Miracle-dz/UniFAR-FTUs huggingface dataset:
CLOUD-BASED SOLUTION FOR NATIONWIDE POWER LINE MAPPING

Abstract. Automatic tools for power line mapping and monitoring are increasingly required by modern societies. Since traditional methods, like ground-based onsite inspections, are very labour- and time-intensive, the use of Geomatics techniques is becoming the most promising solution. However, there is a need for an all-in-one solution that allows the entire 3D mapping pipeline in a nationwide data context. The aim of this paper is to introduce a novel cloud-based solution for nationwide power line mapping. The innovative aspects of the system are threefold. First, to exploit image-based 3D reconstruction algorithms to derive dense point clouds over power line corridors, thus demonstrating the potential of photogrammetry as a promising alternative to costly LiDAR surveys. Second, to supply an all-in-one web-based pipeline that automatically manages all steps of power line mapping, from 3D data generation to clearance anomaly detection. Finally, to exploit cloud-computing technology, to handle massive input data. First tests show promising results for (i) 3D image-based reconstruction, (ii) point cloud classification and (iii) anomaly detection.
Landsat-MODIS image fusion and object-based image analysis for observing flood inundation in a heterogeneous vegetated scene

Typhoon flooding normally occurs suddenly with short duration, and the thick cloud cover limits the ability of one single satellite to timely capture the inundation extent. Landsat satellite data with a spatial resolution of 30 m is spatially applicable for flooding research; however, its 16-day observation frequency is typically insufficient to observe short-term typhoon inundation. Alternatively, despite the coarse spatial resolutions, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor provide daily data, which is well suited for flood-related investigations. Accordingly, the idea of combining these two sources of data to generate a high spatial and temporal image would be useful. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was applied to generate cloud-free Landsat/MODIS synthesized data with a spatial resolution of 30 m for the delineation of the inundated areas during a flood event. This approach produces a Landsat-scale image for fine-scale flood mapping of areas where there are no observed cloud-free Landsat or similar resolution satellite images. The fusion model was implemented on atmospherically corrected surface reflectance, and the resultant reflectance values were validated by comparing with observed Landsat reflectance before further data interpretation. The blending results indicate that the synthetic Landsat-scaled image is highly correlated with Landsat surface reflectance, captured a day after the synthetic image acquisition date, over cloud-free areas. For image interpretation, an object-based image analysis (OBIA) approach with an optimal-scale segmentation and the support vector machine (SVM) classifier was applied for flood classification. The flood mapping result was validated by comparing with a reference flood map derived from an observed Landsat image. This study demonstrates that the techniques of image fusion and object-based image analysis are useful for observing flood inundation in the heterogeneous vegetated area.
BranchGAN: Unsupervised Mutual Image-to-Image Transfer With A Single Encoder and Dual Decoders

Image-to-image translation is a fundamental task for a wide range of applications, such as image style transfer, video effect generation, cross-domain retrieval, etc. Due to the limited number of labeled data, complex scenes, abstract semantics and various involved domains, image translation remains a challenging task. Compared to the supervised approaches for image translation that need a large collection of paired images for training, the unsupervised methods can significantly reduce the training cost. In this paper, an unsupervised end-to-end generative adversarial network is proposed, named BranchGAN, for mutual image-to-image transfer between two domains. A structure with one single encoder and dual decoders is novelly proposed to capture the cross-domain distributions and generate the images in both domains. Three factors, that is, pixel-level overall style, region semantics, and domain distinguishability are comprehensively considered to constrain the training process of the proposed model, corresponding to reconstruction loss, encoding loss, and adversarial loss, respectively. Experiments conducted on three benchmark datasets demonstrate the effectiveness of the proposed method that outperforms the unsupervised state-of-the-art approaches and has the competitive performance as the supervised method.
Protected and Flexible Multi-Keyword Score Search model over Encoded Cloud Data

