Reviewerly/exHarmony huggingface dataset:
Graded Relevance

Sakai, TetsuyaNTCIR was the first large-scale IR evaluation conference series to construct test collections with graded relevance assessments: the NTCIR-1 test collections from 1998 already featured relevant and partially relevant documents. In this chapter, I provide a survey on the use of graded relevance assessments and of graded relevance measures in the past NTCIR tasks which primarily tackled ranked retrieval. My survey shows that the majority of the past tasks fully utilised graded relevance by means of graded evaluation measures, but not all of them; interestingly, even a few relatively recent tasks chose to adhere to binary relevance measures. I conclude the chapter by a summary of my survey in table form.
Analysis of Text Classification with various Term Weighting Schemes in Vector Space Model

Term Weighting Scheme (TWS) is a key component of the matching mechanism when using the vector space model In the context of information retrieval (IR) from text documents, the this paper described a new approach of term weighting methods to improve the classification performance. In this study, we propose an effective term weighting scheme, which gives highest accuracy with compare to the text classification methods. We compared performance parameter of KNN and Naïve Bayes Classification with different Weighting Method, Weight information gain, SVM and proposed method.We have implemented many term-weighting methods (TWM) on Amazon data collections in combination with Information-Gain and SVM and KNN algorithm and Naïve Bayes Algorithm.
Images, sampling and frequency domain processing

In this chapter, we shall look at the basic theory which underlies image formation and processing. We shall start by investigating what makes up a picture and look at the consequences of having a different number of points in the image. We shall also look at images in a different representation, known as the frequency domain. In this, as the name implies, we consider an image as a collection of frequency components. We can actually operate on images in the frequency domain and we shall also consider different transformation processes. These allow us different insights into images and image processing which will be used in later chapters not only as a means to develop techniques but also to give faster (computer) processing.
The Application of Bonang Gamelan Music Based on Mobile Application

The application of bonang gamelan music based on mobile application was developed with the aim of maintaining the tradition of traditional musical instruments in the society of this modern day. Users can recognise and learn how to play the traditional musical instruments, especially bonang gamelan by using mobile phone technology. This application provides convenience for users without spending time and money and they can also access it anywhere. The development of this animated application is based on the ADDIE methodology and uses Adobe Flash software as a platform to develop this music app. The respondents involved in the development of this music app consist of six (6) specialists in bonang gamelan music and IT professionals. The user respondents were ten (10) students of the Faculty of Technical and Vocational Education (FPTV). The evaluation of this application was carried out using questionnaires. Overall, this application succeeds in meeting the objectives and goals set out in the analysis phase although it still has its own weaknesses. It is hoped that the development of bonang gamelan music application based on mobile application will benefit all societies.
Alpaca: AR Graphics Extensions for Web Applications

In this work, we propose a framework to simplify the creation of Augmented Reality (AR) extensions for web applications, without modifying the original web applications. We implemented the framework in an open source package called Alpaca. AR extensions developed using Alpaca appear as a web-browser extension, and automatically bridge the Document Object Model (DOM) of the web with the SceneGraph model of AR. To transform the web application into a multi-device, mixed-space web application, we designed a restrictive and minimized interface for cross-device event handling. We demonstrate our approach to develop mixed-space applications using three examples. These applications are, respectively, for exploring Google Books, exploring biodiversity distribution hosted by the National Park Service of the United States, and exploring YouTube’s recommendation engine. The first two cases show how a 3rd-party developer can create AR extensions without making any modifications to the original web applications. The last case serves as an example of how to create AR extensions when a developer creates a web application from scratch. Alpaca works on the iPhone X, the Google Pixel, and the Microsoft HoloLens.
Improving Performance Parameters of PMBLDC Motor Using Fuzzy Sliding Mode Controller

This paper proposes an intelligent speed control system for PMBLDC motor drive applications. A fuzzy sliding mode controller is designed for the high speed control of BLDC motors, Efficacy of the system can be improved by FSM controller. Due to the non- linear characteristics of BLDC motors PID controllers are not relevant to it. Noise disturbances, harmonics, and antecedent or consequent uncertainties are difficult to handle by Interval Type2 fuzzy PID controller. Whereas Sliding Mode Controller is most attractive control technique for all type of uncertainties, and improves the performance of the system. An effective Mat-lab / Simulation result presents the attractive performance of proposed system.
Innovative aspects of the organization of design and research activity of teachers at the university

