Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Perceived Security Scale for Shared Automated Vehicles (PSSAV) and its Validation in Colombia and Germany

Perceived security is crucial for the widespread adoption of shared automated vehicles (SAVs) and shuttle buses. However, there is currently no validated instrument to measure perceived security in this context, and little research has been done to determine the factors that contribute to perceived security. We propose the Perceived Security Scale for Shared Automated Vehicles (PSSAV), a questionnaire that assesses various aspects of perceived security in SAVs. The scale was evaluated using an exploratory, data-driven approach in a pilot study with 60 German participants, and a main study with 114 German and 101 Colombian participants experiencing a positive or negative ride in an automated shuttle bus (between-subjects design) presented as videos in an online study. The results suggest that trust, privacy, and control are key factors that influence security in the context of SAVs. The PSSAV questionnaire is reliable and sensitive to manipulation, indicating its construct validity.
Long-term phenotypic and genomic instability of an industrial ethanol-producing and C5-utilizing Saccharomyces cerevisiae strain

The production of bio-based compounds can be impacted by stochastic cellular mechanisms acting at both the genetic and non-genetic levels, leading to instabilities in the performance of the producer organisms. Here, we investigated the long-term genomic and phenotypic stability of an ethanol-producing Saccharomyces cerevisiae strain engineered by chromosomal integration of transgenes allowing xylose and arabinose utilization. First, along a series of batch cultures in bioreactor to reach around 100 generations, we observed that, after a period of phenotypic stability, the strain exhibited instabilities in terms of xylose and arabinose consumption. Long-read sequencing did not reveal major genomic modifications at the population level that could explain such fluctuations. However, we isolated clones that have partly or fully lost the ability to use arabinose or xylose, due to copy number variations of the integrated transgenes arising most probably from homologous recombination (HR) events. Based on the cultivation of subpopulations sorted depending on their expression level of RAD52, a gene whose expression is known to be proportional to the HR rate, we did not detect differences in genomic and phenotypic stability in the subpopulations. Thus, this work reveals both phenotypic and genomic variations in an industrial yeast strain that could in the long-term lead to the loss of its production performance.
Impact and centrality of scientific disciplines

The Scimago Journal Rank (SJR) is a metric that captures the centrality of a journal across an all-discipline article network, while the impact factor (IF) is the average incoming citations of a journal. We analyzed SJRs and IFs of the journals belonging to the SJR first quartile from 2013 to 2020 in 7 disciplines: mathematics, biology, physics, medicine, social sciences, chemistry, and engineering. We show that biology is the most central discipline, followed by physics and chemistry. These three disciplines also present the highest IFs. Mathematics journals display a low IF (the second-lowest among disciplines), but possesses an intermediate centrality. While the average IF has increased over the last years, the SJR average has decreased. Gini coefficients show that SJR is a slightly more egalitarian metric than IF. We discuss some possible origins of these findings.
Biology and management of rodent communities in complex agroecosystems—rice terraces

The Ifugao people have been farming the upland rice terraces of Banaue for at least 2,000 years (Barton, 1955). Banaue, Ifugao Province, is situated in the Cordillera Mountain ranges of Central Luzon, 320 km north of Manila. Rice is grown by 90% of farmers in these terraced, upland, rainfed systems (Joshi et al., 2000). The Banaue rice terraces are characteristically situated on the middle and lower slopes of the rugged mountain landscape, with peaks of 1,100 m above sea level (asl) and valleys rapidly falling to 900 m asl (Joshi et al., 2004).
It’s all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera

Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature. In this work, we propose a model-adaptation approach to personalize the training of a CNNLSTM model and improve recognition accuracy while reducing data requirements. Our approach contributes to the field of dynamic hand gesture recognition while driving by providing a more efficient and accurate method that can be customized for individual users, ultimately enhancing the safety and convenience of in-vehicle interactions, as well as driver’s experience and system trust. We incorporate hardware enhancement using a time-of-flight camera and algorithmic enhancement through data augmentation, personalized adaptation, and incremental learning techniques. We evaluate the performance of our approach in terms of recognition accuracy, achieving up to 90%, and show the effectiveness of personalized adaptation and incremental learning for a user-centered design.
On Learning Ring-Sum-Expansions

