Reviewerly/exHarmony huggingface dataset:
Suspicious Event Detection in Real-Time Video Surveillance System

In today's generation, video security is becoming more important in the real-world applications because of the happening of suspicious events in our surroundings, and the safety and security in public places have become a priority. Video surveillance system might be used for enhancing the security in various areas such as offices, mall, theater, organizations, analysis of athletic events, content-based image storage and retrieval and many more. This paper focused research on the automatic analysis of suspicious event detection in the real-time video surveillance system and provided a recommendation on how it can be monitored automatically.
A Study on an Effect of Using Deep Learning in Thai-English Machine Translation Processes

Deep learning has been used in many fields including natural language processing. This paper aims to study the effect of applying deep learning in machine translation processes including word segmentation and translation model generation. We compare the results of the process from traditional statistical method and deep learning and analyze the difference. From experiment, the results indicated that the processes from deep learning obtained higher score in overall. Word segmentation from Bidirectional neural network yielded 0.861 f1 score which was higher than standard n-gram based system for 0.081. The translation results within dataset show that the neural-network-based translation got the best BLEU score in average for 0.43 in which are higher than the traditional statistical approach for 0.16. The result analysis indicates that the neural-network-based translation can translate better for Thai sentences containing unknown words and those with numerical classifier expression.
Counting Attention Based on Classification Confidence for Visual Question Answering

The multi-object counting in visual question answering (VQA) is still a challenging problem. Existing VQA models mainly adopt object detection network to extract image features and combine soft attention mechanism to further increase the model accuracy. However, repeated counting of the same object may occur when the object detection network extracts image features. In addition, the sum of attention weights of all objects calculated by soft attention mechanism is 1, which leads to the constant quantity information of objects being 1. We propose a new counting attention mechanism based on classification confidence. The main idea is to calculate the initial attention with sigmoid function and similarity with the object location generated by object detection network; we introduce classification confidence to calculate a more accurate similarity and solve the problem that the quantity information under existing soft attention mechanism is always 1. The experiment compares the proposed counting attention mechanism with the baseline model and the related work under the VQA v2 dataset. The results show that the counting attention mechanism improves the counting accuracy by 6.4% compared with the baseline model and surpasses most VQA models.
Data Driven Sales Prediction Using Communication Sentiment Analysis in B2B CRM Systems

In this work, we are proposing a methodology for data-driven decision making using sentiment analysis. The analysis of sentiment is done by text mining the activity notes recorded in Customer Relationship Management Systems used to manage complex sales in business to business environments. We built the sentiment enhanced sales prediction models using Artificial Neural Networks, Support Vector Machines and Random Forests and involving different sentiment features. The approach produced meaningful results with Random Forest obtaining the best improvement compared to a baseline model without sentiment features. The best model showed that new attributes incorporating sentiment information improved the accuracy from a baseline of 85.15% to 89.11 %. This model was used to conduct an analysis and an evaluation of the steps needed to be taken to win a possible losing deal in a real-world business to business customer relationship management system.
Cloud Removal from Aerial Images Using Generative Adversarial Network with Simple Image Enhancement

The atmospheric condition of the presence of clouds is one of the biggest problems in most aerial imaging systems. It degrades the visual quality of images leading to the loss of information for ground scenes. Hence, an effective cloud removal algorithm is a significant factor for this kind of problem and other related applications. The proposed cloud removal technique using the generative adversarial network with simple image enhancement (SIE-GAN) is a useful tool in removing cloud formations, most notably in images acquired using Unmanned Aerial Vehicle System (UAVs). This technique showed flexibility in performing the given task with satisfactory results, which is a gauge based on No-Reference Image Quality Metric, specifically the Perception-based Image Quality Evaluator (PIQE). Also, the proposed algorithm outperformed some of existing cloud removal algorithms by producing a better quality output when tested on the too-cloudy satellite images. Overall, the authors introduced a new frontier in generating cloud-free aerial images and added a valuable contribution to the array of cloud removal algorithms.
Testing Coverage Criteria for Deep Forests

In practice, many unknown errors have emerged in deep learning systems. One of the main reasons is that the behaviors of deep learning systems are unpredictable and difficult to test. Proper testing criteria are vitally important to evaluate the adequacy of testing deep learning systems. However, there is no testing criterion available for the deep forest, which is a deep learning model that has achieved good performance on small-scale data sets and low-computing-power platform projects. To address this problem, we propose a set of testing coverage criteria for deep forests in this paper. The set of testing coverage criteria is composed of multi-grained scanning node coverage (MGNC), multi-grained scanning leaf coverage (MGLC), cascade forest output coverage (CFOC) and cascade forest class coverage (CFCC).
BIDS: Blockchain Based Intrusion Detection System for Electoral Process

