Reviewerly/exHarmony huggingface dataset:
Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have been proposed to achieve these goals. However, to the best of our knowledge, there is no suitable method that can be adapted for algorithms that enumerate the rowset lattice, which is effective for biological datasets. Therefore, this paper proposed a method for computing closure compare with the method used in BVBUC algorithm method. Finally, BVBUC_I is proposed and the performances of these algorithms were evaluated using two synthetic datasets and three real datasets. The results of these tests proved the efficiency of the proposed method.
Towards Energy Efficient Servers’ Utilization in Datacenters

Cloud computing datacenters contain hundreds of servers that host different kinds of services for a wide spectrum of customers. These datacenters have substantial energy demands for their operation, thus promoting the need for optimizing their power consumption and energy demands. Resources allocation and optimized scheduling of incoming tasks are at the heart of any successful power management technique used for datacenters. In this work we focus on the efficient utilization of servers in the datacenter to optimize power consumption. The goal is to develop task allocation techniques that contributes to the overall optimization of energy demands by optimizing the consumption of the datacenter servers. The allocation problem is modeled using Integer Linear Programming (ILP) techniques, where models are formulated with the objective of minimizing the total power consumed by the active and idle cores of the servers. Preliminary results show that an optimization driven servers’ allocation strategy produces noticeable improvement in power consumption when compared to scheduling techniques such as round robin.
A New Approach to Developing General Manipulator Control System Application Based on ROS

This paper proposes a new approach to developing control applications for general manipulator using the open source robot operating system (ROS) as the platform. The tool MoveIt! is used to complete the task of the motion planning and the motion data transmission between client and server is realized by setting up the relevant nodes. In combination with the experiment, this paper explains the model building of the robot with a specific manipulator and the motion data is processed by quintic polynomial interpolation algorithm to improve the stability of the manipulator. According to the above methods, it can realize the control of different types of manipulators on the market not specifically referring to certain structures and provides a new way for cross-platform control of robots.
Leveraging Local Optima Network Properties for Memetic Differential Evolution

Population based global optimization methods can be extended by properly defined networks in order to explore the structure of the search space, to describe how the method performed on a given problem and to inform the optimization algorithm so that it can be more efficient. The memetic differential evolution (MDE) algorithm using local optima network (LON) is investigated for these aspects. Firstly, we report the performance of the classical variants of differential evolution applied for MDE, including the structural properties of the resulting LONs. Secondly, a new restarting rule is proposed, which aims at avoiding early convergence and it uses the LON which is built-up during the evolutionary search of MDE. Finally, we show the promising results of this new rule, which contributes to the efforts of combining optimization methods with network science.
Mapping<i>k</i>-combinations and<i>Dih</i><sub>4</sub>in John Cage's<i>Variations I</i>as utilities for determinate and indeterminate realization strategies

This article analyses the 1958 graphic score, Variations I, by John Cage (1912–1992). I firstly trace the resistance that the work has established towards traditional analysis, given its meta-score qualities, and the “distance metric problem,” which arises from the necessity to generate musical parameter data from the measurement of perpendiculars between points and lines printed on six transparent sheets. I propose that an extension to Cage's instructions allows the symmetry of the transparencies to be described as the dihedral group of order 8 (Dih4). I also report on the size of the k-combinations (with and without repetition) of the transparencies and account for the sound densities and sound classes of the work. This analysis then allows for the development of two opposing realization frameworks that are determinate and indeterminate in nature.
Intrinsic surface feature based subaperture stitching of freeform wavefront

The subaperture stitching technique requires the registration of freeform subapertures into global coordinate frame before stitching in order to compute entire freeform wavefront. A scanning Shack-Hartman Sensor (SHS) utilizes translation stages to scan the freeform surface in XY plane and measure the slope data of various subapertures. The measured slope data is then integrated using weighted cubic spline (WCSLI) based integration method to compute the phase data. The positioning error during scanning causes misalignments between the measured subapertures and their nominal values. The least square based subaperture stitching methods are not capable to minimize lateral misalignment errors of freeform subapertures and therefore degrade the performance of subaperture stitching process. In this work, we have utilized fiducial added planes for correction of angular and rotation misalignments of an extended cubic phase plate. An intrinsic surface feature (ISF) based registration method is used for lateral misalignment corrections. Gaussian curvature is used as an intrinsic pattern which can be defined as one of the fundamental second order geometric properties of a surface. Any shift in the peaks of the Gaussian curvature of reference and measured subaperture corresponds to lateral misalignments in X and Y directions and need to be minimized before registration of subaperture into global frame of reference. After precise registrations, all the subapertures are stitched with consistent overlapping area by using least square fitting method. A numerical validation of the proposed scheme is carried out which demonstrates the effectiveness of the proposed method to improve the subaperture stitching accuracy.
On the Nature of Pragmatic Increments at the Truth-Conditional Level

