bluuebunny/crossref_metadata_2025 huggingface dataset:
Nuclear saturation energy in effective chiral model within Fermi liquid theory approach
Authors: Pal Kausik
Published: May 2020

We calculate the relativistic Fermi liquid parameters (RFLPs) for the description of the nuclear saturation energy using a chiral effective Lagrangian. Analytical expressions of Fermi liquid parameters (FLPs) are presented both for the direct and exchange contributions by retaining the meson masses. We present a comparative study of perturbative calculation with mean field results. The FLPs, so determined, are then used to calculate the chemical potential, energy densities, and binding energy for dense nuclear matter interacting via the exchange of σ, ω, and π mesons. In addition, we also estimate bulk quantities like incompressibility and first sound velocity in terms of RFLPs for dense nuclear matter.
Coupling effect and charge redistribution of cyclo[18]carbons and cyclocarbon oxides on NaCl surface
Authors: Liang Zefen , He Tianxiang , An Junpeng , Xue Hongtao , Tang Fuling , Fan Ding
Published: May 2020

The electronic structure, energy barrier and charge redistribution of cyclo[18]carbon and cyclocarbon oxides [Formula: see text], [Formula: see text], [Formula: see text] on NaCl(001) surface were computationally studied by using MOPAC. The HOMO electronics are mainly distributed at the inner and outer circumferential half circle of the cyclo[18]carbon along triple C–C bonds. However, the LUMO electronics are distributed at above and below surfaces of the cyclo[18]carbon circle along single C–C bonds. The energy barrier is different along different direction. The energy barrier of cyclo[18]carbon on the NaCl surface is the smallest (0.23 eV/per atom). When the cyclo[18]carbon and cyclo[18]carbon oxides molecular are put on the NaCl surface, they show different amounts of positive charge, no longer neutral.
Mitigate Parasitic Resistance in Resistive Crossbar-based Convolutional Neural Networks
Authors: Zhang Fan , Hu Miao
Published: May 2020

Traditional computing hardware often encounters on-chip memory bottleneck on large-scale Convolution Neural Networks (CNN) applications. With its unique in-memory computing feature, resistive crossbar-based computing attracts researchers’ attention as a promising solution to the memory bottleneck issue in von Neumann architectures. However, the parasitic resistances in crossbar deviate its behavior from the ideal weighted summation operation. In large-scale implementations, the impact of parasitic resistances must be carefully considered and mitigated to ensure circuits’ functionality. In this work, we implemented and simulated CNNs on resistive crossbar circuits with consideration of parasitic resistances. Moreover, we carried out a new mapping scheme for high utilization of crossbar arrays on convolution, and a mitigation algorithm to mitigate parasitic resistances in CNN applications. The mitigation algorithm considers parasitic resistances as well as data/kernel patterns of each layer to minimize the computing error in crossbar-based convolutions of CNNs. We demonstrated the proposed methods with implementations of a 4-layer CNN on MNIST, and residual neural network (ResNet) (20, 32, and 56) on CIFAR-10. Simulation results show the proposed methods well mitigate the parasitic resistances in crossbars. With our methods, modern CNNs on crossbars can preserve ideal (software) level classification accuracy with 6-bit ADCs and DACs implementation.
Practical Quick File Server Migration
Authors: Matsuzawa Keiichi , Hayasaka Mitsuo , Shinagawa Takahiro
Published: May 2020

Regular file server upgrades are indispensable to improve performance, robustness, and power consumption. In upgrading file servers, it is crucial to quickly migrate file-sharing services between heterogeneous servers with little downtime while minimizing performance interference. We present a practical quick file server migration scheme based on the postcopy approach that defers file copy until after switching servers. This scheme can (1) reduce downtime with on-demand file migration, (2) avoid performance interference using background migration, and (3) support heterogeneous servers with stub-based file management. We discuss several practical issues, such as intermittent crawling and traversal strategy, and present the solutions in our scheme. We also address several protocol-specific issues to achieve a smooth migration. This scheme is good enough to be adopted in production systems, as it has been demonstrated for several years in real operational environments. The performance evaluation demonstrates that the downtime is less than 3 seconds, and the first file access after switching servers does not cause a timeout in the default timeout settings; it takes less than 10 seconds in most cases and up to 84.55 seconds even in a large directory tree with a depth of 16 and a width of 1,000. Although the total migration time is approximately 3 times longer than the traditional precopy approach that copies all files in advance, our scheme allows the clients to keep accessing files with acceptable overhead. We also show that appropriate selection of traversal strategy reduces tail latency by 88%, and the overhead after the migration is negligible.
Anomaly Detection Based on RBM-LSTM Neural Network for CPS in Advanced Driver Assistance System
Authors: Wu Di , Zhu Hanlin , Zhu Yongxin , Chang Victor , He Cong , Hsu Ching-Hsien , Wang Hui , Feng Songlin , Tian Li , Huang Zunkai
Published: May 2020

