Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxi High-Income Region Recommendation and Spatial Correlation Analysis

Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Analyzing the variation trend of taxi operation efficiency indicators throughout the day, mining high-income orders hot-spots and high-income regions at different periods, will effectively improve the average hourly incomes (AHI) of drivers. This paper selects the order data for each day of holidays, working days, and non-working days through the taxi order dataset of October 2019 in Xi’an. Firstly, we analyze the variation trend of taxi operation efficiency indicators in the three days. We next divide the orders into four income levels based on the Natural Breaks accordingly. Then, we use Tyson polygon and mash map matching methods to visualize the high-income orders hot-spots and high-income regions. It is significantly to analyze and summarize the visualization results. Finally, we compute the Moran’I index to measure the spatial correlation between high-income orders regions and high-income regions. The results show that (1) the number and the spatial distribution of high-income orders hot-spots and high-income regions at different periods are different. (2) Some places are hot-spots, but neither high-income orders hot-spots nor high-income regions. (3) The high-income orders regions and high-income regions have a strong correlation in spatial distribution. This study provides suggestions and insights to taxi companies and taxi drivers to increase their average hourly income (AHI) and enhance the efficiency of the taxi industry.
Planning Optimal Rejuvenation Policy for Aging Software Systems via a Two-Layer Model

Software rejuvenation is a proactive fault management technique widely used for preventing performance degradation and failures due to software aging. It works by proactively terminating the system, cleaning the internal state of the system, and restarting the system. Since rejuvenation incurs extra system overhead and downtime cost, it is important to determine the optimal time to trigger rejuvenation. This paper proposes a two-layer model to characterize the system failure behavior and rejuvenation process under aging condition, planning a time-based rejuvenation policy maximizing the system availability and minimizing downtime cost. The lower layer is a failure model that uses an analytical model and runtime measurements to build the failure distribution of the aging system. The upper layer is a rejuvenation model that takes the failure distribution from the lower layer as input to formulate the availability function and downtime cost function. Taking these two functions as optimization targets, we can obtain the optimal rejuvenation time. Compared with the traditional analytical model, the two-layer model modeling software failure considers runtime measurements, which can describe the aging behavior more accurately. In the experimental part, we comprehensively evaluate the two-layer model by studying the aging of the web search system. The results show that the two-layer model reduces unavailability by 18.18% and reduces downtime cost by 31.22% in comparison with the traditional analytical model.
Exploration on Task Scheduling Strategy for CPU-GPU Heterogeneous Computing System

For a CPU-GPU heterogeneous computing system, different types of processors have load balancing problems in the calculation process. What's more, multitasking cannot be matched to the appropriate processor core is also an urgent problem to be solved. In this paper, we propose a task scheduling strategy for high-performance CPU-GPU heterogeneous computing platform to solve these problems. For the single task model, a task scheduling strategy based on load-aware for CPU-GPU heterogeneous computing platform is proposed. This strategy detects the computing power of the CPU and GPU to process specified tasks, and allocates computing tasks to the CPU and GPU according to the perception ratio. The tasks are stored in a bidirectional queue to reduce the additional overhead brought by scheduling. For the multi-task model, a task scheduling strategy based on the genetic algorithm for CPU-GPU heterogeneous computing platform is proposed. The strategy aims at improving the overall operating efficiency of the system, and accurately binds the execution relationship between different types of tasks and heterogeneous processing cores. Our experimental results show that the scheduling strategy can improve the efficiency of parallel computing as well as system performance.
Design of a Humanoid Bipedal Robot Based on Kinematics and Dynamics Analysis of Human Lower Limbs

This paper proposes a humanoid bipedal robot based on kinematics and dynamics analysis of human lower limbs. The robot has a high-torque hip joint which combines two-degree-of-freedom parallel mechanisms and a lightweight high-torque knee joint with variable damping. The introduction of prosthesis makes the robot much closer to the real human body. In this paper, the overall structural design and the machine construction of the bipedal robot are presented, and experimental research was implemented to validate the rationality of the structural design of the humanoid biped robot and the feasibility of the control system.
Current status of mycotoxicoses in India and their control

