Miracle-dz/UniFAR-FTUs huggingface dataset:
Debiased Offline Evaluation of Active Learning in Recommender Systems

Active Learning (AL) when applied to Recommender Systems (RSs) aims at proactively acquiring additional ratings data from the RS users in order to improve subsequent recommendation quality. AL strategies are typically evaluated ofﬂine ﬁrst, but the classic AL ofﬂine evaluation methodology does not take into account the bias problem in RS ofﬂine evaluation. This problem affects the evaluation of an RS, as brought to light by recent literature. But, we argue, it also affects the evaluation of AL strategies as well. For this reason, in paper, we propose a new AL ofﬂine evaluation methodology for RSs which mitigates the bias and thus facilitates a truer picture of the performances of the AL strategies under evaluation. We illustrate our proposed methodology on two datasets and with three simple and well-known AL strategies from the literature. Our experimental results differ from those reported previously in the literature, which shows the importance of our approach to AL evaluation.
Targeting SIRT1 to improve metabolism: all you need is NAD+?

SIRT1 is an evolutionary conserved NAD+-dependent deacetylase that is at the pinnacle of metabolic control, all the way from yeast to humans. SIRT1 senses changes in intracellular NAD+ levels, which reflect energy level, and uses this information to adapt the cellular energy output, such that the it matches cellular energy requirements. Generally, but not exclusively, the changes induced by SIRT1 activation are transcriptional in nature and are related to an increase in mitochondrial metabolism and antioxidant protection. These attractive features have validated SIRT1 as a therapeutic target in the management of metabolic disease and prompted an intensive search to identify pharmacological SIRT1 activators. In this review we will first give an overview of the SIRT1 biology with a particular focus on its role in metabolic control. We will then analyze the pros and cons of the current strategies used to activate SIRT1 and explore the emerging evidence indicating that modulation of NAD+ levels could provide an effective way to achieve such goals.
Properties of Sorbitol Dehydrogenase in Strawberry Fruit and Enhancement of the Activity by Fructose and Auxin

Strawberry, belonging to the Rosaceae family, translocates sucrose as the main carbohydrate to fruit, although many Rosaceae fruit trees use sorbitol as translocated sugar; however, we have found that strawberry has genes encoding sorbitol-metabolizing enzymes. In this study, the property of NAD+-dependent sorbitol dehydrogenase (NAD-SDH) and the effect of sugars and phytohormones on NAD-SDH activity and its mRNA accumulation were investigated to elucidate sorbitol metabolism and its regulation. The Km values of NAD-SDH for substrates of fructose and sorbitol were 78.7 and 7.3 mM, respectively, similar to those of maize, which synthesizes sorbitol by the reduction of fructose by NAD-SDH. This result suggested that NAD-SDH in strawberry fruit can catalyze the reduction of fructose, but the activity is not enough to accumulate sorbitol in fruit. Therefore, we investigated the effect of sugars and phytohormones on NAD-SDH activity and the transcript level by adding various sugars and phytohormones to sliced fruit discs. Tissues incubated in 100 mM fructose or sorbitol stimulated NAD-SDH activity by about 2.5-fold compared with the control. Sucrose also stimulated NAD-SDH activity, but the increase was not as high as sorbitol or fructose. Of the phytohormones treated, 100 μM indole-3-acetic acid (IAA) had a marked stimulatory effect on NAD-SDH activity, but the other phytohormones (abscisic acid, gibberellic acid and 6-benzyladenine) had no stimulative effect. The mRNA transcript level in all sugar and phytohormone treatments showed no marked increase compared with the control. Thus, the kinetics and induction profiles by phytohormones of NAD-SDH showed that sorbitol metabolism in strawberry fruit was different from that in apple and Japanese pear fruits.
Through the Lens of Sequence Submodularity

Several real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them.
An S-system Parameter Sensitivity Analysis of Biohydrogen Production by the Microalga Chlamydomonas reinhardtii

Producing biohydrogen on a commercial scale will likely require the genetic re-engineering of natural hydrogen-producing organisms. Kinetic modeling of hydrogen-producing metabolic pathways can costeffectively help to characterize systemic (e.g., mass/energy/charge conservation) constraints in these organisms. In vitro kinetic studies suggest that the activity of the hydrogenases in several photolytic biohydrogen producers (PBPs) could be increased to as much as four times their nominal in vivo rate. It is much less clear, however, whether the in vitro activity maximum could be realized in vivo. Here I use an Ssystem photosynthesis-based PBP (PS-PBP) simulator to survey the sensitivity of C. reinhardtii to variation in system parameters. The analysis strongly suggests that the H2 production efficiency of the alga cannot be increased by more than a factor of two through single-enzyme genetic modifications.
Scenario-based Evolvability Analysis of Service-oriented Systems: A Lightweight and Tool-supported Method

