Reviewerly/exHarmony huggingface dataset:
Emoji Accessibility for Visually Impaired People

Emoji are graphical symbols that appear in many aspects of our lives. Worldwide, around 36 million people are blind and 217 million have a moderate to severe visual impairment. This portion of the population may use and encounter emoji, yet it is unclear what accessibility challenges emoji introduce. We first conducted an online survey with 58 visually impaired participants to understand how they use and encounter emoji online, and the challenges they experience. We then conducted 11 interviews with screen reader users to understand more about the challenges reported in our survey findings. Our interview findings demonstrate that technology is both an enabler and a barrier, emoji descriptors can hinder communication, and therefore the use of emoji impacts social interaction. Using our findings from both studies, we propose best practice when using emoji and recommendations to improve the future accessibility of emoji for visually impaired people.
A Semi-Fragile Reversible Watermarking for Authenticating 2D Engineering Graphics Based on Improved Region Nesting

To achieve high tampering localization precision and low distortion, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is proposed based on an improved region nesting and a novel watermark generation. First, an intensive investigation is performed to the recently proposed region nesting (RN) partition. It is found that the original vertex and its mapped one cannot be guaranteed to be on the same line, which means that it still has room for improvement in term of distortion. Based on this, an improved region nesting partition (IRN) is proposed. Secondly, inspiring by the idea of soldiers parading, a novel watermark generation based on the short hash of the adjacent geometric features is developed. Then, a new coordinate system is constructed to achieve invariability of translation, scaling, rotation and entity re-arrangement. Based on the above techniques, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is put forward. Experimental results and analysis show that IRN can obtain at least 15% distortion reduction compared with RN, the precision of tampering localization can be improved to a single vertex, and a certain semi-fragility in translation, scaling, rotation, and entity re-arrangement can be achieved. Furthermore, it has no file size expansion, and can obtain a good balance among imperceptibility, semi-fragility and tampering localization precision.
On the effect of geographical, topographic and climatic conditions on feed-in tariff optimization for solar photovoltaic electricity generation: A case study in Iran

One of the important factors of sustainable development is renewable energies penetration in the energy systems. The present study evaluates the optimum feed-in tariff of photovoltaic electricity production based on the available downward solar radiation potential of each province of Iran while this potential calculated considering geographical, topographic and climatic conditions. For downward solar radiation modeling a set of mathematical, geometric and spatial models were used. Also, the net present value model was applied for evaluates the optimum feed-in tariff of photovoltaic electricity production. The results showed that downward solar radiation potential varies from 380 to 578 W m−2 across the Iran country. Furthermore, the optimal photovoltaic electricity generated feed-in tariff varies from 0.0835 to 0.1272 United States dollar for a different region in the country. Based on government approved feed-in tariff and the current study the provinces of Ardebil and Kohkiluyeh and Boyer-Ahmad are the riskiest and the securest regions for investors in case of photovoltaic project, respectively.
Using ASR Methods for OCR

Hybrid deep neural network hidden Markov models (DNN-HMM) have achieved impressive results on large vocabulary continuous speech recognition (LVCSR) tasks. However, the recent approaches using DNN-HMM models are not explored much for text recognition. Inspired by the current work in automatic speech recognition (ASR) and machine translation, we present an open vocabulary sub-word text recognition system. The sub-word lexicon and sub-word language model (LM) helps in overcoming the challenge of recognizing out of vocabulary (OOV) words, and a time delay neural network (TDNN) and convolution neural network (CNN) based DNN-HMM optical model (OM) efficiently models the sequence dependency in the line image. We present results on 12 datasets with training data varying from 6k lines to 600k lines. The system is built for 8 languages, i.e., English, French, Arabic, Chinese, Farsi, Tamil, Russian, and Korean. We report competitive results on several commonly used handwritten and printed text datasets.
Constrained Interval Type-2 Fuzzy Sets

