Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Resonant-Cavity-Enhanced GeSn Photodetectors in Short-Wavelength Infrared Regime

GeSn with a 10% Sn content for resonant-cavity-enhanced photodetectors (RCE PDs) was grown and the RCE PDs was designed for 2000-nm operation, exhibiting a quantum efficiency up to 91%. This work shows RCE GeSn PDs can be a promising route toward high-response SWIR detection.
Application-Grained Block I/O Analysis for Edge Computational Intelligent

Block I/O workload is widely used in studies on storage performance. However, few studies have focused on the workload characteristics of edge intelligent computing. A discrepancy exists between the workload and the storage, resulting in performance loss. Most studies have focused on the overall block I/O workload characteristics or the performance of the storage; however, a fine-grained analysis is required to understand the workload more comprehensively. In this paper, we propose the concept of an application-grained block I/O analysis for edge computational intelligent. Firstly describe the five steps of the application-grained block I/O analysis, i.e., trace collection, application isolation, application weighting, application trace analysis, and results. Subsequently, discuss the key points of the application-grained block I/O analysis. An application-grained block I/O analysis is performed using five off-the-shelf trace analysis tools and four evaluation parameters.
Online Congestion Measurement and Control in Cognitive Wireless Sensor Networks

A lightweight distributed MAC protocol is proposed in this paper to regulate the coexistence of high-priority (primary) and low-priority (secondary) wireless devices in cognitive wireless sensor networks. The protocol leverages the available spectrum resources while guaranteeing stringent quality of service requirements. By sensing the congestion level of the channel with local measurements and without any message exchange, a novel adaptive congestion control protocol is developed by which every device independently decides whether it should continue operating on a channel, or vacate it in case of saturation. The proposed protocol dynamically changes the congestion level based on variations of the non-stationary network. The protocol also determines the optimal number of active secondary devices needed to maximize the channel utilization without sacrificing latency requirements of the primary devices. This protocol has almost no signaling and computational overheads and can be directly implemented on top of existing wireless protocols without any hardware/equipment modification. Experimental results show substantial performance enhancement compared to the existing protocols and provide useful insights on low-complexity distributed adaptive MAC mechanism in cognitive wireless sensor networks.
Interchange-Based Privacy Protection for Publishing Trajectories

Information extracted from trajectory data is very useful in many practical application scenarios. Before trajectories for data mining are published, they need to be processed to protect the privacy of the trajectories’ bodies. In this paper, a method for such privacy protection is proposed. Our method guarantees that the generated trajectory points satisfy the k-anonymity by interchanging the positions of the trajectory points on the k-core subnet of the relation network. The method treats the trajectory points as the privacy protection object. It overcomes the curse of dimensionality resulting from the K-anonymity of trajectories, and reduces the distortion of the generated trajectories significantly. Moreover, our proposed strategy can preserve the original positions of the trajectory points. Experiments on both real-life and synthetic data sets are carried out with different methods for comparison. The results show that our method has greater efficiency and lower distortion of the processed trajectories.
Dynamic Scheduling of EDA Scientific Workflows in Hybrid Computing Environments

With the increasing scale and complexity of Very-Large-Scale Integration (VLSI), Electronic Design Automation (EDA) technology is undergoing rapid development, and it's tools are becoming ever more diverse. The EDA scientific workflow refers to the combination of tasks at each stage in EDA design, e.g., RTL simulation, logic synthesis, layout (placement and routing), and timing simulation, etc. The execution of these tasks is quite inflexible and consumes a lot of time and resources, because EDA design is computing intensive. Due to the diversity of computing resource requirements, single computing environment cannot support the execution of EDA scientific workflows, so hybrid computing environments (HCE) are indispensable for the integration of multiple EDA tools. Besides, the licenses of EDA tools are limited and expensive, which limit the available resources required for EDA tasks. However, the existing dynamic algorithms are not suitable for task characteristics and computing environments of EDA scientific workflows. To solve these problems, we propose an efficient algorithm to schedule EDA scientific workflows dynamically based on HCE, including Cloud and High Performance Computing (HPC) clusters, which takes into account characteristics of each task and constraints of licenses for using EDA commercial tools. In this paper, first, multiple Directed Acyclic Graphs (DAGs) are adopted for representing the EDA scientific workflows and task dependencies. Then, a dynamic scheduling algorithm is used to implement task selection and resource allocation. Moreover, we achieve a tradeoff between efficiency and user fairness. Finally, the proposed algorithm is evaluated in simulated experiments. Experimental results show that it can reduce overall makespan by up to 26.9% and improve user fairness at least 23.04% than other algorithms.
A Heuristic Hybrid Optimization Approach for Spare Parts and Maintenance Workers Under Partial Pooling

