Reviewerly/exHarmony huggingface dataset:
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters

How does the uncertainty of the value function propagate when performing temporal difference learning? In this paper, we address this question by proposing a Bayesian framework in which we employ approximate posterior distributions to model the uncertainty of the value function and Wasserstein barycenters to propagate it across state-action pairs. Leveraging on these tools, we present an algorithm, Wasserstein Q-Learning (WQL), starting in the tabular case and then, we show how it can be extended to deal with continuous domains. Furthermore, we prove that, under mild assumptions, a slight variation of WQL enjoys desirable theoretical properties in the tabular setting. Finally, we present an experimental campaign to show the effectiveness of WQL on finite problems, compared to several RL algorithms, some of which are specifically designed for exploration, along with some preliminary results on Atari games.
Re-examination of the Role of Latent Variables in Sequence Modeling

With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence. However, opposite results are also observed in other domains, where standard recurrent networks often outperform stochastic models. To better understand this discrepancy, we re-examine the roles of latent variables in stochastic recurrent models for speech density estimation. Our analysis reveals that under the restriction of fully factorized output distribution in previous evaluations, the stochastic variants were implicitly leveraging intra-step correlation but the deterministic recurrent baselines were prohibited to do so, resulting in an unfair comparison. To correct the unfairness, we remove such restriction in our re-examination, where all the models can explicitly leverage intra-step correlation with an auto-regressive structure. Over a diverse set of univariate and multivariate sequential data, including human speech, MIDI music, handwriting trajectory, and frame-permuted speech, our results show that stochastic recurrent models fail to deliver the performance advantage claimed in previous work. %exhibit any practical advantage despite the claimed theoretical superiority. In contrast, standard recurrent models equipped with an auto-regressive output distribution consistently perform better, dramatically advancing the state-of-the-art results on three speech datasets.
Correlation Priors for Reinforcement Learning

Many decision-making problems naturally exhibit pronounced structures inherited from the characteristics of the underlying environment. In a Markov decision process model, for example, two distinct states can have inherently related semantics or encode resembling physical state configurations. This often implies locally correlated transition dynamics among the states. In order to complete a certain task in such environments, the operating agent usually needs to execute a series of temporally and spatially correlated actions. Though there exists a variety of approaches to capture these correlations in continuous state-action domains, a principled solution for discrete environments is missing. In this work, we present a Bayesian learning framework based on Polya-Gamma augmentation that enables an analogous reasoning in such cases. We demonstrate the framework on a number of common decision-making related problems, such as imitation learning, subgoal extraction, system identification and Bayesian reinforcement learning. By explicitly modeling the underlying correlation structures of these problems, the proposed approach yields superior predictive performance compared to correlation-agnostic models, even when trained on data sets that are an order of magnitude smaller in size.
DiAna-AD: Dialog Analysis for Adjusting Duration During Face-to-Face Collaborative Discussion

This research proposes a DiAna-AD (dialog analysis for adjusting duration) system that adjusts the duration time of discussions based on the discussion situations. Participants may be dissatisfied with excessive or insufficient fixed time of discussion. The proposed system is intended to terminate the discussions at the appropriate time. The situations of the discussions are estimated only from nonlinguistic acoustic features collected by wearable devices. The criteria of termination by the system are obtained from the analysis of an experiment of satisfaction. The analysis result revealed that the subjective satisfaction of the participants is reduced by the desire to shorten the duration time of the discussion. An algorithm to terminate discussions is implemented via the proposed system. Furthermore, an experiment with the system is conducted to evaluate the effectiveness of the system for improving participants’ satisfaction in a discussion. The result of the experiment with the system is compared with that of the experiment on satisfaction, and there are no significant differences between the results. On the other hand, from the result of the interview, the proposed DiAna-AD system is found to be effective in shortening the duration time of inactive discussions.
Oblivious Sampling Algorithms for Private Data Analysis

