Reviewerly/exHarmony huggingface dataset:
Accuracy Prediction Using Analysis Methods and F-Measures

Abstract Accuracy prediction is basically used in machine learining for evaluating the accuracy of data to get better results during analysis of data for various purposes like financial analysis, credit card fraud detection and sales prediction. Predicting the accuracy of data is necessary for making better decisions in field of business, engineering, medical science and analytics. We introduce a methodology for analysis that improves the accuracy of data while ensuring that the performance of the algorithm also improves so that it improves decision making so that it can be used in real world applications. The analysis involves three phases, first is product analysis phase which involves product analysis and SWOT analysis. Then comes analysis phase where we use various techniques like Straight line method of depreciation, moving average technique, simple linear regression and multiple linear regression. These methods are used for analyzing the trend in data and for comparison. Then comes the next phase where we calculate accuracy and find optimal value. For that we first add more data, then we select essential features for getting accurate results. For that we use multiple algorithms. Multiple algorithms basically consists of algorithms that are used for clustering, classification and comparison. These algorithms are used for creating a better machine learning model by using ensemble method. Ensemble method is basically a method of combining various weak algorithms to create a more accurate algorithm that gives better performance. For checking and performance and getting an accurate value we use Algorithm tuning. Algorithm tuning is used for getting an improved algorithm that gives less error percentage is assists in making predictions. This gives an accurate and optimized model for training the data.
Non-native speech recognition using audio style transfer

Recently automatic speech recognition (ASR) systems achieve higher and higher accuracy rates. However, the score drops significantly, when the ASR system is being used with a non-native speaker of the language to be recognized, mainly because of specific pronunciation and accent features. A limited volume of labeled datasets containing samples of a non-native speech makes it difficult to train any new ASR systems targeted for non-native speakers. In our research, we tried tackling the problem of a non-native accent and its influence on the accuracy of ASR systems, using the style transfer methodology. We designed a pipeline for modifying the speech produced by a nonnative speaker, so that it resembles the native speech to a higher extent, i.e. a method for accent neutralization. Our methodology can be used as a wrapper for any existing ASR system, which reduces the necessity of training new speech recognizers, adapted for non-native speech. The modification can be thus performed on the fly, before passing the data forward to the speech recognition system itself.
Characterization of local regions for wavelet-based image denoising using a statistical approach

The shape of local window plays a vital role in the estimation of original signal variance, which is used to shrink the noisy wavelet coefficients in wavelet-based image denoising algorithms. This paper presents an anisotropic-shaped region-based Wiener filtering (ASRWF) and BayesShrink (ASRBS) algorithms, which exploit the region characteristics to estimate the original signal variance using a statistical approach. The proposed approach divides the region centered on a noisy wavelet coefficient into various non-overlapping subregions. The Euclidean distance-based measure is considered to obtain the similarities between reference subregion and adjacent subregions. An appropriate threshold value is estimated by applying a statistical approach on these distances and the sets of similar and dissimilar subregions are obtained from a defined region. Thus, an anisotropic-shaped region is obtained by neglecting the dissimilar subregions in a defined region. The variance of every similar subregion is calculated and then averaged to estimate the original signal variance to shrink noisy wavelet coefficients effectively. Finally, the estimated signal variance is utilized in Wiener filtering and BayesShrink algorithms to improve the denoising performance. The performance of the proposed algorithms is analyzed qualitatively and quantitatively on standard images for different noise levels.
Improving Teachers' Digital Competence to Bridge the Language Gap

This chapter aims to examine the digital skills that foreign language teachers must have in our global interconnected society and the ways in which they can acquire them. Pedagogical and digital innovation has been traveling along the same lines for some time. In recent years, however, in all fields of education—and more specifically in the didactic of foreign languages—several methods and techniques that increase the use of digital technology have been developed. Consequently, all teachers must keep up with the times. In this light, the objective of this chapter is to reflect on the pedagogical scenario that the digital age has set up, as well as on the ideal portrait that teachers must have. Then, the authors will focus on the specific digital skills that teachers who want to “flip” their teaching process should have: What are the basic knowledge and skills that a teacher must master if they wish to rethink their practices and embark on a flipped classroom approach?
Activity Classifier: A Novel Approach Using Naive Bayes Classification

