Reviewerly/exHarmony huggingface dataset:
Electricity Usage Clustering with K-means++ Algorithm

Global electricity consumption is increasing every year. Electricity has become a necessary need for every sector, from a household to government and industry. With today's technology, data also has become more important, including electricity data. The right tools and techniques can extract valuable information from data. Using a K-Means++ algorithm to cluster electricity data can help to determine when the usage is low, moderate, and high. In this study, there three scenarios of clustering; hourly, daily, and monthly. The silhouette score of this experiment ranges from 0.68 to 0.71, and the DB Index ranges from 0.30 to 0.51.
Video Tracking System Using Midrange Exploration Exploitation Searching-Particle Swarm Optimization (MEESPSO) in handling occlusion and similar appearance due to crowded environment

Detecting the correct object plays a key role in generating an accurate and precise object tracking result. In addition, the usage of conventional method still brings the limitation in term of the accuracy and precision of the detected object. Besides, the process of object tracking in an individual frame is also challenging due to the problems such as occlusion, crowded environment, and similar appearance Therefore, a Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) algorithm with color-shape feature pattern matching methods was introducing to address the problem of the similar appearance or color that comes close to target object in crowded environment, and the presence of occlusion problem cause motion of the crowded object or the camera views. The proposed method is tested by using the MOT16-11 benchmark video dataset. This benchmark video faced the challenges such as partial occlusion, fully occlusion and similar appearance due to crowded environment in the video scene. The experiment has shown that the tracking performance of the proposed method has increased more than 92.69% accuracy and 94.67% precision.
Incorporating bidirectional interactive information and regional features for relational facts extraction

Extracting entity and relation jointly is often complicated since the relational triplets may be overlapped. In this paper, we propose a novel unified joint extraction model that considers the significant information which is useful for relation extraction between a pair of entities. We also consider bidirectional interaction between named entity recognition and relation extraction. To this end, we apply Bi-LSTM to capture sequential information and use Graph Convolutional Network to capture significant regional information in our encoding part. We use multi-layer structure in decoding part including first decode layer, interactive layer and final decode layer to fuse bidirectional interactive information between named entity recognition and relation extraction. In this way, our method can simultaneously extract all entities and their relations including overlapping relations. Experimental results show that our model performs better comparing with other baseline models in this task, and we achieve state-of-the-art performance on two public datasets.
Personal Cognitive Assistant: Personalisation and Action Scenarios Expansion

This paper examines the problem of insufficient flexibility in modern cognitive assistants for choosing cars. We believe that the inaccuracy and lack of content information in the synthesised responses negatively affect consumer awareness and purchasing power. The study's main task is to create a personalised interactive system to respond to the user's preferences. The authors propose a unique method of supplementing the car buying scenario with deep learning and unsupervised learning technologies to solve the issues, analyse the user's utterances, and provide a mechanism to accurately select the desired car based on open-domain dialogue interaction. The paper examines the problem of changing user interest and resolves it using a non-linear calculation of the desired responses. We conducted a series of experiments to measure the assistant's response to temporary changes in user interest. We ensured that the assistant prototype had sufficient flexibility and adjusted its responses to classify user group interests and successfully reclassify them if they changed. We found it logical to interact with the user in open-domain dialogue when there is no certain response to the user's utterance in the dialogue scenario.
Post Quantum Secure Command and Control of Mobile Agents Inserting Quantum-Resistant Encryption Schemes in the Secure Robot Operating System

The secure command &amp; control (C&amp;C) of mobile agents arises in various settings including unmanned aerial vehicles, single pilot operations in commercial settings, and mobile robots to name a few. As more and more of these applications get integrated into aerospace and defense use cases, the security of the communication channel between the ground station and the mobile agent is of increasing importance. The development of quantum computing devices poses a unique threat to secure communications due to the vulnerability of asymmetric ciphers to Shor’s algorithm. Given the active development of new quantum resistant encryption techniques, we report the first integration of post-quantum secure encryption schemes with robotic operating system (ROS) and C&amp;C of mobile agents, in general. We integrate these schemes in the application and network layers and study the performance of these methods by comparing them to present-day security schemes such as the widely used RSA algorithm.
Understanding Drug Abuse Social Network Using Weighted Graph Neural Networks Explainer

