Reviewerly/exHarmony huggingface dataset:
O-Net: Dangerous Goods Detection in Aviation Security Based on U-Net

Aviation security X-ray equipment currently searches objects through primary screening, in which the screener has to re-search a baggage/person to detect the target object from overlapping objects. The advancements of computer vision and deep learning technology can be applied to improve the accuracy of identifying the most dangerous goods, guns and knives, from X-ray images of baggage. Artificial intelligence-based aviation security X-rays can facilitate the high-speed detection of target objects while reducing the overall security search duration and load on the screener. Moreover, the overlapping phenomenon was improved by using raw RGB images from X-rays and simultaneously converting the images into grayscale for input. An O-Net structure was designed through various learning rates and dense/depth-wise experiments as an improvement based on U-Net. Two encoders and two decoders were used to incorporate various types of images in processing and maximize the output performance of the neural network, respectively. In addition, we proposed U-Net segmentation to detect target objects more clearly than the You Only Look Once (YOLO) of Bounding-box (Bbox) type through the concept of a “confidence score”. Consequently, the comparative analysis of basic segmentation models such as Fully Convolutional Networks (FCN), U-Net, and Segmentation-networks (SegNet) based on the major performance indicators of segmentation-pixel accuracy and mean-intersection over union (m-IoU)-revealed that O-Net improved the average pixel accuracy by 5.8%, 2.26%, and 5.01% and the m-IoU was improved by 43.1%, 9.84%, and 23.31%, respectively. Moreover, the accuracy of O-Net was 6.56% higher than that of U-Net, indicating the superiority of the O-Net architecture.
On the Usage of Semantic Text-Similarity Metrics for Natural Language Processing in Russian

This work is devoted to the analysis of the applicability of text processing methods in natural languages to the problem of assessing the similarity of text sequences of arbitrary length in Russian. This task is a particular problem that finds its application in many areas of computational linguistics, primarily in the analysis of the effectiveness of the computer-generated text. The analysis revealed the advantage of using the BERTScore method, based on the semantic similarity of text attachments, especially in terms of the reliability and sequence of detecting semantic similarity of sentences compared to traditional text metrics based on character-by-character analysis of texts.
Toward Campus Mail Delivery Using BDI

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system.
Design of Control Algorithm of Wheeled Robot Based on Wi-Fi and Inertial navigation

This paper design a path planning smart car system based on STM32. It can use MPU6050 module to adjust the direction of route and the angle of the turning. Moreover, We use infrared modules and obstacle avoidance algorithms to complete the obstacle avoidance function, and the robot can move along a U-shaped trajectory. While the Wi-Fi module is used as communication to transmit data to the App developed on the Android platform, so as to control the moving distance of the car. We build the software system and the hardware system to test the control algorithm and the ability of avoiding obstacle. The robot runs 20 meters at a speed of 0.3m/s, and it is obtained that its yaw offset is about 1.3 degrees, and its trajectory offset is about 5cm. The results show that the robot can avoid obstacles on the ground and has certain stability, which provides a reference for the further development of a more intelligent robot car.
Real-Time Gesture Classification System Based on Dynamic Vision Sensor

A biologically inspired event camera being able to produce more than 500 pictures per second [1], has been proposed in recent years. Event cameras can achieve profound efficiency in addressing many drawbacks of traditional cameras, for example redundant data and low frame rate during classification. In this paper, we apply a Celex IV DVS camera to fabricate a four-class hand gesture dataset for the first time. Meanwhile, we propose a real-time workflow for reconstructing Celex event data into intensity images while implementing gesture classification with a proposed LeNet-based [2] network and keyframe detection method. More than 30 fps has been achieved with our proposed workflow on a laptop. Compared to the state-of-art work [3] with an accuracy of 99.3%, our proposed network achieves a competent accuracy of 99.75%.
Mixed Reality-Based Equipment Simulator for Construction Operations

