Reviewerly/exHarmony huggingface dataset:
Optical Character Recognition with Word Prediction Feature using First Order Hidden Markov For The Blind

There are at least one billion people worldwide that encountered a certain degree of visual impairment as according to the World Health Organisation (WHO). This category of people will experience difficulties in learning when accessing any reading materials for study. In addition, some books do not have the Braille version to assist the visually impaired person to read and convert the text to audio output. To address the problem, this paper demonstrates the design and development of an Optical Character Recognition (OCR) system using a Raspberry Pi microcontroller and a camera module to capture the texts from books. A text-to-speech engine is also integrated in the system to read and convert the text to audio at the output. To detect the misspelling error of words in the detected texts, a Word Prediction feature using first order Hidden Markov model is also implemented. In this work, the English novels, English short stories books and a Malay article are used to investigate the accuracy of the OCR system. Based on the results, the average accuracy obtained from the text to speech conversion is 94.05%.
A Transformer Network for CAPTCHA Recognition

Websites can improve their security and prevent harmful Internet attacks by incorporating CAPTCHA verification to dictate whether the end-user is a human being or a robot. It is critical to improve the CAPTCHA design method and promote the CAPTCHA design level that its recognition technology can drive. In this paper, the neural network algorithm is used to study CAPTCHA recognition. First, to address the issues in the traditional BP neural network, such as uncertain structural parameters, low convergence rate, and quickly accept a local minimum. This paper proposes to use a convolutional neural network (CNN) to learn the words’ feature in an image. Second, existing methods are inadequate for CAPTCHAs with colored impedimental lines, character adhesion, rotation, distortion, and scaling interference. This paper presents a new method-based transformer scheme for CAPTCHA identification. The image is divided into individual letters firstly with image pre-processing as an option to improve accuracy and then the detected letters are spelled into words. The proposed method is more efficient and verified by a collection of data.
Tracking a Target on a 450 Degree Turn with the Multi-fractional Order Estimator (MFOE)

This paper presents a method of tracking a maneuvering target during a 450 turn with the polynomial multi-fractional order estimator (MFOE) from statistically corrupted measurement samples. The novel concept of the MFOE is to trade-off noisy measurements for fractions of estimated position, velocity, acceleration, jerk, etc. (fractions of estimated polynomial coefficients) to improve tracking accuracy by reducing the size of the MSE (variance plus bias-squared). Although the direct or ostensible trade-off is between the number of measurement samples vs. fractions of polynomial coefficients, the secondary or actual trade-off is between the variance and bias-squared. The MFOE creates a set of weights and applies them to the noisy measurement samples to yield the predicted position and MSE. MFOE features: The sum of the weights equals 1 and the sum of the squares of the weights is the variance normalized with respect to the noise variance. Of course, the bias is the square root of the difference between the MSE and the variance. Plots included are the target trajectory, the range and azimuth of the target as seen by the sensor (radar), as well as the individual range and azimuth tracking errors: measurement noise standard deviation (SD), prediction noise SD, RMSE, and bias. Particularly surprising is that the azimuth MFOE predictor SD is smaller during constant velocity after the turn than is the SD of the conventional polynomial second order predictor; i.e., first degree polynomial representing a constant velocity trajectory. (Note: Estimator order equals polynomial degree plus 1 from the Kalman filter.
Improving Knowledge Numbers Ability with Card Media in Early Age Children

The initial developmental stages are the basis for further developmental stages, if a stage is interrupted, it will inhibit the next stage. Likewise in the educational process, for children aged 4 to 6 years can be formally pursued in kindergarten, and is expected to obtain physical-motor, cognitive, social, and emotional stimuli in accordance with their age level. Every child has differences, so students who cannot learn as they should are called learning difficulties. Some learning difficulties experienced by young children are reading, writing and arithmetic. Especially in learning to count, mathematics is very useful for developing thinking processes in early childhood. It also cannot be separated from the role of parents who provide physical and psychological needs at the beginning of a child's life. Therefore the purpose of this study is to find out the cause of a 5-year-old child who cannot memorize numbers 1-10. The method in this study uses a qualitative research design with a single case experience with the provision of interventions, namely the introduction of numbers with the media card and assistance and counseling to parents. The intervention was carried out at the subject's home for two weeks for six meetings. The results obtained during six meetings, subjects can recognize the symbol numbers 1-10. The subject better understands numerical symbols by looking at cards or visually. This affects the subject's learning process at school and at home. The subject can continue the process of understanding number symbols using cards or learning visually
Web-Based Laptop Sparepart Information System

