Reviewerly/exHarmony huggingface dataset:
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination

In a multi-agent system, an agent’s optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predicting the behaviours of others, and responding promptly to changes in such behaviours. One obvious possibility is for each agent to broadcast their current intention, for example, the currently executed option in a hierarchical reinforcement learning framework. However, this approach results in inflexibility of agents if options have an extended duration and are dynamic. While adjusting the executed option at each step improves flexibility from a single-agent perspective, frequent changes in options can induce inconsistency between an agent’s actual behaviour and its broadcast intention. In order to balance flexibility and predictability, we propose a dynamic termination Bellman equation that allows the agents to flexibly terminate their options. We evaluate our models empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.
Model and Feature Aggregation Based Federated Learning for Multi-sensor Time Series Trend Following

In the industrial field, especially the work or environment condition monitoring, it is crucial but difficult to follow the trend of the time series monitoring data (TSD) when the TSD come from different kinds of sensors and are collected by different companies. The privacy of the multi-sensor TSD must be carefully treated. Few studies, however, have been devoted to solving such problems. Federated learning (FL) is a good structure developed by Google for well keeping the personal privacy. Motivated by this, we here present an improved FL structure for not only keeping the data privacy but also extracting and fusing the trends features of the multi-sensor TSD. In our work, the client models of FL are first designed and optimized for getting the initial parameters and features w.r.t. the corresponding sensor’s TSD, and then both the model parameters and the extracted features of all the activated clients (sensors) are sent to the central server and aggregated. The fused parameters and features are returned to the clients and used to update the optimization of the model. Finally, the fused features of all multi-sensor TSD are put into an echo state network (ESN) to fulfill the trend following of the multi-sensor TSD. The proposed algorithm is applied to the multi-sensor electromagnetic radiation intensity TSD sampled from an actual coal mine, and its superiority in promoting the accuracy on every sensor is demonstrated.
On the p-adic denseness of the quotient set of a polynomial image

The quotient set, or ratio set, of a set of integers A is defined asR(A):={a/b:a,b∈A,b≠0}. We consider the case in which A is the image of Z+ under a polynomial f∈Z[X], and we give some conditions under which R(A) is dense in Qp. Then, we apply these results to determine when R(Smn) is dense in Qp, where Smn is the set of numbers of the form x1n+⋯+xmn, with x1,…,xm≥0 integers. This allows us to answer a question posed in Garcia et al. (2017) [5]. We end leaving an open question.
A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website’s active user base directly affects its value. Thus, it is important to monitor and influence a user’s likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation. Code related to this paper is available at: https://github.com/grobgl/rnnsm .
A Perceptually Inspired New Blind Image Denoising Method Using $L_{1}$ and Perceptual Loss

Recently, discriminative learning-based denoising methods have received much attention and have been studied to a large extent because of their high denoising performance with significantly shorter inference time compared to model based denoising methods.In this paper, we consider a perceptually motivated blind image denoising problem, which removes various levels of noise from an observed noisy image with a single model and produces visually pleasant images.It is well known that very few blind image denoising methods are available and they have shown the limited quality of restored images because they sacrifice the fine image details during the denoising process due to over-smoothing, resulting in visually unpleasant images.To overcome these problems, we propose a novel loss function that encourages the network to restore noise free images by focusing on the perceived visual quality.Then, the proposed loss function is adopted to an encoder-decoder network with skip connections that produces visually pleasant images with highly preserved fine image details.Further, this method is robust and constantly performs well for several unknown noise levels.Our extensive experimental results show the superiority of the proposed method both quantitatively and qualitatively.
Speckle noise reduction for ultrasound images by using speckle reducing anisotropic diffusion and Bayes threshold

Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this algorithm, SRAD is employed as a preprocessing filter, and the Bayes threshold is used to remove the residual noise in the resulting image. Compared to the conventional filtering techniques, experimental results showed that the proposed algorithm exhibited superior performance in terms of peak signal-to-noise ratio (average = 28.61 dB) and structural similarity (average = 0.778).
TV Channels in Your Pocket! Linking Smart Pockets to Smart TVs

We present a gesture-based user interface for smart TVs that employs deictic gestures to control the content displayed on the TV screen. Our interface implements an instance of the “Smart-Pockets” interaction technique, where links to digital content, in our case to users’ preferred television channels and shows, are stored inside users’ pockets and readily accessible with a mere pointing of the hand to those pockets. Pointing gestures to the pockets and towards the TV screen are detected using the Inertial Measurement Unit embedded in Myo, a smart armband. We discuss the ways in which our prototype opens new opportunities for hybrid, gesture- and pointing-based interactions for smart TVs as well as opportunities for designing interactions that take place at the periphery of user attention.
Understanding User Attention In VR Using Gaze Controlled Games

