Reviewerly/exHarmony huggingface dataset:
Assertive Vision Using Deep Learning and LSTM

Image captioning is an elemental which needs correct understanding of images and also the ability of generating description sentences with proper and proper structure. The captioning of an image must identify the objects within the image, their actions, and their relationships, in addition as any silent features that will be absent from the image. This has been a challenging task within the field of computing throughout the years. Using any tongue sentences to automatically create a picture description may be a difficult issue. This paper explores the numerous picture captioning models that are available. Within the past few years, the matter of generating descriptive sentences automatically for images has garnered a rising interest in tongue processing and computer vision research. The LSTM units are intricate and naturally sequential in nature. Our model can study long-time period visual-language interactions the usage of ancient and information about the future in a high-level semantic domain via combining two different LSTM networks and a deep CNN. We used two publicly available datasets to demonstrate the utility of this strategy: Flickr8k and Flickr30k. Flickers has an API that may be used for image collections. We will use the APIs to search out images and explore for them by tag as an example, you&#x0027;ll search images for common day items/places and buildings like burger, temple, etc. We provide multiple approaches to picture captioning supported deep learning similarly as distinct evaluation strategies during this survey work. Since present deep learning may be a black-box model, it&#x0027;s critical to look at the impact on each module to completely comprehend the model.
An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting

Although the existing algorithms have done a lot of progress in extended target tracking (ETT), most algorithms are limited by the assumption of the target shape, and there are problems with parameter calculations. To solve the problems above, an extended target tracking algorithm based on level set and Gaussian Surface fitting (GSF), is proposed. Different from the existing extended target tracking algorithms, the proposed algorithm uses a level set to implicitly describe the evolution of the estimated shape, eliminates the parameterization process, and uses the GSF to construct the measurement source spatial distribution function (MSSDF) of the extended target. The proposed algorithm can obtain the estimated shape of the extended target without knowing the prior shape and size of the target. The simulation results verify the effectiveness and reliability of the proposed algorithm.
Face Mask Classification Based on Deep Learning Framework

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem around the world. This pandemic is having devastating effects on societies and economies. Due to the lack of health resources in a short period, all countries and continents are likely to face particularly severe damage that could lead to a large epidemic. Wearing a face mask that stops the transmission of droplets in the air can still be helpful in combating this pandemic. Therefore, this chapter focuses on implementing a face mask detection model as an embedded vision system. The six pre-trained models, which are MobileNet, ResNet-50, MobileNet-V2, VGG-19, VGG-16, and DenseNet, are used in this context. People wearing or not wearing masks were detected. After implementing and deploying the models, the selected models achieved a confidence score. Therefore, this study concludes that wearing face masks helps reduce the virus spread and fight this pandemic.
FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs

The fundamental matrix estimation is a classic problem in computer vision. The traditional algorithms require high-precision correspondences. However, correspondences in biplanar radiographs are difficult to match accurately.We propose an end-to-end network to estimate the F-Matrix directly from BR, which includes feature extraction and regression prediction. There is no publicly available dataset of biplanar radiographs. We produce the dataset in this paper to train and test the proposed network. Four metrics, Mean Square Error, Calculating R-squared, Square Value of Extreme Constraint, and Absolute Value of Extreme Constraint are used to measure the performance of the approaches.The best Square Value of Extreme Constraint and Absolute Value of Extreme Constraint values we obtained on the datasets were 0.20 and 0.43, respectively. Compared with other methods, the estimation accuracy of FM-Net is improved by more than 53.53%.The results of experiments demonstrate that the proposed network can estimate the fundamental matrix successfully. It outperforms the classical algorithms and other deep learning-based methods.
Pedestrian Detection and Tracking Through Kalman Filtering

There are a lot of challenges associated with - autonomous driving, and one such challenge is pedestrian detection and tracking, especially in this complex world, multiple people are involved in complicated and cluttered backgrounds. In this paper a novel method is proposed to detect, track and predict pedestrians based on Histograms of Oriented Gradients (HOG) algorithm and the Camshift algorithm respectively. These two algorithms run on top of the Kalman filtering framework. The Kalman filter is used as a tracker for precisely localizing and tracking the pedestrians. Then triangle similarity is used to calculate distance.
Design and Evaluation of Travel and Orientation Techniques for Desk VR

