Reviewerly/exHarmony huggingface dataset:
Comparing Emotional States Induced by 360° Videos Via Head-Mounted Display and Computer Screen

In recent years 360° videos have been becoming more popular. For traditional media presentations, e.g., on a computer screen, a wide range of assessment methods are available. Different constructs, such as perceived quality or the induced emotional state of viewers, can be reliably assessed by subjective scales. Many of the subjective methods have only been validated using stimuli presented on a computer screen. This paper is using 360° videos to induce varying emotional states. Videos were presented 1) via a head-mounted display (HMD) and 2) via a traditional computer screen. Furthermore, participants were asked to rate their emotional state 1) in retrospect on the self-assessment manikin scale and 2) continuously on a 2-dimensional arousal-valence plane. In a repeated measures design, all participants (N = 18) used both presentation systems and both rating systems. Results indicate that there is a statistically significant difference in induced presence due to the presentation system. Furthermore, there was no statistically significant difference in ratings gathered with the two presentation systems. Finally, it was found that for arousal measures, a statistically significant difference could be found for the different rating methods, potentially indicating an underestimation of arousal ratings gathered in retrospect for screen presentation. In the future, rating methods such as a 2-dimensional arousal-valence plane could offer the advantage of enabling a reliable measurement of emotional states while being more embedded in the experience itself, enabling a more precise capturing of the emotional states.
The new learning ecology in the curriculum: the digital age at school

The expansion and growth of learning environments as a result of ICT integration have facilitated the emergence of different forms of educational interrelationship, leading to an interest in the study of the ecologies of learning and digital educational ecosystems. There is a growing need for a profound change in the teaching-learning process as students currently in the classroom demand an active, experiential and digital type of learning. The objective of this work is to analyze the new models or learning paradigms based on the interests of new generations learning in a context surrounded by ICT. The methodology used was qualitative. A systematic review of the scientific literature was carried out based on the adaptation of the PRISMA methodology. The results show us that the technological revolution has changed the way we do, to think and also to teach and learn. ICTs take on a special relevance throughout this process. With the entry of technology into the classroom, we are faced with a new ecology of learning, so the current school and curriculum model evolves to respond to the needs of society.&#x0D;
Chinese Named Entity Recognition Based on Character-Word Vector Fusion

Due to the lack of explicit markers in Chinese text to define the boundaries of words, it is often more difficult to identify named entities in Chinese than in English. At present, the pretreatment of the character or word vector models is adopted in the training of the Chinese named entity recognition model. Aimed at the problems that taking character vector as an input of the neural network cannot use the words’ semantic meanings and give up the words’ explicit boundary information, and taking the word vector as an input of the neural network relies on the accuracy of the segmentation algorithms, a Chinese named entity recognition model based on character word vector fusion CWVF-BiLSTM-CRF (Character Word Vector Fusion-Bidirectional Long-Short Term Memory Networks-Conditional Random Field) is proposed in this paper. First, the Word2Vec is used to obtain the corresponding dictionaries of character-character vector and word-word vector. Second, the character-word vector is integrated as the input unit of the BiLSTM (Bidirectional Long-Short Term Memory) network, and then, the problem of an unreasonable tag sequence is solved using the CRF (conditional random field). By using the presented model, the dependence on the accuracy of the word segmentation algorithm is reduced, and the words’ semantic characteristics are effectively applied. The experimental results show that the model based on character-word vector fusion improves the recognition effect of the Chinese named entity.
A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems

Reliable signal detection plays an essential role in enhancing the quality of signal transmission in wireless communication systems. In this paper, we combine signal detection theory with a deep learning model and propose a novel signal detection scheme based on adaptive ensemble long short term memory (AE-LSTM) neural network to handle wireless single carrier frequency domain equalization (SC-FDE) systems in an end-to-end manner. The feature information used for offline training of the deep learning model is extracted from the received signal containing channel state information (CSI) after the multi-path channel and fast Fourier transform (FFT), and the labels are assigned according to the constellation map adopted at the transmitter. To improve the adaptability of the system, we utilize the received power under different delays as the adaptive factor to integrate the output of each sub-network. Then the original data generated by the channel model is recovered by using the trained model instead of channel estimation and frequency domain equalization. Comparative experiments on SC-FDE symbol detection demonstrate that the proposed scheme achieves better performance in terms of reliability than the traditional scheme and the similar deep learning scheme.
Regularized Graph Matching for Correspondence Identification under Uncertainty in Collaborative Perception

