Reviewerly/exHarmony huggingface dataset:
Improving Data Quality from Remote Eye Tracking Systems Using Real Time Feedback

This study proposes a solution to improve data quality from remote desktop eye trackers. Poor data quality from these systems regularly occurs as a result of participants unknowingly moving outside of the functional data collection area, i.e. the eye tracking box. Researchers are often not aware of the low quality data until after it has been recorded. As a result potentially large amounts of data are unusable. To alleviate this concern, we propose a real-time feedback system that alerts participants when poor eye tracking data are detected, thus enabling them to adjust their position in front of the eye tracker as soon as they move out of the functional data collection area. This capability allows researchers to acquire a higher percentage of useful data over the course of an experiment. Our approach utilized a Raspberry Pi that collected and interpreted data quality from an eye tracker in real time. Data quality from each eye was mapped to a light emitting diode (LED) placed above the computer monitor. The color of LED reflected the current quality of eye tracking data with green and red indicating high and low quality respectively. To determine if the system was effective, we compared the data quality for participants who used the system relative to participants who did not while they performed a cognitive task. Results show increased data quality for those participants using the feedback system. Our results suggest that future studies using remote desktop eye trackers can increase data quality by providing real-time data quality feedback to the participants.
A Study of the Set of Probability Measures Compatible with Comparative Judgements

We consider a set of comparative probability judgements over a finite possibility space and study the structure of the set of probability measures that are compatible with them. We relate the existence of some compatible probability measure to Walley’s behavioural theory of imprecise probabilities, and introduce a graphical representation that allows us to bound, and in some cases determine, the extreme points of the set of compatible measures. In doing this, we generalise some earlier work by Miranda and Destercke on elementary comparisons.
Zero-Shot Image Classification via Consistent Subspace Learning

Recent years have witnessed great advances in image classification due to the availability of high-volume data and the development of deep neural networks. Nevertheless, the classification performance strongly depends on abundant training samples for the specific category, and there is no ability to recognize categories that do not appear during the training process. In this paper, a novel formulation based on consistent subspace learning for zero-shot image classification is proposed, which pursues a common subspace of the visual features and semantic representations of images. Particularly, such a subspace is defined by class label information, in which a similarity metric is utilized for the zero-shot image classification. In addition, a novel mechanism is developed to efficiently capture the consistent information between visual and semantic embeddings in the learned subspace. An iterative optimization algorithm based on block coordinate descent is designed to solve the proposed formulation. Ultimately, comprehensive experimental analyses on five public datasets demonstrate that the proposed algorithm gains promising performance, and even outperforms several state-of-the-art works.
Reversible Hiding with Quick Response Code - A Responsible Security

In this paper, Reversible data hiding using difference statistics technique incorporating QR codes was proposed. Here, Quick Response (QR) codes were employed as an additional feature and were hidden in the corners of the original image to direct to the hyperlink after authentication and then embedding the secret data bits was carried out. At the receiver side, when the QR codes were scanned by the user, the link to the webpage was accessed, and then the original image and the secret data bits were recovered by using the proposed reversible data hiding scheme. In the proposed scheme, the pixels of the cover image were scanned in row-major order fashion, and the differences between the adjacent pixels were computed, keeping the first pixel unaltered to maintain the size of the host and the difference image same. Now, the histogram was shifted towards the right or left to reduce the redundancy and then to embed the secret data bits were done. Due to the similarity exists between the pixel values, the difference between the host and the secret image reconstructs the marked image. The proposed scheme was carried out using MATLAB 2013. PSNR (Peak Signal to Noise Ratio) and payload have been computed for various test images to validate the proposed scheme and found to be better than the available literature.
Tracking of Underwater Maneuvering Target via M-SIMMUKF Algorithm

For localizing and tracking of an underwater maneuvering target by sonobuoys, a novel algorithm is proposed based on the scalar weight interactive multiple model (SIMM) and the unscented Kalman filter (UKF). Firstly, the SIMMUKF algorithm is directly obtained by combining the SIMM with the UKF. However, the filter estimation errors corresponding to each model are assumed to be uncorrelated in the SIMMUKF algorithm. In order to solve this problem, the multi-sensor optimal information fusion under the criterion of linear minimum variance is utilized to correct the estimated probability in the SIMMUKF algorithm. Then, a target motion model probability correction algorithm referred to as Modified-SIMMUKF (M-SIMMUKF) is obtained. The tracking accuracy of the M-SIMMUKF algorithm is higher. Numerical results validated the proposed scheme.
Prediction Model Development using Neural Network Approach

