Reviewerly/exHarmony huggingface dataset:
An Efficient Chicken Search Optimization Algorithm for the Optimal Design of Fuzzy Controllers

A proposed architecture to design the optimal parameters of Membership Functions (MFs) of Type-1 Fuzzy Logic Systems (T1FLSs) using the Chicken Search Optimization (CSO) is applied to three Fuzzy Logic Controllers (FLCs) in this paper. Two types of MFs are considered in the study: triangular and trapezoidal ones. The performance and efficiency of the CSO algorithm are particularly good when perturbations are added during the execution in each control problem. Two benchmark control problems: Water Tank Controller and Inverted Pendulum Controller are considered for testing the proposed approach. Also, the optimal design of a fuzzy controller for trajectory tracking of an Autonomous Mobile Robot (AMR) is considered to test the CSO. The main goal is to highlight the efficiency of CSO algorithm in finding optimal fuzzy controllers of non-linear plants. Two types of perturbations are considered in each control problem. Results show that the CSO algorithm presents excellent results in the field of Fuzzy Logic Controllers. Two types of Fuzzy Inference Systems: Takagi-Sugeno and Mamdani FLSs, are implemented in this paper. The most important metrics usually applied in control are used in this paper, such as: Integral Time Absolute Error (ITAE), Integral Time Squared Error (ITSE), Integral Absolute Error (IAE), Integral Square Error (ISE), Mean Square Error (MSE), and Root Mean Square Error (RMSE).
Opinion Mining on US Airline Twitter Data Using Machine Learning Techniques

The Airline sector is an important field nowadays in the market. In order to keep that sector alive and up to date we have to consider opinion mining. Text sentiment analysis is a Natural Language Processing (NLP) technique to analyze text. In this research, we will use opinion mining one of the text sentiment applications to investigate customer feedback about airline services. One of the largest opinion mining sources is Twitter which contains a huge number of tweets that needs to be processed and analyzed to make a decision and enhance a certain service. In this research, we proposed a machine learning model to categorize Twitter posts into positive, negative and neutral categories. We implemented our model on a dataset containing tweets of 6 different Airlines in the US. We started our model by preprocessing steps where we cleaned tweets and extracted features to represent them as a feature vector and finally, we built our Bag of Words (BoW) model. In the classification phase, we applied 6 machine learning techniques Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XgBoost (XGB), Naïve Bayes (NB) and Decision Tree (DT) to classify tweets. Finally, in the validation phase, we split data into 70% training and 30% testing, for the purpose of testing and validating the data we used the K-Fold Cross-Validation technique. Finally, we calculated Accuracy, Precision, Recall and F1-score for each classifier. After comparing the results of each classifier, we found that SVM had the highest accuracy of 83.31 %.
Monocular Depth Estimation With Improved Long-Range Accuracy for UAV Environment Perception

Environment perception by computing the depth is a key task for unmanned aerial vehicle (UAV) type systems. Due to the limited load they can carry, most drones are equipped with a single camera. This prevents general-purpose depth perception methods based either on light detection and ranging (LiDAR) or stereo reconstruction to be effectively used on such platforms. Due to the success of convolutional neural networks (CNNs), monocular depth estimation (MDE) methods have become more and more trustworthy, so their usage on drones is convenient. However, very few such methods have been proposed in the literature, mainly due to the few existing constraints and high diversity that unstructured aerial environments pose. To bridge this gap, we propose a novel approach for MDE, capable to work on aerial images. The method initially proposes an original CNN, particularly adapted to such scenarios. This is done by finding an optimal feature extractor, introducing a new scene understanding module, a new loss and a novel softmax transformation layer that facilitate a better convergence. Furthermore, since both short- and long-range accuracy is required for a robust UAV perception, we introduce a learning-based correction method that redistributes the depth points across the entire depth interval. The proposed CNN gives accurate results, while the additional refinement further improves the accuracy with only a few additional computational resources (around 1–2 ms). We initially show the capabilities of our method on synthetic images captured in unstructured aerial scenarios. Then, we prove that our method can work in real-life situations, computing depth from a single image (at multiple pitch angles) captured by a drone flying in a series of field and forest-like environments. In all these situations, the depth is densely estimated with increased accuracy and reliability.
Global context aware RCNN for object detection