Advanced Driver Assistance System (ADAS) is a typical Cyber Physical System (CPS) application for human–computer interaction. In the process of vehicle driving, we use the information from CPS on ADAS to not only help us understand the driving condition of the car but also help us change the driving strategies to drive in a better and safer way. After getting the information, the driver can evaluate the feedback information of the vehicle, so as to enhance the ability to assist in driving of the ADAS system. This completes a complete human–computer interaction process. However, the data obtained during the interaction usually form a large dimension, and irrelevant features sometimes hide the occurrence of anomalies, which poses a significant challenge to us to better understand the driving states of the car.

To solve this problem, we propose an anomaly detection framework based on RBM-LSTM. In this hybrid framework, RBM is trained to extract general underlying features from data collected by CPS, and LSTM is trained from the features learned by RBM. This framework can effectively improve the prediction speed and present a good prediction accuracy to show vehicle driving condition. Besides, drivers are allowed to evaluate the prediction results, so as to improve the accuracy of prediction. Through the experimental results, we can find that the proposed framework not only simplifies the training of the entire neural network and increases the training speed but also greatly improves the accuracy of the interaction-driven data analysis. It is a valid method to analyze the data generated during the human interaction.
Shuffled ImageNet Banks for Video Event Detection and Search
Authors: Mettes Pascal , Koelma Dennis C. , Snoek Cees G. M.
Published: May 2020

This article aims for the detection and search of events in videos, where video examples are either scarce or even absent during training. To enable such event detection and search, ImageNet concept banks have shown to be effective. Rather than employing the standard concept bank of 1,000 ImageNet classes, we leverage the full 21,841-class dataset. We identify two problems with using the full dataset: (i) there is an imbalance between the number of examples per concept, and (ii) not all concepts are equally relevant for events. In this article, we propose to balance large-scale image hierarchies for pre-training. We shuffle concepts based on bottom-up and top-down operations to overcome the problems of example imbalance and concept relevance. Using this strategy, we arrive at the shuffled ImageNet bank, a concept bank with an order of magnitude more concepts compared to standard ImageNet banks. Compared to standard ImageNet pre-training, our shuffles result in more discriminative representations to train event models from the limited video event examples. For event search, the broad range of concepts enable a closer match between textual queries of events and concept detections in videos. Experimentally, we show the benefit of the proposed bank for event detection and event search, with state-of-the-art performance for both tasks on the challenging TRECVID Multimedia Event Detection and Ad-Hoc Video Search benchmarks.
Adapting Recursive Sinusoidal Software Oscillators for Low-power Fixed-point Processors
Authors: Smeets Hugues , Ceriotti Matteo , Marrón Pedro José
Published: May 2020

The growing field of the Internet of Things relies at the bottom on components with very scarce computing resources that currently do not allow complex processing of sensed data. Any computation involving Fast Fourier Transforms (FFT), Wavelet Transforms (WT), or simple sines and cosines is considered impractical on low-end devices due to the lack of floating point and math libraries. This article presents new techniques that make it possible to use these functions also on severely constrained target platforms.

Current literature abounds with schemes to compute sine and cosine functions, with focus on speed, hardware footprint, software size, target type, or precision. Even so, there is no practical exploration of the design space available for embedded devices with limited resources, in particular when only integer operations are possible. We select an efficient set of recursive sine and cosine generators and measure the frequency, amplitude, and phase error over a wide parameter range. We show that their simplicity allows them to be implemented on the most bare targets with good precision, reducing power consumption and size while being the fastest on integer-only processors. We also introduce specially tailored FFT and WT algorithms and show that they are usable in practice while having an extremely small code footprint, good precision, and high speed.
HUCDO
Authors: Huang Ke , Zhang Xiaosong , Wang Xiaofen , Mu Yi , Rezaeibagha Fatemeh , Xu Guangquan , Wang Hao , Zheng Xi , Yang Guomin , Xia Qi , Du Xiaojiang
Published: May 2020

