Reviewerly/exHarmony huggingface dataset:
Depth-Adaptive Transformer

State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.
Binarization of ESPI fringe patterns based on local entropy

The fringe skeleton method is the most straightforward analysis method for phase extraction and widely used in dynamic measurement. Binarization is often required in this method. In the traditional binarization methods, filtering is often a necessary step prior to binarization due to the influence of intrinsic speckle noises in ESPI fringe patterns. In this paper, we propose a binarization method based on local entropy and fuzzy c-means (FCM) clustering algorithm. In this method, the pixels in the given ESPI fringe pattern are clustered into white fringes and black fringes according to their local entropy instead of the original intensity information. There is no need to perform the filtering preprocessing, because the intrinsic speckle noises are utilized as essentials. We evaluate the performance of our method by applying it to the computer-simulated and real fringe patterns. Experimental results demonstrate that the proposed method can achieve the desired binarization results, and the binarization results can give desired skeleton results.
Gender Prediction from Images Using Deep Learning Techniques

Mechanized human age estimation by means of facial pictures is a vital and testing errand and has bunches of potential in certifiable applications, especially since the ascent of social stages and online networking. Hearty face affirmation structures are in unbelievable demand to help fight bad behavior and mental mistreatment. Distinctive applications join giving customer affirmation to get the opportunity to control to physical and virtual spaces to ensure higher security. In any case the issue of recognizing a man by taking a data stand up to picture and organizing with the known face pictures in a database is now an amazingly troublesome issue. This is a result of the variability of human faces under different operational circumstance conditions. For instance edification, insurgencies, appearances, camera see centers, developing, beauty care products, and eyeglasses. In this paper, a significant increase in accuracy of Gender prediction can be obtained through the use of convolution neural networks (CNN) for extracting features. Making the use of Convolution neural network (CNN) encompassed with deep learning methods, state-of the art performance has been achieved. The image-based Gender estimation is determined by performing extensive experiments on the largest public available datasets of face images with Gender labels-IMDB-WIKI dataset.
Integrated Velocity Measurement Algorithm Based on Optical Flow and Scale-Invariant Feature Transform

The pyramid Lucas-Kanade (LK) optical flow algorithm has been widely used in velocity measurement applications. However, these applications are limited by some shortcomings of the algorithm, such as its slow calculation speed and susceptibility to illumination changes. To solve these problems, a data fusion scheme based on the scale-invariant feature transform (SIFT) and optical flow is proposed to alleviate the dependence of the optical flow on the illumination conditions. In addition, an improved cubature Kalman filter (CKF) based on multi-rate residual correction (CKF-MRC) is proposed to solve the problem of inconsistency between the sampling frequencies of the SIFT and the optical flow, and takes full advantage of the high sampling frequency of SIFT. The experimental results demonstrate that the proposed CKF-MRC method can effectively improve the accuracy of velocity measurement under variable illumination conditions with a high sampling frequency.
A Vision-Based Amateur Drone Detection Algorithm for Public Safety Applications

Drones will be widely used in the smart cities of the future for the wireless approach, supplying goods, and for conserving the security of smart cities. Apart from the various benefits of the drones, they pose significant challenges and public concerns that need to be tackled. We propose an amenable framework to detect malicious drone and ensure public safety. The proposed model is validated using 400 images with various challenges such as occlusion, scale variation, blurriness, background clutter, and low illumination. After extracting the features using ResNet50 model, we applied SVM's RBF kernel for the classification. The classification accuracy presented in the confusion matrix for this model came out to be the highest in comparison to other state-of-the-art models.
Management City Model Based on Blockchain and Smart Contracts Technology

Recent developments of Information and Communication Technologies (ICT) have led to new and innovative ways to face the challenges of modern societies. We could even say that a revolution is taking place the world over as a consequence of the new capabilities of provided by technology. Blockchain are one of these new disruptive technologies emerging in modern world. This technology provides a decentralized, efficient and secure management that enables new asset administration models. The main contribution of this work is the development of a management model based on blockchain technology and smart contracts. This research is within the research track smart cities and smart villages development and aims to facilitate the city management, the development of e-government and improve the quality of life of citizens when they interact with the administration.
Superior properties in room-temperature colloidal-dot quantum emitters revealed by ultralow-dark-count detections of temporally-purified single photons

