Reviewerly/exHarmony huggingface dataset:
Dealing with Mislabeling via Interactive Machine Learning

We propose an interactive machine learning framework where the machine questions the user feedback when it realizes it is inconsistent with the knowledge previously accumulated. The key idea is that the machine uses its available knowledge to check the correctness of its own and the user labeling. The proposed architecture and algorithms run through a series of modes with progressively higher confidence and features a conflict resolution component. The proposed solution is tested in a project on university student life where the goal is to recognize tasks like user location and transportation mode from sensor data. The results highlight the unexpected extreme pervasiveness of annotation mistakes and the advantages provided by skeptical learning.
Student Perceptions of Virtual Reality in Higher Education

Continuous advancements in technology provide an opportunity for higher educational institutions to enhance the electronic learning experiences of students. Following a review of literature, this research explored student perceptions on the possible uses of virtual reality (VR) in their universities, as a way of easing their access to learning material. The research was conducted with the aim of answering the question of how VR can be used to enhance learning in higher learning institutions. Using a mixed-method research method, online surveys were distributed to registered university students in a South African university. The study used a simple random sampling strategy to obtain a diverse and non-biased data set. The quantitative and qualitative responses were analyzed separately, before being triangulated, and used to inform the discussion and conclusion. The research found that VR can be used to support teaching and learning in South African Higher Education Institutions. The study further found the importance of considering the various resource access challenges when introducing such technologies particularly in under resourced environments.
A Delay-Based Neuromorphic Processor for Arrhythmias Detection

Cardiovascular disease is the leading cause of global mortality, with 17.5 Million deaths per annum (World Health Authority, WHO). Innovative hardware based cardiac recording devices could help elevate this burden. Delay-based reservoir computing is a novel computational framework with only a single nonlinear node. This feature makes it a strong candidate for the hardware implementation of an analogue cognitive system. Such a system can be exploited to improve the energy efficiency of data processing in implantable bioelectronic devices. This paper presents a system modelling of this network that is capable of cognitively processing Electrocardiograph (ECG) signals from the MIT-BIH arrhythmia database. The proposed single-input single-output model receives an encoded ECG signal while the output amplitude pattern aids the diagnostic interpretation. The information processor is an analogue circuit with the dynamic properties of Mackey-Glass nonlinearity and fading memory. To validate this system and mimic real-time operation, the simulation is designed to detect ventricular ectopic beats, an ectopic heartbeat type, using a continuous ECG signal without any signal segmentation or feature extraction. After training, the model successfully locates ventricular ectopic beat with 87.51% sensitivity and 94.12% accuracy for the testing dataset from three patients.
Retrieval of Relevant Data for Measuring the Impact of Spaced-Repetition Algorithms on the Learning Success in Mobile Learning Games

This paper presents an approach on how to retrieve relevant data from a huge set of game-data in order to find evidence for the impact of using spaced-repetition algorithms on the learning success in a mobile learning game. After having collected approximately 12 million sets of playing data, the database needs to be preprocessed before analyzing it in order to filter out any data that is irrelevant or useless for our analysis or may even dilute its results. One structured and established way to do this is to follow the KDD process, which includes several consecutive steps of consolidating the available data, with preprocessing it being one of them. In order to be able to define the data were are looking for, we set up some proposals about how the relevant data should look like and how to retrieve it from our database.
Effectiveness of Synchronization and Cooperative Behavior of Multiple Robots based on Swarm AI

We investigated the effectiveness of Swarm AI, which allows multiple artificial intelligence (AI) robots to autonomously collaborate and achieve more advanced functions as a group. We built two models of robot groups, each equipped with its own AI, and ran simulations. The first one showed that multiple robots can sense the situation of another party and acquire synchronous behavior by reinforcement learning. The second one clarified that the efficiency of cleaning improved by 30-40 % as a result of introducing cooperative behavior based on Swarm AI into the group of cleaning robots. These findings demonstrate that the cooperation of multiple robots using Swarm AI is effective.
Classify Motion Model via SVM to Track Underwater Maneuvering Target

