app is provided that guides the user in five steps from garnet composition to prediction results including data visualization.
PSC-TG: RTL Power Side-Channel Leakage Assessment with Test Pattern Generation

Power side-channel attacks (SCAs) exploit leakage from cryptographic implementations to recover secrets in a non-invasive manner. Existing power side-channel assessment techniques mostly focus on post-silicon stages, suffering from the extremely low flexibility in changing designs to address identified leakages. In this paper, we propose a framework called PSC-TG which supports side-channel leakage assessment at the earliest stage of design cycle, i.e., RTL, allowing the maximum flexibility for countermeasure deployment. The assessment starts with RTL information flow tracking to identify the most sensitive variables according to pre-defined SCA-aware properties. Then, formal assertions are generated based on these variables and the presumed attack model to derive the corresponding test patterns. Next, the sidechannel vulnerability (SCV) metric is calculated using the estimated power with as low as two patterns to quantify the first-order sidechannel leakage. Besides, PSC-TG can give pass/fail indication for masked implementations at higher orders with t-test. We experimentally evaluate the leakage of multiple non-protected benchmarks at RTL, and validate with gate-level and FPGA results. Also, the t-test results of the masked Simon implementation are consistent with the post-silicon findings.
LGCNet: A local-to-global context-aware feature augmentation network for salient object detection

Recent works on salient object detection (SOD) mainly focus on pixel-level classification by leveraging fully convolutional network (FCN)-based encoder-decoder models. In this paper, considering that the context relation plays a critical role in defining a salient object appearing in a scene, we propose a local-to-global context-aware feature augmentation network, namely LGCNet. A two-branch attention-based context relation modeling structure is designed by considering global context-aware information based on foreground/background cues and global feature representations. A pixel-wise self-attention mechanism is then incorporated for both branches to propagate global context information to local feature representations. As a result, a coarse-to-fine salient object detection model is formulated. The whole framework can be trained in an end-to-end manner under a deeply supervised framework. Experimental results demonstrate the effectiveness of key components proposed in our LGCNet. Our LGCNet achieves promising results in comparison with 18 state-of-the-art methods on six widely-used benchmark datasets.
Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.
Continuous Training and Deployment of Deep Learning Models

Abstract Deep Learning (DL) has consistently surpassed other Machine Learning methods and achieved state-of-the-art performance in multiple cases. Several modern applications like financial and recommender systems require models that are constantly updated with fresh data. The prominent approach for keeping a DL model fresh is to trigger full retraining from scratch when enough new data are available. However, retraining large and complex DL models is time-consuming and compute-intensive. This makes full retraining costly, wasteful, and slow. In this paper, we present an approach to continuously train and deploy DL models. First, we enable continuous training through proactive training that combines samples of historical data with new streaming data. Second, we enable continuous deployment through gradient sparsification that allows us to send a small percentage of the model updates per training iteration. Our experimental results with LeNet5 on MNIST and modern DL models on CIFAR-10 show that proactive training keeps models fresh with comparable—if not superior—performance to full retraining at a fraction of the time. Combined with gradient sparsification, sparse proactive training enables very fast updates of a deployed model with arbitrarily large sparsity, reducing communication per iteration up to four orders of magnitude, with minimal—if any—losses in model quality. Sparse training, however, comes at a price; it incurs overhead on the training that depends on the size of the model and increases the training time by factors ranging from 1.25 to 3 in our experiments. Arguably, a small price to pay for successfully enabling the continuous training and deployment of large DL models.
Spoofing Attacks Against Vehicular FMCW Radar

The safety and security of the passengers in vehicles in the face of cyber attacks is a key concern in the automotive industry, especially with the emergence of the Advanced Driver Assistance Systems (ADAS) and the vast improvement in Autonomous Vehicles (AVs). Such platforms use various sensors, including cameras, LiDAR and mmWave radar. These sensors themselves may present a potential security hazard if exploited by an attacker.
Culture Sustainability through Co-Curricular Learning Program: Learning Batik Cross Review

As the application of colored designs on cloth using wax in certain areas in decorating items of clothing, Batik is made through several steps that come from drawing the pattern on the paper followed by imitating the pattern on fabric, which is called ngeblat. The next phase is followed by drawing the pattern using wax, which is called mencanting. The subsequent step is the process of coloring the pattern of Batik, called pencoletan, and then subsequently followed by color-locking on Batik through covering the Batik with wax, namely basic color dyeing. This process is called menembok. The final step is making the panting process through washing, called nglorod. This process should attempt to maintain Batik with cultural sustainability, as its process contains several values, such as discipline, creativity, independence, patriotism, responsibility, cooperation, and environmental care. Based on this background, this paper attempts to examine the Batik’s cultural design and to explore its sustainability through co-curricular school program activities. This study focuses on examining the potential in the process of the application of colored design through elaborating the supporting and interfering factors in a co-curricular learning program of Batik. The analysis was made through several points, namely a co-curricular learning program of Batik for enhancing cultural sustainability, a co-curricular learning program of Batik for enhancing national culture and community responsibility, and co-curricular learning of Batik for sustainability and environmental accountability.
Appenzeller to Brie: Efficient Zero-Knowledge Proofs for Mixed-Mode Arithmetic and Z2k

Zero-knowledge proofs are highly flexible cryptographic protocols that are an important building block for many secure systems. Typically, these are defined with respect to statements that are formulated as arithmetic operations over a fixed finite field. This inflexibility is a disadvantage when it comes to complex programs, as some fields are more amenable to express certain operations than others. At the same time, there do not seem to be many proofs with a programming model similar to those found in modern computer architectures that perform arithmetic with 32 or 64 bit integers.
AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

