Miracle-dz/UniFAR-FTUs huggingface dataset:
Aerial Robotic Simulations for Evaluation of Multi-Agent Pl anning in GaTAC (Demonstration)

Georgia Testbed for Autonomous Control of Vehicles (GaTAC) [7] is a test bed for multi-agent planning under uncertainty. Previously GaTAC simulations have been performed in FlightGear, a flight simulation software, using realistically simulated aircraft. We enhance GaTAC’s versatility by enabling simulations with fully autonomous off-the-shelf robots. Scenarios may include multiple robotic drones interacting with each other while flying over a grid of configurable size. Each drone is controlled via a networked client process. Modules are available for simulations with virtual drones in a robotic simulator and autonomous quadcopters as we show in Figs. 1 and 2. Virtual agents allow for scenarios to be constructed and tested quickly and easily while robotic ones allow for higher impact demonstrations and exhibitions. The networked, modular architecture of GaTAC allows a client program to control either type of agent with no additional overhead for the user. GaTAC agents may be controlled either interactively by a human or with a pre-specified policy. Policies may be generated by an optimal decision-making framework or manually specified as desired. In our demonstration scenarios, we use an interactive POMDP (IPOMDP) [3] framework or DEC-POMDP [1] to find optimal policies for all agents as appropriate.
The Application of Genetic Algorithm for the Design of a Broadband Microstrip Antenna

Genetic Algorithm combined with FDTD method is applied to design a broadband microstrip antenna.We discuss how to operate genetic algorithm and introduce some techniques to reduce the time of optimizing a microstrip antenna with genetic algorithm and FDTD method.Comparing with the original square microstrip antenna,the optimized patch antenna achieves more than three times improvement in impedance bandwidth.
Using Similarity in Context and Access Patterns to Improve Space Efficiency and Performance in Storage Systems

In the past few years, we have seen a tremendous increase in digital data being generated. By 2011, storage vendors had shipped 905 PB of purpose-built backup appliances. By 2013, the number of objects stored in Amazon S3 had reached 2 trillion. Facebook had stored 20 PB of photos by 2010. All of these require an efficient storage solution. To improve space efficiency, compression and deduplication are being widely used. Compression works by identifying repeated strings and replacing them with more compact encodings while deduplication partitions data into fixed-size or variable-size chunks and removes duplicate blocks. While we have seen great improvements in space efficiency from these two approaches, there are still some limitations. First, traditional compressors are limited in their ability to detect redundancy across a large range since they search for redundant data in a fine-grain level (string level). For deduplication, metadata embedded in an input file changes more frequently, and this introduces more unnecessary unique chunks, leading to poor deduplication. Cloud storage systems suffer from unpredictable and inefficient performance because of interference among different types of workloads. This dissertation proposes techniques to improve the effectiveness of traditional compressors and deduplication in improving space efficiency, and a new IO scheduling algorithm to improve performance predictability and efficiency for cloud storage systems. The common idea is to utilize similarity. To improve the effectiveness of compression and deduplication, similarity in content is used to transform an input file into a compressionor deduplication-friendly format. We propose Migratory Compression, a generic data transformation that identifies similar data in a coarse-grain level (block level) and then groups similar blocks together. It can be used as a preprocessing stage for any traditional compressor. We find metadata have a huge impact in reducing the benefit of deduplication. To isolate the impact from metadata, we propose to separate metadata from data. Three approaches are presented for use cases with different constrains. For the commonly used tar format, we propose Migratory Tar: a data transformation and also a new tar format that deduplicates better. We also present a case study where we use deduplication to reduce storage consumption for storing disk images, while at the same time achieving high performance in image deployment. Finally, we apply the same principle of utilizing similarity in IO scheduling to prevent interference between random and sequential workloads, leading to efficient, consistent, and predictable performance for sequential workloads and a high disk utilization.
Method of Android Applications Permission Detection Based on Static Dataflow Analysis

Android applications that have access to crucial system resources are the targets of attackers.An application applies the access rights when it is installed,and users always ignore that.This paper proposes a new method to detect overprivilege in compiled Android applications,which leverages dataflow analysis to get the parameters of an API call.A static detection tool "Brox" is implemented based on this method.And Brox is tested using multiply Android applications.The test results on the accuracy and performance are quite encouraging.
Adaptive Secure Revocable Identity-based Signature Scheme over Lattices

