Reviewerly/exHarmony huggingface dataset:
Discovering Partial Periodic High Utility Itemsets in Temporal Databases

High Utility Itemset Mining (HUIM) is an important model with many real-world applications. Given a (non-binary) transactional database and an external utility database, the aim of HUIM is to discover all itemsets within the data that satisfy the user-specified minimum utility (minUtil) constraint. The popular adoption and successful industrial application of HUIM has been hindered by the following two limitations: (i) HUIM does not allow external utilities of items to vary over time and (ii) HUIM algorithms are inadequate to find recurring customer purchase behavior. This paper introduces a flexible model of Partial Periodic High Utility Itemset Mining (PPHUIM) to address these two problems. The goal of PPHUIM is to discover only those interesting high utility itemsets that are occurring at regular intervals in a given temporal database. An efficient depth-first search algorithm, called PPHUI-Miner (Partial Periodic High Utility Itemset-Miner), has been proposed to enumerate all partial periodic high-utility itemsets in temporal databases. Experimental results show that the proposed algorithm is efficient.
Surprisingly Popular Algorithm-Based Comprehensive Adaptive Topology Learning PSO

The surprisingly popular decision in social science fields is a wisdom of the crowd technique that taps into the expert minority opinion within a crowd, which has been demonstrated to be remarkably effective for multiple questions. Most of the existing PSO variants construct the exemplars by solely using fitness, which could be viewed as the democratic approaches or methods. However, the democratic methods tend to highlight the most popular opinion, not necessarily the most correct, which might lead the population into a local trapping region in the scenarios of swarm intelligent computing and evolutionary computation. This paper proposes a method to implement the surprisingly popular decision in PSO to facilitate the exemplar construction, cooperating with the dynamic topology maintenance. The proposed PSO variant is called the Surprisingly Popular Algorithm-based Comprehensive Adaptive Topology Learning Particle Swarm Optimization (SPA-CatlePSO). By using the dynamic topological connection and surprisingly popular decision strategy, the proposed SPA-CatlePSO could adjust the degree of small world topology, mimicking the mechanism of knowledge conversion in the crowd, and guide the direction of the exploitation by constructing exemplars with the largest surprisingly popular degree. We evaluate the proposed SPA-CatlePSO on the full CEC2014 benchmark suite and compare its validity with OLPSO, TSLPSO, ASDPSO, HCLPSO, OptBees and L-shade. The experimental results show that the SPA-CatlePSO algorithm is competitive with the most advanced swarm-based intelligent algorithms.
Lifting Methods for Manifold-Valued Variational Problems

Lifting methods allow to transform hard variational problems such as segmentation and optical flow estimation into convex problems in a suitable higher-dimensional space. The lifted models can then be efficiently solved to a global optimum, which allows to find approximate global minimizers of the original problem. Recently, these techniques have also been applied to problems with values in a manifold. We provide a review of such methods in a refined framework based on a finite element discretization of the range, which extends the concept of sublabel-accurate lifting to manifolds. We also generalize existing methods for total variation regularization to support general convex regularization.
Echo State Networks: analysis, training and predictive control

The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a condition guaranteeing incremental global asymptotic stability is devised. Then, a modified training algorithm allowing for dimensionality reduction of ESNs is presented. Eventually, a model predictive controller is designed to solve the tracking problem, relying on ESNs as the model of the system. Numerical results concerning the predictive control of a nonlinear process for pH neutralization confirm the effectiveness of the proposed algorithms for the identification, dimensionality reduction, and the control design for ESNs.
Evaluation of Visual Perception Manipulation in Virtual Reality Training Environments to Improve Golf Performance

In the real world, prior research in the field of perception and action has shown that individuals visually perceive objects differently based on their actual performance of an action on those objects, especially for sporting activities. For example, a golfer who performs well on putting a ball into the hole, will perceive that ball as larger (easier to hit) and the hole as larger (easier to put the ball in). We asked the following research question, can manipulation of visual perception of objects influence actual performance in the real world? Virtual objects are easily manipulated in Virtual Reality environments, therefore we investigated the use of Virtual Reality training where the properties of objects, such as size, were manipulated to influence perception on a golf putting task. In this paper, we present the results of our experimental user study. Putting performance increased after virtual reality training exposure when virtual objects were larger (perceived as easier to hit) and decreased when virtual objects were smaller (more difficult to hit). Our research has the potential to broaden the study of how virtual reality training can be used to improve sports training in a unique way.
Early Failure Detection of Deep End-to-End Control Policy by Reinforcement Learning

