Miracle-dz/UniFAR-FTUs huggingface dataset:
A three-dimensional shape measurement method: structured light space-time stereo

The three-dimensional shape measurement has been widely used in a lot of applications such as traffic, entertainment, architecture design, manufacturing and archeology. The paper simplifies the principle of structured-light triangulation with the constraints of light-plane and takes the radial lens distortion during CCD imaging into account, which is able to improve the system accuracy. In order to release the limit of spatial and temporal stereo in the structured light system and improve the process rate and accuracy, the system utilizes the space-time stripe boundary coded patterns. ICP (Iterative Closest Points) is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is given, or the relative motion is small. According to the features of data from structured light acquisition system, the paper utilizes the advanced matching algorithm, which is based on projection. This algorithm is easer and more accurate than conventional ICP.
High-Trusted-Software-Oriented Automatic Testing for Integer Overflow Bugs: High-Trusted-Software-Oriented Automatic Testing for Integer Overflow Bugs

This paper presents an automatic testing method, DAIDT (dynamic automatic integer-overflow detection and testing), for finding integer overflow fatal bugs in binary code. DAIDT can thoroughly test the binary code and automatically find unknown integer overflow bugs without necessarily knowing their symbol tables. It is formally proved in this paper that DAIDT can theoretically detect all the high-risk integer overflow bugs with no false positives and no false negatives. In additional, any bugs find by DAIDT can be replayed. To demonstrate the effectiveness of this theory, IntHunter has been implemented. It has found 4 new high risk integer overflow bugs in the latest releases of three high-trusted applications (two Microsoft WINS services in Windows 2000 and 2003 Server, Baidu Hi Instant Messager) by testing each for 24 hours. Three of these bugs allow arbitrary code execution and have received confirmed vulnerabilities numbers, CVE-2009-1923, CVE-2009-1924 from Microsoft Security Response Center and CVE-2008-6444 from Baidu.
Intangible asset contribution to company performance: the “hierarchical assessment index”

Purpose – This paper aims to define a theoretical model that assesses and measures the intangible asset contribution to company performance. The model keeps in focus the most meaningful elements that reflect the success factors, crucial to company business strategy and value creation.Design/methodology/approach – The model adopts a hierarchical structure. The strategic intangible assets of the company have been divided into value drivers; a series of measurement indicators have been selected to describe the characteristics of each aspect of the intangible company performance. The measurements obtained from numerical indicators, which express the totality of the results achieved by the organizational strategies, are combined to create the hierarchical assessment index (HAI), by assessing quantitative and qualitative company features, through the analytic hierarchy process (AHP). Findings – The HAI identifies the sources of added value and competitive advantage in each business context; it traces the subordi.
Automated Annotation and Visualization of Rhetorical Figures

Linguistic annotation provides additional information asserted with a particular purpose in a document or other piece of information. It is widely used in various fields, from computing and bioinformatics, through imaging, to law and linguistics. There is also a clear distinction between what is communicated through the written/spoken natural language and how this is passed on. A new problem of linguistic annotation is the annotation of classical rhetorical figures – patterns of text in which a characteristic syntactic form modifies the standard meanings of words, and leads to a change or an extension of meaning. Rhetoric studies the effectiveness of language comprehensively, including its emotional impact, as much as its propositional content. The annotation of rhetorical figures is therefore important not only for the linguistic point of view, but also for discovering different styles of writing, purpose and effect of written documents, and for better natural language understanding in general. The purpose of this thesis is the automated annotation of rhetorical figures. In the thesis we primarily focus on the figures of repetition, which include the repetition of words, phrases, and clauses. Additionally, we also describe the work we have done on the detection and annotation of figures of parallelism, as well as those that pertain more to the semantics than to the syntax, or positioning. We have developed a rhetorical figure annotation tool dubbed JANTOR (Java ANnotation Tool Of Rhetoric), which enables manual and automated annotation of files in HTML format. We have applied a lexicalized probabilistic context-free grammar parser for the recognition of the figures of repetition. We also describe a simple parse tree distance used for calculating the difference between similarly structured phrases, which is necessary for the recognition of some of the figures of parallelism. Moreover, we have applied the semantic relationships contained in the WordNet lexical database and extended Porter stemmer algorithm for finding derivationally related words. Finally, we present a method for finding pairs of words which are ordinarily contradictory, which is crucial for detecting the interesting figure of speech: oxymoron. For this purpose typed dependency grammars together with WordNet are used. The experiments we have conducted on the detection of selected subset of rhetorical figures have yielded very promising results. Lastly, we present the visualization of the occurrences of the figures and comparison between 14 American presidents' inaugural addresses including the most recent one by President Barack Obama. The provocative results of this comparison show that a) automated analysis of meaningful rhetorical information is possible and tractable, and b) help us with understanding what creates a successful orator.
Automatic Identification of the Activity Area in Brain with Deep Neural Networks

