Reviewerly/exHarmony huggingface dataset:
Double-Layer Image Security Scheme with Aggregated Mathematical Sequences

Information security has been a major concern since the evolution of technology. Nowadays, internet users' are increasing and their information is being shared almost every second. Two possible information security techniques are cryptography and steganography. This paper proposes a hybrid use of both of these techniques. As a first security layer, one of the following cryptography techniques is used: AES-128, AES-192, AES-256, Blowfish or a Logistic Map, to encrypt the secret message. The second layer is steganographic, employing a number of complex mathematical sequences that determine the color in which the enciphered bits would be embedded in. Visual representation of the stego and the cover images after embedding is presented and the performance of the proposed scheme is measured in terms of the mean squared error (MSE), peak signal to noise ratio (PSNR), structural similarity index metric (SSIM), image fidelity, embedding capacity, R measurement, and entropy values of the cover and stego images.
Look at Me! Correcting Eye Gaze in Live Video Communication

Although live video communication is widely used, it is generally less engaging than face-to-face communication because of limitations on social, emotional, and haptic feedback. Missing eye contact is one such problem caused by the physical deviation between the screen and camera on a device. Manipulating video frames to correct eye gaze is a solution to this problem. In this article, we introduce a system to rotate the eyeball of a local participant before the video frame is sent to the remote side. It adopts a warping-based convolutional neural network to relocate pixels in eye regions. To improve visual quality, we minimize the L2 distance between the ground truths and warped eyes. We also present several newly designed loss functions to help network training. These new loss functions are designed to preserve the shape of eye structures and minimize color changes around the periphery of eye regions. To evaluate the presented network and loss functions, we objectively and subjectively compared results generated by our system and the state-of-the-art, DeepWarp, in relation to two datasets. The experimental results demonstrated the effectiveness of our system. In addition, we showed that our system can perform eye-gaze correction in real time on a consumer-level laptop. Because of the quality and efficiency of the system, gaze correction by postprocessing through this system is a feasible solution to the problem of missing eye contact in video communication.
Granular Modeling of Fuzzy Color Categories

In this paper, we introduce fuzzy granular colors for modeling color categories. Fuzzy granular colors are built by aggregating fuzzy colors having semantic relationships with regard to a certain color category. Our proposal allows us to model color categories which comprise disjoint fuzzy subsets of colors, as well as those having a nonconvex representation in the color space, among other advantages. Such categories are used very often by humans in different real contexts. Fuzzy granular colors are appropriate to provide color models able to deal with ill-defined boundaries, subjectivity, and context-dependence. We illustrate the advantages of our approach with respect to current state of the art with several experiments.
Immersive Virtual Environments and Learning Assessments

The purpose of this systematic literature review is to give a state-of-the-art overview of how learning assessments have been used in studies using Immersive Virtual Environment (IVE) and its applications in education. Forty-six studies were reviewed and categorized according to: type of knowledge, technological immersion, skills learned, context, techniques, processes and methods of assessment. This review identified scarcity of studies focusing conceptual knowledge assessment, few studies using qualitative assessments as well as paucity of artificial intelligence methods applied in learning assessments. Research gaps are discussed and future studies on the use of IVE in education are suggested.
Abductive innovations in information security policy development: an ethnographic study

Developing organisational information security (InfoSec) policies that account for international best practices but are contextual is as much an opportunity for improving InfoSec as it is a challenge. Previous research indicates that organisations should create InfoSec policies based on best practices (top-down) and simultaneously encourages participatory development (bottom-up). These contradictory suggestions place managers in a dilemma: Should they follow a top-down or bottom-up approach? In this research, we build on an ethnographic approach to study how an innovative engineering company (MachineryCorp) managed the contradiction when the firm developed an InfoSec policy. Drawing on the dialectical theory of organisations as a lens, the findings suggest the InfoSec policy development is a recurrent process consisting of three phases: (1) drawing interpretations of InfoSec requirements from best practices (deductive adoption) and (2) constructing possibilities for local implementation (inductive adjustment) (3) that engender tensions between best practices and local contingencies facilitating innovative local resolutions (synthetic innovation). We call this process abductive innovation. At MachineryCorp, a triangle of tensions surfaced due to economic realities, infrastructure affordances, and social arrangements, and were necessary in explaining how the InfoSec policy gradually and iteratively materialised and resulted in an organisationally contingent policy.
Diversifying the Domestic