Typical VR interactions can be tiring, including standing up, walking, and mid-air gestures. Such interactions result in decreased comfort and session duration compared with traditional non-VR interfaces, which may, in turn, reduce productivity. Nevertheless, current approaches often neglect this aspect, making the VR experience not as promising as it can be. As we see it, desk VR experiences provide the convenience and comfort of a desktop experience and the benefits of VR immersion, being a good compromise between the overall experience and ergonomics. In this work, we explore navigation techniques targeted at desk VR users, using both controllers and a large multi-touch surface. We address travel and orientation techniques independently, considering only continuous approaches for travel as these are better suited for exploration and both continuous and discrete approaches for orientation. Results revealed advantages for a continuous controller-based travel method and a trend for a dragging-based orientation technique. Also, we identified possible trends towards task focus affecting overall cybersickness symptomatology.
A Model for Selecting Media Type of Memory Cues in Ubiquitous Prostheses

Memories shape our interactions with the world. Thus, there is a new trending research direction to build memory prostheses that help us augment our cognitive capabilities and become better versions of ourselves. Such prostheses alter our memories through augmentation or degradation. Nevertheless, there is a research gap in systematic processes to design such systems. In this work, we reflect on our experience designing several memory prostheses and present a two-dimensional design space to contextualize existing literature. We also present eight criteria to choose media types to capture, store and present memory cues. Our work is one step forward to identifying elements of a holistic framework to build targeted memory prostheses.
Human Vision vs. Computer Vision: A Readability Study in a Virtual Reality Environment

Text plays an important role in conveying information to users in a virtual reality (VR) environment. Both VR software and hardware are evolving rapidly to improve text display quality. However, evaluation of text readability still relies on human participants. In this study, cloud computer vision was used to evaluate text readability in VR. Human subjects were recruited to test the same text scenarios. The cloud computer vision-based approach produced results that were consistent with human vision-based recognition. The use of computer vision to automate text readability evaluation could significantly reduce the overall effort and time in developing readable text in VR.
Slide Attack on Full-Round ULC Lightweight Block Cipher Designed for IoT

To meet the security demand for IoT devices, many new lightweight block ciphers are proposed. ULC is a lightweight block cipher designed for the IoT. It has many advantages in terms of memory use, efficiency, and security. In this paper, a slide attack on full-round ULC is proposed in a related key setting. First, two properties on ULC are discovered. The first property is presented to illustrate the property of a slid pair for ULC. The second property is introduced to construct a link between some round key bits and some master key bits. Based on these properties, a key recovery attack on ULC is proposed. In this attack, with the condition of one related key, all the 80 master key bits can be recovered with the data complexity of O(232), the memory complexity of O(232), and the time complexity of O(263). The result of this paper indicates that ULC can’t resist slide attack in related key settings.
Interpretation of Chemical Analyses and Cement Modules in Flysch by (Geo)Statistical Methods, Example from the Southern Croatia

This study included the testing of normal (Gaussian) distribution of input data and, consequently, spatially interpolating maps of chemical components and cement modules in the flysch. This deposit contains the raw material for cement production. The researched area is located in southern Croatia, near Split, as part of the exploited field “St. Juraj–St. Kajo”. There are six lithological units: (1) alternation of marls and sandstones with inclusions of conglomerates, (2) marl, (3) calcsiltite, (4) calcarenite, (5) marl with nummulites, (6) debrites, and (7) clayey marl. All of them are deposited in the (a) northern and (b) southern beds. Only debrites are divided into the (a) western and (b) eastern layers. Those lithological units were divided technologically based on their cement modules (lime saturation factor (LSF), silicate module (SM), and aluminate module (AM)). The average thicknesses were analysed, followed by normality tests (Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W)) of the chemical analyses: CaO, SiO2, Al2O3, Fe2O3, MgO, SO3, Na2O, K2O, CaCO3 (%) and three cement modules (LSF, SM, AM), available in the six lithological units. The normality tests were applied based on a number of input data. The further interpolation was performed using two methods, kriging and inverse distance weighting, mapping CaO (%), SiO2 (%), and LSF (−) in three different lithological units. The interpolation methods were selected based on two criteria: (a) normality test pass or fail and (b) the amount of data. In total, 144 tests were calculated, including sets from 7 to 36 points. The results show the current situation in the quarry, after decades of production, making reliable the future predictions of cement raw material exploitation.
Ransomware: To Pay or Not to Pay? The results of what IT professionals recommend

