Miracle-dz/UniFAR-FTUs huggingface dataset:
BUILDING EXTRACTION AND RECONSTRUCTION FROM LIDAR DATA

This paper presents an approach for building extraction and reconstruction from high quality terrain surface, such as LIDAR data generated surface. The approach takes terrain surface data as input and goes through edge detection, edge classification, building points extraction, TIN model generation, and building reconstruction to extract and reconstruct buildings and building related information. For building detection, it detects edges from the surface data and classifies edges to distinguish building edges from other edges based on their geometry and shapes, including orthogonality, parallelism, circularity and symmetry. The classified building edges are then used as boundaries to extract building points and TIN models are generated with the extracted points. Each building has its own TIN model and its surfaces are derived from the TIN model. The test results demonstrate that the approach is capable to extract and reconstruct 3-D building models from high quality terrain surface. The paper presents the concept, algorithms and processes/procedures of the approach and discusses its advantages and limitations. The paper also shows experimental results and discusses the performance/effects of the geometric and shape constraints utilized in the experiments. At the end, the paper concludes with some thoughts for future development.
A pilot study of dual 7-axis -arm robot in small part assembly application

A dual 7-axis-arm bionic robot with intrinsic safety has been studied in a close-to-real consumer product assembly pilot case for its design concept. The design aims to have the robot to be easily interchanged with a human coworker in agile production scenarios. Within this pilot study, two-hand cooperation for small part assembly, human-robot safety, installation space, productivity, and robot programming effort are evaluated and discussed. The dual arm robot is competent for assembly tasks requiring two-hand cooperation. Its intrinsic safety feature allows side-by-side human-robot collaboration. It has similar size and working range as human being which can minimize production disruption when interchanging with a human coworker. However, its programming method still needs significant improvement to meet fast human-robot interchanging requirement.
Investigating an aggressive mode for drowsy cache cells

Applying an aggressive policy to a traditional drowsy cache block management is investigated, where each cache line is allowed to remain in low leakage, low voltage state as long as it is idle. Only for write and read operations, the normal high voltage is applied to the cache line. There is no need for extra cycles or extra control signals to awake the drowsy cache cell, before accessing it. Thus, the performance penalty associated with traditional drowsy caches is reduced. Furthermore, there is no need for complex control circuitry to estimate the best time when to put the cache cells into drowsy mode. Best of all, the aggressive drowsy mode can reduce the leakage power consumption more than the traditional drowsy according to the cache access rate and operating frequency.
Gas and stars in compact (young) radio sources

Gas can be used to trace the formation and evolution of galaxies as well as the impact that the nuclear activity has on the surrounding medium. For nearby compact radio sources, we have used observations of neutral hydrogen - that we detected in emission distributed over very large scales - combined with the study of the stellar population and deep optical images to investigate the history of the formation of their host galaxy and the triggering of the activity. For more distant and more powerful compact radio sources, we have used optical spectra and H I - in absorption - to investigate the presence of fast outflows that support the idea that compact radio sources are young radio loud AGN observed during the early stages of their evolution and currently shredding their natal cocoons through extreme circumnuclear outflows. We will review the most recent results obtained from these projects. (C) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Process-Based Assistance Method for Learner Academic Achievement.

How can we learn to use properly business software, digital environments, games or intelligent tutoring systems (ITS) ? Mainly, we assume that the new user will learn by doing. But what about the efficiency of such a method? Our approach proposes an answer by introducing on-line coaching. In learning process, learners may need guidance to help them in their academic achievement. In this paper, we introduce a process-based assistance method to provide this help. Our method proposes to build a model using process mining upon the observations collected during previous users’ experiences with the considered application. It represents the steps chaining and their impacts on the states of the overall process and is used to recommend the most suitable step to guide the current user or learner. We implemented our coaching approach in the La Rochelle University Institute of Technology jury decision system to show its relevance.
Supporting separation of concerns in requirements artifacts

In order to improve requirements specifications, this paper presents an approach to support the separation of concerns in requirements artifacts. Our goal is to be able to represent non-functional concerns apart from the requirements they affect. We provide means to specify the composition among requirements in a noninvasive way. An Internet Banking System example is used to illustrate our proposal.
Hydrophobicity at Small and Large Length Scales

We develop a unified and generally applicable theory of solvation of small and large apolar species in water. In the former, hydrogen bonding of water is hindered yet persists near the solutes. In the latter, hydrogen bonding is depleted, leading to drying of extended apolar surfaces, large forces of attraction, and hysteresis on mesoscopic length scales. The crossover occurs on nanometer length scales, when the local concentration of apolar units is sufficiently high, or when an apolar surface is sufficiently large. Our theory for the crossover has implications concerning the stability of protein assemblies and protein folding.
Ultrametric Turing Machines with Limited Reversal Complexity ?