: Scenario-based analysis is a comprehensive technique to evaluate software quality and can provide more de-tailed insights than e.g. maintainability metrics. Since such methods typically require signiﬁcant manual effort, we designed a lightweight scenario-based evolvability evaluation method. To increase efﬁciency and to limit assumptions, the method exclusively targets service- and microservice-based systems. Additionally, we implemented web-based tool support for each step. Method and tool were also evaluated with a survey (N=40) that focused on change effort estimation techniques and hands-on interviews (N=7) that focused on usability. Based on the evaluation results, we improved method and tool support further. To increase reuse and transparency, the web-based application as well as all survey and interview artifacts are publicly available on GitHub. In its current state, the tool-supported method is ready for ﬁrst industry case studies.
Optimizing Discount & Reputation Trade-Offs in E-Commerce Systems: Characterization and Online Learning

Feedback-based reputation systems are widely deployed in E-commerce systems. Evidences showed that earning a rep-utable label (for sellers of such systems) may take a substantial amount of time and this implies a reduction of proﬁt. We propose to enhance sellers’ reputation via price discounts. However, the challenges are: (1) The demands from buyers depend on both the discount and reputation; (2) The demands are unknown to the seller. To address these challenges, we ﬁrst formulate a proﬁt maximization problem via a semi-Markov decision process (SMDP) to explore the optimal trade-offs in selecting price discounts. We prove the monotonicity of the optimal proﬁt and optimal discount. Based on the monotonicity, we design a QLFP (Q-learning with forward projection) algorithm, which infers the optimal discount from historical transaction data. We conduct experiments on a dataset from to show that our QLFP algorithm improves the proﬁt by as high as 50% over both the classical Q-learning and speedy Q-learning algorithm. Our QLFP algorithm also improves the proﬁt by as high as four times over the case of not providing any price discount.
Older Adults Testing Assistive Technologies in Living Labs: Guidelines

: Assistive technologies are a promising option for enabling older adults to live independently at home and therefore to meet the challenges of demographic change. An advantageous way to ensure acceptance and successful use by end users is a participatory involvement and testing in real-life environment. This position paper argues for the specific approach of Living Lab testings when testing assistive technologies with older adults at home. The procedure described is intended as a guideline to adequately consider the specifics of the test subjects, the older adults, and the special test setting in the real-life environment. Therefore, the guideline contributes to the quality of future testings.
Explaining the Existential: Functional Roles of Scientific and Religious Explanation

Questions about the origins of life and the universe seem to call out for explanation, with science and religion offering candidate answers. These answers clearly differ in content, but do they also differ in psychological function? In Study 1 (N=501) participants on Amazon Mechanical Turk rated scientific and religious answers to existential questions on dimensions related to epistemic functions (e.g., “This explanation is based on evidence”) as well as moral/social/emotional functions (e.g., “If everyone believed this, the world would be a more moral place”; “This explanation is comforting”). For non-religious participants, only scientific explanations were assigned high values along epistemic dimensions; For religious participants, only religious explanations were assigned high values along non-epistemic dimensions. In Study 2 (N=130), priming a non-epistemic need boosted religious participants’ evaluation of the quality of religious (vs. scientific) explanations. These findings shed light on the functions of scientific and religious cognition and raise new questions about explanatory co-existence and the origins of religious belief.
Clothing Colour & Pattern Recognition for Visually Defective Persons using Grey Level Discrete Wavelet Transform Technique

collecting materials with different colours, patterns are complicated process for visually defective persons. Systematic materials pattern identification is also a denounce investigation highest different pattern deviations. In the study of living human-computer-interaction (HCI) implementation and apply of methods digitalized systems mainly focusing on the particular persons. This system mainly introduces the automatic study on “Blind & visually defective persons HCI and access to GUI produce a recent scenario imminent solution for visually defective persons and deliver together a new investigation. This Proposed system developed camera-captured on the real cloth identification garments patterns into under 4 diperception (plaid, stripped, pattern less, and 11 garments colours. The camera, a computer, a microphone, and of garments colours. Surrounded upon a pair of images. The garments colours are defined to authorizations. Present, managed by input speech m-phone. To recognize garments patterns, novel RSD and a wavelet sub bands to capture main features of garments patterns. To calculated effectiveness proposed accuracy, for apply various Garments Pattern dataset. Our approaches have 95-98% recognition accuracy which gathered output performs.
The Effects of Open Self-Explanation Prompting During Source Code Comprehension

This paper reports the ﬁndings of an empirical study on the effects and nature of self explanation during source code comprehension learning activities in the context of learning computer programming language Java. Our study shows that self explanation helps learning and there is a strong positive correlation between the volume of self-explanation students produce and how much they learn. Furthermore, self-explanations as an instructional strategy has no discrepancy based on student’s prior knowledge. We found that participants explain target code examples using a combination of language, code references, and mathematical expressions. This is not surprising given the nature of the target item, a computer program, but this indicates that automatically evaluating such self-explanations may require novel techniques compared to self-explanations of narrative or scientiﬁc texts.
Not All Samples are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification

An interesting machine learning problem is to learn predictive models that can automatically assign diagnoses or diagnostic categories to patient cases. However, we often do not have enough positive samples for many of diagnoses either due to their rare nature or a limited size of available datasets. This motivates the use of multi-task learning methods that tend to improve model performance by imposing model similarities between related tasks. In this work, we tackle this important problem by exploring the beneﬁts of existing expert-deﬁned diagnostic hierarchies. We argue that related tasks (models) organized in expert-deﬁned hierarchies do not have the same level of similarity for different classes of samples. We discuss how task similarities will be different for positive and negative samples and between parent and child diagnoses. We propose a new asymmetric version of Hierarchical Adaptive Multi-task Learning (HA-MTL) method that allows models to learn separate relatedness coefﬁcient for tasks in the hierarchy based on their class values. Finally, we show that our model outperforms individually trained SVM models and symmetric HA-MTL results.
Extractive Text Summarization for Sports Articles using Statistical Method

The past decade has endorsed a great rise in Artificial Intelligence. Text summarization which comes under AI has been an important research area that identifies the relevant sentences from a piece of text. By Text Summarization, we can get short and precise information by preserving the contents of the text. This paper presents an approach for generating a short and precise extractive summary for the given document of text. A statistical method for extractive text summarization of sports articles using extraction of various features is discussed in this paper. The features taken are TFISF, Sentence Length, Sentence Position, Sentence to Sentence cohesion, Proper noun, Pronoun. Each sentence is given a score known as the predictive score is calculated and the summary for the given document of text is given based on the predictive score or also known as the rank of the sentence. The accuracy is checked using the BBC Sports Article dataset and sports articles of various newspapers like the New York Times, CNN. The precision of 73% is acquired when compared with System Generated Summary (SGS) and manual summary, on an average.
Variance Linear Discriminant Analysis for IRIS Biometrics

Dichotomy transformation in biometric authentication problem creates a two class (“within” or “between”) classiﬁcation problem in multivariate distance space. Linear discriminant analysis, which is a linear classiﬁer, results in good performance in IRIS biometric authentication problem. However, it assumes that the distributions of two classes are normal, whereas they are closely related to the log-normal distributions. Here a modiﬁed variance linear discriminant analysis algorithm is proposed and its superior experimental results on the IRIS biometric database are reported.
Genetic diversity and differentiation in remnant populations of bupleurum latissimum nakai, an endangered endemic plant species to ulleung island, Korea

Bupleurum latissimum is a narrowly endemic and endangered plant, restricted to only two small populations on steep cliffs of a small island, Ulleung Island, inKorea. The genetic diversity and population differentiation in the two remnant populations of the species were investigated using RAPD (random amplified polymorphic DNA) analysis. The Nei's gene diversities were 0.146 in the smaller population of 45 individuals, and 0.151 in the larger population of 61 individuals. The geneticvariation was not significantly different between these two populations. Genetic diversity within populations was not low considering the very small size of populations. Analysis of molecular variance (AMOVA) revealed higher variation with in populations (65.9%) than genetic differentiation between them (34.1%). B. latissimum revealed higher population differentiation than other outbreeding species. The differentiation of the populations corresponded to low gene flow (Nem = 0.482). The cluster and principal coordination analyses provide strong support for high population differentiation, showing that all individuals of the two populations have built up population‐specific clusters. Although gene flow between the two populations ofß. latissimum was limited, they have preserved relatively high levels of genetic variation.
Molecular Characterization of a 313-kb Genomic Region Containing the Self-incompatibility Locus of Ipomoea trifida, a Diploid Relative of Sweet Potato

Diploid Ipomoea trifida is an ancestral wild species of the cultivated hexaploid sweet potato, and displays a sporophytic self-incompatibility (SI) that is controlled by a single multiallelic S-locus. To characterize the genomic region of the S-locus using a map-based cloning method, a BAC library consisting of approximately 40,000 clones was constructed from genomic DNA of S1-homozygote, and screened using S-linked DNA markers which were mapped in our previous study. We constructed a contig covering the S-locus region with additional screening of fosmid and λ phase libraries. RFLP analysis of recombinant plants using terminal end sequences of the BAC clones as probes indicated that the S-locus region was delimited within a map distance of 0.57 cM, spanning approximately 300 kb in physical distance. Remarkable suppression of genetic recombination was detected in the S-locus region. From sequence analysis of the 313-kp region, 43 ORFs, many repetitive sequences and 5 transposable elements were predicted. None of the ORFs, however, showed a high homology with the SI genes reported to date at the S-locus of other plant families, suggesting that a unique molecular mechanism is involved in the SI system of the Convolvulaceae family.
The traffic of proteins between nucleolar organizer regions and prenucleolar bodies governs the assembly of the nucleolus at exit of mitosis