The security of traditional identity-based signatures wholly depends on the security of secret keys.Exposure of secret keys requires reissuing all previously assigned signatures.Based on this,to revoke private key leaks or malicious users in the signature scheme,an adaptive secure revocable identity-based signature over lattices is proposed,which provides an efficient revocation mechanism to revoke misbehaving or compromised users from the systems.The scheme is proved to be strongly Unforgeable against adaptive Chosen-message Attacks(sUFCMA)under Small Integer Solution(SIS)assumption.Security analysis results show that the proposed scheme not only can meet the security of revocable identity-based signature,but also can resist the quantum attack.
An improved geodesic algorithm for trajectory planning of multi-joint robots

The present work proposes an improved geodesic algorithm for the trajectory planning of multi-joint robots. First, all of the joint variables are chosen to set up a generalized local coordinate system for the product of the positional space and the orientational one. Second, by defining a new Riemannian metric that contains both the positional and rational parameters, the traditional geodesic algorithm is improved so that it becomes capable of planning robot trajectories that include both the position and the orientation. To demonstrate the effectiveness of the improvement, trajectories are planned for two typical joint robots: one being a planar 3R and the other a spatial RRPP. It is verified that the improved algorithm can generate not only smooth motions for the joints but also smooth and accurate motions for the end-effector. The improved algorithm applies to multi-joint robots with no more than six degrees of freedom.
A NEW ALGORITHM FOR VEHICLE ROUTING PROBLEM WITH TIME WINDOWS AND A LIMITED NUMBER OF VEHICLES

This paper constructs a modle for the vehicle routing problem with time windows where a limited number of vehicles is given. We propose a route construction heuristic and also a Tabu Search for the problem. Then we use the Solomon's C1? R1? RC1 benchmark instances for the problem. The computational results show that the proposed algorithm is effective.
A Hybrid IP Multicast Based Data Dissemination Allocation Strategy in Large Scale Distributed Simulations

When considering one-to-many or many-to-many transfer methods,people used to assume that only flooding,IP multicasting and multiple unicasting (point-to-point) are available. This article brings a new transmission approach:hybrid IP multicast.Based on a detailed analysis of these data transfer methods,we present an allocation strategy,which outperforms its counterparts.
Blending as a Time Series Forecasting Tool

Problem Improve hourly gas flow forecasting accuracy by combining multiple forecasting strategies at multiple time horizons Importance Hourly gas flow forecasts allows for efficient continuous operation Saves companies and consumers money A generalized forecasting improvement tool benefits anyone making multi-horizon forecasts Current Method GasDay Labs at Marquette University provides many forecasting services to natural gas utilities. One of these services is GasHour, which forecasts hourly gas flow. GasHour takes advantage of the near linearity between temperature and gas flow as well as otherweather and time inputs. GasHour makes forecasts up to 106 hours ahead using 106 distinct models for each time horizon Proposed Method Combine daily profile and multi-horizon model over multiple time horizons Analysis Used software to produce predicted flows based on the multi horizon model, daily profile model, an extrapolation model, a model which simply averages the models and then the blended model.
Improving the Image Encryption-Then-Compression System via Prediction Error Cluster and Random Transformation

In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem of how to design a pair of image encryption and compression algorithms such that compressing the encrypted images can still be efficiently performed. In this project, design a highly efficient image encryption-then-compression (ETC) system, where both lossless and lossy compression are considered. Image encryption scheme operated over the prediction error domain. In gradient adaptive prediction (GAP) is adopted due to its excellent de-correlation capability. Index Terms:
FPGA IMPLEMENTATION OF ROAD NETWORK EXTRACTION USING MORPHOLOGICAL OPERATOR