We use deep max-pooling convolutional neural networks to address a problem of neuroanatomy, namely, the automatic segmentation of cerebral cortex structures of laboratory rat depicted in stacks of Two-photon microscopy images and detect the change areas when stimulation occurs. We classify each pixel in the image by training a CNN network, using a square window to predict the probability of the central pixel for each class. After classification, we perform the post-processing on the output produced by CNN. At last, we depict the areas that we interested through a threshold value.
Sub-component modeling for face image reconstruction in video communications

Emerging communications trends point to streaming video as a new form of content delivery. These systems are implemented over wired systems, such as cable or ethernet, and wireless networks, cell phones, and portable game systems. These communications systems require sophisticated methods of compression and error-resilience encoding to enable communications across band-limited and noisy delivery channels. Additionally, the transmitted video data must be of high enough quality to ensure a satisfactory end-user experience. Traditionally, video compression makes use of temporal and spatial coherence to reduce the information required to represent an image. In many communications systems, the communications channel is characterized by a probabilistic model which describes the capacity or fidelity of the channel. The implication is that information is lost or distorted in the channel, and requires concealment on the receiving end. We demonstrate a generative model based transmission scheme to compress human face images in video, which has the advantages of a potentially higher compression ratio, while maintaining robustness to errors and data corruption. This is accomplished by training an offline face model and using the model to reconstruct face images on the receiving end. We propose a sub-component AAM modeling the appearance of sub-facial components individually, and show face reconstruction results under different types of video degradation using a weighted and non-weighted version of the sub-component AAM.
In Defense of the Fog Index

With the emphasis on &dquo;plain English,&dquo; readability indexes have recently come under a great deal of scrutiny. ~ The complaints are many. Readability indexes do not predict ease of reading 100 percent of the time. They reduce a complex concept to only two variables, sentence and word length, but making sentences shorter does not necessarily make them more readable. They eliminate choice in writing and emphasize the product rather than the process of writing. Although all of these criticisms of our use of the indexes are valid, many of our difficulties with the indexes arise from our misuse of them, not from failings
An Image Classification Algorithm Based on SVM

Image classification is a image processing method which to distinguish between different categories of objectives according to the different features of images. It is widely used in pattern recognition and computer vision. Support Vector Machine (SVM) is a new machine learning method base on statistical learning theory, it has a rigorous mathematical foundation, builts on the structural risk minimization criterion. We design an image classification algorithm based on SVM in this paper, use Gabor wavelet transformation to extract the image feature, use Principal Component Analysis (PCA) to reduce the dimension of feature matrix. We use orange images and LIBSVM software package in our experiments, select RBF as kernel function. The experimetal results demonstrate that the classification accuracy rate of our algorithm beyond 95%.
A Survey of Trends in Massive DDOS Attacks and Cloud-Based Mitigations

Distributed Denial of Service (DDoS) attacks today have been amplified into gigabits volume with broadband Internet access; at the same time, the use of more powerful botnets and common DDoS mitigation and protection solutions implemented in small and large organizations’ networks and servers are no longer effective. Our survey provides an in-depth study on the current largest DNS reflection attack with more than 300 Gbps on Spamhaus.org. We have reviewed and analysed the current most popular DDoS attack types that are launched by the hacktivists. Lastly, effective cloud-based DDoS mitigation and protection techniques proposed by both academic researchers and large commercial cloud-based DDoS service providers are discussed.
Automatic registration of multimodal views on large aerial images

A new automatic method capable of registering multimodal images like terrain maps and multispectral data is presented. In order to speed up the processing, given the large amount of data typical of such settings, the method exploits a multi-resolution approach, which may select different similarity measures in consideration of image resolution and size. In fact, the performances of Cross Correlation and Maximization of Mutual Information (MMI) on images of different resolution and size have been evaluated and are described The adaptive strategy adopted is designed to exploit the strengths and to overcome the limitations of the similarity criteria employed. In case multimodal views are to be registered on 3D models, MMI is to be preferred. The strategies to improve its performance also on smaller images are presented.
KM_GrabCut: a fast interactive image segmentation algorithm

