Reviewerly/exHarmony huggingface dataset:
Classification of Natural Flower Videos Through Sequential Keyframe Selection Using SIFT and DCNN

This paper presents an algorithmic model for automatic selection of keyframes for the classification of natural flower videos. For keyframe selection Scale Invariant Feature Transform and Discrete Cosine Transform are recommended. The selected keyframes are further used for classification process. To extract the features from the selected keyframs Deep Convolutional Neural Network (DCNN) is used as a feature extractor and for classification of flower videos Multiclass Support Vector Machine (MSVM) is applied. For experimentation, we have created dataset of natural flower videos consisting of 1825 flower videos of 20 different classes. Experimental results show that the proposed keyframe selection algorithm gives good compression ratio and the proposed classification system generates good classification accuracy.
White balancing, quad-tree decomposition, and L1 minimization-based single-image dehazing

We present white balancing, quad-tree decomposition, and <italic>L</italic><sub>1</sub> minimization-based dehazing technique. Atmospheric light is estimated through quad-tree decomposition while holistic transmission map is estimated through coarse-scale net. Coarse and fine layers are refined through fusion and a luminance model is used to preserve details and edges. The final dehazed image is refined using <italic>L</italic><sub>1</sub> minimization. Simulation results when equated with existing techniques, verify the effectiveness of the proposed technique.
Recognition of Visual Objects Based on Statistical Distributions for Blocks of Structural Description of Image

In this paper, we investigate a problem of recognizing visual objects in computer vision systems with the use of a feature space as a set of key image points. The set of key point descriptors is determined by binary detectors ORB, BRISK, AKAZE. In order to extract from the existing description the more detailed information of distinguishing objects and reducing the volume of computing compared with the procedure of voting descriptors, it is proposed to form a new system of attributes for the structural description of the object on the basis of the preliminary formation of statistical distributions for the system of fragments description of the image. On the basis of cross-correlation distribution processing for a case of a one-bit size of blocks of binary descriptors, a new system of integrated features was constructed for efficiently calculating the value of the relevance of descriptions in the process of object recognition. By means of software modeling, an experimental evaluation of the time of calculating the relevance value in the built feature space is performed in comparison with the use of acceptable distribution cases and the tradition-based approach of voting for a set of descriptors. Experiments have shown that the implemented block system creates a representative feature space, in which images with the close structure actually demonstrate similarity. The effectiveness of the proposed statistical system of attributes in terms of a significant increase in the rate of performance with an adequate level of recognition quality is confirmed.
LaLo-Check: A Path Optimization Framework for Sampling-Based Motion Planning With Tree Structure

Motion planning is a basic problem in many areas, such as robotics, computer games, and animation. It is a challenge to generate a better solution in less time. Line-of-Sight (LoS) is the straight path between two points, and LoS-Check is often used as a path shortcutting method. Lazy evaluation is a successful strategy in reducing the amount of collision detection, which accelerates the motion planning algorithms, especially in high-dimensional spaces. In this paper, we present Lazy LoS-Check (LaLo-Check), which employs a lazy evaluation strategy with the help of a lower bound technique to delay the LoS-Check until it is necessary. The lower bound technique only accepts the states which could provide a better solution, and it is commonly applied in the sample rejection and candidate path selection. Actually, LaLo-Check can be considered as a general path optimization framework, which could be used in most tree-structure motion planners. We choose three representative sampling-based motion planning algorithms, RRT*, Informed RRT*, and BIT*, to evaluate the performance of LaLo-Check. The experimental results show that the new planners which employ the LaLo-Check could find better solutions than the original algorithms within the equivalent time.
Density-based clustering techniques for object detection and peak segmentation in expanding data fields

