Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Era of Research & Development: Cloud Computing

Cloud computing is a technology that broadly shares the computer resources instead of being sharing a product. It is advancement over the distributed computing where we pay as per usage basis. Instead of installing a suite of software on each pc we would only have to load only one application which allows the cloud users to log in to the web server to access the resources. Cloud computing is based on several other computing technologies such as HPC, Virtualization. It has its own technical, conceptual & economical aspects. It has various areas of research into different aspects such as platform, architecture, scheduling, security, virtualization etc. All these different areas have good future scope. This paper outlines about all the areas of cloud computing mentioned above.
Motion Detection for Rapidly Moving Cameras in Fully 3D Scenes

This paper presents a novel method for detecting motion regions in image sequences obtained by rapidly moving cameras in fully 3-D scenes. The proposed method mainly focuses on the situations that the backgrounds of the image sequences change rapidly. It has three innovations over existing methods: First, it presents a new initialization method to fast and sparsely gather information of the background model, while traditional methods utilize a complicated training step. Second, a novel model updating scheme is proposed for establishing the on-line sparse background model iteratively. This is the main contribution of the proposed method and this enables the method to work in a 3-D scene which totally changes through the image sequence, while most other methods can only work with a pre-modeled scene or with a camera that moving in limited scope. Third, a novel two-stage model of the background and foreground motion regions is proposed and the foreground motion regions are detected using maximum a posterior estimation. The method is tested on various challenging image sequences captured by freely moving cameras and results show that it is very effective and robust.
An Adaptive Least-Squares Mixed Finite Element Method for Elasto-Plasticity

A least-squares mixed finite element method for the incremental formulation of elasto-plasticity using a plastic flow rule of von Mises type with isotropic hardening is presented. This approach is based on the use of the stress tensor, in addition to the displacement field, as independent process variables. The nonlinear least-squares functional is shown to constitute an a posteriori error estimator on which an adaptive refinement strategy may be based. For the finite element implementation under plane strain conditions, quadratic (i.e., next-to-lowest-order) Raviart-Thomas elements are used for the stress approximation, while the displacement is represented by standard quadratic conforming elements. Computational results for a benchmark problem of elasto-plasticity under plane strain conditions are presented in order to illustrate the effectiveness of the least-squares approach.
Finite Element Method Based Modeling of Cardiac Deformation Estimation under Abnormal Ventricular Muscle Conditions

Deformation modeling of cardiac muscle is an important issue in the field of cardiac analysis. Many approaches have been developed to better estimate the cardiac muscle deformation, and to obtain a practical model to be used in diagnostic procedures. But there are some conditions, like in case of myocardial infarction, in which the regular modeling approaches are not useful. In this article, using a point-wise approach, we try to estimate the deformation under some abnormal conditions of cardiac muscle. First, the endocardial and epicardial contour points are ordered with respect to the center of gravity of the endocardial contour and displacement vectors of boundary points are extracted. Then to solve the governing equations of the deformation, which is an elliptic equation, we apply boundary conditions in accordance with the computed displacement vectors and then the Finite Element method (FEM) will be used to solve the governing equations. Using the obtained displacement field of the cardiac muscle, strain map is extracted to show the mechanical behavior of the cardiac muscle. Several tests are conducted using phantom and real cardiac data in order to show the validity of the proposed method.
DETERMINATION OF LAMB GRADES USING TEXTURE ANALYSIS AND NEURAL NETWORKS

This paper presents a lamb carcass classification system based on texture analysis and artificial neural networks (ANNs). Digital images of lamb chops were used to calculate twelve image geometric variables. In addition, a set of ninety texture features was used to extract the textural information. Texture analysis is based on the cooccurrence matrix method. Principal component analysis (PCA) was used to reduce the dimensionality of feature spaces. The reduced feature space, with six geometric variables and eight cooccurrence texture variables, was used for the analysis. Three-, fourand five-layer multilayer perceptron (MLP) networks and Discriminant function analysis (DFA) were performed on the data. The classification accuracy from three-layer MLP (93.1%) was 14% better than that from DFA. This study shows the potential of neural networks combined with texture analysis for lamb grading.
Study on the Waste Water Treatment Technology for Steel Industry Recycle And Reuse