In many contexts, type-2 fuzzy sets (T2 FS) are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation, there is no restriction on the shape of the footprint of uncertainty and the embedded sets (ESs) that can be considered acceptable. This leads, usually, to the loss of the semantic relationship between the T2 FS and the concept it models. As a consequence, the interpretability of some of the ESs and the explainability of the uncertainty measures obtained from them can decrease. To overcome these issues, constrained type-2 (CT2) fuzzy sets have been proposed. However, no formal definitions for some of their key components [e.g., acceptable ESs (AESs)] and constrained operations have been given. In this article, we provide some theoretical underpinning for the definition of CT2 sets, their inferencing and defuzzification method. To conclude, the constrained inference framework is presented, applied to two real-world cases and briefly compared to the standard interval type-2 inference and defuzzification method.
Residual BiRNN Based Seq2Seq Model with Transition Probability Matrix for Online Handwritten Mathematical Expression Recognition

In this paper, we present a Seq2Seq model for online handwritten mathematical expression recognition (OHMER), which consists of two major parts: a residual bidirectional RNN (BiRNN) based encoder that takes handwritten traces as the input and a transition probability matrix introduced decoder that generates LaTeX notations. We employ residual connection in the BiRNN layers to improve feature extraction. Markovian transition probability matrix is introduced in decoder and long-term information can be used in each decoding step through joint probability. Furthermore, we analyze the impact of the novel encoder and transition probability matrix through several specific instances. Experimental results on the CROHME 2014 and CROHME 2016 competition tasks show that our model outperforms the previous state-of-the-art single model by only using the official training dataset.
Efficient and Secure Outsourcing Scheme for RSA Decryption in Internet of Things

Rivest-Shamir-Adleman (RSA) is one of the widely deployed public-key algorithms. Yet, its decryption facet is very time consuming for resource-constrained Internet-of-Thing (IoT) devices, as it is based on the modular exponentiation of a large number. Although several variants of RSA have been designed to accelerate decryption, the outcomes have been far from satisfactory. Therefore, it is of imminent importance to investigate how to securely outsource RSA decryption to computational powerful parties as an alternative solution. In this article, we introduce the first efficient and secure outsourcing scheme for RSA decryption in IoT. Though RSA decryption is achieved via modular exponentiation, existing secure outsourcing schemes for modular exponentiation either assume the modulus to be prime and are not applicable to RSA or incur massive computation costs and are heavy laden in practice. To address these issues, we have designed our scheme based on the Chinese remainder theorem (CRT). In our scheme, the private keys (including the exponent and the modulus) and the plaintext are concealed concurrently, and the proposed scheme is highly efficient for both client and cloud. In addition, our scheme enables the client to detect any misbehavior of the cloud server with a probability of 99.17%. To validate the effectiveness of our proposed scheme, we provide rigorous proofs of security and verifiability, as well as efficiency analysis. The effectiveness and efficiency of our scheme are further confirmed based on experimental results.
Oracle Character Recognition by Nearest Neighbor Classification with Deep Metric Learning

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU.
Diversified keyword search based web service composition

To assist system engineers in efficiently building service-based software systems, the keyword search based service composition approach on service connection graphs (scgraphs) has been proposed recently. However, due to the ambiguity of keywords, a keyword query may represent a bunch of different user requirements. Thus the current approach that only returns the composition with the optimal Quality of Service (QoS) cannot guarantee to hit the spot. In this paper, in order to satisfy the various possible requirements underlying a given keyword query, we formally introduce the top-k diverse service composition problem, and present a novel diversified keyword search approach on scgraphs to address it. Specifically, we firstly propose an All-Then-Diversify (ATD) algorithm that enumerates all potential compositions by searching a scgraph and then derives the top-k diverse subsets by deriving the maximal independent sets of a similarity graph. Then, due to the possibly large number of potential compositions, we present a Pop-And-Diversify (PAD) algorithm that only maintains a similarity graph of the top compositions that have been found so far during the search and computes its maximal independent sets incrementally until convergence, thereby reducing unnecessary computation overheads. Moreover, we propose two composition similarity measurements w.r.t. the categories or descriptions of services respectively. Lastly, the experimental results on ProgrammableWeb.com demonstrate that, our approach outperforms another state-of-the-art composition diversification approach on both metrics of density and redundancy, and meanwhile, improves the efficiency of diversification significantly.
Marine Applications of the Fast Marching Method

