Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Mixed Noise Removal By $\ell _1$-Norm-Based Subspace Representation

This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> data fidelity instead of using the <inline-formula><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal.
Energy-Balanced Clustering Algorithm for Software-Defined Wireless Sensor Networks

The energy supply of sensor nodes is limited, and the sensor nodes near the sink node usually die out earlier than other nodes which causes the imbalance of energy consumption in wireless sensor network (WSN). In order to extend the network lifetime and balance the energy consumption, we present a new clustering algorithm called Energy-balanced Clustering Algorithm (EBCA) based on software-defined wireless sensor network (SDWSN). In our algorithm, the controller redefines the communication radius of each sensor node based on distance from sink node and the number of neighbor nodes, and gives a new clustering method based on the residual energy of itself and its neighbor node. The simulation results show that our algorithm has better performance on balancing energy consumption and extending network lifetime compared with the typical data transmission algorithm.
2.2 AMD Chiplet Architecture for High-Performance Server and Desktop Products

AMO's “Rome” and “Matisse” are second-generation AMD Infinity Fabric-based SoCs using 3 unique hybrid process technology chiplets to achieve leading performance, performance/$ and performance/W, targeting server and client markets, respectively (Fig. 2.2.1). The chiplet architecture enables leading edge 7nm [1] CPUs for multiple markets, while retaining backward compatibility to complex 10 and memory subsystems in a scalable design with high reuse for improved time-to-market. A key benefit is the heterogeneous technology deployed between the CPUs and the 10/mixed-signaIP. It is well known that shrink factors in advanced nodes are much lower for analog circuitry than for digital logic and SRAM. By keeping the memory interfaces and SerOes in mature 12nm technology, costs are mitigated since those circuits see a very small shrink factor to 7nm and very little performance or power gain from advanced nodes. A low-cost 12nm 10 die (IOD) with the high-yielding 8 “Zen2” core, 74mm2 7nm CPU compute die (CCD) combine to provide very cost-effective performance.
Application of Multi-label Learning Model for Chronic Kidney Disease Syndrome Classification

Traditional Chinese Medicine (TCM) has been widely applied in Chronic Kidney Disease (CKD), syndrome classification is a very important step in the diagnosis of TCM. Based on clinical diagnosis experiences and the analogy theory of TCM, we propose a method that is grounded on analogy theory of TCM for syndrome diagnosis (ATTSCM). The ATTSCM method utilizes the feature selection algorithm to select the significant symptoms, and the clustering algorithm combine multi-label learning algorithm to distinguish the syndromes. The proposed method is evaluated by the chronic kidney disease dataset which is collated by University of Chinese Medicine. Three evaluation measures of multi-label classification are taken to evaluate the performance of syndrome classification, including classification accuracy, micro F1 measure and hamming loss. Experimental results show that the proposed method performs well in the application for TCM syndrome differentiation of CKD.
Lifetime Analysis for Ambient RF Energy Harvesting IoT Node

In this paper, we propose a novel theoretical method to analyze the lifetime of IoT node based on RF energy harvesting. We present the RF energy harvesting model and the energy consumption model, and we also measure the RF power density of 400-1000MHz in ambient environment. Then a theoretical model of the duration, during which the IoT node collects enough energy to run, is presented. Furthermore, based on the measurement, we can get the theoretical value and the simulation value of the duration, which shows that under the condition described in this paper the node need to collect RF energy in not less than 150s to support activities in 100ms. At last, some suggestions about the IoT nodes’ operating modes are given to support the application of the RF energy harvesting technology.
Joint User Association and VNF Placement for Latency Sensitive Applications in 5G Networks

