Reviewerly/exHarmony huggingface dataset:
On the Baire Generic Validity of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math>-Multifractal Formalism in Besov and Sobolev Spaces

The<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>t</mml:mi><mml:mo>-</mml:mo></mml:math>multifractal formalism is a formula introduced by Jaffard and Mélot in order to deduce the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>t</mml:mi><mml:mo>-</mml:mo></mml:math>spectrum of a function<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>f</mml:mi></mml:mrow></mml:math>from the knowledge of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mo stretchy="false">(</mml:mo><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>-</mml:mo></mml:math>oscillation exponent of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:mi>f</mml:mi></mml:mrow></mml:math>. The<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mi>t</mml:mi><mml:mo>-</mml:mo></mml:math>spectrum is the Hausdorff dimension of the set of points where<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M8"><mml:mrow><mml:mi>f</mml:mi></mml:mrow></mml:math>has a given value of pointwise<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M9"><mml:mrow><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math>regularity. The<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M10"><mml:mo stretchy="false">(</mml:mo><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>-</mml:mo></mml:math>oscillation exponent is measured by determining to which oscillation spaces<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M11"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>O</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msubsup></mml:mrow></mml:math>(defined in terms of wavelet coefficients)<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M12"><mml:mrow><mml:mi>f</mml:mi></mml:mrow></mml:math>belongs. In this paper, we first prove embeddings between oscillation and Besov-Sobolev spaces. We deduce a general lower bound for the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M13"><mml:mo stretchy="false">(</mml:mo><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>-</mml:mo></mml:math>oscillation exponent. We then show that this lower bound is actually equality generically, in the sense of Baire’s categories, in a given Sobolev or Besov space. We finally investigate the Baire generic validity of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M14"><mml:mi>t</mml:mi><mml:mo>-</mml:mo></mml:math>multifractal formalism.
On the Strength of Uniqueness Quantification in Primitive Positive Formulas.

Uniqueness quantification ($\exists !$) is a quantifier in first-order logic where one requires that exactly one element exists satisfying a given property. In this paper we investigate the strength of uniqueness quantification when it is used in place of existential quantification in conjunctive formulas over a given set of relations $\Gamma$, so-called primitive positive definitions (pp-definitions). We fully classify the Boolean sets of relations where uniqueness quantification has the same strength as existential quantification in pp-definitions and give several results valid for arbitrary finite domains. We also consider applications of $\exists !$-quantified pp-definitions in computer science, which can be used to study the computational complexity of problems where the number of solutions is important. Using our classification we give a new and simplified proof of the trichotomy theorem for the unique satisfiability problem, and prove a general result for the unique constraint satisfaction problem.
Synchronised Visualisation of Software Process and Product Artefacts: Concept, Design and Prototype Implementation

Software visualisation (SV) is now a well-established and growing research domain in software engineering (SE), with numerous application possibilities. Despite this, most prior research related to SE has been limited to the visualisation of software code entities and their properties, typically to reduce the cognitive load on developers understanding the complexity of structures in large code bases. The software development process has received far less attention from SV researchers, and efforts in this problem space have failed to go beyond the visualisation of developer activity data points. This paper presents Conceptual Visualisation, a novel SV approach that builds on the well-known CodeCity metaphor and focuses on agile software practices. Our aim is to bring the benefits of SV to support the agile development process by employing a visual technique that unifies and synchronises software code entities with the original user requirements and their implementation activities. The approach makes the design concepts and original developers' intentions readily and contextually available in an interactive visual environment. The original concepts and developers' intentions are significant sources of information in terms of influencing subsequent software development design and maintenance decisions but are often forgotten over time or as team members change. Our approach is expected to aid agile software developers in carrying out some of their tasks and/or simplifying them. This research is focused on identifying those tasks and applications that would most benefit from this technique, and to verify this empirically.
Biomarkers based on neuroimages to diagnose neurodegenerative diseases

