Reviewerly/exHarmony huggingface dataset:
Networked Event-Based Collision Avoidance of Mobile Objects

This paper proposes an event-based method to ensure a collision-free movement of mobile objects with a certain spatial separation. The vehicles are locally controlled and connected by a communication network. For collision avoidance each object is provided with an event-based trajectory planning unit, which acts on a predictor representing the region in which the neighbouring object is located. If the spatial separation between the two objects deceeds a threshold, communication to the neighbouring object is invoked. The local trajectory is replanned based on Bézier curves to guarantee the collision avoidance. In contrast to existing literature this approach is performed online and with a low communication effort. The advantages of the method are demonstrated by a simulation study with two quadrotors.
Parallelization Strategies for GPU-Based Ant Colony Optimization Solving the Traveling Salesman Problem

Ant Colony Optimization (ACO) is a well known population-based algorithm used for solving combinatorial optimization problems, such as the Traveling Salesman Problem (TSP). The parallelization of ACO becomes necessary when tackling bigger instances of the TSP due to the high number of calculations performed. Many parallel approaches have been already proposed for ACO, in particular for contemporary high-performance hardware, such as GPUs. Typically, the ants are treated in parallel, since they are largely independent. In the case of the TSP, this concerns in particular the tour construction phase. Furthermore, strategies for parallelizing the pheromone deposit and evaporation phase were proposed. The achieved overall speedup hence depends on a combination of different parallelization strategies, where the impact of each strategy depends on the characteristics of the considered application problem and the hardware used. In the present paper, we aim to compare and analyze the performance of ACO implementations using distinct parallelization strategies when solving TSP instances of different magnitudes. At first, a comparison is made between a coarse-grain and a fine-grain parallel ACO. Furthermore the impact of the parallelization of the pheromone deposit process is also analyzed. The results show that there is no overall best parallelization strategy. Also, they highlight the importance of key-points that lead to a reduction of the execution time, such as the occupancy of the GPU and the work load shared among threads.
Flying through Gates using a Behavioral Cloning Approach

Drone racing presents a challenge to autonomous micro aerial vehicles (MAV) because usually the track is not known in advance and it is affected by the environment light. In such scenarios, the vehicle has to act quickly depending on the information provided by its sensors. In this work, we want to predict the movement of the drone so that it passes through a gate. Unlike previous approaches where the task is decomposed into perception, estimation, planning, and control, we are proposing a behavioral cloning approach. In this method, a convolutional neural network is trained with the flights of a human operator. So that the output of the trained network is directly the desired MAV state so that it leads the drone through the gate. We have tested the method using a validation set where we obtained a low loss. Furthermore, we have tested the trained network with unseen data obtaining promising results.
Towards Eye-Friendly VR: How Bright Should It Be?

Visual information plays an important part in the perception of the world around us. Recently, head-mounted displays (HMD) came to the consumer market and became a part of everyday life of thousands of people. Like with the desktop screens or hand-held devices before, the public is concerned with the possible health consequences of the prolonged usage and question the adequacy of the default settings. It has been shown that the brightness and contrast of a display should be adjusted to match the external light to decrease eye strain and other symptoms. Currently, there is a noticeable mismatch in brightness between the screen and dark background of an HMD that might cause eye strain, insomnia, and other unpleasant symptoms. In this paper, we explore the possibility to significantly lower the screen brightness in the HMD and successfully compensate for the loss of the visual information on a dimmed screen. We designed a user study to explore the connection between the screen brightness in HMD and task performance, cybersickness, users' comfort, and preferences. We have tested three levels of brightness: the default Full Brightness, the optional Night Mode and a significantly lower brightness with original content and compensated content. Our results suggest that although users still prefer the brighter setting, the HMDs can be successfully used with significantly lower screen brightness, especially if the low screen brightness is compensated.
Sense-and-avoid system development on an FPGA

In this paper, the first steps of development towards a collision avoidance system for UAV are introduced. The system is based on camera sensors controlled by an FPGA SoC, running an image processing algorithm, controlling a quadcopter to avoid mid-air collisions with other aircraft. The algorithm was already tested in a real mid-air close encounter scenario between two UAVs, but in that scenario, the implementation platform was a GPU-based SoC which had serious limitations concerning processing frame rate and power consumption. Our aim is to realize the same algorithm with higher framerate and lower power consumption. The paper introduces fundamental considerations for the selection of tools used during this process.
Analysis of Transaction Logs from National Museums Liverpool