Mycotoxins are a relatively large, diverse group of naturally occurring, secondary metabolites mainly produced by certain fungi (e.g. Aspergillus sp., Penicillium sp., and Fusarium sp.) that grow on human food and animal feed. Mycotoxins produce carcinogenic, immunotoxic, neurotoxic and teratogenic effects. The mycotoxins of greatest concern include: aflatoxin B1, which is generally produced by Aspergillus mould; deoxynivalenol, zearalenone, T-2 Toxin, and fumonisin b1, which are produced by Fusarium moulds; ochratoxin A and citrinin produced by Aspergillus and Penicillium moulds. There are hundreds of different mycotoxins which are diverse in their chemistry and effects on animals. Feed samples may be contaminated with more than one mycotoxins simultaneously. Numerous toxin binding agents like, HSCAS (Hydrated sodium calcium alumino silicate), zeolite, bentonites, clay and activated carbons (charcoal) have currently been used to counteract the mycotoxicoses in animals and its carry over effect through animal products like milk, meat and eggs. Mannan oligosaccharide (MOS) extracted from the cell wall of Saccharomyces cerevisiae has shown broad spectrum efficacy against most of the mycotoxins including ochratoxin A specially in conjunction with antioxidants like vitamin C. Application of several natural (vitamins, provitamins, carotenoid, chlorophyll and its derivatives, phenolics and selenium) and synthetic (butylated hydroxynisole and butylated hydroxy toluene) compounds with antioxidant properties seem to be considerably efficacious. Dietary modifications such as enhancing the protein level in diet and supplementation with phenylalanine and vitamin A have been found promising in alleviating the adverse effect of mycotoxins to a great extent. However, the best way to control mycotoxicosis problem is by prevention of toxigenic fungal contamination and mycotoxin elaboration through improved agricultural practices and storage of livestock/poultry feed.
Large-Scale MIMO Receiver Based on Finite-Alphabet Sparse Detection and Concave-Convex Optimization

In this paper, we propose a new receiver for detecting signals in large-scale Spatially Multiplexed (SP) Multiple-Input-Multiple-Output (MIMO) systems that may have fewer receive antennas than transmitted symbols (overloaded case). Relying on the idea of Finite-Alphabet Sparse (FAS) detection, we formulate the Maximum Likelihood (ML) criterion as a Difference-of-Convex (DC) programming problem that can be simply and efficiently solved using the Concave-Convex Procedure (CCP) technique. Since, the considered problem is non-convex, we theoretically discuss the behavior of the derived algorithm. Numerical experiments confirm the superiority of the proposed detection scheme, when compared with recent detection methods based on convex optimization, in a variety of large-scale MIMO transmission scenarios including the overloaded case.
Assessing the impact of controllable open learner models on student engagement

As poor student engagement with online learning remains an open issue, this research aims to assess the impact that scrutable and controllable open learner models have on enhancing student engagement. This paper presents personalized visual narratives that display the learner’s engagement with his/her course content, the time spent on course tasks, resources used, and peer comparisons. The visual narratives include the student’s learner model which presents his/her performance and development to date and supports visual scrutinization. It also enables the student to challenge his/her system calculated knowledge levels. The research discussed in this paper shows how the visual narratives, specifically how the visual scrutinization and challenging of knowledge levels enhanced the engagement levels of undergraduate students enrolled in an online learning module.
Analysis, Modeling, and Implementation of Publisher-side Ad Request Filtering

Online advertising has been a great driving force for the Internet industry. To maintain a steady growth of advertising revenue, advertisement (ad) publishers have made great efforts to increase the impressions as well as the conversion rate. However, we notice that the results of these efforts are not as good as expected. In detail, to show more ads to the consumers, publishers have to waste a significant amount of server resources to process the ad requests that do not result in consumers’ clicks. On the other hand, the increasing ads are also impacting the browsing experience of the consumers.In this paper, we explore the opportunity to improve publishers’ overall utility by handling a selective number of requests on ad servers. Particularly, we propose a publisher-side proactive ad request filtration solution Win2. Upon receiving an ad request, Win2 estimates the probability that the consumer will click if serving it. The ad request will be served if the clicking probability is above a dynamic threshold. Otherwise, it will be filtered to reduce the publisher’s resource cost and improve consumer experience. We implement Win2 in a large-scale ad serving system and the evaluation results confirm its effectiveness.
Hybrid Kinematics Modelling for an Aerial Robot with Visual Controllable Fluid Ejection

This paper studies the projection of high velocity fluid stream at a point target by modelling it (for precision) as an entity of the unmanned aerial vehicle (UAV). The ‘point-and-shoot’ capability is homogeneous to a visual servo system in robotics. The proposed concept presents a hybrid kinematics modelling for an aerial platform when performing fluidic jetting. The hybrid kinematics studies the rigid and fluid modelling, essentially allowing the fluidic projectile to resemble an entity of the UAV. Alongside the kinematics model, is a visual compensator which describes how the system can employ Computer Vision (CV) techniques + Convolutional Neural Network (CNN); to accurately track the object in view and manipulate the nozzle control for an active closed-loop visual compensation. This paper also addresses how hybrid kinematics can be exploited for precision nozzle control. The results are evaluated through means of simulations and experiments with actual and synthetic data. This pipeline can be extended to almost any UAV with the similar concept. The prototype was deployed on a public sheltered link-way with staged debris.
Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Crowdsourced Binary-Choice Question Answering

