Reviewerly/exHarmony huggingface dataset:
Applications of Nature-Inspired Algorithms

Nature-inspired algorithms have become powerful and popular for solving problems in optimization, computational intelligence, data mining, machine learning, transport and vehicle routing. After the theoretical analyses in earlier chapters, it would be useful to provide examples and case studies to show that these algorithms can indeed work well in practice.
Research on a new Smart Pedestrian Detection Sensor for Vehicles

This paper explains theory and design of a new smart pedestrian detection sensor by means of a sophisticated analysis of ultrasonic signals. The main objective is to enable driver assistance systems in vehicles to recognize whether an obstacle in front is a pedestrian or a car. In case of a likely collision the sensor activates pedestrian protection systems in an early stage. It consists of an ultrasonic transducer, an embedded system for transducer control and signal acquisition as well as a computer for signal analysis, feature extraction and classification. Results from laboratory experiments as well as an application of the sensor on a car are presented. The smart pedestrian detection sensor is able to detect moving and non-moving persons and even children.
A general approach to reasoning with probabilities

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The Mobile payment based on public-key security technology

This paper mainly concentrates on data security technology applied on the mobile payment application in China. The widely used technology of public-key encryption will be introduced in this paper and it also includes the mobile payment system mode applied in Chinese mobile payment application. And it aims at how the technology serves for different business to meet the users' requirement, and how the technology adjusts to different market requirement.
Bootstrapping Autonomous Skill Learning in the MDB Cognitive Architecture

This paper is concerned with motivation in autonomous robots. In particular we focus on the basic structure that is necessary for bootstrapping the initial stages of multiple skill learning within the motivational engine of the MDB cognitive architecture. To this end, taking inspiration from a series of computational models of the use of motivations in infants, we propose an approach that leverages two types of cognitive motivations: exploratory and proficiency based. The latter modulated by the concept of interestingness. We postulate that these make up the minimum set of motivational components required to initiate the unrewarded learning of a skill toolbox that may later be used in order to achieve operational goals. The approach is illustrated through an experiment with a real robot that is learning skills in a real environment.
Tibetan historical document recognition of uchen script using baseline information

A method is proposed for recognition of uchen script Tibetan historical document using baseline information. The baseline is a significant feature of uchen script Tibetan characters, and the result of Tibetan document recognition can be improved effectively by using baseline information. Firstly, the baseline is detected accurately by the upper edge of character strokes in the document. Secondly, the baseline is used for segmentation of text line and character, and the text line adhesion can be handled also. Finally, the character part above baseline and that below baseline are recognized individually, and the final results are synthesized. The experiment shows that the proposed method is effective for the recognition of Tibetan historical document, and the recognition accuracy is better than that of the whole character recognition.
The Problem of Comprehension in Digital Communication

The present paper deals with the problem of comprehension within the framework of digital communication. The digital communications functioning within the framework of the post-information society are characterized by interactivity, unlimited capacity, the exchange of communicants' roles and only a conditional possibility for determining directions, values and possible consequences of communication. The authors come to a conclusion that new technologies (blockchain, AI, Big data) contribute to implementation of efficient communicative strategies, in which each communicant takes into account the social and personal characteristics of communication participants. The choice of discursive strategies should be based on the possibility of forming multiple identities of all communication participants. But achieving full understanding between the communication participants is difficult due to the impossibility of merging the communicating “Ego” and “the Other”.
Second-order induction in prediction problems

Agents make predictions based on similar past cases, while also learning the relative importance of various attributes in judging similarity. We ask whether the resulting "empirically optimal similarity function" (EOSF) is unique and how easy it is to find it. We show that with many observations and few relevant variables, uniqueness holds. By contrast, when there are many variables relative to observations, nonuniqueness is the rule, and finding the EOSF is computationally hard. The results are interpreted as providing conditions under which rational agents who have access to the same observations are likely to converge on the same predictions and conditions under which they may entertain different probabilistic beliefs.
Nature-Inspired Algorithms

The literature of nature-inspired algorithms and swarm intelligence is expanding rapidly, here we will introduce some of the most recent and widely used nature-inspired optimization algorithms.
Anticipatory development processes for reducing total ownership costs and schedules

