Reviewerly/exHarmony huggingface dataset:
Brass instruments as a cascade of two-port networks: Transfer functions, chain parameters, and power efficiency in theory and practice

This paper investigates how two-port network theory as a means for system identification can be applied to the analysis of brass instruments. A special focus is placed on the energy conversion efficiency as this is limited by inner damping, which receives much attention by expert players and makers of brasses. Theory suggests that a reconstruction of the 2 × 2 matrix representing the network requires input impedance and transfer function for two different boundary conditions. Besides the normal case of free sound radiation, instruments are also analyzed with the bell closed by a spherical cap. For this purpose, a customized 3D-printed spherical cap was fabricated and attached to the bell. Four measured spectra and the passivity condition over-determine the set of system equations. It is shown how to take advantage of this freedom when analyzing wind instruments. Measurements and simulations of a trumpet and a trombone are presented and compared.
Striffs: Architectural Component Diagrams for Code Reviews

Despite recent advancements in automated code quality and defect finding tools, developers spend a significant amount of time completing code reviews. Code understandability is a key contributor to this phenomenon, since engineers need to understand both microscopic and macroscopic level details of the code under review. Existing tools for code reviews including diffing, inline commenting and syntax highlighting provide limited support for the macroscopic understanding needs of reviewers. When reviewing code for architectural and design quality, such tools do not enable reviewers to understand the code from a top-down lens which the original architects of the code would have likely used to design the system. To overcome these limitations and to complement existing approaches, we introduce structure diff (striff) diagrams. Striffs provide reviewers with an architectural understanding of the incoming code in relation to the existing system, allowing reviewers to gain a more complete view of the scope and impact of the proposed code changes in a code review.
ImplementationAnalysis of Load Balancing Procedures for Cloud Computing Domain

Cloud facility suppliers such as IBM, Alibaba Cloud, etc. have perceived expanding evolution above the preceding some decades as new enterprises are migrating its procedures to the Cloud. Since Cloud assists numerous trades and operators instantaneously, the aforementioned is vital nevertheless perplexing to evaluate the performance of the load balancing technique of jobs executing on the Cloud. The study has inspected First Come First, Shortest Job First in addition to Least Connection load balancing procedures employing CloudSim 4.0 agenda plus representative prototypes for Cloud platform such as Amazon Web Services. The simulation outcomes illustrate that the job implementation interval for the space-shared scheduling strategy is near to the hypothetical interval as compared to time-shared scheduling. Besides, it has been detected that Least Connection executes superior to the First Come First Served and the Shortest Job First at lesser load.
PECAN: A Platform for Searching Chat Conversations

Often, existing chat services that organisations and individuals use today provide a way to search through previously sent messages. However, many of these chat services provide far-limited search functionalities, typically exact matching on individual messages. In this paper, we introduce a new task for addressing this problem, called searching for conversations, whereby the aim is to retrieve and rank groups of related messages given a search query. We promote this task by providing a platform for research and development called PECAN. Our platform provides all the necessary functionality researchers need to conduct experiments on searching for conversations. Our system is also generic so as to support organisations and individuals who wish to search through their chat message archives.
Giving the Value of a Variable

Abstract What does it mean to ‘give’ the value of a variable in an algebraic context, and how does giving the value of a variable differ from merely describing it? I argue that to answer this question, we need to examine the role that giving the value of a variable plays in problem-solving practice. I argue that four different features are required for a statement to count as giving the value of a variable in the context of solving an elementary algebra problem: the variable must be in the scope opened by the problem statement; the values given must be in the range of the variable, which is determined by the problem; the statement giving the values must represent a complete solution; and it must be in a canonical form . This account helps us better understand elementary algebra itself, as well as the use of algebraic tools to analyze phenomena in natural language.
Lightweight Security In IoT: A Survey

As the world is moving towards the enhanced connectivity in our day-to-day activities with the handheld devices, PDAs and many other resource constrained devices. The scope for the intrusion into those devices is also increasing rapidly. From days when computing something is not just completed in one machine but on a shared platform with the use of Internet, regardless of time and place. In order to perform such tasks, first the networks of connected devices are formed. As the strength of the network grows in terms of the number of connected devices, it becomes more susceptible to the outside threats. Inadvertently it paves way to access/modify the information stored in the computer. This challenge can be overcome with the use of cryptographic algorithms to transmit/receive or access the data stored in the system. Usually, the normal cryptographic algorithms require lots of computation/resources in the end devices. The problem of unauthorized data access/modification is becoming more worrisome when the end device is not equipped enough to tackle the security intrusion as in the case with the resource constrained devices. Therefore, this paper discusses about the different lightweight algorithms that remain suitable to be used for such resource constrained devices.
Enabling Interactive Visualizations in Industrial Big Data

