Reviewerly/exHarmony huggingface dataset:
Discussion on Technical Review System of Report for Soil and Water Conservation in Binzhou

Abstract Suffering severe soil and water loss, the Binzhou City needs efficient technical review system of report for soil and water conservation to curb erosion and improve the urban environment. In this article, the implementation, work contents, process of the current report review system are systematically summarized, and three suggestions are made to improve the system: firstly, clarify the review criteria; secondly, strengthen the management of expert database; thirdly, promote experts training.
OBSTACLE AWARE COOPERATIVE COMMUNICATION AND MOBILITY MODEL FOR AGRICULTURE AUTONOMOUS ROBOTS

Robots/agents mobility in agriculture environment with presence of obstacle is challenging. This paper reviews and understand the mobility problem of multiple mobile robot (MMR) in agriculture environment. Here we study problem related to localization, mapping and path planning of mobile agentsand also summarize existing methodologies benefits and drawbacks. Especially in agriculture environment, accurate mapping of navigation of mobile robots is needed for carrying out different operation. This paper particularly focused on addressing the navigational problem of multiple agents specific to agriculture environment and presentsobstacle aware cooperative communication and mobility (OACCM) model. Experiment result shows the proposed obstacle aware cooperative communication and mobility model for agriculture autonomous robots achieves much better result than existing path planning model in terms of collision reduction, energy efficiency, and path search efficiency.
A Content Dictionary for In-Object Comments

It is observed that some OpenMath objects may benefit from containing comments. A content dictionary with suitable attribution symbols is proposed. This content dictionary also provides application symbols for constructing comments that are somewhat more than just plain text strings.
Prediction of subsidence deformation of subway project by improved markov chain and optimized wavelet neural network model

Abstract In view of the fact that the factors affecting the subsidence deformation of subway engineering are complex and the subsidence monitoring data are random, this paper constructs an optimized wavelet neural network model with markov chain improvement to improve the accuracy of the predicted data. Firstly, the wavelet neural network is optimized by adding momentum and adjusting learning rate adaptively. Finally, based on markov chain theory, the residual between the predicted value and the actual monitoring value of the optimized wavelet neural network is improved. Taking the settlement data of a settlement monitoring point of xi'an metro line 14 as the research object, the prediction accuracy of the model was compared and analyzed by C++ program. The results of the program show that the additional momentum and the adaptive learning rate are effective for the optimization of the wavelet neural network model, and the prediction accuracy of the optimized wavelet neural network model improved by the markov chain is higher than that of the single model, which is suitable for the prediction of the subway engineering settlement.
Revisiting December Hollow: Unearthing emotive shape

The project is a developed realisation of Zinovieff's 1969 December Hollow 'fold-out score' concept.The compositional system is designed to generate electronic music and/or conventional scores by slicing through a three-dimensional topographical score of 'emotional zones'.Each zone is associated with a vector of musical parameters and by selecting various trajectories through the shape, countless pieces may be generated.This paper briefly outlines the approach taken to realize this idea, and develop it with the implementation of real-time processing, the mapping of score space with RGB space and vector transformations of Schumann's Mein Herz ist Schwer, within an adaptive performance model.
Adaptive and Dynamic Knowledge Transfer in Multi-task Learning with Attention Networks

Multi-task learning has shown promising results in many applications of machine learning: given several related tasks, it aims to generalize better on the original tasks, by leveraging the knowledge among tasks. The knowledge transfer mainly depends on task relationships. Most of existing multi-task learning methods guide learning processes based on predefined task relationships. However, the associated relationships have not been fully exploited in these methods. Replacing predefined task relationships with the adaptively learned ones may lead to superior performance as it can avoid the misguiding of improper pre-definition. Therefore, in this paper, we propose Task Relation Attention Networks to adaptively model the task relationships and dynamically control the positive and negative knowledge transfer for different samples in multi-task learning. To evaluate the effectiveness of the proposed method, experiments on various datasets are conducted. The experimental results demonstrate that the proposed method outperforms both classical and state-of-the-art multi-task learning baselines.
Efficient Web Archive Searching

EfficientWebArchiveSearchingReport.pdf: The project report in PDF format.EfficientWebArchiveSearchingPresentation.pptx: The project presentation in Microsoft PowerPoint format.EfficientWebArchiveSearchingPresentation.pdf: The project presentation in PDF format.EfficientWebArchiveSearchingReportLaTex.zip: The LaTex project of the project report.EfficientWebArchiveSearchingSourceCode.zip: The project source code.
Research on Social Service Education System from the Perspective of Chinese Welfare Culture

