Reviewerly/exHarmony huggingface dataset:
Bangla Abusive Language Detection using Machine Learning on Radio Message Gateway

In the era of modern technology, machine learning and natural language processing has been adopted to be applied in several application areas. Natural language processing consists of diversified techniques such as text classification, text summarization, named entity recognition, sentiment analysis. Text classification is considered to be the area of research where the text gets segmented into different category sentences or paragraphs from a single text genre. This paper presents a mechanism for detecting Bangla abusive language from a realtime radio message gateway. Online radio stations nowadays accept communications and voices of their target audience from web-based applications or social media platforms, such as Facebook or Twitter pages. This paper has created a dataset with more than 45000 Bangla sentences, which are labeled as abusive and non-abusive. Sample online radio message gateway has been been introduced and machine learning algorithms such as multinomial naive bias (MNB), logistic regression (LR), and random forest (RF) classifiers are utilized to predict the abusive languages. One of the significant prospects of this work would be applied during live radio programs where listeners try to communicate by sending live messages. Our proposed mechanism can check and map the live messages with the dataset and segregate the positive comments or messages only, by filtering the abusive comments. Among the applied classifiers, it has been found that the random forest classifier has performed better than the other two classifiers by leveraging approximately 76% accuracy.
Elliptic-Curve Crypto Processor for RFID Applications

This work presents an Elliptic-curve Point Multiplication (ECP) architecture with a focus on low latency and low area for radio-frequency-identification (RFID) applications over GF(2163). To achieve low latency, we have reduced the clock cycles by using: (i) three-shift buffers in the datapath to load Elliptic-curve parameters as well as an initial point, (ii) the identical size of input/output interfaces in all building blocks of the architecture. The low area is preserved by using the same hardware resources of squaring and multiplication for inversion computation. Finally, an efficient controller is used to control the inferred logic. The proposed ECP architecture is modeled in Verilog and the synthesis results are given on three different 7-series FPGA (Field Programmable Gate Array) devices, i.e., Kintex-7, Artix-7, and Virtex-7. The performance of the architecture is provided with the integration of a schoolbook multiplier (implemented with two different logic styles, i.e., combinational and sequential). On Kintex-7, the combinational implementation style of a schoolbook multiplier results in power-optimized, i.e., 161 μW, values with an expense of (i) hardware resources, i.e., 3561 look-up-tables and 1527 flip-flops, (ii) clock frequency, i.e., 227 MHz, and (iii) latency, i.e., 11.57 μs. On the same Kintex-7 device, the sequential implementation style of a schoolbook multiplier provides, (i) 2.88 μs latency, (ii) 1786 look-up-tables and 1855 flip-flops, (iii) 647 μW power, and (iv) 909 MHz clock frequency. Therefore, the reported area, latency and power results make the proposed ECP architecture well-suited for RFID applications.
Fair and Secure Multi-Party Computation with Cheater Detection

Secure multi-party computation (SMC) is a cryptographic protocol that allows participants to compute the desired output without revealing their inputs. A variety of results related to increasing the efficiency of SMC protocol have been reported, and thus, SMC can be used in various applications. With the SMC protocol in smart grids, it becomes possible to obtain information for load balancing and various statistics, without revealing sensitive user information. To prevent malicious users from tampering with input values, SMC requires cheater detection. Several studies have been conducted on SMC with cheater detection, but none of these has been able to guarantee the fairness of the protocol. In such cases, only a malicious user can obtain a correct output prior to detection. This can be a critical problem if the result of the computation is real-time information of considerable economic value. In this paper, we propose a fair and secure multi-party computation protocol, which detects malicious parties participating in the protocol before computing the final output and prevents them from obtaining it. The security of our protocol is proven in the universal composability framework. Furthermore, we develop an enhanced version of the protocol that is more efficient when computing an average after detecting cheaters. We apply the proposed protocols to a smart grid as an application and analyze their efficiency in terms of computational cost.
Enhanced MultiResolution Analysis for MultiDimensional Data Utilizing Line Filtering Techniques

In this article we introduce line Smoothness-Increasing Accuracy-Conserving Multi-Resolution Analysis. This is a procedure for exploiting convolution kernel post-processors for obtaining more accurate multidimensional multiresolution analysis in terms of error reduction. This filtering-projection tool allows for the transition of data between different resolutions while simultaneously decreasing errors in the fine grid approximation. It specifically allows for defining detail multiwavelet coefficients when translating coarse data onto finer meshes. These coefficients are usually not defined in such cases. We show how to analytically evaluate the resulting convolutions and express the filtered approximation in a new basis. This is done by combining the filtering procedure with projection operators that allow for computational implementation of this scale transition procedure. Further, this procedure can be applied to piecewise constant approximations to functions, as it provides error reduction. We demonstrate the effectiveness of this technique in two and three dimensions.
CVS health faces a new wave of disruption

