Miracle-dz/UniFAR-FTUs huggingface dataset:
Leaders for California's schools

There is every reason to believe that the role of the principal is increasing in importance. Positioned between policymakers and teachers, principals are critical actors in virtually any effort to improve the performance of schools and students. Reformers rely on principals to embrace, translate, and enforce their goals. In many ways, California’s schools can only be as great as the individuals who lead them.
Architectural and compiler optimization for network processors

In today's capital constrained environment, routers now must support continually evolving requirements on aggregating a range of network protocols and traffic types. To meet such requirement, network processors (NPs) have emerged as a new class of programmable processors for network applications. New generation NPs offer high performance through parallel processing architecture, which incorporates multiple processing elements (PEs) configured as either independent or pipelined units. Being programmable, NPs support new applications with improved time to market at lower cost. Performance and power consumption are two primary concerns for NPs. However, to conduct research for high performance and low power, we should find an efficient processor simulation method. The existing cycle-accurate simulators faithfully model the micro archi tecture, but they take extremely long time to simulate. The slow speed makes cycle accurate simulation impractical for architectural evaluation. We thus propose a statistical input sampling algorithm that extracts representative traffic patterns from traffic traces. With shortened traffic trace, the simulation can finish in a short time with sufficient accuracy. With the proposed fast simulation method, we further exploit the power saving opportunities in NPs. Traffic variation is typically observed on routers. This traffic variation implies that much less processing power is required during light workload. A number of low power techniques, Dynamic Voltage Scaling (DVS), Clock Gating and Power Gating are studied in NP system. These techniques are shown to be very effective in reducing power in NP system. To increase throughput of NPs, compiler optimization plays a critical role in finding a good program mapping. We propose to use a divide-and-conquer approach to recursively bipartition the task graph and allocate processing elements (PEs). The recursive min-cut algorithm can find the optimal number of pipeline stages with minimum communication overhead. Then we propose a hierarchical refinement method to migrate tasks from bottleneck stage to non-bottleneck stages. The experiment shows that our algorithm achieves averagely 18% throughput improvement than previous compiler algorithms.
Content based texture image classification

An image can be regarded as a combination of different texture areas. Texture is usually defined as a certain local feature, or it measures the relation between pixels in a local area. It is also an important feature in content based image retrieval. Based on moment feature, a new method for content based texture image classification is proposed using support vector machine and directional information of the image, which combines the characteristics of Brushlet and wavelet transform. Experiments show our method is practicable and effective.
All That Heaven Allows

Thank you for downloading all that heaven allows. Maybe you have knowledge that, people have look numerous times for their favorite readings like this all that heaven allows, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some harmful bugs inside their computer. all that heaven allows is available in our book collection an online access to it is set as public so you can download it instantly. Our books collection hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the all that heaven allows is universally compatible with any devices to read.
Error Mitigation in RSSI-Based Fingerprinting Localization Using Multiple Communication Channels

Location data from radio signal strength indication (RSSI) based wireless networks has been used in various applications such as creating smart home behavioral monitoring systems, tracking health care workers for the spread of hospital-associated infections, and providing location-aware tour guide systems. Because RSSI-based systems are inexpensive and can be used with most wireless devices without requiring additional hardware, they are a popular choice for localization. Unfortunately, multipath fading dramatically degrades the performance of an RSSI-based system's ability to locate a target indoors. This thesis endeavors to reduce localization error for RSSI-based ngerprinting localization systems in an indoor environment through frequency diversity by using multiple communication channels. By creating a mul-tichannel ngerprint of the environment using ngerprinting calibration techniques, ne-grained, 5 centimeter, 2-dimensional localization accuracy is achieved in an indoor environment under certain restrictions. 3 DEDICATION To my father. Gracias por todos los días que me obligaste a ayudarte con las repara-ciones de la casa y trabajos de mecánica. Aunque a veces no me gustaba y odiaba hacerlo, cada día era una oportunidad nueva para enfrentar un reto y poder formar la mente de un ingeniero.
Real-Time License Plate Detection Based on Faster R-CNN

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.
Accelerating separable footprint (SF) forward and back projection on GPU

