Reviewerly/exHarmony huggingface dataset:
Raising Awareness about Cloud Security in Industry through a Board Game

Today, many products and solutions are provided on the cloud; however, the amount and financial losses due to cloud security incidents illustrate the critical need to do more to protect cloud assets adequately. A gap lies in transferring what cloud and security standards recommend and require to industry practitioners working in the front line. It is of paramount importance to raise awareness about cloud security of these industrial practitioners. Under the guidance of design science paradigm, we introduce a serious game to help participants understand the inherent risks, understand the different roles, and encourage proactive defensive thinking in defending cloud assets. In our game, we designed and implemented an automated evaluator as a novel element. We invite the players to build defense plans and attack plans for which the evaluator calculates success likelihoods. The primary target group is industry practitioners, whereas people with limited background knowledge about cloud security can also participate in and benefit from the game. We design the game and organize several trial runs in an industrial setting. Observations of the trial runs and collected feedback indicate that the game ideas and logic are useful and provide help in raising awareness of cloud security in industry. Our preliminary results share insight into the design of the serious game and are discussed in this paper.
Open quantum dynamics with singularities: Master equations and degree of non-Markovianity

Master equations describing open quantum dynamics are typically first order differential equations. When such dynamics brings the trajectories in state space of more than one initial state to the same point at finite instants in time, the generator of the corresponding master equation becomes singular. The first-order, time-local, homogeneous master equations then fail to describe the dynamics beyond the singular point. Retaining time-locality in the master equation necessitates a reformulation in terms of higher-order differential equations. We formulate a method to eliminate the divergent behavior of the generator by using a combination of higher-order derivatives of the generator with suitable weights and illustrate it with several examples. We also present a detailed study of the central spin model and we propose the average rate of information inflow in non-Markovian processes as a quantity that captures a different aspect of non-Markovian dynamics.
Development of Real-Time Fuzzy Synchronization of Chaos Based System for Image Encryption

The concept of chaos, which has entered the literature since the 19th century, has been used for the protection of information with the development of information technologies in recent years. Chaos circuits are frequently used in secure communication systems because of its unpredictability and ease of modeling. In this paper, real-time fuzzy control method was used for master-slave synchronization of Rucklidge chaotic circuit. Also, sinusoidal signal and image encryption were used for application of secure communication process in LabVIEW platform. Data acquisition system was used to implement real time application. Fuzzy control performed synchronization at the end of the specified period and image encryption and decryption were successfully obtained in the developed system.
Superpixel algorithm for objects tracking in satellite video

Moving objects tracking in dynamic scenes is the most important study in satellite video processing. This paper's target is to propose an improved algorithm to track and detect the moving object. The object in the satellite image is a small size, and the ratio between the moving object and the whole frame is tiny. Because the satellite is continuously in motion, the motion vector of object has a high interfere with satellite motion. In order to obtain an identical motion vector for each object, this proposed algorithm presents the architecture based on a superpixel algorithm using simple linear iterative clustering(SLIC) before optical flow motion estimation. The simulation result shows the moving object labeling and prediction succeeded by using proposes algorithm.
CSSR: A Context-Aware Sequential Software Service Recommendation Model

We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive experiments have been conducted on a large dataset collected from GitHub against a list of existing methods. The results illustrate the superiority of our method in various aspects.
Factors Impacting Work Engagement of Gen Z Employees: A Regression Analysis

This study examined the factors that impacted work engagement for Gen Z employees. Six factors were identified for analysis and hypothesized to be positively related to employee engagement, including: corporate social responsibility, leadership – transformational and transactional, work-life balance, autonomy, and technology. A survey was sent out through Amazon Mechanical Turk in April 2020. From the responses, Gen Z respondents accounted for 69. This study used a correlation analysis and a multiple regression analysis. Results revealed positive correlations between the variables and employee engagement, with transformational leadership showing the highest correlation and transactional leadership the lowest. Technology, autonomy, and work-life balance fell in between these two extremes. The results of the multiple regression analysis indicated that transformational leadership had the strongest influence on employee engagement, while transactional leadership had no impact. Autonomy and work-life balance were shown to have positive influences on employee engagement; technology and corporate social responsibility were not significant.
PENGGUNAAN E-BOOK UNTUK MENINGKATKAN MINAT BACA SISWA SEKOLAH MENENGAH PERTAMA