In the remote sensing analysis, automatic extraction of road network from satellite or aerial images is the most needed approach for efficient road database creation, refinement, and updating. Mathematical morphology is a tool for extracting the features of an image that are useful in the representation and description of region shape. Morphological operator plays a significant role in the extraction of road network from satellite images. Most of the image processing algorithms need to handle large amounts of data, high repeatability, and general software is relatively slow to implement, so the system cannot achieve real-time requirements. In this paper, field programmable gate array (FPGA) architecture designed for automatic extraction of road centerline using morphological operator is proposed. Based on simulation and implementation, results are discussed in terms of register transfer level (RTL) design, FPGA editor and resource estimation. For synthesis and implementation of the above architecture, Spartan 3 XC3S400TQ144-4 device is used. The hardware implementation results are compared with software implementation results. The performance of proposed method is evaluated by comparing the results with ground truth road map as reference data and performance measures such as completeness, correctness and quality are calculated. In the software imple-mentation, the average value of completeness, correctness, and quality of various images are 90%, 96%, and 87% respectively. In the hardware implementation, the average value of completeness, correctness, and quality of various images are 87%, 94%, and 85% respectively. These measures prove that the proposed work yields road network very closer to reference road map.
Data Filtering of Students' Perfunctory Teaching Evaluation in College P.E.

Students evaluation of teaching is a process to measure the teacher's teaching effect by its own judgment.As the students' evaluation is subjective,their attitudes have a direct impact on the results of the evaluation.Some students provide inaccurate ratings of teaching.Perfunctory teaching evaluation data is negative.In this paper,per functory data was analyzed to compare correlation coefficient to filter perfunctory.The result proved to be effective.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Novel Digital Forensic Framework for Cloud Computing Environment

Cloud computing is a transformative computing model for businesses that deliver computer based services over the Internet. Cloud computing faces major concerns due to its architectural characteristics despite the technological innovations that have made it a feasible solution. The huge popularity and utility of the cloud environment has made it the soft target of cloud crimes. Investigating cloud crimes and fixing the responsibility of the cyber crimes committed in the cloud platforms help instill confidence and trust in the stake holders be the clients, the cloud service providers or the third party entities. Cyber crime investigation is incomplete without the proper detection of the digital evidence in cloud. In general, cloud computing is characterized by its highly virtualized nature. As virtualization provides many benefits, it also makes it difficult to detect digital evidence when it is in the cloud environment. The approach used for the traditional digital forensic cannot be directly applied to the cloud environment due to the presence of virtualization, and hence cloud crime investigation is more difficult to perform than a traditional physical computer investigation. The existing research in cloud forensics has only focused on the organizational and the legal aspects, where as our work aims to contribute towards the technical aspects of forensics in cloud. The aim of this research is to design a generic digital forensic framework for the cloud crime investigation by identifying the challenges and requirements of forensics in the virtualized environment of cloud computing, address the issues of dead/live forensic analysis within/outside the virtual machine that runs in a cloud environment, and to design a digital forensic triage using parallel processing framework to examine and partially analyze the virtual machine data to speed up the investigation of the cloud crime. To analyze the evidence within the virtual machine, we designed various methods of examining the file system metadata, the registry file content, and the physical memory content. For the evidence which is outside a virtual machine (cloud logs), various methods of log data segregation and collection have been devised.
Unified Framework for Energy-proportional Computing in Multicore Processors: Novel Algorithms and Practical Implementation

Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a lower power consumption rate. With multi-cores, there is increased need for dynamic energy management (DEM), much more than for single-core processors, as DEM for multicores is no more a mechanism just to ensure that a processor is kept under specified temperature limits, but also a set of techniques that manage various processor controls like dynamic voltage and frequency scaling (DVFS), task migration, fan speed, etc. to achieve a stated objective. The objectives span a wide range from maximizing throughput, minimizing power consumption, reducing peak temperature, maximizing energy efficiency, maximizing processor reliability, and so on, along with much more wider constraints of temperature, power, timing, and reliability constraints. Thus DEM can be very complex and challenging to achieve. Since often times many DEMs operate together on a single processor, there is a need to unify various DEM techniques. This dissertation address such a need. In this work, a framework for DEM is proposed that provides a unifying processor model that includes processor power, thermal, timing, and reliability models, supports various DEM control mechanisms, many different objective functions along with equally diverse constraint specifications. Using the framework, a range of novel solutions is derived for instances of DEM problems, that include maximizing processor performance, energy efficiency, or minimizing power consumption, peak temperature under constraints of maximum temperature, memory reliability and task deadlines. Finally, a robust closed-loop controller to implement the above solutions on a real processor platform with a very low operational overhead is proposed. Along with the controller design, a model identification methodology for obtaining the required power and i thermal models for the controller is also discussed. The controller is architecture independent and hence easily portable across many platforms. The controller has been successfully deployed on Intel Sandy Bridge processor and the use of the controller has increased the energy efficiency of the processor by over 30%.
Active learning of neural network from weak and strong oracles