The joint planning of spare parts and maintenance workers in a multiple echelon inventory system with lateral and cross-echelon transshipment can reduce the total cost of the system. However, partial pooling is not extensively considered in the joint optimization problem of maintenance resources. In this paper, the objective is to determine the optimal inventory of spare parts and the number of maintenance workers to minimize the total cost of the system considering partial pooling. First, a greedy heuristic is used to obtain the initial inventory. Then, the cat swarm optimization algorithm is formulated to produce nearly optimal results, which can solve larger instances with a faster computation time. Furthermore, a maintenance system with 4 local warehouses, 3 central warehouses and 1 plant serving 5 machine groups is analyzed, in which each machine consists of 5 key components that can breakdown independently. The results verify the effectiveness of the proposed optimization approach. Finally, the total cost for different resource provision scenarios, failure rates and system parameters are discussed.
Attention-Based Dense Point Cloud Reconstruction From a Single Image

Three-dimensional Reconstruction has drawn much attention in computer vision. Generating a dense point cloud from a single image is a more challenging task. However, generating dense point clouds directly costs expensively in calculation and memory and may cause the network hard to train. In this work, we propose a two-stage training dense point cloud generation network. We first train our attention-based sparse point cloud generation network to generate a sparse point cloud from a single image. Then we train our dense point cloud generation network to densify the generated sparse point cloud. After combining the two stages and finetuning, we obtain an end-to-end network that generates a dense point cloud from a single image. Through evaluation of both synthetic and real-world datasets, we demonstrate that our approach outperforms state of the art works in dense point cloud generation. Our source code is available at https://github.com/VIM-Lab/AttentionDPCR.
AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation

In this paper, we propose a novel intelligent model, called AttentiveHerb, for simulating the doctor’s inquiry and prescription that is composed by a series of herbs. It can automatically simulate some principles and learns the interaction between symptoms and herbs from clinical records of traditional herbal medicine. This model consists of two different attention mechanisms for distinguishing the main symptoms and paying different attention to different symptoms. By experiments, in terms of the predicted prescriptions, 51% of the total cases are in full accordance with the labels; in 1.09% of cases, all herbs of a label can be found in the predicted prescription and the predicted prescription have other additional herbs; in 15.4% of cases, all herbs of a predicted prescription can be found in their corresponding label; in 22.41% of cases, several herbs in each predicted prescription overlap with its label; and 10.1% of cases are completely different from the label. In summary, 67.49% of the predicted prescriptions are close to the labels, and 89.9% contain the same herbs with the labels, which indicates that the prescriptions generated by our model are close to those by doctors. Besides, our model can recommend herbs that do not appear in the label prescriptions but are useful for relieving symptoms. It shows that our model can learn some interactions between herbs and symptoms. With enough normalized traditional herbal medical records, this model works more accurately. This study also provides a benchmark for the upcoming researches in intelligent inquiry and prescription generation of traditional herbal medicine.
Square Coil Geometry Evaluation on Power Efficiency for Wireless Power Charging Applications

Wireless power charging includes interesting physics from low frequencies and RF analysis which tends to be complex and tedious to model and simulate. Full wave simulations has been performed in order to evaluate the wireless power efficiency. A square coil was used to perform this analysis and simulations shows pick values of efficiency of 70% around 5 MHz, which is higher than most found at this frequency. The method has been proved, it is simple to implement in a commercial electromagnetic simulator. Then, it can easily be replicated and optimized to find optimal geometries of coil for specific applications such mobile phones, wearable devices or even electric car charging.
An Graph-Based Adaptive Method for Fast Detection of Transformed Data Leakage in IOT Via WSN

As statistics show that most threats to information security in Internet of Things (IOT) are caused by data leakage, lots of methods have been developed to address the problem of data leakage prevention (DLP). However, most of these methods do not work well when the confidentiality of data changes frequently. We propose an Adaptive Feature Graph Update model (AFGU) to solve the problem by mapping the features of confidential data to the feature graph. First, the feature graph are built to record the features of confidential data which involve the sensitive terms and their context. Then, the improved evaluation method for the importance of each term is employed to update the feature graph according to the importance degree of each term. Finally, the confidentiality of data are determined by matching the features of the data with the feature graph. Experiments results show that the proposed method can detect confidential data effectively and efficiently.
Security and Efficiency Enhanced Revocable Access Control for Fog-Based Smart Grid System

