Reviewerly/exHarmony huggingface dataset:
Hierarchical Deep Reinforcement Learning for Multi-robot Cooperation in Partially Observable Environment

Many real-world applications require multi-robot coordination in partially-observable domains such as package delivery, search, and rescue. One typical way to address partial observability is to enable information sharing among robots via dedicated communication protocols. However, designing commu-nication protocols is difficult due to the dynamic environments and complex interactions among robots. Existing broadcasting-based approaches are communication-inefficient, and they usually introduce redundant information that might impair the learning process and action selection. In this paper, we propose a hierar-chical reinforcement learning approach, called COM-cooperative HRL for multi-robot cooperation in a partially observable en-vironment. Specifically, COM-cooperative HRL addresses the above gaps by introducing a partner selector to learn high-level communication strategy using short-term task-execution rewards. Besides, a low-level controller is trained to select actions based on shared information and individual observation. Extensive empirical results show a faster convergence rate and higher team performance over alternative baselines. Our approach can not only improve learning efficiency but also be adaptive to large-scale multi-robot systems.
Deep Unrolling for Light Field Compressed Acquisition Using Coded Masks

Compressed sensing using color-coded masks has been recently considered for capturing light fields using a small number of measurements.Such an acquisition scheme is very practical, since any consumer-level camera can be turned into a light field acquisition camera by simply adding a coded mask in front of the sensor.We present an efficient and mathematically grounded deep learning model to reconstruct a light field from a set of measurements obtained using a color-coded mask and a color filter array (CFA).Following the promising trend of unrolling optimization algorithms with learned priors, we formulate our task of light field reconstruction as an inverse problem and derive a principled deep network architecture from this formulation.We also introduce a closed-form extraction of information from the acquisition, while similar methods found in the recent literature systematically use an approximation.Compared to similar deep learning methods, we show that our approach allows for a better reconstruction quality.We further show that our approach is robust to noise using realistic simulations of the sensing acquisition process.
Mixtures of Gaussian Processes for Robot Motion Planning Using Stochastic Trajectory Optimization

Robot motion planning methods based on trajectory optimization can efficiently generate feasible and optimal trajectories by minimizing a suitable cost function, even in high-dimensional spaces. However, the main drawback of these methods lies in their proneness to infeasible local minima, especially in complex environments. To mitigate this issue, we propose a novel motion planning method that represents trajectories as samples from a mixture of continuous-time Gaussian processes (MGP) and employs stochastic optimization in order to update the MGP parameters in a cost-minimizing manner. The contributions of the proposed trajectory optimization method arise from the introduced mixture representation and stochastic gradient estimation, dominantly enabling better exploration of the trajectory space and including nondifferentiable optimizing costs. We evaluated the proposed method in multiple simulation benchmarks featuring 7 degree-of-freedom (DOF) robot arms and a 10DOF mobile manipulator. We also conducted a real-world experiment with a 14DOF dual-arm robot. The experimental results demonstrated that the proposed method achieves higher success rate than several state-of-the-art methods, while the advantages stemming from MGPs and stochastic optimization, like trajectory smoothness, support of nondifferentiable cost functions, multiple trajectory solutions, and the ability to tackle high-dimensional planning problems, are inherently kept.
Linac: A Smart Environment Simulator of Human Activities

Abstract The identification and construction of datasets of human activities is an extremely time-consuming and resource intensive task, yet researchers cannot refrain from such datasets. The publicly available datasets may not reflect all the researchers’ requirements and are not scrupulously documented. In addition, these datasets can cope with just a limited and predefined set of behaviors. To address these challenges, we developed an instrument that allows to simulate the behavior of agents interacting with an environment. The environment is a customized configuration, equipped with sensors. The simulation generates as output a stream of events stemming from activated sensors. In addition, the agents behavior is not fully deterministic, so as to reflect the dynamic nature of human beings and to be as realistic as possible.
Quantized Gaussian JPEG Steganography and Pool Steganalysis