The number of ransomware attacks has been on the rise. Ransomware is a malware attack in which the cybercriminal encrypts a file or a device and then demands a payment from the victim. There are many different variants of ransomware, and they continue to evolve. The number of IoT devices has been increasing over the years. IoT devices also face a considerable challenge from ransomware. In this study, a survey was administered to 27 Information Technology (IT) professionals. They were asked if they would recommend an organization attacked by ransomware to pay the ransom. The data collected from the professionals was analyzed, and the results indicated that only 4% would pay. Furthermore, the number of those who would not pay or were not sure was evenly divided at 48%. Moreover, the results indicate that the decision to pay or not to pay is difficult even for IT professionals with many years of experience in the field. The literature review revealed that the decision whether to pay or not to pay is a business decision. Some factors to consider when choosing to pay or not pay were presented. The use of cybersecurity insurance with ransomware coverage is recommended. Practices that can prevent ransomware attacks were covered and they include educating users, backups, and multi-layer security.
ArtScape: Gamified Virtual Reality Art Exploration

This paper presents multiple Virtual Reality 3D interaction, manipulation, and simulation techniques in the context of the 2022 3DUI contest of the IEEE VR conference. The proposed work provides the integration of 3DUI techniques and aims to familiarize and inform potential users about various artworks and artists. All techniques are implemented into the context of a virtual reality escape-room or treasure-hunt game, where the information provided for the paintings is necessary to solve the puzzles and as a result, contributes to an immersive experience where 2D paintings break into the 3D world creating a living artwork.
Computational Simulation Analysis for Physical Identifiable Teaching Technology

In order to improve the identifiable teaching effect of physics, this paper studies the identifiable teaching technology of physics combined with computer simulation analysis technology, changes the traditional physics teaching method, and studies the identifiable teaching effect of computer simulation algorithm. The advantages are obvious; that is, it overcomes scaling, translation, and rotation and does not require a lot of time to learn samples, etc. In addition, this paper combines the computer simulation analysis technology and the actual needs of physics teaching to construct an intelligent and identifiable teaching system. The experimental research results show that the physics identifiable teaching system based on computer simulation analysis proposed in this paper can play an important role in physics teaching.
Practical Classes in English in the Form of a Conference Lesson as a Means of Professional Foreign-Language Competence Formation in Non-Linguistic Students at a University

The research aims to substantiate the importance of interactive teaching methods in modern education at higher education institutions using the example of foreign language classes. The paper addresses the use of practical conference lessons as a form of teaching in the educational space of a university. The work gives a brief overview of interactive methods of teaching a foreign language at a university, determines the specificity of organising a conference lesson, describes the stages of preparation and conduct of such a lesson, as well as provides criteria for assessing students’ work in the classroom. Scientific novelty of the research lies in developing the structure, stages of preparation, methods of conducting and assessing a conference lesson as a form of teaching a foreign language at a university through the integration of project-based learning, the case method, discussion, business simulation games involving non-linguistic students at a higher education institution as exemplified by students studying at the Institute of Mathematics and Information Technology of the Federal State Autonomous Educational Institution for Higher Education “Volgograd State University”. As a result, the main stages of a conference lesson and its advantages in the development of students’ professional foreign-language competence have been characterised.
Learning to classify relations between entities from noisy data - A meta instance reweighting approach

Relation classification between entities is a fundamental problem in knowledge extraction. It aims at determining if a semantic relation holds between a pair of entities based on textual descriptions. In general, the training data for each relation is limited. Distant supervision has thus been widely used to generate abundant weakly labeled data for the task. These data are noisy, containing many errors. A crucial problem is to select reliable instances for training or weigh them adequately. However, most existing approaches rely solely on statistics among the noisy data to generate classification features. They could be easily trapped by frequent wrong instances. To deal with this problem, we use a small set of manually annotated samples as reference data to guide the selection/weighting process. In this paper, we propose a new meta instance reweighting framework, which automatically adjusts the instance weights under the guidance of the reference data. The principle is that the classifier trained with the weighted data should perform well on the reference data. To further improve the approach, we propose to augment the limited reference data with a set of highly reliable instances — elite instances, selected from noisy data. The selection of elite instances is based on both their labeling quality and diversity so as to maximize the coverage of diverse expressions by the reference set. Extensive experiments on two public datasets by applying our method to two base neural models demonstrate the effectiveness of our method: It outperforms previous state-of-the-art approaches. We also show that the use of both reference data and elite instances is beneficial. The proposed approach is model-agnostic: it can be used on other classification models. It can also be applied to other classification tasks under distant supervision.
Self Supervised Lesion Recognition for Breast Ultrasound Diagnosis

