Reviewerly/exHarmony huggingface dataset:
The Statistical Learning Methods In image processing and Facial Recognition

The aim of this paper is to develop a new approach for The Statistical Learning Methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. We will investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small low-resolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space (“face space”) to find the corresponding coordinators. The face space is composed of “Eigenfaces” or “Fisherfaces” which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files.
Interactive Volumetric Region Growing for Brain Tumor Segmentation on MRI using WebGL

Volumetric segmentation of medical images is an essential tool in treatment planning and many longitudinal studies. While machine learning approaches promise to fully automate it, they most often still depend on manually labeled training data. We thus present a GPU-based volumetric region growing approach for semi-automatic brain tumor segmentation that can be interactively tuned. Additionally, we propose multidimensional transfer functions for ray tracing that allow users to judge the quality of the grown region. Our implementation produces a full brain tumor segmentation within a few milliseconds on consumer hardware. The visualization uses adaptive resolution scaling and progressive, asynchronous shading computation to maintain a stable 60 Hz refresh rate.
Using Spatial Analysis Methods to Evaluate the Soil Contamination of Baghdad City, Iraq

Abstract The current study aims to identify soil pollutants from heavy metals The study utilized 40 topsoil (5 cm) samples, which adapted and divided into seven regions lies in Baghdad governorate, included (Al-Husainya,(Hs) Al-Doura (Do), Sharie Al-Matar (SM), Al-Waziria (Wz), Nharawan (Nh), Abu Ghraib (Abu) and Al-Mahmoodyia (Mh)). Spatial distribution maps of Nickel (Ni), Manganese (Mn), Lead (Pb) and Zinc (Zn) were created for Baghdad city using Geographic Information Systems (GIS). The concentrations of four heavy metals in the soil of different area of Baghdad were measured and observed using XRF instrument. The result found highest values of Pb and Zn at the middle of the Baghdad in (Wz) area and it reached to 35110 and 720 ppm respectively which exceeded allowed limits values. . While the measurements of the Mn and Ni recorded highest values in (Do) area and reached to 1020 and 170ppm respectively and was within the allowed limits of standard values of soil contamination. The prediction maps of Zn, Pb showed high accumulations in the center and western part of Baghdad city. While the maps of the elements of Mn, Ni showed low accumulations in the eastern and southern part of Baghdad. By using prediction models through which pollution levels can be determination in areas unsampled, which contributes in economic benefits, such as decrease of the required amount of samples in any project, decrease cost of analyzing and using friendly tools. The methodologies incorporated with GIS helps to give a clear picture for decision-makers to take the necessary treatments for polluted areas.
Tri-training for Dependency Parsing Domain Adaptation

In recent years, the research on dependency parsing focuses on improving the accuracy of the domain-specific (in-domain) test datasets and has made remarkable progress. However, there are innumerable scenarios in the real world that are not covered by the dataset, namely, the out-of-domain dataset. As a result, parsers that perform well on the in-domain data usually suffer from significant performance degradation on the out-of-domain data. Therefore, to adapt the existing in-domain parsers with high performance to a new domain scenario, cross-domain transfer learning methods are essential to solve the domain problem in parsing. This paper examines two scenarios for cross-domain transfer learning: semi-supervised and unsupervised cross-domain transfer learning. Specifically, we adopt a pre-trained language model BERT for training on the source domain (in-domain) data at the subword level and introduce self-training methods varied from tri-training for these two scenarios. The evaluation results on the NLPCC-2019 shared task and universal dependency parsing task indicate the effectiveness of the adopted approaches on cross-domain transfer learning and show the potential of self-learning to cross-lingual transfer learning.
Managing High Privileged Accounts in IT Enterprise: Enhanced Security Infrastructure

Account credentials play a vital role in the continuity of an organisation. However, studies concluded that there are still many organisations that fail to protect their high privileged accounts. While some companies are starting to be aware of this issue, many organisations still pay little attention to it. As a result, high privileged accounts in an organisation can get hacked easily, leading to data breaches. Organisations are now looking for a service that can help manage all these high privileged accounts inside one solution. Therefore, the authors have proposed a high privileged account management solution to ease the administrators in handling high privileged accounts.
Multi-agent Story-based Settlement Generation

