Computational Operations Research Articles

A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems

Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix–vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing.

Reconfigurable 2, 3 and 5‐point DFT processing element for SDF FFT architecture using fast cyclic convolution algorithm

In this Letter, a reconfigurable processing element (PE) for pipelined SDF FFT architecture is presented, which can be configured to compute 2, 3 and 5-point DFTs. Foremost, the proposed PE architecture for the 5-point DFT computation is designed by factorising the 5-point DFT computation operation into cyclic convolution units and then the 2- and 3-point DFTs structures are mapped on to it using multiplexers. Thus, all three configurations are possible. In the case of prior 5-point PE designs, the PE can start its operation only after the arrival of all the five-input data, whereas the proposed PE completes a part of computation after the arrival of the first three inputs and reuse the same hardware to process the next two inputs. As a result, the proposed PE requires less hardware, at the same time, preserving the throughput of prior PE. The proposed PE required 25% less multiplier and one adder less compared to the Winograd algorithm based 5-input PE.

Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets

An accurate method based on evolutionary correlation filtering to solve pose estimation of highly occluded targets is presented. The proposed method performs multiple correlation operations between an input scene and a bank of filters designed in frequency-domain. Each filter is computed with statistical parameters of a real-world scene and a template that contains information of the target in a single pose parameter configuration. A vast set of templates is generated from multiple views of a three-dimensional model of the target, which are created synthetically with computer graphics. An evolutionary approach in the bank of filter construction for optimizing the pose estimation parameters is implemented. The evolutionary computation technique based on a pseudo-bacterial genetic algorithm yields high estimation accuracy finding the best filter that produces the highest matching score. The proposed evolutionary correlation filtering yields good convergence of the bank of filter optimization, which produces a reduction of the number of computational operations. Experimental results demonstrate the robustness of the proposed method in terms of detection performance and pose estimation of highly occluded targets compared with state-of-the-art methods.

The intergenerational transmission of mathematics achievement in middle childhood: A prospective adoption design.

The present study uses a parent-offspring adoption design to examine the dual roles of heritable and environmental influences on children's mathematics achievement. Linked sets (N=195) of adopted children, adoptive parents, and birth parents each completed a measure of mathematics fluency (i.e., simple computational operations). Birth parent mathematics achievement and adoptive father mathematics achievement positively correlated with child achievement scores at age 7, whereas adoptive mother and adopted child mathematics achievement scores were not significantly associated with one another. Additionally, findings demonstrated no significant effects of gene-environment interactions on child mathematics achievement at age 7. These results indicate that both heritable and rearing environmental factors contribute to children's mathematics achievement and identify unique influences of the paternal rearing environment on mathematics achievement in middle childhood.

CSRR Based Sensors for Relative Permittivity Measurement With Improved and Uniform Sensitivity Throughout [0.9–10.9] GHz Band

This paper proposes miniaturized, lightweight and high sensitivity planar metamaterial based sensors for relative permittivity measurement within [0.9–10.9] GHz band. Each proposed sensor is designed using a thin-substrate microstrip line loaded with a single complementary split ring resonator (CSRR). The loaded resonator is excited using maximum electric/magnetic (cross polarization) excitation to maximize the sensors’ sensitivity. Each proposed sensor operates at different frequency range within [0.9–10.9] GHz band based on the size of the loaded resonator. Compared to similar state-of-the-art sensors, the proposed ones are at least 30% more sensitive. The minimum transmission frequency shifts (50)% as the sample’s relative permittivity changes from 1 to 10. The paper proposes a condition of sensitivity uniformity to maintain a uniform sensitivity over the specified band irrespective of the resonator size. Utilizing the proposed condition, the sensitivity of all sensors remains uniform throughout [0.9–10.9] GHz band for all dielectric samples with relative permittivity between 1 and 10. This vital feature allows the practical realization of resonant probes with less computational operations and consistent measurements over a wide dynamic range of the sensing-related frequency. Experimental measurements are in good agreement with the numerical findings. The paper includes a comprehensive sensitivity analysis which investigates the effect of resonator’s excitation scheme, resonator’s order (i.e. single or double CSRR) and substrate thickness on the sensitivity of CSRR based sensors. The proposed sensing platforms are recommended for the development of highly sensitive, consistent and reliable planar sensors such as microfluidics, displacement, nondestructive and biomedical sensors.

