Reviewerly/exHarmony huggingface dataset:
Analyzing Spatial Differences in the TLS Security of Delegated Web Services

To provide secure content delivery, Transport Layer Security (TLS) has become a de facto standard over a couple of decades. However, TLS has a long history of security weaknesses and drawbacks. Thus, the security of TLS has been enhanced by addressing security problems through continuous version upgrades. Meanwhile, to provide fast content delivery globally, websites (or origin web servers) need to deploy and administer many machines in globally distributed environments. They often delegate the management of machines to web hosting services or content delivery networks (CDNs), where the security configurations of distributed servers may vary spatially depending on the managing entities or locations. Based on these spatial differences in TLS security, we find that the security level of TLS connections (and their web services) can be lowered. After collecting the information of (web) domains that exhibit different TLS versions and cryptographic options depending on clients' locations, we show that it is possible to redirect TLS handshake messages to weak TLS servers, which both the origin server and the client may not be aware of. We investigate 7M domains with these spatial differences of security levels in the wild and conduct the analyses to better understand the root causes of this phenomenon. We also measure redirection delays at various locations in the world to see whether there are noticeable delays in redirections.
How does emotional intelligence improve employee satisfaction and performance with mediating effect of employee engagement? Perspective from Saudi Arabian private companies

The main focus of this study is to examine how Emotional Intelligence (EI) improves Employee Job Satisfaction and Performance (EJSP) with mediating effect of Employee Engagement (EE) in private sector organizations of the Kingdom of Saudi Arabia (KSA). The nature of the research study is quantitative, and a correlational design has been adopted for the study. Primary data was collected from a sample of 537 employees of private organizations in major cities of KSA. SPSS and Smart PLS were used to run different statistical techniques to test the proposed model. The results of this research study reveal that EI positively impacts the performance and satisfaction level of employees where EE also mediates the relationship between EI and performance and satisfaction level of employees. Also, age, gender, and experience of employees act differently as a moderator in this study. The contribution from this research study would be an addition to existing literature. This research study holds a significant involvement towards theory, practice, and methodological progression in the field of learning and creating EI abilities and its vital influence on performance and satisfaction level of employees in the Saudi Arabian context, as no such study has been conducted in Saudi Arabia, to the best of our knowledge. The study is supposed to provide useful insights into the methodological advances in the field of EI as a learnable ability that can be created. EI should be enhanced to employees to get a significant level of performance and satisfaction in the working environment.
HMRL

In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward. In this respect, we develop a novel meta reinforcement learning framework called Hyper-Meta RL(HMRL), for sparse reward RL problems. It is consisted with three modules including the cross-environment meta state embedding module which constructs a common meta state space to adapt to different environments; the meta state based environment-specific meta reward shaping which effectively extends the original sparse reward trajectory by cross-environmental knowledge complementarity and as a consequence the meta policy achieves better generalization and efficiency with the shaped meta reward. Experiments with sparse-reward environments show the superiority of HMRL on both transferability and policy learning efficiency.
Color bizarreness effects in object memory: Evidence from a recall test and eye tracking

Abstract The main purpose of this study was to examine whether we could find a color bizarreness effect in object memory. Given robust results that objects with bizarre shapes are remembered better than those with common shapes, we would also expect to find a color bizarreness effect. In experiment 1, as in most studies of bizarreness effects, participants were presented pictures one by one in the learning phase, and recalled the object names in the test phase. In experiment 2, we presented multiple objects simultaneously in the learning phase and measured how long participants looked at each object by eye tracking. If participants paid attention to bizarre color objects, it would provide strong support for a color bizarreness effect. The results supported the existence of this effect. Our results strengthen research findings on bizarreness effects in pictorial materials in a different aspect. Not only an object's shape, but also its color can produce the bizarreness effect. Furthermore, we clearly demonstrated that bizarre colors attract attention and enhance object memory. This could be applied to making memorable advertisements and creating learning materials.
IMAGE ENCRYPTION BASED ON TWO-DIMENSIONAL FRACTIONAL QUADRIC POLYNOMIAL MAP

A new fractional two-dimensional quadric polynomial discrete chaotic map (2D-QPDM) with the discrete fractional difference is proposed. Afterwards, the new dynamical behaviors are observed, so that the bifurcation diagrams, the largest Lyapunov exponent plot and the phase portraits of the proposed map are given, respectively. The new discrete fractional map is exploited into color image encryption algorithm and it is illustrated with several examples. The proposed image encryption algorithm is analyzed in six aspects which indicates that the proposed algorithm is superior to other known algorithms as a conclusion.
Not very competent but connected: Leaders’ use of employee social networks as prisms to make delegation decisions.