We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imitation learning to simultaneously learn a control policy as well as a threshold over the predictive uncertainty of the learned model, with no hand-tuning required. Corrective action, such as a return of control to the model predictive controller or human expert, is taken before the failure of tasks, when the uncertainty threshold is exceeded. We validate our method on fully-observable and vision-based partially-observable systems using cart-pole and autonomous driving simulations using deep convolutional Bayesian neural networks. We demonstrate that our method is robust to uncertainty resulting from varying system dynamics as well as from partial state observability.
Short Term Solar Energy Forecasting by Using Fuzzy Logic and ANFIS

Accurate forecasting of solar energy is a key issue for a meaningful integration of the solar power plants into the grid. Solar photovoltaic technology is most preferable and vital technology in comparison with all other sources of renewable energy. We know that the solar energy is very irregular so the output of solar photo voltaic system (SPV) is diverted by the atmospheric conditions like temperatures, humidity, wind velocity, solar irradiance and other climatological facts. It’s necessary to predict solar energy to minimize the uncertainty in power harnessing from solar photovoltaic system. In this work fuzzy logic model and ANFIS model have been developed for manipulating solar irradiation (w/m2) data to forecast short duration solar irradiation. Further the normalization in between the input and output is set in between 0.1 and 0.9 for reducing confluence problems. Acquired results are matched up to the manipulated data and valid results are found out.
Image denoising method and evaluation based on mixed wavelet algorithm

The principle and evaluation method of wavelet threshold denoising are analyzed aiming at the problem that Fourier transform cannot represent the abrupt change of image effectively and wavelet transform cannot represent the texture and slow change of image effectively in the process of image denoising. Through the quantitative comparison of Fourier image denoising and wavelet image denoising, a mixed Fourier-wavelet denoising algorithm is proposed based on the different characteristics of Fourier denoising and wavelet denoising. Experimental results show that the mixed wavelet algorithm is superior to simple Fourier denoising and wavelet denoising algorithm separately, which makes up for the disadvantages of the two algorithms, and has a good application prospect in the field of image denoising.
The Effects of Stereopsis and Immersion on Bimanual Assembly Tasks in a Virtual Reality System

Assembly tasks are an essential component in complex operations done by a large number of humans on a regular basis; examples include system maintenance (preventive and corrective), industrial production lines, and teleoperation. Having access to superior and low-cost solutions that can be used to train personnel who need to conduct these tasks is essential. Virtual reality (VR) technology, with its immersive and non-immersive display solutions combined with hand controllers suitable for bimanual operations, is especially appealing for training purposes in this domain. We designed and executed a user study in which we tested the influence of stereopsis and immersion on execution of bimanual assembly task and examined the effects of tested system configurations on symptoms of cybersickness. Our user study, with its between-subjects format, collected comprehensive data sets in four distinct experimental conditions: immersive stereoscopic (IS), immersive non-stereoscopic (INS), non-immersive stereoscopic (NIS), and non-immersive non-stereoscopic (NINS). The results of this study suggest that IS platforms are the most promising contenders for an efficient system solution, and that NINS solutions that use larger screens (like a TV set, in our case) may also be considered. It is encouraging that no significant simulator sickness issues were recorded in any condition. The results of this study provide important input and guidance that people who work in the training domain need to have before making decisions about the acquisition of new solutions for assembly task training.
Foundations of Collaborative, Real-Time Feature Modeling

Feature models are core artifacts in software-product-line engineering to manage, maintain, and configure variability. Feature modeling can be a cross-cutting concern that integrates technical and business aspects of a software system. Consequently, for large systems, a team of developers and other stakeholders may be involved in the modeling process. In such scenarios, it can be useful to utilize collaborative, real-time feature modeling, analogous to collaborative text editing in Google Docs or Overleaf. However, current techniques and tools only support a single developer to work on a model at a time. Collaborative and simultaneous editing of the same model is often achieved by using version control systems, which can cause merge conflicts and do not allow immediate verification of a model, hampering real-time collaboration outside of face-to-face meetings. In this paper, we describe the formal foundations of collaborative, real-time feature modeling, focusing on concurrency control by synchronizing multiple actions of collaborators in a distributed network. We further report on preliminary results, including an initial prototype. Our contribution provides the basis for extending feature-modeling tools to enable advanced collaborative feature modeling and integrate it with related tasks.
Practical Group-Signatures with Privacy-Friendly Openings