Outsourcing helps relocate data from the cyber-physical system (CPS) for efficient storage at low cost. Current server-based outsourcing mainly focuses on the benefits of servers. This cannot attract users well, as their security, efficiency, and economy are not guaranteed. To solve with this issue, a hybrid outsourcing model that exploits both cloud server and edge devices to store data is needed. Meanwhile, the requirements of security and efficiency are different under specific scenarios. There is a lack of a comprehensive solution that considers all of the above issues. In this work, we overcome the above issues by proposing the first hybrid user-centric data outsourcing (HUCDO) scheme. It allows users to outsource data securely, efficiently, and economically via different CPSs. Brielly, our contributions consist of theories, implementations, and evaluations. Our theories include the first homomorphic collision-resistant chameleon hash (HCCH) and homomorphic designated-receiver signcryption (HDRS). As implementations, we instantiate how to use our proposals to outsource small- or large-scale data through distinct CPS, respectively. Additionally, a blockchain with proof-of-discrete-logarithm (B-PoDL) is instantiated to help improve our performance. Last, as demonstrated by our evaluations, our proposals are secure, efficient, and economic for users to implement while outsourcing their data via CPSs.
PPNet
Authors: Akpinar Kutalmiş , Hua Kien A.
Published: May 2020

Software-defined networking introduces opportunities to optimize the Internet Service Provider’s network and to improve client experience for the Video-on-Demand applications. Recent studies on SDN frameworks show that traffic engineering methods allow a fair share of bandwidth between adaptive video streaming clients. Additionally, ISPs can make better estimations of bandwidth and contribute to the bitrate selection for the clients. This study focuses on another aspect of network assistance in video delivery: CDN server selection. In a typical framework where the ISP contributes to the CDN selection, the video provider and the network provider interfaces are merged together. Clients connect to the ISP to get the best CDN server candidate for a given video. This exposes client requests to the ISP. However, video providers have been investing large resources for encrypted video provisioning to preserve their client’s information from third parties, especially network providers. The typical approach is not practical due to privacy concerns. In this study, we present a framework called PPNet to allow CDN-ISP collaboration while preventing the ISP’s access to the video request and availability information. Our framework introduces an isolation between the video provider’s and the ISP’s web interfaces. Clients connect to both of the interfaces and deliver information on a need-to-know basis. As a second contribution, PPNet introduces a practical optimization method for CDN selection. Real-time data collection capabilities of a typical OpenFlow network is used as the input for optimization. Congestion-awareness has been the priority. To adapt for changing network conditions, capability of utilizing multiple servers simultaneously for a single video is introduced. Instead of directing each video client into a CDN node, the proposed system performs request routing per video segment. Finally, we present a system prototype of PPNet and show that our multiple-host adaptive streaming method introduces a significant improvement in quality of experience when compared to the state of the art.
Integrating Security in Resource-Constrained Cyber-Physical Systems
Authors: Lesi Vuk , Jovanov Ilija , Pajic Miroslav
Published: May 2020

Defense mechanisms against network-level attacks are commonly based on the use of cryptographic techniques, such as lengthy message authentication codes (MAC) that provide data integrity guarantees. However, such mechanisms require significant resources (both computational and network bandwidth), which prevents their continuous use in resource-constrained cyber-physical systems (CPS). Recently, it was shown how physical properties of controlled systems can be exploited to relax these stringent requirements for systems where sensor measurements and actuator commands are transmitted over a potentially compromised network; specifically, that merely intermittent use of data authentication (i.e., at occasional time points during system execution), can still provide strong Quality-of-Control (QoC) guarantees even in the presence of false-data injection attacks, such as
Man-in-the-Middle
(MitM) attacks. Consequently, in this work, we focus on integrating security into existing resource-constrained CPS, in order to protect against MitM attacks on a system where a set of control tasks communicates over a real-time network with system sensors and actuators. We introduce a design-time methodology that incorporates requirements for QoC in the presence of attacks into end-to-end timing constraints for real-time control transactions, which include data acquisition and authentication, real-time network messages, and control tasks. This allows us to formulate a mixed integer linear programming-based method for direct synthesis of schedulable tasks and message parameters (i.e., deadlines and offsets) that do not violate timing requirements for the already deployed controllers, while adding a sufficient level of protection against network-based attacks; specifically, the synthesis method also provides suitable intermittent authentication policies that ensure the desired QoC levels under attack. To additionally reduce the security-related bandwidth overhead, we propose the use of cumulative message authentication at time instances when the integrity of messages from subsets of sensors should be ensured. Furthermore, we introduce a method for the opportunistic use of the remaining resources to further improve the overall QoC guarantees while ensuring system (i.e., task and message) schedulability. Finally, we demonstrate applicability and scalability of our methodology on synthetic automotive systems as well as a real-world automotive case-study.
Device-aware Circuit Design for Robust Memristive Neuromorphic Systems with STDP-based Learning
Authors: Sayyaparaju Sagarvarma , Adnan Md Musabbir , Amer Sherif , Rose Garrett S.
Published: May 2020