PC-based equipment simulators are a safe, inexpensive, and efficient means of provide hands-on training to heavy equipment operators in the construction, mining, and agricultural sectors. However, the degree of immersion and realism for these simulators is very sub-par given their projection onto static computer screens. On the other hand, the advent of head mounted displays promises fully immersive, engaging, and realistic virtual environments to train equipment operators. The primary limitation of using HMDs for training operators is the inability to see their own hands inside the virtual environment without requiring special handheld controllers or gloves, which affects the fidelity of the simulated experience for the user and thereby negatively affects transferability of knowledge from the virtual to the real world settings. This paper provides an implementation of augmented virtuality to overlay real content in a virtual space by using a green screen-based mixed-reality application resulting in user being able to see and use only their hands to control equipment through realistic joystick controls as would be found inside equipment. This paper describes the framework used to create a reconfigurable virtual environment for training equipment operators and the system architecture to utilize the growing availability of head mounted displays to create high-fidelity equipment simulators that can be applied to train construction, mining, and agricultural equipment operators.
Class-Dependent Weighted Feature Selection as a Bi-Level Optimization Problem

Feature selection aims at selecting relevant features from the original feature set, but these features do not have the same degree of importance. This can be achieved by feature weighting, which is a method for quantifying the capability of features to discriminate instances from different classes. Multiple feature selection methods have shown that different feature subset can reduce the data dimensionality and maintain or even improve the classification accuracy. However, different features can have different abilities to distinguish instances of one class from the other classes, which makes the feature selection process a difficult task by finding the optimal feature subset weighting vectors for each class. Motivated by this observation, feature selection and feature weighting could be seen as a BLOP (Bi-Level Optimization Problem) where the feature selection is performed in the upper level, and the feature weighting is applied in the lower level by performing mutliple followers, each of which generates a set of weighting vectors for each class. Only the optimal feature subset weighting vector is retrieved for each class. In this paper, we propose a bi-level evolutionary approach for class-dependent feature selection and weighting using Genetic Algorithm (GA), called Bi-level Class-Dependent Weighted Feature Selection (BCDWFS). The basic idea of our BCDWFS is to exploit the bi-level model for performing upper level feature selection and lower level feature weighting with the aim of finding the optimal weighting vectors of a subset of features for each class. Our approach has been assessed on ten datasets and compared to three existing approaches, using three different classifiers for accuracy evaluation. Experimental results show that our proposed algorithm gives competitive and better results with respect to the state-of-the-art algorithms.
Machine Learning Enabled Sentiment Index Estimation Using Social Media Big Data

The right to speak is guaranteed by the First Amendment to the U.S. Constitution that includes the right to criticize others, and share opinion, comment on any matter. Comments on Social Media display people's political views related to specific racial, ethnic and religious minorities. People strong beliefs and views on any public interest can be determined by their comments on Social Media. This paper investigates and predicts user's views on sensitive and controversial topics such as racism and discrimination, using Twitter as a micro-blogging site. The process includes sentiment analysis and machine learning methods such as Named Entity Recognition (NER) and Polarity algorithm. Due to the unstructured nature of comments and the inaccuracy of the sentiment analysis a preprocessing routine must be developed prior to determining the sentiment. The sentiment Index can help to determine about what content people like or which content has the most sensitive issue.
Energy Forecasting Using an Ensamble of Machine Learning Methods Trained Only with Electricity Data

This work presents case studies on forecasting PV power production and electricity demand in Portugal. We study an ensemble of different machine learning methods to exploit the growing collection of energy supply and demand records. The ensemble uses only electricity data to forecast, since this data is available online for any forecasting horizon. The ensemble relies on offline training and online forecasting, by applying the most recent power measurements to trained models. The different machine learning methods perform different non-linear transformations to the same electricity data, thus introducing diversity in the ensemble. To assess the forecasting performance of this system, we consider two forecasting horizons relevant to the Internal Electricity Market, namely 36 hours ahead, relevant to the single day-ahead coupling, and 2 hours ahead, relevant to the single intraday coupling. The forecasting performance using only electricity data compares gracefully with the state-of-the-art and improves the reference accuracy in our case studies. Since the ensemble relies only on energy data, the results show that machine learning methods are useful to exploit energy big data towards efficient energy forecasting systems.
Sentiment-Based Deep Auto Encoder Recommender System (DAERS)