The thesis aim is the generation of quantitative biomarkers based on neuroimages for the study of dementia. Hence, through global and structural brain connectivity will be possible to build biomarkers to identify differences between healthy controls and patients. Additionally, with the identification of brain networks altered, would understand how cognition and behavior are altered by these diseases.We have analyzed 160 images from patients with dementia and healthy controls from Hospital Universitarlo San Ignacio, and we have built networks of structural brain connectivity by each patient. All patients had diagnosis by a multidisciplinary group in a memory clinic according toInternational guidelines of frontotemporal dementiain their variants. To this study we used two types: structutal and diffusion weighted images. With respect to processing we had several had several steps: ¬model estimation, a whole structural connectivity with atlas and statistical analysis.To analyze the differences between variants of frontotemporal dementia and controls, we have made analyze from topological measures from networks and network based statistical. Also, to measure the impasct of this method, we have built a webpage with the main results and askedexperts in dementia and neuroradiology about the clinical impact and use of these results.The main results showed the importance of a systematic evaluation with different scales in order to find differences between variant, in other words, the structural connectivity might be confounded by the scale of analysis. The expert raters indicated that the better way to represent connectivity networks were brain maps translucent or circular networks.
Research on Multiple Blind Watermarking Algorithm Based on Double Scrambling

Embedding only one kind of watermarking in a digital product has not effectively prevented forgery of digital works and identified intellectual property of digital media. Based on the good confidentiality of the Zigzag-Logistic double scrambling algorithm, this paper proposes a multi-blind watermarking algorithm for color images, which combines spatial domain and DCT domain. The algorithm performs block DCT on the R, G and B components of the color image, and then the robust watermarking after double scrambling is embedded in the intermediate frequency portion of each component. These watermarks are used to complete the copyright authentication and additional information detection. The experimental results show that the proposed algorithm is robust to white noise attacks, Gaussian low-pass filtering and JPEG attacks, and the watermarking information is not easily cracked.
Frequency Shift Operator and Its Application in Multi-scale Data Preprocessing

Summary In order to overcome the problem that wavelets of each scale data are unknown in multi-scale FWI, we define a frequency shift (FS) operator to decompose seismic data to multi-scale data corresponding to arbitrary theoretical wavelet as we expect. Application of FS operator includes two steps: first, source wavelet is obtained by wavefield reconstruction, in which the direct wave in near offset is set as the boundary condition; second, we apply FS operator to seismic data and the source wavelet extracted above, and construct multi-scale data corresponding to a set of theoretical wavelets. Compared with digital filter, multi-scale data computed by our FS operator can overcome the wavelet mismatch problem in FWI. A layered model is used to demonstrate the feasibility and accuracy of FS operator, in which stable multi-scale results is consistent with the forward modeling results using theoretical wavelet. The successful application for actual data proves that FS operator is stable, but the existence of low frequency noise indicates the high requirements for SNR.
Uniformización y automorfismos en la solución de una ecuación funcional en el bicírculo (Colaboración de divulgación)

In the present work we analyze the conditions and formulas for the general solution of particular cases of the linear functional equation A(z,w)ξ(z,w)−A(z,0)ξ(z,0)−A(0,w)ξ(0,w)+A(0,0)ξ(0,0)=zwη(z,w),|z|≤1,|w|≤1,with
Draw this object: a study of debugging representations

Domain-specific debugging visualizations try to provide a view of a runtime object tailored to a specific domain and highlighting its important properties. The research in this area has focused mainly on the technical aspects of the creation of such views so far. However, we still lack answers to questions such as what properties of objects are considered important for these visualizations, whether all objects have an appropriate domain-specific view, or what clues could help us to construct these views fully automatically. In this paper, we describe an exploratory study where the participants were asked to inspect runtime states of objects displayed in a traditional debugger and draw ideal domain-specific views of these objects on paper. We describe interesting observations and findings obtained during this study and a preliminary taxonomy of these visualizations.
Development of a system for detecting video duplicates based on their color maps

This article describes the development of a system for detecting duplicate video files based on the method of obtaining and analyzing the color map of a video sequence, as an approach that does not require large computational resources, is applicable to a wide range of types of video clips and has small distortions relatively to the original.
3D Super-Resolution Reconstruction Based on Multi-view Representation

Despite the progressed achievement on three-dimensional generation, the results of the research on high-resolution models are not satisfactory. In order to generate 3D objects in high resolution, we implement a 3D model super-resolution reconstruction system. First, a set of orthogonal depth maps are acquired based on each input 3D model, which has a low resolution. Then we upscale these images by training a super-resolution network. The high-resolution orthogonal depth maps we obtained include rich information and are used to carve models in high resolution. Absolutely, our system is a reconstruction process from images to high quality 3D objects in voxel space. Considering the effect of the resolution of the image on the reconstruction results, we design a suitable super-resolution network to obtain a high-resolution depth map. We have conducted experiment on a subset of ShapeNet dataset and the experimental results show the effectiveness of our method.
Improving Emulation of Quantum Algorithms using Space-Efficient Hardware Architectures