Statistical image reconstruction (SIR) methods for X-ray CT can improve image quality and reduce radiation dosages over conventional reconstruction methods, such as filtered back projection (FBP). However, SIR methods require much longer computation time. The separable footprint (SF) forward and back projection technique simplifies the calculation of intersecting volumes of image voxels and finite-size beams in a way that is both accurate and efficient for parallel implementation. We propose a new method to accelerate the SF forward and back projection on GPU with NVIDIA’s CUDA environment. For the forward projection, we parallelize over all detector cells. For the back projection, we parallelize over all 3D image voxels. The simulation results show that the proposed method is faster than the acceleration method of the SF projectors proposed by Wu and Fessler.13 We further accelerate the proposed method using multiple GPUs. The results show that the computation time is reduced approximately proportional to the number of GPUs.
CoolCloud: improving energy efficiency in virtualized data centers

viii CHAPTER 1. OVERVIEW . 1 CHAPTER 2. A COOL SCHEDULER FOR MULTI-CORE SYSTEMS EXPLOITING PROGRAM PHASES . 5 2.
Modelling of Ka-band satellite communication channel and system performance simulation

This paper presents a general method for satellite communication system design,modelling and simulation.Stress is placed on modelling and simulation of a Ka band satellite communication channel.Statistical characteristics and model of the channel at Ka band are given firstly,then we conducted the system performance simulations based on the channel and system simulation model proposed by this paper.Finally the analytical derivation of the performance of coherent BPSK signal at Ka band is described and a useful conclusion is drawn by comparing with the simulation results.
Integrating Apache Spark and External Data Sources Using Hadoop Interfaces

Requirements of data-processing are undergoing a profound transition with the dramatic increase of various application data. Along with this demands, sorts of storage systems with highly scalable are developed for large-scale data sources, and Apache Spark for immense amounts of data calculation has also captured attentions and excitements of the industry since release for its excellent performance. Technologies of combining Spark and external data sources have lots of potential to produce valuable data analysis platforms for solving a wide range of data-handling needs, accordingly it's very easy to shift from Hadoop to Spark application development since the industry pour a lot in these datastore system for Hadoop in the past and Spark can work well with Hadoop-supported systems. Paper delves into integration mechanism of spark and external data sources, such as NoSQL and relational databases (RDBs) and proposes a reference for tight and efficient integration of the two using Hadoop interfaces.
Threshold voltage in pentacene field effect transistors with parylene dielectric

Organic field effect transistors (OFETs) offer a suitable building block for many flexible, large-area applications such as display backplanes, electronic textiles, and robotic skin. Besides the organic semiconductor itself, an important area in the development of OFETs is the gate dielectric material. In this thesis the organic polymer parylene is studied as a gate dielectric for pentacene OFETs. The three main areas of study were: (1) parylene's performance as a dielectric, (2) possible improvement of OFETs by surface treatments, and (3) the effects of interface traps on threshold voltage and parasitic bulk conductivity. Parylene was found to provide a favorable, hydrophobic interface for pentacene growth, yielding transistors with mobilities > 0.5cm 2 /Vs at -100V. While the two surface treatments explored did increase contact angle by 10-20', neither the ammonium sulfide nor the polystyrene treatment significantly improved pentacene packing or mobility. Modification of the parylene surface using an oxygen plasma introduced traps at the semiconductor-dielectric interface, observable through a variety of characterization techniques. A model is developed to explain how the fixed and mobile charges these traps introduce influence the threshold voltage and parasitic conductivity in the device. Thesis Supervisor: Akintunde Akinwande Title: Professor of Electrical Engineering Thesis Supervisor: Ioannis Kymissis Title: Postdoctoral Associate
Road Markings Image Segmentation Method

The optimal threshold method,maximum class square error method and minimum error threshold method were used to segment the distressed road markings from some typical images in this paper.From these segmented images,it is concluded that the segmentation by using minimum error threshold method is the best,though some details cannot be segmented accurately.In order to achieve more perfect segmentation effect,the dynamic threshold method based on the minimum error has been discussed in this paper.Through comparing the global threshold method with dynamic threshold method,it is concluded that the dynamic threshold method integrated with global threshold to segment the road markings has better segmentation effect.
Towards an Automated System for Music Event Detection

