Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Endurable, Reliable and Secure Flash Memories-a Coding Theory Application

Storage systems are experiencing a historical paradigm shift from hard disk to nonvolatile memories due to its advantages such as higher density, smaller size and nonvolatility. On the other hand, Solid Storage Disk (SSD) also poses critical challenges to application and system designers. The first challenge is called endurance. Endurance means flash memory can only experience a limited number of program/erase cycles, and after that the cell quality degradation can no longer be accommodated by the memory system fault tolerance capacity. The second challenge is called reliability, which means flash cells are sensitive to various noise and disturbs, i.e., data may change unintentionally after experiencing noise/disturbs. The third challenge is called security, which means it is impossible or costly to delete files from flash memory securely without leaking information to possible eavesdroppers. In this dissertation, we first study noise modeling and capacity analysis for NAND flash memories (which is the most popular flash memory in market), which gains us some insight on how flash memories are working and their unique noise. Second, based on the characteristics of content-replication codewords in flash memories, we propose a joint decoder to enhance the flash memory reliability. Third, we explore data representation schemes in flash memories and optimal rewriting code constructions in order to solve the endurance problem. Fourth, in order to make our rewriting code more practical, we study noisy write-efficient memories and Write-Once Memory (WOM) codes against inter-cell interference in NAND memories. Finally, motivated by the secure deletion problem in flash memories, we study coding schemes to solve both the endurance and the security issues in flash memories. This work presents a series of information theory and coding theory research studies on the aforesaid three critical issues, and shows that how coding
“Physically‐Accurate Fur Reflectance:Modeling,Measurement and Rendering”の実装報告

毛の繊維構造は,キューティクル層とコルテックス層と メデュラ層の 3 層で構成されている. 毛を表現するモデル として Marschner モデル[2]がある. このモデルは人の髪の 毛を対象にしたもので,毛の繊維をキューティクル層とコ ルテックス層のみで表現しており,メデュラ層を無視して いる. しかしながら,動物の毛ではメデュラ層が大きく, 毛の構造の大部分を占めているため動物の毛を表現する際 に無視することができない. Yan らが提案したダブルシリンダモデル[1]では,動物の 毛の構造をキューティクル層,コルテックス層,メデュラ 層の 3 層で表現している(図 1). ダブルシリンダモデルにお いて,各層は以下の性質を考慮している.  キューティクル層は,傾きがあり,表面に粗さと層の 厚さがある.  コルテックス層は,色の波長ごとに光を吸光する性質 を持つ.  メデュラ層は,入射した光が散乱する性質を持つ.  キューティクル層とメデュラ層を 2 つのシリンダで 表現し,シリンダに衝突した光は反射,及び屈折する. ダブルシリンダモデルでは,毛の表面の光の分布を散乱 分布関数で表す. 散乱分布関数は,シリンダでの反射と透 過の組み合わせによって経路を分割し,全ての経路の分布 の和で表現する. ダブルシリンダモデルでは,キューティ クル層での反射を R,透過を T,メデュラ層での反射を r,透過 を t としてR,TT,TRT,TrT,TttT,TtrtT,TrRrT,TttRttTの 8 経 路を考慮する. また,ダブルシリンダモデルでは,メデュ ラ層を透過する経路において,散乱成分を考慮する. この 散乱成分は,前計算されたテーブルを参照して計算する. この前計算テーブルはこの論文の著者の Web ページにて 公開されている.
The psychosocial management of rights restitution: Tracing technologies for reparation in post-conflict Colombia