The integration of sensing technologies and cloud computing signifies the design perspectives of electronic healthcare systems. It has its own application domain to upload the clinical data of the patients and treatment procedures to the cloud server. Moreover, the data user may process the queries with suitable sensing parameters to obtain an appropriate medical record. As a result, the development of the Industrial Internet of Things (I-IoT) demands practical insights, trustworthiness, and reliability of intelligent automation to prevent the occurrence of potential risks in the process of production. In the past, multi-keyword searching (MKS) over encrypted cloud data has attracted researchers' attention. As cloud computing is highly practicing, data owners may easily outsource any kind of system data to commercial sites using the Industrial Internet of Things (I-IoT). However, data privacy and protection should be ensured using encryption techniques before any sensitive data is outsourced over insecure public networks. Providing cloud data encryption and secure keyword searching still exist as challenging issues. In I-IoT, cloud computing deals with a large amount of data users and documents, thus a technique like MKS is highly necessitated to process the search request and secure query processing. Thus, this paper presents a privacy-preservation phrase with multi-keyword ranked searching (PPP-MKRS) that introduces optimized filtering, binary tree index structure, and conjunctive keyword search to achieve secure searching efficiency. The experimental analysis shows that the proposed PPP-MKRS scheme consumes less computation, storage, and verification time in comparison with other searching encryption techniques.
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis

Both the issues of data deficiencies and semantic consistency are important for data augmentation.Most of previous methods address the first issue, but ignore the second one.In the cases of aspect-based sentiment analysis, violation of the above issues may change the aspect and sentiment polarity.In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments.We then substitute the unimportant tokens with two replacement strategies without altering the aspect-level polarity.Our approach is evaluated on several publicly available sentiment analysis datasets and the real-world stock price/risk movement prediction scenarios.Experimental results show that our methodology achieves better performances in all datasets.
Blockchain-Based Healthcare Information Preservation Using Extended Chaotic Maps for HIPAA Privacy/Security Regulations

A healthcare information system allows patients and other users to remotely login to medical services to access health data through the Internet. To protect the privacy of patients and security over the public network, secure communication is required. Therefore, the security of data in transmission has been attracting increasing attention. In recent years, blockchain technology has also attracted more attention. Relevant research has been published at a high rate. Most methods of satisfying relevant security-related regulations use modular and exponential calculation. This study proposes a medical care information preservation mechanism that considers the entire process of data storage in devices from wearable devices to mobile devices to medical center servers. The entire process is protected and complies with HIPAA privacy and security regulations. The proposed scheme uses extended chaotic map technology to develop ID-based key negotiation for wearable devices, and thereby reduces the amount of computing that must be carried out by wearable devices and achieve lightness quantify. It also uses the non-tamperability of the blockchain to ensure that the data have not been tampered with, improving data security. The proposed mechanism can resist a variety of attacks and is computationally lighter than the elliptic curve point multiplication that has been used elsewhere, while retaining its security characteristics.
Renal tumors segmentation in abdomen CT Images using 3D-CNN and ConvLSTM

Renal tumor is one of the common tumors with high incidence, and accurate segmentation of renal tumors is helpful for preoperative evaluation. Computed Tomography (CT) plays an important role in the treatment of renal tumors and accurate segmentation of tumors in CT images may provide critical information for surgery. In this paper, a segmentation approach based on deep learning with limited computation cost is proposed to improve the segmentation accuracy for kidneys and renal tumors. Firstly, a pre-trained restruction network is presented to alleviate small samples problems, which utilizes abdominal CT data to transfer network model effectively; Then, prior contour-assisted channel is introduced in two-dimensional network to segment the region of interest which contains kidneys and renal tumors and act as the input of the subsequente fine segmentation network; Finally, convolutional long short-term memory (ConvLSTM) is employed to extract spatial correlation information between slices and combined with a three-dimensional convolutional neural networks for fine segmentation. Several experiments on the 2019 renal tumor segmentation challenge(Kits19) dataset are designed to evaluate the performance of the proposed method, and the mean segmentation accuracy for kidneys and renal tumors are 96.39% and 78.91% for cross validation tests, which outperforms the other neural network algorithms, including 3D Res-Unet with 95.4% and 72.35%.
Usable User Authentication on a Smartwatch using Vibration

Smartwatches have come into wide use in recent years, and a number of smartwatch applications that improve convenience and user health are being developed and introduced constantly. Moreover, the latest smartwatches are now designed to operate without their paired smartphones, and as such, it is necessary for smartwatches to independently authenticate users. In these current devices, personal identification numbers (PIN) or patterns are entered to authenticate users, but these methods require inconvenient interaction for the user and are not highly secure. Particularly relevant to smartwatch technology, even user authentication based on biometric information needs either special sensors capable of measuring biometric information or user interaction. In this paper, we propose a usable method for user authentication on smartwatches without additional devices. Based on the fact that vibration is absorbed, reflected, and propagated differently according to the physical structure of each human body, our method is designed as a challenge-response scheme, in which the challenge is a random sequence of multiple vibration types that are already built into current smartwatches. The responses to vibrations are measured by the default gyroscope and accelerometer sensors in smartwatches. Moreover, our method is the first working model for commercial smartwatch models with low specifications when vibrating and measuring responses. We evaluated our method using a commercial smartwatch, and the results show that our method is able to authenticate a user with an equal error rate (EER) of 1.37%.