Reviewerly/exHarmony huggingface dataset:
Medical Assistance Using Drones for Remote Areas

In this paper, we have proposed a system of drones that can transfer blood and other medical supplies from a designated medical facility to remote areas/villages where road travel can be expensive and time taking. The novel system of drones works on autonomous navigation using PX4 flight stack on Pixhawk and uses GPS to be aware of its location in x-y axis whereas sharp sensors along with ultrasonic sensors are used to understand the environment. Proposed system has been developed on Robot Operating System (ROS) using its modular approach. Analyzed environment is used to control altitude, speed and direction of a drone when an obstruction such as trees, building or any other object is detected using sharp sensors.
Homological Fault Attack on AES Block Cipher and Its Countermeasures

As the physical security of hardware systems becomes more and more serious, a large number of physical attacks and countermeasures against on-chip cryptographic algorithms are proposed. Clock glitch injection is an easy-to-implement and effective fault type. This paper presents a novel clock glitch-based fault attack on hardware-implemented encryption algorithm called homological fault attack (HFA). It allows us to attack with coarse-grained clock glitches and can extract the key only by the plaintext and whether the encryption result is correct. At the same time, this paper carries out HFA experiment on AES-128 encryption algorithm implemented on FPGA in the real physical environment. Experimental results show that HFA can be used for serial and parallel implementation of AES hardware implementation. And this method can be easily extended to attack other block encryption algorithms.
Research of Fault Diagnosis System for Remote Sensing Satellite Receiving System Base on Fault Tree

The stable operation of the remote sensing satellite receiving system is related to the success or failure of satellite data reception. Accurate fault diagnosis can promptly and effectively troubleshoot and reduce the loss of economic and social benefits. Aiming at the characteristics of fault diagnosis for remote sensing satellite receiving system, a fault diagnosis expert system based on fault tree is designed in this paper. The system builds a fault tree model and expert knowledge base for a remote sensing satellite receiving system, and uses a combination of forward reasoning based on a rule framework and quantitative fault tree analysis to locate the fault. The fault diagnosis expert system has been applied to practical engineering projects, which effectively improves the efficiency of fault diagnosis of remote sensing satellite receiving systems, and is conducive to preventing and controlling faults of remote sensing satellite receiving systems.
Galaxy Morphology Classification Based On An Improved Deep Convolutional Neural Network

Abstract With each passing day telescopes around and above the Earth capture more and more images of distant galaxies. As better and bigger telescopes continue to collect these images, the datasets begin to explode in size. In order to better understand how the different shapes (or morphologies) of galaxies relate to the physics that create them, such images need to be sorted and classified. Combining with Kaggle public datasets called Galaxy Zoo, this paper devised a deep convolutional neural network for galaxy morphology image classification. The network contains eight convolutional layers, five max_pooling layers, one flatten layer, a fully connected layer contains 150 neurons, as well as the final layer which outputs galaxy category of probability distribution of each image. The experiment results show that our neural network on the validation set of cosine similarity reached to -0.8652 (the closer to -1 means that the closer the predicted output and the expected output), implying that our model for galaxy morphology classification is very effective.
An Enhanced Experimental Study of GPS Based Path Tracking Non-holonomic Robot with SAWOA Algorithm

In this research work a path following, obstacle avoiding quad wheel robot has been implemented using one 32-bit ARM processor (LPC2148 Board), one Global Positioning System (GPS) sensor module (L80) and one Ultrasonic (HC SR04) sensor module. A unique path-navigation algorithm named Smart Algorithm with Obstacle Avoidance (SAWOA) has been introduced offering higher speed and optimum path coverage compared to the existing algorithms. Several comparative studies have been performed to showcase and prove the genuineness and efficacy of the algorithm. Effective implementation of the algorithm has been realized using MATLAB and C programming language. Different results obtained in simulation prove stability and efficiency of the robot. Additionally, a real time experimental setup has been realized in laboratory environment to provide credible results regarding trajectory of the robot as per SAWOA.
Research on Situation System Based on Big Data Image -- Taking Road Traffic as an Example

Abstract In this paper, based on convolution neural network, object class awareness is carried out, Relu is selected as the activation function, which speeds up the training speed and overcomes the problem of gradient disappearance; using situation awareness technology and database technology, all object classes in the image scene are identified, and its location is specified. This paper takes the road traffic situation awareness system as the research object, and analyzes the information collection, processing and analysis process of the road traffic situation awareness system. CNN, R-CNN, Fast R-CNN and Faster R-CNN are used for vehicle classification and location recognition in road image big data.
Implementation of Naive Bayes Algorithm in Analyzing Acceptance of Poor Student Assistance