The purpose of this study was to analyze the increase in reading interest through the use of e-books in class IX D SMPN 2 Mayang.The method used in this research is Guidance and Counseling Action Research (PTBK) which was carried out in two cycles. Each cycle includes four stages of planning, implementing action, observing and reflecting. The subjects in this study were students of class IX D in the 2020/2021 academic year as many as 30 students. Data collection techniques using test, observation and interview techniques. Data were analyzed qualitatively and quantitatively.Based on the observation of the initial conditions before the study was conducted, the percentage of students' interest in reading was only 37%. After the action was taken, namely the use of e-books as a source of literacy in guidance and counseling services, the percentage of students' interest in reading increased. In cycle 1 the percentage of students' completeness increased to 50% while in cycle 2 it reached 70%. This increase in the percentage of students' reading interest shows that the use of e-books as a reading source in digital literacy has succeeded in increasing students' reading interest.
ANALYSIS OF METHODS FOR DETECTING FACES IN AN IMAGE

In this article, computer vision is considered as modern technology of automatic processing of graphic images, and the relationship between the terms “computer vision” and “machine vision” is investigated. A diagram of a typical computer vision system is given and the possibility of using a system based on an artificial neural network for image analysis is considered. The article analyses the current situation with the use of computer vision systems and the possibility of its application. This article presents face recognition algorithms for existing categories, including: empirical method; feature method – invariant feature; use the template specified by the developer for identification; study the method of detecting the system by external signs. The empirical method of “top-down knowledge-based methods” involves creating an algorithm that implements a set of rules that image segments must satisfy in order to be recognized as faces. Feature-invariant approaches (Feature-invariant approaches) based on bottom-up knowledge constitute the second group of face detection methods. The methods of this group have the ability to recognize faces in different places as an advantage. Use the template set by the developer for identification (template matching method). Templates define specific standard images of face images, for example, describing the attributes of different areas of the face and their possible mutual positions. A method for detecting faces by external signs (a method for performing the training stage of the system by processing test images). The image (or its fragments) is somehow assigned a calculated feature vector, which is used to classify the image into two categories – human face/non-human face.
The Impact of Learning Loss due to Family Income Factors on Students' Achievement during the COVID-19 Pandemic

The purpose of this study is to examine online learning during the pandemic period from the learner's economic standpoint. Seeing how far family income influences student learning achievement from the perspective of mastering English. The data is derived from English academic achievement and a questionnaire focusing on students' economic situations, and it is analyzed using the SPSS version 23 application. After being determined to be homogeneous and normally distributed, the data was collected and analyzed to test the hypothesis using R Square and ANOVA techniques. The results show that family economic factors influence Riau University students' English proficiency as much as 69.7 % in 2021 during the COVID-19 pandemic which may result in learning loss for the students
Semantically Consistent Image-to-Image Translation for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to adapt models trained on a source domain to a new target domain where no labelled data is available.In this work, we investigate the problem of UDA from a synthetic computer-generated domain to a similar but real-world domain for learning semantic segmentation.We propose a semantically consistent image-to-image translation method in combination with a consistency regularisation method for UDA.We overcome previous limitations on transferring synthetic images to real looking images.We leverage pseudo-labels in order to learn a generative image-to-image translation model that receives additional feedback from semantic labels on both domains.Our method outperforms state-of-the-art methods that combine image-to-image translation and semi-supervised learning on relevant domain adaptation benchmarks, i.e., on GTA5 to Cityscapes and SYNTHIA to Cityscapes.
GHZ-like states in the Qubit-Qudit Rabi model

We study a Rabi type Hamiltonian system in which a qubit and a d-level quantum system (qudit) are coupled through a common resonator. In the weak and strong coupling limits the spectrum is analysed through suitable perturbative schemes. The analysis show that the presence of the multilevels of the qudit effectively enhance the qubit-qudit interaction. The ground state of the strongly coupled system is a found of Greenberger-Horne-Zeilinger (GHZ) type. Therefore, despite the qubit-qudit strong coupling, the nature of the specific tripartite entanglement of the GHZ state suppress the bipartite entanglement. We analyze the system dynamics under quenching and adiabatic switching of the qubit-resonator and qudit-resonator couplings. In the quench case, we found that the non-adiabatic generations of photons in the resonator is enhanced by the number of levels in the qudit. The adiabatic control represents a possible route for preparation of GHZ states. Our analysis provides relevant information for future studies on coherent state transfer in qubit-qudit systems.
Direct generation of entangled photon pairs in nonlinear optical waveguides