With rapid advancement in quantum computing technology, continuous efforts are being directed to simulation and emulation of quantum algorithms on classical platforms. A well-known limitation to classical emulation of quantum circuits is scalability. Existing hardware emulators implement gate-based circuit models of quantum circuits that result in heavy resource utilization and degrade the scalability of the system. Also, current quantum emulation hardware use fixedpoint arithmetic, which has an adverse effect on accuracy when the system is scaled up. In this work, we employ a complexmultiply-and-accumulate (CMAC) and lookup-based emulation approach that greatly reduces resource utilization and improves system scalability in terms of number of emulated qubits. We demonstrate emulation of up to 16 fully-entangled qubits which is highest among existing work. We design fully-pipelined, highthroughput hardware architectures that use floating-point precision for higher accuracy. Experimental evaluation and analysis of the architectures in terms of speed and area is also provided. The emulator is prototyped on a high-performance reconfigurable computing (HPRC) system and our results demonstrate quantitative improvement over existing Field-Programmable-Gate-Array (FPGA)-based hardware emulators.
A multidimensional framework for cloud adoption of SMEs in India

The purpose of this article is to develop an integrated, multidimensional and theory-driven conceptual model from established technology adoption theories. Owing to a dissimilar set of factors influencing the process, under same market conditions, the adoption process calls for a comprehensive understanding of the determinants of cloud adoption. The research accounts for the intricacy and idiosyncrasy of Indian SME practice. Building on interpretive research, the article links the antecedents of cloud adoption and the consequences of field data to capture 12 significant factors. Interestingly, field data reveal critical components that were specific to cloud service providers, namely trust, data residency, and offline function. Cumulatively, the conceptual outlook of the model discusses the following; significant factors to guide Indian SMEs towards cloud adoption, economic performance as an impact of cloud adoption and pointers for cloud service providers to deal effectively with the business ecosystem of the SMEs.
Chaotic based Image Encryption - A Neutral Perspective

Today, there are several applications which allow us to share images over the internet. All these images must be stored in a secure manner and should be accessible only to the intended recipients. Hence it is of utmost importance to develop efficient and fast algorithms for encryption of images. This paper uses chaotic generators to generate random sequences which can be used as keys for image encryption. These sequences are seemingly random and have statistical properties. This makes them resistant to analysis and correlation attacks. However, these sequences have fixed cycle lengths. This restricts the number of sequences that can be used as keys. This paper utilises neural networks as a source of perturbation in a chaotic generator and uses its output to encrypt an image. The robustness of the encryption algorithm can be verified using NPCR, UACI, correlation coefficient analysis and information entropy analysis.
Concealing the User Identity in Cloud Services

Cloud computing is a way of providing facility to the users according to their requirements and it also has the ability to fulfil the user’s required necessities. Cloud computation service may have to direct towards three of the most important security related risks which are integrity of user, the confidential needs of a user and the availability of the user needs. In this paper, a proposition is suggested for the user’s management of his/her identification and it’s required protocols along with the question of anything extra that we can do for these already existing protocols, which may be currently engaged for the cloud computation. The integration of confidentiality with the availability of the focused web based services can be said to be the important apprehensions. The newly trending two factor based identity authentication method and technologies that surround the encryption structure may be considered favourable in order to conceal one’s identification in cloud computation amenity.
Target Tracking Based on SAGBA Optimized Particle Filter

In the field of target tracking, particle filter technology has the advantage of dealing with nonlinear and non-Gaussian problems, but standard particle filter will cause particle depletion when using re-sampling method to solve the degradation phenomenon, and the filter precision is unstable. In response to this problem, particle filter is improved by using a bat algorithm that combines simulated annealing Gaussian perturbation. In this paper, the bat individual is characterized by particle. The bat group search for the optimal bat in the image area by adjusting the frequency, loudness and pulse emissivity, and this can dynamically control the mutual conversion between the global search and the local search, thereby improving the overall quality of the particles and the rationality of the distribution. The fused simulated annealing Gaussian perturbation strategy can enhance the ability of the algorithm to jump out of local optimum. In order to verify the optimization performance of the proposed algorithm, the performance of this paper and the standard particle filter algorithm are compared. Experimental results show that the filter performance of this algorithm is better than the standard particle filter algorithm. ...
A COMPARATIVE STUDY OF STUDENTS’ LISTENING COMPREHENSION ACHIEVEMENT TAUGHT THROUGH VIDEO AND AUDIO AT THE FIRST GRADE STUDENTS OF SMA NEGERI 1 PRINGSEWU

