Miracle-dz/UniFAR-FTUs huggingface dataset:
User misrepresentation in online social networks: how competition and altruism impact online disclosure behaviours

ABSTRACT Their sheer size and scale give social networks significant potential for shaping popular opinions. While the spread of information and influence within social networks has been popular area of research for some time, more recently a research trend has appeared in which the researcher seeks to understand how users can aggressively influence community opinions, often using misrepresented or false information. Such misrepresentations by users are deeply troubling for any social network, where revenue-generation and their reputation depend on accurate and reliable user generated information. This study investigates the individual motivations that both promote and inhibit intentions towards personal information misrepresentation. These motivations are hypothesised to result from the dichotomy of competitive and altruistic attitudes existing with social network communities. Results of a survey analysis involving 502 users of Facebook offer insights useful for understanding social network information sharing practices. Marketing strategies, in particular, should benefit from the careful evaluation of the factors that lead to honesty (or dishonesty) among OSN users.
A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

Regaining the lost functionality of limbs is the top priority for people with motor skills impairment as it directly affects their ability to execute activities of daily living and hence, worsens their quality of life. In the last two decades, a great deal of research has focused on error-related potential (ErrP) based brain-computer interfaces (BCIs). Many applications have been developed to assist motor-impaired people in their rehabilitation and among these are robots, spellers, gesture recognition systems, and brain-controlled wheelchairs. In this paper, we present a review of various ErrP based BCI that can potentially aid motor-disabled people in their rehabilitation and execution of their daily activities. First, we describe the ErrP phenomenon and its characteristics followed by a comprehensive application-driven discussion on ErrP based rehabilitation and assistive strategies for motor-impaired people, including studies conducted since the inception of ErrP to the current state-of-the-art applications. Lastly, we discuss the potential issues and challenges being faced by current state-of-the-art applications as well as important future pathways and research directions that might be adopted for advanced ErrP-BCIs used in clinical settings.
Emerging trends in data analytics and knowledge management job market: extending KSA framework

Purpose This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design. Design/methodology/approach This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM. Findings Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company’s characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels. Practical implications The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula. Originality/value This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.
Analysis of data-at-rest security In smartphones

With almost two billion users worldwide, smartphones are used for almost everything – booking a hotel, ordering a cup of coffee, or paying in a shop. However, small size and high mobility makes these devices prone to theft and loss. In this work we aim to broaden our understanding of how smartphone users and application developers protect sensitive data on smartphones. To understand how well users are protecting their data in smartphones, we conducted several studies. The results revealed that 50% of the subjects locked their smartphone with an unlocking secret and 95% of them chose unlocking secrets that could be guessed within minutes. To understand how well application developers protect sensitive data in smartphones, we analyzed 132K Android applications. We focused on identifying misuse of cryptography in applications and libraries. The study results revealed that developers often misuse cryptographic API. In fact, 9 out of 10 Android applications contained code that used a symmetric cipher with a static encryption key. Further, source attribution revealed that libraries are the main consumer of cryptography and the major contributor of misuse cases. Finally, an in-depth analysis of the top libraries highlighted the need for improvement in the way we define and detect misuse of cryptography. Based on these results we designed and evaluated a system for encryption keys management that uses wearable devices as an additional source of entropy. Evaluation results showed that the proposal introduces insignificant overhead in power consumption and latency.
Evolution of Collective Fairness in Hybrid Populations of Humans and Agents

Fairness plays a fundamental role in decision-making, which is evidenced by the high incidence of human behaviors that result in egalitarian outcomes. This is often shown in the context of dyadic interactions, resorting to the Ultimatum Game. The peculiarities of group interactions – and the corresponding effect in eliciting fair actions – remain, however, astray. Focusing on groups suggests several questions related with the effect of group size, group decision rules and the interrelation of human and agents’ behaviors in hybrid groups. To address these topics, here we test a Multiplayer version of the Ultimatum Game (MUG): proposals are made to groups of Responders that, collectively, accept or reject them. Firstly, we run an online experiment to evaluate how humans react to different group decision rules. We observe that people become increasingly fair if groups adopt stricter decision rules, i.e., if more individuals are required to accept a proposal for it to be accepted by the group. Secondly, we propose a new analytical model to shed light on how such behaviors may have evolved. Thirdly, we adapt our model to include agents with fixed behaviors. We show that including hardcoded Pro-social agents favors the evolutionary stability of fair states, even for soft group decision rules. This suggests that judiciously introducing agents with particular behaviors in a population may leverage long-term social benefits.
Instagram as an Education Platform for EFL Learners.

