Reviewerly/exHarmony huggingface dataset:
A Regression Approach to Certain Information Transmission Problems

A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual information. When the encoder and the decoder know the statistical channel model, it is found that the optimal channel output processing function is the conditional expectation operator, thus hinting a potential role of regression, a classical topic in machine learning, for this model. Without utilizing the statistical channel model, a problem formulation inspired by machine learning principles is established, with suitable performance metrics introduced. A data-driven inference algorithm is proposed to solve the problem, and the effectiveness of the algorithm is validated via numerical experiments. Extensions to more general information transmission models are also discussed.
See and Be Seen – Rapid and Likeable High-Definition Camera-Eye for Anthropomorphic Robots

While many social robots already include carefully designed robotic faces, functional robot eyes that meet human expectations are still an open challenge. As a consequence, many robots either have cameras separated from their robot eyes or active camera heads missing an anthropomorphic face. In this paper, we propose a new robot eye that is integrated in the anthropomorphic robot head Floka and fulfills a similar technical specification as a human eye including zero backlash, an increased range of motion, high velocities and accelerations, an integrated high resolution camera, and fast actuated eyelids. The robot eye is built using state-of-the-art off-the-shelf components and the CAD model of our prototype is available free of charge on request for non-commercial applications. We evaluate the technical properties of the robot eye and show that it meets and partially outperforms human eye movements and saccades.
Privacy-awareness of Users in our Cloudy Smart World

Recent advances in Information and Communications Technology (ICT) are expanding the world of Internet services with smart applications running on interconnected smart devices. Cloud computing already started to dominate the Internet, and with the appearance of Internet of Things (IoT), and Fog and Edge computing IoT-Fog-Cloud systems are formed. Beside such architectural developments, Artificial Intelligence (AI) methods receive new interests by managing Big Data coming from these complex, heterogeneous systems. In May 2018, the General Data Protection Regulation (GDPR) came into force in the European Union (EU) to strengthen users' influence on their personal data. Though the initiative of the regulation was known two years before, the 25th May, 2018 was a shock for many ICT actors. The privacy awareness of end-users of the Internet of Services is also wanting, and the emergence of new technologies in this field further complicates the identification of data protection responsibilities. In this paper summarize the GDPR novelties affecting the design and operation of IoT applications in fogs and clouds, we present the results of a recent survey on the GDPR awareness of international students on privacy issues related to the usage of IoT-Fog-Cloud systems and AI solutions. Based on the results of the survey, we also state our implications for safer utilization of these complex ICT systems in the future.
Discrete Layer Jamming for Safe Co-Robots

High injury severity occurs when a stiff robot arm hits an operator. Introducing compliance into robot systems reduces the impact and enables safe interaction, but at the expense of positioning performance and payload capacity. This paper presents a tunable stiffness mechanism for safe human-robot interaction based on discrete layer jamming. The proposed design of a discrete layer jamming mechanism is a robot link made of multiple thin layers of ABS and multiple clamps. By applying high clamping pressure to the laminates, the link behaves like a rigid link; reducing the clamping pressure softens the link which yields safer human-robot interaction. Compared to conventional pneumatic layer jamming, discrete layer jamming allows for simplicity of installation with dynamic actuators, faster control, greater portability since no air supply is needed, and no sealing issues. To validate the concept, this paper investigates a discrete layer jamming beam made of ten ABS laminates and two aluminum clamps that cover 10% of the surface of the beam. Stiffness tests have been performed, showing that around 17 times bending stiffness change is achieved by increasing the clamping pressure of two clamps from 0 to 1 MPa.
Map My Murder

The ongoing popularity of health and fitness applications catalyzes the need for exploring forensic artifacts produced by them. Sensitive Personal Identifiable Information (PII) is requested by the applications during account creation. Augmenting that with ongoing user activities, such as the user's walking paths, could potentially create exculpatory or inculpatory digital evidence. We conducted extensive manual analysis and explored forensic artifacts produced by (n = 13) popular Android mobile health and fitness applications. We also developed and implemented a tool that aided in the timely acquisition and identification of artifacts from the examined applications. Additionally, our work explored the type of data that may be collected from health and fitness web platforms, and Web Scraping mechanisms for data aggregation. The results clearly show that numerous artifacts may be recoverable, and that the tested web platforms pose serious privacy threats.
The Mechanics and Control of Leaning to Lift Heavy Objects with a Dynamically Stable Mobile Robot