Currently, algorithms for compressed image steganography mainly embed hidden message by minimizing the resulting distortion or statistical detectability. However, as a result of purely heuristic distortion definitions and numerically solvable equations in statistical models, there are no closed-form solutions for JPEG steganography. The absence of closed-form expression to model JPEG steganography is the main limitation on understanding single image and pool steganalysis behavior. In this study, building upon our previously proposed framework for spatial steganography, we develop a statistical framework for JPEG steganography in which the cover and the hidden message are modeled by multivariate Gaussian distribution. Based on this statistical model, we propose a novel quantized Gaussian JPEG steganography that is able to accomplish embedding using any costs defined in spatial or discrete cosine transform (DCT) domain as well as residual variances. We conduct our experiments using a popular database with different compression qualities to determine the effectiveness of the proposed model. The experimental results show that the proposed model improves the security of previous works and outperforms the state-of-the-art JPEG steganography algorithms. Furthermore, we extend the closed-form expression of single image steganalysis error to pool steganalysis for an omniscience optimal detector. We employ the derived expression to approximate the empirical results of pool steganalysis based on the empirical detection error of single image steganalysis. The practical advantage of the approximation is that even though it is derived based on the adopted statistical model, it is accurate regardless of payload, embedding domain, embedding method, and steganalysis feature as long as the pooling strategy is optimal. In addition to approximation of the error, we employ the proposed model to make predictions about the variance behavior of pool steganalysis error. We mathematically show that the variance increases as the pool size increases in small payloads. The same behavior is observed in experimental results which re-validates our analytical model. We conclude that although pooling improves detector’s performance, it makes the detector less stable in low payloads and high pool sizes.
HAVPTAT: A Human Activity Video Pose Tracking Annotation Tool

We propose a new semi-automatic annotation software: Human Activity Video Pose Tracking Annotation Tool (HAVPTAT). It can automatically detect and track multiple people and their pose in the video to improve work efficiency. HAVPTAT also provides the dynamical visualization of human pose, bounding boxes, person tracking ID, and possible prediction results together. The lightweight software can be launched in a few seconds and easily distributed. Its ease of use will allow non-professionals to get started quickly. This software will accelerate the development of human activity recognition models and service robots.
Abusive words Detection in Persian tweets using machine learning and deep learning techniques

Regarding the development of the web and increasing user interaction, different users&#x0027; opinions about different phenomena have been observed. In recent years, the detection of Abusive language in online content used by users has become a necessity. Twitter is a platform in which users can share text messages. On Twitter, different people express their opinion on different topics with different kinds of literature, some of which are accompanied by Abusive words. On the one hand, Abusive comments can be derogatory and harmful to those who share content. On the other hand, filtering these comments in languages other than English is difficult and time-consuming. Most social media platforms are still looking for more efficient ways to filter comments because the manual method is expensive, slow, and risky. Automating helps better identify and filter Abusive comments and increase user safety. In the present article, a deep learning method is presented to detect users&#x0027; Abusive words in Persian tweets. Due to the lack of appropriate data in Persian, we created a database of 33338 Persian tweets, of which 10&#x0025; contained Abusive words and 90&#x0025; were non-Abusive. Perhaps the easiest way is to use a fixed list and filter comments. So, a list of 648 Abusive words in Persian was prepared and used to test the database (accuracy of 76&#x0025;). Finally, a deep neural network is implemented to detect Abusive words using the Bert language model, and it had the best performance with an accuracy of 97.7&#x0025;.
Reading digital- versus print-easy texts: a study with university students who prefer digital sources

The transition from on-paper to on-screen reading seems to make it necessary to raise some considerations, as a greater attentional effort has been claimed for print texts than digital ones. Not surprisingly, most university students prefer this digital medium. This research aims to examine reading times by contextualizing this phenomenon into two processes: namely, word recognition and reading comprehension task on paper and on screen. Thus, two different tasks-counterbalanced into digital and print mediums-were carried out per each participant with a preference for a digital medium: a reading comprehension task (RCT) and a lexical decision task (LDT) after reading a specific story. Participants were slower reading print texts and no statistically significant differences were found in RCT accuracy. This result suggests that the task required more cognitive resources under the print medium for those with a worse comprehension performance in reading, and a more conservative pattern in digital RCT for those with a better performance.
Training, Optimization and Validation of a CNN for Room Retrieval and Description of Omnidirectional Images

