Reviewerly/exHarmony huggingface dataset:
A chaos-based probabilistic block cipher for image encryption

Traditional encryption is based on secrecy provided by secret-key. But this leads to generation of same cipher text when the encryption scheme is applied to same plaintext with same key. Thus, replay of messages can be effortlessly identified by an adversary which can be a weak link in any communication. Probabilistic encryption is an approach to overcome this weakness where different cipher texts are generated each time same plaintext is encrypted using the same key. Extending the probabilistic approach, which is generally employed in asymmetric encryption, this paper proposes a new chaos-based probabilistic symmetric encryption scheme with customizable block-size suitable for image encryption. It employs a Random Bits Insertion phase followed by four rounds of two-staged diffusion involving simple XOR (exclusive-OR) operation making it computationally efficient. Random Bits Insertion makes the scheme probabilistic. This phase also helps in increasing entropy and making intensity distribution more uniform in cipher. The generated cipher text is twice the size of plain text. An increase in cipher text space is inevitable for probabilistic encryption and it provides an advantage as the apparent message space for the attacker is increased. The observations show that the scheme offers high strength to resist statistical and cryptanalytic attacks.
New Techniques for Efficient Trapdoor Functions and Applications

We develop techniques for constructing trapdoor functions (TDFs) with short image size and advanced security properties. Our approach builds on the recent framework of Garg and Hajiabadi [CRYPTO 2018]. As applications of our techniques, we obtain Prior to our work, all constructions of deterministic encryption based even on the stronger DDH assumption incurred a quadratic gap between the ciphertext and plaintext sizes. Moreover, all DDH-based constructions of lossy TDFs had image size quadratic in the input size. At a high level, we break the previous quadratic barriers by introducing a novel technique for encoding input bits via hardcore output bits with the use of erasure-resilient codes. All previous schemes used group elements for encoding input bits, resulting in quadratic expansions.
Determination of the optimal camera distance for cloud height measurements with two all-sky imagers

All-sky imager based systems can be used to measure a number of cloud properties. Configurations consisting of two all-sky imagers can be used to derive cloud heights for weather stations, aviation and nowcasting of solar irradiance. One key question for such systems is the optimal distance between the all-sky imagers. This problem has not been studied conclusively in the literature. To the best of our knowledge, no previous in-field study of the optimal camera distance was performed. Also, comprehensive modeling is lacking. Here, we address this question with an in-field study on 93 days using 7 camera distances between 494 m and 2562 m and one specific cloud height estimation approach. We model the findings and draw conclusions for various configurations with different algorithmic methods and camera hardware. The camera distance is found to have a major impact on the accuracy of cloud height determinations. For the used 3 megapixel cameras, cloud heights up to 12,000 m and the used algorithmic approaches, an optimal camera distance of approximately 1500 m is determined. Optimal camera distances can be reduced to less than 1000 m if higher camera resolutions (e.g. 6 megapixel) are deployed. A step-by-step guide to determine the optimal camera distance is provided.
Provably secure public-key encryption with conjunctive and subset keyword search

Public-key encryption with keyword search (PEKS) schemes enable public key holders to encrypt documents, while the secret key holder is able to generate queries for the encrypted data. In this paper, we present two PEKS schemes with extended functionalities. The first proposed scheme supports conjunctive queries. That is, it enables searching for encrypted documents containing a chosen list of keywords. We prove the computational consistency of our scheme, and we prove security under the asymmetric DBDH assumption. We show that it improves previous related schemes in terms of efficiency and in terms of index and trapdoor size. The second proposed scheme supports subset queries and some more general predicates. We prove the computational consistency of our scheme, and we prove our scheme secure under the p-BDHI assumption. We show that it improves previous related schemes in terms of efficiency and expressiveness. Moreover, unlike previous related schemes, it admits an arbitrary keyword space.
Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces

High Dynamic Range (HDR) and Wide Color Gamut (WCG) screens are able to render brighter and darker pixels with more vivid colors than ever. To assess the quality of images and videos displayed on these screens, new quality assessment metrics adapted to this new content are required. Because most SDR metrics assume that the representation of images is perceptually uniform, we study the impact of three uniform color spaces developed specifically for HDR and WCG images, namely, I C t C p , J z a z b z and H D R - L a b on 12 SDR quality assessment metrics. Moreover, as the existing databases of images annotated with subjective scores are using a standard gamut, two new HDR databases using WCG are proposed. Results show that MS-SSIM and FSIM are among the most reliable metrics. This study also highlights the fact that the diffuse white of HDR images plays an important role when adapting SDR metrics for HDR content. Moreover, the adapted SDR metrics does not perform well to predict the impact of chrominance distortions.
Monocular Depth Estimation: A Survey

