Reviewerly/exHarmony huggingface dataset:
A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT

In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.
Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4

Remote eye tracking has become an important tool for the online analysis of learning processes. Mobile eye trackers can even extend the range of opportunities (in comparison to stationary eye trackers) to real settings, such as classrooms or experimental lab courses. However, the complex and sometimes manual analysis of mobile eye-tracking data often hinders the realization of extensive studies, as this is a very time-consuming process and usually not feasible for real-world situations in which participants move or manipulate objects. In this work, we explore the opportunities to use object recognition models to assign mobile eye-tracking data for real objects during an authentic students' lab course. In a comparison of three different Convolutional Neural Networks (CNN), a Faster Region-Based-CNN, you only look once (YOLO) v3, and YOLO v4, we found that YOLO v4, together with an optical flow estimation, provides the fastest results with the highest accuracy for object detection in this setting. The automatic assignment of the gaze data to real objects simplifies the time-consuming analysis of mobile eye-tracking data and offers an opportunity for real-time system responses to the user's gaze. Additionally, we identify and discuss several problems in using object detection for mobile eye-tracking data that need to be considered.
Decentralized Electronic Vote Based on Blockchain P2P

Voting has become one of the methods used by humans to determine decisions in a matter. As technology has developed, conventional Voting has also followed the transformation in the adaptation of today's digital age. There is an electronic version of this Voting, often referred to as e-voting (electronic Voting). Of course, the application of technology in Voting will lead to public opinion about concerns about maintaining network privacy and security in e-voting. Security problems can be overcome with technology that is currently being maximally utilized in various fields of life, namely blockchain technology. In improving the security of e-voting, Blockchain is used in P2P networks. Implementing the blockchain system in e-voting will result in a secure e-voting system to reduce the percentage of vote forgery in the e-voting system. Implementing a blockchain-based e-voting system will involve cryptography, software engineering theory, and voting theory. In realizing blockchain-based e-voting, several stages are required. (1) Counterfeit votes avoid adjusting the voting record model based on ledger technology, (2) in terms of authentication. The technology complemented by designing a user model based on Elliptic Curve Cryptography (ECC), (3) providing a model designed for voters to change their votes before the time limit specified. The implementation of Blockchain in this e-voting system is transparent with the existence of a blockchain system that provides notifications to all nodes to approve an incoming vote data. With this transparency, it can reduce fraud and the election of leaders according to people's choices.
Vision-based vehicle tracking on highway traffic using bounding-box features to extract statistical information

In this study, a new bounding-box based vehicle tracking algorithm is presented to extract statistical information in the highway traffic. A novel shaking filter and a new voting approach are employed in the vehicle detection and tracking phases to reduce camera shaking effects that cause misdetection, misclassification, and mistracking. The algorithm uses image streams captured via ordinary cameras and successfully classifies and determines the time-dependent vehicle trajectory through successive frames. The novel tracking algorithm utilizes the Euclidean distance-based similarity measure to associate the detected vehicles in successive frames, or it predicts the next state of vehicles using the linear/polynomial prediction functions obtained from the trajectory vector when the observed vehicles are disappeared from the scene due to the occlusion or illusion problems. The comparative vehicle counting results show that the proposed algorithm performs approximately 7% better than the Kalman filter-based tracker.
Synthetic-to-Real Domain Adaptation Joint Spatial Feature Transform for Stereo Matching

Most deep learning-based state-of-the-art stereo matching methods significantly depend on large-scale datasets. However, it is implausible to collect sufficient real-world samples with dense and clear ground-truth disparity maps in practice. Although synthetic datasets’ appearance has alleviated the demand for extensive real data, there is a domain shift between synthetic and real sets. To tackle this problem, we propose an individually trained synthetic-to-real domain adaptation (SDA) network that maps synthetic images into the real domain. Specifically, our approach translates the data style from synthetic domain to real domain while maintaining the content and the spatial information. First, edge cues are leveraged to guide domain adaptation in preserving the spatial consistency between input and the generated image. Second, we combine the spatial feature transform (SFT) layer to effectively fuse features from the edge map and the source image. Extensive experiments demonstrate that: 1) when only trained on synthetic data and generalized to real data, our model evidently outperforms many state-of-the-art domain adaptation methods; 2) our translated synthetic datasets (TSD) help to improve the generalization capability of any stereo matching CNNs. Codes and data will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Archaic-Atom/SDA_network</uri> .
Predicting Non-Markovian Superconducting-Qubit Dynamics from Tomographic Reconstruction

