The newly proposed orthogonal time frequency space (OTFS) system exhibits excellent error performance on high-Doppler fading channels. However, the rectangular prototype window function (PWF) inherent in OTFS leads to high out-of-band emission (OOBE), which reduces the spectral efficiency in multi-user scenarios. To this end, this paper presents an OTFS system based on bi-orthogonal frequency division multiplexing (OTFS-BFDM) modulation. In OTFS-BFDM systems, PWFs with bi-orthogonal properties can be optimized to provide lower OOBE than OTFS, which is a special case with rectangular PWF. We further derive that the OTFS-BFDM system is sparsely-connected so that the low-complexity message passing (MP) decoding algorithm can be adopted. Moreover, the power spectral density, peak to average power ratio (PAPR) and bit error rate (BER) of the OTFS-BFDM system with different PWFs are compared. Simulation results show that: i) the use of BFDM modulation significantly inhibits the OOBE of OTFS system; ii) the better the frequency-domain localization of PWFs, the smaller the BER and PAPR of OTFS-BFDM system.

At Eurocrypt 2011, Lindell presented practical static and adaptively UC-secure commitment schemes based on the DDH assumption. Later, Blazy et al. (at ACNS 2013) improved the efficiency of the Lindell's commitment schemes. In this paper, we present static and adaptively UC-secure commitment schemes based on the same assumption and further improve the communication and computational complexity, as well as the size of the common reference string.

The problem of enumerating connected induced subgraphs of a given graph is classical and studied well. It is known that connected induced subgraphs can be enumerated in constant time for each subgraph. In this paper, we focus on highly connected induced subgraphs. The most major concept of connectivity on graphs is vertex connectivity. For vertex connectivity, some enumeration problem settings and enumeration algorithms have been proposed, such as k-vertex connected spanning subgraphs. In this paper, we focus on another major concept of graph connectivity, edge-connectivity. This is motivated by the problem of finding evacuation routes in road networks. In evacuation routes, edge-connectivity is important, since highly edge-connected subgraphs ensure multiple routes between two vertices. In this paper, we consider the problem of enumerating 2-edge-connected induced subgraphs of a given graph. We present an algorithm that enumerates 2-edge-connected induced subgraphs of an input graph G with n vertices and m edges. Our algorithm enumerates all the 2-edge-connected induced subgraphs in O(n3m|SG|) time, where SG is the set of the 2-edge-connected induced subgraphs of G. Moreover, by slightly modifying the algorithm, we have a O(n3m)-delay enumeration algorithm for 2-edge-connected induced subgraphs.

Smart contracts are becoming more and more popular in financial scenarios like medical insurance. Rather than traditional schemes, using smart contracts as a medium is a better choice for both participants, as it is fairer, more reliable, more efficient, and enables real-time payment. However, medical insurance contracts need to input the patient's condition information as the judgment logic to trigger subsequent execution. Since the blockchain is a closed network, it lacks a secure network environment for data interaction with the outside world. The Data feed aims to provide the service of the on-chain and off-chain data interaction. Existing researches on the data feed has solved the security problems on it effectively, such as Town Crier, TLS-N and they have also taken into account the privacy-preserving problems. However, these schemes cannot actually protect privacy because when the ciphertext data is executed by the contract, privacy information can still be inferred by analyzing the transaction results, since states of the contract are publicly visible. In this paper, based on zero-knowledge proof and Hawk technology, a on-and-off-chain complete smart contract data feed privacy-preserving scheme is proposed. In order to present our scheme more intuitively, we combined the medical insurance compensation case to implement it, which is called MIPDF. In our MIPDF, the patient and the insurance company are parties involved in the contract, and the hospital is the data provider of data feed. The patient's medical data is sent to the smart contract under the umbrella of the zero-knowledge proof signature scheme. The smart contract verifies the proof and calculates the insurance premium based on the judgment logic. Meanwhile, we use Hawk technology to ensure the privacy of on-chain contract execution, so that no information will be disclosed due to the result of contract execution. We give a general description of our scheme within the Universal Composability (UC) framework. We experiment and evaluate MIPDF on Ethereum for in-depth analysis. The results show that our scheme can securely and efficiently support the functions of medical insurance and achieve complete privacy-preserving.