Abstract The realization of high-quality quantum emitters that can operate at room temperature is important for accelerating the application of quantum technologies, such as quantum communication, quantum information processing, and quantum metrology. In this work, we study the photon-antibunching properties on room-temperature emission from individual colloidal quantum dots (CQDs) using superconducting-nanowire single-photon detectors and temporal filtering of the photoluminescence decay curve. We find that high single-photon purities and high photon-generation rates can be simultaneously achieved by removing the signals originating from the sequential two-photon emission of biexcitons created by multiple excitation pulses. We successfully demonstrate that the ultrahigh performance of the room-temperature single-photon sources showing g (2) (0) ≪ 10 −2 can be confirmed by the ultralow-dark-count detection of the temporally purified single photons. These findings provide strong evidence for the attractiveness of CQDs as candidates for high-quality room-temperature quantum light sources.
Computer Vision System Based for Personal Protective Equipment Detection, by Using Convolutional Neural Network

The number of work accidents happened in Indonesia does not decrease, in this moment. One of the cause of work accidents is human negligence in wearing personal protective equipment or what is called PPE. A system that can monitor the completeness of PPE worn by workers in an industrial environment automatically is needed. Computer vision technology one method known as convolutional neural network is used, this is a system to monitor directly and detect workers who do not wear PPE. The level system precision have seen by compare between the real condition and the system condition red which is shown the people without PPE completely. The resulted of the system resulted would compare with the examiner's. The result shown that this system is able to know and distinguish between workers who wear PPE in full and not. With the number of dataset 14512 images, the accuracy of this system is 79.14% with the precision of the detection resulted 80%. This system can give a warning if there are workers who do not wear PPE completely and also could be implemented into several workplaces that have the requirements for the detection. This system give the contribution to decrease work accidents and to increase the safeness of the workers.
Graph-Based Semi-Supervised Learning for Natural Language Understanding

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data.Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited.In this work, we propose transductive graphbased semi-supervised learning models as well as their inductive variants for NLU.We evaluate the approach's applicability using publicly available NLU data and models.In order to find similar utterances and construct a graph, we use a paraphrase detection model.Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%.
Methodological strategy to develop the state of the art as a product of educative research

Elaborating the state of the art represents an indispensable investigative process to establish a theoretical and methodological position that supports the development of a scientific investigation. In this document a proposal is presented to prepare the state of the art starting from the analysis of its conceptualization, also the moments that must be followed to develop this activity in the investigative process are explored. To achieve this, an investigation was carried out using a qualitative and interpretative design of documentary type. The result represents a computer scheme that can help consolidate the state of the art as a product of scientific research of an analytical-interpretative nature categorized into five moments 1) seed, 2) approximation to the object of study, 3) context and inventory, 4) analytical, and 5) interpretive.
Readability of English Wikipedia's health information over time

Objective: To assess and compare the readability of the twenty-five most accessed English medical articles on Wikipedia 0, 1, 5 and 10 years ago. Design:The twenty-five most accessed Wikipedia articles on diseases in August 2018 were identified for this study.The content of the lead paragraphs was formatted to remove any hyperlinks, decimals, colons, semicolons and periods used in abbreviations.An online tool was then used to assign a score to the readability of each text sample using the following formulae: Gunning FOG (Frequency of Gobbledygook) index, Flesch-Kincaid Grade Level (F-K), Simple Measure of Gobbledygook (SMOG) and Flesch Reading Ease (FRE).A single reading grade (RG) was calculated for each passage by averaging scores from the FOG, SMOG and F-K tests to facilitate interpretation.These steps were repeated for the lead paragraph of the same medical articles as visible 1, 5 and 10 years ago on Wikipedia.Main Outcome Measures: Readability grade (RG) and reading ease (FRE score) Results:The average (mean) RG of the twenty-five most accessed Wikipedia articles on diseases in 2018 was 12.73 (95% CI = 12.07-13.38),and the average FRE score was 39.91 (95% CI = 36.09-43.74),a score considered "difficult".The number of articles that were easier to read (lower RG and higher FRE) in 2018 was significantly higher when compared to 2013 and 2008 (p<0.0001),but not significantly different when compared to 2017.When paired by titles and compared over time, a statistically significant difference in readability (RG and FRE) was seen in 2018 when compared to earlier years: 2017 (Friedman Chi-squared=13.70,p=0.0002), 2013 (Friedman Chi-squared=46.08,p<0.0001) and 2008 (Friedman Chi-squared=33.03,p=0.0001).None of the pages were written at the 7th or 8th grade level as recommended by the U.S.
Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot's onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot's state estimation, task planing and task execution. The robot's onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human's requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.
Reflections on the reporting of fraud in Australia