Truth discovery is an effective tool to unearth truthful answers in crowdsourced question answering systems. Incentive mechanisms are necessary in such systems to stimulate worker participation. However, most of existing incentive mechanisms only consider compensating workers’ resource cost, while the cost incurred by potential privacy leakage has been rarely incorporated. More importantly, to the best of our knowledge, how to provide personalized payments for workers with different privacy demands remains uninvestigated thus far. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in crowdsourced question answering systems, named PINTION, which provides personalized payments for workers with different privacy demands as a compensation for privacy cost, while ensuring accurate truth discovery. The basic idea is that each worker chooses to sign a contract with the platform, which specifies a privacy-preserving level (PPL) and a payment, and then submits perturbed answers with that PPL in return for that payment. Specifically, we respectively design a set of optimal contracts under both complete and incomplete information models, which could maximize the truth discovery accuracy, while satisfying the budget feasibility, individual rationality and incentive compatibility properties. Experiments on both synthetic and real-world datasets validate the feasibility and effectiveness of PINTION.
PDL: A Data Layout towards Fast Failure Recovery for Erasure-coded Distributed Storage Systems

Erasure coding becomes increasingly popular in distributed storage systems (DSSes) for providing high reliability with low storage overhead. However, traditional random data placement causes massive cross-rack traffic and severely unbalanced load during failure recovery, degrading the recovery performance significantly. In addition, various erasure coding policies coexisting in a DSS exacerbates the above problem. In this paper, we propose PDL, a PBD-based Data Layout, to optimize failure recovery performance in DSSes. PDL is constructed based on Pairwise Balanced Design, a combinatorial design scheme with uniform mathematical properties, and thus presents a uniform data layout. Then we propose rPDL, a failure recovery scheme based on PDL. rPDL reduces cross-rack traffic effectively and provides nearly balanced cross-rack traffic distribution by uniformly choosing replacement nodes and retrieving determined available blocks to recover the lost blocks. We implemented PDL and rPDL in Hadoop 3.1. 1. Compared with existing data layout of HDFS, experimental results show that rPDL reduces degraded read latency by an average of 62.83%, delivers 6.27× data recovery throughput, and provides evidently better support for front-end applications.
Reinforcement Learning Control for Multi-axis Rotor Configuration UAV

This paper proposes an multiusability reinforcement learning controller design method in low-level control of multi-axis rotor configuration unmanned aerial vehicle (UAV). In other reinforcement learning applications, the trained neural network controllers were designed to a model with specific dynamic properties and it usually cannot play a role while the system configuration changed. To fix this problem, we use a six-degree-of-freedom (6-DOF) dynamic model as the prototype of various vehicles to learn the force and moment required for stability during training, and employs a deep neural network directly mapping the states to the actuator commands. The force and moment can be adjusted according to different conditions and applied to different axis multi-rotor. Using reinforcement learning, this paper demonstrate the flight control of quadrotor and hexrotor using trained policy in simulator to present the stability on different multi-rotor structure, and compared the performance with the one previously introduced by trained quadrotor.
Development and Reliability study of 3D WLCSP for automotive CMOS image sensor using TSV technology

With the rise of intelligent driving market, automotive CMOS image sensor is more and more widely used. Compared with cameras in mobile phones, automotive CMOS image sensor need to pass critical reliability test, namely, AEC-Q100. In this paper, a novel and highly reliable wafer level packaging technology using through via technology (TSV) was developed for automotive CMOS image senor. Several new key process such as wafer level bonding, backside grinding, via etching, low temperature PECVD, spray coating passivation layer, RDL formation and ball drop were investigated. Reliability of the developed WLCSP based on AEC-Q100 was characterized by Temperature Humidity Bias Test (THB), Temperature Humidity Test (THS), Temperature Cycling Test (TC), High Temperature Storage Life Test (HTS) and Low Temperature Storage Life Test (LTS). No failure was found after the reliability rest. Good package reliability results demonstrate that WLCSP technology can meet the requirements of low cost and high reliability for automotive CMOS image sensors.
Dependable Deep Learning: Towards Cost-Efficient Resilience of Deep Neural Network Accelerators against Soft Errors and Permanent Faults