Purpose The purpose of this study is to understand how Amazon’s threat may impact the Pharmacy Industry as a whole and whether traditional drugstore chains such as consumer value stores (CVS) Pharmacy will need to re-think their business strategy, especially in the digital space, to account for potential disruption. Design/methodology/approach This is a hypothetical case study used as a teaching exercise to guide the learner through a decision-making process. The case starts by presenting a disruption in the retail pharmacy business that the main character must navigate by using real-world data and insights, provided in the case, to formulate a recommendation. Findings In an extremely competitive and consolidated pharmacy market, Amazon has the potential to change the business entirely. CVS Health will potentially face strong headwinds from Amazon’s PillPack and a downward trend in prescription sales. Regardless of the new competition, CVS Health continues to be innovative in the space. Instead of being a one-trick pony, CVS has encompassed the mentality of becoming a one-stop-shop by expanding into areas such as specialty pharmacy, health clinics, pharmacy benefits management and innovative digital capabilities. Originality/value This paper provides the reader with existing and known information about the evolving retail pharmacy business and allows the reader to interpret the new information to make their own decision on how a digital business strategy team can account for potential disruption.
Analysis of Best Sampling Strategy in Credit Card Fraud Detection Using Machine Learning

research-article Share on Analysis of Best Sampling Strategy in Credit Card Fraud Detection Using Machine Learning Author: Hanbing Zou Dalian University of Technology, China Dalian University of Technology, ChinaView Profile Authors Info & Claims ICIIT '21: 2021 6th International Conference on Intelligent Information TechnologyFebruary 2021 Pages 40–44https://doi.org/10.1145/3460179.3460186Online:10 August 2021Publication History 0citation107DownloadsMetricsTotal Citations0Total Downloads107Last 12 Months107Last 6 weeks11 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
Admitting the addressee detection faultiness of voice assistants to improve the activation performance using a continuous learning framework

The main promise of voice assistants is their ability to correctly interpret and learn from user input as well as the ability to utilize this knowledge to achieve specific goals and tasks. These systems need predetermined activation actions to start a conversation. Unfortunately, the typically used solution, wake-words, force an unnatural interaction. Furthermore, this method can also confuse when the wake-word, or a phonetically similar phrase, has been said but no interaction with the system is intended by the user. Thereby, the system not only lacks the adequacy of interpersonal interaction, it moreover suffers from an addressee detection faultiness. Although various aspects have already been investigated in this field of acoustic addressee detection research, we demonstrated that the test data used so far rely on ideal conditions: The dialog complexity between human–human and human–device interactions is essentially different while in reality, the behavior of each individual addressing either another human or a device is of large variation. Thus the problem of addressee detection is simplified too much. Our approach works with a specifically designed dataset comprising of human–human and human–computer interactions of similar dialog complexity. Our proposed addressee detection faultiness framework actively communicates the system’s uncertainty that may arise. In connection with a continuous learning framework, this enables a voice assistant system to adapt itself to the users’ individual addressee behavior. This approach achieves significantly improved classification rates of 85.77%, which gives an absolute improvement of 32.22% in comparison to similar experiments employing human annotations as ground truth.
A Study of Voice Print Recognition Technology

Voice print recognition, also known as speaker recognition, is a non-contact emerging technology based on the biological characteristics of the speaker's voice. It recognizes the speaker's identity through voice and is widely used in military, public security, justice, finance, commerce, the IoT and the Internet and other fields. This paper mainly studies speaker recognition technology, and compares and analyzes the advantages and disadvantages of existing text-independent speaker recognition technology, and gives the directions for the next research.
Deep Neural Networks Based Error Level Analysis for Lossless Image Compression Based Forgery Detection

The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the efficiency of distinguishing copy-move images produced by Deep Fake from the real ones. Error Level Analysis is used on images in-depth for identifying whether the photograph has long passed through changing. This Model uses CNN on the dataset of images for training and to test the dataset for identifying the forged image. Convolution neural network (CNN) can extract the counterfeit attribute and detect if images are false. In the proposed approach after the tests were carried out, it is displayed with the pie chart representation based on percentage the image is detected. It also detects different image compression ratios using the ELA process. The results of the assessments display the effectiveness of the proposed method.
Prediction of Risk Percentage in Software Projects by Training Machine Learning Classifiers

