Reviewerly/exHarmony huggingface dataset:
Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care

The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of sensor data that rapidly accumulate, there is a need to employ automated algorithms to label biosignal sensor data. In many real-life clinical applications, no such expert labels are available, and algorithms for processing sensor data must be relied upon, without access to the "ground truth." It is therefore extremely difficult to choose which algorithms to trust or discard at any point in time, where different algorithms may be optimal for different patients, or even for different points in time for the same patient. We propose two fully Bayesian approaches for fusing labels from independent and potentially correlated annotators (i.e., algorithms or, where available, experts). These are generative models to aggregate labels (i.e., the outputs of the algorithms, such as identified ECG morphology) in an unsupervised manner, to estimate jointly the assumed bias and precision of each algorithm without access to the ground truth. The latter fused estimate may then be used to infer the underlying ground truth. For the first time in the biomedical context, we show that modeling correlations between annotators, and fusing information concerning task difficulty (such as the estimated quality of the sensor data), improve these estimates with respect to commonly employed strategies in the literature. Also, we adopt a strongly Bayesian approach to inference using Gibbs sampling to improve estimates over the existing state of the art. We present results from applying the proposed pair of models to simulated and two publicly available biomedical datasets, to demonstrate proof-of-principle. We show that our proposed models outperform all existing approaches recreated from the literature. We also show that the proposed methods are robust when dealing with missing values (as often occurs in real-life biomedical applications), and that they are suitably efficient for use in real-time applications, thereby providing the basis for the reliable use of sensors for personalizing the care of the individual.
Hierarchical Concept Score Postprocessing and Concept-Wise Normalization in CNN-Based Video Event Recognition

This paper is focused on video event recognition based on frame level convolutional neural network (CNN) descriptors. Using transfer learning, the image trained descriptors are applied to the video domain to make event recognition feasible in scenarios with limited computational resources. After fine-tuning of the existing CNN concept score extractors, pretrained on ImageNet, the output descriptors of the different fully connected layers are employed as frame descriptors. The resulting descriptors are hierarchically postprocessed and combined with novel and efficient pooling and normalization methods. As major contributions of this paper to the video event recognition, we present a postprocessing scheme in which the hierarchy and the relative shortest distance of concepts in WordNet concept tree is taken into account to alleviate uncertainty of the resulting concept scores at the output of the CNN. Besides, we propose a concept-wise power law normalization method that outperforms the widely used power law normalization. The integration of these approaches results in a high performance average (max) pooling-based video event recognition. Compared to the average (max) pooling combined with the state-of-the-art normalization methods and fine-tuned support vector machine classification, the proposed processing scheme improves the event recognition accuracy in terms of mean average precision over the Columbia consumer video and unstructured social activity attribute datasets, where achieves a pretty comparable result on UCF101 and ActivityNet datasets.
Semisupervised Learning Based on a Novel Iterative Optimization Model for Saliency Detection

In this paper, we propose a novel iterative optimization model for bottom-up saliency detection. By exploring bottom-up saliency principles and semisupervised learning approaches, we design a high-performance saliency analysis method for wide ranging scenes. The proposed algorithm consists of two stages: 1) we develop a boundary homogeneity model to characterize the general position and the contour of the salient objects and 2) we propose a novel iterative optimization model, termed gradual saliency optimization, for further performance improvement. Our main contribution falls on the second stage, where we propose an iterative framework with self-repairing mechanisms for refining saliency maps. In this framework, we further develop a more comprehensive optimization function applying a novel semisupervised learning scheme to enhance the traditional saliency measure. More elaborately, the iterative method can gradually improve the output in each iteration and finally converge to high-quality saliency maps. Based on our experiments on four different public data sets, it can be demonstrated that our approach significantly outperforms the state-of-the-art methods.
Recurrent neural network language generation for spoken dialogue systems