When implementing deep learning techniques in real-world applications acquiring labeled data can be the most difficult and expensive part. Sometimes in this process there is both a weak (and cheap to ask) oracle as well as a strong (but expensive to ask) oracle available to which one can ask for labels to examples. The weak oracle can for example be non-expert mechanical turks or a rule-based system, whereas the strong oracle may be human experts that provide very reliable labels. We propose an algorithm to do active learning in the presence of a weak and a strong oracle. A central part of the algorithm is an agreement classifier which predicts the probability of the weak oracle knowing the correct label for an example. However, at prediction time the agreement classifier is only assumed to be able to sort the examples after how much the weak oracle can be trusted, which is believed to be a key reason behind why the algorithm works in practice. A second key idea in the algorithm is that the agreement classifier does not only condition the classification on the information from the input space, but also on the label proposed by the weak oracle. A third idea that is examined is to leverage the probabilities supplied by the agreement classifier directly in the cost function of the main classifier. To test the algorithm we built a test environment based on binary sentence classification as well as three types of synthetic weak oracles. The algorithm performs well with these three synthetic oracles. It manages to decrease the cross entropy on the learning task more per example queried to the strong oracle than what standard active learning do.
Eyetracking evidence for heritage speakers’ access to abstract syntactic agreement features in real-time processing

This paper presents the results of an eyetracking study that uses the Visual World Paradigm to determine whether heritage speakers of Polish can use grammatical gender cues to facilitate lexical retrieval of the subsequent noun during real time processing. Previous work has investigated this question for heritage speakers of Spanish with gender cues located on definite articles, which are highly frequent in Spanish; the results are therefore consistent both with a grammatical account, wherein heritage speakers access abstract syntactic gender features during processing, and a probabilistic account, wherein facilitation is due to transition probabilities between frequently co-occurring elements. In Polish, gender cues appear on adjectives, which are optional and infrequent. Results of the present study show that heritage speakers of Polish can use gender on inflected adjectives to fixate on the target noun faster in trials where that gender cue uniquely identifies the target noun. This finding supports a grammatical rather than probabilistic account of the facilitative use of grammatical gender in this population: heritage speakers are able to access abstract syntactic information in real time to aid word recognition in a target-like manner.
Design of Instruction Decoder of Reconfigurable Embedded DSP Processor

Based on a brief introducing of CoStar, which is a reconfigurable embedded 32-bit Digital Signal Processor designed by us, we will discuss in detail the design of its Instruction Decoder. The paper will focus on the novel techniques employed in the design. On the one hand, in order to cooperate with the complex pipeline, SIMD (Single Instruction stream over Multiple Data streams), and reconfigurable structure of CoStar, we present the techniques of Distributed Decoding which has a relatively concentrative characteristic, and Instruction Sharing under Multiple-Mode. On the other hand, in order to reduce the chip area and power consumption, we present Nesting Type-Separated Instruction Decoding, Type Merging, Instruction Pre-decoding, and some other techniques. These techniques are also suitable to design other processors.
The protective effect of grit on clinical nurses’ occupational psychological distress: Mediating and suppressing effects of Hope

As at a high-risk group of psychological distress, nurses generally experience varying degrees of stress, anxiety, and depression. This paper identifies the positive factors that may negatively regulate the psychological pain of clinical nurses and their mechanisms of action, providing reliable references for clinical nurse support management. The effects and mechanisms of hope and the two components of grit consistency of interest and perseverance of effort) on clinical nurses’ psychological distress (depression, anxiety, and stress) were observed in this study. A total of 635 Chinese clinical nurses (90.4% female) completed an anonymous questionnaire for the survey. As expected, hope, consistency of interest, and perseverance of effort were negatively correlated with the three indicators of psychological distress (r = −0.21 ~ −0.38, p < 0.01). Path analysis results showed that hope significantly mediated the negative effect of consistency of interest on psychological distress, with an effect of 12.96%. Hope also covered up the perseverance of effort on psychological distress, the effect of 110.63%. In the influence of consistency of interest and perseverance of effort on psychological distress, hope contributed a vital mediating. Based on these results, it can be concluded that grit and hope have protective effects on psychological distress in clinical nurses. Significantly increasing the level of hope or grit may effectively prevent and reduce psychological distress in clinical nurses.