Reviewerly/exHarmony huggingface dataset:
SOCIAL ENGINEERING AS AN EVOLUTIONARY THREAT TO INFORMATION SECURITY IN HEALTHCARE ORGANIZATIONS

Information security in healthcare settings is overlooked even though it is the most vulnerable for social engineering attacks. The theft of hospital information data is critical to be monitored as they contain patients’ confidential health information. If leaked, the data can impact patients’ social as well as professional life. The hospital data system includes administrative data, as well as employees’ personal information hacked, which can cause identity theft. The current paper discusses types and sources of social engineering attacks in healthcare organizations. Social engineering attacks occur more frequently than other malware attacks, and hence it is crucial to understand what social engineering is and its vulnerabilities to understand the prevention measures. The paper describes types of threats, potential vulnerabilities, and possible solutions to prevent social engineering attacks in healthcare organizations. Keywords: social engineering, hospitals, healthcare organizations, information security.
Lightweight Software Process Improvement Using Productivity and Sustainability Improvement Planning (PSIP)

Productivity and Sustainability Improvement Planning (PSIP) is a lightweight, iterative workflow that allows software development teams to identify development bottlenecks and track progress to overcome them. In this paper, we present an overview of PSIP and how it compares to other software process improvement (SPI) methodologies, and provide two case studies that describe how the use of PSIP led to successful improvements in team effectiveness and efficiency.
Wavelet as a Viable Alternative for Time Series Forecasting

Analysis of financial data is always challenging due to the non-linear and non-stationary characteristics of the time series which is further complicated by volatility clustering effect and sudden changes such as jump, steep slopes and valleys. Classical regression based analysis techniques often entail rigorous mathematical treatments albeit with little success in exploiting the differing frequency characteristics to uncover hidden but valuable trending information. Wavelet, on the other hand provides an efficient way to represent time series with such complex dynamics by decomposing it into time-frequency space and at the same time preserve both temporal and spectral information. This property enables analysts to identify the dominant modes (spectral information) of a time series and observe how those modes vary over time (temporal information). Most importantly, wavelet transform is computationally efficient, only a small number of wavelet coefficients are needed to describe complicated signals. This paper seeks to establish cases for the use of wavelets as viable tools in time series forecasting. Two time series, Kijang Emas Daily Index and Bit Coin Daily Price with differing characteristics are used as subjects of study. Out-of-sample dynamic forecasting of 20 points is made using best-fit ARIMA and prior-point imitation follow by wavelet de-noising methods (imitate-wavelet). Comparisons made with MAPE measurements of ARIMA and imitate-wavelet methods indicated comparable forecasting performance between the simpler imitate-wavelet techniques and ARIMA model.
Adaptive Group Sparse Regularization for Continual Learning.

We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties. Our method selectively employs the two penalties when learning each node based its the importance, which is adaptively updated after learning each new task. By utilizing the proximal gradient descent method for learning, the exact sparsity and freezing of the model is guaranteed, and thus, the learner can explicitly control the model capacity as the learning continues. Furthermore, as a critical detail, we re-initialize the weights associated with unimportant nodes after learning each task in order to prevent the negative transfer that causes the catastrophic forgetting and facilitate efficient learning of new tasks. Throughout the extensive experimental results, we show that our AGS-CL uses much less additional memory space for storing the regularization parameters, and it significantly outperforms several state-of-the-art baselines on representative continual learning benchmarks for both supervised and reinforcement learning tasks.
Active contours for multiregion segmentation with a convolutional neural network initialization

Active contour-based methods are widely popular in the image segmentation field. Basically, they perform a semiautomatic region identification by partitioning the image content mainly into the foreground and background. Nevertheless, the accurate delimitation still remains as an important challenge which usually depends on how close the initial contour is placed to the object of interest (OI). Several applications of active contours require the user interaction to give prior information about the initial position as the first step, which drives the tool substantially dependent on a manual process. This paper describes how to overcome this limitation by including the expertise provided by the training stage of a Convolutional Neural Network (CNN). Despite CNN methods require a large dataset or data augmentation techniques to improve their results, the combined proposal accomplishes a presegmentation task with a reduced number of images to obtain the assumed locations for each OI. These results are used to initialize a multiphase active contour model that follows a level set scheme to lead a smoother multiregion segmentation with less effort. Experiments of this approach are included to compare classic techniques of contour initialization and show the benefits of our proposal.
Air Pollution Model using Neutrosophic Cubic Einstein Averaging Operators

