Reviewerly/exHarmony huggingface dataset:
Monitoring Behavioral Compliance with Architectural Patterns Based on Complex Event Processing

Architectural patterns assist in the process of architectural decision making as they capture architectural aspects of proven solutions. In many cases, the chosen patterns have system-wide implications on non-functional requirements such as availability, performance, and resilience. Ensuring compliance with the selected patterns is of vital importance to avoid architectural drift between the implementation and its desired architecture. Most of the patterns not only capture structural but also significant behavioral architectural aspects that need to be checked. In case all properties of the system are known before runtime, static compliance checks of application code and configuration files might be sufficient. However, in case aspects of the system dynamically evolve, e.g., due to manual reconfiguration, compliance with the architectural patterns also needs to be monitored during runtime. In this paper, we propose to link compliance rules to architectural patterns that specify behavioral aspects of the patterns based on runtime events using stream queries. These queries serve as input for a complex event processing component to automatically monitor architecture compliance of a running system. To validate the practical feasibility, we applied the approach to a set of architectural patterns in the domain of distributed systems and prototypically implemented a compliance monitor.
An Efficient Hyperchaotic based Image Encryption Model based on DNA Encoding and Bit Scrambling

In recent times data security has been given more attention in communication and storing data, especially multimedia data/image. Digital data has been utilized in wide range of application services such as for providing secure access control mechanism, payment gateway service, in providing border security control system, forensic, fraud detection and prevention and so on. Subsequently, wide interest has been shown in providing or enhancing degree of security of multimedia data. Thus, efficient cryptography model for multimedia image is most desired. The traditional cryptography mechanism such as Data encryption standard (DES), advanced encryption standard (AES) and asymmetric encryption method such as RSA are not efficient in meeting digital image security requirement due to their low encryption security efficiency. Recently, deoxyribose nucleic acid (DNA) sequences and hyperchaotic sequence are jointly used for building secure and efficient image encryption model. However, the state-of-art model are not efficient (robust) against noise and cropping attack. Since in existingmodel most digits of each pixel are not altered. For enhancing security for encrypting high dimensional images, this work use both hyperchaotic and deoxyribose nucleic acid sequence. Firstly, pseudorandom sequence is generated using hyperchaotic system. This is done to use hyperchaotic sequences for each possible cases of the cryptography process where intensity parameters of a high dimensional images are transformed to a serial binary digit stream. Then, this stream of bits is scrambled using hyperchaotic sequence. Deoxyribose nucleic acid complementation and algebraic function are conducted among the deoxyribose nucleic acid sequences and the hyperchaotic sequences for attaining a dynamic and efficient image encoding outcomes. The experimental outcome shows proposed image encryption model attain superior performance than stat-of-art model in terms of robustness against entropy, statistical, cropping, noise, plain and differential attack.
초등학생 놀 권리 확보를 위한 정책적 지원 방안 모색

To search for policy-related support plans for the securement of the right to play of elementary students, observations were made of domestic and foreign right to play policy operation bases and policy cases along with right to play policy problems and policy-related support plans. Conclusions and proposals of literary studies conducted for this purpose are as follows.International regulations include the international children’s charter, the declaration on the rights of children, the declaration on the rights of children to play, and the convention on the rights of the child while internal regulations include the pledge of children’s rights, the children’s charter, the 1st general plan on child policies, the children’s policy of tolerant nations, the charter of children’s play, and ordinances. England operates experience-centered playgrounds that are easy to access while Germany operates atypical adventure playgrounds of Japan and Korea executes various policies according to each education office and representative cases are ‘essential play empathy school’ and ‘100 minutes of essential play’, which are conducted under the policy name of ‘Hey friend, let’s play’ by the Gangwon-do Office of Education. Problems and policy-related support plans of right to play policies include guaranteed play time, the securement of play spaces, the provision of play experiences, and play resources for adults.
Competition as a Means of Musical Education

The article focuses on piano performance among amateur pianists and their further creative development. Special attention is paid to amateur musical creativity, which contributes to increasing the importance of the system of music schools and additional musical education in Russia.
A Monte Carlo comparison of categorical tests of independence

