Reviewerly/exHarmony huggingface dataset:
The exact multiple pattern matching problem solved by a reference tree approach

Given a text T and a set of r patterns P1,P2,…,Pr, the exact multiple pattern matching problem reports the ending positions of all occurrences of Pi in T for 1≤i≤r. By transforming all substrings with a fixed length of T into a reference tree such that each internal node stores a reference string, the exact multiple pattern matching problem can be efficiently solved by searching patterns in the tree via the guidance of the reference strings. We design elegant algorithms to construct the reference tree (the preprocessing phase) and to search patterns in the tree (the searching phase) using bitwise operations. The experiments involving problem instances from the DNA sequence and the English language are conducted to compare the performance of our approach against those of the suffix tree and suffix array algorithms. The computational results demonstrate the advantage of our approach over these algorithms. In spite of the simplicity, our approach is quite efficient, flexible and robust.
Visual Saliency Prediction on 360 Degree Images With CNN

With the rapid development of virtual reality techniques, understanding the visual attention in 360° images has attracted tremendous interest. Due to the lack of a sufficient large-scale 360° image saliency database, it is not straightforward to extend the existing saliency prediction methods from traditional 2D images to 360° images. In this paper, an end-to-end model based on Convolutional Neural Network (CNN) is proposed for 360° images saliency prediction. Firstly, we sample the omnidirectional image into equally-sized patches as the input, so as to reduce the error caused by stretching during equirectangular projection. Afterwards, a model containing two networks is proposed, i.e., basenet and refinenet. The basenet is designed to learn the saliency feature from the planar 2D patch, while the refinenet is to refine the saliency from the basenet by incorporating the observer bias towards the equator in spherical space. In addition, a new loss function is proposed, where both the distribution and location based evaluation metric are considered. Finally, we project each pixel of each patch back to the equirectangular image to generate the complete saliency map. Our experiments show that the proposed model outperforms the state-of-the-art method.
Research on Named Entity Recognition Technology of Knowledge Graph for Flipped Classroom

Knowledge Graph is a kind of storage form that uses graphic models to store entities and the relationship between entities. In the process of building Knowledge Graph for flipped classroom, it is necessary to use Named Entity Recognition technology to identify keywords in textbooks and generate entities in Knowledge Graph. In the process of calculating the Position Vectors of words after segmentation, some laws that can be used for Chinese Named Entity Recognition. Based on these laws, a named entity recognition algorithm based on the textbook position vectors is designed without training set. The test results show that the Precision of the algorithm is 90.59% and the Recall is 91.73%, and the algorithm, to some extent, can recognize nested named entities.
CCTL: Cascade Classifier Text Localization Algorithm in Natural Scene Image

Natural scene images often contain a lot of important texts, which carry the information we need, so it has important practical value to locate text information. This paper proposes a Cascade Classifier Text Localization (CCTL) algorithm. Firstly, a cascade classification algorithm based on Real AdaBoost is proposed to improve the accuracy of text localization. Secondly, a perceptual-based grouping algorithm is proposed to establish a perceptual organization framework. The use of adjacency and similar rules to group texts can improve the effect of text grouping. The proposed algorithm is verified and evaluated on the ICDAR dataset. At the same time, it is compared with other algorithms. The experimental results show that the proposed method has superiority in natural scene text detection.
Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River Water Quality

Modeling real-world phenomena is a focus of many science and engineering efforts, from ecological modeling to financial forecasting. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. Knowledge-driven modeling builds a model based on human expertise, yet is often suboptimal. At the opposite extreme, data-driven modeling learns a model directly from data, requiring extensive data and potentially generating overfitting. We focus on an intermediate approach, model revision, in which prior knowledge and data are combined to achieve the best of both worlds. We propose a genetic model revision framework based on tree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG formalism and GP operators in an effective mechanism making data-driven revisions while incorporating prior knowledge. Our framework is designed to address the high computational cost of evolutionary modeling of complex systems. Via a case study on the challenging problem of river water quality modeling, we show that the framework efficiently learns an interpretable model, with higher modeling accuracy than existing methods.
A VC'T Discovery Algorithm of Renju

The VCT discovery of Renju represents a set of searching problems and is worthy of further study. The RAOTS algorithm proposed in this paper discovers VCT by building an AND/OR tree and finding the solution tree. Useless moves are filtered by move classification to reduce the size of the tree. Move scoring and sorting improve the probability of hitting VCT moves. Transition, pruning and prediction are used to optimize the search process. Depth-first, breadth-first and best-first strategies are implemented to adapt to various situations. Experiments show that RAOTS is able to discover very complex VCT.
A Novel Image Classification Algorithm

