Reviewerly/exHarmony huggingface dataset:
FedCRI: Federated Mobile Cyber-Risk Intelligence

In the present era of ubiquitous digitization more and more services are becoming available online which is amplified by the Corona pandemic.The fast-growing mobile service market opens up new attack surfaces to the mobile service ecosystem.Hence, mobile service providers are faced with various challenges to protect their services and in particular the associated mobile apps.Defenses for apps are, however, often limited to (lightweight) application-level protection such as app hardening and monitoring and intrusion detection.Therefore, effective risk management is crucial to limit the exposure of mobile services to threats and potential damages caused by attacks.In this paper, we present FedCRI, a solution for sharing Cyber-Risk Intelligence (CRI).At its core, FedCRI transforms mobile cyber-risks into machine learning (ML) models and leverages ML-based risk management to evaluate security risks on mobile devices.FedCRI enables fast and autonomous sharing of actionable ML-based CRI knowledge by utilizing Federated Learning (FL).FL allows collaborative training of effective risk detection models based on information contributed by different mobile service providers while preserving the privacy of the training data of the individual organizations.We extensively evaluate our approach on several real-world user databases representing 23.8 million users of security-critical mobile apps (since Android 4 and iOS 6) provided by nine different service providers in different European countries.The datasets were collected over the course of six years in the domains of financial services, payments, and insurances.Our approach can successfully extract accurate CRI models, allowing effective identification of cybersecurity risks on mobile devices.Our evaluation shows that the federated risk detection model can achieve better than 99% accuracy in terms of F1-score in most risk classification tasks with a very low number of false positives.
A reasonably gradual type theory

Gradualizing the Calculus of Inductive Constructions (CIC) involves dealing with subtle tensions between normalization, graduality, and conservativity with respect to CIC. Recently, GCIC has been proposed as a parametrized gradual type theory that admits three variants, each sacrificing one of these properties. For devising a gradual proof assistant based on CIC, normalization and conservativity with respect to CIC are key, but the tension with graduality needs to be addressed. Additionally, several challenges remain: (1) The presence of two wildcard terms at any type---the error and unknown terms---enables trivial proofs of any theorem, jeopardizing the use of a gradual type theory in a proof assistant; (2) Supporting general indexed inductive families, most prominently equality, is an open problem; (3) Theoretical accounts of gradual typing and graduality so far do not support handling type mismatches detected during reduction; (4) Precision and graduality are external notions not amenable to reasoning within a gradual type theory. All these issues manifest primally in CastCIC, the cast calculus used to define GCIC. In this work, we present an extension of CastCIC called GRIP. GRIP is a reasonably gradual type theory that addresses the issues above, featuring internal precision and general exception handling. By adopting a novel interpretation of the unknown term that carefully accounts for universe levels, GRIP satisfies graduality for a large and well-defined class of terms, in addition to being normalizing and a conservative extension of CIC. Internal precision supports reasoning about graduality within GRIP itself, for instance to characterize gradual exception-handling terms, and supports gradual subset types. We develop the metatheory of GRIP using a model formalized in Coq, and provide a prototype implementation of GRIP in Agda.
Expert System for Learning Disability Classification in School-Age Children

Learning disabilities are generally defined as disorders of one or more basic psychological processes including the use of spoken or written language shown by children with poor ability to hear, think, read, write, spell, or solve mathematic problems.The purpose of this research is to classify learning disabilities in children using a fuzzy expert system as a step in recognizing the learning abilities of school-age children so that treatment can be done early on.This expert system is built with several modules, namely a knowledge base, memory, fuzzy inference engine, and a web-based interface.Answers from users will be processed using a fuzzy expert system based on the scores obtained from each category of reading, writing, math and logic, and heredity.The output of this system answers whether the child is normal, dyslexia, dysgraphia, dyscalculia, or a combination of the types of learning disabilities.The expert system was conducted on 20 school-age children (6-12 years old).The classification results of three types of learning disabilities using a fuzzy expert system showed an accuracy of 95%.
Efficient Argument Structure Extraction with Transfer Learning and Active Learning

