Reviewerly/exHarmony huggingface dataset:
The Formation of a New Paradigm in Education: From Convergent Technologies to Understanding Social and Technogenic Transformations in the World

At present, dramatic changes in the modern scientific picture of the world are taking place, which urgently require a review of the existing worldview and its social and humanitarian component. The study of a multifaceted reality led to the fact that instead of an integral picture of the world, science obtained a kind of mosaic with varying degrees of completeness of the studied and understood phenomena due to the isolation of the nature model segments accessible to analysis. Nanotechnology led to a change in the research paradigm: if earlier scientific cognition was of an analytical nature (“from top to bottom”), now it has moved to a synthetic level (“from the bottom up”), which required the narrow specialization abandonment and the transition to the creation of various materials and systems on atomic-molecular level. Convergent technologies, being a material foundation of the convergent approach, based on nanotechnological methodology, changed the paradigm of cognition from analytical to synthetic, giving rise to modern industrial technologies that erased narrow interdisciplinary boundaries. The technologies existing today are able to change the evolution of nature in the context of the development of the technogenic society. Misconceptions about social processes in sociology, economics and fragmentation in different sciences, contribute to a misunderstanding of the modern world, which has an impact on the content of educational curricula in education. In the formation of the scientific and theoretical foundations for the creation of a new paradigm of education, it is necessary to take into account the features of modern socio-technogenic development of the world.
An Ensemble Filter Feature Selection Method and Outlier Detection Method for Multiclass Classification

Feature selection methods facilitate removal of irrelevant attributes. Ineffective features may contain outliers that degrade performance of classifiers. We propose an ensemble filter base feature selection technique for multiclass classification. The technique combines results of four selection methods to create an ensemble list. The study uses a red wine dataset drawn from UC Irvine machine learning data repository and WEKA, a collection of machine learning algorithms for data mining tasks. The multiclass red wine dataset is binarized using WekaMulticlassClassifier utilizing the 1against 1 with pairwise coupling decomposing scheme. Using random forest algorithm and root mean square error values, a learning curve is generated that establishes an optimal ensemble sub-list. Outliers are detected using the Tukey statistical method. The proposed ensemble method outperformed the single feature methods. The study concludes by showing that unnecessary features and presence of outliers degrades classifiers performance. We recommend further studies on the effect of gradual selective removal of outliers on classification.
Learning-focused TA Training on a Budget

Teaching assistants are critical to the student experience, especially in large computer science courses, where they may be the only teaching staff students interact with directly. However, institutional restrictions on TA work, and practical limits on instructor time mean that TA training needs to be cost-effective, maximizing impact on the quality of TA work while minimizing instructor and TA time required.
Cognitive bots and algorithmic humans: toward a shared understanding of social intelligence

Questions of social behavior are simultaneously among the most fundamental in neuroscience and the most challenging in artificial intelligence. Yet despite decades of work, a unified perspective from the cognitive and computational approaches to the problem has yet to emerge. Recently, however, excitement around the challenges posed to reinforcement learning by multiplayer video games, coupled with the adoption of more complex modeling strategies in social neuroscience, has broadened the interface between the two fields. Here, we review recent progress from both directions, arguing that advances in artificial intelligence provide neuroscientists with valuable tools for modeling social interactions. At the same time, the study of humans as efficient social learners can inform the design of new algorithms for multi-agent systems. We conclude by encouraging a joint approach that incorporates the best of both domains to advance a shared picture of social intelligence.
Heavy-tails in Kalman filtering with packet losses

In this paper, we study the existence of a steady-state distribution and its tail behaviour for the estimation error arising from Kalman filtering for unstable linear dynamical systems. Although a large body of literature has studied the problem of Kalman filtering with packet losses in terms of analysis of the second moment, no study has addressed the actual distribution of the estimation error. First we show that if the system is strictly unstable and packet loss probability is strictly less than unity, then the steady-state distribution (if it exists) must be heavy tail, i.e. its absolute moments beyond a certain order do not exist. Then, by drawing results from Renewal Theory, we further provide sufficient conditions for the existence of such stationary distribution. Moreover, we show that under additional technical assumptions and in the scalar scenario, the steady-state distribution of the Kalman prediction error has an asymptotic power-law tail, i.e. P[|e|>s]∼s−α, as s → ∞, where α can be explicitly computed. We further explore how to optimally select the sampling period assuming an exponential decay of packet loss probability with respect to the sampling period. In order to minimize the expected value of the second moment or the confidence bounds, we illustrate that in general a larger sampling period will need to be chosen in the latter case as a result of the heavy tail behaviour.
An improved KFDD based reversible circuit synthesis method

