Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Statement A : Approved for public release ; distribution unlimited . Multiple-modality Program for Standoff Detection of Roadside Hazards

The U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) is executing a program to assess the performance of a variety of sensor modalities for standoff detection of roadside explosive hazards. The program objective is to identify an optimal sensor or combination of fused sensors to incorporate with autonomous detection algorithms into a system of systems for use in future route clearance operations. This paper provides an overview of the program, including a description of the sensors under consideration, sensor test events, and ongoing data analysis.
A Mobile Indoor Navigation System Based on J2ME

With the rapid development of global positioning system(GPS)and the wide application of digital map,the navigation system on mobile phone made a rapid development.But the existing GPS navigation system mostly applied on outdoor navigation,the indoor navigation is relatively a blank field.Based on the mobile phone positioning and digital map transmitting functions,a research of the mobile indoor navigation system based on J2ME was applied,and a feasible solution was put forward.
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images

Light-field cameras (LFC) have received increasing attention due to their wide-spread applications. However, current LFCs suffer from the well-known spatio-angular trade-off, which is considered as an inherent and fundamental limit for LFC designs. In this paper, by doing a detailed geometrical optical analysis of the sampling process in an LFC, we show that the effective sampling resolution is generally higher than the number of micro-lenses. This contribution makes it theoretically possible to break the resolution trade-off. Our second contribution is an epipolar plane image (EPI) based super-resolution method, which can super-resolve the spatial and angular dimensions simultaneously. We prove that the light field is a 2D series, thus, a specifically designed CNN-LSTM network is proposed to capture the continuity property of the EPI. Rather than leveraging semantic information, our network focuses on extracting geometric continuity in the EPI. This gives our method an improved generalization ability and makes it applicable to a wide range of previously unseen scenes. Experiments on both synthetic and real light fields demonstrate the improvements over state-of-the-art, especially in large disparity areas.
Research on preceding vehicle detection for intelligent vehicle

In order to implement the front vehicle detection and determine the distances between the host vehicle and the target vehicles accurately,one kind of algorithm for vehicle detection using fuzzy logic is proposed.Considering the importance of the features,the membership function and confidence are combined to verify the hypothesis.After vehicle hypothesis formation,features are fuzzed with membership functions and the vehicle detection is realized according to the confidence obtained by fusion of different features.The experiments indicated that this algorithm has the good adaptive ability and anti-jamming ability.It can detect the proceeding vehicles intimely and accurately.
Imaging method for spinning targets based on Bayesian compressive sensing

Narrow-band radar which emits the signal restricted by bandwidth limitation has a low resolution in range profile so that it usually applies to target detection and tracking. However, the rotating target’s micro-doppler is used to image by Narrow-band radar that provides a new idea for target recognition. Due to the characteristics of narrow-band radar echoes from spinning targets, an imaging method based on Bayesian Compressive Sensing (BCS) is proposed according to the sparsity nature of narrow-band radar echoes from spinning targets. Simulation results show that the proposed approach is able to provide a sharp and sparse image absence of side-lobes which is the common problem in conventional complex-valued back-projection method and has fewer artifacts compared to the previous version of Compressive Sensing (CS) based methods.
ANALYSIS OF INTEGRATED SCIENCE AND COMPUTER SCIENCE STUDENTS' ACADEMIC PERFORMANCES IN PHYSICS IN COLLEGES OF EDUCATION, NIGERIA

Seventy four students of Integrated Science/Physics and Computer Science/Physics were sampled in four Colleges of Educations to analyze their performance in Physics. Frequency counts, percentages and T-test were used to analyse their results from year one to final year in Physics. Results shows that there was no significant difference between Integrated Science and Computer Science students’ academic performance in Physics: mean difference in academic performance of Integrated Science/Physics and Computer Science/Physics students was not significantly difference and there was no relationship between students’ academic performance of Integrated Science/Physics and Computer Science/Physics. The paper made some recommendations based on the findings.
A Human–Machine Interface Design to Control an Intelligent Rehabilitation Robot System

The growing world population and the increasing number of problems people have with their limbs have intensified the need for rehabilitation particularly for lower limbs. Reestablishing and improving the limb functionality and strength are major issues. Furthermore, it is most important to help patients to return to society, to reintegrate them into social life and thus to improve the patients’ ABSTRACT
Prosody and local discourse structure in a polysynthetic language