In this paper the bag-of-words model is applied to image classification and improves the existing problems of the traditional bag-of-words method. We propose a method of combination of corner detection and graph theory for ROI region extraction and fuzzy membership degree. First using corner detection for images, then the ROI region is defined by the method of graph theory. Then the SIFT features of the ROI region are extracted and the visual dictionary is generated. The visual dictionary can be more accurate to describe the image features, which can reduce the influence of background information and other interference information. Secondly, the concept of fuzzy membership function and information of feature space is introduced to improve the image of the visual histogram. Finally, support vector machine classifier is used to classify. Through the experiment of the Caltech 100 database, the result shows that the method improves the accuracy of classification compared with the traditional method.
A Novel Hand Gesture Recognition Method for FMCW Radar

This paper focuses on the hand gesture recognition method based on interference suppression and fusion neural networks using a frequency modulated continuous wave (FMCW) radar. Firstly, the range and Doppler information of the target is obtained by estimating the parameters of the collected radar data. We apply a non-extreme interference suppression (NEIS) method to remove the static and dynamic interference. Then, a hand gesture extraction method based on multi-feature fusion neural network is proposed. Finally, experiments are carried to verify the effectiveness of the proposed algorithm and the results show that the proposed algorithm not only effectively suppresses the interference, but also achieves the hand gesture recognition accuracy as high as 97.26%.
Entity Relation Extraction Based on Multi-attention Mechanism and BiGRU Network

Entity relationship extraction is the main task in information extraction, and its purpose is to extract triples <entity e1, relationship r, entity e2> from unstructured text. The current relationship extraction model is mainly based on the BiLSTM neural network, and most of the introduced are sentence-level attention mechanisms. The structural parameters of this model are complex, which easily leads to over-fitting problems, and lacks the acquisition of word-level information within the sentence. In response to these problems, we propose a model based on the multi-attention mechanism and BiGRU network. The model mainly uses BiGRU as the main coding structure. By reducing the parameter settings, the training efficiency can be effectively improved. At the same time, a multi-attention mechanism is introduced to learn the influence of different features on relationship classification from the two dimensions of word level and sentence level, and to improve the effect of relationship extraction through different weight settings. The model is tested on the SemVal 2010 task8 dataset. The experiment shows that our model is significantly better than the baseline method.
Internet-based Knowledge Graph Construction Technology in Air Defense Field

Air defense security is an important part of our military work. In order to solve the contradiction between "data explosion" and "knowledge lack" existing in the field of military air defense, in this paper, we adopt a more scientific way to organize and manage big data, namely, constructing knowledge graph. In this paper, we construct the ontology model of knowledge graph of air defense domain at first. Secondly, we use information extraction technology to sort, extract and clean the open source data on the Internet. Then the data that meet the requirements are added to the knowledge graph. Finally, we use the Neo4j graph database to manage and store the data in the knowledge graph.
Statistical analysis of geological faults for characterizing mineral deposits

The aim of this work is to estimate the fractal dimension in fault images of two regions of Chile. Since these regions are rich with metal deposits, we think the fractal dimension could help to characterize them. The well-known box counting method has been used for estimating the fractality in small areas with dimensions of 10×20 km in the north of Chile.
INTERDISCIPLINARY APPROACH IN THE TRAINING OF MASTER'S DEGREE STUDENTS (ON THE EXAMPLE OF THE COURSE “EDUCOLOGY”)

The article is devoted to the general characteristics of the interdisciplinary approach as a way of interaction between sciences and the main factor in the growth of interdisciplinary links in the educational process. The authors emphasize the fact that interdisciplinarity is not a phenomenon that is unique to a particular science or a particular field of science, it applies to all fields of knowledge. The article traces the trends in the development of interdisciplinarity in the humanities on the example of the course "Educology". Considerable attention is paid by the authors to the effectiveness of the interdisciplinary approach in the training of higher education students of the educational program "Management of educational institutions" based on the experience of teaching the course "Educology" as an interdisciplinary subject. It is proved that the interdisciplinary approach in the process of professional training of future managers in the field of education allows to expand their scientific worldview and increase the level of methodological culture, promotes awareness of current problems in the field of education and search for solutions and practical life situations.
A New Approach to Machine Learning Based on Functional Decomposition of Multi -Valued Functions