In the current digital era, with the advancement of an era where technology is progressing rapidlyto facilitate the activities of the Indonesian people incommunicating.Where at this time many people make buying and selling transaction online so that people are just waiting for the arrival of the goods purchased.From here, many entrepreneurs take advantage of entrepreneurial opportunitieswith this online method, making it easier for people to buy and sell transactions and new business ideas.The purpose of making this web-based system is to make it easier for sellers and buyers, ordering an item and providing information on the existence of spare parts with several types and prices listed as well as available stock. In this data collection method, it is done by analyzing the system that is running to find out the weaknesses of the system.In this research, an application has been produced that can make it easier for HD Comp customers to make spare parts transactions easily for HD Comp parties for data management from transaction data carried out. The application runs smoothly and without problems. The benefits of this research are for future reference materials.
Automatic Selection of Analytic Platforms with ASAP-DM

The plethora of available analytic platforms escalates the difficulty of selecting the most appropriate platform for a certain data mining task and datasets with varying characteristics. Especially novice analysts experience difficulties to keep up with the latest technical developments. In this demo, we present the ASAP-DM framework. ASAP-DM is able to automatically select a well-performing analytic platform for a given data mining task via an intuitive web interface, thus especially supporting novice analysts. The take-aways for demo attendees are: (1) a good understanding of the challenges of various data mining workloads, dataset characteristics, and the effects on the selection of analytic platforms, (2) useful insights on how ASAP-DM internally works, and (3) how to benefit from ASAP-DM for exploratory data analysis.
Bayesian Dialysis of the Evidence in Fraud Detection

In this paper we propose a methodology that combines Bayesian and “big data” tools designed to optimize the investigation of fraud that we call “Bayesian dialysis”. Its use improves the results of any statistical selection technique previously used, capitalizing on the evidence obtained by decision makers. Its effectiveness is shown using as an exemplary case the selection for VAT control in the “Agencia Estatal de Administración Tributaria” (AEAT), that is the State Tax Agency. Although in Spain the existing systems for the selection of taxpayers in the control of fraud in VAT, in a context in which the impact of the fraud is estimated at 2%2, get excellent results, the proposed methodology, which has the characteristics of novelty and generality, improves accuracy in the detection of fraudsters in a 12.29%, from an average of 82.28% fraudsters detected in the collectives inspected to 94.36%. In addition, the confidence interval in the detection of fraud, at the 0.95 level, improves by 13.72%, which represents a qualitative leap in the effectiveness of the control reaching the threshold of 95.51%
Towards an approach for resource-driven adaptation

Resource-driven systems have tasks that are bound by limited resources. These systems must adapt their tasks that cannot gain access to sufficient resources. This dissertation proposes a new resource-driven adaptation approach, which aims to support (1) task prioritisation using multiple criteria such as the time of day that a task should be executed, the role of involved users, and selection of the least costly adaptation types; (2) collaboration between a human user and a software tool for preparing adapted task behaviour to be used when resources are substituted; and (3) resource extensibility and heterogeneity. The proposed approach is being implemented and will be evaluated with scenarios from enterprise applications.
Squeeze And Reconstruct: Improved Practical Adversarial Defense Using Paired Image Compression And Reconstruction

As shown in the previous literature, non-robust features of an image such as texture are both the secrets why deep neural networks achieve outstanding classification performance and the sources of adversarial examples. Image compression methods such as JPEG can be used to effectively defend against diverse adversarial attacks by eliminating these non-robust features in the pre-processing stage while significantly sacrificing clean accuracy. To address this issue, we present a squeeze-and-reconstruct framework which first performs image compression followed by image reconstruction to recover necessary details for the improved clean and robust accuracies. With extensive experiments on the challenging ImageNet dataset, the evaluation results demonstrate the effectiveness of the proposed method to defend against the Fast Gradient Sign Method and the powerful Projected Gradient Descent attacks in the white-box scenarios. In addition, the proposed approach also outperforms other common and off-the-shelf defense models in terms of both clean and robust accuracies.
A DNS Security Policy for Timely Detection of Malicious Modification on Webpages