Announcements of events are regularly spread using the Internet, e.g., via online newspapers or social media. Often, these events involve playing music publicly that is protected by international copyright laws. Authorities entrusted with the protection of the artists’ interests have to find unregistered music events in order to fully exercise their duty. As a requirement, they need to find texts in the Internet that are related to such events like announcements or reports. However, event detection is a challenging task in the field of Text Mining due to the enormous variety of information that needs to be considered and the large amount of data that needs to be processed. Because no benchmark data is available for the domain of music event detection, in this paper a gold standard dataset is presented and made publicly available for further development and improvement. Subsequently, a process chain for the detection of music events incorporating external knowledge is proposed. Finally, the performance of three classification models is compared using various feature sets and two different datasets. The best performances reach an F1-measure of 0.94 and 0.946 for the classification of music and music event relevance, respectively. Keywords–Event Detection; Text Classification; Categorization; Named Entity Recognition.
RACE for retrieval: Competitive effects in memory retrieval

When a question is stated such as “What is the capital of Australia? ”, various answers start competing for retrieval from declarative memory. If a hint is given during this retrieval process (“The name of the capital begins with the letter C.”), retrieval may be facilitated, but if a distractor is presented (“Amsterdam”), retrieval may be inhibited. A well-known example of these kinds of effects is Picture-Word Interference, a task similar to the Stroop-task (Glaser & Dungelhoff, 1984; Glaser & Glaser, 1989; Schriefers et al., 1990). In these tasks, it has been shown that SOAs have differential effects. For example, presenting a drawing that depicts a concept 50 ms after a word-form of that concept has appeared, speeds up processing of that word in comparison with a neutral condition, whereas in other conditions SOAs might have a negative effect. The ACT-R Latency Equation as it is defined now,
Spatial-Temporal Correlation-Based Low-Latency Compressed Sensing in WSNs

Wireless Sensor Networks (WSNs) have characteristics of large size, limited resources, large amount of transmission data, and so on. In order to reduce the redundancy of sensed data and decrease network data traffic. We applied CS to clustered structure, proposed Low-Latency Compressed Sensing model (LLCS) which is based on the spatialtemporal correlation of sensed data, the model is also capable of processing sparse abnormal events which is a crucial feature in WSNs. We analyzed the relationship between compression ratio and sampling rounds and verified the abnormal event processing method. The results of simulation experiments using the real data show that LLCS could reduce data transfer volume significantly and process abnormal readings effectively.
Sonar image interpretation for sub-sea operations

Mine Counter-Measure (MCM) missions are conducted to neutralise underwater explosives. Automatic Target Recognition (ATR) assists operators by increasing the speed and accuracy of data review. ATR embedded on vehicles enables adaptive missions which increase the speed of data acquisition. This thesis addresses three challenges; the speed of data processing, robustness of ATR to environmental conditions and the large quantities of data required to train an algorithm. The main contribution of this thesis is a novel ATR algorithm. The algorithm uses features derived from the projection of 3D boxes to produce a set of 2D templates. The template responses are independent of grazing angle, range and target orientation. Integer skewed integral images, are derived to accelerate the calculation of the template responses. The algorithm is compared to the Haar cascade algorithm. For a single model of sonar and cylindrical targets the algorithm reduces the Probability of False Alarm (PFA) by 80% at a Probability of Detection (PD) of 85%. The algorithm is trained on target data from another model of sonar. The PD is only 6% lower even though no representative target data was used for training. The second major contribution is an adaptive ATR algorithm that uses local sea-floor characteristics to address the problem of ATR robustness with respect to the local environment. A dual-tree wavelet decomposition of the sea-floor and an Markov Random Field (MRF) based graph-cut algorithm is used to segment the terrain. A Neural Network (NN) is then trained to filter ATR results based on the local sea-floor context. It is shown, for the Haar Cascade algorithm, that the PFA can be reduced by 70% at a PD of 85%. Speed of data processing is addressed using novel pre-processing techniques. The standard three class MRF, for sonar image segmentation, is formulated using graph-cuts. Consequently, a 1.2 million pixel image is segmented in 1.2 seconds. Additionally, local estimation of class models is introduced to remove range dependent segmentation quality. Finally, an A* graph search is developed to remove the surface return, a line of saturated pixels often detected as false alarms by ATR. The A* search identifies the surface return in 199 of 220 images tested with a runtime of 2.1 seconds. The algorithm is robust to the presence of ripples and rocks.
VLSI Implementation of Eye Detection System

