Miracle-dz/UniFAR-FTUs huggingface dataset:
3-D Networks for Mining Knowledge from Energy Policy Literatures

Data mining is a hot research topic over the last twenty years or more. In recent decades, network graphs that have represented knowledge of a focus topic have gained increasing attention. These maps include term network, concept map, topic map or knowledge map. A concept map is one of the visualization tools to show the relationships among concepts. It is a graphical tool for organizing and representing knowledge. Global warming poses a grave threat to the world’s ecological system. Economic development has led to a huge increase in energy demand and therefore energy efficiency and saving has become a key issue for most countries. In many countries, they tried hard to find renewable and sustainable energy supplies and sources. This study tries to analyze trends of energy policy literatures from the international literature database within last three years to be visualized in 3-D concept map layouts; besides, measuring keyword relation linkages though control variables of concept maps, such as size of node, linkage, relations and dynamic figure layout, are the main contributions to academics. This research adapts an IP (integer programming) model to maximize relation linkages for each node among a term network. The more linkages, the more useful information offered for mining knowledge from a term network. The 3-D concept map was demonstrated. Future research suggests applying this approach to other research literatures from international literature databases.
Optimization of Castor Oil Transesterification using Central Composite Design (CCD); Chemical Composition of Fatty acids content using Gas Chromatography

International Journal of Advanced Science and Engineering www.mahendrapublications. com Optimization of Castor Oil Transesterification using Central Composite Design (CCD); Chemical Composition of Fatty acids content using Gas Chromatography P.A. Satyanarayana1, V.Sridevi2*, Chaman Mehtha3, B.Venu Gopal4 1 ,2,4Department of Chemical Engineering, AU College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India. 3Indian Institute of Science Education and Research, (An Autonomous Institution, Ministry of H.R.D) Mangalam B.O, Karkambadi Road, Tirupati-517501.
A new CUDA-based GPU implementation of the two-dimensional Athena code

We present a new version of the Athena code, which solves magnetohydrodynamic equations in two-dimensional space. This new implementation, which we have named Athena-GPU, uses CUDA architecture to allow the code execution on Graphical Processor Unit (GPU). The Athena-GPU code is an unofficial, modified version o the Athena code which was originally designed for Central Processor Unit (CPU) architecture. We perform numerical tests based on the original Athena-CPU code and its GPU counterpart to make a performance analysis, which includes execution time, precision differences and accuracy. We narrowed our tests and analysis only to double precision floating point operations and two-dimensional test cases. Our comparison shows that results are similar for both two versions of the code, which confirms correctness of our CUDA-based implementation. Our tests reveal that the Athena-GPU code can be 2 to 15-times faster than the Athena-CPU code, depending on test cases, the size of a problem and hardware configuration.
Research on Real-time Driving Method of Shearer’s Remote Monitoring Platform

Shearer works in the abominable condition, remote monitoring for the shearer could reduce fatalness of the mining workers. In order to reappear shearer’s working status and control it, the shearer’s virtual prototype was built by using virtual reality technology, which makes the mining workers feel the shearer’s working condition in the remote distance. Real-time driving problem of the shearer’s virtual prototype is required to solve for the sake of realizing action consistency between the virtual prototype in the remote monitoring platform and the real shearer, OPC technology was applied to solve the inconsistent problem of the communication interface between the shearer’s virtual reality platform and S7-300 PLC in the shearer, and the data interaction between virtual reality platform and S7-300 PLC was realized by use of C# programming. The experiment result shows that shearer’s virtual reality platform is able to acquire the immediate data from S7-300 PLC in the shearer by OPC technology, which assures the reliability of the shearer’s remote monitoring system. Keywords-virtual reality; OLE for Process Control; communication interface; remote monitoring; real-time driving
Online Bayesian Time-Varying Parameter Estimation of HIV-1 Time-Series

Abstract Nonlinear Bayesian filtering offers various online tools for system identification of (parametric) ordinary differential equation models. Since parameters may change with time, it is a relevant question to assess how well time-varying parameters can be estimated. For this purpose we tested two filtering methods, Extended Kalman Filter and Particle Filter for joint state and time-varying parameter estimation on a dynamic model of HIV-1 virus immune response. After evaluating the methods on simulated time-series we applied them to clinical datasets. Estimated time-varying parameters on clinical data are consistent with previously reported results with offline algorithms.
Medical Volumes Segmentation Approach Based on Level Set Method