Since reversible logic has promising applications in domains like low-power design and quantum computing, it is necessary to efficiently design high-performance reversible logic circuits. In this paper, in order to reduce the number of qubits while achieving low quantum cost, the Kronecker functional decision diagram (KFDD) based reversible circuit synthesis method is improved by using two strategies. One is to generate a locally optimal reversible cascade for each of the KFDD nodes by performing transformations on the function represented by a node and using a gate library consisting of NOT, CNOT, Toffoli gates and mixed-polarity Peres gates. This strategy helps reduce the quantum cost and the number of qubits. The other is to map KFDD nodes to their locally optimal reversible cascades level by level via breadth-first traversal of the KFDD. This strategy reduces the number of qubits by using the circuit line labeled by an input variable of the KFDD as the target line of a reversible gate. Comparison results are presented in tabular and graphical forms. Compared to the existing decision diagram based reversible circuit synthesis methods, the proposed method can reduce both the quantum cost and the number of qubits in many cases. Compared to other state-of-the-art synthesis methods for reversible circuits, the proposed method can achieve much lower quantum cost but still incurs a high number of qubits for many functions. However, it can achieve the minimum number of qubits for a few irreversible functions while achieving much lower quantum cost. In addition, the proposed method is very time efficient.
Hardware Implementation of Compact Genetic Algorithm for Robot Path Planning in Globally Static Environment in 8-bit Microcontroller

In this paper, hardware implementation of genetic algorithm for Robot path planning in Globally static Environment is presented. Genetic algorithm is modified and implemented in 8-bit Microtontroller (MCU) PIC18F452. The genetic algorithm is designed to decrease number of iteration and processing power by using predefined priorities for parenti initial path generation rather than creating parent paths randomly. The unmanned ground vehicle (UGV) is designed which receives starting node, final destination and obstacles wirelessly, it then create multiple parent paths by using different priorities, cross over to create new child paths, uses distance as fitness function for determining Optimal or shortest path while avoiding the obstacles and uses stepper motors with three- dimensional movements to reach its destination. The environment is 5×5 static Grid Map in which obstacles are known before path planning. The MCU determines optimal path with no obstacles and require minimum distance to reach its destination.
Precise String Analysis for JavaScript Programs Using Automata

Existing static analyzers for JavaScript use constant propagation domains to analyze strings. The simplicity of these domains results in a huge loss of precision when dealing with features such as dynamic property access. This paper presents a string analysis for the full JavaScript language based on abstract interpretation. The analysis uses finite state automata to track all possible strings a variable might hold during execution. We present an empirical performance and precision evaluation on some JavaScript benchmarks and show that the analysis achieves a higher level of precision especially when handling dynamic property access.
Two-dimensional wavelet synopses with maximum error bound and its application in parallel compression

Wavelet synopses with maximum error bound is an effective quality-guaranteed compression method that restricts the approximation error of each data does not exceed a given error bound. In this paper, we focus on the study of constructing efficient two-dimensional wavelet synopses with maximum error bound. First, we propose a linear-time two-dimensional F-shift algorithm (TDFS), then present a general parallel framework for two-dimensional data array and generate a parallel two-dimensional F-shift algorithm (PTDFS). We have proven that the size of a synopsis constructed from PTDFS is always no larger than that of the existing methods and can reduce up to 66.7% at most. The experimental results indicate that the synopsis sizes can be reduced from 40% to 60% in most situations. Moreover, PTDFS can not only improve the quality of reconstruction image, but also reduce the running time.
NavAS: Navigation Approaches for Answer Sets

Answer set programming (ASP) is one of the most popular modeling languages in knowledge representation. In recent years, many integrated development environments (IDE) for ASP programs including editors and debuggers are developed. However, none of them focuses on analyzing the answer sets. With the availability of a huge number of answer sets, it is increasingly important to provide a solution to navigate them. In this paper, we study and analyze the answer sets to perform user access to specific answers. To this end, we aim at conducting and exploring different navigation approaches, such as, filtering, sorting, finding diverse/similar solutions, and faceted browsing. Afterward, we implement a tool performing the above approaches in order to simplify the search task. We conclude by testing the performance of the proposed tool into a real world example of ASP programs.
Document Categorization Engine Based on Machine Learning Techniques