In the past decade, complementary metal oxide semiconductor-memristor hybrid neuromorphic systems have gained importance owing to the advantages of memristors such as nano-scale size, non-volatility, and low-power operation. However, they are often accompanied by non-ideal properties that can impact the system’s performance. This article presents device-aware circuit design to mitigate such effects. A bi-memristor synapse with a robust spike-timing-dependent plasticity (STDP) is designed. A mixed-mode neuron is presented whose accumulation rate is tunable on-chip and can be used with a variety of memristors without needing a re-design. The proposed designs are employed together in an example pattern recognition system. A scalable winner-takes-all circuit is presented for the output stage. A pattern recognition task based on a simple STDP-based learning is demonstrated such that the recognition rate is directly dependent on the learnt weights. Device-level issues such as switching speed/threshold asymmetry, limited switching resolution, endurance, and varying resistance range (across devices) are shown to adversely affect learning at the system level and it is demonstrated that the proposed circuits can mitigate them. Last, the area and energy costs of the proposed designs are evaluated and compared against other implementations in the literature.
FIN
Authors: Luo Xiaofan , Wong Fukoeng , Hu Haifeng
Published: May 2020

Multi-layer detection is a widely used method in the field of object detection. It extracts multiple feature maps with different resolutions from the backbone network to detect objects of different scales, which can effectively cope with the problem of object scale change in object detection. Although the multi-layer detection utilizes multiple detection layers to alleviate the burden of one single detection layer and can improve the detection accuracy to some extent, this method has two limitations. First, manually assigning anchor boxes of different sizes to different feature maps is too dependent on the human experience. Second, there is a semantic gap between each detection layer in multi-layer detection. The same detector needs to simultaneously process the detection layers with inconsistent semantic strength, which increases the optimization difficulty of the detector. In this article, we propose a feature integrated network (FIN) based on single layer detection to deal with the problems mentioned above. Different from the existing methods, we design a series of verification experiments based on the multi-layer detection model, which shows that the shallow high-resolution feature map has the potential to simultaneously and effectively detect objects of various scales. Considering that the semantic information of the shallow feature map is weak, we propose two modules to enhance the representation ability of the single detection layer. First, we propose a detection adaptation network (DANet) to extract powerful feature maps that are useful for object detection tasks. Second, we combine global context information and local detail information with a verified hourglass module (VHM) to generate a single feature map with high resolution and rich semantic information so that we can assign all anchor boxes to this detection layer. In our model, all the detection operations are concentrated on a high-resolution feature map whose semantic information and detailed information are enhanced as much as possible. Therefore, the proposed model can solve the problem of anchor assignment and inconsistent semantic strength between multiple detection layers mentioned above. A large number of experiments on the Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets show that our model has good detection performance for objects of various sizes. The proposed model can achieve 81.9 mAP when the size of the input image is 300 × 300.
Do Users Behave Similarly in VR? Investigation of the User Influence on the System Design
Authors: Rossi Silvia , Ozcinar Cagri , Smolic Aljosa , Toni Laura
Published: May 2020

With the overarching goal of developing user-centric Virtual Reality (VR) systems, a new wave of studies focused on understanding how users interact in VR environments has recently emerged. Despite the intense efforts, however, current literature still does not provide the right framework to fully interpret and predict users’ trajectories while navigating in VR scenes. This work advances the state-of-the-art on both the study of users’ behaviour in VR and the user-centric system design. In more detail, we complement current datasets by presenting a publicly available dataset that provides navigation trajectories acquired for heterogeneous omnidirectional videos
and
different viewing platforms—namely, head-mounted display, tablet, and laptop. We then present an exhaustive analysis on the collected data to better understand navigation in VR across users, content, and, for the first time, across viewing platforms. The novelty lies in the user-affinity metric, proposed in this work to investigate users’ similarities when navigating within the content. The analysis reveals useful insights on the effect of device and content on the navigation, which could be precious considerations from the system design perspective. As a case study of the importance of studying users’ behaviour when designing VR systems, we finally propose a user-centric server optimisation. We formulate an integer linear program that seeks the best stored set of omnidirectional content that minimises encoding and storage cost while maximising the user’s experience. This is posed while taking into account network dynamics, type of video content, and also user population interactivity. Experimental results prove that our solution outperforms common company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a savings up to 70%. More importantly, we highlight a strong correlation between the storage cost and the user-affinity metric, showing the impact of the latter in the system architecture design.
Region-Level Visual Consistency Verification for Large-Scale Partial-Duplicate Image Search
Authors: Zhou Zhili , Wu Q. M. Jonathan , Yang Yimin , Sun Xingming
Published: May 2020