Non-Markovian noise presents a particularly relevant challenge in understanding and combating decoherence in quantum computers, yet is challenging to capture in terms of simple models. Here we show that a simple phenomenological dynamical model known as the post-Markovian master equation (PMME) accurately captures and predicts non-Markovian noise in a superconducting qubit system. The PMME is constructed using experimentally measured state dynamics of an IBM Quantum Experience cloud-based quantum processor, and the model thus constructed successfully predicts the non-Markovian dynamics observed in later experiments. The model also allows the extraction of information about cross-talk and measures of non-Markovianity. We demonstrate definitively that the PMME model predicts subsequent dynamics of the processor better than the standard Markovian master equation.
Pedestrian detection algorithm based on multi-scale feature extraction and attention feature fusion

Despite rapid development in anchor-free pedestrian detection algorithms, an optimistic trade-off between detection accuracy and efficiency is still far from being achieved. In this study, we proposed a new pedestrian detection algorithm based on multi-scale feature extraction and attention feature fusion, which is called MSAF-Net. Firstly, we designed a multi-scale dilate residual module to expand receptive fields while maintaining feature map sizes and improving the spatial sensitivity of information features. Secondly, through a joint attention feature fusion mechanism, the interaction of channel and space joint information features was captured. The context and initial features were integrated into strengthen extracted image features. Finally, a channel attention guidance mask branch was added to a detector to locate pedestrian position information accurately, and to improve the model's robustness. The experimental results demonstrated that our algorithm achieved satisfactory results on two well established datasets, i.e., CityPersons and Caltech. For CityPersons, the values of MR−2 were 9.04% and 40.41% under reasonable and heavy occlusion conditions, which are 0.36% and 3.89% better than the suboptimal comparison detection methods, proving the effectiveness and advancement of the proposed algorithm in this study.
Robust multipartite entanglement without entanglement breaking

Entangled systems in experiments may be lost or off-line in distributed quantum information processing. This inspires a general problem to characterize quantum operations, which result in breaking of entanglement or not. Our goal in this paper is to solve this problem both in single entanglement and network scenarios. We firstly propose a local model for characterizing all entangled states that are breaking for losing particles. This implies a simple criterion for witnessing single entanglement such as generalized GHZ states and Dicke states. It further provides an efficient witness for entangled quantum networks depending on its connectivity such as $k$-independent quantum networks, completely connected quantum networks, and $k$-connected quantum networks. These networks are universal resources for measurement-based quantum computations. The strong nonlocality can be finally verified by using nonlinear inequalities. These results show distinctive features of both single entangled systems and entangled quantum networks.
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks

While deep neural networks show unprecedented performance in various tasks, the vulnerability to adversarial examples hinders their deployment in safety-critical systems. Many studies have shown that attacks are also possible even in a black-box setting where an adversary cannot access the target model’s internal information. Most black-box attacks are based on queries, each of which obtains the target model’s output for an input, and many recent studies focus on reducing the number of required queries. In this paper, we pay attention to an implicit assumption of query-based black-box adversarial attacks that the target model’s output exactly corresponds to the query input. If some randomness is introduced into the model, it can break the assumption, and thus, query-based attacks may have tremendous difficulty in both gradient estimation and local search, which are the core of their attack process. From this motivation, we observe even a small additive input noise can neutralize most query-based attacks and name this simple yet effective approach Small Noise Defense (SND). We analyze how SND can defend against query-based black-box attacks and demonstrate its effectiveness against eight state-of-the-art attacks with CIFAR-10 and ImageNet datasets. Even with strong defense ability, SND almost maintains the original classification accuracy and computational speed. SND is readily applicable to pre-trained models by adding only one line of code at the inference.
Constant-Overhead Zero-Knowledge for RAM Programs

