Reviewerly/exHarmony huggingface dataset:
On Development of An Autonomous Ball Collecting Wheeled Mobile Robot

This paper presents the technological details of an autonomous ball collecting wheeled mobile robot for tennis and table tennis. A novel mechanical structure of ball collecting device is designed and fabricated. Two wheel differential driving mechanism with an omnidirectional wheel is implemented to actuate and steer the robot. Lidar and RGB-D camera are used to detect environmental information as well as scattered balls in the training field to provide robot with navigation information. Experimental results verify the effectiveness of each subsystem.
An Experimental Study on Decision Tree Classifier Using Discrete and Continuous Data

Classification is one of the fundamental tasks of pattern recognition, data mining, and big data analysis. It spans across the domain for classifying novel instances whose class labels are unknown prior to the development of model. Decision trees like ID3, C4.5, and other variants for the task of classification have been widely studied in pattern recognition and data mining. The reason is that decision tree classifier is simple to understand, and its performance has been comparable with many promising classifiers. Therefore, in this work, we have developed a two-phase method of decision tree classifier for classifying continuous and discrete data effectively. In phase one, our method examines the database, whether it is a continuous-valued or discrete-valued database. If it is a continuous-valued database, then the database is discretized in this phase. In the second phase, the classifier is built and then classifies an unknown instance. To measure the performance of these two phases, we have experimented on a few datasets from the University of California, Irvine (UCI) Machine Learning repository and one artificially created dataset. The experimental evidence shows that this two-phase method of constructing a decision tree to classify an unknown instance is effective in both continuous and discrete cases.
Hierarchical Temporal Memory Based Anomaly Detection for Hydrological Monitoring of Unmanned Surface Vehicle

Intelligent hydrological automatic monitoring of the target area under all-weather and unattended conditions is of great significance for preventing water disasters and protecting water resources. An application of intelligent hydrological monitoring is developed based on unmanned surface vehicle (USV). Abnormal detection is an important part of hydrological monitoring. Hierarchical temporal memory (HTM) network imitates the structure of mammalian cortex, and it is used for abnormal detection of hydrological sensor data collected from USV. The prediction HTM network consists of initialization, computation of cell states, and updating of synapses on dendritic segments. The hydrological data of rivers, such as water depth, velocity and flow, can be easily obtained by USV. And a hydrological dataset is established with the mark of normal or abnormal sample. The HTM based abnormal detection method is detailed and it is compared with other machine learning based methods. The experimental results indicate that the HTM based method has the best anomaly detection performance.
The Effect of Two Different Types of Human-Computer Interactions on User's Emotion in Virtual Counseling Environment

Previous studies have suggested that different types of human-computer interactions (HCI) have different impact on use's emotional responses in virtual environment. In this study, we investigat the effects of two different types of HCI, including controller-based interaction and speech-based interaction, on user's emotion in a virtual counseling environment. To achieve this end, we first develop a virtual counseling environment which enable each participant acted as a counselor to help a depressed virtual woman. We then assess the emotional intensity of each participant after they experienced 20-30 minutes' consultation in either controller-based or speech-based interaction condition. Our results show that, although there is no significant difference in emotional intensity between two different interaction conditions, participants in speech-based condition show relatively stronger emotion than these in controller-based condition.
A Modified Ant Colony Optimisation based Optimal Path Finding on a Thematic Map

Increasing demands for unmanned systems and the availability of high resolution satellite images have been promoting researchers to contribute innovations to increase the robustness and efficiency of the optimal path planning. An effectively classified satellite image and a robust path planning strategy are highly desirable in finding an optimal path. In this paper, satellite images from ISPRS are classified to identify the traversable areas using a Deep Convolutional Encoder-Decoder architecture-Seg Net and cost map is generated. Using the cost map, Modified Gain based Ant Colony optimization(MGACO) is introduced to find an energy efficient path. The path is finally smoothened using Bezier Spline approximation. MGACO has been compared with up-to-date algorithms and results outperform existing methods in terms of run time and length of the path.
Feature extraction and Classification of Learners using Multi-Context Recurrent Neural Networks

