Miracle-dz/UniFAR-FTUs huggingface dataset:
Unlock with Your Heart : Heartbeat-based Authentication on Commercial Mobile Phones

LEI WANG, State Key Laboratory for Novel Software Technology, Nanjing University, China KANG HUANG, State Key Laboratory for Novel Software Technology, Nanjing University, China KE SUN, State Key Laboratory for Novel Software Technology, Nanjing University, China WEI WANG, State Key Laboratory for Novel Software Technology, Nanjing University, China CHEN TIAN, State Key Laboratory for Novel Software Technology, Nanjing University, China LEI XIE, State Key Laboratory for Novel Software Technology, Nanjing University, China QING GU, State Key Laboratory for Novel Software Technology, Nanjing University, China
Oğuzhan Urhan Extended Constraint Mask Based One-Bit Transform for Low-Complexity Fast Motion Estimation

Abstract—In this paper, an improved motion estimation (ME) approach based on weighted constrained one-bit transform is proposed for block-based ME employed in video encoders. Binary ME approaches utilize low bit-depth representation of the original image frames with a Boolean exclusive-OR based hardware efficient matching criterion to decrease computational burden of the ME stage. Weighted constrained one-bit transform (WC-1BT) based approach improves the performance of conventional C-1BT based ME employing 2-bit depth constraint mask instead of a 1-bit depth mask. In this work, the range of constraint mask is further extended to increase ME performance of WC-1BT approach. Experiments reveal that the proposed method provides better ME accuracy compared existing similar ME methods in the literature.
Learning Mean Field Games: A Survey

Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases. Introduced by Lasry and Lions (2007) and Huang et al. (2006), Mean Field Games (MFGs) rely on a mean-ﬁeld approximation to allow the number of players to grow to inﬁnity. Traditional methods for solving these games generally rely on solving partial or stochastic differential equations with a full knowledge of the model. Recently, Reinforcement Learning (RL) has appeared promising to solve complex problems. By combining MFGs and RL, we hope to solve games at a very large scale both in terms of population size and environment complexity. In this survey, we review the quickly growing recent literature on RL methods to learn Nash equilibria in MFGs. We ﬁrst identify the most common settings (static, stationary, and evo-lutive). We then present a general framework for classical iterative methods (based on best-response computation or policy evaluation) to solve MFGs in an exact way. Building on these algorithms and the connection with Markov Decision Processes, we explain how RL can be used to learn MFG solutions in a model-free way. Last, we present numerical illustrations on a benchmark problem, and conclude with some perspectives.
IT Employees' Organizational Identification: Examining its Antecedents and Impact on Turnover

Non-IT organizations increasingly rely on IT to achieve both operational excellence and strategic competitiveness. IT employees play pivotal roles in helping non-IT organizations realize the potential of IT. Yet, the turnover culture within the IT profession has posed challenges to organizations. How to effectively manage and retain IT human capital has become a critical issue. The current study examines turnover issue among IT employees in non-IT organizations from an organizational identification perspective. We propose that IT employees’ organizational identification could weaken their turnover behavior. Further and more importantly, the study explores organization-, job-, and relation-related antecedents to IT employees’ organizational identification. A survey with 126 IT employees provides significant empirical support. IT employees’ organizational identification has a significant negative effect on turnover intention. Business-IT alignment, boundary spanning activities, and the closeness of the relationships with non-IT employees can significantly improve IT employees’ organizational identification.
A Graphical Characterization of Structurally Controllable Linear Systems With Dependent Parameters

One version of the concept of structural controllability defined for single-input systems by Lin and subsequently generalized to multi-input systems by others, states that a parameterized matrix pair <inline-formula><tex-math notation="LaTeX">$(A, B)$</tex-math></inline-formula> whose nonzero entries are distinct parameters, is structurally controllable if values can be assigned to the parameters that cause the resulting matrix pair to be controllable. In this paper, the concept of structural controllability is broadened to allow for the possibility that a parameter may appear in more than one location in the pair <inline-formula><tex-math notation="LaTeX">$(A, B)$</tex-math></inline-formula>. Subject to a certain condition on the parameterization called the “binary assumption,” an explicit graph-theoretic characterization of such matrix pairs is derived.
Statistical Analysis of Unlabeled Point Sets: Comparing Molecules in Chemoinformatics

