Reviewerly/exHarmony huggingface dataset:
A New Database Encryption Model Based on Encryption Classes

Data encryption is one of the advanced measures used to consolidate data security inside Databases.It is an essential technique to protect data against theft, disclosure or modification from different typologies of attacks.In this work, we will propose a new database encryption model.It is based on a novel concept called "Encryption Classes".The proposed model is full compared to the existing models; it integrates many security mechanisms starting from the keys generation and their protection until the data encryption.Furthermore, our proposed model performed a new feature which is the Database structure encryption.
On Mersenne polynomials over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>

We work over the field with two elements. We establish a new correspondence between Mersenne polynomials and trinomials so that corresponding polynomials have the same number of irreducible factors. This allows us to get a partial but nontrivial result about the factorization of M2h+1+1, for a Mersenne prime M and for a positive integer h.
DeClassifier

Recovering class inheritance from C++ binaries has several security benefits including in solving problems such as decompilation and program hardening. Thanks to the optimization guidelines prescribed by the C++ standard, commercial C++ binaries tend to be optimized. While state-of-the-art class inheritance inference solutions are effective in dealing with unoptimized code, their efficacy is impeded by optimization. Particularly, constructor inlining---or worse exclusion---due to optimization render class inheritance recovery challenging. Further, while modern solutions such as MARX can successfully group classes within an inheritance sub-tree, they fail to establish directionality of inheritance, which is crucial for security-related applications (e.g. decompilation). We implemented a prototype of DeClassifier using Binary Analysis Platform (BAP) and evaluated DeClassifier against 16 binaries compiled using gcc under multiple optimization settings. We show that (1) DeClassifier can recover 94.5% and 71.4% true positive directed edges in the class hierarchy tree (CHT) under O0 and O2 optimizations respectively, (2) a combination of constructor-destructor (ctor-dtor) analysis provides a substantial improvement in inheritance inference than constructor-only (ctor-only) analysis.
Effect of STEAM Education Program Using Flexible Display

The need for STEAM education has increased as an educational law of Mathematics, Science and Information Education was promulgated in April 2018, and applied to the revised curriculum of 2015 in Korea.Development of a STEAM program using flexible display expected to be a suitable tool for mathematics, science and software classes.In this research, we developed Flexible Display Programming tool based on scratch 2.0.Based on that software we designed STEAM educational program for elementary, middle school students.Satisfaction measurement of this program shows that there is enhancement of the attitude to science and mathematics.Moreover, qualitative measurement confirms the possibility of the suggested method of education in Science, Technology, Engineering, Arts and Mathematics.
Web-based Virtual Reality with A-Frame

Virtual Reality has become an accessible technology but tools to create Virtual Reality experiences are only now becoming accessible to the general public. This work evaluates the development effort of creating a virtual tour based on 360° photos using the web-based VR framework A-Frame. To evaluate the framework we implemented a virtual tour with common interactive features and analysed the development process. We found that A-Frame can be a viable framework to develop VR experiences but there are still aspects that could be improved to reduce the required development effort.
Suppressing nonadiabatic transitions during adiabatic generation of highly entangled states in bosonic Josephson junctions

We study suppression of nonadiabatic transitions during adiabatic generation of a catlike state (a superposition of different-sized cat states) and a spin squeezed state in a bosonic Josephson junction. In order to minimize the adiabatic error, we use the quantum adiabatic brachistochrone, which enables us to track a geometrically efficient path in parameter space under given conditions without requiring additional terms. For the creation of a catlike state, divergence of the quantum geometric tensor associated with gap closing at the critical point is avoided because of the parity conservation. The resulting schedules of parameters are smooth and monotonically decreasing curves. Use of these schedules offers a reduction in the time to generate both a catlike state and a spin squeezed state.
Automatic Classification and Analysis of Multiple-Criteria Decision Making

As part of the automatic decision-making process, we propose to highlight the importance of business intelligence and its contribution to management and decision-making in companies. The multi-criteria automatic analysis proposes to set up a complete computer chain that automates all the classic steps of the multi-criteria decision-making. The automatic multi-criteria decision relies mainly on the two learning techniques. Unsupervised classification is used to find two compact and well-separated groups in a dataset. Supervised classification is a learning method for automatically generating rules from a learning database. Both techniques must have existed to produce comprehensive and automatic classification procedures by the user. In this context, we will focus on showing how business intelligence, particularly through data mining and integrated software packages, can be an important decision-support tool for companies.
Implementasi Gerakan Literasi Sekolah dalam Pembelajaran di Kelas Tinggi