Social media platforms have gained an exceptional popularity especially in the last ten years and become an important part of human lives. Among the popular social media platforms, Instagram is used by millions of people every day especially by the young adults. Given this significance, social media platforms have been used for educational purposes, too. Thus, this study aimed at exploring university students’ opinions about Instagram as an educational platform with respect to educational and language learning purposes and its effect on students’ language learning process supplementary to formal classes. Using mixed methodology, 219 university students learning English were included in the survey, and 80 students took part in the experimental part of the study which was designed to explore the impact of Instagram on students’ language learning process. Six students from the experimental group were also interviewed for their opinions about the Instagram use during the experimental part of the study. Carrying out quantitative and qualitative analyses, the findings show that Instagram is the most frequently used social media platform among the participants and they favor using it for educational and language learning purposes. Additionally, it was found that Instagram had a positive impact on students’ language learning based on the achievement scores. Thus, it is concluded that Instagram can be used to enhance learning of English supplementary to formal teaching by exposing the students to language while they are using this platform as part of their every day practices.
Graph analysis techniques and applications to bitcoin forensics

Flow based networks have been studied over the years, which have considered different interpretations of a flow, for instance where a flow may be based on transfer (e.g., package delivery), or serial replication (e.g., one-to-one gossip), or parallel duplication (e.g., epidemic spread). However, the study of flows which may split while conserving the overall volume at each split is relatively sparse. In this thesis we look at certain aspects of such split-and-transfer based flows with volume conservation. Specifically, we (i) design general purpose flow based graph analysis techniques and demonstrate their applicability by studying a wide range of real networks, including the network induced by Bitcoin transactions; and (ii) design analysis techniques which are specific to the Bitcoin network, and showcase the e cacy of these techniques in conjunction with our novel flow-based graph analysis algorithms with case studies. Pertaining the general purpose graph analysis, we first consider the notion of entropic centrality which measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination (i.e., larger the spread of the flow), the more well connected and thus central the node is deemed. Prior works on entropic centrality implicitly assumed that the flow is indivisible, and at every node, the flow is transferred from one edge to another. In this thesis, we propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can actually be arbitrarily split across choices of neighbours, while conserving the overall volume. This interpretation of volume conserved non-atomic flows better reflects certain applications, including Bitcoin transactions. We consider two variations. In one, we constrain that a down-stream flow cannot revisit a node that has already been visited upstream (no cycles), and show how to relate this model to an equivalent transfer based entropic centrality one for computational tractability. We then relax the ‘no cycle’ constraint, and explore a Markov process of random walk which simplifies the computation. These entropic centrality models can easily accommodate directed and weighted graphs. Based on the latter variant of (Markov) entropic centrality model, we next design a 2stage clustering algorithm, which in the first stage is informed by the entropic centrality to detect local communities based on random walks, and then performs a bottom-up iterative aggregation. We furthermore explore graph clustering by using an entropic distance based approach as an alternative to distribution of flow based approach as described above. To
Prevalence and risk factors of gallbladder polyps in health screening subjects