Abstract The cost of education that is not insignificant is felt by the community, especially among the lower-middle-class people who have low-income levels. MA Hizbul Wathan NW Semaya Sikur is one of the schools where some students still have a lower-middle economic level. To overcome the existing problems the government provides assistance to poor students (BSM) in schools, but the problems felt in schools are still difficult in analyzing students who are entitled to receive BSM, therefore it is necessary to build a data processing system using the principles of data mining with the aim of helping the school in analyzing students who are entitled to receive assistance. The Naive Bayes method was chosen because it can predict future opportunities based on experience. The test results obtained were 95.27% of 169 student data with an AUC value of 1,000. This value is categorized as an excellent classification. Therefore the Naive Bayes method can be used as a reference in analyzing poor student assistance data very well.
Name-Nationality Classification Technology under Keras Deep Learning

To improve the classification efficiency of personnel nationality information, the classification method of personnel nationality information is explored. First, a new classification method is proposed using the Keras model and deep learning theory. Two methods based on support vector machine (SVM) and convolutional neural network classification are proposed. (1) The personal name is input and a set of numbers corresponding to the positions in the alphabet are output orderly. (2) The personal name is input and the number output relies on the number of occurrences of each character of a name, regardless of order. Second, for the problem that the classification accuracy of nationality information by name is not high, the adaptive moment estimation (Adam) algorithm is used to optimize it. Finally, to prove the reliability of the proposed methods, these methods are used to verify the nationality information of Olympic personnel. The results show that comparing the two methods, the classification method that relies on the number of occurrences of characters in the name gets good grades, and the ultimate average score is 90.35. The score of the first method is only 79.34. Through this investigation, it is found that the second method proposed can effectively use the name of the person to determine the nationality information. Applying it in real life can improve the classification efficiency of personnel nationality information.
Evaluating human corrections in a computer-assisted speaker diarization system

In this paper, we present a framework to evaluate the human corrections of a speaker diarization system. We propose four elementary actions to correct the diarization (“Create a boundary”, “Delete a boundary”, “Create a speaker label” and “Change the speaker label”) and we propose an automaton to simulate the correction sequence. A metric is described to evaluate the correction cost. The framework is evaluated using French broadcast news drawn from the following campaigns: REPERE, ESTER and ETAPE.
Occluded Face Restoration Based on Generative Adversarial Networks

In recent years, the combination of Convolutional Neural Networks and Generative Adversarial Networks has played a huge potential in the field of face restoration. In order to effectively repair the large area of random occlusion face, this paper constructs an improved Generative Adversarial Networks model based on the Context Encoder, and proposes a self-localization occlusion face image restoration algorithm. Firstly, the occluded part of the face is marked by occlusion locator, and then the marked face image is sent to the generator of Generative Adversarial Networks for restoration. The model generator uses the Convolutional Neural Networks of the Variational Autoencoder structure, and adds the Batch Normalization layer in the model to enhance the information prediction ability of the generator. At the same time, the discriminator is constructed by combining with VGG19, and the discriminator is trained against the generator. Through the experiment on CelebA face data set, this algorithm is significantly better than other methods in the aspect of random large area occlusion face image restoration.
Performance Comparison of SVM and ANN for Reversible ECG Data Hiding

In the telemedicine industry, data security is a prime aspect because medical data is frequently transferred and stored over networks. In such scenario watermarking emerges as an important technique that ensures data security and integrity. In this work, use of SVM and ANN is investigated for a high capacity ECG data hiding technique. Watermarking is done using prediction error expansion (PEE). SVM and ANN are used to predict the ECG values. Two separate regression SVM models with linear kernel and Gaussian kernel are trained. ANN model used is a feed-forward deep neural network with three hidden layers. The proposed method provides a completely reversible watermarking. The patient’s confidential data is used as a watermark. For testing and training of algorithms, signals from MIT-BIH arrhythmia database are used. Performance of both SVM and ANN-based prediction models are evaluated using normalized cross-correlation, signal-to-noise ratio, and percentage residual difference. Linear SVM and deep ANN are comparable in performance and are better than Gaussian SVM.
Documentation and Scientific Archiving: Digital Repository

