Reviewerly/exHarmony huggingface dataset:
Reproducible Analyses in Education Research

A reproducible analysis is one in which an independent entity, using the same data and the same statistical code, would obtain the exact same result as the previous analyst. Reproducible analyses utilize script-based analyses and open data to aid in the reproduction of the analysis. A reproducible analysis does not ensure the same results are obtained if another sample of data is obtained, often referred to as replicability. Reproduction and replication of studies are discussed as well as the overwhelming benefits of creating a reproducible analysis workflow. A tool is proposed to aid in the evaluation of studies to describe which element in a study has a strong reproducible workflow and areas that could be improved. This tool is meant to serve as a discussion tool, not to rank studies or devalue studies that are unable to share data or statistical code. Finally, discussion surrounding reproducibility for qualitative studies are discussed along with unique challenges for adopting a reproducible analysis framework.
Paraphrase Detection with Dependency Embedding

Paraphrase detection is an important task with implications for machine translation, information retrieval, summarization and question answering, among other applications. Inspired by previous work utilizing syntactic information for this task, we propose a novel approach that combines dependency relations with a simple neural network. The dependencies are extracted from a sentence pair with an off-the-shelf natural language processing toolkit, then they are divided into two sets by alignment, one for the dependencies that are common to both sentences, the other set contains unmatched dependencies. Both sets of dependencies are then embedded, using two different embedding schemes for dependency types and head-modifier word pairs. Finally, these dependency embeddings are fed into neural nets to get the classification results. Our model learns a soft weighting scheme for similarities and dissimilarities between a sentence pair automatically. Through extensive experimentation, we show that by embedding syntactic dependency, we can effectively combine linguistic information with neural networks for the task of sentential paraphrase detection.
Onboard Real Time Image Processing System for Water Bodies Extraction

In this article, hardware and software tools is proposed to process image in satellite image processing systems so that the parameters can be calculated and make image viewable according to user requirements. This is a basic approach to solve real-time problems for water bodies like oceans. The extraction of Water bodies is difficult because of mountain and building shadow. There are three water indices for water bodies extraction namely, Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automated Water Extraction Index (AWEI) that extract water in real-time with onboard image processing system using FPGA. By selecting any input image there are given three options of NDWI, MNDWI and AWEI. When any one option is selected then the output is obtained according to selected indices. The proposed system of onboard real-time image processing using hardware software combination reduces total time to procure satellite image and extraction of water bodies.
Comparative Analysis of CNN-Based Spatiotemporal Reasoning in Videos

Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling. In this survey paper, we have made a comparative analysis of different ST modeling techniques for action and gesture recognition tasks. Since Convolutional Neural Networks (CNNs) are proved to be an effective tool as a feature extractor for static images, we apply ST modeling techniques on the features of static images from different time instants extracted by CNNs. All techniques are trained end-to-end together with a CNN feature extraction part and evaluated on two publicly available benchmarks: The Jester and the Something-Something datasets. The Jester dataset contains various dynamic and static hand gestures, whereas the Something-Something dataset contains actions of human-object interactions. The common characteristic of these two benchmarks is that the designed architectures need to capture the full temporal content of videos in order to correctly classify actions/gestures. Contrary to expectations, experimental results show that Recurrent Neural Network (RNN) based ST modeling techniques yield inferior results compared to other techniques such as fully convolutional architectures. Codes and pretrained models of this work are publicly available ( https://github.com/fubel/stmodeling ).
Design and implementation of graft database on MSME information system using Neo4j

Abstract This study aims to implement a database of information systems for small and medium enterprises (MSME) using database graphs. In the conducted application development, the used database mostly uses the relational database. With the development of large amounts of data, the tendency of ever increasing data volume has an impact on the increasingly large table size and the number of merging data on the query (JOIN) has an impact on the time of accessing the query. This data speed is a problem in the relational database, and one solution is to use the noSQL database. In this research, the implementation of noSQL used a graph database and used the Neo4j application. The research methodology and database development carried out in this study is by collecting and analyzing, conceptual, logical, physical level databases on data related to the needs of MSME information systems such as MSME locations, MSME actors and MSME products. The data is then carried out conceptual level database design and proceed with logical level database design. This research resulted in a graft database that stored the MSME data. The results of the node from the graph database implementation were the nodes of city, sub-district MSME, and product and connector of the sub-district_entrance node which connecting the MSME location (sub-village) with the sub-district, the node connector of industrial_typed_entrance which connecting the MSME actors with the industry types.
Project of the Russian Historical and Cultural Reference Institute of P. K. Simoni: scientific information network in the precomputer era