A Model for Structured Information Representation in Neural Networks of the Brain.

Humans can reason at an abstract level and structure information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the brain from which structural information can be decoded. Based on this data, we introduce the concept of assembly projections, a general principle for attaching structural information to content in generic networks of spiking neurons. According to the assembly projections principle, structure-encoding assemblies emerge and are dynamically attached to content representations through Hebbian plasticity mechanisms. This model provides the basis for explaining a number of experimental data and provides a basis for modeling abstract computational operations of the brain.

Design of a real-time face detection architecture for heterogeneous systems-on-chips

Object detection represents one of the most important and challenging task in computer vision applications. Boosting-based approaches deal with computational intensive operations and they involve several sequential tasks that make very difficult developing hardware implementations with high parallelism level. This work presents a new hardware architecture able to perform object detection based on a cascade classifier in real-time and resource-constrained systems. As case study, the proposed architecture has been tailored to accomplish the face detection task and integrated within a complete heterogeneous embedded system based on a Xilinx Zynq-7000 FPGA-based System-on-Chip. Experimental results show that, thanks to the proposed parallel processing scheme and the runtime adaptable strategy to slide sub-windows across the input image, the novel design achieves a frame rate up to 125fps for the QVGA resolution, thus significantly outperforming previous works. Such a performance is obtained by using less than 10% of on-chip available logic resources with a power consumption of 377 mW at the 100 MHz clock frequency.

A secure, efficient and verifiable multimedia data sharing scheme in fog networking system

As an emerging and efficient paradigm for multimedia systems, fog networking has attracted widespread attention over the last few years. However, an increasing number of attacks in the current virtualized environments underlines the importance of secure data sharing. Unfortunately, existing multimedia data sharing schemes are not suitable for the networking systems because of the heavy computational operations, latency-sensitive service, and resource-limited devices. Additionally, authenticated and secure communications are crucial issues related to privacy and trust. In this paper, we propose a secure and efficient data sharing scheme with the computation outsourcing capability in a fog networking system by employing ciphertext-policy attribute-based encryption. The scheme supports dynamic policy updating and delegates attribute revocation processes to the cloud and fog by proxy re-encryption. In particular, we build a secure communication protocol for the revocation parameter transmission. We adopt a chaotic map to generate a one-time key, by which the revocation parameters are encrypted. Then, to ensure legal user accessing to the system, we establish a privacy-preserving communication architecture between the user and cloud, which provides authentication. In addition, our scheme provides a verifiable auditing service for the decryption key and shared file, thus ensuring its correctness. Finally, we analyze the security of the scheme, evaluate its performance, and compare it with related works.

ANALYSIS OF THE SPEED OF THE DEFUZZIFIER IN THE TASK OF CONTROLLING THE ROBOT-MANIPULATOR

Linear, nonlinear, modified, high-speed defuzzifiers based on the area ratio method are presented in this paper. The proposed defuzzifiers are used in a fuzzy digital filter device and make it possible to ensure the additivity of the robotic manipulator control system, since traditional models do not have this property. The essence of this development is to find a crisp value of the output fuzzy variable, which in this case are the regulation coefficients of the fuzzy digital filter. Reducing the number of computational operations provides an increase in the performance of the defuzzifier. The reduction in number of computational operations is carried out by eliminating the output variable’s truncated term’s height calculation, thereby reducing the computation time. A simulation model which was implemented in the MatLab Simulink system, for a neuro-fuzzy device of the robotic arm using linear and non-linear defuzzifiers is presented. The dependence of time graphs on the angle of rotation of the joints of the robotic manipulator are compared, based on the traditional center of gravity method and the method shown in this paper. It was found that the traditional center of gravity method does not ensure the fulfillment of the specified rotation angles of the links of the robotic arm, while the proposed models of defuzzifiers have this property, which can be seen from the presented dependency graphs. The simulation model of the device was also designed as a parallel-conveyor device for implementation in the field-programmable gate array of the Xilinx Spartan 3Е family. The analysis showed that the calculation time for a crisp value with high-speed defuzzification is 130 ns, which is two orders of magnitude higher than existing models. The experiment was conducted at a frequency of 100 MHz.