With the advent of 5G systems, telecommunication service providers (TSPs) have been facing a tremendous transition by the raised expectations of supporting billions of IoT devices and an unprecedented amount of generated data. This revolutionary transformation necessitates innovative approaches such as multi-access edge computing (MEC) to meet the requirements of many novel applications in terms of their high data rate and low latency. The idea behind MEC is to move data, virtualization, and processing capabilities from central data centers to the edge of the network. However, resources at the network edge are very scarce and costly to provision. Therefore, TSPs have to make smart decisions on how to utilize the network resources such as to make sure that the user service requirements (e.g., data rate, latency) are satisfied while the network resources are used most efficiently. In this paper, we study the problem of joint user association, VNF placement, and resource allocation, employing mixed-integer linear programming (MILP) technique. The objectives of the formulations are to minimize (i) the service provisioning cost, (ii) the number of VNF instances, and (iii) the transport network utilization, having an overarching goal of drawing a comparison between these different approaches.
Structure-from-Motion Retrieval from VideoSAR Sequences: Toward a Unified Framework

Video synthetic aperture radar (VideoSAR) has attracted significant attention in the computer vision-based information retrieval field. In this paper, a unified three-dimensional reconstruction framework is proposed for both the straight and circular VideoSAR modes, and is suitable for height retrieval with/without geometric prior knowledge. First, two VideoSAR imaging modes are presented to explain the formation of dynamic observation. Then, structure-from-motion retrieval method with/without geometric prior knowledge is proposed by exploiting shadow information. Finally, experimental results based on VideoSAR fragments measured by the airborne and MiniSAR systems demonstrate the effectiveness of the unified retrieval method with a higher accuracy compared with that of a single SAR imagery.
15.2 A 28nm 64Kb Inference-Training Two-Way Transpose Multibit 6T SRAM Compute-in-Memory Macro for AI Edge Chips

Many Al edge devices require local intelligence to achieve fast computing time (tAC), high energy efficiency (EF), and privacy. The transfer-learning approach is a popular solution for Al edge chips, wherein data used to re-train the Al in the cloud is used to fine-tune (re-train) a few of the neural layers in edge devices. This enables the dynamic incorporation of data from in-situ environments or private information. Computing-in-memory (CIM) is a promising approach to improve EF for Al edge chips, existing CIM schemes support inference [1]–[5] with forward (FWD) propagation; however, they do not support training, requiring both FWD and backward (BWD) propagation, due to differences in weight-access flow for FWD and BWD propagation. As Fig. 15.2. 1 shows, efforts to increase the precision of the input (IN), weight (W), and/or output (OUT) tend to degrade rAC and EF for training operations irrespective of scheme: digital FWD and BWD (DF-DB) or CIM-FWD-digital-BWD (CiMF-DB). This work develops a two-way transpose (TWT) SRAM-CIM macro supporting multibit MAC operations for FWD and BWD propagation with fast rAC and high EF within a compact area. The proposed scheme features (1) A TWT multiply cell (TWT-MC) with a high resistance to process variation; and (2) a small-offset gain-enhancement sense amplifier (SOGE-SA) to tolerate a small read margin. A 28nm 64Kb TWT SRAM-CIM macro was fabricated using a foundry-provided compact 6T-SRAM cell for SRAM-CIM devices supporting both inference and training operations for the first time. This macro also demonstrates the fastest tAC (3.8 – 21ns) and highest EF (7 – 61.1TOPS/w) for MAC operations using 2 – 8b inputs, 4 – 8b weights and 12 − 20b outputs.
3D Deployment of Multi-UAV for Energy-Saving: A Game-Based Learning Approach

In this paper, we investigate the problem of three-dimensional (3D) coverage deployment of unmanned aerial vehicles (UAVs). The downlink scenario of air-to-ground communication networks is considered, where UAVs are supposed to maximize the coverage utility of mission area with minimal transmission power. We decompose the problem into two sub-problems, which are UAV deployment and power allocation, respectively. In the first sub-problem, we adjust the 3D locations of UAVs to maximize the coverage utility. In the second sub-problem, we adjust the transmission power to maximize the energy efficiency. By constructing marginal utility function, we prove that these two problems are exact potential games (EPG) and exist Nash equilibrium (NE) points. After that, an algorithm based on binary log-linear learning is proposed to achieve the NE point. The simulation results verify the efficiency of the algorithm and the stability of the model.
Controller Based Detection Scheme of Interest Flooding Attack in Named Data Networking