Video game content creation is a creative task that has typically been performed by 3D artists. While procedurally generated worlds provide the opportunity to create arbitrarily large cohesive environments, if the generated content lacks an integrated narrative, the environment may begin to feel generic and auto-generated; human artists can use story narratives to help them create 3D worlds which feel more "alive". This paper describes the StoryViz system which uses a short story as inspiration for generating a 3D settlement in Minecraft, leveraging swarm intelligence to optimize a set of rule-based interest functions. A user survey evaluating the system’s generated settlements provides a baseline for further development of the story visualization task.
Application of regression function model based on panel data in bank resource allocation financial risk management

Abstract Based on the traditional form of the endogenous growth model, and for it to increase the micro-foundation that includes the homogeneous and representative bank resource allocation, this paper constructs an endogenous economic growth model that includes the investment structure of the residential sector and financial deepening. Using China’s prefecture-level data proves that due to the inherent difference between the central planner’s single equilibrium solution and the family’s decentralised equilibrium solution, when the residential sector’s preference for real estate investment causes the investment structure to deviate from the optimal level of society, the increase in the proportion of real estate investment The allocation efficiency of financial resources has a significant inhibitory effect and drags down the realisation of long-term potential economic growth. In the absence of a central planner in a market economy, increasing leverage may not mean financial deepening, but may reduce financial efficiency (FEt) and accumulate systemic financial risks.
Stacked Autoencoders Deep Learning Approach for Left Ventricular Localization in Magnetic Resonance Slices

Deep learning (DL) is an effective method for medical object detection. Studies show that deep networks can achieve accuracy in medical segmentation and detection tasks. This is due to the depth and training methods of deep networks which allows them to derive different levels of abstractions of input mages. In this paper, the left ventricle detection task is carried out using a deep network called stacked auto-encoder (SAE). The networks take off this task as a binary classification task wherein left and non-left ventricles cropped images are being recognized by the SAE. Once the network recognizes left and non-left ventricles, the whole task starts by initiating a sliding window that moves through the whole magnetic resonance (MR) slice till a left ventricle is detected. Experimentally, the network showed effective detection performance when target images are noisy as it is seen that it can detect left ventricles in target images with up to 10% of salt and pepper noise.
Volcano distribution and tectonics: A planetoidic perspective

ABSTRACT Volcanic activity is ultimately controlled by processes that take place many kilometers beneath the surface of a planet. The deeper processes are unlikely to reach the surface without some degree of modification at shallower levels. Nevertheless, traces of those deeper processes may still be found when examining the final products at the surface. In this work, it is shown that it is possible to gain insights concerning the integrated contribution of deep structures through the study of the spatial distribution of volcanic vents at the surface. The method here described relies on the systematic use of increasing smoothing factors in Gaussian kernel estimations. The sequences of probability density functions thus generated are equivalent to images obtained with an increasing wavelength, which therefore have the power to penetrate deeper below the surface. Although the resolution of this method is much smaller than the resolution provided by seismic or other geophysical surveys, it has the advantages of ease of implementation, extremely low cost, and remote application. Thus, the reported method has great value as a first-order exploration tool to investigate the deep structure of a planet, and it can make important contributions to our understanding of the volcano-tectonic relationship, not only on Earth, but also across the various bodies of the solar system where volcanic activity has been documented.
Multilevel Security Embedded Information Retrieval and Tracking on Cloud Environments

Cloud Computing is emerging technology that provides services of storage and platform software to large organizations, but some of them are still hesitant to shift their setups on the cloud due to security issues and risks. Thus, it is important to address the security issues and problems in cloud systems. In this research we contributed to a multilevel security (MLS) framework based on data sensitivity and security that provides adequate level of data security based on various classifications and categories. The proposed multilevel security embedded information retrieval tool in this paper encompasses suitable access control combined with Security Enhanced Linux (SELinux) that facilitates classification of the data based on subsequent changes in the sensitivity levels of the data and changes in the security measures to cope with the dynamic and vulnerable changes in cloud security threats. To implement the proposed MLS framework, the SELinux system is applied as a testbed to retrieve information and track the history of the data retrieved.
Effect on model performance of regularization methods

