Reviewerly/exHarmony huggingface dataset:
An adaptive surrogate assisted differential evolutionary algorithm for high dimensional constrained problems

Differential evolution (DE) is a competitive algorithm for constrained optimization problems (COPs). In this study, in order to improve the efficiency and accuracy of the DE for high dimensional problems, an adaptive surrogate assisted DE algorithm, called ASA-DE is suggested. In the ASA, several kinds of surrogate modeling techniques are integrated. Furthermore, to avoid violate the constraints and obtain better solution simultaneously, adaptive strategies for population size and mutation are also suggested in this study. The suggested adaptive population strategy which controls the exploring and exploiting states according to whether algorithm find enough feasible solution is similar to a state switch. The mutation strategy is used to enhance the effect of state switch based on adaptive population size. Finally, the suggested ASA-DE is evaluated on the benchmark problems from congress on evolutionary computation (CEC) 2017 constrained real parameter optimization. The experimental results show the proposed algorithm is a competitive one compared to other state-of-the-art algorithms.
Estimating and Controlling the False Discovery Rate of the PC Algorithm Using Edge-specific P-Values

Many causal discovery algorithms infer graphical structure from observational data. The PC algorithm in particular estimates a completed partially directed acyclic graph (CPDAG), or an acyclic graph containing directed edges identifiable with conditional independence testing. However, few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG. In this article, we introduce PC with p-values (PC-p), a fast algorithm that robustly computes edge-specific p-values and then estimates and controls the FDR across the edges. PC-p specifically uses the p-values returned by many conditional independence (CI) tests to upper bound the p-values of more complex edge-specific hypothesis tests. The algorithm then estimates and controls the FDR using the bounded p-values and the Benjamini-Yekutieli FDR procedure. Modifications to the original PC algorithm also help PC-p accurately compute the upper bounds despite non-zero Type II error rates. Experiments show that PC-p yields more accurate FDR estimation and control across the edges in a variety of CPDAGs compared to alternative methods.
An extremal problem for integer sparse recovery

Motivated by the problem of integer sparse recovery we study the following question. Let A be an m×d integer matrix whose entries are in absolute value at most k. How large can be d=d(m,k) if all m×m submatrices of A are non-degenerate? We obtain new upper and lower bounds on d and answer a special case of the problem by Brass, Moser and Pach on covering m-dimensional k×⋯×k grid by linear subspaces.
BM3D‐GT&amp;AD: an improved BM3D denoising algorithm based on Gaussian threshold and angular distance

Block-matching and three-dimensional filtering (BM3D) is generally considered as a milestone for its outstanding performance in the area of image denoising. However, it still suffers from the loss of image detail due to the utilisation of hard thresholding on transform domain during the phase of the basic estimate. In the frequency domain, a large amount of image detail information is in high frequency, which tends to be mixed with noise. Since its low amplitude is below the threshold, some image detail is filtered out with the noise. To retain more details, this study proposes an improved BM3D. It adopts an adaptable threshold with the core of Gaussian function during hard thresholding, which can filter out more noise while retaining more high-frequency information. When grouping, the normalised angular distance is taken as a measure of similarity to relieve the interference of noise further and achieve a higher peak signal-to-noise ratio (PSNR). The experimental results show that under the background of Gaussian noise with standard deviation of 20–60, the PSNR of denoised images (with a large amount of detail), applied with the authors’ improved algorithm, can be improved by compared with original BM3D.
Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models

This paper presents a novel unsupervised segmentation method for the three-dimensional microstructure of lung cancer specimens in micro-computed tomography (micro-CT) images. Micro-CT scanning can nondestructively capture detailed histopathological components of resected lung cancer specimens. However, it is difficult to manually annotate cancer components on micro-CT images. Moreover, since most of the recent segmentation methods using deep neural networks have relied on supervised learning, it is also difficult to cope with unlabeled micro-CT images. In this paper, we propose an unsupervised segmentation method using a deep generative model. Our method consists of two phases. In the first phase, we train our model by iterating two steps: (1) inferring pairs of continuous and categorical latent variables of image patches randomly extracted from an unlabeled image and (2) reconstructing image patches from the inferred pairs of latent variables. In the second phase, our trained model estimates te probabilities of belonging to each category and assigns labels to patches from an entire image in order to obtain the segmented image. We apply our method to seven micro-CT images of resected lung cancer specimens. The original sizes of the micro-CT images were $$1024 \times 1024 \times (544{-}2185)$$ voxels, and their resolutions were 25–30 $$\upmu $$ m/voxel. Our aim was to automatically divide each image into three regions: invasive carcinoma, noninvasive carcinoma, and normal tissue. From quantitative evaluation, mean normalized mutual information scores of our results are 0.437. From qualitative evaluation, our segmentation results prove helpful for observing the anatomical extent of cancer components. Moreover, we visualize the degree of certainty of segmentation results by using values of categorical latent variables.
Deploying Health Campaign Strategies to Defend Against Social Engineering Threats