Documentation and archiving tools were developed for using with descriptive linguistics and then became a discipline called Linguistics of Documentation in 1998. The aim of this discipline is to preserve documents and primary data of linguistic, anthropological, archaeological and humanistic research. In 2018 the University of Azuay signed a Framework Agreement for Scientific and Technological Cooperation with several partners: CONICET Argentina, the Peruvian Amazon Research Institute, the University of Chile, the National University of Formosa, the Center for Anthropological Studies of the Catholic University of Paraguay, the National University of San Juan, Argentina; and the Pontifical Catholic University of Peru to contribute to the documentation, preservation and dissemination of linguistic and cultural heritage. The main objective of this research is to create a website called ‘Documentation and scientific Archiving’ that facilitates the use and exchange access to a corpus of resources from various disciplines for scientific and educational purposes. The premise of the methodology used for the development of the website is an orientation to various disciplines. The synthesis focused on the review of the characteristics of sites of similar purpose. The study considered three major stages for the synthesis of the web application: 1) the detailed review of 11 websites allowed identification of the entities and attributes used as the basis for development; 2) the quantification of entities and the attributes found in those pages were the key to deciding that the website covered multiple disciplines or lines of research i.e. the key attributes for handling multimedia files were evidenced; 3) the identification of user requirements. The “Documentation and scientific Archiving” website is expected to be functional for both researchers and users to help maintain and preserve the country’s linguistic and cultural heritage. In addition to UDA researchers, other local and national universities can file their research inventories on the site where they can be preserved, reviewed, exchanged and used by other Latin American researchers. Finally, today, technology has become a fundamental aid for the development of research projects, building bridges between linguistic, anthropological, archaeological, humanistic research and the development and creation of applications and websites.
Hardware Implementation and Verification of the Sensor Defective Pixels Correction Algorithm

Algorithms for correction of defective pixels of sensor raw data are considered. A modification of the directed average sum algorithm is proposed. Implemented module includes multiple stages conveyor. Two parameters that can influence on module’s and conveyor’s performance: size of color component and number of pixels per clock. A numerical study of the quality of image correction by the selected algorithm depending on the module parameters was carried out, as well as a comparison of the result with correction by the average method. A verification environment has been developed, and the module has been verified via UVM (IEEE 1800.2-2017 Universal Verification Methodology).
Human-Neural Net Collaborative Programming

Current program generation models suffer from the challenges of poor generalization, narrow program coverage and being incompetent to dealing with complex program structures (e.g., branch, loop, recursion.), because these models only get single information input (input-output samples) and rely entirely on neural networks. Obviously, there is ambiguity to reverse program behaviors through input-output pairs alone. Thus, we propose a Human-Neural Net Collaborative Programming (HNCP) paradigm, that integrates the strengths of human's experience and perception with the advantages of neural network's automatic learning from data, in which programmers compose the overall program framework, but let the neural network learn to generate the local trivial dirty detail. The advantages are to seamlessly integrate neural network components as a construction of advanced programming language, and to promote the practical application of program generation. Experiments indicate that our approach is effective and outperforms the state-of-the-art in program generation.
The Optimal Implementation of Khudra Lightweight Block Cipher

In this paper, we discuss the area and speed balance optimization of Khudra lightweight block cipher. There are three major ingredients. Firstly, because of the whole structure of the Khudra block cipher and its F-function is based on a Feistel structure, we make the main module that directly calls the F-function in the hardware optimization implementation. Secondly, through the clock control, the plaintext can be XORed with the key and the round constant under certain conditions. At the same time, the three rounds of the F-function are merged into one module, whose F-function module contains the F-function of the left and right branches of the algorithm. Thus, three rounds of operations can be completed with only one S-box, which can effectively reduce the number of registers and the number of iterations to increase the encryption and decryption rate. Finally, because of the particularity of key expansion in Khudra, we separately calculated the round key and stored its results in the register, which can effectively save the time of key expansion.
Cognitive Agriculture—Novel Approach for Sustainability

Technology changes in agriculture have done many favors for the humanity and at the same time has some side effects on the ecosystem. So the attention of the world now turned towards the concept of sustainability. Agriculture is a major Profession in developing countries like India. Growth in population and Industrialization has lead to the scarcity of cultivation lands. In order to satisfy the demands of large population with the lower cultivation lands, there is a necessity to increase the productivity. So many techniques have been for seen in agriculture like the usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. The changes are not eco friendly. Cognitive Agriculture introduces basic principles for producing agricultural products that are environment friendly, resource saving and as well as highly efficient. This paper introduces the concepts of Cognitive agriculture and how it leads to sustainable agriculture. A case study on use of Intelligence in Indian Agriculture is also included.
Développement financier et croissance économique en RDC : Supply leading ou demand folowing ? [Financial development and economic growth in the DRC : Supply leading or demand folowing ?]