The $X^2$ and $G^2$ tests are the most frequently applied tests for testing the independence of two categorical variables. However, no one, to the best of our knowledge has compared them, extensively, and ultimately answer the question of which to use and when. Further, their applicability in cases with zero frequencies has been debated and (non parametric) permutation tests are suggested. In this work we perform extensive Monte Carlo simulation studies attempting to answer both aforementioned points. As expected, in large sample sized cases ($&gt;1,000$) the $X^2$ and $G^2$ are indistinguishable. In the small sample sized cases ($\leq 1,000$) though, we provide strong evidence supporting the use of the $X^2$ test regardless of zero frequencies for the case of unconditional independence. Also, we suggest the use of the permutation based $G^2$ test for testing conditional independence, at the cost of being computationally more expensive. The $G^2$ test exhibited inferior performance and its use should be limited.
IMPLEMENTASI MODEL BELAJAR BERBASIS FORTOFOLIO DALAM MENINGKATKAN PEMAHAMAN MAHASISWA DALAM PENGEMBANGAN MEDIA PEMBELAJARAN

For managing learning and teaching there needs to be a suitable strategy to be able to organize learning so that it becomes better. One of the strategies that must be used by kindergarten teachers is the strategy in developing learning media. Learning media should be educational, economical, practical and contextual. To support this problem, the lecture who teach “Strategi Pembelajaran PAUD”, with based on fortfolio learning model. The method used in this research is quasy experiment with one group pre test posttest design model with independent t test. The results of this study indicate that the learning based on portfolio model can improve student understanding in terms of development strategies for kindergarten learning media with an average pretest of 54.00 and posttest increasing to 84.33. Based on the t test through SPSS it was concluded that there was a significant influence on the use of fortfolio-based learning on student understanding in developing learning media
스마트러닝 기반 학습이 치위생학과 학생들의 학업성취도와 학습만족도에 미치는 영향

Objectives: This study aimed to analyze the effects of smart-learning on the education of dental hygiene students. Methods: This study was conducted on 135 students in the dental hygiene department from a single university, with a pre-test implemented in order to verify the homogeneity of the experimental (smart-learning exposed) and control groups. Following each group’s lecture, we measured the study achievements of the students via a post-test, and their learning satisfaction through a questionnaire. An independent t-test was conducted to examine the homogeneity of the two groups and to compare both study achievements and learning satisfaction. Furthermore, a paired t-test was performed in order to compare the study achievements before and after lectures. Results: The average learning satisfaction level of the experimental group was 4.65±0.43, which was higher than that of the control group (4.46±0.54) (p<0.05). After comparing the study achievements of the groups, there was no statistically significant difference between them, both before and after lectures. Paired comparisons conducted on the overall study achievements showed that they significantly increased following lectures when compared to what they were beforehand, in both the smart-learning and control groups (p<0.001). Conclusions: Smart-learning can be expected to result in positive effects for dental hygiene education, and further structural studies on the various types of smart-learning are needed.
Neutral Network Adaptive Filter with Application to Ocean Current Energy Estimation

This chapter proposes a new approach for the design of an adaptive filter (AF), which is based on an artificial neural network (NN) structure for estimating the system state.The NNs are now widely used as a technology offering a way to solve complex and nonlinear problems such as time-series forecasting, process control, parameter state estimation, and fault diagnosis.The proposed NN-based adaptive filtering (NNAF) is designed by considering the filtering algorithm as an inputoutput system and two-stage optimization procedure.The first concerns a learning process where the weights of the NNAF are estimated to minimize the error between the filtered state and the state samples generated by a numerical model.The adaptation is carried out next to minimize the mean prediction error (MPE) of the system outputs (error between the observations and the system output forecast) subject to the coefficients associated with the estimated NN weights.Simulation results for different numerical models, especially for state estimation of the chaotic Lorenz system as well as for the ocean current at different deep layers which is important for renewable energy device placements, are presented to show the efficiency of the NNAF.
Study of inundation control on Kelayan Regional Inundation Handling Unit in Banjarmasin, South Borneo