Relational data are universal in today's society, such as social media network, citation network, and molecules. They can be transformed into graph data consisting of nodes and edges. To classify the class of nodes in graph, GNNs is a state-of-the-art model in this domain. For example, anti-drug police rely on social data to identify hidden drug abusers. However, in the deployment of GNNs, it still require interpretability techniques to support. Because GNNs embed the node information and pass them through the adjacency network, resulting in the lack of transparency in its model. However, the common explanation technique used in other models are not directly applicable on GNNs. And those explanation methods in CNNs are not applicable either considering the topological variety of graph. In order to explain our GNN model on drug abuse social network, we proposed a framework to preprocess graph data, train GNN model, and apply our Maximal Mutual Information (MMI) method to help explain the prediction made by the GNN model. We evaluate the performance of the explanation model with evaluation metrics consistency and contrastivity. The explanation model performs impressively in both real world data and two synthetic data compared to other baselines. The results also show that our model can mimic GNN explanation made by human in node classification.
GSSF: A Generative Sequence Similarity Function Based on a Seq2Seq Model for Clustering Online Handwritten Mathematical Answers

Toward a computer-assisted marking for descriptive math questions, this paper presents clustering of online handwritten mathematical expressions (OnHMEs) to help human markers to mark them efficiently and reliably. We propose a generative sequence similarity function for computing a similarity score of two OnHMEs based on a sequence-to-sequence OnHME recognizer. Each OnHME is represented by a similarity-based representation (SbR) vector. The SbR matrix is inputted to the k-means algorithm for clustering OnHMEs. Experiments are conducted on an answer dataset (Dset_Mix) of 200 OnHMEs mixed of real patterns and synthesized patterns for each of 10 questions and a real online handwritten mathematical answer dataset of 122 student answers at most for each of 15 questions (NIER_CBT). The best clustering results achieved around 0.916 and 0.915 for purity, and around 0.556 and 0.702 for the marking cost on Dset_Mix and NIER_CBT, respectively. Our method currently outperforms the previous methods for clustering HMEs.
Speeding Up Reinforcement Learning by Exploiting Causality in Reward Sequences

This paper presents a methodology to exploit causation in deep reinforcement learning (DRL). We take advantage of a cognitive architecture that automatically decomposes the game world in proto-objects and records their position in 2D space across time. Therefore, while playing the game, proto-objects' locations define internal time sequences that can be compared with the reward sequence to select the proto-object that caused the reward. We propose a novel non-parametric information theoretic learning Granger causality (ITL-GC) estimator of directed information using Reny's entropy that is accurate in high dimensions. We integrate this module in a state-of-the-art DRL architecture (A3C) and show substantial improvement in the speed of convergence compared with conventional training.
Real-time Monocular 3D People Localization and Tracking on Embedded System

Localizing people in 3D space, rather than in original 2D image plane, provides a more comprehensive understanding of the scene and brings up more potential applications. However, inferring 3D locations usually requires stereo camera or additional sensors since deriving depth information from single image is regarded as an ill-posed problem. With recent progress in deep learning methods, depth estimation neural network can provide convincing depth map by a single RGB image. This work develops a people localization and tracking method based on a monocular camera. Specifically, an efficient self-supervised monocular depth estimation method is adopted to generate pseudo depth map. Afterwards, 2D object detection results are adopted for finding accurate people location. Finally, a filter based tracking method is adopted to fuse temporal information and improve the accuracy. Aiming to provide a real time solution for people tracking on embedded system, our methods are deployed and tested on a NVIDIA Jetson Xavier NX develop kit. The proposed efficient localization and tracking method is validated by a group of field tests. The overall performance reaches 12 fps with an acceptable accuracy compared to ground truth.
Microblog Analysis with Machine Learning for Indic Languages: A Quick Survey

