Reviewerly/exHarmony huggingface dataset:
GR-GAN: Gradual Refinement Text-To-Image Generation

A good Text-to-Image model should not only generate high quality images, but also ensure the consistency between the text and the generated image. Previous models failed to simultaneously fix both sides well. This paper proposes a Gradual Refinement Generative Adversarial Network (GR-GAN) to alleviates the problem efficiently. A GRG module is designed to generate images from low resolution to high resolution with the corresponding text constraints from coarse granularity (sentence) to fine granularity (word) stage by stage, a ITM module is designed to provide image-text matching losses at both sentence-image level and word-region level for corresponding stages. We also introduce a new metric Cross-Model Distance (CMD) for simultaneously evaluating image quality and image-text consistency. Experimental results show GR-GAN significant outperform previous models, and achieve new state-of-the-art on both FID and CMD. A detailed analysis demonstrates the efficiency of different generation stages in GR-GAN.
Employee Satisfaction and its Impact on Faculty Members' Performance at Al Ain University of Science and Technology in the UAE

This study aims to determine the level of job satisfaction and examine factors affecting job satisfaction among Faculty members at Al Ain University of Science and Technology in the United Arab Emirates. A sample of 110 faculty members, responded to the study. Job satisfaction was measured by The Brayfield Rothe Job Satisfaction Index as modified by Warner and self-efficacy by the General Self-efficacy Scale. T-test analysis, Pearson correlation, one-way analysis of variance with Tukey post hoc and multiple regression analyses were employed. Results indicated that faculty members had a high level of job satisfaction. Moreover, results showed differences in the level of job satisfaction for men with higher academic ranks and more teaching experience. No significant difference in job satisfaction could be seen among married and bachelor faculty members. Therefore, this variable was excluded from the further analysis. The result of stepwise multiple linear regression showed that the independent variables (self-efficacy, gender, age, teaching experience, and academic rank) accounted for 46.7% of the variance in the job satisfaction of faculty members. Findings showed that higher self-efficacy aligned with high levels of job satisfaction hence self-efficacy emerged as the most significant predictor of job satisfaction. Recommendations include that higher education policymakers in the United Arab Emirates must focus attention on self-efficacy and its inclusion in recruitment review and training &amp; development of faculty, as an effort to improve faculty members’ job satisfaction.
Public Key Encryption with Equality Test in a Cloud Environment

With the rapid development and wide application of cloud computing and 5G communication, the number of mobile users is increasing rapidly, meaning that cloud storage services are receiving more and more attention. The equality test technology of retrievable encrypted data has become a hot research topic among scholars in recent years. In view of the problem of offline keyword-guessing attacks (KGAs) caused by collusion between internal servers and users, a public key encryption with equality test scheme (RKGA-CET) with higher security against KGAs is proposed. Based on the assumed difficulty of the discrete logarithm problem (DLP) and the properties of bilinear mapping, a specific encryption algorithm that encrypts the keyword twice is designed. In the first encryption stage, we convert the keyword according to the property of isomorphism of a finite field. In the second encryption stage, we encrypt the converted keyword vector and embed the user’s private key, and then perform the equality test. The algorithm ensures that the adversary cannot generate legal ciphertexts and implement KGAs when the secondary server is offline. At the same time, the algorithm also supports two authorization modes, in which case users can flexibly choose the corresponding authorization mode according to their own needs. Performance analysis shows that this scheme has overall superiority compared with other similar ones.
Cybersecurity in business education: The ‘how to’ in incorporating education into practice

Cybersecurity threats have been intensifying and becoming more diverse. Social engineering can enable or enhance these technical attacks. This interplay between technical attacks and social engineering, when used, makes every employee, manager and board member a part of the security infrastructure of an organization. As such, it is incumbent upon systems of higher education to educate both information technologists and organizational leaders on the broader issues: a multidisciplinary approach is needed. This article contributes to the literature by providing a comparison of industry-identified needs to protect against cybersecurity threats and what should be offered by business school academic programs.
Abstractions of Abstractions: Metadata to Infrastructure-as-Code