The steel industry is one of the most important and vital Industry of the present and the future. It is the asset of a nation. Steel plants use a tremendous amount of water for waste transfer, cooling and dust control. The steel plants have sintering mills, coke plants, blast furnaces, chemical byproducts and chemical processes, water cooled rolls, pumps, extrusion experiment, transfer lines for sludges and slurries. All these plants use a tremendous amount of water to cool the products and flush the impurities away from the finished stock. Wastewater is generated in huge quantity in steel industries. It contains many dissolved, undisclosed substances and chemicals in the wastewater. The steel industries produce wastewater and sludge during different industrial processes. The development of innovative technologies for treatment of wastewaters from steel industries is a matter of alarming concern for us. Although many research papers have been reported on wastewater pollution control studies, but a very few research work is carried out for treatment of wastewater of steel industries, especially in reference to development of design of industrial effluent Treatment Plants (ETP) system. Another beneficial aspect of this research work will be recycling, reuse of water and sludge from steel industry The whole technologies for treating industrial wastewater can be divided into four categories: Chemical, Physical, Biological and mathematical approaches. Physical treatment methods include sedimentation, Floatation , filtering , stripping, ion – exchange, adsorption and other processes that accomplish removal of dissolved and undisclosed substances without necessarily changing their chemical structure. The mathematical approaches are very useful and more realistic for developing a well operating cost–effective treatment system for industrial wastewater treatment.
An improved general amino acid replacement matrix.

Amino acid replacement matrices are an essential basis of protein phylogenetics. They are used to compute substitution probabilities along phylogeny branches and thus the likelihood of the data. They are also essential in protein alignment. A number of replacement matrices and methods to estimate these matrices from protein alignments have been proposed since the seminal work of Dayhoff et al. (1972). An important advance was achieved by Whelan and Goldman (2001) and their WAG matrix, thanks to an efficient maximum likelihood estimation approach that accounts for the phylogenies of sequences within each training alignment. We further refine this method by incorporating the variability of evolutionary rates across sites in the matrix estimation and using a much larger and diverse database than BRKALN, which was used to estimate WAG. To estimate our new matrix (called LG after the authors), we use an adaptation of the XRATE software and 3,912 alignments from Pfam, comprising approximately 50,000 sequences and approximately 6.5 million residues overall. To evaluate the LG performance, we use an independent sample consisting of 59 alignments from TreeBase and randomly divide Pfam alignments into 3,412 training and 500 test alignments. The comparison with WAG and JTT shows a clear likelihood improvement. With TreeBase, we find that 1) the average Akaike information criterion gain per site is 0.25 and 0.42, when compared with WAG and JTT, respectively; 2) LG is significantly better than WAG for 38 alignments (among 59), and significantly worse with 2 alignments only; and 3) tree topologies inferred with LG, WAG, and JTT frequently differ, indicating that using LG impacts not only the likelihood value but also the output tree. Results with the test alignments from Pfam are analogous. LG and a PHYML implementation can be downloaded from http://atgc.lirmm. fr/LG.
Efficient Compressed Sensing using Lossless Expander Graphs with Fast Bilateral Quantum Recovery Algorithm

Compressed Sensing is a novel approach to bypass the Nyquist sampling limits whenever the signals are sparse, and to compress them simultaneously. In this paper, improving our previous results, we will propose a compressed sensing algorithm based on the high-quality lossless unbalanced vertex expander graphs, with a fast and simple quantum decoding algorithm. Exploiting the unique neighborhood property of the lossless expander graphs in combination with the efficient quantum algorithm for finding distinct elements among a set we will prove the validity and efficiency of the algorithm and will show how the combination of the lossless expander graphs and quantum recovery algorithm leads to a general compressed sensing framework which is as good as the previous results in terms of the sketch size, encoding time, update time, and having explicit construction; furthermore, it is superior to the previous recovery algorithms in both the recovery time and simplicity of implementation. Finally we will show how the algorithm can be modified to be robust for a well-structured family of almost sparse signals. The robust algorithm will first find the position of the largest elements of the signal, and then using this information finds efficiently an explicit sparse approximation for the original signal.
A history sensitive cascade model in diffusion networks

Diffusion is a process by which information, viruses, ideas and new behavior spread over social networks. The traditional independent cascade model gives activated nodes a one-time chance to activate each of its neighboring nodes with some probability. This paper extends the traditional cascade model to be history dependent. We propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM provides 1) a polynomial algorithm for calculating the probability of activity for any arbitrary node at any arbitrary time in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.
Interaction of Planetary Nebulae with the Interstellar Medium