We report on a design research inquiry aimed at understanding and exploring the values, practices, and perspectives of people that actively embrace and choose to live within collective houses and mobile vehicles as their homes. A goal of our work is to inquire into how such lived alternatives of 'home' can take a step toward broadening possibilities for conceptualizing 'domestic' technology and provoking questions around how it might be critiqued, imagined, and designed. We offer a brief overview of our ongoing research with a sample of collective and mobile dwellers, and propose three themes that extend prior generative work in this area: critique through living, taking time to adapt, and the transitional home. We use these themes in a design-led approach to propose six fictional future technology concepts that aim to (i) critically reflect on and provoke questions about commitments in current mainstream visions of domestic technology and (ii) explore new possibilities for engaging with the material, social, and technological conditions shaping the lives of our collective and mobile dweller participants. We conclude with a reflection on our work, its limitations, and opportunities it suggests for future research and practice.
Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

Splitting and combination are two important events of group target motion.However, the existing tracking approaches for group target splitting and combination events suffer the problems of high-computational cost and low accuracy.Under the random finite set framework, with target extent modeled by random matrix, the algorithms for group target splitting and combination tracking based on δ-generalized labeled multi-Bernoulli filter are researched.Three classical splitting modes of group target are discussed.With appropriate splitting criteria developed, e.g., the setting of the splitting gate, the chosen of the splitting dimension, the compensation of the subgroup's centroid position, and so on.According to the characteristics of each mode, the efficiency and the accuracy of the algorithm for group target splitting event are improved.The group combination approach is derived, where the representation of labels under the tack complicatedly changed condition, e.g., the group splitting and combination events jointly exist are given.With the velocity combination criterion established according to the target motion trend, a decreased sensitivity of the algorithm for target splitting event is avoided.The results show that the proposed algorithms have improved the tracking performance for group target splitting and combination events.
Towards the Identification of Propaedeutic Relations in Textbooks

As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge.
Item Ordering Biases in Educational Data

Data collected in a learning system are biased by order in which students solve items. This bias makes data analysis difficult and when not properly addressed, it may lead to misleading conclusions. We provide clear illustrations of the problem using simulated data and discuss methods for analyzing the scope of the problem in real data from a learning system. We present the data collection problem as a variant of the explore-exploit tradeoff and analyze several algorithms for addressing this tradeoff.
Steward

With the increase of HTTP-based adaptive video streaming over the Internet, multiple clients may compete for a shared bottleneck bandwidth, which brings some damage to the fairness and stability of Quality of Experience (QoE). This paper presents Steward, a system that enforces multi-client joint QoE optimization for bottleneck bandwidth sharing. Joint QoE optimization refers to improving QoE fairness among clients with various video devices and providing differentiated service for clients with different priorities. Steward deploys the adaptive bitrate (ABR) algorithm based on neural networks (NN) and reinforcement learning at the network edge. The ABR agent trains the NN model through experience and makes appropriate bitrate guidance for video chunks to be requested by clients sharing the same bottleneck bandwidth. We compare Steward with state-of-the-art algorithms under different network conditions. Compared with all considered algorithms and conditions, Steward reduces 30%~85% QoE unfairness under the premise of differentiated service.
A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR

Electronic Health Records (EHR) containing longitudinal information about millions of patient lives are increasingly being utilized by organizations across the healthcare spectrum. Studies on EHR data have enabled real world applications like understanding of disease progression, outcomes analysis, and comparative effectiveness research. However, often every study is independently commissioned, data is gathered by surveys or specifically purchased per study by a long and often painful process. This is followed by an arduous repetitive cycle of analysis, model building, and generation of insights. This process can take anywhere between 1 - 3 years. In this paper, we present a robust end-to-end machine learning based SaaS system to perform analysis on a very large EHR dataset. The framework consists of a proprietary EHR datamart spanning ~55 million patient lives in USA and over ~20 billion data points. To the best of our knowledge, this framework is the largest in the industry to analyze medical records at this scale, with such efficacy and ease. We developed an end-to-end ML framework with carefully chosen components to support EHR analysis at scale and suitable for further downstream clinical analysis. Specifically, it consists of a ridge regularized Survival Support Vector Machine (SSVM) with a clinical kernel, coupled with Chi-square distance-based feature selection, to uncover relevant risk factors by exploiting the weak correlations in EHR. Our results on multiple real use cases indicate that the framework identifies relevant factors effectively without expert supervision. The framework is stable, generalizable over outcomes, and also found to contribute to better out-of-bound prediction over known expert features. Importantly, the ML methodologies used are interpretable which is critical for acceptance of our system in the targeted user base. With the system being operational, all of these studies were completed within a time frame of 3-4 weeks compared to the industry standard 12-36 months. As such our system can accelerate analysis and discovery, result in better ROI due to reduced investments as well as quicker turn around of studies.
Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