Correspondence identification is a critical capability for multi-robot collaborative perception, which allows a group of robots to consistently refer to the same objects in their own fields of view.Correspondence identification is a challenging problem, especially caused by non-covisible objects that cannot be observed by all robots and the uncertainty in robot perception, which have not been well studied yet in collaborative perception.In this work, we propose a principled approach of regularized graph matching that addresses perception uncertainties and non-covisible objects in a unified mathematical framework to perform correspondence identification in collaborative perception.Our method formulates correspondence identification as a graph matching problem in the regularized constrained optimization framework.We introduce a regularization term to explicitly address perception uncertainties by penalizing the object correspondences with a high uncertainty.We also design a second regularization term to explicitly address non-covisible objects by penalizing the correspondences built by the non-covisible objects.The formulated constrained optimization problem is difficulty to solve, because it is not convex and it contains regularization terms.Thus, we develop a new samplingbased algorithm to solve our formulated regularized constrained optimization problem.We evaluate our approach in the scenarios of connected autonomous driving and multi-robot coordination in simulations and using real robots.Experimental results show that our method is able to address correspondence identification under uncertainty and non-covisibility, and it outperforms the previous techniques and achieves the state-of-the-art performance.
Quantum-classical dynamical distance and quantumness of quantum walks

We introduce a fidelity-based measure, ${\text{D}}_{\mathrm{QC}}(t)$, to quantify the differences in the dynamics of classical versus quantum walks over a graph. We provide universal, graph-independent, analytic expressions of this quantum-classical dynamical distance, showing that at short times ${\text{D}}_{\mathrm{QC}}(t)$ is proportional to the coherence of the walker, i.e., a genuine quantum feature, whereas at long times it depends only on the size of the graph. At intermediate times, ${\text{D}}_{\mathrm{QC}}(t)$ does depend on the graph topology through its algebraic connectivity. Our results show that the difference in the dynamical behavior of classical and quantum walks is entirely due to the emergence of quantum features at short times. In the long-time limit, quantumness and the different nature of the generators of the dynamics, e.g., the open-system nature of classical walks and the unitary nature of quantum walks, are instead contributing equally.
A modified LOF-based approach for outlier characterization in IoT

The Internet of Things (IoT) is a growing paradigm that is revolutionary for information and communication technology (ICT) because it gathers numerous application domains by integrating several enabling technologies. Outlier detection is a field of tremendous importance, including in IoT. In previous works on outlier detection, the proposed methods mainly tackled the efficacy and the efficiency challenges. However, a growing interest in the interpretation of the detected anomalies has been noticed by the research community, and only a few works have contributed in this direction. Furthermore, characterizing anomalous events in IoT-related problems has not been conducted. Hence, in this paper, we introduce our modified Local Outlier Factor (LOF)–based outlier characterization approach and apply it to enhance the IoT security and reliability. Experiments on both synthetic and real-world datasets show the good performance of our solution.
Probabilistic Swarm Guidance Subject to Graph Temporal Logic Specifications

As the number of agents comprising a swarm increases, individual-agent-based control techniques for collective task completion become computationally intractable.We study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expressed in a recently proposed language called graph temporal logic (GTL).By constraining the distribution of the swarm over the nodes of the graph, GTL specifies a wide range of properties, including safety, progress, and response.In contrast to the individual-agent-based control techniques, we develop an algorithm to control, in a decentralized and probabilistic manner, a collective property of the swarm: its density distribution.The algorithm, agnostic to the number of agents in the swarm, synthesizes a time-varying Markov chain modeling the time evolution of the density distribution of a swarm subject to GTL.We first formulate the synthesis of such a Markov chain as a mixed-integer nonlinear program (MINLP).Then, to address the intractability of MINLPs, we present an iterative scheme alternating between two relaxations of the MINLP: a linear program and a mixed-integer linear program.We evaluate the algorithm in several scenarios, including a rescue mission in a high-fidelity ROS-Gazebo simulation 1 .
Universal Self-Correcting Computing with Disordered Exciton-Polariton Neural Networks

