Locally repairable codes (LRCs) with locality r and availability t are a class of codes which can recover data from erasures by accessing other t disjoint repair groups, that every group contain at most r other code symbols. This letter will investigate constructions of LRCs derived from cyclic codes and generalized quadrangle. On the one hand, two classes of cyclic LRC with given locality m-1 and availability em are proposed via trace function. Our LRCs have the same locality, availability, minimum distance and code rate, but have short length and low dimension. On the other hand, an LRC with $(2,(p+1)lfloorrac{s}{2} floor)$ is presented based on sets of points in PG(k, q) which form generalized quadrangles with order (s, p). For k=3, 4, 5, LRCs with r=2 and different t are determined.

With our ever increasing dependence on computers, many governments around the world have started to investigate strengthening the regulations on vulnerabilities and their lifecycle management. Although many previous works have studied this problem space for mainstream software packages and web applications, relatively few have studied this for consumer IoT devices. As our first step towards filling this void, this paper presents a pilot study on the vulnerability disclosures and patch releases of three prominent consumer IoT vendors in Japan and three in the United States. Our goals include (i) characterizing the trends and risks in the vulnerability lifecycle management of consumer IoT devices using accurate long-term data, and (ii) identifying problems, challenges, and potential approaches for future studies of this problem space. To this end, we collected all published vulnerabilities and patches related to the consumer IoT products by the included vendors between 2006 and 2017; then, we analyzed our dataset from multiple perspectives, such as the severity of the included vulnerabilities and the timing of the included patch releases with respect to the corresponding disclosures and exploits. Our work has uncovered several important findings that may inform future studies. These findings include (i) a stark contrast between how the vulnerabilities in our dataset were disclosed in the two markets, (ii) three alarming practices by the included vendors that may significantly increase the risk of 1-day exploits for customers, and (iii) challenges in data collection including crawling automation and long-term data availability. For each finding, we also provide discussions on its consequences and/or potential migrations or suggestions.

This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.

Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.

This paper presents a real-time body motion classification system using the radio channel characteristics of a wearable body area network (BAN). We developed a custom wearable BAN radio channel measurement system by modifying an off-the-shelf ZigBee-based sensor network system, where the link quality indicator (LQI) values of the wireless links between the coordinator and four sensor nodes can be measured. After interpolating and standardizing the raw data samples in a pre-processing stage, the time-domain features are calculated, and the body motion is classified by a decision-tree based random forest machine learning algorithm which is most suitable for real-time processing. The features were carefully chosen to exclude those that exhibit the same tendency based on the mean and variance of the features to avoid overfitting. The measurements demonstrated successful real-time body motion classification and revealed the potential for practical use in various daily-life applications.

Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.

This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.

When people learn a handicraft with instructional contents such as books, videos, and web pages, many of them often give up halfway because the contents do not always assure how to make it. This study aims to provide origami learners, especially beginners, with feedbacks on their folding operations. An approach for recognizing the state of the learner by using a single top-view camera, and pointing out the mistakes made during the origami folding operation is proposed. First, an instruction model that stores easy-to-follow folding operations is defined. Second, a method for recognizing the state of the learner's origami paper sheet is proposed. Third, a method for detecting mistakes made by the learner by means of anomaly detection using a one-class support vector machine (one-class SVM) classifier (using the folding progress and the difference between the learner's origami shape and the correct shape) is proposed. Because noises exist in the camera images due to shadows and occlusions caused by the learner's hands, the shapes of the origami sheet are not always extracted accurately. To train the one-class SVM classifier with high accuracy, a data cleansing method that automatically sifts out video frames with noises is proposed. Moreover, using the statistics of features extracted from the frames in a sliding window makes it possible to reduce the influence by the noises. The proposed method was experimentally demonstrated to be sufficiently accurate and robust against noises, and its false alarm rate (false positive rate) can be reduced to zero. Requiring only a single camera and common origami paper, the proposed method makes it possible to monitor mistakes made by origami learners and support their self-learning.

Periodic sequences, used as keys in cryptosystems, plays an important role in cryptography. Such periodic sequences should possess high linear complexity to resist B-M algorithm. Sequences constructed by cyclotomic cosets have been widely studied in the past few years. In this paper, the linear complexity of n-periodic cyclotomic sequences of order 2 and 4 over 𝔽p has been calculated, where n and p are two distinct odd primes. The conclusions reveal that the presented sequences have high linear complexity in many cases, which indicates that the sequences can resist the linear attack.

With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.

This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.

