Reviewerly/exHarmony huggingface dataset:
Hybrid Strategy of Multiple Optimization Algorithms Applied to 3-D Terrain Node Coverage of Wireless Sensor Network

The key to the problem of node coverage in wireless sensor networks (WSN) is to deploy a limited number of sensors to achieve maximum coverage. This paper studies the hybrid strategies of multiple evolutionary algorithms, and applies them to the problem of WSN node coverage. We first proposed the hybrid algorithm SFLA-WOA (SWOA) based on Shuffled Frog Leaping Algorithm (SFLA) and Whale Optimization Algorithm (WOA). The SWOA algorithm combines the advantages of SFLA and WOA; that is, it retains the unique evolution model of WOA and also has the excellent co-evolution capability of SFLA. Secondly, using the mutation, crossover and selection operations of the differential evolution (DE) algorithm to further optimize this hybrid algorithm, the SWOA-based SFLA-WOA-DE (SWOAD) algorithm is proposed. In addition, the performance of SWOA and SWOAD has been tested by 30 benchmark functions in the CEC 2017 test set. Experimental results show that the optimization effects of these two algorithms are very outstanding. Finally, the simulation results show that the optimization algorithm proposed in this paper has a good effect on improving the signal coverage of WSN under the actual three-dimensional terrain.
UJI VALIDITAS DAN RELIABILITAS KUESIONER PERILAKU PROSOSIAL

Good prosocial behavior is important for students to support student success in both the social and academic fields. The one of the measuring tools that can be used is a questionnaire. The purpose of this research is to make a questionnaire along with the validity and reliability test of the questionnaire. The research used is a R&amp;D method with 13 respondents of class XI at SMK Aloer Wargakusumah. Based on the results of the research, it is known that the questionnaire test with 50 statement items shows that there are 36 valid statement items with a comparison between rcount&gt; rtable. While the reliability test shows the Cronbach Alpha value&gt; 0.60, then the questionnaire can be said to be reliable or consistent with the moderate category according to Sugiyono's criteria table. Keywords: prosocial behavior, questionnaire, validity and reliability test
Vision-Less Sensing for Autonomous Micro-Drones

This work presents a concept of intelligent vision-less micro-drones, which are motivated by flying animals such as insects, birds, and bats. The presented micro-drone (named BAT: Blind Autonomous Tiny-drone) can perform bio-inspired complex tasks without the use of cameras. The BAT uses LIDARs and self-emitted optical-flow in order to perform obstacle avoiding and maze-solving. The controlling algorithms were implemented on an onboard micro-controller, allowing the BAT to be fully autonomous. We further present a method for using the information collected by the drone to generate a detailed mapping of the environment. A complete model of the BAT was implemented and tested using several scenarios both in simulation and field experiments, in which it was able to explore and map complex building autonomously even in total darkness.
A Stackelberg Game-Theoretic Exploration Rendering Robustness and Optimality for Performance Improvement of Fuzzy Mechanical Systems

We consider mechanical systems with uncertainty. The uncertainty may be time varying. The bound of the uncertainty is described by its fuzzy characteristics. To design a feasible control, we start with a robust phase, which renders a control scheme that guarantees the system performance regardless of the actual value of the uncertainty. This robust phase is then followed by an optimal phase. There are design parameters in the control, which can be fine-tuned. We proposed multiple performance objectives. The goal of the choice of the control design parameters is to minimize the performance objectives. However, since these objectives are nonconciliating (meaning one's minimum is not the other one's minimum), we invoke the Stackelberg strategy for the optimal parameters. The game strategy mimics two players: one is the leader and one is the follower. Through the interplay between the two players, we show how to select the design parameters. The design procedure in both robust and optimal phases is demonstrated by a coupled inverted pendulum system.
Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks

Classroom teaching quality evaluation system can enable the school’s functional departments to accurately assess the performance of the teaching staff and current teaching operations. As per the requirements for cultivating high-quality talents, planned teaching staff construction and teaching reforms need to be carried out to promote teachers’ appointments. Improving the system makes the appointment process more scientific by giving due attention to the individual characteristics of all types of teachers while hiring them for related jobs. The system motivates the love of teaching, high academic level, high teaching level, and competitive teaching. In recent years, the rapid development of artificial intelligence and deep learning caused many colleges and universities to put forward the target of campus digitization and education informatization. The state of the classroom is a critical reference factor throughout the teaching and learning process for evaluating students’ acceptance of the course and the quality of the teaching. However, at present, the analysis of the classroom status is mainly conducted manually, which distracts teachers and is also not much precise. Therefore, finding a method that can improve the efficiency of classroom status analysis has great research significance. This study uses the deep neural network method to read each class’s video recording and analyze it from the aspects of students’ behavior and attendance. The system can realize class behavior and eventually evaluate the course quality employed to motivate teachers to improve teaching and overall quality of education.
Error bounds for variational quantum time evolution

Variational quantum time evolution allows us to simulate the time dynamics of quantum systems with near-term compatible quantum circuits. Due to the variational nature of this method the accuracy of the simulation is a priori unknown. We derive a posteriori global phase agnostic error bounds for the state simulation accuracy with variational quantum time evolution that improve the tightness of fidelity estimates over existing a posteriori error bounds. These analysis tools are practically crucial for assessing the quality of the simulation and making informed choices about simulation hyperparameters. The efficient, a posteriori evaluation of the bounds can be tightly integrated with the variational time simulation and, hence, results in a minor resource overhead, which is governed by the system's energy variance. The performance of the error bounds is demonstrated on numerical examples.
A Middleware Supporting Data Movement in Complex and Software-Defined Storage and Memory Architectures

Among the broad variety of challenges that arise from workloads in a converged HPC and Cloud infrastructure, data movement is of paramount importance, especially oncoming exascale systems featuring multiple tiers of memory and storage. While the focus has, for years, been primarily on optimizing computations, the importance of improving data handling on such architectures is now well understood. As optimization techniques can be applied at different stages (operating system, run-time system, programming environment, and so on), a middleware providing a uniform and consistent data awareness becomes necessary. In this paper, we introduce a novel memory- and data-aware middleware called Maestro, designed for data orchestration.
Parallel Evolutionary Algorithm for EEG Optimization Problems

Big data optimization has become an important research topic in many disciplines. These optimization problems involve a large volume of data, from different sources, in different formats, that are generated at a high speed. For example, in the healthcare sector, electroencephalography (EEG), which is a method for monitoring brain signals and typically used to diagnose neurological disorders, generates a large amount of data which, however, is often captured with artifacts added from non-brain sources. Evolutionary algorithms are considered one of the most successful approaches for solving many such complex optimization problems. In this paper, a differential evolution algorithm is developed to remove artifacts from EEG signals of interest, by using the parallel computing ability of a Graphics Processing Unit. Two levels of parallelization, variable and individual, are implemented, with a gradient-based local search and adaptive control parameters incorporated in order to enhance a search's convergence. The proposed algorithm is tested using six single objective problems from the 2015 big data optimization competition problems with 1024, 3072 and 4864 decision variables, as both noise-free and with white noise. The results presented in this paper indicate that the proposed algorithm is capable of achieving high-quality solutions, and is up to 374.7 faster than the state-of-the-art algorithms.
PAN-CANCER INTEGRATIVE HISTOLOGY-GENOMIC ANALYSIS VIA INTERPRETABLE MULTIMODAL DEEP LEARNING

The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics alone and do not address how histology and genomics can be integrated to develop joint image-omic prognostic models. Additionally identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We used multimodal deep learning to integrate gigapixel whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes and discover prognostic features from these modalities that corroborate with poor and favorable outcomes via multimodal interpretability. We compared our model with unimodal deep learning models trained on histology slides and molecular profiles alone, and demonstrate performance increase in risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types. All analyzed data, including morphological and molecular correlates of patient prognosis across the 14 cancer types at a disease and patient level are presented in an interactive open-access database (this http URL) to allow for further exploration and prognostic biomarker discovery. To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.
Blockchain-Based Confidential Multiparty Contract Signing Protocol Without TTP Using Elliptic Curve Cryptography