This paper presents a novel design of a hardware classifier based on combining modified Ashenhurst-Curtis Decomposition and modified multiplexer-based synthesis. The PSUD classifier brings three new contributions: an approach to solve the column multiplicity problem for both types of decomposition, the way how the multiple-valued variables are encoded and used, and a modification of the Ashenhurst-Curtis Decomposition. One of the biggest challenges in Boolean function decomposition is the variable partitioning problem. Thus, we introduce a new representation of two combined classifiers for multiple-valued functions to overcome the variable partitioning problem which allows finding the minimal column multiplicity and consequently to find high quality decompositions leading to good learning. The classifier gives very good accuracy results when tested on multi-valued Machine Learning benchmarks from the UC Irvine repository.
Stochastic Pseudo-Spin Neural Network with Tridiagonal Synaptic Connections

This paper proposes a simplified architecture of the pseudospin neural network, which is based on the transformation of the synaptic link matrix into a tridiagonal Hessenberg Matrix (hessenberg neural network). In this case, the interaction between all pseudospin neurons does not disappear, but is taken into account in renormalized connections between the nearest neighboring pseudospin neurons. It is shown that with an increase in the number of neurons, the time to establish synaptic connections (per iteration) in the Hessenberg neural network relative to the time to establish synaptic connections (per iteration) in a fully connected synaptic neural network decreases according to a hyperbolic law.
Driving Cars Using Webcam By Finding Contours of Two Similarly Coloured Objects

The essential idea of Human Computer Interaction is to improve the interaction among clients and PCs by making the PC more responsive to client needs. The primary objective of this framework is to viably capture the movement of hands with the use of a web camera by utilizing contour, OpenCV libraries and mathematical functions instead of regular input devices such as keyboard, mouse or touch panels. It just takes a progression of images and these binary images are converted to HSV graded images. Those images are processed and contour is trained on it. Pragmatic use will be driverless car, yet rather client can control his car when in home, and for ventures which utilizes high tech imaging framework this will be a low cost option in contrast to an actual sensor which takes electrical signals directly from our ligaments and converts as input signals for pc, yet here, one needn't bother with any wearable gadgets. Thus the primary goal of any automated gesture recognition system is to create an interface that is natural for humans to operate or communicate with a computerized device.
Neural Joint Model for Part-of-Speech Tagging and Entity Extraction

Part-of-speech tagging and named entity recognition (NER) are fundamental sequential labeling tasks in natural language processing (NLP), where joint learning of both tasks is an effective one-step solution. Limited efforts have been made by existing research to meet such needs for Sindhi language. As POS tagging and NER are highly correlative sequence tagging tasks, so most often, a word recognized by the NER system may be recognized as a noun by a POS tagger. Thus, in this paper, we propose a neural joint model based on a bidirectional long-short term memory (BiLSTM) network and adversarial transfer learning to incorporate syntactic information from two tasks by using task-shared information. The syntactic structure captures and provides the information of long-range dependencies among words. Moreover, the self-attention is employed to capture intra-sentence dependencies to the joint model explicitly. Empirical results on two benchmark datasets show that our proposed joint model consistently and significantly surpass the existing methods.
IoT-PSKTS: Public and Secret Key with Token Sharing Algorithm to Prevent Keys Leakages in IoT

In the Internet of Things (IoT) paradigm, devices involved will frequently face different types of attacks, for example flood attacks, eavesdropping attacks and so on. Once attackers have compromised the IoT device, the data materials of the IoT device will not remain confidential, and it will then be captured by the attacker; this will in turn threaten the entire network. Consequently, to safeguard IoTs, this paper proposes a public and secret key with token sharing (IoT-PSKTS) algorithm to avoid key leakages in IoT. Cryptography can be utilized for secure communication in the presence of attackers. In cryptography, a conventional public key cryptosystem will be suitable because they do not require the sender and receiver to deliver the same secret to contact without threat. But, they regularly depend on intricate mathematical calculations and are thus much more incompetent than comparable symmetric key cryptosystems. In a large number of applications, the high price of encrypting lengthy messages in public key cryptography can be prohibitive. A hybrid system tackles it by utilizing a mixture of both. In IoT network, admin generates a public key, private key, secret key and token. The public and secret key will be used for packet encryption in IoT devices and base station side, and the private key will be utilized for decryption in the admin side and token used for IoT devices access control. For encryption purpose, admin shares public and secret key with a token for IoT devices and base station. Therefore, PSKTS algorithm has been used to securely share the public and secret key with a token for IoT devices and base station in a distributed way. The experimental results show that the proposed PSKTS algorithm shares a public and secret key with token in a secured way.
Estimating Savings Potential of Solar Energy

