Reviewerly/exHarmony huggingface dataset:
A Computational Music Theory of Everything: Dream or Project?

This chapter draws future perspectives of music as a cultural achievement of humans. We discuss the role of mathematics and physics in music from Pythagoras to string theory and the music’s global human presence, transcending specific fields of knowledge in its synthetic force that unifies distant fields of knowledge and action in the concrete and abstract realms. We discuss the idea of a Computational Music Theory of Everything (ComMute) that would parallel the physical project of a Theory of Everything (ToE).
Artificial Intelligence for Extended Software Robots, Applications, Algorithms, and Simulators

Robotics has become a rapidly emerging branch of science, addressing the needs of humankind by way of advanced technique, like artificial intelligence (AI). This chapter gives detailed explanation about the background knowledge required in implementing the software robots. This chapter has an in-depth explanation about different types of software robots with respect to different applications. This chapter would also highlight some of the important contributions made in this field. Path planning algorithms are required for performing robot navigation efficiently. This chapter discusses several robot path planning algorithms which help in utilizing the domain knowledge, avoiding the possible obstacles, and successfully accomplishing the tasks in lesser computational time. This chapter would also provide a case study on robot navigation data and explain the significant of machine learning algorithms in decision making. This chapter would also discuss some of the potential simulators used in implementing software robots.
FUNDAMENTAL STUDY ON LIQUID METAL ASSISTED CRACKING OCCURRED AT VENT AND DRAIN HOLES DURING HOT-DIP GALVANIZING

Hot-dipped zinc coating is well recognized as a standard method of long-term protection from corrosion and is widely applied to heavy steel products such as building structures in Japan. In this process, liquid metal assisted cracking (LMAC) has sometimes occurred at vent and drain holes which were located at the corner of welded beam-to-column connection during hot-dip galvanizing. However, the mechanism of the phenomenon has not been well understood. To develop the understanding of the phenomenon, This study focuses on the effect of geometrical parameters such as the flange-to-web thickness ratio, the web thickness, the diameter of the holes, and their position on LMAC to develop the understanding of the mechanisms through which LMAC that initiated from the edge of holes and seek to identify an effective way for preventing them in practice. As the pilot study, Immersion tests were performed with scaled test specimens to investigate the thermal and strain histories due to the thermal expansion and contraction in hot-dip galvanizing process. A companion analytical model shown in Fig. 4, which is capable of simulating the test results was developed. The verified numerical model was used to develop the helpful index for evaluating the effect of geometric parameters on LMAC, in term of the tensile strain at the point corresponding to LMAC occurrence shown in Fig, 11. From the investigations, the following conclusions were drawn:
Learning Low-Rank Images for Robust All-Day Feature Matching

Image-based localization plays an important role in today's autonomous driving technologies. However, in large scale outdoor environments, challenging conditions, e.g., lighting changes or different weather, heavily affect image appearance and quality. As a key component of feature-based visual localization, image feature detection and matching deteriorate severely and cause worse localization performance. In this paper, we propose a novel method for robust image feature matching under drastically changing outdoor environments. In contrast to existing approaches which try to learn robust feature descriptors, we train a deep network that outputs the low-rank representations of the images where the undesired variations on the images are removed, and perform feature extraction and matching on the learned low-rank space. We demonstrate that our learned low-rank images largely improve the performance of image feature matching under varying conditions over a long period of time.
Accurate Edge Location Identification Based on Location-Directed Image Modeling

This paper introduces a new approach for determining accurate locations of edges in natural images based on a location-directed image modeling framework. The inability to identify accurate locations follows from the inability to characterize the continuous location variation of edges. We exploit the linear phase-location characteristics in a complex-valued image representation, which guarantees even minuscule location variation be captured by changes in phases. In each band of complex-valued coefficients, the fields of phases represent the locations of edges with particular resolution and along particular direction, and thereby the representation offers a nonparametric framework for gathering multiresolution and multi-directional pieces of evidence about an edge's locations to jointly locate the edge. Our approach quantifies an edge's spatial shift that is less than 0.01 pixel and demonstrates its periodic movement within 0.35-pixel range in a series of natural images. This remarkable performance verifies our framework's ability in identifying accurate locations of edges and opens the door to unveil imperceptible phenomena that are not previously detected.
Bionics Based Camouflage Clothing for Soldiers