However, this is not possible in some cases, so using regularization techniques is a good solution.Regularization prevents the model from learning a more complex or flexible model by adjusting the coefficient estimates towards zero for the case of overfitting and underfitting the data.In this study, the issue of overfitting is discussed.Regularization layers such as data augmentation[2], Dropout[3] and batch normalization[4], group normalization[5], Layer normalization[6], Instance Normalization[7] methods are used to prevent the overfitting problem.In addition, the methods such as dropconnect[8 ], maxout[ 9], Lasso [10], and weight normalization [11] are also used to prevent overfitting problems.However, these methods, which have high network resilience and can fail in low data applications, cannot take benefit of input invariants.Data augmentation, an effective technique to increase the amount and variety of data, is used to create more data from existing data by
Fabric-Based Computing: (Re)examining the Materiality of Computer Science Learning Through Fiber Crafts

Abstract Fiber crafts, such as weaving and sewing, occupy a tension-filled space within computing. While associated with domestic practices, fiber crafts have been recognized as a precursor of the earliest computers and continue to present sources of computational inspiration. The connections between fiber crafts and computing have the potential to uncover possibilities for computing to become more diversified in terms of materials, cultural practices, and ultimately people. To explore the promises of fiber crafts for STEM education, this qualitative dissertation built on constructionist and posthumanist perspectives to examine two fiber crafts (i.e., weaving and fabric manipulation) as contexts for computer science learning. Collectively, the dissertation effectively aligned fiber crafts with computational concepts and showed their potential as a promising context for computer science learning. The work further showed that materials used for STEM learning are non-neutral. Materials matter in what can be learned computationally. Lastly, guided by posthumanist perspectives, the dissertation uncovered computational learning as the process of producing physical expansions and highlighted learning as the process of how computational concepts physically change. The work has implications for theorizing learning, designing for learning, and educational practice. For example, the dissertation presents the utility of posthumanist perspectives as an additional theoretical approach to the study of learning that can surface and help address ongoing relational deficit orientations.
Cross-Organizational Data Sharing Technology for Data Management Platform in the Manufacturing Industry

This paper proposes a cross-organizational data sharing technology for data management platform in the manufacturing industry in order to improve and optimize manufacturing operations. We examined a technology that enables requesting and acquiring data among organizations by modeling the manufacturing process and providing models to other organizations. By specifying the range of data that can be provided to the outside for the created data model at the manufacturing site, it enables sharing the necessary and sufficient data with other organization and to keep the data confidential. We evaluated the proposed technology from the viewpoint of storage capacity. The data sharing method has two management type, centralized management and distributed management. As a result of examining for the feasibility of the proposed distributed management technology, the required maximum storage capacity is 128.6 GB per each organization' data management system. Therefore, we got a certain outlook that distributed management technology is feasible.
Identifying Valid Robot Configurations via a Deep Learning Approach

Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures an occupancy grids representation of the robot's workspace, and a Multilayer Perceptron (MLP), which efficiently predicts the collision state of the robot using the output from the CAE. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces. The results show that (1) the framework is computationally efficient in all investigated problems, and (2) the framework generalizes well to new workspaces.
Hand Gesture Recognition Using CNN &amp; Publication of World's Largest ASL Database

Sign language is used throughout the world by the hearing impaired to communicate. Recent advancements in Computer Vision and Deep Learning has given rise to many machine learning based translators. In this research paper, a solution to recognize the English alphabet presented as static signs in the American Sign Language (ASL) is proposed. The classifications are achieved by a four layer CNN. The model is trained and tested on a dataset created for this paper. This dataset will be published as a contribution to the community and is currently the world's largest ASL database consisting of 624,000 images. Split into two sections, the database contains images in both the IR and RGB spectrum. Classifications on both sets of data achieve state-of-the-art results when compared to similar research. An accuracy of 99.89% and 99.91 % are achieved when classifying the IR and RGB datasets respectively.
Evaluating the role of environment management system based on artificial intelligence