In an era where customer relationship management plays an important role in business success, firms endeavour to develop profitable associations with their existing and future consumer base. With customer-driven industries becoming highly competitive, there is a huge demand for efficient business intelligence (BI) approaches to facilitate high performance results while optimising on the costs invested, mainly in marketing activities. Therefore, researchers around the world are exploring BI techniques to maximise customer satisfaction and customer loyalty. Recently, machine learning models are being adopted to guide businesses for investing on new marketing strategies through intelligent mining of their existing customer data. This paper adopts a neural network approach to analyse and develop a model of business rules establishing relationships that can exist between customer demographics and the final purchase made by the customers. Then this model can be employed for the prediction of the purchasing patterns that might occur in the future. The dataset obtained from Kaggle's `Allstate Purchase Prediction challenge' consisting of a car insurance customer base is employed for developing our prediction model. The experimental results confirm that the neural network approach adopted is able to extract the purchasing rules exhibited by the customers of a firm by discovering the customer behaviour patterns and their relationships with different demographic variables. An efficient prediction of the customer purchasing patterns would help organisations to make appropriate business proposals to their prospective customers in order to achieve higher customer satisfaction.
A Novel Image Encryption Based on Hyper-chaotic Financial System

This paper have proposed a hyperchaotic financial system with lorenz-like singular attractor. We using the analysis methods of three-dimensional phase diagram, time-domain diagram and projection diagram to studied the nonlinear dynamic characteristics of the hyperchaotic financial system. And then we proposed a new image encryption algorithm based on the hyperchaos equation. In the first phase, it left mapping position of each pixel exchanged. In the second phase, it using the differential equation of chaotic financial system to generate chaotic sequence and optimize the chaotic sequence. Last but not least, the optimized sequence is used for pixel value diffusion encryption. Experimental results show that the encryption algorithm is not only sensitive to plaintext, but also sensitive to ciphertext. The algorithm relies on the plaintext very sensitively and improves the ability to resist the plaintext attack effectively.
surgXplore: Interactive Video Exploration for Endoscopy

Accumulating recordings of daily conducted surgical interventions such as endoscopic procedures for the long term generates very large video archives that are both difficult to search and explore. Since physicians utilize this kind of media routinely for documentation, treatment planning or education and training, it can be considered a crucial task to make said archives manageable in regards to discovering or retrieving relevant content. We present an interactive tool including a multitude of modalities for browsing, searching and filtering medical content, demonstrating its usefulness on over 140 hours of pre-processed laparoscopic surgery videos.
Chaotic Based Adaptive Inter Planar Confusion for Color Images

Data security remains to be one of the most discussed issues in the modern, fast-paced technological ecosystem. This paves way to a lot of endangerment to the transfer of data over various channels, which can be considered reliable and confidential. Classified information cannot be compromised by transmitting it through common channels as they are prone to attack by certain groups, which might to lead to catastrophic circumstances. The most commonly transmitted type of data is in the form of images and classified images have to be transmitted in such a way that it is highly imperceptible. This paper discusses about an encryption algorithm which follows an unconventional method of encryption, followed by diffusion by Ex-OR addition. The confusion is carried out among different planes of the image and can be termed as inter-planar confusion. There are two different ways in which inter-planar confusion can be done and that depends on a chaotic tent map random number generator. The viability and the overall performance of the proposed algorithm is marked as a trade-off between various combinational metrics and good results shows the imperceptibility of the encrypted image
Visual Story Ordering with a Bidirectional Writer

This paper introduces visual story ordering, a challenging task in which images and text are ordered in a visual story jointly. We propose a neural network model based on the reader-processor-writer architecture with a self-attention mechanism. A novel bidirectional decoder is further proposed with bidirectional beam search. Experimental results show the effectiveness of the approach. The information gained from multimodal learning is presented and discussed. We also find that the proposed embedding narrows the distance between images and their corresponding story sentences, even though we do not align the two modalities explicitly. As it addresses a general issue in generative models, the proposed bidirectional inference mechanism applies to a variety of applications.
Realizing Book Retrieval Using Image Matching Based on HWT-IQA and DSP-SIFT

In order to reduce the interference perspective changes and nonlinear deformation caused by camera shooting, we proposed an approach based on HWT-IQA and DSP-SIFT to realize image matching in book retrieval. Extensive experiments are conducted with different IQA algorithms and DSP-SIFT features in image matching for book retrieval. Experimental results demonstrated the effectiveness of our proposed approach. Adding HWT-IQA algorithm before using DSP-SIFT in image matching, the accuracy rate of image matching for book retrieval is improved greatly.
Secure Data Time-Stamping