Abstract Many systems and software processes overfocus on getting a project and product from an initial set of requirements to an Initial Operational Capability (IOC). Examples are most waterfall and V models. Projects following such processes may pass acceptance tests for functionality and performance, but may leave the product with serious maintainability shortfalls. Many agile processes focus on users' initial usage priorities, but often make development commitments for earlier needs that are incompatible with achieving later critical needs (eg, security, safety). Incremental development process models can do better, but often later increments may find that the earlier increments have not prepared them for ease of modification and repair. Besides increasing Total Ownership Costs (TOCs), long mean times to repair result in long downtimes, which can be critical to an organization's income and reputation. Furthermore, many of these shortfalls take the form of Technical Debt (TD), in that the later they are fixed, the more slow and expensive will be the fixes. This paper summarizes three process frameworks and tools providing more anticipatory ways to improve systems and software maintainability and life‐cycle cost‐effectiveness. The first framework is an Opportunity Tree for identifying and anticipating such ways. The second framework, Software Quality Understanding by Analysis of Abundant Data (SQUAAD), is a toolset for tracking a software project's incremental code commits, and analyzing and visualizing each commit's incremental and cumulative TD. The third framework is a Software/Systems Maintenance Readiness Framework (SMRF), that identifies needed maintenance readiness levels at development decision reviews, similar to the Technology Readiness Levels (TRLs) framework.
Introducing the Run Support Strategy for the Bison Algorithm

Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to ensure, that the newly discovered solutions can affect the overall optimization process. In this paper, we propose a new Run Support Strategy for the Bison Algorithm, that should enhance the utilization of newly discovered solutions, and should be suitable for both continuous and discrete optimization.
Implementation and research of a substation command interaction method using third party validation

This paper describes a method to improve the security of control signal transmission in substation by introducing third party authentication and applying encryption technology. We can use asymmetric encryption technology to ensure the safety of data transmission process. A third party authentication device is introduced to verify the rights and identities of both sides of the command and prevent the interception and tampering of command information. Use dynamic key and authorization expiration system to further protect control security. Finally, the character code is checked by the command and the error data is filtered effectively. This method can effectively prevent signals from being truncated or tampered and ensure the security of data. Restricting the operation crowd, isolating illegal users, protecting information security, greatly improve the information security of substation control process.
Octopus: Context-Aware CNN Inference for IoT Applications

Modern convolutional neural networks (CNNs) in computer vision are trained on a large number of images from numerous categories to form rich discriminative feature extractors. Inference using such models on resource-constrained Internet-of-Things (IoT) platforms poses a challenge and an opportunity. Having limited computation, storage, and energy budgets, most IoT platforms are not capable of hosting such compute intensive models. However, typical IoT applications demand detection of a relatively small number of categories, albeit the specific categories of interest may change at runtime as the context evolves dynamically. In this letter, we take advantage of the opportunity to address the challenge. Specifically, we develop a novel transformation to the architecture of a given CNN, so that the majority of the inference workload is allocated to class-specific disjoint branches, which can be dynamically executed or skipped, based on the context, to fulfill the application requirements. Experiments demonstrate that our approach preserves the classification accuracy for the classes of interest, while proportionally decreasing the model complexity and inference workload.
Research on the Random Corresponding of Privacy Data Mining in the Association Rules of Cloud Computing

To strengthen data privacy protection, and improve data mining accuracy, random response mode is adopted to design privacy protection mining method based on association rules. Granular computing method and technology are applied to mining fields of association rules in data mining, and mining to association rules is researched in more extensive way from another perspective in this paper. Firstly, partial concealing mode is adopted to conceal and transform original privacy data and improve data security; secondly, associated frequent item set is utilized to construct simple and efficient privacy protection mining algorithm; finally, algorithm proposed is verified to have higher privacy and accuracy through theoretical analysis and experimental verification. After classical association rules mining algorithm is analyzed and researched in detail with its characteristics and restrictions summarized through examples in this paper, association rules mining model based on granular computing is proposed, which makes theoretical preparation for proposal and construction of association rules pick-up algorithm based on granular computing. Experimental result shows that association rules mining method based on granular computing is feasible and effective.
Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition

In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influence between the given sample and the pseudo-neighbor samples with the different classes. Then, the global distribution constraints are introduced to the projection objective function for seeking the optimal subspace which compacts intra-classes samples and alienates inter-classes samples at the same time. Extensive experiments are carried out on AR, Extended Yale B, LFW and PubFig databases which are four representative face datasets, and the corresponding experimental results illustrate the effectiveness of our proposed method.
Solving discrete logarithm problems faster with the aid of pre-computation

A trapdoor discrete logarithm group is an algebraic structure in which the feasibility of solving discrete logarithm problems depends on the possession of some trapdoor information, and this primitive has been used in many cryptographic schemes. The current designs and applications of this primitive are such that the practicality of its use is greatly increased by methods that allow for discrete logarithm problems of sizes that are barely solvable to be solved faster. In this article, we propose an algorithm that can reduce the time taken to solve discrete logarithm problems through a one-time pre-computation process. We also provide a careful complexity analysis of the algorithm and compare its performance with those of existing algorithms for solving discrete logarithm problems with the aid of pre-computation. Our new method performs much better than the most widely known algorithm and is advantageous over a more recently proposed method in view of pre-computation cost.
Activity-dependent neuron model for noise resistance

Activity-dependent plasticity plays an important role in biological neural network learning. Unlike the backpropagation-based learning in artificial neural networks that depends on supervised signals, biological neurons adjust themselves using their own historical behaviors as a clue to benefit information processing. Inspired by this biological neural mechanism, this paper proposes a novel neuron model, named Activity-Dependent Neuron (ADN). The basic idea of ADN is to add a gate in the neuron model to control its signal conduction capability, that is, the gate will facilitate transmission of important signals while suppress trivial signals. This idea can be achieved by self-tuning of activation behaviors. We also develop an iterative training algorithm, so that the ADNs can be smoothly incorporated into deep neural networks to jointly learn with the network weights. Experimental results found that the ADNs can efficiently improve the noise-resistant capability. Compared with the state-of-the-art, it is more robust to unforeseen noises. It does not need noise data for training.
Boundary Layer Detection Techniques Applied to Edge Detection

In this paper, we describe a novel algorithm for edge detection on a digital image, which is based locally on the directional averaged gradient properties of the intensity function, and produces very satisfactory results in high-resolution digital images in low execution time. Several examples show results which are comparable to those obtained by Canny and Sobel methods.
An Immersive 3D Virtual Environment to Support Collaborative Learning and Teaching

This paper reflects on a Virtual Learning Environment (VLE)Virtual Learning Environment (VLE) in the context of architectureArchitecture , urban planningUrban planning and designDesign . The paper aims to critically assess the ability of virtualVirtual environments to support experiential online learning. It contributes to the literature by providing the experimental results of implementing the TERF virtual worldVirtual world for undergraduate and graduate Built EnvironmentEnvironment courses. TERF virtual worldVirtual world provides videos and other communicationCommunication toolsTool to support collaborationCollaboration among students. FeedbackFeedback on the usage and functionality of this 3D3D virtualVirtual platform was collected from students through post evaluationEvaluation surveys. This experimentExperiment provided opportunities to facilitate team communicationTeam communication and a route to more collaborative leaning. It is also discussed the strengths and limitations of the 3D3D collaborative virtualVirtual environmentEnvironment to support deeper learning environmentEnvironment .
Hierarchical Bayesian mixture models of processing architectures and stopping rules

Systems Factorial Technology is a methodology that allows researchers to identify properties of cognitive processing systems, such as the system’s architecture and the decisional stopping rule. It assumes that the cognitive system will use the same architecture and decisional stopping rule on every trial of an experiment. Through simulation, we aim to explore the predictions of models that allow for a mixture of architectures and decisional stopping rules across trials. Our simulation reveals a pattern of interaction contrasts across different mixtures, some of which mimic each other. Given the mimicry, we developed a hierarchical Bayesian inference method that can identify mixture probabilities for mixtures of architectures and stopping rules. The method is also a useful inference tool that can determine what architecture and stopping rule a participant is using and has many promising future developments, such as simultaneous group and subject level inference.
Determination Issues of Data Mining Process of Failures in the Production Systems

