Reviewerly/exHarmony huggingface dataset:
GSPSO-LRF-ELM: Grid Search and Particle Swarm Optimization-Based Local Receptive Field-Enabled Extreme Learning Machine for Surface Defects Detection and Classification on the Magnetic Tiles

Machine vision-based surface defect detection and classification have always been the hot research topics in Artificial Intelligence. However, existing work focuses mainly on the detection rather than the classification. In this article, we propose GSPSO-LRF-ELM that is the grid search (GS) and the particle swarm optimization- (PSO-) based local receptive field-enabled extreme learning machine (ELM-LRF) for the detection and classification of the surface defects on the magnetic tiles. In the ELM-LRF classifier, the balance parameter C and the number of feature maps K via the GS algorithm and the initial weight A init via the PSO algorithm are optimized to improve the performance of the classifier. The images used in the experiments are from the dataset collected by Institute of Automation, Chinese Academy of Sciences. The experiment results show that the proposed algorithm can achieve 96.36% accuracy of the classification, which has significantly outperformed several state-of-the-art approaches.
Zero-Time Windowing Cepstral Coefficients for Dialect Classification

In this paper, we propose to use novel acoustic features, namely zero-time windowing cepstral coefficients (ZTWCC) for dialect classification.ZTWCC features are derived from high resolution spectrum obtained with zero-time windowing (ZTW) method, and were shown to be useful for discriminating speech sound characteristics effectively as compared to a DFT spectrum.Our proposed system is based on i-vectors trained on static and shifted delta coefficients of ZTWCC.The i-vectors are further whitened before classification.The proposed system is compared with i-vector baseline system trained on Mel frequency cepstral coefficient (MFCC) features.Classification results on STYRIALECT database (German) and UT-Podcast (English) database revealed that the system with proposed features outperformed aforementioned baseline system.Our detailed experimental analysis on dialect classification shows that the i-vector system can indeed exploit high spectral resolution of ZTWCC and hence performed better than MFCC features based system.
GLDH: Toward more efficient global low-density locality-sensitive hashing for high dimensions

Despite decades of intensive effort, the current solutions for efficiently searching high-dimensional data spaces are not entirely satisfactory. This paper proposes a more efficient global low-density locality sensitive hashing search algorithm (GLDH) based on the minimal cut hyperplane and ensemble learning. The innovation is that a novel global low-density hyperplane candidate set is constructed by the graph cut method, the minimum information gain method and random maximum entropy method are used to greedily select the hyperplane, and the ensemble learning method is used to query the global approximate nearest-neighbors data. This paper proves that the GLDH algorithm produces a low error hyperplane partition. The results of extensive experiments show that the proposed GLDH method performs better than the latest methods when using the same hash coding length for datasets from different fields.
A Study of Bug Management Using the Stack Exchange Question and Answering Platform

Traditional bug management systems, like Bugzilla, are widely used in open source and commercial projects. Stack Exchange uses its online question and answer (Q&A) platform to collect and manage bugs, which brings several new unique features that are not offered in traditional bug management systems. Users can edit bug reports, use different communication channels, and vote on bug reports, answers, and their associated comments. Understanding how these features manage bug reports can provide insights to the designers of traditional bug management systems, like whether a feature should be introduced? and how would users leverage such a feature? We performed a large-scale analysis of 19,151 bug reports of the bug management system of Stack Exchange and studied the in-place editing, the answering and commenting, and the voting features. We find that: 1) The three features are used actively. 2) 57 percent of the edits improved the quality of bug reports. 3) Commenting provides a channel for discussing bug-related information, while answering offers a channel for explaining the causes of a bug and bug-fix information. 4) Downvotes are made due to the disagreement of the reported “bug” being a real bug and the low quality of bug reports. Based on our findings, we provide suggestions for traditional bug management systems.
Online Tracking and Relocation Based on a New Rotation-Invariant Haar-Like Statistical Descriptor in Endoscopic Examination