Volumes segmentation is an important part of computer based medical application for diagnosis and analysis of anatomical data. A segmentation approach based on the level set method is proposed for accurately extracting vasculature from magnetic resonance angiography (MRA) volumes in this paper. The proposed model has a boundary alignment term that is used for segmentation of thin structures. Finally the proposed model is applied to the segmentation of MRA volumes. The result shows that the proposed model by us can complete the segmentation task of vascular structure and cannot complete the same task by the model without boundary alignment term
A quantitative approach to compare collocational patterns in translated and non‑translated texts

The primary objective of this paper is to propose a corpus-based research methodology for comparing quantitative aspects of collocational patterning in translated and non-translated texts of the same language. The main issue under investigation is whether collocational patterns tend to be less diverse (i.e. reduced in range) in translated than non-translated texts. A pilot study is carried out with a view to examining the overall number of collocates and the distribution of collocations in relation to a given node in the translated and non-translated subcorpora. The data is drawn from a comparable corpus of Brazilian Portuguese which consists of two separate subcorpora: one made up of translated Brazilian Portuguese and the other consisting of non-translated Brazilian Portuguese. Both subcorpora are designed according to the same criteria and specifications and are of similar size.
Japanese sentence pattern learning with the use of illustrative examples extracted from the web

It is quite difficult for a foreigner to compose a Japanese sentence comparing with the case of speaking Japanese. Two main factors are supposed to be leading to this situation. One is the scale of Japanese vocabulary and another is the variousness of Japanese sentence patterns. As most Kanji characters are common in Chinese and Japanese, the more confusing issue for native Chinese‐speaking foreigners is the latter one. There are many sentence patterns in Japanese to express different meanings in various situations. In this paper, we first make a survey on existing strategies to support foreigners to compose Japanese sentences, and then develop a system for native Chinese‐speaking foreigners to learn Japanese sentence patterns with the use of illustrative examples extracted from the Web in a practical and convenient way. Finally, we conduct some experiments to examine the effectiveness of our approach. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Cbufferless: A Novel Congestion Control for Bufferless Networks On-Chip

In this paper, we propose a novel distributed source- throttling congestion control mechanism for bufferless NoC, called Cbufferless. Our strategy uses a novel congestion detection and control model, computing deflection rate of routing flit and throttling message injection. The congestion information can be directly obtained inside network interface (NI), which allows the mechanism to be fully distributed without requiring any transmission of global congestion information among neighbor routers and within router. Simulation results show that the proposed mechanism improves system throughput by up to ~30%, saves energy consumption by up to ~40% and keeps low message latency under congested load compared with a baseline bufferless NoC.
Supervised Object Knowledge Learning for Image Understanding

An object learning system for image understanding is proposed in this paper. The knowledge acquisition system is designed as a supervised learning task. Therefore, the role of the user as teacher of the system is emphasized, which allows to obtain the object description as well as to select the best recognition strategy for each specific object. An object description is acquired by considering different model representations provided from several representative examples in training images. Moreover, different recognition strategies are built and applied to obtain initial results. Next, teacher evaluates these results and the system automatically selects the specific strategy which best recognise each object. We provide a feedback with the user until he/she is satisfied with the knowledge achieved by the system. Experimental results are shown and discussed.
Entropy-driven structural adaptation in sample-space self-organizing feature maps for pattern classification

The relationship of the self-organizing map to the general problem of non-parametric density estimation has brought about diverse applications of this network to vector quantization and pattern recognition problems. However, the requirement of deciding a priori the number of processing units to use limits the ability of the network to deliver satisfactory solutions. In this paper we consider a new structural adaptation approach that is based on the measurement and monitoring of the relative entropy during the learning phase of self-organizing feature maps with sample-space neighborhoods and trained in a batch mode. Results on the classification accuracy of the networks built with the proposed scheme are presented, together with some application examples.
A New Online New Event Detection Algorithm Based on Event Merging and Event Splitting

The current Online New Event Detection (ONED) algorithms based on the elements of news can effectively detect new events; however, these methods have two limitations: First, events are tend to be split into several small similar events at the beginning of events; Second, those events about the same describing object but different subject are opt to be merged into one large event. This paper proposes a new improved ONED algorithm that could effectively solve the above limitations; the new algorithm makes two improvements at the basis of the current ONED algorithms: First, at the beginning of events, it not only compares stories with detected events but also compare events with events to make sure that whether certain events should to be merged; Second, it makes a secondary analysis of those events those last for a long time to see whether they should be split. The experimental results show that comparing to the current ONED algorithms the new algorithm can effectively reduce the miss probability and false-alarm probability.
AA-stacked bilayer square ice between graphene layers?

