Miracle-dz/UniFAR-FTUs huggingface dataset:
ASSESSMENT OF INFLUENCE OF IMAGE PROCESSING ON FULLY AUTOMATIC UAV PHOTOGRAMMETRY

Abstract. Nowadays UAVs have been widely used for large scale surveying and mapping. Compared with traditional surveying techniques, UAV photogrammetry is more convenient, cost-effective, and responsive. Aerial images, Position and Orientation System (POS) observations and coordinates of ground control points are usually acquired during a surveying campaign. Aerial images are the data source of feature point extraction, dense matching and ortho-rectification procedures. The quality of the images is one of the most important factors that influence the accuracy and efficiency of UAV photogrammetry. Image processing techniques including image enhancement, image downsampling and image compression are usually used to improve the image quality as well as the efficiency and effectiveness of the photogrammetric data processing. However, all of these image processing techniques bring in uncertainties to the UAV photogrammetry. In this work, the influences of the aforementioned image processing techniques on the accuracy of the automatic UAV photogrammetry are investigated. The automatic photogrammetric data processing mainly consists of image matching, relative orientation, absolute orientation, dense matching, DSM interpolation and orthomosaicing. The results of the experiments show that the influences of the image processing techniques on the accuracy of automatic UAV photogrammetry are insignificant. The image orientation and surface reconstruction accuracies of the original and the enhanced images are comparable. The feature points extraction and image matching procedures are greatly influenced by image downsampling. The accuracies of the image orientations are not influenced by image downsampling and image compression at all.
Nonlinear model predictive control of a batch polymerization process

A Newton-type nonlinear model predictive control (NMPC) algorithm is applied through a simulation study to a batch reactor for polymerization of phenol-formaldehyde resole resins. The NMPC algorithm is combined with a recursive state and parameter estimation algorithm and a nonlinear state-space model, which is used both for the estimation and the prediction. The state-space process model is developed from first principles, and uncertain parameters are estimated off-line from logged process measurements. Some of the parameters can not be assumed to be constant during the batch, and should be estimated online during operation. NMPC strategies are compared with the conventional reactor control. The basic strategy is to maximize the reaction rate, with constraints on the reactor temperature and the heat of reaction. The latter constraint is determined by available cooling capacity. The simulation study indicates a potential reduction in batch cycle time with good safety margins.
Constrained coding techniques for soft iterative decoders

Soft iterative decoding of turbo codes and low-density parity check codes has been shown to offer significant improvements in performance. To apply soft iterative decoding to digital recorders, where binary modulation constraints are often used, modifications must be made to allow reliability information to be accessible by the decoder. A solution is proposed which uses a modified concatenation scheme, in which the positions of the modulation and error-correcting codes are reversed. In addition, a soft decoder based on the BCJR algorithm is introduced for the modulation constraint, and improved performance is obtained by iterating with this soft constraint decoder.
Sub-Topic Segmentation in Multi-Document

The similar sentences in multi-document set are combined into one class, and each class is one sub-topic. Describing the sub-topics from the perspective of understanding makes the multi-document summarization become the one with greater coverage and less redundancy. This paper presents a sub-topic segmentation method based on maximum tree algorithm. And based on sentences similarity matrix, maximum tree is calculated, as well as the sub-topic segmentation is realized through the analysis of the different communities for the sub-topic. The experiment shows that the method achieves the desired result.
A 7 ns 1 Mb BiCMOS ECL SRAM with shift redundancy

A 7-Mb BiCMOS ECL (emitter coupled logic) SRAM was fabricated in a 0.8 mu m BiCMOS process. An improved buffer with a high-level output of nearly V/sub CC/ is adopted to eliminate the DC current in the level converter circuit, and the PMOS transistor has a wide operating margin in the level converter. The configurable bit organization is realized by using a sense-amplifier switch circuit with no access degradation. A wired-OR demultiplexer for the *1 output, having the same critical path as the *4 output circuit, allows for the same access time between the two modes. The *1 or *4 mode is electrically selected by the external signal. A simplified programming redundancy technology, shift redundancy, is utilized. Address programming is performed by cutting only one fuse in the shift redundancy. The RAM operates at the ECL-10K level with an access time of 7 ns. and the power dissipation at 50 MHz is 600 mW for the * mode. >
Table-Based versus Shift-And-Add Constant Multipliers for FPGAs