Abstract Signed information is difficult to manage and this difficulty is even bigger when pieces of signed digital information have to be exchanged in an atomic procedure. Multiparty contract signing is an application of fair exchange of values, where the information to be exchanged are signed versions of a contract. Traditionally, this trust service requires the involvement of a trusted third party (TTP). This is a point of weakness for the generalization of the use of electronic contract signing protocols. Another usual problem for multiparty contract signing is that the protocols for multiparty exchanges are far more complex that those for two-party exchanges. Additionally, it is common that the signers demand protocols that assure the confidentiality of the contract. In this proposal blockchain-based technologies and smart contracts are used to obtain a protocol for electronic contract signing that does not need a TTP in any stage of the procedure. The protocol satisfies the ideal requirements for the contract signing service, even allowing the signature of confidential contracts. Moreover, the proposed solution is far more efficient than previous solutions for fair exchange protocols using blockchain presented up to the date thanks to the use of elliptic curve cryptography.
Coordinate Descent on the Orthogonal Group for Recurrent Neural Network Training

We address the poor scalability of learning algorithms for orthogonal recurrent neural networks via the use of stochastic coordinate descent on the orthogonal group, leading to a cost per iteration that increases linearly with the number of recurrent states. This contrasts with the cubic dependency of typical feasible algorithms such as stochastic Riemannian gradient descent, which prohibits the use of big network architectures. Coordinate descent rotates successively two columns of the recurrent matrix. When the coordinate (i.e., indices of rotated columns) is selected uniformly at random at each iteration, we prove convergence of the algorithm under standard assumptions on the loss function, stepsize and minibatch noise. In addition, we numerically show that the Riemannian gradient has an approximately sparse structure. Leveraging this observation, we propose a variant of our proposed algorithm that relies on the Gauss-Southwell coordinate selection rule. Experiments on a benchmark recurrent neural network training problem show that the proposed approach is a very promising step towards the training of orthogonal recurrent neural networks with big architectures.
Neural networks trained with high-dimensional functions approximation data in high-dimensional space

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.
Distributed trust‐based unscented Kalman filter for non‐linear state estimation under cyber‐attacks: The application of manoeuvring target tracking over wireless sensor networks

This paper is concerned with secure state estimation of non-linear systems under malicious cyber-attacks. The application of target tracking over a wireless sensor network is investigated. The existence of rotational manoeuvre in the target movement introduces non-linear behaviour in the dynamic model of the system. Moreover, in wireless sensor networks under cyber-attacks, erroneous information is spread in the whole network by imperilling some nodes and consequently their neighbours. Thus, they can deteriorate the performance of tracking. Despite the development of target tracking techniques in wireless sensor networks, the problem of rotational manoeuvring target tracking under cyber-attacks is still challenging. To deal with the model non-linearity due to target rotational manoeuvres, an unscented Kalman filter is employed to estimate the target state variables consisting of the position and velocity. A diffusion-based distributed unscented Kalman filtering combined with a trust-based scheme is applied to ensure robustness against the cyber-attacks in manoeuvring target tracking applications over a wireless sensor network with secured nodes. Simulation results demonstrate the effectiveness of the proposed strategy in terms of tracking accuracy, while random attacks, false data injection attacks, and replay attacks are considered.
Quantum key recovery attack on SIMON32/64

Abstract The quantum security of lightweight block ciphers is receiving more and more attention. However, the existing quantum attacks on lightweight block ciphers only focused on the quantum exhaustive search, while the quantum attacks combined with classical cryptanalysis methods haven’t been well studied. In this paper, we study quantum key recovery attack on SIMON32/64 using Quantum Amplitude Amplification algorithm in Q1 model. At first, we reanalyze the quantum circuit complexity of quantum exhaustive search on SIMON32/64. We estimate the Clifford gates count more accurately and reduce the T gate count. Also, the T-depth and full depth is reduced due to our minor modifications. Then, using four differentials given by Biryukov in FSE 2014 as our distinguisher, we give our quantum key recovery attack on 19-round SIMON32/64. We treat the two phases of key recovery attack as two QAA instances separately, and the first QAA instance consists of four sub-QAA instances. Then, we design the quantum circuit of these two QAA instances and estimate their corresponding quantum circuit complexity. We conclude that the quantum circuit of our quantum key recovery attack is lower than quantum exhaustive search. Our work firstly studies the quantum dedicated attack on SIMON32/64. And this is the first work to study the complexity of quantum dedicated attacks from the perspective of quantum circuit complexity, which is a more fine-grained analysis of quantum dedicated attacks’ complexity.
Enhanced Security Authentication Based on Convolutional-LSTM Networks

