Reviewerly/exHarmony huggingface dataset:
Sustainable agricultural online course development framework for community-based transformative learning

This paper examines the change drivers and challenges involved in the transitioning of the Water Research Commission’s Amanzi for Food training of agricultural educators and farmers’ face-to-face co-engaged course to an online course aligned with Education for Sustainable Development (ESD) (www.amanziforfood.co.za). The study drew on system thinking to analyse components within the course development activity system. Insights from stakeholder engagement and expansive learning processes fed into key emergent themes which informed the e-learning processes. The research highlights how the development and implementation of a sustainable training programme using Information and Communication Technology (ICT) tools bring together learning processes occurring within different interconnected dimensions and in complex and unpredictable ways. It became clear that to utilise e-learning as a mediating artifact capable of facilitating social transformation towards suitability expansively, certain conditions needed to be in place. The drivers that impacted the development of e-learning as a mediating tool included the need to find an alternative learning platform for broader dissemination of Rain Water Harvesting &amp; Conservation (RWH&amp;C) knowledge and alignment with the United Nations Sustainable Development Goals (SDGs). This study concludes that this transition requires specific tools, adequate time, an understanding of e-learning pedagogical processes and learning platform functionalities (requiring upskilling of the digital literacy of the development team and other actors), and workable data-efficient or data-free, smartphone-friendly training and communication platforms.
Knowledge Graph Augmentation for Increased Question Answering Accuracy

This research work presents a new augmentation model for knowledge graphs (KGs) that increases the accuracy of knowledge graph question answering (KGQA) systems. In the current situation, large KGs can represent millions of facts. However, the many nuances of human language mean that the answer to a given question cannot be found, or it is not possible to find always correct results. Frequently, this problem occurs because how the question is formulated does not fit with the information represented in the KG. Therefore, KGQA systems need to be improved to address this problem. We present a suite of augmentation techniques so that a wide variety of KGs can be automatically augmented, thus increasing the chances of finding the correct answer to a question. The first results from an extensive empirical study seem to be promising.
Facebook for Sentiment Analysis: Baseline Models to Predict Facebook Reactions of Sinhala Posts

Research on natural language processing in most regional languages is hindered due to resource poverty. A possible solution for this is utilization of social media data in research. For example, the Facebook network allows its users to record their reactions to text via a typology of emotions. This network, taken at scale, is therefore a prime dataset of annotated sentiment data. This paper uses millions of such reactions, derived from a decade worth of Facebook post data centred around a Sri Lankan context, to model an eye of the beholder approach to sentiment detection for online Sinhala textual content. Three different sentiment analysis models are built, taking into account a limited subset of reactions, all reactions, and another that derives a positive/negative star rating value. The efficacy of these models in capturing the reactions of the observers are then computed and discussed. The analysis reveals that the Star Rating Model, for Sinhala content, is significantly more accurate (0.82) than the other approaches. The inclusion of the like reaction is discovered to hinder the capability of accurately predicting other reactions. Furthermore, this study provides evidence for the applicability of social media data to eradicate the resource poverty surrounding languages such as Sinhala.
CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal

Information visualization refers to the practice of representing data in a meaningful, visual way that users can interpret and easily comprehend. Geometric or visual encoding shapes such as circles, rectangles, and bars have grown in popularity in data visualization research over time. Circles are a common shape used by domain experts to solve real-world problems and analyze data. As a result, data can be encoded using a simple circle with a set of labels associated with an arc or portion of the circle. Labels can then be arranged in various ways based on human perception (easy to read) or by optimizing the available space around the circle. However, overlaps can occur in one or more arrangements. This paper proposes CircleVis, a new visualization tool for label arrangement and overlap removal in circle visual encoding. First, a mathematical model is presented in order to formulate existing arrangements such as angular, path, and linear. Furthermore, based on user interaction, a new arrangement approach is proposed to optimize available space in each circle arc and delete label overlaps. Finally, users test and evaluate the designed tool using the COVID-19 dataset for validation purposes. The obtained results demonstrate the efficacy of the proposed method for label arrangement and overlapping removal in circular layout.
Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors

