Reviewerly/exHarmony huggingface dataset:
Mining Scientific and Technical Literature

In this chapter, the authors study text mining technologies such as knowledge extraction and summarization on scientific and technical literature. First, they analyze the needs of scientific information services and intelligence analysis on massive scientific and technical literature. Second, terminology recognition and relation extraction are important tasks of knowledge extraction. Third, they study knowledge extraction based on terminology recognition and relation extraction. Fourth, based on terminology and relational network, they study the text summarization techniques and applications. Last, they give comments on current research and applications on text summarization and give their viewpoints for the possible research directions in the future.
A Multi-Objective Recognition Algorithm with Feature Dimension Fusion

Multi-target recognition technology plays an important role in the field of intelligent driving. In this paper, we propose a novel recognition algorithm with feature dimension fusion to further improve the detection accuracy of road targets and accelerate the detection speed of the recognition system. The algorithm proposed in this paper consists of three steps. Firstly, the initial target position is estimated by a full convolutional lightweight network. Then the feature heatmap is obtained by the symmetric feature fusion network. Finally, the initial position estimation is combined with the heatmap to recognize multiple targets. We evaluate our proposed algorithm on the KITTI data set as well as many real scene experiments. Compared to other multi-target recognition methods, our algorithm provides much higher accuracy under different scenes. The experimental results demonstrate that the fusion feature of multi-dimensional feature map can comprehensively describe the overall sensory characteristics of the target, and extend the learning and analysis of neural networks to different levels of vision.
Revised DBLC Model for Chinese Word Segmentation

[Background] The accuracy of word segmentation is an essential premise in specific or professional domain text mining. [Methods] A Revised DBLC model combining dictionary, statistical and deep learning approach was proposed and implemented by programming. Using professional domain corpora in the China Management Case Library, this paper compared the Revised DBLC Model and other existing word segmentation models for experimentation and analysis. [Results] The segmentation effect of each model on the experimental corpus were obtained through experiments. The Revised DBLC model was superior to other models in each evaluation index, and the accuracy of word segmentation reached 97.6%. [Conclusions] The Revised DBLC model proposed in this paper improved the segmentation accuracy of specific domain texts. However, the time complexity of the Revised DBLC model calculation is high, and the accuracy of word segmentation is positively correlated with the dictionary size.
Learning Deep Structured Multi-scale Features for Crisp and Object Occlusion Edge Detection

The key challenge for edge detection is that predicted edges are thick and need Non-Maximum Suppression to post-process to obtain crisp edges. In addition, object occlusion edge detection is an important research problem in computer vision. To increase the crispness and accuracy of occlusion relationships effectively, we propose a novel method of edge detection called MSDF (Multi Scale Decode and Fusion) based on deep structured multi-scale features to generate crisp salient edges in this paper. The decoder layer of MSDF can fuse the adjacent-scale features and increase the affinity between the features. We also propose a novel loss function to solve the class imbalance in object occlusion edge detection and a two streams learning framework to predict edge and occlusion orientation. Extensive experiments on BSDS500 dataset and the larger NYUD dataset show that the effectiveness of the proposed model and of the overall hierarchical framework. We also surpass the state of the art on the BSDS ownership dataset in occlusion edge detection.
A Survey on the Current Status and Future of IUGPs in Qatar: Challenges, Opportunities, and Recommendations

Qatar’s vision for innovation-driven knowledge economy and sustainable development has been the key motivation for its leadership to strategize and invest in the knowledge ecosystem. Although the outlook of knowledge infrastructure in Qatar, including national and international universities, research and incubation centers, science park, and technology transfer officesTechnology Transfer Office (TTO) , resembles that of the United States, Singapore, and Norway, the country is yet to achieve the same level of excellence in terms of the outcomes. The primary reason for the lack of outputs is that the concept of economic transformation is still new for Qatar and its citizens, and since the overall system is at an infancy stage, some crucial elements of Industry-University-Government partnerships are missing at the execution level. We have already discussed the areas of concern for Qatar in the previous chapter in the light of the Global Innovation IndexGlobal Innovation Index (GII) (Chap. 6 ), which include regulatory environment, research and development, market and business sophistication, and creativity. In order to learn the reasons behind these shortcomings–albeit the country’s commitment to transform to a knowledge-based economy–we conducted interviews and survey with the experts that have firsthand experience of working in the R&D and Industry-University-Government settings in Qatar. In the first part of this chapter, we present the findings of the interviews and survey while in the second part, we propose recommendations to make the knowledge ecosystem in Qatar more effective and efficient.
Building Text Summary Generation System Using Universal Networking Language, Rhetorical Structure Theory, Sangatis and Sutra