In the gastrointestinal biopsy, online tracking and relocation of the region-of-interest are essential to early diagnosis and surgical intervention of colorectal cancer. However, it is challenging for the examiner to track and retarget the optical biopsy site due to interfering factors, e.g. violent rotation of the lens, illumination variation, shape deformation, and target long-time-lost. Previous works may not effectively handle the mentioned challenges due to the complexity of gastrointestinal environment and the limitation of data. In this work, we construct an online tracking and relocation framework based on the concept of detection and tracking, which is dramatically adapted to the inherent characteristics of the gastrointestinal biopsy image. To effectively distinguish the target area from the gastrointestinal biopsy, we designed a new rotated invariant Haar-like statistical descriptor which is robust for rotating and illumination changes. The descriptor is based on the sector-ring difference under the circular sampling area. A simplified statistical random forest discriminator based on confidence statistics is proposed to complete the preliminary screening of the potential tracking target. In order to further estimate the location of the target, a supervised support vector machine is introduced to rank the candidate target regions. Based on proposals of Siamese network and the random forest, a location refinement fusion has been proposed to determine the location and the confidence of the tracking area. Extensive experiments on various gastrointestinal videos, which consists of open source and self-collected data, demonstrate that the proposed framework is superior to the mainstreams methods in accuracy and robustness.
The Geo-accumulation Index of Some Heavy Metals in the Roadsides Soils of Onitsha South Local Government Area Anambra State, Nigeria

Heavy metals have a great ecological significance due to their toxicity and accumulative behaviour. Monitoring of their concentrations in the soils of our environment is very important. The geo-accumulation index (Igeo) in 11 stations in Onitsha South Local Government Area, (<i>i.e</i> Modeebelu Avenue, Ozomagana, Zik Avenue, Port Harcourt Road, Oguta Road, Iweka Road, Venn Road, Niger Street, Creek /Okwaraji Moore Street and Bida Road) were analysed. The soil pollution status of the area was investigated by analyzing the concentration of eight heavy metals (Mn, Ni, Zn, Cd, Cr, Fe, Cu and Pb) using Atomic Absorption spectroscopy (AAS). From the geo-accumulation index (I<sub>geo</sub>) results obtained were; Mn values were mainly in class 5 for wet season and class 4 in dry season, Ni fell within the range of classes 2-3 in both seasons, Zn was in the range of classes 1-2 while Cr fell within the ranges of class 4-5 for both seasons. Cu was mainly within the range of class 2-3 in both seasons, while Pb fell within class 0 (with the exception of few locations). The Fe geo-accumulation for all locations was within the class of 0. These results revealed that Fe, Cd and Pb did not pollute the area but were polluted by Mn, Cr, Zn and Ni at various levels in both seasons.
Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video

Histogram shifting (HS) has been proved to be a great success in reversible data hiding (RDH). To reduce the quality loss of marked media and the increase in file size, several two-dimensional (2D) HS schemes based on the characteristics of cover media have been proposed recently. However, our analysis shows that the embedding strategies used in these methods can be further optimized. In this paper, two new 2D HS schemes for RDH in H.264/AVC video are developed, one of which uses the DCT coefficient pairs with both values 0 and the other does not. The embedding efficiency of a DCT coefficient pair in different embedding modes is firstly calculated. Then, based on the obtained embedding efficiency along with the statistical distribution of DCT coefficient pairs, two better embedding strategies are proposed. The secret data is finally embedded into the pairs of DCT coefficients of the middle and high frequencies using our proposed strategies. The comparison experiment results demonstrate that our schemes can achieve enhanced visual quality in terms of PSNR, SSIM, and entropy in most cases, and the increase in file size is smaller.
Professional Competencies of a Higher Education Teacher in an Information Educational Environment

