Reviewerly/exHarmony huggingface dataset:
A Novel Method for Video Moving Object Detection Using Improved Independent Component Analysis

Moving object detection is a hot and difficult problem. How to separate the changed part from the unchanged one is the key. Also, the connection between the multi-dimensional information of input frames often plays an important role in moving object detection. In this article, we presented a novel and effective video moving object detection method. The innovation of this method lies in the calculation of the connection between multi-dimensional information considering time and space domain at the same time. At present, most moving object detection algorithms consider the time dimension and the space dimension in an independent manner. It is possible to cause some detection problems, such as, if the background information of a static or low-speed object is updated too quickly, only part of the foreground region is detected correctly. To avoid such deficiencies, the improved ICA method is adopted to separate the continuous multi-frame images in the time domain, and then to integrate these signals in the space domain. It could separate and detect the difference between the input signals to extract the correct foreground target, especially in some situations that insignificant changes happen. The proposed algorithm is named VTD-FastICA. A large number of experiments have shown that our method is more competitive than most of the moving object detection methods, especially in detecting long-span, occlusion environments, and objects with weak changes. Moreover, we also analyze the efficiency of our method in different situations, indicating that the proposed algorithm can be applied to real situations.
Automated areas of interest analysis for usability studies of tangible screen-based user interfaces using mobile eye tracking

Abstract The user's gaze can provide important information for human–machine interaction, but the analysis of manual gaze data is extremely time-consuming, inhibiting wide adoption in usability studies. Existing methods for automated areas of interest (AOI) analysis cannot be applied to tangible products with a screen-based user interface (UI), which have become ubiquitous in everyday life. The objective of this paper is to present and evaluate a method to automatically map the user's gaze to dynamic AOIs on tangible screen-based UIs based on computer vision and deep learning. This paper presents an algorithm for automated Dynamic AOI Mapping (aDAM), which allows the automated mapping of gaze data recorded with mobile eye tracking to the predefined AOIs on tangible screen-based UIs. The evaluation of the algorithm is performed using two medical devices, which represent two extreme examples of tangible screen-based UIs. The different elements of aDAM are examined for accuracy and robustness, as well as the time saved compared to manual mapping. The break-even point for an analyst's effort for aDAM compared to manual analysis is found to be 8.9 min gaze data time. The accuracy and robustness of both the automated gaze mapping and the screen matching indicate that aDAM can be applied to a wide range of products. aDAM allows, for the first time, automated AOI analysis of tangible screen-based UIs with AOIs that dynamically change over time. The algorithm requires some additional initial input for the setup and training, but analyzed gaze data duration and effort is only determined by computation time and does not require any additional manual work thereafter. The efficiency of the approach has the potential for a broader adoption of mobile eye tracking in usability testing for the development of new products and may contribute to a more data-driven usability engineering process in the future.
Enhancement and Fusion of Multi-Scale Feature Maps for Small Object Detection

In recent years, deep convolutional neural networks have made breakthrough progress in object recognition and object detection tasks in the field of computer vision, and have achieved great results both in accuracy and speed. However, the detection of small objects is still difficult in the field of object detection, and the accuracy on the common dataset MS COCO is very low. This paper briefly reviews some work in multi-scale object detection algorithms, and then proposes a method of feature enhancement and fusion based on multi-scale feature maps, improving detection accuracy of small objects on MS COCO.
Conceptual Space Modeling for Space Event Characterization

This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data is also taken into account, for example data about space weather effects, by drawing on the Space Domain Ontologies, a large system of ontologies designed to support all aspects of space situational awareness. The framework is coupled with a mathematical programming scheme that frames a mathematically optimal approach for decision support, providing a quantitative basis for ranking potential for collision across multiple satellite pairs. The goal is to provide the broadest possible information foundation for critical assessments of collision likelihood.
Software Cost Estimation – A Comparative Study of COCOMO-II and Bailey-Basili Models

