Reviewerly/exHarmony huggingface dataset:
A Shape Retentive Filtering Algorithm for Post-processing of Instance Contour of Cervical Cell Based on Level Set Method

A novel filtering algorithm is proposed based on level set method (LSM) and linear time Euclidean distance transform (LET) algorithm in this paper, which has the property of shape retention and thus is suitable for post-processing of the initial contours for contacting instances in digital Pap image. As one of our contributions, we propose two new metrics based on the pixel-level average false positive rate and false negative rate that used by baseline method. A significant decrease in pixel-level average false positive rate (FP) by 62% can obtain by our proposed method. The result of quantitative and qualitative evaluation shows that our proposed shape retentive filtering algorithm (SRFA) can effectively filter out the false positive fragments of the initial instance contour of cervical cells from the ISBI-2014 dataset.
WITHDRAWN: “A novel approach using elliptic curve cryptography to mitigate Two-Dimensional attacks in mobile Ad hoc networks”

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Who is in the Room? Notification for Intrusions While in Virtual Reality

The development of head-mounted display virtual reality systems (e.g., Oculus Rift, HTC Vive) has resulted in an increasing need to represent the physical world while immersed in the virtual. Current research has focused on representing static objects in the physical room, but there has been little research into notifying VR users of changes in the environment. This study investigates how different sensory modalities affect noticeability and comprehension of notifications designed to alert head-mounted display users when a person enters his/her area of use. In addition, this study investigates how the use of an orientation type notification aids in perception of alerts that manifest outside a virtual reality users’ visual field. Results of a survey indicated that participants perceived the auditory modality as more effective regardless of notification type. An experiment corroborated these findings for the person notifications; however, the visual modality was in practice more effective for orientation notifications.
Square-root filtering for discrete-time systems with multiplicative and additive noises

Abstract The paper addresses the problem of constructing numerically efficient linear filtering algorithms for discrete-time linear stochastic systems with multiplicative and additive noises. A new square-root covariance algorithm for optimal linear filtering is proposed. We demonstrate that the developed algorithm is algebraically equivalent to the standard covariance filter. Also it has the improved computational properties inherent to all square-root algorithms. The results of numerical experiments confirming the operability of the proposed algorithm are presented.
Flight Data of Airplane for Wind Forecasting

Understanding and predicting weather behavior is vital for informing pilots about changing flight conditions. This paper presents a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare two datasets for prediction suitability, and a collinearity analysis between these datasets reveals a better model performance with smaller test error with one of them. We then apply machine learning and a genetic algorithm to process this data further and arrive at a competitive error rate. Finally, we create an offline software for wind prediction using the best performing classifier.
A Research on Big Data Analytics in Healthcare Industry

Big Data has changed the way we manage, analyze and leverage data in any industry. One of the most promising areas where it can be applied to make a change is healthcare. Healthcare analytics have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Average human lifespan is increasing along world population, which poses new challenges to today's treatment delivery methods. In this article, we would like to address the need of big data in health care industry, big data analytics advantages, big data applications and so on.
Broadcast distributed voting algorithm in population protocols

The authors consider the problem of multi-choice majority voting in a network of n agents where each agent initially selects a choice from a set of K possible choices. The agents try to infer the choice in the majority merely by performing local interactions. Extending the popular ‘Population Protocol’ framework for pairwise interactions between agents, in this study, they propose a new model called ‘Broadcasting Population Protocol’. In the proposed model, each agent broadcasts its messages in such a way that all its neighbours will receive the message simultaneously. They design two distributed algorithms for solving the multi-choice majority voting problem in this new model. They show that these algorithms return the correct output, i.e. the choice in the majority. They also analyse their performances in terms of time and message complexities. They establish via simulations that the proposed algorithms improve both time and message complexities significantly with respect to previous algorithms proposed in conventional population protocols, and they can be utilised in networks where messages can be transmitted to a subset of agents simultaneously, such as wireless networks.
Thai Calligraphy using Adversarial Generative Network

Designing a new font is time-consuming because it involves designing hundreds or thousands of characters in the same style. Adding a new set of characters in another language to an existing font is also not easy, as these characters have to match the existing style. In this paper, a generative adversarial network (GAN) is used for aiding the font style transfer of an existing set of characters in one language to another set of characters in another language. We experimented with 11 calligraphy fonts that contain English and Thai characters. On average, the algorithm produces images with a structure similarity index measure of 0.93 for English to Thai character style transfer.
Parameterless Semi-supervised Anomaly Detection in Univariate Time Series