Vote theft and fraud is a deliberate activity which is predominantly perpetrated at every level of electoral process by misrepresenting the actual value of vote count either manually or electronically. The emergence of technology within voting system remains a welcome development because of the comfort and speed it offer, however remains vulnerable to cyber-attack and has failed to deliver a trusted and reliable system. Therefore, blockchain technology in this paper is proposed at election result collation stages to ensure that result counted remains unchanged from the lower collation edge to the final stage of collation and announcement. In this paper, we examine the influence of technology on electoral system, e-voting system its pros and cons, which forms the basis for undertaking this work and the blockchain technology and its application of election result collation.
Using Solar Photovoltaic Systems to Significantly Reduce Power Production Problems in Nigeria and Create a Greener Environment

As the world develops it looks for a greener way to produce energy. Here we take a look at the present and previous ways in which Nigeria produces energy and we compare with a particular alternative renewable source, solar photovoltaic system. Solar photovoltaic system uses a method of photoelectric effect in order to convert the energy from the sun into electricity by absorbing and utilizing it. We go further in this project by reviewing some calculations to see how solar energy compares to other forms of electricity supply over a period of 20 years. Finally, reasons were given why it is preferable to use solar PV systems as compared to other forms.
A Fast Image Dehazing Algorithm for Highway Tunnel Based on Artificial Multi-exposure Image Fusion

Abstract Single image dehazing has been a challenging problem due to its ill-posed nature. This paper proposed a fast image dehazing algorithm for highway tunnel based on artificial multi-exposure image fusion. The original blurred images are transformed into under-exposed images using a series of gamma-correction operations. Meanwhile, a multi-exposure image fusion algorithm based on two-scale decomposition is proposed. It exploits the guided filter to separate each multi-exposure image into base and detail layers. The fused base and detail layers are integrated into the fused images. Then, a linear saturation adjustment method is utilized to enhance the saturation in spatial domain. Through the above image processing steps, the final haze-free images can be obtained. In this paper, the superiority of the proposed image dehazing algorithm based on artificial multi-exposure image fusion is demonstrated through theoretical analysis and experimental verification. It can quickly improve the visibility of foggy images, and make the processed images colorful and clear.
Elliptic Curve Cryptography-Based Signcryption Scheme with a Strong Designated Verifier for the Internet of Things

The Internet of Things (IoT) is an emerging technology that grows across the World Wide Web. Nayak, BiswojitIt has scenarios in which the real-world object is transferring data over an insecure wireless network. The security in IoT became more challenging because of the low computational and communication capacity of the object. The proposed signcryption scheme is a combination of a digital signature and symmetric key encryption in a single logical unit, which reduces the computational complexity as compared to the traditional signature, then encryption process along with the digital signature of the sender can only verify by the designated verifier. The computational and communication overhead of the elliptic curve cryptography (ECC) scheme have less because of short key length with the same security level as compared to other public key cryptosystem. The security hardness of the scheme is based elliptic curve discrete logarithm (ECDLP) and also provided various security requirements. The proposed scheme has low computational and communication overhead with low-power efficiency for IoT scenarios.
Utilize Discourse Relations to Segment Document for Effective Summarization

This paper proposes a clause-based extractive summarization algorithm by ranking and extracting semantic clauses from the original document. Discourse structure relation is useful for identifying semantically important parts of the source document. We segment the document into clauses and evaluate the importance of clauses based on semantic relations, and then, rank and extract them coarsely, and utilize graph rank to refine the extracted clauses. This way can create a more concise summary with more information and less redundancy. Research reach the following results: 1) compared with the other summarization algorithms on different granularity, the clause-based summarization achieves higher recall score; and, 2) different discourse relations have different importance.
Real Time Embedded Target Detection and Warning System

Abstract This paper presents the design and implementation of target detection, tracking and warning system. The proposed framework detects and warns about stationary and moving targets. The function of the implemented frame work is to extract specified targets features from specific offline video file or an online video streaming form a Universal Serial Port (USB) camera. A weighted gain filter is utilized for noise cancelation which efficiently enhances the image quality and then improves the detection process. A series of experiments for stationary and moving targets for an online video streaming are carried out on personal computer to evaluate proposed system efficiency. The proposed system efficiently detected and warned about stationary and moving objects in the presence of object occlusion and utilizing low cost USB camera. The potential of the proposed system lies on its simplicity, minimized cost and the software is designed and implemented using python language to be integrated with the USB camera on an embedded board or personal computer for wide range of applications.
The Real-time Control Framework for a Modular Snake Robot