To safeguard critical services and assets in a distributed environment, collaborative intrusion detection systems (CIDSs) are usually adopted to share necessary data and information among various nodes, and enhance the detection capability. For simplifying the network management, software defined networking (SDN) is an emerging platform that decouples the controller plane from the data plane. Intuitively, SDN can help lighten the management complexity in CIDSs, and a CIDS can protect the security of SDN. In practical implementation, trust management is an important approach to help identify insider attacks (or malicious nodes) in CIDSs, but the challenge is how to ensure the data integrity when evaluating the reputation of a node. Motivated by the recent development of blockchain technology, in this work, we design BlockCSDN — a framework of blockchain-based collaborative intrusion detection in SDN, and take the challenge-based CIDS as a study. The experimental results under both external and internal attacks indicate that using blockchain technology can benefit the robustness and security of CIDSs and SDN.

In this letter, firstly, a novel adaptive beamformer using independent component analysis (ICA) algorithm is proposed. By this algorithm, the ambiguity of amplitude and phase resulted from blind source separation is removed utilizing the special structure of array manifolds matrix. However, there might exist great calibration error when the powers of interferences are far larger than that of desired signal at many applications such as sonar, radio astronomy, biomedical engineering and earthquake detection. As a result, this will lead to a significant reduction in separation performance. Then, a new method based on the combination of ICA and primary component analysis (PCA) is proposed to recover the desired signal's amplitude under strong interference. Finally, computer simulation is carried out to indicate the effectiveness of our methods. The simulation results show that the proposed methods can obtain higher SNR and more accurate power estimation of desired signal than diagonal loading sample matrix inversion (LSMI) and worst-case performance optimization (WCPO) method.

Many cloud service providers employ specialized hardware accelerators, called neural processing units (NPUs), to accelerate deep neural networks (DNNs). An NPU scheduler is responsible for scheduling incoming user requests and required to satisfy the two, often conflicting, optimization goals: maximizing system throughput and satisfying quality-of-service (QoS) constraints (e.g., deadlines) of individual requests. We propose Layerweaver+, a low-cost layer-wise DNN scheduler for NPUs, which provides both high system throughput and minimal QoS violations. For a serving scenario based on the industry-standard MLPerf inference benchmark, Layerweaver+ significantly improves the system throughput by up to 266.7% over the baseline scheduler serving one DNN at a time.

Construction of multiple output functions is one of the most important problems in the design and analysis of stream ciphers. Generally, such a function has to be satisfied with several criteria, such as high nonlinearity, resiliency and high algebraic degree. But there are mutual restraints among the cryptographic parameters. Finding a way to achieve the optimization is always regarded as a hard task. In this paper, by using the disjoint linear codes and disjoint spectral functions, two classes of resilient multiple output functions are obtained. It has been proved that the obtained functions have high nonlinearity and high algebraic degree.

Controlled optimization of phases (COP) is a core implementation in the future intelligent traffic signal system (I-SIG), which has been deployed and tested in countries including the U.S. and China. In such a system design, optimal signal control depends on dynamic traffic situation awareness via connected vehicles. Unfortunately, I-SIG suffers data spoofing from any hacked vehicle; in particular, the spoofing of the last vehicle can break the system and cause severe traffic congestion. Specifically, coordinated attacks on multiple intersections may even bring cascading failure of the road traffic network. To mitigate this security issue, a blockchain-based multi-intersection joint defense mechanism upon COP planning is designed. The major contributions of this paper are the following. 1) A blockchain network constituted by road-side units at multiple intersections, which are originally distributed and decentralized, is proposed to obtain accurate and reliable spoofing detection. 2) COP-oriented smart contract is implemented and utilized to ensure the credibility of spoofing vehicle detection. Thus, an I-SIG can automatically execute a signal planning scheme according to traffic information without spoofing data. Security analysis for the data spoofing attack is carried out to demonstrate the security. Meanwhile, experiments on the simulation platform VISSIM and Hyperledger Fabric show the efficiency and practicality of the blockchain-based defense mechanism.

