Reviewerly/exHarmony huggingface dataset:
Voiceproof: Speaker Verification with Captcha A Novel Biometric Access Control Mechanism

Authentication on the digital platform has been a serious concern in today&#x0027;s world. Though fingerprint and facial recognition are being widely used, many methods to circumvent them exist. Voice being another biometric that can be used for individual identification also finds its place in authentication applications because of the promising results reported by various state-of-the-art Automatic Speaker Verification (ASV) systems. Also, voice being the only time-variant remotely available biometric, is unique for authentication applications. The vulnerability of ASV systems to spoofing attacks is also undeniable. Thus, a robust access control system is the need for many applications. This article presents two novel methods to fuse the robustness of CAPTCHA, a well-known method to distinguish human from computer, and uniqueness of voice-biometric for access control applications. It is believed that proposed systems would be efficient for real-time access control systems.
Improving the User Experience for Manual Data Labeling using a Graph-based Approach

Even though the ever-increasing demand for large amounts of data makes automated data labeling indispensable in many cases, labels must still be entered manually in various areas. In these cases, an acquisition process that is as simple as possible is essential to ensure high data quality even for complex data sets. We present a graph-based approach that guides a user step-by-step through the process, minimizing the number of possible choices at each step using a defined terminology. A case study demonstrates the capabilities of this approach and statistically evaluates the simplification achieved by this method.
Scale Recurrent Network for Single Image Dehazing

The quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the image, haze removal methods play a critical role. In this paper, we propose a new multi-scale network structure that is rarely used in haze removal and a new loss function to generate high-quality haze-free images. Despite a number of proposed dehazing methods, they are not able to capture the haze accurately without being confused by other objects that are similar in color to haze and completely remove the haze throughout the image. Our proposed network architecture takes the Scale Recurrent Network structure and we incorporate dilated convolution to catch the more global features like haze with the wide receptive field. Additionally, we train our network with a new loss function using dark channel prior to more effectively learn the haze features. Compared with state-of-the-art methods, our proposed method achieves better results on both synthetic and real-world images.
A Study on a Teaching &amp; Learning Method for Basic and General Education in the Post-Corona－Focusing on the Blended Learning

Considering the number of non-face-to-face classes occurring in the Covid-19 era, it is now widely recognized that blended learning will become the new normal for education in the post-Corona era. However, detailed discussions have not sufficiently taken place as to why blended learning is necessary in the post-Corona era, nor how this type of learning should be applied and practiced. Therefore, this paper discusses the necessity of applying blended learning as a teaching and learning method for basic and general education in the post-Corona era. It also examines the methods and requirements for effectively implementing blended learning for these future classes. As blended learning enables ‘'student-centered teaching’ or ‘active learning’in order to develop the various competencies without excluding knowledge education, it is suitable for basic and general education that aims to strengthen competencies as well as to acquire knowledge. As a method for practicing blended learning for basic liberal arts education, first, we suggest that it is necessary to develop standardized video-lectures for the uniformity of the students' learning experience and for the improvement of learning effectiveness, as well as the development of modular content in the form of micro-learning. Second, considering the characteristics of basic and general education subjects and and the learners themselves, it is suggested that we customize teaching-learning methods for each student level using learning analytics and artificial intelligence. Third, we emphasize that it is necessary to develop an educational model for blended learning for each subject and to strengthen the competencies of professors in order to effectively operate blended learning classes in basic and general education. Fourth, we suggest that an organization-oriented curriculum redesign project be implemented to change the educational method to blended learning and to spread it on a large scale at the university level.
Target tracking: method and comparison