Delegation is a critical tool for busy managers. Early delegation research suggests that managers are reluctant to delegate beyond a few highly competent employees or those with whom they have a strong relationship. Extending this line of research, we integrate signaling theory with a view of social networks as "prisms," to demonstrate the relevance of employees' network ties in the work unit for delegation. Signaling theory argues that when direct data about employee competence are mixed or ambiguous, decision makers will look for more indirect signals with which to make inferences about quality and reputation. One such signal is suggested by the networks as "prisms" perspective, which argues that network ties can operate as reputational signals in the absence of more direct quality data. Combining these insights and data from a field study and two follow-up laboratory studies, we find that in situations of moderate employee competence, managers will draw positive reputational inferences and be more willing to delegate to employees when such employees are more central in the friendship network of the work unit as well as when they share common memberships in friendship cliques with the managers. These network ties, however, do not matter for delegation decisions when there are direct data to indicate that employees are highly competent or clearly poor performers. Our lab studies elucidate the mediating role of perceived reputation. Implications for theory and practice are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Skeleton-Based Human Action Recognition on Large-Scale Datasets

Skeleton-based Human Action Recognition (SHAR) is one of the most trending research topics in computer vision, which relies on the investigation of multi-modal data acquired from different sensory devices. Due to its faster execution speed, skeleton-based automated SHAR systems are widely adopted in real-time applications such as surveillance systems, behavior analysis, gesture recognition, and security systems. To build such an efficient system, the recognition model needs to be trained on a large-scale multi-modal dataset to accurately identify multi-class actions. However, the recognition of multi-class actions with higher accuracy requires the extraction of the spatio-temporal discriminative features, which is a challenging task. Moreover, the traditional handcrafted feature-based Machine Learning (ML) models when dealing with a large dataset, fail to preserve spatio-temporal correlations, resulting in poor recognition accuracy. While exploring the recent literature, a paradigm shift in SHAR research from traditional ML-based models to deep learning models is observed, which intrigues us to hypothesize that deep learning is the future avenue for SHAR research on large datasets. To establish it, we perform an exhaustive study on the recent works published in 2017 onwards that utilize deep learning models in SHAR on large skeletal datasets, notably alleviating the tedious task of feature engineering and extraction. The comparative performance analysis of the recent models shows that the successor models outperform its predecessors in terms of recognition accuracy, confirming the prospects of the deep learning-based SHAR research on large skeletal datasets.
An FCM-based Dynamic Modeling of Operability and Maintainability Barriers in Road Projects

Building a new road infrastructure in the country leads to economic and industrial growth. A massive amount of money is paid by governments to build them; however, they fail due to many reasons related to operability and maintainability (O&M) issues. They are not also completed within the expected budget, time, and quality; so they are not justifiable. As these factors have a strong impact on projects, to reduce the final cost and other mentioned problems, it is necessary to identify the existing O&M barriers, their interrelationships, and their effects on the three mentioned factors. An in-depth literature review is conducted to identify the barriers. The fuzzy cognitive mapping (FCM) technique is used to model O&M barriers using real case data analyses. The findings reveal that managerial factors have more significanct impacts on the project’s success compared to other factors such as organizational, human resource, technology, and project management. Therefore, management methods are very important in developing integration in the project. Identifying, classifying, and determining the effects of barriers to entry of O&M contractors on the cost, time, and quality of road infrastructure projects show the signifcance of conducting this research, which is necessary to deal with the existing barriers. All these ultimately increase quality and reduce time and cost in road infrastructure projects.
Breast Cancer Detection and Classification Using Improved FLICM Segmentation and Modified SCA Based LLWNN Model

Breast cancer death rates are higher due to the low accessibility of early detection technologies. From the medical point of view, mammography diagnostic technology increases are essential in the detection process. This research work proposes a segmentation for each image by using improved Fuzzy Local Information C-Means (FLICM) algorithms and classification by using the novel local linear wavelet neural network (LLWNN-SCA) model. Further, the weights of the LLWNN model is optimized by using the modified Sine Cosine Algorithm (SCA) to improve the performance of the LLWNN algorithm. By applying an improved FLICM algorithm, the segmented images have undergone the process of feature extraction. The statistical features are extracted from the segmented images and fed as input to the SCA based LLWNN model. The improved FLICM segmentation achieves an accuracy of about 99.25%. Classifiers such as Pattern Recognition Neural Network (PRNN), Feed Forward Neural Network (FFWNN), and Generalized Regression Neural Network (GRNN) are also utilized for classification, and comparison results are presented with the proposed SCA-LLWNN model.
A Novel Data-Driven Approach to Autonomous Fuzzy Clustering