The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To address this problem, this paper introduces a deep learning-based authentication approach to learn and track the variations of channel characteristics, and thus improving the adaptability and convergence of the physical layer authentication. Specifically, an intelligent detection framework based on a Convolutional-Long Short-Term Memory (Convolutional-LSTM) network is designed to deal with channel differences without knowing the statistical properties of the channel. Both the robustness and the detection performance of the learning authentication scheme are analyzed, and extensive simulations and experiments show that the detection accuracy in time-varying environments is significantly improved.
Bayesian Nonparametric Space Partitions: A Survey

Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that the data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject. We explore the current progress of BNSP research through three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions and discuss their theoretical foundation, `self-consistency'; (2) Applications, where we detail the current mainstream usages of BNSP models and identify some potential future applications; and (3) Challenges, where we discuss current unsolved problems and possible avenues for future research.
Stochastic optimization with momentum: Convergence, fluctuations, and traps avoidance

In this paper, a general stochastic optimization procedure is studied, unifying several variants of the stochastic gradient descent such as, among others, the stochastic heavy ball method, the Stochastic Nesterov Accelerated Gradient algorithm (S-NAG), and the widely used Adam algorithm. The algorithm is seen as a noisy Euler discretization of a non-autonomous ordinary differential equation, recently introduced by Belotto da Silva and Gazeau, which is analyzed in depth. Assuming that the objective function is non-convex and differentiable, the stability and the almost sure convergence of the iterates to the set of critical points are established. A noteworthy special case is the convergence proof of S-NAG in a non-convex setting. Under some assumptions, the convergence rate is provided under the form of a Central Limit Theorem. Finally, the non-convergence of the algorithm to undesired critical points, such as local maxima or saddle points, is established. Here, the main ingredient is a new avoidance of traps result for non-autonomous settings, which is of independent interest.
Efficient verification of anticoncentrated quantum states

Abstract I present a method for estimating the fidelity F ( μ , τ ) between a preparable quantum state μ and a classically specified pure target state $$\tau =\left|\tau \right\rangle \left\langle \tau \right|$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>τ</mml:mi> <mml:mo>=</mml:mo> <mml:mfenced> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> </mml:mfenced> <mml:mfenced> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:math> , using simple quantum circuits and on-the-fly classical calculation (or lookup) of selected amplitudes of $$\left|\tau \right\rangle$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfenced> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> . The method is sample efficient for anticoncentrated states (including many states that are hard to simulate classically), with approximate cost 4 ϵ −2 (1 − F ) d p coll where ϵ is the desired precision of the estimate, d is the dimension of the Hilbert space, and p coll is the collision probability of the target distribution. This scaling is exponentially better than that of any method based on classical sampling. I also present a more sophisticated version of the method that uses any efficiently preparable and well-characterized quantum state as an importance sampler to further reduce the number of copies of μ needed. Though some challenges remain, this work takes a significant step toward scalable verification of complex states produced by quantum processors.
Using Denotational Mathematics for the Formal Description of Home UbiHealth Decision-Support Systems With Knowledge Flow

Home UbiHealth applications require support from decision-making and decision-support components. The decision process must possess a holistic perspective about the individual's healthcare condition and needs at home. Also, it shall consider and evaluate the available data, producing cognitive intelligence through appropriate processing. The present work describes a model for decision-making and decision-support in UbiHealth environments based on denotational mathematics. The decision-making component of the model provides the necessary actions to handle the connected devices. The decision-support component suggests actions to the user (physician). The decisions are drawn from a simulated operation of cooperating and co-existing semantic networks. The model is thoroughly described, and its positive impact is explained for all participating stakeholders including medical professionals, patients, information technology scientists, and technical personnel.