The Robot Operating System (ROS) is widely adopted for the control of the robots which is usually running on the Linux system with a standard kernel. But snake robot has a high requirement for the real-time of the control framework since it has several modules which contain a variety of sensors. To solve the problem, the control frame based on the Xenomai real-time system is developed by inserting a patch to the kernel and the real-time performance can be further enhanced by priority scheduling. The proposed control framework of the snake robot is applied to deal with the non- real-time communication thread with ROS packages and the real-time communication thread with the joint controller. So the application programs based on the proposed control framework can realize the multi-task function and be compatible with the ROS system. At last, the experiments validate that the control framework can control the hardware of the snake robot efficiently.
A Critical Analysis of Brokering Services (Scheduling as Service)

Cloud of things integrates cloud computing and Internet of things. The cloud broker acts as an interface between the cloud service providers and service consumer. The objective of this paper is to provide an in-depth knowledge on various aspects of a cloud broker. It aims to focus on scheduling as a service provided as a brokering service. This paper will provide initial background knowledge to researchers on cloud brokering services and platform to explore the open research issues related to scheduling as a service in cloud of things.
Iris Region Matching for Visible-Spectrum Gaze Trackers

The iris region matching would be the core of the visible-spectrum gaze tracker. The design challenge is much more difficult than the one of the traditional infra-ray gaze tracker due to the imperfection of the eye images. In practical application environments, the iris regions on the eye images may be influenced by the reflection light, and the matching process is also affected by the eyelids and wrinkles near the eyes. In this paper, we further improve the fitness function used for iris region matching. The experimental results show that the new one can effectively prevent the matching results from being dominated by the wrong textures near the eyes.
Dual-Mode Training with Style Control and Quality Enhancement for Road Image Domain Adaptation

Dealing properly with different viewing conditions remains a key challenge for computer vision in autonomous driving. Domain adaptation has opened new possibilities for data augmentation, translating arbitrary road scene images into different environmental conditions. Although multimodal concepts have demonstrated the capability to separate content and style, we find that existing methods fail to reproduce scenes in the exact appearance given by a reference image. In this paper, we address the aforementioned problem by introducing a style alignment loss between output and reference image. We integrate this concept into a multimodal unsupervised image-to-image translation model with a novel dual-mode training process and additional adversarial losses. Focusing on road scene images, we evaluate our model in various aspects including visual quality and feature matching. Our experiments reveal that we are able to significantly improve both style alignment and image quality in different viewing conditions. Adapting concepts from neural style transfer, our new training approach allows to control the output of multimodal domain adaptation, making it possible to generate arbitrary scenes and viewing conditions for data augmentation.
A Novel Interactive System for Quadrotor Path Planning in Cluttered Outdoor Environments

In this paper, we present a new system framework for generating collision-free trajectories based on Octomap transformed from 3D point cloud for quadrotor's autonomous flight in cluttered outdoor environments. In order to make the quadrotor autonomously navigate in woods, we utilize a 3D laser scanner to acquire laser point cloud and build Octomap of the woods. Then a modified RRT* algorithm is proposed to generate a set of collision-free waypoints and a curve fitting algorithm is adopted to get a smooth minimum snap trajectory. Finally, simulation and experiments are both conducted to show the validity and practicality of the proposed approach.
Defining Privacy-Preserving Data Analysis

With the growing focus on instrumenting our environment and monitoring our activities, there is a need to implement privacy-preserving algorithms into our technological systems. Defining privacy formally is a delicate task but is a necessary first step to be able to provide clear guarantees to the individuals being monitored. In this chapter, after discussing the pitfalls of naive approaches to data privacy, we review the notion of differential privacy, a state-of-the-art definition of privacy that we adopt in the rest of this monograph, and which provides guarantees against adversaries with arbitrary side information. Privacy-preserving data analysis has a relatively long history in fields such as econometrics and statistics or for the processing of sensitive static data stored for example in medical databases. Current trends emphasize the need to work with streams of data originating from many sources and requiring sanitization in real-time, which brings new challenges to the field.
A Novel IMM Filter for VideoSAR Ground Moving Target Tracking