The websites of Cultural Heritage institutions attract the full range of users, from professionals to novices, for a variety of tasks. However, many institutions are reporting high bounce rates and therefore seeking ways to better engage users. The analysis of transaction logs can provide insights into users' searching and navigational behaviours and support engagement strategies. In this paper we present the results from a transaction log analysis of web server logs representing user-system interactions from the seven websites of National Museums Liverpool (NML). In addition, we undertake an exploratory cluster analysis of users to identify potential user groups that emerge from the data. We compare this with previous studies of NML website users.
Face and Upper-Body Emotion Recognition Using Service Robot’s Eyes in a Domestic Environment

The population of the elderly/disabled people of the world is increasing rapidly. Taking care of these people has become a major issue since lack of professional caregivers or family members. Hence, the only feasible solution for this is using humanoid service robots. Available care-giving service robots lack the proper human emotion recognition. Hence, they cannot communicate with people as humans do. In addition, it is not preferable to people when robots are not androids. Therefore, this paper has proposed a method to recognize a face and upper-body emotions by using service robot's eyes. The service robot's eyes model is able to track a particular person in a domestic environment to mimic human eyes' behavior while providing visual feed for the system to recognize emotions. Face emotional expressions and upper-body gestures are recognized by using supervised learning methods. Finally, the results show that the trained system recognizes the emotions effectively in the domestic environment for a particular person.
GOOFeD: Extracting Advanced Features for Image Classification via Improved Genetic Programming

Feature extraction is widely considered one of the most critical components to classification performance in computer vision. In the past, human-designed features, such as the histogram of oriented gradients, were used for extracting statistically rich features. Recently, there has been a movement away from human-designed features to machine-learned features. Herein, we propose a novel genetic programming (GP) approach, coined GOOFeD, to automatically generate discriminative-rich features for image classification. This is achieved by greatly advancing GP in three ways: (1) promoting population diversity and redundancy removal, (2) introducing a unique adaptive mutation approach, and (3) controlling tree bloat through a new crossover technique. These improvements also lead to a population size required for learning that is smaller than that commonly used in the literature. To assess performance, GOOFeD is tested on the MIT urban and nature scene data set and a real-world buried explosive hazard data set. Experiments verify that, in terms of classification accuracy, GOOFeD outperforms many of the state-of-the-art human-designed features and feature learning techniques.
Large-scale Optimization Using Center-based Differential Evolution with Dynamic Mutation Scheme

For several years the Differential Evolution (DE) algorithm has been an effective method for solving complex real-world optimization problems. However, when it comes to solving large-scale problems its performance deteriorates. In this paper, we propose five different dynamic center-based DE mutation schemes (DCDE) to solve large-scale optimization problems. In each generation, the proposed dynamic centerbased mutation strategies linearly divide the population into two different groups. Then, the first sub-population group utilizes center-based mutation scheme and the second sub-population employs the classical DE mutation. The proposed dynamic schemes are benchmarked on CEC 2013 large-scale optimization problems. The experimental results show that the overall performance of the proposed dynamic center-based mutation schemes better than the compared algorithms in solving LSGO problems.
A Paradox in ML Design

Many data scientists are currently pointing out that the amount of Machine Learning (ML) research that will cross into practice will depend, not just on the ability of the specialized algorithms used to scrutinize positive/negative examples, but also on the quality of the data exploited for training those algorithms. Our experience, while training a neural network with a huge dataset comprised of over fifteen million water meter readings, confirms such conjecture. In this paper, we report on the actions we took to extrapolate from that database just those data that could correctly represent the complex statistical phenomenon in play. With an adequate re-organization of those data, we got an interesting, yet controversial, result. On the one hand, we improved the accuracy on the prediction when a water meter fails/needs disassembly based on a history of water consumption measurements, thus making smarter a meter maintenance process; on the other hand, all this came with the paradox of a (statistical) transformation of the initial dataset: while we alleviate a problem with a restructured and better interpretable data model, we simultaneously change the replicated form of those data.
Evaluation of Maslows Hierarchy of Needs on Long-Term Use of HMDs - A Case Study of Office Environment

Long-term exposure to VR will become more and more important, but what we need for long term immersion to meet users fundamental needs is still under-researched. In this paper, we apply the theory of Maslows Hierarchy of Needs to guide the design of VR for longterm immersion based on the normal biological rhythm of human beings (24 hours). An office environment is designed to verify those needs. The efficiency, the physical and the psychological effects of this VR office system are tested. The results show that the VR office environment is as comfortable as the physical environment at short-term immersion and it can support users basic immersion. It means that the Maslows Hierarchy of Needs can be a guideline for long-term immersion.
Applying QKD to improve next-generation network infrastructures

