Reviewerly/exHarmony huggingface dataset:
An Effective Randomized Hough Transform Method to Extract Ground Plane from Kinect Point Cloud

This article presents a method to improve plane segmentation on a 3D point cloud acquired and converted from Kinect data. This method utilizes Randomized Hough Transform (RHT) combined with angle and distance constraints to eliminate non-ground elements. The algorithm randomly selects 3 points to form a plane model, then estimates the surface normal on one of the three points to compare with the selected model and ensure the model fits a present plane in the point cloud. The planes that satisfy the predefined angle and distance constraints are selected out from all plane models found. Finally, all valid ground points which satisfy the best ground plane model can be found. The test results show that the elapsing time of the proposed modified RHT method is about to 10% of that running with the RANSAC algorithm and it is negligible in comparison with original RHT, while the benchmark completeness, correctness and quality are stable at high level. The result of this work is promising to apply for indoor robot navigation application using 3D computer vision.
Visual Saliency Aided SAR and Optical Image Matching

This paper proposes a novel deep neural network training procedure for matching optical image and synthetic aperture radar image. This deep neural network architecture uses convolutional Siamese neural network and visual saliency map to enhance convolutional Siamese neural network’s power of features. Computational visual saliency maps can be thought as an indicator for neural network architecture to emphasize some important neural network features. Firstly, well-known visual saliency map extraction algorithms have analyzed then discussed to determine fusion strategy with main neural network, convolutional Siamese neural network. Another core idea is about Siamese network. Two different Siamese networks have studied, Pseudo-Siamese CNN and Identical-Siamese CNN, and experiments are resulted in detail. Experiments show that computational visual saliency map can help to Siamese networks to select more informative features in matching process. Incorporation with visual saliency map increases matching accuracy whether Siamese network shares its weights or not.
Lightweight Attribute-Based Keyword Search with Policy Protection for Cloud-Assisted IoT

The following topics are dealt with: cryptography; Internet of Things; data privacy; security of data; cloud computing; cryptocurrencies; learning (artificial intelligence); computer network security; telecommunication security; authorisation.
The Item Response Theory Model for an AI-based Adaptive Learning System

Item characteristics (e.g. item difficulty), and students' latent traits (e.g. student ability) are essential in a personalized learning system. In such system, items of different characteristics need to be recommended according to students' latent traits. The item response theory can fulfill such requirements. In this paper, we present a detailed description of the student ability and the item difficulty measurement based on an item response theory model. The model is evaluated using real data regarding the math and the English curriculum gathered from students' learning processes during the usage of the Squirrel AI Learning System. The data is regarding the math and the English curriculum. It is proved that the difficulty evaluated by teachers minus the difficulty estimated from the data approximately follows a normal distribution. The mean value of students' abilities approaches 0.50 as the numbers of their attempts on questions increase.
Connections of Evidence: Using Best Practices of Assessment in an Ongoing Serials Analysis Project

Making decisions about continuing or cancelling serials subscriptions is a difficult process under the best of circumstances. In these situations, academic librarians can borrow from assessment practices in terms of gathering data, examining emergent patterns, comparing quantitative research and qualitative feedback, closing the loop by making changes, and then preparing to re-evaluate these changes. This article addresses a case study from an academic library at a state university in the midwestern United States as a model of best assessment practices for determining the future of print and online periodical subscriptions. The result is an updated, refocused serials collection that supports learning and research outcomes.
Conceptualization of a Measurement Method Proposal for the Assessment of IT Security in the Status Quo of Micro-Enterprises