Recently, artificial intelligence (AI) based on IoT sensors has been widely used, which has increased the risk of attacks targeting AI. Adversarial examples are among the most serious types of attacks in which the attacker designs inputs that can cause the machine learning system to generate incorrect outputs. Considering the architecture using multiple sensor devices, hacking even a few sensors can create a significant risk; an attacker can attack the machine learning model through the hacked sensors. Some studies demonstrated the possibility of adversarial examples on the deep neural network (DNN) model based on IoT sensors, but it was assumed that an attacker must access all features. The impact of hacking only a few sensors has not been discussed thus far. Therefore, in this study, we discuss the possibility of attacks on DNN models by hacking only a small number of sensors. In this scenario, the attacker first hacks few sensors in the system, obtains the values of the hacked sensors, and changes them to manipulate the system, but the attacker cannot obtain and change the values of the other sensors. We perform experiments using the human activity recognition model with three sensor devices attached to the chest, wrist, and ankle of a user, and demonstrate that attacks are possible by hacking a small number of sensors.
Taming the round efficiency of cryptographic protocols for private web search schemes

Private web search (PWS) schemes allow users to find information on the internet while preserving their privacy. Among them, cryptographic PWS schemes guarantee strong privacy at the cost of performance. Their design principle is that each user takes a list of ciphertexts as inputs from its predecessor and forwards a shuffled list of the input to its successor. This idea is quite reasonable but requires O(n) round complexity with respect to the number of users n. Because these cryptographic PWS schemes are interaction-intensive, this long delay causes network latency and dictates the local computational cost. This linear round complexity is the main obstacle for deploying these schemes in real-world applications. In this work, our primary goal is to improve existing cryptographic PWS solutions to reduce their complexity to O(1) rounds, thus ensuring their practicality. To this end, we first introduce the notion of vertical mixing, which hides the linkability between messages and their sources without horizontally cascaded shuffling. Second, we design a method to achieve vertical mixing by adopting additive secret sharing and modifying the widely used cryptographic PWS framework, CVH, originally developed by Castellà-Roca et al. (Computer Communications 2009). This yields enhanced round efficiency while ensuring security in the semi-honest model. Furthermore, we provide a formal proof of our PWS solution using the standard simulation-based proof. In contrast, the existing works only provide heuristic proofs. Finally, we perform a set of microbenchmarks by varying the number of users and evaluate the effectiveness of our construction. Our PWS scheme runs 1.48∼3.63× faster than the original CVH framework.
COVID-19 Pandemic and Special Education Teacher Voices

The pandemic caused by COVID-19 created havoc in schools across the world as it necessitated school closures and lockdowns, and/or a sudden switch to an online mode of learning. Although all children and teachers were adversely affected, children with disabilities faced additional problems. Special education teachers bore the burden of having to continue providing high quality special education in the face of these challenges. In this paper, I present findings from a survey of special education teachers through use of qualitative and arts-based research technics.
Special Issue “Selected Algorithmic Papers From CSR 2020”

The 15th International Computer Science Symposium in Russia (CSR 2020) was organized by the Ural Federal University located in Ekaterinburg, Russian Federation [...]
Smooth-RRT*: An Improved Motion Planner for Underwater Robot

In underwater search and rescue, it is very important for underwater robot to reach the rescue position quickly. Planning path in advance is very important to save rescue time and energy consumption. Therefore, it is meaningful to find a better and shorter path as soon as possible. As a common method of path planning , RRT* has the disadvantages of high cost and slow convergence. To solve these flaws, an improved motion planner for underwater robots is proposed in this paper. In this study, the simulation experiments were divided into two-dimensional conditions and three-dimensional conditions, where used point cloud of real underwater scene to find a better initial solution. Based on RRT*, this paper finds the ancestor node farthest from the sampling point and without collision in the random tree as parent node, adds intermediate nodes in the path according to the step size, and uses trigonometric inequality many times throughout the process, so as to obtain an optimized path. Through a large number of simulation experiments, the results show that the cost of path is less and the convergence speed is faster than RRT* and Q-RRT*.
A Survey on the Integration of Machine Learning with Sampling-based Motion Planning