Anomaly detection algorithms that operate without human intervention are needed when dealing with large time series data coming from poorly understood processes. At the same time, common techniques expect the user to provide precise information about the data generating process or to manually tune various parameters. We present SIM-AD: a semi-supervised approach to detecting anomalies in univariate time series data that operates without any user-defined parameters. The approach involves converting time series using our proposed Sojourn Time Representation and then applying modal clustering-based anomaly detection on the converted data. We evaluate SIM-AD on three publicly available time series datasets from different domains and compare its accuracy to the PAV and RRA anomaly detection algorithms. We conclude that SIM-AD outperforms the evaluated approaches with respect to accuracy on trendless time series data.
When Burstable Instances Meet Mobile Computing: Performance Modeling and Economic Analysis

This paper proposes a tandem fluid queue model for a mobile computing system with computation offloading and analytically derives its key quality-of-service (QoS) metric. Based on the performance model, we further evaluate the economic benefits of burstable instances, a new type of cloud instances that are recently introduced to the market, and make suggestions on whether burstable instances should be used for mobile computing to save users' costs under different wireless channel conditions and QoS requirements.
Usage and Effect of Eye Tracking in Remote Guidance

Eye gaze information is an important and effective input modality for human-human communication. As eye tracker is becoming more affordable for remote collaboration, a systematic literature review about eye-tracking supported collaboration systems on physical works will improve the collaboration and benefit system design in the future. Toward this end, we review publications since year 2000 and categorize the reported prototypes and systems with respect to eye gaze functionality, eye-tracked subjects, physical task types and gaze visualisations, demonstrating an overview of the usage and effect of eye tracking in remote guidance over time. We identify the usage of eye tracking in remote guidance as gaze behavior and intention indicator, fast and effective input modality, physical referential pointer and communication cues for social presence. Finally we analyze and discuss the effect and limitation of eye tracking in remote guidance with respect to different gaze visualisations in a variety of applied scenarios. The technical and social challenges identified will improve collaborations under remote guidance and benefit eye-tracking supported collaboration system design in the future.
MultiModal, Robust and Accurate Hand Tracking

3D hand tracking has a wide application prospect, for instance, in the field of human-computer interaction. Existing generative methods based on model fitting typically obtain good accuracy, but are far less robust to rapid hand motions and gestures with self-occlusions. We thus present a multimodal hand tracking system aiming to boost both accuracy and robustness. An efficient initialization strategy based on image data and inertial sensor data is designed to improve the robustness. A new optimization algorithm is proposed to improve the accuracy further by introducing gradient descent into APSO (Adaptive Particle Swarm Optimization). In addition, we construct a cylinder-cuboid hand model and establish a corresponding cost function. The proposed hand tracking method is evaluated on MSRA14 Dataset and achieves the state-of-the-art result.
Exploiting Word Embeddings for Recognition of Previously Unseen Objects

A notable characteristic of human cognition is its ability to derive reliable hypotheses in situations characterized by extreme uncertainty. Even in the absence of relevant knowledge to make a correct inference, humans are able to draw upon related knowledge to make an approximate inference that is semantically close to the correct inference. In the context of object recognition, this ability amounts to being able to hypothesize the identity of an object in an image without previously having ever seen any visual training examples of that object. The paradigm of low-shot (i.e., zero-shot and few-shot) classification has been traditionally used to address these situations. However, traditional zero-shot and few-shot approaches entail the training of classifiers in situations where a majority of classes are previously seen or visually observed whereas a minority of classes are previously unseen, in which case the classifiers for the unseen classes are learned by expressing them in terms of the classifiers for the seen classes. In this paper, we address the related but different problem of object recognition in situations where only a few object classes are visually observed whereas a majority of the object classes are previously unseen. Specifically, we pose the following questions: (a) Is it possible to hypothesize the identity of an object in an image without previously having seen any visual training examples for that object? and (b) Could the visual training examples of a few seen object classes provide reliable priors for hypothesizing the identities of objects in an image that belong to the majority unseen object classes? We propose a model for recognition of objects in an image in situations where visual classifiers are available for only a limited number of object classes. To this end, we leverage word embeddings trained on publicly available text corpora and use them as natural language priors for hypothesizing the identities of objects that belong to the unseen classes. Experimental results on the Microsoft Common Objects in Context (MS-COCO) data set show that it is possible to come up with reliable hypotheses with regard to object identities by exploiting word embeddings trained on the Wikipedia text corpus even in the absence of explicit visual classifiers for those object classes. To bolster our hypothesis, we conduct additional experiments on larger dataset of concepts (themes) that we created from the Conceptual Captions dataset. Even on this extremely challenging dataset, our results, though not entirely impressive, serve to provide an important proof-of-concept for the proposed model.
A Comparative Study of Image Dehazing Based on Attention-Net and U-Net Atmospheric Light Estimation