Information-centric networking (ICN), which is believed to be one of the most promising technologies in the next-generation network, has drawn lots of attention from both academia and industry. While ICN solves many problems of the TCP/IP architecture, new security issues arise. The interest flooding attack is one of them. In this paper, we discuss the influences of this type of attack in Named Data Networking (NDN) and propose a novel detection and mitigation scheme. In this scheme, a controller is introduced in an NDN domain to collect routing and forwarding statistics from routers. These statistics are then analyzed by the controller to detect interest flooding attack as well as trace the adversaries and malicious prefixes. Simulation experiments are conducted using ndnSIM, and the results demonstrate that the proposed scheme is both effective and efficient.
Fine-grained Chinese Named Entity Recognition in Entertainment News Using Adversarial Multi-task Learning

Named Entity Recognition (NER) systems are often used as the first step for most of Natural Language Processing (NLP) tasks like question answering, information retrieval, etc. In our paper, the fine-grained NER for entertainment news is the task of identifying nine named entities like person, TV, music, etc. It is challenging not only because of inherent noisiness but the data contains numerous informal abbreviations. We proposed a novel adversarial multi-task learning framework by employing two auxiliary tasks named Part-of-Speech (POS) and Chinese Word Segmentation (CWS) together with a primary NER task. Our model can use task-shared features better and filter task-specific information of CWS and POS. To the best of our knowledge, our work is the first study that incorporate POS and CWS into fine-grained NER task in entertainment news
An In-vehicle Camera Based Traffic Estimation in Smart Transportation

Traffic condition estimation services a critical role on assisting vehicles to estimate current traffic states and determine an optimal route for their journey, thereby improving the traffic efficiency in smart transportation. To accurately estimate traffic conditions of roads, a number of efforts focusing on fixed-camera based traffic estimation has been developed. However, fixed-cameras in smart transportation usually are deployed on road-side units, leading to limited visual areas. Additionally, these existing schemes also are lack of flexibility and cannot achieve great traffic estimation efficiency to roads with less cameras deployed. To address these issues, in this paper an In-vehicle Camera based Traffic Estimation scheme (ICTE) is proposed for smart transportation system to assist vehicles to estimate traffic conditions with traffic videos collected by in-vehicle cameras. Integrating with edge computing and V2X (vehicle-to-vehicle (or infrastructure)) networks, our scheme can obtain extra computation and communication resource to accurately estimate traffic conditions anywhere vehicles reach. Particularly, YOLO-based vehicle detection and Hough-based lane detection are introduced in our ICTE to detect the vehicles and lanes in traffic videos captured by in-vehicle cameras. Then, a lane-based vehicle counting model is developed to count the number of vehicles that travels on different lanes. Finally, a density-based traffic estimation model is conducted to estimate traffic conditions based on the vehicle density. Via simulation experiments, our data shows that ICTE can achieve great accuracy and efficiency in terms of vehicle counting and traffic condition estimation.
Performance Comparison of Ryu and Floodlight Controllers in Different SDN Topologies

Ryu and Floodlight controllers are being used in academia and industries for different research and development purposes. The objective of this paper is to compare the performance of these controllers under different topologies namely single, linear, tree, torus and custom. Experiments are performed under a much known emulator, mininet. Four parameters were valuated under this experimental setups namely throughput, jitter, latency and packet loss. Valuation of results clearly shows that Ryu is giving better throughput as compared to Floodlight in all the topologies. In case of latency and jitter Ryu is performing better in all the topologies except Torus. Similar is the case with packet loss.
Long short-term memory based on random forest-recursive feature eliminated for hot metal silcion content prediction of blast furnace

