Reviewerly/exHarmony huggingface dataset:
Adversarial Reinforcement Learning for Procedural Content Generation

Training RL agents to solve novel environments is a notoriously difficult task. Here we present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable. The procedurally generated environments induces state diversity which increases the generalizability of the trained agents. ARLPCG deploys an adversarial model with one PCG RL agent (called Generator) and one solving RL agent (called Solver). The Generator receives a reward signal based on the Solver's performance, which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose using auxiliary inputs for the Generator. The benefit is two-fold: Firstly, the Solver achieves better generalization through the Generator's generated challenges. Secondly, the trained Generator can be used as a creator of novel environments that, together with the Solver, can be shown to be solvable. We create two types of 3D environments to validate our model, representing two popular game genres: a third-person platformer and a racing game. In these cases, we show that ARLPCG has a significantly better solve ratio, and that the auxiliary inputs renders the levels creation controllable to a certain degree. For a video compilation of the results please visit https://youtu.be/z7q2PtVsT0I.
First person video summarization using different graph representations

First-person video summarization has emerged as an important research problem for computer vision and multimedia communities. In this paper, we show how different graph representations can be developed for accurately summarizing first-person (egocentric) videos in a computationally efficient manner. Each frame in a video is first represented as a weighted graph. A shot boundary detection method using graph based mutual information is developed. We next construct a weighted graph for each shot. A representative frame from each shot is selected using a graph centrality measure. A new way of characterizing egocentric video frames using a graph based center-surround model is shown next. Here, each representative frame is modeled as a union of a center region (graph) and a surround region (graph). By exploiting spectral measures of dissimilarity between the two (center and surround) graphs, optimal center and surround regions are determined. Optimal regions for all frames within a shot are kept the same as that of the representative frame. Center-surround differences in entropy and optical flow values along with PHOG (Pyramidal HOG) features are extracted from each frame. All frames in a video are finally represented by another weighted graph, termed as a Video Similarity Graph (VSG). The frames are clustered by applying a Minimum Spanning Tree (MST) based approach with a new measure for inadmissible edges. Frames closest to the centroid of each cluster are captured to build the summary. Experimental evaluation on two benchmark datasets indicate the advantage of the proposed formulation.
Locally Weighted Principal Component Analysis-Based Multimode Modeling for Complex Distributed Parameter Systems

Global principal component analysis (PCA) has been successfully introduced for modeling distributed parameter systems (DPSs). In spite of the merits, this method is not feasible due to parameter variations and multiple operating domains. A novel multimode spatiotemporal modeling method based on the locally weighted PCA (LW-PCA) method is developed for large-scale highly nonlinear DPSs with parameter variations, by separating the original dataset into tractable subsets. This method implements the decomposition by making full use of the dependence among subset densities. First, the spatiotemporal snapshots are divided into multiple different Gaussian components by using a finite Gaussian mixture model (FGMM). Once the components are derived, a Bayesian inference strategy is then applied to calculate the posterior probabilities of each spatiotemporal snapshot belonging to each component, which will be regarded as the local weights of the LW-PCA method. Second, LW-PCA is adopted to calculate each locally weighted snapshot matrix, and the corresponding local spatial basis functions (SBFs) can be generated by the PCA method. Third, all the local temporal models are estimated using the extreme learning machine (ELM). Thus, the local spatiotemporal models can be produced with local SBFs and corresponding temporal model. Finally, the original system can be approximated using the sum form of each local spatiotemporal model. Unlike global PCA, which uses global SBFs to construct a global spatiotemporal model, LW-PCA approximates the original system by multiple local reduced SBFs. Numerical simulations verify the effectiveness of the developed multimode spatiotemporal model.
HCANet: A Hierarchical Context Aggregation Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Many practical applications of high-resolution remote sensing images (HRRSIs) are based on semantic segmentation. However, due to the complex ground object information contained in remote sensing images, it is difficult to make precise semantic segmentation of HRRSIs. In this letter, we proposed a hierarchical context aggregation network (HCANet) for the semantic segmentation of HRRSIs. The HCANet has an encoder-decoder structure which is similar to UNet. In the HCANet, we designed two Compact Atrous Spatial Pyramid Pooling (CASPP and CASPP+) modules. The CASPP modules replace the copy and crop operation in UNet to extract the multiscale context information of the multisemantic features of ResNet. The CASPP+ module is embedded in the middle layer of HCANet’s decoder to provide a strong aggregation path of contextual information. In the decoder of HCANet, the multiscale context information obtained by CASPP modules is hierarchically merged layer by layer for the semantic segmentation of HRRSIs. We compared our method with several of the most advanced methods on the ISPRS Vaihingen and Potsdam data sets. The final results demonstrate that our method can achieve outstanding performance.
Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments

Reinforcement learning (RL) based methods are an upcoming approach for the control of power systems such as electric drives. These data-driven techniques do not need an explicit plant model like most common state-of-the-art approaches. Instead, the control policy is continuously improved solely based on measurement feedback pursuing optimal control performance through learning. While the general feasibility of RL-based drive control algorithms has already been proven in simulation, this work focuses on transferring the methodology to real-world experiments. In the case of electric motor control, a strict real-time requirement, safety constraints, system delays and the limitations of embedded hardware frameworks are hurdles to overcome. Hence, several modifications to the general RL training setup are introduced in order to enable RL in real-world electric drive control problems. In particular, a rapid control prototyping toolchain is introduced allowing fast and flexible testing of arbitrary RL algorithms. This simulation-to-experiment pipeline is considered an important intermediate step towards introducing RL in embedded control for power electronic systems. To highlight the potential of RL-based drive control, extensive experimental investigations addressing the current control of a permanent magnet synchronous motor utilizing a deep deterministic policy gradient algorithm have been conducted. Despite the early state of research in this domain, promising control performance could be achieved.
Named entity recognition for extracting concept in ontology building on Indonesian language using end-to-end bidirectional long short term memory

Information Extraction has been widely used to extract information from text. Named Entity Recognition (NER) is one of the primary tasks of Information Extraction to extract entities such as person, location, and organization. Extraction from text collection is essential to obtain information from unstructured text. Moreover, Named Entity Recognition is part of ontology building, which is the main objective of this research. Ontology can be built on the basis of a collection of concepts and relation between concepts. Concepts in ontology usually consist of a group of entities and are obtained using Noun Phrase Extraction or Named Entity Recognition. Our main focus in this research is to extract concepts in Ontology Building automatically using Named Entity Recognition. In this paper, Named Entity Recognition was chosen as our approach due to the lack of results from the previous Noun Phrase Extraction works, which is not all nouns obtained are entities. Our proposed methodology for Named Entity Recognition is applying an end-to-end model using Bidirectional Long Short Term Memory (Bi-LSTM). Bi-LSTM is able to perform a sequence classification task by understanding the context of the input. Named Entity Recognition approaches in the previous study uses Part-of-Speech (POS) Tagging in the preprocessing phase by using other tools or models. This Part-of Speech is also used as a feature to improve the performance of Named Entity Recognition. Our proposed methodology provides an end-to-end system that can be used for both POS Tagging and Named Entity Recognition. By using our proposed end-to-end model, no additional tool is needed for Part-of-Speech Tagging. This the advantage of our model compared to other models. Experiments were conducted on news documents that were labeled with four types of entity classes and 35 types of part-of-speech. The target entities that we have extracted in this study are person, location, organization, and miscellaneous. We evaluated the performance of our model using F1-Score. We have achieved the best F1-Score for Part-of-Speech Tagging of 91.79% and Named Entity Recognition of 83.18%.
Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Abstract The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.
CP-PSIS: CRT and polynomial-based progressive secret image sharing

In (k, n) Progressive Secret Image Sharing (PSIS) schemes, n shares are obtained by sharing a secret image. With k to n shares, the secret image can be recovered progressively. No information of the secret image can be constructed by k-1 or fewer shares. Many Secret Image Sharing (SIS) schemes have been developed to achieve the progressive reconstruction property. However, such property is usually based on the pre-processing of the secret image which is inconvenient and would increase the cost of the algorithm. Moreover, in existing PSIS schemes, only if all of n shares are involved, the secret image can be completely reconstructed. That makes the schemes not fault-tolerant. Thus, a solution for the flexible and extendable PSIS scheme is required. In this paper, we propose a Chinese Remainder Theorem (CRT) and Polynomial-based Progressive Secret Image Sharing (CP-PSIS) scheme and a modified version of CP-PSIS scheme. The proposed schemes are the first to utilize the homomorphism of CRT in the Lagrange interpolation operation. The progressive property of the schemes is achieved by CRT. In the (k, n) threshold SIS, any k participants can obtain different versions of the secret image by choosing different parameters. Our proposed schemes achieve a high security based on CRT and polynomial. Theoretical analyses and experimental results have demonstrated that the proposed schemes are general, flexible and extendable for different PSIS applications.
A Regularized Model to Trade-off between Accuracy and Diversity in a News Recommender System

News recommender systems are usually designed to provide accurate and personalized recommendations to the readers. The diversity of the recommended results has received much less attention in this field. When it is considered, the current state-of-the-art models often apply the re-ranking mechanisms to promote the diversified results to the individual users. In this work, we propose a latent factor model to achieve the requisite level of accuracy while maintaining a reasonable level of diversity in a news recommender system. The existing latent factor methods mostly rely on Tikhonov regularization to improve the generality of the learnt models. These methods tend to focus mainly on accuracy measures, i.e., generating recommendations highly aligned with a user's past preference, which may cause a decrease in the diversity of information to which news readers are exposed. In our work, we make effective use of elastic-net regression to regularize the model for both the accuracy and the diversity in a single optimization framework. We demonstrate the effectiveness of our model over the state-of-the-art methods by conducting extensive experiments on a real-world news dataset.
A Hindi Image Caption Generation Framework Using Deep Learning

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .
A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