The multiplication by a constant is a frequently used operation. To implement it on Field Programmable Gate Arrays (FPGAs), the state of the art offers two completely different methods: one relying on bit shifts and additions/subtractions, and another one using look-up tables and additions. So far, it was unclear which method performs best for a given constant and input/output data types. The main contribution of this work is a thorough comparison of both methods in the main application contexts of constant multiplication: filters, signal-processing transforms, and elementary functions. Most of the previous state of the art addresses multiplication by an integer constant. This work shows that, in most of these application contexts, a formulation of the problem as the multiplication by a real constant allows for more efficient architectures. Another contribution is a novel extension of the shift-and-add method to real constants. For that, an integer linear programming (ILP) formulation is proposed, which truncates each component in the shift-and-add network to a minimum necessary word size that is aligned with the approximation error of the coefficient. All methods are implemented within the open-source FloPoCo framework.
Emerging Applications of Link Analysis for Ranking

The booming growth of digitally available information has thoroughly increased the popularity of search engine technology over the past years. At the same time, upon interacting with this overwhelming quantity of data, people usually inspect only the very few most relevant items for their task. It is thus very important to utilize high quality ranking measures which efficiently identify these items under the various information retrieval activities we pursue. In this thesis we provide a twofold contribution to the Information Retrieval field. First, we identify those application areas in which a user oriented ranking is missing, though extremely necessary in order to facilitate a qualitative access to relevant resources. Second, for each of these areas we propose appropriate ranking algorithms which exploit their underlying social characteristics, either at the macroscopic, or at the microscopic level. We achieve this by utilizing link analysis techniques, which build on top of the graph based representation of relations between resources in order to rank them or simply to identify social patterns relative to the investigated data set. We start by arguing that Ranking Desktop Items is very effective in improving resource access within Personal Information Repositories. Thus, we propose to move link analysis methods down to the PC Desktop by exploiting usage analysis statistics, and show the resulted importance ordering to be highly beneficial for the particular scenario of Desktop Search. We then apply the same technique for Spam Detection. We connect people across email social networks based on their email exchanges and induce a reputation metric which nicely isolates malicious members of a community. Similarly, we model several higher level artificial constructs which could negatively manipulate generic link analysis ranking algorithms, and indicate how to remove them in the case of Web page ranking. Finally, we exploit manually created large scale information repositories in order to Personalize Web Search. We investigate two different types of such repositories, namely globally edited ones and individually edited ones. For the former category we project link analysis onto public taxonomies such as the Open Directory and define appropriate similarity measures which order the search output in accordance to each user’s preferences. For the latter one, we propose to expand Web queries by utilizing both text and link analysis on top of Personal Information Repositories. Extensive experiments analyzing both approaches show them to yield significant improvements over regular Google search.
Compositional software reuse with case-based reasoning

Case-based reasoning can be applied to software reuse. The approach presented goes beyond furnishing a library of potentially reusable modules and provides a tool that supports the process of reusing software. It uses case-based reasoning to add flexibility and adaptability to the compositional model of reuse. The authors describe the structure of the case base, emphasizing the case acquisition process during which high level functional information is associated with its components. The system performs advanced data-flow analysis of source code to guide acquisition of the functional specification of a library of software modules. The system then decomposes a user's problem, retrieves matching cases, and adapts and assembles their code. The data-flow of the result is again analyzed to produce test cases which can be used to evaluate the success of case-based reasoning. <<ETX>>
Enhancing User-friendliness of the User Taste Prediction Service Using MapReduce Framework

In our previous work, a user similarity-based contents recommendation service using NFC was proposed for the same goal. This service used a small sample because it used only information of the user who had watched a museum. However, it has been shown that there are some limitations resulting from the difficulty of accurately predicting the user's preference. In order to lift this drawback, this paper introduces a user taste prediction service using big data for improving user-friendliness to a maximum. The proposed service predicts the user's taste using big data such as Twitter and blogs. It is possible to predict the exact user's preference and might recommend more suitable contents to the user's taste because it
Managerial Entrenchment and Debt Specialization