The variation of hot metal silicon content simultaneously reflects the product quality and the thermal state of blast furnace (BF), and it is a vital economic index to monitor in the iron-making process. This paper proposes a model to predict the change trend of silicon content, which integrates random forest-recursive feature eliminated (RF- RFE) with long short-term memory (LSTM). In the process of feature selection, the ensemble learning method of RF can obtain subsets with high classification accuracy, and RFE further removes some irrelevant features to determine the best input variables. Then, LSTM is presented as the predictor, which conquers the shortcoming of traditional RNN vanishing gradient, and the gating mechanism processes the correlation information before and after the time series adequately. The effectiveness of the proposed solution for silicon content prediction of a nonlinear process is proved by a case study of real production data from BF. The results also show that the proposed method has better prediction accuracy than other data-driven models.
A Simulation Study of Gate-All-Around Nanowire Transistor With a Core-Substrate

In this letter, a novel Core-Substrate Gate-All-Around (CSGAA) nanowire structure has been proposed, investigated and simulated systematically based on 3D numerical simulation. This new structure is characterized by a Core-Substrate directly connected to the substrate wafer or well which is formed by a bulk region inside the tubular channel. Comparisons are carried out between conventional Gate-All-Around (GAA) nanowire transistor and newly proposed CSGAA structure. This budding structure exhibits a maximum of four orders of magnitude of reduction in off-state current, high saturation current and low performance degradation. With the help of Core-Substrate, the CSGAA structure is able to maintain excellent performance despite geometric and structural variation. Its special structure is very suitable for the use of vertical Gate-All-Around nanowire structure, making it a promising candidate of future high performance and low power CMOS devices.
A Normal Wiggly Pythagorean Hesitant Fuzzy Bidirectional Projection Method and Its Application in EV Power Battery Recycling Mode Selection

As a useful mathematical tool to solve the multi-attribute decision-making (MADM) problems, Pythagorean hesitant fuzzy set (PHFS) permits decision makers (DMs) to give several evaluation values in membership and non-membership degrees. Given that much cognitive preferences of DMs may be hidden in the original evaluation information and cannot be fully expressed, we propose the normal wiggly Pythagorean hesitant fuzzy set (NWPHFS) to comprehensively mine the uncertain preferences from original Pythagorean hesitant fuzzy information. NWPHFS can help DMs more accurately express their potential valuable preferences for objects while giving the original evaluation information. We put forward some operational laws and comparison rules of NWPHFS, then present the formulas of projection and bidirectional projection measures between normal wiggly Pythagorean hesitant fuzzy elements (NWPHFEs). Furthermore, we propose the normal wiggly Pythagorean hesitant fuzzy bidirectional projection (NWPHFBP) method and its specific application steps to solve MADM problems. The proposed approach can accurately reflect the projection relationship between the alternatives evaluated and ideal solutions by the NWPHF information. Through constructing a reasonable criteria system, we apply the NWPHFBP method to the issue of electric vehicle (EV) power battery recycling mode selection. Finally, we conduct the sensitivity analysis and verify the effectiveness of the proposed method by comparisons with other approaches.
An LSTM Acceleration Engine for FPGAs Based on Caffe Framework

Recently, Long Short Term Memory (LSTM) networks have been widely used in sequence-related problem. LSTMs outperform conventional feed-forward neural networks and RNNs in many ways, since they remember patterns selectively for long durations of time. However, due to the recurrent property of LSTMs, it is hard to implement a high computing parallelism on general processors such as CPUs and GPUs. Besides, the huge energy consumption of GPU and CPU computing is a non-negligible issue for data centers. In order to solve the problems above, FPGA emerges as an ideal solution. It has the characteristics of low power and latency, which has natural advantages for the implementation of recurrent neural networks, such as LSTMs. In this paper, we propose to implement an acceleration engine for LSTM network based on FPGAs. By employing fixed-point arithmetic, systolic arrays for matrix multiplication and look up table for activate function, we optimize the LSTM on FPGA in depth. Additionally, we integrate the acceleration engine into Caffe, one of the most popular deep learning framework, to make it easier to deploy. According to the experimental results, our acceleration engine achieves 8.8X and 2.2X gains for performance, 16.9X and 9.6X gains for energy efficiency compared with CPU and GPU, respectively.
Student Modeling Using Educational Data Mining Techniques