To support cloud newcomers and empower them with the full suite of benefits afforded by the DevOps toolkit, we propose a solution for generating infrastructure-as-code from metadata. Key-value pairs will describe the containers, volumes, configurations and virtual machines that make up a complex microservices architecture. This metadata will be first compiled down to an intermediate template based on the OASIS TOSCA Specification. There, it will be processed by a deployment and execution engine called MiCADO, which will further compile relevant sections of the template into the respective infrastructure-as-code for tools like Kubernetes, Terraform and Ansible. An implementation of the solution is currently being developed for a European project investigating Manufacturing-as-a-Service and Digital Twins, with the hopes of providing an approachable interface for users who are new to the unfamiliar environment of the cloud.
Energy-efficient scheduling algorithms based on task clustering in heterogeneous spark clusters

Spark is widely used for its fast in-memory processing. It is important to improve energy efficiency under deadline constrains. In this paper, a Task Performance Clustering of Best Fitting Decrease (TPCBFD) scheduling algorithm is proposed. It divides tasks in Spark into three types, with the different types of tasks being placed on nodes with superior performance. However, the basic computation time for TPCBFD takes up a large proportion of the task execution time, so the Energy-Aware TPCBFD (EATPCBFD) algorithm based on the proposed energy consumption model is proposed, focusing on optimizing energy efficiency and Service Level Agreement (SLA) service times. The experimental results show that EATPCBFD increases the average energy efficiency in Spark by 77% and the average passing rate of SLA service time by 14% compared to comparison algorithms. EATPCBFD has higher energy efficiency on average than comparison algorithms under deadline. The average energy efficiency of EATPCBFD with the deadline constraint is higher than the comparison algorithm.
A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret image. We suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold transform. The notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in payload. By scrambling the cover image, Arnold transform adds security to the information that gets embedded and also allows embedding more information in each iteration. The hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure system. The proposed method employs a set of keys to ensure that information cannot be decoded by an attacker. The experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats, including medical images. Various performance metrics proves that the retrieved cover image and hidden image are both intact. This System is proven to withstand rotation attack as well.
An Survey Analysis of Security Issues in the Cloud Data Storage

In When it comes to meeting the key security needs of a cloud data centre, blowfish is used for the encryption of file slices since it is the quickest and has the maximum throughput when compared to other symmetric algorithms for encryption and decryption. It is the concept of splitting and merging that adds to the principle of data security. Because it is utilized in a cloud environment, the hybrid approach helps to make the distant server more secure, allowing cloud providers to acquire the trust of their customers. In terms of data security and privacy protection, the fundamental difficulty of distinguishing sensitive data from access control has been overcome. With the help of cryptography technology, original data can be converted into an unreadable format. Symmetric key cryptography and public key cryptography are the two types of encryption algorithms that are available. This method makes use of keyboard shortcuts to turn data into an unintelligible format. As a result, only those who have been granted access to the cloud server can access the data. The cypher text data is visible to everyone.
Tracking daily routines of elderly users through acoustic sensing: An unsupervised learning approach

Assistive technologies that can passively track people’s daily activities with dementia can deliver significant benefits for the patients themselves and their carers. This work investigates the feasibility of developing a system for the unsupervised tracking of daily activities at home through acoustic sensing. Motivated by the wide adoption of intelligent voice assistant devices in home environments, we developed a prototype algorithm to identify diversions from typical activities using the captured sounds, without the need for activity labeling. The system relies on sound embeddings through a pre-trained model, a novel dimensionality reduction algorithm, and the application of dynamic time warping for pattern matching. Our evaluation through synthetic activity sequences using data from our data collection in addition to public datasets shows very good performance (precision 0.99, recall 0.95).
Prediction of the Resource Consumption of Distributed Deep Learning Systems

Predicting resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users of how long their training would take and enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings" such as GPU types and also by "workloads" like deep learning models. Previous studies have attempted to derive or model such a prediction, but they fall short of accommodating the various combinations of settings and workloads together. This study presents Driple, which designs graph neural networks to predict the resource consumption of diverse workloads. Driple also designs transfer learning to extend the graph neural networks to adapt to differences in settings. The evaluation results show that Driple effectively predicts a wide range of workloads and settings. In addition, Driple can efficiently reduce the time required to tailor the prediction for different settings by up to 7.3×.
MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS

Abstract. Depth estimation from a single image is a challenging task, especially inside the highly structured forest environment. In this paper, we propose a supervised deep learning model for monocular depth estimation based on forest imagery. We train our model on a new data set of forest RGB-D images that we collected using a terrestrial laser scanner. Alongside the input RGB image, our model uses a sparse depth channel as input to recover the dense depth information. The prediction accuracy of our model is significantly higher than that of state-of-the-art methods when applied in the context of forest depth estimation. Our model brings the RMSE down to 2.1 m, compared to 4 m and above for reference methods.
Detection of Trust Shilling Attacks in Recommender Systems

Most research on detecting shilling attacks focuses on users' rating behavior but does not consider that attackers may also attack the users' trusting behavior. For example, attackers may give a low score to other users' ratings so that people would think the ratings from the users are not helpful. In this paper, we define the trust shilling attack, propose the behavior features of trust attacks, and present an effective detection method using machine learning methods. The experimental results demonstrate that, based on our proposed behavior features of trust attacks, we can detect trust shilling attacks as well as traditional shilling attacks accurately.
A Keypoint-Based and Block-Based Fusion Method for Image Copy–Move Forgery Detection

Currently, image copy–move forgery issues have brought increasingly serious problems to the social fields. Many research have been devoted to address the image copy–move forgery issues. However, image copy–move forgery detection (CMFD) is still a challenging problem to image forensics. This paper proposes a coarse-to-fine detection method fusing the superiorities of both keypoint-based and block-based methods. The fusion method gets good geometric invariances of keypoint-based methods and good matching robustness with the invariant moment of block-based methods. In the coarse detection stage, a robust SIFT descriptor is used to extract the candidate keypoints, and then the 2NN test is applied to match the suspicious keypoint couples. The proposed three-pass filtering relying on the Euclidean distance, scaling coefficient ratio, and correlation coefficient of block-based features, remove the false-positive outlier couples. Finally, the scaling coefficient ratio statistics of the remaining keypoint couples get the scaling coefficient ratio between the size of copied/pasted or pasted/copied snippets. In the fine detection stage, the block-based thought relying on the scaling coefficient ratio uses the DAFMT to extract the block-based features of multiple scaling levels. Subsequently, LSH is presented to classify block features and finally indicate the fine forgery snippets. Finally, the morphological operations are presented to indicate the forgery accurately. The benchmark IMD and CoMoFoD image copy–move datasets are used to measure the performances between the proposed fusion method and the state-of-the-art CMFD methods. The numerous experimental results demonstrate that the proposed fusion method achieves nearly the best performances to resist various attacks, especially large-scaling attacks, among the compared state-of-the-art methods.
Constraints between concurrence and polarization for mixed states subjected to open system dynamics

Entanglement and polarization are mutually constrained by the relationship ${C}^{2}+{P}^{2}=1$, which engages the concurrence $C$ of a pure, two-qubit state and the degree of polarization $P$ of either of its two subsystems. How the above constraint generalizes for mixed states is an open question. We address mixed, two-qubit states of the $X$ type, i.e., those whose density matrix has nonzero elements only in the two main diagonals. We focus on mixed states that arise out of a pure, two-qubit state that is subjected to either the amplitude damping channel or the depolarizing channel. We derive alternative constraints for concurrence and polarization and test them experimentally with polarization-entangled photons. We argue that our theoretical results hold also for classical light, whenever two binary degrees of freedom can be entangled.
ORB Features and Isophotes Curvature Information for Eye Center Accurate Localization

Pupil center recognition and location is an essential branch of ergonomics. It can be applied to emotion analysis and attention judgment. How to get the position of the pupil center from eye photos is the core of this field. Previous studies provided a helpful method, using scale-invariant feature transform (SIFT) to extract relevant features and combine them with the K-Nearest Neighbor (KNN) classifier. However, this method’s accuracy is not satisfying, and under some conditions, it will be position drift and other problems. We put forward a new idea to solve it by using Oriented FAST and Rotated BRIEF (ORB) features and Random Forest (RF) classifies. It is proved by experiment that our method improves the robustness of localization and the use of isophotes yields low computational cost, allowing for real-time processing. Meanwhile, we found that the ORB and RF are nearly as good, yielding an accuracy of 92.88% (BioID database).
How can a robot help people draw?