Abstract The aim of this paper is to achieve the optimal hyperparameters setup of a convolutional neural network (CNN) to address the localization of a mobile robot. The localization problem is solved with a hierarchical approach by using omnidirectional images as provided by a catadioptric visual sensor, with no panoramic conversion. In this way, we propose adapting and re-training AlexNet with a double purpose. First, to perform the rough localization step by means of a room retrieval task. Second, to carry out the fine localization step within the retrieved room, in which the CNN is used to obtain a holistic descriptor that is compared with the visual model of the retrieved room by means of a nearest neighbour search. To achieve this, a CNN has been adapted and re-trained to address both the room retrieval problem and the obtention of holistic descriptors from raw omnidirectional images. The novelty of this work is the use of a data augmentation technique and Bayesian optimization to address the training process robustly. As shown in the present paper, these tools have proven to be an efficient and robust solution to the localization problem even with substantial changes of the lighting conditions of the target environment.
Statistical-mechanical Study of Deep Boltzmann Machine Given Weight Parameters after Training by Singular Value Decomposition

Deep learning methods relying on multi-layered networks have been actively studied in a wide range of fields in recent years, and deep Boltzmann machines(DBMs) is one of them. In this study, a model of DBMs with some properites of weight parameters obtained by learning is studied theoretically by a statistical-mechanical approach based on the replica method. The phases characterizing the role of DBMs as a generator and their phase diagram are derived, depending meaningfully on the numbers of layers and units in each layer. It is found, in particular, that the correlation between the weight parameters in the hidden layers plays an essential role and that an increase in the layer number has a negative effect on DBM's performance as a generator when the correlation is smaller than a certain threshold value.
Human-object interaction detection with depth-augmented clues

Human object interaction (HOI) detection aims to localize and classify triplets of human, object and relationship from a given image. Different from previous methods that only extract vision information in RGB images, we propose a Depth-augmented Relationship Reasoning (DRR) method that focuses on the RGB images and corresponding depth messages simultaneously. Rethinking principles of photography, we argue that RGB images discard spatial depth carrying third dimension relative distance information between instances. In light of this, we beforehand estimate the depth information for each image, yielding a corresponding depth map. Then we leverage multiple representations encoded by depth information and RGB images to enrich semantic interpretation. Subsequently, we explore a hierarchical attention strategy to fuse these semantic representations and further generate depth-augmented features, being used to reason about fine-grained human-object interactions. Extensive experiments on the benchmark datasets V-COCO, HICO-DET and HCVRD verify the effectiveness of our method and demonstrate the importance of spatial depth information for HOI.
Patient-centered cross-enterprise document sharing and dynamic consent framework using consortium blockchain and ciphertext-policy attribute-based encryption

Patient-centered healthcare data sharing and data usage consent are gaining popularity. Cross-enterprise document sharing (XDS) is the crucial system of sharing personalized healthcare data. Furthermore, dynamic consent is vital to the XDS system, because it respects people's autonomy and achieves recognition of data sovereignty. Because of its transparency, blockchain is a powerful system for managing storage and computing without a trusted third party. Besides, ciphertext-policy attribute-based encryption (CP-ABE) extends public-key encryption by implying access control policies in ciphertexts, making it suitable for protecting the privacy of individual healthcare data in versatile cases. Particularly, we use hospital name, "date" and "department" as attribute strings in the access control policies. Consequently, based on consortium blockchain and CP-ABE, we propose a patient-centered XDS and a dynamic consent framework. Compared with previous related literature, we make the proposed framework consistent with current practices and achieve favorable criteria, such as data confidentiality, data recoverability and time-aware ciphertext. Further, we conduct comprehensive experiments to show the feasibility and practicality.
Category-universal witness discovery with attention mechanism in social network

Discovering witnesses of real-world events in social networks based on posts is of great significance for crime investigation, rumor dispelling and emergency management during disasters. However, a unified modeling method for posts of all event categories is scarce. Towards this end, we propose an advanced category-universal witness discovery model (L-CA). By automatically learning the description patterns of witnesses and non-witnesses for different event categories, such as sentiment polarity, language style, etc., our model can recognize witnesses of all event categories with tweets. The model consists of three modules: feature extraction, category-enhanced tweet representation learning and multilayer perceptron classifier. The first module extracts keywords of each category and tweet textual features using TextRank and long short-term memory network respectively, while the second module aims to learn the category-enhanced tweet representation through twice attention calculations, one for category feature learning over keywords and another for tweet representation learning over the concatenation of category features and tweet textual features, and passed into the third module for classification. We construct a dataset with 3205 witnesses and 8455 non-witnesses through Twitter API, covering 14 event categories. A wealth of comparative experiments with advanced methods and model variants are performed on it. The results demonstrate that our model outperforms the state-of-the-art method on average by 9.6%, 2.3% and 2.5% in accuracy on CF_VF, HC and our dataset, respectively. Moreover, the performance of various deep neural architectures in witness discovery is analyzed in this paper.
Impact of aerosols on photovoltaic energy production using a spectrally resolved model chain: Case study of southern West Africa

