Reviewerly/exHarmony huggingface dataset:
A comparative study of meta-heuristics for local path planning of a mobile robot

Recent trends in path planning have led to a proliferation of studies that find solutions to the path planning problems in an unknown cluster environment. This study aims to find an optimum impact-free path length for a mobile robot with a multi-objective optimization approach. The multi-objective optimization problem is formulated by using path length and a safety aspect as the two objectives. A hybrid population-based optimization algorithm, i.e. the hybrid particle swarm and chemical reaction optimization (HPCRO) algorithm, has been used to obtain a smooth path for the robot in an unknown environment with circular and/or polygonal obstacles. The results of the HPCRO algorithm are then compared with those of genetic algorithms, chemical reaction optimization and particle swarm optimization. Some statistical tests are performed to illustrate the superiority and potential applicability of the hybrid algorithm. The results of the hybrid algorithm are encouraging in terms of cost function value and computational cost.
Speech to Text Translation enabling Multilingualism

Speech acts as a barrier to communication between two individuals and helps them in expressing their feelings, thoughts, emotions, and ideologies among each other. The process of establishing a communicational interaction between the machine and mankind is known as Natural Language processing. Speech recognition aids in translating the spoken language into text. We have come up with a Speech Recognition model that converts the speech data given by the user as an input into the text format in his desired language. This model is developed by adding Multilingual features to the existent Google Speech Recognition model based on some of the natural language processing principles. The goal of this research is to build a speech recognition model that even facilitates an illiterate person to easily communicate with the computer system in his regional language.
Single Image Reflection Removal with Edge Guidance, Reflection Classifier, and Recurrent Decomposition

Removing undesired reflection from an image captured through a glass window is a notable task in computer vision. In this paper, we propose a novel model with auxiliary techniques to tackle the problem of single image reflection removal. Our model takes a reflection contaminated image as input, and decomposes it into the reflection layer and the transmission layer. In order to ensure quality of the transmission layer, we introduce three auxiliary techniques into our architecture, including the edge guidance, a reflection classifier, and the recurrent decomposition. The contributions and the efficacy of these techniques are investigated and verified in the ablation study. Furthermore, in comparison to the state-of-the-art baselines of reflection removal, both quantitative and qualitative results demonstrate that our proposed method is able to deal with different kinds of images, achieving the best results in average.
Coherent perfect absorption of quantum light

Coherent perfect absorption (CPA) was introduced as a classical optics phenomenon of a standing-wave absorption by a subwavelength film. In this paper, we develop a theory of CPA of quantized standing waves taking account of a subwavelength thickness of the absorber. This approach allows us to merge all known quantum effects of CPA under a single theory and introduce other regimes of CPA including CPA of NOON states with arbitrary number of entangled photons, orthogonally squeezed vacuum states, continuous variable entangled states, and Schr\"odinger cat states. Detailed analysis of the quantum regime of CPA sheds light on fundamental aspects of quantum light dissipation and its practical implementation in quantum optics and quantum information.
Estimation of Hourly Global Solar Radiation Using Deep Learning Algorithms

Lately, the environmental impacts of fossil energy have forced many countries to begin advocating for renewable energy sources (RES). Solar energy is one of the RES that has gained worldwide acceptability because it is cheap and available. However, solar power is characteristically unpredictable. Accurate prediction of global solar radiation (GSR) continues to be a great need both in the field of physical simulations and artificial intelligence. Both statistical and machine learning methods have proved to be very useful, but researchers are still working on algorithms that can give the least possible error. In this paper, four deep learning algorithms were used to successfully to estimate GSR. Historical meteorological data for Johannesburg city were used as the dataset. The dataset was split into training and testing data. Results obtained showed that Convolutional Long Short-Term Memory (ConvLSTM) with nRMSE value of 1.62% performed better than the other models. Other models used were convolutional neural network (CNN), Long Short-Term Memory Network (LSTM), and a hybrid of CNN and LSTM known as CNN-LSTM network. Real-time implementation of these results to solar plant operations in South Africa could go long to generate a stable electric power for the smart grid.
Improvement of the Interaction Model Aimed to Reduce the Negative Effects of Cybersickness in VR Rehab Applications

Virtual reality (VR) has the potential to be applied in many fields, including medicine, education, scientific research. The e-health impact of VR on medical therapy for people cannot be ignored, but participants reported problems using them, as the capabilities and limitations of users can greatly affect the effectiveness and usability of the VR in rehabilitation. Previous studies of VR have focused on the development and use of the technology itself, and it is only in recent years that emphasis has been placed on usability problems that include the human factor. In this research, different ways of adapting interaction in VR were tested. One approach was focused on means of navigating through a VR, while the second dealt with the impact of the amount of animation and moving elements through a series of tests. In conclusion, the way of navigation and the amount of animation and moving elements, as well as their combination, are proven to have a great influence on the use of VR systems for rehabilitation. There is a possibility to reduce the occurrence of problems related to cybersickness if the results of this research are taken into consideration and applied from an early stage of designing VR rehabilitation applications.
A classification of Broken Hill-type deposits: A critical review