A control algorithm is developed to enable dynamically stable spherical-wheel robots (ballbots) with arms to detect a heavy object of unknown mass, navigate to it, lift it, transport it, and place it in a desired location semi-autonomously. Previous work has successfully demonstrated two-wheeled dynamically stable mobile manipulator robots transporting heavy objects. We report here the first ballbot to reliably achieve such a task. A successful semi-autonomous lift and transport of a 15 kg heavy box whose actual mass was unknown was achieved using a combination of feedforward and feedback control laws based on a quasi-static center of mass computation. The ballbot's pan and tilt sensor turret tracked fiducial markers on the box. Ballbot-to-human and human-to-ballbot exchanges of a 10 kg heavy object was achieved while dynamically balancing.
Towards a Framework for Composition Design for Music-Led Virtual Reality Experiences

Virtual reality is an exciting new medium for artists and content creators, including composers. The ease of content delivery coupled with the capacity for immersive sound diffusion makes VR of particular interest to composers. However, a framework would be useful to support the composition and designing of music-led virtual reality experiences. This paper begins that process by exploring perspectives such as acoustic ecology, the phenomenology of embodiment, affordances and discoverability in design, instrument design, the techno-somatic dimension, and 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century aleotoric compositional poetics that can help to establish a theoretical framework for composing engaging sonic experiences in virtual environments.
Saliency-Guided Image Style Transfer

In this paper, we propose an automatic saliency-guide style transfer method, which deploys style transfer to the salient object in an image. Our method can generate a new artwork form of image, which aggregates the characteristics of real-world images and artistic images. First, the existing style transfer method is utilized to combine the content of an arbitrary real-world image with the appearance of a well-known artwork, and to produce the stylized image with high perceptual quality. Then, the saliency map of real-world image is used to fuse the real-world image and the stylized image. In this way, the fusion image not only contains the style of artwork but also highlights the salient object. Finally, the saliency-based refinement is employed to improve the quality of object boundaries, and to enable the stylized object blends into the surroundings naturally. Experimental results demonstrate the effectiveness of the proposed saliency-guided image style transfer method.
Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-Supervised Regression

In the task of Autonomous aerial filming of a moving actor (e.g. a person or a vehicle), it is crucial to have a good heading direction estimation for the actor from the visual input. However, the models obtained in other similar tasks, such as pedestrian collision risk analysis and human-robot interaction, are very difficult to generalize to the aerial filming task, because of the difference in data distributions. Towards improving generalization with less amount of labeled data, this paper presents a semi-supervised algorithm for heading direction estimation problem. We utilize temporal continuity as the unsupervised signal to regularize the model and achieve better generalization ability. This semi-supervised algorithm is applied to both training and testing phases, which increases the testing performance by a large margin. We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced. We also discuss several important details on improving the performance by balancing labeled and unlabeled loss, and making good combinations. Experimental results show that our approach robustly outputs the heading direction for different types of actor. The aesthetic value of the video is also improved in the aerial filming task.
Two Stage Particle Filter Based Terrain Referenced Navigation for Computational Efficiency

A novel particle filter (PF) based terrain referenced navigation (TRN) is presented for computational efficiency in this paper. TRN is a navigation method using terrain elevation information, mainly using a Bayesian filter such as a PF.PF based TRN has a disadvantage in that it requires a large number of particles to achieve a good estimation performance, which increases the computationalburden.To solve this disadvantage, two stage particle filter (TSPF) is proposed. TSPF consists of two stages and estimates the nonlinear state variables and the linear state variables separately. In the first stage, the nonlinear state variables are estimated by PF. Then, the linear state variables are estimated by a Kalman filter in the second stage considering a correlation between nonlinear and linear state variables. Since TSPF estimates only nonlinear state variables using PF and estimates linear state variables with only one Kalman filter, the computational efficiency can be greatly improved although the estimation performance is degraded a bit.In order to verify the proposed method, simulationsare performed to compare TSPF with PF andRao Blackwellized PF (RBPF)which has the same purpose as TSPF. The simulation results show similar estimation performance with fewer particles than with all state variables estimated using PF, resulting in less computational burden.
A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information

Collaborative filtering (CF), the most efficient technique in recommendation systems, can be classified into two types: neighborhood-based model and latent factor model. Both are only based on the user-item interaction, or rating information, and do not take into account the item's content-based information which may contain valuable knowledge. In this work, we propose a hybrid content-based and neighborhood-based recommendation system which utilizes the genome tag associated with each movie in the MovieLens 20M dataset. Experiment results show that our proposed system not only achieves a comparable accuracy but also performs at least 2 times faster than the "pure" CF methods.
Overhead Aware Task Scheduling for Cloud Container Services