Summary We consider Bayesian methodology for comparing two or more unlabeled point sets. Application of the technique to a set of steroid molecules illustrates its potential utility involving the comparison of molecules in chemoinformatics and bioinformatics. We initially match a pair of molecules, where one molecule is regarded as random and the other fixed. A type of mixture model is proposed for the point set coordinates, and the parameters of the distribution are a labeling matrix (indicating which pairs of points match) and a concentration parameter. An important property of the likelihood is that it is invariant under rotations and translations of the data. Bayesian inference for the parameters is carried out using Markov chain Monte Carlo simulation, and it is demonstrated that the procedure works well on the steroid data. The posterior distribution is difficult to simulate from, due to multiple local modes, and we also use additional data (partial charges on atoms) to help with this task. An approximation is considered for speeding up the simulation algorithm, and the approximating fast algorithm leads to essentially identical inference to that under the exact method for our data. Extensions to multiple molecule alignment are also introduced, and an algorithm is described which also works well on the steroid data set. After all the steroid molecules have been matched, exploratory data analysis is carried out to examine which molecules are similar. Also, further Bayesian inference for the multiple alignment problem is considered.
Mapping tree canopy cover in the semi-arid Sahel using satellite remote sensing and Google Earth imagery

Tree vegetation is an essential element in the daily life of the people in the Sahel region of Africa. It is also considered as a robust indicator of the Sahel ecosystem status and health. In this thesis, a method to estimate tree vegetation was developed and used in mapping the tree canopy cover in semi-arid Sahel. The developed method utilized Normalized Difference Vegetation Index (NDVI) as the predictor variable coupled with estimations of tree canopy cover from Google Earth imagery as the response variable. The developed estimation regression was applied in the Sahel of Africa for the dry season (November to May). The results showed a strong correlation between NDVI derived from Landsat 8 imagery and Tree Canopy Cover (TCC) estimations from Google Earth imagery. The developed method errors were evaluated using two different validation approaches. Moreover, two estimation regressions were developed using NDVI products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modeling and Mapping Studies (GIMMS). The correlation between MODIS and GIMMS NDVI was weak which could be due to the coarse spatial resolution of these NDVI products. Mapping tree cover in the Sahel using Landsat 8 derived NDVI require high computational power and large storage capacity. Therefore, Landsat 8 NDVI based estimation regression was applied to MODIS and GIMMS NDVI products to the map tree canopy cover for the entire Sahel. All the datasets used in this study is available for public use, and therefore this method is applicable for more development and improvements. (Less)
Experiments

Experiments permit rigorous testing of auction theory. In single-unit private value auctions the Revenue Equivalence Theorem fails, but the comparative static predictions of Nash bidding theory hold, indicating that bidders are responsive to the primary economic forces at work in the theory. In single-unit common value auctions inexperienced bidders invariably suffer from a “winner’s curse,” and the comparative static predictions of the Nash bidding model fail. More experienced bidders do substantially better. Recent research dealing with Internet auctions, mixed private and common value auctions, and multi-unit demand auctions are surveyed as well. Auctions: Experiments: Experimental work in auctions interacts with theory, providing a basis for testing and modifying theoretical developments. It has advantages and disadvantages relative to empirical work with field data, so that we view the two as complimentary. Experimental work is used increasingly as a test-bed for new auction formats such as the Federal Communication Commission’s (FCC) sale of spectrum (airwave) rights. Until recently most of theoretical and experimental work was devoted to singleunit demand auctions. With the success of the FCC’s spectrum auctions much of the interest has shifted to auctions in which individual bidders demand multiple units. Experimental work in this area is still in its infancy. In keeping with the historical development of the field we first report on single-unit demand auctions and then move to multi-unit demand auctions and Internet auctions. Single-unit, private-value auctions: Initial experimental research on auctions focused on the independent private values (IPV) model investigating the Revenue Equivalence Theorem. In the IPV model each bidder knows his valuation of the item with certainty, bidders' valuations are drawn independently from each other, and bidders know the distribution from which their rivals' values are drawn (but not their values) and the number of bidders. Under the revenue equivalence theorem the four main auction formats – firstand second-price sealed-bid auctions, English and Dutch auctions – yield the same average revenue for risk neutral bidders. Further, the first-price sealed-bid and Dutch auctions are theoretically isomorphic – they yield the same revenue for each auction trial regardless of risk preferences – with the second-price sealed-bid and English clock auction isomorphic to 1 The Dutch auction starts with a high price which is lowered until a bidder accepts at that price. In English auctions price starts low and increases until only one bidder is left standing and pays the price where the next to last bidder dropped out. In a first(second-) price sealed-bid auction the high bidder wins the item and pays the highest (second-highest) bid.
Enhancing nursing students’ reflection through Padlet: an action research

