Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxi Demand Prediction with LSTM-Based Combination Model

Taxi demands to predict in urban city helps to guide the drivers to cruise and pickup riders, which increases drivers' income and decreases riders' waiting time. The taxi demands prediction problem is usually cast as a time series prediction task, and deep models such as recurrent neural network (RNN) or long short-term memory (LSTM) is adopted for the prediction. In this paper, we present a LSTM-based combination model to predict taxi demand based on the recent demand and other relevant information. More specifically, we use a spatio-temporal component to capture the spatio-temporal information and use an attribute component to obtain external information (e.g. weather, point of interest), and these two components are combined to make the final predictions. We evaluate our method of two real-world datasets, and the results show that the proposed approach outperforms other prediction methods.
Separation Principle for Linear Sampled-Data Control Systems Under Observer-Based Output Feedback With Aperiodic Sampling

This paper studies a separation principle for a class of linear sampled-data control systems with aperiodic sampling. The concerned problem is to show that in aperiodic sampling, the design of observer-based output feedback controller can be broken into two separate designs: aperiodic sampled-data static state feedback controller and observer designs. It is proved that asymptotic (respectively, exponential) stabilization by the sampled-data static state feedback and asymptotic (respectively, exponential) observation by the sampled-data Luenberger observer imply asymptotic (respectively, exponential) stabilization by the sampled-data observer-based output feedback. An example is given to check the validity of the proposed theoretical claims.
Dimensionality Reduction on Grassmannian: A Good Practice

Representing images and videos as linear subspaces for visual recognition has made a great success which benefits from the Riemannian geometry named the Grassmann manifold. However, subspaces in vision are high-dimensional, which leads to a high computational expense and limited applicability of existing techniques. In this paper, we propose a generalized model to learn a lower-dimensional and more discriminative Grassmann manifold from the high dimensional one through an orthonormal projection for a better classification. We respect the Riemannian geometry of the Grassmann manifold and search for this projection directly from one Grassmann manifold to another face-to-face without any additional transformations. In this natural geometry-aware way, any metric on the Grassmann manifold can be resided in our model theoretically. We have combined different metrics with our model and the learning process can be treated as an unconstrained optimization problem on a Grassmann manifold. Experiments on several action datasets demonstrate that our approach can improve a more favorable accuracy over the state-of-the-art algorithms.
Parallel Stochastic Portfolio Search for Constraint Solving

It is not uncommon to observe that the performance of constraint solving on a particular problem can be easily influenced by altering the search strategy, restart policy and their parameter settings, etc. In the multicore era, this lack of robustness can be exploited to speed up the constraint solving by designing a parallel portfolio search which simultaneously executes different incarnations of a sequential solver on the same problem. In this paper, we first investigate the techniques of existing single-solver-based portfolio approach in detail. On this basis, we gained insight into how to improve the portfolio approach. We then present the parallel stochastic portfolio search that benefits from the explicit early diversity resulted from randomization and parallelism. Performance evaluation on some classical constraint satisfaction problems benchmarks shows that our technique can solve harder instances compared to the existing portfolio; and not only that, it has advantages of simplicity for implementation and good scalability.
An SDN-Based QoS Guaranteed Mechanism for Geospatial Flows

As a significant portion of big data, geospatial big data is experiencing extremely rapid growth, which brings tremendous pressure and great challenges on geospatial data servers. To provide better services for users, it is an important approach to enable the transmission of geospatial data with QoS, which mainly focus on bandwidth. Therefore, based on the software-defined network(SDN), this paper puts forward a bandwidth guaranteed mechanism for geospatial flows for the first time. In our proposed mechanism, taking advantage of the fuzzy analytic hierarchy process(FAHP), a prioritization model for geospatial flows is proposed to calculate the comprehensive priority. With the comprehensive priority, geospatial flows are mapped into slices with different QoS levels. And then, a dynamic routing algorithm and a bandwidth adjustment mechanism are proposed to guarantee the transmission of geospatial flows with high-priority in sliced SDN. Experimental results show that the proposed QoS guaranteed mechanism has the ability to provide differentiated services for geospatial flows, and it can help to alleviate congestion in the geospatial cloud data center as well.
Delta Models of Frequency-Selective Structures

Models of oscillatory circuits and resonant transmission line sections as impedance resonant 8-inhomogeneities called $\delta-$models are proposed. Delta-models are characterized by three parameters — direction determining the resonance nature — series or parallel, resonant frequency and parameter directly proportional to the quality factor. Delta-models of coupled oscillatory circuits and passband filters are given. Filters are formed by series and parallel oscillatory circuits and by oscillatory structures with quarter-wavelength links. Passband filter 6-model “prompt” possibility of filter design improving. Filters frequency responses are given.
Batch-Fabricated WSe₂-on-Sapphire Field-Effect Transistors Grown by Chemical Vapor Deposition