The automation of extracting argument structures faces a pair of challenges on (1) encoding long-term contexts to facilitate comprehensive understanding, and (2) improving data efficiency since constructing high-quality argument structures is time-consuming.In this work, we propose a novel context-aware Transformer-based argument structure prediction model which, on five different domains, significantly outperforms models that rely on features or only encode limited contexts.To tackle the difficulty of data annotation, we examine two complementary methods: (i) transfer learning to leverage existing annotated data to boost model performance in a new target domain, and (ii) active learning to strategically identify a small amount of samples for annotation.We further propose model-independent sample acquisition strategies, which can be generalized to diverse domains.With extensive experiments, we show that our simple-yeteffective acquisition strategies yield competitive results against three strong comparisons.Combined with transfer learning, substantial F1 score boost (5-25) can be further achieved during the early iterations of active learning across domains.
A cluster validity for optimal configuration of Kohonen maps in e-learning recommendation

the first block of our unsupervised deep collaborative recommendation (UDCF) system and proposes a platform whose goal is to try to find the adequate parameters of the Kohonen maps, to create homogeneous clusters in profile data and results, the homogeneity is verified thanks to the very low variance rate of the results obtained by the cluster population and a second criterion which is the high prediction rate of collaborative recommendation. Although the revision concerns only the clustering block, and the use of a symmetrical autoencoder without searching for its optimization, the result obtained (82.33%) for the optimal configurations with high homogeneity of the Kohonen map is equivalent to the optimized result of the UDCF and even better than the classical recommendation methods
Spatio-temporal analysis of East Asian seismic zones based on multifractal theory

Abstract In this article, the integrity of the seismic catalog obtained (1970–2014, M &gt; 2.8) was verified according to the Gutenberg–Richter relation, the appropriate minimum magnitude was determined, and the whole region was divided into five areas according to the geological structure background of the whole research object and the trend of the historical seismic zone. We applied multifractal analysis in each partition. The results showed that although in different geological backgrounds, before major earthquakes, the earthquake time series information dimension had different degrees of growth, and the parameter after the flame had different degrees of decline. To a certain extent, this reflected the seismic energy accumulation and release process. In addition, the variation of fractal parameters in scale-free regions of time series and spatial distribution series also indicated that these two kinds of sequences in different regions show the characteristics of a multifractal structure rather than a single and uniform fractal structure.
The effect of electronic human resources practices on employee satisfaction in private hospitals

The objective of the study was to determine the impact of electronic human resources practices on Employees Satisfaction in private hospitals in Jordan. The dimensions of electronic human resources practices are (e-recruitment, e-training, e- performance evaluation, e-rewards system, and e-communication). Data were analyzed using IBM SPSS and AMOS software. The population of the study involves all the physicians working in private hospitals in Jordan. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a purposive sample of physicians via email. To achieve the objectives of the study and test hypotheses, the researcher used SPSS and path analysis. The study results showed that there is a statistically significant impact of electronic human resources practices on employee’s satisfaction. Considering study finding, the researcher recommends decision makers to provide the largest possible investment in modern technology, and to subscribe to databases that qualify doctors to practice electronic human resource management dimensions that were mentioned in the study and ensuring doctors’ job stability.
Automatic Parameter Selection for Non-Redundant Clustering

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Automatic Parameter Selection for Non-Redundant ClusteringCollin Leiber, Dominik Mautz, Claudia Plant, and Christian BöhmCollin Leiber, Dominik Mautz, Claudia Plant, and Christian Böhmpp.226 - 234Chapter DOI:https://doi.org/10.1137/1.9781611977172.26PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract High-dimensional datasets often contain multiple meaningful clusterings in different subspaces. For example, objects can be clustered either by color, weight, or size, revealing different interpretations of the given dataset. A variety of approaches are able to identify such non-redundant clusterings. However, most of these methods require the user to specify the expected number of subspaces and clusters for each subspace. Stating these values is a non-trivial problem and usually requires detailed knowledge of the input dataset. In this paper, we propose a framework that utilizes the Minimum Description Length Principle (MDL) to detect the number of subspaces and clusters per subspace automatically. We describe an efficient procedure that greedily searches the parameter space by splitting and merging subspaces and clusters within subspaces. Additionally, an encoding strategy is introduced that allows us to detect outliers in each subspace. Extensive experiments show that our approach is highly competitive to state-of-the-art methods. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Demonstration of VegaPlus: Optimizing Declarative Visualization Languages