String theory in physics and molecular biology use p-adic numbers as a tool to describe properties of microworld in a more adequate way. We consider a notion of automata and Turing machines using p-adic numbers to describe random branching of the process of computation. We prove that Turing machines of this type can have advantages in reversal complexity over deterministic and probabilistic Turing machines.
New multi‐standard dual‐wideband and quad‐wideband asymmetric step impedance resonator filters with wide stop band restriction

New multi‐standard wide band filters with compact sizes are designed for wireless communication devices. The proposed structures realize dual‐wideband and quad‐wideband characteristics by using a new skew‐symmetrical coupled pair of asymmetric stepped impedance resonators, combined with other structures. The first and second dual‐wideband filters realize fractional bandwidths (FBW) of 43.2%/31.9% at the central frequencies (CF) of 1.875/1.63 GHz, and second bandwidths of 580 MHz/1.75 GHz at CF of 5.52/4.46 GHz, respectively. The proposed quad‐band filter realizes its first/second/third/fourth pass bands at CF 2.13/5.25/7.685/9.31 GHz with FBW of 46.0%/11.4%/4.6% and 5.4%, respectively. The wide pass bands are attributed to the mutual coupling of the modified ASIR resonators and their bandwidths are controllable by tuning relative parameters while the wide stop band performance is optimized by the novel interdigital cross coupled line structure and parallel uncoupled microstrip line structure. Moreover, the quad band is generated by introducing the novel defected rectangle structure. These multi‐standard filters are simulated, fabricated and measured, and measured results agree well with both simulated results and theory predictions. The good in‐band and out‐of‐band performances, the miniaturized sizes and simple structures of the proposed filters make them very promising for applications in future multi‐standard wireless communication.
Decision Templates with Gradient based Features for Farsi Handwritten Word Recognition

This paper proposes a new classification method for Farsi handwritten word recognition using a scale invariant gradient based features. The extracted feature vectors classified using MLPs. Outputs of MLPs are then combined by Decision Templates. The experiments were achieved using the Iranshahr dataset. This dataset consist of 780 samples of 30 city names of Iran which 600 samples for train and 180 samples for test was used. A set of experiments were conducted to compare Decision Templates with some combination rules. Results show that template based fusion method is superior to the other schemes.
School principals’ leadership types and student achievement in the Italian context: Empirical results from a three-step latent class analysis

This study investigates the existence of various leadership types in a sample of lower secondary school principals across Italy (n = 1073). Information is obtained using a questionnaire about instructional practices and leadership perceptions administered by the National Evaluation Committee for Education (INVALSI). Employing a latent class analysis (LCA) for the specification of the educational production function (EPF), we identify three subgroups of school leaders: educative leaders (49% of the total sample), who support school improvement that utilises teachers’ skills; leaders who teach (35%), who are actively present in the classroom; and transactional leaders (16%), who support the school and balance various viewpoints. We then analyse the data to ascertain if some principals’ individual characteristics and school context factors are statistically correlated with the probability of having certain leadership attitudes. Finally, we provide evidence that schools with ‘leaders who teach’ report lower academic test scores; this third step is novel as few previous studies have attempted to link leadership styles and test scores. However, the difference in test scores across groups disappears when the geographical factor is accounted for, pointing to the importance of contextual factors in that they affect both leadership types and educational outcomes.
Word Co-occurrence Matrix and Context Dependent Class in LSA based Language Model for Speech Recognition

