Reviewerly/exHarmony huggingface dataset:
Data-dependent compression of random features for large-scale kernel approximation

Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods typically require cubic running time in the data set size, a prohibitive cost in the large- data setting. Random feature maps (RFMs) and the Nystrom method both consider low- rank approximations to the kernel matrix as a potential solution. But, in order to achieve desirable theoretical guarantees, the former may require a prohibitively large number of features J+, and the latter may be prohibitively expensive for high-dimensional problems. We propose to combine the simplicity and generality of RFMs with a data-dependent feature selection scheme to achieve desirable theoretical approximation properties of Nystrom with just O(log J+) features. Our key insight is to begin with a large set of random features, then reduce them to a small number of weighted features in a data-dependent, computationally efficient way, while preserving the statistical guarantees of using the original large set of features. We demonstrate the efficacy of our method with theory and experiments—including on a data set with over 50 million observations. In particular, we show that our method achieves small kernel matrix approximation error and better test set accuracy with provably fewer random features than state-of-the-art methods.
FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions

Explaining the decisions of a Deep Learning Network is imperative to safeguard end-user trust. Such explanations must be intuitive, descriptive, and faithfully explain why a model makes its decisions. In this work, we propose a framework called FLEX (Faithful Linguistic EXplanations) that generates post-hoc linguistic justifications to rationalize the decision of a Convolutional Neural Network. FLEX explains a model’s decision in terms of features that are responsible for the decision. We derive a novel way to associate such features to words, and introduce a new decision-relevance metric that measures the faithfulness of an explanation to a model’s reasoning. Experiment results on two benchmark datasets demonstrate that the proposed framework can generate discriminative and faithful explanations compared to state-of-the-art explanation generators. We also show how FLEX can generate explanations for images of unseen classes as well as automatically annotate objects in images.
Feasibility of single-shot realizations of conditional three-qubit gates in exchange-coupled qubit arrays with local control

We investigate the feasibility of single-shot Toffoli- and Fredkin-gate realizations in qubit arrays with Heisenberg-type exchange interactions between adjacent qubits. As follows from the Lie-algebraic criteria of controllability, such an array is rendered completely controllable -- equivalent to allowing universal quantum computation -- by a Zeeman-like control field with two orthogonal components acting on a single "actuator" qubit. Adopting this local-control setting, we start our analysis with piecewise-constant control fields and determine the global maxima of the relevant figure of merit (target-gate fidelity) by combining the multistart-based clustering algorithm and quasi-Newton type local optimization. We subsequently introduce important practical considerations, such as finite frequency bandwidth of realistic fields and their leakage away from the actuator. We find the shortest times required for high-fidelity Toffoli- and Fredkin-gate realizations and provide comparisons to their respective two-qubit counterparts -- controlled-NOT and exponential-SWAP. In particular, the Toffoli-gate time compares much more favorably to that of controlled-NOT than in the standard decomposition-based approach. This study indicates that the use of the single-shot approach can alleviate the burden on control-generating hardware in future experimental realizations of multi-qubit gates.
Audiovisual aids application in the secondary-level vocational education establishments

Purpose The purpose of this paper is to analyze how the audiovisual teaching aids are applied in the modern educational environment and to assess their application efficiency in the context of the secondary-level vocational education establishments. Design/methodology/approach A pedagogical experiment was conducted to confirm this hypothesis. At the preparatory stage, the authors have analyzed the teaching and learning process, as well as students learning at the secondary-level vocational education establishment. Statistical sample was 300 people. Findings Based on the research results, main mistakes made while applying the audiovisual teaching aids were identified, formulated and investigated. These mistakes were related to the insufficient methodological preparation. As these mistakes were eliminated, student achievements and learning skills have increased by 15–20 percent (experiment data). The average marks, obtained by students before and after eliminating the methodological mistakes, were taken in points (from 2 to 5) as achievement and learning skill criteria. Research conclusion is that audiovisual aids application quality can be improved only through the research on students’ educational and creative potential, their perception of various learning materials, and their preferences in the information structure, composition, types and forms. Originality/value Applying audiovisual teaching aids in the learning process is a challenge. This paper is driven by the need of new unique methods for applying audiovisual aids related to identifying the optimal temporal lesson structure, as well as the composition and the amount of auxiliary teaching materials, interactive communication level and ways to stimulate the emotional and creative activity of students.
Effects of Limited Computational Precision on the Discrete Chaotic Sequences and the Design of Related Solutions