И. В. Абрамова, Е. М. Вечтомов, З
Professional deficits of teachers in the field of subject competencies (Russian), manifested in the conditions of digital transformation of education

Introduction. The relevance of the research undertaken is determined by the fact that the ambiguous VUCA world creates a changeable and complex environment of the modern educational system, in which there is an obvious gap between the needs of the digital economy and the content of school education, as a result of which new professional deficits of teachers arise, including subject ones. Purpose of the study is analysis and generalization of the results of the monitoring of professional issues of teachers in the field of subject competences (Russian language), manifested in the context of the digital transformation of education. Materials and methods. The main research methods are a survey of 1075 Russian language teachers from Belgorod, Volgograd, Lipetsk, Rostov, Krasnodar, Stavropol regions, including Chechen Republic (by Google-forms); method of scaling empirical data; interpretation method. Research results. Data were obtained on the different degrees of severity of professional deficits in Russian language teachers, which are manifested in the context of the digital transformation of education. There was a lack of awareness of teachers about the components of the methodological system (more than 50%) in the field of methodological competencies; on the means of individualization, differentiation of educational programs; on the principles of teaching the Russian language (over 60%). We state small deficits associated with ideas about meta and subject skills (more than 20%), about the structure of the Final State Examination (less than 20%). Problematic issue is knowledge in the field of theoretical foundations of the morphemic structure of a word, its word-formation capabilities (more than 70%) in the field of subject competences; transitional phenomena in the field of morphology (more than 60%), syntactic connections and relationships (more than 50%). The scientific novelty of the research is in fixing and stating whether teachers of the Russian language have professional issues (methodological and subject). The obtained results of the research can be used in the organization of a multilateral system of support for teaching staff in the subject area "Russian language".
BOOSTING LIFELONG LEARNING FOR GENERAL SECONDARY SCHOOLTEACHERS: DIGITAL COMPETENCE DEVELOPMENT AMID BLENDED LEARNING

The research is targeted at examining general secondary schoolteachers’ strategies for assessing, self-assessing and improving their digital competence. The authors also define the scope of the most effective strategies for developing schoolteachers’ digital competence amid blended learning caused by the Covid-19 pandemic. The main aim of research is to clarify the most effective ways of improving schoolteachers’ digital competence amid blended learning caused by the Covid-19 pandemic. The research activities have been organized and realized taking into account the undisputable fact that digital competence is the key one for lifelong learning. The research methodology for generalizing theoretical material and empirical data collecting implies combining theoretical (analysis and synthesis) and empirical (a web-based questionnaire, individual interviews, conversations with respondents and analysis of reflexive texts) methods of investigation. The research sample consists of 1284 general secondary schoolteachers from different regions of Ukraine. The authors examine information obtained by using the mentioned combination of empirical methods of investigation. As a result of research some certain strategies for developing schoolteachers’ digital competence are found out, described and recommended for being implied into both the system of general secondary school education and the system of general secondary schoolteachers’ postgraduate education. It is also proved that the process of developing general secondary schoolteachers’ digital competence amid blended learning organized effectively is the basis for boosting their further lifelong learning.
Anchored Constrained Clustering Ensemble

In the context of semi-supervised learning and clustering ensemble methods, we introduce a novel strategy to consider not only pairwise constraints, but also triplet constraints. As far as we are aware, the latter have not been addressed in the literature of semi-supervised clustering ensembles. The strategy consists of a post-processing applied once a consensus partition has been built. Taking into account the fact that the clusters of the consensus partition are usually not spherical, in order to maintain their complex shapes, we first generate anchors, which are data points judged representative of the clusters, in such a way that every point in the cluster has an anchor close to it. These anchors are then used to create a data structure that we call an allocation matrix, which measures the assignment score of each point to each cluster in the consensus partition. Such a matrix is provided to an Integer Linear Programing (ILP) model to find the partition which is closest to the consensus partition, while satisfying the constraints. The experimental results show that our method, given an initial consensus partition and a set of constraints, allows to satisfy all the given constraints, modifying the initial partition, but without deteriorating considerably its quality.
Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset's characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed.
Geography student knowledge of flood disaster risk reduction in Sampang, Indonesia