Stepper-based batch fabrication of top-gated field-effect transistors (FETs) is demonstrated on few-layer WSe<sub>2</sub>-on-sapphire grown by chemical vapor deposition. This article reports the electrical characterization of 94 transistors with three different transistor structures. ON-/ OFF-current ratios exceeding 10<sup>5</sup> at room temperature with noise-floor-limited gate current and contact-limited ON-current density of 15 nA/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> was achieved for FETs using a structure in which the gate overlaps the source and drain contacts. Two transistors with open channel access regions are used to allow ion doping through polyethylene oxide: cesium perchlorate (PEO: CsClO<sub>4</sub>) to induce electron and hole conductivity and allow characterization of the intrinsic FET characteristics. In these geometries, the ON-current increased by four orders of magnitude with ON-/ OFF-current ratio greater than 10<sup>6</sup> at <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}} = -0.05$ </tex-math></inline-formula> V, compared to gate overlapped FETs. An analytic 2-D FET model was used to model the full current-voltage characteristics. This allowed the field-effect mobility to be extracted revealing the gate and drain field dependence for the first time. The fit of the measured characteristics from subthreshold to saturation is made with only four fitting parameters, revealing a field-dependent electron mobility ranging from 1 to 2.6 cm<sup>2</sup>/Vs.
Parallel-Log-Single-Compaction-Tree: Flash-Friendly Two-Level Key-Value Management in KVSSDs

Log-Structured Merge-Tree (LSM-tree) based key-value store applications have gained popularity due to their high write performance. To further pursue better performance for key-value applications, various researches were conducted by adopting different architectures of flash devices, such as key-value solid-state drives (KVSSDs). However, since LSM-trees were originally designed based on the architecture of hard disk drives (HDDs), true potential of SSDs can not be well exploited without re-designing the management strategy. In this work, we propose Parallel-Log-Single-Compaction-Tree (PLSC-tree), which is a two-level and flash-friendly key-value management strategy specially tailored for KVSSDs. In particular, the first layer takes advantage of the massive internal parallelism of SSDs for maximizing the write performance via logging, while the second layer is designed to alleviate the internal recycling (i.e., compaction) overheads of flash devices for ultimately optimizing the performance on managing key-value pairs. A series of experiments were conducted based on a well-known SSD simulator with realistic workloads, and the results are very encouraging.
Decentralized and Secure Cooperative Edge Node Grouping to Process IoT Applications in Heterogeneous Smart Cyber-Physical Systems

Smart Cyber-Physical Systems (SCPS) has enhanced use of smart devices with numerous applications including smart cities, smart traffic management, smart cars, smart health care and smart grids. Core logic behind these applications usually require huge processing or massive data handling which are normally performed at cloud but the application will suffer from latency. Edge computing offers solution for latency aware computations at edge of the network but with limited resources available at edge nodes. This problem can be resolved by leveraging edge resources in a group and executing resource intensive task. Most previous studies deploy centralized methods for clustering but comes with overhead of cluster formation and management. In this article, key idea is to group heterogeneous edge nodes on task arrival in a decentralize way, and handle task allocation and execution in parallel on group devices to achieve its deadline. Our methodology will help to reduce traffic amount travelling towards cloud in case of resource intensive big tasks of SCPS applications. We have proposed an algorithm for decentralized group formation and presented task division and allocation methodology for parallel execution. Our results show that our technique is working while providing desired goals of reducing overall latency, and limiting network traffic as well as achieving higher ratio for number of tasks meeting their deadline.
Fine Grained Named Entity Recognition via Seq2seq Framework

Fine-grained Named entity recognition (NER) is crucial to natural language processing (NLP) applications like relation extraction and knowledge graph construction. Most existing fine-grained NER systems suffer from inefficiency problem as they use manually annotated training datasets. To address such issue, our NER system could automatically generate datasets from Wikipedia in distant supervision paradigm through mapping hyperlinks in Wikipedia documents to Freebase. In addition, previous NER models can not effectively process fine-grained labels with more than 100 types. So we introduce a ‘BIO’ tagging strategy which can identify the position and type attributes simultaneously. Such tagging scheme transfers NER problem into a sequence-to-sequence (seq2seq) based issue. We propose a seq2seq framework to comprehend the input sentence in a comprehensive way. Specifically, we adopt a Bi-LSTM as the encoder to equally process the past and future information of the input. Then we add a self-attention mechanism to handle the long-term dependency problem in a long sequence. When classifying the entity tags, we choose CRF model as it adds more constraints to avoid position logical problem. Experiments are performed on large-scale datasets for fine-grained NER tasks. Experimental results verify the effectiveness of FSeqC, and it outperforms other state-of-the-art alternatives consistently and significantly.
A Task Offloading Solution for Internet of Vehicles Using Combination Auction Matching Model Based on Mobile Edge Computing