Understanding user’s intent has a pivotal role in developing immersive and personalised media applications. This paper introduces our recent research and user experiments towards interpreting user attention in virtual reality (VR). We designed a gaze-controlled Unity VR game for this study and implemented additional libraries to bridge raw eye-tracking data with game elements and mechanics. The experimental data show distinctive patterns of fixation spans which are paired with user interviews to help us explore characteristics of user attention.
Mapping Hydrothermal Zoning Pattern of Porphyry Cu Deposit Using Absorption Feature Parameters Calculated from ASTER Data

Identifying hydrothermal zoning pattern associated with porphyry copper deposit is important for indicating its economic potential. Traditional approaches like systematic sampling and conventional geological mapping are time-consuming and labor extensive, and with limitations for providing small scale information. Recent developments suggest that remote sensing is a powerful tool for mapping and interpreting the spatial pattern of porphyry Cu deposit. In this study, we integrated in situ spectral measurement taken at the Yudai copper deposit in the Kalatag district, northwestern China, information obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as well as the spectra of samples (hand-specimen) measured using an Analytical Spectral Device (ASD) FieldSpec4 high-resolution spectrometer in laboratory, to map the hydrothermal zoning pattern of the copper deposit. Results proved that the common statistical approaches, such as relative band depth and Principle Component Analysis (PCA), were unable to identify the pattern accurately. To address the difficulty, we introduced a curve-fitting technique for ASTER shortwave infrared data to simulate Al(OH)-bearing, Fe/Mg(OH)-bearing, and carbonate minerals absorption features, respectively. The results indicate that the absorption feature parameters can effectively locate the ore body inside the research region, suggesting the absorption feature parameters have great potentials to delineate hydrothermal zoning pattern of porphyry Cu deposit. We foresee the method being widely used in the future.
Learning interpretable continuous-time models of latent stochastic dynamical systems

We develop an approach to learn an interpretable semi-parametric model of a latent continuous-time stochastic dynamical system, assuming noisy high-dimensional outputs sampled at uneven times. The dynamics are described by a nonlinear stochastic differential equation (SDE) driven by a Wiener process, with a drift evolution function drawn from a Gaussian process (GP) conditioned on a set of learnt fixed points and corresponding local Jacobian matrices. This form yields a flexible nonparametric model of the dynamics, with a representation corresponding directly to the interpretable portraits routinely employed in the study of nonlinear dynamical systems. The learning algorithm combines inference of continuous latent paths underlying observed data with a sparse variational description of the dynamical process. We demonstrate our approach on simulated data from different nonlinear dynamical systems.
Composing WoT services with uncertain data

The Web of Things (WoT) represents a set of technologies that allow everyday objects to connect to the Web. Often, the creation of new applications requires the composition of multiple objects. However, the interconnection of these objects poses a great problem of heterogeneity. In this paper, we propose to use WoT services as a solution to the heterogeneity problem, such that the composition of connected objects (things) will become a composition of WoT services. During the composition process, data exchanged between WoT services is often uncertain due to various reasons. To handle the problem of data uncertainty, we propose a probabilistic approach to model and evaluate WoT service composition. We also propose an efficient algorithm to execute uncertain WoT service compositions. Our proposal is implemented and evaluated, and the results of the evaluation prove the scalability and efficiency of our approach.
Medical Concept Representation Learning from Multi-source Data

Representing words as low dimensional vectors is very useful in many natural language processing tasks. This idea has been extended to medical domain where medical codes listed in medical claims are represented as vectors to facilitate exploratory analysis and predictive modeling. However, depending on a type of a medical provider, medical claims can use medical codes from different ontologies or from a combination of ontologies, which complicates learning of the representations. To be able to properly utilize such multi-source medical claim data, we propose an approach that represents medical codes from different ontologies in the same vector space. We first modify the Pointwise Mutual Information (PMI) measure of similarity between the codes. We then develop a new negative sampling method for word2vec model that implicitly factorizes the modified PMI matrix. The new approach was evaluated on the code cross-reference problem, which aims at identifying similar codes across different ontologies. In our experiments, we evaluated cross-referencing between ICD-9 and CPT medical code ontologies. Our results indicate that vector representations of codes learned by the proposed approach provide superior cross-referencing when compared to several existing approaches.
Big Data Analytics using Machine Learning Techniques

Gigabytes of data is being generated now a days on daily basis which may possess some of the characteristics such as high speed, huge volume, uncertainty, non-stationary data, real time data etc. Conventional Machine Learning Techniques cannot be used for analysis of big data due to its above mentioned features. Also, traditional storage and processing techniques fails to meet the requirements. In this paper, we have discussed the various challenges that may occur while using traditional MLT for Big Data Analytics and its possible solutions. As per our survey, Parallel Processing, Dimensionality Reduction techniques, GPUs, Map reduce jobs, Deep learning, Online learning, Incremental learning are some of the possible solution to meet the challenges associated with big data analytics.
Deterministic generation of maximally discordant mixed states by dissipation

