Reviewerly/exHarmony huggingface dataset:
CHEESE: Cyber Human Ecosystem of Engaged Security Education

This Innovative Practice Full Paper presents CHEESE, a platform for cybersecurity education that complements formal classroom instruction with hands-on experience. With the ubiquitous use of computing devices and applications today, the protection of personal and privileged information is a persistent challenge. Modern software applications are typically complex pieces of code that borrow from various preexisting software libraries. Consequently, a flaw in one piece of software can have far-reaching and often unintended security implications that malicious actors can exploit. Thus, cybersecurity education needs to be transformed from a purely academic enterprise for cybersecurity researchers into a necessary skill that is imparted to the current and future IT workforce at large. CHEESE aims to impart such skills. CHEESE is composed of CHEESEHub, a public web-platform hosting demonstrations of cybersecurity concepts, a set of lessons complementing the demonstrations, and a community-driven approach to the contribution of new demonstrations and lessons. CHEESE is intended to supplement and enhance traditional cybersecurity education with hands-on training that has been shown to improve concept retention and understanding. Instructors can incorporate CHEESE into their teaching in several ways: by utilizing one or more of the demonstrations hosted on the publicly-accessible CHEESEHub in conjunction with the web-accessible lessons; by deploying their own version of CHEESEHub with a custom set of demonstrations and lessons; or by developing their own lesson plan which borrows from and combines one or more demonstrations on CHEESEHub. The use of CHEESEHub only requires a web-browser and can hence be employed in a wide variety of educational and training settings from K-12 schools through university.
Hour-Ahead Power Generating Forecasting of Photovoltaic Plants Using Artificial Neural Networks Days Tuning

PV power generation has an important place in sustainable energy production. The proportion of power generation systems in various countries has increased year by year. However, PV power generation is probabilistic and cannot be accurately predicted, so in recent years it has been an important and popular research topic. This paper proposes an ANN with days tuning to predict the PV power generation of hour-ahead using every ten minutes power generation of three hours ago (a total of 19 data). The neural network has an excellent learning ability, which can easily learn when using days tuning to make prediction results more accurate and yield a slight error. Different forecasting conditions have validated the effectiveness and performance of the proposed idea. The result can trigger an early response to the power system dispatcher and solve PV power generation probabilistic and forecasting.
Enhancing Precision of ResNet-RRC with Pruning

Up to date, researchers have proposed various car detection models for driver safety. For each model, trade-off between precision and detection speed was inevitable. Most methods proposed for detecting cars in a scene do not quite satisfy these conditions simultaneously. The objective of this study is to propose a model using Recurrent Rolling Convolution (RRC) by adopting ResNet as the base net and utilizing pruning process within training. The pruned model has detection speed similar to that of the original network while having increased mean average precision (mAP).
Noisy quantum input loophole in measurement-device-independent entanglement witnesses

Entanglement witnesses form an effective method to locally detect entanglement in the laboratory without having the prior knowledge of the full density matrix. However, separable states can be erroneously indicated as entangled in such detections in the presence of wrong measurements or loss in detectors. Measurement-device-independent entanglement witnesses (MDI-EWs) never detect fake entanglement even under wrong measurements and for a particular kind of lossy detectors. A crucial assumption in the case of faithful detection of entanglement employing MDI-EWs is that the preparation devices producing "quantum inputs" - which are inputs additional to the quantum state whose entanglement is to be detected - are perfect and there is no noise during their transmission. Here, we relax these assumptions and provide a general framework for studying the effect of noise on the quantum inputs, invoking uniform and non-uniform noise models. We derive sufficient conditions on the uniform noisy map for retaining the characteristic of MDI-EWs. We find that in the context of non-uniform and entangling noise, fake entanglement detection is possible even by MDI-EWs. We also investigate various paradigmatic models of local noise and find conditions of revealing entanglement in the class of Werner states.
CCA-Secure ABE Using Tag and Pair Encoding