Object contour detection is a task of extracting the shape created by the boundaries between objects in an image. Conventional methods limit the detection targets to specific categories, or miss-detect edges of patterns inside an object. We propose a new method to represent a contour image where the pixel value is the distance to the boundary. Contour detection becomes a regression problem that estimates this contour image. A deep convolutional network for contour estimation is combined with stereo vision to detect unspecified object contours. Furthermore, thanks to similar inference targets and common network structure, we propose a network that simultaneously estimates both contour and disparity with fully shared weights. As a result of experiments, the multi-tasking network drew a good precision-recall curve, and F-measure was about 0.833 for FlyingThings3D dataset. L1 loss of disparity estimation for the dataset was 2.571. This network reduces the amount of calculation and memory capacity by half, and accuracy drop compared to the dedicated networks is slight. Then we quantize both weights and activations of the network to 3-bit. We devise a dedicated hardware architecture for the quantized CNN and implement it on an FPGA. This circuit uses only internal memory to perform forward propagation calculations, that eliminates high-power external memory accesses. This circuit is a stall-free pixel-by-pixel pipeline, and performs 8 rows, 16 input channels, 16 output channels, 3 by 3 pixels convolution calculations in parallel. The convolution calculation performance at the operating frequency of 250 MHz is 9 TOPs/s.

We propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples.

In this paper, we propose a novel Hybrid-Hierarchical spatial-aerial-Terrestrial Edge-Centric (H2TEC) for the space-air integrated Internet of Things (IoT) networks. (H2TEC) comprises unmanned aerial vehicles (UAVs) that act as mobile fog nodes to provide the required services for terminal nodes (TNs) in cooperation with the satellites. TNs in (H2TEC) offload their generated tasks to the UAVs for further processing. Due to the limited energy budget of TNs, a novel task allocation protocol, named TOP, is proposed to minimize the energy consumption of TNs while guaranteeing the outage probability and network reliability for which the transmission rate of TNs is optimized. TOP also takes advantage of the energy harvesting by which the low earth orbit satellites transfer energy to the UAVs when the remaining energy of the UAVs is below a predefined threshold. To this end, the harvested power of the UAVs is optimized alongside the corresponding harvesting time so that the UAVs can improve the network throughput via processing more bits. Numerical results reveal that TOP outperforms the baseline method in critical situations that more power is required to process the task. It is also found that even in such situations, the energy harvesting mechanism provided in the TOP yields a more efficient network throughput.

In this work, we consider a remote estimation system where a remote controller estimates the status of heterogeneous sensing devices with the information delivered over wireless channels. Status of heterogeneous devices changes at different speeds. With limited wireless resources, estimating as accurately as possible requires careful design of status update schemes. Status update schemes can be divided into two classes: centralized and decentralized. In centralized schemes, a central scheduler coordinates devices to avoid potential collisions. However, in decentralized schemes where each device updates on its own, update decisions can be made by using the current status which is unavailable in centralized schemes. The relation between these two schemes under the heterogeneous devices case is unclear, and thus we study these two schemes in terms of the mean square error (MSE) of the estimation. For centralized schemes, since the scheduler does not have the current status of each device, we study policies where the scheduling decisions are based on age of information (AoI), which measures the staleness of the status information held in the controller. The optimal scheduling policy is provided, along with the corresponding MSE. For decentralized schemes, we consider deviation-based policies with which only devices with estimation deviations larger than prescribed thresholds may update, and the others stay idle. We derive an approximation of the minimum MSE under the deviation-based policies and show that it is e/3 of the minimum MSE under the AoI-based policies. Simulation results further show that the actual minimum MSEs of these two policies are even closer than that shown by the approximation, which indicates that the cost of collision in the deviation-based policy cancels out the gain from exploiting status deviations.

Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.