The subject of this article is the project of the &amp;ldquo;Russian Historical and Cultural Reference Institute&amp;rdquo; proposed by the philologist and historian of the Russian booklore P. K. Simoni for consideration by the Academic Council of the Russian Historical Museum in May 1917. The October Revolution triggered the major changes in the infrastructure of historical science. Dozens of scientific and cultural establishments were restructured or ceased their activity, and dozens opened up. The project of P. K. Simoni was one of many in this series of transformations. It was developed by a practicing scholar in the humanities, who knew firsthand about all the difficulties of research work, which defines the uniqueness of this project. This fact makes this document a valuable historical source on the history of the Russian humanities in the late XIX &amp;ndash; early XX centuries. Although the project of P. K. Simoni has been previously mentioned in the special literature, this is the first detailed analysis on the topic. The conducted analysis allowed revealing the challenge faced by scholars in the humanities of the late XIX &amp;ndash; early XX centuries in their research activity, as well as outlining the possibilities for establishment of a new institution to resolve them. The author described the similar in their concept projects, which inspired P. K. Simoni in his work, as well as determined the similarities and differences of the proposed solutions. The conclusion was made that the Russian scientific community requires an institution that would solve the tasks of information support for the humanities, since the 1820s. Such need was formulated, clarified and modified over time, and yet does not fully meet the demands.
Permissive runtime information flow control in the presence of exceptions

Information flow control (IFC) has been extensively studied as an approach to mitigate information leaks in applications. A vast majority of existing work in this area is based on static analysis. However, some applications, especially on the Web, are developed using dynamic languages like JavaScript where static analyses for IFC do not scale well. As a result, there has been a growing interest in recent years to develop dynamic or runtime information flow analysis techniques. In spite of the advances in the field, runtime information flow analysis has not been at the helm of information flow security, one of the reasons being that the analysis techniques and the security property related to them (non-interference) over-approximate information flows (particularly implicit flows), generating many false positives. In this paper, we present a sound and precise approach for handling implicit leaks at runtime. In particular, we present an improvement and enhancement of the so-called permissive-upgrade strategy, which is widely used to tackle implicit leaks in dynamic information flow control. We improve the strategy’s permissiveness and generalize it. Building on top of it, we present an approach to handle implicit leaks when dealing with complex features like unstructured control flow and exceptions in higher-order languages. We explain how we address the challenge of handling unstructured control flow using immediate post-dominator analysis. We prove that our approach is sound and precise.
Comparison of various ML and DL Models for Emotion Recognition using Twitter

Despite recent success of Sentiment Analysis in many fields to detect the emotions expressed in various social media posts are lagging. Previous studies on twitter hardly focused on lexicons and classifiers with bag-of-words model. Emotion detection with variations in expression and perception are key challenges. To address this issue, emotion classes have been defined to train the model such as fear, anger etc. Further classification approach is proposed to automatically classify the tweets to its emotion class. Our approach involves two main tasks: using machine learning classification models and neural network approach for better performance. The first task includes various classifiers like Naive Bayes, Support Vector Machine (SVM), Logistic Regression, and Random Forest are used to classify the emotions in tweets. For the second task, Neural Network Models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) are used to classify the emotions in tweets. According to experimental results Support Vector Machine (SVM) outperforms compared to other models.
Rotation-DPeak: Improving Density Peaks Selection for Imbalanced Data

Density Peak (DPeak) is an effective clustering algorithm. It maps arbitrary dimensional data onto a 2-dimensional space, which yields cluster centers and outliers automatically distribute on upper right and upper left corner, respectively. However, DPeak is not suitable for imbalanced data set with large difference in density, where sparse clusters are usually not identified. Hence, an improved DPeak, namely Rotation-DPeak, is proposed to overcome this drawback according to an simple idea: the higher density of a point p, the larger $$\delta $$ it should have such that p can be picked as a density peak, where $$\delta $$ is the distance from p to its nearest neighbor with higher density. Then, we use a quadratic curve to select points with the largest decision gap as density peaks, instead of choosing points with the largest $$\gamma $$ , where $$\gamma =\rho \times \delta $$ . Experiments shows that the proposed algorithm obtains better performance on imbalanced data set, which proves that it is promising.
Sign Language Recognition Using Cluster and Chunk-Based Feature Extraction and Symbolic Representation