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, we propose a novel end-to-end trainable framework, called global context aware (GCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to improve the information flow of the global context at different stages in the top-down process of FPN, and further use the attention mechanism to refine the global context at each level in the feature pyramid. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach.
Ontology-based knowledge management with verbal interaction for command interpretation and execution by home service robots

This paper describes a system for service robots that combines ontological knowledge reasoning and human–robot interaction to interpret natural language commands and successfully perform household chores, such as finding and delivering objects. Knowledge and context reasoning is essential for providing more efficient service robots, given their diverse and continuously changing environments. Moreover, since they are in contact with humans, robots require such skills as interaction and language. Therefore, we developed a system with specific modules to manage robots' knowledge and reasoning, command analysis, decision-making, and talking interaction. The system relies on inference methods and verbal interaction to understand commands and clarify uncertain information. We tested our system inside a simulated environment where the robot receives commands with missing or unclear information. The system's performance was compared with the average performance of human subjects who completed the same commands in the simulation.
Deep Relighting Networks for Image Light Source Manipulation

Manipulating the light source of given images is an interesting task and useful in various applications, including photography and cinematography. Existing methods usually require additional information like the geometric structure of the scene, which may not be available for most images. In this paper, we formulate the single image relighting task and propose a novel Deep Relighting Network (DRN) with three parts: 1) scene reconversion, which aims to reveal the primary scene structure through a deep auto-encoder network, 2) shadow prior estimation, to predict light effect from the new light direction through adversarial learning, and 3) re-renderer, to combine the primary structure with the reconstructed shadow view to form the required estimation under the target light source. Experimental results show that the proposed method outperforms other possible methods, both qualitatively and quantitatively. Specifically, the proposed DRN has achieved the best PSNR in the "AIM2020 - Any to one relighting challenge" of the 2020 ECCV conference.
An Adaptive Framework for Learning Unsupervised Depth Completion

We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for determining co-visibility (occlusions, disocclusions) and the degree of regularization to impose on the model. We show that regularization and co-visibility are related via the fitness (residual) of model to data and both can be unified into a single framework to improve the learning process. Our method is an adaptive weighting scheme that guides optimization by measuring the residual at each pixel location over each training step for (i) estimating a soft visibility mask and (ii) determining the amount of regularization. We demonstrate the effectiveness our method by applying it to several recent unsupervised depth completion methods and improving their performance on public benchmark datasets, without incurring additional trainable parameters or increase in inference time.
Accelerating Deep Neural Networks implementation: A survey

Recently, Deep Learning (DL) applications are getting more and more involved in different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a challenging task considering the massive requirement of computation and storage. Given that the number of operations and parameters increases with the complexity of the model architecture, the performance will strongly depend on the hardware target resources and basically the memory footprint of the accelerator. Recent research studies have discussed the benefit of implementing some complex DL applications based on different models and platforms. However, it is necessary to guarantee the best performance when designing hardware accelerators for DL applications to run at full speed, despite the constraints of low power, high accuracy and throughput. Field Programmable Gate Arrays (FPGAs) are promising platforms for the deployment of large-scale DNN which seek to reach a balance between the above objectives. Besides, the growing complexity of DL models has made researches think about applying optimization techniques to make them more hardware-friendly. Herein, DL concept is presented. Then, a detailed description of different optimization techniques used in recent research works is explored. Finally, a survey of research works aiming to accelerate the implementation of DNN models on FPGAs is provided.
Towards High-Data-Rate Noncoherent Chaotic Communication: A Multiple-Mode Differential Chaos Shift Keying System