Psychosocial assistance is a crucial aspect of recent state reparation and human rights restitution policies in post-conflict Colombia. Drawing on the methodological tools offered by Science and Technology Studies (STS), we follow the trajectories of a psychosocial protocol for emotional recovery as a technology of reparation deployed in rural communities between 2013 and 2017. We ethnographically describe how psychological and administrative projects are merged in practice and come to shape practices and emotional self-valuations. Building on Serres’ concept of betrayal, we reflect on the potential contours of quantifications embedded in psychosocial assistance as opportunities for different forms of reparation to emerge. These forms of reparation coexist in intertwined epistemic practices of psychosocial assistance. We claim that a potentially alternative form of reparation arises despite the predominance of an administrative design mainly concerned with quantification and efficient policy management.
Differential Contributions of Empathy to Math Achievement in Women and Men

Mathematics forms a foundation for the science, technology, engineering and mathematics (STEM) fields. While considerable work has identified the individual cognitive and external systemic factors that influence math achievement, less is known about personality-like traits that might contribute to success in mathematics, especially among women. This study examines two such traits: systemizing – the tendency to analyze systems and extract underlying rules that govern their behavior – and empathizing – the ability to identify with another’s emotions and respond appropriately. Recently Escovar et al. (2016) found that empathizing was a negative predictor of math skills in children, especially among girls, suggesting that women with higher empathy might be particularly disposed to lower math performance. In the first study, 142 participants (71 female) completed two standardized measures of math achievement and questionnaires to gauge the tendency to empathize and systemize. Surprisingly, higher empathy was associated with better math performance in women, while men displayed the expected pattern of lower empathy being related to higher math scores. In a second study, we extend this finding in women (n = 121) to show that individuals who report higher mathematics achievement in university level course work also have higher empathy scores. Further, while positive attitudes toward mathematics tended to decline from elementary school to college, women whose attitudes increased had higher empathy scores than those who declined. Together, these results suggest that while the tendency to empathize is associated with worse math performance in childhood, it may become a protective factor as women progress through their mathematics education.
Dilemmas of an Aging Society: Family and State Responsibilities for Intergenerational Care in Taiwan

In an aging society with a declining birthrate, there are more and more elderly to care for and fewer adult children to provide them care; these adult children, and the state, are forced to weigh the costs of eldercare against the cost of child care. In Taiwan, these dilemmas may be particularly acute, given the persistence of Confucian norms of filial piety and the extended family structures. In this study, we examine the attitudes of Taiwanese people toward the relative responsibilities of both adult children and the welfare state for eldercare and child care. Data were taken from the Taiwan Social Change Survey in 2011. Using latent class analysis to develop a typology of attitudes toward intergenerational care responsibilities, we found four types: (a) Family cares for elders and children, (b) family cares mainly for children, (c) cooperation between family and government, (d) government cares for the elderly. Findings show that an individual’s attitudes toward welfare state policies are significantly related to both self-interest and sociocultural norms as well as intergenerational family interactions. In Taiwan, filial norms and the quality of family interaction significantly influence attitudes toward the division of intergenerational care responsibilities.
Pet ownership and homelessness: a scoping review

ABSTRACT Between 5 and 25% of people experiencing homelessness have pets. Pet ownership can have a range of impacts in the lives of people experiencing homelessness, which may mitigate or further complicate the many adversities they face. However, there is a need to better understand the benefits and challenges associated with pet ownership to determine how this group can be best supported. Accordingly, a scoping review was conducted using PubMed, PsycINFO, and Google Scholar to address the question: What are the effects of pet ownership on people experiencing homelessness? All of the 18 reviewed studies used either qualitative or cross-sectional research designs. Three domains have been principally examined in relation to pet ownership and homelessness: (1) psychological health and purpose; (2) social support and connection; and (3) access to housing, employment, and service use. Physical health, violence, and crime were less frequently studied. Although the findings offer further support that there are both benefits and liabilities to pet ownership for people experiencing homelessness, there is a critical need for more rigorous research, including longitudinal and intervention studies. Recommendations for developing more pet-friendly services and using a strengths-based approach that considers animal companionship when working with people experiencing housing instability are also discussed.
A Differentially Private Decision Forest