Sampling-based methods are widely adopted solutions for robot motion planning. The methods are straightforward to implement, effective in practice for many robotic systems. It is often possible to prove that they have desirable properties, such as probabilistic completeness and asymptotic optimality. Nevertheless, they still face challenges as the complexity of the underlying planning problem increases, especially under tight computation time constraints, which impact the quality of returned solutions or given inaccurate models. This has motivated machine learning to improve the computational efficiency and applicability of Sampling-Based Motion Planners (SBMPs). This survey reviews such integrative efforts and aims to provide a classification of the alternative directions that have been explored in the literature. It first discusses how learning has been used to enhance key components of SBMPs, such as node sampling, collision detection, distance or nearest neighbor computation, local planning, and termination conditions. Then, it highlights planners that use learning to adaptively select between different implementations of such primitives in response to the underlying problem's features. It also covers emerging methods, which build complete machine learning pipelines that reflect the traditional structure of SBMPs. It also discusses how machine learning has been used to provide data-driven models of robots, which can then be used by a SBMP. Finally, it provides a comparative discussion of the advantages and disadvantages of the approaches covered, and insights on possible future directions of research. An online version of this survey can be found at: https://prx-kinodynamic.github.io/
Complex wave and phase retrieval from a single off-axis interferogram

Single-frame off-axis holographic reconstruction is promising for quantitative phase imaging. However, reconstruction accuracy and contrast are degraded by noise, frequency spectrum overlap of the interferogram, severe phase distortion, etc. In this work, we propose an iterative single-frame complex wave retrieval based on an explicit model of object and reference waves. We also develop a phase restoration algorithm that does not resort to phase unwrapping. Both simulation and real experiments demonstrate higher accuracy and robustness compared to state-of-the-art methods, for both complex wave estimation and phase reconstruction. Importantly, the allowed bandwidth for the object wave is significantly improved in realistic experimental conditions (similar amplitudes for object and reference waves), which makes it attractive for large field-of-view and high-resolution imaging applications.
A Comparative Study of Ensemble Techniques Based on Genetic Programming: A Case Study in Semantic Similarity Assessment

The challenge of assessing semantic similarity between pieces of text through computers has attracted considerable attention from industry and academia. New advances in neural computation have developed very sophisticated concepts, establishing a new state of the art in this respect. In this paper, we go one step further by proposing new techniques built on the existing methods. To do so, we bring to the table the stacking concept that has given such good results and propose a new architecture for ensemble learning based on genetic programming. As there are several possible variants, we compare them all and try to establish which one is the most appropriate to achieve successful results in this context. Analysis of the experiments indicates that Cartesian Genetic Programming seems to give better average results.
TrafficSpy: Disaggregating VPN-encrypted IoT Network Traffic for User Privacy Inference

People have been increasingly deploying the Internet of Things (IoT) devices to monitor and control their environments. Unfortunately, extensive recent research has shown that IoT devices are vulnerable to multiple adversarial attacks, which analyze their network traffic to reveal a wide range of sensitive private information about user in-home activities. Thus, smart home users recently have a keen interest in employing virtual private networks (VPN) to obscure their privacy information in their IoT network traffic. Our key insight is that VPN-encrypted IoT network traffic data is not anonymous, since this aggregate traffic data can still be disaggregated into individual IoT device traffic data. And this individual IoT device traffic may have an identifiable traffic signature that already embeds detailed user sensitive information. To explore the severity and extent of this privacy threat, we design a new factorial hidden Markov model (FHMM)-based smart home network traffic disaggregator-TrafficSpy that can accurately disaggregate VPN-encrypted whole-house IoT network traffic data into individual IoT device network traffic data. We evaluate TrafficSpy using VPN network traffic data from three smart homes. We find that TrafficSpy can disaggregate VPN traffic data into individual IoT device data accurately. We also show that the disaggregated traffic traces can be further attacked by smart and adaptive adversaries and thus reveal user sensitive information. TrafficSpy represents a serious privacy threat, but also a potentially useful tool that provides important contextual information for smart home monitoring and automation.
A framework to recognize the sign language system for deaf and dumb using mining techniques