At present, Internet technology is changing with each passing day, the globalization process is booming, and the rapid development of technology products is inseparable from the meaning given to society by design itself. Convenient life and high-quality service experience make people’s lives happy, which have given new meaning to “well-being”. Welfare services are about social stability and people’s happiness. Social needs and industrial ecology determine the direction of design. From the perspective of social design itself, this is a systematic progress, which determines the new thinking paradigm of innovation, service, design, and education. It is a new direction of designing and educating people to cut into the “social service” from the culture of well-being. Taking the vision of Chinese well-being culture and people’s livelihood service as the research purpose, we will deeply study the principles and methods of educating people about ecological design education. It is the focus of this article to cultivate design talents who are in line with society and take on the future.
CROMqs: An infinitesimal successive refinement lossy compressor for the quality scores

The amount of sequencing data is growing at a fast pace due to a rapid revolution in sequencing technologies. Quality scores, which indicate the reliability of each of the called nucleotides, take a significant portion of the sequencing data. In addition, quality scores are more challenging to compress than nucleotides, and they are often noisy. Hence, a natural solution to further decrease the size of the sequencing data is to apply lossy compression to the quality scores. Lossy compression may result in a loss in precision, however, it has been shown that when operating at some specific rates, lossy compression can achieve performance on variant calling similar to that achieved with the losslessly compressed data (i.e. the original data). We propose Coding with Random Orthogonal Matrices for quality scores (CROMqs), the first lossy compressor designed for the quality scores with the “infinitesimal successive refinability” property. With this property, the encoder needs to compress the data only once, at a high rate, while the decoder can decompress it iteratively. The decoder can reconstruct the set of quality scores at each step with reduced distortion each time. This characteristic is specifically useful in sequencing data compression, since the encoder does not generally know what the most appropriate rate of compression is, e.g. for not degrading variant calling accuracy. CROMqs avoids the need of having to compress the data at multiple rates, hence incurring time savings. In addition to this property, we show that CROMqs obtains a comparable rate-distortion performance to the state-of-the-art lossy compressors. Moreover, we also show that it achieves a comparable performance on variant calling to that of the lossless compressed data while achieving more than 50% reduction in size.
Research on the Application of Internet-based Technology in Integrated Meteorological Services

Abstract With the continuous development of information technology, the rapid progress of the Internet and computer technology, the life of the public has become convenient, fast, intelligent. A lot of computer software has gradually penetrated into people’s work and life, playing an important role in every field. Similarly, Internet technology also plays a very important role in meteorological services, making a great contribution to the improvement of information processing efficiency in the integrated services of meteorological departments. To a certain extent, the application of Internet in meteorological services improves the quality of comprehensive meteorological services. This paper discusses the current situation of the integrated meteorological service, analyzes how the Internet should be applied in the integrated meteorological service and the requirements for its use, looks forward to the future, and considers the application effect of the Internet in the integrated meteorological service in the future
Voice Grounded Gender Identification

This paper designed to predict the gender of a speaker from the voice. It has a variety of applications ranging from voice analytics to personalizing human machine interactions. Various machine learning algorithm can be used to build a learning system that identifies speaker’s gender. The dataset is designed such that, it includes voices of either gender in all kind of emotional reactions. A learning model is built based on the dataset, and have trained it using various machine learning algorithms. The overall prediction is based on the result which is most consistent with models. The observation made from the results is that Random forest decision tree and Support Vector Machine predict the results with best accuracy scores.
One legacy: ONE ICHNOLOGY! Richard Granville Bromley (1939–2018)

It does not sound like much: one legacy… But the legacy Richard Granville Bromley (Figure 1) left is a huge one. Palaeoichnology cannot be conducted without knowledge of neoichnology; at least, beh...
Single Color Sketch-Based Image Retrieval in HSV Color Space

Sketch-based image retrieval is a fundamental computer vision problem. Instead of using hand-designed features to represent sketches and images, recent researches apply deep learning approaches combined with fine-grained matching to retrieve images with fine-grained details. Although these researches allow user to use hand-free sketches drawn for retrieving similar objects, the color matching is ignored which induces a low retrieval precision. To address this problem, we propose a single color sketch-based image retrieval (SCSBIR) approach using HSV color feature considering both shape matching and color matching in this paper. The SCSBIR problem is investigated using deep learning networks, in which deep features are used to represent color sketches and images. A novel ranking method considering both shape matching and color matching is also proposed. In addition, we build a SCSBIR dataset with color sketches and images, and train and test our method by using this dataset. The test results show that our method has a better retrieval performance. The research in this paper can not only promote its application in the commercial field, but also provide reference for the future research in this field.
Performance Benchmark in Febrile Mass Screening Detection