Why do some firms borrow from multiple creditors and employ multiple debt types? This paper shows that entrenched managers exploit the coordination failure and free riding problem amongst multiple creditors. We find that firms with entrenched management have a higher proclivity to employ multiple debt types and have a dispersed debt structure. On the other hand, firms that are well-managed have a tendency to concentrate debt and borrow predominantly from a few creditors. We also find that while bank debt is negatively related to debt specialization, market debt is positively related to debt specialization. Overall, the findings suggest that creditors can discipline managers through debt specialization.
Unequal error protection for digital mobile DS-CDMA radio systems

Unequal error protection (UEP) is investigated for coded direct-sequence code-division multiple access (DS-CDMA) mobile radio systems. The UEP techniques considered include (i) different channel code rates, (ii) different number of chips/symbol, and (iii) different symbol energies. Analytical approximations of the bit error rate (BER) in correlated Rayleigh fading are presented taking carrier phase synchronization into account. Binary PSK modulation with pilot-symbol-aided coherent detection and binary DPSK modulation with differential detection are considered. Both schemes are candidates for an asynchronous up-link, which is the critical link. Technique (i) has advantages in fast fading, otherwise technique (ii) is recommended. Technique (iii), although basically identical with technique (ii), is likely to suffer from side effects. <<ETX>>
ASPIRE: Building a Sentiment Lexicon from Ratings of Social Reviews

Finding semantic orientation and intensity of sentiment phrases and words is a subst antial task of opinion mining. The problem is to give a score to each sentiment phrase, so that different expressions of opinions in different platforms, like social networks, can be processed. There have been several attempts to do this task, and this paper aims to score each sentiment phrase based on its occurrence in reviews with different overall ratings. The idea is that if a sentime nt phrase occurs more in 5-starred reviews than in 3-starred ones, it should be more positive and more intense. The results support this idea. Each sentiment phrase in the corpus is given a score based on a weighted average of their frequency in reviews with different ratings. When a sentiment phrase gets a high score, it means it is more likely to be positive and more likely to be intense. And if a sentiment phrase gets a low score, it means that it is negative. This score sets the threshold of negativity and positivity. The high precision and recall for this feature shows its significance in classifying positive and negative sentiment phrases.
Mental Tasks Classification from Electroencephalogram (EEG) Signal Using Gabor Based Matching Pursuit (MP)

Brain Computer Interface (BCI) Systems have developed for people who are suffer from severe motor disabilities and difficult to communicate with their environment. BCI let them for communication by non muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which are recorded from the human’s brain by mean of electrode. Electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human’s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Mental Tasks Dataset. Independent Component analysis (ICA) method via EEGLab Tools is used for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been performed for both 1-against-1 and 1-against-all methods.
The ADDIE Model: Designing, Evaluating Instructional Coach Effectiveness

The era of accountability throughout the last decade has brought with it a demand for increased capabilities in today’s educators. The elevated need for teachers to engage in the practices of effective instructional design, quality lesson delivery using research-based strategies, and data-enhanced reflection of student results has begun a flurry of interest in a form of professional development known as “instructional coaching. ” Specifically, districts that have begun the improvement journey using the Baldrige Criteria for Performance Excellence may experience an even greater sense of urgency to develop their teachers and leaders as key elements to improving overall organizational results. Recent literature and conversations have demonstrated great strides in identifying the proper role of the instructional coach, as well as key practices coaches can use to support the work of their campuses. Very little information, however, has surfaced in the public education community that describes specific, systematic processes that coaches can use to evaluate the impact and effectiveness of their support. Some of the key questions about instructional coaching that remain for district leaders and funders of these programs include: 1. How does an instructional coach design their work functions to ensure proper alignment to student, teacher, principal and federal needs? 2. How does an instructional coach evaluate his or her effectiveness and impact? 3. How does an instructional coach personally model the usage of a systematic learning process and data-enhanced decision making? By using the Analysis-Design-Develop-Implement-Evaluate (ADDIE) Model of instructional design, the instructional coach can effectively demonstrate a proper return on expectation for each of his or her intended stakeholders.
CROSSLAYER DESIGN·FOR VIDEO STREAMING OVER WIRELESS AD HOC NETWORKS