Recently, software project failures have been increasing due to lack of planning and budget constraints. In this regard, identifying the suitable software model with the consideration of risk factors is imperative. Therefore, this study investigates the key software models utilized in the industry through an interaction with software development experts and literature survey. In this study, 15 standard indicators were chosen where a survey was conducted through a questionnaire. The major performance metrics which were taken into consideration are network, security, software, machine learning, internet of things and application programming interface. We proposed a novel framework for the received dataset through questionnaire in which the machine learning classifiers were applied and risk predictions for each of the identified software models were accomplished. Using this outcome, software product managers can identify the appropriate software model according to the software requirements along with risk prediction percentage.
Research on Image Denoising Based on Median Filter

Medical images provide intuitive visualization methods and provide reliable basis for disease diagnosis and treatment. In the process of medical image acquisition, transmission and recording, noise information will inevitably be introduced due to the influence of transmission medium errors, measurement errors and the quantization of digital information. Noisy images not only reduce the image quality and affect the visual effect, but also blur or obscure the important information of the image. In order to obtain clear and noise-free medical images, this paper proposes an adaptive median filter image denoising method. Firstly, the noise points and signal points in the image are judged, and only the noise points are processed. Then, in the process of removing noise signal, we fully consider the pixel distribution feature information around the noise point and replace the pixel value of the noise point. This method has a good ability to remove noise and it can effectively deal with impulse noise while protecting the details of the image.
Diagnosis of Ear Conditions Using Deep Learning Approach

The lack of medical expertise and facilities makes it difficult to diagnose the diseases and the misdiagnosis can severely affect the health of the patients. This problem can be overcome by automating the process of diagnosis using artificial intelligence algorithms like deep learning. It has been used a lot for developing methods for the diagnosis of diseases with help of medical images. The CNN performs quite well for the diagnosis of diseases using deep learning and has been used for the diagnosis of a lot of diseases. Ear diseases are one of the most common problems that are encountered. They can be easily treated with proper diagnosis and medication using deep learning. This work proposes a deep learning-based method employing CNN for the detection of ear infections. It is capable of classifying ear images belonging to four categories (Chronic otitis media, Earwax plug, Myringosclerosis and Normal) and obtained training and validation accuracies of 99.98% and 97.37% respectively.
Building and enhancing library services: Patrons’ awareness of, and engagement with social media in academic libraries in Ghana

This study sought to examine academic libraries’ patrons’ level of awareness of, and engagement with social media in the use of academic libraries services in Ghana. The study employed the cross-sectional survey whereby quantitative data were collected from a total of 417 respondents, who consisted of students and library staff from public, private and technical universities in Ghana. Findings indicated that the academic libraries have adopted and used a number of social media applications, with Facebook and instant messaging as the most popular and frequently used platforms, for the provision and access to library services and resources as well as for contacting the academic libraries. However, the level of awareness, extent of use, as well as the level of engagement with the platforms was low as social media tools adopted by the libraries were not prevalent among the library patrons. Recommendations and conclusions based on the research findings are offered in the paper. This study uses a cross-sectional survey, with the data being collected at a specific point in time. However, due to rapid technological change and trends, future research studies could consider longitudinal studies to investigate the adoption and use of social media in academic libraries across time and environment. The analysis of social media use for academic library services from both the library staff and library patrons’ perspectives will inform decisions on how to improve the use of such platforms for effective and efficient library services delivery and access.
Image features extractor based on hybridization of fuzzy controller and meta-heuristic

The image recognition task is one of the fundamental aspects of image and video analysis. Recognition of individual objects allows for further inference or analysis. Unfortunately, quite often the detection and recognition itself are difficult tasks. Especially if there are many different objects in the image, or if there is some noise. In this paper, we propose a method for extracting specific features from images. The proposition is a hybridization of two main tools - meta-heuristic and fuzzy system. At first, an objective function is created for a specific object, then the meta-heuristic is used for analyzing an image for finding the best features. The operation of creating an objective function and then interpreting the position of individuals in the metaheuristic is evaluated by a fuzzy controller. The use of fuzzy logic enables the creation of decision sets during data analysis. This is possible through the adaptive technique of improving the value of the membership functions in Takagi-Sugeno systems. A fuzzy approach shows great potential in analyzing the position in the image. The proposed feature extraction mechanism has been tested and discussed due to the possibility of using fuzzy logic as well as its hybridization with meta-heuristics.
SWS-DAN: Subtler WS-DAN for fine-grained image classification