Broken Hill Hill-type Pb-Zn-Ag (BHT) deposits constitute some of the largest and highest grade ore deposits in the world. The Broken Hill deposit, Australia, is the largest accumulation of Pb, Zn, and Ag on Earth and the Cannington deposit is among the largest silver deposits. Characteristic features of BHT deposits include: 1. High Pb + Zn + Ag values; 2. Metamorphism from upper greenschist to granulite facies; 3. Sulfides in Paleoprotorozoic oxidized clastic metasedimentary host rocks in the absence of metamorphosed carbonates/calcsilicate horizons; 4. Sulfides that are spatially associated with bimodal (felsic and mafic) intrusive and volcanic rocks, and stratabound gahnite- and garnet-bearing rocks and iron formations, 5. Stacked orebodies with characteristic Pb:Zn:Ag ratios and skarn-like (generally pyroxenoid dominated) Fe-Mn-Ca-F gangue assemblages, and the presence of Cu, Au, Bi, As, and Sb; 6. Sulfur-poor assemblages, 7. A spatial relationship to continental rifts. 8. A spatial association with alteration zones despite previous proposals to the contrary, and 9. Sulfur isotope compositions of sulfides generally centered around zero per mil. Previous classification schemes of BHT deposits have included deposits that are hosted in marbles/calc-silicates, however this is inappropriate because the type example of this class of deposit (Broken Hill) is not hosted in marble or calc-silicate rocks. Multiple deposits have previously been proposed in the literature as BHT deposits but of the larger deposits (i.e., ~2 Mt or greater), only deposits in the Diamantina Orogen in eastern Australia, which includes those in the three major Proterozoic provinces (Curnamona, Georgetown Inlier, Eastern Succession of the Mt. Isa Inler) are considered to be BHT deposits (Broken Hill, Cannington, Mount Misery (Chloe and Jackson), Pegmont, and Pinnacles). Within the Curnamona Province, which hosts hundreds of minor BHT deposits, there are some occurrences that resemble BHT deposits but can be sub-classified into Parnell- and Corruga-types. Several deposits that have previously been referred to as BHT deposits are instead metamorphosed clastic sedimentary exhalative (SEDEX), marble/calc-silicate-hosted SEDEX, Bergslagen-type bedded, stratiform Zn-Pb-Ag rich mineralization hosted in rhyolitic ash-siltstone (SAS-type)/stratabound volcanic-associated, limestone-skarn (SVALS-type), or are transitional deposits between BHT and these metamorphosed classes of deposits. Although the non-sulfide zinc deposits at Franklin and Sterling Hill, New Jersey, have been considered in the past as possible non-sulfide versions of BHT deposits they do have some affinities to BHT deposits but are essentially Pb-, Ag- and S-free carbonate-hosted SEDEX deposits. In addition, the Menninnie Pb-Zn deposit, South Australia, is not a BHT deposit since mineralization formed post-peak metamorphism.
Webometrics: evolution of social media presence of universities

This paper aims at an important task of computing the webometrics university ranking and investigating if there exists a correlation between webometrics university ranking and the rankings provided by the world prominent university rankers such as QS world university ranking, for the time period of 2005–2016. However, the webometrics portal provides the required data for the recent years only, starting from 2012, which is insufficient for such an investigation. The rest of the required data can be obtained from the internet archive. However, the existing data extraction tools are incapable of extracting the required data from internet archive, due to unusual link structure that consists of web archive link, year, date, and target links. We developed an internet archive scrapper and extract the required data, for the time period of 2012–2016. After extracting the data, the webometrics indicators were quantified, and the universities were ranked accordingly. We used correlation coefficient to identify the relationship between webometrics university ranking computed by us and the original webometrics university ranking, using the spearman and pearson correlation measures. Our findings indicate a strong correlation between ours and the webometrics university rankings, which proves that the applied methodology can be used to compute the webometrics university ranking of those years for which the ranking is not available, i.e., from 2005 to 2011. We compute the webometrics ranking of the top 30 universities of North America, Europe and Asia for the time period of 2005–2016. Our findings indicate a positive correlation for North American and European universities, but weak correlation for Asian universities. This can be explained by the fact that Asian universities did not pay much attention to their websites as compared to the North American and European universities. The overall results reveal the fact that North American and European universities are higher in rank as compared to Asian universities. To the best of our knowledge, such an investigation has been executed for the very first time by us and no recorded work resembling this has been done before.
Geometry-Based Camera Calibration Using Closed-Form Solution of Principal Line