Natural Language Generation (NLG) is a critical component of spoken dialogue systems and it has a significant impact both on usability and perceived quality. Most existing NLG approaches in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. Moreover, these limitations also add significantly to development costs and make the delivery of cross-domain, cross-lingual dialogue systems especially complex and expensive. The first contribution of this paper is to present RNNLG, a Recurrent Neural Network (RNN)-based statistical natural language generator that can learn to generate utterances directly from dialogue act – utterance pairs without any predefined syntaxes or semantic alignments. The presentation includes a systematic comparison of the principal RNN-based NLG models available. The second contribution, is to test the scalability of the proposed system by adapting models from one domain to another. We show that by pairing RNN-based NLG models with a proposed data counterfeiting method and a discriminative objective function, a pre-trained model can be quickly adapted to different domains with only a few examples. All of the findings presented are supported by both corpus-based and human evaluations.
Stereo Video Object Segmentation Using Stereoscopic Foreground Trajectories

We present an unsupervised segmentation framework for stereo videos using stereoscopic trajectories. The proposed stereo trajectory shows favorable properties for modeling the long-term motion information through the whole sequence and explicitly capturing the corresponding relationships between two stereo views. The stereo prior is important for inferring the desired object and guarantees the consistent spatial-temporal segmentation, which contributes to an enjoyable stereo experience. We start by deriving stereo trajectories from left and right views simultaneously, which are represented via a graph structure. Then we detect object-like stereo trajectories via the graph structure to efficiently infer the desired object. Finally, an energy optimization function is proposed to produce the stereo segmentation results via leveraging the object information from stereo trajectories. To benefit potential research, we collected a new stereoscopic video benchmark, which consists of a total of 50 stereo video clips and includes many challenges in segmentation. Extensive experimental results demonstrate that our stereo segmentation method achieves higher performance and preserves better stereo structures, compared with prevailing competitors. The source code and results are available at: https://github.com/shenjianbing/StereoSeg.
Locally optimal measurement-based quantum feedback with application to multiqubit entanglement generation

We present a general approach to measurement-based quantum feedback that employs proportional and quantum state--(PaQS-) based feedback components to obtain locally optimal protocols. To demonstrate the power of the method, we first show that it reproduces many known feedback protocols and then apply it to generation of multipartite entanglement with an emphasis on remote entanglement, which requires spatially local feedback Hamiltonians. The symmetry of both measurement and feedback operators is found to be essential for construction of effective protocols. We show that under perfect measurement efficiency, entangled states can be reached with fidelity approaching unity under non-Markovian feedback control protocols, while Markovian protocols resulting from optimizing the feedback unitaries on ensemble-averaged states still yield fidelities above 94%. Application of the PaQS approach to generation of $N$-qubit $W$, general Dicke, and Greenberger-Horne-Zeilinger states shows that such entangled states can be efficiently generated with high fidelity, for up to $N=100$ in some cases.
Interval-valued membership function estimation for fuzzy modeling

Fuzzy modeling is an important topic in fuzzy sets theory and applications. A powerful method for constructing an interval-valued Takagi–Sugeno fuzzy model (IVFM), based on input–output data of the identified system, is presented. In this investigation, a Takagi–Sugeno fuzzy model is automatically generated in three steps: (1) Structure identification, (2) Envelope detection and (3) parameters identification. In the structure identification phase, a clustering method based on Gustafson–Kessel algorithm is used in order to detect the linear subsystems of the whole nonlinear system (local linearization). Then, an envelope detection algorithm (EDA) based on derivative concept is proposed to estimate both the upper and lower functions of the interval-valued membership function defined point-wise. In the parameter identification step, the least squares algorithm is applied to compute the best parameter values of the premises (Gaussians) and the Kalman Filter algorithm to compute the consequences (straight lines) parameters. The effectiveness of this approach is demonstrated on approximating some nonlinear static functions, real world data and dynamical systems.
Improving Saliency Detection Based on Modeling Photographer's Intention