The building of nuclear bodies after mitosis is a coordinated event crucial for nuclear organization and function. The nucleolus is assembled during early G1 phase. Here, two periods (early G1a and early G1b) have been defined. During these periods, the nucleolar compartments (DFC, GC) corresponding to different steps of ribosome biogenesis are progressively assembled. In telophase, rDNA transcription is first activated and PNBs (reservoirs of nucleolar processing proteins) are formed. The traffic of the processing proteins between incipient nucleoli and PNBs was analyzed using photoactivation. We demonstrate that the DFC protein fibrillarin passes from one incipient nucleolus to other nucleoli but not to PNBs, and that the GC proteins, B23/NPM and Nop52, shuttle between PNBs and incipient nucleoli. This difference in traffic suggests a way of regulating assembly first of DFC and then of GC. The time of residency of GC proteins is high in incipient nucleoli compared to interphase nuclei, it decreases in LMB-treated early G1a cells impairing the assembly of GC. Because the assembly of the nucleolus and that of the Cajal body at the exit from mitosis are both sensitive to CRM1 activity, we discuss the fact that assembly of GC and/or its interaction with DFC in early G1a depends on shuttling between PNBs and NORs in a manner dependent on Cajal body assembly.
Dynamic Control System Design for Autonomous Car

The autonomous vehicle requires higher standards of safety to maneuver in a complex environment. We focus on control of the self-driving vehicle that includes the longitudinal and lateral dynamics of the vehicle. In this work, we have developed a customized controller for our KIA Soul self-driving car. The customized controller implements the PID control for throttle, brake, and steering so that the vehicle follows the desired velocity profile, which enables a comfortable and safe ride. Besides, we have also catered the lateral dynamic model with two approaches: pure pursuit and model predictive control. An extensive analysis is performed between pure pursuit and its adversary model predictive control for the efficacy of the lateral model.
MICROZOOPLANKTON GRAZING IN NORTHERN SAN FRANCISCO BAY MEASURED BY THE DILUTION METHOD

Mlcrozooplankton (defined hereln as phagotrophs <200 pm) grazing was studied in northern San Francisco Bay, California, USA, from September 1993 to July 1994 using the dilution method The focus of the study was the estuarine tui bldity maxilnum zone of northern San Francisco Bay (Suisun Bay), though some experments were also perfoimed In southern San Francisco Bay and Tolnales Bay Three microzooplankton prey Items were e x a m ~ n e d phytoplankton (as chlorophyll a ) , cyanobactena and bactenoplankton (l e non-chlorophyll containing bactena) Stat~stically s ~ g n i f ~ c a n t grazing rates were observed in 5 of 31 northern San Francisco Bay expenments over 7 dates spanning 10 mo Average grazing rates on phytoplankton, cyanobacteria and bactenoplankton were 0 06, 0 00, and 0 22 d l respectively Grazlng rates were statistically significant In 5 of 7 southern San Franc~sco Bay expenments on 3 dates Average grazing lates on phytoplankton and cyanobactena were 0 41 and 1 84 d I respectively Grazing iates were statistically significant In 4 of 4 Tomales Bay experiments performed on 1 date, averaging 0 69 and 0 75 d. ' on phytoplankton and bacterioplankton respectively The low grazlng ~ a t e s in northern San Francisco Bay suggest that microzooplankton may not play a pivotal role in controlling pley biomass although this interpretation is coinplicated by the possibility that one or more 01 the dilutlon method assumptions may not hold ~n this environment We hypothesize that the introduced Asian clam Potamocolbula amurens~s may be controlling the blomass of microzooplankton, their prey or both A review of the literature Indicated that the ddution method regularly yields statistically non-significant grazing rates, suggesting that our results are not unique
User-adaptive Eyelid Aperture Estimation for Blink Detection in Driver Monitoring Systems

: This paper presents a new method for eyelid aperture estimation, suitable to be used in Driver Monitoring Systems (DMS) to measure blink patterns such as microsleeps and any other metric that assess the fatigue level of the driver. The method has been designed to work real-time and in continuous operation, by intro-ducing a novel online Exponential Weighted Moving Average (EWMA)-based Bayesian estimation process, which ensures dynamic adaptability to drivers with different physiognomy features, and also to changes due to physiological states (e.g. drowsiness). Our method has been implemented in the framework of a DMS, to take advantage of existing facial landmark detection and tracking mechanisms, and to provide real-time functionality for driving platforms (such as the NVIDIA Drive PX 2). The method is evaluated against a large labelled dataset, and compared to baseline and previous existing methods, showing an excellent balance between adaptability, performance, and robustness.