In this article, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC first uses all the data samples as microcluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a “one pass” manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles.
Distribution Network Fault Location Based on Intelligent Algorithm Research

With the development of science and technology, many scholars study artificial intelligence technology in distribution network fault location. In recent times, there have been many achievements made by many researchers, of which some were the application of particle swarm optimization [, ], genetic algorithm [, ], ant colony algorithm []. This paper describes the application of a bi-state binary particle swarm optimization algorithm, multiple population genetic algorithm, and hybrid ant colony algorithm, the optimized intelligent algorithm in the distribution network's fault location. In distribution network fault location, traditional algorithms have more limitations, which need further improvement to make them more perfect—comparing the simulation data obtained before and after optimization shows that the optimized algorithm has faster convergence speed, higher accuracy, and higher fault location efficiency.
Characterization of bifurcations in the Zeeman catastrophe machine using singular value decomposition

Stable limit cycles of the dynamic Zeeman Catastrophe Machine are calculated and classified. As the geometry of the device is changed, they evolve into different types at discrete bifurcation points. The process is normally presented as a bifurcation diagram using a stroboscopic map, but this does not provide enough information to uniquely identify the bifurcation event. Three types of bifurcation are common: period-doubling, symmetry-breaking, and turning points. A method is developed to distinguish between these types by calculating the response to a change in device geometry. Assuming that the limit cycle has already been calculated, the response to a change in geometry is defined as the solution of a linear second-order differential equation. The equation is solved in finite-difference form by defining a matrix which contains the second-order response to a change in the limit cycle, as well as an augmented matrix which contains the response to a change in device geometry. At a bifurcation, the solution can be analyzed using singular value decomposition of these two matrices. The resulting singular values contain enough information to uniquely identify, and characterize, each type of event. The corresponding singular vectors can also be used to uniquely characterize the symmetry changes that occur in the response to change at each event.
Align, then memorise: the dynamics of learning with feedback alignment*

Abstract Direct feedback alignment (DFA) is emerging as an efficient and biologically plausible alternative to backpropagation for training deep neural networks. Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the-art models such as transformers. On the other hand, it notoriously fails to train convolutional networks. An understanding of the inner workings of DFA to explain these diverging results remains elusive. Here, we propose a theory of feedback alignment algorithms. We first show that learning in shallow networks proceeds in two steps: an alignment phase, where the model adapts its weights to align the approximate gradient with the true gradient of the loss function, is followed by a memorisation phase, where the model focuses on fitting the data. This two-step process has a degeneracy breaking effect: out of all the low-loss solutions in the landscape, a network trained with DFA naturally converges to the solution which maximises gradient alignment. We also identify a key quantity underlying alignment in deep linear networks: the conditioning of the alignment matrices . The latter enables a detailed understanding of the impact of data structure on alignment, and suggests a simple explanation for the well-known failure of DFA to train convolutional neural networks. Numerical experiments on MNIST and CIFAR10 clearly demonstrate degeneracy breaking in deep non-linear networks and show that the align-then-memorize process occurs sequentially from the bottom layers of the network to the top.
A Novel Data Filtering for a Modified Cuckoo Search Based Movie Recommender

Nowadays, recommender systems are inseparable parts of e-commerce businesses and help in personalizing the offers. Clustering is an unsupervised tool to divide a given dataset into clusters based on a similarity metric. Hybrid recommendations based on clustering and metaheuristic optimization can improve predictions significantly. In our approach, the K-means algorithm applies for clustering the data, and the modified cuckoo search algorithm optimizes the clustering by moving some items into better clusters. the modified cuckoo search optimization we have used here replaces the random selection with tournament selection, which results in better clustering and prevents the algorithm from immature convergence. We also tried new filtering on data instead of modifying the clustering algorithm. With this new filtering, we made the recommendations more focused on the most interesting nearest movies. We compared the performance of our method to the existing methods, and the results show a significant improvement.
H-Stegonet: A Hybrid Deep Learning Framework for Robust Steganalysis

