Reviewerly/exHarmony huggingface dataset:
Optimizing Learning Weights of Back Propagation Using Flower Pollination Algorithm for Diabetes and Thyroid Data Classification

A number of ANN methods are used, but BP is the most commonly used algorithms to train ANNs by using the gradient descent method. Two main problems which exist in BP are slow convergence and local minima. To overcome these existing problems, global search techniques are used. This research work proposed new hybrid flower pollination based back propagation HFPBP with a modified activation function and FPBP algorithm with log-sigmoid activation function. The proposed HFPBP and FPBP algorithm search within the search space first and finds the best sub-search space. The exploration method followed in the proposed HFPBP and FPBP allows it to converge to a global optimum solution with more efficiency than the standard BPNN. The results obtained from proposed algorithms are evaluated and compared on three benchmark classification datasets, Thyroid, diabetes, and glass with standard BPNN, ABCNN, and ABC-BP algorithms. The simulation results obtained from the algorithms show that the proposed algorithm performance is better in terms of lowest MSE (0.0005) and high accuracy (99.97%).
Development of a Virtual View for Processing Complex Natural Language Queries

Natural language interfaces to databases (NLIDBs) allow inexperienced users to formulate questions in natural language (NL) to DB management systems. A survey of the literature on NLIDBs shows that the prevalence of the interfaces developed to date are limited by two aspects: it is difficult to port them from one database (DB) to another, and they fail on some questions, either because they misinterpret the questions or they are unable to respond even when the answer exists. Among the problems that occur in NL questions, there is a problem that may arise when querying complex DBs: semantically implied entities. This problem is related to the semantic meaning of a query, when upon referring to an entity (DB table), another entity (or entities) is semantically implied to which the first entity is related, and usually the user ignores the relationship between the two (or more) entities. This chapter describes a method for using a virtual view in a NLIDB, which allows processing queries that involve this problem more efficiently than a previous version of the interface that uses materialized views. Specifically, experimental results show that the new version reduces by 23% the time for translating questions from NL to SQL.
A Dropout-Based Single Model Committee Approach for Active Learning in ASR

In this paper, we proposed a new committee-based approach for active learning (AL) in automatic speech recognition (ASR). This approach can achieve lower recognition word error rate (WER) with fewer transcription by selecting the most informative samples. Different from previous committee-based AL approaches, the committee construction process of this approach needs to train only one acoustic model(AM) with dropout. Since only one model needs to be trained, this approach is simpler and faster. At the same time, the AM will be improved continuously, we also found this approach is more robust to its improvement. In experiments, we compared our approach with the random sampling and another state-of-the-art committee-based approach: heterogeneous neural networks (HNN) based approach. We examined our approach in WER, the time to construct committee and the robustness of model improvement in the Mandarin ASR task with 1600 hours speech data. The results showed that it achieves 2-3 times relative WER reduction compare with the random sampling, and it only uses 75% the time to achieve close WER with HNN-based approach.
Tail-Biting Berlekamp-Preparata Convolutional Codes for Phased-Burst-Error-Correcting

This paper presents new encoding and decoding methods for Berlekamp-Preparata convolutional codes (BPCCs) based on tail-biting technique. The proposed scheme can correct a single block of n bit errors relative to a guard space of m error-free blocks while no fractional rate loss is incurred. The proposed tail-biting BPCCs (TBBPCCs) can attain optimal complete burst error correction bound. Therefore, they have the optimal phased-burst-error-correcting capability for convolutional codes. Compared with the previous scheme, the proposed scheme can also improve error correcting capability.
A Survey of Physiciansâ€™ Viewpoints on Effective Factors in Internet of Things-Based Smart Hospitals Succes

Background: The Internet of Things (IOT) plays an important role in health care applications and is a system of interoperating computing devices, mechanical and digital machines. Methods: This descriptive study was carried out in 2017 using a pre-designed questionnaire, using Brender and Richards Heeks information system. It contains managerial, cultural, functional, technical, organizational, political and human factors relating to smart hospital based on internet of things. The questionnaire was completed by all 230 physicians working at university hospitals. Validity of the questionnaire was determined using content validity and its reliability was determined by test-retest method with 95% correlation coefficient. Data were analyzed by SPSS statistical software, using descriptive statistics as well as t-test and analysis of variance. Results: In this study, 149 (79.5%) males and 38 (20.5%) females participated. Based on the physicians’ viewpoints, percentage of agreement for managerial, cultural and organizational factors in creating a smart hospital was higher than other studied factors. Moreover, the viewpoint of male and female physicians on managerial, functional, organizational and human factors in implementing a smart hospital was significantly different. Conclusions: It seems that the management, organizational, and cultural factors are the most important factors in the implementation of smart hospital in Hormozgan University of Medical Sciences. It is necessary for planners and system owners to pay more attention to these factors.
Encrypt Images Using Mutation Methods