Analysts, researchers, and other users from different fields may spend a lot of time recognizing their work-related documents and organizing them in a way that allows them to make the best use of their content. In this regard, we propose a Document Categorization Engine (DCE) that utilizes concepts of machine learning techniques and data mining. The project aims to develop a system that is capable of classifying documents based on user defined criteria.
A New Non Linear Inertia Weight Approach in PSO for Faster Rigid Image Registration

A non-linear inertia weight approach is proposed and validated for faster image registration. Inertia weight in Particle Swarm Algorithm controls the movement of the particles and effectively balances the exploitation and exploration mechanism of the swarm. The most efficient approach used in literature is that of linear inertia weight variation during the PSO run. In the present work a Non-linear approach is used for image registration problem. For the registration mutual information is used as a similarity metric. Images are registered using the standard linearly varied inertia weight approach and the new non-linear approach and comparative study is done. The results show that the proposed inertia weight variation is conducive in faster convergence of PSO and hence provide faster image registration. Further, to validate our study the proposed approach is used on twenty standard CEC benchmark functions and the comparison is done with the standard approach. It was found that the convergence rate was improved with the proposed inertia weight variation.
Across-scale process similarity based interpolation for image super-resolution

A pivotal step in image super-resolution techniques is interpolation, which aims at generating high resolution images without introducing artifacts such as blurring and ringing. In this paper, we propose a technique that performs interpolation through an infusion of high frequency signal components computed by exploiting ‘process similarity’. By ‘process similarity’, we refer to the resemblance between a decomposition of the image at a resolution to the decomposition of the image at another resolution. In our approach, the decompositions generating image details and approximations are obtained through the discrete wavelet (DWT) and stationary wavelet (SWT) transforms. The complementary nature of DWT and SWT is leveraged to get the structural relation between the input image and its low resolution approximation. The structural relation is represented by optimal model parameters obtained through particle swarm optimization (PSO). Owing to process similarity, these parameters are used to generate the high resolution output image from the input image. The proposed approach is compared with six existing techniques qualitatively and in terms of PSNR, SSIM, and FSIM measures, along with computation time (CPU time). It is found that our approach is the fastest in terms of CPU time and produces comparable results.
Identifying High Value Opportunities for Human in the Loop Lexicon Expansion

Many real world analytics problems examine multiple entities or classes that may appear in a corpus. For example, in a customer satisfaction survey analysis there are over 60 categories of (somewhat overlapping) concerns. Each of these is backed by a lexicon of terminology associated with the concern (e.g., “Easy, user friendly process” or ”Process confusing, too many handoffs”). These categories need to be expanded by a subject matter expert as the terminology is not always straight forward (e.g., “handoffs” may also include “ping-pong” and “hot potato” as relevant terms).
Promoting Contemplative Culture through Media Arts

This paper presents the practice of designing mediation technologies as artistic tools to expand the creative repertoire to promote contemplative cultural practice. Three art–science collaborations—Mandala, Imagining the Universe, and Resonance of the Heart—are elaborated on as proof-of-concept case studies. Scientifically, the empirical research examines the mappings from (bodily) action to (sound/visual) perception in technology-mediated performing art. Theoretically, the author synthesizes media arts practices on a level of defining general design principles and post-human artistic identities. Technically, the author implements machine learning techniques, digital audio/visual signal processing, and sensing technology to explore post-human artistic identities and give voice to underrepresented groups. Realized by a group of multinational media artists, computer engineers, audio engineers, and cognitive neuroscientists, this work preserves, promotes, and further explores contemplative culture with emerging technologies.
New approach to the fidelity of quantum teleportation based on the entangled state generated by beam splitter

Entangled states play an important role in quantum computation and quantum information processing. Beam splitter, a linear device, is often used to prepare entangled states and quantum mechanics representations. For any two input states, the formula for the fidelity is derived, including the Q function representation, the P function representation, and the Wigner function representation, by using the output states from beam splitter as a source of quantum teleportation. This provides a new way to calculate the fidelity. As applications, two single-mode squeezed vacuum states and coherent superposition state together with coherent state are used as inputs, and the fidelity for teleporting coherent state is discussed. Our method provide more alternatives for calculating fidelity of teleportaion.
The rise of encrypted malware

Increasingly complex multi-cloud architectures and more stringent regulatory environments have increased reliance on encryption – primarily secure sockets layer (SSL) and transport layer security (TLS) – to the extent that over 80% of all network traffic is now encrypted. While in many ways the growth of encryption is a good thing for security, it also creates a backdoor for hackers and threat actors looking to hide from traditional detection tools. Organisations are adopting more stringent encryption practices. And while the growth of encryption might seem a good thing for security, it also creates a backdoor for hackers and threat actors looking to hide from detection tools. Additionally, as organisations continue to move to the cloud, increase their utilisation of APIs and adopt ever-more complex and sensitive systems, they find themselves with ever-more complex multi-cloud architectures. The solution lies in encrypted cognitive analytics, argues Omar Yaacoubi of Barac, which enables the creation of new types of data elements or telemetry that are independent of protocol details.
A new surface preparation method for microscopic digital image correlation applications