In the context of data analytics and signal processing applications, clustering techniques have always played an essential role when it comes to structuring, contextualizing, and analyzing data. The ubiquitous and growing availability of data in novel IoT and big data applications does not only challenge the analysis techniques itself by providing an immense volume of data sets. Together with the persistent connectivity and real-time availability of this data, new requirements for an efficient handling of this continuous growth of analyzed data are coming up. Density-based clustering techniques form a well-known class of algorithms suitable for multidimensional local cluster detection. In this chapter, the reader is first introduced to the formal definitions and practical implications of these techniques. Building on this, the challenges imposed by time-varying data are pointed out in the special case of lattice-based data. Taking these requirements into account, a novel clustering approach and strategy is introduced that is especially suitable for continuously acquired sensor data, such image or audio data. This type of data is especially present with IoT applications, such as smart and connected sensing devices in all kinds of environments. The proposed approach is formalized, discussed, and finally evaluated in terms of algorithmic performance, based on a real-world image processing example.
USEing Transfer Learning in Retrieval of Statistical Data

DSSM-like models showed good results in retrieval of short documents that semantically match the query. However, these models require large collections of click-through data that are not available in some domains. On the other hand, the recent advances in NLP demonstrated the possibility to fine-tune language models and models trained on one set of tasks to achieve a state of the art results on a multitude of other tasks or to get competitive results using much smaller training sets. Following this trend, we combined DSSM-like architecture with USE (Universal Sentence Encoder) and BERT (Bidirectional Encoder Representations from Transformers) models in order to be able to fine-tune them on a small amount of click-through data and use them for information retrieval. This approach allowed us to significantly improve our search engine for statistical data.
Optimization of Production Scheduling and Actual Stock Using Fuzzy Genetic Algorithm

Abstract In this case, fuzzy is applied as a determination optimization of actual stock. Based on the research that has been done, solving the problem of scheduling and actual stock optimization with Fuzzy Genetic Algorithm method has better performance than non-fuzzy Genetic Algorithm method. Production scheduling with Fuzzy Genetic Algorithm shows faster process result. Seen from the iteration that occurs is 1 (one) for scheduling with Fuzzy while on non fuzzy production scheduling, the process is completed in the iteration to 34. Scheduling with Fuzzy also shows better optimum percentage that is with 100% average while in non fuzzy production scheduling is 97%.
Mobile Phones for Assessment

This chapter focuses on using mobile assessment (m-assessment) for teaching and learning in formal and informal education settings. M-assessment has been helpful in making traditional classrooms more interactive and support the individual and class cognitive wealth. Unique affordances of m-assessment can also extend its benefits to digital classrooms, informal and formal learning settings, professional development settings, and other places where learning happens. M-assessment has increased accessibility to assessment, making it available anywhere, anytime, and to anyone.
Seeking Cognitive Convergence: Small Group Collaborative Visualization in the Library Learning Commons

In order to generate a hybrid and ubiquitous approach to integrating multiple visualization modalities within spatial settings, it is essential to understand how small group users select and adapt technology and spatial settings to generate co-located visualization. Insights are needed to guide the design of spatial-visualization hybrid technologies that are ubiquitous, and user intuitive to adjustable to group size as scalable displays interfaces. This study examines the constructed visualization formats of small groups in a library learning commons to understand the visual and social affordances of group generated visualization formats.
Information support of teaching process in the sparsely populated areas of the Russian Arctic

Currently, one of the priority initiatives for the development of the Russian Federation is the development of its Arctic zone. However, this initiative has its own characteristics, which must be considered. This paper studies the existing problems of educational development in the Arctic zone of the Russian Federation and proposes ways to solve them. Based on the analysis carried out, it proposes the use of information technologies to organize the most effective educational process. In this paper, the business processes of the proposed training methodology using distance learning systems was described and conclusions were made about the advisability of using information technology. As a successful example of the implementation of information technology, the work of the network engineering and technical school is presented.
FeatureBand: A Feature Selection Method by Combining Early Stopping and Genetic Local Search

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45$$\times $$ to 17.6$$\times $$ faster than the state-of-the-art wrapper-based methods without accuracy loss.
Polynomial Topic Distribution with Topic Modeling for Generic Labeling

Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with words to select the most relevant labels for topics. Compared to WUP similarity topic labeling system, our methodology is simpler, more effective, and obtains better topic labels.
Human-Machine Interaction