Cognitive impairment is one of the crucial problems elderly people face. Tracking their daily life activities and detecting early indicators of cognitive decline would be necessary for further diagnosis. Depending on the decline magnitude, monitoring may need to be done over long periods of time to detect abnormal behaviour. In the absence of training data, it would be helpful to learn the normal behaviour and daily life patterns of a (cognitively) healthy person and use them as a basis for tracking other patients. In this paper, we propose to investigate Recursive Auto-Encoders (RAE)-based transfer learning to cope with the problem of scarcity of data in the context of abnormal behaviour detection. We present a method for generating synthetic data to reflect on some behavior of people with dementia. An RAE model is trained on data of a healthy person in a source household. Then, the resulting RAE is used to detect abnormal behavior in a target house. To evaluate the proposed approach, we compare the results with the-state-of-the-art supervised methods. The results indicate that transfer learning is promising when there is lack of training data.
Learning motion field of LiDAR point cloud with convolutional networks

This paper proposes a novel method for motion field estimation in two consecutive LiDAR scans with convolutional neural networks (CNNs) wherein object detection, point-wise motion, and object-level motion are learned hierarchically. In the input stage, a unique mapping model serves to describe the LiDAR point cloud and its motion. In the encode network, two input maps are subjected to spatial compression and merged into a correlation layer. In the decode network, the model outputs data encompassing object detection, point-wise motion, and object-level motion. Since existing ground truth datasets are not sufficiently large to train a CNN, we generate a synthetic Vehicle Motion dataset for training. The set contains scenes with several simultaneously moving 3D vehicles recorded by a simulated LiDAR. An experiment conducted on both real and synthetic datasets shows that the proposed method outperforms other state-of-the-art motion field estimation methods.
A Survey of Various Cryptographic Techniques: From Traditional Cryptography to Fully Homomorphic Encryption

With the advent in Internet and networking applications, security is a major concern in the current era of Information Technology. The huge amount of information exchanged over the Internet is vulnerable to security threats and attacks. Cryptography provides secure exchange of encrypted data by sharing a key. The major concern with this approach is data privacy as anybody with the key can access the data. Moreover, user loses control over data once it is uploaded to the cloud and must rely on cloud service provider. User must share a key with cloud service provider to perform any operations like searching, sorting, etc., or need to download and decrypt the data and then perform the operation. These approaches lead to privacy issue and repeated encryption decryption, even for small computation. These concerns are addressed by Homomorphic Encryption (HE), which enables the cloud service provider to carry out computations on ciphered data without decrypting it. With the advent of HE scheme, in 2009, the Fully Homomorphic Encryption (FHE) scheme was invented by C. Gentry, which allows any computational function to operate on encrypted data. But the practical implementation of FHE is still in research. This survey focuses on various traditional and modern cryptography techniques along with the different schemes of HE and FHE.
Computational Detection of Cervical Uterine Cancer

The aim of this work is to provide a computational method of calculation of parameters to analyze the exfoliative cytology of the cervix or Papanicolaou (PAP). The parameters calculated are those currently used to diagnose cervical uterine cancer, checked by a cytologist through visual examination of a PAP sample, under a microscope. The aim is to determine the risk of cancer, based on the calculation of parameters of the cell nucleus of the sample, such as its size and shape. It is suggested that a low-cost automated system implements a 4-stage process: (1)to obtain a digital image as from a cytological sample; (2)preprocessing it, implementing mathematical operations; (3)to apply an edge detection algorithm to segment the image, separating and identifying the cells from the rest of the sample; (4)to identify a cell nuclei, classify and measure it, to determine whether it corresponds to a healthy or sick nuclei. This process made possible to detect and measure the nucleus of a sample with an efficiency level of more than 96%. This validates the possibility of automating the process of anomaly detection to prevent the cervical cancer, given that it involves great benefits for the patient.
Active contour model based on improved fuzzy c-means algorithm and adaptive functions

