Reviewerly/exHarmony huggingface dataset:
Schrödinger cat states and steady states in subharmonic generation with Kerr nonlinearities

We discuss general properties of the equilibrium state of parametric down-conversion in superconducting quantum circuits with detunings and Kerr anharmonicities, in the strongly nonlinear regime. By comparing moments of the steady state and those of a Schr\"odinger cat, we show that true Schr\"odinger cats cannot survive in the steady state if there is any single-photon loss. A delta-function 'cat-like' steady-state distribution can be formed, but this only exists in the limit of an extremely large nonlinearity. The steady state is a mixed state, which is more complex than a mixture or linear combination of delta-functions, and whose purity is reduced by driving. We expect this general behaviour to occur in other driven, dissipative quantum subharmonic non-equilibrium open systems.
An Improved GPU-Based SAT Model Counter

In this paper, we present and evaluate a new parallel propositional model counter, called gpusat2, which is based on dynamic programming (DP) on tree decompositions using log-counters. gpusat2 extends its predecessor by a novel architecture for DP that includes using customized tree decompositions, storing solutions to parts of the input instance during the computation variably in arrays or binary search trees, and compressing solution parts. In addition, we avoid data transfer between the RAM and the VRAM whenever possible and employ extended preprocessing by means of state-of-the-art preprocessors for propositional model counting. Our novel architecture allows gpusat2 to be competitive with modern model counters when we also take preprocessing into consideration. As a side result, we observe that state-of-the-art preprocessors allow to produce tree decompositions of significantly smaller width.
A closed-form solution for multi-view color correction with gradient preservation

Color correction across multiple images for consistency is a challenging problem in image mosaicking. To facilitate the global color optimization, existing methods mainly use less flexible correction models, e.g., linear or gamma function, which often struggle to cover the practically existing color discrepancy. In this paper, we present a novel color consistency correction method that models the color remapping function with a parameterized spline curve for each image. Thanks to the flexible model representation, on the one hand, our method has the capability to handle very challenging cases, i.e. hundreds of images with drastic color discrepancy; On the other, some important image properties, e.g., image gradient, contrast, and dynamic range, can be effective formulated with model parameters. Thus, the visual quality of individual images, along with the global color consistency, are comprehensively considered in our specifically designed cost function, which is solved in a closed form via convex quadratic programming. In addition, considering the possibly existing alteration objects within inter-image overlaps, we also propose an functional change detection algorithm with gradient and color features utilized, which guarantees the accuracy of the extracted color correspondences. We have tested the proposed approach on several challenging dataset of diverse genres, which shows that our method substantially outperforms state-of-the-art methods in both visual quality and quantitative metrics.
A mixture of views network with applications to multi-view medical imaging

This paper examines data fusion methods for multi-view data classification. We present a decision concept that explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. This data fusion concept, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. The single view decisions are combined by a data-driven decision, into a global decision according to the relevance of each view in a given case. The method was applied to two challenging computer-aided diagnosis (CADx) tasks: the task of classifying breast microcalcifications as benign or malignant based on craniocaudal (CC) and mediolateral oblique (MLO) mammography views and segmenting Multiple Sclerosis (MS) white matter lesions. The experimental results show that our method outperforms previously suggested fusion methods.
A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot

Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types.
Statistical-based system combination approach to gain advantages over different machine translation systems

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination. Three steps are involved in the proposed statistical system combination approach, viz., alignment, decoding, and scoring. Pair alignment is done in the first step to prevent duplication so that only a single phrase is chosen from various phrases containing the same information. Thus the alignment and scoring strategy are implemented in our approach. Hypotheses are built in the second step. In the third step, we calculate the scores for all the hypotheses. The hypothesis with the highest score is chosen as the final translated output. Wrong scoring can mislead to identify the best part from different systems. It may be noted that a particular phrase may appear in various ways in different translations. To resolve the challenges, we incorporate WordNet in the alignment phase and word2vec in the scoring phase along with the existing factors. We find that the system combination model using WordNet and word2vec injection improves the machine translation accuracy. In this work, we have merged three systems viz., Hierarchical machine translation system, Bing Microsoft Translate, and Google Translate. The broad tests of translation on eight language pairs with benchmark datasets demonstrate that the proposed system achieves better quality than the individual systems and the state-of-the-art system combination models.
Pen drawing display