The conventional digital video encoding and transmission are inefficient for crowdsourced multi-view video uploading due to its high power consumption, and undesirable quality degradation in unstable wireless channel. Soft video delivery scheme known as SoftCast skips digital video encoding and transmission to decrease power consumption in video encoding and transmission. In addition, it achieves graceful quality improvement with the improvement of wireless channel quality by directly sending linear-transformed video signals. However, there are two typical issues to apply conventional soft video delivery to crowdsourced multi-view video uploading. First, since soft video delivery has been designed for direct path between each contributor and the access point (AP), it may suffer low video quality when a contributor uploads its video to the AP over unstable direct wireless path. Second, conventional soft video delivery may suffer low video quality due to the redundant transmission of correlated videos because it does not exploit inter-camera correlations existed in multi-view videos. In this paper, we propose a cluster-based redirect video uploading scheme for high-quality and low-power crowdsourced multi-view video streaming. The proposed scheme integrates the four approaches of network clustering, delegate selection, soft video delivery, and four-dimensional discrete consine transform (4D-DCT) to redirectly upload the captured videos to the AP. Specifically, network clustering and delegate selection leverage the redirect path between the contributors and the AP. Soft video delivery removes power-hungry digital encoding and transmission by directly sending frequency-domain coefficients using multi-dimensional DCT and near-analog modulation. 4D-DCT exploits the content correlations between the contributors to reduce redundant transmissions. Evaluation results show that our proposed scheme outperforms the conventional soft video delivery scheme when the channel quality difference between the direct and redirect paths increases. In addition, our scheme outperforms the digital-based video uploading schemes in terms of both video quality and power consumption. For example, the proposed scheme yields graceful quality improvement with the improvement of wireless channel quality, however, the digital-based schemes suffer from sudden quality degradation due to synchronization errors in decoding.

A fully integrated 16-phase 8-branch Dickson charge pump is proposed and implemented to decrease the power dissipation due to parasitic capacitance at the bottom plate of the boost capacitor. By using the charge recycling concept, 87% of the power consumption related to parasitic capacitance is saved. In a 4-stage version of this charge pump, a maximum power efficiency of 41% is achieved at 35µA output current and 11V output voltage from a 3.3V supply voltage. The proposed multi-branch charge pump can also reach a very low output voltage ripple of only 0.146% at a load resistance of 1MΩ, which is attributed to the fact that the 8-branch charge pump can transfer charges to the output node eight times consecutively during one clock period. In addition, a high voltage gain of 4.6 is achieved in the 4-stage charge pump at light load conditions. The total chip area is 0.57mm2 in a 0.35µm HV CMOS technology.

This letter proposes a gradient-enhanced softmax supervisor for face recognition (FR) based on a deep convolutional neural network (DCNN). The proposed supervisor conducts the constant-normalized cosine to obtain the score for each class using a combination of the intra-class score and the soft maximum of the inter-class scores as the objective function. This mitigates the vanishing gradient problem in the conventional softmax classifier. The experiments on the public Labeled Faces in the Wild (LFW) database denote that the proposed supervisor achieves better results when compared with those achieved using the current state-of-the-art softmax-based approaches for FR.

This paper analyzes the optimal SSD configuration including emerging non-volatile memories such as quadruple-level cell (QLC) NAND flash memory [1] and storage class memories (SCMs). First, SSD performance and SSD endurance lifetime of hybrid SSD are evaluated in four configurations: 1) single-level cell (SLC)/QLC NAND flash, 2) SCM/QLC NAND flash, 3) SCM/triple-level cell (TLC)/QLC NAND flash and 4) SCM/TLC NAND flash. Furthermore, these four configurations are compared in limited cost. In case of cold workloads or high total SSD cost assumption, SCM/TLC NAND flash hybrid configuration is recommended in both SSD performance and endurance lifetime. For hot workloads with low total SSD cost assumption, however, SLC/QLC NAND flash hybrid configuration is recommended with emphasis on SSD endurance lifetime. Under the same conditions as above, SCM/TLC/QLC NAND flash tri-hybrid is the best configuration in SSD performance considering cost. In particular, for prxy_0 (write-hot workload), SCM/TLC/QLC NAND flash tri-hybrid achieves 67% higher IOPS/cost than SCM/TLC NAND flash hybrid. Moreover, the configurations with the highest IOPS/cost in each workload and cost limit are picked up and analyzed with various types of SCMs. For all cases except for the case of prxy_1 with high total SSD cost assumption, middle-end SCM (write latency: 1us, read latency: 1us) is recommended in performance considering cost. However, for prxy_1 (read-hot workload) with high total SSD cost assumption, high-end SCM (write latency: 100ns, read latency: 100ns) achieves the best performance.