The way politicians communicate with the electorate and run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SNs). Due to the inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using the SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial and many times challenged. In this context, this article aims to investigate and summarize how research on predicting elections based on the SM data has evolved since its beginning, to outline the state of both the art and the practice, and to identify research opportunities within this field. In terms of method, we performed a systematic literature review analyzing the quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation, and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances in process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-the-art machine learning approaches.
Deep monocular depth estimation leveraging a large-scale outdoor stereo dataset

• A novel framework for monocular depth estimation via a student–teacher strategy. • Introducing a data ensemble and stereo confidence-guided regression loss. • Constructing a new large-scale outdoor stereo dataset named the DIML/CVL dataset. • Demonstrating the feature representation of our trained-model for high-level tasks. Current self-supervised methods for monocular depth estimation are largely based on deeply nested convolutional networks that leverage stereo image pairs or monocular sequences during the training phase. However, they often exhibit inaccurate results around occluded regions and depth boundaries. In this paper, we present a simple yet effective approach for monocular depth estimation using stereo image pairs. The study aims to propose a student–teacher strategy in which a shallow student network is trained with the auxiliary information obtained from a deeper and more accurate teacher network. Specifically, we first train the stereo teacher network by fully utilizing the binocular perception of 3-D geometry, and then use the depth predictions of the teacher network to train the student network for monocular depth inference. This enables us to exploit all available depth data from massive unlabeled stereo pairs. We propose a strategy that involves the use of a data ensemble to merge the multiple depth predictions of the teacher network to improve the training samples by collecting non-trivial knowledge beyond a single prediction. To refine the inaccurate depth estimation that is used when training the student network, we further propose stereo confidence guided regression loss that handles the unreliable pseudo depth values in occlusion, texture-less region, and repetitive pattern. To complement the existing dataset comprising outdoor driving scenes, we built a novel large-scale dataset consisting of one million outdoor stereo images taken using hand-held stereo cameras. Finally, we demonstrate that the monocular depth estimation network provides feature representations that are suitable for high-level vision tasks. The experimental results for various outdoor scenarios demonstrate the effectiveness and flexibility of our approach, which outperforms state-of-the-art approaches.
The ICT usage in teaching mathematics to students with special educational needs

Abstract In the article, authors have considered the implication of ICT into mathematics educational strategies in the inclusive class of secondary school. The research includes also a presentation of a teacher’s tool, prepared by an author group, on the use of ICT in teaching planimetry and stochastics of children with special educational needs (SEN) in secondary schools. The research has introduced the model of implication ICT and the influence of effective strategies and methods of inclusive education on the math studying process by children with hearing disorder. The possibilities of using the method of educational projects as one of socialization directions are described. The efficiency of the distance learning was discovered that facilitates the self-representation of students with SEN.
Efficient face detection and tracking in video sequences based on deep learning

Video-based face detection and tracking technology has been widely used in video surveillance, safe driving, and medical diagnosis. In video sequences, most existing face detection and tracking methods face interference caused by occlusion, ambient illumination, and changes in human posture. To accurately track human faces in video sequences, we propose an efficient face detection and tracking framework based on deep learning, which includes a SENResNet face detection model and a Regression Network-based Face Tracking (RNFT) model. Firstly, the SENResNet model integrates the Squeeze and Excitation Network (SEN) with the Residual Neural Network (ResNet). To solve the problem that deep neural networks are difficult to train, we use ResNet to overcome the problem of gradient disappearance in deep network training. To fuse the features of each channel during the convolution operation, we further integrate the SEN module into the SENResNet model. SENResNet accurately detects facial information in each frame and extracts the position of the target face, thereby providing an initialization window for face tracking. Then, the RNFT model extracts facial features from adjacent frames and predict the position of the target face in the next frame. To address the problem of feature scaling, we add a correction network to the RNFT model. The improved RNFT model extracts the rectangular frame of the target face in the previous frame and strengthens the perception of feature scaling, thereby improving its accuracy. Extensive experimental results on public facial and video datasets show that the proposed SENResNet and RNFT models are superior to the state-of-the-art comparison methods in terms of accuracy and performance.
Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview

Privacy has gained a growing interest nowadays due to the increasing and unmanageable amount of produced confidential data. Concerns about the possibility of sharing data with third parties to gain fruitful insights beset enterprise environments; value not only resides in data, but also in the intellectual property of algorithms and models that offer analysis results. This impasse locks both the availability of highperformance computing resources in the "as-a-service" paradigm and the exchange of knowledge with the scientific community in a collaborative view. Privacy-preserving data science enables the use of private data and algorithms without putting their privacy at risk. Conventional encryption schemes are not able to work on encrypted data without decrypting them first. Homomorphic Encryption (HE) is a form of encryption that allows the computation on encrypted data while preserving the features and the format of the plaintext. Against the background of interesting use cases for the Central Bank of Italy, this article focuses on how HE and data science can be leveraged to design and develop privacy-preserving enterprise applications. We propose a survey of main Homomorphic Encryption techniques and recent advances in the conubium between data science and HE.