The problem of learning ring-sum-expansions from examples is studied. Ring-sum-expansions (RSE) are representations of Boolean functions over the base $\{ \wedge , \oplus ,1 \}$, which reflect arithmetic operations in $GF(2)$. k-RSE is the class of ring-sum-expansions containing only monomials of length at most k. k-term is the class of ring-sum-expansions having at most k monomials. It is shown that k-RSE, $k \geq 1$, is learnable while k-term-RSE, $k \geq 2$, is not learnable if $RP \ne NP$. Without using a complexity-theoretical hypothesis, it is proven that k-RSE, $k \geq 1$, and k-term-RSE, $k \geq 2$ cannot be learned from positive (negative) examples alone. However, if the restriction that the hypothesis which is output by the learning algorithm is also a k-RSE is suspended, then k-RSE is learnable from positive (negative) examples only. Moreover, it is proved that 2-term is learnable by a conjunction of a 2-CNF and a 1-DNF. Finally the paper presents learning (on-line prediction) algorithms for k-.
Automatic Transferring between Ancient Chinese and Contemporary Chinese

During the long time of development, Chinese language has evolved a great deal. Native speakers now have difficulty in reading sentences written in ancient Chinese. In this paper, we propose an unsupervised algorithm that constructs sentence-aligned ancient-contemporary pairs out of the abundant passage-aligned corpus. With this method, we build a large parallel corpus. We propose to apply the sequence to sequence model to automatically transfer between ancient and contemporary Chinese sentences. Experiments show that both our alignment and transfer method can produce very good result except for some circumstances that even human translators can make mistakes without background knowledge.
Advanced sensor fusion technique for enhanced Intrusion Detection

The existing intrusion detection systems are of varied type and hence show distinct preferences in detecting certain types of attacks with improved accuracy, while performing moderately on the other types. With the advances in sensor fusion, it has become possible to obtain a more reliable and accurate decision for a wider class of attacks, by combining the decisions of multiple Intrusion detection systems. In this paper, an architecture using data-dependent decision fusion is proposed. The method gathers an in-depth understanding about the input traffic and also the behavior of the individual intrusion detection systems by means of a neural network supervised learner unit. This information is used to fine-tune the fusion unit, since the fusion depends on the input feature vector. For illustrative purposes three intrusion detection systems PHAD, ALAD, and Snort have been considered using the DARPA 1999 dataset in order to validate the proposed architecture. The overall performance of the proposed sensor fusion system shows considerable improvement in comparison to the performance of individual intrusion detection systems.
Hierarchical BOA on random decomposable problems

The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems scalably and reliably. This paper describes a class of random additively decomposable problems with and without interactions between the subproblems and tests hBOA on a large number of random instances of the proposed class of problems. The performance of hBOA is compared to that of the simple genetic algorithm with standard crossover and mutation operators, the univariate marginal distribution algorithm, and the hill climbing with bitflip mutation. The results confirm that hBOA achieves quadratic or subquadratic performance on the proposed class of random decomposable problems and that it significantly outperforms all other methods included in the comparison. The results also show that low-order polynomial scalability is retained even when only a small percentage of hardest problems are considered and that hBOA is a robust algorithm because its performance does not change much across the entire spectrum of random problem instances of the same structure. The proposed class of decomposable problems can be used to test other optimization algorithms that address nearly decomposable problems.
Modelling the longitudinal properties of financial ratios

Previous studies provide conflicting evidence on the time series properties of company financial ratios, claiming either that the components of ratios exhibit nonstationarity which is not eliminated by the ratio transformation, or that a unit root in the components may be rejected which implies strong persistence in their ratio. In this article, a generalized model is derived that incorporates stochastic and deterministic trends and allows also for restricted and unrestricted proportionate growth in the ratio numerator and denominator. When the individual firm series are included in a panel analysis for large N and small T, this study is unable to reject convincingly a joint hypothesis of nonstationarity. However, the ratio variables are shown to be cointegrated, which can lead to stationarity in the ratio itself. Furthermore, evidence of cotrending provides support for a parsimonious model, where the financial ratio varies lognormally around its expected value.
Optimisation of biogas production from a microbial perspective

Phil Hobbs, Institute for Grassland and Environmental Research, North Wyke, Okehampton, Devon. England. EX20 2SB Alastair Ward, Sreenivas Ravella, Institute for Grassland and Environmental Research, North Wyke, Okehampton, Devon. England. EX20 2SB Jon Williams Institute for Grassland and Environmental Research, North Wyke, Okehampton, Devon. England. EX20 2SB Andy Retter Institute for Grassland and Environmental Research, North Wyke, Okehampton, Devon. England. EX20 2SB Contact: Phil.hobbs@bbsrc.ac. uk
FAWS: FPGA Acceleration of Large-Scale Wave Simulations

