Reviewerly/exHarmony huggingface dataset:
Approach to Extract Keywords and Keyphrases of Text Resources and Documents in the Kazakh Language

In this paper authors propose a hybrid approach for extracting keywords and keyphrases of text resources and documents in Kazakh. Direct application of the statistical method tf-idf is not the optimal solution to the question of extracting keywords and phrases in the Kazakh language, since the Kazakh language is an agglutinative type of language. The authors developed and used the stemming algorithm in the pre-processing process taking into account the grammatical features of the Kazakh language. In the extraction, we also take into account the syntactic feature of the words or phrases using the morphological analyzer of the Kazakh language. During extraction, the restrictions indicated by the authors are observed as well, as not all words may be key words. When choosing keywords or a phrase, their features are considered (for example, some words that are a numeral name in combination with a noun are selected). The extraction of keywords and phrases specifically for the Kazakh language is an urgent task in classification, clustering, abstracting the text, and searching the information. The results of the research indicate that the presented approach is the best solution on extracting keywords and phrases from texts in the Kazakh language.
Content analytics based on random forest classification technique: An empirical evaluation using online news dataset

In this paper, a study is established for exploiting a document classification technique for categorizing a set of random online documents. The technique is aimed to assign one or more classes or categories to a document, making it easier to manage and sort. This paper describes an experiment on the proposed method for classifying documents effectively using the decision tree technique. The proposed research framework is a Document Analysis based on the Random Forest Algorithm (DARFA). The proposed framework consists of 5 components, which are (i) Document dataset, (ii) Data Preprocessing, (iii) Document Term Matrix, (iv) Random Forest classification, and (v) Visualization. The proposed classification method can analyze the content of document datasets and classifies documents according to the text content. The proposed framework use algorithms that include TF-IDF and Random Forest algorithm. The outcome of this study benefits as an enhancement to document management procedures like managing documents in daily business operations, consolidating inventory systems, organizing files in databases, and categorizing document folders.
A fast technique for hyper-echoic region separation from brain ultrasound images using patch based thresholding and cubic B-spline based contour smoothing

Ultrasound image guided brain surgery (UGBS) requires an automatic and fast image segmentation method. The level-set and active contour based algorithms have been found to be useful for obtaining topology-independent boundaries between different image regions. But slow convergence limits their use in online US image segmentation. The performance of these algorithms deteriorates on US images because of the intensity inhomogeneity. This paper proposes an effective region-driven method for the segmentation of hyper-echoic (HE) regions suppressing the hypo-echoic and anechoic regions in brain US images. An automatic threshold estimation scheme is developed with a modified Niblack's approach. The separation of the hyper-echoic and non-hyper-echoic (NHE) regions is performed by successively applying patch based intensity thresholding and boundary smoothing. First, a patch based segmentation is performed, which separates roughly the two regions. The patch based approach in this process reduces the effect of intensity heterogeneity within an HE region. An iterative boundary correction step with reducing patch size improves further the regional topology and refines the boundary regions. For avoiding the slope and curvature discontinuities and obtaining distinct boundaries between HE and NHE regions, a cubic B-spline model of curve smoothing is applied. The proposed method is 50–100 times faster than the other level-set based image segmentation algorithms. The segmentation performance and the convergence speed of the proposed method are compared with four other competing level-set based algorithms. The computational results show that the proposed segmentation approach outperforms other level-set based techniques both subjectively and objectively.
Fog Forecast Using WRF Model Output for Solar Energy Applications

The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact of discontinuity, the main drawback of renewable energy sources. Considering that information on discontinuity is crucial to optimize power production estimation and plant management efficiency, in this work, a fog forecast method based on the output of the Weather Research and Forecasting (WRF) numerical model is presented. The areal extension and temporal duration of a fog event are not easy to predict. In fact, there are many physical processes and boundary conditions that cause fog development, such as the synoptic situation, air stability, wind speed, season, aerosol load, orographic influence, humidity and temperature. These make fog formation a complex and rather localized event. Thus, the results of a fog forecast method based on the output variables of the high spatial resolution WRF model strongly depend on the specific site under investigation. In this work, the thresholds are site-specifically designed so that the implemented method can be generalized to other sites after a preliminary meteorological and climatological study. The proposed method is able to predict fog in the 6–30 h interval after the model run start time; it has been evaluated against METeorological Aerodrome Report data relative to seven selected sites, obtaining an average accuracy of 0.96, probability of detection of 0.83, probability of false detection equal to 0.03 and probability of false alarm of 0.18. The output of the proposed fog forecast method can activate (or not) a specific fog postprocessing layer designed to correct the global horizontal irradiance forecasted by the WRF model in order to optimize the forecast of the irradiance reaching the photovoltaic panels surface.
Hybrid Collaborative Filtering with Attention CNN for Web Service Recommendation