This paper aims to verify the causal relationship between financial development and economic growth in the Democratic Republic of Congo (DRC) using data from 2004 to 2019. The dynamic Granger causality analysis is used to analyze the variables. The results indicate that there is a robust, one-way relationship from economic growth to financial development. This result confirms the Demand-following hypothesis that economic growth drives the development of the financial system. Therefore, policies to promote economic growth, such as the accumulation of human capital, macroeconomic stabilization, rehabilitation of key infrastructure, structural reforms and the creation of a good economic environment for the private and regulatory sector, are crucial to improve financial development in the DRC.
Question Chunk Extraction for Improving Question Similarity

Intention classification is one of the challenging issues of question understanding. The same intention could be described in different styles e.g., "when", "what time." It is more challenging for a language that has no word separator, flexible grammar and lack of annotated corpus. In Thai, a word "when" could be described as "เมื่อไหร่", "ตอนไหน", "กี่โมง", "เวลาไหน." The underlining text are question identifier i.e., "when" "which" "how many" and "which", respectively. With above limitations, traditional word segmentation could not tokenize question words properly especially on question tokens. As a result, word similarity or word suggestion services using word embedding, which heavily relies on word segmentation could not provide a decent result when it is a question. We proposed a question chunk extraction method since question should be tokenized into a chunk instead of a word. This can improve quality of word segmentation and can improve question similarity results. A question chunk (QC) is a combination of question word (QW) and focus word (FW.) We compared our question chunk based word embedding (QCWE) using QC and traditional word embedding (TWE) using traditional word segmentation, the results showed that our QCWE outperformed the TWE in all aspects.
Mobile Learning: An Approach to Improve the Quantity of Educational Attainment in Conflict Hit Areas Like J&amp;K

Mobile Learning: An Approach to Improve the Quantity of Educational Attainment in Conflict Hit Areas Like J&amp;K The days are gone when mobile was considered a big distraction in the study. We can make best use of mobile in achieving educational attributes by improving both quantity and quality of education and this approach is simply called mobile learning. This paper presents an outlook of making best use of mobile learning to increase the educational attainment, especially in conflict hit areas, where strikes, curfew and processions, demonstrations become the hurdle in the normal working hours in the educational system. Here we have taken an example of Jammu and Kashmir, where the security climate is vulnerable from last more than thirty years and peace is a major concern at present. In this situation a fear among the student community to go schools has risen to a level, which ultimately affects the education system badly. In most of the cases, the syllabus is not completed in the schools due to shortage of effective class hours. To tackle this situation, the mobile learning could be the best remedial measure to address this issue.
Deep Domain Adaptation for Low-Resource Cross-Lingual Text Classification Tasks

Recently, the data-driven machine learning approaches have shown their successes on many text classification tasks for a resource-abundant language. However, there are still many languages that lack of sufficient enough labeled data for carrying out the same specific tasks. They may be costly to obtain high-quality parallel corpus or cannot rely on automated machine translation due to unreliable or unavailable machine translation tools in those low-resource languages. In this work, we propose an effective transfer learning method in the scenarios where the large-scale cross-lingual data is not available. It combines transfer learning schemes of parameter sharing (parameter based) and domain adaptation (feature based) that are joint trained with high-resource and low-resource languages together. We conducted the cross-lingual transfer learning experiments on text classification on sentiment, subjectivity and question types from English to Chinese and from English to Vietnamese respectively. The experiments show that the proposed approach significantly outperformed the state-of-the-art models that are trained merely with monolingual data on the corresponding benchmarks.
An Improved Deep Belief Network for Chinese Emergency Recognition

Abstract Aiming at the defects that the RBM module in DBN can only re-represent information but cannot extract information features, and can only handle one-dimensional data, the DBN network is improved, and a Conv-DBN model is proposed to recognize emergencies. First, the text corpus is preprocessed, and the word vector matrix generated by Word2Vec is used as input, and then the word vector features are extracted through the visible layer integrated into the convolution operation. Word vector features are used as the input of the next layer. Finally, every layers are fine-tuned through back-propagation at the top layer. The softmax function is used to activate, and the recognition result is output. Simulation results show that the method proposed in this paper has improved accuracy and recall, and F value is better than other methods.