Miracle-dz/UniFAR-FTUs huggingface dataset:
A Confidence-Based Approach for Balancing Fairness and Accuracy

We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain the high accuracy of these learning algorithms while reducing the degree to which they discriminate against individuals because of their membership in a protected group. Our first contribution is a method for achieving fairness by shifting the decision boundary for the protected group. The method is based on the theory of margins for boosting. Our method performs comparably to or outperforms previous algorithms in the fairness literature in terms of accuracy and low discrimination, while simultaneously allowing for a fast and transparent quantification of the trade-off between bias and error. Our second contribution addresses the shortcomings of the bias-error trade-off studied in most of the algorithmic fairness literature. We demonstrate that even hopelessly naive modifications of a biased algorithm, which cannot be reasonably said to be fair, can still achieve low bias and high accuracy. To help to distinguish between these naive algorithms and more sensible algorithms we propose a new measure of fairness, called resilience to random bias (RRB). We demonstrate that RRB distinguishes well between our naive and sensible fairness algorithms. RRB together with bias and accuracy provides a more complete picture of the fairness of an algorithm.
Gender and Conceptual Change 1

Three hundred and forty one children (Mage = 9,0 years) engaged in a series of science tasks in collaborative, same-sex pairs or did not interact. All children who collaborated on the science tasks advanced in basic level understanding of the relevant task (motion down an incline). However, only boys advanced in their conceptual understanding at a three week post-test. Discussion of concepts and procedural aspects of the task led to conceptual development for boys but not girls. Gender differences in behavioral style did not influence learning. Results are discussed in terms of the links between gender and engagement in conversations, and how gender differences in collaboration may relate to differences in participation in science. Gender and conceptual change 2 Peer collaboration facilitates learning in science (Phelps & Damon, 1989). However, relatively little research has explored whether this learning is influenced by gender. The lack of research into gender and collaborative learning is surprising because gender has a profound influence on children's interactions and conversation dynamics (Leaper & Smith, 2004) and peer collaboration is frequently used in classroom teaching. The present study examines whether gender influences learning of concepts in a collaborative science task. Questions about when and how children grasp scientific concepts have been a focus of research for developmental psychologists for many years (e.g., Piaget, 1967). By examining collaborative learning we can better understand how conceptual and social factors connect with developmental change. A central objective of the present study was to establish whether any gender differences in the style and content of peer conversations are associated with gender differences in learning, or with different routes to learning. Gendered communication dynamics in children's interactions reinforce and sustain gender differences and may have longer term consequences for social relationships (Di Donato, Martin, & England, 2014). However, by middle childhood children can anticipate and " work around " the influence of gender on interactions. For instance, a 7-year-old girl can anticipate that a boy will seek to dominate in discussion and as a consequence use a more circumspect approach to persuade him of the merits of her position (Leman, Ahmed, & Ozarow, 2005). By the end of middle childhood, children are quite adept social actors who interact in gendered ways and can respond to a partner's gender. Children at this age can also use peer conversation to make decisions together and to focus on appropriately using varied forms of information in …
Association Study of Candidate Gene Polymorphisms with Amnestic Mild Cognitive Impairment in a Chinese Population

To investigate the relationship between amnestic mild cognitive impairment (aMCI) and candidate gene polymorphisms in a Chinese population, 116 aMCI patients and 93 normal controls were recruited. Multi-dimensional neuropsychological tests were used to extensively assess the cognitive functions of the subjects. MassARRAY and iPLEX systems were used to measure candidate single nucleotide polymorohisms (SNPs) and analyse allelic, genotypic or haplotypic distributions. The scores of the neuropsychological tests were significantly lower for the aMCI patients than for the normal controls. The distributions of SNPs relating to the amyloid cascade hypothesis (TOMM40 rs157581 G and TOMM40 rs2075650 G), to the cholesterol metabolism hypothesis (ApoE rs429358 C, LDLR rs11668477 G and CH25H rs7091822 T and PLAU rs2227564 CT) and to the tau hypothesis (MAPT/STH rs242562 GG) in aMCI were significantly different than those in normal controls. Interactions were also found in aMCI amongst SNPs in LDLR rs11668477, PLAU rs2227564, and TOMM40 rs157581, between SNPs in TOMM40 rs157580 and BACE2 rs9975138. The study suggests that aMCI is characterised by memory impairment and associated with SNPs in three systems relating to the pathogenesis of AD-those of the amyloid cascade, tau and cholesterol metabolism pathways. Interactions were also observed between genes in the amyloid pathway and between the amyloid and cholesterol pathways.
ARNTL (BMAL1) and NPAS2 Gene Variants Contribute to Fertility and Seasonality