Jutla and Roy (Asiacrypt 2013) proposed the first tag-based identity-based encryption which Chen and Gong (Asiacrypt 2017) extended to construct CPA-secure attribute-based encryption (ABE) in prime-order groups. However, Chen and Gong used predicate encoding for ABE construction. In literature, there exists a more powerful encoding, namely, pair encoding which Attrapadung (Eurocrypt 2014, Asiacrypt 2016) introduced as a generic framework to construct CPA-secure attribute-based encryptions in prime order groups. In reality, however, CPA-secure encryptions are not always sufficient as a security requirement. Yamada et al. (PKC 2011, PKC 2012) suggested generic techniques to get CCA secure attribute-based encryptions from CPA-secure attribute-based encryptions. In this work, we achieve two-fold improvements. We provide a tag-based ABE construction that uses pair encoding and at the same time achieves CCA security from the matrix Diffie-Hellman assumption in the standard model. As a result, our tag-based ABE can be utilized to construct new attribute-based encryptions with multiple randomnesses required in both the secret key and the ciphertext. Moreover, the cost we pay for CCA security is still significantly less than the cost of generic approach of Yamada et al.
A Gaussian Process Assisted Offline Estimation of Multivariate Gaussian Distribution Algorithm

Surrogated assisted evolutionary algorithms are commonly used to solve real-world expensive optimization problems. However, in some situations, no online data is available during the evolution process. In this situation, we have to build surrogate models based on offline historical data, which is known as offline data-driven optimization. Since no new data can be used to improve the surrogate models, offline data-driven optimization remains a challenging problem. In this paper, we propose a Gaussian process assisted offline estimation of multivariate Gaussian distribution algorithm to address the offline data-driven optimization problem. Instead of using surrogate models to predict the fitness values of individuals, we utilize a surrogate model to predict the rankings of individuals based on the frequently used lower confidence bound. In this way, the robustness of the proposed algorithm could be enhanced. Experiments are conducted on five commonly used benchmark problems. The experimental results demonstrate that the proposed offline surrogate model and the multivariate Gaussian estimation of distribution algorithm are able to achieve competitive performance.
Clustering ensemble of massive high dimensional data based on BLB and stratified sampling framework

Clustering is an important research content for data analysis. It can obtain the underlying structure of data from the original data set. Clustering on massive high-dimensional data sets is still a huge challenge. In this paper, we propose a clustering ensemble algorithm based on BLB and stratified sampling framework for massive high-dimensional data. From two aspects of sample and feature, we use the BLB (Bag of Little Bootstrap) algorithm to divide the original data set into several small-scale data subsets, then use feature stratified sampling to obtain a low-dimensional subset. we generate base clustering results on multiple small-scale low-dimensional subsets, and finally obtain a cluster integration results by link-based consensus functions. The experimental results on synthetic data sets and UCI real data sets show that the algorithm proposed in this paper is effective for clustering massive high-dimensional data.
Improving the Performance of Blockchain Based Digital Contract Using Niederreiter Method

Nowadays the use of internet in daily life is increasing such that data exchange is frequently conducted. Especially in the case of business transactions, data communication security (such as for banking transaction, contract) is necessary. Sony, et al. [1] proposed a method for securing a contract by introducing ECDSA for signing a contract based on blockchain. However, Sony's method requires high time complexity and not resistant against quantum computing attacks. Therefore, this research proposed a method to reduce the time complexity and resistant against quantum computing attack. To overcome the quantum attack and reduce the time complexity of digital signing based on blockchain, McElliece Cryptosystem and digital signature based on Niederreiter are introduced for signing the contract. Since McElliece Cryptosystem and Niederreiter based signature uses scalar multiplication then the processing time is less than ECDSA. Based on the experiment result, it is proven that the execution time for signing process using the proposed method may reach up to 51.1% less than signing execution time of Sony's method, and for verification process up to 77.8% less than verification execution time of Sony's method. It is also proven that the proposed method is resistant against quantum attack.
Investigation of the k-ary GCD Algorithm for Natural Numbers

In our paper we investigate the k-ary Algorithm for finding GCD of long natural numbers. We suggest an effective modification for the Algorithm which gives an essential acceleration for the whole procedure of finding GCD. The common task of GCD calculation plays an important role in different spheres of Number Theory and Cryptography. It has applications in arithmetic of Finite Fields and key generation for the Cryptography.
How Does Computer Animation Affect Our Perception of Emotions in Video Summarization?