Efficiently solving large-scale scientific problems described by partial differential equations (PDEs) is a critical task in high-performance computing. Application-specific hardware design is a well-known solution, but the wide range of kernels makes it infeasible to provision supercomputers with accelerators for all applications. This makes reconfigurable platforms a promising direction. In this work, we focus on wave simulations using discontinuous Galerkin solvers, as an important class of PDE applications. Existing work using FPGAs is limited to accelerating specific kernels or small problems that fit into FPGA BRAM. We present FAWS, a generic and configurable architecture for large-scale accelerated wave simulation problems running on FPGAs out of DRAM. FAWS exploits fine- and coarse-grain parallelism using a scalable array of application-specific processing cores, and incorporates novel dataflow optimizations, including prefetching, kernel fusion, and memory layout optimizations to minimize data transfers and maximize DRAM bandwidth utilization. We demonstrate FAWS on the simulation of elastic wave equations. Results show that a single FPGA core achieves 2.2x higher performance than 24 Xeon cores with 18.64x better energy efficiency, when given ~1.94x less peak DRAM bandwidth. Scaling to the same peak DRAM bandwidth, an FPGA is 4.27x and 1.96x faster than 24 CPU cores and an Nvidia P100 GPU, with 31.33x and 5.29x better efficiency, respectively.
An analytical 2DEG model considering the two lowest subbands

In this paper, a physics-based single-piece solution for the two-dimensional electron gas (2DEG) charge density (ns) for the generic metal-insulator-semiconductor highelectron-mobility transistor (MIS-HEMT) is derived. The ns model includes the two lowest subbands as well as the subthreshold region and varying temperatures. It is validated by comparison with the (exact) numerical ns solutions.
STATEMENT OF RESEARCH INTERESTS

Generic absoluteness. Paul Cohen’s work in the 1960s struck a blow against the idea of mathematics as absolute truth. Cohen showed that there is a natural mathematical statement, namely the Continuum Hypothesis, whose truth cannot be proved or disproved in ZFC. (Gödel sentences were discovered earlier but are arguably not natural mathematical statements.) Cohen’s method, called forcing, enlarges a given model of set theory by adjoining an ideal “generic” object, producing a new model that typically exhibits a different set theory. Applications of this method now constitute a major area of research. However, there are limits to what it can do. For example, forcing cannot change the truth of arithmetic statements simply because it cannot change the structure (N; 0, 1,+,×) of arithmetic. Accordingly, we say that arithmetic statements are “generically absolute. ” As we go up the complexity hierarchy, the nature of mathematical truth undergoes a transition from absolute (such as arithmetic) to relative (such as the Continuum Hypothesis.) For statements about real numbers, the question of generic absoluteness is quite subtle and is tied to the existence of tree representations for sets of real numbers. A tree representation for a set A of “logician’s reals” (integer sequences) is a continuous function assigning to every real x a tree Tx such that x ∈ A if and only if Tx has an infinite branch. The existence of infinite branches is absolute, and the classical absoluteness theorems of Mostowski and Shoenfield can be proved using the existence of tree representations for Σ1 and Σ 1 2 sets of reals respectively. For more complex statements about real numbers, the connection between generic absoluteness phenomena and tree representations persists for a while, but now to get a complete picture we need to go beyond ZFC and introduce large cardinals. This is because while the trees for Σ1 sets of reals are countable and the trees for Σ2 sets of reals have size א1, the trees for more complicated sets typically have very large cardinality. For example, Σ3 sets of reals are represented by trees of size equal to a measurable cardinal if one
Does nutrient enrichment contribute to coral bleaching? A Fijian case study

This study investigated the effects of nutrient enrichment and thermal stress on the cellular and photosynthetic response of a dominant, reef-building coral in Fiji. Porites cylindrica corals were collected from Votua, Fiji, placed in individual mesocosms, and enriched with one of three concentrations of labelled nitrate (NO3) for two weeks. Then, colonies were gradually heated above the identified bleaching threshold for Fijian corals for a final seven days. Stable isotope values confirmed that NO3 had a significant impact on symbiotic algal populations (Symbiodinium spp.) within P. cylindrica colonies, and demonstrated rapid uptake and cycling of nutrients between algae and coral host. Regression analysis showed a strong, positive relationship between algae populations and nitrate concentration exposure. While no visual bleaching events were observed in this experiment, there was a tendency for corals in highly enriched seawater to lose proportionately more symbiotic algae than control samples after exposure to heat stress, suggesting greater potential for bleaching events in nutrient-enriched waters.
Relationship of spectral data to grain yield variation

Two-band hand-held radiometer data from a winter wheat field, collected on 21 dates during the spring growing season, were correlated with within-field final grain yield. Significant linear relationships were found between various combinations of the red and photographic infrared radiance data collected and the grain yield. The spectral data explained about 64 percent of the within-field grain yield variation. This variation in grain yield could not be explained using meteorological data as these were similar for all areas of the wheat field. Most importantly, data collected early in the spring were highly correlated with grain yield, a five-week time window existed from stem elongation through anthesis in which the spectral data were most highly correlated with grain yield, and manifestations of wheat canopy water stress were readily apparent in the spectral data.
Empirical Analysis of Security Protocol in Mobile VoIP System