In this paper, we focus on designing a high-data-rate noncoherent chaotic communication scheme to satisfy the demand of the explosive growth in data traffic. To achieve this goal, a joint time-frequency index modulation assisted multiple-mode DCSK (JTFIM-MM-DCSK) system is proposed, where a pair of distinguished signal modes are modulated into the selected subcarriers and another signal mode is transmitted by the unselected subcarriers. In this configuration, besides the modulated bits used for physical transmission, the carrier index bits and time slot index bits, conveyed by the state of whether subcarriers and time slots are selected or not, are also transmitted implicitly to achieve higher data rate. Moreover, the design of multiple-mode signals ensures that it is taking full advantage of all subcarriers and time slots to transmit the information bits, thereby improving the data rate significantly. The bit error rate (BER) expressions of the JTFIM-MM-DCSK system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels, which are checked by computer simulations. Finally, the advantage of the JTFIM-MM-DCSK system over other up-to-date noncoherent chaotic communication systems is demonstrated in terms of BER performance through extensive computer simulations.
A Reference Architecture for Blockchain-Based Crowdsourcing Platforms

Companies increasingly tender knowledge-intensive tasks using crowdsourcing platforms to gain access to scarce knowledge and skills otherwise out of reach, and in this way, gaining competitive advantage. Despite its potential, existing crowdsourcing platforms encounter several challenges, including (1) fragmentation of expertise, as there are many platforms, (2) distrust between task providers and crowdsourcing participants, as identity and past performance are often not known, and (3) inability to learn from experience due to a lack of openness. A reference architecture for blockchain-based knowledge-intensive crowdsourcing platforms to mediate transactions between demand and supply of knowledge is designed in this paper to overcome these challenges. A design science research method is followed to develop the architecture. The reference architecture shows how blockchain and smart contract components can be integrated to support and coordinate knowledge-intensive crowdsourcing activities. By removing traditional e-commerce intermediaries, blockchain reduces search friction, knowledge transfer costs, and cheating by task providers or crowdsourcing participants.
A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions

Automated filtering of toxic conversations may help an Open-source software (OSS) community to maintain healthy interactions among the project participants. Although, several general purpose tools exist to identify toxic contents, those may incorrectly flag some words commonly used in the Software Engineering (SE) context as toxic (e.g., `junk', `kill', and `dump') and vice versa. To encounter this challenge, an SE specific tool has been proposed by the CMU Strudel Lab (referred as the `STRUDEL' hereinafter) by combining the output of the Perspective API with the output from a customized version of the Stanford's Politeness detector tool. However, since STRUDEL's evaluation was very limited with only 654 SE text, its practical applicability is unclear. Therefore, this study aims to empirically evaluate the Strudel tool as well as four state-of-the-art general purpose toxicity detectors on a large scale SE dataset. On this goal, we empirically developed a rubric to manually label toxic SE interactions. Using this rubric, we manually labeled a dataset of 6,533 code review comments and 4,140 Gitter messages. The results of our analyses suggest significant degradation of all tools' performances on our datasets. Those degradations were significantly higher on our dataset of formal SE communication such as code review than on our dataset of informal communication such as Gitter messages. Two of the models from our study showed significant performance improvements during 10-fold cross validations after we retrained those on our SE datasets. Based on our manual investigations of the incorrectly classified text, we have identified several recommendations for developing an SE specific toxicity detector.
Vector representation based on a supervised codebook for Nepali documents classification

Document representation with outlier tokens exacerbates the classification performance due to the uncertain orientation of such tokens. Most existing document representation methods in different languages including Nepali mostly ignore the strategies to filter them out from documents before learning their representations. In this article, we propose a novel document representation method based on a supervised codebook to represent the Nepali documents, where our codebook contains only semantic tokens without outliers. Our codebook is domain-specific as it is based on tokens in a given corpus that have higher similarities with the class labels in the corpus. Our method adopts a simple yet prominent representation method for each word, called probability-based word embedding. To show the efficacy of our method, we evaluate its performance in the document classification task using Support Vector Machine and validate against widely used document representation methods such as Bag of Words, Latent Dirichlet allocation, Long Short-Term Memory, Word2Vec, Bidirectional Encoder Representations from Transformers and so on, using four Nepali text datasets (we denote them shortly as A1, A2, A3 and A4). The experimental results show that our method produces state-of-the-art classification performance (77.46% accuracy on A1, 67.53% accuracy on A2, 80.54% accuracy on A3 and 89.58% accuracy on A4) compared to the widely used existing document representation methods. It yields the best classification accuracy on three datasets (A1, A2 and A3) and a comparable accuracy on the fourth dataset (A4). Furthermore, we introduce the largest Nepali document dataset (A4), called NepaliLinguistic dataset, to the linguistic community.
Latent Discriminative Low-Rank Projection for Visual Dimension Reduction in Green Internet of Things