Educational Data Mining (EDM) is a research area which applies machine learning, data mining and statistical techniques to different types of data that come from educational settings. Student modeling is one of EDM applications that used to recommend or adapt the educational system resources. This paper explores the different educational systems and their characteristics. In addition, modeling of the student learning style, affective state, and prediction of the student performance are addressed. Profiling, grouping students and collaboration analysis are also explored.
Visual counts as an index of White-Tailed Prairie Dog density

of North American small mammals. Am. Midl. Nat. 37:223-249. OLSEN, G. H. , S. B. LINHART, R. A. HOLMES, G. J. DASCH, AND C. B. MALE. 1986. Injuries to coyotes caught in padded and unpadded steel foothold traps. Wildl. Soc. Bull. 14:219-223. QUAYLE, C. L. 1983. The behavioral development of scent-marking in captive male coyotes (Canis latrans). M.S. Thesis. Central Michigan Univ., Mount Pleasant. 104pp. ROBINSON, W. B. 1943. The "humane coyote-getter" vs. the steel trap in control of predatory animals. J. Wildl. Manage. 7:179-189. 1948. Thallium and compound 1080 impregnated stations in coyote control. J. Wildl. Manage. 12:279-295. Roy, L. D. , AND M. J. DORRANCE. 1985. Coyote movements, habitat use, and vulnerability in central Alberta. J. Wildl. Manage. 49:307-313. SAMUEL, M. D. , D. J. PIERCE, E. 0. GARTON, L. J. NELSON, AND K. R. DIXoN. 1985. User's manual for program HOME RANGE. For. Wildl. Range Exp. Stn. Tech. Rep. 15. Univ. Idaho, Moscow. 70pp. SAVARIE, P. J., AND R. T. STERNER. 1979. Evaluation of toxic collars for selective control of coyotes that attack sheep. J. Wildl. Manage. 43:780-783. TURKOWSKI, F. J., M. L. POPELKA, B. B. GREEN, AND R. W. BULLARD. 1979. Testing the responses of coyotes and other predators to odor attractants. Pages 255-269 in J. R. Beck, ed. Spec. Tech. Publ. 680, Am. Soc. Testing and Materials, Philadelphia, Pa. -, i-iAND R. W. BULLARD. 1983. Efficacy of odor lures and baits for coyotes. Wildl. Soc. Bull. 11:136-145. WINDBERG, L. A. , H. L. ANDERSON, AND R. M. ENGEMAN. 1985. Survival of coyotes in southern Texas. J. Wildl. Manage. 49:301-307. AND F. F. KNOWLTON. 1988. Management implications of coyote spacing patterns in southern Texas. J. Wildl. Manage. 52:632-640. WOODRUFF, R. A. , AND B. L. KELLER. 1982. Dispersal, daily activity, and home range of coyotes in southeastern Idaho. Northwest Sci. 56:199-207.
Color Constancy Algorithms for Multi-source Non-uniform Scenes Based on Local Estimation

This paper presents a color constancy solution for multi-source non-uniform scenes. Firstly, the image partitioning strategy based on Tk-means is used to realize the conversion of multi-light source non-uniform scene to single source uniform scene. A single source color estimate is then performed on the local scene using the BP neural network algorithm. Aiming at the characteristics of multi-light source non-uniform scene, it is proposed to use FF attention mechanism to estimate the weight of the light source color estimated by each local scene, and obtain a composite light source as an approximate estimation of the scene light source. Finally, the paper reconstructs the image through the diagonal model. The experimental results show that the proposed strategy can solve the color constancy problem of multi-source non-uniform scenes.
Readmission Prediction of Diabetic based on Convolutional Neural Networks

Unplanned readmission expenses have always accounted for a large part of the expenditure of the health care system, and reducing hospital readmission rate is important for the government to reduce the financial burden and improving hospital efficiency. First of all, we preprocess the readmission records of patients, including feature creation, feature selection, imbalanced data processing, and data reduction. Then we compare the traditional machine learning algorithms and propose a convolution neural networks model combined with PCA. The experimental results show that the accuracy of the readmission prediction is effectively guaranteed, and the AUC reaches 0.97.