Abstract Inundation occurred in high density urban areas is a major issue especially in low land because it potentially damage the affected infrastructure. In this research, a case was taken in Kelayan Regional Inundation Handling Unit in Banjarmasin City, South Borneo. The purpose of this study is to determine cause of the inundation and provide recommendation for reduced it. The hydrological analysis was calculated by using the rational method, using data from Sungai Tabuk Rain Station that 24 years long. From the discharge obtained, then the required channel dimensions could be calculated and compared with the existing channel dimensions. The hydraulic analysis was performed by HEC-RAS model with the discharge as boundary condition, and topographic data to determine channel elevation. As the results, the existing drainage channels can only cover 39.85% of the entire area, and almost all of these existing channels cannot drain the discharge that occurs, therefore it becomes an inundation. For example, channel number 4.C.14c the existing channel has 0.5m width and 0.7m depth, it caused inundation as high as 39cm upstream. After normalized the channel by increasing the dimensions to 2.1m width and 1.3m depth, the discharge does not overflow so there is no inundation.
Applying Social-Gamification for Interactive Learning in Tuberculosis Education

There are several methods of education for tuberculosis, and one of them is through the DOTS (Direct Observed Treatment Shortcourse) program.The management of tuberculosis education through the DOTS program is performed in clinics and hospitals only to patients and their families.The purpose of this study is to describe the development and testing of a prototype (social-game education) for interactive education for tuberculosis patients in particular and the general public.The data collection process is through direct observation of tuberculosis patients and health professionals (doctors, nurses, and DOTS health workers).Challenge the game in the prototype was provided with content that contained tuberculosis information that had been previously validated by a specialist.In addition to tuberculosis information as the main content, two important elements are making up this prototype, which are gamification and social media elements.In the game elements, this study adopted elements of the leaderboard, badge/achievement, challenge, and level.As for the third element, social media includes likes, comments, and shares.Prototype application testing was conducted on two participant groups (N = 48) consisting of 23 tuberculosis patients and 25 random participants.By using the user experience questionnaire (UEQ) technique, this research focuses on identifying the user's motivation in capturing compositional information as well as the clarity of the prototype.With a confidence interval of 5% (p = 0.05) per scale.The results indicate that participants have a high level of motivation towards the prototype; this is seen in the rating scale of stimulation with an average of 1.578.Likewise the effectiveness level of information in the rating scale of perspicuity has a mean of 1.224 also has a rate that is quite effective.
E-commerce Design Interaction with Voice User Interface using User-centered Design Approach

The rapid expansion of e-commerce has encouraged many platforms to serve their consumers better, including by providing state-of-the-art user interaction. Voice user interface is integrated in the e-commerce in order to allow users doing multitask while having handful activities and simplify features whose discoverability is low. The interface is designed using user-centered design approach, specifically ISO 9241-210:2010 methodology. In addition, the interface is verified by usability testing conducted in three iterations for two personas. Verification process of the design shows that high-fidelity prototype is 83.0% helpful and 70.0% effective.
Symmetric Frame Cracking: A Powerful Dynamic Textual CAPTCHAs Cracking Policy

In this work, we analyze the vulnerability of the dynamic textual CAPTCHA ( http://fexteam.gz01.bdysite.com/blog/2014/07/captcha-gif/ .) and propose a new method to automatically identify the CAPTCHA, which is based on Basic Vector Space Search Engine (BVSSE) and Convolutional Neural Network (CNN). Specifically, by exploiting the specific “Symmetric Frame Vulnerability”, we can remove most of the noise, therefore greatly reducing the difficulty of cracking. In the process of cracking, we first use the BVSSE to identify the CAPTCHA . The method is simple and fast, but there are problems such as a low recognition rate. Then we choose the CNN to identify the CAPTCHA, and finally get a recognition rate of 99.98% with the average speed of 0.092 s/gif. To have a deeper understanding of the internal recognition process, we visualize the intermediate output of the CNN model. In general, by comparing the two identification methods and visualizing the model, the entire recognition process becomes easier to understand. Based on the above experimental results and analyses, we finally summarize a new and general CAPTCHA attack method and discuss the security of the dynamic textual CAPTCHA .
Quantum computing using continuous-time evolution