Usually, courses are given on human-computer interaction, while in recent years there was a major shift towards (mobile) devices and machines, with new human interfaces. Of course, they include embedded computers and software, but their interaction with users poses many new challenges and offers new solutions. This tutorial shows manifold usability problems as observed by the presenter in daily life, beyond those usually known from graphical user interfaces (GUIs) of traditional PCs (including laptop computers). It explains them by human factors usually unknown to embedded engineers and motivates user experience. User-centered and Usage-centered Design are compared with the result that they typically overlap but have a different focus each on Interaction Design. Usability Test and Usability Study are explained and contrasted as well. In addition, this tutorial explains key properties of Multimodal Interfaces and UIs of Mobile Devices. Finally, it culminates in a sketch of specific challenges of Human-Robot Interaction.
Comparative Analysis of Flight Search Representations

Visualization has been extensively used in almost all the fields, but unfortunately, there is very little work on the visualization of flight search. The literature discusses only two representations: textual representation and linear representation. In this study, the authors introduce a new form of visualization called circular representation and compare it with the existing visualization techniques. The authors found that each representation has its own merits and demerits. More specifically, they found that users were able to find required flight circular efficiently when using circular representation, while most errors were committed by the user when using textual representation, and users ranked circular representation low in terms of qualitative satisfaction.
Final Year Students' Approaches to Implementing Complex Distributed Systems

Understanding how final year students build complex software systems is critical for determining whether desired graduate outcomes have been met, for identifying curriculum gaps, and for designing scaffolding and support structures. A large body of work focuses on the programming strategies employed by novice programmers, with few existing research in understanding programming strategies and development focus of final year students, in particular with respect to non-functional requirements. In this paper, we analyse consecutive revisions of 77 students across two cohorts that implemented a large and complex Distributed Systems assignment with several non-functional requirements. To obtain a qualitative overview of the students' approach to software development, we manually read and tagged all sourcefiles in all assignment revisions with specific development focus categories. Our analysis identifies how the students' development focus evolves throughout the assignment timeline. We visualise the software development process and identify several areas that require further support.
Robust image registration using subspace method in Radon transform domain

Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.
Object Recognition and Localization Base on Binocular Vision

Autonomous grasping of manipulator is a challenging problem, due to the difficulties and accuracy of object recognition and localization. This paper presents a binocular vision-based approach, which enables to recognize and locate object accurately with a binocular. This approach, which is based on the limitation of the application of the traditional manipulator, aims to achieve autonomous grasp of manipulator. The main contribution of this paper is to construct the recognition and localization system. Firstly, the left camera of the binocular is used to collect the image information, and the TensorFlow is used to imply the ResNet to build the recognition system; Then the principle of the ranging of the binocular is used for locating the target object; Finally, tests have been performed in three different indoor environments to achieve autonomous grasping of manipulator. The proposed object recognition and localization approach is testified by successfully autonomous grasping of manipulator, and experimental results show that the accuracy reaches 89.83% averagely.
3D Surface Splicing Based on Principal Component Feature Extraction

In order to achieve automatic splicing for complex 3D surfaces, the parametric surface representation method for fractured surfaces of fragmented objects with thickness was analyzed and then a surface splicing method based on eigenvector of mixed subscript was proposed in this paper. In this method, geometric features of a surface were extracted based on principal components and directed subscripts and undirected subscripts were adopted to form mixed eigenvectors, and then the matching relationship between two surfaces was determined based on two similarity judgments. This method effectively lowered surface splicing error. Surface splicing experiments of broken cultural relic fragments verified the feasibility and effectiveness of this method.
The statistical eigenvector analysis technique (SEAT) for dip data analysis