As a result of the tremendous development of information and communications technology, especially&#x0D; after the wide spread of the internet, the process of preserving images has become a major and important subject. The&#x0D; most important method to preserve images is the use of encryption, which aims to ensure the preservation of the&#x0D; privacy of images and not allowing anyone to tamper with or view them because they are either confidential or very&#x0D; special. Also, no one can understand the content of those images unless they have a means of encryption.&#x0D; In this research, a new methods has been applied in images encryption using mutation methods. Different analysis&#x0D; like correlation coefficient, and histogram analysis is applied for the verification of the performance of the suggested&#x0D; algorithm. Matlab language has been used to implement the suggested algorithm, because of its efficiency, accuracy&#x0D; and flexibility in giving results.
Analysis of Different Image Processing Techniques for Classification and Detection of Cancer Cells

This chapter gives an analysis of various methodologies for detecting cancer cells through image processing techniques. The challenges during such detections are over-segmentation and computational complexities. Therefore, the algorithms dealing with such problems are analyzed in this chapter. In these algorithms, a watershed and setting up threshold are helpful to overcome segmentation issues. A support vector machine is discussed to detect subtypes of pneumoconiosis for disjointing segments of lungs. For finding lung cancer cells, a segmentation weighted fuzzy probabilistic-based clustering has been used. Multiple variants of thresholding along with classifiers are proposed to detect lungs and liver cancer. Other than that, noise-removal, feature extraction and watershed are used to detect breast cancer. For leukemia, a bimodal thresholding over enhanced images of cytoplasm and nuclei regions has been discussed. kNN classifier, k-mean clustering, and feed-forward neural networks have also been discussed. Results from these techniques vary from 60%-100% depending on the proposed methodology.
Turns of the centuries. The Transkribus automated tool for recognition, transcription and translation of handwritten historical documents

Abstract The translation of handwritten historical documents faces many challenges due to variation in the writing style, local language, and an inevitable language change. Even the transliteration from Cyrillic to Latin characters is standardized by the bijective transliteration standard ISO 9. This presentation introduces a number of tools offered by Transkribus for the automated processing of documents, such as Handwritten Text Recognition (HTR) and Document Understanding, which are needed for the translation of historical documents. Next to the problem of decoding handwritten documents, written for example in Kurrentschrift using ancient terminology, changed meanings and different spelling have additionally to be considered during the translation of texts from earlier centuries. Resolution strategies on a case study show different methods for ensuring quality translations.
An Adversarial Attack Based on Multi-objective Optimization in the Black-Box Scenario: MOEA-APGA II

Various approaches have been proposed to exploit the vulnerability to challenge the robustness of victim models, in the black-box scenario, it is difficult to generate barely noticeable adversarial examples while guaranteeing the attack success rate. Although some methods could solve this problem to some extent, the imperceptibility of the generated perturbations is still far from that of the most advanced attack, worse still, it is infeasible to attack the color image datasets due to its inefficiency. In MOEA-APGA II, We propose the new objective function and the novel population evolution strategies to reduce the average distortion without sacrificing the attack success rate, and compared to the state-of-the-art black-box attack (ZOO), our method achieves a better attack success rate under fewer queries on the benchmark datasets.
Data Mining Techniques for Videos Subscribers of Google YouTube