A data sparseness problem for modeling a language often occurs in many language models (LMs). This problem is caused by the insufficiency of training data, which in turn, makes the infrequent words have unreliable probability. Mapping from words into classes gives the infrequent words more confident probability, because they can rely on other more frequent words in the same class. In this research, we investigates a class LM based on a latent semantic analysis (LSA). A word-document matrix is commonly used to represent a collection of text (corpus) in LSA framework. This matrix tells how many times a word occurs in a certain document. In other words, this matrix ignores the word order in the sentence. We propose several word co-occurrence matrices that keep the word order. By applying LSA to these matrices, words in the vocabulary are projected to a continues vector space according to their position in the sentences. To support this matrices, we also define a context dependent class (CDC) LM. Unlike traditional class LM, CDC LM distinguishes classes according to their context in the sentences. Experiments on Wall Street Journal (WSJ) corpus show that the word co-occurrence matrix works 3.62%-12.72 better than worddocument matrix. Furthermore, the CDC improves the performance and achieves better perplexity than the traditional class LM based on LSA. When the model is linearly interpolated with the word-based trigram, it gives improvements about 2.01% for trigram model and 9.47% for fourgram model on relative perplexity against a standard word-based trigram LM. Keywords— Latent semantic analysis, Language model, Word cooccurrence matrix, Context dependent class
Frequency Domain Equalization using EHW based Neural Network

This paper describes an evolvable hardware (EHW) system for modelling a adaptive neural network. We have developed an adaptive RBF neural network which can be used to model plenty of systems. This approach is particularly suited to applications requiring ability to cope with time-varying problems and realtime timing constraints. FPGA as a reconfigurable hardware is used for hardware implementation purpose of EHW_based NN. The GA is used to determine the characteristic of neurons in RBF and the adaptation of weights is done with a steepest descend method. Simulation results for an adaptive frequency domain equalizer in digital mobile communication are given. Our system shows better results comparing to time domain one.
Waves around almost periodic arrangements of scatterers : Analysis of positional disorder

Much is known about the propagation of waves through periodic arrangements of identical scatterers, such as through a photonic crystal. Here, we consider a simple realization: scalar waves through a regular two-dimensional array of identical small circles. We are interested in the effect of random disorder: the circles remain identical, but their centres are given small random displacements. We derive asymptotic approximations that can be used to quantify the effect of positional disorder. Extension to more complicated problems seems feasible and is expected. Copyright © 2010 John Wiley & Sons, Ltd.
Helping Students Develop Coherence in Writing

ideas in a text to create meaning for the readers. Although coherence is crucial to effective writing, it is often considered an abstract, elusive, and controversial concept that is difficult to teach and difficult to learn (Connor 1990; Connor and Johns 1990). Research has found that in their writing, ESL/EFL students focus almost exclusively on the word and sentence levels rather than the level of the whole discourse, that is, textual coherence (Bamberg 1984; Ferris and Hedgecock 1998). The majority of ESL/EFL students feel that " their only sense of security comes from what they have learned about grammar " (Leki 1996:34) and that grammar is the only tool they can use in writing English essays (Silva 1992). It is important that students be taught alternative strategies to improve their writing. A pedagogical focus on coherence can shift students' attention from sentence-level grammar to discourse features such as textual structuring and propositional unity, which are crucial to creating meaning in texts. Indeed, helping students improve the coherence of their writing ought to be a significant aspect of L2 writing instruction.
Query expansion based on term similarity tree model

We propose a new method for query expansion called "term similarity tree model" (TSTM). Term similarity tree is built to represent and estimate similarities between terms. Based on TSTM, we use similarity restriction and overlay restriction to implement query expansion. This method can cluster terms automatically, make the process of query expansion more flexible and controllable, and control noise effectively. In addition, the parameters of TSTM can be adjusted easily to meet the requirements of different types of queries. TREC data is used to test the method. The experiments show that TSTM method outperforms the existing methods in query expansion, such as WordNet-based method, local cooccurrence method and latent semantic indexing (LSI-based) method.
Robust function approximation based on fuzzy sets and rough sets

The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. Fuzzy set and the rough set theories turned out to be particularly adequate for the analysis of various types of data, especially, when dealing with inexact, uncertain or vague knowledge. In this paper, we propose an novel algorithm, which termed as Rough-Fuzzy C-regression model (RFCRM), that define fuzzy subspaces in a fuzzy regression manner and also include Rough-set theory for TSK modeling with robust capability against outliers.
Vaccination with Recombinant RNA Replicon Particles Protects Chickens from H5N1 Highly Pathogenic Avian Influenza Virus