In this paper, we analyzed the periodicity of discrete Logistic and Tent sequences with different computational precision in detail. Further, we found that the process of iterations of the Logistic and Tent mapping is composed of transient and periodic stages. Surprisingly, for the different initial iterative values, we first discovered that all periodic stages have the same periodic limit cycles. This phenomenon has seriously affected the security of chaotic cipher. To solve this problem, we designed a novel discrete chaotic sequence generator based on m-sequence and discrete chaotic mapping. The experimental results indicated that the chaotic sequence generator can generate pseudorandom chaotic sequences with large periodicity and good performance under the condition of limited computational precision.
The programming of sequences of saccades

Saccadic eye movements move the high-resolution fovea to point at regions of interest. Saccades can only be generated serially (i.e., one at a time). However, what remains unclear is the extent to which saccades are programmed in parallel (i.e., a series of such moments can be planned together) and how far ahead such planning occurs. In the current experiment, we investigate this issue with a saccade contingent preview paradigm. Participants were asked to execute saccadic eye movements in response to seven small circles presented on a screen. The extent to which participants were given prior information about target locations was varied on a trial-by-trial basis: participants were aware of the location of the next target only, the next three, five, or all seven targets. The addition of new targets to the display was made during the saccade to the next target in the sequence. The overall time taken to complete the sequence was decreased as more targets were available up to all seven targets. This was a result of a reduction in the number of saccades being executed and a reduction in their saccade latencies. Surprisingly, these results suggest that, when faced with a demand to saccade to a large number of target locations, saccade preparation about all target locations is carried out in parallel.
AI-based targeted advertising system

&lt;span&gt;The most common technology used in targeted advertising is facial recognition and vehicle recognition. Even though there are existing systems serving for the targeting purposes, most propose limited functionalities and the system performance is normally unknown. This paper presents an intelligent targeted advertising system with multiple functionalities, namely facial recognition for gender and age, vehicle recognition, and multiple object detection. The main purpose is to improve the effectiveness of outdoor advertising through biometrics approaches and machine learning technology. Machine learning algorithms are implemented for higher recognition accuracy and hence achieved better targeted advertising effect.&lt;/span&gt;
A modified comprehensive learning particle swarm optimizer and its application in cylindricity error evaluation problem

Particle swarm optimizer was proposed in 1995, and since then, it has become an extremely popular swarm intelligent algorithm with widespread applications. Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very powerful one. In order to enhance its performance further, a local search based on Latin hypercube sampling is combined with it in this work. Due to that a hypercube should become smaller and smaller for better local search ability during the search process, a control method is designed to set the size of the hypercube. Via numerical experiments, it can be observed that the comprehensive learning particle swarm optimizer with the local search based on Latin hypercube sampling has a strong ability on both global and local search. The hybrid algorithm is applied in cylindricity error evaluation problem and it outperforms several other algorithms.
Multi-Agent Deep Reinforcement Learning in Cognitive Inter-Domain Networking with Multi-Broker Orchestration

This paper proposes, for the first time, a cognitive inter-domain networking framework with multi-broker orchestration and multi-agent deep reinforcement learning for multi-domain optical networks. Simulation results show > 17% blocking reduction compared to the baselines.
System Integration and Control for 3D Scanning Laser Metrology