The threat of social engineering is one that affects not only the financial and reputational well-being of corporate entities but also the basic rights of individuals to privacy. Methods common to health campaigns used to disseminate information warning against social engineering have the potential for success. By providing potential scenarios and exact descriptions of threats, the recipients of the messages might become more aware of the dangers posed through the Internet. Case studies of effective health campaigns will be examined, as well as specific threats that could be avoided by more awareness on the part of the individual. Psychological effects of different techniques and the way that general knowledge can help to prevent social engineering attacks will also be examined.
Video-based learning assistant scheme for sustainable education

Recently, owing to the development of information technology, there have been many changes in learning activities and equipment. For instance, compared with traditional textbook-based learning, e-learning has freed learners from spatial-temporal restrictions, and other educational media are replacing textbooks. Specifically, educational videos have become very popular, as they are effective at conveying their content or meaning. Furthermore, their accessibility has been improved because of the emergence of platforms that enable the sharing and delivery of such videos. However, when learners use such platforms, they tend to face difficulty in finding videos they want, as these platforms have a lot of videos. They also experience the inconvenience of performing additional searches because of the lack of information relevant to the video. To solve these problems, we propose a learning assistant system for educational video platforms. Our system helps learners find videos based on their readability level and contents. Moreover, it provides learners with useful information such as keywords and named entities for further learning activities. To demonstrate the effectiveness, we implemented a prototype system on a website dealing with speech videos and performed various experiments. By reporting some results, we show that our scheme can be used to implement sustainable education.
A Feasible Method for the Trajectory Measurement of Radial Jet Drilling Laterals

Summary Radial jet drilling (RJD) is an unconventional drilling technology that uses high-pressure liquid jet to drill multiple radial laterals. It is a cost-effective alternative for bypassing damage zones near the wellbore, restimulating the production of old wells, and developing unconventional reservoirs. However, because of the structure of the deflector and the diameter of the radial laterals, traditional well-trajectory measuring tools cannot be applied in RJD wells, which hinders the conduct of some significant operations. The unknown trajectory is a crucial limitation to further development and field application of RJD technology. In this paper, along with an introduction to RJD technology, a measuring system and a mini-tool are proposed for attitude measurement and motion-state recognition. With an extra tripping stage, the tool is tripped down to the bottom of the RJD laterals after the jet drilling is completed, and then tripped out at a constant speed. The measured data stored in memory media could be downloaded after the measurement. On the basis of navigation theory, a reckoning method was proposed to obtain the trajectory parameters by using the recorded data of the tool and the operational data of the coiled tubing (CT). After that, an experimental study was carried out to test the performance of the measuring tool and the reckoning method. As results of the experiments demonstrate, the average errors of the measured length, inclination, and azimuth are 3.94, 2.62, and 4.54%, respectively.
Bayesian Intent Prediction for Fast Maneuvering Objects using Variable Rate Particle Filters

The motion of a tracked object often has long term underlying dependencies due to premeditated actions dictated by intent, such as destination. Revealing this intent, as early as possible, can enable advanced intelligent system functionalities for conflict/opportunity detection and automated decision making, for instance in surveillance and human computer interaction. This paper presents a novel Bayesian intent inference framework that utilises sequential Monte Carlo (SMC) methods to determine the destination of a tracked object exhibiting unknown jump behaviour. The latter can arise from the object undertaking fast maneuvers (e.g. for obstacle avoidance) and/or due to external uncontrollable environmental perturbations. Suitable intent-driven stochastic models and inference routines are introduced. The effectiveness of the proposed approach is demonstrated using synthetic and real data.
On the Exploitability of the Ka Band for Spaceborne Radar Debris Detection and Tracking Measurements

This paper follows the increasing demands for augmenting space-based monitoring capabilities for near-Earth Space Situational Awarness (SSA). The outline comprises an introduction to current SSA as an operative framework, with a particular focus on the possible tantalized and complementary role of Spaceborne Radars (SBR) in the Ka band.
“I Recall this Picture”: Understanding Picture Password Selections based on Users’ Sociocultural Experiences

Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic user interaction realms. Image content related to users’ past sociocultural experiences assists users with the creation of more secure and memorable passwords. Aiming to shed light on the effects of sociocultural-related image content towards graphical password selections, we conducted a between-subjects eye-tracking study (N=37) in which users selected one image among a set of images from their assigned image group (sociocultural-related vs. generic) that would be used for creating their graphical password. Results revealed differences in users’ interaction and visual behavior during image selection. Initial users’ feedback regarding the likeability and users’ engagement with the sociocultural-related image content is also presented.
Research on Construction and Application of TCM Knowledge Graph Based on Ancient Chinese Texts

At present, the "document-centered" literature retrieval and information acquisition methods can no longer meet the demand, and instead are "knowledge-centric" structured, networked and intelligent knowledge services. In order to more effectively analyze the connection between Chinese medicine knowledge, optimize the retrieval of knowledge, share knowledge in the field of Chinese medicine, and make Chinese medicine better carry on. This paper builds a knowledge graph based on the texts of ancient traditional Chinese medicine books through the key concepts of diseases, symptoms, prescriptions, drugs, medical records, and related content such as regions, doctors, and dynasties in traditional Chinese medicine texts, to form a comprehensive knowledge service system, providing multi-dimensional and personalized knowledge customization services.
Transfer Learning Methods for Spoken Language Understanding

In this paper, we present a series of methods to improve the performance of spoken language understanding in the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU 2019) which is aimed to improve the robustness for automatic speech recognition (ASR) errors and to solve the problem of not enough labeled data in new domains. We combine word information and char information to improve the performance of the semantic parser. We also use some transfer learning methods like correlation alignments to improve the robustness of the spoken language understanding system. Then we merge the rule method and the neural network method to raise system output performance. In video and weather domains with few training data, we use both the transfer learning model trained on multi-domain data and the rule-based approach. Our approaches achieve F1 scores of 86.83%, 92.84%, 94.16%, and 93.04% on the test sets of map, music, video and weather domains.
Task-independent Multimodal Prediction of Group Performance Based on Product Dimensions

This paper proposes an approach to develop models for predicting the performance for multiple group meeting tasks, where the model has no clear correct answer. This paper adopts "product dimensions" [Hackman et al. 1967] (PD) which is proposed as a set of dimensions for describing the general properties of written passages that are generated by a group, as a metric measuring group output. This study enhanced the group discussion corpus called the MATRICS corpus including multiple discussion sessions by annotating the performance metric of PD. We extract group-level linguistic features including vocabulary level features using a word embedding technique, topic segmentation techniques, and functional features with dialog act and parts of speech on the word level. We also extracted nonverbal features from the speech turn, prosody, and head movement. With a corpus including multiple discussion data and an annotation of the group performance, we conduct two types of experiments thorough regression modeling to predict the PD. The first experiment is to evaluate the task-dependent prediction accuracy, in the situation that the samples obtained from the same discussion task are included in both the training and testing. The second experiments is to evaluate the task-independent prediction accuracy, in the situation that the type of discussion task is different between the training samples and testing samples. In this situation, regression models are developed to infer the performance in an unknown discussion task. The experimental results show that a support vector regression model archived a 0.76 correlation in the discussion-task-dependent setting and 0.55 in the task-independent setting.
SEO: A Scientific Events Data Model

Scientific events have become a key factor of scholarly communication for many scientific domains. They are considered as the focal point for establishing scientific relations between scholarly objects such as people (e.g., chairs and participants), places (e.g., location), actions (e.g., roles of participants), and artifacts (e.g., proceedings) in the scholarly communication domain. Metadata of scientific events have been made available in unstructured or semi-structured formats, which hides the interconnected and complex relationships between them and prevents transparency. To facilitate the management of such metadata, the representation of event-related information in an interoperable form requires a uniform conceptual modeling. The Scientific Events Ontology (OR-SEO) has been engineered to represent metadata of scientific events. We describe a systematic redesign of the information model that is used as a schema for the event pages of the OpenResearch.org community wiki, reusing well-known vocabularies to make OR-SEO interoperable in different contexts. OR-SEO is now in use on thousands of OpenResearch.org events pages, which enables users to represent structured knowledge about events without having to deal with technical implementation challenges and ontology development themselves.
Filling OpenStreetMap data gaps in rural Nepal: a digital youth internship and leadership Programme