Activity movements have been recognized in various applications for elderly needs, athletes activities measurements and various fields of real time environments. In this paper, a novel idea has been proposed for the classification of some of the day to day activities like walking, running, fall forward, fall backward etc. All the movements are captured using a Light Blue Bean device incorporated with a Bluetooth module and a tri-axial acceleration sensor. The acceleration sensor continuously reads the activities of a person and the Arduino is designed to continuously read the values of the sensor that works in collaboration with a mobile phone or computer. For the effective classification of a person’s activity correctly, Naïve Bayes Classifier is used. The entire Arduino along with acceleration sensor can be easily attached to the foot of a person right at the beginning of the user starts performing any activity. For the evaluation purpose, mainly four protocols are considered like walking, running, falling in the forward direction and falling in the backward direction. Initially five healthy adults were taken for the sample test. The results obtained are consistent in the various test cases and the device showed an overall accuracy of 90.67%.
The Multi-task Fully Convolutional Siamese Network with Correlation Filter Layer for Real-Time Visual Tracking

In recent years, the trackers based on the siamese network have achieved good performance on various benchmarks. However, most siamese trackers have difficulty in discriminating the similar objects and cannot benefit from the shallow features in the neural network. In this paper, we used three methods to solve the above problems. We use the VGGNet as the backbone of our networks instead of the most used AlexNet. We jointly train the correlation filter and the embedding similarity learning. The multi-task learning makes our tracker benefit from both the shallow and deep features in the neural network. We use the correlation filter as an attention module to make the tracker pay more attention to the object being tracked. Extensive experiments on benchmarks show that our approach yields 11.4% relative gain in OTB2015 and 33% relative gain in VOT2017 compared with the SiamFC. The proposed tracker can be real-time while achieving leading performance in OTB2013, OTB2015 and VOT2017.
Designing Safe and Secure Land Registration- Ownership Using Blockchain Technology with a Focus on Mutual Authentication

This paper presents a scheme for electronic land registration using blockchain technology. A blockchain is a comprehensive approach in industries and academia which need to be protected. The objective is to maintain an open ledger (blockchain) to record the land registration data. Idea is to offer the right person the right ownership. This paper highlights the need of blockchain technology in an application and addresses the need for adopting the mechanisms for confidentiality, authentication, integrity, and non-repudiation which are basics of any security systems referred to as CIA-R in short. This paper discusses the implementation of three blockchains namely People, Land and Transaction with more emphasis on mutual authentication. The application uses the following tools/technology for implementation.
Handling Disagreement in Ontologies-Based Reasoning via Argumentation

Ontologies are at the heart of the Semantic Web technologies. This paper introduces a framework for reasoning under uncertainty in the context of ontologies represented in description logics; these ontologies could be inconsistent or incoherent. Conflicts are addressed through a form of logic-based argumentation. We examine how the number of attacks and the weights of arguments can be used to define various labelling functions that identify the justification statuses of arguments. Then, different inference relations are distinguished to obtain meaningful answers to queries from imperfect ontologies without extra computational costs compared to classical DL reasoning. Lastly, we study the properties of these new entailment relations and their relationships with other well-known existing ones.
ShutterApp: Spatio‐temporal Exposure Control for Videos

Abstract A camera's shutter controls the incoming light that is reaching the camera sensor. Different shutters lead to wildly different results, and are often used as a tool in movies for artistic purpose, e.g., they can indirectly control the effect of motion blur. However, a physical camera is limited to a single shutter setting at any given moment. ShutterApp enables users to define spatio‐temporally‐varying virtual shutters that go beyond the options available in real‐world camera systems. A user provides a sparse set of annotations that define shutter functions at selected locations in key frames. From this input, our solution defines shutter functions for each pixel of the video sequence using a suitable interpolation technique, which are then employed to derive the output video. Our solution performs in real‐time on commodity hardware. Hereby, users can explore different options interactively, leading to a new level of expressiveness without having to rely on specialized hardware or laborious editing.
Possible Impact of Facebook’s Libra on Volatility of Bitcoin: Evidence from Initial Coin Offer Funding Data

Abstract This paper examines the impact of Libra on volatility of Bitcoin using the classical framework of C. G. Lamoureux and W. D. Lastrapes (1990). ARCH and GARCH effects disappear when lagged ICO funding size is included in the variance equation. A negative association between volatility and funding size and the disappearance of volatility persistence (long-term volatility effect) suggest that Libra, as a dominant new currency, is likely to stabilize the cryptocurrency market and enhance potential for currency diversification. Furthermore, it is revealed that the stability cannot be ensured merely by backing decentralized blockchain instruments, such as Bitcoin, with bank deposits, government securities or exchange rate.
Dynamic Online Course Recommendation Based on Course Network and User Network