Software Cost Estimation (SCE) is a hot area of research in the field of development of software projects. Precise estimation of the efforts put on development of software projects, in term of person-month (PM) and development time, is an essential earlier startup of projects. There are several software cost estimation techniques, such as algorithmic and non-algorithmic. This study presents a comparison among two algorithmic methods, namely Baily-Based model and Constructive Cost Models (COCOMO-II). The simulation is conducted on Turkish and Nasa datasets. From the excremental results, it is evident that COCOMO-II is better than the Bailey-Basili in term of Mean Magnitude of Relative Error (MMRE).
KTransGAN: Variational Inference-Based Knowledge Transfer for Unsupervised Conditional Generative Learning

Class-conditional generative models have gained popularity due to their characteristics of learning disentangled representations. However, these models typically require labeled examples in training. In this paper, we explore the feasibility of training these models on completely unlabeled data, under the assumption that we have access to other labeled data. The labeled data share the same label space, while their domain is shifted. Our model, which we refer to as KTransGAN, incorporates a classifier to transfer knowledge from the labeled data and performs collaborative learning with the conditional generator. By adopting these measures, KTransGAN is able to approximate the conditional distribution of the unlabeled data and simultaneously introduces a new solution to the unsupervised domain adaptation problem. To mitigate the training difficulty of our generative adversarial networks-based model, variational encoding and feature matching are also considered. From the empirical results, KTransGAN exhibits outstanding performance on a number of synthetic datasets and multiple real-world benchmarks. The quality of the synthesized instances is far superior to the pure variational autoencoding model. For example, on the CIFAR-10 dataset, our model scores 35.3 in FID, while the other model scores 128.45. In addition, the synthesis quality is close to the case when the model is trained in a fully supervised setting over the same number of training iterations. Regarding the classification performance, for instance, our model surpasses the highest state-of-the-art results (89.19%) by a large margin and achieves a test accuracy of 95.31% on the unlabeled data SVHN, while MNIST represents the labeled data. These results highlight the effectiveness of our proposed framework.
A debate about implementing immersive technology for higher education: Pre-study examining the usability of virtual reality for lectures

BACKGROUND: As the COVID-pandemic has shown, the need for innovative (digitalized) solutions is in high demand across almost every field of interest. The implementation of advanced technologies in higher education provides an intriguing opportunity to expand its scope by reaching new audiences as well as ensuring a high quality of learning outcome. OBJECTIVE: In this article we tried to examine if virtual reality can be a suitable option by placing lectures into a virtual setup. METHODS: First, we explored the theoretical background if and how virtual reality has been adapted for usage in higher education. We then asked five lecturers from the IMC University of Applied Sciences Krems to test a virtual environment (Mozilla Hubs) and evaluate the platform for their teaching purposes. RESULTS: Among one of the results was, that 80 percent would recommend using the platform for lectures to their colleagues. Due to the small sample size the findings need to be further evaluated. CONCLUSIONS: In the foreseeable future virtual reality will become a valuable teaching assistance in higher education. Findings show that the response rate when training with virtual reality applications is much higher than to common studying methods.
Usable and Acceptable Response Delays of Conversational Agents in Automotive User Interfaces

With an increasing ability to answer and fulfill user requests, voice-enabled Conversational Agents (CAs) are becoming more and more powerful. However, as the complexity of the requests increase, the time for the CAs to process and fulfill the tasks can become longer. In other cases where input prediction is available, some requests can be processed and answered even before the user is finished saying the command. However, the effects of these positive and negative delays in system response time are still under-explored. In this paper, we systematically analyze the effects of different response delays on usability and acceptability considering three common interaction techniques for voice-enabled CAs. Our results reveal that an unnaturally long positive delay in system response time leads users to assume that an error occurred, while a negative delay is perceived by the users as rude. Based on our findings, we present design guidelines for voice-enabled CAs.
A Deep-Learning Model with Task-Specific Bounding Box Regressors and Conditional Back-Propagation for Moving Object Detection in ADAS Applications