While many visualization specification languages are user-friendly, they tend to have one critical drawback: they are designed for small data on the client-side and, as a result, perform poorly at scale. We propose a system that takes declarative visualization specifications as input and automatically optimizes the resulting visualization execution plans by offloading computational-intensive operations to a separate database management system (DBMS). Our demo emphasizes live programming of visualizations over big data, enabling users to write or import Vega specifications, view the optimized plans from our system, and even modify these plans and compare their performance via a dedicated performance dashboard.
Meta-Learning for Online Update of Recommender Systems

Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over time, the update strategy should be flexible to quickly catch users' current interest from continuously generated new user-item interactions. Existing update strategies focus either on the importance of each user-item interaction or the learning rate for each recommender parameter, but such one-directional flexibility is insufficient to adapt to varying relationships between interactions and parameters. In this paper, we propose MeLON, a meta-learning based novel online recommender update strategy that supports two-directional flexibility. It is featured with an adaptive learning rate for each parameter-interaction pair for inducing a recommender to quickly learn users' up-to-date interest. The procedure of MeLON is optimized following a meta-learning approach: it learns how a recommender learns to generate the optimal learning rates for future updates. Specifically, MeLON first enriches the meaning of each interaction based on previous interactions and identifies the role of each parameter for the interaction; and then combines these two pieces of information to generate an adaptive learning rate. Theoretical analysis and extensive evaluation on three real-world online recommender datasets validate the effectiveness of MeLON.
Facial Emotion Recognition using Convolutional Neural Networks

Abstract: Humans use their facial expressions to communicate their emotions, which is a strong tool in communication. Facial expression identification is one of the most difficult and powerful challenges in social communication, as facial expressions are crucial in nonverbal communication. Facial Expression Recognition (FER) is just an important study topic in Artificial Intelligence, with numerous recent experiments employing Convolutional Neural Networks (CNNs). The emotions that have grown in the face image have a significant impact on judgments and debates on a variety of topics. Surprise, fear, disgust, anger, happiness, and sorrow are the six basic categories in which a person's emotional states can be categorized according to psychological theory. The automated identification of these emotions from facial photos can be useful in human-computer interaction and a variety of other situations. Deep neural networks, in particular, are capable of learning complicated characteristics and classifying the derived patterns. A deep learning-based framework for human emotion recognition is offered in this system. The proposed framework extracts feature with Gabor filters before classifying them with a Convolutional Neural Network (CNN). The suggested technique improves both the speed of CNN training and the recognition accuracy, according to the results of the experiments. Keywords: convolutional neural network (CNN), Gabor filter
A state of the art and scoping review of embodied information behavior in shared, co-present extended reality experiences

We present a state of the art and scoping review of the literature to examine embodied information behaviors, as reflected in shared gaze interactions, within co-present extended reality experiences.Recent proliferation of consumer-grade head-mounted XR displays, situated at multiple points along the Reality-Virtuality Continuum, has increased their application in social, collaborative, and analytical scenarios that utilize data and information at multiple scales.Shared gaze represents a modality for synchronous interaction in these scenarios, yet there is a lack of understanding of the implementation of shared eye gaze within co-present extended reality contexts.We use gaze behaviors as a proxy to examine embodied information behaviors.This review examines the application of eye tracking technology to facilitate interaction in multiuser XR by sharing a user's gaze, identifies salient themes within existing research since 2013 in this context, and identifies patterns within these themes relevant to embodied information behavior in XR.We review a corpus of 50 research papers that investigate the application of shared gaze and gaze tracking in XR generated using the SALSA framework and searches in multiple databases.The publications were reviewed for study characteristics, technology types, use scenarios, and task types.We construct a state-of-the field and highlight opportunities for innovation and challenges for future research directions.
The Inviting-Disinviting Index

As invitational theory continues to develop and invitational practices continue to be investigated, researchers will seek reliable and valid measures upon which to base their findings and conclusions. This article summarizes validity and reliability studies of the Inviting-Disinviting index (Wiemer &amp; Purkey, 1994) and an adapted version of the index for children (Schmidt, 1996). Results of these studies show weak to strong test-retest reliability for the 101 and strong content validity. Further investigation of both the original and adapted version of the IDI is recommended.
Internalizing Knowledge for Anticipatory Classifier Systems in Discretized Real-Valued Environments