As the access to computerized correspondence turns out to be progressively basic around the globe, it ends up less demanding to trade thoughts between societies, crossing once-restrictive geographic separations and national limits. Online video is especially interesting as a potential vector for social correspondence. As a visual resource, video offers the possibility to cross semantic and education hindrances. Video resources are additionally ready to catch social encounters, for example, move and music, which are hard to pass on through content-based media. Information mining is an expansive region that incorporates strategies from a few fields including AI, insights, design acknowledgment, man-made consciousness, and database frameworks, for the investigation of substantial volumes of information. There have been countless mining calculations attached in these fields to perform distinctive information examination tasks. Google give most intriguing thing and significant thing which is valued by each one whose most youthful to most established one in the time of present-day era. Google gives that thing is. YouTube is where sound and video has been seen by the clients. Loads of audio and recordings materials are on YouTube. Every client need to watch their fascinating sound and recordings material they visit. YouTube is where there is no bar of age or sexual orientation. Each one inquiries their very own decision. They can download or transfer the sound and recordings material. Presently in current age channels like Zee TV, Sony Entertainment, DD News, and so on their sound and recordings are transferred to YouTube and the greater part of clients watch their most loved channels and they can see sound recordings materials. In this paper, we need to dissected Video Subscribers or channel Subscribers with various information mining classifiers and there calculations. In the information mining procedures a bunches of grouping strategies are accessible. In this paper select managed and unsupervised characterization techniques. Bayes in calculations, Support Vector Machine, Deep learning, and Random Forest. In these order strategies accomplish best exactness rate out of four three 100% which are Navies Bayes, Deep Learning, and Support Vector Machine and rest Random Forest technique accomplish 95% in 4 min 48 s runtime yet in the three characterization strategies accomplish their objective at 4, 10, and 1 min 29 s.
Gene Permutation: A new Probabilistic Genetic Operator for Improving Multi Expression Programming

Multi-expression Programming (MEP) encodes multiple genes through linear representation and is a widely useful technique for tangible applications like classification, symbolic regression and digital circuit designing. MEP uses only two genetic operators (mutation, crossover) to explore the search space and exploit genetic materials. However, after going through multiple generations and due to its naturally inspired fitness-based selection procedure, MEP significantly reduces genetic diversity in the population and ultimately produces homogeneous individuals; hence, leading to poor convergence and an ultimate fall into the local minimum. Gene-permutation, the newly proposed Probabilistic Genetic Operator, breakouts the homogeneity by rearranging and inducing new genetic materials in the individuals which in turn maintains the healthy genetic diversity in the population. Moreover, it also assists other genetic operators to produce more effective chromosomes and fully explore the search space. The experiments point out that Gene-permutation improves training efficiency as well as reduces test errors on several well-known symbolic regression problems.
Prediction and Estimation of Dominant Factors Contributing to Lesion Malignancy Using Neural Network

Cancer is among one of the major causes of death worldwide. Any region when infected experiences uncontrollable growth of cells, resulting in unstoppable growth of protrusions or lesions. Lesions are categorized as benign or malignant. Imaging techniques have gained a potential rise over last two decades in diagnosis and detection of cancer cells. Automated classifiers could upgrade the diagnosis process substantially, in terms of both time consumption and accuracy by automatically distinguishing benign and malignant patterns. The paper presents a statistical conclusion clubbed with artificial neural network (ANN) tool for early detection of disease, problem addressed is of breast lesions; however, the same can be addressed to any category of lesions appearing in any region of body. The statistical analysis on sample data of 699 values was evaluated for the purpose of establishing the dependence on selected microscopic attributes having a higher percentage contribution on cell malignancy, further a technique for prediction of breast lesion into benign and malignant categories using ANN is performed, that achieved sensitivity, specificity and classification accuracy as 96.94%, 98.75% and 97.70% for complete nine microscopic attributes and 96.70, 96.72 and 96.68 for selected microscopic attributes, respectively, having a higher percentage contribution on cell malignancy. Reduction in features resulted in lesser number of epoch’s and hence reduction in processing time for identification of infection and thus early detection.
ROBO - Autonomous Football Robot

The autonomous football robot (ROBO) was designed to enhance the knowledge in the field of robotics and computer vision through the robot development. The goal was to learn the use of robotic kits, the Raspberry Pi micro-controller to control the robot motion, the Raspbian operating system (Linux based OS), the python programming language and the computer vision library (OpenCV). The robot design consists of hardware and software designs. For the robot hardware design and implementation, the robotic kit were built, tested, and attached to a 3D custom printing gripper and kicker along with the 180 degrees rotational Raspberry Pi camera by servo motor. Lastly, a small breadboard was attached to set up the pulse, voltage, and the ground for the servo motors. For the robot software design, there are two algorithms or modes in the program, ball mode and goal mode. The ball mode start with ball detection and end with the ball grab. The goal mode start with the goal search and end with kick the ball into the goal. A 150 experiments done for three different configuration of the robot. Each configuration consists of 50 experiments. The results were 2% task error for the first configuration, 10% for the second, and 22% for the third. The task errors were because of the mechanical failure of the kicker. The kicker was unable to reach the ball into the goal from far distance. A future work can be a better kicker with more power.
Preliminary Scouting Reports from the Outer Limits of Conceptual Engineering

