Reviewerly/exHarmony huggingface dataset:
Using Virtual Reality Technology to Visualize Management of College Assets in the Internet of Things Environment

This article studies the visual management of college assets under the changing environment of the Internet of Things, uses virtual reality technology to discuss the mechanism of its visualization, and studies the technical selection from the aspects of system architecture, database, and message middleware to provide reliable colleges and universities asset management, provide a complete authority distribution management mechanism, have a safe and reliable data storage mechanism and good scalability. And according to this goal, the overall architecture of the system is designed. Under this architecture, the main modules of the college asset management system divided into college asset information management, information release management, and other modules. To meet these functional modules, related diagrams are designed. The diagram creates a data table corresponding to it. The emphasis is on the systematic analysis of system performance and uses case modeling, static model, and dynamic model based on UML. The overall design of a three-tier logical structure including presentation layer, business logic layer, and data access layer, six functional modules including business process management, platform visualization management, application management, data storage, data processing, and data asset security control. Using the MySQL database and the database design method based on the E-R model, the definition of data entities was completed and the table structure design was carried out. It proved that the functions of the platform are in line with actual needs, and the performance can also meet the business requirements of the visualized management of college assets. It can manage data assets well and has high practical application value.
A Network for Makeup Face Verification Based upon Deep Learning

Makeup, derived from the human pursuit of beauty, it changes the image of people appearance, brings more beautiful enjoyment and spiritual pleasure. However, recent studies have shown that facial makeup have a negative effect on face verification. To solve this problem, we formulate an end-to-end deep learning network which is composed of a stem CNN and a novel mapping module. Specifically, we pre-train our framework on a comprehensive dataset and fine-tune our mapping module on makeup datasets. Then we experimentally validate the proposal on these datasets. Experimental results demonstrate that the proposal achieves promising performance compared to the existing state-of-the-art methods.
Secure Agile Software Development: Policies and Practices for Agile Teams

In recent years there has been a significant shift from traditional development towards agile. Agile adoption has been reported to result in more efficient and productive projects. Information security is becoming an important entity for most organizations. Cyber security has been a major concern in the digital world. However, despite its importance, agile methodologies hardly address the security requirements. In result, many security problems have been exposed in recent years, often leading to financial and social losses. As software is one of the computational assets most exposed to security threats, it is imperative that its development process includes special attention to security requirements. In this scenario, the need arises to include safety practices in the daily life of agile teams. In this paper, we highlight a security policies that presents practices suitable for agile teams. In order to do that this study (a) synthesizes the extant literature in this domain; (b) prioritizes the agile security practices; (c) groups these practices in policies based on the results of a workshop in a cyber security company (d) evaluates the policies based on security and agile specialist assessment; and (e) considering the feedback, presents the final security policies for software development agile teams.
Jupiter: A Modern Federated Learning Platform for Regional Medical Care

In this paper we propose Jupiter, an easy-to-use, secure and high-performance federated learning platform for regional medical care. Jupiter provides innovative programming abstractions to make data tunning more efficient as well as high-performance infrastructures to accelerate secure aggregations of parameters. Jupiter employs a stateful design with a bunch of optimizations and leverages popular techniques like SDN, DPDK and Intel SGX. The experiments show that with a low memory footprint, the throughput of single aggregator can reach 300MB/sec(with slice size fixed to 64KB), and the aggregation primitive we built can process 11k aggregations per second.
L-CO-Net: Learned Condensation-Optimization Network for Segmentation and Clinical Parameter Estimation from Cardiac Cine MRI

In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology patient groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool), and 90.0% (LV Myocardium) with five-fold cross-validation and yielded similar clinical parameters as those estimated from the ground-truth segmentation data. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning, and guidance applications.
A Literature Review: A Novel Human Vital Sign based Wheel Chair Cum Stretcher for Disabled Person

In the recent years a census indicates that around 700 million persons suffer from some kind of disability or handicap. There is an increase in the elderly population around the globe. This increase their need of support structures for enabling comfort and independent life. The phenomenon increases attention to the scientific community. A wheelchair cum Stretcher the pre-existing technique in this domain includes either wheel cum stretcher that are joined by linkages or mechanical systems. The disadvantage of this technique mainly involves high mechanical strength needed for modifying or conversion. So therefore a solution by incorporates wheelchair cum stretcher. Therefore the goal of the survey is to criticize the researchers and compare between the various types of mechanisms involved in the designing of wheelchair cum stretcher in the previous decade. Thus by this way we would like to compare between various approaches their pros and cons, thus helping the upcoming researchers to design a device as per the necessities overcoming the problems or constraints faced in the existing design.
Developing Dialog Manager in Chatbots via Hybrid Deep Learning Architectures