Background Circadian clocks guide the metabolic, cell-division, sleep-wake, circadian and seasonal cycles. Abnormalities in these clocks may be a health hazard. Circadian clock gene polymorphisms have been linked to sleep, mood and metabolic disorders. Our study aimed to examine polymorphisms in four key circadian clock genes in relation to seasonal variation, reproduction and well-being in a sample that was representative of the general population, aged 30 and over, living in Finland. Methodology/Principal Findings Single-nucleotide polymorphisms in the ARNTL, ARNTL2, CLOCK and NPAS2 genes were genotyped in 511 individuals. 19 variants were analyzed in relation to 31 phenotypes that were assessed in a health interview and examination study. With respect to reproduction, women with ARNTL rs2278749 TT genotype had more miscarriages and pregnancies, while NPAS2 rs11673746 T carriers had fewer miscarriages. NPAS2 rs2305160 A allele carriers had lower Global Seasonality Scores, a sum score of six items i.e. seasonal variation of sleep length, social activity, mood, weight, appetite and energy level. Furthermore, carriers of A allele at NPAS2 rs6725296 had greater loadings on the metabolic factor (weight and appetite) of the global seasonality score, whereas individuals with ARNTL rs6290035 TT genotype experienced less seasonal variation of energy level. Conclusions/Significance ARNTL and NPAS2 gene variants were associated with reproduction and with seasonal variation. Earlier findings have linked ARNTL to infertility in mice, but this is the first time when any polymorphism of these genes is linked to fertility in humans.
Cross-Validation of Representational Structures Using the Attribute-Challenge Technique and the Test of Context Independence: The Social Representation of Health

In connection with the structural approach of social representations, Lo Monaco, Lheureux, and Halimi-Falkowicz (2008) showed that the Test of Context Independence (TCI) allows to find the same central core components as those retrieved with the Attribute-Challenge Technique (ACT, Moliner, 1989, 2002). As this study was carried out on two ‘unavoidable’ objects of study of the structural approach (higher education and the ideal group), we aimed to carry out a cross-validation of the representational structure of an object of representation that had never been studied from a structural perspective (i.e., health). An initial study (N = 223) was conducted in order to compare structural diagnoses highlighted by the ACT (N = 105) and the TCI (N = 118). Contrary to Lo Monaco et al. (2008), no element emerged as central using the ACT while the TCI uncovered five central elements (i.e., healthy lifestyle, food, essentiality, prevention, and equilibrium). To ensure that results weren’t due to a methodological artefact (misunderstanding of items and phrasing effect), a second study was conducted (N = 123). Fifty-two participants completed the ACT and 71 participants to the TCI. In spite of the methodological changes, as in Study 1, no central elements were highlighted by the ACT, whereas three elements (i.e., healthy lifestyle, food, and equilibrium) were found to form the central core by using the TCI. These results were discussed by focusing on the link between the characteristics of the methods used to study the social representations and the characteristics of the objects studied.
Boundary conditions for the 2D linearized PEs of the ocean in the absence of viscosity

The linearized Primitive Equations with vanishing viscosity are considered. Some new boundary conditions (of transparent type) are introduced in the context of a modal expansion of the solution which consist of an infinite sequence of integral equations. Applying the linear semi-group theory, existence and uniqueness of solutions is established. The case with nonhomogeneous boundary values, encountered in numerical simulations in limited domains, is also discussed.
Generating particular classes of connected graphs