Systems for Internet of Things (IoT) have generated new requirements in all aspects of their development and deployment, including expanded Quality of Service (QoS) needs, enhanced resiliency of computing and connectivity, and the scalability to support massive numbers of end devices in a variety of applications. The research reported here concerns the development of a reliable and secure IoT/cyber physical system (CPS), providing network support for smart and connected communities, to be realized by means of distributed, secure, resilient Edge Cloud (EC) computing. This distributed EC system will be a network of geographically distributed EC nodes, brokering between end-devices and Backend Cloud (BC) servers. This paper focuses on three main aspects of the CPS: a) resource management in mobile cloud computing; b) information management in dynamic distributed databases; and c) biological-inspired intrusion detection system.

On the Internet, end hosts and network nodes interdependently work to smoothly transfer traffic. Observed traffic dynamics are the result of those interactions among those entities. To manage Internet traffic to provide satisfactory quality services, such dynamics need to be well understood to predict traffic patterns. In particular, some nodes have a function that sends back-pressure signals to backward nodes to reduce their sending rate and mitigate congestion. Transmission Control Protocol (TCP) congestion control in end-hosts also mitigates traffic deviation to eliminate temporary congestion by reducing the TCP sending rate. How these congestion controls mitigate congestion has been extensively investigated. However, these controls only throttle their sending rate but do not reduce traffic volume. Such congestion control fails if congestion is persistent, e.g., for hours, because unsent traffic demand will infinitely accumulate. However, on the actual Internet, even with persistent congestion, such accumulation does not seem to occur. During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model. We also show that by using ULCL, we can predict the degree of QoS degradation and traffic reduction as an equilibrium of the feedback loop. We applied our model to traffic and packet-loss ratio time series data gathered in an actual network and demonstrate that it effectively estimates actual traffic and packet-loss ratio.

Due to the superior performance, deep learning has been widely applied to various applications, including image classification, bioinformatics, and cybersecurity. Nevertheless, the research investigations on deep learning in the adversarial environment are still on their preliminary stage. The emerging adversarial learning methods, e.g., generative adversarial networks, have introduced two vital questions: to what degree the security of deep learning with the presence of adversarial examples is; how to evaluate the performance of deep learning models in adversarial environment, thus, to raise security advice such that the selected application system based on deep learning is resistant to adversarial examples. To see the answers, we leverage image classification as an example application scenario to propose a framework of Evaluating Deep Learning for Image Classification (EDLIC) to conduct comprehensively quantitative analysis. Moreover, we introduce a set of evaluating metrics to measure the performance of different attacking and defensive techniques. After that, we conduct extensive experiments towards the performance of deep learning for image classification under different adversarial environments to validate the scalability of EDLIC. Finally, we give some advice about the selection of deep learning models for image classification based on these comparative results.

This paper presents a Siamese architecture model with two identical Convolutional Neural Networks (CNNs) to identify code clones; two code fragments are represented as Abstract Syntax Trees (ASTs), CNN-based subnetworks extract feature vectors from the ASTs of pairwise code fragments, and the output layer produces how similar or dissimilar they are. Experimental results demonstrate that CNN-based feature extraction is effective in detecting code clones at source code or bytecode levels.

This paper analyzes the system-level performance of Storage Class Memory (SCM)/NAND flash hybrid solid-state drives (SSDs) and SCM/NAND flash/NAND flash tri-hybrid SSDs in difference types of NAND flash memory. There are several types of NAND flash memory, i.e. 2-dimensional (2D) or 3-dimensional (3D), charge-trap type (CT) and floating-gate type (FG) and multi-level cell (MLC) or triple-level cell (TLC). In this paper, the following four types of NAND flash memory are analyzed: 1) 3D CT TLC, 2) 3D FG TLC, 3) 2D FG TLC, and 4) 2D FG MLC NAND flash. Regardless of read- and write-intensive workloads, SCM/NAND flash hybrid SSD with low cost 3D CT TLC NAND flash achieves the best performance that is 20% higher than that with higher cost 2D FG MLC NAND flash. The performance improvement of 3D CT TLC NAND flash can be obtained by the short write latency. On the other hand, in case of tri-hybrid SSD, SCM/3D CT TLC/3D CT TLC NAND flash tri-hybrid SSD improves the performance 102% compared to SCM/2D FG MLC/3D CT TLC NAND flash tri-hybrid SSD. In addition, SCM/2D FG MLC/2D FG MLC NAND flash tri-hybrid SSD shows 49% lower performance than SCM/2D FG MLC/3D CT TLC NAND flash tri-hybrid SSD. Tri-hybrid SSD flash with 3D CT TLC NAND flash is the best performance in tri-hybrid SSD thanks to larger block size and word-line (WL) write. Therefore, in 3D CT TLC NAND flash based SSDs, higher cost MLC NAND flash is not necessary for hybrid SSD and tri-hybrid SSD for data center applications.