Reviewerly/exHarmony huggingface dataset:
An Effective Search Based Algorithm for Structural Test Data Generation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Investigating vowel production with computational physical modelling and glottal inverse filtering

Speech serves a fundamental function in society as one of the main tools enabling conveyance of information from one human to another. The ease with which humans use this tool is deceptive, however, as the physiological process of producing speech sounds is far from simple. Understanding this production process can, for example, bring valuable information to the development of speech and language technology applications and help in diagnosing and treating speech disorders. Obtaining information about speech production can be challenging, however, due to the location of the speech organs and the nature of the physiological processes involved. This dissertation focuses on the production of one specific aspect of speech, vowels, which are a major component of all spoken languages. Two methodologies, computational physical modelling and glottal inverse filtering (GIF), are used here to investigate vowel production phenomena. Computational speech production models enable simulation of vowel production with virtually complete control of all variables of interest which is not possible with human speakers. In contrast, GIF offers a tool to investigate the natural vowel production process. Both physical modelling and GIF benefit from the utilisation of multichannel data of natural speech. In this dissertation, two multichannel datasets were collected. The first dataset comprises simultaneously acquired speech pressure signals and magnetic resonance imaging (MRI) data of the vocal tract (VT). The second dataset consists of speech pressure signals recorded simultaneously with high-speed videoendoscopy (HSV) of the vocal folds and electroglottography (EGG). The two datasets were utilised together with computational physical modelling and GIF in order to investigate two dynamic phenomena: the onset of phonation in vowel utterances and fundamental frequency glides, which are vowel utterances in which the fundamental frequency increases or decreases over time. The results of using HSV and GIF together to analyse onsets indicate that the amplitudes of glot-tal area and flow are closely correlated during phonation initiation despite the presence of non-linear processes and possible dynamic control by the speakers. Simulations of fundamental fre-quency glides with a computational physics model utilising MRI data for VT modelling reveal that the perturbations occurring when the fundamental frequency crosses a resonance of the VT follow distinct patterns. In addition to these two dynamic phenomena, HSV, GIF, and computational physical modelling were also used to study the relationship between the glottal area and flow during steady phonation. These investigations show how different elements of phonation and articulation contribute to the complex process of vowel production, and it would not be possible to obtain this information without novel combinations of data acquisition, analysis methods, and modelling.; Puhe palvelee yhteiskunnassa keskeista tehtavaa yhtena tarkeimmista tyokaluista, jotka mahdollistavat tiedon valittamisen…
Object-Level Salience Detection by Progressively Enhanced Network

Saliency detection plays an important role in computer vision area. However, most of the previous works focus on detecting the salient regions, rather than the objects, which is more reasonable in many practical applications. In this paper, a framework is proposed for detecting the salient objects in input images. This framework is composed of two main components: (1) progressively enhanced network (PEN) for amplifying the specified layers of the network and merging the global context simultaneously; (2) object-level boundary extraction module (OBEM) for extracting the complete boundary of the salient object. Experiments and comparisons show that the proposed framework achieves state-of-the-art results. Especially on many challenging datasets, our method performs much better than other methods.
Game Solving Through Deep Learning Agents

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Mobile SMS Spam Recognition Using Machine Learning Techniques with the help of Biasian and Spam Filters

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Novel Approach Using Incremental Fusion Sampling for Data Stream Mining

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Struktur dan Ciri Kebahasaan Teks Prosedur di Media Cetak sebagai Bahan Ajar Pembelajaran Bahasa Indonesia Kelas XI SMA

Riska Dewi Purwanti / A310150170. STRUCTURE AND CHARACTERISTICS OF TEXTILE FACILITIES PROCEDURES IN PRINT MEDIA AS A TEACHER OF INDONESIAN CLASS XI CLASSROOM LEARNING. Essay. Faculty of Teacher Training and Education. Muhammadiyah Surakarta university. May, 2019.Learning text procedures in schools is based on the 2013 curriculum. Based on KI. KD 2013 Curriculum class XI SMA, there is KD 3.2 Analyzing the structure and linguistic characteristics of the procedure text. The basic competency is the reason the researchers conducted this research, because the results were used as teaching materials for Indonesian Language learning in the eleventh grade of SMA. The purpose of this study is to (1) determine the type of procedural text contained in the print media, (2) describe the structure and form of the language of the procedure text contained in print media, (3) describe the use of procedural texts for learning Indonesian in class XI SMA. This type of research is qualitative descriptive. The design used is content analysis. This study uses secondary data obtained from documents or archives. The data of this study are twenty-text procedure texts. The results of this study are: (1) Procedure text consists of five types, namely commands / instructions, protocols, tips / guidelines, procedures / directions for making things, and recipes, (2) a. Each type of procedure text has a varied structure, b. Nominalization and topicalisation is used as an element of microstructure analysis of procedure text data, c. It is found that there is a conformity of findings on the language characteristics of procedure texts which are analyzed with the theory referred to, (5) The results of this study can be applied in learning Indonesian
DEEP LEARNING METHODS FOR ENGINEERING APPLICATIONS.