The rise of artificial intelligence (AI) and its increasingly broad impact on various industries necessitates an examination of its impact on the accomplishment of the Sustainable Development Goals. We discovered, to use a consensus-based expert information gathering method, that AI may facilitate the achievement of objectives throughout all objectives, but it can also hinder aims. However, present study priorities neglect critical issues. AI's rapid progress must be accompanied with the appropriate legislative understanding and supervision for AI-based technologies that enable long growth. Neglect to do so may results in inconsistencies in openness, safety, and ethical norms. Appropriate environmental management is a crucial element in the optimal use of energies. Environmental management increases the standard for process innovation. If a substitute is employed, the environmental management transition, from ecologically to actuality, must be accompanied by sustainable economic growth. Through environmental and economic policies, as well as market administration, the assets management systems understand the main advantages of the ecological environment. Existing protection of the environment quantifies and environmental management strategies centered on (Field Programmable Gate Arrays) FPGA-based machine having to learn environmental ecological significance overview of fast development must construct an ecological compensatory method via current FPGA environmental administration.
Sentiment Analysis of Facebook Posts Towards Good Governance Using SVM Algorithm: A Framework Proposal

Good governance means that the institutions are able to meet the needs of its stakeholders. It is participatory and inclusive through considering the opinions and sentiments of the stakeholders for decision-making. To analyze the massive number of opinions and sentiments, sentiment analysis is necessary. Thus, this study intends to devise and propose a framework for sentiment analysis that will be used by the City College of Calamba (CCC) towards the attainment and maintenance of good governance. The proposed framework presented the phases for sentiment analysis to develop an application/software: data acquisition, data pre-processing, data classification, validation and evaluation, and integration utilizing the Support Vector Machine (SVM) algorithm.
Evolutionary Method for Two-dimensional Associative Local Distribution Rule Mining

In this paper, we propose a rule discovery method that can reveal a combination of attributes that provide characteristic distribution of two consecutive variables of interest directly at high speed in a database having many attributes. In numerical association rule mining (NARM), when using association rules that handle consecutive numerical data values, it is difficult to heuristically extract rules that focus on statistical distributions of numerical data. The proposed method enables quick discovery of the number of rules necessary for prediction purposes using evolutionary calculations characterized by a network structure and a strategy to pool solutions throughout generations. This effectively finds attribute combinations in which the values taken by two consecutive variables of interest are both narrow ranges and can address instance-based two-dimensional regression problems in a short time. As an evaluation experiment, a prediction task using musical data linked with map data was carried out, and the discovery condition of the flexible rule was set. This resulted in realizing a high coverage rate in the instance-based regression problem, and the proposed method was effective in rule discovery based on the statistical distribution in NARM.
Denoising 3D Human Poses from Low-Resolution Video using Variational Autoencoder

We tackle the problem of refining and denoising a series of 3D human poses estimated from a low-resolution video. Low-resolution often causes the wrong pose estimation, e.g., left-right switching and the absence of keypoints. We propose to use the variational autoencoder (VAE) to remove these challenging noises. The VAE model utilizes time-series information and motion priors in denoising. From our experiments, the VAE model can reduce the pose estimation error (MPJPE) for poor-quality images by 24.37mm, from the original 105.53mm. This improves about 6.5 times over the traditional DCT approach. In addition, it removes jitters and generates smooth movements, which is helpful in recognition of human behaviors.
Optimal resource scheduling for SMEs using cloud in manufacturing sectors

Defining on a common policy which effectively suits all the production units, stakeholders and suppliers is very difficult to figure-out in highly populated countries like India. Manufacturing industries are ineffective in managing services, this is due to the communication gap between stakeholders. Approximately 48 million small and medium businesses are registered in India, according to a report by the Ministry of Micro, Small and Medium Enterprises (MSME). Among these numbers, SMEstend to contribute 40% of the total exports to India's industrial output, which is nearly 45%. As scheduling process majorly impacts on the time and cost of job completion, it is a critical part of the project. To efficiently schedule resources related to the casting industry, we propose a hybrid Ant Colony Optimization strategy. The efficiency of this enhanced algorithm was simulated to verify its effectiveness and the results show that, the proposed model minimizes both cost and time. It also maintains workload balance to maintain optimum resource utilization.