Maneuvering target tracking is one of the most important topics in marine research, and it is vital to track a target accurately. Tracking accuracy is closely related to the veracity of estimation of target's motion model. To improve the accuracy of tracking single underwater maneuvering target, this paper brings up a new method to reduce tracking error caused by switching of motion models. A support vector machine (SVM) is used to classify current motion model of the target, then state of the target is estimated by a Kalman filter (KF) with dynamic parameters. Simulation results suggest that compared with the classical interacting multiple model (IMM), algorithm proposed in this paper leads to a more satisfactory tracking root mean square error (RMSE) no matter the target maneuvers or not.
Tropical Reservoir Computing Hardware

In recent years Reservoir Computing has arisen as an emerging machine-learning technique that is highly suitable for time-series processing. Nevertheless, due to the high cost in terms of hardware resources, the implementation of these systems in one single chip is complex. In this brief, we propose a hardware implementation of a reservoir computing system with morphological neurons that allows us to reduce considerably the area cost associated with the neural synapses. The main consequence of using tropical algebra is that input multipliers are substituted by adders, leading to much lower hardware requirements. The proposed design is synthesized on a Field-Programmable Gate Array (FPGA) and evaluated for two classical time-series prediction benchmarks. The current approach achieves significant improvements in terms of energy efficiency and hardware resources, as well as an appreciably higher precision compared to classical reservoir systems.
NeXLink: Node Embedding Framework for Cross-Network Linkages Across Social Networks

Users create accounts on multiple social networks to get connected to their friends across these networks. We refer to these user accounts as user identities. Since users join multiple social networks, therefore, there will be cases where a pair of user identities across two different social networks belong to the same individual. We refer to such pairs as Cross-Network Linkages (CNLs). In this work, we model the social network as a graph to explore the question, whether we can obtain effective social network graph representation such that node embeddings of users belonging to CNLs are closer in embedding space than other nodes, using only the network information. To this end, we propose a modular and flexible node embedding framework, referred to as NeXLink, which comprises of three steps. First, we obtain local node embeddings by preserving the local structure of nodes within the same social network. Second, we learn the global node embeddings by preserving the global structure, which is present in the form of common friendship exhibited by nodes involved in CNLs across social networks. Third, we combine the local and global node embeddings, which preserve local and global structures to facilitate the detection of CNLs across social networks. We evaluate our proposed framework on an augmented (synthetically generated) dataset of 63,713 nodes & 817,090 edges and real-world dataset of 3338 Twitter-Foursquare node pairs. Our approach achieves an average Hit@1 rate of 98% for detecting CNLs across social networks and significantly outperforms previous state-of-the-art methods.
Machine Learning Classification Algorithms for Phishing Detection: A Comparative Appraisal and Analysis

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.
Fuzzy Utility Mining Under Minimum Weight Constraint of Multiple Item Weights

In this paper, we extend Lan et al.'s fuzzy utility mining approach to handle different weights for items. To address this issue, an efficient algorithm called the weighted fuzzy utility mining is proposed to efficiently discover high weighted fuzzy utility itemsets. However, the weighted fuzzy utility mining problem cannot hold the downward-closure property. We thus design an upper-bound model to prevent information loss in mining. Based on this model, we suggest a two-stage method for weighted fuzzy utility mining. Finally, some experiments are conducted to show the performance of the proposed approach.
Development of a Model of Cyberattacks Identification Based on the Analysis of Device States in the Network of a Telecommunications Service Provider

This paper is aimed in development of an attack detection model based on an analysis of the state chains of network nodes. In our research, we solve the problem of a typical attack profile in a network of telecommunication service providers formalization while constructing a sequence of network nodes state shiftings and a state change time of particulary investigated devices. The research includes the most popular types of attacks. We use the decision tree algorithm for the construction of the chain of security events. The ROC analysis is used for analyzing the results of security events binary classification, which is found using developed approach. The experimental study allowed evaluating the efficacy of the developed model for network attacks in the telecommunication service providers infrastructure recognition. Experimental results show high accuracy of determining the popular type of attacks. This gives an advantage to reduce the response time to security incidents in a larger network.
Control and Ergonomic Problems of Collaborative Robotics