Service-oriented computing has significantly affect the software development in Web 2.0 era, computing diagram and architectures based on Web services were comprehensively developed. As the Web services were continuously increasing, it becomes more difficult for users to screen out Web services that meet their needs and with good quality while facing with a large amount of Web Services. Therefore, how to recommend the best Web services for users has become a hot research direction in the domain of service computing. Many machine-learning approaches, especially CF (collaborative filtering) models based on matrix factorization, has been widely used in service recommendation tasks. However, it is tough for CF models to deal with sparse invocation matrix when capturing the complicate interaction relation between mashups and services, which would result in a bad performance. To solve this problem, we proposed a hybrid collaborative filtering with attention CNN model for web service recommendation by combining collaborative filtering and attention CNN. The mashup-service invocation matrix as well as attention-based CNN are seamlessly integrated into deep neural nets, which could be used to capture the complicated mashup-service relationships. The experiment result we gain could validate that our proposed models performs better than several state-of-the-art approaches in service recommendation tasks, and further demonstrate the effectiveness of our models.
Research on the Application of BP Neural Network in Vocal Music Teaching Quality Evaluation

To solve the problem that the evaluation of vocal music teaching is influenced by multiple factors and subjective factors in scoring, BP neural network is proposed to simulate the nonlinear mapping of various factors and establish the vocal music evaluation system. At the same time, aiming at the disadvantage of slow training speed of BP network, a momentum model with adjustment coefficient is put forward, and a new weight adjustment formula is given. The idea of the algorithm and the optimization measures it takes are summarized, the establishment of improved algorithm model is completed, and the steps of network training algorithm are realized. Finally, taking the vocal music evaluation system as the input, BP neural network is used to establish the teaching quality evaluation model. The simulation results show that the improved scheme has the characteristics of fast convergence, strong optimization ability and generalization ability, which effectively weakens the influence of human factors in the determination of index weight.
Detecting Disk Sectors Data Types Using Hidden Markov Model

File carving is process of recovering data without knowledge of file system like recovering files from a formatted disk. Sometime the file systems do not write a file to disk in continues sectors and may split it to more than one chunks. Recovering of such a fragmented file can be difficult because if we found the first chunk of the file, the second chunk can be anywhere on disk. If the disk is large, the search process for finding the second chunk of file will be a time-consuming process. Data type classification help to classify disk sectors based on the type stored on them. Understanding the type of stored data on disk sectors, help to search for a specified file only on area of disk that more likely have the file type we want. In this article we propose an approach to create a hidden markov model that can help classifying disk sector based on their type and detect the point of disk that a fragmentation probably happened. The created hidden markov model classify sectors based on their entropy. The results show 52% of correct data type detection on disks with 512Bytes sector size.
<scp>ASTER</scp> mapping of gypsum deposits of Thumrait region of southern Oman

Abstract This study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. The measurement of spectra over samples of gypsum in the 1,300–2,500 nm wavelength using a PIMA spectrometer showed the presence of distinct absorptions at 1400–1600, 1750, 1940, 2,100, and 2,400 nm characteristics to OH stretching, H 2 O combinations, and SO bending overtones and stretching, respectively. Studying the unique spectral absorption characters of gypsum samples, we developed a false color composite (FCC) and an image by decorrelation stretch using the spectral bands 7, 3, and 2 of ASTER. The results FCC showed the regions of gypsum occurrences, and the decorrelated image discriminated the gypsum occurrences from other geological formations of the area. The study of surface mineralogy of the region using the VNIR‐SWIR bands by the spectral angle mapper method showed the presence of sulfate, carbonate, and clay minerals of the geological formations in the study area. We compared the results of ASTER with the results obtained using spectral bands 12, 8, and 4 of Sentinel‐2A processed by the same methods. The study showed that the spectral bands of ASTER can be used for mapping the gypsum and associated geological formations.
RETRACTED: Light music online system based on FPGA and Convolutional Neural Network

