Reviewerly/exHarmony huggingface dataset:
Building Reusable Canvas Components

Components enable us to work more efficiently by reducing duplication. With components, we can encapsulate canvas controls and logic in a single place and reuse those components multiple times in the same app or between different apps. For readers with some development experience, they behave like the user controls that exist in Microsoft products such as ASP.NET, Windows Forms, and WPF (Windows Presentation Foundation).
Research Guides: CYBR-500-OLA (Hawthorne): Search for Reliable Articles

Research guide for Rider University's M.S. in Cybersecurity program and Beyond Code: Cybersecurity in Context online course with Dr. Hawthorne
De interactie tussen gender en homofoondominantie bij dt-fouten in de informele online communicatie van jongeren

We examine unintentional spelling errors (“dt-fouten”) on regular verb homophones in informal social media writing of Flemish adolescents. Our study reveals a high overall error rate: 28 percent of all target forms were spelled incorrectly. In addition, we found some clear patterns with regard to gender and the psycholinguistic variables that played a role in previous experimental research. Boys made significantly more verb spelling errors than girls. This effect of gender suggests that girls display greater error awareness or norm sensitivity. However, in both gender groups we found the same error pattern: a psycholinguistic effect of homophone dominance which causes more errors on the lower frequency form. These findings reveal that a greater focus on spelling errors may reduce the number of dt-errors, while it has no effect on the nature of those errors.
A Secure White Box Implementation of AES Against First Order DCA

The white box threat model considers an attacker with complete access to the implementation and execution environment of a cryptographic algorithm. Aiming towards secure implementation of cryptographic algorithms in this context, several implementations of the AES cipher were proposed in the literature. However, they were proven vulnerable to implementation speciﬁc attacks, as well as to reﬁned side-channel and more robust attacks that do not rely on implementation knowledge of the cipher, such as DCA (differential computation analysis). In this paper we present a white box implementation of the AES cipher with recently proposed DCA countermeasures [Lee et al. 2018]. We provide a comparison of the performance difference these countermeasures incur in practice and report some preliminary experimental results on the security of our implementation.
Boundary Attributions Provide Normal (Vector) Explanations.

Recent work on explaining Deep Neural Networks (DNNs) focuses on attributing the model's output scores to input features. However, when it comes to classification problems, a more fundamental question is how much does each feature contributes to the model's decision to classify an input instance into a specific class. Our first contribution is Boundary Attribution, a new explanation method to address this question. BA leverages an understanding of the geometry of activation regions. Specifically, they involve computing (and aggregating) normal vectors of the local decision boundaries for the target input. Our second contribution is a set of analytical results connecting the adversarial robustness of the network and the quality of gradient-based explanations. Specifically, we prove two theorems for ReLU networks: BA of randomized smoothed networks or robustly trained networks is much closer to non-boundary attribution methods than that in standard networks. These analytics encourage users to improve model robustness for high-quality explanations. Finally, we evaluate the proposed methods on ImageNet and show BAs produce more concentrated and sharper visualizations compared with non-boundary ones. We further demonstrate that our method also helps to reduce the sensitivity of attributions to the baseline input if one is required.
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation

It is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. In this paper, we consider a zero shot generalization problem setup that complies with biological intelligent agents' learning and generalization processes. The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards. Later when it is placed in the new testing environment, it is asked to perform the task without any interaction with the testing environment. We find this setting natural for biological creatures and at the same time, challenging for previous methods. Behavior cloning, state-of-art RL along with other zero-shot learning methods perform poorly on this benchmark. Given a set of experiences in the training environment, our method learns a neural function that decomposes the sparse reward into particular regions in a contingency-aware observation as a per step reward. Based on such decomposed rewards, we further learn a dynamics model and use Model Predictive Control (MPC) to obtain a policy. Since the rewards are decomposed to finer-granularity observations, they are naturally generalizable to new environments that are composed of similar basic elements. We demonstrate our method on a wide range of environments, including a classic video game -- Super Mario Bros, as well as a robotic continuous control task. Please refer to the project page for more visualized results.
Blockchain Myths Busted: It's More than Bitcoin, and SMBs Can Benefit Too

