Miracle-dz/UniFAR-FTUs huggingface dataset:
EMOSS: An Efficient Algorithm to Hide Sequential Patterns

Nowadays data mining is the way of extracting hidden knowledge from raw data whereas sequence mining aims to find sequential patterns that are frequent in the database, so publishing these data may lead to the disclosure of private information about organizations or individuals. Knowledge hiding is the process of hiding sensitive knowledge extracted previously from the database, to ensure that no abuse will be caused. This paper addresses the problem of sequential pattern hiding and proposes an efficient algorithm which uses a multi-objective approach to overcome the problem of sequence hiding as well as maintaining database fidelity as much as possible. It also shows that the proposed algorithm outperforms existing methods in terms of both speed and memory usage.
Recommender Systems: A Review

:The way in which the growth in the available digital information and the number of users on the Internet have created a challenge of data overload causes timely access to item of interest on the Internet. Many of the information retrieval systems try to solve this problem but prioritization of item and user were absent in most of the systems. The purpose of this paper is to develop a recommender system using collaborative filtering technique and Fuzzy C-mean (FCM). Collaborative filtering is the most successful algorithm in the recommender system’s field. Recommender systems are the systems for filtering the information that handle with the problem of overloaded information by filtering important information fragment out of huge amount of dynamically generated information according to user’s interest, preferences or observed behaviour about them. This paper considers m users, n number of items and present an approach based on FCM clustering to produce a recommendation for the active user by a new approach. Here FCM clustering algorithm to categorize users based on their interests.
MIST: A multi-resolution parcellation of functional brain networks

The functional architecture of the brain is organized across multiple levels of spatial resolutions, from distributed networks to the localized areas they are made of. A brain parcellation that defines functional nodes at multiple resolutions is required to investigate the functional connectome across these scales. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution group level parcellation of the cortical, subcortical and cerebellar gray matter. The individual MIST parcellations match other published group parcellations in internal homogeneity and reproducibility and perform very well in real-world application benchmarks. In addition, the MIST parcellations are fully annotated and provide a hierarchical decomposition of functional brain networks across nine resolutions (7 to 444 functional parcels). We hope that the MIST parcellation will accelerate research in brain connectivity across resolutions. Because visualizing multiresolution parcellations is challenging, we provide an interactive web interface to explore the MIST. The MIST is also available through the popular nilearn toolbox.
Improving Virtual Machine Performance Usin g aCross-Ru nProfil eRepository

Virtual machines for languages such as the Java programming language make extensive use of online proling and dynamic optimization to improve program performance. But despite the important role that proling plays in achieving high performance, current virtual machines discard a program’s prole data at the end of execution, wasting the opportunity to use past knowledge to improve future performance. In this paper, we present a fully automated architecture for exploiting cross-run prole data in virtual machines. Our work addresses a number of challenges that previously limited the practicality of such an approach. We apply this architecture to address the problem of selective optimization, and describe our implementation in IBM’s J9 Java virtual machine. Our results demonstrate substantial performance improvements on a broad suite of Java programs, with the average performance ranging from 8.8% { 16.6% depending on the execution scenario.
Spillover Effects of Financial Incentives on Non-Incentivized User Engagement: Evidence from an Online Knowledge Exchange Platform

Abstract Online knowledge exchange platforms have become an important information technology (IT) artifact that empowers online learning for the Internet users. A key challenge for knowledge exchange platforms is how to motivate the desirable user engagement behaviors. Based on the motivation theory and the equity theory, we propose a set of hypotheses regarding the spillover effects of financial incentives on three types of desirable yet non-incentivized user engagement, namely, voluntary knowledge sharing, knowledge seeking, and social engagement. We obtain an archival data set from a major online knowledge exchange platform (Zhihu.com) to evaluate our hypotheses. Leveraging a quasi-natural experiment wherein the platform implemented a paid knowledge sharing feature, we employ difference-in-differences models in tandem with propensity score matching to evaluate the spillover effects of financial incentives on the abovementioned types of engagements. Our results show that the initial financial incentives on the paid knowledge sharing activities further motivate users to voluntarily share more knowledge and increase their social engagement in the platform. However, the financial incentives have no significant impact on users’ knowledge seeking behavior. Our study suggests that the financial incentives not just have an effect on incentivized engagement, but they spillover to users’ desirable non-incentivized online engagement behaviors. Therefore, the overall positive effect of financial incentives to a platform is likely under-estimated in prior research. Our research offers implications to practice that financial incentives can be an effective strategy to nurture users, to seed content, and to enhance sociality of a platform.
Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies

The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.
Design of Cancelable Palmprint Templates Based on Look Up Table

A novel cancelable palmprint templates generation scheme is proposed in this paper. Firstly, the Gabor filter and chaotic matrix are used to extract palmprint features. It is then arranged into a row vector and divided into equal size blocks. These blocks are converted to corresponding decimals and mapped to look up tables, forming final cancelable palmprint features based on the selected check bits. Finally, collaborative representation based classification with regularized least square is used for classification. Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable templates can achieve very high performance and security levels. Meanwhile, it can also satisfy the needs of real-time applications.
Effect of Electrode Geometry and Intensity Control Method on Comfort of Electrotactile Stimulation on the Tongue

Electrotactile (electrocutaneous) stimulation for sensory substitution and augmentation possesses a variety of sensory attributes, including intensity, pitch, and more qualitative factors relating to the degree of comfort or discomfort of the stimulus. This study examined the roles of electrode geometry (size and presence or absence of a coaxial ground plane), and intensity control method (voltage or current), on the relative comfort of the electrotactile stimulus on the tongue. Only electrode size was found to significantly affect comfort, with larger electrodes resulting in more preferable percepts.
Semantics all the way down: the Semantic Web and open science in big earth data

Abstract Semantic technologies have emerged as a prominent research area within Big Earth Data. These technologies have provided significant benefits for data discovery and integration. Yet, the formality of the Semantic Web, in languages such as the Web Ontology Language (OWL), does not always integrate well with the numerical, statistical, and geometric methods of the geosciences. Two prominent challenges in this area are how to semantically model individual measurements and what to do when geoscience needs are not addressed by languages such as OWL. This has led to a fragmented Big Earth Data community with either no solution or incompatible semantic solutions. We use an oceanographic example to highlight the limitations and challenges surrounding the semantic encoding of observations and the use of semantics during analysis. We then present potential solutions to each challenge showing that a full end-to-end application of semantic technologies is not only feasible, but beneficial to Big Earth Data.
Methods for Nonlinear Impairments Compensation in Fiber-Optic Communication Systems

Fiber optic links are the backbone of the current high-speed teleand data communication networks. Two major factors which cause degradation in the performance of this type of communication systems are fiber optic link nonlinear impairments, self-phase and cross-phase modulation, and linear additive noise. To tackle with linear noise, the average energy of constellation points should be increased and this increment also increases the nonlinear impairments. This research explores some possible methods of tackling the issues caused by nonlinear impairments without changing the average energy of constellation points. In chapter 1, we will review mathematical models introduced in literature for modelling nonlinearities in fiber optic links. In Chapter 2, we will introduce a simplified model for modelling nonlinearities which can help us with compensating procedure by reducing computational complexity and providing feasibility for parallel computations. In Chapter 3, a novel method called Masking Data Samples will be introduced which can reduce the power of self-phase modulation by sampling from that, which is a random variable, and choosing the best sample in sense of SPM noise between all obtained ones. In Chapter 4, a modified version of known tree-structure codes will be introduced which can reduce the power of SPM noise by a new sorting method in the phase of producing look-up table for codes. At the last chapter, a method for exploiting the statistical characteristics and memory of the samples of cross-phase modulation noise will be introduced which can help us with detecting data symbols more accurately.
PAL: Personal Assistant System Using Low-Cost Computer