End users consider the data available through web as unmodified. Even when the web is secured by HTTPS, the data can be tampered in numerous tactical ways reducing trust on the integrity of data at the clients' end. One of the ways in which the web pages can be modified is via client side browser extensions. The extensions can transparently modify the web pages at client's end and can include new data to the web pages with minimal permissions. Clever modifications can be addition of a fake news or a fake advertisement or a link to a phishing website. We have identified through experimentation that such attacks are possible and have potential for serious damages. To prevent and detect such modifications we present a novel domain expressiveness based approach that uses DNS (Domain Name System) TXT records to express the Hash of important web pages that gets verified by the browsers to detect/thwart any modifications to the contents that are launched via client side malicious browser extensions or via cross site scripting. Initial experimentation suggest that the technique has potential to be used and deployed.
Mobile Application with Augmented Reality for the learning of the cell

This study shows the effects resulting from the implementation of a mobile application with augmented reality for the learning of the cell. The Mobile-D methodology was used for this study, whose focus and properties are mainly targeted at the mobile device market. The quasi-experimental design was used in a sample of 40 fifth-grade students from a private school in Lima, which was divided into two groups of 20 students: a control and an experimental group. And it was found the experimental group achieved a significant improvement in the learning level compared to the control. Lastly, it is concluded that the use of a mobile application with augmented reality has significantly improved in three aspects of learning.
Recurrent RLCN-Guided Attention Network for Single Image Deraining

Single image deraining is an important yet challenging task due to the ill-posed nature of the problem to derive the rain-free clean image from a rainy image. In this paper, we propose Recurrent RLCN-Guided Attention Network (RRANet) for single image deraining. Our main technical contributions lie in threefold: (i) We propose rectified local contrast normalization (RLCN) to apply to the input rainy image to effectively mark candidates of rain regions. (ii) We propose RLCN-guided attention module (RLCN-GAM) to learn an effective attention map for the deraining without the necessity of ground-truth rain masks. (iii) We incorporate RLCN-GAM into a recurrent neural network to progressively derive the rainy-to-clean image mapping. The quantitative and qualitative evaluations using representative deraining benchmark datasets demonstrate that our proposed RRANet outperforms existing state-of-the-art deraining methods, where it is particularly noteworthy that our method clearly achieves the best performance on a realworld dataset.
A mathematical expression model of network asset data

When the data sources of network assets collected by network detection technology need to use the traditional machine learning technology for research and analysis, it requires to carry out a comprehensive numerical conversion. However, this kind of data presents as a collection of various heterogeneous features and complex textual data. When numerical conversion is performed in order to be trained by machine learning algorithms, there is a lack of unified and comprehensive rules for the conversion of various heterogeneous features. At the same time, the numerical transformation of text features lacks rationality and validity. So the application of machine learning to network asset data is greatly restricted. In response to the above problems, this paper proposes a mathematical expression model for network asset data. Firstly, it defines the rules of timestamp, segment transfer method etc to convert various heterogeneous features, and then constructs the AWK (Adaptive-Word2vec-Kmeans) algorithm to convert the complex text data into numerical values. Based on the expression model, the data source obtained by Nmap is processed to a trainable data set. The full feasibility and effectiveness of the model are proved through experiments, and the converted data set is available and is more challenging.
Covert timing channels: analyzing WEB traffic

In case there is a communication contrary to the system security policies, a covert channel has been created. The attacker can easily disclosure information from the victim’s system with just one public access permission. Covert timing channels, unlike covert storage channels, do not have memory storage and they draw less attention. Different methods have been proposed for their identification, which generally benefit from the shape of traffic and the channel’s regularity. The application nature of HTTP protocol allows the creation of a covert timing channel based on different features of this protocol (or different levels) that has not been addressed in previous researches. This research tries to study the effect of using different features (or levels) of HTTP protocol on identifying the covert channel. The amount of channel’s entropy could be manipulated by changing the channel’s level or adding intentional noise on the channel to protect from the analyzer’s detection. The difference in the placement of the covert channel and the detector causes the amount of channel entropy to be far from the detection threshold. Therefore, we concluded that the analyzer must investigate traffic at all possible levels. Adding noise on the covert channel decrease its capacity, but as entropy increases, it would be harder to detect it.
On the benefits and costs of offloading among cooperating clouds