Task scheduling in cloud computing is an NP-complete problem. It concerns how to properly arrange task execution process using a set of necessary cloud resources. Existing works assume that tasks can be interrupted without any overhead, based on which task schedules can be optimized to achieve some objectives (e.g., maximize resource utilization). We observe that interrupting tasks, as well as subsequent task recovery process, will inevitably impose overheads in terms of consuming additional CPU time on corresponding physical machines. In addition, not all tasks can be interrupted. Those observations motivate us to consider a more general scenario where a job consists of both interruptible and non-interruptible tasks with specific deadline and resource requirements. Accordingly, we design algorithms to minimize task interruption overhead while ensuring task completion deadline. Specifically, we first formulate an Integer Linear Program (ILP) for offline optimization. A heuristic algorithm is then proposed for online task scheduling, and is compared with the optimal ILP solution. Numerical results confirm the correctness of our ILP and show the efficiency of the proposed heuristic.
Natural Evolution Speciation for NEAT

The Neural Evolution of Augmenting Topologies (NEAT) algorithm is a popular technique to evolve neural networks. While NEAT has been applied to many problems, such as control systems and game playing, the underlying speciation rules governing NEAT have not been explored. This paper proposes a new speciation technique, called Natural Evolution NEAT (NENEAT), that replaces NEAT's speciation strategy with a new stategy inspired by cladistics. The cladistic formation of species forms a tree of common descent. NENEAT can find smaller network solutions in fewer generation and population member evaluations than NEAT using the same number of children.
Flight Test Validation of Collision Avoidance System for a Multicopter using Stereoscopic Vision

This paper presents the flight test validation of collision avoidance system for a multicopter using stereovision technique. Stereoscopic vision offers an affordable, accessible, and effective method of collision detection. Using a ZED stereoscopic camera, depth maps were generated using image disparity to calculate the distance between the camera and obstacles in its view. An NVIDIA Jetson TX1 computer aboard the multicopter was used to process the depth maps to detect any obstacles in its flight path. The algorithm partitions the continuously updated depth maps in equal sections to determine the section containing obstacles. Once this section is chosen, a new vector is calculated to determine the “path of least resistance” and communicated to the onboard Pixhawk flight controller using DroneKit. Using these commands, the UAS maneuvers to safely correct its course and avoid the obstacle autonomously. Flight test objectives, procedures, and results are presented.
Blockchain Based Authentication and Authorization Framework for Remote Collaboration Systems

Due to the advantages of blockchain technologies, including decentralization, immutability, transparency and security, people try to replace existing problematic architectures /frameworks with blockchain based ones. In this paper we propose a novel authentication and authorization framework based on blockchain technologies to control access to the resources of an IoT device. In this paper, we focus on devices such as the Cyber Handyman used in remote collaboration applications to develop our framework. We tested our smart contracts on the Ropsten test network. Our results showed that it can handle 25 service requests simultaneously.
Automatic Modulation Classification in the Presence of Interference

A modulation recognition method based on a convolutional neural network (CNN) architecture is assessed through classification of synthetic baseband signals in the presence of a second interfering signal source. The complexity and adaptability of CNNs is leveraged so as to forgo statistical feature extraction procedures and efficiently classify based on raw signals or their modified forms. Both scenarios with the interfering signal's modulation scheme known and unknown, are considered. Simulation results show that the CNN architecture achieves considerable accuracy despite the presence of interference, and the knowledge of the modulation scheme of the interfering signal significantly improves the accuracy.
Sentiment Analysis and Deep Learning Based Chatbot for User Feedback

Recently, the conversational agents like Chatbots are widely employed for achieving a better Human-Computer Interaction (HCI). In this paper, a retrieval based chatbot is designed using Natural Language Processing (NLP) techniques and a Multilayer Perceptron (MLP) neural network. The purpose of the chatbot is to extract user's feedback based on the services provided to them. User feedback is a very essential component for the betterment of the service. Chatbot serves as a better interface for obtaining an appropriate user feedback. Furthermore, sentiment analysis is done on the feedback as a result a suitable response is delivered to the user. A Long Short Term Neural Network (LSTM) is used to classify the sentiment of the feedback.
Detection of Triple JPEG Compressed Color Images