Solution to alleviate the impact of line resistance on the crossbar array

The crossbar array implementing the weighted sum computation and weight update operation is a promising hardware accelerator for neuromorphic computing. However, the voltage drop caused by the current flowing through the access lines could be aggressive for the resistive crossbar array in a fully parallel fashion. In this study, the authors analysed the impact of the line resistance on the crossbar array based on the SPICE simulation. It implies that the scale of the crossbar array and the ratio of line resistance to resistive random access memory resistance bring great influence on the performance of the crossbar array. Also an scheme of optimisation has been proposed to diminish its influence. Furthermore, considering line resistance, multi-layer perceptron based on crossbar array has been simulated with SPICE on Modified National Institute of Standards and Technology dataset. Those results could provide design guidelines for the practical hardware implementation of the neuromorphic accelerator.

ArchiText: Interactive Hierarchical Topic Modeling.

Human-in-the-loop topic modeling allows users to explore and steer the process to produce better quality topics that align with their needs. When integrated into visual analytic systems, many existing automated topic modeling algorithms are given interactive parameters to allow users to tune or adjust them. However, this has limitations when the algorithms cannot be easily adapted to changes, and it is difficult to realize interactivity closely supported by underlying algorithms. Instead, we emphasize the concept of tight integration, which advocates for the need to co-develop interactive algorithms and interactive visual analytic systems in parallel to allow flexibility and scalability. In this article, we describe design goals for efficiently and effectively executing the concept of tight integration among computation, visualization, and interaction for hierarchical topic modeling of text data. We propose computational base operations for interactive tasks to achieve the design goals. To instantiate our concept, we present ArchiText, a prototype system for interactive hierarchical topic modeling, which offers fast, flexible, and algorithmically valid analysis via tight integration. Utilizing interactive hierarchical topic modeling, our technique lets users generate, explore, and flexibly steer hierarchical topics to discover more informed topics and their document memberships.

Provable secure lightweight attribute‐based keyword search for cloud‐based Internet of Things networks

AbstractAs more data from Internet of Things (IoT) devices is outsourced to cloud service providers, the need for secure and efficient fine‐grained access control and data retrieving mechanisms will be increasingly pressing. Attribute‐based keyword search (ABKS) is a promising tool to provide the required infrastructures. However, most of the existing ABKS schemes suffer from expensive computational overhead on the user side. This feature makes such schemes unsuitable for IoT applications as IoT devices usually have limited computational resources. To address this problem, we design a lightweight ABKS scheme called LW‐ABKS. In our scheme, by performing a constant number of efficient computational operations, a data owner can generate a searchable ciphertext. Indeed, in generating a searchable ciphertext, the number of operations performed by data owners modeling IoT devices does not depend on the number of attributes in the access control policy. Moreover, in LW‐ABKS, an authorized data user can retrieve the outsourced data files by performing very lightweight computational operations. We also provide security definitions for the new primitive and prove its security in the standard model and under the hardness assumption of the discrete logarithm and the decisional bilinear Diffie‐Hellman problems.

Lightweight Fine-Grained Access Control for Wireless Body Area Networks.

Wireless Body Area Network (WBAN) is a highly promising technology enabling health providers to remotely monitor vital parameters of patients via tiny wearable and implantable sensors. In a WBAN, medical data is collected by several tiny sensors and usually transmitted to a server-side (e.g., a cloud service provider) for long-term storage and online/offline processing. However, as the health data includes several sensitive information, providing confidentiality and fine-grained access control is necessary to preserve the privacy of patients. In this paper, we design an attribute-based encryption (ABE) scheme with lightweight encryption and decryption mechanisms. Our scheme enables tiny sensors to encrypt the collected data under an access control policy by performing very few computational operations. Also, the computational overhead on the users in the decryption phase is lightweight, and most of the operations are performed by the cloud server. In comparison with some excellent ABE schemes, our encryption mechanism is more than 100 times faster, and the communication overhead in our scheme decreases significantly. We provide the security definition for the new primitive and prove its security in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem.