Previous deep learning based Computer Aided Diagnosis (CAD) system treats multiple views of the same lesion as independent images. Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views. In this paper, we propose a multi-task framework that complements Benign/Malignant classification task with lesion recognition (LR) which helps leveraging relationship among multiple views of a single lesion to learn a complete representation of the lesion. To be specific, LR task employs contrastive learning to encourage representation that pulls multiple views of the same lesion and repels those of different lesions. The task therefore facilitates a representation that is not only invariant to the view change of the lesion, but also capturing fine-grained features to distinguish between different lesions. Experiments show that the proposed multi-task framework boosts the performance of Benign/Malignant classification as two sub-tasks complement each other and enhance the learned representation of ultrasound images.
Editorial for the Special Issue on Meaningful Human Control and Autonomous Weapons Systems

Global discussions on the legality and ethics of using Artificial intelligence (AI) technology in warfare, particularly the use of autonomous weapons (AWS), continue to be hotly debated [...]
Linearity Graph in the Prediction of Granular Active Carbon (GAC) Adsorption Ability

Abstract Detergent waste or greywater is the waste generated from most cleaning products. The existing cleaning products generally contain compounds that threaten the life of aquatic biota, so special treatment is needed in the disposal of their waste. Several studies have stated that activated carbon is a good medium for the adsorption of detergent waste. This research aims to find out maximum ability of activated carbon as a waste adsorption medium detergent. So that the necessary initial analysis of the quality of activated carbon for maximizing the performance of activated carbon as an adsorption medium detergent waste. Iodine absorption analysis can be used as an initial reference in determining the quality of adsorption media. Iodine absorption linearity test using concentrations of 0.025 N, 0.05 N, 0.1 N, 0.15 N, 0.2 N carried out on granular activated carbon which will be used as an adsorption medium. Based on the graphic linearity determination coefficient values obtained at 0, 99. In other words, the absorption of GAC is influenced by iodine concentration, with the correlation between the two variables being opposite. Increasing the concentration of iodine will be accompanied by a decrease in the absorption of iodine. This shows that using GAC as an adsorption medium, it is necessary to take into account the pollutant content of the detergent waste and the contact time of GAC to the pollutant (adsorbate). To be effective as an adsorption medium, the calculation of pollutant levels of detergent waste needs to be analyzed as an initial reference in determining the required contact time. Estimated contact time will affect the design of the adsorbent media for activated carbon-based detergent waste.
Effects of Transfer Functions and Body Parts on Body-Centric Locomotion in Virtual Reality

Body-centric locomotion allows users to control both movement speed and direction with body parts (e.g., head tilt, arm swing or torso lean) to navigate in virtual reality (VR). However, there is little research to systematically investigate the effects of body parts for speed and direction control on virtual locomotion by taking in account different transfer functions(L: linear function, P: power function, and CL: piecewise function with constant and linear function). Therefore, we conducted an experiment to evaluate the combinational effects of the three factors (body parts for direction control, body parts for speed control, and transfer functions) on virtual locomotion. Results showed that (1) the head outperformed the torso for movement direction control in task completion time and environmental collisions; (2) Arm-based speed control led to shorter traveled distances than both head and knee. Head-based speed control had fewer environmental collisions than knee; (3) Body-centric locomotion with CL function was faster but less accurate than both L and P functions. Task time significantly decreased from P, L to CL functions, while traveled distance and overshoot significantly increased from P, L to CL functions. L function was rated with the highest score of USE-S, -pragmatic and -hedonic; (4) Transfer function had a significant main effect on motion sickness: the participants felt more headache and nausea when performing locomotion with CL function. Our results provide implications for body-centric locomotion design in VR applications.
Assessing Students’ Perception, Engagement and Metacognitive Skills on Mobile Learning

research-article Share on Assessing Students’ Perception, Engagement and Metacognitive Skills on Mobile Learning Authors: Janina C. Sercenia Science Education Department, De La Salle University, Philippines Science Education Department, De La Salle University, PhilippinesView Profile , Maricar S. Prudente Science Education Department, De La Salle University, Philippines Science Education Department, De La Salle University, PhilippinesView Profile Authors Info & Claims IC4E 2022: 2022 13th International Conference on E-Education, E-Business, E-Management, and E-Learning (IC4E)January 2022 Pages 209–215https://doi.org/10.1145/3514262.3514270Published:19 April 2022Publication History 0citation23DownloadsMetricsTotal Citations0Total Downloads23Last 12 Months23Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access