Background Reflective practice is encouraged amongst healthcare students, including nursing students. However, students do not have a ‘safe space’ to practice reflection before being assessed. Padlet is an interactive platform that can potentially facilitate students’ reflection via its features that enables anonymous participation, asynchronous participation and collaborative learning environment. This study aims to explore the influence of current reflection teaching method on students’ reflective practice and how Padlet can facilitate students’ reflective practice. Methods An action research was undertaken with 22 first year nursing students from Feb to May 2023. Participants answered questions anonymously pre-class and post-class in two Padlet boards. The researcher gave constructive feedback and signposted good examples of reflection to participants thus enabling ‘champion’ students to emerge during the process. Anonymous texts from two Padlet boards were analysed using reflexive thematic analysis technique. Results Three themes were generated: i) Unpacking variation in students baseline understanding of reflection; ii) Co-constructed understanding of reflection and iii) Prompting reflective practice through tools and triggers. Students joined the study with different presumptions and understanding of reflection, ranging from descriptive understanding of the concept, a total misunderstanding of the concept and in-depth understanding of reflection. They all indicated a changed understanding of reflection post-class and emphasized the benefits of a socially constructed learning process. Participants suggested the use of reflective tools (via reflective models) and triggers (via probing questions and feedback) as useful to facilitate their reflection. Conclusions This study indicates that the current teaching materials enable students to enhance their understanding of reflection. Nevertheless, students could potentially benefit from tools and triggers that will initiate and support their reflection. To that end, Padlet proves a promising tool to enhance students’ reflection via its function to enable anonymity, asynchronous participation and socially constructed learning environment.
BADENIAN (LANGHIAN - EARLY SERRAVALLIAN) PALYNOFLORA FROM THE CARPATHIAN FOREDEEP AND VIENNA BASIN (CZECH AND SLOVAK REPUBLICS)

The Badenian (Langhian - Early Serravallian) marine sediments from the adjacent areas within the Central Paratethys and NE part of the Vienna Basin, were studied from a palynological perspective. The pollen data document a subtropical climate during the Mio- cene Climatic Optimum with dominant representation of zonal vegetation being evergreen broadleaved forests. Higher differentiation of oak type pollen, increasing number of Platanus pollen and different types of herb were observed. Some thermophilous elements (especially Sapotaceae, Palmae, Mastixiaceae and Lygodiaceae) decreased and there was an increase of the warm to cold temperate zone taxa which were first registered during the Late Badenian. These findings together with a higher pro- portion of extrazonal vegetation (Tsuga, Picea and Abies) in the Late Badenian pollen spectra document changes due to the uplift of the Carpathian Mountain chain.
Harvesting Image Databases from the Web

The objective of this work is to automatically generate a large number of images for a specified object class. A multimodal approach employing both text, metadata, and visual features is used to gather many high-quality images from the Web. Candidate images are obtained by a text-based Web search querying on the object identifier (e.g., the word penguin). The Webpages and the images they contain are downloaded. The task is then to remove irrelevant images and rerank the remainder. First, the images are reranked based on the text surrounding the image and metadata features. A number of methods are compared for this reranking. Second, the top-ranked images are used as (noisy) training data and an SVM visual classifier is learned to improve the ranking further. We investigate the sensitivity of the cross-validation procedure to this noisy training data. The principal novelty of the overall method is in combining text/metadata and visual features in order to achieve a completely automatic ranking of the images. Examples are given for a selection of animals, vehicles, and other classes, totaling 18 classes. The results are assessed by precision/recall curves on ground-truth annotated data and by comparison to previous approaches, including those of Berg and Forsyth [5] and Fergus et al. [12].
UvA-DARE (Digital Academic Repository) Foundations of structural causal models with cycles and latent variables

Structural causal models (SCMs), also known as (nonparametric) structural equation models (SEMs), are widely used for causal modeling purposes. In particular, acyclic SCMs, also known as recursive SEMs, form a well-studied subclass of SCMs that generalize causal Bayesian networks to allow for latent confounders. In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles. We show that in the presence of cycles, many of the convenient properties of acyclic SCMs do not hold in general: they do not always have a solution; they do not always induce unique observational, interventional and counterfactual distributions; a marginalization does not always exist, and if it exists the marginal model does not always respect the latent projection; they do not always satisfy a Markov property; and their graphs are not always consistent with their causal semantics. We prove that for SCMs in general each of these properties does hold under certain solvability conditions. Our work generalizes results for SCMs with cycles that were only known for certain special cases so far. We introduce the class of simple SCMs that extends the class of acyclic SCMs to the cyclic setting, while preserving many of the convenient properties of acyclic SCMs. With this paper, we aim to provide the foundations for a general theory of statistical causal modeling with SCMs.
A Delphi study on collaborative learning in distance education