There is a great attention to quantum technologies in the ICT environment. In particular, when dealing with security matters, the most prominent quantum technology is Quantum Key Distribution (QKD). QKD allows the sharing of symmetric keys with information theoretic security (ITS, i.e. independently of the computational power of the attacker) between two remote network nodes. QKD is the only known method to share a key able to reach ITS. During the last two decades, there has been a tremendous technological progress in QKD research, that has led to the availability of QKD network demonstrators.Software Defined Networks (SDN) enables the automation of service provisioning within network operator infrastructures. With the advent of web-scale services and dynamic network requirements, operators can not anymore deploy their services based on manual intervention or using proprietary vendor solutions. Programmability is key in the next-generation network infrastructure and any new technology must be integrated with this paradigm. Let us highlight that this requirement is even more important with virtual environments, where a Virtual Network Function (VNF) can be deployed in any point-of-presence of the operator. The new 5G deployments will enable operators to have edge computing services supporting different capabilities, thus increasing even more the complexity to deliver services without any automation.In spite of the high potential of QKD, this technology has not yet found its path to wide adoption, commercialization and deployment. QKD is a physical technology that requires the existence of a quantum channel, a physical connection able to transmit quantum bits without perturbation, making hard its integration in networks. The aim of this document is to present how QKD can be deployed in next generation infrastructures, based on realistic scenarios. To do so, this paper describes the technological components of the solutions, as well as the use cases that motivate such effort. These use cases are described from a telecommunication provider's point of view, as they are the actors in charge of deploying QKD systems in their networks.
Security-Aware Synthesis of Human-UAV Protocols

In this work, we synthesize collaboration protocols for human-unmanned aerial vehicle (H-UAV) command and control systems, where the human operator aids in securing the UAV by intermittently performing geolocation tasks to confirm its reported location. We first present a stochastic game-based model for the system that accounts for both the operator and an adversary capable of launching stealthy false-data injection attacks, causing the UAV to deviate from its path. We also describe a synthesis challenge due to the UAV's hidden-information constraint. Next, we perform human experiments using a developed RESCHU-SA testbed to recognize the geolocation strategies that operators adopt. Furthermore, we deploy machine learning techniques on the collected experimental data to predict the correctness of a geolocation task at a given location based on its geographical features. By representing the model as a delayed-action game and formalizing the system objectives, we utilize off-the-shelf model checkers to synthesize protocols for the human-UAV coalition that satisfy these objectives. Finally, we demonstrate the usefulness of the H-UAV protocol synthesis through a case study where the protocols are experimentally analyzed and further evaluated by human operators.
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model

Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Current popular recommendation algorithms, typically based on matrix factorizations, often focus on achieving high accuracy. While achieving good clickthrough rates, diversity of the recommended items is often overlooked. Moreover, most algorithms do not produce interpretable uncertainty quantifications of the recommendations. In this work, we propose the Bayesian Mallows for Clicking Data (BMCD) method, which simultaneously considers accuracy and diversity. BMCD augments clicking data into compatible full ranking vectors by enforcing all the clicked items clicked by a user to be top-ranked regardless of their rarity. User preferences are learned using a Mallows ranking model. Bayesian inference leads to interpretable uncertainties of each individual recommendation, and we also propose a method to make personalized recommendations based on such uncertainties. With a simulation study and a real life data example, we demonstrate that compared to state-of-the-art matrix factorization, BMCD makes personalized recommendations with similar accuracy, while achieving much higher level of diversity, and producing interpretable and actionable uncertainty estimation.
A Presence- and Performance-Driven Framework to Investigate Interactive Networked Music Learning Scenarios

Cooperative music making in networked environments has been subject of extensive research, scientific and artistic. Networked music performance (NMP) is attracting renewed interest thanks to the growing availability of effective technology and tools for computer-based communications, especially in the area of distance and blended learning applications. We propose a conceptual framework for NMP research and design in the context of classical chamber music practice and learning: presence-related constructs and objective quality metrics are used to problematize and systematize the many factors affecting the experience of studying and practicing music in a networked environment. To this end, a preliminary NMP experiment on the effect of latency on chamber music duos experience and quality of the performance is introduced. The degree of involvement, perceived coherence, and immersion of the NMP environment are here combined with measures on the networked performance, including tempo trends and misalignments from the shared score. Early results on the impact of temporal factors on NMP musical interaction are outlined, and their methodological implications for the design of pedagogical applications are discussed.
Pretty good state transfer on Cayley graphs over dihedral groups