Abstract Entangled photons are pivotal elements in emerging quantum information technologies. While several schemes are available for the production of entangled photons, they typically require the assistance of cumbersome optical elements to couple them to other components involved in logic operations. Here, we introduce a scheme by which entangled photon pairs are directly generated as guided mode states in optical waveguides. The scheme relies on the intrinsic nonlinearity of the waveguide material, circumventing the use of bulky optical components and their associated phase-matching constraints. Specifically, we consider an optical waveguide under normal illumination, so that photon down-conversion can take place to excite waveguide states with opposite momentum in a spectral region populated by only two accessible modes. By additionally configuring the external illumination to interfere different incident directions, we can produce maximally entangled photon-pair states, directly generated as waveguide modes with conversion efficiencies that are competitive with respect to existing macroscopic schemes. These results should find application in the design of more efficient and compact quantum optics devices.
A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. Resulted by suspended particles scattering and wavelength-dependent light attenuation, underwater images are always hazy and color-distorted. To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. Furthermore, the FPGA implementation of various convolution in the proposed network is optimized based on the Winograd algorithm. An efficient upsampling engine is presented, and the FPGA implementation of squeeze-and-excitation module in MobileNetV3 is optimized. The accelerator is implemented on a Zynq XC7Z045 device running at 150 MHz and achieves 23.68 frames per second (fps) and 33.14 fps when using MobileNetV3-Large and MobileNetV3-Small as the feature extractor. Compared to CPU, our accelerator achieves 7.5×–8.7× speedup and 52×–60× energy efficiency.
Evaluation of GAN Architectures For Visualisation of HPV Viruses From Microscopic Images

Human papillomavirus (HPV) remains a leading cause of virus-induced cancers and has a typical size of 52 to 55nm in diameter. Hence conventional light microscopy that usually sustains a resolution at $\sim$ 100nm per pixel falls short of detecting it. This study explores four state of the art generative adversarial networks (GANs) for visualising HPV. The evaluation is achieved by counting the HPV clusters that are corrected identified as well as drug treated cultured cells, i.e. no HPVs. The average sensitivity and specificity are 78.81%, 76.37%, 76.62% and 84.71% for CycleGAN, Pix2pix, ESRGAN and Pix2pixHD respectively. For ESRGAN, the training takes place by matching pairs between low and high resolution (x4) images. For the other three networks, the translation is performed from original raw images to their coloured maps that have undertaken Gaussian filtering in order to discern HPV clusters visually. Pix2pixHD appears to perform the best.
An Evaluation of E-Learning Implementation During Covid-19 Pandemic in Elementary School

This study aims to evaluate the implementation of online learning at SD 3 Wergu Wetan. This evaluative research uses the Stake model from the planning, implementation stages, and which is then linked to the online learning implementation guide from the Ministry of Education and Culture. Subjects were taken purposively consisting of principals, teachers, and students, as well as parents as measured by interviews, documentation, and observation. Data were analyzed descriptively qualitatively. The results showed that in planning, the teacher emphasized the achievement of the main indicators, reading, writing, and arithmetic skills, using student worksheets. The implementation aspect, learning process uses WhatsApp, YouTube, and study groups. Parents and internet connections are critical success factors for online learning. Teachers need the training to create their own media. In the output aspect, the average score of students is good but students' abilities are not in accordance with the results achieved because the exam is done at home so that an online assessment is needed that students can do independently. Implementation of online learning has been going quite well and is included in the B category.
Brno urban dataset: Winter extension

This paper presents our latest extension of the Brno Urban Dataset (BUD), the Winter Extension (WE). The dataset contains data from commonly used sensors in the automotive industry, like four RGB and single IR cameras, three 3D LiDARs, differential RTK GNSS receiver with heading estimation, the IMU and FMCW radar. Data from all sensors are precisely timestamped for future offline interpretation and data fusion. The most significant gain of the dataset is the focus on the winter conditions in snow-covered environments. Only a few public datasets deal with these kinds of conditions. We recorded the dataset during February 2021 in Brno, Czechia, when fresh snow covers the entire city and the surrounding countryside. The dataset contains situations from the city center, suburbs, highways as well as the countryside. Overall, the new extension adds three hours of real-life traffic situations from the mid-size city to the existing 10 h of original records. Additionally, we provide the precalculated YOLO neural network object detection annotations for all five cameras for the entire old data and the new ones. The dataset is suitable for developing mapping and navigation algorithms as well as the collision and object detection pipelines. The entire dataset is available as open-source under the MIT license.
Reliable Go Game Images Recognition Under Strong Light Attack