Microblogs (MB) are the new powerhouses of insights for the coming decade, and hence, microblog analysis holds a valuable position in the minds of researchers in both India and abroad. Advanced computational resources along with abundant data availability make MB analysis a reality. Though there is a surge of ML algorithms being used in MB analysis, natural language processing (NLP) tools are still considered preferable for analysing many stages. Hence, it becomes important to identify the better approach among the two. Special emphasis on employing ML algorithms at the language identification phase is also important, as it becomes complex when Indic languages are considered. This paper discusses the stage-wise breakdown for MB analysis in situations where the dataset has multiple languages having distinct dialects and promotes the shift from statistical tools towards holistic usage of ML for all the future MB text analysis.
Learning-‘N-Flying: A Learning-Based, Decentralized Mission-Aware UAS Collision Avoidance Scheme

Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft Systems (UASs) carry out a wide variety of missions (e.g., moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-‘N-Flying (LNF), a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on the fly, and allows autonomous Unmanned Aircraft System (UAS)s managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UASs as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining (1) learning-based decision-making and (2) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UASs on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds) and under certain assumptions has failure rates of less than 1% in the worst case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.
Equal Baseline Camera Array—Calibration, Testbed and Applications

This paper presents research on 3D scanning by taking advantage of a camera array consisting of up to five adjacent cameras. Such an array makes it possible to make a disparity map with a higher precision than a stereo camera, however it preserves the advantages of a stereo camera such as a possibility to operate in wide range of distances and in highly illuminated areas. In an outdoor environment, the array is a competitive alternative to other 3D imaging equipment such as Structured-light 3D scanners or Light Detection and Ranging (LIDAR). The considered kinds of arrays are called Equal Baseline Camera Array (EBCA). This paper presents a novel approach to calibrating the array based on the use of self-calibration methods. This paper also introduces a testbed which makes it possible to develop new algorithms for obtaining 3D data from images taken by the array. The testbed was released under open-source. Moreover, this paper shows new results of using these arrays with different stereo matching algorithms including an algorithm based on a convolutional neural network and deep learning technology.
Modeling interference using Monte Carlo ray trace

Monte Carlo ray tracing has been shown to be both a robust and reliable method for optical modeling of illumination systems. The typical approach traces rays through an optical system and averages the rays in spatial and angular bins to provide estimates of the illuminance and intensity. While Monte Carlo methods are commonly used with incoherent sources, they can also be used with coherent sources. The primary difference between the two simulation types is the inclusion of the phase difference between binned rays in the illuminance and intensity calculations. The phase information can be stored in a ray’s optical path length, which is updated as a ray is traced through a system and when a ray encounters specific interfaces, such as the π phase shift due to a reflection from a surface of higher index. Thus, when careful consideration is given to the phase changes that occur during a ray trace, it is possible to use Monte Carlo methods to model illumination systems that require coherent sources, such as interferometers. Furthermore, with the ability to model coherent sources, beamlet based approaches are also effective. Comparisons are presented. In general, a common challenge for Monte Carlo simulations is the large number of needed rays to overcome statistical noise. However, it is possible to reduce the number of traced rays using various methods, such as: source aiming, scatter aiming, and Backwards Ray Trace. These methods minimize the number of traced rays by using knowledge of the system to selectively avoid tracing rays which will not contribute meaningful data to the output distribution. Monte Carlo ray tracing can also be computationally expensive. To prevent rerunning a costly simulation, it is advantageous to save the traced rays and post process the data for further analysis and studies. For example, the rays can be defocused to a plane at a different distance, re-binned for a difference receiver resolution, or filters can be used to remove rays based on specific criteria like whether they passed through a specific surface. This paper is organized with three main sections. First, we discuss Monte Carlo ray tracing to model interference. The second discusses Monte Carlo ray tracing for modeling diffraction using a Huygens-Fresnel approach. The last summarizes the paper.
Using Tools for the Analysis of the Mental Activity of Programmers

Programmers are the most important part of software production and individual developers are hard to substitute. The essential part of the knowledge intensive development process is the developers mind state. Understanding the mental states of software developers has become a main interest of software production companies since it is the most valuable resource for software development. However the main challenge in analysing the software developers mental states is that most precise equipment, such as fMRI, is extremely expensive and not portable. Thus, fMRI approximation from EEG readings tools such as MNE, have been developed over the years. The idea of recreating the fMRI based on EEG signal is the main motivation for the current work. This research explains how we used this tool in our studies.
Test time augmentation by regular shifting for deep denoising autoencoder networks

