Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximation algorithms for probabilistic $k$-center clustering

Uncertainty about data appears in many realworld applications and an important issue is how to manage, analyze and solve optimization problems over such data. An important tool for data analysis is clustering. When the data set is uncertain, we can model them as a set of probabilistic points each formalized as a probability distribution function which describes the possible locations of the points. In this paper, we study $k$-center problem for probabilistic points in a general metric space. First we present a fast greedy approximation algorithm that builds $k$ centers using a farthest-first traversal in $k$ iterations. This algorithm improves the previous approximation factor of the unrestricted assigned $k$-center problem from 10 (see [1]) to 6. Next we restrict the centers to be selected from all the probabilistic locations of the given points and we show that an optimal solution for this restricted setting is a 2-approximation factor solution for an optimal solution of the assigned $k$-center problem with expected distance assignment. Using this idea, we improve the approximation factor of the unrestricted assigned $k$-center problem to 4 by increasing the running time. The algorithm also runs in polynomial time when $k$ is a constant. Additionally, we implement our algorithms on three real data sets. The experimental results show that in practice the approximation factors of our algorithms are better than in theory for these data sets. Also we compare the results of our algorithm with the previous works and discuss about the achieved results.
A Flexible Attentive Temporal Graph Networks for Anomaly Detection in Dynamic Networks

Anomaly Detection in Dynamic Networks plays a critical role in various real-world applications such as cyberse-curity, e-commerce, and social media. Recent approaches based on graph neural networks have achieved fruitful results in static networks, and most of the researches focus on learning node embeddings to represent the large-scale complex networks, thereby facilitating downstream anomaly detection task. However, these methods require the whole network to extract intrinsic properties, so they are sensitive to the frequent changes of nodes and incapable of capturing the dynamism as realworld networks evolve over time. In this paper, we propose a novel framework DynAD for anomalous edge detection on time-evolving networks, which performs adaptive parameter learning in an end-to-end manner. In particular, DynAD first extracts fixed-size node embedding from each snapshot with temporal graph convolution and pooling operations. Then, it captures the temporal information of the graph sequence using Gated recurrent units (GRU) for anomaly detection, where an attention mechanism is employed to highlight the differences between the dynamic patterns. Experimental results on three real-world datasets illustrate that DynAD significantly outperforms the state-of-the-art baseline methods in anomaly detection.
Health State Estimation and Remaining Useful Life Prediction of Power Devices Subject to Noisy and Aperiodic Condition Monitoring

Condition monitoring of power devices is highly critical for safety and mission-critical power electronics systems. Typically, these systems are subjected to noise in harsh operational environment contaminating the degradation measurements. In dynamic applications, the system duty cycle may not be periodic and results in aperiodic degradation measurements. Both these factors negatively affect the health assessment performance. In order to address these challenges, this article proposes a health state estimation and remaining useful life prediction method for power devices in the presence of noisy and aperiodic degradation measurements. For this purpose, three-source uncertainties in the degradation modeling, including the temporal uncertainty, measurement uncertainty, and device-to-device heterogeneity, are formulated in a Gamma state-space model to ensure health assessment accuracy. In order to learn the device degradation behavior, a model parameter estimation method is developed based on a stochastic expectation-maximization algorithm. The accuracy and robustness of the proposed method are verified by numerical analysis under various noise levels. Finally, the findings are justified using SiC metal–oxide-semiconductor field-effect transistors (MOSFETs) accelerated aging test data.
SAT-Based Data-Flow Mapping Onto Array Processor

Recently, it has been common to perform parallel processing in machine learning. Reconfigurable array processor is drawing attention in terms of easy custom adjustment and high performance. We propose a method to map a data-flow onto an array processor using a SAT solver. The proposed method is combined with an automatic transformation method, which changes the order of calculations, to generate a more efficient computation scheme. We have solved mapping problems of matrix-vector multiplication. In our experiment, a SAT solver was more scalable than an ILP solver. Our method handled a dataflow of more than a hundred nodes using MAC operation. The automatic transformation under the associative and commutative laws is less scalable but successfully reduced calculation time. We have also mapped sparse matrix multiplication with varying latency and throughput and generated faster schedules utilizing the sparsity.
A Model-based Approach to Acoustic Reflector Localization with a Robotic Platform