Disbursed computing is contemporary innovation as any other processing model in wide variety of business organization areas. Expansive portions of sizeable scale divisions are startingto pass the information on to the cloud situation. With the benefit of functionality as an management numerous undertakings are shifting their worthwhile facts to the cloud, since it costs much less, correctly adaptable and may be gotten to from anywhere on every occasion. advanced dynamic multi-watchword positioning inquiry plot with fine key by the use of encoded cloud records that all the whilst underpins dynamic refresh responsibilities as erasing and embeddings information. eager profundity first experiment calculation is accommodated effectiveness multi catchphrases on spot and listing shape. Cryptography is one of the building up believe fashions. on hand protection is a cryptographic technique to provide protection. In variety of scientists have been taking a shot at growing protection and effective on hand encryptiontypes. We take new possible cryptographic techniques relying on statistics structures like CRSA and BTree to improve the size of safety. We advise new multi-watchword are in search of for inquiry over encoded cloud records in recovering nice okay scored files.The vector area model and TFIDF display are applied to carry collectively document and question age. This paper facilities spherical multi catchphrase are looking for dependent on positioning over an encoded cloud facts. The pursuit utilizes the element of evaluation and internal item closeness coordinating. We advocate to assist the super ok Multi-full-content material search for safety and execution examination show off that the proposed model guarantees a immoderate health and not unusual sense and dynamic refresh activities, for example, erasing and such as files. The exploratory results show that the overhead in calculation and correspondence is low.
Fast Robot Learning using Prospection and Experimental Knowledge : A Cognitive Approach with Narrative-Enabled Episodic Memories and Symbolic Knowledge

Humans employ data-efficient learning mechanisms to obtain new skills and to improve the existing ones. Robots have already replaced humans in terms of labor in performing repetitive and dangerous tasks in structured environments like factories. On the other hand, developing completely autonomous robotic systems for unstructured environments, like a household, is still a challenge for roboticists due to the infeasibility of programming every possible case and today’s data-hungry machine learning approaches. In order to assist humans in such environments, I believe that robots should be able to gain and improve skills using human-like learning mechanisms regularly. For this purpose, I present a cognition-enabled fast learning framework in this dissertation which makes use of symbolic knowledge, episodic memories, and cloud robotics services along with a cutting-edge deep imitation learning methodology in order to reduce the dependency on big experiment data. Using this framework, robots can (1) imitate tasks demonstrated by a human demonstrator in virtual-reality, (2) adapt the actions of itself and others to new conditions, and (3) prospect which task parameters lead to the desired goal. To validate these abilities, I have provided some experimental results. These experiments were conducted with four different service robots in various kitchen environments. The human demonstrations were recorded inside a game-with-a-purpose using virtual-reality equipment. Such a setup enables roboticists to crowdsource their training data by eliminating the requirement of being in the same environment with the robot.
Ni-assisted Fabrication of GaN Based Surface Nano-textured Light Emitting Diodes for Improved Light Output Power

Light enhancement of GaN based light emitting diodes (LEDs) have been investigated by texturing the top p-GaN surface. Nano-textured LEDs have been fabricated using self-assembled Ni nano mask during dry etching process. Experimental results were further compared with simulation data. Three types of LEDs were fabricated: Conventional (planar LED), Surface nano-porous (porous LED) and Surface nano-cluster (cluster LED). Compared to planar LED there were about 100% and 54% enhancement of light output power for porous and cluster LED respectively at an injection current of 20 mA. Moreover, simulation result showed consistency with experimental result. The increased probability of light scattering at the nano-textured GaN-air interface is the major reason for increasing the light extraction efficiency.
Applied Research on Improved Dijkstra Algorithm to the Optimal Vehicle Path Selection in Emergency Command

In view of serious traffic jam, how to make the rescue vehicle arrive at the scene in the shortest time has been one of key problems to the success of emergency handling. This paper puts forward a kind of improved Dijkstra algorithm applied to optimal vehicle path selection for emergency command, which builds a relationship between node and adjacent edge to improve the searching rate and optimize the search area. The simulation results show the improved algorithm proposed has better effects in query speed, the storage space and the selection of the optimal vehicle path.
Smooth trajectory planning for anthropomorphic industrial robots employed in continuous processes

The first robotic arm was employed in an automotive assembly line in the 1961. Since then, the industrial robots became progressively more widespread and now they can be commonly found even in small manufacturing enterprises. The trajectory planning is an essential aspect of the motion programming for a robotic arm. In collaboration with Comau, it was decided to focus on the study of a particular type of process, namely continuous process. Continuous processes are tasks that require a continuous motion of the tool in terms of positions and velocity profile and are very common in industrial manufacturing. Some examples are arc welding, spray painting and adhesive sealing. A smooth execution and a precise control of the Cartesian speed is essential to guarantee a good quality of the final product. The objective of the thesis is to propose a novel trajectory planner for this kind of processes. After a brief overview of the techniques currently employed in the trajectory planning for some common industrial processes, part of the efforts are dedicated to the geometrical analysis of the trajectory itself. This is important to find the critical parts of a path that may be difficult for the machine to follow with high precision. In this parts a lower speed can be imposed in order to obtain smoother results. This kind of work is generally done manually by trial and error, but it requires a lot of work and it can not achieve high precisions. Instead a method is proposed to simulate and automatize this process. After that, the actual trajectory planner and its implementation is described. It is based on the spline interpolation and different techniques are analysed in order to study their performance in terms of quality of path obtained and velocity profile. The planner is proposed in two versions, one that considers all the points of the trajectory at once an another that works only on a small subset at a time. The second version is proposed having in mind a possible on-line implementation, which limits the number of points that can be used to plan the trajectory. The proposed methods are implemented in MatLab and have been tested on real processes proposed by Comau. The trajectory computed with the novel planner have been properly simulated and then fed to a real robot bypassing the on-board planner. The results obtained have been analysed and verified in order to validate the approach. The work done until now do not fully resolves the complex problem of trajectory planning for continuous processes, but the results are encouraging and they can be used as a basis for a future development and implementation.
Dependentzia-ereduan oinarritutako baliabide sintaktikoak: zuhaitz-bankua eta gramatika konputazionala