Background : Gallbladder (GB) polyps are now increasingly detected by ultrasonography. We investigated the prevalence and risk factors of GB polyps in health screening population. Methods : An ultrasonographic study of GB polyps were conducted in 35,012 subjects who received a paid health screening examination at Samsung Medical Center. Controls, twice the subjects with GB polyp, were randomly selected from the cases with normal GB. The demographic characteristics and biochemical parameters were recorded. Results : The overall prevalence of GB polyps was 2.94% (1,330/15,573). Among them, 3.63% were men, 2.09% were women. In both males and females, the prevalence was highest in their 40s. The diameter of GB polyps ranged from 2 mm to 20 mm (mean 4.5 mm). Most polyps (98.3%) were less than 10 mm in diameter. Of 1,030 subjects, 64% had a single polyp and 36% had multiple polyps. Multiple logistic regression, with reference group of body mass index (BMI, <22.5 kg/m2), showed odds ratio of 1.074 and 1.864 for BMI groups of 22.5-24.9 and 25 or more respectively. In contrast, other demographic characteristics and biochemical parameters, such as age, smoking, alcohol use, glucose, liver function tests, lipid profiles and HBsAg carriage were lack of any relation to GB polyps. Conclusions : GB polyps are not likely to be rare in Korean adults despite their low prevalence compared to other reports. The body mass index may be a risk factor for GB polyps. (Korean J Med 57:1014-1020, 1999)
Predicting Patient Falls

OverviewThe Hendrich II Fall Risk Model is used to assess a hospitalized patient's risk of falling. Designed to be administered quickly, it focuses on eight independent risk factors: confusion, disorientation, and impulsivity; symptomatic depression; altered elimination; dizziness or vertigo; male sex; administration of antiepileptics (or changes in dosage or cessation); administration of benzodiazepines; and poor performance in rising from a seated position in the Get-Up-and-Go test. (This screening tool is included in a series, Try This: Best Practices in Nursing Care to Older Adults, from the Hartford Institute for Geriatric Nursing at New York University's College of Nursing.) For a free online video demonstrating the use of this tool, go to http://links.lww. com/A111.
Raptor Codes for BIAWGN Channel: SNR Mismatch and the Optimality of the Inner and Outer Rates

Fountain codes are a class of rateless codes with two interesting properties, first, they can generate potentially limitless numbers of encoded symbols given a finite set of source symbols, and second, the source symbols can be recovered from any subset of encoded symbols with cardinality greater than the number of source symbols. Raptor codes are the first implementation of fountain codes with linear complexity and vanishing error floors on noisy channels. Raptor codes are designed by the serial concatenation of an inner Luby transform (LT) code, the first practical realization of fountain codes, and an outer low-density parity-check (LDPC) code. Raptor codes were designed to operate on the binary erasure channel (BEC), however, since their invention they received considerable attention in order to improve their performance on noisy channels, and especially additive white Gaussian noise (AWGN) channels. This dissertation considers two issues that face Raptor codes on the binary input additive white Gaussian noise (BIAWGN) channel: inaccurate estimation of signal to noise ratio (SNR) and the optimality of inner and outer rates. First, for codes that use a belief propagation algorithm (BPA) in decoding, such as Raptor codes on the BIAWGN channel, accurate estimation of the channel SNR is crucial to achieving optimal performance by the decoder. A difference between the estimated SNR and the actual channel SNR is known as signal to noise ratio mismatch (SNRM). Using asymptomatic analysis and simulation, we show the degrading effects of SNRM on Raptor codes and observe that if the mismatch is large enough, it can cause the decoding to fail. Using the discretized density evolution (DDE) algorithm with the modifications required to simulate the asymptotic performance in the case of SNRM, we determine the decoding threshold of Raptor codes for different values of SNRM ratio. Determining the threshold under SNRM enables us to quantify its effects which in turn can be used to reach important conclusions about the effects
Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches

Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches Ikram Boukhedimi By endowing the base station with hundreds of antennas and relying on spatial multiplexing, massive multiple-input-multiple-output (MIMO) allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate and optimize the performance of massive MIMO in different contexts. First, we explore the spectral efficiency of massive MIMO in large-scale multi-tier heterogeneous networks that aim at network densification. This latter is epitomized by the joint implementation of massive MIMO and small cells to reap their benefits. Our interest is on the design of coordinated beamforming that mitigates cross-tier interference. Thus, we propose a regularized SLNR-based precoding in which the regularization factor is used to allow better resilience to channel estimation errors. Second, we move to studying massive MIMO under Line-of-Sight (LoS) propagation conditions. To this end, we carry out an analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician factor. We start by analyzing conventional processing schemes such as LMMSE and MRC under training-based imperfect-channel-estimates, and then, propose a statistical combining 5 technique that is more suitable in LoS-prevailing environments. Finally, we look into the interplay between LoS and the fundamental limitation of massive MIMO systems, namely, pilot contamination. We propose to analyze and compare the performance using single-cell and multi-cell detection methods. In this regard, the single-cell schemes are shown to produce higher SEs as the LoS strengthens, yet remain hindered by LoS-induced interference and pilot contamination. In contrast, for multi-cell combining, we analytically demonstrate that M-MMSE outperforms both single-cell detectors by generating a capacity that scales linearly with the number of antennas, and is further enhanced with LoS.
Prophylactic intra-aortic balloon pump reduces C-reactive protein levels and early mortality in high-risk patients undergoing percutaneous coronary intervention

Objective Intra-aortic balloon pump (IABP) plays a pivotal role in the treatment of cardiogenic shock complicating acute myocardial infarction. However, the usefulness of prophylactic IABP support in high-risk patients during percutaneous coronary intervention (PCI) is still controversial, and its influence on the inflammatory response following PCI has not been well evaluated. In this study we sought to assess the impact of prophylactic IABP support upon C-reactive protein (CRP) level and clinical prognosis in high-risk patients undergoing PCI. Methods and results A total of 106 high-risk patients diagnosed with acute ST-elevation or non-ST-elevation myocardial infarction (Cardiogenic shock was excluded) were enrolled and divided into two groups at random: 51 cases receiving PCI accompanied by prophylactic IABP support, and the remaining 55 cases undergoing PCI without IABP insertion served as the control group. CRP levels were determined on admission, day 3 and day 7, respectively. The troponin I (TNI) peak, left ventricular functions and major adverse cardiovascular events (MACE) were compared during follow-up. We found that the IABP group had a lower TNI peak as well as CRP level after PCI. Left ventricular function was improved at 2-week instead of 3-month followup. Although the mortality did not reach a significant decline after 6-month follow-up, it had improved in-hospital and at 30-day follow-up. Conclusion The use of a prophylactic IABP in high-risk patients before PCI could reduce the CRP level and reduce mortality during the early phase following PCI.
Morphology of Memory: Creating a Web-Based 3D Interactive Resource to Teach the Anatomy of the Human Hippocampus

The hippocampus is a critical region of the brain involved in memory and learning. It has been widely researched in animals and humans due to its role in consolidating new experiences into long-term declarative memories and its vulnerability in neurodegenerative diseases. The hippocampus is a complex, curved structure containing many interconnected regions that consist of distinct cell types. Despite the importance of understanding the normal state of hippocampal anatomy for studying its functions and the disease processes that affect it, didactic educational resources are severely limited. The literature on the hippocampus is expansive and detailed, but a communication gap exists between researchers presenting hippocampal data and those seeking to improve their understanding of this part of the brain. The hippocampus is typically viewed in a two-dimensional fashion; students and scientists have difficulty visualizing its three-dimensional anatomy and its structural relationships in space. To improve understanding of the hippocampus, an interactive, web-based educational resource was created containing a pre-rendered 3D animation and manipulatable 3D models of hippocampal regions. Segmentations of magnetic resonance imaging data were modified and sculpted to build idealized anatomical models suitable for teaching purposes. These models were animated in combination with illustrations and narration to introduce the viewer to the subject, and the completed animation was uploaded online and embedded into the interactive. A separate section of the interactive allows the user to rotate the models, hide and show different regions, and access additional explanatory text. The user interface and interactivity were coded to allow exploration of hippocampal regions and navigation between sections of the resource. The resources developed in this project provide a didactic and accessible visualization for graduate students, researchers, clinicians, and other individuals involved in neuroscience. The animation and interactive models allow users to reinforce their understanding of 3D hippocampal anatomy and connectivity. By improving visual understanding of the hippocampus, this project aims to advance the communication and scientific study of hippocampus-related topics, such as epilepsy and Alzheimer's disease.