Reviewerly/exHarmony huggingface dataset:
Pose Variation Adaptation for Person Re- identification

Person re-identification (reid) aims at matching pedestrians observed from non-overlapping camera views. It has important applications in surveillance video analysis such as human retrieval, human tracking and activity analysis. Although a large number of effective feature learning and distance metric optimizing approaches have been proposed, it still suffers from pedestrian appearance variations caused by pose changing. Most of the previous methods address this problem by learning a pose-invariant descriptor subspace. In this paper, we propose a pose variation adaptation method for person reid. It can reduce the probability of deep learning network over-fitting. Specifically, we introduce a pose transfer generative adversarial network with a similarity measurement module. With the learned pose transfer model, training images can be transferred to any given poses, and with the original images, forming an augmented training dataset. It increases data diversity against over-fitting. In contrast to previous GAN-based methods, we consider the influence of pose variations on similarity measures to generate shaper and more realistic samples for person reid. Besides, we optimize hard example mining to introduce a novel manner of samples used with the learned pose transfer model. It focuses on the inferior samples which are caused by pose variations to increase the number of effective hard examples for learning discriminative features and improving the generalization ability. We extensively conduct comparative evaluations to demonstrate the advantages and superiorities of the proposed method over the state-of-the-art person reid approaches on Market-1501 and DukeMTM C- reID.
SPURIOUS REGRESSION ANALYSIS ON TIME SERIES DATA FROM FACTORS AFFECTING INDONESIAN HUMAN DEVELOPMENT INDEXS IN 1990 – 2017

In a spurious regression conditions occur linear regression equations that are not stationary on the mean and variance. If the variables are not stationary, there will be cointegration, so it can be concluded that there is a long-term equilibrium relationship between the two research variables and in the short term there is a possibility of an imbalance, so to overcome it in this study using the Error Correction Model. The purpose of this study is to apply a cointegration test to see whether there is a long-term non-equilibrium relationship between the time series between the Human Development Index and life expectancy at birth, average school year for adults aged 25 years and over and gross national income per capita. The data used in this study are time series data between 1990-2017. The statistical management is carried out using Eviews 10. Based on the results obtained, it was concluded that 81.7% and it can be said that the types of independent variables included in the model are already good, because only 18.3% of the diversity of the dependent variable is influenced by the independent variables outside this research model. Keywords: spurious regression, stationary, cointegration, error correction model, equilibrium
메타분석을 통한 4차 산업혁명 시대 대학무용교육의 방향

This study aims to analyze the research studies regarding to direction of the college dance education in the 4th industrial revolution by using meta-analysis method. This study found that first, the curriculum should focus on convergent subject, video education, emotional education, creative subject, and field related education. Second, inclusive education, collaborative learning, and techology-based education should be used for educational method. Third, various educational content and technology- based educaional environment should be developed for future educaion. Therefore, the college dance education should consider continuous development especially in curriculum, educational method, and educational enviornment.
Random effects regression trees for the analysis of INVALSI data

Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem &amp; Larocque, 2011; Sela &amp; Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci &amp; Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.
Deep Block Transform for Autoencoders

We discover that a trainable convolution layer with a stride over 1 and kernel ≥ stride is identical to a trainable block transform. A block transform is performed when we use a convolution layer with a stride ≥ 2 and a kernel ≥ the stride. For instance, if we use the same widths, such as a 2×2 convolution kernel and stride-2, there are no overlaps between sliding windows, so this layer operates a block transform on the partitioned 2×2 blocks. A block transform reduces the computational complexity due to a stride ≥ 2. To keep the original size, we apply a transposed convolution (stride $=$ kernel ≥ 2), an adjoint operator of a forward block transform. Based on this relationship, we propose a trainable multi-scale block transform for autoencoders. The proposed method has an encoder consisting of two sequential convolutions with stride-2, a 2×2 kernel, and a decoder consisting of the encoder's two adjoint operators (transposed convolution). Clipping is used for nonlinear activations. Inspired by the zero-frequency element in the dictionary learning method, the proposed method uses DC values for residual learning. The proposed method shows high-resolution representations, whereas the stride-1 convolutional autoencoder with 3×3 kernels generates blurry images.
PneuMat: Pneumatic Interaction System for Infant Sleep Safety Using Shape-Changing Interfaces