Micro-enterprises are an essential part of any national economy. With the advancement of digitization, it has become necessary to protect these enterprises from the current and future dangers of technology. Due to their limited financial and infrastructural resources, micro-enterprises tend to be unable to use complex and cost-intensive industrial solutions. In addition, many micro-enterprises lack the technical knowledge and awareness to correctly assess the dangers of their used technology. Nevertheless a micro-enterprise needs to be able to protect its IT-components from external attacks and internal failures. Therefore, ways and solutions must be worked out to help the micro-enterprises with improving their IT security. For this goal, a proposal for a generic self-measurement method for the assessment of IT security in micro-enterprises will be developed. with an internal and an external perspective, practicable for micro-enterprises. This measurement method will use the framework of German industrial requirements and standards within the scope on German micro-enterprises, in particular craft enterprises.
TagStack: Automated System for Predicting Tags in StackOverflow

Stack Overflow is one of the popular online media for the programmers to share their knowledge and experience. Developers seeks help from online communities such as Stack Overflow. The objective of our research is to ease the tagging of questions on Stack Overflow. This work proposes TagStack system, a machine learning and feedback-based framework for predicting tags on Stack Overflow. We perform experiment on real world and publically available dataset, and results shows that TagStack system is effective in predicting tags on Stack Overflow.
An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques

Abstract Background Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch. Result An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus ) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure. Conclusions Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures.
CREATIVE DIRECTIONS IN DIGITALIZATION OF EDUCATION IN RUSSIA

The article deals with the issues of improving the efficiency of education in Russia. To this end, a set of measures in five areas was formulated and a list of tasks for the implementation of the proposed measures was compiled. Recommendations on the organization of continuing education are formulated in the following sequence: school; University (professional education); additional vocational education; mass online education. Two forms of continuing education are considered in more detail: additional vocational education and mass online training. The organization of additional vocational education is based on the state system of personal digital certificates and other programs for the development of digital literacy. Depending on the level of training, available skills, work experience and availability of free time, potential students can choose the course that they need to develop competencies for work in the field of digital economy. The online education market in Russia is actively developing. In 2016, it amounted to 20.7 billion rubles, and in 2021, experts expect it to increase to 53.3 billion rubles. The concept of continuing education can be implemented in the best way if the structure of online education is developed and approaches to motivation in education are improved. Self-assessment of the level of professional qualification, the desire to obtain a new profession, change of residence are the motivations of readiness for training. It is only necessary to provide citizens with conditions for learning, depending on each age group.
Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution

Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates. Modern variance reduction techniques mostly consider categorical distributions and have limited applicability when the number of possible outcomes becomes large. In this work, we consider models with latent permutations and propose control variates for the Plackett-Luce distribution. In particular, the control variates allow us to optimize black-box functions over permutations using stochastic gradient descent. To illustrate the approach, we consider a variety of causal structure learning tasks for continuous and discrete data. We show that our method outperforms competitive relaxation-based optimization methods and is also applicable to non-differentiable score functions.
Corona fault detection in switchgear with extreme learning machine

Switchgear is a very important component in a power distribution line. Failure in a switchgear can lead to catastrophic danger and losses. In this research, a fault detection system is proposed with the implementation of Extreme Learning Machine (ELM). This algorithm is capable to identify faults in a switchgear by analyzing the sound wave generated. Experiments are carried out to investigate the performance of the developed algorithm in identifying Corona faults in switchgears. The performances are analyzed in time and frequency domains, respectively. In time domain analysis, the results show 90.63%, 87.5%, and 87.5% of success rates in differentiating the Corona and non-Corona cases in training, validation and testing phases respectively. In frequency domain analysis, the results show 89.84%, 83.33%, and 87.5% success rates in training, validation and testing phases respectively. It can thus be concluded that the developed algorithm performed well in identifying Corona faults in switchgears.
On the Learnability of Random Deep Networks

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)On the Learnability of Random Deep NetworksAbhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, and Rina PanigrahyAbhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, and Rina Panigrahypp.398 - 410Chapter DOI:https://doi.org/10.1137/1.9781611975994.24PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper we study the learnability of random deep networks both theoretically and experimentally. On the theoretical front, assuming the statistical query model, we show that the learnability of random deep networks with sign activation drops exponentially with their depths; under plausible conjectures, our results extend to ReLu and sigmoid activations. The core of the arguments is that even for highly correlated inputs, the outputs of deep random networks are near-orthogonal. On the experimental side, we find that the learnability of random networks drops sharply with depth even with the state-of-the-art training methods. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Improving Data Security in Message Communication between ACT and Aircraft using Private Blockchain

