Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel nanometric fault tolerant reversible divider

Quantum and reversible logic circuits have more advantages than the common circuits, like low power consumption. These circuits are good choice to design future computers. One of the important issues in reversible logic is parity preservation. If parity of inputs and outputs are equal in reversible gate, this gate will be parity preserve. Reversible circuits made by these gates are parity preserve. In this paper we propose a new fault tolerant reversible divider. The proposed fault tolerant reversible divider is the first effort to design fault tolerant reversible division circuit. In this circuit, we use some fault tolerant reversible components like fault tolerant reversible parallel adder, fault tolerant reversible shift register and fault tolerant reversible n-bit register. Hence, we also propose a new fault tolerant reversible full adder, a new fault tolerant reversible n+1-bit parallel adder and a new basic cell for PIPO fault tolerant reversible left-shift register. These parity preserving reversible components are also proposed for the first time in the literature. All the scales are in the nanometric area.
Application of monopulse techniques in angle-measuring of single-beam mechanical scanning radar

Due to large computation cost and low accuracy of traditional angel measurement in single-beam mechanical scanning radar, a new application of monopulse techniques is proposed. First of all, this paper introduces the principle of monopulse and analyzes the difference in accuracy between monopulse and the traditional methods. Based on that, engineering implementation is then discussed. Finally, experimental data obtained in detection mode for inverse synthetic aperture radar (ISAR) is processed by this method, and results demonstrate effectiveness of the proposed method.
Anthropocentric Visualisation of Optimal Cover of Association Rules

Visual mining as an emerging field is gradually witnessing approaches where visual capabilities are of use to mine manageable and useful knowledge. In this respect, the implication of the user at the core of the mining process seems to be one of the key factors in the success of any visual mining system. In this paper, we introduce a new anthropocentric visual mining approach for the extraction and the visualization of a reduced cover of association rules. Given that getting out such a reduced cover is NP-hard problem, we introduce a greedy algorithm that relies on correlation assessment metrics to flag discovered formal concepts as optimal. In addition, we present some snapshots illustrating the key features of the implemented visualization tool that relies on the virtual reality paradigm.
Detect The ultima shape of a based on u into four ca An Efficient Algorithm for Face Localizatio

and localizing a face in a single image is the most important part of almost all face systems. Face localization aims to determine the image position of a face for verification documents such as passport, driving license, ID cards, etc. In this paper an entropy-based roposed for detecting the high information region of the image which may include eyes, e, etc. The derived regions in this stage of recognition are sent to feature extraction and n phase. The method has been tested on the ORL database. The results show the ss and robustness of the proposed method for face detection and localization in presence dditive Gaussian noise up to 25 dbw. We have achieved localization rate 99.75% for f faces in the ORL data set that we had which means 1 miss over 400 ORL faces. Image processing, Face localization, Recognition, Entropy.
Introducing CURRENNT: the munich open-source CUDA recurrent neural network toolkit

In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA's Computed Unified Device Architecture (CUDA). CURRENNT supports uni- and bidirectional RNNs with Long Short-Term Memory (LSTM) memory cells which overcome the vanishing gradient problem. To our knowledge, CURRENNT is the first publicly available parallel implementation of deep LSTM-RNNs. Benchmarks are given on a noisy speech recognition task from the 2013 2nd CHiME Speech Separation and Recognition Challenge, where LSTM-RNNs have been shown to deliver best performance. In the result, double digit speedups in bidirectional LSTM training are achieved with respect to a reference single-threaded CPU implementation. CURRENNT is available under the GNU General Public License from http://sourceforge.net/p/currennt.
Seroevidence for a High Prevalence of Subclinical Infection With Avian Influenza A(H5N1) Virus Among Workers in a Live-Poultry Market in Indonesia

Background. In Indonesia, highly pathogenic avian influenza A(H5N1) virus has become endemic in poultry and has caused sporadic deadly infections in human. Since 2012, we have conducted fixed-point surveillance of avian influenza viruses at a live-poultry market in East Java, Indonesia. In this study, we examined the seroprevalence of avian influenza A(H5N1) virus infection among market workers. Methods. Sera were collected from 101 workers in early 2014 and examined for antibody activity against avian A(H5N1) Eurasian lineage virus by a hemagglutination-inhibition (HI) assay. Results. By the HI assay, 84% of the sera tested positive for antibody activity against the avian virus. Further analysis revealed that the average HI titer in 2014 was 2.9-fold higher than in 2012 and that seroconversion occurred in 44% of paired sera (11 of 25) between 2012 and 2014. A medical history survey was performed in 2016; responses to questionnaires indicated that none of workers had had severe acute respiratory illness during 2013. Conclusions. This study provides evidence of a high prevalence of avian A(H5N1) virus infection in 2013 among workers at a live-poultry market. However, because no instances of hospitalizations were reported, we can conclude the virus did not manifest any clinical symptoms in workers.
A New Federal Funds Rate Target Series: September 27, 1982 - December 31, 1993