Most recent large-scale image search approaches build on a bag-of-visual-words model, in which local features are quantized and then efficiently matched between images. However, the limited discriminability of local features and the BOW quantization errors cause a lot of mismatches between images, which limit search accuracy. To improve the accuracy, geometric verification is popularly adopted to identify geometrically consistent local matches for image search, but it is hard to directly use these matches to distinguish partial-duplicate images from non-partial-duplicate images. To address this issue, instead of simply identifying geometrically consistent matches, we propose a region-level visual consistency verification scheme to confirm whether there are visually consistent region (VCR) pairs between images for partial-duplicate search. Specifically, after the local feature matching, the potential VCRs are constructed via mapping the regions segmented from candidate images to a query image by utilizing the properties of the matched local features. Then, the compact gradient descriptor and convolutional neural network descriptor are extracted and matched between the potential VCRs to verify their visual consistency to determine whether they are VCRs. Moreover, two fast pruning algorithms are proposed to further improve efficiency. Extensive experiments demonstrate the proposed approach achieves higher accuracy than the state of the art and provide comparable efficiency for large-scale partial-duplicate search tasks.
A Global Routing Method for Graphene Nanoribbons Based Circuits and Interconnects
Authors: Das Subrata , Das Debesh Kumar , Pandit Soumya
Published: May 2020

With extreme miniaturization of traditional CMOS devices in deep sub-micron design levels, the delay of a circuit, as well as power dissipation and area are dominated by interconnections between logic blocks. Interconnect today is causing major problems such as delay, power dissipation, and so on. In an attempt to search for alternative materials, Graphene nanoribbons have been found to be potential for both transistors and interconnects due to its outstanding electrical and thermal properties. Graphene nanoribbons provide better options as materials used for global routing trees in VLSI circuits. However, certain special characteristics of Graphene nanoribbon prohibit direct application of existing VLSI routing tree construction methods. In this article, we address this issue and propose heuristic methods for construction of Graphene nanoribbon--based minimum hybrid cost and minimum-delay Steiner trees. We compute the delays for the trees using Elmore delay approximation. Experimental results demonstrate the effectiveness of our proposed methods, which are quite encouraging.
Design Space Exploration for Ultra-Low-Energy and Secure IoT MCUs
Authors: Aerabi Ehsan , Bohlouli Milad , Livany Mohammad Hasan Ahmadi , Fazeli Mahdi , Papadimitriou Athanasios , Hely David
Published: May 2020

This article explores the design space of secure communication in ultra-low-energy IoT devices based on Micro-Controller Units (MCUs). It tries to identify, benchmark, and compare security-related design choices in a Commercial-Off-The-Shelf (COTS) embedded IoT system which contributes to the energy consumption. We conduct a study over a large group of software crypto algorithms: symmetric, stream, hash, AEAD, MAC, digital signature, and key exchange. A comprehensive report of the targeted optimization attributes (memory, performance, and specifically energy) will be presented from over 450 experiments and 170 different crypto source codes. The article also briefly explores a few system-related choices which can affect the energy consumption of secure communication, namely, architecture choice, communication bandwidth, signal strength, and processor frequency. In the end, the article gives an overview of the obtained results and the contribution of all. Finally, it shows, in a case study, how the results could be utilized to have a secure communication in an exemplary IoT device. This article gives IoT designers insight into ultra-low-energy security, helps them to choose appropriate cryptographic algorithms, reduce trial-and-error of alternatives, save effort, and hence cut the design costs.
Context-Aware Recommendations Based on Deep Learning Frameworks
Authors: Unger Moshe , Tuzhilin Alexander , Livne Amit
Published: May 2020

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets.
Hyperspectral Reconstruction with Redundant Camera Spectral Sensitivity Functions
Authors: Han Xian-Hua , Zheng Yinqiang , Sun Jiande , Chen Yen-Wei
Published: May 2020

High-resolution hyperspectral (HS) reconstruction has recently achieved significantly progress, among which the method based on the fusion of the RGB and HS images of the same scene can greatly improve the reconstruction performance compared with those based on the individually spectral or spatial enhancement. It is well known that the HS image is obtained only via the costly hypersoectral sensor, whereas the RGB images can be provided by low-price RGB cameras and the spectral sensitivity (SS) functions of RGB cameras are usually different. Thus, this study proposes a HS reconstruction, which fuses merely two RGB images with redundant spectral responses. In this work, we design a new RGB camera via shifting the SS of an existed RGB camera, which can provide similar strength of spectral response with different spectral centers of SS, and fuse the new achieved color image with an existed RGB image by a deep ResNet. Experiments validate that fusion of two existed RGB images can provide impressive HS reconstruction performance and further improvement can be achieved by integrating the color image of the simulated SS with the RGB image.
Joint Model of Entity Recognition and Relation Extraction with Self-attention Mechanism
Authors: Liu Maofu , Zhang Yukun , Li Wenjie , Ji Donghong
Published: May 2020