Sampang floods are caused because of urban floods, flash floods, and tidal floods. In addition to morphology, the Kemoning River flow pattern triggers flooding. Sampang floods cause losses in the educational, economic, and social aspects. Efforts to reduce disaster risk can be made through education. Education has a role in sensitizing geography students to disaster response. One way is to provide students disaster knowledge and disaster mitigation techniques. Therefore, it is essential to examine how high the level of knowledge and perception of geography students is in reducing the risk of floods in Sampang. The method used is a descriptive cross-sectional survey. This research was conducted in high school in Sampang. More precisely, researchers choose schools that the majority of students are affected by the flood in Sampang. Using descriptive data analysis, data were used for histogram analysis, bivariate correlation analysis, distribution analysis, and independent sample testing. Although students learned about the disaster flood in Sampang, many students do not have complete knowledge about it.
Multi-residual generative adversarial networks for QR code deblurring

Motion blur and ambient noise are the main reasons that affect quick response (QR) code recognition. In this paper, we propose a novel deep learning approach to deblur the QR codes and realize the effective recognition of deblurring QR codes by using generative adversarial networks (GANs). We estimate the blur kernel and ambient noise of the blur QR code in the dataset using GANs, so as to realize the transformation from the blur QR code image to the sharp image. We also propose an expansion method of QR codes dataset, and achieve better generalization performance of the model. The experimental results show that our approach can effectively estimate the blur kernel and ambient noise that can realize the deblurring of QR code.
College English Teaching Evaluation Model Using Natural Language Processing Technology and Neural Networks

College English is one of the most important basic courses in college education, and classroom evaluation is one of the effective means to improve teaching efficiency. Multidimensional classroom evaluation system, which reflects the characteristics of college English courses, is the premise to standardize the evaluation process and ensure the fair and reasonable evaluation results. Based on NLP (natural language processing) technology and neural network, we use NLP to optimize BPNN (BP neural network) method to construct CETE (college English teaching evaluation) system model, which quantifies the concept of teacher evaluation index as input, makes the data clear, and takes the teaching effect as output. The training results show that the network can fit the training data well, and the prediction effect is remarkable, which indicates that the CETE model based on the BPNN method optimized by NLP is reasonable and feasible.
Tsunami Early Warning System Based on Maritime Wireless Communication

Tsunami buoy, linked to satellite, is commonly used as a tsunami early warning system but has been discovered to have several drawbacks such as the need for approximately 5 minutes to issue an early warning for a tsunami after detecting the initial wave as well as its fragility. It was also reported that the twenty-two buoys placed in the Indonesian seas from 2012 to 2018 were damaged and missing. Therefore, this study proposes a new method for tsunami early warning by integrating ship-to-ship maritime wireless communication. It is important to note that vessels or fishing boats with over 30 GT have the ability to travel more than 100 nmi (approximately 180 km) from the shoreline and can be equipped with point-to-multipoint VHF radio communication. Meanwhile, smaller boats on the fishing ground located approximately 2-5 km from the shore can use a WiFi network to communicate like a wireless mesh while the existing terrestrial network can be used for the ship-to-shore communication between boats and land stations. This system is expected to provide significant benefits for a fishing town such as Pangandaran, West Java, Indonesia which is directly facing Java Megathrust in the Indian Ocean. Therefore, a tsunami numerical simulation was conducted in this study using Shallow Water Equation which involved a hypothetical tsunami simulated from the possible fault source which is approximately 250 km from the source. Moreover, the vessel’s location was assumed to be in line with the fishing ground while the arrival time of the tsunami was estimated from the model to be 22.5 minutes and compared to the relay time of the proposed system which was approximately 5.4 seconds. This is faster in terms of delay than the existing system which relays information through satellite at approximately 5 minutes in an ideal condition and also has the ability to reduce the need for tsunami buoys.
Medical image segmentation using U-Net and progressive neuron expansion