Together with the development of information technology and artificial intelligence robotics intended to enlighten the more and more area of human life and work. It results in a change of the method of communication between human and “intelligence robot” capable of autonomous behavior—from control to collaboration. The new branch of robotics has been formed nowadays as collaborative robotics. The new ways of application of robotics create the new problems of communication of human and “intelligent” robot which are not only technical but also of a psychological kind because human-operator now becomes a part of the human-robot system. The new problems demand the development of new methods in the field of cognitive ergonomics. Some of the problems are under discussion below.
Robust trimap generation based on manifold ranking

In this paper, we propose a simple and effective method for creating accurate trimaps based on input images. Most advanced matting algorithms require the user to provide prior information to estimate high-quality alpha masks, where the prior information is primarily in the form of trimaps. A precise trimap is one of the most important factors affecting the performance of the matting algorithm. It is a very tedious task for users to specify a large number of accurate trimaps, and it is even impractical in some applications. Based on manifold ranking, we use strokes to mark the superpixel nodes to create high-quality trimaps. The experimental results show that the method given in this paper can generate high-quality trimaps, thus ensuring the accuracy of the alpha masks that are estimated by the matting algorithm. We verify the performance of the trimaps that are created using the method given in this paper for various matting algorithms.
A Community-Enhanced Retrieval Model for Text-Rich Heterogeneous Information Networks

In recent years, we have witnessed a large increase in the number of available text-rich heterogeneous networks, in which text documents, users and other objects are interconnected in various ways. Examples include social networks, bibliographic information networks, and collaboration networks. Text-rich heterogeneous networks contain text nodes (e.g., user comments in social networks; scientific publications in bibliographic networks), non-text nodes (e.g., users and companies in social networks), as well as rich relationships between different types of nodes. An important task, which is very useful on its own right, as well as often serves as a preprocessing to mining and analytical tasks, is identifying relevant text information from the heterogeneous information networks given user queries. Unlike Web search and many related problems, the effects of the underlying semantically meaningful patterns in heterogeneous networks play an important role in determining relevant answers for user search queries. For example, there are several structural patterns we can explore to more effectively identify relevant text nodes for a given query. The relevant nodes are likely to come from the same user-topic communities as the query. In addition, the relevant nodes on the same query topic tend to form tight local relationships with each other in the same user-topic community. In this paper, we present a unified and principled framework that effectively integrates network community and local relationship detection with retrieval model construction in a mutually enhancing manner, which leads to a community-enhanced retrieval model for text-rich heterogeneous information networks. Experimental results show that this new model significantly outperforms state-of-the-art baselines.
Cloud-Based Weather Monitoring System

Weather analysis plays a crucial role in daily activity to predict the weather system. Weather affects many fields, such as agriculture, industrial and aviation department. Temperature and humidity alarm systems are integrated with the monitoring system(s) at high and low set points. There should be an alert and also, most likely, a detectable alert, together with automated telephone dial-up or SMS text warnings to key personnel. The aforementioned material prefers constitution of actual time weather elements track setup and makes alert after a desired degree of temperature. This system is associated to the Thingspeak cloud service with sensors, to gather data and the data are accumulated in the desired channel. The channel data is analyzed by MATLAB analysis option on the Thingspeak cloud. Using IFTTT, which is a free web-based service to create the chain of simple conditional statements used to create the notification on mail, messages, tweet and so on. This observation of the temperature data plays a key role in server maintenance, and agriculture weather data is compulsory to predict the rainfall. In this paper we assert a solution that uses technologies like cloud computing, embedded systems and internet of things to improvise the productivity and at the same time decrease the usage. This architecture has been implemented in Neemrana, Rajasthan.
A Deeper Look at Human Visual Perception of Images

How would one describe an image? Interesting? Pleasant? Aesthetic? A number of studies have classified images with respect to these attributes. A common approach is to link lower level image features with higher level properties, and train a computational model to perform classification using human-annotated ground truth. Although these studies generate algorithms with reasonable prediction performance, they provide few insights into why and how the algorithms work. The current study focuses on how multiple visual factors affect human perception of digital images. We extend an existing dataset with quantitative measures for human perception of 31 image attributes under 6 different viewing conditions: images that are intact, inverted, grayscale, inverted and grayscale, and images showing mainly low- or high-spatial frequency information. Statistical analyses indicate varying importance of holistic cues, color information, semantics, and saliency on different types of attributes. Building on these insights we build an empirical model of human image perception. Motivated by the empirical model, we designed computational models that predict high-level image attributes. Extensive experiments demonstrate that understanding human visual perception helps create better computational models.
Controversy Score Calculation for News Articles