Mechatronic imaging systems, ranging from nanoscale metrology to telescope systems and adaptive optics for astronomy, are complex machines that demand continual improvement of system speed, range, and precision. This demand requires advanced designs of the mechatronic components and a motion control scheme that carefully considers the interplay of a physical plant and the target application. A proper data acquisition system is required to synchronously acquire and process measurement and position data, and a sophisticated system integration is needed to obtain the maximum performance of the resulting system. This paper discusses the interplay between process and control design, as well as the system integration with the example of a scanning laser triangulation system for high precision 3D metrology. The integration process can be tailored to individual applications, and is discussed for raster and Lissajous scan trajectories, considering their individual requirements for the system and control design. Further it is demonstrated how these individually tailored system components can improve the performance in terms of precision and efficiency by several orders of magnitude.
Concept of a learning knowledge-based system for programming industrial robots

A major challenge for the use of industrial robots has been its programming, which requires expert knowledge. Offline programming tools, based on simulation models, are normally used to facilitate the robot programming. However, in many industrial applications the intervention of an operator is still necessary to correct the robot programs. This paper presents a concept based on a knowledge-based system (KBS) that integrates the capability to learn from manual adjustments conducted by an operator. Based on the learned data the KBS will be able to imitate the adjustments of the operator, enabling a full automation of the robot programming.
Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.
Modification of calibration and image processing procedures for precise 3-D measurements in arbitrary spectral bands by means of a stereoscopic prism-based imager

Nowadays, stereoscopic devices are widely used for precise three-dimensional (3-D) measurements of hard-to-reach elements in industrial and biomedical applications. The most common approach for their implementation is the utilization of prism-based optical systems, which allows simultaneous acquisition of two images from different viewpoints on a single sensor. Normally, they are equipped with a wideband (WB) white-light source, but contrast visualization of the inspected object and, therefore, accurate quantitative characterization of its parameters, often requires narrow-band (NB) spectral imaging. We show that the standard geometrical calibration may lead to significant measurement errors when obtained using WB illumination and applied to NB images. As the criterion to evaluate the calibration error, we have chosen the difference between reference and measured lengths of the 1 mm segment along transverse x, y, and longitudinal z axes. If WB calibration data are applied to NB images, the measurement bias increases from the middle of the working spectral range to its edges and can reach significant values: up to 0.1 mm along x axis and 0.15 mm along z axis in 10 to 25 mm distance range. To overcome this, we propose the calibration and image processing procedures based on a proper choice of a few spectral bands for calibration and interpolation of the calculated calibration parameters. Results of multiple experiments using stereo video endoscope confirmed that the proposed technique allows a decrease in the measurement bias by three times in comparison to conventional WB calibration for all wavelengths of the visible range, which essentially improves the measurement accuracy. The impact of WB calibration on random errors of measurements and the quality of image rectification was also analyzed and shown to be insignificant.
Ensembles of instance selection methods: A comparative study

Abstract Instance selection is often performed as one of the preprocessing methods which, along with feature selection, allows a significant reduction in computational complexity and an increase in prediction accuracy. So far, only few authors have considered ensembles of instance selection methods, while the ensembles of final predictive models attract many researchers. To bridge that gap, in this paper we compare four ensembles adapted to instance selection: Bagging , Feature Bagging , AdaBoost and Additive Noise . The last one is introduced for the first time in this paper. The study is based on empirical comparison performed on 43 datasets and 9 base instance selection methods. The experiments are divided into three scenarios. In the first one, evaluated on a single dataset, we demonstrate the influence of the ensembles on the compression–accuracy relation, in the second scenario the goal is to achieve the highest prediction accuracy, and in the third one both accuracy and the level of dataset compression constitute a multi-objective criterion. The obtained results indicate that ensembles of instance selection improve the base instance selection algorithms except for unstable methods such as CNN and IB3 , which is achieved at the expense of compression. In the comparison, Bagging and AdaBoost lead in most of the scenarios. In the experiments we evaluate three classifiers: 1 NN, k NN and SVM. We also note a deterioration in prediction accuracy for robust classifiers ( k NN and SVM) trained on data filtered by any instance selection methods (including the ensembles) when compared with the results obtained when the entire training set was used to train these classifiers.
Effects of Augmented Reality in Teaching Old Turkish Language Mementoes on Student Achievement and Motivation