Deep Learning has enabled machines to learn computational models (i.e., Deep Neural Networks – DNNs) that can perform certain complex tasks with claims to be close to human-level precision. This state-of-the-art performance offered by DNNs in many Artificial Intelligence (AI) applications has paved their way to being used in several safety-critical applications where even a single failure can lead to catastrophic results. Therefore, improving the robustness of these models to hardware-induced faults (such as soft errors, aging, and manufacturing defects) is of significant importance to avoid any disastrous event. Traditional redundancy-based fault mitigation techniques cannot be employed in a wide of applications due to their high overheads, which, when coupled with the compute-intensive nature of DNNs, lead to undesirable resource consumption. In this article, we present an overview of different low-cost fault-mitigation techniques that exploit the intrinsic characteristics of DNNs to limit their overheads. We discuss how each technique can contribute to the overall resilience of a DNN-based system, and how they can be integrated together to offer resilience against multiple diverse hardware-induced reliability threats. Towards the end, we highlight several key future directions that are envisioned to help in achieving highly dependable DL-based systems.
Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in particular are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, by replacing the complex convolutional operation by a low-cost multiplexer. We perform experiments on CIFAR10, CIFAR100 and SVHN datasets and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints compared to the baselines. We also propose an efficient hardware architecture, implemented on Field Programmable Gate Arrays (FPGAs), to accelerate inference, which works as a pipeline and accommodates multiple layers working at the same time to speed up the inference process. In contrast with most proposed approaches which have used external memory or software defined memory controllers, our work is based on algorithmic optimization and full-hardware design, enabling a direct, on-chip memory implementation of a DNN while keeping close to state of the art accuracy.
Static linearity BIST for $V_{cm}$-based switching SAR ADCs using a reduced-code measurement technique

This work presents a reduced-code strategy for the static linearity self-testing of $V_{cm}$ -based successive-approximation analog to digital converters (SAR ADCs). These techniques take advantage of the repetitive operation of SAR ADCs for reducing the number of necessary measurements for static linearity testing. In this paper we discuss the application of these techniques for the $V_{cm}$ -based SAR ADC topology and present a practical BIST implementation based on an embedded incremental ADC. Electrical simulation results at transistor level are presented to validate the feasibility of the proposed on-chip reduced-code static linearity test.
Crop Yield Prediction based on Indian Agriculture using Machine Learning

In India, we all know that Agriculture is the backbone of the country. This paper predicts the yield of almost all kinds of crops that are planted in India. This script makes novel by the usage of simple parameters like State, district, season, area and the user can predict the yield of the crop in which year he or she wants to. The paper uses advanced regression techniques like Kernel Ridge, Lasso and ENet algorithms to predict the yield and uses the concept of Stacking Regression for enhancing the algorithms to give a better prediction.
Automated Dimensional Extraction Of Different Regions Using Single Monocular Camera In Pseudo-Stereo Configuration

This paper describes the development of an automated dimensional extraction algorithm of specific regions, implemented on BINO: Bearing Inspector for Narrow-space Observation. BINO is a tethered Unmanned Aerial Vehicle (UAV) system designed to eliminate the labor-intensive process and to improve the efficiency of inspecting infrastructures in confined remote locations. The UAV platform provides an effective method to access these hard to reach areas, and the algorithm proposed provides a dimensional analysis of the targeted infrastructure. The algorithm takes in several stereo pair images captured from only a single monocular camera. The availability of the multiple stereo images is possible due to a customized linear actuator where the camera sits on a rack-and-pinion configuration. The algorithm adopts a convolution network for image segmentation to extract crucial areas and, at the same time, generate disparity maps to obtain depth information. A pinhole camera model using the depth information and a single reference object of the bearing enables the derivation of the measurements of the segmented areas. Through experiments, the algorithm has an accuracy of an average RMSE of 1.23mm and a max error of 2.23mm. The ease of conducting inspection using BINO and more accurate measurements from the algorithm ultimately leads to an increase in inspection quality, safety and efficiency.
On Using k-means Clustering for Test Suite Reduction

Testing large software or other systems where test execution is time consuming or requires high computational resources is difficult and requires the selection of appropriate test cases. In the context of testing, appropriate means to have tests that most likely reveal faults or at least indicate when passing that the important functionality of the system works. In practice it is often the case that we have to reduce available test suites in order to finalize testing in a given time not exceeding other resources. In this paper, we introduce a machine learning based algorithm for test suite reduction that combines k-means clustering with binary search. The idea behind the algorithm is to cluster test cases that are close together and to select a representative test case from each of the clusters to be used in the new reduced test suite. We use binary search for looking for the proper number of clusters that allows to reduce the test suite under the condition of not substantially deviating from coverage or mutation score obtained from the initial tests suite. Besides discussing the algorithm, we present experimental results using small to larger Java programs with different types of inputs and outputs. For all example cases we were able to considerably reduce the number of test cases requiring a short reduction time especially compared to other test suite reduction approaches.