From the 2016 election continuing to the present, a strong focus has developed on the US news media landscape. From issues such as fake news to ideological wars between news sources, helping readers become more discerning news consumers remains a priority for technologists and journalists alike. This paper is a summary of our contribution to the continuing discussion of public empowerment towards making more informed news consumption decisions. In this paper we introduce NewsPhi, a news-feed application designed to provide topic-based contextual information on a changing corpus of daily news. NewsPhi's main contribution is a "controversy score" of news topics - a score that summarizes how controversial a certain news topic is according to the larger media environment. We find that this approach to building products for news readers is both unique and in line with ongoing bias and opinion formation research in the social sciences. Rather than seeking to correct or standardize news opinions, NewsPhi provides important contextual information to inform readers of existing opinions on existing and new topics while also lightly encouraging further exploration.
Augmented Environment Mapping for Appearance Editing of Glossy Surfaces

We propose a novel spatial augmented reality (SAR) framework to edit the appearance of physical glossy surfaces. The key idea is utilizing the specular reflection, which was a major distractor in conventional SAR systems. Namely, we spatially manipulate the appearance of an environmental surface, which is observed through the specular reflection. We use a stereoscopic display to present two appearances with disparity on the environmental surface, by which the depth of the specularly reflected visual information corresponds to the glossy surface. We refer to this method as augmented environment mapping (AEM). The paper describes its principle, followed by three different implementation approaches inspired by typical virtual and augmented reality approaches. We confirmed the feasibility of AEM through both quantitative and qualitative experiments using prototype systems.
An Intelligent Agent Framework for SCADA

The world is moving towards Industry 4.0, where an industrial automation will be integrated with artificial intelligence. In this paper we investigate how SCADA software, which is an essential part of Industry 4.0, will be formulated as an intelligent agent under a framework based on object-oriented modelling and logic. The agent framework is, in turn, developed under an open standard web platform based on HTML5, JavaScript, and Python.
Improving Rolling Horizon Evolutionary Algorithm in a Fighting Game

In this paper, we examine the performance of a game AI using Rolling Horizon Evolutionary Algorithm (RHEA) for a fighting game. We propose two methods for improving RHEA. Our experimental results show that the proposed AI is superior to an AI using standard RHEA.
Control of Wheeled Mobile Robot Using Centroid Algorithm

In today’s developing technology, industries are playing important role for the betterment of life and society. But in industries many functions are running under hazardous conditions, so demand of human free interaction arises, which can be fulfilled by robotics. In Chemical, Oil & Gas, Petrochemicals and many manufacturing industries today we need human safety on head, and it can be done by robots. This paper implies such type of mobile robot which can be used in complex industries. The work is based on Image processing and Centroid Algorithm. The physical quantities like (temperature, pressure, humidity, stress, PH) and leakage of gases and liquids, fire, spark and many human safety related things which can be encountered by this mobile robot. By using this robot, the main achievement is human safety and simultaneously cost effective, precision and all far away data can be easily available through IoT (Cloud) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . Also, it works without any disincentive at particular of desired time in day or night, where particular place requires physical quantities measurement. In this project the main components are Controller (Raspberry Pi), Four DC Motor for each wheel, Pi Camera for image detection and processing, motor driver logic and physical quantities sensor <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> .
A Formalization of Group Decision Making in Multi-Viewpoints Design

Complex systems are typically designed collaboratively by stakeholders from different domains. This multi viewpoints paradigm promotes the separation of concerns since separate teams, from different business viewpoints, build partial models describing the system. These partial models are naturally heterogeneous. So, it is difficult to ensure their inter-model consistency if kept separately. For that, we propose a collaborative approach that combines Group Decision Making (GDM) and Model-Based Engineering (MBE). This paper highlights the GDM part of our approach and especially the concept of decision policy that enables coming up with collective decisions in group decision-making contexts.