This chapter discusses how text summaries could be generated by using a high-level semantic representation. The semantic representation is built using the discourse structure which is comprised of three text representation techniques, namely, universal networking language (UNL), rhetorical structure theory (RST), and Saṅgatis. Sangati is an ancient concept that is used in Sanskrit language literature to capture coherence. This discourse structure is indexed using a concept called sūtra which has been used in both Tamil language and Sanskrit literatures. The chapter mainly focusses on how summary could be generated by using this unique discourse structure and the indexing technique concept, sūtra. Forum for information retreival (FIRE) corpus has been used to test the system and a performance comparison has been done with the one of the state-of-art summary generation systems that is built on discourse structure.
Search for Information in Text Files

As information becomes increasingly abundant and accessible on the web, researchers do not have a need to go to excavate books in the libraries. These require a knowledge extraction system from the text (KEST). The goal of authors in this chapter is to identify the needs of a person to do a search in a text, which can be unstructured, and retrieve the terms of information related to the subject of research then structure them into classes of useful information. These may subsequently identify the general architecture of an information retrieval system from text documents in order to develop it and finally identify the parameters to evaluate its performance and the results retrieved.
Synthesize Models for Quantitative Analysis Using Automata Learning

We apply a probably approximately correct learning algorithm for multiplicity automata to generate quantitative models of system behaviors with a statistical guarantee. Using the generated model, we give two analysis algorithms to estimate the minimum and average values of system behaviors. We show how to apply the learning algorithm even when the alphabet is not fixed. The experimental result is encouraging; the estimation made by our approach is almost as precise as the exact reference answer obtained by a brute-force enumeration.
Fast Implementation of Image Denoising Algorithm Based on GPU

In this paper, we propose a novel fast image denoising algorithm based on GPU in directionlet domain. Firstly, the noisy image is decomposed into two sub-images which are denoted as two cosets. Then the two sub-images are transformed by directionlet respectively to obtain different sub-band coefficients. Secondly, the GMM (Gaussian Mixture Model) is used to fit these sub-band coefficients by analyzing these coefficients and directionlet transform (DT) process. Also, the EM (Expectation Maximization) algorithm is used to estimate the parameters of the GMM including variance, mean and weight and a general estimation algorithm is used for the noise model parameters. After that, the noise is filtered by constructing a Bayesian MAP (Maximum a Posteriori) estimator and non-noise sub-band coefficients will be obtained. Finally, the noise-free image is reconstructed by inverse DT so as to achieve the purpose of removing noise. The algorithm proposed in this paper not only has clear steps, but also can be quickly implemented on the GPU, which greatly improves the efficiency of the algorithm. In comparison with the CPU-based implementation, the proposed method achieves a significant speed-up.
Covariance Matrix adaptation based on Opposition learning for multimodal optimization

Although CMA-ES is effective in locating a single global optimum, it cannot perform well on multi-optima problems. In order to improve the performance of CMA-ES, this paper proposed Covariance Matrix adaptation based on Opposition learning (CMA-OL). In CMA-OL, an improved dynamic peak identification method is introduced in CMA-OL to identify the various peaks dynamically. Opposition learning method is employed in CMA-OL to improve the probability of visiting unproductive regions of the search space. To verify the effectiveness of CMA-OL, numerical experiments are carried on six benchmark problems from CEC2013. The experimental results show that CMA-OL is competitive with respect to other compared algorithms for solving multi-optima problems.
Design of Graphical Representations of Dynamic 3D Gestures

Like tool-tips, graphical representations of 3D gestures are an important component of intuitive user interfaces of gesture controlled systems. The purpose is to communicate how the gestures are to be performed. Although the representations aim at increasing usability, they themselves may lead to usability problems. Based on this experience, different graphics were examined in order to find out the extent to which users correctly imitate the displayed gestures. Two cases proved to be particularly problematic: Too detailed graphics and a lack of differentiation of the phases of a gesture performance. Besides results of a user test this paper presents hints for future guidelines for the design of gesture representations.
Breaking Randomized Mixed-Radix Scalar Multiplication Algorithms