Abstract If we distinguish between the conceptual engineering task of determining what concepts can be built and the conceptual ethics task of determining what concepts ought to be used once built, we make clear the possibility of a conceptual maximalist position that avoids conceptual ethics by holding that the norms of theorizing require a big theory including all truths expressible using any concepts. But the conceptual maximalist then assumes the burden of saying what the range of possible concepts (equivalently, possible languages) is. Despite the attractions of conceptual maximalism, we lack even the beginnings of an answer to the question of what concepts there could be.
Analysis of Forensic Fingerprints in Facebook Images, the Universal Antiforensic Attack

Now days Facebook is used as one of the most effective communication media. Cheaper digital devices have made it easy, to capture, edit and share images on Facebook. Consequently, image integrity is often an issue for these images. Most of the internet images do not use any embedded active security mechanism, such as watermarking and steganography. Hence, passive digital image forensic techniques are often used to investigate these images. These techniques analyze various fingerprints introduced during tampering attacks, e.g. Noise, JPEG fingerprints, resampling artefacts, etc. Anitforensic attacks searches techniques to hide these forensic fingerprints. Most of the antiforensic techniques target a single forensic fingerprint. In this paper, we have investigated some of the very common forensic techniques on Facebook images. We found that JPEG fingerprints fail on Facebook images. In addition, the common resampling detector and noise inconsistency also fail on these images. Hence, Facebook can be considered as the universal antiforensic attack without degrading visual quality of an image.
LEGO CONSTRUCTION SETS AS A MEANS OF PRIMARY SCHOOLCHILDREN’S CRITICAL THINKING DEVELOPMENT

Aim: To evaluate the level of development of critical thinking elements in the process of working with a constructor in elementary school students. The main tasks are:1) to analyze the concept of "critical thinking" in the works of researchers; 2) to establish the main characteristics of critical thinking and its ability; 3) to identify the impact of "LEGO" on the development of critical thinking in younger students. Research Methods. Comparative analysis of sources of scientific pedagogical literature, interpretation, formulation of conclusions. The results are processed mathematically and presented in the format of comparative tables. Results. The article presents the analysis of psychological and pedagogical research. The concept of "critical thinking" is analyzed, its signs are shown. The peculiarities of critical thinking of the younger student are formulated, the educational possibilities of the designer are considered. The peculiarities of the development of critical thinking in younger students in the process of tasks with the designer "LEGO" are revealed. LEGO classes provide opportunities for diverse development of students and the formation of the most important competences reflected in the concept of the New Ukrainian School. critical thinking is formed and developed when processing information, solving problems, problems, assessing the situation, choosing rational ways of activity. It is in the process of working with the designer, first of all, is the processing of information, assessment of the situation. Therefore, this process can be considered a condition for the development of critical thinking. The study contains empirical results obtained during the work with the experimental data. In the process of completing the tasks, the following indicators are observed: the ability to put ideas into words; ability to find cause and effect; the ability to combine several ideas into one, to create a new idea. Conclusions. School practice requires the development of new technologies for the formation of students' critical thinking in the learning process, the use of a variety of different pedagogical techniques that encourage students to creative activity, create conditions for them to become aware of the educational material, generalization of the acquired knowledge. Tasks with "LEGO" should be used in working with younger students to develop critical thinking.
The Development of Machine Translation in Big Data Era

This paper probes into Machine Translation and its developments from theoretical and practical perspectives in Big Data era. By analyzing the history of Machine Translation, it is found that Machine Translation with the assistance of Artificial Intelligence and Big Data has changed dramatically in the following aspects: translating subject, texts’ formats, contents, methods and translated text type and style. By differentiating Machine Translation from Computer Aided Translation, we can find the relationship between Machine Translation, computers and other aids to translation, and human translators, to justify the establishment and development of the Integrated Translation Environment of Machine Translation.
Design of Intelligent Unmanned Vehicle Handling Simulation System

The intelligent unmanned vehicle handling simulation system is designed to automatically follow the designated path to achieve the purpose of delivery. Intelligent vehicle system adopts the modular design idea, including MCU minimum system, the sensor module, the drive unit, the power supply unit and the human-computer interaction module. The intelligent vehicle acquires path information through multiple electromagnetic sensors and cameras (mainly electromagnetic, supplemented by the camera) in front of the vehicle body to identify the path and control the vehicle body to carry and drive. The PD controller is used to realize the steering direction control, and the incremental PID is used to realize the speed control to ensure the smooth operation of intelligent vehicle. This paper presents the design of the hardware part and the implementation of the software algorithm. After actual test, the intelligent vehicle runs reliably and stably, and can adapt to various track elements to complete independent operation, providing a practical and good solution for the operation of the unmanned truck.