Sleep plays an integral role in human health and is vitally important for neurological development in infants. In this study, we propose the PneuMat, an interactive shape-changing system integrating sensors and pneumatic drives, to help ensure sleep safety through novel human-computer interaction. This system comprises sensor units, control units and inflatable units. The sensor units utilize information exchange between infants and the system, and detect the infant's sleeping posture, sending raw data to control units. For better sleep experience, the inflatable units are divided into nine areas. The inflatable units are multi-mode, can be independently inflated in different areas, and can be inflated in different areas together. We aim to ensure sleep safety by ensuring that infants stay in a safe sleeping position while in bed, by autonomously actuating the PneuMat's shape-changing capability. In this article, we describe the division of PneuMat, the design of the control unit, integration of the sensors and our preliminary experiments to evaluate the feasibility of our interaction system. Finally, based on the results, we will discuss future work involving the PneuMat.
Super Resolution of Medical Images Using Generative Adversarial Networks

This paper aims to present a new model called SRMIGAN that performs super-resolution for MRI and CT medical images to help doctors reach a better diagnosis. This model, SRMIGAN adopts deep learning by applying generative adversarial networks technique. It is developed using MSE loss and by exploiting different optimization techniques. This model is compared to other adopted models by using both objective and subjective metrics. Hence PSNR, SSIM, and mean opinion score results are included. The results show that our model beats the other examined models.
Transformation from Web Pages to Optimal Normal Form Database Schema Using a Conceptual Schema Approach

Web pages and their embedded documents are a good source of information. However, issuing complex queries directly on web pages using direct pattern-matching techniques are challenging tasks that require lengthy procedural programming. Also, programmers working directly on web pages are in charge of the search path and routing, which depend on individual document structures and affect the correctness, completeness, and performance of the results. In contrast, relational databases are well-structured and backed by mathematical principles. Well-designed relational database structures are known to be anomalies-free. The standard relational database language, SQL, is a non-procedural language that defines the required results precisely. Query results are both correct and complete. Performance issues are handled by intelligent query optimizers employed by modernday Database Management System (DBMS). This paper suggests an approach that transforms documents embedded on web pages in HTML format to corresponding relational database structures and populations. Functional dependencies (FDs) and multi-valued dependencies (MVDs) obtained from documents on the webpages are used to construct conceptual schema diagrams, which are further transformed into the Optimal Normal Form (ONF) relational database structures. In this research project, the Object Role Model (ORM) conceptual schema model is employed. The paper discusses the ORM and the rationales behind its usage. The detection of FDs and MVDs from webpage documents and the technical properties of the ONF relational database structures. Illustrated examples are also provided.
Self-Selective Context for Interaction Recognition

Human-object interaction recognition aims for identifying the relationship between a human subject and an object. Researchers incorporate global scene context into the early layers of deep Convolutional Neural Networks as a solution. They report a significant increase in the performance since generally interactions are correlated with the scene (i.e., riding bicycle on the city street). However, this approach leads to the following problems. It increases the network size in the early layers, therefore not efficient. It leads to noisy filter responses when the scene is irrelevant, therefore not accurate. It only leverages scene context whereas human-object interactions offer a multitude of contexts, therefore incomplete. To circumvent these issues, in this work, we propose Self-Selective Context (SSC). SSC operates on the joint appearance of human-objects and context to bring the most discriminative context(s) into play for recognition. We devise novel contextual features that model the locality of human-object interactions and show that SSC can seamlessly integrate with the State-of-the-art interaction recognition models. Our experiments show that SSC leads to an important increase in interaction recognition performance, while using much fewer parameters.
Information Security Using the Ensemble Approach of Steganography and Cryptography

Information security is a buzz world that has made the working philosophy of businesses, administration and society more complex. Cryptography and steganography are the twin technologies that form the building block of data security in various operating environments including the networked systems. We find that most of the security endeavors use these technologies in isolation. We propose that data can be made more secure by applying the multifold fold techniques wherein we convert the plain text into cipher text and then embed it to other media by applying steganography technique. We establish that this ensemble approach can make the security feature more robust.
Development of Personal Identification Number Authorization Algorithm Using Real- Time Eye Tracking &amp; Dynamic Keypad Generation