Collaborative Learning (CL) is increasingly being used in Distance Education (DE), as it has been identified as an effective solution to known weaknesses such as high average rates of dropout and low quality of learning attainment. Information Technology is a core component of this type of learning as it not only provides the means to collaborate over distance but also has the potential to enable higher learning outcomes. There are a rapidly growing number of technologies in use today and the importance of these to collaborative learning initiatives, and the role they play, is an area of active research in the Information Systems (IS) community. IS educators and practitioners face an increasing challenge therefore to successfully implement CL in DE, precipitated not only from technical advances but also from wider social and organisational concerns. Using a Delphi study, this research is the first to investigate the factors that influence collaborative learning in distance education by surveying the opinions of an expert panel in this area. The aim was to produce an integrated list of the most important implementation factors and to investigate the role technology is perceived to contribute. The findings identified seventeen of the most important factors. These factors cover a range of themes including course rationale and design, instructor characteristics, training, group dynamics, the development of a learning community and technology. The potential of technology however does not seem to be fully realised and newer technologies such as multi-user environments would seem to be of limited use in practice, according to the expert panel.
CASCADED FILTERING FOR BIOMETRIC IDENTIFICATION USING RANDOM PROJECTION Thesis submitted in partial fulfillment of the requirements for the degree of MS by Research in Computer

Biometrics has a long history, and is possibly as old as the human race itself. It is often used as an advanced security measure to safeguard important artifacts, buildings , information, etc. Biometrics is increasingly being used for secure authentication of individuals and is ma king its presence felt in our lives. With the fast increase in computational power, such systems can now be deployed on a very large scale. However, efficiency in large scale biometric matching is still a con cern as the problem of deduplication (removal of duplicates) of a biometric database with N entries isO(N2), which can be extremely challenging for large databases. To make this problem tractable, ma ny indexing methods have been proposed that would speed up the comparison process. However , th xpectation of accuracy in such systems combined with the nature of biometric data makes the problem a very challenging one. Biometric identification often involves explicit comparison of a probe template aga inst each template stored in a database. An effective approach to speed up the process is that of filtering, where a lightweight comparison is used to reduce the database to smaller set of candidate s for explicit comparison. However, most existing filtering schemes use specific features that are ha nd-cr fted for the biometric trait at each stage of the filtering. In this work, we show that a cascade of simple linear projections on random lines can achieve significant levels of filtering. Each stage of filter ing consists of projecting the probe onto a specific line and removal of database samples outside a window a round the probe. The approach provides a way of automatic generation of filters and avoids the n eed of developing specific features for different biometric traits. The method also provides us with a va riety of parameters such as the projection lines, the number and order of projections, and the window siz es to customize the filtering process to a specific application. The experiments are performed on the fin gerpri ts, palmprints and iris. For both iris and palmprint datasets, the representation that we use (befor e projection) is the popularly used thresholded filter response from pre-defined regions of the image . Exp rimental results show that using an ensemble of projections reduce the search space by 60% without increasing the false negative identification rate in palmprint. However for stronger biometrics such as iris, th e approach does not yield similar results. We further explore this problem to find a solution, specific ally for the case of fingerprints. The fundamental approach here is to explore the effectiveness of wea k features in a cascade for filtering fingerprint databases. We start with a set of potential indexing fea tures computed from minutiae triplets and minutiae quadruplets. We show that by using a set of random lines a nd the proposed fitness function, one can achieve better results that optimized projection methods suc h a PCA or LDA. Ex-
Hierarchical multi-label prediction of gene function

MOTIVATION Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies, such as the Gene Ontology (GO). Over a hierarchy of functional classes, a group of independent classifiers where each one predicts gene membership to a particular class can produce a hierarchically inconsistent set of predictions, where for a given gene a specific class may be predicted positive while its inclusive parent class is predicted negative. Taking the hierarchical structure into account resolves such inconsistencies and provides an opportunity for leveraging all classifiers in the hierarchy to achieve higher specificity of predictions. RESULTS We developed a Bayesian framework for combining multiple classifiers based on the functional taxonomy constraints. Using a hierarchy of support vector machine (SVM) classifiers trained on multiple data types, we combined predictions in our Bayesian framework to obtain the most probable consistent set of predictions. Experiments show that over a 105-node subhierarchy of the GO, our Bayesian framework improves predictions for 93 nodes. As an additional benefit, our method also provides implicit calibration of SVM margin outputs to probabilities. Using this method, we make function predictions for multiple proteins, and experimentally confirm predictions for proteins involved in mitosis. SUPPLEMENTARY INFORMATION Results for the 105 selected GO classes and predictions for 1059 unknown genes are available at: http://function.princeton. edu/genesite/ CONTACT ogt@cs.princeton. edu.