With the ubiquity of data collection in today’s society, protecting each individual’s privacy is a growing concern. Differential Privacy provides an enforceable definition of privacy that allows data owners to promise each individual that their presence in the dataset will be almost undetectable. Data Mining techniques are often used to discover knowledge in data, however these techniques are not differentially privacy by default. In this paper, we propose a differentially private decision forest algorithm that takes advantage of a novel theorem for the local sensitivity of the Gini Index. The Gini Index plays an important role in building a decision forest, and the sensitivity of it’s equation dictates how much noise needs to be added to make the forest be differentially private. We prove that the Gini Index can have a substantially lower sensitivity than that used in previous work, leading to superior empirical results. We compare the prediction accuracy of our decision forest to not only previous work, but also to the popular Random Forest algorithm to demonstrate how close our differentially private algorithm can come to a completely non-private forest.
Implementation of bi-directional blue-fi gateway in IoT environment

In the world of possibilities, IoT is playing a crucial role in development and automation of things which is making life easier, comfortable and most importantly reliable. This paper implements a new IoT Gateway, BLUE-FI designed to allow interconnection between Bluetooth and Wi-Fi protocols. This gateway gives us significant advantages. It enables us to transfer data between Bluetooth and Wi-Fi devices by renovating the protocols making it more simple and reliable, which obtains information from various sensors and convert them into a uniform format. With the huge possibilities in IoT, this paper implements an application of Blue-Fi gateway through Smart health monitoring system (SHMS). Here in SHMS we use different components to help us determine the health of a patient which is updated real time. This information, i.e., Bio-metric data is transferred to concerned Doctor and is also saved in cloud for future references in diagnosis. Our proof of notion demonstrates the performance and ability of the Bi-Directional Blue-Fi through smart health monitoring system (SHMS).
Multi Objective Particle Swarm Optimization

several optimization techniques are proposed in artificial intelligence. This paper we contrast performance of Swarm Intelligence based PSO search strategy to optimize the multiple objective functions. Experimental analysis also demonstrated the effect of the inertia weight for multiple objective functions in the algorithm. And optimize time for all particles are detected and calculated by Particle Swarm Optimization. Keywords—PSO,Inertia Weight,Multi Object PSO(MOPSO)
Location Aware Social Networks User Profiling Using Big Data Analytics

Social Network Analytics (SNA), on the other hand, provides insights of many perspectives of the society through the sample users participated in social network. The exponential growth of the social network and correspondingly its data leads to the demand for big data computational environments. One such popular and useful big data in SNA is GIS (Geographical Information System) data that provides geographical location data of the record generated in social networks. In order to profile a user in social networks according to GIS data, the existing methodologies uses the centroid measures such as mean, median of the GIS data available for the user. These methods failed to mention and solve the serious issues such as cold user as well does not able to consider the weightage of the data item in analytics. The proposed method in this paper focuses to define the weightage of a data item in the available GIS data according to the application and also proposed a method to identify and solve cold user problem. The experiments carried on cutting edge technologies of the big data analytics shown significance of the proposed method in profiling user w.r. t. GIS data. The results of the proposed user profiling is measured with Within Sum of Squares (WSS) measure and compared over exiting profiling methodologies shown consistent improvement for any number of clusters formed over benchmark datasets.
THE PHONOGRAPHIC NETWORK OF LANGUAGE: USING NETWORK SCIENCE TO INVESTIGATE THE PHONOLOGICAL AND ORTHOGRAPHIC SIMILARITY STRUCTURE OF LANGUAGE