Steganalysis can be characterized as detecting a weak noise signal (hidden information) in textured regions of naturally occurring images. These noise signals are typically not perceptible to human eyes, which renders steganalysis a challenging task. On the other hand, recent breakthroughs in deep learning have seen remarkable progress in many applications, ranging from object recognition and segmentation to image generations. While there were efforts to build deep learning networks to perform steganalysis, the proposed architectures exhibit some limitations and a high tendency to overfit. We propose a hybrid deep learning architecture, namely H-StegoNet, to perform spatial steganalysis in this work. Precisely, by combining two different neural networks inspired by handcrafted features and the U-Net, we design a robust architecture that outperforms the existing approaches. Moreover, the experiments we performed under more realistic assumptions, including encoding with the syndrome trellis codes and assuming no prior knowledge of the payload used, thereby defining a rigorous and standard operation procedure for evaluating any steganalysis algorithm.
BWIN: A Bilateral Warping Method for Video Frame Interpolation

Flow-based video frame interpolation approaches typically adopt forward or backward warping to approximate the intermediate frames. And a synthesis network is used to refine the interpolation results. Optical flows indicate motion between two input frames, but both forward and backward warping only utilize the first frame. In this work, we propose bilateral warping to make full use of optical flows. Specifically, the proposed bilateral warping yields intermediate candidates from not only the first frame but also the second frame. Our model first applies bilateral warping on the input frames and contextual features. Then, we add skip connections from the input frames and contextual features to the synthesis network. Finally, the synthesis network generates the interpolation results by integrating the original and warped representations. The experimental results on a wide variety of datasets demonstrate the superiority of the proposed approach over the state-of-the-art video frame interpolation methods.
Substation Object Detection Based on Enhance RCNN Model

In the object detection task of substation, the low resolution object would suffer from serious information loss problem, so some low resolution objects with potential security risks cannot be detected by object detection models such as Faster RCNN. We combine Faster RCNN model with Wasserstein GAN model, and propose Enhance RCNN model especially for the low resolution object detection in the substation. In our model, discriminator in GAN is used to distinguish the abstract feature difference between the high resolution object and the low resolution object after supplementing feature. And generator is used to supplement the abstract feature for low resolution object, so that its feature distribution is consistent with the feature distribution of high resolution object, thus improving the overall detection effect. The experimental results show that for the typical object in the substation such as person, bicycle and vehicle, Enhance RCNN model averagely improves mAP (Mean Average Precision) and IoU (Intersection-over-Union) by 7.79% and 6.57% respectively when is compared with the other models including Faster RCNN, Fast RCNN and SSD. For the low resolution object whose ratio of the object pixel to total image pixel less than 0.2%, Enhance RCNN model averagely improves mAP by 10.44%.
Recognition-Based Graphical Password Algorithms: A Survey

User Authentication is an important aspect of information security. Alphanumeric passwords are the most common and widely adopted means of user authentication. Nevertheless, there are several disadvantages attached to the alphanumeric forms of authentication. Example, users choose passwords that are easy to guess (dates of births, their names, car plate number) in other to remember them, because difficult passwords are not easily remembered. This brought about the alternative of graphical passwords because research have been carried out to proof that humans find it easier to recall images. This paper reviews 10 recognition-based graphical password algorithms, and the common usability and security threats of these systems based on these algorithms were analyzed. This paper also suggests future research directions.
Real-Time Content-Based Cyber Threat Detection with Machine Learning

Due to the digitalization of our lives and the connection of almost all types of devices to the Internet, cybersecurity has become one of the hottest research areas in computer engineering. The anonymous structure of the Internet results in inevitable increase not only in the number of attacks but also in their types. Attackers generally prefer the use of webpages for deceiving the end-users. These pages are designed as if they are legitimate webpages, and they aimed to reach some important credentials of their victims, such as userIds, passwords, credit card numbers, etc. Therefore, the systems which detect and decrease this type of cyber threat can easily reduce the number of victims/losses and also increase the security of the computer network. To catch this type of malicious web pages, different approaches are proposed in the literature. Due to the dynamic structure of Machine Learning (ML) approaches, they are generally preferred for the detection of these pages. In this paper, we proposed a cyber threat detection system, which analyzes the content of the web pages to understand whether it is a legitimate or malicious page. The experimental results depict that the proposed model detects the malicious web pages with a very good level of accuracy.