Reviewerly/exHarmony huggingface dataset:
A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services

Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio.
Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder

We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals.Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier.We evaluate the proposed approach on three biomedical events and show that the decoder generates interpretable rules that serve as accurate explanations for the event classifier's decisions, and, importantly, that the joint training generally improves the performance of the event classifier.Lastly, we show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rulebased system.
CIMA: A Large Open Access Dialogue Dataset for Tutoring

One-to-one tutoring is often an effective means to help students learn, and recent experiments with neural conversation systems are promising.However, large open datasets of tutoring conversations are lacking.To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns.In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors.The CIMA collection, which we make publicly available, is novel in that students are exposed to overlapping grounded concepts between exercises and multiple relevant tutoring responses are collected for the same input.CIMA contains several compelling properties from an educational perspective: student roleplayers complete exercises in fewer turns during the course of the conversation and tutor players adopt strategies that conform with some educational conversational norms, such as providing hints versus asking questions in appropriate contexts.The dataset enables a model to be trained to generate the next tutoring utterance in a conversation, conditioned on a provided action strategy.
Adaptive weighted splines

Genetic Programming for Symbolic Regression is often prone to overfit the training data, resulting in poor generalization on unseen data. To address this issue, many pieces of research have been devoted to regularization via controlling the model complexity. However, due to the unstructured tree based representation of individuals the model complexity cannot be directly computed, rather approximation of the complexity must be taken. This paper proposes a new novel representation called Adaptive Weighted Splines which enables explicit control over the complexity of individuals using splines. The experimental results confirm that this new representation is significantly better than the tree-based representation at avoiding overfitting and generalizing on unseen data, demonstrating notably better and far more consistent generalization performances on all the benchmark problems. Further analysis also shows that in most cases, the new Genetic Programming method outperforms classical regression techniques such as Linear Regression, Support Vector Regression, K-Nearest Neighbour and Decision Tree Regression and performs competitively with state-of-the-art ensemble regression methods Random Forests and Gradient Boosting.
A LOF K-Means Clustering on Hotspot Data

K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method. The object detected outliers are then removed. Then new centroid for each group is obtained using the K-Means method again. This dataset was taken from the FIRM are provided by the National Aeronautics and Space Administration (NASA). Clustering was done by varying the number of clusters (k = 10, 15, 20, 25, 30, 35, 40, 45 and 50) with cluster optimal is k = 20. The result based on the value of Sum of Squared Error (SSE) shown the LOF K-Means method was better than the K-Means method.
Motivational Aspect of Student’s Language Learning Style in Differentiated Instruction of English for Specific Purposes

This study aims to research the opportunity of applying the motivational aspect of the language learning style in differentiated instruction of English for specific purposes (ESP) to information technology students (IT) through defining their intrinsic and extrinsic motivation; to determine the students' levels of motivation; to compare motivation of the third-and fourth-year students, their desire to master the IT profession and study ESP; to define the factors which balance students' motivation.A mixed research design was employed.The participants were 234 thirdyear students and 231 fourth-year students, mostly males, aged from 20 to 22 years who studied at technical university during 2017-2019 academic years and volunteered to take part in the study.To collect the data, the questionnaires by Rean and Iakunіn "Studying the motives of students' learning", Ilina "Learning motivation at the university" and Dubovitskaya "Methodology for diagnosing the learning motivation orientation" were used.Although we revealed that students' intrinsic motivation to study ESP and master the IT profession was stronger than extrinsic motivation, we also found that the dominance of intrinsic or extrinsic motives in students may depend on the situation and particular ESP activity.The support of students' high level of motivation and development of students' medium and low levels of motivation should rely on the factors which help to balance students' motivation.
The development of numerical forecasting systems of primary sources of gold on the results of placer sampling in the example Vagran placer cluster (North Urals)

Based on the results of field research, as well as data from stock reports, two types of placer gold were identified within the Vagran placer cluster, which are indicators of primary mineralization. They are used as benchmarks for developing a digital system for predicting parameters and localizing primary sources of placer gold. Formalized typomorphic characteristics of placer gold (size, roundness, fineness, sorting and content of impurity elements), combined in multiplicative indicators, make it possible to forecast the composition and localization of the primary mineralization with greater confidence than ordinary parameters separately. The data required for such an assessment do not require additional field and highly qualified laboratory studies, they are contained in standard reports on the heavy minerals testing, and, in contrast to the characteristics of individual indicator types of placer gold, they give more stable results. The study of the correlation system allowed to identify characteristic indicators for the primary mineralization of gold-sulfide-quartz and hypogenic-hypergenic types, and to give recommendations for conducting prospecting and exploration in order to identify the primary gold content of the cluster. The proposed method of creating forecast estimates allows to computerize the process of determining the prospects for primary mineralization of territories.
PPaaS: Privacy Preservation as a Service

Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is one of the important components in data science. However, due to privacy concerns, data should be enforced with strong privacy guarantees before sharing. Different privacy-preserving approaches were developed for privacy preserving data sharing; however, identifying the best privacy-preservation approach for the privacy-preservation of a certain dataset is still a challenge. Different parameters can influence the efficacy of the process, such as the characteristics of the input dataset, the strength of the privacy-preservation approach, and the expected level of utility of the resulting dataset (on the corresponding data mining application such as classification). This paper presents a framework named Privacy Preservation as a Service (PPaaS) to reduce this complexity. The proposed method employs selective privacy preservation via data perturbation and looks at different dynamics that can influence the quality of the privacy preservation of a dataset. PPaaS includes pools of data perturbation methods, and for each application and the input dataset, PPaaS selects the most suitable data perturbation approach after rigorous evaluation. It enhances the usability of privacy-preserving methods within its pool; it is a generic platform that can be used to sanitize big data in a granular, application-specific manner by employing a suitable combination of diverse privacy-preserving algorithms to provide a proper balance between privacy and utility.
OsamorSoft: clustering index for comparison and quality validation in high throughput dataset

Abstract The existence of some differences in the results obtained from varying clustering k-means algorithms necessitated the need for a simplified approach in validation of cluster quality obtained. This is partly because of differences in the way the algorithms select their first seed or centroid either randomly, sequentially or some other principles influences which tend to influence the final result outcome. Popular external cluster quality validation and comparison models require the computation of varying clustering indexes such as Rand, Jaccard, Fowlkes and Mallows, Morey and Agresti Adjusted Rand Index (ARI MA ) and Hubert and Arabie Adjusted Rand Index (ARI HA ). In literature, Hubert and Arabie Adjusted Rand Index (ARI HA ) has been adjudged as a good measure of cluster validity. Based on ARI HA as a popular clustering quality index, we developed OsamorSoft which constitutes DNA_Omatrix and OsamorSpreadSheet as a tool for cluster quality validation in high throughput analysis. The proposed method will help to bridge the yawning gap created by lesser number of friendly tools available to externally evaluate the ever-increasing number of clustering algorithms. Our implementation was tested alongside with clusters created with four k-means algorithms using malaria microarray data. Furthermore, our results evolved a compact 4-stage OsamorSpreadSheet statistics that our easy-to-use GUI java and spreadsheet-based tool of OsamorSoft uses for cluster quality comparison. It is recommended that a framework be evolved to facilitate the simplified integration and automation of several other cluster validity indexes for comparative analysis of big data problems.
Kinematic skeleton graph augmented network for human parsing

Human parsing, which is a task of labeling pixels in human images into different fine-grained semantic parts, has achieved significant progress during the past decade. However, there are still several challenges in human parsing, due to occlusions, varying poses and similar appearance between the left/right parts. To tackle these problems, a Human Kinematic Skeleton Graph Layer (HKSGL) is proposed to augment regular neural networks with human kinematic skeleton information. The HKSGL has two major components: kinematic skeleton graph and interconnected modular neural layer. The kinematic skeleton graph is a user pre-defined skeleton graph, which models the interconnections between different semantic parts. Then the skeleton graph is passed to the interconnected modular neural layer which is composed of a set of modular blocks corresponding to different semantic parts. The HKSGL is a lightweight, low costs layer which can be easily attached to any existing neural networks. To demonstrate the power of the HKSGL, a new dataset on human parsing in occlusions is also collected, termed the RAP-Occ. Extensive experiments have been performed on four datasets on human parsing, including the LIP, the CIHP, the ATR and the RAP-Occ. And two popular baselines, i.e., the Deeplab V3+ and the CE2P, are agumented by the proposed HKSGL. Competitive performance of the augmented models has been achieved in comparison with state-of-the-art methods.
Evaluation of resampling methods in the class unbalance problem

The purpose of many real world applications is the prediction of rare events, and the training sets are then highly unbalanced.In this case, the classifiers are biased towards the correct prediction of the majority class and they misclassify a minority class, whereas rare events are of the greater interest.To handle this problem, numerous techniques were proposed that balance the data or modify the learning algorithms.The goal of this paper is a comparison of simple random balancing methods with more sophisticated resampling methods that appeared in the literature and are available in R program.Additionally, the authors ask whether learning on the original dataset and using a shifted threshold for classification is not more competitive.The authors provide a survey from the perspective of regularized logistic regression and random forests.The results show that combining random under-sampling with random forests has an advantage over other techniques while logistic regression can be competitive in the case of highly unbalanced data.
Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera

This paper proposes a technique to estimate the distance between an object and a rolling shutter camera using a single image. The implementation of this technique uses the principle of the rolling shutter effect (RSE), a distortion within the rolling-shutter-type camera. The proposed technique has a mathematical strength compared to other single photo-based distance estimation methods that do not consider the geometric arrangement. The relationship between the distance and RSE angle was derived using the camera parameters (focal length, shutter speed, image size, etc.). Mathematical equations were derived for three different scenarios. The mathematical model was verified through experiments using a Nikon D750 and Nikkor 50 mm lens mounted on a car with varying speeds, object distances, and camera parameters. The results show that the mathematical model provides an accurate distance estimation of an object. The distance estimation error using the RSE due to the change in speed remained stable at approximately 10 cm. However, when the distance between the object and camera was more than 10 m, the estimated distance was sensitive to the RSE and the error increased dramatically.
Rule-Based Effective Collaborative Recommendation Using Unfavorable Preference