Literacy in learning is an activity that aims to maintaim students’ reading interest to reading and reading activities and improve literacy skills (reading and writing) students by using enrichment books and learning textbooks. With respect to to the importance of literacy in learning then the author conducts research on high class teachers SDN 164 Pekanbaru with purpose for describe the implementation of the school literacy movement in learning in high class SDN 164 Pekanbaru. The type of research used is descriptive research. The subjects in this study were IVC class teacher, one IVC class student, and principal of SDN 164 Pekanbaru. Data collection is done by structured observation, structured interview and documentation. Data analysis techniques used are by reducing data, displaying data and concluding data. This study concluded that literacy has not been implemented in high class learning SDN 164 Pekanbaru namely the lack of facilities and infrastructure for schooly literacy movement, small classroom, one class used by two study groups, limited time and not fully understood by literacy class teachers.
An Image Processor Bill Acceptor for Iraqi Currency

Automatic Teller Machines (ATMs) and vending machines is a rapidly growing trade in the Iraqi market.Bill acceptors\validators, are considered the heart of these machines have a great significance.Due to the inflation in Iraq with numerous forgery attempt to the currency, an efficient way to accept and validate the Iraqi currency is very important.This paper suggests a bill acceptor\validator to accept, recognize the amount, and validate the bill based solely on image processing functions rather than the magnetic field detection that is used in the regular Bill Acceptors.The program couples the faked bill with a photo graph of the person who presents it.The program uses the Matlab computer vision abilities to recognize the bill.The distinctive characteristics that appears on the banknote when an ultraviolet light is shed on the bill it used for the authentication part with the same Matlab pattern recognition functions.The proposed system include all the mechanical and electronical parts that are used in the proposed system.The results was 100% accurate, which implies that there is great possibility to use this technique in industrial bill acceptors.
A population entropy based adaptation strategy for differential evolution

We introduce a new enhanced algorithm with a population entropy based population adaptation strategy under the framework of SHADE (PE-SHADE). The distribution state of the population is characterized, and then the population size is adapted with a population increasing strategy and a population reduction strategy. Experimental results on CEC2014 statistically support the effectiveness of the proposed algorithm.
Towards Blockchain-based Multi-Agent Robotic Systems: Analysis, Classification and Applications

Decentralization, immutability and transparency make of Blockchain one of the most innovative technology of recent years. This paper presents an overview of solutions based on Blockchain technology for multi-agent robotic systems, and provide an analysis and classification of this emerging field. The reasons for implementing Blockchain in a multi-robot network may be to increase the interaction efficiency between agents by providing more trusted information exchange, reaching a consensus in trustless conditions, assessing robot productivity or detecting performance problems, identifying intruders, allocating plans and tasks, deploying distributed solutions and joint missions. Blockchain-based applications are discussed to demonstrate how distributed ledger can be used to extend the number of research platforms and libraries for multi-agent robotic systems.
Scaling Productivity and Innovation on the Path to Exascale with a “Team of Teams” Approach

One of the core missions of the Department of Energy (DOE) is to move beyond current high performance computing (HPC) capabilities toward a capable exascale computing ecosystem that accelerates scientific discovery and addresses critical challenges in energy and national security. The very nature of this mission has drawn a wide range of talented and successful scientists to work together in new ways to push beyond the status-quo toward this goal. For many scientists, their past success was achieved through efficient and agile collaboration within small trusted teams that rapidly innovate, prototype, and deliver. Thus, a key challenge for the ECP (Exascale Computing Project) is to scale this efficiency and innovation from small teams to aggregate teams of teams. While scaling agile collaboration from small teams to teams of teams may seem like a trivial transition, the path to exascale introduces significant uncertainty in HPC scientific software development for future modeling and simulation, and can cause unforeseen disruptions or inefficiencies that impede organizational productivity and innovation critical to achieving an integrated exascale vision. This paper identifies key challenges in scaling to a team of teams approach and recommends strategies for addressing them. The scientific community will take away lessons learned and recommended best practices from examples for enhancing productivity and innovation at scale for immediate use in modeling and simulation software engineering projects and programs.
Implementation of high speed hash function Keccak on GPU