Real-valued environments are challenging for learning systems because of a significant increase in the input space size of the problem.This work demonstrates that Anticipatory Learning Classifier Systems (ALCS) can successfully build sets of conditional rules foreseeing the consequences of executed actions.Three major classes of Learning Classifier Systems -Anticipatory Classifier System (ACS), ACS2, Yet Another Classifier System (YACS), alongside the traditional Dyna-Q algorithm implementations were adapted to handle real-valued input signal discretization.Aspects like the ability to capture all possible interactions, model generalization capabilities, size of the solution of relative execution times were compared in four different problems using probabilistic modelling, providing unbiased judgments.Results proved that the examined ALCS are capable of solving selected problems.Despite increased input size, all possible environmental transitions were learned latently, without obtaining any explicit incentives.Such an internal representation provides a more compact solution representation and can optimize learning speed further by executing imaginary environmental interactions or performing action planning for a new set of potential problems.
Curriculum learning for safe mapless navigation

This work investigates the effects of Curriculum Learning (CL)-based approaches on the agent's performance. In particular, we focus on the safety aspect of robotic mapless navigation, comparing over a standard end-to-end (E2E) training strategy. To this end, we present a CL approach that leverages Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the Robotnik Kairos as a robotic agent. For a fair comparison, our evaluation considers an equal computational demand for every learning approach (i.e., the same number of interactions and difficulty of the environments) and confirms that our CL-based method that uses ToL outperforms the E2E methodology. In particular, we improve the average success rate and the safety of the trained policy, resulting in 10% fewer collisions in unseen testing scenarios. To further confirm these results, we employ a formal verification tool to quantify the number of correct behaviors of Reinforcement Learning policies over desired specifications.
The Digital Detective's Discourse - A toolset for forensically sound collaborative dark web content annotation and collection

In the last decade, the proliferation of machine learning (ML) algorithms and their application on big data sets have benefited many researchers and practitioners in different scientific areas.Consequently, the research in cybercrime and digital forensics has relied on ML techniques and methods for analyzing large quantities of data such as text, graphics, images, videos, and network traffic scans to support criminal investigations.Complete and accurate training data sets are indispensable for efficient and effective machine learning models.An essential part of creating complete and accurate data sets is annotating or labelling data.We present a method for law enforcement agency investigators to annotate and store specific dark web content.Using a design science strategy, we design and develop tools to enable and extend web content annotation.The annotation tool was implemented as a plugin for the Tor browser.It can store web content, thus automatically creating a dataset of dark web data pertinent to criminal investigations.Combined with a central storage management server, enabling annotation sharing and collaboration, and a web scraping program, the dataset becomes multifold, dynamic, and extensive while maintaining the forensic soundness of the data saved and transmitted.To manifest our toolset's fitness of purpose, we used our dataset as training data for ML based classification models.A five cross-fold validation technique was used to evaluate the classifiers, which reported an accuracy score of 85 -96%.In the concluding sections, we discuss the possible use-cases of the proposed method in real-life cybercrime investigations, along with ethical concerns and future extensions.
Pelatihan scratch programming untuk anak-anak SD Umeanyar

The ability to read, write, and arithmetic is something that must be mastered by all students. Computational thinking is considered as one of the basic competencies to complete these skills. After entering a higher level students will be introduced to learning that involves technology and it requires sufficient understanding to easily understand the basics of computer technology logic. Some elementary schools still do not use technology to encourage students' computational thinking skills. During the Covid-19 pandemic, students had difficulty understanding learning materials and decreased student interest in learning. The elementary school curriculum in Umeanyar Village has not implemented computational thinking, this makes it difficult for students to understand the logic of computer technology at higher school levels and material concepts that require critical thinking logic. Therefore, the purpose of this activity is (1) to educate students on how to use the scratch application as a method to introduce mathematical and computational thinking in elementary schools and (2) to provide hands-on experience for students to introduce simple programming concepts by creating animation projects and games and fostering cooperation between each other so that the learning atmosphere is more interesting. These objectives can be achieved by means of preliminary studies, implementation of training and mentoring, and evaluation. After providing material and training, students are invited to make a games and animation project for 3 meetings which are held every Saturday. The results obtained by students after participating in this training are increased understanding and experience of how to use computers and students can create animations and simple games with the scratch application. Previously, students were still unfamiliar with using computers and computer applications and had difficulty understanding how to operate the software. Students still need assistance and support from the surrounding environment in implementing this training. For this reason, village heads and schools need to work together to support their students through various training activities that provide knowledge and insight for the next level of education. As for the universities that can help realize the program.