Monocular depth estimation is often described as an ill-posed and inherently ambiguous problem. Estimating depth from 2D images is a crucial step in scene reconstruction, 3Dobject recognition, segmentation, and detection. The problem can be framed as: given a single RGB image as input, predict a dense depth map for each pixel. This problem is worsened by the fact that most scenes have large texture and structural variations, object occlusions, and rich geometric detailing. All these factors contribute to difficulty in accurate depth estimation. In this paper, we review five papers that attempt to solve the depth estimation problem with various techniques including supervised, weakly-supervised, and unsupervised learning techniques. We then compare these papers and understand the improvements made over one another. Finally, we explore potential improvements that can aid to better solve this problem.
A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics

With the rapid increase of publishable research articles and manuscripts, the pressure to find reviewers often overwhelms the journal editors. This paper incorporates the major entity level metrics found in the heterogeneous publication networks into a pattern mining process in order to recommend academic reviewers and potential research collaborators. In essence, the paper integrates authors' h-index and papers' citation count and proposes a quantification to account for the author diversity into one formula duped impact to measure the real influence of a scientific paper. Thereafter, this paper formulates two kinds of target patterns and mines them harnessing the high-utility itemset mining (HUIM) framework. The first pattern, researcher-general topic patterns (RGP), is a pattern that includes only researchers; whereas, the researcher-specific topic patterns (RSP) is comprised of combinations of researchers and keywords that summarize their niche of expertise. The HUI algorithms of Two Phase, IHUP, UP-Growth, FHM, FHN, HUINIV-Mine, D2HUP, and EFIM were compared on two real-world citation datasets related to Deep Learning and HUIM, in addition to the open source mushroom dataset. The EFIM algorithm showed good performance in terms of run time and memory usage. Consequently, it was then used to mine the patterns within the proposed framework. The discovered patterns of RGP and RSP showed high coverage, proving the efficiency of the proposed framework.
Fast Heuristics for the 3-D Multi-Goal Path Planning Based on the Generalized Traveling Salesman Problem With Neighborhoods

In this letter, we address the multi-goal path planning problem to determine a cost-efficient path to visit a set of three-dimensional regions. The problem is a variant of the traveling salesman problem with neighborhoods (TSPN) where an individual neighborhood consists of multiple regions, and the problem is to determine a shortest multi-goal path to visit at least one region of each neighborhood. Because each neighborhood may consist of several regions, it forms a neighborhood set, and the problem is called the generalized TSPN (GTSPN) in the literature. We propose two heuristic algorithms to provide a feasible solution of the GTSPN quickly. The first algorithm is based on a decoupled approach using a solution of the generalized TSP that is further improved by a quick post-processing procedure. Besides, we propose to employ the existing unsupervised learning technique called the growing self-organizing array to quickly find a feasible solution of the GTSPN that can be further improved by more demanding optimization. The reported results on existing benchmarks for the GTSPN indicate that both proposed heuristics provide better or competitive solutions than the state-of-the-art reference algorithm, but they are up to two orders of magnitude faster.
On the consistency of supervised learning with missing values

In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing values appear in both training and testing data. We show the consistency of two approaches in prediction. A striking result is that the widely-used method of imputing with a constant, such as the mean prior to learning is consistent when missing values are not informative. This contrasts with inferential settings where mean imputation is pointed at for distorting the distribution of the data. That such a simple approach can be consistent is important in practice. We also show that a predictor suited for complete observations can predict optimally on incomplete data, through multiple imputation. Finally, to compare imputation with learning directly with a model that accounts for missing values, we analyze further decision trees. These can naturally tackle empirical risk minimization with missing values, due to their ability to handle the half-discrete nature of incomplete variables. After comparing theoretically and empirically different missing values strategies in trees, we recommend using the "missing incorporated in attribute" method as it can handle both non-informative and informative missing values.
The effect of market-sensing capability on knowledge creation process and innovation Evidence from SMEs in Jordan