The potential for photovoltaic (PV) power generation in the sun rich countries of West Africa is vast, and could play a vital role in increasing the region's electricity supply. In this context it is important to study the effect of atmospheric aerosols on solar irradiance and PV power. Based on an analysis of the aerosol optical properties for Cotonou, Benin from the DACCIWA measurement campaign, we investigate the impact of aerosols on PV power for polycrystalline silicon and amorphous silicon technology using a spectrally resolved model chain. The model considers both spectral effects on global irradiance due to different aerosol properties as well as the spectral response of different PV technologies. First, we study the contribution of various aerosol types (mineral dust, biomass burning and anthropogenic pollution) that were classified based on the DACCIWA data analysis. Subsequently, we present differences between these imaginary aerosol scenarios and the real case of a biomass burning outbreak on July 13, 2016 in Benin. The results show that aerosol emissions due to the biomass outbreak on the day of the case study in Cotonou lead to solar flux losses of up to 55%, which correspond to a reduction in photovoltaic power of up to 81% for the polycrystalline cell and 78% for the amorphous cell. The relative impact of aerosols differs depending on aerosol type and concentration, and is larger for high solar zenith angles than at noon. For the situation studied in Cotonou, Benin, we are able to show that the inclusion of spectral aspects makes a significant difference when calculating the PV power. Comparing the effects of aerosols on the photovoltaic power between the two technologies, we find that during the morning and evening hours, when there is more diffuse irradiance, the amorphous cell suffers a greater reduction in power (36%) than the polycrystalline cell (27%). Conversely, in the middle of the day, we observe a greater PV power reduction of 12% for the polycrystalline cell compared to that for the amorphous cell (8%).
Comparing Bayesian and Frequentist Models of Language Variation

This chapter compares standard frequentist and more recent Bayesian approaches to logistic regression analyses. Starting out from a multifactorial case study of the verb help complemented by either the bare infinitive or the to-infinitive, the key components and the main conceptual differences of frequentist and Bayesian inference are discussed. Conceptually, the Bayesian rationale of directly testing hypotheses on the effects of multiple factors on an outcome variable is argued to be preferable and more sensitive than the conventional approach of testing null hypotheses. On the practical side, Bayesian statistics enables the researcher to recycle and integrate the results of previous analyses based on different datasets as informative priors, which can help improve and stabilize statistical modelling. Recourse to prior research can thus produce synergies and reduce data preparation expense. In cases of data sparsity, it can by the same token enable researchers to analyse small samples. Bayesian methods are thus put forward as powerful tools for overcoming the limitations of isolated corpus studies and for promoting synergies between data collected by individual researchers.
Learn multi-step object sorting tasks through deep reinforcement learning

Abstract Robotic systems are usually controlled to repetitively perform specific actions for manufacturing tasks. The traditional control methods are domain-dependent and model-dependent with cost of much human efforts. They cannot meet the new requirements of generality and flexibility in many areas such as intelligent manufacturing and customized production. This paper develops a general model-free approach to enable robots to perform multi-step object sorting tasks through deep reinforcement learning. Taking projected heightmap images from different time steps as input without extra high-level image analysis and understanding, critic models are designed to produce a pixel-wise Q value map for each type of action. It is a new trial to apply pixel-wise Q value-based critic networks to solve multi-step sorting tasks that involve many types of actions and complex action constraints. The experimental validations on simulated and realistic object sorting tasks demonstrate the effectiveness of the proposed approach. Qualitative results (videos), code for simulated and realistic experiments, and pre-trained models are available at https://github.com/JiatongBao/DRLSorting
Online Spam Review Detection: A Survey of Literature

Abstract The increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers’ purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants’ profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research.