Image segmentation is critical for image processing. Among several algorithms, GrabCut is well known by its little user interaction and desirable segmentation result. However, it needs to take a lot of time to adjust the Gaussian Mixture Model (GMM) and to cut the weighted graph with Max-Flow/Min-Cut Algorithm iteratively. To solve this problem, we first build a common algorithmic framework which can be shared by the class of GrabCut-like segmentation algorithms, and then propose KM_GrabCut algorithm based on this framework. The KM_GrabCut first uses K-means clustering algorithm to cluster pixels in foreground and background respectively, and then constructs a GMM based on each clustering result and cuts the corresponding weighted graph only once. Experimental results demonstrate that KM_GrabCut outperforms GrabCut with higher performance, comparable segmentation result and user interaction.
Oxywater (water oxide): new evidence for the existence of a structural isomer of hydrogen peroxide

Ab initio molecular electronic structure theory has been applied in an investigation of the oxywater-hydrogen peroxide isomerization. Oxywater, hydrogen peroxide, and the transition state connecting them have been located using the self-consistent-field (SCF), configuration interaction including all single and double excitations (CISD), and coupled cluster with single and double excitations (CCSD) methods with several basis sets, the largest being of triple-zeta plus double-polarization (including f functions on the oxygen atoms) quality (TZ2P+f). Harmonic vibrational frequencies have been evaluated at the SCF, CISD, and CCSD levels of theory and the stationary points characterized as minima or transition states. The CCSD method with connected triple excitations [CCSD(T)] also has been used to obtain oxywater`s equilibrium geometry and frequencies as well as to compute single-point energies of all CCSD optimized structures. A classical barrier to isomerization of 5.7 kcal mol{sup {minus}1} has been predicted at the highest level of theory. After correction for zero-point vibrational energies, the comparable ground-state activation energy is 3.2 kcal mol{sup {minus}1}. Although these ab initio predictions could be decreased by 1 or 2 kcal mol{sup {minus}1} at yet higher levels of theory, there can be little doubt that oxywater is a genuine minimum of the H{sub 2}O{sub 2}more » potential energy hypersurface. « less
Listener performance in speaker verification tasks

The ability of listeners to perform some speaker verification tasks has been measured experimentally and compared with the performance of an automatic system for speaker verification. A test presentation in the subjective experiments consists of a pair of utterances. One of these is drawn from the recordings of a group of speakers designated customers while the second utterance is either a distinct recording from the same customer or the recording of an impostor. Listeners must respond whether the utterances are from the same or different speakers. The impostor classes that have been considered are casual impostors making no attempt to mimic customers, trained professional mimics, and an identical twin of a customer. Listener performance is specified by the two types of error that can be committed.
A biocybernetic method to learn hand grasping posture

In this paper, a biocybernetic method to learn hand grasping posture definition with few knowledge about the task is proposed. The developed model is composed of two stages. The first is dedicated to the fingers inverse kinematics learning in order to locally define a single finger posture given its desired fingertip position. This motor function is fulfilled by a modular neural network architecture that tackles the discontinuity problem of inverse kinematics function (called Fingers Configuration Neural Network (FCNN)). Following the concept of direct associative learning, a second neural model is used to search the space of hand configuration and optimize it according to an evaluative function based on the results of the FCNN. Simulation results show good learning of grasping posture determination of various object types, with different numbers of fingers involved and different contact configurations.
How to Find Out What People Really Want to Know

L'auteur essaye de mettre au point une methodologie d'interrogation efficace des utilisateurs dans le cadre de l'animation d'un service de renseignements en bibliotheque; et developpe le concept de «question neutre» ou «ouverte» par opposition aux questions fermees, qui induisent un comportement passif de l'utilisation
Evaluasi Kinerja IEEE 802.11e HCCA untuk Dukungan QoS pada WLAN Menggunakan NS-2

Wireless Local Area Network (WLAN) digunakan oleh trafik multimedia yang seharusnya memerlukan persyaratan jaringan yang lebih baik terhadap delay , jitter dan packet losses . IEEE 802.11 Task Group E memperkenalkan perbaikan protokol MAC 802.11, yaitu Hybrid Coordination Function (HCF), yang terdiri dari dua mekanisme akses: Enhanced Distributed Channel Access (EDCA) dan HCF Controlled Channel Access (HCCA) yang memberikan dukungan Kualitas Layanan/ Quality of Service (QoS) bagi trafik multimedia. Tujuan dari penelitian ini adalah untuk memahami algoritma protokol MAC IEEE 802.11e HCCA pada jaringan WLAN, dan menganalisis kinerja protokol MAC 802.11e HCCA pada aplikasi multimedia dengan menggunakan metode simulasi pada NS-2. Metrik kinerja yang diukur adalah jitter dan throughput -nya. Hasil penelitian ini menunjukkan bahwa protokol MAC HCCA dapat memberikan jaminan QoS dibanding protokol MAC DCF, dimana jitter HCCA lebih stabil dari pada jitter DCF. Begitupula dengan throughput HCCA yang tidak berubah selama trafik berlangsung, tidak seperti pada DCF yang masih mengalami fluktuasi yang besar.
Segmentation of Brain Tumors using Meta Heuristic Algorithms