A photographer's intention towards a photo evokes the viewer's attentional response. By analyzing the photographer's intention, we can have a better understanding of what the photographers want to convey to the viewers, thus helping to improve image analyzing and understanding. In this paper, a novel method is presented to improve saliency detection based on exploring the relationship between the photographer's intention and the viewer's attention. An intention rate is derived to quantify the intention and integrated with traditional saliency detection in a unified framework accordingly. We evaluate the proposed scheme with several classic saliency detection algorithms on different datasets. The experimental results demonstrate the superior improvements of our method.
Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations

Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but suffer from approximating a potentially indefinite Hessian with a positive-definite matrix. Hessian-free methods leverage the ability to perform Hessian-vector multiplication without needing the entire Hessian matrix, but each iteration's complexity is significantly greater than quasi-Newton methods. In this paper we propose an alternative approach for solving ML problems based on a quasi-Newton trust-region framework for solving large-scale optimization problems that allow for indefinite Hessian approximations. Numerical experiments on a standard testing data set show that with a fixed computational time budget, the proposed methods achieve better results than the traditional limited-memory BFGS and the Hessian-free methods.
Basis-updating for data compression of displacement maps from dynamic DIC measurements

The extraction of useful information and removal of redundant noise from data has become a major research topic in recent years. Data compression is necessary for all kinds of analysis, and the demand for efficient compression techniques has gained much attention. Digital image correlation is a camera-based measuring system, which has been widely applied in strain analysis because of the convenience of measuring displacement fields by simply selecting a region of interest. Currently, there is interest in applying such methods to engineering structures in dynamics. However, one of the major issues related to the integration of camera-based systems with dynamic measurement is the generation of huge amounts of data, typically extending to many thousands of data points, because of the requirements of high sampling rate, spatial resolution, and long duration of recording. In this paper a new algorithm is presented that addresses the need for efficiency in full-field data processing. By making use of the data itself and combining the concept of sparse representation with Gram-Schmidt orthogonalisation, the number of basis function used to represent the data can be reduced and a concise decomposition established. In both simulated and experimental cases, the compression ratios for data size and number of signals used in operational modal analysis are substantially diminished, thereby demonstrating the effectiveness of the proposed algorithm. A reduced number of new basis functions is determined for the representation of data under the condition that the reconstructed displacement map reproduces the raw measured data to within a chosen threshold on the coefficient of correlation.
An efficient Map Reduce-Based Hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus – A big data approach

The increase in the usage of internet and social media has enabled people exchange views, opinions and thoughts as never before. This exchange of data has paved the way for sentiment analysis. The basic task of sentiment analysis is to classify the data into positive, negative and neutral. In this paper an effective MapReduce-Based Hybrid NBC-TFIDF (Naive Bayes Classifier -Term Frequency Inverse Document Frequency) algorithm is proposed to mine people sentiment. A Map Reduce-Based Hybrid NBC is employed to classify the data based on the polarity score of each sentence in social media data. The polarity score is calculated using the emotion corpus and the Diabetic corpus is created using food Glycemic Index and physical activity index. This study analyses the correlation of food habits, physical activity and diabetic risk factors among Indian population using social network data. Around two million data has been identified for the study and the study is restricted to India. The experimental result shows that MapReduce-Based Hybrid NBC–TFIDF performs efficiently in multimode cluster. The results reveal that no individual factor is associated with diabetic risk and also a group of common factors contribute to diabetes mellitus. It is found that 60% of the social media data had positive polarity about the food items that are high in Glycemic Index which is the main root cause for type – 2 Diabetes. This Big-Data analysis reveals that the young generations of India are unaware of risk factors of Diabetes mellitus.
The 2017 AIBIRDS Level Generation Competition

This paper presents an overview of the second AIBIRDS level generation competition, held jointly at the 2017 IEEE Conference on Computational Intelligence and Games and the 26th International Joint Conference on Artificial Intelligence. This competition tasked entrants with developing a level generator for the physics-based puzzle game Angry Birds. Submitted generators were required to deal with many physical reasoning constraints caused by the realistic nature of the game's environment, in addition to ensuring that the created levels were fun, challenging, and solvable. This year's competition was a significant improvement over the previous year, with a greater number of participants and more advanced generators. In this paper, we describe the framework, rules, submitted generators, and results for this competition. We also provide some background information on related research and other video game AI competitions and discuss what can be learned from this year's competition. There are several game and real-world applications for this type of research, and we provide some examples of the types of levels we would like future competition entries to generate.
Sequential Gaussian Approximation Filter for Target Tracking With Nonsynchronous Measurements