This research paper emphasizes on the bionics based colour changing clothing for camouflage of soldiers which changes colour based on the concept used in chameleon and octopus. It is made up of smart thread which is coated with photochromic paint and with the change in light it changes colour as in case of chameleon. And the other type of clothing is based on Octopus skin which changes its colour based on temperature variation. This clothing can be used for military purposes or for changing the colour of clothing of groom and bride in wedding ceremony. The application can be further enhanced to changing colour of the day today clothing depending on the mood of person wearing the clothing. As the person enters the office his clothing colour could be changed to some formal colour and as he enters out of office in evening to the lighter different colour. Thus, instead of having fifty different T-Shirts, he can have only one T-shirt, and further, this concept can be further extended to fashion industry.
Scaling and Semantically-Enriching Language-Agnostic Summarization

This chapter describes the evolution of a real, multi-document, multilingual news summarization methodology and application, named NewSum, the research problems behind it, as well as the steps taken to solve these problems. The system uses the representation of n-gram graphs to perform sentence selection and redundancy removal towards summary generation. In addition, it tackles problems related to topic and subtopic detection (via clustering), demonstrates multi-lingual applicability, and—through recent advances—scalability to big data. Furthermore, recent developments over the algorithm allow it to utilize semantic information to better identify and outline events, so as to offer an overall improvement over the base approach.
Testing Modified Confusion Entropy as Split Criterion for Decision Trees

Confusion Entropy (CEN) has been proposed as a performance measure for classification showing a better discrimination against other metrics. Many works use CEN for other purposes. Recently, an improvement in the definition of CEN has been proposed, a modified CEN (MCEN). The aim of this work is to review a previous work based on a classification tree that uses CEN as a pruning criterion, replacing this criterion with the newly defined MCEN metric.
Spatial Temporal Attentional Glimpse for Human Activity Classification in Video

Recently, the Convolutional Networks (ConvNet) has become the dominated approach to the human activity classification problem. We investigate current standard ConvNet architectures and pinpoint one of their main limitations: the spatial-temporal dependency is simply captured by global pooling operation, which may not well capture the complex long term spatial-temporal relationships in videos. For this work, we propose a Spatial Temporal Attentional Glimpse (STAG) module to overcome this shortcoming. Specifically, the input to this STAG module is a 3D tensor which is first processed by a spatial-temporal attention block. Spatial Temporal Glimpse block decomposes the resulting tensor into two low dimensional tensors and then fuses their operation results. The proposed STAG module is pluggable, easy to learn, and effective in computation. We conduct extended ablation studies to show that our model incorporated with the STAG block substantially improves the performance over the state-of-the-art. All the experimental results, the trained models, and the complete source codes will be released to facilitate further studies on this problem.
Analytic and synthetic based on the paradox of knowability

The purpose of this paper is to show how the paradox of knowability loses its paradoxical character when we correctly interpret one of its premises. It is then shown how this new interpretation can be used to logically define analytical and synthetic truths. In this way, the paradox of knowability is traced back to the harmless affirmation that, in order to know every proposition with certainty, there must be no propositions whose truth is synthetic.
Measurement of Performance Value for New Student Admission Information Systems

Abstract New Student Admission for Universities is the frontline in recruiting new students, therefore the New Student Admission performance needs to be measured to provide excellent service for prospective students as consumers who achieve customer satisfaction values, and for that it requires proportional and professional use of Information Technology, so that it is fulfilled speed and acceleration in winning market competition, and this is inseparable from the quality of information produced from a New Student Admissions Information System, while measurements are made using the Time Motion Study method, which measures the time needed to complete one type of activity in a given period (T) in the form of measurement of productivity time (tp) and response time (tr), so that productivity volumes can be known. This measurement is expected to provide an increase in the value of the University in society in general as well as an increase in the accreditation of the University in particular, as a result of which has an impact on the selling value of the products of the wider community.
Real-Time HDR Video Tone Mapping Using High Efficiency Video Coding

High-dynamic-range (HDR) video streams have been delivered through high efficiency video coding (HEVC). HDR video tone mapping is additionally required but is operated separately to adjust the content's dynamic range for each display device. HDR video tone mapping and HEVC encoding share common computational processes for spatial and temporal coherence in a video stream; however, they have been developed and implemented independently with their own computational budgets. In this work, we propose a practical HDR video tone-mapping method that combines two overlapping computational blocks in HDR video tone-mapping and HEVC compression frameworks with the objective of achieving real-time HDR video tone mapping. We utilize precomputed coding blocks and motion vectors so as to achieve spatial and temporal coherence of HDR video tone-mapping in the decoding stage, even without introducing substantial computational cost. Results demonstrate that our method can achieve real-time performance without compromising the video quality, which is highly comparable to that of state-of-the-art video tone-mapping methods.
Isomorphic transformation and its application to the modulo (2n + 1) channel for RNS based FIR filter design

