Input Length Research Articles

Determination of Freight Rates Based on Package Dimension and Distance of Delivery Using Fuzzy Logic System in Angkotin Application

Angkot.in is a website for goods delivery service by “angkot”. “Angkot” is abbreviation from Angkutan Kota. Angkutan Kota (City Transport) is a mode of transportation with a predetermined route set by the government. The price of goods delivery is influenced by a number of factors. This research aims to solve this problem using fuzzy logic system, using it will be built an automated system to calculate the transportation price needed to be paid by the customer. The rate is determined by taking into consideration the dimension of the goods and the number of angkot used. This is done because fuzzy logic system can solve problems related to calculation system that is not detailed about changes in the input. The method used in this fuzzy logic system is Tsukamoto’s. This system weights on the factors that influence the rate to calculate the accurate number. With the existence of this rate calculation system, it is hoped that the rate determined will be accurate and could maintain the rate in the normal range of angkot usage, which is economical. Tsukamoto method is one example of a method in a fuzzy logic system. In Fuzzy Design, The Rules are made to map out the conditions obtained from the user. Creating accurate rules is vital because it will affect the result. Knowledge base as an important fact source to create rules will determine the total rate need to be paid by the user. Before creating rules, first the variables that influence need to be determined. From the result of the tests on the features that supports the rate calculation system, was obtained a conclusion that almost all features created are already running as intended and support the performance of this system. The Rate calculation system built by implementing fuzzy logic system using initial location and destination as inputs to obtain distance value and goods size input (length, width, height) and weight to obtain dimension.

Bach 2.0 - generating classical music using recurrent neural networks

The main incentive of this paper is to approach the sensitive subject of classical music synthesis in the form of musical scores by providing an analysis of different Recurrent Neural Network architectures. We will be discussing in a side-by-side comparison two of the most common neural network layers, namely Long-Short Term Memory and Gated Recurrent Unit, respectively, and study the effect of altering the global architecture meta-parameters, such as number of hidden neurons, layer count and number of epochs on the categorical accuracy and loss. A case study is performed on musical pieces composed by Johann Sebastian Bach and a method for estimating the repetition stride in a given musical piece is introduced. This is identified as the primary factor in optimizing the input length that must be fed during the training process.

Fusion Methods for CNN-Based Automatic Modulation Classification

An automatic modulation classification has a very broad application in wireless communications. Recently, deep learning has been used to solve this problem and achieved superior performance. In most cases, the input size is fixed in convolutional neural network (CNN)-based modulation classification. However, the duration of the actual radio signal burst is variable. When the signal length is greater than the CNN input length, how to make full use of the complete signal burst to improve the classification accuracy is a problem needs to be considered. In this paper, three fusion methods are proposed to solve this problem, such as voting-based fusion, confidence-based fusion, and feature-based fusion. The simulation experiments are done to analyze the performance of these methods. The results show that the three fusion methods perform better than the non-fusion method. The performance of the two fusion methods based on confidence and feature is very close, which is better than that of the voting-based fusion.

FPGA-based Acceleration of FT Convolution for Pulsar Search Using OpenCL

The Square Kilometre Array (SKA) project will be the world’s largest radio telescope array. With its large number of antennas, the number of signals that need to be processed is dramatic. One important element of the SKA’s Central Signal Processor package is pulsar search. This article focuses on the FPGA-based acceleration of the Frequency-Domain Acceleration Search module, which is a part of SKA pulsar search engine. In this module, the frequency-domain input signals have to be processed by 85 Finite Impulse response (FIR) filters within a short period of limitation and for thousands of input arrays. Because of the large scale of the input length and FIR filter size, even high-end FPGA devices cannot parallelise the task completely. We start by investigating both time-domain FIR filter (TDFIR) and frequency-domain FIR filter (FDFIR) to tackle this task. We applied the overlap-add algorithm to split the coefficient array of TDFIR and the overlap-save algorithm to split the input signals of FDFIR. To achieve fast prototyping design, we employed OpenCL, which is a high-level FPGA development technique. The performance and power consumption are evaluated using multiple FPGA devices simultaneously and compared with GPU results, which is achieved by porting FPGA-based OpenCL kernels. The experimental evaluation shows that the FDFIR solution is very competitive in terms of performance, with a clear energy consumption advantage over the GPU solution.