Dialog Manager has played a great role in conversational AI so much, so that it is also called the heart of a dialog system. It has been employed in task-oriented Chatbot to learn the context of a conversation and come up with some representation which helps in executing the task. For example, booking a restaurant table, flight booking, movie tickets, etc. In this paper, a dialog manager is trained in a supervised manner in order to predict the best response given the latent state representation of the user message. The latent representation is formed by the Convolution Neural Network (CNN) and Bidirectional Long Short Term Memory network (BiLSTM) with attention. An ablation study is conducted with three different architectures. One of them achieved a state-of-the-art result in turn accuracy on babI6 dataset and dialog accuracy equivalent to the baseline model.
EPIE Dataset: A Corpus for Possible Idiomatic Expressions

Idiomatic expressions have always been a bottleneck for language comprehension and natural language understanding, specifically for tasks like Machine Translation (MT). MT systems predominantly produce literal translations of idiomatic expressions as they do not exhibit generic and linguistically deterministic patterns which can be exploited for comprehension of the non-compositional meaning of the expressions. These expressions occur in parallel corpora used for training, but due to the comparatively high occurrences of the constituent words of idiomatic expressions in literal context, the idiomatic meaning gets overpowered by the compositional meaning of the expression. State of the art Metaphor Detection Systems are able to detect non-compositional usage at word level but miss out on idiosyncratic phrasal idiomatic expressions. This creates a dire need for a dataset with a wider coverage and higher occurrence of commonly occurring idiomatic expressions, the spans of which can be used for Metaphor Detection. With this in mind, we present our English Possible Idiomatic Expressions (EPIE) corpus containing 25,206 sentences labelled with lexical instances of 717 idiomatic expressions. These spans also cover literal usages for the given set of idiomatic expressions. We also present the utility of our dataset by using it to train a sequence labelling module and testing on three independent datasets with high accuracy, precision and recall scores.
Digital Procurement on Systems Applications and Products (SAP) Cloud Solutions

The number of businesses has exploded over the past few decades. This increased competition in the market between different products. The final price of the product is what distinguishes it from all its competitors. To cut down the final costs a business needs to optimize its internal processes. Procurement is the obtaining of goods to create value for spend. Traditional procurement is a haphazard process when done manually. Digitalizing the procurement process can exponentially optimize the process by increasing spend visibility. But the problem arises when choosing from among the hundreds of procurement tools available in the market. The objective of this paper is to spot out why SAP Ariba is the best choice for the problem of procurement. SAP Ariba is a cloud-based solution. Cloud solutions like Ariba provide a low risk of data loss, redundancy, and older data. This paper concentrates on the working of SAP Ariba operational and strategic procurement tools, its differentiating factors, and its advantages.
Surface Electromyography based Pakistani Sign Language Interpreter

Electromyography signal is the electrical activity of the signals that quantitatively measured and it provides a significant amount of information related to various muscular activities. This research focuses on surface Electromyography (sEMG) technique for the detection of electrical signals that are produced as a result of hand movements for various alphabetical signs of Pakistani Sign Language (PSL). The data set was gathered using the BIOPAC system and 30 signals per alphabet were collected giving us a total of 26 classes. The pre-processing of data was established using Empirical Mode Decomposition and a total of 15 features were extracted for developing distinct characteristics in discrete classes. Different features from the time domain, spectral domain, statistical domain, and many other domains were fed to Linear Discriminant Classifier for the classification of data set. Statistical evaluation parameters were utilized to validate the results obtained through classification along 10-fold cross-validation. The results obtained from the study show an accuracy of 81.0%, a sensitivity of 84.08%, and specificity of 84.7%. Moreover, this study serves as a stepping stone towards the use of EMG for the interpretation of Pakistani Sign Language.
Improved Sensors Based on <i>Scheimpflug</i> Conditions and Multi-Focal Constraints