The digital financial transaction is on a continuous rise and going to be the order of the day. These transactions rely on the entry of the Personal Identification Number (PIN) by the user. The PIN is a common user authentication method for many applications such as ATM's, unlocking personal devices. Cyber-crimes are committed by shoulder surfing or thermal tracking. PIN entry is found vulnerable to password attacks such as shoulder surfing or thermal tracking. Intruders try to gain passwords or personal identification numbers (PIN) by glancing over the user's shoulder and observing the pattern of PIN entry (shoulder surfing). Thermal tracking is another method followed by cyber thieves, using the heat traces to decode the entered PIN by the user. This paper proposes a novel approach to authorization of PIN. To demonstrate the same, a web application is developed with trio-based authentications using machine learning techniques. Initially, the application detects and recognizes the user's face. A dynamic keypad is displayed which prompts the user to provide input PIN via eye blink. User's eye is detected and monitored by the application in order to capture the PIN and verifies the same with the existing PIN in the database. On a successful PIN verification process, the application allows the user to proceed with the transaction. The results show the proposed novel approach is better than existing approaches.
Path Planning Algorithm for UAV in Complex Environment Based on State Change

Based on the physical phenomenon that the voltage across the ideal inductance element changes with the ideal power supply voltage in the circuit, a new path planning algorithm in the complex environment of UAVs is proposed. Aiming at the tracking problem of dynamic targets, a state transition matrix is proposed to realize the tracking of dynamic targets; for the obstacle avoidance problem of dynamic obstacles, the state change of the UAV is used to define the amount of state change to achieve dynamic obstacle avoidance; using state superposition, to achieve obstacle avoidance and tracking of dynamic targets in complex environments. The simulation results show that this method can effectively avoid obstacles and track dynamic targets, with low computational complexity and strong real-time performance.
PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection

Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms. Liquid State Machine (LSM) is a bio-inspired RC model that mimics the cortical microcircuits and uses spiking neural networks (SNN) that can be directly realized on neuromorphic hardware. In this paper, we present a novel Parallelized LSM (PLSM) architecture that incorporates spatio-temporal read-out layer and semantic constraints on model output. To the best of our knowledge, such a formulation has been done for the first time in literature, and it offers a computationally lighter alternative to traditional deep-learning models. Additionally, we also present a comprehensive algorithm for the implementation of parallelizable SNNs and LSMs that are GPU-compatible. We implement the PLSM model to classify unintentional/accidental video clips using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Oops</i> dataset. From the experimental results on detecting unintentional action in a video, it can be observed that our proposed model outperforms a self-supervised model and a fully supervised traditional deep learning model. All the implemented codes can be found in our repository <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sadimanna/Parallelized_LSM_for_Unintentional_Action_Recognition</uri> .
Evaluating Bug Prediction under Realistic Settings

Bug prediction is expected to reduce the cost of quality assurance. To build a reliable bug prediction model, we should use realistic settings that satisfy all three of the following conditions. (1) We should build a dataset in a way that allows us to evaluate the prediction performance of the model correctly. (2) We should adopt the optimal granularity of bug prediction to minimize the cost of quality assurance. (3) We should use a dependent variable that correctly represents the presence or absence of bugs in the software modules to be predicted. However, no research has been conducted on bug prediction models built under the above realistic settings. Consequently, we established the following two objectives in this research. (1) We experimentally evaluate the prediction performance of bug prediction models built under realistic settings. (2) We propose techniques to improve the prediction performance of bug prediction models built under realistic settings. The first objective has now been achieved. Our experimental results show that the F-Measure of the bug prediction models built under realistic settings is only 0.19. Thus, there are still some issues to be solved to build a high-performance bug prediction model under realistic settings.
How Do Users Answer MATLAB Questions on Q&amp;A Sites? A Case Study on Stack Overflow and MathWorks