Recently, detecting and tracking people using infrared sensors in public surveillance has been given attention by many researchers since the global outbreak of severe acute respiratory syndrome, and machine vision is bound to play an important role. In machine vision, sliding window approach has appeared as most promising approach. Since Papageorgious et al who proposed the first sliding window detetectors until Dalal et al who have come out with a large gains detector based on histogram adoption, the ideas of sliding in human detections continue to serve as a root for modern detectors. While there is increasing research in thermal spectrum, very little research focus on the effectiveness of state of art detectors in febrile mass screening application. The exposed area of head-to-shoulder is an important body part for region of interest detection priorly before the temperature of febrile person is measured by the thermal camera. In addition, some of the thermal datasets used in previous research are more fit to surveillance or safety applications where the targets are mostly far-scales. One of the challenges in detecting the pedestrian in thermal images is the nature and quality of image in infrared spectrum as well as the real crowd situation in public area that cause occlusion. Therefore in this paper, we are interested to evaluate the top three detectors’ performance on thermal images taken during fever screening in Kuala Lumpur International Airport. We also analyze the best approach to be adopted in the detection using a new context of training and evaluation.
Tatar WordNet: The Sources and the Component Parts

We describe an ongoing project of construction of the Tatar Wordnet. The Tatar Wordnet is being constructed on the base of three source resources, developed by us. The first source is TatThes, a bilingual Russian-Tatar Social-Political Thesaurus. TatThes, in turn, has been constructed by manual translation and extension of RuThes, a linguistic ontology for Russian. The second source is a Tatar translation of RuWordNet, a wordnet for Russian. This translation was carried out automatically on the base of a Russian-Tatar dictionary, and then was manually verified. The third source is a semantic classification of Tatar verbs, developed from scratch. We discuss the structure, methodology of compilation and the current state these source resources, and justify the choice of them as the initial resources for building the Tatar Wordnet. Our ultimate goal is to publish Tatar Wordnet on the Linguistic Linked Open Data cloud and integrate it to the Global WordNet Grid.
A Framework to Analyze Comments for Educational Apps on Google Play Store

School going students are often juggling between their studies and their parents’ expectations to perform well, often leading to educational stress. After a quick survey of 10th Grade students in India, it was found that difficulty in understanding mathematics is one of the leading causes of stress. To moderate such stress and help towards making mathematics a joyful experience, ReviseOnTheGo, a suite of mobile apps were developed. These applications were published to the Google Play Store and there are about 30000+ downloads in total to date. A lot of feedback through 484 ratings, about 100+ ratings with comments were received. Young students express their feedbacks differently, for instance, their ratings and comments may not be aligned. These comments and ratings are the only interface between the developer and the students, hence they are crucial for the fine-tuning of User Interface, Information Architecture, and its contents. There is a lack of analytics frameworks which differentiate between the choice of words and their intended meaning of these young students. The developed framework attempts to bridge this gap by performing sentiment analysis based on parameters such as discrepancy between the rating and tone of the comment, the student’s gender, feedback persona, use of slang, number of words used and grammatical errors. The framework provides a better view by objectively looking at these parameters and unequivocally classifying the emotional response.
Credit Card Fraud Detection Technique Based on Hybrid Classification

The credit card fraud detection is the prediction analysis method which can predict future possibilities based on the current information. The credit card fraud detection is the problem of prediction analysis. The credit card fraud detection has the various phases which are pre-processing, feature extraction and classification. In the previous research work, the voting classification method is designed. To reduce complexity of voting classification method and to improve accuracy ensemble classification is designed in this research work. The existing and proposed methods are implemented in Python and results are analyzed in terms of accuracy. It is analyzed that accuracy of proposed method is high as compared to existing method.
Towards Efficient FPGA Implementation of Elliptic Curve Crypto-Processor for Security in IoT and Embedded Devices