Water confined between two layers with separation of a few Angstrom forms layered two- dimensional ice structure. Using large scale molecular dynamics simulations with the adoptable ReaxFF interatomic potential we found that flat monolayer ice with a rhombic-square structure nucleates between graphene layers which is non-polar and non-ferroelectric. Two layers of water are found to crystallize into a square lattice close to the experimental found AA-stacking [G. Algara- Siller et al. Nature 519, 443445 (2015)]. Each layer has a net dipole moment which are in opposite direction. Bilayer ice is also non-polar and non-ferroelectric. For three layer ice we found that each layer has a crystal structure similar to monolayer ice.
3-D Scene Generation On A Shared Memory Parallel Processor

Realistic 3-D scene generation is now a possibility for many applications. One barrier to increased use of this technique is the large amount of computer processing time needed to render a scene. With the advent of parallel processors that barrier may be overcome if efficient parallel scene generation algorithms can be developed. In general, this has not been true because of restrictions imposed by non-shared memory and limited processor interconnect architectures. In addition, vector processors do not efficiently support the adaptive nature of many of the algorithms. A new parallel computer, the NYU Ultracomputer, has been developed which features a shared memory with a combining network. The com-bining network permits simultaneous reads and writes to the same memory location using a new instruction the Fetch and_Op. These memory references are resolved in the memory access network and result in particularly efficient shared data structures. Basic elements of this architecture are also being used in the design of the gigaflop range RP3 at IBM. Some algorithms typical of image synthesis are explored in the paper and a class of equivalent queue based algorithms are developed. These algorithms are particularly well suited to the Ultra-computer class processor and hold the promise for many new applications of realistic scene generation.
Design of CNTFET-based 2-bit ternary ALU for nanoelectronics

This article presents a hardware-efficient design of 2-bit ternary arithmetic logic unit (ALU) using carbon nanotube field-effect transistors (CNTFETs) for nanoelectronics. The proposed structure introduces a ternary adder–subtractor functional module to optimise ALU architecture. The full adder–subtractor (FAS) cell uses nearly 72% less transistors than conventional architecture, which contains separate ternary cells for addition as well as subtraction. The presented ALU also minimises ternary function expressions with utilisation of binary gates for optimisation at the circuit level, thus attaining a simple design. Hspice simulations results demonstrate that the ALU ternary circuits achieve great improvement in terms of power delay product with respect to their CMOS counterpart at 32 nm.
Human interaction recognition through two-phase sparse coding

In this paper, we propose a novel method to recognize two-person interactions through a two-phase sparse coding approach. In the first phase, we adopt the non-negative sparse coding on the spatio-temporal interest points (STIPs) extracted from videos, and then construct the feature vector for each video by sum-pooling and l2-normalization. At the second stage, we apply the label-consistent KSVD (LC-KSVD) algorithm on the video feature vectors to train a new dictionary. The algorithm has been validated on the TV human interaction dataset, and the experimental results show that the classification performance is considerably improved compared with the standard bag-of-words approach and the single layer non-negative sparse coding.
A blind detection technique for CPM signals transmitted over frequency-flat fading channels

We propose a novel detection technique for CPM signals transmitted over frequency-flat fading channels. Its most significant feature is that it operates without statistical information on the fading channel and, for this reason, it is nicknamed "blind detector". Its error rate performance is assessed with CPFSK schemes and is compared with the performance of other decoders.
Understanding multi-articled documents

A document understanding method based on the tree representation of document structures is proposed. It is shown that documents have an obvious hierarchical structure in their geometry which is represented by a tree. A small number of rules are introduced to transform the geometric structure into the logical structure which represents the semantics. The virtual field separator technique is employed to utilize the information carried by special constituents of documents such as field separators and frames, keeping the number of transformation rules small. Experimental results on a variety of document formats have shown that the proposed method is applicable to most of the documents commonly encountered in daily use, although there is still room for further refinement of the transformation rules. <<ETX>>
Ranch: a dynamic network topology