Interaction of planetary nebulae (PNe) with the interstellar medium (ISM) is quite common, in accord with our understanding of the large-scale structure of the ISM. The characteristic signature of the interaction is a dipole asymmetry resulting from a distortion and compression of the nebula in the direction of stellar motion. Studies of interacting PNe provide us with information on the structure of the ISM, and on the evolution of old PNe and PN halos.
The Hatfield-system versus the weekly undulating periodised resistance training in trained males

The purpose of this study was to compare training adaptations attained during six weeks of using the Hatfield-system and six weeks of the weekly undulating periodisation strength training. Twenty-six recreationally strength trained men were randomly assigned to either a Hatfield-system group (n = 13; age = 26.8 ± 7.2 years) or a weekly undulating periodisation group (n = 13; age = 29.2 ± 9.0 years). Anthropometric measures and strength testing were performed before (PRE) and after six weeks (POST) of training. To subjectively quantify the individual’s perception of the physical demands of the intensity of resistance training, a category-ratio scale (CR10) was used by the subjects after each training session. The participants of both groups trained twice a week for six weeks. The Hatfield-system and weekly undulating periodisation programmes used the same exercises, the same total training volume and the same total intensity in these six weeks. The difference between the two programmes was in the distribution within each training phase. The Hatfield-system and weekly undulating periodisation groups trained using a periodised strength programme with all programme variables controlled (e.g. volume and intensity). The Hatfield-system group used a linear not varying intensity, whereas the weekly undulating periodisation group had a varied intensity. The results show that both the Hatfield-system and weekly undulating periodisation groups made significant (p ≤ 0.05) increases in strength and power. The results of this study indicate that the Hatfield-system model is no more effective than the weekly undulating periodisation model for increasing strength, power and muscle size.
HITSZ-ICRC at NTCIR-11 Temporalia Task

Temporal Information Access (Temporalia) task is a pilot task at NTCIR-11 for the first year. HITSZ-ICRC group participated in Temporalia task, worked in both Temporal Query Intent Classification (TQIC) subtask and Temporal Information Retrieval (TIR) subtask. In TQIC subtask, firstly, we extracted different linguistic level features from user query, extracted expanding features for the query by downloading search results from search engine Bing; then we designed rule based method and multi-classifier voting method to classify user query intent separately; in formal run step, we combined the classification results produced by rule based method and multi-classifier voting method as final classification result to submit. In TIR subtask, firstly, we built an index for documents in the aim corpus using Lucene tool kit; secondly we calculated the content relevant score using BM25 model and the temporal relevant score based on the date distance between the query date and time expression tagged in document content; thirdly, we developed two rank methods, relevant score weight sum method and learning to rank method, to calculate the final relevant score for each document and rank relevant documents based on the final score; the subtopic classification method we used in TIR subtask is same as in TOIC subtask.
A Compound Control for PMSM Based on Fuzzy Sliding-mode and Neural Network

Permanent magnet synchronous motor (PMSM) system has many characteristics such as nonlinearity, time-varying parameters and complex modeling. PMSM system is required to become quick response and insensitivity to the variation of parameters and disturbance especially in the electric vehicle. In order to solve the problems, this paper presents a new compound control method containing fuzzy control, sliding-mode variable structure control and neural network control. The effectiveness of the proposed controller is demonstrated by MATLAB/SIMULINK. The result shows that this scheme has not only excellent dynamics and steady-state performance but also excellent robustness to external disturbance.
A fast top-down visual attention method to accelerate template matching

This paper presents a fast top-down visual attention method to downsize the search space of template matching. Such a method first generates patterns representing the local structures, and then calculates the pattern distributions representing the template and its surroundings. From here two separate operations are performed: the "pattern weight" is first introduced, which describes how well a certain pattern is correlated to the template, and then weights of all patterns are calculated for later reference. This is the "off-line" operation, and in comparison the "on-line" operation only calculates the pattern of each pixel, whose weights can be indexed conveniently from the off-line results. With all pixels' pattern weights calculated, the weight image is ready, from which we can extract the region of interest for subsequent matching. Experiments showed that our method obtained at least 6.21 times speed-ups over the state-of-the-art methods with little or no loss in performance.
Three-Dimensional Surface Curvature Estimation using Quadric Surface Patches