As has been known since at least Chafe 1994, spoken language is produced in spurts, or quanta, or elementary discourse units (EDUs; see Kibrik and Podlesskaya 2009). EDUs are identified on the basis of prosodic criteria, such as tempo, loudness, intonation contours, pitch accents, and pausing. Cognitively, EDUs correspond to foci of consciousness (Chafe 1994). Local discourse structure, consisting of EDUs, has been studied in a number of languages. In this paper I apply this approach to a polysynthetic language of Alaska, North America, called Upper Kuskokwim Athabaskan (UKA). Polysynthetic languages are those in which morphological complexity of the verb substantially exceeds crosslinguistic average. Much of what is encoded by function words of nominal morphology in other languages, is encoded in verbs of polysynthetic languages. As a result, polysynthetic verbs often consist of lengthy sequences of morphemes. One typical aspect of polysynthetic languages is that clause arguments are encoded by pronominal affixes inside the verb. Whereas in a language like English the clause She saw him consists of three words, in a polysynthetic language it would be one word that can be schematically represented as she-him-saw. It is an interesting research question how these grammatical peculiarities relate to local discourse structure. This study is based on a corpus of UKA discourses including several genres, such as personal stories, folk stories, conversations, interviews, etc. The overall length of UKA talk that was transcribed and served as the basis for this study is 3 hours 20 minutes. The corpus of discourses has been divided into EDUs. This procedure did not meet with major difficulties. The familiar set of prosodic criteria (see above) worked for UKA successfully. For example, in the EDU shown in (1) the beginning of the clause (the noun) was pronounced by the speaker with the pace of 240 ms per syllable, and in the final part of the clause (the verb and the ensuing particle) the pace is 450 ms per syllable.
Facilitating the programming from the perspective of agile methodologies

The agile idea of pair programming implicitly obeys this theory. The idea of this paper is to provide that a larger set of people can share the development of a source code in a collaborative manner and that the history and the whole intellectual process that leads to changes in certain parts of a source code will be record in the environment. This allows a N-N relationship can be established in a development environment for distributed software, leveraging the theory of the more capable peer. The article presents a tool developed as a NetBeans’ plug-in, functionality that incorporates several of collaborative environments to support distributed programming in real time.
Comparison of tracking algorithms implemented in OpenCV

Computer vision is very progressive and modern part of computer science. From scientific point of view, theoretical aspects of computer vision algorithms prevail in many papers and publications. The underlying theory is really important, but on the other hand, the final implementation of an algorithm significantly affects its performance and robustness. For this reason, this paper tries to compare real implementation of tracking algorithms (one part of computer vision problem), which can be found in the very popular library OpenCV. Moreover, the possibilities of optimizations are discussed.
Continuing the Scaling of Digital Computing Post Moore’s Law

Author(s): Michelogiannakis, George; Shalf, John; Donofrio, David; Bachan, John | Abstract: The approaching end of traditional CMOS technology scaling that up until now followed Moore's law is coming to an end in the next decade. However, the DOE has come to depend on the rapid, predictable, and cheap scaling of computing performance to meet mission needs for scientific theory, large scale experiments, and national security. Moving forward, performance scaling of digital computing will need to originate from energy and cost reductions that are a result of novel architectures, devices, manufacturing technologies, and programming models. The deeper issue presented by these changes is the threat to DOE’s mission and to the future economic growth of the U.S. computing industry and to society as a whole. With the impending end of Moore’s law, it is imperative for the Office of Advanced Scientific Computing Research (ASCR) to develop a balanced research agenda to assess the viability of novel semiconductor technologies and navigate the ensuing challenges. This report identifies four areas and research directions for ASCR and how each can be used to preserve performance scaling of digital computing beyond exascale and after Moore's law ends.
As Google Goes, So Goes the Nation

ou don't get to be a verb unless you're doing something right. Do a Google search on "ford," for example, and the first batch of results includes the pages for the Ford Motor Company, the Ford Foundation, the Betty Ford Center, Harrison Ford and Gerald R. Ford  all good guesses at what a user would be looking for, particularly considering that Google estimates its index holds more than 16 million pages including the word.
Tackling bufferbloat in 3G/4G mobile networks

With the exponential growth of hand-held devices like smart phones and tablet computers, a deep investigation of TCP performance in cellular networks is becoming increasingly important. In this paper, we conducted extensive measurements over the 3G/4G networks of four major U.S. carriers as well as the largest carrier in Korea and discovered a significant problem: the bufferbloat in cellular networks nullifies loss-based congestion control and allows excessive growth of the TCP congestion window, resulting in extremely long delays and throughput degradation. To alleviate this issue, smart phone vendors put an upper bound on the sender’s congestion window by advertising a static receive window smaller than the actual receive buffer size. This simple trick helps mitigate the problem, but has fundamental limitations due to its static nature. In this paper, we propose a dynamic receive window adjustment (DRWA) algorithm to improve TCP performance over bufferbloated cellular networks. According to our extensive real-world tests, DRWA may reduce the delay by 25 ∼ 49% in general cases and increase TCP throughput by up to 51% in some specific scenarios. Since DRWA requires modification at the client side (e.g., smart phones) only and is fully compatible with existing TCP protocol, it is immediately deployable.
A Bayesian approach to the interpretation of climate model ensembles