In this paper, the implementation of a Finite Impulse Response (FIR) filter in the Residue Number System (RNS), is presented, in which a modulo multiplier based on the isomorphism technique is used to perform multiplication in the (2 n + 1) channel.An RNS modular multiplication in the Galois Field GF(2 n + 1) is presented in detail in this paper.The multiplication is based on the isomorphic mapping technique adapted to the residue arithmetic.The isomorphic encoder and decoder look-up tables in the GF(2 8 + 1) are given.An architecture for FIR filter design based on distributed arithmetic for multiplication and accumulation in mentioned (2 n + 1) channel is also presented.This architecture is discussed in details and compared with with architecture based on isomorphing technique.
Privacy-Preserving Smart Surveillance Using Local Color Correction and Optimized ElGamal Cryptosystem over Cloud

The emergence of cloud computing in integration with smart multimedia devices has created an attractive business model today. However, due to the involvement of third party servers, there is a risk of privacy for highly confidential data like surveillance images/videos. Moreover, due to inconsistent lightning conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. Addressing these problems, we propose a novel cloud based privacy-preserving approach for image color enhancement in this paper. Unlike existing color correction schemes, where colors of the test image are processed in plain domain with visible image contents, we propose to perform color correction operations in the encrypted domain over cloud. As a consequence, superior results are achieved along with complete privacy assurance. In addition, we propose a block-based image encryption method using logistic-tent system and ElGamal cryptosystem. As a result, size of the encrypted image is significantly reduced as compared to the naive approach. Experimental results are performed under various tests and the proposed approach is found to be highly effective as compared to state-of-the-art schemes. Moving ahead, security strength of the proposed approach is demonstrated through a challenge response game model.
An Optimal Case-Base Maintenance Method for Compositional Adaptation Applications

Case-base maintenance method aims at maintaining a compressed case-base which is useful for solving future problems effectively. In this paper, we propose an optimization formulation to arrive at a compressed case-base that can find a solution for the rest of the cases in the case-base that involves compositional adaptation process. The objective of the optimization problem is to minimize the footprint set size and maximize the quality of solutions that can be adapted from the footprint set. We empirically studied the proposed formulation on four different datasets and the results show that the proposed model is effective and overcomes the limitation of the existing optimal footprint method in compositional adaptation applications.
Safety Performance Evaluation of High Speed Railway Traction Substation Grounding Grid

With the continuous development of high-speed railways, more and more electronic equipment is introduced into high-speed railway traction substations, and the grounding grid is an important facility to ensure the normal operation of electrified railways and the safety of people and equipment in the station. The safety performance evaluation of the grounding grid of high-speed railway traction substation is essential for the design of the grounding engineering. In this study, The safety performance of the grounding grid was evaluated by a method of moment under a power frequency grounding fault and the lightning strike of the substation. The results show that while the grounding impedance of the traditional evaluation standard grounding grid meets the requirements, the contact voltage under the grounding grid power frequency grounding fault and the mesh potential difference under the lightning strike do not meet the safety standard.
License Chain - An Identity-Protecting Intellectual Property License Trading Platform

This paper proposes a design for privacy-critical blockchain applications with a focus on license trading: Observing parties shall neither know about the licenses nor the identity of the parties involved. On the other hand trading partners, themselves shall only get the information disclosed after the deal is completed. The proposed platform-concept enables trading intellectual property licenses while simultaneously decreasing transaction costs. The manuscript contains a concept analysis regarding privacy and security issues.
Using the LORETA Method for Localization of the EEG Signal Sources in BCI Technology

Proper localization of a given signal generation can be useful for the construction of precise brain-computer interfaces. In order to obtain that, for the purposes of the conducted works, the LORETA technique was used based on the idea of solving the inverse problems and estimating distribution of electrical activity of neurons in three-dimensional space.
Robust Distributed Pseudorandom Functions for mNP Access Structures

Distributed pseudorandom functions (DPRFs) formally defined by Naor et al. (EUROCRYPT’99) provide the properties of regular PRFs as well as the ability to distribute the evaluation of the PRF function; rendering them useful against single point of failures in multiple settings (e.g., key distribution centres). To avoid the corruption of the partial PRF values computed by distributed servers, Naor et al. proposed the notion of robust distributed PRFs, which not only allows the evaluation of the PRF value by a set of distributed servers, but also allows to verify if the partial evaluation values are computed correctly. In this paper, we investigate different approaches to build non-interactive robust distributed PRFs for a general class of access structures, going beyond the existing threshold and monotone span programs (MSP). More precisely, our contributions are two fold: (i) we first adapt the notion of single round robust distributed PRFs for threshold access structures to one for any mNP access structure (monotone functions in NP), and (ii) we provide a provably secure general construction of robust distributed PRFs by employing puncturable PRFs, a non-interactive witness indistinguishable proof (NIWI) and indistinguishable obfuscation. We compare our robust DPRF with existing DPRFs in terms of security guarantees, underlying assumptions and required primitives.
The Development of Media Application Physics Learning Based Smartphone and Its Effects on Students’ Learning Outcomes on Kinematics Materials