Target tracking technology is an important research direction in computer vision, which has a wide range of applications, such as video surveillance, human-computer interaction, unmanned driving and so on. At present, target tracking technology has made great progress, especially the target tracking method using deep learning has achieved satisfactory results in the last two years, which has made a breakthrough in target tracking technology. This paper discusses the target tracking system from the two frameworks of correlation filter and Siamese network, and further examines the new target tracking method proposed. In the correlation filter, we analyze the improved algorithms such as CACF, ECO, MOSSE and SRDCF, However, all the above methods based on correlation filtering are affected by boundary effect. In order to overcome this problem, SRDCF came into being. SRDCF uses spatial regularization to punish the correlation filter coefficients, and obtains results comparable to deep learning tracking methods. In Siamese network, we analyze the algorithm frameworks of SiamR-CNN, SiamMask, SiamRN, SiamRPN++, SiamFC. In this paper, we review the object tracking task and split it into two main aspects: CF, Siamese network. For each framework, we survey the work and draw some opinions. Finally, we hope to help sort out ideas and help beginners get started quickly.
The MESSIR Flexible Scientific Approach to Requirements Engineering

Among the software engineering development phases, requirements engineering is the one that has the most impact on project success or failure. To be executed in various contexts, there is an important need for flexibility and efficient tool support. A flexible requirements engineering method should include several levels allowing for more or less completeness and precision. Some project contexts would need a lightweight activity using structured natural language but still being guided and grounded partly on professional standards. Some more advanced projects would need more complete requirements documents and would benefit from a description language based on scientific notions allowing for better precision for specific system operations. Some business or safety critical systems would need an approach allowing for requirements simulation and verification. Requirements engineering education is an important objective to prepare future engineers to understand those requirements engineering needs and be prepared for practice in a professional setting. In the last five years, we have developed a requirements engineering method called Messir with a tool Excalibur and experiments in academia have been made to see how it was solving actual software engineering problems focusing first on requirements engineering education. Messir components represent in themselves some improvements w.r.t. the state of the art of the “standard” theories, methods and tools, mainly by introducing an improved requirements engineering process, language and verification support based on executable requirements specifications. Furthermore, the Messir approach solves also some actual problems related to software engineering education by offering a product line framework for setting up or improving courses in computer science curricula. The main result being to contribute to develop the software engineering capabilities of engineers and scientists that feed the job market in industry, research or education.
Interview by Martin Ford

chapter Share on Interview by Martin FordProbabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 29–42https://doi.org/10.1145/3501714.3501719Online:04 March 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 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
The Probability Density Function of Bearing Obtained From a Cartesian-to-Polar Transformation

The problem of tracking a two-dimensional Cartesian state of a target using polar observations is well known.At a close range, a traditional extended Kalman filter (EKF) can fail owing to nonlinearity introduced by the Cartesian-to-polar transformation in the observation prediction step of the filter.This is a byproduct of the nonlinear transformation acting on the state variables, which make up a bivariate Gaussian distribution.The nonlinear transformation in question is the arctangent of Cartesian state variables X and Y , which corresponds to the target bearing.At long range, the bearing behaves as a wrapped Gaussian random variable, and behaves well for the EKF.At close range, the bearing is shown to be non-Gaussian, converging to the wrapped uniform distribution when X and Y are uncorrelated.This study provides a concise derivation of the probability density function (PDF) for bearing for the EKF observation prediction step and explores the limiting behavior for this distribution while parameterizing the target range.
Advanced Driver Assistant System Using Image Processing

In recent years, it is observed that driving can occupy a large span of one’s daily life. Dangerous road accidents can occur due to various reasons. Most importantly, the driver should be attentive so that collisions can be avoided. Image processing method areused to process an image for getting some valuable information from it. In this work, a driving assistant system using image processing method has been proposed, where the facial expression that reveals the exhaustion level of the driver can be identified and alerts him accordingly.
Homogenization of earthquake magnitudes using bivariate fit shell approach: Eastern Himalaya region

An earthquake catalog is an essential requirement for seismic hazard estimation, which contains data from various sources or agencies (e.g., ISC, NEIC, HRVD). A myriad of earthquake magnitude scales (e.g., MS, mb MW) are given in these catalogs. These different magnitudes need to be homogenized for seismic hazard assessment. Here, we studied seismicity of the Eastern Himalayan region, within latitudes 26°N-30°N and longitudes 89°E −98°E, to obtain a set of relations for the homogenization. Development of regression relationships using Williamson-York Iterative Bivariate Fit shell method is executed. The conversions are implemented to magnitude scales MS and mb, and homogenized to moment magnitude MW, which is accepted as a more stable magnitude scale. This study further involves the development of a robust function in MATLAB that makes the homogenization of an extensive data set much more effective and more accessible. The earthquake catalog data for the period 1900–2020 are compiled for homogenization to MW. The code and the homogenized earthquake catalog would be helpful for seismic hazard assessment studies in the Eastern Himalayan region.
Videos2Doc: Generating Documents from a Collection of Procedural Videos