We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset. Trusted execution environments (TEEs) can be used to protect the content of the data during query computation, while supporting differential-private (DP) queries in TEEs provides record privacy when query output is revealed. Support for sample-based queries is attractive due to \emph{privacy amplification} since not all dataset is used to answer a query but only a small subset. However, extracting data samples with TEEs while proving strong DP guarantees is not trivial as secrecy of sample indices has to be preserved. To this end, we design efficient secure variants of common sampling algorithms. Experimentally we show that accuracy of models trained with shuffling and sampling is the same for differentially private models for MNIST and CIFAR-10, while sampling provides stronger privacy guarantees than shuffling.
Co-Generation with GANs using AIS based HMC

Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones -- which we refer to as co-generation -- is an important challenge that is computationally demanding for all but the simplest settings. This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction. In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs). Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm. The presented approach significantly outperforms classical gradient based methods on a synthetic and on the CelebA and LSUN datasets.
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration

Deep neural networks achieve outstanding results for challenging image classification tasks. However, the design of network topologies is a complex task, and the research community is conducting ongoing efforts to discover top-accuracy topologies, either manually or by employing expensive architecture searches. We propose a unique narrow-space architecture search that focuses on delivering low-cost and rapidly executing networks that respect strict memory and time requirements typical of Internet-of-Things (IoT) near-sensor computing platforms. Our approach provides solutions with classification latencies below 10~ms running on a low-cost device with 1~GB RAM and a peak performance of 5.6~GFLOPS. The narrow-space search of floating-point models improves the accuracy on CIFAR10 of an established IoT model from 70.64% to 74.87% within the same memory constraints. We further improve the accuracy to 82.07% by including 16-bit half types and obtain the highest accuracy of 83.45% by extending the search with model-optimized IEEE 754 reduced types. To the best of our knowledge, this is the first empirical demonstration of more than 3000 trained models that run with reduced precision and push the Pareto optimal front by a wide margin. Within a given memory constraint, accuracy is improved by more than 7% points for half and more than 1% points for the best individual model format.
Nonconvex Bayesian Restoration of Blurred Foreground Images

Recently, some techniques for deblurring images having arbitrarily shaped boundaries have been proposed. However, when we observe a blurry foreground (FG) object on a sharp background (BG) there is no abrupt transition between them. Because previous techniques are not designed to cop with a smooth transition area between FG and BG, they avoid it by artificially discarding it from the computations. Here we construct, instead, an observation model that accounts for the occlusion photometric effect of the FG object on the BG, and we illustrate its realism by comparing a simulation with a real capture. Then we use that observation model to pose a Bayesian MAP estimation problem with an L2-relaxed L0 sparse prior and two Gaussian likelihood terms (for the noise, and for the BG interference in the smooth transition area), which we solve by alternating optimizations. Using simulations, we demonstrate a higher performance of our method, compared to two state-of-the-art unconstrained boundary restoration techniques.
Backprop with Approximate Activations for Memory-efficient Network Training

Training convolutional neural network models is memory intensive since back-propagation requires storing activations of all intermediate layers. This presents a practical concern when seeking to deploy very deep architectures in production, especially when models need to be frequently re-trained on updated datasets. In this paper, we propose a new implementation for back-propagation that significantly reduces memory usage, by enabling the use of approximations with negligible computational cost and minimal effect on training performance. The algorithm reuses common buffers to temporarily store full activations and compute the forward pass exactly. It also stores approximate per-layer copies of activations, at significant memory savings, that are used in the backward pass. Compared to simply approximating activations within standard back-propagation, our method limits accumulation of errors across layers. This allows the use of much lower-precision approximations without affecting training accuracy. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that our method yields performance close to exact training, while storing activations compactly with as low as 4-bit precision.
Frequent Itemsets as Descriptors of Textual Records

The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. the process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we propose to use frequent itemsets as descriptors and classifying them by using K-Medoids algorithm and Hierarchical cluster. We present how they can be identified and used to define a level of similarity between several segments. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments.
Bidirectional One-Shot Unsupervised Domain Mapping

