Reviewerly/exHarmony huggingface dataset:
CARs-Lands: An associative classifier for large-scale datasets

Associative classifiers are one of the most efficient classifiers for large datasets. However, they are unsuitable to be directly used in large-scale data problems. Associative classifiers discover frequent/rare rules or both in order to produce an efficient classifier. Discovery rules need to explore a large solution space in a well-organized manner; hence, learning of the associative classification methods of large datasets is not suitable on large-scale datasets because of memory and time-complexity constraints. The proposed method, CARs-Lands, presents an efficient distributed associative classifier. In CARs-Lands, first, a modified dataset is generated. This new dataset has sub-datasets that are completely appropriate to produce classification association rules (CARs) in a parallel manner. The produced dataset by CARs-Lands contains two types of instances: main instances and neighbor instances. Main instances can be either real instances of training dataset or meta-instances, which are not in the training dataset; each main instance has several neighbor instances from the training dataset, which together form a sub-dataset. These sub-datasets are used for parallel local association rule mining. In CARs-Lands, local association rules lead to more accurate prediction, because each test instance is classified by the association rules of their nearest neighbors in the training datasets. The proposed approach is evaluated in terms of accuracy on six real-world large-scale datasets against five recent and well-known methods. Experiment results show that the proposed classification method has high prediction accuracy and is highly competitive when compared to other classification methods.
Chaos-based steganography technique to secure information and integrity preservation of smart grid readings using wavelet

In recent years, smart grids have attracted much attention due to their digital properties. Smart grids play a vital role in transmitting the collected readings of meter and other sensitive information of customers through the public network. Therefore, the confidentiality of consumers' information and integrity protection of periodically collected readings are required for better assessment. This paper presents a novel technique of steganography based on wavelet and Hénon chaotic map to achieve integrity and authentication. Periodically collected readings are used as a cover object for hiding information of smart meter and consumer. The discrete wavelet transform is used to decompose readings into sub-band coefficients and used as a cover object. Sensitive information is converted into cipher using Hénon chaotic map. Stego readings are constructed with negligible distortion in original readings after concealing bits into a cover object by applying double-precision floating-point format. Percentage residual difference (PRD) test has been performed between stego and original readings as well as reconstructed readings. PRD test values have been observed to be very less than other existing algorithms. Experimental results illustrate that the proposed algorithm is reliable, robust and highly sensitive to its initial key, and retrieval of information at receiver's end is not labyrinthine.
A Robot with Decoupled Mechanical Structure and Adapted State Machine Control for Both Ground and Staircase Situations

The rapid development of e-commerce makes the last-mile problem more and more prominent. To meet the requirements of such a scenario, this paper proposes a robot with a decoupled mechanical structure. Such a hexapod robot is divided into a chassis frame with a pair of auxiliary wheel-legs for horizontal movement, a tetrapod for vertical movement, and a slide-and-rail mechanism to connect the former two parts. Such a structure is simple and, thus, cost-efficient; by managing the horizontal and vertical movements, such a robot can keep the balance and safety of the load in the various operation environments, such as roads, staircases, and even indoor ones. Meanwhile, a state-machine-based controller, which adapts well to the unique structure of the proposed robot, is also proposed, simplifying the sequential control of the robot. A prototype robot with 30-kg load ability was built, and the experimental results prove the validity of the design.
Finding Active Experts for Question Routing in Community Question Answering Services

In this article, we propose a method for finding active experts for a new question in order to improve the effectiveness of a question routing process. By active expert for a given question, we mean those experts who are active during the time of its posting. The proposed method uses the query likelihood language model, and two new measures, activeness and answering intensity. We compare the performance of the proposed method with its baseline query likelihood language model. We use a real-world dataset, called History, downloaded from Yahoo! Answers web portal for this purpose. In every comparing scenario, the proposed method is found to outperform the corresponding baseline model.
An Abstract Framework for Non-Cooperative Multi-Agent Planning

In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents’ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.
Memetic Algorithm based Similarity Metric for Recommender System