With the popularity of smart grids, plentiful of smart devices have been put into use, such as smart meters and power assets. Due to limited computation capabilities and storage spaces of these devices, the collected data need to be “outsourced” towards the data server for processing and storage. The data owners, therefore, lose direct control over these “outsourced” data, leading to significant security issues of the users’ data. In this paper, aiming at solving this problem, we propose a multi-authority Ciphertext Policy Attribute-based Encryption (CP-ABE) scheme with revocation for the fog-based smart grid system. Specifically, in order to achieve attribute revocation without requiring users to be always online, we use the DH (Diffie-Hellman) tree to distribute the group key statelessly, which also solves the problem of collusion attack initiated by revoked user and valid user. To improve security of our proposed scheme, we remove the trusted key authority (KA) by using a secure two-party computation (2PC) protocol between the KA and the cloud service provider to generate user private key. To improve efficiency of our proposed scheme, we combine user and attribute revocation, and outsource complex calculations to fog nodes. Furthermore, our proposed scheme uses attribute group key and leaf private key together to protect user proxy key, which reduces the storage overhead of the system and improves the security. Both security analysis and experimental results demonstrate that our proposed scheme can balance the security objectives with the efficiency.
Optimization of Graphene-Based Slot Waveguides for Efficient Modulation

We systematically investigated graphene-based slot and multi-slot waveguides. A tri-slot waveguide which significantly enhances the light-graphene interaction was experimentally demonstrated with low insertion loss. By integrating a dual-layer graphene h-BN heterostructure on the tri-slot waveguide, a high efficiency modulator is designed, which combines the advantages of high modulation efficiency (0.376 dB/μm), low insertion loss (0.01 dB/μm), large modulation bandwidth (∼0.13 THz) and low power consumption (∼0.128 pJ/bit). Our work may promote the development of future high efficient graphene-based optical devices.
Throughput Analysis of MRT-ZF Wireless Body Area Network with Multi-Antenna AP

This paper analyzes the throughput performance in a wireless body area network (WBAN) consisting of a multiantenna access point (AP), which intends to broadcast radio frequency (RF) power to several single-antenna on-body sensors then receives physiological information from them. Different path loss and shadowing values are considered in channel models between AP and sensors to be more reliable. In this paper, we design an improved time-splitting protocol which is adaptive according to channel conditions. The whole protocol is divided into two parts: power broadcasting phase and information receiving phase. In power broadcasting phase, the AP broadcasts RF power to sensors using maximal ratio transmission (MRT) method. In the information receiving phase, the AP receives information from all sensors simultaneously after sensors harvest RF energy in last power broadcasting phase, and zero-forcing (ZF) method is applied when the AP decodes information. In each time block, considering signal to noise plus interference power ratio (SINR) threshold, the sum information throughput is formulated as a relative entropy problem subject to the timesplitting ratio constraint. We propose the optimal solution to this problem, and numerical results demonstrate the effectiveness of the optimal time-splitting ratio.
Bulk Acoustic Wave Resonator-Oscillators and Tests of Fundamental Physics

Quartz Bulk Acoustic Wave resonator-oscillators are amongst the best oscillators for frequency control applications. We will present our latest results for using such oscillators and resonators to improve the precision and test fundamental physics. For instance, we have set up new experiments to test for Lorentz invariance violations, to search for Dark Matter and high frequency Gravitational Wave radiation. The former uses stable room temperature oscillators and the later uses cryogenic resonators cooled to low temperatures. At cryogenic temperatures such resonators have been shown to have record acoustic $Q$ factors of more than a billion near 4 K and above 100 MHz. Such high values also suggest low phase noise and highly frequency-stabilized oscillators should be possible. We have realized a Pound stabilized cryogenic oscillator from one such mode near 116 MHz. Despite the extremely high Q-factors, we find that the power-frequency sensitivity is increased by a factor of 1000 at cryogenic temperatures, which limits the purity of the oscillator signal at low temperatures. To realise the full potential of such a stable oscillator, this dependence must be understood and dealt with. Details of the oscillator stabilisation system and noise floor analysis will be presented.
PositNN Framework: Tapered Precision Deep Learning Inference for the Edge

The performance of neural networks, especially the currently popular deep neural networks, is often limited by the underlying hardware. Computations in deep neural networks are expensive, have a large memory footprint, and are power hungry. Conventional reduced-precision numerical formats, such as fixed-point and floating point, are not optimal to represent deep neural network parameters with a nonlinear distribution and small dynamic range. The recently proposed posit numerical format with tapered precision represents small values more accurately than the other formats. In this work, we propose a deep neural network framework, PositNN, that uses the posit numerical format and exact-dot-product operations during inference. The efficacy of the ultra-low precision version of PositNN as compared to other frameworks (which use fixed-point and floating point) is demonstrated on three datasets (MNIST, Fashion MNIST, and CIFAR-10), where an {5-8}-bit PositNN outperforms other {5-8}-bit low-precision neural networks across all tasks.
Improving Read Performance of LSM-Tree Based KV Stores via Dual Grained Caches