This paper focuses on sign language recognition with respect to the hand movement trajectories at a sentence level. This is achieved by applying two proposed methods namely Chunk-based and Cluster-based feature representation techniques in order to extract the desired keyframes. The features are extracted based on hands and head local centroid characteristics such as velocity, magnitude and orientation. A set of experiments are conducted on a large self-curated sign language sentence data set (UOM-SL2020) in order to evaluate the performance of the proposed methods. The results clearly show the high recognition rate of 75.51% in terms of F-measure which is achieved by combining the proposed method with symbolic interval-based representation and validation of feature sets.
Fault-Diagnosing Monocular-SLAM for Scale-Aware Change Detection

In this paper, we present a new fault diagnosis (FD) -based approach for image change detection (ICD) that can detect significant changes as inconsistencies between different visual experiences of monocular-SLAM. Unlike classical change detection approaches such as pairwise image comparison (PC) and anomaly detection (AD), neither the memorization of each map image nor the maintenance of up-to-date placespecific anomaly detectors are required in this FD approach. A significant challenge that is encountered when incorporating different visual experiences into FD involves dealing with the varying scales of changed objects. To address this issue, we reconsider the bag-of-words (BoW) image representation, and focus on the state-of-the-art BoW-based SLAM paradigm. As a key advantage, the local feature -based representation enables to re-organize the BoW into any different scales without modifying the database entries (i.e., the map). Furthermore, it enables to control discriminative power and expected inconsistencies of local features. Experiments on challenging cross-season ICD using publicly available NCLT dataset, and comparison against state-of-the-art ICD algorithms validate the efficacy of the proposed FD approach with/without combining AD and/or PC.
An Architecture Method of Electromagnetic Spectrum Monitoring System Software

The paper provides an architecture method of electromagnetic spectrum monitoring system software, which mainly includes: a method for creating an application program framework, functional plugins and a public data interface between the two using Qt plugin. Practice has shown that the application program framework and functions included in the software can be developed quickly and efficiently using this method to meet the complex functional requirements of this kind of large-scale system software.
Complex Gradient Function Based Descriptor for Iris Biometrics and Action Recognition

Image gradient has always been a robust characteristic of digital image which possesses localised spatial information of each pixel in all the directions. Exploiting the gradient information at the pixel level is a good old technique applied in various fields of digital image processing. In this paper, the magnitude and direction of image gradient is explored to design a local descriptor. We propose a novel local feature descriptor based on Complex Gradient Function (CGF), which maps each pixel from the spatial plane into its complex extension involving the magnitude and direction of image gradient at that pixel. We exploit the proposed descriptor for human action recognition from depth sequences and human authentication using iris biometrics. The efficiency of descriptor is demonstrated with experimental results on benchmark datasets IITDelhi, MMU-v2, CASIA-Iris, UBIRIS, and MICHE-I for iris authentication and MSR Action 3D dataset for human action recognition (HAR).
Interactive multimedia – based mobile learning prototipe development

Abstract Mobile learning (m-learning) is a learning paradigm utilizing mobile technology and devices which is expected to experience rapid and potential development along with the development of mobile technology itself. The flow of information that is so swift requires each individual to be able to access learning activities more flexibly meaning without being limited by time and space. Therefore, we need an application that is able to accommodate the needs of every individual with a variety of activities but is still able to participate in various learning activities without limited space and time.
Prashant Pardeshi, Yosuke Momiyama, Yuriko Sunakawa, Shingo Imai, and Yasunari Imamura: <i>Tagidōshi-bunseki no shin-tenkai to nihongo-kyōiku eno ōyō</i> [New developments in the analysis of polysemous words and their application to Japanese language education]

Article Prashant Pardeshi, Yosuke Momiyama, Yuriko Sunakawa, Shingo Imai, and Yasunari Imamura: Tagidōshi-bunseki no shin-tenkai to nihongo-kyōiku eno ōyō [New developments in the analysis of polysemous words and their application to Japanese language education] was published on May 1, 2021 in the journal Journal of Japanese Linguistics (volume 37, issue 1).
Copula Particle Filters

A novel analysis of the state space model is presented. It is shown that by modifying the standard recursive update it is possible to apply a copula model to eliminate a particular integral, which is typically performed using importance sampling. With Bayesian models, copulas have recently been shown to provide predictive densities directly, avoiding integrals altogether. As in every particle filter algorithm particles are generated; hence the proposed algorithm is named the Copula Particle Filter (CPF). As a by-product, the likelihood function of the model is obtained and used for parameter inference. Several illustrations and comparisons made with the standard updating schemes are provided. Supplementary material for this article, containing code, are available online.
Construction of the Mechanism of Flexible Management of Teachers Under the Background of “Double First-Class”: A Case Study of Nanyang Technological University

