Reviewerly/exHarmony huggingface dataset:
Automated Linux Secure Host Baseline for Real-Time Applications Requiring SCAP Compliance

With the emergence of Linux real-time applications and the ever-growing cyber security requirements being imposed on those systems in high security environments, a need has developed for an automated and quantitative approach for developing a solution. The development and mandate for a Department of Defense Windows Secure Host Baseline has provided a framework for how to approach an equivalent Linux Secure Host Baseline. However, a method for automating a cyber security compliant Linux distribution with real-time capability has not yet been created. Utilizing National Institute of Standards and Technology (NIST) approved tools and security guidelines (DISA STIG), this work has produced an automated Linux distribution capable of performing real-time functions with a 96.15% security compliance performance metric. The compliance deficiencies have been addressed to a satisfactory level based on their context. The promising results show that this approach can be utilized by other organizations for Linux real-time application development and deployment with customized environments specific to their needs while maintaining a high level of security posture. The methodology proposed provides a template for designing, customizing, and maintaining the solution.
Research on Power Harmonic Reconstruction Using Compressed Sensing

With the development of modem industry, more and more power harmonics threaten the safety and stability of power systems. Aiming at the extremely sparse characteristic of the harmonic signal in the Fourier domain, this paper proposes a method of harmonic signal reconstruction based on compressed sensing, which performs compression sampling on the original harmonic and Interharmonics signals respectively. Simulations show that using the compressed sensing algorithm, for the entire harmonic, the reconstruction error reaches 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> , and the RSNR is improved by 34.66%. For interharmonics, the reconstruction effect is improved by an order of magnitude over FFT, and the RSNR is improved by 22.15%. And further study the impact of the number of sampling points, sampling frequency and sparsity on the reconstruction of harmonic signals. It can be seen that the compressed sensing algorithm retains the sparse features of the original signal during the observation process, and as the sampling frequency increases, the reconstruction effect improves accordingly. It provides conditions for the next step of feature extraction of harmonic signals.
An Unsupervised Searching Scheme over Encrypted Cloud Database

In the progression of the imaging tools, like computerized cameras, cell phones, and medical imaging equipment the world has been seeing development in quantity, accessibility and significance of images. Significantly more storage space is required for the images over the text document. But in the storage outsourcing scenario, there is a potential hazard for clients that everyone may have the entrance to gain useful information from these image data. For the most part, the encryption mechanism is embraced to avoid such situations. Thus, it is a decent practice to encrypt the confidential information before outsourcing to the untrusted outsiders. Also, query processing for image retrieval is a huge task. Therefore, the proposed paper executes a methodology for secure image encryption using a chaotic system and machine learning based approach for searching similar images proficiently over encrypted image database without leaking any sensitive data.
Meningkatkan Minat Membaca melalui Gerakan Literasi Sekolah

The literation will affect the nation's competitiveness in global competition. Literacy ability is very important for the success of individuals and countries in the level of knowledge-based on economi in area global in the future. It is give reinforcement that the compulsory reading curriculum is important to be applied in Indonesia education. The literation on SMA Negeri 1 Bungura Timur Laut Kabupaten Natuna Provinsi Kepulaua Riau is beneficial for formating of noble character, developing a love of reading, stimulating the growth of reading activities outside of school, adding knowledge and opening ideas and ideas concept that can also be contained in a piece of writing.The literation on SMA Negeri 1 Bunguran Timur Laut applied with the form of socialization has conducted to educators, students, for completing reading material that has begun to be difficult to obtain, presenting a digital library in accordance with the needs of current technological developments. The participants who visit the library will get a doorprize, besides that each twice a year will be announced on reading ambassador , to develop the Gelige library at SMA Negeri 1 Bunguran Timur Laut there is a Suggestion box and Feedback Box to be filled in by visitors who want to submit their suggestions.
A System for Predictive Data Analytics Using Sequential Rule Mining

In the current scenario of the business world, the importance of data analytics is quite large. It certainly benefits the businesses in the decision-making process. Sequential rule mining can be widely utilized to extract important data having variety of applications like e-commerce, stock market analysis, etc. Predictive data analytics using the sequential rule mining consists of analyzing input sequences and finding sequential rules that can help businesses in decision making. This article presents an approach called M_TRuleGrowth that generates partially-ordered sequential rules efficiently. The authors conducted an experimental evaluation on real world dataset that provides strong evidence that M_TRuleGrowth performs better in terms of execution time.
PROSES PENANAMAN KARAKTER GEMAR MEMBACA PADA SISWA KELAS III MELALUI PELAKSAAAN PROGRAM LITERASI DI SDN BANJAR SARI 5