Nowadays, a hash function is used for password management. The hash function is desired to possess the following three characteristics: Pre-Image Resistance, Second Pre-Image Resistance, and Collision Resistance. They are set on the assumption that it is computationally difficult to find the original message from a given hash value. However, the security level of the password management will be further reduced by implementing a high speed hash function on GPU. In this paper, the implementation of high speed hash function Keccak-512 using the integrated development environment CUDA for GPU is proposed. The following four techniques are used in order to speed up its implementation. The first one is reforming lookup tables from 2 dimensional arrays to 1 dimensional arrays at step ρ and π. The second is an investigation into the effect of using constant memory and shared memory for constant values. The third is the finding out the optimal configuration of blocks-threads, then evaluate the implementation according to the occupancy. And the last one is using CUDA streams with overlapping to hide the overhead of data transfer and GPU processing.
Investigating algorithms for finding nash equilibria in cyber security problems

Distributed Denial of Service (DDoS) cyber attacks continue to increase and cause disruptions in both industry and politics. As more critical information and services are provided through networks, it is important to keep these networks available. However, since adversaries are continuously changing and adapting, stationary defense strategies do not effectively secure networks against attacks. We investigate Nash equilibria in cyber security problems by modeling attacker-defender interactions using competitive coevo-lutionary algorithms. In particular, we examined the performances of two algorithms (and their variations) that look for Nash equilibria, NashSolve and HybridCoev, and compared their performances against other existing heuristics. Using two evaluation techniques, one that looked at average fitness scores and one that created a compendium of MEU, MinMax, and inverse Pareto front ratio scores, we found that NashSolve and HybridCoev did not perform significantly better for both attacker and defender populations relative to other heuristics.
A Comparative Study of Automatic Programming Techniques

Automatic programming, an evolutionary computing technique, forms the programs automatically and is based on higher level features that can be easily specified than normal programming languages. Genetic Programming (GP) is the first and best-known automatic programming technique that is applied to solve many practical problems. Artificial Bee Colony Programming (ABCP) is one of the latest proposed automatic programming method that combines evolutionary approach with swarm intelligence. GP is an extension version of Genetic Algorithm (GA) and ABCP is based on Artificial Bee Colony (ABC) algorithm. The main differences of these automatic programing techniques and their conventional algorithms (GA and ABC) are modeling solution. In ABC same as GA, the solutions are represented fixed code blocks. In GP and ABCP, the positions of food sources are expressed in tree structure that is composed of different combinations of terminals and functions that are specifically defined as problems. This paper presents a review on GP and ABCP and they are worked in symbolic regression, prediction and feature selection problems which are widely tackled by researchers. The results of the ABCP compared with results of GP show that this algorithm is a powerful optimization technique for structural design.
U.S. Macroeconomic Policy Evaluation in an Open Economy Context Using Wavelet Decomposed Optimal Control Methods

It is widely recognized that the policy objectives of fiscal and monetary policymakers usually have different time horizons, and this feature may not be captured by traditional econometric techniques. In this paper, we first decompose U.S macroeconomic data using a time-frequency domain technique, namely discrete wavelet analysis. We then model the behavior of the U.S. economy over each wavelet frequency range and use our estimated parameters to construct a tracking model. To illustrate the usefulness of this approach, we simulate jointly optimal fiscal and monetary policy with different short-term targets: an inflation target, a money growth target, an interest rate target, and a real exchange rate target. The results determine the reaction in fiscal and monetary policy that is required to achieve an inflation target in a low inflation environment, and when both fiscal and monetary policy are concerned with meeting certain economic growth objectives. The combination of wavelet decomposition in an optimal control framework can also provide a new approach to macroeconomic forecasting.
Correction to: Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

In the original publication of this article [1], the Acknowledgements and Funding section in Declarations need to be revised.
Improving Facial Image Recognition based Neutrosophy and DWT Using Fully Center Symmetric Dual Cross Pattern

Face recognition is one of the most commonly used biometric features in the identification of people.In this article, a novel facial image recognition architecture is proposed with a novel image descriptor which is called as fully center symmetric dual cross pattern (FCSDCP) The proposed architecture consists of preprocessing, feature extraction and classification phases.In the preprocessing phase, discrete wavelet transform (DWT) and Neutrosophy are used together to calculate coefficients of the face images.The proposed FCSDCP extracts features.LDA, QDA, SVM and KNN are utilized as classifiers.4 datasets were chosen to obtain experiments and the results of the proposed method were compared to other state of art image descriptor based methods and the results clearly shows that the proposed method is a successful method for face classification.
Automating Instagram Activities and Analysis: A Survey of Existing Tools