The Turkish history has a very rich content in literary context as well as political and administrative achievements. The Turkish language, which dates back to B.C., has become one of the great languages that continues its existence until modern day, leaving priceless reference guide behind. The purpose of this research is to investigate the effects of lessons taught using activities designed with Augmented Reality Technology on the academic achievement and motivation of students in the teaching of the subjects of Yusuf Has Hacib, Kasgarli Mahmud, Kutadgu Bilig and Divanu Lugati’t-Turk which are among the cultural heritages called “Old Turkish Language Mementoes.” In this context, Augmented Reality environments were designed in relation to the mentioned works and authors and these designs were uploaded to AURASMA 3D application and made available for smart phones. Quantitative research model was adapted in the study and the quasi-experimental method with pre-test-post-test control group was used. At the end of the study whose procedures took 6 weeks in total with the involvement of 50 college students, it was determined that AR activities has a positive effect on the academic success of the students and increases the academic success of the students, and it creates a positive environment which motivates the students towards the lesson and learning.
Efficient-Scheduling Parallel Multiplier-Based Ring-LWE Cryptoprocessors

This paper presents a novel architecture for ring learning with errors (LWE) cryptoprocessors using an efficient approach in encryption and decryption operations. By scheduling multipliers to work in parallel, the encryption and decryption time are significantly reduced. In addition, polynomial multiplications are conducted using radix-2 and radix-8 multiple delay feedback (MDF) architecture-based number theoretic transform (NTT) multipliers to speed up the multiplication operation. To reduce the hardware complexity of an NTT multiplier, three bit-reverse operations during the NTT and inverse NTT (INTT) processes are removed. Polynomial additions in the ring-LWE encryption phase are also arranged to work simultaneously to reduce the latency. As a result, the proposed efficient-scheduling parallel multiplier-based ring-LWE cryptoprocessors can achieve higher throughput and efficiency compared with existing architectures. The proposed ring-LWE cryptoprocessors are synthesized and verified using Xilinx VIVADO on a Virtex-7 field programmable gate array (FPGA) board. With security parameters n = 512 and q = 12,289, the proposed cryptoprocessors using radix-2 single-path delay feedback (SDF), radix-2 MDF, and radix-8 MDF multipliers perform encryption in 4.58 μ s, 1.97 μ s, and 0.89 μ s, and decryption in 4.35 μ s, 1.82 μ s, and 0.71 μ s, respectively. A comparison of the obtained throughput and efficiency with those of previous studies proves that the proposed cryptoprocessors achieve a better performance.
Digital Educational Footprint as a Way to Evaluate the Results of Students’ Learning and Cognitive Activity in the Process of Teaching Mathematics

Overcoming the effects of the global economic crisis and focusing on innovative development demanded the modernization of the education system, a key factor in improving various areas of human activity. Digital technologies nowadays are a comprehensive platform for the development of all sectors of the economy, including the field of education. The use of digital technologies in higher education has a didactic potential in organizing the educational and cognitive process, providing new qualitative opportunities through the implementation of the principles of virtualization, mobility, adaptability, instant feedback. Digital education dictates new requirements for subjects of the educational process, for the content of the information and educational space, for the regulation of the interaction of all participants in the educational process, for methods and parameters for evaluating the educational and cognitive activity of students. Thus, the purpose of the article is to study the specificity of evaluating the results of students’ educational and cognitive activity in a digital educational environment. The leading method here is the design of students’ digital educational footprint, which combines motivational, cognitive and reflective components. As a result of the study, the authors have determined the structural and informative characteristics of the digital educational footprint as a way to assess the results of students’ learning and cognitive activity in the process of teaching mathematics; revealed the didactic potential of digital technologies in organizing the process of teaching mathematics; developed a methodological system that allows to evaluate students’ digital educational footprint in teaching mathematics in a digital educational environment. Practical use of the research results contributes to the improvement of teaching mathematics in higher educational establishments.
Reliable Data Analysis through Blockchain based Crowdsourcing in Mobile Ad-hoc Cloud

Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource-constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit-based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC. This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluating the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting reliable data analysis through the blockchain based computational crowdsourcing in MAC.
Learning from data streams and class imbalance

With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Applications in various domains such as risk manag...