The aim of this chapter is to reflect on the necessity of the pragmatic development of propositional forms and to reach a better understanding of the level of meaning that Sperber and Wilson and Carston famously call ‘explicatures’ (or ‘explicature’) and to support the claim that (the pragmatically conveyed elements of) explicatures are not cancellable – unlike conversational implicatures (Someone alleged that my claim is not original; however, I have to modestly assert that I put forward this claim in my 2003 paper, which was revised and reprinted in 2006.). While Capone (RASK: Int J Lang Commun 19:3–32, 2003) (A paper that antecedes Burton-Roberts claim that explicatures are non-cancellable.) addressed the issue of the cancellability of explicatures from the point of view of Grice’s circle, a number of important theoretical questions are raised and discussed here. In particular, I propose that the analysis of the notion of intentionality and of the nature of pragmatic intrusion will settle the question concerning the non-cancellability of explicatures. An explicature can be considered to be a two-level entity, in that it consists of a logical form and a pragmatic increment which this logical form gives rise to (in the context of utterance). However, both the initial logical form and the pragmatic increment are the target of pragmatic processes, in that we need a pragmatic process to promote the initial logical form to a serious intended interpretation and another pragmatic process to derive further increments starting from this initial logical form and being promoted to serious utterance interpretations.
A combination of Rivest Shamir Adlemann (RSA) and Affine Cipher method on improvement of the effectiveness and security of text message

Abstract Rivest Shamir Adlemann (RSA) method is one of the popular methods in the field of public key cryptography. The strength of the RSA method is based on the difficulty level of factoring of two large prime numbers. If they are factored easily, then we need to use brute force to break the system. In this present, we combined RSA method with classical method, namely Affine Cipher method to improve the level of security on text message. The process of combination RSA method and Affine Cipher method is as follow: first, text message was encryption using Affine Cipher method, then the encryption output is used as input of the RSA method. The results certified that the improvement of the security level of text message can be realized by using the combined method.
Robot Localization Through Optical Character Recognition of Signs

Optical character recognition (OCR) is the process by which the textual content of an image is converted into strings. Localization is the problem of figuring out where one is in a given environment. In this work we approach the application of OCR in robot localization. We develop and test a vision based localization system that is capable of detecting room identification signs present in the environment, recognizing their textual contents and apply them to determine its location referent to a topological map of the environment. A sign detection method based on image segmentation by color and corner detection by contour analysis is developed. The recognition of characters is performed with the application of an open-source OCR engine. Localization is performed through the comparison of sign readings with the textual information embedded in the topological representation of the environment. The algorithm was tested in a dataset of images acquired in a corridor. Experimental results show that the system successfully determines its localization in 83.33% of tested cases.
Exer-model

A user model that is built from the data of multiple physical activity sensors has the potential to enable people to answer important questions about their long-term physical activity. Our work provides a way to do this for the case of exercise from virtual reality gaming and from incidental daily walking. Our approach is based two parts: 1) a carefully designed a user model ontology, Exer-model; 2) an interface for navigating the model and comparing components of the model. We evaluated the Exer-model ontology and the scrutiny interface in a study with 16 users: 8 viewing their own user models, from 8 weeks of their sensor data, and the other 8 scrutinising the model of a hypothetical user. Our core contributions are the insights about designing the ontologies and interfaces for scrutable user models from multiple physical activity sensors.
Effect of Video Game Violence on Playing with Real Guns

Due to the serious problem of children being injured or killed with guns in the home, concern over the possible contribution of violent video games to
Transformation of Value Positions in the Youth Attitude of to Work: A Philosophical Analysis