Orthographic effects in spoken word recognition and phonological effects in visual word recognition have been observed in a variety of behavioral experimental paradigms, strongly suggesting that a close interrelationship exists between phonology and orthography. However, the metrics used to investigate these effects, such as consistency and neighborhood size, fail to generalize to words of various lengths or syllable structures, and do not take into account the more global similarity structure that exists between phonological and orthographic representations in the language. To address these limitations, the tools of Network Science were used to simultaneously characterize the phonological as well as orthographic similarity structure of words in English within a phonographic multiplex. In this paper, I analyze a section of the phonographic multiplex known as the phonographic network of language, where links are placed between words that are both phonologically and orthographically similar to each other, i.e., a link would be placed between words such as ‘pant’ (/p@nt/) and ‘punt’ (/p^nt/). Conventional psycholinguistic experiments (auditory naming and auditory lexical decision) and an archival analysis of the English Lexicon Project (visual naming and visual lexical decision) were conducted to investigate the influence of two network science metrics derived from the phonographic network—phonographic degree and phonographic clustering coefficient—on spoken and visual word recognition. Results indicated a facilitatory effect of phonographic degree on visual word recognition, and a facilitatory effect of phonographic clustering coefficient on spoken word recognition. The present findings have implications for theoretical models of spoken and visual word recognition, and for increasing our understanding of language learning and language disorders.
ADDICTA: THE TURKISH JOURNAL ON ADDICTIONS

To cite this article: Çebi, A. , Reisoğlu, İ. , & Bahçekapılı, T. (2019). The relationships among academic procrastination, self-control, and problematic mobile use: Considering the differences over personalities. Addicta: The Turkish Journal on Addictions, 6, 449–470. http://dx.doi. org/10.15805/addicta.2019. 6.3. 0082 1 Department of Computer Education and Instructional Technology, Trabzon University, Trabzon Turkey. Email: aycacebi@trabzon.edu. tr 2 Correspondence to: İlknur Reisoğlu, Department of Computer Education and Instructional Technology, Recep Tayyip Erdoğan University, Rize Turkey. Email: ilknur.reisoglu@erdogan.edu. tr 3 Department of Computer Education and Instructional Technology, Trabzon University, Trabzon Turkey. Email: tugbabahcekapili@trabzon.edu. tr Abstract Only a few studies in the literature have dwelt on the relationships among problematic mobile phone use, academic procrastination, and self-control based on personality traits and how these three affect one another. In this sense, the purpose of this study is to reveal how the relationships among the variables of problematic mobile use, self-control, and academic procrastination change based on the Big Five personality traits. Data have been collected from 571 university students. The Academic Procrastination Scale, Problematic Mobile Phone Use Scale, Self-Control Scale, and Adjective-Based Personality Test have been used as data collection tools. Path analysis is used for analyzing the data. Firstly, descriptive statistics for the mean, standard deviation, skewness, and kurtosis values have been calculated for each variable in the model. Secondly, the data set of the study is examined in terms of multivariate normality. Thirdly, the path model is estimated using the IBM Statistical Packages for the Analysis of Moment Structures. As a result of the data analysis, the negative effect of experiential self-control on problematic mobile use has been seen to be significantly higher in the more neurotic. Also, the negative effect of experiential self-control on academic procrastination has been found to be significantly higher in the less extraverted and less agreeable individuals. The negative effect of reformative self-control on academic procrastination has been determined to be statistically higher for those with lower conscientiousness and openness. The significant positive effect of redressive self-control on academic procrastination has been identified to be higher in the less neurotic and the less conscientious individuals. This study is believed to contribute to the field as it offers measures on taking self-control into account in order to prevent problematic mobile phone use and academic procrastination in people with various personality traits.
Evaluative Observation in a Concealed Information Test