We show theoretically that neural networks based on disordered exciton-polariton systems allow the realization of Toffoli gates. Noise in input signals is self-corrected by the networks, such that the obtained Toffoli gates are in principle cascadable, where their universality would allow for arbitrary circuits without the need of additional error-correcting codes. We further find that the exciton-polariton reservoir computers can directly simulate composite circuits, such that they are a highly efficient platform allowing circuits to operate in a single step, minimizing the delay of signal transport between elements and error-correction overhead.
Explainable Agents for Less Bias in Human-Agent Decision Making

As autonomous agents become more self-governing, ubiquitous and sophisticated, it is vital that humans should have effective interactions with them. Agents often use Machine Learning (ML) for acquiring expertise, but traditional ML methods produce opaque results which are difficult to interpret. Hence, these autonomous agents should be able to explain their behaviour and decisions before they can be trusted by humans. This paper focuses on analyzing the human understanding of the explainable agents behaviour. It conducts a preliminary human-agent interaction study to investigate the effect of explanations on the introduced bias in human-agent decision making for the human participants. We test the hypothesis where different explanation types are used to detect the bias introduced in the autonomous agents decisions. We present three user groups: Agents without explanation, and explainable agents using two different algorithms which automatically generate different explanations for agent actions. Quantitative analysis of three user groups (n = 20, 25, 20) in which users detect the bias in agents’ decisions for each explanation type for 15 test data cases is conducted for three different explanations types. Although the interaction study does not give significant findings, but it shows the notable differences between the explanation based recommendations and non-XAI recommendations in human-agent decision making.
Tool Path Optimization for Robotic Surface Machining by Using Sampling-Based Motion Planning Algorithms

Abstract This paper develops a tool path optimization method for robotic surface machining by sampling-based motion planning algorithms. In the surface machining process, the tool-tip position needs to strictly follow the tool path curve and the posture of the tool axis should be limited in a certain range. But the industrial robot has at least six degrees-of-freedom (Dof) and has redundant Dofs for surface machining. Therefore, the tool motion of surface machining can be optimized using the redundant Dofs considering the tool path constraints and limits of the tool axis orientation. Due to the complexity of the problem, the sampling-based motion planning method has been chosen to find the solution, which randomly explores the configuration space of the robot and generates a discrete path of valid robot state. During the solving process, the joint space of the robot is chosen as the configuration space of the problem and the constraints for the tool-tip following requirements are in the operation space. Combined with general collision checking, the limited region of the tool axis vector is used to verify the state’s validity of the configuration space. In the optimization process, the sum of the path length of each joint of the robot is set as the optimization objective. The algorithm is developed based on the open motion planning library (OMPL), which contains the state-of-the-art sampling-based motion planners. Finally, two examples are used to demonstrate the effiectiveness and optimality of the method.
Video Popularity Prediction: An Autoencoder Approach With Clustering

Autoencoders implemented by artificial neural networks (ANNs) are utilized to learn the latent space representation of data in an unsupervised manner, and they have been widely used in recommender systems. For instance, several collaborative denoising autoencoder (CDAE) models have shown that their performance gains outperform that of the collaborative filtering based (CF-based) models. In this work, a near-optimal Top-K forecasting solution is proposed for our advanced autoencoder recommender systems. We propose a method which utilizes CDAE model in predicting the Top-K popular videos in an upcoming time period. In order to improve the prediction accuracy, we also propose an autoencoder based recommendation algorithm with the help of K-means clustering that upgrades the performance of the original autoencoder model. The experimental results show that our method increases significantly the Average Precision (AP) and Recall values by nearly 30%. We then further utilize our proposed autoencoder model with clustering in predicting Top-K popular videos. The applications of predicting Top-K popular videos can be used in the video delivery for the Mobile Edge Computing (MEC) environment to avoid bottleneck in the constricted capacity of backhaul link. Namely, the performance gain will be upgraded if our proposed method precisely predicts and caches the Top-K popular videos in advance with the help of a better forecasting model.
Guided User Research Methods for Experience Design—A New Approach to Focus Groups and Cultural Probes