Entanglement can be considered as a special quantum correlation, but not the only kind. Even for a separable quantum system, it is allowed to exist non-classical correlations. Here we propose two dissipative schemes for generating a maximally correlated state of two qubits in the absence of quantum entanglement, which was raised by [F. Galve, G. L. Giorgi, and R. Zambrini, {\color{blue}Phys. Rev. A {\bf 83}, 012102 (2011)}]. These protocols take full advantages of the interaction between four-level atoms and strongly lossy optical cavities. In the first scenario, we alternatively change the phases of two classical driving fields, while the second proposal introduces a strongly lossy coupled-cavity system. Both schemes can realize all Lindblad terms required by the dissipative dynamics, guaranteeing the maximally quantum dissonant state to be the unique steady state for a certain subspace of system. Moreover, since the target state is a mixed state, the performance of our method is evaluated by the definition of super-fidelity $G(\rho_{1},\rho_{2})$, and the strictly numerical simulations indicate that fidelity outstripping $99\%$ of the quantum dissonant state is achievable with the current cavity quantum electrodynamics parameters.
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation

The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters.This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task.In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph.The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global).Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information.Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.
Pearson correlation coefficient as a measure for certifying and quantifying high-dimensional entanglement

A scheme for characterizing entanglement using the statistical measure of correlation given by the Pearson correlation coefficient (PCC) was recently suggested that has remained unexplored beyond the qubit case. Towards the application of this scheme for the high-dimensional states, a key step has been taken in a very recent work by experimentally determining PCC and analytically relating it to Negativity for quantifying entanglement of the empirically produced bipartite pure state of spatially correlated photonic qutrits. Motivated by this work, we present here a comprehensive study of the efficacy of such an entanglement characterizing scheme for a range of bipartite qutrit states by considering suitable combinations of PCCs based on a limited number of measurements. For this purpose, we investigate the issue of necessary and sufficient certification together with quantification of entanglement for the two-qutrit states comprising maximally entangled state mixed with white noise and colored noise in two different forms, respectively. Further, by considering these classes of states for $d=4$ and 5, extension of this PCC-based approach for higher dimensions ($d$) is discussed.
Optimal clock speed of qubit gate operations on open quantum systems

Efficient implementation of quantum algorithms requires single- or multiple-qubit gates with high fidelity. Here we report that the fidelity of gate operations on open quantum systems has a maximum value corresponding to an optimum value of the drive amplitude in the presence of drive-induced decoherence. To demonstrate this, we use a previously reported fluctuation-regulated quantum master equation [A. Chakrabarti and R. Bhattacharyya, Phys. Rev. A 97, 063837 (2018)]. The fidelity is found to be a function of the drive-induced dissipative terms as well as the relaxation terms arising from the qubit-environment coupling; as a result, it behaves nonmonotonically with the drive amplitude. The existence of an optimum drive amplitude implies that the qubit gate operations on open quantum systems would have an optimal clock speed. We demonstrate the universality of the results for the gate operations on single- and multiple-qubit gates.
One-Stop Physics E-Book Package Development for Senior High School Learning Media

Many e-Book versions of Physics learning have used to support for gaining more information. Mostly, these e-Books didn’t contain virtual laboratory environment due to the student’s visualization in Physics concept. For this objection, an e-Book package was involved to developing, branded as Beboo (Bilingual e-Book), one stop e-Book package completed by physics visualize animation, virtual laboratory environment, student’s worksheet, supporting video and self-final test. Beboo uses to assist the second-year student in senior high school to learn static fluids concept, including of the condition in static fluids and the Pascal-Archimedes principle applying in an everyday situation. This e-Book was developed by a range of multimedia including text, pictures, diagrams, sound effect, music, video, and animation in interactive and effective elements and required multiskilling that are content crating and programming. Besides, it contains the interesting part in physics learning, a laboratory activity, designed in two simulators on U-Pipe and Hydraulic Lift due to the student’s understanding of the hydrostatic and pascal law systems. For a common self-final test, Beboo provides the final test feature, shown in multiple choice for concept achievement. In general, Beboo develops in three main stages (needs assessment, design and development &amp; implementation) known as Hannafin and Peck Model and have the validation score in a range of 84%- 94%. Furthermore, Beboo should enlarge team due to more chapter coverage and Android platform support. Overall, Beboo uses for an alternative use for school with no real laboratory and as the minimal point to the next e-Book development. Moreover, this media developed as the answer in technology integration in 21st-century learning.
LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach

Abstract In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5–89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions.
Sentiment Aware Neural Machine Translation

Sentiment ambiguous lexicons refer to words where their polarity depends strongly on context.As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data.While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence.We investigate the translation variation in two sentiment scenarios.We perform experiments to study the preservation of sentiment during translation with three different methods that we propose.We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods.We show that NMT can generate both positive-and negative-valent translations of a source sentence, based on a given input sentiment label.Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequenceto-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.