In this paper we present a novel, powerful attack on a recently introduced randomized scalar multiplication algorithm based on covering systems of congruences. Our attack can recover the whole key with very few traces, even when those only provide partial information on the sequence of operations. In an attempt to solve the issues raised by the broken algorithm, we designed a constant-time version with no secret dependent branching nor memory access based on the so-called mixed-radix number system. We eventually present our conclusions regarding the use of mixed-radix representations as a randomization setting.
Automate RFP Response Generation Process Using FastText Word Embeddings and Soft Cosine Measure

Companies frequently receive RFP (Request for Proposal) from interested customers requiring technical expertise, specialized capability regarding to their products or services. Appropriately responding to these RFPs is heavily influential in buyer decision-making. Currently most companies answer RFPs manually, and they (including some major RFP solution providers) mainly use key word(s) matching algorithm to search for similar questions in the knowledge base and choose the one the working analyst thinks most relevant. However, the biggest challenge is the same question can be rephrased using different wordings, so key word matching may lead to irrelevant result(s). The current process is very time-consuming, inefficient, ineffective and sometimes can create inconsistencies. In this paper, we propose using a method which combines state-of-the-art Word Embedding solution in Natural Language Processing (NLP) and AI with the promising new idea of Soft Cosine Measure (SCM) in finding the matching responses. This is by far the first effort to introduce the state-of-the-art technologies in RFP process to improve accuracy, efficiency and effectiveness. From our study of applying this method, we observed high response relevancy rates for non-standardized questionnaires which are out of the knowledge base.
EUSKOR: End-to-end coreference resolution system for Basque

This paper describes the process of adapting the Stanford Coreference resolution module to the Basque language, taking into account the characteristics of the language. The module has been integrated in a linguistic analysis pipeline obtaining an end-to-end coreference resolution system for the Basque language. The adaptation process explained can benefit and facilitate other languages with similar characteristics in the implementation of their coreference resolution systems. During the experimentation phase, we have demonstrated that language-specific features have a noteworthy effect on coreference resolution, obtaining a gain in CoNLL score of 7.07 with respect to the baseline system. We have also analysed the effect that preprocessing has in coreference resolution, comparing the results obtained with automatic mentions versus gold mentions. When gold mentions are provided, the results increase 11.5 points in CoNLL score in comparison with results obtained when automatic mentions are used. The contribution of each sieve is analysed concluding that morphology is essential for agglutinative languages to obtain good performance in coreference resolution. Finally, an error analysis of the coreference resolution system is presented which have revealed our system's weak points and help to determine the improvements of the system. As a result of the error analysis, we have enriched the Basque coreference resolution adding new two sieves, obtaining an improvement of 0.24 points in CoNLL F1 when automatic mentions are used and of 0.39 points when the gold mentions are provided.
Enhanced Ant Colony Algorithm for Best Features Selection for a Decision Tree Classification of Medical Data

Classification algorithms are widely applied in medical domain to classify the data for diagnosis. The datasets have considerable irrelevant attributes. Diagnosis of the diseases is costly because many tests are required to predict a disease. Feature selection is one of the significant tasks of the preprocessing phase for the data. It can extract a subset of attributes from a large set and exclude redundant, irrelevant, or noisy attributes. The authors can decrease the cost of diagnosis by avoiding numerous tests by selection of features, which are important for prediction of disease. Applied to the task of supervised classification, the authors construct a robust learning model for disease prediction. The search for a subset of features is an NP-hard problem, which can be solved by the metaheuristics. In this chapter, a wrapper approach by hybridization between ant colony algorithm and adaboost with decision trees to ameliorate the classification is proposed. The authors use an enhanced global pheromone updating rule. With the experimental results, this approach gives good results.
Semantic Network Modeling

A method of developing of information systematization of the subject domain of automated systems for technical preparation of production is proposed. A network semantic model of the knowledge base of a machine-building enterprise is formed taking into account all possible operations. To solve the problem of integrating computer-aided design, managing technological and organizational-economic processes, the concept of localized surfaces is used, which are the main elements of the information structure of an enterprise and combine five types of relationships: material, dimensional, temporal, informational and economic properties.
Facial Landmark Detection for Expression Analysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Hyper-spherical Reservoirs for Echo State Networks

In this paper, we propose a model of ESNs that eliminates critical dependence on hyper-parameters, resulting in networks that provably cannot enter a chaotic regime and, at the same time, denotes nonlinear behaviour in phase space characterised by a large memory of past inputs, comparable to the one of linear networks. Our contribution is supported by experiments corroborating our theoretical findings, showing that the proposed model displays dynamics that are rich-enough to approximate many common nonlinear systems used for benchmarking.
SandExplorer