The Temporal role in Grammatical Performance of Foreign Language Learners of English

This study evaluates the effects of length of formal learning time on the grammatical performance of English morphosyntactic structures among Iranian foreign language learners. Among different factors affecting language learning, the length of formal language exposure or language input is introduced as an important factor in language learning process. Thus, length of receiving input exposure in the form of formal classroom instruction in the current study is carefully studied by categorizing the participants in four different groups. The participants represented different exposure time groups by having various formal instruction hours at school and/or language center. In order to investigate the role of formal learning time on the performance of the individual morphosyntactic structures, the participants were presented to a timed Grammaticality Judgment Task. The task tested eleven different morphosyntactic structures and contained 132 sentences. The participants were asked to judge the grammaticality of the sentences and do the necessary corrections if needed. Repeated Measures ANOVA and regression analyses were performed to analyse the data. Results revealed the contribution of time on the performance of the individual structures. Keywords: Input exposure; Formal learning time; Grammaticality Judgment Task; Foreign Language Learning; Grammatical performance

Increase lattice Boltzmann method ability to simulate slip flow regimes with dispersed CNTs nanoadditives inside

In this study, the mixed convection of flow in a microchannel containing nanofluid is simulated by the Lattice Boltzmann Method. The water/functionalized multi-wall carbon nanotubes nanofluid is selected as the working fluid. The cold nanofluid passes through the warm walls of the microchannel to cool them down. The buoyancy forces caused by the mass of the nanofluid change the hydrodynamic properties of the flow. Accordingly, the gravitational term is included as an external force in the Boltzmann equation and Boltzmann’s hydrodynamic and thermal equations are rewritten under new conditions. The flow analysis is performed for different values of slip coefficient and Grashof number. The results are expressed in terms of velocity and temperature profiles, contours of streamlines and isotherms beside the slip velocity and temperature jump diagrams. It is observed that the effect of buoyancy force changes the motion properties of the flow in the input region and increases the hydrodynamic input length of flow. These changes are particularly evident at higher values of Grashof numbers and create a rounded circle in the opposite direction of the flow at the microchannel input. The negative slip velocity caused by the vortex resulted in a temperature jump at the input flow region.

Indistinguishability Obfuscation from Functional Encryption

Indistinguishability obfuscation (IO) is a tremendous notion, powerful enough to give rise to almost any known cryptographic object. Prior candidate IO constructions were based on specific assumptions on algebraic objects called multi-linear graded encodings. We present a generic construction of indistinguishability obfuscation from public-key functional encryption with succinct encryption circuits and subexponential security. This shows the equivalence of indistinguishability obfuscation and public-key functional encryption, a primitive that has previously seemed to be much weaker, lacking the power and the staggering range of applications of indistinguishability obfuscation. Our main construction can be based on functional encryption schemes that support a single functional key , and where the encryption circuit grows sub-linearly in the circuit-size of the function. We further show that sublinear succinctness in circuit-size for single-key schemes can be traded with sublinear succinctness in the number of keys (also known as the collusion-size ) for multi-key schemes. We also show that, under the Learning with Errors assumption, our techniques imply that any indistinguishability obfuscator can be converted into one where the size of obfuscated circuits is twice that of the original circuit plus an additive overhead that is polynomial in its depth, input length, and the security parameter.

Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network

Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, the influence of different numbers of modes and the input length of the convolutional neural network are discussed to select the optimal value through calculating the errors. During the regression step, each mode is treated as a channel that constitutes the input of the forecasting model. The convolution operations in convolutional neural networks extract helpful local features in each mode and the relationships between modes for forecasting. We take advantage of the convolutional neural network and directly output multi-step forecasting results. In order to show the forecasting and generalization performance of the proposed method, wind seed data from two wind farms in Inner Mongolia, China and Sotavento Galicia, Spain with different statistical information were employed. Some classic statistical approaches were adopted for comparison. The experimental results show the satisfactory performance for all of the methods in single-step forecasting and the advantages of using decomposed modes. The root mean squared errors range from 0.79 m/s to 1.64 m/s for all of the methods. In the case of multi-step forecasting, our proposed method achieves an outstanding improvement compared with the other methods. The root mean squared error of our proposed method was 1.30 m/s while the worst performance of the other methods was 9.68 m/s. The proposed method is able to directly predict the variation trend of wind speed based on historical data with minor errors. Hence, the proposed forecasting schemes can be utilized for wind speed multi-step forecasting to cost-effectively manage wind power generation.

High-Speed Implementation of Length-Compatible Privacy Amplification in Continuous-Variable Quantum Key Distribution

Privacy amplification is an indispensable step in postprocessing of continuous-variable quantum key distribution (CV-QKD), which is used to distill unconditional secure keys from identical corrected keys between two distant legal parties. The processing speed of privacy amplification has a significant effect on the secret key rate of CV-QKD system. We report the high-speed parallel implementation of length-compatible privacy amplification algorithm based on graphic processing unit. Length-compatible algorithm is used to satisfy the security requirement of privacy amplification at different transmission distances when considering finite-size effect. We achieve the speed of privacy amplification over 1 Gbps at arbitrary input length and the speed is one to two orders of magnitude faster than previous demonstrations, which supports high-speed real-time CV-QKD system and ensures the security of privacy amplification.