Theneutrosophic cubic averaging and Einstein averaging aggregation operators are presented andappliedto the air pollution model of the city of Peshawar, Pakistan. Neutrosophic cubic set (NCS) isamore generalized version of the neutrosophic set (NS) and an interval neutrosophic set (INS). Itis in a better position to express consistent, indeterminant and incomplete information, thus it is able to be applied to aggregate the air pollution model.Aggregation operators have a key role in science and engineering problems. Firstly, the neutrosophic cubic weighted averaging (NCWA) operator, neutrosophic cubic ordered weighted averaging (NCOWA) operator, neutrosophic cubic hybrid aggregation (NCHA) operator, neutrosophic cubic Einstine weighted averaging (NCEWA) operator, neutrosophic cubic Einstine ordered weighted averaging (NCEOWA) operator and neutrosophic cubic Einstine hybrid aggregation (NCEHA) operator are defined. Secondly,these operators areapplied to the air pollution model of particulate matter with the size of less than 10 micron(PM10) in Peshawar. Subsequently,the results are compared with the World Health Organization (WHO) standards using score/accuracy function.The pollution of PM10 is found to be very much higher than WHO standards. Hence, strong measures are required to control air pollution.
Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.
Entropy: An Essential Component of Cryptographic Security

Digital computers are deterministic, that is, for every input n, they will produce output x. Cryptographic systems use complex math to encrypt data using non-deterministically generated random keys, or pseudorandom keys, which are statistically indistinguishable from a random sample.  Protecting data and ensuring integrity are core principals of information security. How essential is the use of secure cryptographic ciphers and truly random keys to modern cryptographic security?
A Real-time Mobile Notification System for Inventory Stock out Detection using SIFT and RANSAC

&lt;p&gt;Object detection and tracking is one of the most relevant computer technologies related to computer vision and image processing. It may mean the detection of an object within a frame and classify it (human, animal, vehicle, building, etc) by the use of some algorithms. It may also be the detection of a reference object within different frames (under different angles, different scales, etc.). The applications of the object detection and tracking are numerous; most of them are in the security field. It is also used in our daily life applications, especially in developing and enhancing business management. Inventory or stock management is one of these applications. It is considered to be an important process in warehousing and storage business because it allows for stock in and stock out products control. The stock-out situation, however, is a very serious issue that can be detrimental to the bottom line of any business. It causes an increased risk of lost sales as well as it leads to reduced customer satisfaction and lowered loyalty levels. On this note, a smart solution for stock-out detection in warehouses is proposed in this paper, to automate the process using inventory management software. The proposed method is a machine learning based real-time notification system using the exciting Scale Invariant Feature Transform feature detector (SIFT) and Random Sample Consensus (RANSAC) algorithms. Consequently, the comparative study shows the overall good performance of the system achieving 100% detection accuracy with features’ rich model and 90% detection accuracy with features’ poor model, indicating the viability of the proposed solution.&lt;/p&gt;
Multi-objective Consensus Clustering Framework for Flight Search Recommendation

In order to provide personalized recommendations for travel search queries to online customers, an appropriate segmentation of customers is required using information from the search query. Clustering ensemble approaches have been proposed to address well-known issues of classical clustering methods that each relies on a different theoretical model and can thus identify in the data space only clusters corresponding to this model, and ensemble methods aggregate diverse clustering solutions from dissimilar algorithmic configurations to generate more robust consensus clusters corresponding to agreements between initial clusters. We put forward a new clustering ensemble multi-objective optimization-based framework developed to improve personalized recommendations generated by the flight search engine of the company Amadeus. This framework optimizes diversity in the clustering ensemble search space and finds an appropriate number of clusters automatically without requiring any user input. Experimental results compare the efficacy of this method with other existing approaches on Amadeus customer flight search data in terms of the adjusted Rand index and a business metric defined and used by the company.
Neural Analogical Matching

Analogy is core to human cognition. It allows us to solve problems based on prior experience, it governs the way we conceptualize new information, and it even influences our visual perception. The importance of analogy to humans has made it an active area of research in the broader field of artificial intelligence, resulting in data-efficient models that learn and reason in human-like ways. While cognitive perspectives of analogy and deep learning have generally been studied independently of one another, the integration of the two lines of research is a promising step towards more robust and efficient learning techniques. As part of a growing body of research on such an integration, we introduce the Analogical Matching Network: a neural architecture that learns to produce analogies between structured, symbolic representations that are largely consistent with the principles of Structure-Mapping Theory.
Energy modeling of Hoeffding tree ensembles

Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average.
A study of using task-based artificial intelligence(AI) chatbot for further interaction in English and the analysis of students’ production

This study aimed to explore the possibility of using artificial intelligence(AI) Chatbot in English class to facilitate communication. Participants were 19 students in 6 th grade and performed 3 interactive tasks with Google Dialogflow API Chatbot. Tasks were exchanging small talk(task1), information gap activity(task2), and solving problems(task3), adopted from elementary English textbooks. The conversations were transcribed by the history function of Dialogflow and analyzed quantitatively to examine the production of participants and the success rates of each task and discourse. Furthermore, quantitative and qualitative analyses were executed to investigate how the prompts, activated by Chatbot, work as negotiation of meaning and elicitation to proceed interaction. The results revealed that the average t-unit of participants’ production showed 10, and they tended to speak 3-4 words in conversation. Next, the success rates of discourse and task were different for each task. The rate of task1 and task2 showed more than 65% compared to the task3. Despite the rate, Chatbot’s prompts provided meaningful effect on developing communicative ability by leading continual talk. Eventually, this study gave suggestion for integrating AI Chatbot into classroom to provide affordances for students’ using English, and for future progress of AI Chatbots as tools in foreign language education.
There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training.

Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes. These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases. Previous work proposed tackling this problem via adversarial training, but this leads to learned sentence representations that still suffer from the same biases. As a solution, we propose using an ensemble of adversaries during the training, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data. We show that using an ensemble of adversaries can prevent the bias from being relearned after the model training is completed, further improving how well the model generalises to different NLI datasets. In particular, these models outperformed previous approaches when tested on 12 different NLI datasets not used in the model training. Finally, the optimal number of adversarial classifiers depends on the dimensionality of the sentence representations, with larger dimensional representations benefiting when trained with a greater number of adversaries.
FRAUD TRIANGLE ANALYSES IN DETECTING FRAUDULENT FINANCIAL STATEMENT USING FRAUD SCORE MODEL

Company’s financial condition reflected in the financial statements. However, there are many loopholes in the financial statements which can become a chance for the management and certain parties to commit fraud on the financial statements. This study aims to detect financial statement fraud as measured using fraud score model that occurred in issuers entered into the LQ-45 index in 2014-2016 with the use of six independent variables are financial stability, external pressure, financial target, nature of industry, ineffective monitoring and rationalization. This study using 27 emiten of LQ-45 index during 2014-2016. However, there are some data outlier that shall be removed, thus sample results obtained 66 data from 25 companies. Multiple linear regression analysis were used in this study. The results showed that the financial stability variables (SATA), nature of industry (RECEIVBLE), ineffective monitoring (IND) and rationalization (ITRENDLB) proved to be influential or have the capability to detect financial statement fraud. While the external pressure variables (DER) and financial target (ROA) are not able to detect the existence of financial statement fraud. Simultaneously all variables in this study were able to detect significantly financial statement fraud.
Foundations of Real World Economics: What Every Economics Student Needs to Know

"Foundations of Real World Economics: What Every Economics Student Needs to Know." Journal of Human Development and Capabilities, 21(2), pp. 209–210
The Conditional Multiway Mapped Tree: Modeling and Analysis of Hierarchical Data Dependencies

In many areas of scientific studies, the processed data show hierarchical dependencies and can be arranged in various tree structures. The shape of such structures depends mainly on the rules defining the ways to organize or systematize the data. Often there is a need to modify these rules many times. In the presented work, the model of the Conditional Multiway Mapped Tree (CMMTree) is proposed. This model was developed to obtain two fundamental functionalities. The first was the possibility to build any tree structures that reflect any relationships between the input data elements, assuming that the model should be able to operate on different types of data and that the node connection rules can be easily changed. The second was the ability to quickly identify the characteristic places that determine the shape of trees, including trees that can contain tens of millions of nodes, and also the ability to quickly reach precisely selected fragments of these trees. To generalize the proposed model the generic programming technique, in C++ language, was used.
GD-StarGAN: Multi-domain image-to-image translation in garment design

In the field of fashion design, designing garment image according to texture is actually changing the shape of texture image, and image-to-image translation based on Generative Adversarial Network (GAN) can do this well. This can help fashion designers save a lot of time and energy. GAN-based image-to-image translation has made great progress in recent years. One of the image-to-image translation models––StarGAN, has realized the function of multi-domain image-to-image translation by using only a single generator and a single discriminator. This paper details the use of StarGAN to complete the task of garment design. Users only need to input an image and a label for the garment type to generate garment images with the texture of the input image. However, it was found that the quality of the generated images is not satisfactory. Therefore, this paper introduces some improvements on the structure of the StarGAN generator and the loss function of StarGAN, and a model was obtained that can be better applied to garment design. It is called GD-StarGAN. This paper will demonstrate that GD-StarGAN is much better than StarGAN when it comes to garment design, especially in texture, by using a set of seven categories of garment datasets.
Preprocessing approaches in DTW based online signature verification

Abstract Amongst different approaches, dynamic time warping has shown promising results during the online signature verification competitions of previous years. To improve the results of dynamic time warping, different preprocessing steps may be applied and different dimensions of the samples may be compared. The choice of preprocessing steps and comparing dimensions may significantly influence the results. Thus, to aid researchers with these decisions, a comparison made between the results of promising preprocessing algorithms as horizontal scaling, vertical scaling and alignment using dynamic time warping in different dimensions and their combinations on two datasets (SVC2004 and MCYT-100). The results showed that preprocessing methods made a very promising improvement in the verification accuracy.
Exploration of Reinforcement Learning to Play Snake Game

In this research, we explore the hypothesis that Reinforcement Learning applications are not amenable to conduct close analysis. With the combination of Deep Reinforcement Learning and neural network, the snake game agent is trained. The dependences of this game are on python, Keras, sci-kit image, and tensor flow. For the training of the agent in the game, SARSA is used to control the moving capabilities of the snake as per the user's desire. Deep Q-learning is used in the learning module and the learning rate of the SARSA algorithm shows the effective results in the different agent iterations.