This study presents about a new procedure for extraction and classification of learners in a class using the neural networks. It is necessary to provide learning support corresponding to the understanding degree of each learner to improve learning process efficiency. For this purpose, this study develops a procedure to predict the achievement level of learners at the end of the class and classify them. A Multi-Context Recurrent Neural Network (MCRNN) is used for predicting achievement level and classifying learners. By providing additional education for the learners who are classified as a low degree by the proposed method, it is expected to be able to take countermeasures for not becoming dropout in early stage. In this study, numerical experiments are executed to verify the usefulness of the proposed method. To gather enough number of learners' data, this study generates the learners' growth process data that used as training and test data of MCRNN. The experimental result indicates that the proposed method succeeded in classifying learners into three groups based on the understanding degree at the end of a class.
Cloud Computing: A Paradigm of More Insider Threats

Insider threats are one of the most challenging issues in the world of computer networks. Now a day, most of the companies are relying on cloud services to get scalable data services and to reduce cost. The inclusion of cloud environment has spread the canvas for insider threats because cloud service providers are also there in addition to the organization that outsourced for cloud services. In this paper, multiple existing approaches to handle the insider threats in cloud environment have been investigated and analyzed thoroughly. The comparison of these techniques depicts which better approaches in the paradigm of cloud computing exist.
Classical 2D Face Recognition: A Survey on Methods, Face Databases, and Performance Evaluation

The visual system is the ultimate model for computer vision systems. Face recognition is one of the essential biometric-based methods of computer vision from the perspective of safety and security. The research in face recognition has improved significantly during the way back 1970 to present based on the various classification technique. This paper presents a survey of some most significant classical 2D classification techniques in face recognition, the well-known face databases for evaluation of methods, and performance evaluation techniques.
A year in statistics—the view from the trenches

Last year we launched the EBM Methods Verdicts as part of an attempt to keep on top of developments in statistics that will impact on the way we do research.1 2 We focused on statistical methods and in particular on the output from five different journals: Journal of Clinical Epidemiology , Statistics in Medicine , Statistical Methods in Medical Research , BMC Medical Research Methodology and Biostatistics .The idea is simple: identify papers that we believe are likely to change the way we analyse or report evidence syntheses, randomised trials or studies related to diagnosis, prognosis or monitoring. Our editorial team typically focuses on one of these per month and aims to write a short commentary with a ‘verdict’ explaining how this will change how we do things. We are also interested in people submitting proposals for similar short pieces.Bayesian neural networks were the rage in the early 90s. Work in this area seemed to go quiet for most of the 00s but has exploded back into our consciousness in the last few years. This is clear by the number of papers published on the topic (figure 1) with nearly half of the total appearing in the last 2 …
Monotone recursive types and recursive data representations in Cedille

Guided by Tarksi's fixpoint theorem in order theory, we show how to derive monotone recursive types with constant-time roll and unroll operations within Cedille, an impredicative, constructive, and logically consistent pure typed lambda calculus. This derivation takes place within the preorder on Cedille types induced by type inclusions, a notion which is expressible within the theory itself. As applications, we use monotone recursive types to generically derive two recursive representations of data in lambda calculus, the Parigot and Scott encoding. For both encodings, we prove induction and examine the computational and extensional properties of their destructor, iterator, and primitive recursor in Cedille. For our Scott encoding in particular, we translate into Cedille a construction due to Lepigre and Raffalli that equips Scott naturals with primitive recursion, then extend this construction to derive a generic induction principle. This allows us to give efficient and provably unique (up to function extensionality) solutions for the iteration and primitive recursion schemes for Scott-encoded data.
Improving the measurement of thread parameters by use of corundum elements

Abstract Considered an advanced method of the three wires to measure the average diameter d av and pitch P of the thread using three elements corundum (synthetic sapphire, sapphire, ruby): two fibers and tip with the ability to visualize and measure the size of the contact zone with the product to calculate the contact elastic deformation. It is proposed to use two fibers incoherent and one coherent interferometers to measure the position of corundum fibers and the tip. A method for calculating the diameter d av and pitch P of the thread based on the results of interferometer measurements by using the compensation of contact elastic deformations is presented.
Image-Based Clone Code Detection and Visualization

Currently, research mainly focuses on four perspectives of text, vocabulary, grammar and semantics in the field of clone code detection. However, few breakthroughs have been made in the effect of clone code detection for a long time. For this purpose, a new image-based clone code detection and visualization (ICCV) is proposed with the inspiration of image processing. First, the source code is preprocessed by removing comments, whitespace, etc. from which a "clean" function fragment can be obtained, and the identifiers, keywords, etc. in the function can also be highlighted; Then the processed source code is converted into images and these images are normalized; Finally, Jaccard distance and perceptual hash algorithm are used to detect and visualize the clone code information. In order to verify the validity of the experiment, six open source software were used to constitute the evaluation data set for testing. The experimental results show that ICCV can detect 100% type-1 clone code, 88% type-2 clone code and 60% type-3 clone code, which proves the good effect of ICCV on clone code detection.
Dealing with Producing and Consuming Expressions in Italian Sentiment Analysis