In recent years, the joint model of entity recognition (ER) and relation extraction (RE) has attracted more and more attention in the healthcare and medical domains. However, there are some problems with the prior work. The joint model cannot extract all the relations for a specific entity, and the majority of joint models heavily rely on complex artificial features or professional natural language processing (NLP) tools. In this article, we construct a novel joint model that can simultaneously extract all medical entities and relations from medicine Chinese instructions. Moreover, the self-attention mechanism is introduced to the joint model to learn word intra-sentence dependencies. The proposed model is evaluated using a medicine Chinese instruction dataset that we collect and an open dataset provided in CoNLL-2004. Experimental results show that the model with self-attention achieves the state-of-the-art performance.
A User-centric Approach toward Resilient Frequency-regulating Wind Generators
Authors: Arani Mohammadreza , Kundur Deepa
Published: May 2020

Smart microgrids are rapidly being developed and deployed, even as concerns over their cyber-physical security are increasing. The high penetration of these power electronic-interfaced energy resources has resulted in weaker power grids and an increase in cyberattack surface. The implementation of frequency regulation in these new resources—particularly in wind generators—is on the rise. This article investigates how malicious controllable loads can threaten the integrity of frequency-regulating wind generators. Adopting a user-centric approach and benefiting from small-signal analyses, the article shows for the first time how these wind generators can be the target of attackers. Effective methods to enhance system resilience are sought by mitigating the attack risk in the extended end-users, wind generators. The article models and explores how proper tuning and design of the physical system can improve cyber-physical security. The work also extends the user-centric method to the physical layer of smart grids. Detailed time-domain simulations verify the results of the analyses.
A Time-space Efficient Algorithm for Parallel
k
-way In-place Merging based on Sequence Partitioning and Perfect Shuffle
Authors: Salah Ahmad , Li Kenli , Liao Qing , Hashem Mervat , Li Zhiyong , Chronopoulos Anthony T. , Zomaya Albert Y.
Published: May 2020

The huge data volumes, big data, and the emergence of new parallel architectures lead to revisiting classic computer science topics. The motivation of the proposed work for revisiting the parallel
k
-way in-place merging is primarily related to the unsuitability of the current state-of-the-art parallel algorithms for multicore CPUs with shared memory. These architectures can be profitably employed to solve this problem in parallel. Recently, Intel introduced the parallel Standard Template Library (STL) implementation for multicore CPUs, but it has no in-place merge function with the in-place property. We propose Partition-Shuffle-merge (
PS-merge
) to address this problem.
PS-merge
includes combining sequence partitioning with the in-place perfect shuffle effect to address the
k
-way merge task. At first, each sequence is divided into
t
equal-sized partitions or ranges. Thus, each partition is spread over at most
k
sequences. Then, perfect shuffle is utilized as a replacement for the classic block rearrangement. Finally, range subpartitions are merged using a sequential in-place merging algorithm. To evaluate the proposed algorithm, as
PS-merge
produces the standard merging format, we compare this algorithm against the state-of-the-art methods, bitonic merge, a parallel binary merge tree, and lazy-merge.
PS-merge
shows a significant improvement in overall execution time.
A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection
Authors: Huang Yapei , Tian Yun , Liu Zhijie , Jin Xiaowei , Liu Yanan , Zhao Shifeng , Tian Daxin
Published: May 2020

Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing. However, this way lead to a greater risk of leaking users’ privacy. To avoid the risk, this article proposes a traffic density estimation model based on crowdsourcing privacy protection. First, in the acquisition process of the track data by crowdsourcing, dual servers are employed for transmission, and homomorphic encryption is carried out to encrypt the data to protect the data from being leaked during transmission. Second, sampling is implemented for randomization and anonymization to reduce the spatial continuity and temporal continuity of position data. In this way, the intermediate server cannot acquire users’ original data, and the main server cannot obtain users’ personal information. Finally, before data transmission, Laplace noising is performed on the users’ local position data to further protect the original location information. The proposed algorithm in this study realizes that only users have their original track data, and the servers involved in the work cannot infer the original track data, which ensures the real security of user privacy. The proposed algorithm was verified with the track data from the Didi Gaia Data Opening Plan. The experimental results showed that the proposed algorithm could still maintain the validity of data analysis results and the security of user data privacy after homomorphic encryption, noise addition, and sample collection, and displayed good robustness and scalability.
Runtime Identification of Hardware Trojans by Feature Analysis on Gate-Level Unstructured Data and Anomaly Detection
Authors: Vijayan Arunkumar , Tahoori Mehdi B. , Chakrabarty Krishnendu
Published: May 2020