The digitalization of all spheres of modern society has led to the widespread use of information and communication technologies in the professional activities of university teachers, which is currently one of the priorities for modernizing Russian education.In this regard, the role of purposeful training and advanced training, which should be based on a thorough knowledge of the whole system of the main factors in the informatization of education, has sharply increased in the system of higher education.This is due to the fact that at present the activities of a teacher of higher education include both activities to build competencies among students and activities to create educational resources placed in the information educational environment, new ways of interacting with students, and the use of information technologies in scientific and research work.The article considers the psychological and pedagogical aspects of the professional activity of a teacher of higher education and advanced training.The article highlights the experience of the Russian regional university in building a system of continuing education for a university teacher in the field of information and communication technologies in professional pedagogical activity in the conditions of an information educational environment.On the basis of an empirical study, in which 1003 teachers of various faculties have taken part (a representative sample was 300 people), it is concluded that targeted training in the field of education informatization affects the motivation and sustained interest in the use of information and communication technologies significantly.At the same time, a high level of cognitive and activity components can be formed only if there is a combination of continuing education courses, high motivation, independent knowledge acquisition in this area, as well as individual additional consultations and targeted support of motivation from the administration.
A framework to alleviate common problems from recommender system : A case study for technical course recommendation

Abstract Recommender systems face multiple challenges like cold-start, sparsity, first-rater and scalability. This study proposes an ontology based framework which can resolve these challenges simultaneously. The Ontology is prepared using protégé and visualized online using Graphical Ontology Editor OWLGrEd .Ontology is trust based where degree of trust is the ratings given by users. The framework is used for technical course recommendation. To eliminate cold start, user will be provided a form with top ‘k’ factors. These top “k” factors are the significant factors obtained as a result of partial correlation analysis. The selection criterion was level of significance <0.05.The recommendation will be done on the basis of the ratings user provides to these “k” factors. Sparsity and First-rater is handled by using the Knowledge of trust from the knowledge base made from trust base ontology and provides the recommendation. The proposed framework is scalable and is implemented using offline forms. The precision rate of the proposed system is more than 95% for 3 algorithms out of total 6 implemented algorithms. The only limitation of the proposed framework is that it is domain specific because it is based on ontology.
Intelligent driving system of robot based on computer vision and neural network algorithm

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
Real-time Illumination Estimation for Mixed Reality on Mobile Devices

We present a lightweight lighting estimation method for the purpose of realistic mixed reality (MR) on mobile devices. Given a single RGB image, our method estimates the environment lighting and renders the virtual object in real-time. While previous works tackled this problem by reconstructing the complicated high dynamic range (HDR) environment maps, our deep neural network directly infers the corresponding spherical harmonics in extensive environments. Compared to previous approaches, our method is more robust and efficient as it works for both indoor and outdoor scenes in real-time. Experiments show that our approach achieves realistic rendering in various MR scenarios.
Hyperparameter tuning methods in automated machine learning

In this paper, we make a comprehensive review of the challenging task of hyperparameter optimization in automated machine learning. The commonly used hyperparameter optimization methods include single-shot sampling strategies, e.g., grid search, random search and sequential strategies where new trials are gradually augmented based on existing information, including Bayesian optimization, evolutionary algorithms, reinforcement learning-based methods.We find the sequential number-theoretic optimization (SNTO) algorithm proposed by Kai-Tai Fang and Yuan Wang in 1990 can also be applied in hyperparameter optimization, where sequential uniform designs are utilized to search the global optima in complex response surfaces. For illustration, various hyperparameter optimization methods are tested with two widely-used machine learning models including the support vector machine (SVM) and extreme gradient boosting (XGBoost), on two classical binary classification datasets. By analyzing the experimental results, we find a modified SNTO algorithm is quite promising in the hyperparameter optimization task.
Optimal Path Planning for Drone Inspections of Linear Infrastructures

Autonomous Beyond Visual Line of Sight (BVLOS) flights represent a huge opportunity in the drone industry due to their ability to monitor larger areas.Autonomous navigation and path planning are essential capabilities for BVLOS flights.In this paper, we introduce the routing component of a path planning system for inspecting linear infrastructures.We explore both a direct algorithm and a transformation algorithm.The direct algorithm is an extension of A* to allow limited routing through air as well as the use of non-logic intersections.The transformation algorithm pre-computes a graph that include edges for routing through air and nodes for non-logic intersections.We implemented both algorithms for routing along a particular type of linear infrastructure, power lines, and validated them through an empirical evaluation at three different scales: the Danish power grid, the French power grid, and the entire European power grid.The test results show that the transformation algorithm allows for sub-second routing performance for a small-to-medium sized power grid.Larger power grids can be routed in less than five seconds, and even an optimal route of more than six thousand kilometers along linear infrastructures from Portugal to Sweden via Russia is found in less than half a minute.All algorithms have been implemented and are available as an open-source Python package for Linear-infrastructure Mission Control (LiMiC).
Color image steganalysis based on embedding change probabilities in differential channels