From a global perspective, an Internet of Vehicles task offloading solution based on mobile edge computing is proposed, which satisfies the application requirements (high reliability) strictly. The average time for completing a task can be minimized with the reasonable task offloading solution. Firstly, we model the wireless network, the transmission time and the movement of vehicles. Besides, heterogeneous wireless network architecture is adopted, data centers are deployed at Small-cell Base Stations, Macro-cell Base Stations and Internet. Then considering the limitedness, heterogeneity and task diversity of resources, we utilize matching model based on combination auction to design the offloading model. Furthermore, the multi-round sequential combination auction mechanism is proposed, which equals the matching problem to the multi-dimensional grouping knapsack problem and uses dynamic programming to get the optimal match. This solution is based on virtual machine technology and voltage scaling technology in the task execution time model. Moreover, the computing resources can be measured by CPU frequency. We propose an optimization problem for the shortest average task completion time with limited resources. Finally, the effects of these parameters (such as the number of tasks per unit time, the amount of data offloaded and the number of CPU cycles) on the task execution efficiency are analyzed and compared with other algorithms by simulation experiments. Compared to existing schemes, simulation results show that the proposed algorithm can reduce system overhead and shorten task execution time effectively.
Application of an Adaptive Genetic Algorithm for Task Mapping Optimisation on a Wormhole-based Real-time Network-on-Chip

The task mapping problem of real-time applications onto a homogeneous multiple processors system-on-a-chip (MPSoC) is an NP-complete problem that can be addressed using search-based meta-heuristic methods. A particular case is the priority preemptive wormhole-based Network-on-a-Chip (NoC) communication architecture that takes into consideration the end-to-end schedulability analysis. In this paper, we present a Adaptive Genetic Algorithm (AGA) with adaptive parameter control strategies that is capable of efficiently achieving schedulable task placements. In our experiments, we perform analytic methods in different scenarios with multiple synthetically generated real-time applications, as well as various platforms with a ranging number of processors to provide a statistical study comparing our algorithm against a standard genetic algorithm approach.
Convolutional Neural Network-Based Approach for Citrus Diseases Recognition

For the development of agricultural modernization, an automatic recognition network for crop diseases with the advantages of high efficiency, non-destructiveness and continuity is indispensable. In this paper, we construct a 13-layers convolutional neural network for common citrus diseases recognition, which improving the efficiency and reducing overfitting by using a stack of small kernel and dropout, etc. Finally, we conduct comparative experiments with other networks. The experiment results show that the performances of our network are better than CNN and AlexNet. It indicates that the network we constructed is an effective citrus diseases recognition method, which can provide technical support for the identification and prevention of citrus diseases.
Channel Access Scheduling for Wireless Network Coding Operation in Ad Hoc Networks

Network coding can significantly improves the performance of wireless networks. How to encode more packets in the intermediate node is a critical design issue. In this paper, we proposed a revised Time Division Multiple Address (TDMA) protocol to adapt network coding operation in wireless ad hoc networks. The proposed protocol efficiently and evenly assigns slot to each node in the network. The performance of the network is improved by reducing unsigned slot and balancing slot distribution among nodes. More available packets can be encoded in the intermediate node when operating network coding. The simulation result shown that, compared with DCF protocol, the proposed protocol obtained better performance. In the end of the paper, we discussed the unsolved problems.
Parallelized Top-k Route Search with User's Preferences

In this paper, we consider the top-k route search with user's preferences. Specifically, given a set of POIs, our problem is to find k different routes from a source POI to a target POI such that the constraint on route cost and the POIs covered by the route can optimally satisfy the user-defined weighted feature preference. It has been shown that the problem is NP-hard. The challenge is how to select from plenty of POIs and construct an optimal route especially when the size of candidate POIs is large. In order to support top-k route search on a large dataset or with a looser budget constraint, we propose a parallel method on a single machine to speed up the search. Moreover, we adopt further effective pruning strategies to reduce the search space. The experimental results on real-world datasets show that our proposed parallel method is much efficient, about 10 times faster than the existing serial algorithm at best.
A Multi-Hop VANETs-Assisted Offloading Strategy in Vehicular Mobile Edge Computing