Every day, tech companies and businesses find seemingly new use cases for blockchain, creating almost limitless ways that the technology can be leveraged to solve business problems. And while Bitco...
Database Repairing with Soft Functional Dependencies

A common interpretation of soft constraints penalizes the database for every violation of every constraint, where the penalty is the cost (weight) of the constraint. A computational challenge is that of finding an optimal subset: a collection of database tuples that minimizes the total penalty when each tuple has a cost of being excluded. When the constraints are strict (i.e., have an infinite cost), this subset is a of an inconsistent database; in soft interpretations, this subset corresponds to a most probable world of a probabilistic database, a most likely intention of a probabilistic unclean database, and so on. Within the class of functional dependencies, the complexity of finding a cardinality repair is thoroughly understood. Yet, very little is known about the complexity of finding an optimal subset for the more general soft semantics. This paper makes a significant progress in this direction. In addition to general insights about the hardness and approximability of the problem, we present algorithms for two special cases: a single functional dependency, and a bipartite matching. The latter is the problem of finding an optimal almost matching of a bipartite graph where a penalty is paid for every lost edge and every violation of monogamy.
PENDAMPINGAN METODE PEMBELAJARAN JARAK JAUH (PPJ) DI ERA NEW NORMAL PADA SISWA TAMAN BACA MASYARAKAT (TBM) CARITA, BANTEN

The closure of all teaching and learning activities at educational institutions throughout Indonesia starting March 2020, to assist in efforts to contain the spread of the Covid-19 virus. Hundreds of thousands and even millions of students throughout Indonesia began to feel the impact of teaching and distance learning activities carried out online at their respective homes, many problems began to arise from mounting homework, incomprehensible assignments, psychologically shaken children because they were not put up with online learning, and parents who are busy working to buy their children online learning needs. Therefore, the opening of several community reading parks or TBM for short, if it can help a little bit the burden of parents and the burden of the students themselves, in doing the tasks given by the Educational Institution, with face-to-face offline learning methods.
Working with Tables, Rows, and Collections

Tables and rows play a crucial role in the vast majority of apps. Most apps require us to build functionality that can process and manipulate data in tables.
A Visualized Analysis of Hot Spots in the Studies of Fine Traditional Chinese Culture in the Past 40 Years

This paper is based on CNKI and CSSCI database, using VOSviewer visualization software, analyzes and explores the thematic features in the studies of fine traditional Chinese culture from the number of publications, time and journal distribution, the co-occurrence of key words, and the co-citation of Authors and documents. The research found that: the research on fine traditional Chinese culture has a broad social foundation and full academic discussion; its explosive growth in recent years is closely related to the encouragement and promotion of national power. The research topics focus on the four aspects: national governance and cultural construction, educational development, contemporary value and significance of traditional culture, cultivation and practice. It has strong real-world social concerns, interweaving and interaction with national cultural construction, as well as the concentration of direction and the diversity of horizons. It is an academic response to the development of the country and the times, and promotes the inheritance and development of contemporary Chinese cultural values.
Student teachers’ pedagogical reasoning in TPCK-based design tasks. A multiple case study

Teachers’ professional expertise cannot ignore anymore a technological component to it. Technology is nowadays accessible more and more widely, but it does not automatically translate into learning improvement. It is crucial to understand how educators give meaning to technology integration in their practices, i.e. investigate teachers’ professional reasoning. The paper reports on part of a wider study on Initial Teacher Education (ITE) institutions’ capability to engage student- teachers’ reasoning. Within the broader multiple case study across Europe, the paper reports on data emerging from document analysis and focused interviews with pre-service teachers (Ntot 36). The findings suggest an activation of reasoning whose roots might find place outside ITE influence, encouraging further research.
Modeling Sums of Exchangeable Binary Variables

We introduce a new model for sums of exchangeable Bernoulli random variables. The proposed distribution is an approximation to the exact distributional form, and relies on the theory of completely monotone functions and the Laplace transform of a gamma distribution function. Using Monte Carlo methods, we show that this new model compares favorably to the beta-binomial model with respect to estimating the success probability of the Bernoulli trials and the correlation between any two variables in the exchangeable set. We apply the new methodology to two classic data sets and the results are summarized.
Classical Quantum Optimization with Neural Network Quantum States