We present an algorithm to generate all connected graphs in a recursive and efficient manner. This algorithm is restricted subsequently to 1-particle irreducible graphs and to connected graphs without selfloops. The recursions proceed by loop order and vertex number. The main result of [1] is a recursion formula to generate all tree graphs. The underlying structure is a Hopf algebraic representation of graphs defined in the context of quantum field theory. This recursion formula is generalized to all connected graphs in [2]. In both cases, graphs are generated together with weight factors, which correspond to the inverses of their symmetry factors as considered in [3, 4]. In this paper, we reformulate that recursion formula to generate all connected graphs, in terms of abstract graphs. In addition, we restrict this result successively to 1PI graphs and to connected graphs without self-loops. Crucially, as in [1, 2], the correct weights of graphs are obtained. The (double) recursions proceed by loop order and vertex number. Our method is based on three basic operations on graphs, which are suitable to produce graphs with, say, e internal edges from a graph with e−1 internal edges. Namely, attaching a self-loop to a vertex; connecting a pair of adjacent vertices with an edge; and splitting a vertex in two, distributing ∗email: amestre@teor.fis. uc.pt
Temporospatial identification of language-related cortical function by a combination of transcranial magnetic stimulation and magnetoencephalography

Identification of language‐related cortical functions can be carried out noninvasively by transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG), which allow for lesion‐based interrogation and global temporospatial investigation of cortices, respectively. Combining these two modalities can improve the accuracy of the identification, but the relationships between them remain unclear. We compared TMS and MEG responses during the same language task to elucidate their temporospatial relationships and used the results to develop a novel method to identify language‐related cortical functions.
Characteristics of Eye-Position Gain Field Populations Determine Geometry of Visual Space

We have previously demonstrated differences in eye-position spatial maps for anterior inferotemporal cortex (AIT) in the ventral stream and lateral intraparietal cortex (LIP) in the dorsal stream, based on population decoding of gaze angle modulations of neural visual responses (i.e., eye-position gain fields). Here we explore the basis of such spatial encoding differences through modeling of gain field characteristics. We created a population of model neurons, each having a different eye-position gain field. This population was used to reconstruct eye-position visual space using multidimensional scaling. As gain field shapes have never been well-established experimentally, we examined different functions, including planar, sigmoidal, elliptical, hyperbolic, and mixtures of those functions. All functions successfully recovered positions, indicating weak constraints on allowable gain field shapes. We then used a genetic algorithm to modify the characteristics of model gain field populations until the recovered spatial maps closely matched those derived from monkey neurophysiological data in AIT and LIP. The primary differences found between model AIT and LIP gain fields were that AIT gain fields were more foveally dominated. That is, gain fields in AIT operated on smaller spatial scales and smaller dispersions than in LIP. Thus, we show that the geometry of eye-position visual space depends on the population characteristics of gain fields, and that differences in gain field characteristics for different cortical areas may underlie differences in the representation of space.
Approximation by Superpositions of a Sigmoidal Function

We generalize a result of Gao and Xu [4] concerning the approximation of functions of bounded variation by linear combinations of a fixed sigmoidal function to the class of functions of bounded φ-variation (Theorem 2.7). Also, in the case of one variable, [1: Proposition 1] is improved. Our proofs are similar to that of [4].
Riesz exponential families on homogeneous cones

In this paper, we introduce, for a multiplier $\chi$, a notion of generalized power function $x\mapsto \Delta_{\chi}(x),$ defined on the homogeneous cone ${\mathcal{P}}$ of a Vinberg algebra ${\mathcal{A}}$. We then extend to ${\mathcal{A}}$ the famous Gindikin result, that is we determine the set of multipliers $\chi$ such that the map $\theta \mapsto \Delta_{\chi}(\theta ^{-1})$, defined on ${\mathcal{P}}^{\ast}$, is the Laplace transform of a positive measure $R_{\chi}$. We also determine the set of $\chi $ such that $R_{\chi}$ generates an exponential family, and we calculate the variance function of this family
Automatic Isolation of Cause-Effect Chains with Machine Learning