Currently, weakly supervised data augmentation network (WS-DAN) has been proved to be one of the state-of-the-art methods for fine-grained image classification due to its effectiveness on attention-guided data augmentation and bilinear attention pooling. Taking WS-DAN as the backbone, in this paper, we further propose a subtler WS-DAN recognition network, namely, SWS-DAN. Specifically, we first construct a novel "salience-guided data augmentation" scheme composed of cutblock, part-aware cropping, and SCutMix operations, which can more effectively expand the number of training dataset and improve the weakness addressed in the data augmentation procedure of WS-DAN. Meanwhile, the novel data-augmentation manner reduces background noise and mines more discriminative regions simultaneously, thereby avoiding the overfitting. In caring about the key issue in fine-grained image classification task is how to distinguish the extremely similar subclasses (e.g., Artic Tern, Elegant Tern, and Forsters Tern), we then design a "Top-k" loss function that mainly focuses on the similar classes so as to find their extraordinary subtle differences. Extensive experiments carried out on common fine-grained image datasets demonstrate that SWS-DAN can surpass WS-DAN with a significant margin in the classification performance.
A Privacy Preserving Federated Learning Framework for COVID-19 Vulnerability Map Construction

This paper presents a federated learning (FL) framework that uses multiple self-reporting crowdsourcing mobile and web apps to collaboratively construct a fine-grained COVID-19 vulnerability prediction map. The use of FL provides a reliable prediction by aggregating training results from multiple apps, while at the same time circumventing data privacy regulations that prevent user information from multiple apps to be shared with each other. Such a fine-grained vulnerability map identifies early on high-risk areas, helping to reduce the spread of the disease. To mitigate data bias from each self-reporting app, an adaptive worker selection algorithm that leverages neighbouring datasets to obtain a balanced data distribution is proposed. Further, a differential privacy scheme is adopted to protect user information. The simulation results show that the proposed framework outperforms the widely used FedAvg FL algorithm by 6% on prediction accuracy while preserving user privacy.
RLTIR: Activity-Based Interactive Person Identification via Reinforcement Learning Tree

Identity recognition plays an important role in ensuring security in our daily life. Biometric-based (especially activity-based) approaches are favored due to their fidelity, universality, and resilience. However, most existing machine learning-based approaches rely on a traditional workflow where models are usually trained once for all, with limited involvement from end users in the process and neglecting the dynamic nature of the learning process. This makes the models static and cannot be updated in time, which usually leads to high false positive or false negative. Thus, in practice, an expert is desired to assist with providing high-quality observations and interpretation of model outputs. It is expedient to combine both advantages of human experts and the computational capability of computers to create a tight-coupling incremental learning process for better performance. In this study, we develop RLTIR, an interactive identity recognition approach based on reinforcement learning, to adjust the identification model by human guidance. We first build a base tree-structured identity recognition model, and an expert is introduced in the model for giving feedback upon model outputs. Then, the model is updated according to strategies that are automatically learned under a designated reinforcement learning framework. To the best of our knowledge, it is the very first attempt to combine human expert knowledge with model learning in the area of identity recognition. The experimental results show that the reinforced interactive identity recognition framework outperforms baseline methods with regard to recognition accuracy and robustness.
Improving the accuracy of solar radiation estimation from reanalysis datasets using surface measurements

This paper assesses accuracy of solar radiation from different generation reanalysis datasets against ground measurements for different climatic conditions. The range of rMBE for NCEP-DOE, CFSR, MERRA, MERRA-2, ERA-Interim and ERA-5 is 4–34, 12–24, 14–29, 8–19, 5–14, 6–13% respectively, using sub-daily data. The performance of newer versions of the datasets is better than previous versions. The performance of datasets varies in different months depending upon seasonal and monthly meteorological conditions. The datasets perform better under clear-sky conditions and estimations are poor for overcast conditions. The evaluation of three datasets was performed under different sky conditions. The rMBE using hourly data under clear-sky, intermediate-sky and cloudy-sky range −3–2, 4–17 and 134–170% respectively. The performance of MERRA-2 is better than ERA-5 under clear-sky and intermediate-sky whereas ERA-5 performs better under cloudy-sky conditions for region under study. A method based on clearness index, solar zenith angle and periodicity factor is developed to correct hourly GHI estimated by datasets. Twenty models were used to correct data considering monthly variation of errors. The rMBE and rRMSE were reduced to 100% and upto 50% for months having lower clearness index and higher errors in the original data.