Many companies are facing the task for radical innovations—totally new concepts and ideas for products and services, which are successful at the market. One major factor for success is a positive user experience. Thus, design teams need, and are challenged to integrate, an experience-centered perspective in their human-centered design processes. To support this, we propose adjusted versions of the well-established user research methods focus groups and cultural probes, in order to tailor them to the specific needs and focus of experience-based design, especially in the context of solving “wicked design problems”. The results are experience focus groups and experience probes, which augment the traditional methods with new structuring, materials, and tasks based on the three principles experience focus, creative visualization, and systematic guidance. We introduce and describe a two step-approach for applying these methods, as well as a case study that was conducted in cooperation with a company that illustrates how the methods can be applied to enable an experience-centered perspective on the topic of “families and digital life”. The case study demonstrates how the methods address the three principles they are based on. Post-study interviews with representatives of the company revealed valuable insights about their usefulness for practical user experience design.
GeoFusion: Geometric Consistency Informed Scene Estimation in Dense Clutter

We propose GeoFusion, a SLAM-based scene estimation method for building an object-level semantic map in dense clutter. In dense clutter, objects are often in close contact and severe occlusions, which brings more false detections and noisy pose estimates from existing perception methods. To solve these problems, our key insight is to consider geometric consistency at the object level within a general SLAM framework. The geometric consistency is defined in two parts: geometric consistency score and geometric relation. The geometric consistency score describes the compatibility between object geometry model and observation point cloud. Meanwhile, it provides a reliable measure to filter out false positives in data association. The geometric relation represents the relationship (e.g. contact) between geometric features (e.g. planes) among objects. The geometric relation makes the graph optimization for poses more robust and accurate. GeoFusion can robustly and efficiently infer the object labels, 6D object poses, and spatial relations from continuous noisy semantic measurements. We quantitatively evaluate our method using observations from a Fetch mobile manipulation robot. Our results demonstrate greater robustness against false estimates than frame-by-frame pose estimation from the state-of-the-art convolutional neural network.
An Interactive Visualization for Feature Localization in Deep Neural Networks

Deep artificial neural networks have become the go-to method for many machine learning tasks. In the field of computer vision, deep convolutional neural networks achieve state-of-the-art performance for tasks such as classification, object detection or instance segmentation. As deep neural networks become more and more complex, their inner workings become more and more opaque, rendering them a "black box" whose decision making process is no longer comprehensible. In recent years, various methods have been presented that attempt to peek inside the black box and to visualize the inner workings of deep neural networks, with a focus on deep convolutional neural networks for computer vision. These methods can serve as a toolbox to facilitate the design and inspection of neural networks for computer vision and the interpretation of the decision making process of the network. Here, we present the new tool Interactive Feature Localization in Deep neural networks (IFeaLiD) which provides a novel visualization approach to convolutional neural network layers. The tool interprets neural network layers as multivariate feature maps and visualizes the similarity between the feature vectors of individual pixels of an input image in a heat map display. The similarity display can reveal how the input image is perceived by different layers of the network and how the perception of one particular image region compares to the perception of the remaining image. IFeaLiD runs interactively in a web browser and can process even high resolution feature maps in real time by using GPU acceleration with WebGL 2. We present examples from four computer vision datasets with feature maps from different layers of a pre-trained ResNet101. IFeaLiD is open source and available online at https://ifealid.cebitec.uni-bielefeld.de.
Short Codes for Quantum Channels With One Prevalent Pauli Error Type

One of the main problems in quantum information systems is the presence of errors due to noise, and for this reason quantum error-correcting codes (QECCs) play a key role. While most of the known codes are designed for correcting generic errors, i.e., errors represented by arbitrary combinations of Pauli X, Y and Z operators, in this paper we investigate the design of stabilizer QECC able to correct a given number e <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> of generic Pauli errors, plus e <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</sub> Pauli errors of a specified type, e.g., Z errors. These codes can be of interest when the quantum channel is asymmetric in that some types of error occur more frequently than others. We first derive a generalized quantum Hamming bound for such codes, then propose a design methodology based on syndrome assignments. For example, we found a [[9, 1]] quantum error-correcting code able to correct up to one generic qubit error plus one Z error in arbitrary positions. This, according to the generalized quantum Hamming bound, is the shortest code with the specified error correction capability. Finally, we evaluate analytically the performance of the new codes over asymmetric channels.
Distilling the Evidence to Augment Fact Verification Models

The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims.Most approaches perform a claimevidence classification without providing any insights about why the claim is trustworthy or not.We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim.We show that the spans are informative for the classifier, improving performance and robustness.Tested on several state-of-the-art models over the FEVER dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.