This paper presents an adaptive sequential fusion estimation method for the target tracking with nonsynchronous measurements in wireless sensor networks (WSNs). Based on Gaussian assumption and Bayesian inference, a sequential cubature Kalman filtering (SCKF) method, as well as its square root form (SR-SCKF), is presented by applying the cubature rule to approximate the function × Gaussian integrals. By taking into consideration the time-varying properties of the measurement noise and the linearization errors, some adaptive factors are introduced into the SCKF to compensate for the measurement uncertainties based on Chi-square tests. The convergence analysis of the SCKF is presented. It is proved that the adaptive SCKF (ASCKF) has a better convergence property than the SCKF. Both simulations and experiments of a target tracking example are presented to show the effectiveness and superiority of the proposed ASCKF method.
List Decodability of Symbol-Pair Codes

We investigate the list decodability of symbol-pair codes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> in this paper. First, we show that the list decodability of every symbol-pair code does not exceed the Gilbert-Varshamov bound. On the other hand, we are able to prove that with high probability, a random symbol-pair code can be list decoded up to the Gilbert-Varshamov bound. Our second result of this paper is to derive the Johnson-type bound, i.e., a lower bound on list decoding radius in terms of minimum distance. Finally, we present a list decoding algorithm of Reed-Solomon codes beyond the Johnson-type bound in the pair metric. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A symbol-pair code is referred to a code in the pair metric.
Lackadaisical Quantum Walks with Multiple Marked Vertices

The concept of lackadaisical quantum walk – quantum walk with self loops – was first introduced for discrete-time quantum walk on one-dimensional line [8]. Later it was successfully applied to improve the running time of the spacial search on two-dimensional grid [16]. In this paper we study search by lackadaisical quantum walk on the two-dimensional grid with multiple marked vertices. First, we show that the lackadaisical quantum walk, similarly to the regular (non-lackadaisical) quantum walk, has exceptional configuration, i.e. placements of marked vertices for which the walk has no speed-up over the classical exhaustive search. Next, we demonstrate that the weight of the self-loop suggested in [16] is not optimal for multiple marked vertices. And, last, we show how to adjust the weight of the self-loop to overcome the aforementioned problem.
Quantifying quantum reference frames in composed systems: Local, global, and mutual asymmetries

The Page-Wootters mechanism questioned the fundamental nature of time in quantum physics. The mechanism explored the notion that a given physical quantity is always defined and measured relative to a reference frame, in general, not explained in the theoretical description of quantum physical experiments. Recently, the resource theory of asymmetry deals explicitly with the physical conditions necessary for a quantum system to serve as a good reference frame. Nonetheless, to quantify a quantum reference frame in relation to another one, it is important to establish an internal description of quantum theory, i.e., without the need for a classical reference frame. In this work, we address this issue with the concept of mutual asymmetry and use this machinery in the Page-Wootters mechanism by identifying the concept of mutual asymmetry as mutual or internal coherence. To do so, the notion of quantum coherence in relation to a quantum reference frame is revisited and a quantifier is proposed in this scenario. Also, this opens space to investigate the link of internal coherence and correlations, as proposed by Page and Wootters, under a resource theory approach.
Drivers of usability in product design practice: Induction of a framework through a case study of three product development projects

In a case study of the development of three electronic consumer products, we traced the origins of usability issues. Based on the data collected, an initial, explanatory framework was induced of important drivers of usability in product design. We conclude that – while usability methods mostly focus on gaining knowledge about users and usability issues – in many instances the primary cause of usability problems seems to be a lack of design freedom to implement usability-improving design changes. In addition, the organisational context seemed to influence the design process considerably. Thus, it can be concluded that to conduct user-centred design effectively, the design process should be considered holistically and the organisational context should be taken into account.