With fast-growing computing power and large amounts of data availability, deep learning (DL) algorithms are achieving unprecedented success across different fields. One of its great achievements in health care is medical imaging. Medical image segmentation, such as lung cancer segmentation, is an important tool that facilitates clinical decision systems. U-Net, a recent innovative DL architecture, has shown great promise in segmenting regions of interest in medical images. One of the key advantages of U-Net is that it effectively constructs contracting and expanding paths with symmetric network connection which allows for capturing context information and enabling precise localization in a single network. Although U-Net and its variants have been widely adopted in the medical image segmentation task, there are some limitations that need to be addressed to meet specific requirements, including hardware memory consumption and segmentation accuracy. In this work, we propose a new U-Net based DL architecture, U-PEN (U-net with Progressively Expanded Neuron), that requires less memory on hardware while achieving highly accurate segmentation. The underlying hypothesis behind the proposed architecture is that this model, when compared to existing models, can efficiently capture image context via incorporating nonlinear functions into hidden neurons expansion in the encoder path. The proposed network can eliminate additional convolutional layers thus producing less trainable parameters compared to U-Net. The proposed DL model was tested on two benchmark datasets, namely DRIVE and CHASH DB1, for different medical image segmentation problems. The experimental results show that the suggested architecture is effective, yielding better performance over U-Net and Residual U-Net in most of the experiments.
Transfer Learning-based Hybrid Approach for Bayesian Network Structure Learning

Bayesian network is a graphical model that is widely used to perform probabilistic reasoning. However, learning the structure of Bayesian network is a complex task. In this paper, we propose a hybrid structure learning algorithm that has two phases: a constraint-based phase to reduce the search space and a score-and-search phase that employs case-injected genetic algorithms for determining the optimal structure from the reduced space of structures. We use a case-injected genetic algorithm-based hybrid approach for the structure learning in order to improve the learning accuracy over similar problems. A case-injected genetic algorithm is the augmentation of a case-based memory with the Genetic Algorithm (GA). Thereby, it finds near-optimal solutions in fewer generations compared to GA. Our method stores relevant or partial solutions in a case-base while solving the problems and utilizes those stored solutions on new similar problems. We use small-to-very large networks for assessing our viability of our approach. In this paper, a series of experiments are conducted on datasets generated from four benchmark Bayesian networks. We compare our method against GA-based hybrid approach and a state-of-the-art algorithm, Max-Min Hill Climbing (MMHC). Presented results indicate an enhanced improvement of our approach over GA and MMHC in learning the Bayesian network structures.
Asking for help in community question-answering

Community question-answering (CQA) enables both information retrieval and social interactions. CQA questions are viewed as goal-expressions from the askers' perspectives. Most prior studies mainly focused on the goals expressed in the questions, but not on how responders' expectations and responses are influenced by the goal-expressions. To fill the gap, this research proposes the use of framing theory to understand how different expressions of goals influence responses. Cues of questions were used to identify goal-frames in CQA questions. Social network analysis was used to construct response networks whose nodes represent postings and connections represent responses. Our results reveal that goal-frames with high complexity, high specificity, and rewards tend to increase the centrality of questions. In contrast, low complexity and low specificity tend to generate extensive conversations. Implications for both researchers and practitioners are discussed in the final section.
Analysis of the Unexplored Security Issues Common to All Types of NoSQL Databases

NoSQL databases outperform the traditional RDBMS due to their faster retrieval of large volumes of data, scalability, and high performance. The need for these databases has been increasing in recent years because data collection is growing tremendously. Structured, unstructured, and semi- structured data storage is allowed in NoSQL, which is not possible in a traditional database. NoSQL needs to compensate with its security feature for its amazing functionalities of faster data access and large data storage. The main concern exists in sensitive information stored in the data. The need to protect this sensitive data is crucial for confidentiality and privacy problems. To understand the severity of preserving sensitive data, recognizing the security issues is important. These security issues, if not resolved, will cause data loss, unauthorized access, database crashes by hackers, and security breaches. This paper investigates the security issues common to the top twenty NoSQL databases of the following types: document, key-value, column, graph, object- oriented, and multi-model. The top twenty NoSQL databases studied were MongoDB, Cassandra, CouchDB, Hypertable, Redis, Riak, Neo4j, Hadoop HBase, Couchbase, MemcacheDB, RavenDB, Voldemort, Perst, HyperGraphDB, NeoDatis, MyOODB, OrientDB, Apache Drill, Amazon, and Neptune. The comparison results show that there are common security issues among the databases. SQL injection security issues were detected in eight databases. The names of the databases were MongoDB, Cassandra, CouchDB, Neo4j, Couchbase, RavenDB, OrientDB, and Apache Drill.