Recommender Systems (RS) are web-based intelligent decision-making tools, which narrow down the user's choices based on their defined and undefined behavior. An evolutionary algorithm, namely, Genetic Algorithm (GA) has shown significant results in the field of RS in the past. Despite its huge success, it suffers from the limitation of premature convergence. Memetic Algorithm (MA), also called parallel or hybrid GA is one such technique which introduces local search to reduce the likelihood of premature convergence. This work presents a novel MA-based Similarity Metric (MASM) for RS, leveraging the collaborative behavior of memes. We use publicly available Movielens dataset (100K ratings) to conduct experiments. Results demonstrate that the proposed metric outperforms the conventional GA-based Similarity Metric (GASM). The precision of RS using MASM is improved by 28% over RS using GASM, resulting in improved predictive recommendation accuracy.
Self-Paced Multi-View Clustering via a Novel Soft Weighted Regularizer

Multi-view clustering (MVC), which can exploit complementary information of different views to enhance the clustering performance, has attracted people's increasing attentions in recent years. However, existing multi-view clustering methods typically solve a non-convex problem, therefore are easily stuck into bad local minima. In addition, noisy data and outliers affect the clustering process negatively. In this paper, we propose self-paced multi-view clustering via a novel soft weighted regularizer (SPMVC) to address these issues. Specifically, SPMVC progressively selects samples to train the MVC model from simplicity to complexity in a self-paced manner. A novel soft weighted regularizer is proposed to further reduce the negative impact of outliers and noisy data. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method.
Human Following of Mobile Robot With a Low-cost Laser Scanner

Human-following robots can bring a lot of convenience to our lives. The function of human-following can be decomposed into three processes: human detection, tracking and following. Considering cost and adaptability. In this paper, a low-cost laser scanner is used to achieve it. And an improved method of threshold-based clustering is proposed, which can effectively solve the problem of excessive clustering when it is far away from the laser scanner. Three types of features are extracted to recognize the legs from other objects by the random forest classifier. The position in the middle of the legs is used to indicate the position of the target person, which will be tracked by the robot using a Kalman filter algorithm and nearest neighbor algorithm. After inputting the coordinates of the followed target into the controller, the linear velocity and angular velocity of the following can be obtained. Experiments show that the robot can follow the target person stably.
Utilizing Steering Entropy as a Measure of Performance in Head-mounted Augmented Reality

The next phase of augmented reality (AR) technologies suggest that as both the hardware and software continue to improve, we can expect that AR will become more commonly used as a tool for a variety of applications in complex operational contexts (i.e. training, manufacturing, mission planning). As new applications are designed and developed within these contexts, there is a necessity to be able to measure the effectiveness of these systems and to understand their impact on human performance and workload, so that only the most appropriate designs are selected for use, growing the technology in usefulness, not novel hindrances. A unique opportunity presented by the Microsoft HoloLens platform, as an example of head-worn AR systems, is the ability to collect positional and movement data, which lends itself to the computation of behavioral (or steering) entropy data, which can be related to human workload and performance within the system environment. However, little reference exists to be able to verify the accuracy of tracking of the device with regards to the output data available for collection. Within this practitioner-oriented paper, we extend current entropy measurement theory typically used in control settings within a heads-up display type ‘controls' environment. Our findings indicate that in-situ measurements of entropy utilizing the onboard sensors within the AR platform are more accurate to those collected within a Motion Capture Facility. Extending this work, these measurements can be used as a correlate of performance.
A Convolutional Neural Network for Sentiment Analysis of TripAdvisor reviews

We live in a world deluged with data, most of which however is inconceivable by humans. Sentiment analysis has been proved to be invaluable in many diverse scientific fields to provide insight in such unstructured data. Convolutional Neural Networks are popular and powerful models that excel at different tasks, including sentiment analysis. We develop a CNN with the objective of analyzing TripAdvisor user review data. The well-known large IMDB dataset is used for training and predictions are obtained for our extracted hotel review dataset, demonstrating transfer learning. Hotel review classification results by this fully automatic sentiment analysis system are shown to be very close to TripAdvisor’s 5-star rating and ranking system.
Detecting correlation between extreme probability events

As classical definitions of correlation give rise to counterintuitive statements when extreme probability events are involved, we introduce enhanced notions of positive and negative correlation in the general framework of coherent conditional probability. These notions allow to handle extreme probability events in a principled way by accommodating the different levels of strength of the zero probabilities involved (namely, zero layers). Since the detection of correlations by means of zero layers is computationally challenging, we provide a full characterization relying on only conditional probability values.
A Lwt-Firefly Algorithm Based Approach for Smooth Images Watermarking