In order to extend the detection of JPEG compressed color images to solve the real-life problem, three-class classification forensics of JPEG compressed color images with the same quantization matrix is proposed. Since the previous methods treat detection of JPEG compressed color images as binary classification and JPEG compression with the same quantization matrix leaves slight tracks, three-class classification forensics of JPEG compressed color images with the same quantization matrix is a new and challenging problem. In this paper, two aspects are considered to solve this problem. First, if images are compressed, rounding and truncation error will occur. Thus, preprocessing of images is performed to extract error to highlight statistical difference which can help to classify. Second, the support vector machine (SVM) algorithm is originally designed for the binary classification problem, so dealing with a three-class problem, it is necessary to reconstruct a suitable three-class classifier. Besides, convolutional neural network (CNN) parallelly deal with three channels of the color image. The relationship of the three channels is terminated. However, quaternion convolutional neural network (QCNN) which utilizes quaternion algebra not only is directly used to three-class classification but also retain the relationship between three channels. Experimental results demonstrate that the proposed method achieves good performance and is better than the state-of-the-art approaches investigated.
Sphere in Hand: Exploring Tangible Interaction with Immersive Spherical Visualizations

The emerging possibilities of data analysis and exploration in virtual reality raise the question of how users can be best supported during such interactions. Spherical visualizations allow for convenient exploration of certain types of data. Our tangible sphere, exactly aligned with the sphere visualizations shown in VR, implements a very natural way of interaction and utilizes senses and skills trained in the real world. This work is motivated by the prospect to create in VR a low-cost, tangible, robust, handheld spherical display that would be difficult or impossible to implement as a physical display. Our concept enables it to gain insights about the impact of a fully tangible embodiment of a virtual object on task performance, comprehension of patterns, and user behavior. After a description of the implementation we discuss the advantages and disadvantages of our approach, taking into account different handheld spherical displays utilizing outside and inside projection.
Static deep neural network analysis for robustness

This work studies the static structure of deep neural network models using white box based approach and utilizes that knowledge to find the susceptible classes which can be misclassified easily. With the knowledge of susceptible classes, our work has proposed to retrain the model for those classes to achieve increased robustness. Our preliminary result has been evaluated on MNIST, F-MNIST, and CIFAR-10 (ImageNet and ResNet-32 model) based datasets and have been compared with two state-of-the-art detectors.
Trusted D2D-Based IoT Resource Access Using Smart Contracts

We present and evaluate models that allow clients to access IoT resources using secure and trusted device-to-device (D2D) communication, while utilizing smart contracts to obtain the benefits of blockchain technology. These benefits include decentralized trust, immutability, transparency, and high availability. The models consider different network connection capabilities of the clients and the IoT resources, namely continuous network connectivity and D2D-only connectivity. We describe two approaches for utilizing blockchains and smart contracts in the authorization process: in the first approach, only hashes of the authorization information are recorded on the blockchain. In the second approach, a smart contract handles authorization requests. We implement the approaches using the OAuth 2.0 delegated authorization framework and evaluate the implementations on the public Ethereum testnet Rinkeby, in terms of execution cost, contract creation cost, and delay. Our evaluation quantifies the tradeoffs of blockchain cost and smart contract functionality, such as blocking and non-blocking operation, and the reduction of the transaction cost that can be achieved when multiple authorization requests are concatenated in a single transaction.
Secure 5G Network Slicing for Elderly Care

In the time of an ageing world, aging at home is both an economically and socially viable and sustainable solution which may also improve the elderly’s well-being. There are currently a few Home-based Elderly Care systems which although operational are not yet optimal in terms of efficiency and security. The paper propose a Home-based Elderly Care solution which makes use of the 5G mobile network slicing with two secure and isolated slices, namely the Health Care slice and the Smart Home slice to provide an inherent secure connection. Further, the solution includes an efficient and secure Emergency Call which ensures that the appropriate caregivers can dispatched and provide help in shorter times. A proof-of-concept implementation is described thoroughly.
Towards a New Repository for Feature Model Exchange

Feature models are one of the most important contributions to the field of software product lines, feature oriented software development or variability intensive systems. Since their invention in 1990, many feature model dialects appeared from less formal to more formal, from visual to textual, integrated in tool chains or just as a support for a concrete research contribution. Ten year ago, S.P.L.O.T. a feature model online tool was presented. One of its most used features has been the ability to centralise a feature model repository with its own feature model dialect. As a result of MODEVAR, we hope to have a new simple textual feature model language that can be shared by the community. Having a new repository for that language can help to share knowledge. In this paper we present some ideas about the characteristics that the future feature model repository should have in the future. The idea is to discuss those characteristics with the community.