Reviewerly/exHarmony huggingface dataset:
Stream Cipher from Chaotic Systems and High Gain Observers

This paper proposes an advanced secured transmission of information method founded on synchronizing chaotic system with an optimal high gain observer. The challenging idea investigated in this work is to insulate the encryption process from the synchronization operations relying on the same chaotic system but with two different roles. First, the chaotic system is a part of the encryption process and generate pseudo-random sequence of signals, which will be treated with Exclusive-OR operations to form the cipher, and then the dynamic system will acts as a transmitter. In fact, the designed transmission scheme is established using an improved optimal nonlinear observer having the advantage to reconstruct the transmitter. Finally, the decryption operation is carried out by replacing the chaotic system by its estimation one. Numerical simulation based on an image encryption/decryption is conducted as an illustration to demonstrate the outstanding performances of the designed transmission method.
Experimental Analysis of the Scalability of Ethereum Blockchain in a Private Network

Blockchain technology has aroused the interest of researchers as it ensures security and privacy in decentralized applications. One of the platforms currently used for the development of new blockchain-based solutions is the Ethereum network. This article presents an experimental analysis of the scalability of this network to identify its cost and performance for several contracts in a simulated private Ethereum network. The results obtained demonstrate that storage operations for large amounts of data can prevent the use of blockchain due to the high cost of gas charged for this type of operation. The experiments also pointed out that processing operations which do not store information have a small cost of gas.
$\beta$-fuzzy filters in MS-algebras

In this paper, we introduce the concept of $ \beta $-fuzzy filters in MS-algebras and $ \beta $-fuzzy filters are characterized in terms of boosters. It is proved that the lattice of  $ \beta $-fuzzy filters is isomorphic to the fuzzy ideal lattice of boosters.
Big Data Privacy, Ethics, and Enterprise Continuous Monitoring Systems

There is a delicate balance associated with ethics and privacy in “enterprise continuous monitoring systems.” On the one hand, it can be critical to enterprises to continuously monitor the ethical behavior of different agents, and thus, facilitate enterprise risk management, as noted in the KPMG quote. In particular, continuous monitoring systems help firms monitor related internal and external agents to make sure that the agents hired by or engaged by the enterprise are behaving ethically. However, on the other hand, such continuous monitoring systems can pose ethical and privacy risks to those being monitored and provide risks and costs to the company doing the monitoring. For example, inappropriate information can be assembled, stored, and inferred about a range of individuals. Thus, information obtained by continuous monitoring generally should follow privacy principles that require that the data be up-to-date and conform to the purpose for which the data was originally gathered, and other constraints.
Solving MAXCUT Optimization Problems with a Coherent Ising Machine Based on Opto-Electronic Oscillators

We propose and test a new concept for a coherent Ising machine (CIM) with the goal of solving NP-hard optimization problems with a photonic system that is small in size and cheap to fabricate. CIMs are a promising concept for solving difficult optimization problems faster than on conventional digital computers [1]. Current CIMs are based on degenerate optical parametric oscillators (DOPOs) and use the optical phase of short laser pulses circulating in a ring cavity (∼1km circumference) to implement large-scale artificial Ising spin networks, which can in turn be used to implement the cost function of various optimization problems [2,3]. The natural tendency of the optical system to evolve to its ground state is then used to find the optimal solution. DOPO-based CIMs offer many advantages over quantum annealing hardware [1] and have demonstrated significant speed ups compared to conventional computers [3]. However, the large external cavity and the nonlinear optical processes required for the DOPO generation result in large and complex setups, which makes DOPO-based CIMs expensive and challenging to build. Additionally, the cavity has to be actively phase-locked, making it highly susceptible to external perturbations.
Apprenticeship Learning Based Load Balancing Technique for Cloud Environment

Cloud Computing is accessing and handling data and documents from the internet rather than from any individual computer hard drive. The issues faced by cloud computing are security, privacy, vendor lock-in, server downtime, network connectivity, dependency, vulnerability to attacks, load balancing, etc. Load balancing in cloud computing is one of the important issues as huge amount of load need to be efficiently distributed among the servers. The existing approaches to address load balancing issue are throttled technology, active clustering, central policy for virtual machine, round robin technology, max-min min-min, fuzzy monitoring, honeybee foraging behavior, reinforcement learning, etc. The primary drawbacks of above-mentioned approaches towards load balancing are lowered throughput, high migration rate, overloading and under-loading of resources. This paper proposes a novel architecture which applies apprenticeship learning for load balancing in the cloud. Its performance is found to be good with respect to parameters like response time, accuracy, learning rate and speed.
A Digital WATERMARKING Algorithm with High Efficiency Based on DWT and LSB