Abstract As advancements in science and technology, such as the Internet of things, smart home systems, and automobile displays, become increasingly embedded in daily life, there is a growing demand for displays with customized sizes and shapes. This study proposes a pen drawing display technology that can realize a boardless display in any form based on the user’s preferences, without the usual restrictions of conventional frame manufacturing techniques. An advantage of the pen drawing method is that the entire complex fabrication process for the display is encapsulated in a pen. The display components, light-emitting layers, and electrodes are formed using felt-tip drawing pens that contain the required solutions and light-emitting materials. The morphology and thickness of each layer is manipulated by adjusting the drawing speed, number of drawing cycles, and substrate temperature. This study is expected to usher in the upcoming era of customized displays that can reflect individual user needs.
Measuring Quantum Entropy

The entropy of a quantum system is a measure of its randomness and is useful in quantifying entanglement. We study the problem of measuring the von Neumann and Rényi entropies of an unknown mixed quantum state given access to independent copies of the state. For Rényi entropy of integral order exceeding one, we determine the order-optimal copy complexity and show that it is strictly lower than the number of copies required to learn the underlying state. The main technical innovation is a concentration result for certain polynomials that arise in the Kerov algebra of Young diagrams, which is proven using the cycle structure of compositions of certain types of permutations. For von Neumann entropy and Rényi entropy of non-integral orders, we provide upper and lower bounds on the sample complexity of the Empirical Young Diagram (EYD) algorithm, which is the analogue of the empirical plug-in estimator in classical estimation.
“My smartwatch told me to see a sleep doctor”: a study of activity tracker use

Purpose The purpose of this paper is to investigate the use of wearable health and fitness trackers in everyday life, and users’ motivations and their understanding and use of the data derived from devices, and understand the results using the lens of information behaviours. Design/methodology/approach The study used a qualitative, constructionist approach, based on 21 interviews with users of a range of wearable activity trackers used for health and fitness. Findings Findings show that the lifelogging devices have become companion tools that enable users to take information from their bodily indicators and make some decisions about their health and fitness, and also track the results when they act on it, thus giving them a sense of gratification and a sense of control over their own health. Practical implications The findings have implications on how health professionals can talk to their lifelogging patients about how to deal with and understand the information provided by their activity-tracking devices. Some participants in the study already discuss these data regularly with their health professionals. Originality/value As the self-tracking practices attract wide range research interests from human–computer interaction, information systems, digital sociology, health informatics and marketing among others. This study provides important everyday information-seeking perspective that contributes to the understanding of the practices of how people make sense of the data, how the data improves their wellbeing, i.e. physical health improvement or fitness, and implications to users health behaviour. Additionally the study adds to the lifelogging literature through a constructionist, qualitative approach rather than a technological deterministic approach.
E-mail classification with machine learning and word embeddings for improved customer support

Abstract Classifying e-mails into distinct labels can have a great impact on customer support. By using machine learning to label e-mails, the system can set up queues containing e-mails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve a manually defined rule-based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> -score and classification rate. Integrating or migrating from a manually defined rule-based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct a number of experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings to investigate how they work together. A long short-term memory network showed best classification performance with an $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> -score of 0.91. The authors conclude that long short-term memory networks outperform other non-sequential models such as support vector machines and AdaBoost when predicting labels for e-mails. Further, the study also presents a Web-based interface that were implemented around the LSTM network, which can classify e-mails into 33 different labels.
Photo2Trip: Exploiting Visual Contents in Geo-Tagged Photos for Personalized Tour Recommendation

Recently accumulated massive amounts of geo-tagged photos provide an excellent opportunity to understand human behaviors and can be used for personalized tour recommendation. However, no existing work has considered the visual content information in these photos for tour recommendation. We believe the visual features of photos provide valuable information on measuring user / Point-of-Interest (POI) similarities, which is challenging due to data sparsity. To this end, in this paper, we propose a visual feature enhanced tour recommender system, named ‘Photo2Trip’, to utilize the visual contents and collaborative filtering models for recommendation. Specifically, we propose a Visual-enhanced Probabilistic Matrix Factorization model (VPMF), which integrates visual features into the collaborative filtering model, to learn user interests by leveraging the historical travel records. We then extend VPMF to End-to-End training framework to incorporate users (POIs) latent factors into the learning process of the visual content of photos, which generalizes the applicability of the proposed VPMF framework in tour recommendation. Extensive empirical studies verify that our proposed visual-enhanced personalized tour recommendation method outperforms other benchmark methods in terms of recommendation accuracy. The results also show that visual features are effective in alleviating the data sparsity and cold start problems on personalized tour recommendation.
Position-aware context attention for session-based recommendation