Group signatures allow creating signatures on behalf of a group, while remaining anonymous. To prevent misuse, there exists a designated entity, named the opener, which can revoke anonymity by generating a proof which links a signature to its creator. Still, many intermediate cases have been discussed in the literature, where not the full power of the opener is required, or the users themselves require the power to claim (or deny) authorship of a signature and (un-)link signatures in a controlled way. However, these concepts were only considered in isolation.
Learning Click-Based Deep Structure-Preserving Embeddings with Visual Attention

One fundamental problem in image search is to learn the ranking functions (i.e., the similarity between query and image). Recent progress on this topic has evolved through two paradigms: the text-based model and image ranker learning. The former relies on image surrounding texts, making the similarity sensitive to the quality of textual descriptions. The latter may suffer from the robustness problem when human-labeled query-image pairs cannot represent user search intent precisely. We demonstrate in this article that the preceding two limitations can be well mitigated by learning a cross-view embedding that leverages click data. Specifically, a novel click-based Deep Structure-Preserving Embeddings with visual Attention (DSPEA) model is presented, which consists of two components: deep convolutional neural networks followed by image embedding layers for learning visual embedding, and a deep neural networks for generating query semantic embedding. Meanwhile, visual attention is incorporated at the top of the convolutional neural network to reflect the relevant regions of the image to the query. Furthermore, considering the high dimension of the query space, a new click-based representation on a query set is proposed for alleviating this sparsity problem. The whole network is end-to-end trained by optimizing a large margin objective that combines cross-view ranking constraints with in-view neighborhood structure preservation constraints. On a large-scale click-based image dataset with 11.7 million queries and 1 million images, our model is shown to be powerful for keyword-based image search with superior performance over several state-of-the-art methods and achieves, to date, the best reported NDCG@25 of 52.21%.
EEG/EMG based Architecture for the Early Detection of Slip-induced Lack of Balance

In this paper, we propose the preliminary version of a novel pre-impact fall detection (PIFD) strategy, optimized for the early recognition of balance loss during the steady walking. The technique has been implemented in a multi-sensor architecture aiming to jointly analyzes the muscular and cortical activity. The physiological signals were acquired from 10 electromyography (EMG) electrodes on the lower limbs and 13 electroencephalography (EEG) sites all along the scalp. Data from the EMGs are statistically treated and used both to identify abnormal muscular activities and to trigger the cortical activity assessment. The EEG computation branch evaluate the rate of variation of the EEG power spectrum density, named m, to describe the cortical responsiveness in five bands of interest. Then, a logical conditions network allows the system to recognize the loss of balance induced by the slippage, by considering both the evaluated muscular parameters and the cortical ones. Experimental validation on six adults (supported by the motion capture system) showed that the system reacts in a time compliant with the fall dynamic request (403.16 ms), ensuring a competitive detection accuracy (Sensitivity =93.33%, Specificity=99.82 %).
Industrial and Academic Software Product Line Research at SPLC

We present preliminary insights into the perception of researchers and practitioners of the software product line (SPL) community on previous, current, and future research efforts. We were particularly interested in up-and-coming and outdated topics and whether the views of academics and industry researchers differ. Also, we compared the views of the community with the results of an earlier literature survey published at SPLC 2018. We conducted a questionnaire-based survey with attendees of SPLC 2018. We received 33 responses (about a third of the attendees) from both, very experienced attendees and younger researchers, and from academics as well as industry researchers. We report preliminary findings regarding popular and unpopular SPL topics, topics requiring further work, and industry versus academic researchers' views. Differences between academic and industry researchers become visible only when analyzing comments on open questions. Most importantly, while topics popular among respondents are also popular in the literature, topics respondents think require further work have often already been well researched. We conclude that the SPL community needs to do a better job preserving and communicating existing knowledge and particularly also needs to widen its scope.
Behavio2Auth

Many mobile applications use mobile built-in sensors to provide users with a plethora of services to collect and store a mass of sensitive data. These mobile devices need to be protected from unauthorized access and allow access for legitimate users only. In this paper, the problem of unauthorized access is addressed by identifying the user during activities under two considered scenarios: walking and sitting.We authenticate users continuously and implicitly based on micro-movements by leveraging the typing activity information on the screen. These micro-movements come from the user's typing on the touchscreen while walking or sitting. Accelerometer data were analyzed to capture these micro-movements and build the proposed authentication model, named Behavio2Auth. Assuming that each individual has a distinct movement pattern, this hypothesis is used to differentiate between users. A set of experiments were conducted on 100 participants which show that Behavio2Auth is efficient for recognizing smartphone users during walking and sitting scenarios and achieves low Equal Error Rate of only (10%, 16%) with an Area Under the Curve of (95%, 91%).
Automatic Generation and Stylization of 3D Facial Rigs