As the globalization of chip design and manufacturing process becomes popular, malicious hardware inclusions such as hardware Trojans pose a serious threat to the security of digital systems. Advanced Trojans can mask many architectural-level Trojan signatures and adapt against several detection mechanisms. Runtime Trojan detection techniques are considered as a last line of defense against Trojan inclusion and activation. In this article, we propose an offline analysis to select a subset of flip-flops as surrogates and build an anomaly detection model based on the activity profile of flip-flops. These flip-flops are monitored online, and the anomaly detection model implemented online analyzes the flip-flop data to detect any anomalous Trojan activity. The effectiveness of our approach has been tested on several Trojan-inserted designs of the Leon3 processor. Trojan activation is detected with an accuracy score of above 0.9 (ratio of the number of true predictions to total number of predictions) with no false positives by monitoring less than 0.5% of the total number of flip-flops.
A Hierarchical HVAC Control Scheme for Energy-aware Smart Building Automation
Authors: Jana Rajib Lochan , Dey Soumyajit , Dasgupta Pallab
Published: May 2020

Heating ventilation and air conditioning (HVAC) systems usually account for the highest percentage of overall energy usage in large-sized smart building infrastructures. The performance of HVAC control systems for large buildings strongly depend on the outside environment, building architecture, and (thermal) zone usage pattern of the building. In large buildings, HVAC system with multiple air handling units (AHUs) is required to fulfill the cooling/heating requirements.

In the present work, we propose an energy-aware building resource allocation and economic model predictive control (eMPC) framework for multi-AHU-based HVAC system. The energy consumption of a multi-AHU-based HVAC system significantly depends on how long the AHUs are running, which again is governed by the zone usage demands. Our approach comprises a two-step hierarchical technique where we first minimize the running time of AHUs by suitably allocating building resources (thermal zones) to usage demands for zones. Next, we formulate a finite receding horizon control problem for trading off energy consumption against thermal comfort during HVAC operations. Given a high-level building specification and usage demand, our computer-aided design framework generates building thermal models, allocates usage demands, formulates the control scheme, and simulates it to generate power consumption statistics for the given building with usage demands. We believe that the proposed framework will help in early analysis during the design phase of energy-aware building architecture and HVAC control. The framework can also be useful from a building operator point of view for energy-aware HVAC control as well as for satisfying smart grid demand-response events by HVAC system peak power reduction through automated control actions.
Does It Ping or Pong? Auditory and Tactile Classification of Materials by Bouncing Events
Authors: Pra Yuri De , Fontana Federico , Järveläinen Hanna , Papetti Stefano , Simonato Michele
Published: May 2020

Two experiments studied the role of impact sounds and vibrations in classification of materials. The task consisted of feeling on an actuated surface and listening through headphones to the recorded feedback of a ping-pong ball hitting three flat objects respectively made of wood, plastic, and metal, and then identifying their material. In Experiment 1, sounds and vibrations were recorded by keeping the objects in mechanical isolation. In Experiment 2, recordings were taken while the same objects stood on a table, causing their resonances to fade faster due to mechanical coupling with the support. A control experiment, where participants listened to and touched the real objects in mechanical isolation, showed high accuracy of classification from either sounds (90% correct) or vibrations (67% correct). Classification of reproduced bounces in Experiments 1 and 2 was less precise. In both experiments, the main effect of material was statistically significant; conversely, the main effect of modality (auditory or tactile) was significant only in the control. Identification of plastic and especially metal was less accurate in Experiment 2, suggesting that participants, when possible, classified materials by longer resonance tails. Audio-tactile summation of classification accuracy was found, suggesting that multisensory integration influences the perception of materials. Such results have prospective application to the nonvisual design of virtual buttons, which is the object of our current research.
An Artifact Evaluation of NDP
Authors: Zilberman Noa
Published: May 2020

Artifact badging aims to rank the quality of submitted research artifacts and promote reproducibility. However, artifact badging may not indicate inherent design and evaluation limitations.

This work explores current limits in artifact badging using a performance-based evaluation of the NDP artifact. We evaluate the NDP artifact beyond the Reusable badge's level, investigating the effect of aspects such as packet size and random-number seed on throughput and flow completion time.