This article aims to investigate the effect of market-sensing capability among knowledge creation and process and product innovation in small and medium-sized enterprises (SMEs) in Jordan.We propose that market-sensing capability will influence knowledge creation and process as well as product innovation.The study develops a model that contributes to the flourishing marketing management literature, i.e. the higher the knowledge creation, the higher the performance of SMEs.Data are collated from valid (n=304) respondents of Jordanian SMEs.The study uses structural equation modeling (SEM) to explore the analysis and the results indicate a positive effect of market-sensing capability on knowledge creation, and process and product innovation.Knowledge creation process has also maintained a positive effect on process and product innovation.Knowledge creation process mediated the link between market-sensing capability and process and product innovation.The study suggests that market sensing abilities of an enterprise are important for SME's improvement which requires learning-positioning for firm effectiveness.In addition, managers need to harmonize their procedures concerning market sensing and learning.Future study can streamline the findings with a longitudinal design and insights can be acquired by exploring the impact of environmental factors surrounding strategic entrepreneurial orientation.
Deep Analysis of CNN Settings for New Cancer Whole-slide Histological Images Segmentation: The Case of Small Training Sets

Accurate analysis and interpretation of stained biopsy images is a crucial step in the cancer diagnostic routine which is mainly done manually by expert pathologists. The recent progress of digital pathology gives us a challenging opportunity to automatically process these complex image data in order to retrieve essential information and to study tissue elements and structures. This paper addresses the task of tissue-level segmentation in intermediate resolution of histopathological breast cancer images. Firstly, we present a new medical dataset we developed which is composed of hematoxylin and eosin stained whole-slide images wherein all 7 tissues were labeled by hand and validated by expert pathologist. Then, with this unique dataset, we proposed an automatic end-to-end framework using deep neural network for tissue-level segmentation. Moreover, we provide a deep analysis of the framework settings that can be used in similar task by the scientific community.
Generative Adversarial Networks as an Advanced Data Augmentation Technique for MRI Data

This paper presents a new methodology for data augmentation through the use of Generative Adversarial Networks. Traditional augmentation strategies are severely limited, especially in tasks where the images follow strict standards, as is the case in medical datasets. Experiments conducted on the ADNI dataset prove that augmentation through GANs outperforms traditional methods by a large margin, based both on the validation accuracy and the models’ generalization capability on a holdout test set. Although traditional data augmentation did not seem to aid the classification process in any way, by adding GAN-based augmentation an increase of 11.68% in accuracy was achieved. Furthermore, by combining traditional with GAN-based augmentation schemes, even higher accuracies can be reached.
Image steganography using exploiting modification direction for compressed encrypted data

Building a balanced relation between image quality and the payload, the robustness of the method in facing electronic attacks and securing data, all the mentioned processes represent the main challenge in steganography. Here, a novel approach to steganography is suggested using Vigenere Cipher and Huffman Coding methods to encrypt and compress the mystery message content. This approach will raise the security and ensure the message content cannot be extracted without earlier knowledge of decrypting rules and the Huffman Dictionary Table. Later, the image is segmented into blocks, size (w*h) groups for each block and with each group having n pixels. Subsequently, the knight tour algorithm and arbitrary function are utilized to select which blocks and groups can be used to conceal the mystery digit within a specific pixel in the group randomly. This is to address the weakness of the Exploiting Modification Direction (EMD) technique that uses a serial selection to enhance the robustness of the suggested scheme. The EMD technique is then utilized to insert the mystery digits inside a specific pixel. Later, the chi-square method is employed to apply statistical attacks on the stego-image to estimate the suggested scheme robustness. The empirical outcomes show that the suggested scheme is more efficient compared to the old Steganography schemes with respect to Imperceptibility by PSNR of 55.71 dB, the Payload of 52,400 bytes and the robustness.
Verifiable fully outsourced attribute-based signcryption system for IoT eHealth big data in cloud computing