Binocular vision sensor composed of two traditional camerasis widely utilized in many fields. However, there are still twodeficiencies, i.e. limited measurement area and weakconstraint for feature matching. In this paper, we firstintroduced measurement principle of the Scheimpflug camera and its calibrationmethod. Then an improved binocular vision sensor composed of two Scheimpflug cameras is constructed. Measurement area is maximized.Then, a trinocular vision sensor as an example of the multi-viewstereo vision sensor is analyzed and multi-focal constraint basedmeasurement model is given. Feature matching according to theconstraint of a trifocal tensor is strict, which is verified by relatedexperiments. Calibration procedures for the camera and improved visionsensor are detailed. Experiment results show that measurement area ofour improved vision sensor increases up to 86.2% (when tilted angleis 4°). Root mean square error of distance is0.107 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> / <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">00</sub> with regard to the measurementarea.
Approach to Robotic Mobile Platform Path Planning Upon Analysis of Aerial Imaging Data

The research is focused on solving one of the main problems of robotic system navigation—building an energy-efficient trajectory. A method of trajectory planning is proposed, according to which images obtained using an unmanned vehicle camera are stitched into an orthomosaic image, on which the Mask R-CNN neural network detects all static obstacles. In addition to creating a map of the area, the resulting images are also used to create a 3D model of the area. The developed method enables the robotic to find all static obstacles, as well as to identify all terrain features. Based on the obtained data on the state of the terrain and the presence of static obstacles, the system finds the most energy-efficient path.
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity

This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity. The proposed method aims to recommend the best FS method from four candidate FS methods for any given dataset. This is achieved by firstly constructing a large training data repository using data synthesis. Six meta features that represent the characteristics of the training dataset are then extracted. The best FS method for each of the training datasets is used as the meta label. Both the meta features and the corresponding meta labels are subsequently used to train a classification model using a fuzzy similarity measure based framework. Finally the trained model is used to recommend the most suitable FS method for a given unseen dataset. This proposed method was evaluated based on eight public datasets of real-world applications. It successfully recommended the best method for five datasets and the second best method for one dataset, which outperformed any of the four individual FS methods. Besides, the proposed method is computationally efficient for algorithm selection, leading to negligible additional time for the feature selection process. Thus, the paper contributes a novel method for effectively recommending which feature selection method to use for any new given dataset.
Feature Point Extraction and Matching Method Based on Akaze in Illumination Invariant Color Space

Visual SLAM is the technology that complete self-localization and build environment map synchronously. The feature point extraction and matching of the input image is very important for visual SLAM to achieve pose calculation and map building. For most of the literature feature point extraction and matching algorithms, the change of illumination may have a great impact on the final matching results. To address the issue, this paper proposes a novel feature point extraction and matching method based on Akaze algorithm (IICS-Akaze). Histogram equalization and dark channel prior theory are combined to construct a color space with constant illumination. Akaze algorithm is adopted for fast multi-scale feature extraction to generate feature point descriptors. The feature points are then quickly matched through the FLANN, and RANSC is introduced to eliminate the mismatches. In addition, the experiments on open data set are conducted in terms of feature extraction quantity, matching accuracy, and illumination robustness among the related methods. The experimental results show that the proposed method is able to accurately extract and match image feature points when the illumination changes dramatically.
Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks

In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user's intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered one of the most effective indicators among recently developed interaction techniques (e.g., face orientation estimation, body tracking, and gesture recognition). Previous gaze estimation techniques, however, are known to be effective only in a controlled lab environment under normal lighting conditions. In this study, we propose a novel deep learning-based approach to achieve a successful gaze estimation under various low-light conditions, which is anticipated to be more practical for smart interaction scenarios. The proposed approach utilizes a generative adversarial network (GAN) to enhance users' eye images captured under low-light conditions, thereby restoring missing information for gaze estimation. Afterward, the GAN-recovered images are fed into the convolutional neural network architecture as input data to estimate the direction of the user gaze. Our experimental results on the modified MPIIGaze dataset demonstrate that the proposed approach achieves an average performance improvement of 4.53%-8.9% under low and dark light conditions, which is a promising step toward further research.
An Efficient Software Defect Prediction Model Using Neuro Evalution Algorithm based on Genetic Algorithm