Sentiment analysis, an eminent domain of data mining, refers to elucidation and classification of sentimental contents exhibited on social media platform. Twitter is a popular micro blogging platform where many user tweet to exhibit their thoughts regarding different domains. Immense research has been carried out in the field of sentiment analysis varying from lexicon-based approach to intricate deep learning models. Information from user tweet-feeds can be used to make cross domain recommendations. User generated content (reviews and comments) crawled from web requires analysis to discover the latent thematic structure in the corpora of both source and target domains, with no user-item overlapping between the given domains. Our proposed approach incorporates the power of deep learning to exploit user sentiments that helps to infer ratings based on user reviews and thereby resolving data sparsity issue in target domain. Also, to address Cold Start Recommendations (CSR) for new users we put forward a low-rank Deep Auto-Encoder (DAE), trained back-to-back with no prior training of individual layers. Encoder in DAE maps the behaviour space to attributes space and Decoder reconstructs the behaviour space from attribute space. Three cruxes challenged in this paper include; domain-shift, CSR and spurious correlations. We hypothetically exhibit that: a) DAE models generalize exceptionally than the linear models, and b) Use of normalization technique such as Dropout at the representation layer is necessary to prevent over fitting.
A Study About the Use of OWL 2 Semantics in RDF-Based Knowledge Graphs

RDF-based knowledge graphs have been attracting increasing attention since Google popularized the term in 2012. However, historically, knowledge graphs are based on Semantic Web technologies. Many years ago, several works pointed out the lack of semantics in some RDF graph. So the question is whether semantics is there somewhere. Hence, we conducted an up-to-date large-scale study of the current state of the Web of data regarding the OWL 2 semantics to confirm or deny older results. Moreover, we propose an ontology to capture which OWL 2 features are defined or used in a given RDF-based knowledge graph and the tools to instantiate such an ontology.
Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies

We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with annotation projection. We use syntactic parsing as the auxiliary task in our multitask setup. Our annotation projection experiments from English to Czech show that our multitask setup yields 3.1% (4.2%) improvement in labeled F1-score on in-domain (out-of-domain) test set compared to a single-task baseline.
Enhancing Proportional IO Sharing on Containerized Big Data File Systems

Big Data platforms recently employ resource management systems, such as YARN, Mesos, and Google Borg, to provision computational resources. These systems adopt containerization to share the computing resources in a multi-tenant setting with low performance overhead and interference. However, it may be observed that tenants often interfere with each other on the underlying Big Data File Systems (BDFS), e.g., Hadoop File System, which have been widely deployed as a persistent layer in current data centers. A solution with systematic generality is to containerize BDFS itself to isolate and allocate its IO sources to multiple tenants. To this end, we conduct analysis on the ineffectiveness of proportionally sharing BDFS IO resource via containerization. This ineffectiveness is due to the scheduler of containerization in “pseudo-starvation” status, in which most of IO requests are backlogged in BDFS rather than in containerization scheduler. Without enough backlogged IO requests, existing schedulers might have to maximize device utilization rather than enforce proportional sharing policy. To resolve this ineffectiveness issue, we develop a cross-layer system called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BDFS-Container</i> , which containerizes BDFS at the Linux block IO level. Central to BDFS-Container, we propose and design a proactive IOPS throttling-based mechanism named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IOPS Regulator</i> , which achieves a trade-off between maximizing IO utilization and accurately proportional IO sharing. The evaluation results show that our method can improve proportionally sharing BDFS IO resources by 74.4 percent on average.
Tortuosity in two-dimensional and three-dimensional fractal porous media: A numerical analysis

Tortuosity is an important parameter to characterize flow and transport in porous media. It is desired to sufficiently understand the definition of tortuosity and its calculation in different materials while the description for the travel length of the electrical/hydraulic flow in porous media is difficult in two-dimensional and three-dimensional spaces. In this chapter, we introduce the definition of tortuosity in fractal porous media, and describe its theoretical relation with length scale based on fractal geometry. The structure of a given Sierpinski carpet/sponge is used to illustrate the calculating process of tortuosity and the fractal dimension, and the numerical results show tortuosity is variable in different numerical methods. In those simulation examples of tortuosity, the logarithmic relation between tortuosity and fractal dimensions provides a framework for understanding the behavior of tortuosity in fractal porous media.
Non-cryptographic Approaches for Collaborative Social Network Data Publishing - A Survey