E-learning attracts much attentions and gains sustainable development in recent years. Course recommendation tries to recommend proper courses to users from a large number of online courses. Existing works usually focus on improving the accuracy, neglecting to match the recommended course with user’s knowledge level. It results in a high enrollment rate but low grades, indicating poor learning results. Moreover, course recommendation also faces the challenges of sparse user-rating matrix and sparse social learning network. In this paper, we try to recommend courses that are fit to user’s knowledge level. To this end, we (1) propose to construct social learning network, for which we first build the user network and the course network, and combine them together; (2) explore the social learning network to extend the user-rating matrix by HITS algorithm, so as to overcome the sparsity challenge; (3) sort the recommendation list to meet user’s knowledge level, exploiting the course network. Experiments in a real e-learning dataset show that our model performs well in online course recommendation, and the learning results are better, validating the effectiveness of considering user’s knowledge level.
Information support for modeling the functioning of defense industries

When processing huge amounts of information arising in the process of activities of individual organizations and entire sectors of the economy, new approaches and solutions are needed.For effective conversion of national defense industry, an information and analytical system is being developed.The paper discusses the created tool for systematization of open data on military products and defense industry potential.In particular, a resource is being developed that will be a kind of aggregator of this information.
Channel Feature Enhanced Detector for Small Ball Detection

We propose the channel feature enhanced detector (CFED) for ball detection, a challenging small object detection task. The proposed method achieves a good performance on small ball detection since we design a channel feature enhanced module to increase the discriminability of the target features. Moreover, we set up the BALL dataset for training and evaluation. Experimental results show that our method achieves a mAP of 90.2% on BALL dataset with an inference time of 11.4 milliseconds per image. The proposed lightweight network makes it possible to apply real-time detection to mobile devices.
A Taxonomy and Survey of Manifold Resource Allocation Techniques of IaaS in Cloud Computing

In cloud computing environment there are Cloud Service Providers (CSP)/Vendor, and Cloud User/Client. CSP provides application, infrastructure, and/or software. Cloud user demand for a service to the CSP via the internet which is accounted on a pay-per-usage basis. Resource allocation related parameters are optimization, cost efficiency, security, quality of service (QoS), reliability, compatibility, efficiency, and delay. In this survey, we have reviewed resource allocation algorithms and mechanisms used by researchers in the recent past and classified these techniques according to the parameters considered in the approach. According to the survey, we noticed that few parameters are well addressed by many of the researches while some are yet not much investigated. The survey will guide the researchers to achieve more vision in the field of resource allocation for IaaS in Cloud Computing.
Collaborating on pre-competitive geophysical projects in the Northern Territory, Australia

SummaryThis paper reviews three different collaborative approaches that have been applied to acquire precompetitive geophysical data in the Northern Territory. The collaborative structures involve the resource exploration industry, Northern Territory Geological Survey (NTGS) and Geoscience Australia (GA), classified as Industry-led, NTGS-led and GA-led dependent on which group takes the lead role in survey design and project management.Each collaborative structure has different benefits, trading off between project targeting, impact and scale. All three collaborative approaches have successfully contributed to increasing the coverage, resolution, quality and variety of pre-competitive geophysical data within the NT and will continue to be refined and applied into the future.
A generalized ghost detection and segmentation method for double-joint photographic experts group compression

The versatility of digital photographs and vast usage of image processing tools have made the image manipulation accessible and ubiquitous. Thus, there is an urgent need to develop digital image forensics tools, specifically for joint photographic experts group (JPEG) format which is the most prevailing format for storing digital photographs. Existing double JPEG methods needs improvement to reduce their sensitivity to the random grid shifts which is highly common in manipulation scenario. Also, a fully automatic pipeline, in terms of segmentation followed by the classifier is still required.First, a low-pass filter (with some modifications) is used to distinguish between high-textured and low-textured areas. Then, using the inconsistency values between the quality-factors, a grayscale image, called the ghost image, is constituted. To automate the whole method, a novel segmentation method is also proposed, which extracts the ghost borders. In the last step of the proposed method, using Kolmogorov-Smirnov statistic, the distance between two separated areas (ghost area and the rest of the image) is calculated and compared with a predefined threshold to confirm the presence of forgery/authenticity.In this study, a simple yet efficient algorithm to detect double-JPEG compression is proposed. This method reveals the sub-visual differences in the quality factor in the different parts of the image. Afterward, forgery borders are extracted and are used to assess authenticity score. In our experiments, the average specificity of our segmentation method exceeds 92% and the average precision is 75%.The final binary results for classification are compared with six state-of-the-art methods. According to several performance metrics, our method outperforms the previously proposed ones.
CLASSROOM VIGNETTESWhat kind of computing program(s) should my school offer?