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.
Realistic simulation of quantum computation using unitary and measurement channels

The implementation and practicality of quantum algorithms highly hinge on the quality of operations within a quantum processor. Therefore, including realistic error models in quantum computing simulation platforms is crucial for testing these algorithms. Existing classical simulation techniques of quantum information processing devices exhibit a trade-off between scalability (number of qubits that can be simulated) and accuracy (how close the simulation is to the target error model). In this paper, we introduce a new simulation approach that relies on approximating the density matrix evolution by a stochastic sum of unitary and measurement channels within a pure state simulation environment. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known stochastic approaches while allowing to simulate a larger number of qubits compared to the exact density matrix simulation. Furthermore, we used this approach to realistically simulate the Grover's algorithm and the surface code 17 using gate set tomography characterization of quantum operations as a noise model.
Domain-Oriented Topic Discovery Based on Features Extraction and Topic Clustering

Topic detection technology can automatically discover new topics on the Internet. This paper investigates domain-oriented feature extraction methods, and proposes a keyword feature extraction method ITFIDF-LP, a subject word feature extraction method LDA-SLP and a topic clustering model based on vector product similarity. A novel Domain-oriented Topic Discovery based on Features Extraction and Topic Clustering (DTD-FETC) model is proposed to analyze open source web of a domain and identify emerging topics in the domain in real time. This article describes a DTD-FETC system built for cyber security domain. It filters and aggregates web for specical security threat topics such as vulnerability and malware, and helps security staff respond quickly and defends against the emerging cyber threats as early as possible. The recall rate, accuracy and F1 value results of the DTD-FETC method applied to the cyber security dataset are all above 0.99.
Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.
Application of Data Mining Methods in Internet of Things Technology for the Translation Systems in Traditional Ethnic Books

In order to translate the ethnic classics, based on the research on the Internet of things, machine learning, and translation technology of ethnic classics, the log-linear model is combined with the national corpus scale and the grammatical structure characteristics, and the phrase statistical machine translation is used to establish a discontinuous phrase extraction model. Then, the translation technology is studied from the three aspects of model definition, training, and decoding. Finally, the algorithm is compared with the traditional phrase extraction algorithm to verify its effectiveness. The results show that the extraction number of discontinuous phrase extraction model is significantly higher than that of traditional phrase extraction model, and the model can extract more phrases, handle larger and more complex text, and score higher in translation fluency. From the evaluation indexes scores of Bilingual Evaluation Understudy (B.L.E.U.) and National Institute of Standards and Technology (N.I.S.T.), it can be found that the B.L.E.U. and N.I.S.T. values of the traditional phrase extraction algorithm are lower than those of the discontinuous phrase extraction model algorithm. The discontinuous phrase extraction algorithm can not only extract the regular continuous phrase, but also obtain the discontinuous text, and the translation effect is better. In conclusion, the combination of Internet of things and machine learning can be used in the translation of ethnic classics to achieve high-quality translation of discontinuous phrases, which is of guiding significance for the study of machine translation.
Integrating Educational Design Research and Design Thinking to Enable Creative Pedagogies

&#x0D; &#x0D; &#x0D; This paper explores the interrelationship between educational design research, and design thinking that guides the design stage, enabling the design of authentic collaborative mobile learning environments. As an example the article outlines the design thinking principles and processes that informed the development of wireless mobile presentation systems (MOAs) designed to create a flexible infrastructure to enable the exploration of new pedagogies in different educational contexts. The project used design thinking within an educational design research methodology to provide an in house solution to creating a supporting infrastructure to enable the implementation of a new framework for creative pedagogies and curriculum redesign. The article reflects upon example implementations of using mobile social media and MOAs as a catalyst for implementing our framework for creative pedagogies, and propose collaborative curriculum design principles for integrating the use of mobile social media within new pedagogical paradigms.&#x0D; &#x0D; &#x0D;