Image restoration, which is the process of denoising noisy images in order to recover their latent clean images, has been frequently addressed. The importance of this field resides in the impact of noisy images on the performance of computer vision systems. In this work, a deep autoencoder neural network architecture is proposed to denoise images affected by Gaussian noise. The performance of the system is enhanced by using a test time augmentation scheme. Experiments have been carried out by considering different levels of Gaussian noise. Results demonstrate the suitability of the proposed methodology in order to enhance the quality of the image restoration process in images affected by Gaussian noise.
PA4ALL—Innovative Learning Methods for Education in Agriculture: An ICT Based Learning Programme for High Schools

Abstract PA4ALL addresses the topic of introducing precision agriculture tools in high schools specialised in agriculture, exploring the benefits of using the ICT in the field and encouraging high school students to uptake new trends and innovations.
Modifying Voice-User Interfaces for Resiliency and Offline Management of IoT Devices

Voice-user interfaces (VUIs) have gained popularity in recent years since these provide ease of access to users. The problem with current VUIs, however, is that they become unusable once Internet connection is cut off since processes such as speech-to-text decoding of commands and Internet of Things (IoT) management are implemented on cloud servers. This, however, does not necessarily have to be the case since there are commands, such as managing IoT devices like smart lights, smart thermostats, and smart locks, which do not need Internet connection for it to be executed. In this work, a proposal for a VUI which can determine when Internet connection is cut off and locally execute tasks which do not need information retrieval from the Internet, is presented. The proposed VUI runs 2 systems: the full system and the offline system. The full system is used when there is Internet connection while the offline system is used when Internet connection is cut off. Through experiments, the difference in performance between the two systems, along with the amount of time that should be given to the VUI as leeway in order for it to process all incoming voice inputs once it switches from the full system to the offline system and vice versa, were quantified.
Recurrent Neural Networks with Mixed Hierarchical Structures for Natural Language Processing

Hierarchical structures exist in both linguistics and Natural Language Processing (NLP) tasks. How to design RNNs to learn hierarchical representations of natural languages remains a long-standing challenge. In this paper, we define two different types of boundaries referred to as static and dynamic boundaries, respectively, and then use them to construct a multilayer hierarchical structure for document classification tasks. In particular, we focus on a three-layer hierarchical structure with static word- and sentence- layers and a dynamic phrase-layer. LSTM cells and two boundary detectors are used to implement the proposed structure, and the resulting network is called the Recurrent Neural Network with Mixed Hierarchical Structures (MHS-RNN). We further add three layers of attention mechanisms to the MHS-RNN model. Incorporating attention mechanisms allows our model to use more important content to construct document representation and enhance its performance on document classification tasks. Experiments on five different datasets show that the proposed architecture outperforms previous methods on all the five tasks.
THE ROLE OF PYTHON PROGRAMMING COURSE IN NATURAL SCIENCES

The study of Natural Sciences often leads to Data Science, a trending topic among skilled professionals and organizations. Data Science is the newest field that promises to revolutionize industries, businesses, government, health care, and academia. A data scientist should own mathematical expertise, strong business and technology skills.With the development of higher education, teaching methods, courses content, the technology used in the learning process, and the curriculum system should reform to adapt to the needs of society. Some data scientists have studied Computer Science. Meanwhile, many others come from Statistics, Mathematics, Physics, or Biology backgrounds and don’t have programming skills. This article shows the role of programming in Natural Sciences study programs in Universities and suggests the programming course content.Many data scientists and software developers use Python to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. Python provides many functions to Data visualize and data analysis. Python syntax is easy to understand and write, which makes it very popular.With the advantages of Python, the Natural Sciences students create the main skills in data science, mathematical computations libraries, and data analysis.The methodology used in this research is the SciPy, the main Python library that includes linear algebra, integration, optimization, and statistics modules. Another library is NumPy. It is a perfect tool for scientific computing and performing basic and advanced array operations. Matplotlib is a standard data science library that generates data visualizations, such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs).This article aims to emphasize the role of the Python programming language in all the study programs in Natural Sciences. It discusses the course content of Python Programming, the main ideas to cover to create the basic knowledge of Data Science. It suggests that Natural Sciences students should master these three libraries: NumPy, SciPy, and matplotlib. It describes each of these libraries with main functionalities.