Constructing a spatial map of an indoor environment, e.g., a typical office environment with glass surfaces, is a difficult and challenging task. Current state-of-the-art, e.g., camera- and laser-based approaches are unsuitable for detecting transparent surfaces. Hence, the spatial map generated with these approaches are often inaccurate. In this paper, a method that utilizes echolocation with sound in the audible frequency range is proposed to robustly localize the position of an acoustic reflector, e.g., walls, glass surfaces etc., which could be used to construct a spatial map of an indoor environment as the robot moves. The proposed method estimate the acoustic reflector’s position, using only a single microphone and a loudspeaker that are present on many socially assistive robot platforms such as the NAO robot. The experimental results show that the proposed method could robustly detect an acoustic reflector up to a distance of 1.5 m in more than 60% of the trials and works efficiently even under low SNRs. To test the proposed method, a proof-of-concept robotic platform was build to construct a spatial map of an indoor environment.
NLabel: An Accurate Familial Clustering Framework for Large-scale Weakly-labeled Malware

Automatic family labeling for malware is in demand, especially for today's malware scale. While business Anti-Virus engines provide an efficient family labeling method, the raw labels tend to be inconsistent. Prior works mitigate such inconsistency by detecting the aliases and majority voting to obtain the final family label. However, these methods solve the inconsistency in a coarse-grained and vulnerable manner, and the obtained family label is inaccurate sometimes. In this work, we propose NLabel to conduct familial clustering based on AV engines' raw labels. On the one hand, NLabel uses word embedding techniques to capture the similarity among raw labels, transform the inconsistent labels of the same family into similar semantic representations, and mitigate the inconsistency at finer granularity. On the other hand, we propose a hierarchical family clustering method to boost the performance of large-scale data sets. Experimental results show that our method outperforms the SOTA.
To Become Fashionable: A Brief Review of Outfit Compatibility

How to dress fashionably has become a major concern for people nowadays. In order to match a fashionable outfit, we need to make our outfit as compatible as possible. Thus, we need to exploit an effective outfit matching scheme and apply it to help people match their own clothes. Outfit compatibility is an emergent computer vision field which tackles this problem. It refers to whether multiple fashion items look good if worn together. Recently, researchers have proposed different compatibility algorithms and recommendation algorithms to make an outfit as compatible as possible and fulfill the users' personal need. In this paper, we present a detailed overview of major compatibility algorithms, such as neural network, and major recommendation algorithms, such as collaborative filtering in outfit compatibility. This paper evaluates the models based on different tasks and datasets and concludes current applications, challenges and possible solutions in outfit compatibility.
Real-Time Constrained Nonlinear Model Predictive Control on SO(3) for Dynamic Legged Locomotion*

This paper presents a constrained nonlinear model predictive control (NMPC) framework for legged locomotion. The framework assumes a legged robot as a floating base single rigid body with contact forces being applied to the body as external forces. With consideration of orientation dynamics evolving on the rotation manifold SO(3), analytic Jacobians which are necessary for constructing the gradient and the Gauss-Newton Hessian approximation of the objective function are derived. This procedure also includes the reparameterization of the robot orientation on SO(3) to orientation error in the tangent space of that manifold. Obtained gradient and Gauss-Newton Hessian approximation are utilized to solve nonlinear least squares problems formulated from NMPC in a computationally efficient manner. The proposed algorithm is verified on various types of legged robots and gaits in a simulation environment.
S-Shaped Metasurface-Based Wideband Circularly Polarized Patch Antenna for C-Band Applications

This research proposed an S-shaped metasurface (MTS)-based wideband circularly polarized (CP) patch antenna for C-band uplink frequency spectrum. The proposed MTS-based CP patch antenna was of low profile and fabricated on three substrate layers: upper, middle, and lower. The upper substrate contained $4\times 4$ periodic S-shaped MTS elements, the middle substrate functioned as ground plane with a rectangular-shaped slot at the center, and the lower substrate contained a coplanar waveguide with microstrip and ground. The S-shaped MTS elements converted linearly polarized (LP) into CP wave. Simulations were performed, and an antenna prototype was fabricated and experiments carried out. The measured impedance bandwidth and axial ratio bandwidth (ARBW) at the center frequency of 5.9 GHz were 43.22% (4.05 – 6.6 GHz) and 22% (5.3 – 6.6 GHz), respectively, rendering the proposed antenna suitable for satellite communication applications. The proposed antenna achieved the maximum gain of 6.16 dBic at 5.6 GHz. The novelty of this research lies in the use of S-shaped MTS elements to efficiently convert LP into CP wave and achieve wider ARBW for the C-band uplink spectrum.
Laser Speckle Integrated Multispectral Imaging System for In-Vivo Assessment of Diabetic Foot Ulcer Healing: A Clinical Study