Industries are considered data rich but information poor environments. Mainly due to systems design restrictions, to the lack of adequate processing power and to a sector culture notably focused on collecting, selecting, storing and preserving historical series in on-demand access repositories, massive data generated in industrial operations is traditionally neglected (or simply took aside). This huge amount of unprocessed data resting in these repositories is a latent and rich source of information that could be used to improve industrial processes. This work then proposes an approach in which an elastic processing engine is designed to be plugged-in to currently installed industrial information infrastructure to provide it with the ability of performing visual analytics on massive industrial data. A case study where an interactive visualization application is made possible in real-world industrial data scenario of over 100 million records is presented to attest the effectiveness and potential of the proposed approach in enabling interactive visualizations to Industrial Big Data.
Visual Saliency Models Applied to ROI Detection for Brain MR Images: A Critical Appraisal and Future Prospects

Visual saliency models are widely used for salient region detection in natural images. However, limited work has been carried out for detecting diagnostically important region in medical images with saliency models. This article aims at exploring the potential of visual saliency models in the context of region of interest (ROI) detection and to thoroughly test the performance of existing algorithms to optimize their usage for detection tasks in medical images. The main objective is to provide an overview for the researchers who are beginners in this field and are not well equipped to understand its complete technical aspects. In this study, various saliency models, primarily designed for salient region detection in natural images, are critically evaluated for detecting ROI in MR images. The performance of various models is evaluated under varying conditions and tested on brain tumor image segmentation dataset. The experiment exploits different concepts in visual attention by adopting models that include Itti–Koch, graph-based visual saliency, spectral residual, frequency tuned, saliency by induction mechanisms and image signature. These models, which perform aptly on natural images, were not found suitable in delineating the tumorous region when evaluated empirically, except the FT model that exhibits exceptionally good performance for the high-grade and low-grade gliomas cases for real and synthetic images.
User’s perception on library automation: A survey

We all know that use of ICT spread over in the information age. Libraries are atomizing their housekeeping operations. The paper aims to highlight user’s perceptions on library automation. It does not include aspects like how to do automation, what software is used for the automation etc. The objective of the study is to find out user’s awareness about library automation. Impact of automation of library housekeeping activities on a usage of the library and academic work of users. Find out pros and cons of library automation and to get users suggestions for further improvement. Data have been collected from the 100 users of academic library with the help of web based questionnaire. It is analysed with the help of tables and diagrams. It is found from analysis of the data that users are aware of the automated system of a library, and they are satisfied with the quick service of the library after automation. The conclusion drawn is users are habituated with the automated library sources and services like webopac and due book reminder. They are aware of the different library automation software. They said that with the automation their working capacity is increase and they will complete their task in less time.Keywords: ICT, Academic Library, Computerization
Investigating and improving the utility of probabilistic linear discriminant analysis for acoustic signal classification

Probabilistic linear discriminant analysis (PLDA) has achieved good performance in face recognition and speaker recognition. However, the computation of PLDA using the original formulation is inefficient when there are many training data, especially when the dimensionality of the data is high. Faced with this inefficiency issue, we propose scalable formulations for PLDA. The computation of PLDA using the scalable formulations is more efficient than using the original formulation when dealing with many training data. Using the scalable formulations, the PLDA model can significantly outperform other popular classifiers for speaker recognition, such as Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). Besides of directly using PLDA as a classifier, we may also use PLDA as a feature transformation technique. This PLDA-based feature transformation technique can reduce or expand the original feature dimensionality, and at the same time keep the transformed feature vector approximately following the Gaussian distribution. Our experimental results on speaker recognition and acoustic scene classification demonstrate the effectiveness of applying PLDA for feature transformation. It is then promising to combine PLDA with other classification models for improved performance, extending the utility of PLDA to a wider range of areas.
Research of Weibo Text Classification based on Knowledge Distillation and Joint Model