In medical field, Magnetic Resonance Image (MRI) is used to differentiate pathological tissues from normal tissues, especially for brain tumors. These days, millions of medical images have been produced routinely in medical care centers. Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Image analysis is still performed manually which is often a difficult and time consuming task. As a result, there is an increasing need for computerized image analysis to facilitate image based diagnosis. In Computer Aided Systems (CAS), the analyzed computer based output has been used as a second opinion for physicians and radiologists to analyze and diagnose the patient details in a faster manner as compared to manual process. Using the automated CAS, identification of different tissues and pathologies is clear, accurate and more certain. In this paper different metaheuristic methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bacteria Foraging Optimization Algorithm (BFOA), and Artificial Bee Colony Optimization (ABCO) are analyzed for segmenting brain tumors in 2D magnetic resonance images.
Analysis And Implementation Of Non-Kolmogorov Phase Screens Appropriate To Structured Environments

In order to statistically represent the turbulence encountered in a structured environment, the established procedure has been to employ simple mathematical generalizations of models that are known to perform well for atmospheric turbulence. However, for the non-Kolmogorov regimes of interest, this approach encounters theoretical as well as practical difficulties of a fundamental nature. These will be described in detail, along with new analytical methods better suited to the particular problem. Finally, results of wave optics computer codes implementing these techniques will be presented for specific realizations of the environment as computed by the hydrodynamic models.
Twin-VCM Controller Design for the Nutator System with Evolutionary Algorithms

Abstract We present an evolutionary algorithm to optimize controller parameters for a nonlinear nutator system which is implemented in twin voice coil motors (VCMs). In this paper, genetics-based algorithm is applied to tune the proportional-integral-derivative (PID) controller for the multi-input multi-output (MIMO) coupling system. The result has been validated that this algorithm can find out all six PID control parameters in optimal and robust senses. The controller architecture of the nutator system is constructed to meet the specifications of the coupled mirror and rocker parallel loops, both adopting PID control. Two main linear VCMs operating in a push-pull organization drive the nutator subreflector. The mandatory operation mode for the nutator system is two-position switching. The required technical specifications for the nutator control system are extremely precise requirements, system tracking control with a disturbance force more challenging than that in ordinary systems. Simulation results have demonstrated the superiority of the evolutionary algorithm, satisfying the Atacama large millimeter/submillimeter array (ALMA) severe conditions and requirements.
Reductive Alkylation of Aromatic Amines with Enol Ethers

Reductive alkylation of aromatic amines with 2-meth­oxypropene using 1.0 equivalent of HOAc and NaBH(OAc)3 in 1,2-dichloroethane (DCE) at room temperature furnished N-isopropyl amines in 50-98% yields. This method was successfully extended to trimethylsilyl enol ethers. The mild reaction conditions provide a new alternative procedure for the reductive amination of electron deficient aromatic amines.
Research on Feature Extraction and Match Method based on the Surf Algorithm for Mobile Augmented Reality System

According to the characteristics of constrained mobile devices, an improved Surf feature extraction and matching algorithm is proposed. First, dynamic pyramid is established in order to meet the real-time requirements; then reducing redundant feature points and strengthening the feature points of big difference individuality is done based on the Niche ideas; finally improved classification BBF matching algorithm is used to speed the matching. The contrast experiment on Surf and its improved algorithm is done based on the iOS platform. The experimental results demonstrate that the improved Surf algorithm has strong robustness, fast speed, high accuracy, and lays the foundation for mobile augmented reality technology in video, animation, text, 3D virtual scene registration, so it has great application value.
Node Localization Algorithm Based on DV-HOP in Wireless Sensor Networks

Node self-positioning is one of the core technologies in wireless sensor networks and is the premise of many applications. It is significant to achieve highly efficient and reliable node localization for event observation, target tracking and efficient routing. The DV-Hop localization algorithm which is one of the Range-Free algorithms is researched deeply. Firstly, algorithmic process, error sources, and the amount of communication and computing for Traditional DV-Hop algorithm are analyzed. For low positioning accuracy in Conventional DV-Hop localization algorithm, an improved DV-Hop localization algorithm based on estimate of average hop distance, Min-Max and position correction is brought forward. The simulation results show that the improved algorithm can improve the positioning accuracy more effectively without increasing hardware consumption in nodes comparing with conventional DV-Hop algorithm. So it is a practical and effective node localization method in wireless sensor networks.