—With the rapid growing of Voice over Internet Protocol services, security issues of VoIP become more and more important. Also, popularization of smartphone, such as Android and iPhone, makes it necessary to provide security to mobile VoIP. However, because of the limitations in mobile environment, providing security could bring more overhead than in wired environment. Hence, it is necessary to develop proper security solution to mobile VoIP. In order to analyze whether standard security protocol is suitable to be used in mobile environment, in this paper, we set up a test-bed and implement security protocol using A sterisk as the server and Sipdroid as the client. The necessity of developing a proper mVoIP security protocol is also suggested.
Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets: The TETSC Method

Due to the explosive growth in the amount of text snippets over the past few years and their sparsity of text, organizations are unable to effectively and efficiently classify them, missing out on business opportunities. This paper presents TETSC: the Topically-Enriched Text Snippet Classification method. TETSC aims to solve the classification problem for text snippets in any domain. TETSC recognizes that there are different types of text snippets and, therefore, allows for stop word removal, named-entity recognition, and topical enrichment for the different types of text snippets. TETSC has been implemented in the production systems of a personal finance organization, which resulted in a classification error reduction of over 21%. Highlights: The authors create the TETSC method for classifying topically-enriched text snippets; the authors differentiate between different types of text snippets; the authors show a successful application of Named-Entity Recognition to text snippets; using multiple enrichment strategies appears to reduce effectivity.
Navigating Higher Education: Factors Contributing to Former Foster Care Youth’s Graduation from Post-Secondary Education

This study identifies factors contributing to the success of foster care alumni (FCA) who graduate from post-secondary education (PSE), despite institutions devoting little effort to making PSE accessible to FCA. Nine FCA PSE graduates were interviewed about their success. Responses were analyzed using an exploratory method to identify themes related to factors deemed critical to theirgraduation from PSE. Ferguson’s (2019) conceptual model of a Student Development Pathway was used to organize findings; success factors were analyzed accordingly to their role in the Preparation, Awareness, Engagement, Transition, and Persistence phases. Findings suggest the role of coaching and caring adults was most notable in the Preparation, Engagement, and Transitional phases to PSE. Determination factored predominantly during the Persistence phase, and Resistance was most associated with the Preparation and Persistence phases. Findings also indicated numerous untapped opportunities and initiatives that could make PSE more accessible and achievable for FCA.
Generalized Collective Algorithms for the Exascale Era

Exascale supercomputers have renewed the exigence of improving distributed communication, specifically MPI collectives. Previous works accelerated collectives for specific scenarios by changing the radix of the collective algorithms. However, these approaches fail to explore the interplay between modern hardware features, such as multi-port networks, and software features, such as message size. In this paper, we present a novel approach that uses system-agnostic, generalized (i.e., variableradix) algorithms to capture relevant features and provide broad speedups for upcoming exascale-class supercomputers.We identify hardware commonalities found on announced exascale systems and three omnipresent communication kernels (binomial tree, ring, and recursive doubling) that can be generalized to better leverage these features, creating 10 total implementations. For each kernel, we develop analytical models to intuit algorithm performance with varying radix values.Experiments on the world’s first exascale supercomputer (Frontier at ORNL) and a pre-exascale system (Polaris at ANL) show that our generalized algorithms outperform the baseline open-source and proprietary vendor MPI implementations by a significant margin, up to over 4.5x. We empirically determine optimal algorithms and parameter values, identifying where the analytical models are accurate and where hardware features directly determine performance. Most notably, we show how a single, system-agnostic implementation of a generalized algorithm can optimize for multiple hardware/software features across multiple systems.
Subprime Mortgage Pricing: The Impact of Race, Ethnicity, and Gender on the Cost of Borrowing

The subprime lending boom increased the ability of many Americans to get credit to purchase a house. Yet concerns persist that not all borrowers have been treated equally. Previous research suggests that subprime loans were particularly concentrated in neighborhoods with a high concentration of black and Hispanic residents (Mayer and Pence 2007). Some commentators have been concerned that minority borrowers were steered into subprime loans in some cases when they might have qualified for cheaper conforming loans or that minority borrowers were given subprime loans that had fees or rates that were too high. Previous research on housing markets suggests that such concerns might be warranted. Beginning in the early 1990s, data collected from lenders through the Home Mortgage Disclosure Act (HMDA) indicate that black or Hispanic applicants were more likely to be rejected for a mortgage relative to a white applicant, even when controlling for credit scores or other observable individual risk factors (Munnell and others 1996). Subsequent research showed that minority borrowers might also have been more likely to default on loans, but these findings were less clear in that they did not control for basic exante risk factors (Ladd 1998). Even controlling for the likelihood of default, Canner,