The high prevalence of the world’s population diagnosed with diabetes mellitus, with a significant number suffering from diabetic foot ulcer (DFU), has always been a global concern over the years. The rapid rise of this disease in the last decades is deemed increasingly alarming as it has left deleterious effects not only on affected patients but also on the society and nation. This paper aims to develop a reliable diagnostic tool to address the tremendous need for coordinated and efficient DFU management via prediction of transcutaneous oxygen saturation (<inline-formula> <tex-math notation="LaTeX">$\text{S}_{\mathrm {t}}\text{O}_{2}$ </tex-math></inline-formula>) and relative blood perfusion (<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>) in the affected limb. This system integrates the use of a multispectral imaging and laser speckle contrast imaging technique for two-dimensional (2D) mapping of tissue oxygen and blood perfusion level in ulcerated foot. Longitudinal study revealed a slightly higher mean <inline-formula> <tex-math notation="LaTeX">$\text{S}_{\mathrm {t}}\text{O}_{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> level in healed ulcer than in impaired healing, despite the data indicating no statistical significance between these two groups <inline-formula> <tex-math notation="LaTeX">$\left ({{p-\textrm {value}>0.05} }\right)$ </tex-math></inline-formula>. It was observed that a mean <inline-formula> <tex-math notation="LaTeX">$\text{S}_{\mathrm {t}}\text{O}_{2}$ </tex-math></inline-formula> of at least 70 % and <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> value of 1.5 (<inline-formula> <tex-math notation="LaTeX">$\times 10^{3}$ </tex-math></inline-formula>) are necessary during the proliferative phase to ensure progressive healing. Based on these findings, this study concluded that high tissue oxygenation and perfusion levels are pivotal to ensure progressive wound healing. This work provides a rationale for evaluating the healing outcomes of skin grafting procedures in diabetic ulcers based on observation of quantitative changes in blood perfusion and tissue oxygen level during its revascularization phase.
KR-Net: A Dependable Visual Kidnap Recovery Network for Indoor Spaces

In this paper, we propose a dependable visual kidnap recovery (KR) framework that pinpoints a unique pose in a given 3D map when a device is turned on. For this framework, we first develop indoor-GeM (i-GeM), which is an extension of GeM [1] but considerably more robust than other global descriptors [2]–[4], including GeM itself. Then, we propose a convolutional neural network (CNN)-based system called KR-Net, which is based on a coarse-to-fine paradigm as in [5] and [6]. To our knowledge, KR-Net is the first network that can pinpoint a wake-up pose with a confidence level near 100% within a 1.0 m translational error boundary. This dependable success rate is enabled not only by i-GeM, but also by a combinatorial pooling approach that uses multiple images around the wake-up spot, whereas previous implementations [5], [6] were constrained to a single image. Experiments were conducted in two challenging datasets: a large-scale (12,557 m2) area with frequent featureless or repetitive places and a place with significant view changes due to a one-year gap between prior modeling and query acquisition. Given 59 test query sets (eight images per pose), KR-Net successfully found all wake-up poses, with average and maximum errors of 0.246 m and 0.983 m, respectively.
HypFDI: Fault Domain Isolation for Hosted Hypervisor in ARM

Hosted hypervisor which takes advantage of a vast amount of mature and stable abilities inside Linux Kernel has been widely adopted to manage virtual machines (VM) in cloud system. Due to rich virtualization capabilities, hosted hypervisor already has large code base (e.g. 33.6K SLOC for KVM) and frequently interacts with VMs. However, commodity hosted hypervisor and kernel in a single address space, which implies that a vulnerability in the hypervisor opens a way to jeopardize the host OS and further may take over VMs running in the same server. So current clouds have a a fragile adversary model with serious security issues. To improve that, previous approaches either suffer from suboptimal performance or required hardware support. In this paper, we present a system that aims to isolate, ‘deprivilege’ and constrain the hosted hypervisor inside kernel. By enforcing function access control on hypervisor, disabling writes to virtual memory control registers from hypervisor and developing a trusted switch gate, our system not only reduces the attack surface between hypervisor and the host kernel but also shield the host kernel. We have implemented our prototype on the open source KVM hypervisor. The evaluation suggests that our system achieves protection against attacks from malicious guest with moderate performance overheads.
Programmable RF Receiver Related On-Chip Photonic Processor

This study reports and experimentally demonstrates a programmable on-chip photonic processor towards different RF receiver configurations. The Si3N4 processor is built by interconnecting independent subunits such as simple and complex Mach-Zehnder interferometers and Ring resonators. The design, fabrication, and characterization of a dedicated chip are described. As a proof of concept, it can be used as a reconfigurable filter with distinct frequency response. More importantly, a thorough RF receiver related passive processor including out-of-band suppression, carrier-sideband routing, channelization filtering, as well as I-Q mixing prior to an optical detection stage. This is effective proof of a programmable integrated processor encompassing most passive filtering and routing subunits for broadband photonic RF receiver.
Modeling and Simulation of an Improved Resistive Random Access Memory Array