MATLAB is an engineering programming language with various toolboxes that has a dedicated Question and Answer (Q&A) platform on the MathWorks website, which is similar to Stack Overflow (SO). Moreover, some MATLAB users ask their questions on SO. This paper aims to compare these two Q&A platforms to see what kind of questions are asked and how developers answer these questions in each platform. The result of our analysis on 80,382 MATLAB questions on SO and 266,367 questions on MathWorks show that MATLAB questions on topics ranging from the MATLAB software installation to questions related to programming received high votes and accepted answers on MathWorks. However, the questions about basics of programming such as plots, functions, and variables and questions on converting MATLAB code to other programming languages are very likely to receive answers on SO. Our detailed analysis on SO shows that users answer MATLAB questions with the same rate of the accepted answer as other popular programming languages like Java and Python, but the rate of unanswered questions and questions without an accepted answer for Simulink and the three most popular MATLAB toolboxes -- image processing, signal processing, and computer vision -- are very high. To analyze the evolution of MATLAB questions on SO, we studied 80,382 MATLAB questions using the SOTorrent dataset. The patterns in MATLAB questions' evolution are: 1) Most of the revisions to questions are text-related and not on code snippets. 2) Most of the code-related revisions were performed by the original poster (OP). 3) Non-original posters (Non-OPs) usually revise code snippets' appearance, while OPs usually revise code snippets' content and logic.
An Enhanced Mathematical Model For Cloud Based Data Oriented Job Analysis

Abstract Data processing or data analytics is the common functionality that is attached to most of the real world applications. The amount of processing required for data oriented tasks or jobs are quiet high. To resolve the processing issue the most common approach deployed is by using a high performance cluster. Setting a cluster over real time infrastructure leads to a very expensive solution. A Cloud based infrastructure remains as an ideal support for setting up a cluster. Managing the cluster over a Cloud is a challenging task as allocation of infrastructure based on the task schedule is a critical parameter. The proposed mathematical model introduces a strategy to allocate the infrastructure and manage the load of the cluster based on Queuing model. The experimental setup is made on top of private cloud and Hadoop based data processing jobs are tested. The proposed data oriented resource optimizer enhances the performance of the cluster by balancing the increased load due to data processing jobs. The result shows the enhanced improvement in performance compared to default resource manager.
Semantic Segmentation Refinement Using Entropy and Boundary-guided Monte Carlo Sampling and Directed Regional Search

Semantic segmentation requires both a large receptive field and accurate spatial information. Although existing methods based on a fully convolutional network have greatly improved the accuracy, the prediction results still do not show satisfactory when parsing small objects and boundary regions. We propose a refinement algorithm to improve the result generated by the front network. Our method takes a modified double-branches network to generate both segmentation masks and semantic boundaries, which serve as refinement algorithms' input. We creatively introduce information entropy to represent the confidence of the neural network's prediction corresponding to each pixel. The information entropy combined with the semantic boundary can capture those unpredictable pixels with low-confidence through Monte Carlo sampling. Each selected pixel will serve as the initial seed for directed local search and refinement. According to the initial seed, our purpose is tantamount to searching the neighbor high-confidence regions, and the re-labeling approach is based on high-confidence results. Remarkably, our method adopts a directed regional search strategy based on gradient descent to find the high-confidence region effectively. Our method can be flexibly embedded into the existing encoder backbone at a trivial computational cost. Our refinement algorithm can further improve the state of the art method's accuracy both on Cityscapes and PASCAL VOC datasets. In evaluating some small objects, our method surpasses most of the state of the art methods.
Synthesized 3D models with smartphone based MR to modify the PreBuilt environment

The past few years have seen an increase in the number of products that use AR and VR as well as the emergence of products in both these categories i.e. Mixed Reality. However, current systems are exclusive to a market that exists in the top 1% of the population in most countries due to the expensive and heavy technology required by these systems. This project showcases a system in the field of Smartphone Based Mixed Reality through an Interior Design Solution that allows the user to visualise their design choices through the lens of a smartphone. Our system uses Image Processing algorithms to perceive room dimensions alongside a GUI which allows a user to create their own blueprints. Navigable 3D models are created from these blueprints, allowing users to view their builds. Following this, Users switch to the mobile application for the purpose of visualising their ideas in their own homes (MR). This System/POC showcases the potential of MR as a field that can be explored for a larger portion of the population through a more efficient medium.