A model of two multi-class service provisioning clouds which cooperate by offloading jobs from one to the other is analysed in the steady state. The costs of successful and unsuccessful offloading are set against the revenues obtained by serving local and offloaded jobs. The solution is based on approximate class decomposition and fixed-point iterations. It may be generalized to more than two cooperating clouds. The results of several numerical and simulation experiments are presented.
Illumination Coverage and Optimization of the 3-D Visual Task

In this paper, illumination coverage is proposed to optimize the illumination performance of the visual task. The coverage of illumination is considered on the basis of traditional visual coverage task. A detailed illumination model is proposed to describe the parameters of the spot illumination sensor. Three criteria called Illumination Attenuation, Field of Illumination, and Shading are introduced and combined with other visual criteria to define the illumination coverage strength. The illumination coverage performance of a single-camera coverage task with multi-illumination sensor is optimized by maximizing the constructed cost function. The simulation and experiment verify the effectiveness of the proposed optimization method of illumination coverage.
Exploring and Analyzing the Optimization of the Ways of College Social Service Training Functions under the Background of Artificial Intelligence

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Using Convolutional Neural Networks to Restore Audiosignals

In this article, we will explore the possibility of using neural networks to solve the problem of audio signal recovery. Based on the previously obtained results of using convolutional neural networks for the extraction of the voice part, the concept of a convolutional neural network was developed, designed to correct a distorted audio signal. This article presents the initial concept of this neural network architecture, which unfortunately gave unsatisfactory results. Nevertheless, based on the concept of this network, several new neural network architectures have been developed, specifically focused on the restoration of distorted audio signal, but at the same time, the shortcomings of the basic architecture were taken into account. The article contains descriptions of all these architectures and the results of their application to restore the drummer part in a musical composition from which it was deleted. We also studied the effect of increasing the number of neural networks on the efficiency of signal recovery.
Feature Fusion Ensemble Architecture With Active Learning For Microscopic Blood Smear Analysis

The blood smear analysis provides vital information and forms the basis to diagnose most of the diseases. With recent developments, deep learning methods can analyze the microscopic blood sample using image processing and classification tasks with less human effort and increased accuracy. In this work, embarking upon domain-specific feature extraction and active learning, we propose a compact, yet efficient feature fusion ensemble-based architecture for WBC sub-class classification and WBC disease identification which can automate and speed up the process of blood smear analysis with the help of digital image slide scanners.The proposed architecture is a three-stage multi-channel architecture with shallow feature inputs, deep feature extractors, and classification stages, respectively. The trainable parameters are quite less in our architecture when compared to deeper networks like ResNet 152, VGG19, which are the State of the Art (SOTA) methods [1], [2], [3]. However, labeling large medical data sets has been challenging and very costly. To mitigate huge labeling requirements and costs, Active Learning is employed to train this architecture and demonstrated much higher accuracy with quite less labeled data than SOTA. The proposed approach is shown to be quite general and yields better performance in terms of accuracy in WBC classification and Disease identification as well, with much fewer labeled samples for training, when compared with recent approaches employing deeper models.
Brain image conversion method based on generative adversarial network

In recent years, brain image conversion technology has become popular in the field of medical imaging, and plays a very important auxiliary role in clinical diagnosis and disease detection of brain diseases, such as brain image reconstruction, brain image noise reduction and brain image segmentation. The brain image conversion technology aims to learn the mapping relationship between two different modal brain images, and can convert one modal brain image into another modal brain image. However, there are complex relationships between different modal data, and there is no unified theoretical framework to realize the conversion between different modal data. Therefore, we propose a brain image conversion method based on generative adversarial network to realize the conversion between different modal brain images, and prove the superiority of this method through experiments.
Non-local network coding in interference channels

Network information theory establishes communication protocols among multiple senders and multiple receivers in the presence of correlations introduce noise over a network. We here present a framework of non-local network coding of multiple senders whereby network communication capacities improve over their classical counterparts. The framework exploits a Bell scenario and shows the usefulness of non-local and quantum resources in network information theory. Two-sender and two-receiver interference channels are considered in particular, for which network coding is characterized by two-input and four-outcome Bell scenarios. Resources for the network coding are classified It is shown that non-signaling (quantum) correlations lead to strictly higher channel capacities in general than quantum (local) correlations. It is also shown that, however, more non-locality does not necessarily imply a higher channel capacity. The framework can be generally applied to network communication protocols.