Distance regularized level set evolution (DRLSE) model, which solves the re-initialization problem in early active contours, is a ground breaking edge-based model for image segmentation. However, it has the disadvantages of unsatisfactory robustness to initialization and noise, unidirectional movement, slow convergence and poor stability. In this paper, we propose an active contour model driven by improved fuzzy c-means algorithm (FCM) and adaptive functions. An adaptive sign function based on image clustering information not only increases stability, but also solves the problem of unidirectional movement. Furthermore, it gives our model the ability to selectively segment targets in image. An adaptive edge indicator function accelerates convergence with better function performance. To further increase stability, a novel double-well potential function and the corresponding evolution speed function are proposed. Due to the improved FCM, the proposed model is robust to initialization and noise. In addition, our model exhibits an edge-based and region-based characteristic. Experimental results have proved that the proposed model can not only effectively segment images with intensity inhomogeneity, but also show a good robustness to initialization. Moreover, it has shorter time spent and higher segmentation accuracy compared with other models.
Capturing Joint Label Distribution for Multi-Label Classification Through Adversarial Learning

Label correlations are important for multi-label learning. Although current multi-label learning approaches can exploit first-order, second-order, and high-order label dependencies, they fail to exploit complete label correlations, which are included in the joint label distribution of the ground truth labels. However, directly modeling the complex and unknown joint label distribution is very challenging, if not impossible. In this paper, we propose an adversarial learning framework to enforce similarity between joint distribution of the ground truth multi-labels and the predicted multiple labels. Specifically, the proposed multi-label learning method includes a multi-label classifier and a label discriminator. The classifier minimizes error between predicted labels and corresponding ground truth labels and gives the discriminator room for error. The object of the discriminator is to distinguish the predicted labels from the ground truth labels. The classifier and discriminator are trained simultaneously through an alternate process. By adversarial learning, the joint label distribution of the predicted multi-labels converges to the joint distribution inherent in the ground truth multi-labels, and thus boosts the performance of multi-label learning as demonstrated in the experiments on 11 benchmark databases.
Computing the Statistical Significance of Overlap between Genome Annotations with iStat

Genome annotation remains a fundamental effort in modern biology. With reducing costs and new forms of sequencing technologies, annotations specific to tissue type and experimental conditions are continually being generated (e.g., histone methylation marks). Computing the statistical significance of overlap between two different annotations is key to many biological findings but has not been systematically addressed previously. We formalize the problem as follows: let I and If each describe a collection of n and m intervals of a genome with particular annotation. Under the null hypothesis that genomic intervals in I are randomly arranged with respect to If, what is the significance of k of m intervals of If intersecting with intervals in I? We describe a tool iSTAT that implements a combinatorial algorithm to accurately compute p values. We applied iSTAT to simulated and real datasets to obtain precise estimates and contrasted them against previous results using permutation or parametric tests.
Multimodal Fusion of Speech and Text using Semi-supervised LDA for Indexing Lecture Videos

Lecture videos are the most popular learning materials due to their pedagogical benefits. However, accessing a topic or subtopic of interest requires manual examination of each frame of the video and it is more tedious when the volume and length of videos increases. The main problem thus becomes the efficient automatic segmentation and indexing of lecture videos that enables faster retrieval of specific and relevant content. In this paper, we present automatic indexing of lecture videos using topic hierarchies extracted from slide text and audio transcripts. Indexing videos based on slide text information is more accurate due to higher character recognition rates but, text content is very abstract and subjective. In contrast to slide text, audio transcripts provide comprehensive details about the topics, however retrieval results are imprecise due to higher WER. In order to address this problem, we propose a novel idea of fusing complementary strengths of slide text and audio transcript information using semi-supervised LDA algorithm. Further, we strive to improve learning of the model by utilizing words recognized from video slides as seed words and train the model to learn the distribution of video transcriptions around these seed words. We test the performance of proposed multimodal indexing scheme on 500 number of class room videos downloaded from Coursera, NPTEL and KLETU (KLE Technological University) classroom videos. The proposed multimodal fusion based scheme achieves an average percentage improvement of 44.49% F-Score compared with indexing using unimodal approaches.