This paper proposes a deep-learning model with task-specific bounding box regressors (TSBBRs) and conditional back-propagation mechanisms for detection of objects in motion for advanced driver assistance system (ADAS) applications. The proposed model separates the object detection networks for objects of different sizes and applies the proposed algorithm to achieve better detection results for both larger and tinier objects. For larger objects, a neural network with a larger visual receptive field is used to acquire information from larger areas. For the detection of tinier objects, the network of a smaller receptive field utilizes fine grain features. A conditional back-propagation mechanism yields different types of TSBBRs to perform data-driven learning for the set criterion and learn the representation of different object sizes without degrading each other. The design of dual-path object bounding box regressors can simultaneously detect objects in various kinds of dissimilar scales and aspect ratios. Only a single inference of neural network is needed for each frame to support the detection of multiple types of object, such as bicycles, motorbikes, cars, buses, trucks, and pedestrians, and to locate their exact positions. The proposed model was developed and implemented on different NVIDIA devices such as 1080 Ti, DRIVE-PX2 and Jetson TX-2 with the respective processing performance of 67 frames per second (fps), 19.4 fps, and 8.9 fps for the video input of 448 × 448 resolution, respectively. The proposed model can detect objects as small as 13 × 13 pixels and achieves 86.54% accuracy on a publicly available Pascal Visual Object Class (VOC) car database and 82.4% mean average precision (mAP) on a large collection of common road real scenes database (iVS database).
Open Borders and the COVID-19 Pandemic

This paper considers the implications of COVID for open borders. It notes that while COVID concerns do not directly challenge arguments for open borders, the pandemic has revealed two more general phenomena that are salient for such arguments. The first concerns the increasing unmooring of legal borders from physical spaces and the interaction of surveillance and identification technologies with this process. The second addresses the issue of interdependency and the potentially negative implications of open borders if not underpinned by a global basic structure.
Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models.
Surveillance planning with safe emergency landing guarantee for fixed-wing aircraft

In this paper, we study Emergency Landing Aware Surveillance Planning (ELASP) to determine a cost-efficient trajectory to visit a given set of target locations such that a safe emergency landing is possible at any point of the multi-goal trajectory. The problem is motivated to guarantee a safe mission plan in a case of loss of thrust for which it is desirable to have a safe gliding trajectory to a nearby airport. The problem combines computational challenges of the combinatorial multi-goal planning with demanding motion planning to determine safe landing trajectories for the curvature-constrained aerial vehicle. The crucial property of safe landing is a minimum safe altitude of the vehicle that can be found by trajectory planning to nearby airports using sampling-based motion planning such as RRT*. A trajectory is considered safe if the vehicle is at least at the minimum safe altitude at any point of the trajectory. Thus, a huge number of samples have to be evaluated to guarantee the safety of the trajectory, and an evaluation of all possible multi-goal trajectories is quickly computationally intractable. Therefore, we propose to utilize a roadmap of safe altitudes combined with the estimation of the trajectory lengths to evaluate only the most promising candidate trajectories. Based on the reported results, the proposed approach significantly reduces the computational burden and enables a solution of ELASP instances with tens of locations in units of minutes using standard single-core computational resources.
Lattice-Based Information Flow Control-by-Construction for Security-by-Design

Many software applications contain confidential information, which has to be prevented from leaking through unauthorized access. To enforce confidentiality, there are language-based security mechanisms that rely on information flow control. Typically, these mechanisms work post-hoc by checking whether confidential data is accessed unauthorizedly after the complete program is written. The disadvantage is that incomplete programs cannot be interpreted properly and information flow properties cannot be built in constructively. In this work, we present a methodology to construct programs incrementally using refinement rules to follow a lattice-based information flow policy. In every refinement step, confidentiality and functional correctness of the program is guaranteed, such that insecure programs are prohibited by construction. Our contribution is fourfold. We formalize refinement rules for the constructive information flow control methodology, prove soundness of the refinement rules, show that our approach is at least as expressive as standard language-based mechanisms for information flow, and implement it in a graphical editor called CorC. Our methodology is also usable for integrity properties, which are dual to confidentiality.
D-VPnet: A network for real-time dominant vanishing point detection in natural scenes