Security and privacy remain a major challenge for data communication between an Air Controller Tower (ACT) and Flight mainly due to the immense scale of data transfer and distributed nature of this Internet of Things (IoT) network. Blockchain and its related approaches provide decentralized security and privacy, however yet they involve significant energy, delay, and computational overhead. The aforementioned costs are not feasible for most resource-constrained IoT devices. The proposed security models of this paper acknowledged these drawbacks by circumventing the computational resource-intensive Proof of Work (PoW) concepts. The models consist of three main components: Customized Digital Certificate (CC), Validator (Smart Contract), and Storage (Blockchain Network). A set of new algorithms are proposed to outline various phases during communication and embedding message security. The proposed models are evaluated in a simulated environment for secure data communication between Flights and ACTs. Each ACT and Flight need to be equipped with IoT devices and Validator (SC) that can send and receive validated messages and stored in the Blockchain network at both ends. This paper explores the communication realm and outlines Cryptography and Blockchain-based approaches. Extensive empirical work has been done to demonstrate that the proposed models are viable with respect to confidentiality, integrity, and availability. The presented models can be further utilized for tracing of message exchange for integrity and non-repudiation.
Measuring Crowd Collectiveness via Global Motion Correlation

Crowd collectiveness refers to the behavior consistency of crowd scenes, which reflects the degree of collective movements among massive individuals in crowd systems. The existing methods focus on measuring the discrepancy of motion direction among the individuals. However, few studies consider the magnitude discrepancy of velocity in a crowd and the collectiveness among different crowds, which can also affect the overall crowd collectiveness. In this paper, we propose a novel descriptor which combines intra-crowd collectiveness with inter-crowd collectiveness to solve the problem. For intra-crowd collectiveness, we introduce the energy spread process to identify the impacting factors of collectiveness, then measure the collectiveness of individuals within a crowd cluster by computing their similarities of magnitude and direction from the optical flow. For inter-crowd collectiveness, we assess the motion consistency among various crowd clusters generated from collective merging. Experimental results demonstrate that how the new collectiveness descriptor improves performance on three different crowd datasets, thus validating the superiority of the proposed descriptor.
Geography-Enhanced Link Prediction Framework for Knowledge Graph Completion

Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Recent years have witnessed great advance of representation learning (RL) based link prediction models, which represent entities and relations as elements of a continuous vector space. However, the current representation learning models ignore the abundant geographic information implicit in the entities and relations, and therefore there is still room for improvement. To overcome this problem, this paper proposes a novel link prediction framework for knowledge graph completion. By leveraging geographic information to generate geographic units and rules, we construct geographic constraints for optimizing and boosting the representation learning results. Extensive experiments show that the proposed framework improves the performance of the current representation learning models for link prediction task.
WeFreS: weighted frequent subgraph mining in a single large graph

Natural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba
Education Cloud Data Integrity Verification Based on Mapping-Trie Tree

There are privacy data leakage risks and low verification efficiency against the existing public cloud data integrity audit model. Based on the characteristics of the education cloud, this paper designs an education cloud data integrity verification scheme based on the Mapping-Trie tree. Remove the Third Party Auditor platforms to prevent disclosure of private information. The method of random sampling is used to audit the data, which greatly saves the calculation and communication resources of users and servers. Both the Hash scheme and the Trie scheme have been improved in terms of audit efficiency and saves the space of storage.
An Analysis of the Contents of Proclamation of Indonesian Independence in Semantic Role