Peer-to-peer computing is an emerging paradigm that has the potential of harnessing enormous amounts of under-utilized computational resources (e.g., home computers). A central problem in peer-to-peer computing is how to organize the network nodes so that sophisticated applications can be efficiently supported. The cornerstone of a peer-to-peer network is a dynamic network topology that determines the neighbor relationships to be maintained by the network nodes. This dissertation is concerned with algorithmic and concurrency issues in dynamic network topologies. We present Ranch (random cbyclic hbypercube), a simple, recursive topology consisting of a collection of rings. Ranch is a scalable topology. In particular, it has logarithmic in-degree, out-degree; and diameter; and it uses only a logarithmic number of messages for a node to join or leave the network. Ranch also has a number of additional desirable properties, including locality awareness and fault tolerance. We show how to build a name resolution scheme for Ranch that enables the peer-to-peer network to find data items efficiently. Our results include a name replication scheme and a fault-tolerant lookup algorithm. We address the problem of topology maintenance in peer-to-peer networks, that is, how to properly update the neighbor variables when nodes join and leave the network, possibly concurrently. We design, and prove the correctness of, protocols that maintain the ring topology; the basis of several peer-to-peer networks, in the fault-free environment. Our protocols handle both joins and leaves actively (i.e., they update the neighbor variables as soon as a join or a leave occurs). We use an assertional method to prove the correctness of our protocols; that is, we first design a global invariant for a protocol and then show that every action of the protocol preserves the invariant. Our protocols are simple and our proofs are rigorous and explicit. We extend our results on the maintenance of rings to address the maintenance of Ranch. We present active and concurrent maintenance protocols that handle both joins and leaves for Ranch, along with their assertional correctness proofs. The protocols for Ranch use the protocols for rings as a building block. The protocols and the correctness proofs for Ranch substantially extend those for rings. We present simulation results that demonstrate the scalability and locality awareness of Ranch.
Study of Physicochemical Characters of in-situ Gel of Tramadole Hydrochloride

In situ gel forming systems have been widely investigated as vehicles for sustained drug delivery. In situ gel formation occurs due to one or combination of different stimuli like pH change, temperature modulation and solvent exchange. So, In situ gelling system via different route such as oral, nasal, ophthalmic etc can be formulated. In the present research work Oral Floating Insitu gel of Tramadol Hydrochloride was formulated using Eudragit L100, Eudragit S100, Eudragit RSPO, Ethyl cellulose, HPMC K100M. The optimized batch gave drug release for 12 hrs in the polymer combination of Sodium Alginate 2% and Ethyl Cellulose 2.5%. In vivo study was performed by providing the formulation to rabbit and then X-ray was taken for the confirmation of formation of gel in stomach and floating of dosage form for 12 hrs. And it was found to be floating for more than 12 hrs. The batch was found to be stable for 6 months in accelerated stability study.
A New Write Caching Algorithm for Solid State Disks

Flash-based solid state disks (SSD) is a performance based data storage technology that optimizes the use of flash-based technology to implement its data storage capabilities compared with mechanically available data storage technologies. It has been argued in theory and practice that SSD devices are better performers compared with mechanical devices. To improve the efficiency of a flash memory SSD device, it is important for it to be designed to be computationally support parallel operations.
Java Code Coverage Test Technology Based on EMMA

Traditionally, at the end of functional testing, testers resort to the numbers of problems reported and the test cases executed to evaluate the adequacy of the testing. This method generally results in that testers have no confidence in the test results. Therefore, we introduce coverage to settle this problem. EMMA is an open source testing tool which detects and reports Java code coverage. In this paper, two actual functional testing as examples to explain the processes of using EMMA command line and EMMA with Ant to obtain code coverage, further to explain how to increase test cases to accumulate code coverage based on EMMA. The final code coverage analysis reports testify that using EMMA can greatly ensure the integrity of the functional testing.
Time series analysis and prediction by neural networks

Neural network algorithms have been shown to provide good solutions for a variety of non-linear optimization problems, ranging from classification to function approximation in high dimension space. These algorithms are capable of “learning” a target function from a set of “training examples”without strong assumption about the function. In this paper we show an example of applying neural networks to time series analysis and prediction. Backpropagation algorithm is used to train layered, feed-forward networks to model a complex, non-linear time series. A general state space formulation is adopted to analyze the problem and a Cascaded Method is used to predict multiple steps into the future. A fast parallel implementation of Backpropagation on the Connection Machine allowed us to do extensive exploratory data analysis to search for good neural net predictive models on large data sets.
Development of dual sensor hand-held detector

In this paper hand-held dual sensor detector development requirements are considered dedicated to buried object detection. Design characteristics of such a system are categorized and listed. Hardware and software structures, ergonomics, user interface, environmental and EMC/EMI tests to be applied and performance test issues are studied. Main properties of the developed system (SEZER) are presented, which contains Metal Detector (MD) and Ground Penetrating Radar (GPR). The realized system has ergonomic structure and can detect both metallic and non-metallic buried objects. Moreover classification of target is possible if it was defined to the signal processing software in learning phase.
Applying genetic algorithms and neural networks to the container loading problem

In recent years, the container loading problem has important industrial and commercial application because the cost of physical distribution has risen. This paper combines genetic algorithms and neural networks to solve this problem. The searching purpose of genetic algorithm is to seek the weight of some important factors of packing for minimizing the unused space in the container. In addition, this research extracts the features from different problems and obtains the optimal weight combinations by using generic algorithms. Finally, these solutions are learned by neural network, so that the results of this research can be used widely