Abstract The ability of teachers in managing learning environments and utilizing cellular technology in learning will affect students’ interest in learning. The purpose of this research is to develop mobile learning media. This research uses two methods. First, it used a development research method with model Analysis, Design, Development, Implementation, Evaluation (ADDIE) and quasi-experimental design. This application is validated by experts for technical aspects and the students were also as an application user test. The results showed that both technically given a very good response to a mobile learning application, it means that can be used to increase student’ interest in learning. Finally, the effect of media application physics learning based smartphone to student’ learning outcomes can help in increasing the students’ learning outcomes.
Understanding a Key Dichotomy in Enterprise Blockchain Advancement: A Case Study of Insolar within the Crypto-Enterprise Grade Ethos

Blockchains can be public, permissionless networks implementing novel cryptocurrency-based technology features or permissioned, interorganizational networks championed by industry consortia. Some ventures operationalize a hybrid of these two network types to enhance adoption of their blockchain platforms by broadening their base of stakeholders or facilitating interoperability between heterogeneous blockchains. In this paper, we synthesize literature and industry writings to identify four hybrid blockchain architectures: hybrid blockchain approach, connected hybrid blockchain, interoperable blockchain architecture, and hard-forked blockchain for enterprise use. We then analyze these architectures along dimensions of semantic modeling support between private and public networks; data connectivity between networks; syntactic interoperability support between networks with heterogeneous codebases; governance model, and technical features. We find that hybrid blockchain ventures make trade-offs: support APIâ€™s, tools, and customized development so that a codebase is useful for private and public networks or provide such support for interoperation between heterogeneous codebases. We then conduct a case study of an exemplar for a hybrid blockchain approach, the startup Insolar. We identify characteristics that have led Insolar to be idiosyncratically agile and effective in its blockchain development, which together with our architecture analysis may be timely and prescriptive as enterprises grow interested in addressing blockchain hybridity and interoperability.
An Improved Genetic Algorithm based on Immune Theory in Target Segmentation of Infrared Images

In this paper, an improved genetic algorithm based on immune thought is proposed. The adaptive ability of the immune system is used to improve the optimization ability of the genetic algorithm. The immune genetic algorithm is divided into antigen memory, antibody encouragement and restraint, antibody diversity-keeping and other processes, and is used for infrared image segmentation. Compared with many classical segmentation methods, the immune genetic algorithm proposed in this paper has better effect and more advantageous performance indexes.
Bounded-Search Pathfinders Based on Influence Maps Generated by Attractors and Repulsors

This article combines pathfinding and influence fields (or influence maps) in a way that the influence costs replace the traditional distance costs. By placing repulsors and attractors in the game world, we are mixing their influence fields into an overall game influence field, which determines the influence costs in finding a path between a start and a goal node. For that purpose, we introduce an automated method to place such attractors and repulsors, which builds upon the concept of thinning graph (and its dual). This thinning graph is a sort of medial axis of the set of passable tiles of the game map. As a result, we obtain bounded-search pathfinders that avoid obstacles (repulsors) and follows checkpoints (attractors) in an intuitive manner. Moreover, the well-known problem underlying the influence fields, called local extremum trap (i.e., an agent may enter into a place from where there is no escape away), is solved in an elegant way. Also, our influence-driven bounded-search pathfinders are comparable to other influence-based pathfinders in terms of memory expenditure and time performance.
Auto Adaptive Differential Evolution Algorithm

Differential Evolution algorithm has proved to be effective and best method for solving various optimization challenges. It has been proved to be rather cumbersome to manually set control parameters in DE. This paper sketch a new variant of the DE algorithm that provides an environment to auto-adjust the control parameters settings. For the past years, DE has captured the attention in many practical cases. It makes use of a few control parameters that are bound to the same value throughout the evolutionary process. Manual control parameters setting is a time-consuming process, so the proposed work provides a reliable, accurate and fast technique to optimize numerical function. This work is tested against various numerical set functions. Final results show that this proposed algorithm performs a cut above when compared with the classical Differential Evolution algorithm, and the other control parameter setting variant of DE considered in the literature.