Language is used to know how to produce and understand and acquire sentences with particular meanings in communicational interaction. In language we would find for how to analyze the meaning of languages which uttered or written that is the linguistic that meant to the study of language. How to analyze the meaning of the written or spoken language in contextual meaning of a linguistic semantics? Semantics is the study of how languages organize and express meanings. Semantics is the systematically study of meaning in language. Semantics is described as concerned with the relation of linguistic forms which have variety of aspects. Either of those aspects is semantic role that is going to described as the objective of this research. This research aimed to analyze the contents of proclamation of Indonesian independence in semantic role. This research conducted by the researcher is qualitative research. The data of this research are declarative sentences which taken on the contents of proclamation of Indonesian independence published in January 17th 1945. The method used by the researcher in collecting the data is documentation with a variety of steps such reading, collecting, and selecting the data. In analyzing the data, the researcher classifies the arguments have on sentences into semantic role aspect in chart table. The result of this research shows that there are five aspects of semantic role used in an analysis of the content’s proclamation of Indonesian independence they are agent, theme, source, instrument and goal.
Forecasting Analysis of GMDH model with LSSVM and MARS models for Hydrological Datasets (Case study)

Objectives: To forecast hydrological datasets using time-series forecasting model, namely, Group Method and Data Handling (GMDH).Methods/statistical analysis: The monthly streamflow datasets covering a period of 485 and 550 months have been collected from two well-known rivers of Pakistan, the Indus and the Chenab, respectively, for the endorsement of the GMDH model.Computed results are compared with two other forecasting models: Least Square Support Vector Machine (LSSVM) and Multivariate Adaptive Regression Splines (MARS).The accuracy of the model has been verified by the following three statistical estimations: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Correlation Coefficient (CE).Findings: The GMDH model has the potential to estimate with high precision the forecast real value of the hydrological datasets compared to the other models discussed in the present article.Findings show that the GMDH forecasting model is more robust than the other models discussed here.Applications/improvements: The novelty of this study is that it provides a trustable forecast of streamflow of the rivers.
Phase Measurement of Quantum Walks: Application to Structure Theorem of the Positive Support of the Grover Walk

We obtain a structure theorem of the positive support of the $n$-th power of the Grover walk on $k$-regular graph whose girth is greater than $2(n-1)$. This structure theorem is provided by the parity of the amplitude of another quantum walk on the line which depends only on $k$. The phase pattern of this quantum walk has a curious regularity. We also exactly show how the spectrum of the $n$-th power of the Grover walk is obtained by lifting up that of the adjacency matrix to the complex plain.
Emoji-Text Relations on Instagram

Social-sharing platforms such as Instagram generate two types of image-centricity: central and dominant images of all kinds, and frequent emoji-use mainly in comments to posted images. This chapter presents a corpus-based exploration of emoji-text relations and of the functions emoji fulfil in so-called iconographetic communication. First, emoji are shown to be capable of standing in for all kinds of syntactic and semantic elements of the text – ranging from nouns and adjectives to prepositional phrases and entire propositions or semantic frames. Second, emoji can be seen to function as grapho-stylistic, cohesive, and illocution-structuring/-enhancing devices. They can perform these functions by serving as allographs, semantic specifiers, cohesive devices, deictic elements, structuring cues, and hashtags. The chapter also surveys the state of Instagram-related social media research.
An Implementation of Neural Network Approach for Recognition of Handwritten Odia Text

Although no of arguments available that handwriting can be mimicked or forged, there is a certain level of individuality and uniqueness where the forge is not possible. As on recent studies, the handwritten character recognition (HCR) gaining more importance due to the increase in computational work. Various methods proposed for the identification of written characters. Selection of a relevant feature is probably the essential factor in achieving high recognition performance with much better accuracy in HCR system. Another critical challenge is that there was very less number of research done on Odia character. In this research work, we have used a multilayer feed-forward network with backpropagation for the recognition. Also, a few comparisons we have done on this scenario. In pre-processing, we have applied some basic algorithms for de-noising, segmentation, normalizing of characters, etc.