Our evaluation demonstrates that while the NDP artifact is reusable, it is not robust, and we identify architectural, implementation and evaluation limitations.
An Open Platform to Teach How the Internet Practically Works
Authors: Holterbach Thomas , Bü Tobias , Rellstab Tino , Vanbever Laurent
Published: May 2020

Each year at ETH Zurich, around 100 students collectively build and operate their very own Internet infrastructure composed of hundreds of routers and dozens of Autonomous Systems (ASes). Their goal? Enabling Internet-wide connectivity. AB@We find this class-wide project to be invaluable in teaching our students how the Internet infrastructure practically works. Among others, our students have a much deeper understanding of Internet operations alongside their pitfalls. Besides students tend to love the project: clearly the fact that all of them need to cooperate for the entire Internet to work is empowering. AB@In this paper, we describe the overall design of our teaching platform, how we use it, and interesting lessons we have learnt over the years. We also make our platform openly available.
Workshop on Internet Economics (WIE 2019) report
Authors: claffy kc , Clark David
Published: May 2020

On 9-11 December 2019, CAIDA hosted the 10th interdisciplinary Workshop on Internet Economics (WIE) at UC San Diego's Supercomputer Center. This workshop series provides a forum for researchers, Internet facilities and service providers, technologists, economists, theorists, policymakers, and other stakeholders to exchange views on current and emerging economic and policy debates. This year's meeting had a narrower focus than in years past, motivated by a new NSF-funded project being launched at CAIDA: KISMET (Knowledge of Internet Structure: Measurement, Epistemology, and Technology). The objective of the KISMET project is to improve the security and resilience of key Internet systems by collecting and curating infrastructure data in a form that facilitates query, integration and analysis. This project is a part of NSF's new Convergence Accelerator program, which seeks to support fundamental scientific exploration by creating partnerships across public and private sectors to solve problems of national importance.
Tenon Capsule Grafting versus Autologous Scleral Graft in Ahmed Glaucoma Valve Surgery
Authors: Wagdy Faried M.
Published: May 2020

Objective. To compare between the surgical outcomes of Tenon capsule grafting and autologous scleral graft in Ahmed glaucoma valve (AGV) surgery in the management of refractory glaucoma and prevention of tube exposure. Patients and Methods. This prospective randomised study included 30 eyes of 30 patients with refractory glaucoma, who were aged between 46 and 58 years and diagnosed with refractory glaucoma. This study was conducted in Menofia University Hospital between July 2018 and December 2019. Informed patient consent was obtained. The studied eyes were divided into two groups: the first group included 15 eyes for which AGV with Tenon capsule grafting was performed, while the second group included 15 eyes for which AGV with autologous scleral graft was performed. All patients were followed up for one year after the surgery. The outcomes were evaluated according to intraocular pressure (IOP) and the number of postoperative glaucoma medications. Visual acuity, visual field, number of postoperative glaucoma medications, and postoperative complications were followed throughout the 1-year follow-up period. Results. There was a significant reduction in IOP in both groups, with more reduction in the Tenon graft group where the mean IOP after one year was 11.66 ± 0.89 mmHg, whereas in the scleral graft group, the mean IOP was 14.20 ± 4.0 mmHg (p value < 0.001). However, the difference between the 2 groups in lowering IOP was insignificant. Regarding postoperative complications, tube exposure was observed in one case in the scleral graft group with associated scleral melting and hypotony, postoperative hypotony was more in the scleral graft group with 3 cases (20%), and in the Tenon graft group, hypotony occurred only in 1 case (6.67%). In addition, less vascular blebs were seen in most cases in the Tenon graft group, while most blebs seen in the scleral graft group were vascular blebs. In addition, both groups showed stability in terms of visual acuity and visual field. Conclusion. Tenon capsule grafting and autologous scleral grafting might be effective and safe techniques when applied with AGV in the management of refractory glaucoma. Tenon capsule resection with grafting showed relatively low incidence of tube exposure and hypertensive phase.
Option Pricing under Double Stochastic Volatility Model with Stochastic Interest Rates and Double Exponential Jumps with Stochastic Intensity
Authors: Chang Ying , Wang Yiming
Published: May 2020

We present option pricing under the double stochastic volatility model with stochastic interest rates and double exponential jumps with stochastic intensity in this article. We make two contributions based on the existing literature. First, we add double stochastic volatility to the option pricing model combining stochastic interest rates and jumps with stochastic intensity, and we are the first to fill this gap. Second, the stochastic interest rate process is presented in the Hull–White model. Some authors have concentrated on hybrid models based on various asset classes in recent years. Therefore, we build a multifactor model with the term structure of stochastic interest rates. We also approximated the pricing formula for European call options by applying the COS method and fast Fourier transform (FFT). Numerical results display that FFT and the COS method are much faster than the numerical integration approach used for obtaining the semi-closed form prices. The COS method shows higher accuracy, efficiency, and stability than FFT. Therefore, we use the COS method to investigate the impact of the parameters in the stochastic jump intensity process and the existence of the process on the call option prices. We also use it to examine the impact of the parameters in the interest rate process on the call option prices.