In this paper, we introduce SandExplorer: a device to physically explore geospatial data by creating a 2.5-dimensional, topographic sand sculpture. This sculpture is created semi-manually, by moving a handle over a sandbox, which serves as a plotting canvas. The sandbox' shape represents the geospatial region the data is about. The amount of sand flowing from the system's sand reservoir through the handle is regulated by a mapping algorithm, depending on the underlying data. With SandExplorer, users can playfully create a sand-based data 'physicalization'.
Plugging Brake System as a Hill Descend Control for Electric Powered Wheelchair: Experimental Analysis

The common brake system for the electric powered wheelchair (EPW) is the mechanical brake system. Although the mechanical brake system is compact, the brake performance is depending on the user's hand grip. During descending on the slope, due to the gravitational force, the possibility of EPW to accelerate is high. In this study, by using the plugging brake system strategy, the hill descend control is proposed to control the speed of EPW. The plugging brake system will maintain the speed of EPW same as the desired speed that set by the user. In this plugging brake control system, the PID control is applied to minimize the error between the actual and desired speed. The experiment is performed on the slope based on the requirement from the American Disability Act (ADA) with maximum angle of 8.1 degrees and the slope length is 5.4 m. From the experimental results, the speed of EPW is maintaining at the desired speed during descending on the slope. These results prove the plugging brake control system is suitable for hill descend control.
Construction of M-Subsequence and its Properties

m-subsequence, generated by increasing a few units of logic based on the realization of m-sequence hardware, change the order of output of m-sequence by selecting the four points or more of the cross joins, is non-linear sequence, has great randomness, large quantity, very stable linear complexity, and can be applied to the field of communications.
Automatic Ontology Construction

World wide web has information resources even on unthinkable subjects. This information may be available instantly to anyone having Internet connection. This web is growing exponentially, and it is becoming difficult to locate useful information in such a sheer volume of information. Semantic web extends the current web by emphasizing on interoperable ontologies which are capable of processing high quality information so that the agents placed on top of semantic web can automate the work or curate the content for the user. In this chapter, an extensive research in the area of ontology construction is presented, and after having a critical look over the work done in this field and considering the limitation of each, it has been observed that constructing ontology automatically is a challenging task as this task faces difficulties due to unstructured text and ambiguities in English text. In this work an ontology generation technique is devised covering all important aspects missing in the existing works giving better performance as compared to another system.
A Scene Text Detection Algorithm based on ResNet and Faster R-CNN

Text detection in natural scenes has become a research hotspot due to the continuous development of target detection technology. However, because of the influence of a scene text itself and external environmental factors, the conventional detection algorithm is not very effective. To solve the above problems, we propose in this paper a novel natural scene text detection algorithm based on the Faster R-CNN framework. We utilized a deep ResNet network instead of the VGG-16 network to extract, to a certain extent, more feature maps and improve the accuracy of the detection model. For candidate frames of different scales, we used different scales of pooling processing and classified the candidate boxes by cascading pooling features to achieve accurate positioning of the text. Finally, the Soft-NMS algorithm was used to post-process the region candidate frame. This method only attenuated the detection score of the non-maximum detection frame instead of deleting it, which effectively reduced the NMS threshold selection present in the conventional NMS algorithm. Consequently, false detection and missed detection were considerably reduced, thereby improving the accuracy of the text detection system. Moreover, experimental results revealed that our algorithm has significant advantages in F-value and detection accuracy over other widely used algorithms.
Graph-Boosted Attentive Network for Semantic Body Parsing

Human body parsing remains a challenging problem in natural scenes due to multi-instance and inter-part semantic confusions as well as occlusions. This paper proposes a novel approach to decomposing multiple human bodies into semantic part regions in unconstrained environments. Specifically we propose a convolutional neural network (CNN) architecture which comprises of novel semantic and contour attention mechanisms across feature hierarchy to resolve the semantic ambiguities and boundary localization issues related to semantic body parsing. We further propose to encode estimated pose as higher-level contextual information which is combined with local semantic cues in a novel graphical model in a principled manner. In this proposed model, the lower-level semantic cues can be recursively updated by propagating higher-level contextual information from estimated pose and vice versa across the graph, so as to alleviate erroneous pose information and pixel level predictions. We further propose an optimization technique to efficiently derive the solutions. Our proposed method achieves the state-of-art results on the challenging Pascal Person-Part dataset.