We study the problem of mapping between a domain $A$, in which there is a single training sample and a domain $B$, for which we have a richer training set. The method we present is able to perform this mapping in both directions. For example, we can transfer all MNIST images to the visual domain captured by a single SVHN image and transform the SVHN image to the domain of the MNIST images. Our method is based on employing one encoder and one decoder for each domain, without utilizing weight sharing. The autoencoder of the single sample domain is trained to match both this sample and the latent space of domain $B$. Our results demonstrate convincing mapping between domains, where either the source or the target domain are defined by a single sample, far surpassing existing solutions. Our code is made publicly available at this https URL
A Weak Centralized Consensus Mechanism with More Incentive Effects

Abstract In the blockchain system, the consensus mechanism not only helps the nodes maintain data consistency, but also has certain functions for token issuance and attack prevention. Aiming at the problems of low enthusiasm and weak centralization in Delegated Proof of Stake (DPoS), this paper proposes a weak centralization consensus mechanism with more incentive effect. We replace the voting link with opportunity verification mechanism, and make the voting link that affects the enthusiasm of nodes become the embodiment of their own rights and interests, so as to strive for the maximization of their own interests. And we also introduce the method of random guess to make the selection of representative nodes more random, so as to achieve the situation of weaker center. At last, combined with the existing problems, the paper analyses the feasible solution with this mechanism.
Air Combat Situation Assessment Based on Improved RS Theory

Abstract In order to improve the ability of the extraction and weight distribution of key air combat factors in air combat situation assessment, this paper improves the rough set theory and establishes an improved rough set theory air combat situation assessment model. Firstly, the air combat information is discretized and decoupled. Then, the improved rough set theory is used to reduce and assign the attributes, and the air combat situation is calculated and evaluated. Finally, the effectiveness of the model is verified by simulation.
Fitness Movements Recognition and Evaluation Based on LSTM

In this paper, we use Kinect to obtain human skeleton information, then extract 24-dimensional eigenvalues to represent the dynamic movements. A lighted refined LSTM model is employed to recognition the movement serious, finally we propose a movement evaluation model to mainly avoid sports injured. For experiment, we collect 5 kinds of movements as 250 movement samples totally. Using our model, the accuracy can get 80% as the accuracy we test on MSR dataset is 82%. At last, the vulnerable judgment for fitness movement is calculated based on characteristic angles of each dynamic action which can also enlarge the effect of fitness.
Tunable three-body coupler for superconducting flux qubits

The implementation of many-body interactions is relevant in various areas of quantum information. We present a superconducting device that implements a strong and tunable three-body interaction between superconducting quantum bits, with vanishing two-body interactions and robustness against noise and circuit parameter variations. These properties are confirmed by calculations based on the Born-Oppenheimer approximation, a two-level model for the coupling circuit, and numerical diagonalization. This circuit behaves as an ideal computational basis ZZZ coupler in a simulated three-qubit quantum annealing experiment. This work will be relevant for advanced quantum annealing protocols and future developments of high-order many-body interactions in quantum computers and simulators.
An Assessment Survey of Cloud Simulators for Fault Identification

Cloud computing is a large set of logical computational resources accessible via internet. Cloud computing offers services to obtain coherence, scalability, economy subscale with maximum efficiency and resource optimization. Fault tolerance is the characteristic that enables the system to stay operating and adhere SLA even when the in the system faults and failures. For a system to be fault tolerant the interval of fault identification and removal must be minimum to follow the QoS requirements. virtualization in the Data center can assist in fault prediction that makes the system fault tolerant. A cloud simluator is an extensible tool to analyse, evaluate and measure the system performance of the cloud cloud applications to satisfy the QoS provisions. This paper deals with the survey of the various cloud simulators with emphasis on using CloudSim.
Extracting Reasons for Moral Judgments Under Various Ethical Principles

We present an approach to the computational extraction of reasons for the sake of explaining moral judgments in the context of an hybrid ethical reasoning agent (HERA). The HERA agent employs logical representations of ethical principles to make judgments about the moral permissibility or impermissibility of actions, and uses the same logical formulae to come up with reasons for these judgments. We motivate the distinction between sufficient reasons, necessary reasons, and necessary parts of sufficient reasons yielding different types of explanations, and we provide algorithms to extract these reasons.
Web-Based Geographic Information System (GIS) in Determining Shortest Path of MSME Medan City Using Bellman-Ford Algorithm