Path planning for marine applications is a special case of the more general problem for mobile robots since, in addition to avoid colliding with obstacles, it is necessary to optimize the cost function taking into account water currents. In this paper, several solutions for ship motion planning based on the Fast Marching Method are proposed. The basic method focus on collision avoidance and optimal planning and, later on, using the same underlying method, the influence of marine currents in the optimal path planning is detailed. Finally, the application of these methods to consider marine robot formations is presented.
Multi-sensor System, Gamification, and Artificial Intelligence for Benefit Elderly People

Over the years, the elderly people population will become more than children population; besides since 2018 people over 65 years old outnumbered the population under 5 years old. Growing up involves biological, physical, social and psychological changes that may lead to social isolation and loss of loved ones, or even to the sense of loss of value, purpose or confidence. Moreover, as people are aging, they usually spend more time at home. As a solution, social inclusion through mobile devices and smart home seem to be ideal to avoid that lack of purpose in life, confidence or value. Smart homes collect and analyze data from household appliances and devices to promote independence, prevent emergencies and increase the quality of life in elderly people. In that regard, the multi-sensor system allows the expert to know more about the elderly people needs to propose actions that improve the elderly people’s quality of life, as they can read and analyze through sensors their facial expressions, voice, among others. Gamification in older people may motivate elderly people to socialize with their peers through social interaction and by doing activities as exercising. Thus, this chapter proposes to use an adaptive neural network fuzzy inference to evaluate camera and voice devices that come from the multi-sensor system to propose an interactive and tailored human-machine interface for the elderly people in the home.
A Framework for Learning From Demonstration With Minimal Human Effort

We consider robot learning in the context of shared autonomy, where control of the system can switch between a human teleoperator and autonomous control. In this setting we address reinforcement learning, and learning from demonstration, where there is a cost associated with human time. This cost represents the human time required to teleoperate the robot, or recover the robot from failures. For each episode, the agent must choose between requesting human teleoperation, or using one of its autonomous controllers. In our approach, we learn to predict the success probability for each controller, given the initial state of an episode. This is used in a contextual multi-armed bandit algorithm to choose the controller for the episode. A controller is learnt online from demonstrations and reinforcement learning so that autonomous performance improves, and the system becomes less reliant on the teleoperator with more experience. We show that our approach to controller selection reduces the human cost to perform two simulated tasks and a single real-world task.
Making Two Vast Historical Manuscript Collections Searchable and Extracting Meaningful Textual Features Through Large-Scale Probabilistic Indexing

Textual access to large collections of digitized images remains unfeasible because usually they lack transcripts. Transcribing such collections is in turn typically unattainable in terms of costs. However, the use of probabilistic indices can facilitate textual accessing with only moderate demands of resources. Besides allowing effortless information retrieval, it will be shown that probabilistic indices can also be used to estimate textual features of the indexed but otherwise untranscribed collections, such as running words and Zipf's curves. Complete probabilistic indices have been recently produced for two iconic large collections: "Bentham" (90K images) and "Spanish Golden Age Theater" (40K images). To show the repercussion of making these collections searchable, we provide accessing statistics gathered through their corresponding search interfaces. To the best of our knowledge this is the first publication of large collections of untranscribed manuscripts which are now publicly accessible for effective and efficient textual access.
Integrating Blockchain and IoT/ITS for Safer Roads

Road safety continues to be a great concern despite various advances and initiatives. Sweden introduced the SS approach in 1996 as part of a plan to eliminate road fatalities and injuries. Since then, SS has been internationally adopted by the World Health Organization. In this article, we describe how the SS approach offers a holistic structure of various innovative solutions in the context of IoT and ITS. We also introduce a necessary extension to the SS approach considering emerging technologies such as connected and computing vehicles and vehicle autonomy. To facilitate this extension, we show how blockchains can play a crucial role in ensuring road safety, as well as the safety of its connected elements.
IVOA: Introspective Vision for Obstacle Avoidance