&lt;span lang="EN-US"&gt;One way of communicating with the deaf is to speak sign language. The chief barrier to little Indian sign language (ISL) research was the language diversity and variations in place. It is essential to learn sign language to communicate with them. Most learning takes place in peer groups. There are very few materials available for teaching signs. Thus, signing is very challenging to learn. Fingerspelling is the first step in sign learning and is used whenever there is no appropriate sign or if the signatory is unfamiliar with it. Sign language learning tools currently available use expensive external sensors. Through this project, we will take this field further by collecting a dataset and extracting functionally helpful information used in several supervised learning methods. Our current work presents four validated fold cross results for multiple approaches. The difference from the previous work is that we used different figures for our validation set than the training set in four-fold cross-validations.&lt;/span&gt;
Generation and Classification of Illicit Bitcoin Transactions

Financial fraud is an everyday problem that banking institutions have to face. With the disruption of Bitcoin as a new model which relies on decentralisation and anonymity, attackers have taken advantage of this monetary system. It allows them to obtain funds from illegal activities such as ransomware payments and hide them. At the same time, Law Enforcement Agencies use open-source data to apply network forensics to Blockchain data. The analysis is usually performed by using artificial intelligence. Unfortunately, the current situation shows a scarcity of high-quality data sets to train the detection algorithms. This work tries to overcome this barrier with significant contributions. With nearly 25,000 illicit transactions, we have increased the Elliptic Data Set –the most extensive labelled transaction data publicly available in any cryptocurrency. The former data set only contained 4,545 illicit transactions, resulting in a class imbalance of 9.8:90.2 illicit/licit ratio. Our work has changed that to a 41.2:58.8 illicit/licit ratio. Besides, to show that class imbalance datasets can also be beaten with artificial work, we have studied the use of generative adversarial networks (GAN) for creating synthetic samples. Finally, the last part of this work was dedicated to applying deep learning and, more particularly, long short-term memory networks (LSTM) for the binary classification problem. We show ideal results that can help change the current state-of-the-art trend, mainly focused on machine learning algorithms.
Energy Aware Load Balancing in Cloudlet Federation

With the rapid increase of compute intensive tasks and workload, the need of the hour is to manage the total workload due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in federated cloudlet environment, load balancing is the main challenge. Load balancing means to divide the total workload between all the member nodes to get the maximum benefits from the federated environment. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible to the mobile devices in the vicinity. Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various member cloudlets. Different tasks consume different amount of energy for their execution that results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat, the more performance degradation, so it becomes very vital to keep the energy consumption as minimum as possible. To address this problem, this paper proposes a novel scheduling strategy that will distribute the total load between all the member nodes in a federated cloudlet environment for load balancing. The proposed methodology not only considers the energy level of the system on which the task has to be executed but also considers the energy required by the tasks in queue. The proposed methodology is tested in a Cloudlet Federation environment and the results show an improved load balancing in terms of energy consumption.
A complementary resource relation of concurrence and roughness for a two-qubit state

Quantum resources lie at the core of quantum computation as they are responsible for the computational advantage in many tasks.The complementary relations are a pathway to understanding how the physical quantities are related.Here it was shown that there is a complementary relationship that evolves quantumness of a two qubits state and the degree of entanglement, respectively measured by Roughness and Concurrence.It was shown that there is a R 2 surface that characterizes these quantities and can be understood as a projection of the Bloch Sphere.The distribution of states on the surface shows a concentration in the region of lower roughness and Concurrence, i.e., in the subset of states of greater entropy.
Rationalizing predictions by adversarial information calibration

Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive rationale, i.e., a subset of features of an instance that lead the model to give its prediction on that instance. For example, the subphrase “he stole the mobile phone” can be an extractive rationale for the prediction of “Theft”. Previous works on generating extractive rationales usually employ a two-phase model: a selector that selects the most important features (i.e., the rationale) followed by a predictor that makes the prediction based exclusively on the selected features. One disadvantage of these works is that the main signal for learning to select features comes from the comparison of the answers given by the predictor to the ground-truth answers. In this work, we propose to squeeze more information from the predictor via an information calibration method. More precisely, we train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction. The first model is used as a guide for the second model. We use an adversarial technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features. In addition, for natural language tasks, we propose a language-model-based regularizer to encourage the extraction of fluent rationales. Experimental results on a sentiment analysis task, a hate speech recognition task, as well as on three tasks from the legal domain show the effectiveness of our approach to rationale extraction.