Internet of Things (IoT) terminals have been widely deployed for data sensing and analysis, and efficient data storage and transmission plays an important role in green IoT due to the explosive data growth. To simultaneously reduce the data dimension and preserves the discriminative intrinsic knowledge of data, this paper develops a novel latent discriminative low-rank projection (LDLRP) method for visual dimension reduction. Specifically, a data self-expressiveness model is established by considering the low-rank and discriminative similarity relations of data. Then, the developed model is efficiently optimized and solved via an augmented Lagrange multiplier (ALM) based-iterative algorithm, and a block-diagonal solution can be found for intraclass and interclass graph construction. Afterwards, a discriminative dimension reduced-subspace is derived by concurrently minimizing the intraclass scatter and maximizing the interclass scatter. The experimental results on benchmark datasets show that the proposed method can learn discriminative lower-dimensional expressions of high-dimensional data, and yield promising classification accuracy compared with several state-of-the-art methods. Hence, the effectiveness and efficiency of proposed method in data dimension reduction and knowledge preservation are verified, which will facilitate efficient data storage, transmission and application in green IoT.
Quantum Receiver for Phase-Shift Keying at the Single-Photon Level

Quantum enhanced receivers are endowed with resources to achieve higher sensitivities than conventional technologies. For application in optical communications, they provide improved discriminatory capabilities for multiple non-orthogonal quantum states. In this work, we propose and experimentally demonstrate a new decoding scheme for quadrature phase-shift encoded signals. Our receiver surpasses the standard quantum limit and outperforms all previously known non-adaptive detectors at low input powers. Unlike existing approaches, the receiver only exploits linear optical elements and on-off photo-detection. This circumvents the requirement for challenging feed-forward operations that limit communication transmission rates and can be readily implemented with current technology.
Conditional Generative Adversarial Networks for Optimal Path Planning

Path planning plays an important role in autonomous robot systems. Effective understanding of the surrounding environment and efficient generation of an optimal collision-free path are both critical parts for solving path-planning problems. Although conventional sampling-based algorithms, such as the rapidly exploring random tree (RRT) and its improved optimal version (RRT*), have been widely used in path-planning problems because of their ability to find a feasible path in even complex environments, they fail to find an optimal path efficiently. To solve this problem and satisfy the two aforementioned requirements, we propose a novel learning-based path-planning algorithm which consists of a novel generative model based on the conditional generative adversarial networks (CGANs) and a modified RRT* algorithm (denoted by CGAN-RRT*). Given the map information, our CGAN model can generate an efficient probability distribution of feasible paths, which can be utilized by the CGAN-RRT* algorithm to find an optimal path with a nonuniform sampling strategy. The CGAN model is trained by learning from ground-truth maps, each of which is generated by putting all the results of executing the RRT algorithm 50 times on one raw map. We demonstrate the efficient performance of this CGAN model by testing it on two groups of maps and comparing the CGAN-RRT* algorithm with the Informed-RRT* algorithm and conventional RRT* algorithm.
Augmenting Transformers with KNN-Based Composite Memory for Dialog

Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augmenting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialog modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge required for knowledgeable but engaging dialog from Wikipedia, images, and human-written dialog utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.
Video action detection by learning graph-based spatio-temporal interactions

Action Detection is a complex task that aims to detect and classify human actions in video clips. Typically, it has been addressed by processing fine-grained features extracted from a video classification backbone. Recently, thanks to the robustness of object and people detectors, a deeper focus has been added on relationship modelling. Following this line, we propose a graph-based framework to learn high-level interactions between people and objects, in both space and time. In our formulation, spatio-temporal relationships are learned through self-attention on a multi-layer graph structure which can connect entities from consecutive clips, thus considering long-range spatial and temporal dependencies. The proposed module is backbone independent by design and does not require end-to-end training. Extensive experiments are conducted on the AVA dataset, where our model demonstrates state-of-the-art results and consistent improvements over baselines built with different backbones. Code is publicly available at https://github.com/aimagelab/STAGE_action_detection.