Mobile edge computing (MEC) is a suitable solution to improve computational capabilities in vehicular environments, and the computation offloading plays a critical role in MEC. After the computation task generates, in traditional offloading strategies, vehicles offload data only when they have entered the communication range of the MEC server, which greatly increases the time cost and cannot satisfy the delay requirement of certain applications in many cases. To deploy a large number of MEC servers for full coverage of roads is not a realistic solution due to its high construction cost. In this paper, we make full use of the multi-hop vehicular ad hoc networks (VANETs) to assist in computation offloading of vehicles. In the real-time traffic environment, based on the link correlation theory in VANETs, the reliability model of multi-hop routing path is built. Meeting the delay constraints, the optimal offloading strategy with the lowest relaying and computing cost is executed by the binary search algorithm. The simulation results show that the proposed multi-hop VANETs-assisted offloading strategy (MHVA) shows better performance in offloading delay and cost than existing typical strategies under various environments.
An Adaptive Electromigration Assessment Algorithm for Full-chip Power/Ground Networks

In this paper, an adaptive algorithm is proposed to perform electromigration (EM) assessment for full-chip power/ground networks. Based on the eigenfunction solutions, the proposed method improves the efficiency by properly selecting the eigenfunction terms and utilizing the closed-form eigenfunctions for commonly seen interconnect wires such as T-shaped or cross-shaped wires. It is demonstrated that the proposed method can trad-off well among the accuracy, efficiency and applicability of the eigenfunction based methods. The experimental results show that the proposed method is about three times faster than the finite difference method and other eigenfunction based methods.
Binary Competitive Swarm Optimizer Algorithm for Feature Selection in Identification of Chinese Fir Family

Identification of Chinese fir family is a key procedure in forestry cultivation. However, it is time consuming depending on experts' identification. Therefore, in this paper, the method of image based recognition of Chinese fir family is proposed to replace experts' identification. For the cones of different Chinese fir families have different texture features, the textures of the cone is modeled using dominant rotated local binary patterns and these textures is selected by combination of binary competitive swarm optimization algorithm and probabilistic collaborative representation based classifier for filtered out irrelevant, noisy or redundant information. Experimental results demonstrate that the proposed approach is effective in recognizing Chinese fir family especially with feature selection.
Fast Reroute in Hybrid Segment Routing Network

We consider a hybrid segment routing (SR) network consisting of both IP routers and SR routers. We focus on exploiting the source routing capability of SR to enhance the performance of IP fast reroute. In particular, an IP router detects a local link failure, it can immediately reroute the affected packets by tunneling them to a SR router. The SR router will then forward the rerouted packets along a SR path such that when they exit the SR domain, they can reach the destination without traversing the failed link. In order to maximize the percentage of links that can be protected by fast reroute, two Integer Linear Programming (ILP) formulations are proposed for finding optimal repair paths for rerouted packets. ILP1 aims at minimizing the repair path length and ILP2 focuses on balancing the traffic load in the network. We further show that as the number of SR routers increases, the average repair path length will be shortened and the load balancing performance will be improved.
Recloser-fuse Coordination in Radial Distribution Systems connected with Distributed Generation Using Saturated-core Superconducting Fault Current Limiter

Recently, distributed energy is being introduced into distribution systems for many reasons. This energy is entered in the form of distributed generators (DGs). Incorporating DGs into radial distribution systems has many merits such as improving voltage profiles and reducing power losses. However, if the DG location and penetration level were not well-planned, it can lead to problems in the protection systems such as prevention of auto-reclosing of circuit breakers, recloser-fuse miscoordination, blinding of protection and false tripping. This paper aims to study the problem of recloser-fuse miscoordination of radial distribution systems due to the incorporation of DGs. Different case studies are addressed with different locations and penetration levels of DGs with the 33-bus radial distribution system. Reclosers and fuses are inserted in different positions in the studied distribution system to protect it against all possible locations of fault and in every fault location the coordination between reclosers and fuses are kept. The penetration level of all DG locations is increased until the recloser-fuse coordination is broken. To restore the reclosers-fuses coordination, a saturated-core superconducting fault current limiter (SFCL) is connected in series with the DG to limit the fault current coming from the DG. The simulation results show that the saturated-core SFCL could recover the reclosers-fuses coordination when it is broken by connecting DGs with the studied radial distribution system.
Meteorite Hunting Using Deep Learning and UAVs

In this paper, we present an automated meteorite detection system that employs an autonomous Unmanned Aerial Vehicle (UAV). It is programmed to recognize and locate meteorites using machine learning. In this design, the UAV carries a Single Board Computer (SBC), through which realtime data processing of the live video feed from an optical camera is performed. The integration of a GPS module into the system enables localization of the detected meteorites. The onboard processing, carried out by the SBC, involves analyzing each frame from the video and running it through our deep learning model. This model is trained on images of different types of commonly found meteorites, via Transfer Learning performed on Convolutional Neural Networks (CNN). The results were promising, with an accuracy of approximately 90%. Future enhancements, including the addition of an infrared sensor to capture thermal images, are also discussed.