Automatic Speech Recognition (ASR) describes the ability of a computer to capture, identify and recognize the variety of human speech. It has been applied in many technologies such as personal assistant systems. Unfortunately, many personal assistant systems has been built in a way that may not always be disability-friendly and this causes the affected users whom are blind, disabled, illiterates and those who have physical limitations unable to enjoy the benefits of operating a computer. Hence, PAL is introduced. PAL is a personal assistant system built using low-cost device called the Raspberry Pi and open source voice-controlled software called Jasper. The functionalities of PAL includes searching for information on the internet, check for unread emails, schedule events in the calendar, manage a to-do list and translate texts through voice commands. Apart from that, a friendly graphical user interface (GUI) is also designed to display the output of each of the functional modules. Lastly, a number of tests are conducted to evaluate the performance and accuracy of the functional modules, GUI output display as well as response rate of the system. These tests include GUI output display test, user acceptance testing, the Command Success Rate (CSR) and Word Error Rate (WER) tests as well as response rate test. With the development of this project, it is hoped that PAL will be able to provide users with the benefits of using a computer in a more convenient and cost efficient manner.
Big Data Factories. Collaborative Approaches

The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as "data factoring" emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing. The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools. Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer. com
Health Evaluation of a Domain Name System Based on the Analytic Hierarchy Process

As the vital infrastructure of the Internet, the Domain Name System (DNS) is important from any aspect of Internet actors with the explosive increases of connected things and deployed applications. However, how to monitor and evaluate the healthy state of the DNS is an emergent and difficult issue. In this paper, we propose an Analytic Hierarchy Process (AHP) based DNS evaluation method. We adopt the Point of View (PoV) idea proposed by Global Cyber Security Center (GCSEC) and extend their metric sets for more comprehensive understanding of the DNS health. The AHP is introduced to set the weights for different metrics based on the threat scenario or special PoV. We introduce a monitoring platform with two examples of the actual procedure. The results show that we can adopt this method in any use case for the DNS health evaluation and improvement.
EURL ECVAM WORKSHOP ON NEW GENERATION OF PHYSIOLOGICALLY-BASED KINETIC MODELS IN RISK ASSESSMENT

The European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) Strategy Document on Toxicokinetics (TK) outlines strategies to enable prediction of systemic toxicity by applying new approach methodologies (NAM). The central feature of the strategy focuses on using physiologically-based kinetic (PBK) modelling to integrate data generated by in vitro and in silico methods for absorption, distribution, metabolism, and excretion (ADME) in humans for predicting whole-body TK behaviour, for environmental chemicals, drugs, nano-materials, and mixtures. In order to facilitate acceptance and use of this new generation of PBK models, which do not rely on animal/human in vivo data in the regulatory domain, experts were invited by EURL ECVAM to (i) identify current challenges in the application of PBK modelling to support regulatory decision making; (ii) discuss challenges in constructing models with no in vivo kinetic data and opportunities for estimating parameter values using in vitro and in silico methods; (iii) present the challenges in assessing model credibility relying on non-animal data and address strengths, uncertainties and limitations in such an approach; (iv) establish a good kinetic modelling practice workflow to serve as the foundation for guidance on the generation and use of in vitro and in silico data to construct PBK models designed to support regulatory decision making. To gauge the current state of PBK applications, experts were asked upfront of the workshop to fill a short survey. In the workshop, using presentations and discussions, the experts elaborated on the importance of being transparent about the model construct, assumptions, and applications to support assessment of model credibility. The experts offered several recommendations to address commonly perceived limitations of parameterization and evaluation of PBK models developed using non-animal data and its use in risk assessment, these include: (i) develop a decision tree for model construction; (ii) set up a task force for independent model peer review; (iii) establish a scoring system for model evaluation; (iv) attract additional funding to develop accessible modelling software. ; (v) improve and facilitate communication between scientists (model developers, data provider) and risk assessors/regulators; and (vi) organise specific training for end users. The experts also acknowledged the critical need for developing a guidance document on building, characterising, reporting and documenting PBK models using non-animal data. This document would also need to include guidance on interpreting the model analysis for various risk assessment purposes, such as incorporating PBK models in integrated strategy approaches and integrating them with in vitro toxicity testing and adverse outcome pathways. This proposed guidance document will promote the development of PBK models using in vitro and silico data and facilitate the regulatory acceptance of PBK models for assessing safety of chemicals.
User Study of Emotional Visualization Dashboard for Educational Software