Camera calibration is a crucial prerequisite in many applications of computer vision. In this paper, a new geometry-based camera calibration technique is proposed, which resolves two main issues associated with the widely used Zhang's method: (i) the lack of guidelines to avoid outliers in the computation and (ii) the assumption of fixed camera focal length. The proposed approach is based on the closed-form solution of principal lines with their intersection being the principal point while each principal line can concisely represent relative orientation/position (up to one degree of freedom for both) between a special pair of coordinate systems of image plane and calibration pattern. With such analytically tractable image features, computations associated with the calibration are greatly simplified, while the guidelines in (i) can be established intuitively. Experimental results for synthetic and real data show that the proposed approach does compare favorably with Zhang's method, in terms of correctness, robustness, and flexibility, and addresses issues (i) and (ii) satisfactorily.
A Multi-Sensor Information Fusion Method Based on Factor Graph for Integrated Navigation System

The current navigation systems used in many autonomous mobile robotic applications, like unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The key point is to fuse the information from different sensors efficiently. However, different sensors provide asynchronous measurements, some of which even appear to be nonlinear. Moreover, some sensors are vulnerable in specific environments, e.g., GPS signal is likely to work poorly in interior space, underground, and tall buildings. We propose a multi-sensor information fusion method based on a factor graph to fuse all available asynchronous sensor information and efficiently and accurately calculate a navigation solution. Assuming the sensor measurements and navigation states in a navigation system as factor nodes and variable nodes in a factor graph, respectively, the update of the states can be implemented in the framework of the factor graph. The proposed method is experimentally validated using two different datasets. A comparison with Federated Filter, which has been widely used in integrated navigation systems, demonstrates the proposed method’s effectiveness. Additionally, analyzing the navigation results with data loss verifies that the proposed method could achieve sensor plug and play in software.
Pattern Classification With Corrupted Labeling via Robust Broad Learning System

Most of the existing classification systems assume that the data used is high-quality labeled. However, the labeling process in real-world may inevitably introduce corruptions into labels which can confuse the performances of classifiers. In this paper, based on Broad Learning System (BLS), we propose a novel label noise tolerant method to classify the pattern with corrupted labels. The standard BLS has shown promising efficiency and accuracy in general classification, but its learning process is prone to be affected by the noisy labels. Here, by detailed probabilistic analysis, we first give the reason for lacks of robustness in standard BLS. Then a maximum likelihood estimation-based objective function is derived for robust classification. In addition, a manifold regularization term is integrated to preserve the local geometry of data, which makes the model to be more robust and flexible to learn the output weights. Given some basic assumptions on the approximation errors, the obtained model can be transformed to a graph regularized reweighted BLS problem. The negative effects of noisy labels in data can be inhibited adaptively by assigning reasonable weights. Theoretical analysis and extensive experiments are provided to demonstrate the robustness and effectiveness of the proposed robust BLS model, especially for the case of large amounts of noisy labels.
High-accuracy phase demodulation method compatible to closed fringes in a single-frame interferogram based on deep learning

Interferogram demodulation is a fundamental problem in optical interferometry. It is still challenging to obtain high-accuracy phases from a single-frame interferogram that contains closed fringes. In this paper, we propose a neural network architecture for single-frame interferogram demodulation. Furthermore, instead of using real experimental data, an interferogram generation model is constructed to generate the dataset for the network's training. A four-stage training strategy adopting appropriate optimizers and loss functions is developed to guarantee the high-accuracy training of the network. The experimental results indicate that the proposed method can achieve a phase demodulation accuracy of 0.01 λ (root mean square error) for actual interferograms containing closed fringes.
A Log-Rectilinear Transformation for Foveated 360-degree Video Streaming

With the rapidly increasing resolutions of 360° cameras, head-mounted displays, and live-streaming services, streaming high-resolution panoramic videos over limited-bandwidth networks is becoming a critical challenge. Foveated video streaming can address this rising challenge in the context of eye-tracking-equipped virtual reality head-mounted displays. However, conventional log-polar foveated rendering suffers from a number of visual artifacts such as aliasing and flickering. In this paper, we introduce a new log-rectilinear transformation that incorporates summed-area table filtering and off-the-shelf video codecs to enable foveated streaming of 360° videos suitable for VR headsets with built-in eye-tracking. To validate our approach, we build a client-server system prototype for streaming 360° videos which leverages parallel algorithms over real-time video transcoding. We conduct quantitative experiments on an existing 360° video dataset and observe that the log-rectilinear transformation paired with summed-area table filtering heavily reduces flickering compared to log-polar subsampling while also yielding an additional 10% reduction in bandwidth usage.
Open Set Driver Activity Recognition