Instagram serves as a modern advertising channel for many business sectors. For a business Instagram account to be effective in advertising, they need to be active and reach the current and potential clients constantly. A common way to achieve that is to hire workers to keep the accounts active by, for example, liking and commenting on photos and video. However, this process is time and money consuming. In addition, data associated with accounts present a valuable information to guide business plans. Since the data usually large, data analysis has to be completed in an automated fashion. Therefore, it is important to automate both Instagram activities and data analysis. However, it is not clear if such tools are available. In this paper, we survey the existing tools within the context of automations. We investigate the capability of the current tools to perform Instagram activities and data analysis in an automated fashion. An important factor we added to our investigation is whether the tools are free and open source. We found that there is an urgent need for both free and open source tools to support especially small emerging business. This paper should serve as a reference about current tools for business companies at different scale. Also, it helps tools? developers to design and implement tools that are better serve the current business needs. To understand the automation in Instagram, we proposed a simple automation layered architecture. This should help in understanding current tools and develop new ones.
Blending notions of diversity for MAP-elites

Quality-diversity algorithms focus on discovering multiple diverse and high-performing solutions. MAP-elites is such an algorithm, as it partitions the solution space into bins and searches for the best solution possible for each bin. In this paper, multi-behavior variants of MAP-Elites are tested where the MAP-Elites grid partitions the solution space based on a certain dimension, while selection is guided by measures of diversity on another dimension. Four divergent search algorithms are tested for this selection process, targeting novelty or surprise or their combination, and their performance on a soft robot evolution task is discussed.
Spatial light modulator phase calibration based on spatial mode projection

Spatial light modulators (SLMs), which are devices used to manipulate the phase of an incident wave front, are prolific in fields such as optical trapping, dynamic diffractive optical elements, and display technology. Of the many challenges inherent to using SLMs, one of the most ubiquitous is the calibration of the device's phase-shifting mechanism. In this paper, we present a new SLM calibration method based on spatial mode projection. We also implement a data processing technique to our data to generate accurate look-up tables from our calibration curves. We then evaluate the success of our method by propagating Laguerre-Gauss beams with computer-generated holograms. Our results show that the qualitative analysis of modes propagated using the SLM is a viable method of assessing performance. On the whole, we show that spatial mode projection provides clear performance improvements in the SLM's phase-modulating capabilities.
Plan merging by reuse for multi-agent planning

Multi-Agent Planning deals with the task of generating a plan for/by a set of agents that jointly solve a planning problem. One of the biggest challenges is how to handle interactions arising from agents’ actions. The first contribution of the paper is Plan Merging by Reuse, pmr, an algorithm that automatically adjusts its behaviour to the level of interaction. Given a multi-agent planning task, pmr assigns goals to specific agents. The chosen agents solve their individual planning tasks and the resulting plans are merged. Since merged plans are not always valid, pmr performs planning by reuse to generate a valid plan. The second contribution of the paper is rrpt-plan, a stochastic plan-reuse planner that combines plan reuse, standard search and sampling. We have performed extensive sets of experiments in order to analyze the performance of pmr in relation to state of the art multi-agent planning techniques.
Testing halfspaces over rotation-invariant distributions

We present an algorithm for testing halfspaces over arbitrary, unknown rotation-invariant distributions. Using [MATH HERE] random examples of an unknown function f the algorithm determines with high probability whether f is of the form f(x) = sign(∑iwixi − t) or is ϵ-far from all such functions. This sample size is significantly smaller than the well-known requirement of Θ(n) samples for learning halfspaces, and known lower bounds imply that our sample size is optimal (in its dependence on n) up to logarithmic factors. The algorithm is distribution-free in the sense that it requires no knowledge of the distribution aside from the promise of rotation invariance. To prove the correctness of this algorithm we present a theorem relating the distance between a function and a halfspace to the distance between their centers of mass, that applies to arbitrary distributions.
Breast Cancer Diagnosis in Digital Mammography Images Using Automatic Detection for the Region of Interest

Background: One of the early screening methods of breast cancer that is still used today is mammogram due to its low cost. Unfortunately, this low cost accompanied with low performance rate also. Methods: The low performance rate in mammograms is associated with low capability in determining the best region from which the features are extracted. Therefore, we offer an automatic method to detect the Region of Interest in the mammograms based on maximizing the area under receiver operating characteristic curve utilizing Genetic Algorithms. : The proposed method had been applied to the MIAS mammographic database, which is widely used in literature. Its performance had been evaluated using four different classifiers; Support Vector Machine, Naïve Bayes, K-Nearest Neighbor and Logistic Regression classifiers. Results &amp; Conclusion: The results showed good classification performances for all the classifiers used due to the rich information contained in the features extracted from the automatically selected Region of Interest.