In order to improve the performance of digital watermarking system, a novel watermarking algorithm for blind watermarking based on the least significant bits and support vector machine. Firstly, digital watermarking was pretreated by binaryzation. Subsequently, the watermark signal is embedded into least significant bit of the low frequency coefficients by the Discrete Wavelet Transformation. Finally, the trained support vector machine is used to extract the attacking vetors. Simulation results show that the watermarking algorithm has good invisibility and robustness to general attacks and geometrical distortions.
User Recognition Based on Daily Actigraphy Patterns

The use of inertial sensors such as accelerometers and gyroscopes, which are now often embedded in many wearable devices, has gained attention for their applicability in user authentication applications as an alternative to PINs, passwords, biometric signatures, etc. Previous works have shown that it is possible to authenticate users based on fine-grained kinematic behavior profiles like gait, hand gestures and physical activities. In this work we explore the use of actigraphy data for user recognition based on daily patterns as opposed to fine-grained motion. One of the advantages of the former, is that it does not require to perform specific movements, thus, easing the training and calibration stages. In this work we extracted daily patterns from an actigraphy device and used a random forest classifier and a majority voting approach to perform the user classification. We used a public available dataset collected by 55 participants and we achived a true positive rate of 0.64, a true negative rate of 0.99 and a balanced accuracy of 0.81.
Visualizing Transport and Mixing in Particle-based Fluid Flows

To gain insight into large, time-dependent particle-based fluid flows, we visually analyze Lagrangian coherent structures (LCS), a robust skeleton of the underlying particle dynamics. To identify these coherent structures, we build on recent work that efficiently computes the finite-time Lyapunov exponent (FTLE) directly on particle data. We formulate the LCS definitions for particles based on robust approximations for higher-order derivatives of the FTLE. Based on these formulations, we derive a per-particle distance to the closest coherent structure. This allows us to visually analyze and explore the Lagrangian transport behavior directly on the particle data. We show that this is especially beneficial to detect and visualize flow features on different time scales. Lastly, we apply our approach to study mixing in multiphase flows by visualizing the different types of fluids and their relation to the coherent structures.
Ontology-Based Legal System in Multi-agents Systems

The development of ontologies associated with multi-agent systems provides mechanisms to model and correlate knowledge about the world to agent applications in various domains where simulations involve agents perform social exchanges or resource consumption. Norms that restrict and guide behavior often regulate the actions of agents, and therefore research on normative systems is necessary. Questions about the definition and construction of legal ontologies have been discussed in this research context. Legal ontologies have been proposed to formulate how laws can be modeled to formalize and manage law information in legal systems about regulation like traffic, taxes and other administrative rules. These ontologies have in their modeling formal laws with the purpose of providing information about permission, prohibition, obligation, rewards and punishments for these agents. Firstly, this article presents and discusses researches on ontologies applied in multi-agent systems, more specifically on legal ontologies. Next, we propose a model of legislative ontology related to the Brazilian domain of laws. The ontology of this model is provided by a web service and applied in multi-agent systems through a middleware. We used as a case study the Brazilian legislation that regulates fishing activity in an example scenario where the components simulate the restrictions for fishermen and government agents. Finally, we present how agent actions can be verified on our model and their applicability to the multi-agent systems.
QUALITY OF HUMAN DEVELOPMENT INDEX (HDI) IN MUSLIM COUNTRIES (CASE STUDY OF OIC MEMBERS)

This study analyzes factors or variables that effectof human development index (HDI) in Muslimcountries, a case study of the Organization of Islamic Cooperation (OIC) member countries. The data of 33 selected OIC member countries from 2007-2016 were analyzed using panel data regression analysis. The entire models show the significant influence of some independent variables to the level of HDI in Muslim countries. However, a variable number of the population contributes positively to HDI in all models. Unfortunately, foreign direct investment (FDI) does not add to the increasingof HDI; this is the biggest challenge faced by the government in various Muslim countries. While government fiscal policy reflected in government spending (G) contributes positively to HDI in OIC member countries.
Image Encryption Decryption Using Simple and Modified Version of AES

