Miracle-dz/UniFAR-FTUs huggingface dataset:
Cultural Issues in Offshore Teams: A Categorization based on Existing Studies

Abstract— Cultural and personal issues resulting from dispersed teams are considered to be serious barriers to form trust and organize effective agile teams. However, apart from separate, reported evidence of such issues from work experience, there has been no theoretical classification in literature. This paper provides a list and analysis of common challenges mainly resulting from cultural differences and barriers in Agile Software Development (ASD) offshore teams. The data source comprise articles published in IEEE, mostly of conferences related to ASD. Among the articles, papers with concrete evidence of Agile Methods (AM) implementation were selected. The results show that despite the relative significance of such issues, ASD adopters typically still rely on their own experience, and creativity rather than using well-defined methods. Moreover, this study reveals that the notion of trust, as discussed in the literature, mainly refers to maintaining the pace of communication, which is the focal point in ASD.
Identifying SDC-Causing Instructions Based on Random Forests Algorithm

Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. The identification and protection of the program instructions that cause SDCs is one of the research hotspots in computer reliability field at present. A lot of solutions have already been proposed to solve this problem. However, many of them are hard to be applied widely due to time-consuming and expensive costs. This paper proposes an intelligent approach named SDCPredictor to identify the instructions that cause SDCs. SDCPredictor identifies SDC-causing Instructions depending on analyzing the static and dynamic features of instructions rather than fault injections. The experimental results demonstrate that SDCPredictor is highly accurate in predicting the SDCs proneness. It can achieve higher fault coverage than previous similar techniques in a moderate time cost.
Complex Balancing Reconstructed to the Asymptotic Stability of Mass-Action Chemical Reaction Networks with Conservation Laws

Motivated by the fact that the pseudo-Helmholtz function is a valid Lyapunov function for characterizing asymptotic stability of complex balanced mass-action systems (MASs), this paper develops the generalized pseudo-Helmholtz function for stability analysis for more general MASs assisted with conservation laws. The key technique is to transform the original network into two different MASs, defined by reconstruction and reverse reconstruction, with an important aspect that the original network is dynamically equivalent to the reverse reconstruction. Stability analysis of the original network is then conducted based on an analysis of how stability properties are retained from the original network to the reverse reconstruction. We prove that the reverse reconstruction possesses at most an equilibrium in each positive stoichiometric compatibility class if the corresponding reconstruction is complex balanced. Under this complex balanced reconstruction strategy, the asymptotic stability of the reverse reconstr.
Why Social Comparison on Instagram Matters: Its impact on Depression

Social Networking Sites (SNS) provide people with unique online social interaction environments where users can disclose their thoughts, feelings, and opinions to their personal contacts. Although previous studies have suggested that such activities produce positive effects on SNS user well-being, this study considered potential negative effects by investigating the relationship between SNS use and depression. In particular, This stydy examined how specific activities are related to different types of social comparison (upward/downward/horizontal) and how these different types of social comparison influence depressed moods among college students. The analysis of a survey of 245 Instagram users found that (1) looking at other people’s status updates and commenting on other people’s photos influences upward social comparison, (2) frequency of Instagram use predicts upward/downward/horizontal social comparison, and (3) upward social comparison was postively associated with depression, while downward social comparison was negatively associated with depression. Furthermore, the path anlaysis show that social comparison mediates the effect of Instagram use on depression. It suggests that Instagram use does not directly increase depression but it can lead to depression when social comparison on Instagram triggers depression.
Video Object Segmentation with Weakly Temporal Information

Video object segmentation is a significant task in computer vision, but its performance is not very satisfactory. A method of video object segmentation using weakly temporal information is presented in this paper. Motivated by the phenomenon in reality that the motion of the object is a continuous and smooth process and the appearance of the object does not change much between adjacent frames in the video sequences, we use a feed-forward architecture with motion estimation to predict the mask of the current frame. We extend an additional mask channel for the previous frame segmentation result. The mask of the previous frame is treated as the input of the expanded channel after processing, and then we extract the temporal feature of the object and fuse it with other feature maps to generate the final mask. In addition, we introduce multi-mask guidance to improve the stability of the model. Moreover, we enhance segmentation performance by further training with the masks already obtained. Experiments show that our method achieves competitive results on DAVIS-2016 on single object segmentation compared to some state-of-the-art algorithms.
Semantical analysis of weak Kleene logics

ABSTRACT This paper presents a semantical analysis of the Weak Kleene Logics and from the tradition of Bochvar and Halldén. These are three-valued logics in which a formula takes the third value if at least one of its components does. The paper establishes two main results: a characterisation result for the relation of logical consequence in – that is, we individuate necessary and sufficient conditions for a set Δ of formulas to follow from a set Γ in – and a characterisation result for logical consequence in . The paper also investigates two subsystems of and and discusses the relevance of the results against existing background. Finally, the paper discusses some issues related to Weak Kleene Logics – in particular, their philosophical interpretation and the reading of conjunction and disjunction – and points at some open issues.
Unknowable Unknowns