Front video and sensor data captured by vehicle-mounted event recorders are used for not only traffic accident evidence but also safe-driving education as near-miss traffic incident data. However, most event recorder (ER) data shows only regular driving events. To utilize near-miss data for safe-driving education, we need to be able to easily and rapidly locate the appropriate data from large amounts of ER data through labels attached to the scenes/events of interest. This paper proposes a method that can automatically identify near-misses with objects such as pedestrians and bicycles by processing the ER data. The proposed method extracts two deep feature representations that consider car status and the environment surrounding the car. The first feature representation is generated by considering the temporal transitions of car status. The second one can extract the positional relationship between the car and surrounding objects by processing object detection results. Experiments on actual ER data demonstrate that the proposed method can accurately identify and tag near-miss events.

We present detailed measurements and analysis of the guided acoustic wave Brillouin scattering (GAWBS)-induced depolarization noise in a multi-core fiber (MCF) used for a digital coherent optical transmission. We first describe the GAWBS-induced depolarization noise in an uncoupled four-core fiber (4CF) with a 125μm cladding and compare the depolarization noise spectrum with that of a standard single-mode fiber (SSMF). We found that off-center cores in the 4CF are dominantly affected by higher-order TRn,m modes rather than the TR2,m mode unlike in the center core, and the total power of the depolarization noise in the 4CF was almost the same as that in the SSMF. We also report measurement results for the GAWBS-induced depolarization noise in an uncoupled 19-core fiber with a 240μm cladding. The results indicate that the amounts of depolarization noise generated in the cores are almost identical. Finally, we evaluate the influence of GAWBS-induced polarization crosstalk (XT) on a coherent QAM transmission. We found that the XT limits the achievable multiplicity of the QAM signal to 64 in a transoceanic transmission with an MCF.

Heart rate measurement for mm-wave FMCW radar based on phase analysis comprises a variety of noise. Furthermore, because the breathing and heart frequencies are so close, the harmonic of the breathing signal interferes with the heart rate, and the band-pass filter cannot solve it. On the other hand, because heart rates vary from person to person, it is difficult to choose the basic function of WT (Wavelet Transform). To solve the aforementioned difficulties, we consider performing time-frequency domain analysis on human skin surface displacement data. The PA-LI (Phase Accumulation-Linear Interpolation) joint ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) approach is proposed in this paper, which effectively enhances the signal's SNR, estimates the heart rate, and reconstructs the heartbeat signal. The experimental findings demonstrate that the proposed method can not only extract heartbeat signals with high SNR from the front direction, but it can also detect heart rate from other directions (e.g., back, left, oblique front, and ceiling).

Zero-shot learning (ZSL) aims to classify images of unseen classes by learning relationship between visual and semantic features. Existing works have been improving recognition accuracy from various approaches, but they employ computationally intensive algorithms that require iterative optimization. In this work, we revisit the primary approach of the pattern recognition, ı.e., nearest neighbor classifiers, to solve the ZSL task by an extremely simple and fast way, called SimpleZSL. Our algorithm consists of the following three simple techniques: (1) just averaging feature vectors to obtain visual prototypes of seen classes, (2) calculating a pseudo-inverse matrix via singular value decomposition to generate visual features of unseen classes, and (3) inferring unseen classes by a nearest neighbor classifier in which cosine similarity is used to measure distance between feature vectors. Through the experiments on common datasets, the proposed method achieves good recognition accuracy with drastically small computational costs. The execution time of the proposed method on a single CPU is more than 100 times faster than those of the GPU implementations of the existing methods with comparable accuracies.

There exists a great demand for automatic anomaly detection in industrial world. The anomaly has been defined as a group of samples that rarely or never appears. Given a type of products, one has to collect numerous samples and train an anomaly detector. When one diverts a model trained with old types of products with sufficient inventory to the new type, one can detect anomalies of the new type before a production line is established. However, because of the definition of the anomaly, a typical anomaly detector considers the new type of products anomalous even if it is consistent with the standard. Given the above practical demand, this study propose a novel problem setting, few-shot anomaly detection, where an anomaly detector trained in source domains is adapted to a small set of target samples without full retraining. Then, we tackle this problem using a hierarchical probabilistic model based on deep learning. Our empirical results on toy and real-world datasets demonstrate that the proposed model detects anomalies in a small set of target samples successfully.

Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.