The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the ability to solve the quantum many-body problem in regimes that had previously been intractable for existing exact numerical methods. Here, we demonstrate the utility of the variational representation of quantum states based on artificial neural networks for performing quantum optimization. We show empirically that this methodology achieves high approximation ratio solutions with polynomial classical computing resources for a range of instances of the Maximum Cut (MaxCut) problem whose solutions have been encoded into the ground state of quantum many-body systems up to and including 256 qubits.
14 Formal methods in semantics

Covering almost an entire century, this article reviews in general and non-technical terms how formal, logical methods have been applied to the meaning and interpretation of natural language. This paradigm of research in natural language semantics produced important new linguistic results and insights, but logic also profited from such innovative applications. Semantic explanation requires properly formalized concepts, but only provides genuine insight when it accounts for linguistic intuitions on meaning and interpretation or the results of empirical investigations in an insightful way. The creative tension between the linguistic demand for cognitively realistic models of human linguistic competence and the logicians demand for a proper and explicit account of all and only the valid reasoning patterns initially led to an interesting divergence of methods and associated research agendas. With the maturing of natural language semantics as a branch of cognitive science an increasing number of logicians trained in linguistics and linguists apt in using formal methods are developing more convergent empirical issues in interdisciplinary research programs.
An AR-based conversion system of classical Chinese text with return mark to Japanese reading

The so-called “Kanbun,” the Japanese reading of the classical Chinese text, has been studied as a type of the classical Japanese in secondary education in Japan. In Kanbun the word order is changed based on symbols called “return mark,” which is converted into a Japanese reading. This change is often so complicated that it may cause phobias in learning for junior and senior high school students in Japan. Therefore in this paper, a visualization system of step-by-step change of word order is proposed, which recognizes the return marks on the classical Chinese text and visually superimposes the converted text beside the original one, in the use of the augmented reality (AR) technology. The proposed system is applicable to any Kanbun sentences so that it is expected to contribute to improved student understanding.
INVESTIGATING THE POTENTIAL CONSEQUENCES OF THE MEMBERSHIP FUNCTIONS IN A FUZZY LOGIC CONTROLLER-BASED OBSTACLE CLIMBING ROBOT

Autonomous mobile robots are the robots which can perform desired tasks in unstructured environment without continuous human guidance. This paper deals with the design of an Autonomous obstacle climbing robot that will able to climb an obstacle. This obstacle climbing navigation system is utilized by the intelligent fuzzy logic controller. The controller has 2 inputs and single output system. The inputs are slope and terrain type and the output is the speed of the robot. The membership functions are the building blocks of fuzzy logic system and play a vital role in the performance of mobile robot. This paper describes the effect of different membership functions namely triangular, trapezoidal membership functions are considered.
An Empirical Study to Improve Multiclass Classification Using Hybrid Ensemble Approach for Students’ Performance Prediction

Improving machine learning algorithms has been the interest of data scientists and researchers for the past few years. Among the performance problems raised is the classification imbalance issues listed as the top ten. The present study makes comparison and analysis of 5 state-of-art classifiers, 5 ensembles classifiers and 10 resampling techniques for data imbalance. This is done via the used 4413 instances consisting of demographic, economic, and behavioural data from student information systems and e-learning, as well as engineering faculty for the first semester 2017/2018. The use of three sampling types was adapted for the analysis: oversampling, undersampling and hybrid. The experimental results prove to model students’ behaviour from e-learning data and bagging decision tree ensemble classifier produces the highest results. Lastly, a hybrid resampling technique, SMOTEENN consistently shows the top result compared to other resampling techniques.
Predictive Modeling and Analysis of Logistic Regression and k-Nearest Neighbor for Personal Loan Campaign