In today's world, trillions of persons are providing their data to social network data provider for connecting, interacting and data sharing with other users. The data provider may utilize these collected data for analysis purpose. Alternatively, multiple data providers prefer collaboration to attain enhanced analysis outcomes from the collected collaborated data. For such collaboration, the data providers do not share their data directly due to privacy issues instead they share the collected data with the trusted data publisher. The data publisher combines these collected data and subsequently publishes the data. Data collected at trusted data publisher site from multiple providers contain individuals' information that may be sensitive. Hence, the privacy of individuals may be compromised if it is published by the publisher in its original form. As a consequence, in literature, various non-cryptographic approaches are discussed for privacy-preserving collaborative social network data publishing. The motive of this paper is to emphasize the evaluation of these existing approaches with the help of different parameters.
Optimization Search Using Hypercubes

An optimization search algorithm for multivariate systems is proposed. The proposed optimization search algorithm includes initialization-, displacement-shrink- and searching areas stages. The initialization stage generates initial solutions in the search area that represented by hypercube and then evaluates the function inside this hypercube; the displacement-shrink stage calculates the displacement and updates the parameters of the hypercube; the searching areas stage using certain rules find the next hypercube. The design stages of the proposed hypercube optimization search (HOS) algorithm are presented. The proposed HOS algorithm is tested on specific benchmark functions. The experimental results on different test functions demonstrate that the HOS algorithm has shown to be a promising approach for finding the solutions of a set of optimization problems.
An exploratory study on applicability of cross project defect prediction approaches to cross-company effort estimation

BACKGROUND: Research on software effort estimation has been active for decades, especially in developing effort estimation models. Effort estimation models need a dataset collected from completed projects similar to a project to be estimated. The similarity suffers from dataset shift, and cross-company software effort estimation (CCSEE) gets an attractive research topic. A recent study on the dataset shift problem examined the applicability and the effectiveness of cross-project defect prediction (CPDP) approaches. It was insufficient to bring a conclusion due to a limited number of examined approaches. AIMS: To investigate the characteristics of CPDP approaches that are applicable and effective for dataset shift problem in effort estimation. METHOD: We first reviewed the characteristics of 24 CPDP approaches to find applicable approaches. Next, we investigated their effectiveness in effort estimation performance with ten dataset configurations. RESULTS: 16 out of 24 CPDP approaches implemented in CrossPare framework were found to be applicable to CCSEE. However, only one approach could improve the effort estimation performance. Most of the others degraded it and were harmful. CONCLUSIONS: Most of the CPDP approaches we examined were helpless for CCSEE.
Playing Fair With Time Series Data

Data graphics had to be invented: The idea of using diagrams to represent numerical data emerged only toward the end of the 18th century. While graphs of mathematical functions existed before this time, it fell to a Scottish scoundrel, William Playfair, to popularize the idea of graphically representing empirical data in all of their messiness.
Question Answering System over Knowledge Graph of Weapon Field

Question answering system in the weapon field not only enables users to obtain information on weapons quickly and accurately, but also provides smarter question answering. With military weapons as the research direction, an SVM question classification method based on Chinese character algorithm is proposed, and a question answering system over knowledge graph of weapons is established. The domain word segmentation is used in this system to analyze user questions, extract question features for classification, intelligently generate SPARQL, query RDF to get answers, and return them to the user. Experiments show that the intelligent question answering system with the multi-question classification is effective and highly accurate in answering questions in the field of weapon.
State-of-the-art of Object Detection Model Based on YOLO

Object detection has got rapid development these years, and target classification and location become gradually mature. Fast R-CNN, which is a landmark in accuracy, is a kind of classic two-stage model and has relatively low speed. To improve the detection efficiency, YOLO (You Only Look Once) model is proposed. In this article, the research starts from the evolution of CNN for object detection and YOLO model is sorted out. Then we compare the structures, target outputs and network training of each versions. The functional layers evolved into a structure constructed with residual modules, which keep the accuracy. At last, we discuss the system advantage future develop trend. Form the discussion, we conclude that YOLO learns very general representations of objects, and it integrates various mature new technologies. With the improvement of hardware technology, it will be applied more widely.
Increasing the resolution of a non-negative brightness image distorted by a linear transformation

We consider an image distorted by linear brightness conversion and additive noise. Linear brightness transformation simulates distortions that occur in the video system due to the movement of the camera, the final aperture, etc. The task consists in such a transformation of this image, the result of which can be interpreted as an image obtained by the system with a higher resolution. Linear methods of increasing resolution lead to an increase in the error on the transformed image. One way to reduce this error is to narrow the class of possible images. The paper uses information on the nonnegativity of the image brightness. The numerical implementation of the method takes into account the limitation of computational resources.