The entrance of Internet of Things (IoT) technologies to healthcare industry has impacted the explosion of eHealth big data. Cloud computing is widely considered to be the promising solution to store this data because of the presence of abundant resources at a lower cost. However, the privacy and security of the IoT generated data cannot be ensured as the data is kept far from the owner's phys- ical domain. In order to resolve the underlined issues, a reassuring solution is to adopt attribute-based signcryption (ABSC) due to the desirable cryptographic properties it holds including fine-grained ac- cess control, authentication, confidentiality and data owner privacy. Nonetheless, executing expensive computation such as pairing and modular exponential operations in resource-constrained IoT device platform can be too taxing and demanding. To address the challenges stated above, we proposed in this paper, a more efficient scheme where computation power is borrowed from the cloud server to process expensive computations while leaving simple operations to local users. In order to realize this, trusted attribute authority, signcryptor and designcryptor outsources to the cloud expensive tasks for key gener- ation, signcryption and designcryption respectively. Moreover, validity and correctness of outsourced computations can be verified by employing outsourcing verification server. Security analysis, compar- isons evaluation and simulation of the proposed scheme is presented. The output demonstrates that it is efficient, secure and therefore suitable for application in resource-constrained IoT devices.
Security Pattern for Cloud SaaS: From System and Data Security to Privacy Case Study in AWS and Azure

The Cloud is fast becoming a popular platform for SaaS, a popular software delivery model. This is because the Cloud has many advantages over the traditional private infrastructure, such as increased flexibility, no maintenance, less management burden, easy access and easy to share information. However, there are many concerns around issues like system security, communication security, data security, privacy, latency and availability. In addition, when designing and developing Cloud SaaS application, these security issues need to be addressed in order to ensure regulatory compliance, security and trusted environment for Cloud SaaS users. In this paper, we explore the security patterns for Cloud SaaS. We work on the patterns covering different security aspects from system and data security to privacy. Our goal is to produce the security best practices and security knowledge documentation that SaaS developer can use as a guideline for developing Cloud SaaS applications from the ground up. In addition to that, we also provide a case study of security patterns and solutions in AWS and Azure.
Quantifying quantum coherence based on the generalized $$\alpha $$ α –z-relative R $$\acute{e}$$ e ´ nyi entropy

We present a family of coherence quantifiers based on the generalized $\alpha-z-$relative R$\acute{e}$nyi entropy. These quantifiers satisfy all the standard criteria for well-defined measures of coherence, and include some existing coherence measures as special cases.
VRIA - A Framework for Immersive Analytics on the Web

We report on the design, implementation and evaluation of <VRIA>, a framework for building immersive analytics (IA) solutions in Web-based Virtual Reality (VR), built upon WebVR, A-Frame, React and D3. The recent emergence of affordable VR interfaces have reignited the interest of researchers and developers in exploring new, immersive ways to visualize data. In particular, the use of open-standards web-based technologies for implementing VR in a browser facilitates the ubiquitous and platform-independent adoption of IA systems. Moreover, such technologies work in synergy with established visualization libraries, through the HTML document object model (DOM). We discuss high-level features of <VRIA> and present a preliminary user experience evaluation of one of our use cases.
Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s).
An FBG-Based Sensing Glove to Measure Dynamic Finger Flexure With an Angular Resolution of 0.1$^{\circ }$ up to Speeds of 80$\mathrm{^{\circ }\text{/s}}$

We demonstrate a fiber Bragg grating (FBG) sensor-based highly accurate instrumented glove that can measure finger flexure with an angular resolution of 0.1°. The sensing unit consists of an FBG that is very sensitive to axial strain induced by the flexing of fingers. The spectral shift of the reflection spectrum of the FBG varies linearly with the joint rotation angle. The sensor offers a very high angular resolution of 0.1° with a very high sensitivity of 18.45 pm/degree. The accuracy evaluated using a mechanical setup, and the human hand is 0.13° and 0.67°, respectively. This is much better than many other reported sensors. The sensor showed excellent repeatability with a maximum standard deviation of 0.30° and 0.79° on a mechanical setup and the human hand, respectively. The results are validated using a precalibrated inertial measurement unit (IMU) sensor. The sensor also exhibited much better dynamic response compared to the IMU upto a rotation speed of 80°/s. These results demonstrate that our sensor is a strong potential candidate for the development of high-accuracy instrumented gloves that could be used to monitor the progress in the rehabilitation of stroke survivors. This paper focuses on the careful characterization of the sensor to establish it as a sensitive and practical approach. The accuracy, repeatability, and dynamic response were evaluated using a motorized mechanical model of a finger joint and also on the hands of two human subjects.