Accurate bug localization is important for automated debugging. One attractive approach is to apply statistical-based techniques on large number of evaluation profiles of program predicates to locate bug causes. Previous research has proposed a number of specialized techniques to isolate certain predicates as bug predictors. However, these techniques still require a programmer to manually examine significant fractions of code to locate bugs, and no existing statistical-based technique can discover cause-effect chains. In this paper, we adapt advanced machine learning techniques (such as support vector machines and random forests) and propose an effective algorithm for both isolating bug-related predicates and discovering cause-effect chains. Our technique combines feature selection (to select bug-related predicates) and clustering (to group similar predicates) in a novel way to precisely pinpoint bug causes. We have implemented our technique and validated it on various instrumented code including the Siemens test suite and rhythmbox (a music management application for GNOME). Compared to previous techniques, our algorithm not only discovers more bugs but also requires significantly less manual code inspection. For example, on the Siemens test suite, it discovers 38 bugs when programmers are expected to examine no more than 1% of the code, significantly better than 11, the best previously reported number (see Table 1, Section 5). Our algorithm also discovers previously unknown errors in ryhthmbox that were missed by previous techniques.
A meta-analysis of the effectiveness of ALEKS on learning

Abstract Assessment and Learning in Knowledge Spaces (ALEKS) is one of the widely used online intelligent tutoring systems (ITS) in the USA, but it has rarely been included in meta-analyses of ITS efficacy to help students learn. We conducted a meta-analysis to assess the effectiveness of ALEKS on learning. A total of 15 empirical studies were conducted between 2005 and 2015, containing 24 independent samples, which met the inclusion criteria. Our results revealed that ALEKS was as good, but not better than, traditional classroom teaching. Moderator analyses revealed the effect sizes were not significantly different, when ALEKS was used by students at different schooling levels (i.e. secondary or postsecondary), when it was implemented differently (i.e. used as principal or supportive instruction), or when the learning outcome was measured differently (i.e. measured with standardized tests or instructor-designed tests). Surprisingly, effect sizes were greater, when ALEKS was used for shorter periods of time rather than longer periods. Our paper ends with a discussion on the implications of these findings, and how these results can better inform educators using the ALEKS learning system to better promote student learning.
HA-VMSI: A Lightweight Virtual Machine Isolation Approach with Commodity Hardware for ARM

Once compromising the hypervisor, remote or local adversaries can easily access other customers' sensitive data in the memory and context of guest virtual machines (VMs). VM isolation is an efficient mechanism for protecting the memory of guest VMs from unauthorized access. However, previous VM isolation systems either modify hardware architecture or introduce a software module without being protected, and most of them focus on the x86 architecture. This paper proposes HA-VMSI, a lightweight hardware-assisted VM isolation approach for ARM, to provide runtime protection of guest VMs, even with a compromised hypervisor. In the ARM TrustZone secure world, a thin security monitor is introduced as HA-VMSI's entire TCB. Hence, the security monitor is much less vulnerable and safe from attacks that can compromise the hypervisor. The key of HA-VMSI is decoupling the functions of memory isolation among VMs from the hypervisor into the security monitor. As a result, the hypervisor can only update the Stage-2 page tables of VMs via the security monitor, which inspects and approves each new mapping. It is worth noting that HA-VMSI is more secure and effective than current software approaches, and more flexible and compatible than hardware approaches. We have implemented a prototype for KVM hypervisor with multiple Linux as guest OSes on Juno board. The security assessment and performance evaluation show that HA-VMSI is effective, efficient and practical.
Default priors for density estimation with mixture models