This chapter introduces the secure time-stamping technique and discusses how to construct a secure time-stamping scheme for cloud storage systems. This chapter first introduces the traditional secure time-stamping technique that is mainly applied in traditional data storage systems (where the data are stored locally on the user side). Then, a comprehensive survey on secure time-stamping schemes is provided. Finally, the relationship between the secure time-stamping technique and blockchains is discussed, and the secure time-stamping for cloud storage is studied.
Vision Extension for a Ball Camera by Using Image Completion

A ball camera is a high tech ball equipped with cameras to obtain images from the ball's point of view. To capture 360° images with a minimum number of cameras, we embedded two ultra-wide-angle cameras in the ball. However, the two cameras were set diametrically opposite to each other; therefore, a blind spot was present, which could not be captured by the two cameras. Consequently, the obtained 360° image had a discontinuous part. In this paper, we describe the design and implementation of the ball camera and propose a method for completing the blind spot using image completion. We applied our method to the videos captured by the ball camera and proved this method successfully completes the blind spot and provides more real-life images.
A modified Boneh-Lynn-Shacham signing dynamic auditing in cloud computing

Cloud Computing is an alternative to conventional IT Outsourcing. As a result, cloud computing migration between organizations is rapidly growing. The adoption of this technology brings many positive aspects but prescribes various risks and concerns. An organization that officially provides its cloud computing services to external providers and implies that its IT functions and process are outsourced to third-party providers of BPO services. For privacy-making public audit processes in dynamic cloud data storage, a modified Boneh-Lynn-Shachame Dynamic Auditing (MBLSDA) algorithm is suggested in this paper. The proposed algorithm executes an audit process for multiple users based on a batch audit simultaneously and effectively to enable Third Party Auditing (TPA). This paper integrates the homomorphic authenticator in an algorithm of dynamic signing audit by random marking in terms of the privacy conserving public auditing process. When the user of cloud service, store or update the data, it encrypts using Rijndal algorithm, and Boneh-Lynn-Shacham signature generation is used for key generation. Finally, a hybrid of these a proposed Modified Boneh-Lynn-Shacham Signing Dynamic audit the data to provide the security.
Signal Representation with Optimal Subspace Graph Filtering

Signal representation is a prime problem in signal processing. In this paper, we propose a subspace graph filtering method for signal representation. We demonstrate an extended singular value decomposition (SVD) model of signal essentially is a subspace graph filtering, and build a bridge from SVD to graph filtering. A smoothing subspace graph filtering is sequentially learned in the space provided by SVD. In the experiments, we compare the signal restoration performance between the extended SVD and the proposed subspace graph filtering. It shows that clean signal can be better reconstructed from the noisy signal in our smoothing subspace than the SVD space, where the learned smoothed bases of graph filtering are more robust than the bases of SVD to cope with noise.
Saliency detection based on weighted color contrast of image patch

Image saliency analysis is an important research content in the field of computer vision. At present, the main method of saliency analysis is to measure the saliency of single pixel or regular image patch. It is easy to be affected by image texture, noise and other factors, and some important information is lost in the process of segmentation, which makes it difficult to extract salient objects from the image. Therefore, a saliency detection algorithm based on weighted color contrast of image patch is proposed. Firstly, the original image is divided into different size and non-overlapping image patch structure. Then, the color contrast of the image patch, the number of pixels included and the spatial distance between the two image patches are calculated. Considering the influence of spatial distance between image patches on saliency value, the weighted color contrast model of image patch is used to detect salient region. Finally, considering the influence of spatial distance between pixels on saliency value, the salient region is enhanced by calculating the distance between each pixel and the center of the salient region. In order to evaluate this algorithm, we use the largest publicly available data set in the world for testing. Experimental results show that the proposed method has better precision and recall rate, can significantly suppress the influence of complex texture and noise.
Selection of Code Segments for Exclusion from Code Similarity Detection

When student programs are compared for similarity, certain segments of code are always sure to be similar. Some of these segments are boilerplate code -- public static void main String [] args and the like -- and some will be code that was provided to students as part of the assessment specification. The purpose of this working group is to explore what other code is expected to be reasonably common in student assessments, and should therefore be excluded from similarity checking. The answers will clearly vary with programming language, and perhaps with level of assessment item. Working group members will collect assessment submissions from their own or their colleagues' students, and it is hoped that these submissions will together encompass a wide variety of assessment tasks in a wide variety of programming languages. The working group aims to deliver clear guidelines as to what code can reasonably be excluded from automatic code similarity detection in various circumstances. It also aims to deliver a summary of what sort of code lecturers tend to provide for students when setting an assigned task, and why they provide that code.
Securing Digital Signature Algorithm against Side Channel Attacks