The transition matrix of a graph Γ with the adjacency matrix A is defined by H(t)≔exp(−ıtA), where t∈R and ı=−1. The graph is said to admit a pretty good state transfer between a pair of vertices u and v if for any ε>0, there is a time t such that |evtH(t)eu|≥1−ε. The state transfer is perfect if the above inequality holds for ε=0. Perfect (pretty good) state transfer on graphs has received extensive attention recently due to their significant applications in quantum information processing and quantum computations. In this paper, we study pretty good state transfer on Cayley graphs over dihedral groups. We find that if n is a power of 2, then Cay(Dn,S) exhibits pretty good state transfer for some subset S in Dn, some concrete constructions are provided. We also show that this is basically the only case for a non-integral Cayley graph Cay(Dn,S) to have PGST.
Combining Monotone and Normal Modal Logic in Nested Sequents – with Countermodels

We introduce nested sequent calculi for bimodal monotone modal logic, aka. Brown’s ability logic, a natural combination of non-normal monotone modal logic M and normal modal logic K. The calculus generalises in a natural way previously existing calculi for both mentioned logics, has syntactical cut elimination, and can be used to construct countermodels in the neighbourhood semantics. We then consider some extensions of interest for deontic logic. An implementation is also available.
An imputation measure for data imputation and disease classification of medical datasets

Imputation of missing data values is an important pre-processing task for mining of medical data records. Application of data mining principles, techniques requires the dataset to be free from missing data values. In this paper, there are two contributions. One is the imputation measure for finding nearest optimal record for imputation and another is the algorithm for imputing missing data values. The proposed imputation function is extended by using our previous research in which a similarity measure for temporal pattern mining named as ASTRA is proposed. The measure ASTRA is modified suitably to serve as an imputation measure.
A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System

In this paper, we introduce a hardware architecture to accelerate connected component labeling (CCL) for embedded systems. The proposed CCL architecture scans the given binary image only once, and during the raster scan, the input binary image is compressed with run-length encoding to extract runs. The equivalences between runs are resolved efficiently by merging equivalent label lists, and all the intermediate data generated while labeling the images are stored in on-chip memory to avoid frequent access to off-chip memory. The finished connected components are determined and then output directly to free onchip memory resources early. These freed memory resources can be reused, which saves memory. Our CCL architecture is implemented with Verilog, and a quantitative comparison of memory cost shows that the proposed CCL architecture is memory-efficient and requires significantly fewer memory resources compared to other methods. In addition, our CCL architecture can process more than 25 2048 × 1536 benchmark images per second when it works at 300 MHz.
Applying a description logic of typicality as a generative tool for concept combination in computational creativity

In this work we exploit a nonmonotonic Description Logic of typicality as a tool for the generation and the exploration of novel creative concepts. Our logic, called TCL, is able to deal with the phenomenon of prototypical concept combination, which has been shown to be problematic to be modelled i n formalisms like fuzzy logic. The logic TCL is the result of combining three main ingredients: (i) the logic of typicality ALC+TR , that allows one to describe typical properties of a class by means of a typicality operator, (ii) the distributed semantics of probabilistic Description Logics, that allows one to provide a probability distribution over worlds/scenarios, and (iii) established heuristics used by humans for concept composition coming from the field of cognitive semantics. Our approach could be useful in many applicative scenarios, ranging from video games to the creation of new movie characters.
The multi-tag semantic correlation used for micro-blog user interest modeling

Tags play an important role in expressing the interests and attributes of microblog users, but the implicit semantic meaning of user tags is often ignored. We propose an improved micro-blog user interest modeling approach based on multi-tag semantic correlation via analyzing tag relationship and the limitations of the existing micro-blog user interest models. Firstly, the co-occurrence frequency of tag pair is calculated from the micro-blog user collection to obtain the intra-correlation between tag pair, the path is constructed based on the linking tags for each tag pair and the inter-correlation of tag pair is obtained via the shared entropy. Secondly, usertags are clustered and representative tags are obtained according to the similarity between tags, to construct the tags-representative tags matrix. Finally, we combine the above two correlations to acquire the semantic correlation matrix, based on which the users-tags matrix can be updated, thus the micro-blog user interest model based on multi-tag semantic correlation can be obtained. We evaluate our method through a series of experiments based on a dataset crawled from the open API and the results are analyzed. The results show that the interest expression model of microblog users proposed in this paper can better represent the interest characteristics of users.