This study was designed to describe the process of planting reading characters in grade III students through the literacy implementation program at SDN Banjar Sari 5. The research subjects were teachers and students. The object of this research is the character of reading fondly through the literacy program. This research uses descriptive qualitative. Data collection procedures in research are interviews, observation, and documentation. Data analysis uses analysis techniques namely data reduction, displaying data and drawing conclusions. Checking the validity of the data using triangulas, specifically methodological triangulation. The results of the study complement what is meant by fond of reading at school with habituation activities through the literacy program going pretty well, both in direct learning and online which is held by the school as the school which manages this.
End-to-end Visual Object Tracking with Motion Saliency Guidance

In recent years, deep learning-based object tracking methods have achieved excellent performance. Most of existing object tracking algorithms only focus on target appearance features and ignore motion features. However, most of tracked targets are moving, motion features are important in object tracking tasks. In this work, we are committed to extracting deep features with motion saliency to highlight targets from the background and improve tracking accuracy. To this end, a new object tracking network combining correlation filters, siamese network and optical flow network is proposed. In the proposed network, we apply optical flow to extract motion information of targets, and employ an attention network to integrate motion features and appearance features. The evaluations on OTB2013, OTB2015 and UAV123 demonstrate that the proposed method can track targets accurately while ensuring speed.
Low Power Area Efficient Improved DWT and AES Architectures for Secure Image Coding

This paper unfolds the development of an HDL model on the ASIC platform for the quicker and safer transmission of image data. Secure image encoding was facilitated by the DWT through image compression and AES through encryption. The DWT computation based on lifting scheme algorithm was developed and implemented on ASIC platform. An algorithm that can compute multi-level sub bands using 2D-DWT was developed. Based on compression ratio and data recovery, appropriate sub bands were chosen to reduce the computation time of the AES encryption. The DWT architecture was developed to facilitate high throughput and latency. The HDL model of the DWT architecture and the AES algorithm were developed and validated on the ASIC platform to identify the area, timing and power performance. The ASIC implementation was carried out using 56nm CMOS technology.
An Improved Biology Migration Algorithm with Von Neumann Structure

Biology migration algorithm (BMA) is a newly developed metaheuristic algorithm inspired by the biology migration phenomenon. The paramount challenge in BMA is that it is prone to stagnation in local optima and premature convergence. To solve these issues, this paper proposed an improved biology migration algorithm with Von Neumann structure to help BMA balance exploration and exploitation better, which named BMAVI. First, non-linear initial weights are introduced into BMA to balance exploration and exploitation. Second, the Von Neumann structure was introduced into the BMA to construct the neighbors of each individual, increasing information exchange and enriching population diversity. Finally, the proposed BMAVI has also been evaluated on CEC2017 test set compared with the classic and other novel evolutionary algorithms to confirm the performance of BMAVI. Experimental results show that the performance of the proposed BMAVI algorithm is better than other selected algorithms.
Project BeARCAT : Baselining, Automation and Response for CAV Testbed Cyber Security : Connected Vehicle & Infrastructure Security Assessment

Connected, software-based systems are a driver in advancing the technology of transportation systems. Advanced automated and autonomous vehicles, together with electrification, will help reduce congestion, accidents and emissions. Meanwhile, vehicle manufacturers see advanced technology as enhancing their products in a competitive market. However, as many decades of using home and enterprise computer systems have shown, connectivity allows a system to become a target for criminal intentions. Cyber-based threats to any system are a problem; in transportation, there is the added safety implication of dealing with moving vehicles and the passengers within.
A Vision-based Demonstration and Verification Platform for Path Planning

With the rapid development of robotics, path planning has become a hot issue. From the traditional artificial potential field method, genetic algorithm to the latest deep learning neural network, new path planning algorithms have emerged endlessly. However, there is little research on the construction of verification platform for path planning algorithm. In this paper, a visual based demonstration platform for path planning is designed. The information of scene is captured into the computer through a camera. This system combines perspective transformation and improved Canny detection to identify robots and obstacles, using the improved Hough circle transform and Kalman filter to improve the accuracy of the object position, getting the detailed information of the object in the scene. After using the path planning method to plan the path, the projection equipment will project it to the scene for visual demonstration. Finally, the artificial potential field method is demonstrated as an example.
Çok Boyutlu Sırt Çantası Problemi İçin Yeni Bir Melez Genetik Algoritma Önerisi