Human Splice-Site Prediction with Deep Neural Networks.

Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

A Simple Projection Algorithm for Linear Programming Problems

Fujishige et al. propose the LP-Newton method, a new algorithm for linear programming problem (LP). They address LPs which have a lower and an upper bound for each variable, and reformulate the problem by introducing a related zonotope. The LP-Newton method repeats projections onto the zonotope by Wolfe’s algorithm. For the LP-Newton method, Fujishige et al. show that the algorithm terminates in a finite number of iterations. Furthermore, they show that if all the inputs are rational numbers, then the number of projections is bounded by a polynomial in L, where L is the input length of the problem. In this paper, we propose a modification to their algorithm using a binary search. In addition to its finiteness, if all the inputs are rational numbers and the optimal value is an integer, then the number of projections is bounded by $$L+1$$ , that is, a linear bound.

On the (in)efficiency of non-interactive secure multiparty computation

Secure multi-party computation (MPC) enables multiple players to cooperatively evaluate various functions in the presence of adversaries. In this paper, we consider non-interactive MPC (NIMPC) against honest-but-curious adversaries in the information-theoretic setting, which was introduced by Beimel et al. at CRYPTO 2014. Their main focus is to realize stronger security while completely avoiding interaction, and succeeded to show that every function admits a fully robust NIMPC protocol. In this paper, we further develop the study of NIMPC. We first present a simple lower bound on the communication complexity derived from the correctness requirement of NIMPC. Secondly, we present an efficient NIMPC protocol for indicator functions, which is an important building block of NIMPC protocols. An NIMPC protocol for arbitrary functions is also constructed from the proposed NIMPC for indicator functions by using the generic compiler introduced by Beimel et al. in CRYPTO 2014. The communication complexities of NIMPC protocols presented in this paper are much more efficient than the previous ones. In fact, the gap between the lower and upper bounds of the communication complexity is reduced from exponential in the input length to quadratic. Finally, we show some improvements on the efficiency in the so-called offline-online model. Specifically, for some sets of functions, the exponential amount of offline communication reduces the online communication to almost optimum amount in the standard model.

Key-Recovery Attacks on Full Kravatte

This paper presents a cryptanalysis of full Kravatte, an instantiation of the Farfalle construction of a pseudorandom function (PRF) with variable input and output length. This new construction, proposed by Bertoni et al., introduces an efficiently parallelizable and extremely versatile building block for the design of symmetric mechanisms, e.g. message authentication codes or stream ciphers. It relies on a set of permutations and on so-called rolling functions: it can be split into a compression layer followed by a two-step expansion layer. The key is expanded and used to mask the inputs and outputs of the construction. Kravatte instantiates Farfalle using linear rolling functions and permutations obtained by iterating the Keccak round function.We develop in this paper several attacks against this PRF, based on three different attack strategies that bypass part of the construction and target a reduced number of permutation rounds. A higher order differential distinguisher exploits the possibility to build an affine space of values in the cipher state after the compression layer. An algebraic meet-in-the-middle attack can be mounted on the second step of the expansion layer. Finally, due to the linearity of the rolling function and the low algebraic degree of the Keccak round function, a linear recurrence distinguisher can be found on intermediate states of the second step of the expansion layer. All the attacks rely on the ability to invert a small number of the final rounds of the construction. In particular, the last two rounds of the construction together with the final masking by the key can be algebraically inverted, which allows to recover the key.The complexities of the devised attacks, applied to the Kravatte specifications published on the IACR ePrint in July 2017, or the strengthened version of Kravatte recently presented at ECC 2017, are far below the security claimed.

On Communication Through a Gaussian Channel With an MMSE Disturbance Constraint

This paper considers a Gaussian channel with one transmitter and two receivers. The goal is to maximize the communication rate at the intended/primary receiver subject to a disturbance constraint at the unintended/secondary receiver. The disturbance is measured in terms of the minimum mean square error (MMSE) of the interference that the transmission to the primary receiver inflicts on the secondary receiver. This paper presents a new upper bound for the problem of maximizing the mutual information subject to an MMSE constraint. The new bound holds for vector inputs of any length and recovers a previously known limiting (when the length of the vector input tends to infinity) expression from the work of Bustin et al. The key technical novelty is a new upper bound on the MMSE. This bound allows one to bound the MMSE for all signal-to-noise ratio (SNR) values below a certain SNR at which the MMSE is known (which corresponds to the disturbance constraint). The bound also complements the “single-crossing point property” of the MMSE that upper bounds the MMSE for all SNR values above a certain value at which the MMSE value is known. The MMSE upper bound provides a refined characterization of the phase-transition phenomenon, which manifests, in the limit as the length of the vector input goes to infinity, as a discontinuity of the MMSE for the problem at hand. For vector inputs of size $n=1$ , a matching lower bound, to within an additive gap of order $O ( \log \log ({1}/{\sf MMSE}) )$ (where ${\sf MMSE}$ is the disturbance constraint), is shown by means of the mixed inputs technique recently introduced by Dytso et al.