Text classification is a basic task in natural language processing. In 2018, the BERT(Bidirectional Encoder Representation from Transformers) was proposed. This model greatly improves the effect of natural language processing related tasks. However, the model size of the pre-trained language model is large, and because of its huge network structure, the predict time is long. In order to solve these problems, an improved model that uses knowledge distillation and adversarial perturbation is proposed. In the training phase, RoBERTa-wwm-ext is used as the teacher model, the joint model of Text-CNN and Text-RCNN is used as a student model, combined with label smoothing and adversarial perturbation method to improve the classification accuracy of the student model, compared with Text-CNN on two datasets, is improved by 1.91% and 1.21% respectively. Using the student model to classify texts has the advantages of easy for deployment and taking less prediction time while obtaining ideal accuracy.
AdaEmb-Encoder: Adaptive Embedding Spatial Encoder-Based Deduplication for Backing Up Classifier Training Data

The advent of the AI era has made it increasingly important to have an efficient backup system to protect training data from loss. Furthermore, a backup of the training data makes it possible to update or retrain the learned model as more data are collected. However, a huge backup overhead will result if a complete copy of all daily collected training data is always made to backup storage, especially because the data typically contain highly redundant information that makes no contribution to model learning. Deduplication is a common technique in modern backup systems to reduce data redundancy. However, existing deduplication methods are invalid for training data. Hence, this paper proposes a novel deduplication strategy for the training data used for learning in a deep neural network classifier. Experimental results showed that the proposed deduplication strategy achieved 93% backup storage space reduction with only 1.3% loss of classification accuracy.
NPs, not DPs: The NP vs. DP debate in the context of dependency grammar

Abstract This paper considers the NP vs. DP debate from the perspective of dependency grammar (DG). The message is delivered that given DG assumptions about sentence structure, the traditional NP-analysis of nominal groups is preferable over the DP-analysis. The debate is also considered from the perspective of phrase structure grammar (PSG). While many of the issues discussed here do not directly support NP over DP given PSG assumptions, some do. More importantly, one has to accept the widespread presence of null determiner heads for the DP analysis to be plausible on PSG assumptions. The argument developed at length here is that the traditional NP-analysis of nominal groups is both more accurate and simpler than the DP-analysis, in part because it does not rely on the frequent occurrence of null determiners.
PHENOMENOLOGY OF CINEMA IN PHILOSOPHY OF POSTMODERNISM

The article focuses on the issue of phenomenon of cinema in works of Jean-Francois Lyotard, Henri Bergson, Roland Barth, Michel Foucault and Jean Baudrillard. Postmodern methodology allows one to view cinema as a “deconstruction” of both cultural and socio-political discourse. Based on the analysis of the works of philosophers, the author generalizes the phenomenological characteristics of the cinema of postmodernism, such as the hypnotic ritualism of cinema; devaluation of meta-narratives; and appeal to the absolute “banal” and “hyper-realities”. It is noted that the spread of postmodern tendencies in modern cinema is manifested in drama, imagery, stylistics, montage and other components of the creative process. The author concludes that Jean-Francois Lyotard, Henri Bergson, Roland Barthes, Michel Foucault and Jean Baudrillard have laid the foundations for understanding the unity of philosophy and cinema in the modern world.
Design and Implementation of Visual Employment Recommendation System

Abstract This paper studies the design and implementation of a visual employment recommendation system based on deep learning. The system mainly uses web crawler technology, word segmentation system jieba, Django framework, data analysis algorithm Kmeans, data visualization Echarts framework and other technologies. The system can make two-way precise recommendation for enterprises and fresh graduates to improve employment matching.
Video Chat Application for Mutes

The current situation gives us the clear idea and justification that how much virtual environment for companies, colleges and other campus is important. To deal with this situation we always look forward to video calls and systems which is easy to manage and don't create hindrance in our work. But what about the people who are mute and deaf who couldn't deal with this situation because the best language they know for communication is sign language for which they use different hand gestures called ASL (American Sign Language) and ISL (Indian Sign Language). So, to deal with this problem in this project we have created Video chat application for mutes. In this project our main focus is on ISL (Indian Sign Language). To recognize different hand gestures of users we have used CNN Deep Learning algorithm. As soon as user starts showing hand gestures to camera our algorithm using CNN would detect the correct phrase/number/letter and send it to frontend. Detection and formation of sentence will be done simultaneously for better user experience.
Publishing classification systems on the Internet: representation and use