The construction of “double first-class” puts forward new requirements for the management mode of teachers in universities, which needs innovation in concept and mechanism to meet the needs of teachers management under the new situation. The flexible management mode characterized by humanized management can be well applied to the management of knowledge-based talents. Based on the theory of flexible management, this paper probes into the measures and strategies of Nanyang Technological University in the field of flexible management of teachers, analyzes the necessity of flexible management of teachers in universities under the new normal, and explores the model, path and strategy of teachers management in Chinese universities under the background of “double first-class”.
The Reader at Large: A Computational Approach to London, British Library, Cotton Caligula A.ii (Part One)

Abstract This article draws on computational methods including multi-dimensional scaling and clustering to analyse the contents of the Middle English multi-text manuscript London, British Library, Cotton Caligula A.ii (Part One). It makes the argument that digital linguistic analysis can complement analogue scholarship on collections of this kind by providing new perspectives on the networks of intertextuality and thematic and linguistic resonance these groups of texts engender. In particular, it argues for the use of scatterplots and dendrograms as navigational tools in digital editions that create non-hierarchical frameworks within which readers can navigate the contents of multi-text manuscripts in flexible and self-directed ways.
Complexity Estimation Technique for Large Scale Software Based on Communicating Parameters

Complexity assessment is always an exigent task for the designers of large scale applications and software where software is divided into various components or modules. Complexity accumulates as the size of the software increases. This work proposes an estimation technique for large software where a number of components communicate with each other to fulfill the requirements of the customer. We assess the complexity of software using internal complexity of components as well as the parameters used to communicate information from one component to the other. Components communicate through some parameters. This paper categorizes communication parameters as requesting and responding parameters. Proposed technique considers four factors of complexity, including individual complexities of requesting/responding components, and requesting/responding parameters. We have used McCabe’s complexity formula to assess the individual components complexity.
Unsupervised Image Generation and Manipulation Using Deep Convolutional Adversarial Networks

In recent years, there has been an outburst in the field of Computer Vision due to the introduction of Convolutional Neural Networks. However, Convolutional Neural Networks have been sparsely used for unsupervised learning. The advancement of computational power and large datasets provide large opportunities to apply deep learning for image processing. This paper proposes a new architecture based on Deep Convolutional Generative Adversarial Network (DCGAN) for unsupervised image generation, its usage for image manipulation tasks such as denoising, super-resolution, and deconvolution. This proposed model demonstrates that the learned features can be used for image processing tasks—demonstrating their applications for general use as DCGAN learns from large datasets and adds high-level image details and features where traditional methods cannot be used. While the image results from the proposed network architecture and the original DCGAN architecture are similar in terms of performance, they are visually better when viewed by humans.
DeepHDR-GIF: Capturing Motion in High Dynamic Range Scenes

In this work, we have proposed a novel computational photography application to generate a Graphics Interchange Format (GIF) image corresponding to High Dynamic Range (HDR) scene involving motion. Though HDR image and GIF image are prevalent in the computational photography community for a long time, according to our literature survey, this is the maiden attempt to combine them in a single framework. Like most other HDR image generation algorithms, the first step in the proposed framework is to capture a sequence of multi-exposure (−2EV, 0EV, 2EV) low dynamic range (LDR) images. The decided exposures (−2EV, 0EV, 2EV) are varied in a round-robin fashion, and continuous frames are captured to get adequate information about the motion of the scene. The next step is to combine sets of three consecutive multi-exposure LDR images to generate HDR images. Further, we take two successive HDR images and produced three in-between frames in a binary-search manner. At last, generated HDR frames and interpolated frames are merged in to a GIF image, which depicts the motion in the scene without losing out on the dynamic range of the scene. The proposed framework works on different types of dynamic scenes, Object movement or Camera Movement, and the results are observed to be visually pleasing without any noticeable artifacts.
Instructional media using video scribe for training industrial electronics teachers

Abstract One of the weaknesses in the teacher training program is the minimum use of instructional media and unsuitability of teaching method while in fact those two are really needed. the research is aimed at developing the instructional media using Video Scribe application for teacher trainings. It employed the Design and Development Research (DDR). The data were collected through questionnaires using the rating scale and analysed quantitatively descriptively. The results obtained indicate that the learning media using video scribe for teacher training is very feasible to use.