The Multidimensional Knapsack Problem which is an integer programming problem serves as a mathematical basis for the analysis and solution of various types of problems facing businesses. When the number of variables and constraints that compose the mathematical model of the problem increases, the problem is often solved with heuristic approaches that can find values that converge to the optimum. Genetic Algorithm, which is a population-based heuristic algorithm, is one of the leading approaches in solving the problem. Within the scope of the study, a new hybrid Genetic Algorithm was proposed to solve the problem. The hybrid approach based on local search improvement in the initial population and on repairing unsuitable solutions with a new problem-specific repair operator was compared with the standard Genetic Algorithm through the solution of sample problems. When the results were examined, it was seen that the proposed hybrid Genetic Algorithm achieved higher performance in Multidimensional Knapsack Problem.
A Video-Based Cybersecurity Modular Lecture Series for Community College Students

The shortage of people who can fill open cybersecurity positions is a growing problem, both in the U.S.A. and globally. There is a clear need to grow the number of people who can fill these positions. Building from our experience collaborating with a nearby community college, we have developed a distributable course of video lectures that are designed for community college students with the goal of increasing interest in the career field of cybersecurity. The course is composed of four modules: offensive security and digital forensics, the internet of things, cryptography, and blockchain technologies. Each module consists of three or four video lectures delivered by JHU faculty, staff, and graduate students, along with accompanying reading material and exercises. The course is being made freely available to anyone who would like to have access to it. It has been designed to be put to use in numerous ways including, as a course for credit, a seminar series, individual standalone lectures or modules, and for use as content to complement existing curriculum.
Designing an Internet-of-Things Laboratory to Improve Student Understanding of Secure Embedded Systems

In the U.S., the Department of Defense and the National Security Agency are taking steps to build cybersecurity capacity through workforce training and education. We describe the development of cybersecurity education courseware for internet-of-things (IoT) applications. We selected the domain of healthcare and featured different IoT sensors that are seeing increased usage. These include barcode scanners, cameras, fingerprint sensors, and pulse sensors. These devices cover important functions such as patient identification, monitoring, and creation of electronic health records. We used a password protected MySQL database as a model for electronic health records. We demonstrated potential vulnerabilities of these databases to SQL injection attacks. We administered these labs during a Fall 2019 course where students reported a significant increase in their understanding of cybersecurity issues. The instructional lab material will be uploaded to https://clark.center .
Deficiencies of Compliancy for Data and Storage

There are many reasons to implement data protection security strategies regardless of if the data is financial, personal or confidential, risks are a moving target. The purpose of this systematic literary review was to examine articles and documents pertaining to data and storage security and to compare with modern regulatory compliance requirements to determine if gaps exist within the datacenter. Both academic and applied IT security papers were used as well as online governmental and industry sources. As part of this research, the components of the CIA triad were used as a baseline which resulted in a granular model. The model was applied to both quantitative and qualitative data that exposed deficiencies in data and storage security.
Coherent parallel binary-weighted digital-to-analog converter in silicon photonics

Here, we introduce a coherent parallel photonic DAC concept along with an experimental demonstration based on binary-weighted bits in a silicon PIC platform unlike other concepts capable of performing the conversion without optical-electronic-optical domain crossings.
Polygon graphic recognition based on improved fast corner detection

Abstract The accuracy of polygon graphic recognition based on chain code features and Hough transform is low, and the computation is limited. Therefore, this paper proposes a polygon graphic recognition method based on improved features from accelerated segment test (FAST) corner detection. First, hole filling and Freeman chain code are used to segment the image, and the regular geometric features are obtained. Second, in order to improve the performance of the algorithm, an improved FAST corner detection combined with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is proposed. Through clustering, false corner in the image can be eliminated, and the feature points are quickly filtered by local NMS (non-maximum suppression). Finally, polygon graphic recognition is realized by feature points, experimental results illustrate that this method has high calculation efficiency and recognition rate.
Precipitation Phase Inversion Algorithm for Extremely Unbalanced Data