† The eye detection is one of the most challenging problems in many applications such as image processing, pattern recognition and computer vision. This paper introduces efficient vlsi implementation of eye detection system. Face detection is a very important part of the developed eye detection algorithm. Face detection is done by using skin detection method. Skin detection is an extraction of skin color pixels and regions from an image. In this method, first input image is converted from RGB image to YUV image. With YUV domain, the skin pixels in an image are extracted. Then Morphological operation is done by using erosion method through which noise of an image is also eliminated. From this image, face region is extracted by identifying the skin pixels. Then eye detection is done according to rules of human face proportion. The eye detection system is implemented using Verilog-HDL and simulation is done by using Xilinx ISE.
Research on Tibetan Stop Words Selection and Automatic Processing Method

Stop words processing is a key preprocessing step in the text mining.In this paper,the selection method of stop words in Tibetan based on statistics is studied by combining with the existing techniques.Through experiments,TF,DF,and entropy calculation methods in the selection of Tibetan stop words are analyzed.An approach for the selection of Tibetan stop words is presented by the combination of Tibetan function words,special verb and automatic approach.The experimental results show that the proposed method can determine a reasonable Tibetan stop words list.
GETTING TO GRIPS WITH THE LARGER PATTERNS IN THE REPORT TEXT

This research is aimed to know about what larger patterns are used in the report text and what is the role of larger patterns used for the reader. The researcher found that the report text about Australian senator hit with egg after blaming imigration for mosque attack (Karasz, 2019) used claim-counterclaim patterns.The role of claim-counterclaim patterns in that report text is to make readers easy to understand the text, as the reader will know who is the one who was claimed at the beginning as an anti-imigration person, and why he hit a teenager, and what is the reason the teenager smashed him. The researcher suggests for the futher researcher to use other more interesting sources to find more the larger patterns and to knowabout the role of the patterns.Keywords: Larger patterns, report text
Networks in ATLAS

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks. We will report on a number of networking initiatives in ATLAS including participation in the global perfSONAR network monitoring and measuring efforts of WLCG and OSG, the collaboration with the LHCOPN/LHCONE effort, the integration of network awareness into PanDA, the use of the evolving ATLAS analytics framework to better understand our networks and the changes in our DDM system to allow remote access to data. We will also discuss new efforts underway that are exploring the inclusion and use of software defined networks (SDN) and how ATLAS might benefit from: • Orchestration and optimization of distributed data access and data movement. • Better control of workflows, end to end. • Enabling prioritization of time-critical vs normal tasks • Improvements in the efficiency of resource usage
Effect of Challenge Feeding on Production Performance of Surti Buffaloes

Twenty advanced pregnant Surti buffalo heifers were randomly divided into 2 groups of 10 animals in each to evaluate the effect of challenge feeding on production performance. The animals in control group (CN) were given standard ration while in challenge fed (CF) group, the animals were given additional amounts of concentrate mixture. The mean DM intake of heifers during pre-partum period was similar (10.52 vs. 11.20 kg) in both the groups. However, the post-partum DM intake was higher (P
Utilisation of Audio Mining Technologies for Researching Public Communication on Multimedia Platforms

The number and volume of spoken language corpora which are generally available for research purposes increase signiicantly. That is due to the wide adoption of audio-visual communication on news websites and social web platforms. The respective messages that are published by professional and individual communicators are subject to online content analysis. To date, such analyses strongly rely on manually operated processes which come along with a huge effort for transcribing spoken language corpora into textual content. Hence, challenges like the ever increasing volume, velocity and variety of multimedia content need to be faced. Audio Mining technologies are capable of reducing the effort for turning speech into text signiicantly. Using these technologies via application programming interfaces (APIs), it is demonstrated how a hybrid approach enables researchers to reduce the time that is needed for analysing news content by an order of magnitude. Licence: Creative Commons Attribution 4.0 (CC-BY 4.0) Digital Communication Research.de