In recent years, research in education and study abroad has focused on collaborative research between education and learning, for example, using "diversified education" and "flexible education" in the teaching process. Simultaneously, colleges in numerous nations likewise that undergrad instruction must address self-association and variation models in the training and learning measure. These advanced instructive thoughts and college strategies offer viable hypothetical help for this study. However, the traditional educational model of "diversity" and "cognitive flexibility" pays attention to the difference in student learning knowledge. Personal Career Goals: In other words, this model is designed from educational content instructions, ignoring key factors. It must be a difference in the actual meaning of the development of the "diversity" student personality. Therefore, despite the various social systems, the importance of reforms in foreign universities and education has improved, reflecting previous experimental educational effectiveness assessments, reflecting the development of traditional educational theories based on the FPGA and Convolutional Neural Network (CNN). Teach and summarize your experience. In Reality, it easy to find a universal educational model. Because the actual educational model comes from achieving educational goals, consensus can be obtained from numerous educational theories and practical studies.
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering

Visual Question Answering (VQA) is a challenging multi-modal task that requires not only the semantic understanding of images and questions, but also the sound perception of a step-by-step reasoning process that would lead to the correct answer. So far, most successful attempts in VQA have been focused on only one aspect; either the interaction of visual pixel features of images and word features of questions, or the reasoning process of answering the question of an image with simple objects. In this paper, we propose a deep reasoning VQA model (REXUP- REason, EXtract, and UPdate) with explicit visual structure-aware textual information, and it works well in capturing step-by-step reasoning process and detecting complex object-relationships in photo-realistic images. REXUP consists of two branches, image object-oriented and scene graph-oriented, which jointly works with the super-diagonal fusion compositional attention networks. We evaluate REXUP on the benchmark GQA dataset and conduct extensive ablation studies to explore the reasons behind REXUP's effectiveness. Our best model significantly outperforms the previous state-of-the-art, which delivers 92.7% on the validation set, and 73.1% on the test-dev set. Our code is available at: https://github.com/usydnlp/REXUP/ .
The Secret image hiding schemes using Steganography- Survey

Steganography is a clever technique for concealing information within a digital cover medium, and without causing any significant distortion or modification in this medium. There are many media that can hide information such as text, images, audio and video file, the stronger the method used, the more difficult it was to disclose. Image Steganography is the most common used, as digital images are widely used over the Internet. The importance of Image Steganography lies in maintaining the confidentiality and privacy of the information exchanged. One of the most commonly flexible and efficient used concealment techniques is the Least Significant Bit (LSB) replacement technology, but it is very simple and easy to break, so the hidden data must be unreadable. There are many algorithms employed to raise the security of hidden data. This paper surveyed some of recent researches and highlight modalities and encryption algorithms used to strength in LSB steganography.
MoiPrivacy: Design and Evaluation of a Personal Password Meter

Passwords commonly contain personal information. However, there is limited awareness about its detrimental effect on the user’s online security. Current password meters do not take into account personal information and, therefore, their users are susceptible to targeted password guessing. In this paper, we present the MoiPrivacy password meter, that extends a neural network- and heuristic-based approach and considers a user’s personal information, while calculating the password strength and feedback. To do so, we analyzed the type of personal information used in passwords through an online survey (n = 62). We conducted a second user study (n = 49) for evaluating the MoiPrivacy browser extension. Our results show that MoiPrivacy significantly limits the inclusion of personal information in passwords.
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies for Sustainable Farming and Smart Agriculture

In this chapter, the author describes the main new challenges and opportunities of blockchain technology for digital economy in Russia. The study in Russia showed that the Russian research community has not addressed a majority of these challenges, and he notes that blockchain developer communities actively discuss some of these challenges and suggest myriad potential solutions. Some of them can be addressed by using private or consortium blockchain instead of a fully open network. In general, the technological challenges are limited at this point, in terms of both developer support (lack of adequate tooling) and end-user support (hard to use and understand). The recent advances on developer support include efforts by of the towards model-driven development of blockchain applications sliding mode in intellectual control and communication and help the technological challenges and created tools. The chapter shows how avatars may communicate with each other by utilizing a variety of communications methods for sustainable farming and smart agriculture.
A Fuzzy-Based Customisation of Healthcare Knowledge to Support Clinical Domestic Decisions for Chronically Ill Patients