Advances in big data, artificial intelligence (AI), augmented reality (AR), and user interface development have led to practical digital tools and virtual interactions. However, physical interactions offer a richness that cannot be fully replaced through virtual interactions. I use exploratory systems and empirical approaches to study how data-driven tools and virtual interfaces can augment physical interactions. For my dissertation research, I focus on the activity of drawing: a task that is widely relatable, versatile, and that requires complex perception, conceptualization and motor skills. My initial studies show that the computers are social actors (CASA) paradigm also applies to drawing robots, adding a new dimension to thinking about the interaction.
Removal of Image Steganography using Generative Adversarial Network

Secret messages can be concealed in ordinary media like audio, video and images. This is called as Steganography. Steganography is used by cyber attackers to send malicious content that could harm victims. Digital steganography, or steganography in photographs, is exceedingly difficult to detect. The detection of steganography in images, has been investigated in thoroughly by a variety of parties. The use of steganographic techniques to send more malware to a compromised host in order to undertake different post-exploitation operations that affect the exploited system. Many steganalysis algorithms, on the other hand, are limited to working with a subset of all potential photos in the wild or have a high false positive rate. As a result, barring any suspected image becomes an arbitrary policy. Filtering questionable photos before they are received by the host machine is a more practical policy. In this paper, a Generative Adversarial Network based model is proposed that may be optimized to delete steganographic content while maintaining the original image's perceptual quality. For removing steganography from photos while keeping the maximum visual image quality, a model is built utilizing a combination of Generative Adversarial Network (GAN) and Image Processing. In the future, utilizing a generator to synthesize a picture will become more popular, and detection of steganography in images will become very difficult. In comparison to other models that have been addressed further, the proposed model is able to give a mean square error of 5.4204 between the generated image and the cover image, as well as better outcomes based on several metrics. As a result, a GAN-based steganography eradication method will aid in this endeavor.
Building an Operable Graph Representation of a Java Program as a Basis for Automatic Software Maintainability Analysis

As a part of a research project concerning software maintainability assessment in collaboration with the development team, we were interested in the frequent use of metrics as predictors. Many metrics exist, often with opaque and arguable implementations. We claim metrics mix the assessment of presentation, structure and model. In order to focus on true detectable maintainability defects, we computed metrics solely based on the structure of the program. Our approach was to parse the source code of Java programs as a graph, and to compute metrics in a declarative query language. To this end, we developed Javanalyser and implemented 34 metrics using Spoon to parse Java programs and Neo4j as graph database. We will show that the program graph constitutes a steady basis to compute metrics and conduct future machine-learning studies to assess maintainability.
Experimental study of quantum uncertainty from lack of information

Abstract Quantum uncertainty is a well-known property of quantum mechanics that states the impossibility of predicting measurement outcomes of multiple incompatible observables simultaneously. In contrast, the uncertainty in the classical domain comes from the lack of information about the exact state of the system. One may naturally ask, whether the quantum uncertainty is indeed a fully intrinsic property of the quantum theory, or whether similar to the classical domain lack of knowledge about specific parts of the physical system might be the source of this uncertainty. This question has been addressed in the previous literature where the authors argue that in the entropic formulation of the uncertainty principle that can be illustrated using the so-called, guessing games, indeed such lack of information has a significant contribution to the arising quantum uncertainty. Here we investigate this issue experimentally by implementing the corresponding two-dimensional and three-dimensional guessing games. Our results confirm that within the guessing-game framework, the quantum uncertainty to a large extent relies on the fact that quantum information determining the key properties of the game is stored in the degrees of freedom that remain inaccessible to the guessing party. Moreover, we offer an experimentally compact method to construct the high-dimensional Fourier gate which is a major building block for various tasks in quantum computation, quantum communication, and quantum metrology.
Programmable optical real-time processing system for ultra-wide bandwidth electronic reconnaissance

Electronic reconnaissance is to detect signals, extract their parameters, modulation types or direction of arrival and so on from a wide bandwidth range. It is difficult for digital signal processing device to process in real time under an ultra-wide bandwidth environment. This paper proposed a programmable optical system which can process signals from an instantaneous bandwidth up to 40GHz in real time. In the optical system, the signals are reconstructed at wavefront of a laser beam. The laser beam carrying signals passes through an optical system composed by lens, beam splitter, light modulator, etc. Signal processing operation is accomplished when laser beam arrives at a focal plane, and processing results are acquired by a high-speed camera. Typical pulse description words can be yielded from the results. The proposed optical system has a nano-second processing delay due to its meter-length light path.