Most representatives of the modern youth demonstrate formed clip thinking characterized by a loss of skills to analyze, discourse, set logical connections. This not only affects the process of learning and obtaining professional knowledge, but also expresses a change in the attitude to work as one of the most important values against the background of a significant increase in the value of leisure. The transformation of values due to the influence of external factors such as television, Internet, radio, media on consciousness is obvious. The bulk of the media clip thinking is the youth facing serious difficulties in introducing serious creative work and the creation of new values. At present the structure and types of differences in employment are undergoing epoch-making changes. They are expressed in the mixture of traditional and modern trends, the emergence of new forms of work (freelance, downshifting, etc.), as well as in the formation of new trends in the attitude to work. New values such as "success", "pleasure", "power" are formed on the basis of cultural changes in the youth environment. The value paradigm formed in the youth environment under the influence of mass culture in the conditions of the information revolution has become an expression of new needs in the transformation of social reality. Under these conditions, the study of trends in the youth's value attitudes to work is of great theoretical and practical importance. The theoretical basis of this article is grounded on the works of A. Mole, E. Toffler, D. bell, E. Fromm, G. Marcuse, G. McLuhan. Their research papers reflect the changes that began to occur in society in the second half of the XX century and had an impact on the change of value orientations of young people. In our country, these changes were manifested in the generation of the nineties and noughties, and the representatives of this generation are the carriers of new values associated with the consequences of the information revolution.
METACOMMUNICATION OF VIRTUALITY: EPISTEMOLOGICAL ASPECTS

The article deals with the issues related to the peculiarities of metacommunicative messages of virtual space and their influence on a subject’s cognitive processes. The history of the metacommunication definition emergence and its transformation in the context of various social disciplines and the humanities is described. The author singles out and analyses three groups of metacommunicative messages divided according to the principle of their formation source: technology, interface and virtual communication links. Metacommunicative virtual messages’ influence on a subject’s cognitive processes is analysed.
Conclusion and Future Directions

We envision, for haptic personalization, a suite of tools that are unified by one underlying conceptual model and can be effectively incorporated into users’ workflows with various applications. Our vision is analogous to what we have for color personalization: A simple, yet powerful, suite of tools (color swatches, color gamut, sliders) built on the color theory (e.g., Munsell color system), and seamlessly integrated in a variety of applications (e.g., Microsoft Office, Adobe Creative Suite) for a wide range of users. Below, we first discuss the contributions of this book towards this vision, divided by the three main themes that we presented in Chap. 1 , then we outline the next steps for which the research described here has exposed a need.
Structure for Knowledge Acquisition, Use, Learning and Collaboration Inter Agents over Internet Infrastructure Domains

This article describes an environment for knowledge acquisition, learning, use and collaboration inter agents over Internet Infrastructure. Four agent types are used in a previously applied four-tier model, such as the use case on the Internet Routing Registry. This model, which can be implemented in each Autonomous System domain of the Internet infrastructure, is integrated into an environment with (a) capturing information from unstructured databases, (b) creating and updating training bases appropriate to machine learning algorithms, and (c) creation and feeding of a knowledge base. Such resources become readily available to agents in each domain and to agents in all other domains with the aim of making them autonomous. The agents collaborate and interact with each other, through individual blockchain structures that also take care of operational security and integration aspects. In addition, a test bed to validate the entire model, including the functionalities of the agents, is also proposed and characterized.
Declarative Learning-Based Programming as an Interface to AI Systems

Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models' output and input. However, the current tools are cumbersome not only for domain experts who are not fluent in machine learning but also for machine learning experts who evaluate new algorithms and models on real-world data and develop AI systems. We review key efforts made by various AI communities in providing languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques and their data and knowledge representations, compare the ways the current tools address the challenges of programming real-world applications and highlight some shortcomings and future directions. Our comparison is only qualitative and not experimental since the performance of the systems is not a factor in our study.
A cooperating metaheuristic approach for MR image segmentation