The development of customised healthcare systems is becoming an important issue in the healthcare industry due to the rapid increase in the number of chronically ill patients. These systems aim to deliver effective care to patients having chronic diseases through customised services. However, knowledge bases need also to be customised since systems are confronted with huge amount of personalised and imprecise medical knowledge. Therefore, we propose in this paper a new system to customise medical knowledge according to progressive disease phases and pathological cases. A rule management process first customises rules according to the specificities of every disease phase, and then matches a private knowledge base with each enrolled patient. This base contains only the patient’s customised knowledge. After reasoning, another customisation process is carried out by the component, Result Manager, which ensures the validation of the system outcomes by the pathological case experts, before being recommended. This will better ensure the recommendation of the generated results to the non-professional users. In addition, Result Manager offers fuzzy semantic queries to the experts. In conclusion, our new decision support system makes medical aid decisions not only addressed to physicians, but also to chronically ill patients and persons regarded as caregivers.
Blockchain-Based Solution for Effective Employee Management

The Fourth Industrial Revolution (IR 4.0) aims to disrupt business in various sectors like manufacturing, services, mining, fashion, etc., with the use of digital technology, one of them blockchain. The use of blockchain in the human resource management function will help collaboration between parties in updating employee skills and knowledge. Information generated from the blockchain process can also be used as a source of policy makers to set competency standards among employees. In addition, HR blockchain will run an automated process to make consensus between the parties involved. The purpose of this article is to develop a blockchain framework for managing employee performance and career progression in a particular organization. The blockchain design will focus on improving and developing the quality of the workforce, especially for sharing information between employees and managers. This will facilitate the process of identifying and analyzing employee skills, knowledge, and attitude data. In addition, data transparency and access opportunities by employees and company managers will exist without being misused. This will make managers easily track records of each employee's performance items, which in turn can be used to develop employee careers.
Robot learning through observation via coarse-to-fine grained video summarization

Learning human daily behavior is important for enabling robots to perform tasks and assist people. However, most prior work either requires specific sensors for capturing data or heavily relies on prior knowledge of human motion, which can be difficult to obtain. To alleviate the above problems, we propose a novel pipeline for robots to learn human behavior based on coarse-to-fine video summarization using a single Kinect camera. Specifically, the robot first retrieves information of general interest followed by a task-specific content retrieval, then focuses on fine-grained motion clips of human behavior, and guides itself by using an object-centric learning method to complete the desired task. Our work has three unique advantages: (1) it enables the robot to effectively capture granularity hierarchies of human behavior which efficiently exploits multi-stage information while alleviating disturbances and redundancies in visual data; (2) it obtains knowledge by focusing on object movements in summarized motion clips which does not require any prior knowledge of human motion; (3) it only requires a single Kinect sensor for the robot to learn human behavior which is fully accessible and easy to equip. Experiments in an office environment were performed to validate the efficiency and effectiveness of the proposed framework, and the results indicate that this approach exhibits good learning efficacy for the robot to understand human behavior and learn to perform tasks.
Exploiting BERT with Global-Local Context and Label Dependency for Aspect Term Extraction

Aspect term extraction (ATE) is a subtask of aspect-based sentiment analysis (ABSA), which aims to extract all aspect-specific words in a sentence. Recent neural network methods ignore the problem that word may play different semantic roles in different sentences and have limitation in handling dependencies between labels. In this work, we first exploit BERT as embedding layer to obtain word-level representations and utilize BERT architecture to capture global sequence features. Then, a position-aware attention is proposed to extract local context information. Global-local context representations of words are built by merging the global sequence features and local context information, which can select related information from both sides: global sequence and local context. Finally, to model the label dependency, we construct a label dependency module based on RNN and CRF, where the previous label features are introduced as additional information for label relationship modeling. Experimental results on four benchmark datasets show that our proposed model obtains the state-of-the-art performance.
Component-level Script Classification Benchmark with CNN on AUTNT Dataset

Script identification from camera images is a prerequisite for efficient end-to-end systems in multi-script environment. In recent times, due to the wide usage of digitized multi-lingual documents and images, efficient script identifier becomes an inevitable module in computer vision and pattern recognition applications. Here, a component-level multi-lingual identifier is designed based on CNN to set the benchmark performance on publicly available dataset named AUTNT. The model is evaluated using three different text scripts, viz., Bengali, Latin, and Devanagari. It yields reasonably high accuracy of 92.02% and 89.49% for document and scene component images, respectively, and 92.51% for overall text components irrespective of image sources. This result is first of its kind and it may be convincingly considered as a benchmark for component-level script classification from the said dataset.