In session-based recommendation scenarios where user profiles are not available, predicting their behaviors is a challenging problem. Previous dominant methods to solve this problem are RNN-based models. Recently, attention mechanisms that allow higher parallelization have shown significant improvement on this issue. However, none of the existing attention-based methods explicitly takes advantage of both the position information and context information in a sequence. We assume that one item usually exhibits different levels of importance when it appears in different positions in a sequence. Therefore, a position-aware context attention (PACA) model is proposed as a remedy, which improves the recommendation performance by taking into account both the position information and the context information of items. PACA introduces positional vectors to model the position information and utilizes a pooling function to generate the context feature vectors. Then the two vectors are combined to generate the attention weight for each item in a session. To further improve the performance, we use a multi-head method to combine several parallel attention modules. Extensive experiments on two real-world datasets show that the proposed attention model is able to achieve very promising performance in comparison with the state-of-the-art methods. Finally, we visualize the positional vectors to explicitly analyze the importance of each position in a sequence.
Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: “very engaged”, “nominally engaged” and “not engaged at all”. The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were “very engaged”, “nominally engaged” and “not engaged at all”. Additionally, the results also show that the students with best scores also have higher concentration indexes.
Profile-based Privacy for Locally Private Computations

Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curator. A major barrier, however, towards a wider adoption of this model is that it offers a poor privacy-utility trade-off. In this work, we address this problem by introducing a new variant of local privacy called profile-based privacy. The central idea is that the problem setting comes with a graph G of data generating distributions, whose edges encode sensitive pairs of distributions that should be made indistinguishable. This provides higher utility because unlike local differential privacy, we no longer need to make every pair of private values in the domain indistinguishable, and instead only protect the identity of the underlying distribution. We establish privacy properties of the profile-based privacy definition, such as post-processing invariance and graceful composition. Finally, we provide mechanisms that are private in this framework, and show via simulations that they achieve higher utility than the corresponding local differential privacy mechanisms.
A Generalized Framework for Self-Play Training

Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories. They allow to verify and replicate existing findings, and to link disconnected results. The notion of self-play, albeit often cited in multiagent Reinforcement Learning, has never been grounded in a formal model. We present a formalized framework, with clearly defined assumptions, which encapsulates the meaning of self-play as abstracted from various existing self-play algorithms. This framework is framed as an approximation to a theoretical solution concept for multiagent training. On a simple environment, we qualitatively measure how well a subset of the captured self-play methods approximate this solution when paired with the famous PPO algorithm. The results indicate that throughout training the trained policies exhibit cyclic evolutions, showing that self-play research is still at an early stage.
A vision-based fully automated approach to robust image cropping detection

The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content that is quite insensitive to image processing operations, such as compression and resizing, typical of social media platforms. Robust computer vision techniques are employed throughout, so as to cope with large sources of noise in the data and improve detection performance. The method leverages a novel metric based on robust statistics, and is also capable to decide autonomously whether the image at hand is tractable or not. The results of an extensive experimental evaluation covering several cropping scenarios demonstrate the effectiveness and robustness of our approach.
Application of Graph-based Path Planning for UAVs to Avoid Restricted Areas

Path planning around restricted areas is crucial for Unmanned Aerial Vehicles (UAVs) to avoid flying into areas that raise security or safety concerns. This paper aims to demonstrate the application of a graph-based path planner on a real UAV to avoid polygonal restricted areas. The path planner builds a visibility graph representation of the environment based on GPS information and plans the shortest path using the A* algorithm. It was tested both in simulations and during field tests with a real UAV. The results demonstrate that the path planner can be used successfully to plan paths around polygonal restricted areas of any shape, size or number. It can be generalised to any UAV that supports automatic waypoint navigation and has global localization capabilities. Furthermore, the path planner can be used to successfully eliminate the difficulty of manual operation of UAVs around restricted areas under challenging circumstances.
Attribute evaluation on attack trees with incomplete information

Attack trees are considered a useful tool for security modelling because they support qualitative as well as quantitative analysis. The quantitative approach is based on values associated to each node in the tree, expressing, for instance, the minimal cost or probability of an attack. Current quantitative methods for attack trees allow the analyst to, based on an initial assignment of values to the leaf nodes, derive the values of the higher nodes in the tree. In practice, however, it shows to be very difficult to obtain reliable values for all leaf nodes. The main reasons are that data is only available for some of the nodes, that data is available for intermediate nodes rather than for the leaf nodes, or even that the available data is inconsistent. We address these problems by developing a generalisation of the standard bottom-up calculation method in three ways. First, we allow initial attributions of non-leaf nodes. Second, we admit additional relations between attack steps beyond those provided by the underlying attack tree semantics. Third, we support the calculation of an approximative solution in case of inconsistencies. We illustrate our method, which is based on constraint programming, by a comprehensive case study.
Product Lifecycle Management (PLM)

Product Lifecycle Management (PLM) is the business activity of managing, in the most effective way, a company's products all the way across their lifecycles; from the very first idea for a product all the way through until it is retired and disposed of.