LIKC: A liberty of encryption and decryption through imploration from K-cloud servers

The technological advancement in the field of IoT and cloud computing along with the use of handheld gadgets generate huge amount of data. To process and share these data with fine-grained access control while maintaining confidentiality using cloud servers is a challenging task. Individuals as well as the organizations apprehend to use public cloud to share their private data due to security issues. Ciphertext-policy-attribute-based-encryption(CPABE) achieves one-to-many encryption with data confidentiality and fine-grained access control. Due to the computational intensive operations of encryption and decryption the performance of CPABE as a solution in resource-constraint environment is not encouraging enough. Some of the existing models use the resources of cloud servers to carry out outsourced computation either with compromised security or with communication-overhead. To reduce the computational time of encryption and decryption we propose a scheme LIKC which incorporates the outsourcing mechanism using a proxy server. With the imploration the computation time get reduced without compromising the security. It also supports attribute-revocation to support flexible data sharing. The proposed model is implemented using python charm crypto. The output reveals that LIKC is able to produce promising results in comparison to the data sharing models for this study.

Assessment of Wide-Sense Stationarity of an Underwater Acoustic Channel Based on a Pseudo-Random Binary Sequence Probe Signal

The performances of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Designing the physical layer of a reliable data transmission system requires a knowledge of channel characteristics in terms of the specific parameters of the stochastic model. The Wide-Sense Stationary Uncorrelated Scattering (WSSUS) assumption simplifies the stochastic description of the channel, and thus the estimation of its transmission parameters. However, shallow underwater channels may not meet the WSSUS assumption. This paper proposes a method for testing the Wide-Sense Stationary (WSS) part of the WSSUS feature of a UAC channel on the basis of the complex envelope of a received probe Pseudo-Random Binary Sequence (PRBS) signal. Two correlation coefficients are calculated that can be interpreted, together, as a measure that determines whether the channel is WSS or not. A similar wide-sense stationarity assessment can be performed on the basis of the Time-Varying Impulse Response (TVIR) of a UAC channel. However, the method proposed in this paper requires fewer computational operations in the receiver of a UAC system. PRBS signal transmission tests were conducted in the UAC channel simulator and in real conditions during an inland water experiment. The correlation coefficient values obtained using the method based on the envelope of a probe signal and the method of analysing the TVIR estimates are compared. The results are similar, and thus, it is possible to assess if the UAC channel can be modelled as a WSS stochastic process without the need for TVIR estimation.

Comparison of Kane’s and Lagrange’s Methods in Analysis of Constrained Dynamical Systems

SUMMARYDynamic modeling is a fundamental step in analyzing the movement of any mechanical system. Methods for dynamical modeling of constrained systems have been widely developed to improve the accuracy and minimize computational cost during simulations. The necessity to satisfy constraint equations as well as the equations of motion makes it more critical to use numerical techniques that are successful in decreasing the number of computational operations and numerical errors for complex dynamical systems. In this study, performance of a variant of Kane’s method compared to six different techniques based on the Lagrange’s equations is shown. To evaluate the performance of the mentioned methods, snake-like robot dynamics is considered and different aspects such as the number of the most time-consuming computational operations, constraint error, energy error, and CPU time assigned to each method are compared. The simulation results demonstrate the superiority of the variant of Kane’s method concerning the other ones.

Scaling-up skilled intentionality to linguistic thought

Cognition has traditionally been understood in terms of internal mental representations, and computational operations carried out on internal mental representations. Radical approaches propose to reconceive cognition in terms of agent-environment dynamics. An outstanding challenge for such a philosophical project is how to scale-up from perception and action to cases of what is typically called ‘higher-order’ cognition such as linguistic thought, the case we focus on in this paper. Perception and action are naturally described in terms of agent-environment dynamics, but can a person’s thoughts about absent, abstract or counterfactual states of affairs also be accounted for in such terms? We argue such a question will seem pressing so long as one fails to appreciate how richly resourceful the human ecological niche is in terms of the affordances it provides. The explanatory work that is supposedly done by mental representations in a philosophical analysis of cognition, can instead be done by looking outside of the head to the environment structured by sociomaterial practices, and the affordances it makes available. Once one recognizes how much of the human ecological niche has become structured by activities of talking and writing, this should take away at least some of the motivation for understanding linguistic thinking in terms of content-bearing internal representations. We’ll argue that people can think about absent, abstract or counterfactual because of their skills for engaging with what we will call “enlanguaged affordances”. We make use of the phenomenological analysis of speech in Merleau-Ponty to show how the multiple affordances an individual is ready to engage with in a particular situation will typically include enlanguaged affordances.