With the exponential growth of film productions and the popularization of the web, the summary of films has become a useful and important resource. Movies data specifically has become one of the most entertaining sources for viewers, especially during quarantine. However, browsing a movie in enormous collections and searching for a desired scene within a complete movie is a tedious and time-consuming task. As a result, automatic and personalized movie summarization has become a common research topic. In this paper, we focus on emotion summarization for videos with one shot and apply three independent methods for its summarization. We provide two different ways to visualize the main emotions of the generated summary and compare both approaches. The first one uses the original frames of the video and the other uses an open source facial animation tool to create a virtual assistant that provides the emotion summarization. For evaluation, we conducted an extrinsic evaluation using a questionnaire to measure the quality of each generated video summary. Experimental results show that even though both videos had similar answers, a different technique for each video had the most satisfying and informative summary.
RETRACTED: FPGA processor and visual keyword matching to optimize feature recognition of tourism resources

Confirm information from the web page is a critical issue to support tourism activities. These steps are based on how the analysis and synthesis of a web page article grammatical structure. Semantic structure is a single sentence or a fragment of the marked text. This is a characteristic of developing useful tourist information from a web page to identify the way. Identification, development, and utilization of tourism resources have proved inseparable from the earth sciences. In this case, the literature does not explain Earth's tourism resources' characteristics from a scientific point of almost complete. Still, it is only mentioned in the discussion of the operation of tourism resources and tourism. Machine learning algorithms are used in the proposed architecture, which resembles a field-programmable gate array to achieve. FPGA implementation of a fixed point, comparing classification performance.
Research on the Human-Computer Interaction Design in Mobile Phones

Mobile phones play an important role in our modern life. Meanwhile, the development of human-computer interaction has promoted the progress of the mobile phone industry. A good human-computer interaction design model needs to consider the user's understanding of the product system, but also needs to use ergonomics and cognitive psychology to carry out in-depth analysis and research on the user. The emergence of mobile communications and cloud computing technologies has also changed people's lifestyles. Firstly, through the discussion and research on the relationship between human-computer interaction and ergonomics and cognitive psychology, some key points that human-computer interaction design can influence the development trend of mobile phones are proposed. This article will also point out the impact of communication technology and cloud computing technology on mobile phones and people's lifestyles. Besides, the different operating systems of mobile phones will be compared and analyzed. Due to the different human-computer interaction experience in different user groups, the different requirements of them on a mobile phone will be analyzed. At the same time, the article also pays attention to the social problems caused by people's over-reliance on mobile phones. Finally, this article will make a reasonable prediction of the future development trend of mobile phones.
Fine-grained Task Scheduling in Cloud Data Centers Using Simulated-annealing-based Bees Algorithm

Cloud computing is increasingly implemented by a growing number of organizations in recent years. Their critical business applications are deployed in distributed cloud data centers (CDCs) for fast response and low cost. The ever-increasing consumption of energy makes it highly important to schedule tasks efficiently in CDCs. In addition, many factors in CDCs, e.g., the wind and solar energy and prices of power grid have spatial differences. It becomes a challenging problem of how to achieve the energy cost minimization for CDCs in such a market. This work applies a G/G/1 queuing system to evaluate the optimization of servers in each CDC. Furthermore, a single-objective constrained optimization problem is given and addressed by a proposed Simulated-annealing-based Bees Algorithm to yield a close-to-optimal solution. Based on it, a Fine-grained Task Scheduling (FTS) algorithm is designed to minimize the energy cost of CDCs by intelligently scheduling heterogeneous tasks among distributed CDCs. In addition, it also determines running speeds of servers and the number of switched-on servers in each CDC while strictly meeting tasks' delay bounds. Realistic data-driven results demonstrate that FTS outperforms its typical benchmark scheduling peers in terms of energy cost and throughput.
Cluster-Based Deep Contextual Reinforcement Learning for Top-k Recommendations

Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalized, efficient and dynamic recommendation systems. To sufficiently cater to this need, we propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB scan clustering to tackle vast item space, hence increasing the efficiency multi-fold. Moreover, by using deep contextual reinforcement learning, our proposed work leverages the user features to its full potential. With partial updates and batch updates, the model learns user patterns continuously. The dueling bandit-based exploration provides robust exploration as compared to the state-of-the-art strategies due to its adaptive nature. Detailed experiments conducted on a public dataset verify our claims about the efficiency of our technique as compared to existing techniques.
Enhancing Trust and Immutability in Cloud Forensics