Thermal crosstalk in a high density resistive random access memory (RRAM) array due to the self-heating effect is one of the most critical issues affecting device reliability. In this paper, an improved cross-point array is proposed to optimize the thermal management of the RRAM array. An in-house simulator is employed to study the performance of the improved RRAM array. Simulation results reveal that the improved RRAM array can help to reduce thermal crosstalk in highly integrated RRAM arrays.
A Blockchain-based Vehicle-trust Management Framework Under a Crowdsourcing Environment

Vehicular crowdsourcing networks (VCNs) enable vehicles to provide or obtain traffic-related services in a costefficient and flexible manner. Therefore, it is crucial to provide trusted management in VCNs for high reliability towards both service producers and consumers. However, most recent VCN platforms rely on a third party to manage crowdsourcing services which might be not fully trusted by users. For the issue, this paper proposes a blockchain-based trust management scheme for VCNs to provide a decentralized and trusted service management. A comprehensive trust evaluation model (TEM) is designed to quantify the trust degree of each vehicular node, and a vehicle-trust blockchain framework called VTchain is proposed to preserve the trust values of nodes while guaranteeing transparency and trustworthiness. Particularly, we leverage a trusted execution environment (TEE) to provide secure trust evaluation to tackle possible untrusted road-side units. In addition, we introduce TEM-based Proof of Trust to support blockchain maintenance, which works together with an efficient consensus algorithm Zyzzyva for improved scalability. Finally, extensive experiments are conducted by developing a testbed deployed on cloud servers for measurements.
Malware Classification on Imbalanced Data through Self-Attention

Malware is an ever-growing threat to the Internet. New and mutated malware are appearing with increasing frequency in recent years. In the real-world scenario, new families of malware are often discovered in the cybersecurity protection system. In order to improve the protection capability of the system, it is necessary to add the newly discovered malware identification characteristics to the online system. However, the samples of newly discovered malware families are usually too small to effectively extract the characteristics of new classes, resulting in a low recognition rate of new families. The essence of this problem is a multi-classification problem based on imbalanced datasets. In this paper, we propose a self-attention based malware classification method to solve the malware classification on imbalanced datasets. An open source dataset is used to simulate the classification of malware on balanced and imbalanced datasets. Our method has reached an accuracy of 98.48%, and the F1-Score of the imbalanced Simda class has reached 89.66% on the Microsoft Kaggle dataset. Experimental results have demonstrated the effectiveness and robustness in malware classification with imbalanced datasets.
A Scoring System to efficiently measure Security in Cyber-Physical Systems

The importance of Cyber-Physical Systems (CPS) gains more and more weight in our daily business and private life. Although CPS build the backbone for major trends, like Industry 4.0 and connected vehicles, they also propose many new challenges. One major challenge can be found in achieving a high level of security within such highly connected environments, in which an unpredictable number of heterogeneous systems with often-distinctive characteristics interact with each other. In order to develop high-level security solutions, system designers must eventually know the current level of security of their specification. To this end, security metrics and scoring frameworks are essential, as they quantitatively express security of a given design or system. However, existing solutions may not be able to handle the proposed challenges of CPS, as they mainly focus on one particular system and one specific attack. Therefore, we aim to elaborate a security scoring mechanism, which can efficiently be used in CPS, while considering all essential information. We break down each system within the CPS into its core functional blocks and analyze a variety of attacks in terms of exploitability, scalability of attacks, as well as potential harm to targeted assets. With this approach, we get an overall assessment of security for the whole CPS, as it integrates the security-state of all interacting systems. This allows handling the presented complexity in CPS in a more efficient way, than existing solutions.
Name-Signature Lookup System: A Security Enhancement to Named Data Networking

Named Data Networking (NDN) is a content-centric networking, where the publisher of the packet signs and encapsulates the data packet with a name-content-signature encryption to verify the authenticity and integrity of itself. This scheme can solve many of the security issues inherently compared to IP networking. NDN also support mobility since it hides the point-to-point connection details. However, an extreme attack takes place when an NDN consumer newly connects to a network. A Man-in-the-middle (MITM) malicious node can block the consumer and keep intercepting the interest packets sent out so as to fake the corresponding data packets signed with its own private key. Without knowledge and trust to the network, the NDN consumer can by no means perceive the attack and thus exposed to severe security and privacy hazard. In this paper, the N ame-Signature Lookup System (NSLS) and corresponding Name-Signature Lookup Protocol (NSLP) is introduced to verify packets with their registered genuine publisher even in an untrusted network with the help of embedded keys inside Network Interface Controller (NIC), by which attacks like MITM is eliminated. A theoretical analysis of comparing NSLS with existing security model is provided. Digest algorithm SHA-256 and signature algorithm RSA are used in the NSLP model without specific preference.