Key-value (KV) stores based on the Log-Structure Merge tree (LSM-tree) have been widely used in modern storage systems (e.g. LevelDB and RocksDB) to achieve high write throughput. However, conventional LSM-tree design has high latency for random read operations, especially in the workloads with concurrent read and write operations. This paper proposes a dual-grained cache scheme to improve the performance of LSM-tree based KV stores, in which a coarse-grained cache improves read performance by leveraging spatial locality and a fine-grained cache tracks the frequently accessed KV pairs. We implement the dual-grained caching on a LSM-tree simulator and evaluate its performance using various Yahoo! Cloud Service Benchmark (YCSB) workloads. Our experimental results show that the proposed way of dual-grained caches can significantly improve the read performance of KV stores without sacrificing the advantages of the LSM-tree for write-intensive workloads.
SHC: A Method for Stackable Parallel File Systems in Userspace

We present SHC, a stackable layer that leverages the power and flexibility of middlewares to handle kernel crossings of parallel file systems in userspace. The core of SHC is a dynamic library interception mechanism that improves I/O performance and guarantees consistency. Additionally, our solution uses a server process to handle write synchronizations so as to maintain the data correctness and the data consistency with minimal overheads. We implement an SHC prototype with PLFS on a Lustre system and demonstrate that SHC maintains most interactions between applications and PLFS in userspace while requiring fewer kernel calls. We show that SHC improves write bandwidth up to 30x higher than FUSE, and reduces the latency of up to 90% for high-performance I/O systems.
Solving the Discrete Cost Multicommodity Network Design Problem to Optimality

Multicommodity Network Design problems are a challenging class of problems that arises in the strategic and tactical planning processes and has many applications mainly in the fields of telecommunication and supply chain. Thus, solving this interesting NP-hard problem is fundamental for the profitable business of network operators. In this paper, we focus on a variant of the Discrete Cost Multicommodity Network Design Problem, with multiple discrete facilities (corresponding to different technologies) that could be installed on the edges. Each facility is bidirectional and has prefixed discrete capacity and cost. For this problem, we present a tailored Benders decomposition approach using an arc-flow based formulation and we strengthen it by adding some valid inequalities. Computational experimentations conducted on both randomly generated and real-world instances are reported.
Optimal Anchor Placement for Localization in Large-Scale Wireless Sensor Networks

In wireless sensor networks (WSNs), location information of sensor nodes (or simply sensors) plays a vital role in both of the management of WNSs and many other applications. Due to the constraints on costs and energy, only a small portion of nodes in a WSN is deployed as anchor nodes or simply anchors with their locations a priori known or determined through certain hardware (e.g. GPS) to localize normal sensor nodes. However, the placement of such anchors has significant influence on the localization performance of sensor nodes. This paper tackles the problem of optimal anchor placement for localization in large-scale WSNs. But, differently from existing studies assuming independent and identically distributed measurement noises, this paper takes into account more practical distance dependent measurement noises. Then, provided that sensors' locations satisfy a homogeneous Poisson Point process, a theoretical analysis based on the average Cramer-Rao Lower Bound (CRLB) proves that it is optimal to place anchors in a regular fashion. In particular, given that each sensor can measure distances to nearby 3 anchors, the optimal anchor placement pattern is the equilateral triangle pattern, which is consistent with the optimal node deployment for 3-coverage and 6-connectivity. This study not only provides the knowledge for guiding the deployment of large-scale WSNs in practice, but also paves the way for building the theory of sensor localization in WSNs.
ITO Islands as Floating Electrodes to Deposit Aligned Carbon Nanotubes for Photovoltaic Applications

Recently transparent conductive electrodes (TCEs) have attracted the interest of researchers due to their outstanding optical and electrical properties. Indium tin oxide (ITO) is an oxide material that combines the criteria of high conductance and transmittance. However, ITO is a non-flexible, brittle and expensive material. TCEs are commonly embedded in solar cells to perform high photon absorption and electron collection instantaneously. In this paper, an optimized TCE network is proposed to maintain TCEs' conductivity and transparency and to improve their flexibility and ability to handle mechanical forces. The network is designed by aligning multi-walled carbon nanotubes (MWCNTs) on ITO speckled surface to form ITO-CNT grid. The alignment mechanism is achieved through dielectrophoretic (DEP) force, where an electric field of 10V and 105 Hz is subjected to an ethanol/ MWCNT suspension to assemble the CNT across ITO's floating electrodes.
Investigating Characteristics of Attacks on Public Cloud Systems

In this work, honeypots were set up on several public cloud infrastructures of Amazon, Microsoft and Google located in different regions around the world, including North America, Asia and Europe. The honeypots, simulating different popular services like SSH and VNC, were used to collect data over a period of two month, resulting in over 170 million log entries. Further analysis of the log entries regarding attack patterns and geographic characteristics are presented in this paper. For example, the attacks originated from 216 countries involving 268,614 unique IPs, dominated by China with a share of 25.83%