This thesis is concerned with uncertainty quantification when interpreting ensembles of climate models (MMEs), to learn about the true climate. The work improves a recent Bayesian framework for MME interpretation and applies it for first time on real data. The original framework accounts for shared simulator discrepancy from reality. Inference for the true climate is provided through a posterior distribution. The posterior is obtained based on a computationally efficient implementation which assigns estimates to the covariance matrices expressing variability due to different sources of uncertainty in the MME structure. Three framework improvements are considered. Firstly, an improved estimator of the covariance matrix expressing vari- ability due to shared simulator discrepancy from reality is proposed. It relates future to historical discrepancy using bootstrapping from earlier data, instead of subjectively setting a parameter to relate them. The second improvement incorporates prior in- formation about the framework’s covariance matrices to account for uncertainty in their values, leading to a fully Bayesian implementation. Two such implementations are introduced, to explore sensitivity of the true-climate posterior to different priors. The third improvement proposes an extended framework which accounts for simu- lator grouping. A random effects model is also proposed to estimate within-group variability when singleton groups exist. The improvements are assessed through an application on global surface air temperature, using observations and simulator out- puts. Results suggest that ignoring simulator grouping and uncertainty in the values of the framework’s covariances does not seriously underestimate the uncertainty in inference for real climate. A simulation study is also presented, to compare the performance of the extended framework relative to the simpler. Results suggest that given small shared discrepancy and a well-defined grouping structure, accounting for grouping improves uncertainty quantification in inference for true climate, provided the bias in estimation of shared discrepancy is small.
Malaysian traffic sign dataset for traffic sign detection and recognition systems

Traffic Sign Detection and Recognition (TSDR) plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. It is part of the computer vision which requires a dataset for training and testing the detection and recognition techniques. In this paper, a dataset for Malaysian TS (MTSD) is proposed in order to eliminate the gap in the previously created datasets. The MTSD includes a variety of TS scenes to be used in TS detection and images contain only TS to assist in the recognition of TS.
Performance evaluation of a new single-ended comparator for low-power high-speed flash ADC

ABSTRACT In this paper, a new single-ended comparator is explored for designing the reference ladder-free flash analog-to-digital converter (ADC). This proposed comparator generates inherent embedded threshold voltage. It uses the variable threshold voltage-generation method for producing the reference voltage for the flash ADC design. Employing optimised comparator gives various benefits, as it does not require the necessity of a reference resistor ladder as well as front-end track and hold circuit. This reduces both layout area and power consumption and makes it appropriate for system-on-chip ADC implementation. The basic structure of the single-ended comparator is a modified complementary metal oxide semiconductor (CMOS) inverter. The performance of the modified CMOS inverter circuit is compared with the CMOS inverter. Simulation results show that the modified CMOS inverter-based comparator achieves average power consumption of 1.46 μW with propagation delay of 53.8 ps. To demonstrate the functionality of the new comparator, a 4-bit flash ADC has also been realised. The designed 4-bit flash ADC exhibits significant improvement in terms of power and speed of previously reported flash ADCs. The converter consumes power 274 μW from a 1.8-V supply and achieves the conversion speed of 1 GHz in United Microelectronics Corporation 180-nm CMOS. The measurement of maximum differential and integral nonlinearities of the flash ADC are 0.2 least significant bit (LSB) and 0.6 LSB, respectively.
Real-time acquisition of multi-view face images to support robust face recognition using a wireless camera network