La construccion y obtencion de un Treebank o banco de arboles sintacticos es un paso muy importante dentro de las aplicaciones en el area del Procesamiento del Lenguaje Natural, ya que constituye un recurso indispensable para el desarrollo de herramientas. Asi, en esta tesis se han descrito los criterios de anotacion seguidos en la construccion del banco de arboles sintacticos del euskera Eus3LB (Palomar et al. 2004). Despues de examinar los dos principales formalismos de anotacion sintactica de corpus, por una parte la anotacion basada en constituyentes (o parentizacion) y por otra, la basada en dependencias, se ha optado por el formalismo de la Gramatica de Dependencia (Tesniere, 1959), por ser el mas adecuado para una lengua como el euskera de orden libre en la oracion. Las dependencias representan las relaciones de nucleo-modificador entre los elementos terminales de las oraciones; es decir, entre las hojas de los arboles o palabras de las oraciones. El desarrollo de este Treebank ha permitido la elaboracion de una gramatica computacional de dependencias que lleva a cabo un analisis sintactico total del euskera. Esta gramatica se incluye en el mismo marco que la gramatica de restricciones, Constraint Grammar (Karlsson et al.;Tapanainen, 1996), ya que muchas de sus caracteristicas se derivan de esta ultima. El trabajo realizado supone la primera formalizacion de las estructuras sintacticas representativas del euskera basada en la Gramatica de Dependencia, y el paso de un analisis sintactico parcial a un analisis sintactico total en el tratamiento automatico de textos reales, realizado mediante el analizador sintactico o parser.
Design of fuzzy classification system using genetic algorithms

This paper proposes a GA-based method to construct an appropriate fuzzy classification system to maximize the number of correctly classified patterns and minimize the number of fuzzy rules. In this method, a fuzzy classification system is coded as an individual in the GA. A fitness function is defined such that it can guide the search procedure to select an appropriate fuzzy classification system to maximize the number of correctly classified patterns and minimize the number of fuzzy rules. Finally, a two-class classification problem is utilized to illustrate the efficiency of the proposed method.
The interaction effects of consumer susceptibility to interpersonal influence and type of word-of-mouth on Chinese consumers’ restaurant choices

ABSTRACT This study contributes to the understanding of how the interpersonal influence impacts on consumer behaviors. By leveraging the dual-process theory, the current study demonstrates the interaction effects between individual levels of Consumer Susceptibility to Interpersonal Influence (CSII) and different types of WOM on Chinese consumers’ restaurant consumption choices. Specifically, facing the informational cue of online ratings by anonymous reviewers, Chinese consumers show similar level of consumption intention regardless of their individual levels of CSII. On the other hand, when the WOM comes from the normative cue of friend referrals, high CSII Chinese consumers show significantly higher levels of consumption intention than their low CSII counterparts. Furthermore, preferred identification induced by the normative cue leads to the high CSII Chinese consumers’ restaurant choices. The theoretical and managerial implicates are discussed.
Energy Efficient and High–Performance FIR Filter Design on Spartan–6 FPGA

In this paper, we are going to design the energy efficient Gaussian low pass FIR filter on spartan-6 FPGA. To make an energy efficient filter, we have used different methods in this paper like capacitance scaling, frequency scaling, and then we analysed the demand of power by Gaussian low pass FIR filter. The frequency range which is used in this paper is 1 GHz, 2GHz, 2.5GHz, 5 GHz, 10 GHz and the range of capacitance which we have used un this paper is 5pF, 10pF, 25pF, 40pF and 50 pF. An FIR filter always remnants in linear phase with the help of symmetric co-efficient and this is the very useful feature of the FIR filter for phase sensitive application like data communications etc. At present there are many different methods for communications and networking. So, in this paper, we have designed an energy efficient FIR filter and that design will faster than traditional design.
Comparison of different Ant based techniques for identification of shortest path in Distributed Network