In today’s environment security becomes an important issue for both storage and transmission of multimedia data. Hence there is a need to secure the data from unauthorized access and to thwart the attack by the intruder in between transmission. Images play a great role for multimedia data. It depicts more information by visualization as compared to text data. The application of images in military communication, medical sciences, etc., requires high protection of image data only between the end users. Encryption is the process by which image is unreadable or in determined easily. AES (Advanced Encryption Standard) is an algorithm which is used to encrypt any multimedia data and is used vastly because of its high security. This paper proposes to use simple and simplified steps of the AES algorithm for image encryption. The proposed algorithm provides high randomness in image data for providing high security in minimum number of operations. Java has been used for the implementation.
Diving into STEM with Underwater Robotics

The poster details the development of Rogue Robotics, an underwater robotics organization founded by high school students in Monmouth County, New Jersey. The mission of the organization is to provide members of varying backgrounds and technical skill the opportunity to explore hands-on robotics through the design, construction, and testing of an underwater vehicle in preparation for the annual Marine Advanced Technology Education (MATE) ROV competition. The primary challenge discussed in the paper is the robot development process and methods for addressing the missions tasked. Establishing a company structure based on specialized divisions and focusing on design simplicity maximized efficiency and robot performance. Our approach to addressing the problems tasked by MATE was tested at the regional competition in Villanova, Pennsylvania, in which we qualified for the international level. Our success in competition and positive team reflections demonstrated our solution was effective and met the intended goals.
Perfectly Secure Message Transmission Against Independent Rational Adversaries

Secure Message Transmission (SMT) is a two-party protocol by which the sender can privately transmit a message to the receiver through multiple channels. An adversary can corrupt a subset of channels and makes eavesdropping and tampering over the corrupted channels. Fujita et al. (GameSec 2018) introduced a game-theoretic security notion of SMT, and showed protocols that are secure even if an adversary corrupts all but one of the channels, which is impossible in the standard cryptographic setting. In this work, we study a game-theoretic setting in which all the channels are corrupted by two or more independent adversaries. Specifically, we assume that there are several adversaries who exclusively corrupt subsets of the channels, and prefer to violate the security of SMT with being undetected. Additionally, we assume that each adversary prefers other adversaries’ tampering to be detected. We show that secure SMT protocols can be constructed even if all the channels are corrupted by such rational adversaries. We also study the situation in which both malicious and rational adversaries exist.
Intrinsic Factorization of Ideals in Dedekind Domains

We present a generalization of a polynomial factorization algorithm that works with ideals in maximal orders of global function fields. The method presented in this paper is intrinsic in the sense that it does not depend on the embedding of the ring of polynomials into the Dedekind domain in questi on.
Exploration of BIM Video Diversification Teaching Based on the Theory of MOOC Research

The diversification of teaching videos will improve the enthusiasm and efficiency of learning.The main purpose of the paper is to get the training of BIM short video and considering the learner's psychological and environmental factors.Therefore, this paper firstly combined the willingness research data of multi-course platform, the student data provided by Thsware Company and the profit model research data before short video studio, secondly used the model analysis method to obtain comprehensive results, thirdly investigated the learning status and problems of some college students, then tried to find out the key factors affecting their learning based on empirical data, finally explored the influence mechanism of learning willingness in order to promote and improve their learning efficiency.The innovation of this paper lies in the comprehensive analysis of the internal and external factors affecting students learning on MOOC and the factors related to the surrounding environment, then put forward that BIM short video teaching experience training and platform technology upgrading, to meet the value needs of students.
Электронный фронтир как среда отношений бизнеса и власти

The article deals with the issues of social relations in cyberspace through the metaphor of frontier. The authors postulate a proposal to consider the processes of electronic transformation from the point of view of the frontier theory, compare the features and stages of the development of the historical frontiers of the USA and Russia and the course of the development of the electronic (network) frontier in the USA and Russia. The paper proposes to turn to the experience of developing historical frontiers in the USA and Russia in order to formulate new approaches to the development of cyberspace.
Solving quantum linear system problem with near-optimal complexity.

We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigenstate of a given Hamiltonian, if we have access to an initial state with non-trivial overlap with the target eigenstate and have a reasonable lower bound for the spectral gap. We apply this algorithm to the quantum linear system problem (QLSP), and present two algorithms based on quantum adiabatic computing (AQC) and quantum Zeno effect respectively. Both algorithms prepare the final solution as a pure state, and achieves the near optimal $\mathcal{\widetilde{O}}(d\kappa\log(1/\epsilon))$ query complexity for a $d$-sparse matrix, where $\kappa$ is the condition number, and $\epsilon$ is the desired precision. Neither algorithm uses phase estimation or amplitude amplification.
Predicting Patients at Risk of 30-Day Unplanned Hospital Readmission.