This paper describes a user study plan for the MADE Teacher’s Dashboard, our proposed data visualization dashboard that supports educators to inspect and reflect on the emotional states of students using web learning applications. Our goal was to support the system designer, and indirectly also teachers and students. Our dashboard follows affective learning models and monitors online learner emotions. It uses an open source library that supports tracking of facial features and detection of emotions in real time, identifying six different emotions. We present a user study plan to determine whether the data visualization graph can be interpreted properly.
Effects of Task Experience on Attention to Extraneous Information during Multimedia Learning

markdownabstractThe aim of this dissertation is to provide an answer to the following questions: 1) Does the negative effect of extraneous information on learning decrease or disappear with increasing task experience? 2) Does this effect arise because learners start to ignore the extraneous information? These two questions are addressed both for irrelevant and unnecessary information presentation. An important aspect of this dissertation is the use of eye-tracking methodology to address the second question, which can provide insight into the perceptual and cognitive processes that underlie the effects of different multimedia materials on learning outcomes (Van Gog & Scheiter, 2010).
Cost-aware Optimized Virtual Machine Placement through Cloud Broker in Multi-cloud Architecture

Cloud services through cloud brokers are upcoming services acquisition approach for cloud service consumers, where cloud broker acts as an intermediary between cloud service providers and service consumers. From the viewpoint of service charge, the IaaS cloud service providers impose higher services charges for their services. As the service charge varies dynamically in respect to cloud service providers, in respect to requested cloud resources and timestamp. In this paper, we propose a costaware selection of cloud providers and their virtual machines (VMs) as well from the broker perspective to provide cost effective services to consumers. We consider multi-cloud environment for cloud service provisioning and apply portfolio optimization technique to purchase services from cloud service
Exploring Metacognitive Strategies Utilizing Digital Books: Enhancing Reading Comprehension Among Deaf and Hard of Hearing Students in Saudi Arabian Higher Education Settings

The purpose of this study was to examine evidence-based practices related to reading comprehension through metacognitive strategies utilizing digital books among deaf and hard of hearing (DHH) in higher education. Previous literature has demonstrated that the major educational challenge for DHH students was developing reading comprehension skills. This issue becomes more complex when DHH students start their undergraduate education in Saudi Arabia due to the difficulty of understanding academic books and journals. Based on the preliminary data, an intervention was designed for DHH students with control and treatment groups (n = 36). The control group was taught using metacognitive strategies-based printed books. By contrast, the treatment group received an intervention through metacognitive strategies-based digital books. The procedure of the study was designed through multiple measurements: The Achievement Test of Reading Comprehension was used as a baseline measurement. In addition, classroom observations were used during the study to ensure the fidelity of treatment, and students’ perspectives were collected through a survey at the end of the intervention for the social validity purpose. The results indicated significant improvement for both groups with the largest effect relating to the treatment group in reading comprehension and engagement level compared with that of the control group.
Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison

Eye tracking has commonly been used to investigate how users interact with web pages, with the goal of improving their usability. This article comprehensively revisits the techniques that could be applicable to eye tracking data for analysing user scanpaths on web pages. It also uses a third-party eye tracking study to compare these techniques. This allows researchers to recognise existing techniques for their goals, understand how they work and know their strengths and limitations so that they can make an efficient choice for their studies. These techniques can mainly be used for calculating similarities/dissimilarities between scanpaths, computing transition probabilities between web page elements, detecting patterns in scanpaths and identifying common scanpaths. The scanpath analysis techniques are classified into four groups by their goals so that researchers can directly focus on the appropriate techniques for a sequential analysis of user scanpaths on web pages. This article also suggests dealing with the limitations of these techniques by pre-processing eye tracking data, considering cognitive processing and addressing their reductionist approach.