A Distributed network is one in which the data is distributed and the data from source to destination can be transferred through several nodes. When huge amount of packets are transferred through particular node congestion may occur which may result loss of packets and bandwidth also can't utilize. Hence the shortest path is chosen and routing is done dynamically so that the node can't suffer from congestion. Ant based routing techniques is an efficient one in which routing is done on the behavior of the ants and a shortest path is selected such that the packets can be send quickly and bandwidth also utilizes. Here in this paper we compare different ant based techniques for the shortest path selection from source to destination in a distributed network. Here on the basis of different Ant based techniques such as Max-Min, Rank based and Fuzzy rule based ant technique an efficient algorithm of ant technique is implemented which performs better as compared to other existing ant based techniques.
Domesticating the Beast: A “Resource Profile” Framework of Power Relations in Nonprofit–Business Collaboration

Researchers have addressed the implications of power imbalance for nonprofits engaging in collaborations with businesses. Yet as nonprofit–business collaboration intensifies, nonprofit managers’ perceptions of power asymmetry in these relationships remain scantly studied. We argue that investigating these perceptions can sharpen the understanding of determinants and processes of power relations from a nonprofit perspective. To do so, we studied nonprofit–business collaboration in a network of international cooperation nongovernmental organizations (NGOs). Based on our findings, we designed a nonprofit-centric “resource profile” framework of power relations in cross-sector collaborations. This framework provides an empirically grounded tool to inform nonprofit managers’ decision making as they engage in collaborations with businesses. Based on this framework, we elaborate a set of theoretical propositions to integrate existing knowledge and guide further nonprofit-centric research on power dynamics in cross-sector collaboration.
A comprehensive memory modeling tool for design and analysis of future memory hierarchies

This thesis describes CACTI-D, a memory modeling modeling tool that supports both SRAM and DRAM circuits and technologies. With CACTI-D it is possible to project area, access time, cycle time, dynamic read and write energies per access, and standby leakage power of memories and caches for technology nodes between 90nm and 32nm. CACTI-D supports SRAM, logic process based DRAM (LP-DRAM) and commodity DRAM (COMM-DRAM) technologies. CACTI-D also supports the modeling of main memory DRAM chips. Thus with CACTI-D, modeling of the complete memory hierarchy with consistent models all the way from SRAM based L1 caches through main memory DRAMs on DIMMs becomes possible. CACTI-D is based on the well-known memory and cache modeling tool CACTI. We borrow key circuit models from CACTI but revamp CACTI-D from the ground up with a complete rewrite of the source code. We incorporate a new technology foundation into CACTI-D with device data based on the ITRS roadmap. We incorporate device data for different ITRS device types such as high performance (HP), low standby power (LSTP) and low operating power (LOP). We also incorporate data for interconnect technology based on well-documented models and data from the literature. We have validated CACTI-D by comparing its projections against real designs. For SRAM validation, we compare against two prominent 90nm and 65nm SRAM caches and for DRAM validation, we compare against a 78nm DDR3 DRAM chip. Taking into account the extremely generic nature of CACTI-D, there is good agreement between the projections produced by CACTI-D and the published data. We illustrate the potential applicability of CACTI-D in the design and analysis of future memory hierarchies by carrying out a last level cache study for a multicore multithreaded architecture at the 32nm technology node. In this study we use CACTI-D to model all components of the memory hierarchy including L1, L2, last level SRAM, LPDRAM or COMM-DRAM based L3 caches, and main memory DRAM chips. We carry out architectural simulation using benchmarks with large data sets and present results of their execution time, breakdown of power in the memory hierarchy, and system energy-delay product for the different system configurations. We find that COMM-DRAM technology is most attractive for stacked last level caches, with significantly lower energy-delay products.
CUA: a Flexible and Comprehensive Codon Usage Analyzer

Codon usage bias (CUB) is pervasive in genomes. Studying its patterns and causes is fundamental for understanding genome evolution. Rapidly emerging large-scale RNA and DNA sequences make studying CUB in many species feasible. Existing software however is limited in incorporating the new data resources. Therefore, I release the software CUA which can compute all popular CUB metrics, including CAI, tAI, Fop, ENC. More importantly, CUA allows users to incorporate user-specific data, such as tRNA abundance and highly expressed genes from considered tissues; this flexibility enables computing CUB metrics for any species with improved accuracy. In sum, CUA eases codon usage studies and establishes a platform for incorporating new metrics in future. CUA is available at http://search.cpan. org/dist/Bio-CUA/ with help documentation and tutorial.