In this paper, we present a fully automatic pipeline for generating and stylizing high geometric and textural quality facial rigs. They are automatically rigged with facial blendshapes for animation, and can be used across platforms for applications including virtual reality, augmented reality, remote collaboration, gaming and more. From a set of input facial photos, our approach is to be able to create a photorealistic, fully rigged character in less than seven minutes. The facial mesh reconstruction is based on state-of-the art photogrammetry approaches. Automatic landmarking coupled with ICP registration with regularization provide direct correspondence and registration from a given generic mesh to the acquired facial mesh. Then, using deformation transfer, existing blendshapes are transferred from the generic to the reconstructed facial mesh. The reconstructed face is then fit to the full body generic mesh. Extra geometry such as jaws, teeth and nostrils are retargeted and transferred to the character. An automatic iris color extraction algorithm is performed to colorize a separate eye texture, animated with dynamic UVs. Finally, an extra step applies a style to the photorealis-tic face to enable blending of personalized facial features into any other character. The user's face can then be adapted to any human or non-human generic mesh. A pilot user study was performed to evaluate the utility of our approach. Up to 65% of the participants were successfully able to discern the presence of one's unique facial features when the style was not too far from a humanoid shape.
Planning Coordinated Event Observation for Structured Narratives

This paper addresses the problem of using autonomous robots to record events that obey narrative structure. The work is motivated by a vision of robot teams that can, for example, produce individualized highlight videos for each runner in a large-scale road race such as a marathon. We introduce a method for specifying the desired structure as a function that describes how well the captured events can be used to produce an output that meets the specification. This function is specified in a compact, legible form similar to a weighted finite automaton. Then we describe a planner that uses simple predictions of future events to coordinate the robots' efforts to capture the most important events, as determined by the specification. We describe an implementation of this approach, and demonstrate its effectiveness in a simulated race scenario both in simulation and in a hardware testbed.
ExLeap: Minimal and highly available telepresence system creating leaping experience

We propose “ExLeap”, a minimal telepresence system that creates leaping experience. Multiple “nodes” with an omnidirectional camera, mic and speaker transmit the video to clients, and on the client, videos are rendered in 3D space. When moving to another node, by crossfading two videos, the user can feel as if she/he leaps between two places. Also, on each node, the user can talk with people in that place. Each node consists of very simple hardware, so we can put them on multiple places we want to go to. Moreover, because the system can be used 24/7 by multi-user simultaneously and is very easy to use, it creates various types of chances of communications.
Multi-agent Strategy for Marine Applications via Temporal Planning

This paper proposes a novel strategy for the implementation of complex missions in an underwater domain, based on the combination of a Goal Allocation (GA) strategy and Temporal Planning (TP). We define a new highly constrained planning domain, motivated by the autonomous supervision of an offshore wind farm scenario. The domain introduces actions which enhance the execution of long-term missions, support coordinated actions between surface and underwater vehicles and reduce the risks of failures. The ROSPlan framework, a realistic AUV, and Autonomous Surface Vehicle (ASV) simulators are integrated to implement effective missions, and plan execution is analysed using ROS and Gazebo. Experiments have shown a dynamic refuelling point method enhances long-term missions with small robot fleets. The approach also contributes to an increase in plan quality while relaxing complexity in the planning process, leading to an improvement in the planning time and makespan results for multi-agent problems.
Evolution of the Hype around Agile Hardware Development

Agile development is no silver bullet - therefore, the contribution investigates how the perception of agility is changing over time as well as from different viewpoints. The results discussed in this contribution have been obtained in the course of two studies, which have been carried out in both 2017 and 2018. The focus of that studies' series is the field of agile development of physical products. The 2017 study revealed that agile hardware development shows benefits rather in `soft' factors like transparency, commitment and communication. Expectations in the `hard' factors (controlling KPI's) quality, cost and time-to-market are overestimated and thus hyped, as of today. Based on the findings of 2017, a follow-up study has been conducted to investigate how the field of agile hardware development has matured over time and how the hype is evolving. It turned out that there is a large deviation in terms of the expectations and thus the different perspectives of both project management and research and development, which are the key drivers of the hype.