The availability of user-generated multi-modal content, including videos and images, in abundance makes it easy for users to use it as a reference and source of information. However, several hours may be required for the consumption of this large corpus of data. Particularly, for authors and content creators abstracting out information from videos to then representing it in a textual format is a tedious task. The challenges are multiplied due to the diversity and the variety introduced when there are several videos associated with a given query or topic of interest. We present, Videos2Doc, a machine learning-based framework for automated document generation from a collection of procedural videos. Videos2Doc enables author-guided document generation for those looking for authoring assistance and an easy consumption experience for those preferring the text or document media over videos. Our proposed interface allows the users to choose several visual and semantic preferences for the output document allowing the generation of custom documents and webpage templates from a given set of inputs. Empirical and qualitative evaluations establish the utility of Videos2Doc as well as the superiority over the current benchmarks. We believe, Videos2Doc will ease the task of making multimedia accessible through automation in conversion to alternate presentation modes.
Anonymising pathology data using generative adversarial networks

Anonymising medical data for use in machine learning is important to preserve patient privacy and, in many circumstances, is a requirement before data can be made available. One approach to anonymising image data is to train a generative model to produce data that is statistically similar to the input data and then use the output of the model for downstream tasks, such as image classification, instead of the original sensitive data. In digital pathology, it's not yet well understood how using generative models to anonymise histology slide data impacts the performance of downstream tasks. To begin addressing this, we present an evaluation of a histology image classifier trained using patches extracted from the Camelyon 16 dataset and compare it to a classifier trained on the same number of synthetic images generated with a Deep Convolutional Generative Adversarial Network (DCGAN), from the same data. When predicting the class of an image patch as either cancer or normal it's shown that the accuracy reduces from 0.78 for original alone to 0.59 for synthetic alone, and the recall is significantly reduced from 0.70 to 0.44 when training exclusively on the same amount of synthetic data. If retaining a similar accuracy is required for the downstream task, then either the original data must be used or an improved anonymisation strategy must be devised. We conclude that using this DCGAN to anonymise the dataset, degrades the accuracy of the classifier which implies that it has failed to capture the required variation in the original data to generalise and act as a sufficient anonymisation strategy.
Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.
A Gray Correlation Algorithm for Analysis of Influencing Factors of Film and Television Copyright Export

In this paper, an intelligent model is constructed to facilitate real-time dynamic analysis of the factors affecting the export of film and television copyrights and to track the process of spatiotemporal context. The algorithm analyzes the factors affecting the export of film and television copyrights according to actual needs. Through the comparative analysis of experimental models, the influence and optimization of gamma correction and Laplace distribution weighting on spatial context information and confidence map update are verified. In addition, this paper uses the gray relational algorithm to construct an analysis system of factors affecting the export of film and television copyright. The research shows that the analysis model of the influencing factors of film and television copyright export based on the gray relational algorithm can play a very good role in the analysis of these factors.
Delegation of Security Functions in Heterogeneous Embedded Systems

Various security challenges have been arising with the advent of Internet of Things (IoT) in the past decade. One of the issues is the limited resources for enforcing security functions in heterogeneous embedded IoT systems. This paper aims to cope with this issue by resolving the delegation problems within the scope of systematic security design. Concrete techniques for the delegation of security functions are discussed. Meanwhile, partial delegation and total delegation, and restrictions on the practice of delegation are examined. It also shows that the storage and communication capacities in the system are restriction factors affecting the viability of security function delegation.
Modeling and simulation of imaging through the water-air Snell's window from underwater

Imaging through water-air Snell's window from underwater involves several complicated physical processes hard to model. In this paper, a simplified imaging model is proposed. In this imaging model, CIE sky model is used to calculate the sky background radiance, directional ocean wave spectrum is used to model random ocean surface, raytracing is used to form the refraction equation, and a simplified model based on RTE is used to calculate underwater light scattering and absorption. Simulated data using the proposed imaging model are presented.
Elderly Hajj pilgrims activity recognition based on candidate classification technique