Recent advances in 3D scanning technology have enabled the development of interesting applications of 3D human body modelling and shape analysis, especially in the areas of virtual shopping, custom clothing and sizing surveys for the clothing industry. Most of the current applications have so far been concerned with automatic tape measurement extraction, i.e. simulation of the manual procedure for extracting a set of body measurements by conventional means, such as tapes and callipers. This has been an important advance since it has made it possible to extract in a non-intrusive manner sets of measurements in a few seconds rather than in the usual 40-45 minutes that also involve intrusive physical contact. However, this approach has a number of problems, and also fails to exploit the full potential of 3D imaging technology, as the 3D sets obtained by a scanner are still reduced to a collection of 1D measurements in order to describe body shape. The approach we present is a method for detecting significant geometric features on the 3D body surface. These features (such as ridges and umbilic points) require no a-priori anatomical information and can be used for driving matching and modelling algorithms such as deformable Active Shape Models. The approach is truly hardware-independent and will work on any set of reasonably complete 3D data, whether it is a raw point cloud or a pre-processed, canonical-type representation. Potential applications include sophisticated Shape Analysis techniques that may be used for classification of body types.
Epipole-based visual servoing for nonholonomic mobile robots

A new image-based visual servoing algorithm is presented for nonholonomic mobile robots. The algorithm, based on epipolar geometry, consists of three independent and sequential steps making use of both the estimated epipoles and the image features. In particular, due to the nonlinear dynamics of the camera-robot system, an input-output feedback linearizing control law is used during the second step. Simulations results are presented to validate the proposed visual servoing technique.
Statistical variability vs. probabilistic uncertainty

The concepts of variability and uncertainty came from experience and coexist with different connotations. First, the article reviews the statistical methods for variability assessments of probability distributions. Next, it sums up the entropy concept of uncertainty of systems of events in probability theory. The two concepts are brought closer together on the basis of common experience of predictability. The article also considers the concept of average number of equally probable events based on entropy. Then, it introduces the concept of equivalent number of outcomes based on variability of probability distributions. Finally, the link between variability and uncertainty is illustrated with examples.
Computer Based Communication: Changing Students’ Writing Viewed From Motivation (An Experimental Study at the Eleventh Grade of SMAN 5 Yogyakarta in the Academic Year of 2015/2016)

This research is aimed to reveal whether: (1) Computer BasedCommunication material is more effective than Textbook material to teachwriting to the XI grade of SMAN 5 in the academic year of 2015/2016; (2)the students who have high learning motivation have better writing skillthan those who have low learning motivation of the XI grade of the school;and (3) there is an interaction between teaching materials and learningmotivation to teach writing to the XI grade of the school.This experimental study was conducted at SMAN 5 Yogyakarta inthe academic year of 2015/2016. The population in this research is the XIgrade students of the school. The total number of population is 174 studentscoming from six classes. The samples are XI IPA1 as the experimental classand XI IPA 2 as the control class in which each consists of 30 students.Each class was divided into two groups (the students who have high and lowlearning motivation). In collecting the data, the researcher used test andquestionnaire. The data were analyzed by using Multifactor analysis ofvariance ANOVA 2x2 and Tukey test.Based on the result of analysis, there are some research findings thatcan be taken: (1) Computer Based Communication material is moreeffective than Textbook material to teach writing; (2) the students who havehigh learning motivation have better writing skill than those who have lowlearning motivation; and (3) there is an interaction between teachingmaterials and learning motivation in teaching writing.Finally, it can be inferred that Computer Based Communicationmaterial is an effective material to teach writing for the XI grade students.This material depends on the levels of learning motivation. Hopefully, thisresearch gives contribution for the students and teacher in teaching writing.Keywords: Computer Based Communication Material, Textbook Material,Learning motivation, Experimental study
Quantum Computing Approach for Alignment-Free Sequence Search and Classification

Many classes of algorithms that suffer from large complexities when implemented on conventional computers may be reformulated resulting in greatly reduced complexity when implemented on quantum computers. The dramatic reductions in complexity for certain types of quantum algorithms coupled with the computationally challenging problems in some bioinformatics problems motivates researchers to devise efficient quantum algorithms for sequence (DNA, RNA, protein) analysis. This chapter shows that the important sequence classification problem in bioinformatics is suitable for formulation as a quantum algorithm. This chapter leverages earlier research for sequence classification based on Extensible Markov Model (EMM) and proposes a quantum computing alternative. The authors utilize sequence family profiles built using EMM methodology which is based on using pre-counted word data for each sequence. Then a new method termed quantum seeding is proposed for generating a key based on high frequency words. The key is applied in a quantum search based on Grover algorithm to determine a candidate set of models resulting in a significantly reduced search space. Given Z as a function of M models of size N, the quantum version of the seeding algorithm has a time complexity in the order of O Z ( ) as opposed to O(Z) for the standard classic version for large values of Z.