An Elliptic Curve Crypto-Processor (ECCP) is a favorite public-key cryptosystem due to its small key size and its high security arithmetic unit. It is applied in constrained devices which often run on batteries and have limited processing, storage capabilities and low power. This research work presents an effective ECCP architecture for security in IoT and embedded devices. A finite field polynomial multiplier takes the most implementation effort of an ECCP because it is the most consuming operation for time and area. So, the objective is to implement the main operation of Point Multiplication (PM) 𝑄=𝑘𝑃 using FPGA. The aim is to obtain the optimal registers number for an area optimization of ECCP architecture. Moreover, it proposes a time optimization of ECCP based on the liveness analysis and exploiting forward paths. Also, a comparison between sequential and parallel hardware design of PM based on Montgomery ladder algorithm is provided.The developed ECCP design is implemented over Galois Fields GF (2163) and GF (2409) on Xilinx Integrated Synthesizes Environment (ISE) Virtex 6 FPGA. In case of GF (2163), this work achieved an area saving that uses 2083 Flip Flops (FFs), 40876 Lookup Tables (LUTs) and 19824 occupied slices. The execution time is 1.963 s runs at a frequency of 369.529 MHz and consumes 5237.00 mW. In case of GF (2409), this work achieved an area saving that uses 8129 Flip Flops (FFs), 42300 Lookup Tables (LUTs) and 18807 occupied slices. The execution time is 29 s runs at a frequency of 253.770 MHz and consumes 2 W. The obtained results are highly comparable with other state-of-the-art crypto-processor designs. The developed ECCP is applied as a case study of a cryptography protocol in ATMs.
A Framework for Degraded Kannada Character Recognition

Degraded Character Recognition (DCR) is an important area of research in the field of Document Image Analysis and Recognition (DIAR). The degradation of characters poses lot of challenges like broken characters, characters mixed with noise etc. Kannada language script has curves and complex patterns, which makes recognizing these characters very difficult. The degradation in the document can introduce gaps in these patterns, which complicates the recognition problem. In this paper, the importance of Kannada DCR system for printed scripts is addressed and also it proposes a framework consisting of four stages namely preprocessing, rebuilding, feature extraction and classification. This framework is also supported with an efficient implementation that achieved a recognition accuracy of 99% in characters with degradation.
First Online Physics Olympiads between United Russian-American High-School Teams

The article is dedicated to the twentieth anniversary of two Internet physics olympiads organized by the Physics Department of Novosibirsk State University, in which senior pupils from Russia and the USA participated. For the time when before the advent of popular social networks there were a few more years when the currently popular messengers were not yet widespread, the organization of such competitions was technically and organizationally far from a trivial task. It was also necessary to overcome the problem of different programs and different levels of school physics teaching in Russia and the USA, as well as the problem of the language barrier. All these tasks were successfully solved by the joint efforts of the Russian and American organizing committees, and in 1999 the competitions Novosibirsk – San Diego and in 2000 Novosibirsk – St. Petersburg – San Diego – Seattle were held. A successful invention that allowed equalizing the chances of teams and replacing interethnic rivalry with cooperation was the idea to hold competitions between international teams, consisting of an equal number of Russian and American schoolchildren communicating with each other via direct video communication. Sets of tasks were prepared for the olympiads, both ordinary, written, and video clips with tasks-demonstrations. The latter have been particularly successful in resolving the problem of the language barrier. The great help in conducting these two Olympiads was the many years of experience gained by NSU during the All-Siberian Olympiads and the idea of the demonstration tasks used in entrance examinations at the Physics Department of NSU. We present in this article both the content of the tasks of the Olympiads and the responses of the domestic and American press to the events described.
On finite exchangeability and conditional independence

We study the independence structure of finitely exchangeable distributions over random vectors and random networks.In particular, we provide necessary and sufficient conditions for an exchangeable vector so that its elements are completely independent or completely dependent.We also provide a sufficient condition for an exchangeable vector so that its elements are marginally independent.We then generalize these results and conditions for exchangeable random networks.In this case, it is demonstrated that the situation is more complex.We show that the independence structure of exchangeable random networks lies in one of six regimes that are two-fold dual to one another, represented by undirected and bidirected independence graphs in graphical model sense with graphs that are complement of each other.In addition, under certain additional assumptions, we provide necessary and sufficient conditions for the exchangeable network distributions to be faithful to each of these graphs.
An Application of Block Chain in Examination System, A Case Study

A block chain is a set of unchangeable transactions that usually exist in multiple locations in a network. In a data warehouse, predominant operations are additions and deletions. However, changes in virtual data warehouses are also possible. Therefore, block chain technology can distribute public data in an incontrovertible, secured, and encrypted form to ensure that transactions can never be fiddled. The present system of conducting examination suffers from various inconsistencies like score manipulations, issuance of certificates, and many more. After a detailed study of a large university examination system, an attempt is made to present the block chain technology, starting from blank answer script distribution to the examination centers, script evaluation, and tabulation to auto-generation of certificates upon successful completion of the examination. In this paper, we present the block chain framework of the system and validate the framework with the help of a case study showing the hash-digest at every stage of the transaction added directly on the block chain. It enables the tracing of how exactly a candidate answers the script, script evaluation, score received, and adding more trustworthiness to the obtained certificate.