The biggest challenge in collaborative filtering recommendation system research is data sparsity; it mainly occurs as user rates very few items from widely available options. Data Imputation techniques address the data sparsity problem by filling the missing values and then predicting the likeliness of the user. Most of the existing imputation systems assign high ratings to the items or incorporate additional information to enhance the performance of collaborative filtering recommendations. This paper proposes an association rule-based imputation method (RUBLE) to improve the top-N prediction performance of the collaborative filtering recommendation. The proposed method identifies the unfavorable items of each user using the association rule mining technique and imputes them with low values. The proposed method not only addresses the sparsity problem but also provides a better quality of recommendation by eliminating the unfavorable items in top-N predictions. Existing collaborative methods can quickly adapt to the proposed method as it is method agnostic. The experimental results show that the proposed method enhances the accuracy of the traditional recommender methods by two times on average and significantly outperforms existing imputation based approaches.
WinGrt, a Windows program for garnet supergroup minerals

A Microsoft ® Visual Basic software, called WinGrt, has been developed to calculate and classify wet-chemical and electron-microprobe garnet supergroup mineral analyses.The program evaluates 33 approved species that belong to the tetragonal henritermierite and isometric bitikleite, schorlomite, garnet and berzeliite groups based on the Commission on New Minerals and Mineral Names (CNMMN) of the International Mineralogical Association (IMA-13) nomenclature scheme.WinGrt also evaluates thirty geothermometers using the Fe 2+ -Mg exchange reactions for garnet-biotite, garnet-clinopyroxene and garnet-orthopyroxene pairs within the application range of greenschist-, amphibolite-, granulite-and eclogite-facies metamorphic rocks.As naturally occurring garnet is potentially a useful provenance indicator, the program calculates end-member molecules from chemical compositions on the basis of different approaches and yields pyrope, almandine, spessartine, grossular, andradite and schorlomite phase on various ternary discrimination diagrams used in provenance studies.The ferric and ferrous iron contents from total FeO (wt.%) amount are estimated by stoichiometric constraints.The program allows the users to enter 30 input variables including Sample No, SiO 2 , TiO 2 , ZrO 2 , HfO 2 , Th 2 O, SnO 2 , F and H 2 O (wt. %).WinGrt also enables the user to enter the total REE 2 O 3 (wt.%) as input values from La 2 O 3 to Lu 2 O 3 (wt.%) of garnet supergroup mineral analyses in program's data edit section.WinGrt enables the user to type or load multiple garnet compositions in the data entry section, to edit and load Microsoft ® Excel files in calculating, classifying and naming the garnet species, and to store all the calculated parameters in the Microsoft ® Excel file for further evaluation.
A Comparative Study of Operating Systems: Case of Windows, UNIX, Linux, Mac, Android and iOS

Varieties of operating systems (OS) have emerged over the years having different features and functionalities.Understanding the functionalities of each OS guides users' decisions about the OS to install on their computers.In view of this, the comparative analysis of different OS is needed to provide details on the similarities and difference in recent types of OS vis-à-vis their strengths and weaknesses.This paper focus on the comparative analysis of Windows, Unix, Linux, Mac, Android and iOS operating systems based on the OS features and their strengths and weaknesses.A qualitative analysis of six different operating systems and result showed that Windows 10 had 0.04 malware file present while Windows 7 machine was 0.08.Higher percentage of mobile malware target Androids than iOS.Windows 10, Linux, UNIX and Mac OS are more secured and reliable.Windows and Android are more popular, user-friendly, easy to use and allow more application program than Mac OS.Linux and Android are free while Windows is moderately costly and Mac OS is very costly.Except for Mac and iOS others allow compatibility.Windows 10 and Mac OS integrated firewall.Windows and Android tend to be the most widely used especially the newest versions.It is because they are affordable, secure, reliable, compatible and user friendly.This study helps to provide some guides to both end-users and developers guiding them in taking decisions about operating systems that are most suitable for them.
Defeating data hiding in social networks using generative adversarial network

Abstract As a large number of images are transmitted through social networks every moment, terrorists may hide data into images to convey secret data. Various types of images are mixed up in the social networks, and it is difficult for the servers of social networks to detect whether the images are clean. To prevent the illegal communication, this paper proposes a method of defeating data hiding by removing the secret data without impacting the original media content. The method separates the clean images from illegal images using the generative adversarial network (GAN), in which a deep residual network is used as a generator. Therefore, hidden data can be removed and the quality of the processed images can be well maintained. Experimental results show that the proposed method can prevent secret transmission effectively and preserve the processed images with high quality.