The Concealed Information Test (CIT) is a valid method to detect hidden knowledge by means of psychophysiological measures. Concealing information is always a social behavior; yet, the role of social aspects has barely been investigated in recent CIT research favoring standardized, computer-based experiments. Evaluative observation is known to influence social behavior as well as physiological measures; examining the impact of evaluative observation on physiological responding in a CIT is the aim of this study. Sixty-three students completed a mock-crime and then underwent a CIT. In a between-subjects manipulation, half of the participants were observed through a camera and were faced with the real-time video of the experimenter watching them while completing the CIT. The other half completed the CIT without observation and video. Electrodermal activity, respiration line length, phasic heart rate, and finger pulse waveform length were registered. A specific questionnaire captured the individual fear of negative evaluation. Typical differential CIT responses occurred in both groups and with each measure. Contrary to expectations, differential CIT responses did not differ between groups. No modulatory influence of the fear questionnaire score on physiological responding was found. A ceiling effect, involving high attention and high motivation to avoid detection as well as high arousal in both groups due to the CIT procedure per se is discussed as explanation for these results, while the independence of the orienting reflex of social and motivational influence appears less likely in the light of previous literature.
The improved Apriori algorithm based on matrix pruning and weight analysis

This paper uses the matrix compression algorithm and weight analysis algorithm for reference and proposes an improved matrix pruning and weight analysis Apriori algorithm. After the transactional database is scanned for only once, the algorithm will construct the boolean transaction matrix. Through the calculation of one figure in the rows and columns of the matrix, the infrequent item set is pruned, and a new candidate item set is formed. Then, the item’s weight and the transaction’s weight as well as the weight support for items are calculated, thus the frequent item sets are gained. The experimental result shows that the improved Apriori algorithm not only reduces the number of repeated scans of the database, but also improves the efficiency of data correlation mining.This paper uses the matrix compression algorithm and weight analysis algorithm for reference and proposes an improved matrix pruning and weight analysis Apriori algorithm. After the transactional database is scanned for only once, the algorithm will construct the boolean transaction matrix. Through the calculation of one figure in the rows and columns of the matrix, the infrequent item set is pruned, and a new candidate item set is formed. Then, the item’s weight and the transaction’s weight as well as the weight support for items are calculated, thus the frequent item sets are gained. The experimental result shows that the improved Apriori algorithm not only reduces the number of repeated scans of the database, but also improves the efficiency of data correlation mining.
Mining association rule based on the diseases population for recommendation of medicine need

Selection of medicines that is inappropriate will lead to an empty result at medicines, this has an impact on medical services and economic value in hospital. The importance of an appropriate medicine selection process requires an automated way to select need based on the development of the patient's illness. In this study, we analyzed patient prescriptions to identify the relationship between the disease and the medicine used by the physician in treating the patient's illness. The analytical framework includes: (1) patient prescription data collection, (2) applying k-means clustering to classify the top 10 diseases, (3) applying Apriori algorithm to find association rules based on support, confidence and lift value. The results of the tests of patient prescription datasets in 2015-2016, the application of the k-means algorithm for the clustering of 10 dominant diseases significantly affects the value of trust and support of all association rules on the Apriori algorithm making it more consistent with finding association rules of disease and related medicine. The value of support, confidence and the lift value of disease and related medicine can be used as recommendations for appropriate medicine selection. Based on the conditions of disease progressions of the hospital, there is so more optimal medicine procurement.
Teaching Along the Way: an Ethnographic Study of Faculty Growth and Sensemaking on the Camino De Santiago

xvi CHAPTER 1: TAKING THE FIRST STEPS . 2 My Camino . 3 Background . 5 The Camino de Santiago . 6 The Camino as classroom. 9 The Study . 10 Internationalization of U.S. Higher Education . 11 Study Abroad in American Higher Education . 14 Student learning outcomes in short-term study abroad. 14 Student development and short-term study abroad. 15 Academics and short-term study abroad. 16 Faculty Work in U.S. Higher Education . 17 Internationalization of Faculty Work . 18
Advice on Exploratory Factor Analysis

Exploratory Factor Analysis (EFA) is a process which can be carried out in SPSS to validate scales of items in a questionnaire. The purpose of an EFA is to describe a multidimensional data set using fewer variables. Once a questionnaire has been validated, another process called Confirmatory Factor Analysis can be used. This is supported by AMOS, a ‘sister’ package to SPSS.