Characterizing the performance of continuous-variable Gaussian quantum gates

The required set of operations for universal continuous-variable quantum computation can be divided into two primary categories: Gaussian and non-Gaussian operations. Furthermore, any Gaussian operation can be decomposed as a sequence of phase-space displacements and symplectic transformations. Although Gaussian operations are ubiquitous in quantum optics, their experimental realizations generally are approximations of the ideal Gaussian unitaries. In this work, we study different performance criteria to analyze how well these experimental approximations simulate the ideal Gaussian unitaries. In particular, we find that none of these experimental approximations converge uniformly to the ideal Gaussian unitaries. However, convergence occurs in the strong sense, or if the discrimination strategy is energy bounded, then the convergence is uniform in the Shirokov-Winter energy-constrained diamond norm and we give explicit bounds in this latter case. We indicate how these energy-constrained bounds can be used for experimental implementations of these Gaussian unitaries in order to achieve any desired accuracy.

Optimal Strategies for Computation of Degree ℓn Isogenies for SIDH

This article presents methods and algorithms for the computation of isogenies of degree $\ell^n$ . Some of these methods are obtained using recurrence equations and generating functions . A standard multiplication based algorithm for computation of isogeny of degree $\ell^n$ has time complexity equal to $O(n^2 M \left(n \log{n} \right) )$ , where $M(N)$ denotes the cost of integers of size $N$ multiplication . The memory complexity of this algorithm is equal to $O\left( n \log{\left( n \log{(n)} \right)} \right)$ . In this article are presented algorithms for: \begin {itemize} \item determination of optimal strategy for computation of degree $\ell^n$ isogeny , \item determination of cost of optimal strategy of computation of $\ell^n$ isogeny using solutions of recurrence equations, \item determination of cost of optimal strategy of computation of $\ell^n$ isogeny using recurrence equations, \end {itemize} where optimality in this context means that , for the given parameters , no other strategy exists that requires fewer operations for faster computation of isogeny . Also this article presents a method using generating functions for obtaining the solutions of sequences $\left( u_m \right)$ and $\left( c_m \right)$ where $c_m$ denotes the cost of computations of isogeny of degree $\ell^{u_m}$ computations for given costs $p,q$ of $\ell$ - isogeny computation and $\ell$ - isogeny evaluation . These solutions are also used in the construction of the algorithms presented in this article.

Философские основания прикладного антропоморфизма в социальной робототехнике

This article is devoted to the philosophical foundations of anthropomorphism in the context of Human-Robot Interaction (HRI), a new interdisciplinary field of research. On the basis of modern scientific works, a positive concept of anthropomorphism as a cognitive mechanism ensuring human adaptation to a complex external environment is formulated. The theoretical principles of applied anthropomorphism (AA) are being developed to identify the conditions for activating anthropomorphic projections in a user during an HRI act. There are two key factors in the structure of AA: appearance and autonomous behaviour. Asymmetry principle is formulated: behavioral realism is more important than highly anthropomorphic appearance. The principle of coherence is formulated: the necessity of synchronisation of the appearance and the robot's behaviour, that is, the level of development of behavioral patterns of the robotic system determines the degree of its anthropomorphism. Various forms of anthropomorphism are distinguished. These differences can be described in terms of involvement in cognitive activity. Anthropomorphism as passive ascription and simple projection receives negative assessment in social robotics, while anthropomorphism, which is deduced from autonomous robots behaviour or initiated by them, is assessed positively. The epistemological foundations of the robotic revolution of the late 80s-early 90s of the XX century are analysed; the methodology of "behavior-based robotics" is examined thoroughly. The behavioral approach in robotics is based on the concept of weak artificial intelligence, within which computational operations and functions of a machine represent concatenation of processes and can lead to the illusion of intelligence in a robot, primarily due to projective intelligence from a human-observer side. These questions are analysed in the context of modern philosophical theories, such as second-order cybernetics, autopoiesis. Anthropomorphism as active ascription of cognitive or emotional states to the robot from the observer side in order to rationalise the behaviour of the object is correlated with D. Dennett's intentional stance. At the end of the article, the phenomenon of attribution of human characteristics to non-human entities in Eastern religious cults is studied. The question of the ontological status of gods and robots is raised.