We propose a cross-layer design framework for supporting deJay­ critical traffic over ad hoc wireless networks and analyze its ben­ efits for video streaming. In this framework, link capacitie s and traffic flows are jointly aIlocated to minimize the congestion ex­ perienced by video packets. The optimal solution, calculated via time sharing among different transmission sch emes, concentrates resources only on active links. Experimental results on a simu­ lated network illustrate the advantages of cross-layer design over another method based on oblivious layers. one path, the cross -layer approach yields a lO-fold gain in supported data rate or equivalently 8.5 dB improvement in PSNR of achievable re­ ceived video quality. Using 3 paths, the gain is 3-fold in rate or 5 dB in video quality. While multi path routing is essential to high data rate in Oblivious-layere d design. cross-layer design achieves efficient resource utilization regardless of the number of routes.
Linking and Mining Event-centric Data in the Semantic Web. (Alignement et Fouille de Données Événementielles dans le Web Sémantique)

Recently, the widespread growth of social media has shifted the way people explore and share information of interest. Part of this evolution is the event landscape increasingly augmented by the user-generated content leading to vast amount of event-centric data. In today’s Web, numerous are the websites that provide facilities to organize and publish events, and to share related thoughts and captured media. However, the information about the events, the social interactions and the representative media are all spread and locked into the sites providing limited event coverage and no interoperability of the description. To fully benefit from the event, users are constrained to monitor different channels some of which suffer from the information overload. The goal of this thesis is to provide a unified environment that provides broad event coverage along with complete description and illustrative media, and to investigate efficient approaches that can benefit content personalization. The major challenge is to face the complex nature of events as multifaceted, ephemeral and social entities. Various distributed platforms host a wide variety of scheduled events along with related media and background knowledge, making the user-contributed Web a primary source of information about any real world happening. Mining in real-time the connections between these heterogeneous and spread data fragments is a key factor to improve data quality and to enable opportunistic discovery of events. Towards this goal, we integrate different sources using Linked Data, so that we can explore the information with the flexibility afforded by the Semantic Web technologies. More precisely, we leverage the wealth of information derived from event-based services, media platforms and social networks to build a Web environment that allows users discovering meaningful connections between events, media and people. On the other hand, users tend to be overwhelmed by the massive amount of information available in event-based websites. This fact requires valuable personalization solutions that cope with the information overload and help organize data. In particular, recommendation and community detection are two promising solutions that have been widely investigated in research. Yet their applications in event domain are still elusive. Thus, we propose a hybrid recommender system that capitalizes on ontology-based event representation along with the collaborative filtering techniques. Second, we propose an approach based on semantic modularity maximization to discover overlapping semantic communities in event-based social network.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELS

Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Na・・ve Bayes and RNTN that were published in other works using English datasets.
Christopher Alexander's pattern language

In a series of books published between 1964 and 1987 (C. Alexander, 1964; 1987; C. Alexander et al., 1975; 1977), Christopher Alexander, an urban planner and architect, has inspired object oriented programmers with his idea of a pattern language, which originally catalogued solutions to common problems faced by any community or individual creating livable structures such as a town or a house. His approach might also help technical communicators polish and perfect our own standard rhetorical structures (such as the procedure, user guide, or reference), viewed as common ways of answering frequent, if virtual questions from our users. Alexander's way of describing age-old patterns such as neighbourhoods, streets, paths, and homes may give us a model for creating our own set of patterns in technical communication, whether or not we adopt some of the eager elaborations offered by folks in the object oriented design world.
An Accurate and Robust Flexible Guidance System for Indoor Industrial Environments

This work presents the development of a flexible industrial guidance system used to guide Automated Guided Vehicles (AGVs) in indoor industrial environments. Typically, wireless guidance systems are composed of path-tracking and localization methods linked to follow a certain route. This paper focuses on the localization approach that permits industrial vehicles to operate indoors with the grade of accuracy, repeatability and reliability required by industrial applications. A key point is that, apart from accuracy, the position estimates should be performed at a high sample rate in order to permit the path tracker to follow the route properly. Robustness of absolute positioning is also crucial in industrial applications. An Extended Kalman Filter (EKF) is adopted to fuse the information provided by a laser navigation system and odometry. The effectiveness of the development is tested using a custom modified commercial industrial vehicle operating in an industrial setting.