One of the most popular media used in listening is audio format by which students are facilitated to improve listening skill. It can also give language learning experience and help learners improve their listening skill. On the other side, video has been used in listening. It provides information not only in audio format, but also provides visual clues. Thus, both video and audio can help the learners improve their listening ability particularly in comprehending the text. Therefore, this research aimed (1) to find out whether there is a significant difference of students’ listening comprehension achievement taught through audio and taught through video, and (2) to find out whether there is a significant difference of students’ listening comprehension achievement in terms of macro skills. This study used quantitative approach employing pre-test and post-test design. The population of the research was first grade students of SMA Negeri 1 Pringsewu. The sample was chosen by using purposive sampling. The sample consisted of two groups: X MIPA 6 as a control class (audio) and X MIPA 4 as an experimental class (video). The instrument of the research was 30 items of multiple choice questions with four alternative answers. To analyze the data, the researcher used Independent Sample t-test through SPSS version 16.0 at the level of significance of 0.05. The results showed that students in video class have the gain score of 1.57707, while students in audio class had the gain score of 0.81045. This means that students who were taught using video showed higher result than those who were taught using audio with the difference score of 0.71233. Furthermore, t-test analysis showed that there was statistically significant difference of students’ listening comprehension achievement with the sig. of 0.00 lower than α (0.00 < 0.05). From the result, it can be concluded that (1) there was a significant difference of students’ listening comprehension achievement taught through video and taught through audio and (2) there was also a significant difference of students’ listening comprehension achievement in terms of macro skills, specifically Main Idea and Specific Information.
A Multi-strategy Shuffled Frog-leaping Algorithm for Numerical Optimization

Abstract This study proposes a multi-strategy shuffled frog-leaping algorithm for numerical optimization (MSSFLA), which combines the merits of a frog-leaping step rule, the crossover operator, and a novel recursive programming. First, the frog-leaping step rule depends on the level of attractive effect between the worst frog and other frogs in a memeplex, which utilizes the advantages of frogs around the worst frog, making the worst frog more conducive to the evolution direction of the whole population. Second, the crossover operator of the genetic algorithm is used for yielding new frogs based on the best and worst individual frog instead of the random mechanism in the original shuffled frog-leaping algorithm (SFLA). The crossover operation aims to enhance population diversity and conduciveness to the memetic evolution of each memeplex. Finally, recursive programming is presented to store the results of preceding attempts as basis for the computation of those that succeed, which will help save a large number of repeated computing resources in a local search. Experiment results show that MSSFLA has better performance than other algorithms on the convergence and searching effectivity. Therefore, it can be considered as a more competitive improved algorithm for SFLA on the efficiency and accuracy of the best solution.
The Improved Regularized Extreme Learning Machine for the Estimation of Gas Flow Temperature of Blast Furnace

Gas flow temperature of blast furnace is one of significant indicators of judging the anterograde state of blast furnace. Aiming at the defects of traditional gas flow temperature prediction model, this paper proposes a prediction model for gas flow temperature based on improved regularized extreme learning machine algorithm (RELM). Firstly, influencing factors are chosen through analyzing the distribution of blast furnace gas flow. Considering the blast furnace production data contain high frequency noise, the Fourier transform method is used for spectrum analysis, and the appropriate digital filter is selected according to the spectrum analysis result to eliminate the high frequency noise. Secondly, this paper establishes the prediction model of gas flow temperature. Regularized extreme learning machine and finite memory recursive least squares are combined to overcome Data saturation problem of ordinary extreme learning machine. And the improved algorithm is named as RFM-RELM. Finally, actual production data are used to train and test this model. Experimental result indicate that the model can quickly and accurately predict gas flow temperature ,which provides effective help and support for blast furnace operators to analyze the conditions of blast furnace.
An Improved Infrared Image Contrast Enhancement Method

Abstract Aiming at the characteristics of infrared image, such as concentrated gray level distribution, low contrast and poor resolution, the paper proposes an improved infrared image enhancement algorithm. Firstly, the original image is filtered by adaptive median filtering method, then platform histogram equalization and gamma transformation are performed on the filtered image to improve the overall contrast of the image. At the same time, the filtered image is performed by Laplacian sharpening to get the edge information of the image. Finally, we obtained an enhanced infrared image by weighted combination of gamma transformation and Laplacian sharpening. Experiment shows that target contrast and average brightness are enhanced by the proposed algorithm, furthermore, the overall information is preserved and the edge information of the image is obtained, which improves the visual effect.