Go is a popular global game whose win or loss is only determined by the number of intersection points surrounded by black or white pieces. Among all the counting methods, the traditional manual counting method is time-consuming. Additionally, the current Go game images recognition technology cannot endure light reflection attacks or extreme image capture angles effectively. In this paper, a reliable Go game images recognition method is proposed which not only can resist light reflection attacks but also can endure various image capture angles. To obtain this goal, we propose a detection method based on the optimized CNNs (Convolutional Neural Network) framework. Experiments on recognizing 3220 images show that the average accuracy with our proposed method is over 99.99%, which is 22 times better than the accuracy of the state-of-the-art approach on Go game images recognition. Besides, our study provides potential references for the recognition of interfered small objects in groups that have few features. It provides a reference in similar application scenarios such as the detection of animal crowds, industrial parts, physiological tissues, and micro-particles.
The Future is Near: On the Prospects of Central Bank Digital Currency Adoption in Central and Eastern European Countries

Central banks around the world have started exploring the possibility of issuing Central Bank Digital Currencies (CBDC). A CBDC would be similar to digital cash and could, in theory, substitute for commercial bank deposits as the money used by households and businesses. This paper explored the potential implications for financial stability and monetary policy stemming from potential CBDC adoption in Central and Eastern European countries (CEEs). We conducted an empirical analysis using publicly available data from the European Central Bank’s database to assess how introducing a CBDC could impact the business models of the largest CEE banks. Our findings indicated that a CBDC could support financial stability by accelerating the adoption of digital payments in CEEs, improving anti-money laundering, and thus supporting banks’ ability to finance the economy. We found that the impact of a CBDC on the profitability of commercial banks would be impacted more by any change in interest rates than by the quantity of CBDC replacing stable bank funding. Furthermore, a CBDC could be beneficial for monetary policy by improving the control over inflation and accelerating the implementation of countercyclical instruments when classical monetary policy instruments are no longer effective. Hence, non-Euro CEE countries would benefit the most from the introduction of a CBDC.&#x0D; Keywords: central bank digital currencies; financial stability; monetary policy; financial institutions
Comparative analysis of software optimization methods in context of branch predication on GPUs

General Purpose computing for Graphical Processing Units (GPGPU) technology is a powerful tool for offloading parallel data processing tasks to Graphical Processing Units (GPUs). This technology finds its use in variety of domains – from science and commerce to hobbyists. GPU-run general-purpose programs will inevitably run into performance issues stemming from code branch predication. Code predication is a GPU feature that makes both conditional branches execute, masking the results of incorrect branch. This leads to considerable performance losses for GPU programs that have large amounts of code hidden away behind conditional operators. This paper focuses on the analysis of existing approaches to improving software performance in the context of relieving the aforementioned performance loss. Description of said approaches is provided, along with their upsides, downsides and extents of their applicability and whether they address the outlined problem. Covered approaches include: optimizing compilers, JIT-compilation, branch predictor, speculative execution, adaptive optimization, run-time algorithm specialization, profile-guided optimization. It is shown that the aforementioned methods are mostly catered to CPU-specific issues and are generally not applicable, as far as branch-predication performance loss is concerned. Lastly, we outline the need for a separate performance improving approach, addressing specifics of branch predication and GPGPU workflow.
Taxonomy of cognitive abilities and measures for assessing artificial intelligence and robotics capabilities

This chapter reviews taxonomies of human cognitive abilities and measures of those abilities. It recalls the history of key models and frameworks, analysing their strengths and weaknesses. It gives special attention to the second order of frameworks, which comprises approximately nine distinct abilities, including fluid, crystallised, spatial and broad retrieval/creativity abilities. The primary factors associated with these nine abilities are discussed, along with sample tests and test items reflecting the different abilities. It proposes two additional abilities: emotional intelligence and collaboration/communication. Finally, it discusses the prospects and feasibility of using human abilities and their associated tests to evaluate machine intelligence, discussing the advantage of having a justification for the selection of tasks.