Science is the knowledge which a person understands and that can be taught to a computer.Extensive research has been made to develop appropriate machine learning algorithms for different classification or function approximation problems.Some of the machine learning methods depends on the characteristics of the data set and the requirements of the business domain.This case study provides the predictive performance of different classification methods for identifying the potential customers who have a higher probability of purchasing the loan.For this we will build two statistical model, a logistic regression model and a k-nearest neighbor model.This study works on the following two techniques to build the model and provides a guideline for similar comparison studies and to find the best one out.
Attitude Estimation and Three-Dimensional Computer-Graphics Representation of a Small Hybrid Rocket

Authors are developing a small scale hybrid rocket. It is essential to release a parachute around the apogee point of the flying hybrid rocket, and to obtain the experimental data from the sensors on the rocket for confirmation after the experiment. In this study, authors examined an attitude estimation method of the rocket as a preparatory study for activing attitude control by estimating the attitude of the rocket based on the acquired data. In this study, we made a water rocket with a small computer and sensors and launched it for experiments. The roll, pitch and yaw angles were calculated using the values of acceleration and angular velocity, and Euler angles were obtained from the data acquired during the rocket's ballistic flight. The attitude estimation of the rocket is verified by representing the rocket attitude in 3D graphics.
A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.
An image defogging method using adaptive bi-channel priors

Image is the important source of information for modern war. And making effective use of the information from the image would take full advantage of the reconnaissance capability. When images captured under fog, they are vulnerable to suspended particles in the atmosphere of the light scattering, absorption and other effects, and images suffer from quality degradation problems which lead to many difficulties for battlefield reconnaissance and recognition. Combining the dark and bright channel priors (bi-channel priors), the super-pixels are used as local regions, thus local transmission and atmospheric light values are estimated more reliably and efficiently. Furthermore, adaptive bi-channel priors are developed to rectify any incorrect estimation on transmission and atmospheric light values for both white and black pixels those fail to satisfy the assumptions of the bi-channel priors. Experimental results demonstrate that the white and black pixels on the restored UAV image are with excellent fidelity and the proposed method performs better for restoring images in terms of both quantitation and quality, and leads to great improvements in real-time defogging.
EDUCATIONAL PROCESS TRANSFORMATION FOR THE MULTICULTURAL GROUPS OF THE MASTER STUDENTS

The theme of our investigation is topical because of globalization in the educational process, students and professors’ high mobility, growing interest to the international higher education of the foreign students, and speeding-up e-learning and online technologies in education.The main goal of the investigation is to assess the changes in the educational process for the multicultural groups of the Master students that have been taking place for the last five years.The object of the investigation is the structure of the multicultural groups of the Master students in the Management division at the regional (Siberian) Russian university. The logic and way of the analyses have the following steps:1) collecting the official information about the Master students in the English-speaking programs and use dynamics of the number and structure of the students for the period under investigation to further analyses of different aspects of education;2) analyzing the cultural differences from the international cultural communication theories point of view; 3) analyzing geographical changes for the period and the factors that can influence it such as the global environment, the university’s policy to attract foreign students, the Master programs quality, and the availability of entering the university in general;4) assessing the results of the students’ progress and its factors;5) assessing changing the laboriousness of the educational process for both students and teachers, especially during 2020, and the importance of soft-skills in digital learning. The result of our investigation is the creation of the list of methodological recommendation that pays attention to the following:1) the number of the foreign students’ growth, especially from Asia and the Middle East;2) new realities of using a mix of classical and digital techniques in education;3) new opportunities for the reliable and honest students and hard-working and skilled professors of the university.
An Experiment Study on Federated LearningTestbed

While the Internet of Things (IoT) can benefit from machine learning by outsourcing model training on the cloud, user data exposure to an untrusted cloud service provider can pose threat to user privacy. Recently, federated learning is proposed as an approach for privacy-preserving machine learning (PPML) for the IoT, while its practicability remains unclear. This work presents the evaluation on the efficiency and privacy performance of a readily available federated learning framework based on PySyft, a Python library for distributed deep learning. It is observed that the training speed of the framework is significantly slower than of the centralized approach due to communication overhead. Meanwhile, the framework bears some vulnerability to potential man-in-the-middle attacks at the network level. The report serves as a starting point for PPML performance analysis and suggests the future direction for PPML framework development.