A common obstacle for applying computer vision models inside the vehicle cabin is the dynamic nature of the surrounding environment, as unforeseen situations may occur at any time. Driver monitoring has been widely researched in the context of closed set recognition i.e. under the premise that all categories are known a priori. Such restrictions represent a significant bottleneck in real-life, as the driver observation models are intended to handle the uncertainty of an open world. In this work, we aim to introduce the concept of open sets to the area of driver observation, where methods have been evaluated only on a static set of classes in the past. First, we formulate the problem of open set recognition for driver monitoring, where a model is intended to identify behaviors previously unseen by the classifier and present a novel Open-Drive&Act benchmark. We combine current closed set models with multiple strategies for novelty detection adopted from general action classification [1] in a generic open set driver behavior recognition framework. In addition to conventional approaches, we employ the prominent I3D architecture extended with modules for assessing its uncertainty via Monte-Carlo dropout. Our experiments demonstrate clear benefits of uncertainty-sensitive models, while leveraging the uncertainty of all the output neurons in a voting-like fashion leads to the best recognition results. To create an avenue for future work, we make Open-Drive&Act public at www.github.com/aroitberg/open-set-driver-activity-recognition.
Weakly Supervised Deep Reinforcement Learning for Video Summarization With Semantically Meaningful Reward

Conventional unsupervised video summarization algorithms are usually developed in a frame level clustering manner For example, frame level diversity and representativeness are two typical clustering criteria used for unsupervised reinforcement learning-based video summarization. Inspired by recent progress in video representation techniques, we further introduce the similarity of video representations to construct a semantically meaningful reward for this task. We consider that a good summarization should also be semantically identical to its original source, which means that the semantic similarity can be regarded as an additional criterion for summarization. Through combining a novel video semantic reward with other unsupervised rewards for training, we can easily upgrade an unsupervised reinforcement learning-based video summarization method to its weakly supervised version. In practice, we first train a video classification sub-network (VCSN) to extract video semantic representations based on a category-labeled video dataset. Then we fix this VCSN and train a summary generation sub-network (SGSN) using unlabeled video data in a reinforcement learning way. Experimental results demonstrate that our work significantly surpasses other unsupervised and even supervised methods. To the best of our knowledge, our method achieves state-of-the-art performance in terms of the correlation coefficients, Kendall's and Spearman's p.
Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries

Effective coding of patient records in hospitals is an essential requirement for epidemiology, billing, and managing insurance claims. The prevalent practice of manual coding, carried out by trained medical coders, is error-prone and time-consuming. Mitigating this labor-intensive process by developing diagnostic coding systems built on patients’ Electronic Medical Records (EMRs) is vital. However, developing nations with low digitization rates have limited availability of structured EMRs, thereby necessitating a need for systems that leverage unstructured data sources. Despite the rich clinical information available in such unstructured data, modeling them is complex, owing to the variety and sparseness of diagnostic codes, complex structural and temporal nature of summaries, and prolific use of medical jargon. This work proposes a context-attentive network to facilitate automatic diagnostic code assignment as a multi-label classification problem. The proposed model facilitates information aggregation across a patient’s discharge summary via multi-channel, variable-sized convolutional filters to extract multi-granular snippets. The attention mechanism enables selecting vital segments in those snippets that map to the clinical codes. The model’s superior performance underscores its effectiveness compared to the state-of-the-art on the MIMIC-III database. Additionally, experimental validation using the CodiEsp dataset exhibited the model’s interpretability and explainability.
Linear sets and MRD-codes arising from a class of scattered linearized polynomials

A class of scattered linearized polynomials covering infinitely many field extensions is exhibited. More precisely, the $q$-polynomial over $\mathbb F_{q^6}$, $q \equiv 1\pmod 4$ described in arXiv:1906.05611, arXiv:1910.02278 is generalized for any even $n\ge6$ to an $\mathbb F_q$-linear automorphism $\psi(x)$ of $\mathbb F_q^n$ of order $n$. Such $\psi(x)$ and some functional powers of it are proved to be scattered. In particular this provides new maximum scattered linear sets of the projective line $\mathrm{PG}(1,q^n)$ for $n=8,10$. The polynomials described in this paper lead to a new infinite family of MRD-codes in $\mathbb F_q^{n\times n}$ with minimum distance $n-1$ for any odd $q$ if $n\equiv0\pmod4$ and any $q\equiv1\pmod4$ if $n\equiv2\pmod4$.