In experimental mechanics as well as in industry the digital image correlation is an important method for the evaluation of displacement and strain fields. The resolution of the method is influenced by the fineness of the existing speckle pattern. Sprayed patterns do not provide the required resolution for microscopic applications. Therefore, an alternative pattern production based on abrasive paper was developed. The generated pattern was applied to a test specimen and its function was tested in an experiment. The evaluations of displacement and strain fields were made using GOM Correlate Professional and Matlab
Neural network in a multi-agent system for line detection task in images

Lines are one of the most informative structure elements in any images. For this reason, objects detection and recognition problem are often reduced to edge detection task. Radon transform and Hough transform are widely used in straight-line detection. However, these methods allow estimating only the straight line parameters (but not line segment). It is proposed to split the image into square fragments (blocks) in which straight-line segments are detected to solve this problem. A multi-agent system is used to combine segments into curves and drop false detections. The use of artificial neural networks (NN) for programming a part of agent behavior in the multi-agent system is the main theme of this work.
A Model-driven Approach for Token Based Code Clone Detection Techniques - An Introduction to UMLCCD

Reusing code through copy and paste is a well-known process in software development. The replicated code is known as code clone and the process is known as code cloning. Software clones can cause bug propagation and severe issues related to maintenance. Code clone detection process consist of many steps, which is complex to some extent for user to perform. Therefore, we are trying to propose a generic approach for code clone detection approaches at model level. This study particularly presents Unified Modeling Language profile for Code Clone Detection (UMLCCD) to model the process of token-based code clone detection approaches as token-based approaches has good execution time while maintaining high precision and recall. It automate the clone detection process of token-based approaches which is helpful for user because user simply gives input and whole process is done automatically. It can be used for source code of various programming languages. The proposed UML profile provides the bases to transform the UMLCCD source model into required text. The applicability of UMLCCD is validated by taking 484 lines of source code of Library Management System case study.
Neural Network for Smart Adjustment of Industrial Camera - Study of Deployed Application

Since machine vision is gaining more and more interest lately, it is necessary to deal with correct approaches to visual data acquisition in industry. As a particular part of this complex problematics, a technique for the industrial camera exposure time and image sensor gain tuning is presented in this contribution. In comparison to other approaches, a human expert photographer is used instead of explicitly defined cost function. His knowledge is transformed into an artificial expert system represented by a feedforward neural network. The expert system then provides the suitable exposure time and image sensor gain to gather sharp and balanced images.
Load Balancing Scheme for Effectively Supporting Distributed In-Memory Based Computing

As digital data have increased exponentially due to an increasing number of information channels that create and distribute the data, distributed in-memory systems were introduced to process big data in real-time. However, when the load is concentrated on a specific node in a distributed in-memory environment, the data access performance is degraded, resulting in an overall degradation in the processing performance. In this paper, we propose a new load balancing scheme that performs data migration or replication according to the loading status in heterogeneous distributed in-memory environments. The proposed scheme replicates hot data when the hot data occurs on the node where a load occurs. If the load of the node increases in the absence of hot data, the data is migrated through a hash space adjustment. In addition, when nodes are added or removed, data distribution is performed by adjusting the hash space with the adjacent nodes. The clients store the metadata of the hot data and reduce the access of the load balancer through periodic synchronization. It is confirmed through various performance evaluations that the proposed load balancing scheme improves the overall load balancing performance.
Advancements in Medical Practice Using Mixed Reality Technology

Mixed reality technology is one of the most emerging fields of interactive visual interfaces in recent years. It’s evolution from virtual reality systems brings outstanding performance in highly immersive experiences. Furthermore, the MR addresses the most pressing issue among VR - the space and context awareness of the user. The core of MR, being a holographic projection displayed onto real-world objects, highly increases the number of possible applications. In this paper we discuss the openings for using the leading commercial device for MR - Microsoft HoloLens - in numerous contexts among medical training and practice. We also propose a novel way to facilitate the home-based therapy of the lazy eye syndrome. The proposed solution combines the principles of traditional medical methods with increased ease of use and user-friendliness of exercising. Finally, we discuss the developments required for the method to become valid for medical practice.