Computational methods are the most effective tools we have besides scientific experiments to explore the properties of complex biological systems. Progress is slowing because digital silicon computers have reached their limits in terms of speed. Other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. Quantum computing exploits the coherence and superposition properties of quantum systems to explore many possible computational paths in parallel. This provides a fundamentally more efficient route to solving some types of computational problems, including several of relevance to biological simulations. In particular, optimisation problems, both convex and non-convex, feature in many biological models, including protein folding and molecular dynamics. Early quantum computers will be small, reminiscent of the early days of digital silicon computing. Understanding how to exploit the first generation of quantum hardware is crucial for making progress in both biological simulation and the development of the next generations of quantum computers. This review outlines the current state-of-the-art and future prospects for quantum computing, and provides some indications of how and where to apply it to speed up bottlenecks in biological simulation.
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.
Security Readiness Model of Public Organization in Smart Government: Conceptual Framework

Smart government are becoming one of the dominant and preferred sectors that intensely improving their service and facilities to attract public organization as well as to improve their delivering information. On top of that, security has become the main consent as it involves in every services and technology in smart government. This study aims to provide a new security readiness model of public organization in smart government. The proposed conceptual model for evaluating the level readiness of public organization in security of smart government. The proposed model will improve the security delivery of UAE government departments by enhancing the readiness towards public organization.
Threat-Based Sensor Management For Multi-target Tracking

Multi-sensor management for multi-target tracking is a theoretically and computationally challenging problem. A multi-sensor management algorithm is proposed within the partially observed Markov decision process (POMDP) framework, and cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is applied to track targets. The novelties lie in evaluating the threat degree of the targets using the analytic hierarchy process (AHP) and the threat-based sensor selection algorithm. Considering distance, speed and heading of targets, we use the AHP to evaluate the threat degree of targets at each sampling time. Based on the threat level of targets and detection distance of sensors, the sensor-target matching algorithm is proposed. Numerical studies are presented in the dynamical system. The simulation results show the feasibility of the method.
Exact simulation of diffusion first exit times: algorithm acceleration

In order to describe or estimate different quantities related to a specific random variable, it is of prime interest to numerically generate such a variate. In specific situations, the exact generation of random variables might be either momentarily unavailable or too expensive in terms of computation time. It therefore needs to be replaced by an approximation procedure. As was previously the case, the ambitious exact simulation of exit times for diffusion processes was unreachable though it concerns many applications in different fields like mathematical finance, neuroscience or reliability. The usual way to describe exit times was to use discretization schemes, that are of course approximation procedures. Recently, Herrmann and Zucca \cite{Herrmann-Zucca-2} proposed a new algorithm, the so-called GDET-algorithm (General Diffusion Exit Time), which permits to simulate exactly the exit time for one-dimensional diffusions. The only drawback of exact simulation methods using an acceptance-rejection sampling is their time consumption. In this paper the authors highlight an acceleration procedure for the GDET-algorithm based on a multi-armed bandit model. The efficiency of this acceleration is pointed out through numerical examples.
VoltKey: Using Power Line Noise for Zero-Involvement Pairing and Authentication (Demo Abstract)

We present VoltKey, a method that transparently generates secret keys for colocated devices, leveraging spatiotemporally unique noise contexts observed in commercial power line infrastructure. VoltKey extracts randomness from power line noise and securely converts it into an authentication token. Nearby devices which observe the same noise patterns on the powerline generate identical keys. The unique noise pattern observed only by trusted devices connected to a local power line prevents malicious devices without physical access from obtaining unauthorized access to the network. VoltKey is implemented inside of a standard USB power supply as a platform-agnostic bolt-on addition to any IoT or mobile device or any wireless access point that is connected to the power outlet.
Full-Sum Decoding for Hybrid HMM based Speech Recognition using LSTM Language Model

In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should be applied in decoding. This motivates to reconsider full summation over the HMM-state sequences instead of Viterbi approximation in decoding. We explore the potential gain from more accurate probabilities in terms of decision making and apply the full-sum decoding with a modified prefix-tree search framework. The proposed full-sum decoding is evaluated on both Switchboard and Librispeech corpora. Different models using CE and sMBR training criteria are used. Additionally, both MAP and confusion network decoding as approximated variants of general Bayes decision rule are evaluated. Consistent improvements over strong baselines are achieved in almost all cases without extra cost. We also discuss tuning effort, efficiency and some limitations of full-sum decoding.