Abstract Every year, the rate of elderly people is rising, and monitoring them in mass gatherings like a pilgrimage is a tough task. Millions of Muslims around the world visit Makkah and Madinah for the Hajj pilgrimage. Since 1990, more than 4800 pilgrims have died in the stampede. Aged pilgrims are very much vulnerable to these stampedes. To support them, it is very important to monitor their activities. But only a little is done to monitor the elderly pilgrims. In this article, all the rituals performed in the Hajj pilgrimage have been studied and an activity recognition dataset for elderly pilgrims in line with the rituals of Hajj has been created. A novel classification method called the candidate classification technique (CCT) is proposed to recognize the activities accurately with less response time. Three publicly available activity recognition datasets namely UCI HAR, USC HAD, SKODA, and the proposed elderly hajj pilgrim activity recognition dataset have been used. The accuracy achieved using the proposed classification technique is 98.25%, 96.97%, and 99.29% for the UCI HAR, USC HAD, and SKODA datasets.
Multi-task feature fusion network for monocular depth estimation without joint annotations

Monocular depth estimation refers to recovering the depth information of a 3D scene from a single 2D image taken by a camera. A multi-task training framework combining of semantic segmentation and depth estimation is developed to improve the monocular depth estimation performance in this paper. Nevertheless, joint annotations, namely semantic labels and depth annotations, are necessary for training dataset in the traditional joint training framework of semantics and depth. Unluckily, scarcely any large public datasets that provide the joint annotations can be accessed. To address the problem, a training framework having the feature correlation screening and linkage mechanism based on the linear independence of Gram matrix called GSFA-MDEN (Gram Semantic-Feature-Aided Monocular Depth Estimation Network), which is trained through the TSTB (Two-Stages-Two-Branches) training strategy, is studied and developed. GSFA-MDEN is composed with two brunches namely DepthNet and SemanticsNet, which are firstly trained through two different large datasets having its own respective annotation. Subsequently, the overall network is constructed through the feature fusion of the two brunches based on the Gram nonlinear correlation, which can establish the quantitative representation of the correlation between semantic features and depth features. Compared to the original DepthNet, on the KITTI dataset, GSFAMDEN decreases Root Mean Square Error (RMSE) from 5.808m to 5.370m by adding SemanticsNet assisted depth estimation, and the RMSE is further decreased to 5.167m by creatively employing Gram nonlinear correlation to excavate correlation of different task features. The series experimental results illustrate the superiority of GSFA-MDEN.
Autonomous monitoring of finishing pigs using side-view cameras and deep learning

Monitoring of pigs is currently done by humans observing pig pens to acquire information such as behaviour to back certain actions such as regulation of temperature. These actions can have vital outcomes, as taking the wrong action or not taking an action, can result in fatalities. A vision-based system to automatically monitor pigs, therefore, has the potential of improving the welfare of pigs and enhancing the pig industry. This work investigates such a system and demonstrates several modules that can be utilized to obtain behavioural information and how such information can be interpreted. Moreover, the utilized system modules and side-view data will be evaluated. The code is public and available at: https://github.com/KHML-master/apimos.
A novel multilabel video retrieval method using multiple video queries and deep hash codes

Abstract Most videos consist of multiple clips. Namely, videos usually have multiple spatio‐temporal information. However, existing video datasets are of single video clips. Additionally, content‐based video retrieval methods have been implemented based on a single query clip. However, a single query may not be sufficient for better expressing intention concerning the query. Besides, it narrows the capability to search for multiple semantics. How to deal with retrieving videos having multiple clips has not been adequately investigated. This study proposes a novel multilabel video retrieval method that utilizes multiple video clips as queries. Whatever the number of queries, it transforms a multi‐query video retrieval into a single‐query video retrieval. The method is independent of both the video features and distance metric types. However, we propose a deep video hashing method to achieve speed and efficiency. Experiments performed on three prepared multilabel video datasets have proven the effectiveness of the proposed method. The source codes of the study are public.