The effect of image dehazing depends largely on the estimation effect of atmospheric light. In this paper, the Attention-Unet network is introduced to estimate atmospheric light. The model trained by the attention gate mechanism can suppress the irrelevant area of atmospheric light in the input image through implicit learning, and highlight the significant characteristics of the area that has a large impact on atmospheric light, so as to solve the problem of parameter invariability caused by U-Net's difficulty in self-learning. In this paper, the pyramid dense network estimation network is used to estimate the transmission diagram, and then the attention U-net is used to predict atmospheric light. The results of the two networks are obtained by atmospheric scattering model. Meanwhile, the output transmission map and output figure to haze through joint discriminator, can better use the structure of the correlation between them. The experimental results show that the effect of dehaze is better than that of DCPDN by using UNET to estimate atmospheric light, and the performance of SSIM and PSNR is improved on the same data set NNA-depTH2.
Research on evaluation index system of information system equipment system contribution rate

The construction of index system is the basis of equipment system contribution rate evaluation, and is also one of the main contents of equipment test and appraisal. This paper discusses the concept of systems contribution rate, introduces the evaluation process of system contribution rate of information system equipment, and gives the construction principles and ideas of information system equipment system contribution rate evaluation index system. Then, referring to the theory of operational loop, the evaluation index system of system contribution rate is given from the perspectives of system capability and system effectiveness. The evaluation index of this paper can provide decision support for the development of equipment planning system.
A Machine Learning Pipeline Generation Approach for Data Analysis

Data analysis requires a high level of expertise for domain workers, and AutoML aims to make these decisions in an automated way. But it is still a difficult problem to automatically generate machine learning pipelines with high performance in acceptable time. This paper presents a DFSR (Data Feature and Service Association) approach to automatically generating machine learning pipelines utilizing data features and service associations. The experimental results showed that the performance of the generated pipelines reached the satisfactory level of current AutoML tools, and the time consumption is reduced to the minute level.
THE USE OF ICT IN THE PROCESS OF DISTANCE LEARNING FOR STUDENTS OF HIGHER EDUCATION INSTITUTIONS

The article analyzes the peculiarities of the use of ICT during distance learning. Features of distance learning have been considered, namely its elements, subjects and set of means of realization. The difference between the concepts of "distance learning" and "online learning" has been analyzed and their common and distinctive features have been identified. The advantages and disadvantages of using ICT in the process of distance learning have been highlighted. The main advantages include flexibility (the ability to present the course material based on training, abilities of students), the ability to implement the latest pedagogical, psychological, methodological developments, convenience (the ability to study at a convenient time, in a certain place, education without separation from the main work, no time constraints for mastering the material), modularity (breaking down the material into separate functionally complete topics), costeffectiveness, the ability to simultaneously use a large amount of educational information by any number of students, interactivity (active communication between group students and teacherі), no geographical boundaries for education. The world practice of development and use of ICT in education shows a tendency to change the traditional forms of organization of the educational process in the information society. Therefore, information and communication technologies are highly effective tools that allow to give a large amount of information to a greater extent than traditional sources of information, and in a sequence that corresponds to the logic of cognition and the level of perception of a certain contingent of students. Having solved the problems with ICT, it will be possible to use new technologies and raise the learning process to a qualitatively higher level.
The comparison of accuracy between naïve bayes clasifier and c4.5 algorithm in classifying toddler nutrition status based on anthropometry index