The climatic conditions in India are among the best that can be used maximise solar energy potential and are capable enough to make solar energy serve the purpose as an alternative source of electricity to Thermal and Hydro electricity. However, the high installation costs of the solar panel coupled has prevented solar energy growth in the country. But when looked at from a broader perspective, solar energy can decrease the costs for electricity as well as help to reduce the adverse effects of using non-renewable energy resources like Thermal electricity. The proposed system determines a mechanism to estimate the amount of savings that an individual could make by using a solar panel on their rooftop. Deep Learning is applied to process and segment the rooftop images while removing obstructions such as trees, electric poles etc. and estimate the area of the rooftop. The solar radiation values and weather patterns of a region are considered and the calculated area is used to predict the amount of energy that can be produced by installing a solar panel on the roof.
Building a ‘Sicko’ AI

Using the GPT-2 algorithm developed by OpenAI in February 2019, a skewered or ‘sicko’ AI character was created as a live time entity running in the Google cloud. The algorithm allows imitations of human dialogue that produce fake and often realistic interactions emanating from computer cloud-based agents. The character was created as one of two characters in the emotionally intelligent artificial intelligence brainwave opera AIBO (Artificial Intelligent Brainwave Opera). The spoken word opera rhetorically inquired “Can an AI be fascist?” and “Can an AI have epigenetic or inherited traumatic memory?” through the interplay of human and non-human characters. This paper discusses certain aspects involved in creating the GPT-2 cloud-based character AIBO.
Research on Ontology and Linked Data-oriented Construction of Government Website Web Archive Thematic Knowledge Base

This research aims to explore the application of semantic web technologies such as ontology and linked data in the preservation of government website information resources. By constructing a government website Web Archive thematic knowledge base driven by semantic web technology, it can improve the historical traceability and data mining in the longterm preservation environment. The utilization rate and service quality of the original information of government websites with multiple utilization values such as reference for decision-making. On the basis of summarizing relevant research systems at home and abroad, this paper designs the special knowledge base of the government website Web Archive from three levels, namely, requirement analysis of the knowledge base system, conceptual structure design and logical structure design, and refers to the hierarchical idea of the OAIS model. And the Web Archive thematic knowledge base model for government websites based on ontology and associated data is constructed. Finally, the core process and key technology of the construction of the government website Web Archive thematic knowledge base are proposed, in order to provide reference and reference for research on longterm preservation of government website information resources.
Robust Watermarking Schemes for Copyright Protection of Digital Data: A Survey

Digital watermarking has emerged as a potential solution to the copyright protection-related issues of digital data. This paper presents the key paradigms of research in robust digital watermarking techniques. The main issues that derive research in robust watermarking schemes are imperceptibility and robustness. The purpose of this paper is to provide a gist of robust watermarking schemes in the transform domain with the help of some brief theories and methodologies proposed in the literature. Frequency domain techniques have revealed better efficiency in watermarking applications, to their counterpart spatial domain techniques. The general framework, recent application areas, characteristics, classification of information hiding methods, and various performance evaluation parameters considered by researchers have also been presented in this survey. Broadly, this study offers an introduction to existing literature and provides a well-organized summary of current research being done in robust watermarking, specifically in the frequency domain. This survey paper will be beneficial for the researchers keen to contribute in the field of robust digital watermarking as potential new areas of research in this field have also been recommended.
Automatically Retrieving of Software Specification Requirements Related to Each Actor

It is well-known that success or failure of any software is dependent upon requirement analysis. The most significant problem hidden in natural language software requirements may be ambiguity. This can lead to poor design and performance of the final software product and can be time-consuming at the system analysis stage. Previous studies applied natural language processing (NLP) techniques to identify these ambiguities and reduce them to improve the requirement quality. However, this problem has not yet been fully resolved. This study applies NLP to automatically extract the software system requirement specification and visualize an overview of the software that is being developed. The proposed method clusters relevant software requirement sentences of each system user by text mining technique, and then extracts ‘actors’ and their ‘actions’ from sentences using NLP techniques. The recall technique is used to evaluate the efficacy of the proposed method. Response time and relevancy of the results are significant factors for software product satisfaction.