This paper creates a new series of the FOMC*s Target for the federal funds rate for the period September 27, 1982 through December 31, 1993. The creation of this series was motivated by Thornton (2005). Analyzing the verbatim transcripts of the FOMC, Thornton finds that most of the FOMC believed they began targeting the funds rate even before it deemphasized M1*s role in the Fed*s daily operating procedure. The new series was constructed using the verbatim transcripts of FOMC meetings, the FOMC Blue Book, the Report of Open Market Operations and Money Market Conditions, and data that the author obtained from the Desk for the Federal Reserve Bank of New York dealing with open market operations over the period March 1984 through December 1996. The new series compared with another widely used series presented in Thornton and Wheelock (2000). There are some differences in the dating and magnitude of target changes between the two series prior to but not after August 1989.
Continuous Speech Recognition system for Tamil language using monophone-based Hidden Markov Model

Research in Automatic Speech Recognition (ASR) has attracted a great deal of attention over the past five decades. It aims to provide an efficient way for human to communicate with computers. With the extensive development of these systems permits the user to talk almost naturally with computers. Hence, today's researchers are mainly focusing on developing a system for recognizing continuous speech to accomplish tasks such as answering emails and creating text documents etc. But developing such system is still found to be a difficult task due its own complexity. This paper presents a Continuous Speech Recognition (CSR) system for Tamil language using Hidden Markov Model (HMM) approach. The most powerful and widely used MFCC feature extraction is used as a front-end for the proposed system. The monophone based acoustic model is chosen to recognize the given set of sentences from medium vocabulary. The results are found to be satisfactory with 92% of word recognition accuracy and 81% of sentence accuracy for the proposed developed system.
A Simple Intervention to Reduce Framing Effects in Perceptions of Global Climate Change

This paper explores a technique for mitigating framing effects in perceptions of global climate change. People's opinions on issues ranging from same sex marriage and the environment to free speech and health care have been shown to be influenced by the way in which the issue is presented: the metaphors, images, and catchphrases that communicate implicit normative messages about the issue. The work described here synthesizes insights from experimental research on moderators of framing effects and sociological research on frame reflection. Based on this synthesis, this paper describes a test of a simple highlighting technique for focusing respondents' attention on the framing operating in political materials. Results suggest that this intervention not only can reduce the opinion-shaping impact of frames but may also reduce ideological polarization. Implications include a better understanding of the mechanisms by which frames operate, techniques for making frame reflection possible among political non-elites, and recommendations for strengthening environmental groups' public communication strategies.
A Log-Based Relevance Feedback Scheme for 3D Model Retrieval

Relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. In the past, most research efforts in 3D model retrieval field have focused on designing algorithms for traditional relevance feedback. Given a 3D model retrieval system, it can collect and store users’ relevance feedback information in a history log, retrieval system can take advantage of the log data to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level 3D model features and high-level concepts. In this scheme, we present a learning technique for log-based relevance feedback biased on support vector machine. Experimental results show that this log-based scheme achieves higher search accuracy than traditional query refinement schemes.
Building a conversational model from two-tweets

The current problem in building a conversational model from Twitter data is the scarcity of long conversations. According to our statistics, more than 90% of conversations in Twitter are composed of just two tweets. Previous work has utilized only conversations lasting longer than three tweets for dialogue modeling so that more than a single interaction can be successfully modeled. This paper verifies, by experiment, that two-tweet exchanges alone can lead to conversational models that are comparable to those made from longer-tweet conversations. This finding leverages the value of Twitter as a dialogue corpus and opens the possibility of better conversational modeling using Twitter data.
Interpersonal Vulnerability to Depression in High-Risk Children: The Role of Insecure Attachment and Reassurance Seeking

This study examined the relation between insecure attachment and depression in a sample of 140 children (69 boys and 71 girls; ages 6 to 14) whose parents have a history of major depressive episodes. In addition, we examined whether this relation was moderated by excessive reassurance seeking. Children completed measures assessing insecure attachment to parents, excessive reassurance seeking, and current depressive symptoms. In addition, children and their parents participated in a semistructured clinical interview assessing children's current and past history of depressive symptoms and episodes. In line with hypotheses, children who exhibited high levels of both insecure attachment and excessive reassurance seeking experienced higher levels of current depressive symptoms than children who possessed only one or neither of these interpersonal risk factors. Furthermore, the interaction of insecure attachment with excessive reassurance was associated with a past history and greater severity of depressive episodes even after controlling for current depressive symptoms.
PARALLEL GRASP ALGORITHM WITH DELAY AND DELAY VARIATION FOR RENDEZVOUS POINT SELECTION IN PIM-SM MULTICAST