This paper presents cooperating Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms for MR image segmentation. The problem can be formulated as an optimization problem and the proposed algorithm has been applied to find the best solution. Since image segmentation requires satisfying several criteria, it is important to know how to optimize them in parallel in the same algorithm. This paper is actually a further step of our works, which applies a new mechanism of using metaheuristic algorithms for optimizing a Markov Random Field (MRF) segmentation criterion. The proposed method is validated on both simulated and real MR images. The results indicate that our method can provide better solutions in terms of segmentation quality and efficiency.
Anomaly Detection of Distribution Network Synchronous Measurement Data Based on Large Dimensional Random Matrix

In order to identify faults of power system measurement subsystem, a method based on deep belief network is proposed in this paper. Firstly, data from actual measurement system is collected and divided into training and test samples. And then, the data is used to train a deep belief network. Finally, the model’s fault diagnosis results and actual samples’ labels are combined as a cross-validation set to test the deep belief network. The results show that the method based on deep belief network proposed in this paper can be more stable and reliable identification of electric power measurement equipment fault diagnosis.
Deep Influence Diagrams: An Interpretable and Robust Decision Support System

Interpretable decision making frameworks allow us to easily endow agents with specific goals, risk tolerances, and understanding. Existing decision making systems either forgo interpretability, or pay for it with severely reduced efficiency and large memory requirements. In this paper, we outline DeepID, a neural network approximation of Influence Diagrams, that avoids both pitfalls. We demonstrate how the framework allows for the introduction of robustness in a very transparent and interpretable manner, without increasing the complexity class of the decision problem.
The Burden of Selfhood

The Burden of Selfhood is an interdisciplinary performance artwork exploring the intersection of feminism, identity and technology. By connecting methods from cognitive science, music, poetry, video and performance art, we investigate the experience of viewing and being viewed as a gendered body. Technology has accelerated the recursive gaze to the point that we continually perform and project back onto each other our internalized expectations for unattainable perfection. This poly-vocal performance uses large-scale data visualizations and live performers to make explicit both the collective gaze and the implicit impact of being seen. Select portions of the performance can be found at: https://www.youtube.com/watch?v=UPk2JSt-e9Q https://www.youtube.com/watch?v=j1DXmiBU_3w
Establishing Standards for Consensus on Blockchains

We survey six popular blockchain consensus algorithms, showing that the blockchain community does not have well-established theoretical foundations. We consolidate and unify these foundational notions, establishing high quality standards and exploring the comparative relationship between foundations used on these different algorithms. The framework established here is meant to be used by academic and industrial blockchain researchers as a foundation for the theory of consensus.
On Efficiency of Scrambled Image Forensics Service Using Support Vector Machine

Images can be a very good evidence during investigation of a crime scene. At the same time they can also contain very personal information which should not be exposed without the consent of the involved people. In this paper, We have presented here a practical approach to protect privacy of under investigation images with the use of Arnold's Transform (AT) scrambling and Support Vector Machine, We also provide a new approach towards the whole forensics service provided by the designated agencies with the help of implementation of our approach. We enhanced the security of AT and provided privacy preserving mechanism to ensure protection of privacy. In literature only policies are defined to protect the privacy and lack of a solid approach which we have tried to resolve with a proof of concept implementation. In short, we have provided a full image forensics framework for illegal image detection while preserving the privacy.
Robust Detection of Outdoor Urban Advertising Panels in Static Images

One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images.
360 DEGREES AND K-NEAREST NEIGHBOUR METHODS FOR LECTURER PERFORMANCE APPRAISAL SYSTEMS: 360 DEGREES AND K-NEAREST NEIGHBOUR METHODS FOR LECTURER PERFORMANCE APPRAISAL SYSTEMS

The lecturer performance appraisal system functions to measure and evaluate the performance of the lecturers in a certain period of time. The purpose of this study is to apply the 360-degree and machine learning algorithm using the K-NN (k-nearest neighbour) method to model the lecturer performance appraisal system to be more objective and accountable. DP3 is List of Appraisal of Employee Work Implementation. DP3 data is used as knowledge datasets to be used as training data and test data for the classification process and prediction of lecturer performance values. The results obtained show the 360-degree method and K-NN is able to predict with 90% accuracy right on knowledge datasets that have not been normalized and K = 5 values. Hence, this model can then be used to become a lecturer performance appraisal system application.