Abstract Body Mass Index (BMI), one of which is used to indicate the category of nutrition al status of children under five whether it is proportional or not. Anthropometry has an important role to play in determining the nutritional status of Toddler. The Anthropometric guidelines for determining the nutritional status of Toddler are the parameters chosen which include an assessment of height, age, and weight. On the other hand this ever-increasing amount of data requires several methods to process and draw conclusions and information from these data. Some methods used to process large data to find patterns contained therein are simple, including the Naïve Bayes Clasifier Algorithm and C4.5 Algorithm. Both are methods in the classification of Data Mining Techniques. The Naïve Bayes Clasifier Algorithm and C4.5 Algorithm are included in the Ten Most Popular Data Mining Classifications. This Research will apply the Naïve Bayes Clasifier Algorithm and C4.5 Algorithm which is used for the classification of Toddler Nutrition Status based on the Anthropometry Index so as to produce the accuracy of both algorithms. While the five classes will be classified as thin, very thin, normal, fat, very fat. The results of this classification process show that the C4.5 algorithm has an accuracy rate of 0.93% better than the Naïve Bayes Clasifier Algorithm. The algorithm that has been formed can then be developed and implemented into an application making it easier for stakeholders to make a decision for the Classification of Toddler Nutrition Status based on the Anthropometric Index
Structure and Randomness in Planning and Reinforcement Learning

Planning in large state spaces inevitably needs to balance the depth and breadth of the search. It has a crucial impact on the performance of a planner and most manage this interplay implicitly. We present a novel method Shoot Tree Search (STS), which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random shooting. It also lets the user control the bias-variance trade-off, akin to TD(n), but in the tree search context. In experiments on challenging domains, we show that STS can get the best of both worlds consistently achieving higher scores.
Tunable capacitor for superconducting qubits using an InAs/InGaAs heterostructure

Abstract Adoption of fast, parametric coupling elements has improved the performance of superconducting qubits, enabling recent demonstrations of quantum advantage in randomized sampling problems. The development of low loss, high contrast couplers is critical for scaling up these systems. We present a blueprint for a gate-tunable coupler realized with a two-dimensional electron gas in an InAs/InGaAs heterostructure. Rigorous numerical simulations of the semiconductor and high frequency electromagnetic behavior of the coupler and microwave circuitry yield an on/off ratio of more than one order of magnitude. We give an estimate of the dielectric-limited loss from the inclusion of the coupler in a two qubit system, with coupler coherences ranging from a few to tens of microseconds.
Designing 360 Video for Immersive Journalism

Journalism has seen tremendous change and 360-degree video, a form of virtual reality (VR), continues to present new ways for audiences to experience and engage with stories. Many journalists see 360° video as the future of storytelling. This literature review was conducted to guide UX practitioners and journalism professionals to practical information in an effort to understand the design challenges of VR and 360° video in journalism and identify opportunities to improve the user experience.
Malaria Detection Using Local Composition Pattern

Abstract Malaria is a major global health problem. Every year thousands of people have been died because of this harmful disease. It is a life-threatening disease that is triggered by the “night-biting” mosquitoes known as the Anopheles mosquitoes. They normally bite at day time. In this paper, to detect malaria parasite in the bloodstream, an image processing algorithm has been developed. Our proposed approach can classify malaria-infected images from patient’s blood samples by extracting red blood cells (RBCs) from the images. Malaria detection using Local Composite Pattern (LBP) outperforms in comparison with the existing traditional approaches.
Research on Image Recognition Method of Convolutional Neural Network with Improved Computer Technology

Abstract It has been a goal of artificial intelligence to make computers recognize objects and have the vision similar to human beings. After years of development, considerable progress has been made, but it is not satisfactory. It can be said that deep convolutional network is now the best algorithm for image recognition, which is also the reason why this paper decides to adopt deep convolutional network algorithm. The convolutional neural network USES Shared parameters between the convolutional layers, which not only reduces the required memory size, but also reduces the number of parameters to be trained and improves the performance of the algorithm. The difficulty of image recognition is hard to find a way to from the natural environment the boundary shape, texture, angular point, and image characteristics such as concept, and the image itself are highly susceptible to the influence of natural environment and changes in perspective, dimension, twist deformation, interference, light, shade, difficulties of the difference of background doping and within the class, make the computer more difficult to get about abstraction and understand the expression of natural images.