Video synthetic aperture radar (VideoSAR) combines SAR imaging and video techniques together, enabling continuous surveillance of a specific area, which has the advantages of high frame rate, high resolution, the ability of real-time tracking, etc. For VideoSAR systems, detecting and tracking the shadows of moving targets can effectively avoid the complex algorithms needed for moving target location and refocusing. In this paper, an interactive multiple model (IMM) filter is designed for VideoSAR moving target tracking task. Considering of the maneuverability of ground moving target is more complex than aerial target, we define three different target motion models according to the motion characteristics of ground moving targets, including a stop model and a rotated model. The experimental results show that the proposed IMM filter outperforms conventional filter for ground moving target tracking application.
SuperNCN: Neighbourhood Consensus Network for Robust Outdoor Scenes Matching

In this paper, we present a framework for computing dense keypoint correspondences between images under strong scene appearance changes. Traditional methods, based on nearest neighbour search in the feature descriptor space, perform poorly when environmental conditions vary, e.g. when images are taken at different times of the day or seasons. Our method improves finding keypoint correspondences in such difficult conditions. First, we use Neighbourhood Consensus Networks to build spatially consistent matching grid between two images at a coarse scale. Then, we apply Superpoint-like corner detector to achieve pixel-level accuracy. Both parts use features learned with domain adaptation to increase robustness against strong scene appearance variations. The framework has been tested on a RobotCar Seasons dataset, proving large improvement on pose estimation task under challenging environmental conditions.
Pharos: Query-Driven Schema Inference for the Semantic Web

The practical advantage of a data lake depends on the semantic understanding of its data. This knowledge is usually not externalized, but present in the minds of the data analysts who have used a great deal of cognitive effort to understand the semantic relationships of the heterogeneous data sources. The SQL queries they have written contain this hidden knowledge and should therefore serve as the foundation for a self-learning system. This paper proposes a methodology for extracting knowledge fragments from SQL queries and representing them in an RDF-based knowledge graph. The feasibility of this approach is demonstrated by a prototype implementation and evaluated using example data. It is shown that a query-driven knowledge graph is an appropriate tool to approximate the semantics of the data contained in a data lake and to incrementally provide interactive feedback to data analysts to help them with the formulation of queries.
Feature Reorganization Based Human Activity Recognition in IoT-Enabled Applications

The wider application of the Internet of Things (IoT) technology provides a broader data foundation for human activity recognition (HAR) which has been widely applied in various applications, but the recognition accuracy is susceptible to the employed feature selection methods. To this end, a feature-reorganization-based feature selection method is proposed to achieve more acceptable recognition accuracy. Firstly, the four popular feature selection methods, variance analysis based filter, mutual information based filter, wrapper and embedding, are employed to select respective feature sets which then are combined to form an initial feature set. Subsequently, the features are divided into high-frequency and low-frequency features in terms of the occurrence frequency, and the low-frequency features selected by random traversal selection strategy are reorganized with the high-frequency features to form a new feature set. Finally, support vector machine is employed to conduct the human activity recognition based on the new feature set. Experiments show that the investigation in this paper can achieve more acceptable recognition accuracy, and a better balance between recognition accuracy and time cost while being compared with the four feature selection methods in five categories of activity recognition.
A Time-varying Filtering Algorithm based on Short-time Fractional Fourier Transform

This paper presents a novel time-varying filtering (TVF) algorithm based on order time-varying short-time fractional Fourier transform (OTV-STFrFT) for multi-component signal analysis, which can process non-linear frequency modulated (NLFM). The idea of combining TVF and the order time-varying STFrFT are mainly inspired by the following two aspects: i) NLFM signals can be locally regarded as segmented linear frequency modulated (LFM) signals; ii) the fractional Fourier transform is the optimal sparse representation of LFM signal. The order time-varying STFrFT can overcome several defects of the existing TVF algorithms in dealing with multicomponent signals, of which the mixed components may intersect in the time-frequency distribution. The numerical results shows that the proposed algorithm is superior to the TVF algorithms based on conventional short-time Fourier transform (STFT) and state-of-the-art synchrosqueezed wavelet transforms (SsWT) in multi-component signal analysis.
Uncertainty in Argumentation Schemes: Negative Consequences and Basic Slippery Slope

This study is an approach to encompass uncertainty in the well-known Argumentation Scheme from Negative Consequences and in the more recent "Basic Slippery Slope Argument" proposed by Douglas Walton. This work envisages two new kinds of uncertainty that should be taken into account, one related to time and one related to the material relation between premises and conclusion. Furthermore, it is argued that some modifications to the structure of these Argumentation Schemes or to their Critical Questions could facilitate the process of Knowledge Extraction and modeling from these two argumentative patterns. For example, the study suggests to change the premises of the Basic Slippery Slope related to the Control and the Loss of Control.