Abstract Micro, Small and Medium Enterprises (MSME) are one of the drivers of Indonesian economy but have problems in introducing their products to the public. In addition, public ignorance of MSME and their products is another problem experienced by MSME. Therefore, a system is needed that can become a forum for MSME to introduce their businesses and display complete information about the MSME business in Medan City, such as a geographical information system that contains a mapping of the shortest MSME routes, estimated distances and travel time to these MSME. This study uses Bellman-Ford algorithm and Euclidean Distance method to map the shortest route to MSME. Based on the testing, a system is obtained that allows administrators to input MSME data and allows users to search for information about a MSME and its address along with mapping the shortest route.
A Hybrid Lexicon-Based Sentiment and Behaviour Prediction System

Text mining is the process of extracting and/or deriving high-quality information from unstructured text by properly structuring the raw text. The structured texts serve as ideal candidates for revealing the syntactic and semantic interpretations encapsulated in them. To do so, we need to employ different methods of text mining and text analytics. Text Analytics deals with the objective of evaluating and assessing text by the application of natural language processing and other linguistic-oriented analytical methods. Text-based sentiment analysis aims to determine the attitude and sentimental state of an author by analysing different tokens of the texts in terms of their polarity. The aim of this paper is to propose a hybrid Lexicon-based sentiment and behaviour prediction system which can help one to comprehend the sentimental as well as the behavioural context of the author. We have used two sets of lexicons, viz. SenticNet 4.0 Lexicon and our own manually created Profile Lexicon in order to assess the input text and to predict the sentiment conveyed by the text as well as to identify the behaviour of the author. Our system works fairly in case of predicting both sentiment and behaviour by offering an accuracy of approximately 90%. Such a system has immense potential in identifying the real intention of an author once the behavioural and sentimental patterns of an author are predicted consummately.
Remote monitoring of agricultural land using multispectral satellite imagery Sentinel 2 by contour analysis

This article describes the method of remote monitoring of agricultural land using Sentinel 2 satellite imagery. The NDVI index identifies problem areas of an agricultural field. The local filtering algorithm smoothes images for further processing. An algorithm for filtering images by threshold obtained experimentally is also described. An algorithm for detecting contours by the Rosenfeld method of healthy vegetation is described.
High-performance Cryptographically Secure Pseudo-random Number Generation via Bitslicing.

In this paper, a high-throughput Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) using the bitslicing technique is proposed. In such technique, instead of the conventional row-major data representation, column-major data representation is employed which allows the bitsliced implementation to take full advantage of all the available datapath of the hardware platform. We use LFSR-based (Linear Feedback Shift Register) PRNG for our implementation since its register oriented architecture perfectly suits the GPU's many-core structure and allows for usage of bitslicing technique which can further improve its performance. In our GPU implementation, each GPU thread is capable of generating a remarkable number of 32 pseudo-random bits in each LFSR clock cycle. In order to obtain cryptographically suitable properties, we propose an SIMD vectorized fully parallel bitsliced implementation of the LFSR-based, cryptographically secure MICKEY 2.0 stream cipher algorithm for CSPRNG. To the best of our knowledge, our method not only achieves better performance, but also significantly outperforms optical solutions in terms of performance per cost while maintaining an acceptable measure of randomness. It should be mentioned that our implementation successfully passes the NIST test for statistical randomness and bitwise correlation criteria. Our proposed methodology significantly outperforms the current best implementations in the literature for computer-based PRNG. Moreover, our evaluations show 6.6x improvement over the Nvidia's proprietary high-performance PRNG, cuRAND library, achieving 5.2 Tb/s of throughput on the affordable Nvidia GTX 980 Ti.
Computing Optimal Assignments in Linear Time for Approximate Graph Matching

Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words' representations in natural language processing and computer vision. Solving the assignment problem typically requires cubic time and its pairwise computation is expensive on large datasets. In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance. We employ the method to approximate the edit distance between two graphs by matching their vertices in linear time. To this end, we propose two tree distances, the first of which reflects discrete and structural differences between vertices, and the second of which can be used to compare continuous labels. We verify the effectiveness and efficiency of our methods using synthetic and real-world datasets.