The infinite mixture of normals model has become a popular method for density estimation problems. This paper proposes an alternative hierarchical model that leads to hyperparameters that can be interpreted as the location, scale and smoothness of the density. The priors on other parts of the model have little effect on the density estimates and can be given default choices. Automatic Bayesian density estimation can be implemented by using uninformative priors for location and scale and default priors for the smoothness. The performance of these methods for density estimation are compared to previously proposed default priors for four data sets.
Traffic Density Estimation and Mechanical Condition Determination of Vehicles using Acoustic Signals Ms

In this paper we will estimate the vehicular traffic density by using cummulative acoustic or sound signals. Here we will estimate three probable conditions of traffic that is heavy flow traffic (0-10km/h), medium flow (2040km/h), and free flow (above 40km/h) traffic. Cumulative sound signals consist of various noise signals coming from various parts of vehicles which includes rotational parts, vibrations in the engine, friction between the tires and the road, wind effect, gears, fans. Noise signals are tire noise, engine noise, engineidling noise, occasional honks, and air turbulence noise of multiple vehicles. These noise signals contains spectral content which are different from each other, therefore we can determine the different traffic density states and mechanical condition of vehicle. For example, under a free-flowing traffic condition, the vehicles typically move with medium to high speeds and thereby produces mainly tire noise and air turbulence noise and less engineidling noise and honks. Similar vehicles working in comparable conditions would have a similar sound signals that could be used for recognition.
PRISONER'S DILEMMA IN ONE-DIMENSIONAL CELLULAR AUTOMATA: VISUALIZATION OF EVOLUTIONARY PATTERNS

The spatial Prisoner's Dilemma is a prototype model to show the emergence of cooperation in very competitive environments. It considers players, at sites of lattices, that can either cooperate or defect when playing the Prisoner's Dilemma with z other players. This model presents a rich phase diagram. Here we consider players in cells of one-dimensional cellular automata. Each player interacts with z other players. This geometry allows us to vary, in a simple manner, the number of neighbors ranging from one up to the lattice size, including self-interaction. This approach has multiple advantages. It is simple to implement numerically and we are able to retrieve all the results found in the previously considered lattices, with a faster convergence to stationary values. More remarkable, it permits us to keep track of the spatio-temporal evolution of each player of the automaton, giving rise to interesting patterns. These patterns allow the interpretation of cooperation/defection clusters as particles, which can be absorbed and collided among themselves. The presented approach represents a new paradigm to study the emergence and maintenance of cooperation in the spatial Prisoner's Dilemma.
Intermittency Coding in the Primary Olfactory System: A Neural Substrate for Olfactory Scene Analysis

The spatial and temporal characteristics of the visual and acoustic sensory input are indispensable attributes for animals to perform scene analysis. In contrast, research in olfaction has focused almost exclusively on how the nervous system analyzes the quality and quantity of the sensory signal and largely ignored the spatiotemporal dimension especially in longer time scales. Yet, detailed analyses of the turbulent, intermittent structure of water- and air-borne odor plumes strongly suggest that spatio-temporal information in longer time scales can provide major cues for olfactory scene analysis for animals. We show that a bursting subset of primary olfactory receptor neurons (bORNs) in lobster has the unexpected capacity to encode the temporal properties of intermittent odor signals. Each bORN is tuned to a specific range of stimulus intervals, and collectively bORNs can instantaneously encode a wide spectrum of intermittencies. Our theory argues for the existence of a novel peripheral mechanism for encoding the temporal pattern of odor that potentially serves as a neural substrate for olfactory scene analysis.
Violence Detection in Video Using Spatio-Temporal Features

In this paper we presented a violence detector built on the concept of visual codebooks using linear support vector machines. It differs from the existing works of violence detection in what concern the data representation, as none has considered local spatio-temporal features with bags of visual words. An evaluation of the importance of local spatio-temporal features for characterizing the multimedia content is conducted through the cross-validation method. The results obtained confirm that motion patterns are crucial to distinguish violence from regular activities in comparison with visual descriptors that rely solely on the space domain.