We are living in a digital era where most of us have an online presence with all their data on cloud. “Easy access of data from anywhere” is the biggest advantage of cloud but this same point becomes the cause of cloud-based crimes. There has been a drastic increase in cloud-based crime in the recent past. The immoral from any geolocation can target the victim of any geolocation, even cross-borders. Moreover, hunting down the immorals/suspects in these cross-border attacks is more difficult as suspects are easily able to coat themselves. In the cloud, data is stored at multiple geolocations, and this cross-border cloud data storage is the biggest challenge for law enforcement agencies. When the crime is committed from a different country this geographically sparse data becomes a major hindrance for the forensics investigation. LEA had to request the cloud service providers of all the involved locations (including the ones which are outside the country) to provide data for fetching out the right pieces of evidence. In the current state, LEA performs these forensic investigation requests through web portals or through email conversations. These requests take days or weeks to process which in turn delays the investigation process, and sometimes victims are left out without justice. Also, these web portals/applications through which the LEA demands the pieces of evidence are also based on cloud and thus are highly insecure. These not so secure portals raise a big question on the authenticity of the data and evidence received on them. Evidence could be easily tailored or removed (partially or fully) from the cloud storage.
Age Group Based Document Classification in Bahasa Indonesia

Internet provides articles that may be categorized to various target readers based on genders, ages, hobbies, etc. To make sure that readers consume a proper article based on their age group, methods and training data were proposed and collected to classify the articles. This paper reported a document classification based on age groups using a binary classification method for Indonesian documents. The document classification used the term frequency and inverse document frequency (TF-IDF) features run on the Multinomial Naïve Bayes Classifier. The dataset was crowdsourced from three different sites: bobo.grid.id, hai.grid.id, and www.detik.com for three age group readers such as elementary school children, teenagers, and adults. The experimental results obtained 0.9406, 0.9341, and 0.9374 of precision, recall, and F-score respectively. This experiment also reported that for the datasets that were not stemmed performed better than those that were stemmed. It shows that the stemming process, which usually be done in the document classification, throws some information in the Indonesian texts. However, because this behavior was not happen on nouns, our future work is to elaborate further on the role of affixations in the lower age group documents.
A NEW GLOBAL REVOLUTION IN SCIENCE AND EDUCATION

The article notes that the formation of the global world is taking place and this world is reflected in science and education, and these spheres vice versa the world formation. It is shown that a global research direction has already been formed, including globalistics, global scientific disciplines, globalization studies, global evolutionism and a number of other scientific research areas. Although this global cluster of scientific knowledge is most intensively developed only since the second half of the last century, its origins are found in the works of Vladimir I. Vernadsky at the beginning of the same century. The global cluster of scientific knowledge is associated with the globalization of education and the formation of global education. The main integrative characteristics and features in the forms and content of these global evolutionary processes in education are identified. The authors use global and systemic approaches, integrative-interdisciplinary research methods, historical and evolutionary approaches, methods of forecasting, as well as a number of general scientific methods. It is shown that integrative tendencies related to the global-evolutionary changes of civilization and its interaction with nature are unfolding in education. The article notes that the globalization of education is mainly focused on formal and organizational ways of integrating various systems and forms of education; the most significant directions of this process are highlighted. Global education is characterized by qualitative and meaningful transformations of the educational process, the subject field of which is “filled” with universal and integrative knowledge obtained in the course of global research. It is assumed that the globalization of science and education begins a new global revolution in the scientific and educational space, and the phenomenon of “globality” appears as global processe and system.
Research on the Control Strategy of Robot Fish Game in Dynamic Environment

In order to improve the scoring efficiency and solve the problem that the robot fish was easy to lose the target, we designed the general strategy of robot fish game based on fuzzy control in this paper. According to the dynamic environment of the game, an evaluation function was constructed to select the target ball. And a path planning and dribbling method of the robot fish was put forward to avoid getting stuck in the game. The experimental results showed that the scoring efficiency of the robot fish was improved and it was not easy to lose the ball.
Note on Autocorrelation of the Residuals of the NCV Kalman Filter Tracking a Maneuvering Target

The Kalman filter is the optimal estimator for tracking a nearly constant velocity (NCV) target with white noise acceleration errors. However, when the target maneuvers the white noise input assumption is violated and the filter estimate will become biased or worse, lead to the loss of the target track. Selection of a larger process noise will reduce the bias in the state estimates during a maneuver, but when the target is not maneuvering the filter performance is far from optimal. In decision-directed techniques for tracking maneuvering targets, the estimation process is impacted by hard decisions regarding onset and termination of target maneuvers. In this work, the autocorrelation of consecutive residuals for a maneuvering target is derived and a new test for maneuver detection is proposed. When the process noise covariance of an NCV Kalman filter is artificially high for tracking maneuvering targets, achieving a specified false alarm rate for maneuver detection is a challenge. A remedy to this challenge is also presented.