The main aim of Software Defect Prediction (SDP) is to identify the defect prone in source code, therefore to reduce the effort and time taken as well the cost incurred by it with guaranteeing the quality of software. The machine learning algorithms is used both code and non-code metrics are trained to predict software defects. This paper explores a knowledge of using code profiles as an alternative to traditional metrics to predict software defects. This proposed novel evolution algorithm proves to be more promising than any traditional machine learning approaches. The objective is to derive an efficient machine learning model that can predict the number of bugs the software project can produce while reaching the Quality Assurance (QA) Stage.
Topology-aware virtual machine replication for fault tolerance in cloud computing systems

Extensive use of cloud services has led to the need for service reliability for both the service provider as well as the users. In the Infrastructure as a Service cloud computing model, it is critical to ensure the reliability of resources such as virtual machines (VMs); storage networks etc. The p aper proposes a replication-based fault tolerance method to improve the reliability of VM-based services. The proposed approach utilizes a data centre topology-aware method to select physical machines where replicas of VMs may be placed. The selection criteria for VM replica placement favour the physical machines at lower CPU temperature, more available space and at a lower edge length from the physical machine that primarily hosts the VM. By avoiding deteriorating physical machines, this policy increases the probability of successful recovery if the VM or its host physical machine fails. The proposed approach has been evaluated using two metrics, namely recoverability and the total bandwidth consumed in the replication and recovery process. The performance of the approach has been compared with a random replica placement method as well as a state of art algorithm. The simulation results illustrate that the proposed approach provides higher reliability than the other methods.
Evaluation of appearance-based eye tracking calibration data selection

Eye tracking is a valuable topic in computer vision. Appearance-based eye tracking is a promising research direction in recent years. Convolutional neural networks (CNN) had been used in gaze estimation, which cover the significant variability in eye appearance caused by unconstrained head motion. With computation capability of consumer devices rapidly evolving, accurate and efficient appearance-based eye tracking has the potential for multipurpose applications. Person-independent networks have limit in improving gaze estimation accuracy. Person-specific network with calibration is more effective than person-independent approaches. Unlike classical eye tracking methods, appearance-based eye tracking has not a clear way to calibration. Our goal is to analyze the impact of calibration data selection and calibration target distribution on person-specific gaze estimation accuracy. We trained person-independent network and use SVR to calibration. We choose two kind of typical distribution targets to evaluation. Use different distribution targets to calibration achieves different accuracy.
An Implementation of Face Recognition with Deep Learning based on a Container-Orchestration Platform

As a lightweight alternative to a virtual machine, a container runs applications only with the necessary environmental variables, libraries, etc. Moreover, many more containers can be run on the same computer compared to traditional VMs, which take up a lot of computing resources. Currently, Docker container and Kubernetes (K8s), which is a container-orchestration platform, are very popular tools. In addition, K8s is a high availability (HA) system with many features that can provide containers to implement more applications. In this project, a face recognition application is implemented with deep learning on Kubeflow, which is a machine learning platform running on K8s. Also, the deep learning method output features instead of classifications. This method computes the distance between two images with Triplet loss function and Euclidean distance. K8s runs on the server as a private cloud, on which our face recognition application runs.
Association of Face and Facial Components Based on CNN and Transfer Subspace Learning for Forensics Applications

Face and facial components are the most significant pieces of evidence used in forensic applications. Component-based recognition and age-invariant face recognition are the key factors in forensic face recognition. A major challenge during a forensic investigation is the association of facial components with a relevant face and this paper addresses this concern using the proposed transfer learning methodology. Research has been carried out treating facial components and the face as individual and separate entities. However, they share common auxiliary information which is exploited to bring the face and facial components in a common subspace in the proposed method. This facilitates transfer of knowledge gained from a face and classification of facial components of a person using the same subspace. Stability and invariant features of the facial components catalyze a solution for the problem of recognition. Convolutional neural network (CNN) is used to extract the features of the biometrics and then they are given to a regularization framework for reducing the probability distribution difference between them. The system is trained using the face and facial components and then tested using either the full-face or individual facial components. For the transfer, the proposed Fisher linear discriminant analysis and locality preserving projection, a convolutional neural network-based algorithm gives 91% and 90% accuracy, respectively, which outperforms the Histogram of Gradient and Gabor methods for predicting an association.