Many software implementations for the schemes used for public key cryptosystems, of which elliptic curve cryptography is the most powerful, has been concerned with performance and efficiency. However, the advent of side channel attacks with their diverse categories, such as timing, fault and power analysis attacks, impose us to reconsider new strategies in implementing more secured elliptic curve algorithms to thwart any information leakage that leads to break the security of these algorithms. In this paper, we propose a new optimization on the algorithmic level for computing the arithmetic of elliptic curve point over prime fields to countermeasure side channel attacks that threaten elliptic curve cryptosystems. Indeed, these attacks present nowadays a realistic threat to cryptographic applications and have been proved to be very effective against most cryptosystems. To develop a digital signature algorithm that targets performance and security against side channel attacks, we built a library for the underlying prime fields arithmetic for the common fields specialized by NIST and SECG. Our work can be employed in numerous applications such as, E-health, E-banking, E-commerce and E-governance.
Grouping of Distributed Data at Multiple Sites by Sustaining the Privacy of Data

Data with respect to individuals are available at multiple sites. Essential inferences can be made based on the collective information that is distributed. However, privacy of the data maintained be different organizations becomes a major concern. There is a necessity to construct precise models to mine crucial information from the distributed data without placing the data in a centralized storage. Existing work concentrates on manipulating the data at each of the sites and then clusters them. But it is observed that maneuvering the huge amount of data is expensive. The proposed work discusses a privacy preserving clustering method to group identical data. One of the major challenges while modeling is to compute a representative for each cluster. A secure approach is also proposed to calculate the cluster representative. When compared to the existing privacy preserving distributed clustering approaches, the proposed method provides better accuracy and reduced error rate.
Chapter 2. How less means more in the comments section of vegan food blogs

Recipes in (vegan) food blogs are often advertised as not having certain ingredients such as gluten or refined sugar. In the comments sections of posts topicalising such exgredients (linguistically, no-X constructions like soy-free or no nuts), extreme case formulations (e.g. always or entirely) are employed to (a) construct the urgency of a request for an alternative, (b) index the liability and safety of a suggestion for an alternative as expert advice, (c) construct alternatives as rare and precious finds to share with the community, and (d) compliment the blogger.
Simply E-Teaching And E-Learning In And For A Complex E-World

ICT, storytelling and gamification is supposedly modern; many theoretical writings on them have been published and some concrete examples of them presented, but all three methods nonetheless undoubtedly remains too rarely used in our educational system. Teachers like to play, tell and listen stories, use computer and mobile devices too, although according to the prevailing opinion this is considered to be of fun and leisure activities. We believe in stories and gamification in education! We increasingly include the elements of the game and storytelling in the classes. These elements are not of a digital nature, but many applications today make them easier to use, but above all allow a different approach. We collect several applications and web tools in the Padlet: https://padlet.com/bostjan_kernc/ofcwpf8601qa. The article aims to dispel the prejudice by providing some concrete examples of gamification used with higher grades pupils in primary school. The first part presents some basic possibilities offered by modern applications, which are no more time-consuming than making an ordinary worksheet. The article present examples and free apps with which the teacher or student can build the story itself: Fakebook, GoogleMyMaps, Sutori, Storyboardthat. Additionally, free apps with which it is easy to gamify (making quizzes, riddles, crosswords, memory games, matching mosaic games, interactive maps, word and picture completion tasks, jumbled sentences, comics ...) educational process: H5P, Educaplay, LearningApps and Quizizz. The final part provides also two our examples of educational games (we've linked different gamifications and story into a game). The first one offers discovering a city as a living museum using QR codes, internet contents, cameras and applications, which enable the students to create a story based on the recordings. The second one is the animated computer (serious) game, which guides user through the life of a particular historic person, familiarizes them with the places, prominent figures, events and values of his time - simple virtually trip to history with tasks and animations. We consider gamification and storytelling as state of mind and organisation of learning material. ICT and many web applications just help to gamify! Moreover, it works in the classroom!
An Aging Face Synthesis Method Based on Feature Regions Morphing and Poisson Matting

To meet realistic needs of retrieving lost children many years ago or catching criminals years ago, image morphing method based on Delaunay trigonometric feature region and wavelet decomposition and synthesis method were combined. Face contours were changed by image morphing method, aging textures were transferred by wavelet decomposition and synthesis method and aged face images were synthesized through Poisson matting method. Experimental results show that we can get lifelike aged images with relatively fast speed.