Purpose The purpose of this paper is to reflect on the reporting of cybercrime in Australia, specifically the reporting of fraud. Design/methodology/approach Using an evaluation of the (former) Australian Cybercrime Online Reporting Network (ACORN), this paper provides a commentary on the report findings, including both positive and negative aspects of the reporting mechanism. Findings The paper focuses on three key challenges that were identified in the evaluation: victim satisfaction; quality of data; and unintended consequences. Each of these topics is outlined and located within a broader context to better understand the evaluation findings. Research limitations/implications This viewpoint paper is a commentary based upon an evaluation of ACORN which itself has several limitations of its methodology. Despite this, the evaluation provides important insights into the challenges that currently exist with the online reporting of fraud offences both in Australia, and worldwide. Practical implications This paper emphasises the structural and systemic issues that exist for the reporting of fraud in Australia. It therefore warns against placing blame exclusively on police agencies as responsible for these results. Instead, it advocates the need for society to take a more coordinated and collaborative approach to the policing of fraud, which includes law enforcement, government and industry partners. Social implications The paper documents some of the underlying reasons for additional trauma and harm experienced by victims of fraud in their efforts to report their incident and receive what they perceive to be an appropriate criminal justice response. These must be acknowledged in order to make the required change. Originality/value This paper is a commentary and reflection on the current way in which fraud is reported in Australia. It points to a need to rethink this approach in some key areas. It highlights the critical need for an education campaign to dispel some of the myths that exist in relation to realistic police responses to fraud, and also calls for the need to consider alternatives to the exclusively online system currently in operation, as well as larger questions about notions of justice and appropriate responses to fraud victims.
The Myth of Double-Blind Review Revisited: ACL vs. EMNLP

The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field.The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process.Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind?We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper.Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most.Our results show that the authors of a paper can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP for the top 100 most prolific authors.
Unveiling the indigenous art and craft of <i>bakat</i> and its economic significations

Purpose The purpose of this paper is to unveil the intricacies of bakat art of weaving; its origin; processes; uses; primary materials and principles used; the profile of the weavers; and its economic significations in the system of production and trade. Design/methodology/approach The study used a descriptive–qualitative design with 35 key informants using narrative analysis. The unstructured questions listed in the interview guide were used during the interview. Responses were recorded using an audio–video recorder. Coding sheets were used in the actual transcription of data. Findings The results showed dependency on the bakat art of weaving with the available resources found in the environment. Its economic significations showed a sustainable impact on the weavers’ life. The bakat art of weaving represented the values of resiliency to hardships, adaptability to changes, passion to craftsmanship, sense of community and family centeredness. Aspiring craftsmen and artists may enhance continuously their craftsmanship for sustainable development with government support through the creation of the School of Living Traditions. Research limitations/implications The study was limited to bakat art of weaving based on the responses of the key informants that were subjected to a point of saturation without sacrificing robustness, brevity, credibility and dependability. Practical implications Though the economic side of bakat art of weaving is sustainable within the village, it can create a powerful branding for cultural recognition that would transform the town of Barili into a creative weaving hub in Cebu. This creates the balance of the historical significance of bakat art of weaving and the tourism sector in promoting sustainable development. Social implications Bakat art rekindled the spirit of consciousness among the majority of the Cebuano public for preservation and promotion. Originality/value The study is original because this has not been published.
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power

Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models.In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations.The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications.We show that lexical features are more sensitive to text modifications than syntactic ones.However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features.Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both conversational and written language.
Bend but Don’t Break? Multi-Challenge Stress Test for QA Models

The field of question answering (QA) has seen rapid growth in new tasks and modeling approaches in recent years. Large scale datasets and focus on challenging linguistic phenomena have driven development in neural models, some of which have achieved parity with human performance in limited cases. However, an examination of state-of-the-art model output reveals that a gap remains in reasoning ability compared to a human, and performance tends to degrade when models are exposed to less-constrained tasks. We are interested in more clearly defining the strengths and limitations of leading models across diverse QA challenges, intending to help future researchers with identifying pathways to generalizable performance. We conduct extensive qualitative and quantitative analyses on the results of four models across four datasets and relate common errors to model capabilities. We also illustrate limitations in the datasets we examine and discuss a way forward for achieving generalizable models and datasets that broadly test QA capabilities.
Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data

We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard.We use an end-toend deep neural model designed to deal with context-dependent spelling correction and normalisation.Results indicate that a model with two CNN sub-network encoders and an LSTM decoder performs the best, and that word context matters.Additionally, preprocessing data token-by-token with an editdistance based aligner significantly improves the performance.We get promising results for the spelling correction and normalisation, as a pre-processing step for downstream tasks, on detecting binary Semantic Textual Similarity.
A Multibranch Object Detection Method for Traffic Scenes

The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16 × 16, 32 × 32, and 64 × 64 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33 fps) is fast, which can meet the real-time requirements of industry.