Classification systems are published primarily in the book form, for various purposes and in different versions. They are identified by the number or year of publication. The authors propose another variant of maintaining classification systems, i.e. in the card format. The authors discuss the issues of presenting classification schedules of popular classification systems on the Internet with special focus on their applications. They explain why privately owned classification systems, like Dewey Decimal Classification (DDC) and Universal Decimal Classification (UDC), are not published on the Internet, while the systems supported by government organizations (e.g. national libraries) are traditionally published openly. The authors provide the example of the Library of Congress Classification (LCC), which is the United States’ National Classification System and which is published on open access. The project of the open presentation of the Library-Bibliographical Classification (LBC) in several versions supplemented with indices on the website of the Russian State Library (RSL) is described in more detail. The possibilities for their use are analyzed.
Prediction and control of buildings with sensor actuators of fuzzy EB algorithm

Building prediction and control theory have been drawing the attention of many scientists over the past few years because design and control efficiency consumes the most financial and energy. In the literature, many methods have been proposed to achieve this goal by trying different control theorems, but all of these methods face some problems in correctly solving the problem. The Evolutionary Bat (EB) Algorithm is one of the recently introduced optimization methods and providing researchers to solve different types of optimization problems. This paper applies EB to the optimization of building control design. The optimized parameter is the input to the fuzzy controller, which gives the status response as an output, which in turn changes the state of the associated actuator. The novel control criterion for guarantee of the stability of the system is also derived for the demonstration in the analysis. This systematic and simplified controller design approach is the contribution for solving complex dynamic engineering system subjected to external disturbances. The experimental results show that the method achieves effective results in the design of closed-loop system. Therefore, by establishing the stability of the closed-loop system, the behavior of the closed-loop building system can be precisely predicted and stabilized.
Hierarchical Latent Words Language Models for Automatic Speech Recognition

This paper presents hierarchical latent words language models (h-LWLMs) for improving automatic speech recognition (ASR) performance in out-of-domain tasks. Language models called h-LWLM are an advanced form of LWLM that are one one hopeful approach to domain robust language modeling. The key strength of the LWLMs is having a latent word space that helps to efficiently capture linguistic phenomena not present in a training data set. However, standard LWLMs cannot consider that the function and meaning of words are essentially hierarchical. Therefore, h-LWLMs employ a multiple latent word space with hierarchical structure by estimating a latent word of a latent word recursively. The hierarchical latent word space helps us to flexibly calculate generative probability for unseen words. This paper provides a definition of h-LWLM as well as a training method. In addition, we present two implementation methods that enable us to introduce the h-LWLMs into ASR tasks. Our experiments on a perplexity evaluation and an ASR evaluation show the effectiveness of h-LWLMs in out-of-domain tasks.
Privacy-Preserving Accelerated Clustering for Data Encrypted by Different Keys

k-Means is a widely used clustering algorithm that divides data points into k groups. Previous studies focus on implementing secure k-Means clustering either for single user or for multiple users. The common way is to operate on outsourced and encrypted datasets in the cloud. Two difficulties exist in this procedure - how to handle with different keys and how to compute distance, comparison and division on the ciphertexts. On the other hand, k-Means algorithm includes many unnecessary distance calculations. In this paper, we aim to construct privacy-preserving protocol which accelerates k-Means algorithm for data encrypted by different keys in the cloud. Experiments show that our scheme is effective.
Performance Interference on Key-Value Stores in Multi-tenant Environments: When Block Size and Write Requests Matter

Key-value stores are currently used by major cloud computing vendors, such as Google, Facebook, and LinkedIn, to support large-scale applications with concurrent read and write operations. Based on very simple data access APIs, the key-value stores can deliver outstanding throughput, which have been hooked up to high-performance solid-state drives (SSDs) to boost this performance even further. However, measuring performance interference on SSDs while sharing cloud computing resources is complex and not well covered by current benchmarks and tools. Different applications can access these resources concurrently until becoming overloaded without notice either by the benchmark or the cloud application. In this paper, we define a methodology to measure the problem of performance interference. Depending on the block size and the proportion of concurrent write operations, we show how a key-value store may quickly degrade throughput until becoming almost inoperative while sharing persistent storage resources with other tenants.
Optimal Quantum Realization of a Classical Linear System

Additional noise in a quantum system can be detrimental to the performance of a quantum coherent feedback control system. This paper proposes a Linear Matrix Inequality (LMI) approach to construct an optimal quantum realization of a given Linear Time Invariant (LTI) system. The quantum realization problem is useful in designing coherent quantum feedback controllers. An optimal method is proposed for solving this problem in terms of a finite horizon quadratic performance index, which is related to the amount of quantum noise appearing at the system’s output. This cost function provides a measure of how much the additional quantum noise in the coherent controller will alter the feedback control system.