Many multicast routing protocols has proposed to su pport efficient multimedia application, PIM-SM protocol remains the most used multicast routing pr otocol; they propose using a Shared Rendezvous Poin t Tree SRPT to forward multicast packets. The prime p roblem concerning SRPT construction is to determine an optimal multicast router in the network as root; this problem is called RP selection. This problem influences the multicast routing tree structure, an d therefore influences performances of the multicas session and multicast routing scheme. Determination of a best RP position is an NP complete problem, f irst proposed by Wall, which needs to be solved with a h euristic algorithm. In this paper we propose a new RP selection algorithm based on Parallel GRAS Procedur e. 2DV-PGRASP-RP selects RP by considering cost, delay and delay variation functions and can be easi ly integrated to bootstrap RP protocol used by PIMS . Simulation results show that good performance is ac hieved in multicast cost.
Flipping the Assessment of Cognitive Load: Why and How

Cognitive load theory is typically used to evaluate and improve learning materials, with the goal of optimising students' opportunity to acquire new knowledge and understanding. The cognitive load on a student is typically assessed either objectively, by taking physiological measurements while the student is learning, or subjectively, by asking the student to complete an appropriate questionnaire after the learning experience. However, there are circumstances in which a decision on learning materials must be made before those materials are developed and deployed, whereupon it is not helpful to measure the students during learning or to survey them after learning. Such circumstances necessitate a completely different approach, in which the assessment of the likely imposition of cognitive load is made by the instructors and informs the development of the learning materials. This paper explores such a situation: the choice of a programming language and integrated development environment for an introductory programming course. The paper explains the impracticality of addressing this choice by way of the usual measures of cognitive load. It then presents the approach that was used, flipping the assessment of expected cognitive load from the students to the instructors. The paper explains how this was done, presents the findings, and concludes by suggesting possibilities for future work in the area.
Amphiphilic Telechelics Incorporating Polyhedral Oligosilsesquioxane: 1. Synthesis and Characterization

Well-defined amphiphilic telechelics incorporating polyhedral oligosilsesquioxane (POSS) were synthesized by direct urethane linkage between the diol end groups of poly(ethylene glycol) (PEG) homopolymers and the monoisocyanate group of POSS macromers. The hydrophobicity of the amphiphilic telechelics was varied by using a series of PEG homopolymers of increasing molecular weight, resulting in control over molecular architecture by hydrophilic/hydrophobic balance. The synthesized amphiphilic telechelics were characterized by 1H NMR, FT-IR, GPC, DSC, and TGA, showed a relatively narrow and unimodal molecular weight distribution (Mw/Mn < 1.1) that had close to 2.0 end groups per PEG chain, and revealed strongly modified thermal behavior. Amphiphilic telechelics with POSS contents of 19.8, 23.6, 40.7, 52.7, and 68.1% depending on the molecular weights of PEG homopolymers could be obtained. Modification to crystallization behavior is observed and attributed to the bulkiness of POSS groups with respect to crys.
An in-pipe mobile robot using electro-conjugate fluid

An electro-conjugate fluid (ECF) is a kind of functional fluid, which produces a jet flow (ECF jet) when subjected to high DC voltage. It is known that a strong ECF jet is generated under nonuniform electric field, for example, the field with a pair of needle and ring electrodes. This study introduces the ECF jet to develop a novel inchworm like in-pipe mobile robot which mainly consists of two clamping units (a clamping actuator, an ECF jet generator and an ECF tank) and a propelling unit (a propelling actuator, an ECF jet generator and an ECF tank). First, we characterize the actuators composed of a silicone rubber tube and aramid fibers. Next, we characterize the ECF jet generator which could be a micro fluid pressure source of the actuators, and confirm the effect of the number of electrode pairs on its performance. Finally we develop the ECF in-pipe mobile robot and demonstrate that the robot can move in a φ14 mm acrylic pipe with 0.043 mm/s. The robot is 200mm long with the diameter of 10mm.
Unified Approach to Thermodynamic Optimization of Generic Objective Functions in the Linear Response Regime