We developed a machine learning model to predict 30-day readmissions using the model types; XGBoost, Random Forests and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004) and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR (NZ) models, the proposed model achieved better F1-score by 12.5% compared to LACE and 22.9% compared to PARR (NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 42.4% higher than PARR (NZ). The mean PPV was 15.9% and 13.5% higher than LACE and PARR (NZ) respectively.
Applying Innovative Learning Management System (LMS) with Gamification Framework

The development of ICT technology has delivered significant impact on the development of the game industry. Currently, gamification method is widely applied in the world of education. Gamification learning method means applying of a game into the learning process, with the aim to foster motivation to learn and change student behavior. It makes teachers more creative in designing the learning process. Some of the developers have learned more about psychology science or other sciences that study human motivation and behavior. The aim is to motivate students in the learning process and maximizing feelings of enjoyment and involvement with the learning process, besides this media can be used to capture the things that interest students and inspire them to keep learning. This study aims to propose a gamification model that are flexible in LMS, have good performance and can be improved student performance in their study.
A Multiplication by a Neural Network (NN) with Power Activations and a Polynomial Enclosure for a NN with PReLUs

Since a series of successes of deep neural networks (DNNs) with rectified linear units (ReLUs), many approximations by NNs with ReLUs, parametric rectified linear units (PReLUs), or rectified power units (RePUs) have been focused on. However how to obtain the parameters of NNs with PReLUs, approximating polynomials, have not been fully discussed. In this paper for finding such parameters, a multiplication of NNs with n-th power (P) activation and a polynomial enclosure of NN with PReLUs are discussed. Theorem of Multiplication of n variables by a NN with n-th P, and m-th P enclosure of PReLU are provided. PReLU NN is compared with the polynomial implying the enclosure. Learnings of the approximation by the shallow, or the deep PReLU NNs are also discussed.
Multi-Concern Integrated Engineering of Dependable Intelligent Systems

Dependable intelligent systems should be able to deliver trustworthy services despite changes. It is an important requirement in many application domains. Development of intelligent systems spans over several domains and is inherently heterogeneous and multi-concern. Currently development of dependable intelligent systems relies on a wide diversity of methods and tools, while missing an integrated support for efficient information engineering. In this paper, we identify the challenges pertaining to the engineering of dependable intelligent systems and propose the Problem-Design-Exploration framework as a model of the adaptive service development process. We define the key concepts supporting multi-concern engineering and discuss the opportunities offered by the Open Services for Lifecycle Collaboration (OSLC) framework.
MODEL BAHAN AJAR KAJIAN PUISI BERBASIS PENDIDIKAN KARAKTER (PENELITIAN PENGEMBANGAN DI STKIP MUHAMMADIYAH BOGOR)

The purpose of this study was to produce a product of Poetry Learning subject teaching materials based on character education for second semester students of STKIP Muhammadiyah Bogor. This study uses the Research and Development Research (DRD) model. The effectiveness test results are in the form of pretest and posttest scores. The results of the effectiveness test show that the lecture process using teaching materials for subject book Poetry Learning based on Character Education can improve the quality of the results of the Poetry Learning course, the quality of lectures, and the planting of values of Character Education. This can be proven by the comparison of the pretest and posttest scores that have a significant increase in value. The results of the student questionnaire revealed that students were motivated in lectures, were actively involved in discussions in the classroom, and were able to instill values of character education including showing mutual respect as a reflection of good behavior. With the existence of Poetry Learning subject teaching materials, students are helped and directed in lectures.
Energy-Efficient Signal Processors with Silent Mirror Tracer for Long-Term Activity Monitoring

An energy-efficient biosignal processing (BSP) system with a silent mirror tracer is proposed for long-term activity monitoring in battery-operated wearable devices. We implemented our approach using the PRUDAQ analog front stacked on the BeagleBone base station. A low-power highspeed sampler in PRUDAQ is directly integrated with the digital signal processing (DSP) unit in BeagleBone to reduce the communication overhead on connecting to any cloud-side host computer. The BSP on-chip timer-driven hardware directly drives the PRUDAQ sampler without CPU activation and silently traces samples into a mirrored buffer. The proposed method could significantly reduce the number of temporary pauses by running the interrupt service routines and frequent buffer flush to swap traced samples between the host computer. The proposed BSP system results in a 15% reduction of total energy consumption by silently tracing incoming samples in case of extracting features from the 20 million samples for long-term activity monitoring applications such as electrocardiogram (ECG) signals.