Author(s): Laredo Razo, David | Advisor(s): Sun, Jian-Qiao | Abstract: Neural networks and deep learning are changing the way that engineering is being practiced. New and more efficient deep learning models are having a large impact in many engineering fields. Common engineering applications of deep learn- ing are prognostics and health assessment of mechanical systems, design of optimal control, and surrogate modeling for computational fluid dynamics.However, for a deep learning application to be successful there are a number of key decisions that have to be taken. Some of these decisions include the choice of a certain model architecture or topology, hyper-parameter tuning, and a suitable data pre-processing method. Efficiently choosing these key components is a time- consuming task usually entailing a staggering number of possible alternatives. This thesis aims to develop methods that make this process less cumbersome.In this thesis, we propose a framework for efficiently estimating the remaining useful life (RUL) of mechanical systems. The framework mainly focuses on the data pre-processing stage of a machine learning pipeline. Making use of evolutionary algorithms and strided time windows, the presented framework can help process the data in a way that even simple deep learning models can make good predictions on it. The framework is tested using the C-MAPSS dataset, which consists of data recorded from the sensors of simulated jet engines. The obtained results are competitive compared against some of the recent methods applied to the same dataset.Furthermore, an algorithm for efficiently selecting a neural network model given a specific problem (classification or regression) is also developed. The al- gorithm, named Automatic Model Selection (AMS), is a modified micro-genetic algorithm that automatically and efficiently finds the most suitable neural network model for a given dataset. The major contributions of this development are a simple list based encoding for neural networks as genotypes in an evolutionary algorithm, new crossover and mutation operators, the introduction of a fitness function that considers both, the accuracy of the model and its complexity and a method to measure the similarity between two neural networks. AMS is evaluated on two dif- ferent datasets. By comparing some models obtained with AMS to state-of-the-art models for each dataset we show that AMS can automatically find efficient neural network models. Furthermore, AMS is computationally efficient and can make use of distributed computing paradigms to further boost its performance.
Parallel Computing Approaches for Dimensionality Reduction in the High-Dimensional Data

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Governing the Entrepreneurial Discovery of Blockchain Applications

This paper examines the institutional context of the entrepreneurial discovery of blockchain applications. It draws on institutional and entrepreneurial theory to introduce the economic problem entrepreneurship in the early stages of new technologies, examines the diversity of self-governed hybrid solutions to coordinating entrepreneurial information, and draws policy implications. To perceive a valuable and actionable market opportunity, entrepreneurs must coordinate distributed non-price information under uncertainty with others. One potential class of transaction cost economising solution to this problem is private self-governance of information coordination within hybrids. This paper explores a diverse range of entrepreneurial hybrids coalescing around blockchain technology, with implications for innovation policy. Defining the innovation problem as either choice-theoretic or contract-theoretic changes the remit of innovation policy. Innovation policy and blockchain policy should extend beyond correcting sub-optimal investments or removing barriers to action, to incorporate how polices impact entrepreneurial choices over governance structures to coordinate information.
Diffractive optical neural networks designed by deep learning (Conference Presentation)

We introduce a physical mechanism to perform machine learning by demonstrating a Diffractive Deep Neural Network architecture that can all-optically implement various functions following the deep learning-based design of passive layers that work collectively. We created 3D-printed diffractive networks that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. This passive diffractive network can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using diffractive networks.
Identifying Oversampling and under sampling of Data-A Practical Approach Using R

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
PREPARE-Playback RatE and Priority Adaptive BitRate Selection

HAS (HTTP-based Adaptive Streaming) based on ABR (Adaptive Bitrate Rate selection) has become a prevalent solution to media transmission. In order to improve the performance of HAS, many studies focus on how to reduce the unfairness, the instability and the inefficiency in a multi-clients scenario. However, the unfairness problem of the multi-clients with different priority levels and different playback rates, and the instability problem caused by the changes of the playback rate are still unresolved. To solve these problems, a novel ABR algorithm called PREPARE is proposed in this paper. In addition to normally considered throughput and client buffer information in HAS system model, the client priority and the playback rate are also incorporated to make the system model more accurate. With the feedback from servers, PREPARE algorithm is able to estimate the bandwidth much precisely and to reduce the unfairness among the multi-clients. The stability in bitrate selection is improved by introducing the buffer status in conjunction with the real-time playback rate. Moreover, the buffer underflow is prevented in download scheduling by advancing download according to the real-time playback rate. NS-3 simulator is used to evaluate the performance of PREPARE. The performance results show that PREPARE reduces the unfairness among multi-clients by more than 42% comparing to other three algorithms including ESTC, PANDA, FDASH. Meanwhile, PREPARE simultaneously keeps high stability and high efficiency.
Detection and Recovery of Economic Losses in Sales Person Based Pharmaceutical Bussiness

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Framework for Cognitive CAPTCHA Designing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Machine Learning Analysis of Supernova Light Curves

The next generation of astronomical surveys will revolutionize our understanding of the Universe, raising unprecedented data challenges in the process. One of them is the impossibility to rely on human scanning for the identification of unusual/unpredicted astrophysical objects. Moreover, given that most of the available data will be in the form of photometric observations, such characterization cannot rely on the existence of high resolution spectroscopic observations. We introduce an analysis of anomaly detection in the Open Supernova Catalog (http://sne.space/) with use of machine learning. We developed a strategy and pipeline — where anomalous objects are identified and then submitted to careful individual analysis. This project represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire.
Best Response Dynamics

This chapter introduces a dynamic process called the best response dynamics. This is an evolutionary process that does not require full rationality of the game players.
LibGuides: Time Management: Glossary

Our top tips for getting organised, beating procrastination and effectively managing your time at university.
Internet of Musical Things A Survey

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Usability testing of Moodle application in the Context of M-learning in HE in India with Special Reference to MBA and MCA courses

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.