To perform aspect-level sentiment analysis, in the context of customer reviews, it is necessary to deal not only with subjective opinions, that are well covered by literature, but also with fact-implied opinions, that are less examined since they are less common and harder to handle. This work focuses on a specific type of fact-based opinion called producing and consuming expression. For example, expressions like “this printer consumes a lot of ink” and “this washing-machine makes a lot of noise” are instances of this type of opinion. This research aims to build a tool that can identify these expressions, classify their sentiment polarities and determine their opinion targets. To achieve this task, we started analyzing these expressions from a linguistic point of view. Then we developed a set of linguistic resources using the Nooj software. This set consists of one dictionary and one grammar. The dictionary contains verbs that express usage and production; adjectives and determiners that modify quantity, size, etc. and some generic nouns of resources and wastes. The grammar can recognize and tags simple and complex sentences that are producing and consuming expressions. This grammar produces a tag that allows to determine the sentiment of the expression simply by applying the rules of sentiment composition.
WARP: Contributional Tracking Architecture Towards a Worldwide Augmented Reality Platform

Ubiquitous Tracking (UT) for Augmented Reality (AR) applications shows great potential in becoming a key driving force in the wide scale adoption of AR. However, the implementation and integration of a global sensor network necessary for this technology may seem too daunting of a task to entrust in one central body. We propose an architecture designed to allow both individual and corporate bodies to equally contribute to the construction of a sensor network, providing a platform in which the growth of the AR environment is in the hands of its users. We propose a distributed blockchain based model capable of storing tracking entity pose data. The model allows users to contribute tracking entities to the blockchain and to share this data through peer-to-peer connections. We conclude with a discussion surrounding beneficial contributions and necessary future works.
Accurate estimation of transmission maps for image restoration based on polarimetric parameters and average intensity

Haze limits visibility and degrades image contrast, and images lose color fidelity owing to the scattering and absorption of light caused by particles. Consequently, removing haze is critical for enhancing the image visibility and quality, with estimation of airlight and transmission map being critical steps. We propose a simple and convenient improved scheme, which is polarization-based method for dehazing. The method uses four polarization orientations (0°, 45°, 90°, 135°) to measure the Stokes vector, and obtained the degree of polarization (DOP) of the airlight by angle of polarization (AOP) in three channels. Subsequently, estimating the airlight at infinity by average total intensity and re-constructing a matrix to estimate the transmission maps. Experimental results demonstrate that a high-quality transmission map can be obtained, and this improved method can restore outdoor scene haze images with high quality and contrast. In addition, this method also proved to be useful for enhancing color saturation and restoring color fidelity.
Negation of Croatian Nouns

The purpose of this paper is to describe a morphological grammar for recognizing negation of a noun and annotating its polarity accordingly. Not all nouns can be negated on the morphological level. For example, nouns like ‘activity’ and ‘knowledge’ (aktivnost, znanje) can have negatives (neaktivnost, neznanje respectfully), but the same is not the case for nouns such as ‘battle’ or ‘table’ (bitka or stol). The most common and frequent Croatian prefix for negation of nouns is ‘ne-’ although several more are used either of Slavic (‘be-’, ‘bez-’) or Latin origin (‘anti-’, ‘dis-’). In some cases, negated nouns actually denote positive concepts, whereas their non-negated counterparts are used for expressing concepts with negative connotations. For this purpose, all the nouns in NooJ dictionary, that may have nouns in both polarities, are provided with [Polarity = pos] or [Polarity = neg] marker. This information is used in the grammar to switch the polarity of the opposite noun after the insertion of a negative prefix. The grammar is tested on different types of corpora and results are discussed.
Reflections on Temporal Factors in Interaction Design

This paper reflects on insights from the design process and evaluation of an online tool for supporting professional learning in a higher education setting. The online tool was developed through an iterative user-centred design process to ensure that it supported the needs and goals of academics. However, findings from a semester-long evaluation study showed that it found relatively little uptake despite participants' positive feedback in post-study interviews. This prompted us to conduct a further analysis of our results, which revealed that our process had failed to capture some of the more nuanced factors emerging from the users' work environment relating to time and timing. Drawing on those insights, the paper highlights the need to capture temporal factors during the design process and offers three design considerations to better support the adoption of online tools into daily work practices.
Comparison of Classification Algorithms for Movie Reviews