Highly pathogenic avian influenza viruses (HPAIV) of subtype H5N1 not only cause a devastating disease in domestic chickens and turkeys but also pose a continuous threat to public health. In some countries, H5N1 viruses continue to circulate and evolve into new clades and subclades. The rapid evolution of these viruses represents a problem for virus diagnosis and control. In this work, recombinant vesicular stomatitis virus (VSV) vectors expressing HA of subtype H5 were generated. To comply with biosafety issues the G gene was deleted from the VSV genome. The resulting vaccine vector VSV*ΔG(HA) was propagated on helper cells providing the VSV G protein in trans. Vaccination of chickens with a single intramuscular dose of 2×108 infectious replicon particles without adjuvant conferred complete protection from lethal H5N1 infection. Subsequent application of the same vaccine strongly boosted the humoral immune response and completely prevented shedding of challenge virus and transmission to sentinel birds. The vaccine allowed serological differentiation of infected from vaccinated animals (DIVA) by employing a commercially available ELISA. Immunized chickens produced antibodies with neutralizing activity against multiple H5 viruses representing clades 1, 2.2, 2.5, and low-pathogenic avian influenza viruses (classical clade). Studies using chimeric H1/H5 hemagglutinins showed that the neutralizing activity was predominantly directed against the globular head domain. In summary, these results suggest that VSV replicon particles are safe and potent DIVA vaccines that may help to control avian influenza viruses in domestic poultry.
Vesicular expression and release of ATP from dopaminergic neurons of the mouse retina and midbrain

Vesicular nucleotide transporter (VNUT) is required for active accumulation of adenosine tri-phosphate (ATP) into vesicles for purinergic neurotransmission, however, the cell types that express VNUT in the central nervous system remain unknown. This study characterized VNUT expression within the mammalian retina and brain and assessed a possible functional role in purinergic signaling. Two native isoforms of VNUT were detected in mouse retina and brain based on RNA transcript and protein analysis. Using immunohistochemistry, VNUT was found to co-localize with tyrosine hydroxylase (TH) positive, dopaminergic (DA) neurons of the substantia nigra and ventral tegmental area, however, VNUT expression in extranigral non-DA neurons was also observed. In the retina, VNUT labeling was found to co-localize solely with TH-positive DA-cells. In the outer retina, VNUT-positive interplexiform cell processes were in close contact with horizontal cells and cone photoreceptor terminals, which are known to express P2 purinergic-receptors. In order to assess function, dissociated retinal neurons were loaded with fluorescent ATP markers (Quinacrine or Mant-ATP) and the DA marker FFN102, co-labeled with a VNUT antibody and imaged in real time. Fluorescent ATP markers and FFN102 puncta were found to co-localize in VNUT positive neurons and upon stimulation with high potassium, ATP marker fluorescence at the cell membrane was reduced. This response was blocked in the presence of cadmium. These data suggest DA neurons co-release ATP via calcium dependent exocytosis and in the retina this may modulate the visual response by activating purine receptors on closely associated neurons.
Multiple Device Login Attacks and Countermeasures of Mobile VoIP Apps on Android

Because Android apps are structurally easy to decompile, attackers may, using reverse engineering, modify the source code or inject some code of his choice. If a mobile messenger app were to be attacked in the same nature, the attacker can bypass the authentication mechanism applied on the app to not only view past conversations and Time line records of a particular user but to also receive and view real time conversations. In addition, there are widespread attacks on the apps’ weak points depending on the app such as wiretapping VoIP and other voice messages or illegally use of pay items. Therefore, in this paper, we analyze the security weak points of app A and app B, two representative Android message apps, and propose effective solutions.
A methodology for designing efficient on-chip interconnects on well-behaved communication patterns

As the level of chip integration continues to advance at a fast pace, the desire for efficient interconnects - whether on-chip or off-chip - is rapidly increasing. Traditional interconnects like buses, point-to-point wires and regular topologies may suffer from poor resource sharing in the time and space domains, leading to high contention or low resource utilization. In this paper, we propose a design methodology for constructing networks for special-purpose computer systems with well-behaved (known) communication characteristics. A temporal and spatial model is proposed to define the sufficient condition for contention-free communication. Based upon this model, a design methodology using a recursive bisection technique is applied to systematically partition a parallel system such that the required number of links and switches is minimized while achieving low contention. Results show that the design methodology can generate more optimized on-chip networks with up to 60% fewer resources than meshes or tori while providing blocking performance closer to that of a fully connected crossbar.
An Energy Efﬁcient Protocol for Wireless Sensor Networks

—Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These wireless networks require robust routing protocols that are energy efﬁcient and provide low latency. Starting from the basic idea of classical LEACH (Low Energy Adaptive Clustering Hierarchy), in this paper we introduce some innovations in the algorithm giving origin to LEACH-B. LEACH-B presents a new strategy of cluster heads election and cluster formation. Our results show that LEACH-B optimizes system lifetime in a large range of situations and applications.