Abstract Crowdsourced, open geospatial data such as the data compiled through OpenStreetMap have proven useful in addressing humanitarian, disaster and development needs. However, the existing ways in which volunteers engage in OpenStreetMap have inherent limitations that lead to critical data gaps in economically underdeveloped countries and regions. Various initiatives that target specific geospatial data gaps and engage volunteers for longer periods have emerged to overcome these limitations, yet there has been limited in-depth study of such targeted mapping initiatives. This article reports the findings from Digital Internship and Leadership (DIAL), a programme designed to fill data gaps in rural Nepal by engaging young people in mapping rural Nepal by integrating targeted mapping, a virtual internship strategy and youth leadership development. The findings suggest the potential benefits of targeted mapping initiatives embedded in youth leadership internship programmes to address those critical data gaps.
DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification

Traffic accidents cause over a million deaths every year, of which a large fraction is attributed to drunk driving. An automated intoxicated driver detection system in vehicles will be useful in reducing accidents and related financial costs. Existing solutions require special equipment such as electrocardiogram, infrared cameras or breathalyzers. In this work, we propose a new dataset called DIF (Dataset of perceived Intoxicated Faces) which contains audio-visual data of intoxicated and sober people obtained from online sources. To the best of our knowledge, this is the first work for automatic bimodal non-invasive intoxication detection. Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are trained for computing the video and audio baselines, respectively. 3D CNN is used to exploit the Spatio-temporal changes in the video. A simple variation of the traditional 3D convolution block is proposed based on inducing non-linearity between the spatial and temporal channels. Extensive experiments are performed to validate the approach and baselines.
Fog Image Classification and Visibility Detection Using CNN

The paper aims to find an approach for predicting the visibility percentage of the foggy images based on the factors like image brightness, luminance, intensity, and variance. The scope of using an image for predicting weather and providing information about the other details i.e. predicting if weather is foggy has increased. The idea is to utilize the data images to firstly classify them as being foggy or not. Secondly to detect the image visibility based on the image of a particular location. Thus the paper presents the implementation of convolutional neural network for the task of detecting and classifying the images into fog and non-fog. After getting the classification output, the brightness, luminance, variance and intensity of the images is calculated to find the visibility percent of the fog images.
Dataset bias: A case study for visual question answering

ABSTRACT We examine the issue of bias in datasets designed to train visual question answering (VQA) algorithms. These datasets include a collection of natural language questions about images (aka ‐ visual questions). We consider three popular datasets that are captured by people with sight, people who are blind, and generated by computers. We first demonstrate that machine learning algorithms can be trained to recognize each dataset's bias, and so determine the source of a novel visual question. We then discuss potential risks and benefits of biased VQA datasets and corresponding machine learning algorithms that can identify the source of a visual question; e.g., whether it comes from a person with sight, a person who is blind, or bot (aka ‐ computer). Our ultimate aim is to inspire the development of more inclusive VQA systems.
One Step Forward: Towards a Blockchain Based Trust Model for WSNs

Nowadays, Wireless Sensor Networks (WSNs) are facing various challenges. Cost efficiency, low energy consumption, reliable data communication between nodes and security are the major challenges in the field of WSNs. On the other hand, blockchain is also a very hot domain in this era. Blockchain has a remedy for some challenges, which are faced by the WSNs, e.g., secure data transactions and trustworthiness, etc. By keeping in mind the security issues, we induce blockchain into the WSNs. In short, we have proposed a trust model to avoid the malicious attacks and keep the transact data using the blockchain property of immutability. Moreover, an enhanced version of Proof of Stack (PoS), i.e., the Proof of Authority (PoA) consensus mechanism is being used to add a new node in the network. Additionally, the smart contract is also written to check the working status of nodes. Simulations are performed in order to record the transaction cost and execution cost.
Resources Allocation in Cloud Computing: A Survey

Cloud Computing is a model in which resources (Computing, Networking, Storage…) are consumed on any utility such as computing power, storage space, servers and applications. With the growing of the Cloud resources demand and the user’s number, the need of the quality of service and the resources allocation become a crucial challenge. This paper presents the challenges of resource allocation and some recent contributions to minimize them, hence the need for paper reviews and surveys to identify the area of our research.
A Survey on Security Issues in Iot for Blockchain Healthcare

Blockchain is picking up footing and can be named as one of the furthermost basic topics nowadays. This paper introduces a wide review of the present blockchain customs for the Internet of Things (IoT) structures. It begins by outlining the blockchain and delineating the present examinations that arrangement with blockchain progressions. Even though commentators' question about its adaptability, security, and supportability, it has effectively changed numerous people's way of life in a few zones because of its extreme impact on ventures and organizations. The range of square chain applications ranges from budgetary, human services, car, hazard the executives, Internet of things (IoT) to open and social administrations a few investigations center on using the block chain information structure in different applications. For the most part it depicted here human services application and issue dependent on the ebb and flow study, it features open research difficulties and examine conceivable future research headings in the square chain innovations for IoT.