The objective of this research is to seek data mining techniques that are effective in classification and accurate at acceptable levels. Data from a movie review data set was tested using five algorithms, including Naïve Bayes, Decision Tree, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbor. Compared to other algorithms, it was found that the Support Vector Machine algorithm provided the highest accuracy of 80.20 percent. Based on this data, it suggested that this algorithm is suitable to analyze the review data that will be developed as a system to introduce products and services in the future.
Determining the shape of reflectance reference samples for curved surface reflectors

Abstract Foils made of different materials are often used as reflective insulators. Many manufacturers aim to accurately measure their optical properties to estimate and improve their performance. However, flat reflectance reference samples used in measurements do not correctly represent reflective insulators and cause discrepancies between different measurement techniques. The current work presents a method for modelling surface shape of appropriate reflectance reference samples that could be produced by additive manufacturing. The method is based on studying the reflection distribution of reflective insulators and is described with an example of aluminium foil. The method’s performance is validated using Monte-Carlo simulations.
Research on Technology and Standards of Augmented Reality-Based Auxiliary Maintenance

This paper is prepared to analyze the current application of augmented reality in industrial auxiliary maintenance, study the basic framework of connectivity, data integration model and human-machine interaction way, and explore the standardized methods. The standard verification platform is built from two aspects of system level and typical auxiliary maintenance scenarios. The established standard is tested and verified, and the iterative optimization of the standard is realized through test verification.
Banks of filters for implementation of DMWT of an image

Abstract We describee some possible options for implementation of the Discrete multiwavelet transform (DMWT) of an image by using filter banks. DMWT can be implemented by two channel bank of vector filters which are made by cross-connected scalar filters. The properties of DGHM, CL, BiHermite and SA4 multiwavelets are here analyzed, and compression analysis for output normalization of DMWT is performed. A procedure is design of equivalent replacing of 2 channel multifilters bank by 4 channel bank of single scalar filters. Finally, we deal with a possible reduction and combinations of subbands and suggest their use.
A Dynamic and Informative Intelligent Survey System Based on Knowledge Graph

In the paper we propose a dynamic and informative solution to an intelligent survey system that is based on knowledge graph. To illustrate our proposal, we focus on ordering the questions of the questionnaire component by their acceptance, along with conditional triggers that further customise participants’ experience, making the system dynamic. Evaluation of the system shows that the dynamic component can be beneficial in terms of lowering the number of questions asked and improving the quality of data, allowing more informative data to be collected in a survey of equivalent length. Fine-grained analysis allows assessment of the interaction of specific variables, as well as of individual respondents rather than just global results. The paper explores and evaluates two algorithms for the presentation of survey questions, leading to additional insights about how to improve the system .
Ant Lion-Based Random Walk Differential Evolution Algorithm for Optimization and Clustering

Ant lion optimization algorithm (ALO) is a swarm-based metaheuristic for optimization inspired by the nature of ant lion hunting. One of the main step of hunting is the random walk of ants around the ant lion, which ensures ALO to possess a good local searching ability. Differential evolution (DE) is an evolutionary algorithm with a structure including mutation, crossover, and selection. The operations of DE are randomly executed which makes DE suffering from week exploiting ability. In this paper, a hybrid differential evolution based on the random walk of ants around the ant lion is presented, which combines the advantages of ant lion optimization algorithm and differential evolution, aiming to well balance the exploitation and exploration of the search. The hybrid algorithm is tested on CEC'17 benchmark suit and clustering problems. Experimental results verify the superiority of the proposed algorithm in comparison with other related algorithms.
Capped l<sub>2</sub>,<sub>1</sub>-Norm Regularization on Kernel Regression for Robust Semi-supervised Learning

Graph-based semi-supervised learning has become one of the most important research areas in machine learning and artificial intelligence community. In this paper, we propose a Capped l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -Norm Regularization model for graph based semi-supervised learning (SSL). The new proposed model is aims to fully train the classification function by utilizing all the data points as well as handle the out-of-sample extension for new-coming data points. In addition, in order to enhance the robustness to the outliers, we leverage the capped l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm as the loss function for the classification model. The capped l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm can suppress the bias of outliers that are far away from the normal data distribution. Simulation results show that the proposed method can achieve better performance compared with other state-of-the-art graph based SSL methods.