Dip data acquired from image logs (i.e. the dip/azimuth of planar features) are extensively used in the oil and gas industry to assist in interpreting subsurface geology. Gradual and/or abrupt variations in dip data with depth may indicate different structural features (e.g. faults, folds, etc.) or changes to sedimentary systems (e.g. unconformity). The statistical eigenvector analysis technique (SEAT) and accompanying computer application are useful in statistically calculating the orientation and geometry of subsurface structures from dip data. Poles to planes on a spherical projection over a given depth interval may reveal cluster or girdle distributions, which can be interactively analyzed using statistical distribution functions on the unit sphere. SEAT provides a mathematical solution for as few as two data points, and has the potential to be applied to dip data at the meter-scale. The orientation and geometry of various subsurface geological features (e.g. folds, fault drag deformation, etc.) can be interactively calculated over a chosen depth interval. SEAT is applied to a dip dataset acquired from a modern, 8-pad electronic borehole image log and a previously studied dipmeter data from the discovery well of the Railroad Gap Field to illustrate its higher accuracy in determining the orientation and geometry of subsurface structures compared to older methods. SEAT allows for a vastly improved interpretation of subsurface geological features when combined with high-resolution borehole image logs and accurate dip data. Improved understanding of subsurface geometries may allow for better business decisions in future exploration and appraisal of existing assets.
The Excellent Center: Center for Artificial Intelligence Technology

Abstract To overcome the slow development of knowledge due to the weakness of knowledge management, innovation about organization is done by establishing a center of excellence related to the importance of that knowledge developed. However, without objective derivation and target establishment, such an organization will not achieve its excellence. This paper aims to describe the center of excellence in science, with the example of a review about Center for Artificial Intelligence Technology and with showing a number of measurements that can be done from it.
Quantum Resistant Public Key Encryption Scheme HermitianRLCE

Recently, Wang (2017) introduced a random linear code based quantum resistant public key encryption scheme RLCE which is a variant of McEliece encryption scheme. Wang (2017) analyzed an instantiation of RLCE scheme using Generalized Reed-Solomon codes. In this paper, we introduce and analyze Hermitian code based RLCE schemes HermitianRLCE. Based on our security analysis, we provide HermitianRLCE parameters at the 128, 192, and 256 bits security level. These parameters show that HermitianRLCE has much smaller public keys than GRS-RLCE.
Local Expansion Invariant Operators in Argumentation Semantics

We study invariant local expansion operators for conflict-free and admissible sets in Abstract Argumentation Frameworks (AFs). Such operators are directly applied on AFs, and are invariant with respect to a chosen ‘semantics’ (that is, w.r.t. each of the conflict-free/admissible sets of arguments). Accordingly, we derive a definition of robustness for AFs in terms of the number of times such operators can be applied without producing any change in the chosen semantics.
Folkmun.se: A Study of a User-Generated Dictionary of Swedish

Abstract This study examines the Swedish user-generated web dictionary Folkmun.se, encompassing roughly 5,000 entries. Initially a general overview of the website is presented, followed by an analysis of how the content of Folkmun.se has developed, with particular focus on 190 usernames. The contributors can be grouped together based on two distinct factors: 1) by number of contributions, and 2) by the kind of words they prefer to submit. One conclusion of this study is that a great majority of contributors only publish one or two entries. This entails that a large number of users have contributed to the dictionary. Furthermore, a majority chooses to focus on dialect words or slang words. Many of these entries are not represented in traditional Swedish dictionaries. The advantages of having people of different backgrounds and skills working with word collections are obvious, and their work is an important, albeit often neglected, contribution to general linguistics.
Building Stakeholder Trust in Internet of Things (IoT) Data Services using Information Service Level Agreements (SLAs)

A Service Level Agreement (SLA) defines a contract between network service providers and consumers, specifying the terms of a service which providers will make available and the conditions which consumers will accept. To date, SLAs have been specified using basic terms, such as availability and network performance, with a consumer being compensated in the event that the service provided does not meet the terms agreed. Given changes in the ways which network services are now made available, however, SLA terms are changing to capture both the differences in service provision and, additionally, in the responsibilities of the parties involved. It is this aspect of information SLAs which we respond to in this work, and we propose a SLA model which accommodates the requirements of these new relationships. We also propose a set of metrics, a selection of which are presented in this paper to demonstrate our concept, and recommend that a selection can be adapted by consumers. Finally, due to the intricate relationships between data consumers and data providers in the IoT environment and the fact that metric adaptation may lead to SLA violation, we discuss SLA conflict resolution through prioritizing non-functional metrics on a per-customer basis.