Advancement of digital technologies has led to unauthorized distribution and reproduction of digital media contents. To prevent unrestricted access to digital data, digital watermarking presents an efficient solution for requirements, such as proof of ownership of digital media, protection of copyright. Digital watermarking techniques that exist in the literature often embed the watermark on complex images. In this study, a blind method with entropy, LWT and Firefly Algorithm is proposed to embed the binary logo into smooth images. The embedding and extraction steps of the method are done in the low frequency bands of LWT. The experimental results show the effectiveness of the proposed method. Peak signal to noise ratio, normalized correlation and bit error rate are preferred to interpret the success of the method.
Demo Abstract: LiftRight: Quantifying Training Performance using a Wearable Sensor

There are many reasons why exercise is important, ranging from improvements in physical and psychological wellness to disease management. Fitness trackers today focus on very basic aspects of monitoring exercise, such as tracking the heart-rate and counting steps. Very few monitoring tools exist, especially in the realm of weight training. This demo presents LiftRight, an approach in monitoring strength training sessions to capture performance metrics and form analysis.
Cosine Focal Loss-based Change Detection for Video Surveillance Systems

Change detection is one of the important tasks for video surveillance systems. A variety of learning-based approaches have been proposed, but class imbalance in training data degrades their learning efficiency. In this paper, we propose a cross entropy loss with a modulating term in cosine form to handle this class imbalance. Although the original focal loss focuses only on reducing weights for well-classified data, the proposed function is designed to preserve sufficient gradients for rare hard samples as well. This property allows a network to learn mainly from a few significant samples on which the network should focus. We validate the proposed loss through various experiments on CDNet2014 dataset, and the results show that the network trained with the proposed loss achieves better performance than other state-of-the-arts in various complex scenarios.
Visual saliency with foveated images for fast object detection and recognition in mobile robots using low-power embedded GPUs

This paper presents a visual saliency algorithm for fast object detection and recognition in mobile robots using low power graphics processing units (GPUs), based on human vision foveation. The step of image foveation enables the use of small images, which leads to a much reduced number of computations in deep convolutional neural networks and consequent increase in frame-rate. We demonstrate how using a simple foveated downsampling method, we can maintain a detection-recognition performance level similar to the level at larger image resolutions, even when transforming from 416×416 to 128×128 pixels, for a small high acuity region of the image, which can lead to a 4× speed up in frame rates, maintaining a relatively stable mean Average Precision. The visual saliency algorithm is evaluated on the Stanford drone dataset and our own experimental drone dataset.
Communication Design and Space Narratives

The experience with space is achieved through projects developed according to a design perspective. In addition to collaborating on the creation of spatial narratives by promoting the experience, the design also acts in the sense of enhancing accessibility in both the physical and cognitive domain. Wayfinding systems as well as informational technological systems are informative elements that communicate with citizens, fostering experiences of greater accessibility and the creation of social space. This paper discusses the concept of space from its social dimension, questioning on how spatial narratives, places, and paths can create individual or collective experiences within urban space through Communication Design by the use of technology.
Real-Time Video-Based Person Re-Identification Surveillance with Light-Weight Deep Convolutional Networks

Today's person re-ID system mostly focuses on accuracy and ignores efficiency. But in most real-world surveillance systems, efficiency is often considered the most important focus of research and development. Therefore, for a person re-ID system, the ability to perform real-time identification is the most important consideration. In this study, we implemented a real-time multiple camera video-based person re-ID system using the NVIDIA Jetson TX2 platform. This system can be used in a field that requires high privacy and immediate monitoring. This system uses YOLOv3-tiny based light-weight strategies and person re-ID technology, thus reducing 46% of computation, cutting down 39.9% of model size, and accelerating 21% of computing speed. The system also effectively upgrades the pedestrian detection accuracy. In addition, the proposed person re-ID example mining and training method improves the model's performance and enhances the robustness of cross-domain data. Our system also supports the pipeline formed by connecting multiple edge computing devices in series. The system can operate at a speed up to 18 fps at 1920×1080 surveillance video stream. The demo of our developed systems can be found at https://sites.google.com/g.ncu.edu.tw/video-based-person-re-id/.
Detecting Architectural Issues During the Continuous Integration Pipeline