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115 fps.
Extractive text summarization using F-RBM

The Intense growth of data over the internet and process extracting predominating details from it is a time-consuming task. A new application of making this task simplified is text-summarization. This paper presents the integration of Restricted Boltzmann Machine and Fuzzy Logic methods. Both of these processes have different ways of giving precision to a summary but together being part of the unsupervised world has surpassed their working alone to summarize the text. Integrated algorithm i.e. called FRBM (fuzzy Restricted Boltzmann Machine) provides enhanced depiction in the modeling of probability over visible and hidden units than the RBM.
Online state space generation by a growing self-organizing map and differential learning for reinforcement learning

In this research, we develop a method integrating a growing self-organizing map and differential learning system for online reinforcement learning which adaptively builds the state structure. In the conventional method, models and information on the environment are required beforehand, whereas the proposed method automatically estimates the state transitions from differentials of input signals and from these builds the state space without reference to prior information on the environment. Also, since it is an online learning method, the proposed method requires less computation and no batch memory. Through numerical experiments, we show that the proposed method has the same performance as the conventional method with the information given and that the learning time is shortened by abstraction of the state space.
Autonomous Mobile Robot Obstacle Avoidance Using Fuzzy-PID Controller in Robot’s Varying Dynamics

This paper focuses on the design of a fuzzy-PID controller for a laser sensor-based mobile robot to detect the obstacle and to avoid it. The research discusses two significant problems, i.e. The mobile robot requries high performance so for this purpose; the robots are required to avoid the obstacle with minimum overshoot and settling time, and other is to tune the gains of controller automatically using fuzzy laws even if the parameters of the vehicle are changed. This paper compares the fuzzy tuned PID controller and classic PID controller under two different vehicle dynamics. The results shows that in varying dynamics of the vehicle, the performance of the fuzzy-PID controller possess better adaptability and very promising while results are compared to the classical PID controller.
Development of Sentiment Lexicon in Bengali utilizing Corpus and Cross-lingual Resources

Bengali, one of the most spoken languages, lacks tools and resources for sentiment analysis. To date, the Bengali language does not have any sentiment lexicon of its own; only the translated versions of English lexica are available. Therefore, in this work, we focus on developing a Bengali sentiment lexicon from a large Bengali review corpus utilizing a cross-lingual approach. To build the sentiment dictionary, we first created a Bengali corpus of around 42000 drama reviews; among them, we manually annotated around 12000 reviews. Utilizing a machine translation system, labeled and unlabeled Bengali review corpus, English sentiment lexica, pointwise mutual information (PMI), and supervised machine learning (ML) classifiers in different phases, we develop a Bengali sentiment lexicon of around 1000 sentiment words. We compare the coverage of our lexicon with the translated English lexica in two evaluation datasets. The proposed lexicon achieves 70%-74% coverage in document-level and around 65% coverage in word-level, which is approximately 30%-100% improvement over the translated lexica in word-level and 30%-50% in document-level. The results demonstrate that our developed lexicon is highly effective in recognizing sentiments in the Bengali text.
Why doesn't the conversational agent understand me?

Conversation agents have shifted the way we communicate with ubiquitous services by enabling the use of natural language communication and the analysis of acoustic and linguistic language patterns. Speech skills of children are not yet fully developed; therefore, most conversational agents frequently misunderstand them. In this research, we examined if conversational agents can uncover instances of language discrimination in children. We developed Bolita, a conversational agent using a Google Home and a Sphero Robot to encourage children to practice how to tell a joke. The results of a two week study of the use of Bolita by 37 Mexican children showed a conversational agent is more likely to misunderstand children with speech skills below average. Our results indicate that they speak less, use fewer words, and need more time to answer when interacting with a conversational agent, which may explain the challenges with the conversational agent understanding them. We close discussing the potential of conversational agents to uncover digital markers in children with language differences and suggest ways that conversational agents could be built to be more inclusive.