While many efforts have been devoted to optimizing the power output for a finite-time thermodynamic process, thermodynamic optimization under realistic situations is not necessarily concerned with power alone; rather, it may be of great relevance to optimize generic objective functions that are combinations of power, entropy production, and/or efficiency. One can optimize the objective function for a given model; generally the obtained results are strongly model dependent. However, if the thermodynamic process in question is operated in the linear response regime, then we show in this work that it is possible to adopt a unified approach to optimizing the objective function, thanks to Onsager’s theory of linear irreversible thermodynamics. A dissipation bound is derived, and based on it, the efficiency associated with the optimization problem, which is universal in the linear response regime and irrespective of model details, can be obtained in a unified way. Our results are in good agreement with previous findings. Moreover, we unveil that the ratio between the stopping time of a finite-time process and the optimized duration time plays a pivotal role in determining the corresponding efficiency in the case of linear response.
Parallelization of Encryption and Hashing Algorithm Using GPU

- With the development of the GPGPU (General-purpose computing on graphics processing units), more and more computing problems are solved by using the parallel property of GPU (Graphics Processing Unit). CUDA (Compute Unified Device Architecture) is a framework which makes the GPGPU more accessible and easier to learn for the general population of programmers. This is because it builds on C and hides many of the complicated details of how the GPU works from a CUDA developer. Using the unique properties of the GPU through CUDA has greatly increased the efficiency of many computational problems. The target in this project is to study and analyses the majority of algorithms related to the cryptography and then to design and make an implementation of algorithm in CUDA. Finally, this reach will compare the performance between the GPU implementation and the CPU implementation in order to look into the possibility of improving the performance of algorithms. The survey done in three cryptography algorithm AES, SHA, MD5 .
Downstream and Intermediate Interactions of Synovial Sarcoma-Associated Fusion Oncoproteins and Their Implication for Targeted Therapy

Synovial sarcoma (SS), an aggressive type of soft tissue tumor, occurs mostly in adolescents and young adults. The origin and molecular mechanism of the development of SS remain only partially known. Over 90% of SS cases are characterized by the t(X;18)(p11.2;q11.2) translocation, which results mainly in the formation of SS18-SSX1 or SS18-SSX2 fusion genes. In recent years, several reports describing direct and indirect interactions of SS18-SSX1/SSX2 oncoproteins have been published. These reports suggest that the fusion proteins particularly affect the cell growth, cell proliferation, TP53 pathway, and chromatin remodeling mechanisms, contributing to SS oncogenesis. Additional research efforts are required to fully explore the protein-protein interactions of SS18-SSX oncoproteins and the pathways that are regulated by these partnerships for the development of effective targeted therapy.
Application of a max–min ant system to joint layout and size optimization of pipe networks

The application of a max–min ant algorithm to the layout and size optimization of pipe networks is described in this paper. The formulation conventionally used for the pipe size optimization of networks with fixed layout is extended to account for the layout determination of the networks. This is achieved by including new constraints regarding the reliability of the network and modifying some of the constraints of the optimization problem. A deterministic concept of reliability is used in which the number of independent paths from source nodes to each of the demand nodes is considered as a measure of reliability. The method starts with a predefined layout which includes all possible links. The method is capable of designing the layout and pipe sizes of water distribution networks of predefined reliability including tree-like and looped networks. It is also shown that a layout optimization of a network followed by size optimization does not lead to an optimal or a near-optimal solution. This emphasizes the need for simultaneous layout and size optimization of networks if an optimal or near-optimal solution is desired. The performance of the method for layout and pipe size optimization of pipe networks is tested against two benchmark examples in the literature and the results are presented. The first example is considered to show the necessity of joint layout and size optimization even for the simple tree networks while the second example is considered to illustrate the efficiency of the proposed method for layout and size optimization of real-world networks with different levels of reliability.
An optimal VM resource allocation for near-client-datacenter for multimedia cloud

With the development of cloud computing, datacenter already plays increasingly important role in improving the multimedia user Quality of Experience (QoE). The resource allocation strategy, namely the virtual machines (VMs) allocation strategy, is a major research direction for cloud computing technology. In this paper, we introduce the definition of near-client-datacenter for virtual machines, which is the main environment for multimedia cloud. Specifically we consider a network environment model for the multi-datacenter. Base on this model, we analyze the key factors in the transmission network between datacenters and propose a resource allocation strategy for the VMs in datacenters to minimize the resource cost. Since the constrained optimization problem is a NP-hard problem, we present a heuristic algorithm to get an approximate solution. The evaluation results not only demonstrate the importance of the transmission network between datacenters in the resource allocation of cloud network, but also show the effectiveness of our optimal heuristic algorithm.