The use of a software reference architecture limits possible deviations and errors in the implementation of software projects, as the code must follow predefined rules that developers must respect to guarantee quality. However, when introducing new code to projects these rules can be violated. As a result, architectural erosion, bad smells, or even bugs that can be difficult to find are introduced to the projects. This paper proposes an approach for reviewing compliance to predefined rules that map architectural decisions to code. During the continuous integration process, the automatic analysis raises an issue for each rule violation. Developers can analyze and correct issues, and trace/visualize improvements, or lack thereof, through time. We present a validation experiment carried out in the context of a Software Development course, and we show how the approach helps developers to write better code.
Application research on improved CGAN in image raindrop removal

Rainy weather can greatly reduce the image quality and hinder the subsequent processing of the image. In order to achieve raindrop removal on rainy images, the single image raindrop removal method based on conditional generative adversarial networks (CGAN) is proposed. In this method, CGAN is used as the basic framework. The network receives the raindrop image as an additional condition information and adds Lipschitz constraint on the network. The network model is trained by the combination of condition adversarial loss, content loss, and perception loss to repair the raindrop area and reconstruct the image. The experimental results show that the proposed method has better raindrop removal effect than the existing algorithm and can avoid image blurring on the basis of ensuring the raindrop removal effect.
Incomplete-Data Oriented Dimension Reduction via Instance Factoring PCA Framework

In this paper, we propose an instance factoring PCA (IFPCA) framework for dimension reduction in incomplete datasets. The advantage of IFPCA over the traditional PCA is that, a penalty is imposed on the instance space via a scaling-factor to suppress the effect of outliers in pursuing projections. We geometrically use two scaling-factor strategies, total distance and cosine similarity metrics. Both strategies can learn the relationship between each data point and the principal projection in the feature space. In this way, better low-rank projections are obtained through scaling the data iteratively to suppress the impact of noise in the training set. Extensive experiments on COIL-20, ORL and USPS datasets prove the superiority of the proposed framework over state-of-the-art dimensionality reduction methods such as LSDA, gLPCA, RPCA-OM, PCA, LPP and RCDA.
Teaching Propositional and Syllogistic Logic Using E-learning Tools

This paper is a study of the use of E-learning tools in a course on basic logic. It is a continuation of earlier studies involving practical experiments with students of communication using the Syllog system for syllogistic reasoning. In the present study, we also discuss the use of the Proplog system which we developed to support a part of the course dealing with basic propositional logic. Both systems make it possible to do learning analytics based on logged data. The aim of the present paper is to investigate whether various log-data from the use of the two E-learning tools can be helpful in order to improve the quality of the logic teaching. Furthermore, it is discussed how the use of the tools can be integrated in the logic course including the exam.
The Effect of Cybersickness of an Immersive Wheelchair Simulator

A key challenge that Immersive applications have to overcome is cybersickness. Cybersickness is particularly prevalent in dynamic applications such as vehicles simulators. The work presented here aims to understand the cause of cybersickness symptoms in an assistive technology (AT) application, the virtual wheelchair training simulator. This evaluation is performed in terms of errors made during experience and post-experience Simulator Sickness Questionnaire (SSQ). The performance metrics analyzed are time to complete the proposal task and number of collisions (errors/mistakes). The post-experience questionnaires (subjective measurements) collected the user's experience in terms of simulator sickness by applying the Simulator Sickness Questionnaire (SSQ) and immersion questions. The experiments were conducted with 10 participants. In terms of results, analysis of human factors reveals that the average cybersickness score is slightly higher for women compared to men. However, these differences were not statistically significant. There was an inverse correlation between cybersickness symptoms and task performance as well as between cybersickness symptoms and immersion.
Facial Expression Classification Using Convolutional Neural Network and Real Time Application

Facial expression is an important feature that gives information about a person's psychological situation. People use these expressions while communicating and socializing and they have lots of information related to inner world of an individual. Therefore it is important to understand the meaning of these facial expressions automatically and use this information. This paper presents a method for facial expression classification with grayscale images from Kaggle Face Dataset with a Convolutional Neural Network and a realtime user interface in order to test the performance online. Data augmentation is used to increase the diversity of the samples. Neural network is created with Matlab and user interface is created via App Designer. Different training and fine-tuning techniques are employed in the design. The overall accuracy 61.8% is achieved across seven different facial expression categories with test dataset supplied in Kaggle domain.