Pseudorandomness via the Discrete Fourier Transform

We present a new approach to constructing unconditional pseudorandom generators against classes of functions that involve computing a linear function of the inputs. We give an explicit construction of a pseudorandom generator that fools the discrete Fourier transforms of linear functions with seed-length that is nearly logarithmic (up to polyloglog factors) in the input size and the desired error parameter. Our result gives a single pseudorandom generator that fools several important classes of tests computable in logspace that have been considered in the literature, including halfspaces (over general domains), modular tests, and combinatorial shapes. For all these classes, our generator is the first to achieve near logarithmic seed-length in both the input length and the error parameter. Getting such a seed-length is a natural challenge in its own right, which needs to be overcome in order to derandomize $\mathsf{RL}$---a central question in complexity theory. Our construction combines ideas from a large body of prior work, ranging from the classical construction of [J. Naor and M. Naor, SIAM J. Comput., 22 (1993), pp. 838--856] to the recent gradually increasing independence paradigm of [D. M. Kane, R. Meka, and J. Nelson, Approximation, Randomization, and Combinatorial Optimization, Lecture Notes in Comput. Sci. 6845, Springer, Heidelberg, 2011, pp. 628--639; L. E. Celis et al., SIAM J. Comput., 42 (2013), pp. 1030--1050; P. Gopalan et al., Proceedings of the $53$rd Annual IEEE Symposium on Foundations of Computer Science, 2012, pp. 120--129], while also introducing some novel analytic machinery which might find other applications.

Conveyor Merges in Zone Picking Systems: A Tractable and Accurate Approximate Model

Sequential zone picking systems are popular conveyor-based picker-to-parts order picking systems that divide the order picking area in work zones. When designing a zone picking system, it is important to know whether the throughput capability of the system can meet customer demand. However, the performance and maximum throughput capability of a zone picking system is largely determined by congestion and blocking that occur at the various conveyor merges in the system. In this paper we develop an analytical model to study the impact of conveyor merges in sequential zone picking systems. Because of finite buffers, blocking, recirculation, and merging, the resulting queueing model does not have a product-form stationary queue-length distribution which makes exact analysis practically infeasible. Therefore, we develop an approximate solution by using an aggregation technique and matrix-geometric methods to study the throughput capability of the system. The model is suitable to support rapid design of complex zone picking systems, in terms of number and length of zones, input and output buffer capacities, and storage allocation of products to zones to meet prespecified performance targets. Comparison of the approximation results to simulation show that for a wide range of parameters the mean relative error in the system throughput is typically less than 5%. The model accurately predicts the loss in throughput due to congestion and blocking at the merges, and can be used to allocate input and output buffer spaces to maximize the throughput capability of the system.

FPGA-Based Implementation of Size-Adaptive Privacy Amplification in Quantum Key Distribution

In a quantum key distribution (QKD) system, privacy amplification (PA) is an essential procedure that can effectively eliminate the leaked information to an eavesdropper and distill a secret key. The processing speed of the PA algorithm inevitably affects the final key rate of the QKD system. We propose a high-speed PA algorithm based on field-programmable gate array (FPGA), where the matrix multiplications are divided into a number of rhomboid-block operations. The implementation of the PA reduces significantly the required number of FPGA Block RAMs. Meanwhile, it automatically adapts to different lengths of input and output blocks. Finally, we verified the correctness and high-speed of the algorithm when implemented in a Xilinx Virtex-7 FPGA.

Advances in weld residual stress prediction: A review of the NeT TG4 simulation round robin part 1, thermal analyses

The members of the NeT network have undertaken parallel round-robin activities measuring and predicting transient temperatures, fusion boundary development, weld residual stresses and structural distortions in a benchmark specimen manufactured from AISI 316L(N) austenitic stainless plate with a central finite length slot filled with three superimposed TIG weld beads. This is a strongly 3-dimensional configuration with many of the characteristics of a repair weld. The round-robin finite element predictions of transient temperatures and the extent and shape of the melted zone are compared with thermocouple measurements made during welding, and with the results of destructive metallography. It is found that the majority of thermal simulations achieve high accuracy at weld mid-length, where quasi-steady state conditions apply, and slightly reduced but still acceptable accuracy at the bead ends, where thermal conditions are much more 3-dimensional. With two exceptions, variability in thermal solutions can be discounted as a cause of variation in subsequent mechanical simulations. The two outlier simulations allow investigation of sensitivity to final pass heat input and bead length.

Text Categorization with Dynamic Input Length Using Deep Learning – A Review

Text Categorization with Dynamic Input Length Using Deep Learning – A Review