Vision, as an inexpensive yet information rich sensor, is commonly used for perception on autonomous mobile robots. Unfortunately, accurate vision-based perception requires a number of assumptions about the environment to hold - some examples of such assumptions, depending on the perception algorithm at hand, include purely lambertian surfaces, texture-rich scenes, absence of aliasing features, and refractive surfaces. In this paper, we present an approach for introspective vision for obstacle avoidance (IVOA) - by leveraging a supervisory sensor that is occasionally available, we detect failures of stereo vision-based perception from divergence in plans generated by vision and the supervisory sensor. By projecting the 3D coordinates where the plans agree and disagree onto the images used for vision-based perception, IVOA generates a training set of reliable and unreliable image patches for perception. We then use this training dataset to learn a model of which image patches are likely to cause failures of the vision-based perception algorithm. Using this model, IVOA is then able to predict whether the relevant image patches in the observed images are likely to cause failures due to vision (both false positives and false negatives). We empirically demonstrate with extensive real-world data from both indoor and outdoor environments, the ability of IVOA to accurately predict the failures of two distinct vision algorithms.
A new systematic decision support framework based on solar extended exergy accounting performance to prioritize photovoltaic sites

Determining locations is important for researchers and policymakers involved in developing photovoltaic (PV) plants. Here, solar extended exergy accounting performance is proposed as a new systematic decision support framework for prioritizing PV sites from environmental, technical, financial, and social viewpoints. The space-time solar extended exergy accounting performance of a 500 kW grid-connected PV system is determined for different Iranian climates as an example to show how the proposed tool can enhance site selection for PV power systems. The simulations are carried out for all 12 months of the year using the actual climatic, technical, economic, environmental, and social databases. Results for January, April, July, and October, representing winter, spring, summer, and autumn, respectively, are discussed in detail. The outcomes of the proposed method are compared with those for total irradiance, solar irradiance exergy, power generation, and exergy efficiency. In addition, several space-time ‏maps are developed for facilitating comparison among PV installation priorities obtained using various criteria. The average monthly total irradiance and solar irradiance exergy in Iran respectively varies in the range 120–184 kWh/m2/month and 112–172 kW/m2/month, respectively. The average monthly power generation, exergy efficiency, and extended exergy accounting performance of the developed plant are respectively found to be in the range of 45.3–66.0 MWh/month, 13.6‒14.8%, and 9.46–10.5% for different Iranian climates. Overall, southeast, central, and northwest cities of Iran are shown to be promising candidates for solar PV power systems based on the extended exergy accounting approach. The outcomes show that the proposed tool can be much more helpful than conventional methods for ranking candidate locations for building PV plants. The proposed extended exergy accounting performance framework can be applied for other stand-alone and hybrid renewable energy systems for various environmental and climatic conditions.
Savitzky–Golay filtering as image noise reduction with sharp color reset

Images acquired through photosensors, known to be non-stationary correlated signals, due to its acquisition characteristics, often come attached with uncorrelated, zero-mean, variance σ2, stationary processes. The latter can be built-up over the former, being sourced from two contributions, one dependently, known as photonic noise, while the other independently, if circuitry generated. Either way, a straightforward approach to reduce this noise would be the use of low-pass filters. We propose an improvement of the Moving Average Filter with a polynomial Savitzky–-Golay filter. Based on the obtained results, we believe that, in most cases, this filter can produce better results than the standard FIR filters since it does a better job in preserving the high-frequency signals. The challenge is to choose the best tradeoff between the window frame size, the derivative order and polynomial coefficients. The contour (color sharp) transitions are done through filter resetting every time the next pixel value is outside the variance markers of the filter. Since the processing is intended to be done in real time, we want to stick in time domain using fixed-point calculations. MATLAB is used to compare floating-point results with an integer processing platform, an FPGA. Error and Signal-to-Noise Ratio (SNR) improvements were achieved by several orders of magnitude using this new method.