Real-time acquisition of multi-view face images to support robust face recognition using a wireless camera network by Srikanth Parupati Master of Science in Electrical Engineering West Virginia University Dr. Vinod Kulathumani, Ph.D., Chair Recent terror attacks, intrusion attempts and criminal activities have necessitated a transition to modern biometric systems that are capable of identifying suspects in real time. But real-time biometrics is challenging given the computationally intensive nature of video processing and the potential occlusions and variations in pose of a subject in an unconstrained environment. The objective of this dissertation is to utilize the robustness and parallel computational abilities of a distributed camera network for fast and robust face recognition. In order to support face recognition using a camera network, a collaborative middle-ware service is designed that enables the rapid extraction of multi-view face images of multiple subjects moving through a region. This service exploits the epipolar geometry between cameras to speed up multi view face detection rates. By quickly detecting face images within the network, labeling the pose of each face image, filtering them based on their suitability of recognition and transmitting only the resultant images to a base station for recognition, both the required network bandwidth and centralized processing overhead are reduced. The performance of the face image acquisition system is evaluated using an embedded camera network that is deployed in indoor environments that mimic walkways in public places. The relevance of the acquired images for recognition is evaluated by using a commercial software for matching acquired probe images. The experimental results demonstrate significant improvement in face recognition system performance over traditional systems as well as increase in multi-view face detection rate over purely image processing based approaches.
User Modeling and User Profiling in Adaptive

It is essential for user-adaptive systems to have information about the user. Without any user information an adaptive system is not able to adapt itself to the user's characteristics and preferences. The required information is stored and managed in form of user models. Thus, a user model represents the system's beliefs of the user. This work starts by giving the reader and overview of available and well-founded user modeling methods, standards and existing systems. This theoretical excursion into the field of adaptive user modeling systems allows us to come up with a new approach based on a service-oriented architecture. Service-oriented architecture with its main advantages of modularity and flexibility is and ideal candidate to implement as user modeling system as these are exactly the two main characteristics which we are looking for in such a systems. The technical part of this work compares several service-oriented frameworks and finally describes a service-oriented implementation of a user modeling system.
Automated Identification of Computer Science Research Papers

The fast growing speed of the size of scholarly data have made it necessary to find out efficient machine learning ways to automatically categorize the data. This thesis aims to build a classifier that can automatically categorize Computer Science (CS) papers based on text content. To find out the best method for CS papers, we collect and prepare two large labeled data sets: CiteSeerX and arXiv, and experiment with different classification approaches including Naive Bayes and Logistic Regression, different feature selection schemes, different language models, and different feature weighting schemes. We found that with large size of training set, Bi-gram modeling with normalized feature weight performs the best for all the two data sets. It is surprising that arXiv data set can be classified up to 0.95 F1 value, while CiteSeerX reaches lower F1 (0.764). That is probably caused by labeling of CiteSeerX is not as accurate as arXiv data set.
Tree Based Data Aggregation Algorithm to Increase the Lifetime of Wireless Sensor Network

Wireless sensor networks are becoming increasingly important in recent years due to their ability to detect and convey real time information for many civilian and military applications. The most important challenge faced by WSNs is battery-limited lifetime of the network. Physically replacing batteries is infeasible in most real-life deployments of WSNs. Longer lifetime of the devices can be achieved by reducing the delay and the energy consumption. Hence this project aims to reduce energy during delay on collection of data, and hence by this process gathering information by sensor nodes is achieved. There are strong needs to develop wireless sensor networks algorithms with optimization priorities biased to aspects besides energy saving. In order to minimize delays in the data collection process of wireless sensornetworks, clustering is used. This exploits use of clustering for reducing the delayduring data collection process. The two network formation algorithms, centralized top-down and decentralized bottom-up clustering is considered and analyzed for shortening delays in the data collection process for Single and Multiple-cluster network structure. Cluster data fusion and routing algorithms are considered with data packets generated by sensor nodes.
Human Face Recognition and the Face Image Set's Topology

Abstract If we consider an n × n image as an n2-dimensional vector, then images of faces can be considered as points in this n2-dimensional image space. Our previous studies of physical transformations of the face, including translation, small rotations, and illumination changes, showed that the set of face images consists of relatively simple connected subregions in image space. Consequently linear matching techniques can be used to obtain reliable face recognition. However, for more general transformations, such as large rotations or scale changes, the face subregions become highly non-convex. We have therefore developed a scale-space matching technique that allows us to take advantage of knowledge about important geometrical transformations and about the topology of the face subregion in image space. While recognition of faces is the focus of this paper, the algorithm is sufficiently general to be applicable to a large variety of object recognition tasks
Vibrant Resource Allocation Algorithms usingVirtual Machine in Cloud-Survey

Cloud computing is a usage of very large scalable and virtualized resources in a dynamic way over the internet. Due to the rapid growth of cloud environment usage many tasks require to be executed by the available resources . At the same time it should be possible to achieve better performance, optimizing the servers, reduce migration, support green computing, better resource utilization etc. so resource allocation using virtual machine plays a most important role in cloud environment because it should allocate proper resource to proper resources to various machines to get maximum benefit. In this paper we are going to study different set of resource allocation process and their concerns using virtual machines in cloud computing.