Precipitation phase inversion has important significance for agricultural production, flood mitigation, human activities and power. Due to the high uncertainty of precipitation phase, traditional physical models and, statistical models are difficult to meet the requirements of real-time and accuracy. In the actual inversion process, there are very few samples of a few types, which leads to the poor inversion of a few classes. Aiming at the challenge of data imbalance in precipitation phase inversion, this paper proposes an improved hybrid sampling method ADASYN Borderline-Tomek Link, which uses boundary location and reduces redundancy for most types of large-density regions. After a comparison of several sets of experiments, a few types of G-mean and F-value have been improved, which effectively improved the data imbalance in precipitation phase inversion.
Resolving Seemingly Conflicting Fact Statements Caused by Missing Terms

The Web has become the most useful resource for people to acquire information. However, it still contains much incorrect and inaccurate information. While the problem of resolving conflicting structured Web data has been studied extensively, the corresponding problem involving unstructured text data has rarely been ad-dressed. In this paper, we consider how to resolve seemingly conflicting fact statements (SCFS). A statement is a fact statement if it attempts to state a fact, although the stated fact may not necessarily be correct. In this paper, multiple fact statements are SCFSs if they are identical except that they have different terms at a certain (same) position. In general, SCFSs are not necessarily truly in conflict. Some conflicts may result from imprecise statements and may be resolved by improving the precision of the statement. In this work, we focus on a special type of SCFSs whose corresponding statements are imprecise because they have missing terms. More specifically, given a set of SCFSs, we propose a method to mine the possible missing terms. This method effectively transforms an imprecise fact statement to precise ones. At the same time, our method also resolves the seemingly conflicts among the transformed fact statements. The experimental results show that our best solution for missing term mining on different data sets can achieve close to 90% F-score, which improves a baseline method by about 40%. We also propose a method to place the mined missing term for each SCFS at the right place in the statement and our experiments show that this method has an accuracy of about 73%.
Future of Deep Learning for Cancer Diagnosis

Long-term treatments observed in cancer-derived diseases have a higher risk of death than other diseases. The effectiveness of the developing technology in human life is manifested in the treatment and diagnosis of diseases. ‘Early diagnosis saves lives’, which is frequently heard in all diseases, comes to life in this part. The first of the most important points in cancer and derivative diseases is early detection of the disease. Artificial intelligence is used to simulate and simplify the human life offered by developing technology. This study focuses on the methods of deep learning, which is one of the subfields of artificial intelligence. The aim of this study is to emphasize the deep learning methods used in cancer diagnosis. As a result of emphasizing the methods, the present and future potential of the literature in terms of cancer diagnosis has been revealed. It is thought that the study will be a current reference for the researchers who will conduct research within the scope of the subject.
Integrating Bi-Dynamic Routing Capsule Network with Label-Constraint for Text classification

Neural-based text classification methods have attracted increasing attention in recent years. Unlike the standard text classification methods, neural-based text classification methods perform the representation operation and end-to-end learning on the text data. Many useful insights can be derived from neural based text classifiers as demonstrated by an ever-growing body of work focused on text mining. However, in the real-world, text can be both complex and noisy which can pose a problem for effective text classification. An effective way to deal with this issue is to incorporate self-attention and capsule networks into text mining solutions. In this paper, we propose a Bi-dynamic routing Capsule Network with Label-constraint (BCNL) model for text classification, which moves beyond the limitations of previous methods by automatically learning the task-relevant and label-relevant words of text. Specifically, we use a Bi-LSTM and self-attention with position encoder network to learn text embeddings. Meanwhile, we propose a bi-dynamic routing capsule network with label-constraint to adjust the category distribute of text capsules. Through extensive experiments on four datasets, we observe that our method outperforms state-of-the-art baseline methods.
Assessing Early Grade Mathematics Learner Outcomes Using m-Learning

JumpStart makes use of research-based structured learning material for mathematics together with in-class support by interns, teacher training, real-time formative and summative assessment, through m-learning. To assess its impact on learner outcomes, a cross sectional study design was adopted, whereby performance of a random selection for learners across Grades 1 to 3 in 18 schools was analysed. Grouped learner outcomes (at the level of a grade across groups of schools) prior to the intervention were compared to learner outcomes post the intervention and set alongside a description of the nature of the intervention. Improvements in learning outcomes, as measured by the Early Grade Mathematics Assessment (EGMA) are evident after one year of intervention, and appear to be strengthened after two years of intervention. Overall the JumpStart programme is having a significant impact on all the schools. The use of m-learning by interns for the efficient uptake and appropriate dosage of an early maths intervention; as well as the utility of a tablet interface for the efficient collection of early grade mathematics assessment data should be of interest to the m-learning and broader education community.