We show a constant-overhead interactive zero-knowledge (ZK) proof system for RAM programs, that is, a ZK proof in which the communication complexity as well as the running times of the prover and verifier scale linearly in the size of the memory N and the running time T of the underlying RAM program. Besides yielding an asymptotic improvement of prior work, our implementation gives concrete performance improvements for RAM-based ZK proofs. In particular, our implementation supports ZK proofs of private read/write accesses to 64~MB of memory (224 32-bit words) using only 34~bytes of communication per access, a more than 80x improvement compared to the recent BubbleRAM protocol. We also design a lightweight RISC CPU that can efficiently emulate the MIPS-I instruction set, and for which our ZK proof communicates only ~320 bytes per cycle, more than 10x less than the BubbleRAM CPU. In a 100 Mbps network, we can perform zero-knowledge executions of our CPU (with 64~MB of main memory and 4~MB of program memory) at a clock rate of 6.6 KHz.
Exploring and Slicing Volumetric Medical Data in Augmented Reality Using a Spatially-Aware Mobile Device

We present a concept and early prototype for exploring volumetric medical data, e.g., from MRI or CT scans, in head-mounted Augmented Reality (AR) with a spatially tracked tablet. Our goal is to address the lack of immersion and intuitive input of conventional systems by providing spatial navigation to extract arbitrary slices from volumetric data directly in three-dimensional space. A 3D model of the medical data is displayed in the real environment, fixed to a particular location, using AR. The tablet is spatially moved through this virtual 3D model and shows the resulting slices as 2D images. We present several techniques that facilitate this overall concept, e.g., to place and explore the model, as well as to capture, annotate, and compare slices of the data. Furthermore, we implemented a proof-of-concept prototype that demonstrates the feasibility of our concepts. With our work we want to improve the current way of working with volumetric data slices in the medical domain and beyond.
AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference

This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions. For a given input, AppealNet accurately predicts on-the-fly whether it can be successfully processed by the DL model deployed on the resource-constrained edge device, and if not, appeals to the more powerful DL model deployed at the cloud. This is achieved by employing a two-head neural network architecture that explicitly takes inference difficulty into consideration and optimizes the tradeoff between accuracy and computation/communication cost of the edge/cloud collaborative architecture. Experimental results on several image classification datasets show up to more than 40% energy savings compared to existing techniques without sacrificing accuracy.
Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search

Near-infrared-visual (NIR-VIS) heterogeneous face recognition (HFR) aims to match NIR face images with the corresponding VIS ones. It is a challenging task due to the sensing gaps among different modalities. Occlusions in the input face images make the task extremely complex. To tackle these problems, we present a Saliency Search Network (SSN) to extract domain-invariant identity features. We propose to automatically search the efficient parts of face images in a modality-aware manner, and remove redundant information. Moreover, the searching process is guided by an information bottleneck network, which mitigates the overfitting problems caused by small datasets. Extensive experiments on both complete and partial NIR-VIS HFR on multiple datasets demonstrate the effectiveness and robustness of the proposed method to modality discrepancy and occlusions.
SoK: Context Sensing for Access Control in the Adversarial Home IoT

In smart homes, access-control policies increasingly depend on contexts, such as who is taking an action, whether there is an emergency, or whether an adult is nearby. The vast literature on context sensing could potentially be leveraged to support contextual access control, yet this literature mostly ignores attacks, adversaries, and privacy. In this paper, we reevaluate the literature on home context sensing through a security and privacy mindset. We first describe a novel threat model in smart homes focusing on the capabilities of non-technical adversaries. Replay, imitation, and shoulder-surfing attacks are much more likely in this model. We summarize contexts relevant to access control in homes, mapping them to existing sensors. We then systematize the sensing literature to construct a decision framework for home context sensing that considers security, privacy, and usability. Applying our framework, we find that current sensors do not fully mitigate likely threats in homes. Some sensors are susceptible to simple threats like physical denial-of-service attacks, making it easy to bypass policies relying on the absence of a characteristic. Many sensors collect more data than needed and are not effective for all groups of users or under all situations.
OASIS: One-pass aligned atlas set for medical image segmentation

Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited their extension to customized applications. By revisiting the superiority of atlas based segmentation methods, we present a new framework of One-pass aligned Atlas Set for Images Segmentation (OASIS). To address the problem of time-consuming iterative image registration used for atlas warping, the proposed method takes advantage of the power of deep learning to achieve one-pass image registration. In addition, by applying label constraint, OASIS makes the registration process focus on the regions to be segmented for improving the performance of segmentation. Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion. Our experimental results on the challenging task of prostate MR image segmentation demonstrate that OASIS is able to significantly increase the segmentation performance compared to other state-of-the-art methods.
On functional data analysis and related topics

This paper aims to present the various contributions to the Special Issue of the Journal of Multivariate Analysis on Functional Data Analysis and some related topics including High-Dimensional Statistics and Multivariate Analysis of complex data. The presentation is made by emphasizing on how the contributions are behaving among recent trends in the fields, in such a way that this paper can also be viewed as a selected bibliographical discussion about recent advances in these topics.
A novel animation authoring framework for the virtual teacher performing experiment in mixed reality

Abstract Virtual teachers embedded into reality enable the transfer of skills and knowledge to students by demonstration. However, authoring the animation of virtual characters aligned with real environments is difficult for educators. In this study, a novel animation authoring framework for demonstration teaching is proposed based on human–computer interaction and intelligent algorithm, and its application is demonstrated in mixed reality experimental education. We developed a simulation experiment environment; users could operate equipment using gestures by a Leap Motion controller. The interaction trajectory can be adaptively adjusted by the dynamic motion primitives algorithm to generate a virtual teacher demonstration trajectory aligned with real environments. For the details of the virtual teacher animation, the deep reinforcement learning algorithm performed the adaptive grabbing for objects of different shapes and sizes. Our experimental results showed that the framework is easy to use; users are able to easily author a natural animation of a virtual teacher performing a chemical experiment aligned with real environments. Also, the user feedback showed that the virtual teacher's demonstration animation in MR is impressive, and students can learn from the animation by observation and imitation.
Online multiple object tracking based on fusing global and partial features

Multiple object tracking (MOT) has gained increasing attention due to its academic and commercial interests in computer vision tasks. Most of the existing state-of-the-art MOT methods consider the tracking-by-detection (TBD) framework, which localizes the pedestrian in each frame and connects these object hypotheses into the trajectories without any initial labeling. These methods heavily depend on detection accuracy and data association. However, occlusion often occurs in real surveillance scenes. The frequent occlusion leads to many false detections and inaccurate appearance, decreasing the tracking performance. In this paper, we aim to propose a novel feature matching method that combines the global and partial feature matching model between two bounding boxes to improve the similarity measurement between them. Moreover, the new feature matching method leverages the advantage that global features can illustrate the whole image, and partial features can effectively handle occlusion and noise. In addition, we propose a detection modifier method based on human pose information. This detection method can be used to filter out false pedestrian detections. Finally, the experimental results demonstrate the effectiveness of our proposed method and achieve comparable performance with the state-of-the-art MOT trackers.
Advancing Local Distance Discrimination of Explosions and Earthquakes With Joint P/S and M<sub>L</sub>‐M<sub>C</sub> Classification

Abstract Classification of local‐distance, low‐magnitude seismic events is challenging because signals can be numerous and difficult to characterize with approaches developed for larger magnitude events observed at greater distances. Yet, accurate classification is important to studies of earthquake processes and detection of potential underground nuclear tests. Here, we combine two classification metrics: the three‐component ratio of high‐frequency P/S amplitudes and the difference between local and coda duration magnitudes (M L ‐M C ). The metrics use different parts of the high‐frequency wavefield and exhibit complementary sensitivity for classification of M ∼ 0.5–4 natural earthquakes and borehole explosions, which are the best analog for underground nuclear explosions. Using means from bootstrap resampling across four diverse geologic settings, joint classification achieves &gt;94.4% true positives and &lt;8.4% false positives when using ≥ 8 seismographs within 200 km of the source. This high performance is obtained without local site corrections, indicating that the method may be transportable for local event classification.