column Share on CLASSROOM VIGNETTESWhat kind of computing program(s) should my school offer? Author: Henry M. Walker Grinnell College Grinnell CollegeView Profile Authors Info & Claims ACM InroadsVolume 10Issue 4December 2019 pp 10–14https://doi.org/10.1145/3362808Published:15 November 2019Publication History 1citation170DownloadsMetricsTotal Citations1Total Downloads170Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Comparison of the Methods of Classical and Synergetic Theories of Control of the Movement Autonomous Underwater Machine

The article deals with the problem of nonlinear synthesis of the laws of motion control of an autonomous underwater vehicle (APA) in the vertical plane. The tasks of the synthesis are the output of the underwater vehicle to a predetermined depth at a given speed. Based on the non-linear mathematical model of the APA, the control laws are synthesized by two different approaches: using the classical automatic control theory method, the proportional-integral-differential controller (PID controller), and using the synergetic control theory, the analytical design method for aggregated regulators (ADAR). Classical methods of the theory of automatic control assume a linear or linearized mathematical description of controlled processes and scalar control, which cannot but affect the adequacy of the mathematical description of processes and the efficiency of the developed algorithms. Such structures are ineffective because they do not allow to obtain the necessary stability margin of the system and are approximate. In addition, the scalar control principle often limits the ability to effectively influence the system, ignoring potential control channels. The vector control principle used in the work allows to more effectively influence the system through various control channels. The assumed laws of synergetic control endow the object in question with properties of asymptotic stability in the entire admissible region of change of state variables. The results of computer simulation of the APA motion, which confirm the achievement of control goals, are considered.
Clustering via Nonsymmetric Partition Distributions

Random partition models are widely used to perform clustering, since their features make them appealing options. However, additional information regarding group properties is not straightforward to incorporate under this approach. In order to overcome this difficulty, a novel approach to infer about clustering is presented. By relaxing the symmetry property of random partitions’ distributions, we are able to include group sizes in the computation of the probabilities. A Bayesian model is also given, together with a sampling scheme, and it is tested using simulated and real datasets.
Integrity Checking of Cloud Data with an Auditing Mechanism Using ECC and Merkle Hash Tree

Cloud computing is a major concerning solution for the raising infrastructure costs in IT. There are many benefits of storing data in cloud. Once the data is stored in cloud it can be accessed from any location at any time. Securing the data in cloud is necessary. Cloud data auditing mechanism is very essential to check the data integrity in cloud. The auditing mechanism is very efficient and advantageous because it reduces the computation effort on both client’s and server’s side. But performing auditing with complete file information is a time-consuming process. In this paper we propose ECC-Merkle Integrity checking system by generating hash values and also, there is no need to retrieve the complete file for auditing. Here Provable data possession also known as PDP is used to check the integrity of data at cloud server which uses a spot-checking technique. Also, it uses Merkle hash tree to generate hash of file. Whenever client wants to check the data integrity, he poses a challenge to the server. The server will generate the hash values as a proof and this will be verified by the client. Secure data transmission is supported by Elliptic Curve Cryptography algorithm, which provides a high level of security with small key size. This provides moderately speed encryption and decryption. Merkle hash tree provides efficient and secure verification of the contents of huge data structures. This concrete methodology makes sure that the data is undamaged, unaltered.
Assessing the Impact of Spatial Proximity Data on the Solar Insolation Prediction

Improving the prediction of the availability of solar energy resources became a necessary component in the operation of utilities with a high penetration level of renewable energy resources. In this article, the solar insolation data in spatial proximity is leveraged to investigate the error in the prediction of solar insolation using multiple learning algorithms. Different error measures are utilized to evaluate the accuracy of the presented linear and nonlinear learning algorithms. Essential data preprocessing steps are conducted on the solar insolation data available from multiple meteorological stations in spatial proximity. The impact of utilizing the spatiotemporal data compared with the temporal data is analyzed. A comprehensive analysis based on multiple error measures is presented to compare the prediction error while employing multiple learning algorithms. It is shown that it is possible to identify the particular station and the particular learning algorithm that contribute the most in improving the solar insolation prediction of a specific location.