The implementation of data mining methods is equally important in the industrial area as well as their deployment in other areas. Among the benefits that new knowledge can bring include, for example, optimization of production and other processes, fault prediction, or maintenance prediction. In our research we are focused on production processes and obtaining new knowledge from them. This paper deals with the data from automotive industry and with predicting and identifying failures in such production process in automotive industry. The industrial area is appropriate for KDD research because the processes are fully describable and they have finite number of states. Even possible failures can be described before the process is alive. Even though the number of possible states of the system is finite, it is hard to make predictions by hand. Therefor the methods of data mining are appropriate.
Line and Polygon Clipping Techniques on Natural Images — A Mathematical Solution and Performance Evaluation

The objective of this paper is to apply clipping techniques on natural images and to analyze the performance of various clipping algorithms in computer graphics. The clipping techniques used in this paper is Cohen–Sutherland line clipping, Liang–Barsky line clipping, Nicholl–Lee–Nicholl line clipping and Sutherland–Hodgman polygon clipping. The clipping algorithms are evaluated by using the three parameters: time complexity, space complexity and image accuracy. Previously, there is no performance evaluation on clipping algorithms done. Motivating by this factor, in this paper an evaluation of clipping algorithms is made. The novelty of this paper is to apply the clipping algorithms on natural images. It is justified that the above mentioned clipping algorithms outperform well on clipping the natural images.
Experimental Studies of Network Traffic of Mobile Devices with Android OS

As part of the study of mobile device traffic, the formation of an experimental database of network traffic of selected mobile applications, which can be used for training and testing classifiers using machine learning methods, occupies an important place. To automate the process of studying mobile application traffic classification algorithms, a software package has been developed that allows you to automatically collect network traffic packets from mobile devices and save them to a database; group network traffic packets into streams; upon user request, generate data sets with specified characteristics (number of specific application flows: background traffic; generate a data set based on the already created set with the addition of new flows, excluding repetitions). To collect traffic from mobile devices running the Android operating system, an application was developed that uses the application programming interface to create virtual private networks, collects network traffic packets, identifies the source application and sends them via HTTP to server software. Using the client and server software, the database created was filled with traffic from 18 main applications. During the data collection, 71,667 streams and 6,989,991 packets were received.
Theoretical and Experimental Evaluation of PSO-K-Means Algorithm for MRI Images Segmentation Using Drift Theorem

Image segmentation is the process of subdividing an image into regions that are consistent and homogeneous in some characteristics. An important factor in the recognition of magnetic resonance images is not only the accuracy, but also the speed of the segmentation procedure. Modified Exponential Particle Swarm Optimization algorithm is proposed in paper. The time complexity of proposed algorithm is investigated using consequences from Drift theorem. It is established that the proposed algorithm has a polynomial estimation of complexity. Images from the Ossirix image dataset and real medical images were used for testing.
Deep Support Vector Classification and Regression

Support Vector Machines, SVM, are one of the most popular machine learning models for supervised problems and have proved to achieve great performance in a wide broad of predicting tasks. However, they can suffer from scalability issues when working with large sample sizes, a common situation in the big data era. On the other hand, Deep Neural Networks (DNNs) can handle large datasets with greater ease and in this paper we propose Deep SVM models that combine the highly non-linear feature processing of DNNs with SVM loss functions. As we will show, these models can achieve performances similar to those of standard SVM while having a greater sample scalability.
Quantum radar, quantum networks, not-so-quantum hackers

Many quantum technologies, such as quantum computers, rely on a phenomenon called entanglement. One reason why quantum networks are being studied is because they can distribute entanglement to their users. In this paper, we describe how quantum radars, particularly the recently-developed quantum two-mode squeezing radar (QTMS radar), can be used with quantum networks. On a related note, we also point out how QTMS radar can be vulnerable to interception if an adversary has access to the measurement record that the radar uses to distinguish signal from noise.