K urt Gödel proved that there are problems for which it is impossible to construct an algorithm that always leads to a correct yes-or-no answer; those problems are undecidable. Alan Turing proved that the halting problem is undecidable in Turing machines. Alfred Tarski proved that truth in the standard model of a system cannot be defined within that system. Olav Lysne proved that it is not possible to verify electronic equipment procured from untrusted vendors, and that a vendor cannot build a system that supports verification by untrusting customers. Ben-David et al. proved that scenarios exist where learnability can neither be proved nor refuted. Finally, Donald Rumsfeld made commonplace the phrase unknown unknowns. And so we come to artificial intelligence (AI), which is to say self-modifying algorithms, which is to say machine learning. Readers of IEEE Security & Privacy are well aware of the interrogatability problem “Monsieur Algorithme, why did you make this decision? ” an acute concern in multiple subject matter areas, of which cybersecurity is assuredly one. Most IEEE Security & Privacy readers agree that all security tools are dual-use, freighting “Why did you make this decision? ” with significantly more than mere curiosity or a search for optimality. There are some who say that a selfmodifying algorithm, if purposefully and skillfully constructed, can tell the “why” of its decisions, tell that why in a form we humans can appreciate, and then, perhaps, nod in knowing acceptance. One hopes that this will soon be true, but as of now, it is not. In other words, and for the time being, black-box interrogation of AI models—similar in spirit to a statistician’s sensitivity testing—has the potential to become the default method of assessing an AI model’s behaviors. This default will last at least so long as no one has success in understanding a priori how a model works. It is merely stop-gap, relative to Rumsfeld’s unknown unknowns: the probability of not asking enough of the right type of questions to characterize a data-driven model is as great as the probability that the model was trained on incomplete or biased data. It is logical to presume that as AI models increase in complexity, they become more opaque. This parallels a problem we in cybersecurity know only too well: that of trying to understand the attack surface of growing and/ or dynamic software installations. Unprovability thus becomes acute, including in the case of cybersecurity, where the mutation rate for offense and defense alike mean not just learning but unlearning. In some areas other than cybersecurity, handing off the keys to AI models offers immediate, iterative improvement in tailored
Legendre Transforms of Electrostatic Free-Energy Functionals

In the Poisson--Boltzmann (PB) theory, the electrostatic free-energy functional of all possible electrostatic potentials for an ionic solution is often formulated in such a way that the Euler--Lagrange equation of such a functional is exactly the PB equation. However, such a PB functional is concave downward and maximized at its critical point, making it inconsistent in many applications where a macroscopic free-energy functional is minimized. Maggs [Europhys. Lett. , 98 (2012), 16012] proposed a Legendre transformed form of the electrostatic free-energy functional of all possible dielectric displacements. This new functional is convex and minimized at the displacement corresponding to the critical point of the PB functional, and the minimum value is exactly the equilibrium electrostatic free energy. In this work, we study mathematically the Legendre transformed electrostatic free-energy functionals and the related variational principles. We first prove that the PB functional and its Legendre transformed f.
Maximum margin learning under uncertainty

In this thesis we study the problem of learning under uncertainty using the statistical learning paradigm. We first propose a linear maximum margin classifier that deals with uncertainty in data input. More specifically, we reformulate the standard Support Vector Machine (SVM) framework such that each training example can be modeled by a multi-dimensional Gaussian distribution described by its mean vector and its covariance matrix – the latter modeling the uncertainty. We address the classification problem and define a cost function that is the expected value of the classical SVM cost when data samples are drawn from the multi-dimensional Gaussian distributions that form the set of the training examples. Our formulation approximates the classical SVM formulation when the training examples are isotropic Gaussians with variance tending to zero. We arrive at a convex optimization problem, which we solve efficiently in the primal form using a stochastic gradient descent approach. The resulting classifier, which we name SVM with Gaussian Sample Uncertainty (SVM-GSU), is tested on synthetic data and five publicly available and popular datasets; namely, the MNIST, WDBC, DEAP, TV News Channel Commercial Detection, and TRECVID MED datasets. Experimental results verify the effectiveness of the proposed method. Next, we extended the aforementioned linear classifier so as to lead to non-linear decision boundaries, using the RBF kernel. This extension, where we use isotropic input uncertainty and we name Kernel SVM with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU), is used in the problems of video event detection and video aesthetic quality assessment. The experimental results show that exploiting input uncertainty, especially in problems where only a limited number of positive training examples are provided, can lead to better classification, detection, or retrieval performance. Finally, we present a preliminary study on how the above ideas can be used under the deep convolutional neural networks learning paradigm so as to exploit inherent sources of uncertainty, such as spatial pooling operations, that are usually used in deep networks.
Experience in the Adaptive Immunity Impacts Bone Homeostasis, Remodeling, and Healing

Bone formation as well as bone healing capacity is known to be impaired in the elderly. Although bone formation is outpaced by bone resorption in aged individuals, we hereby present a novel path that considerably impacts bone formation and architecture: Bone formation is substantially reduced in aged individual owing to the experience of the adaptive immunity. Thus, immune-aging in addition to chronological aging is a potential risk factor, with an experienced immune system being recognized as more pro-inflammatory. The role of the aging immune system on bone homeostasis and on the bone healing cascade has so far not been considered. Within this study mice at different age and immunological experience were analyzed toward bone properties. Healing was assessed by introducing an osteotomy, immune cells were adoptively transferred to disclose the difference in biological vs. chronological aging. In vitro studies were employed to test the interaction of immune cell products (cytokines) on cells of the musculoskeletal system. In metaphyseal bone, immune-aging affects bone homeostasis by impacting bone formation capacity and thereby influencing mass and microstructure of bone trabeculae leading to an overall reduced mechanical competence as found in bone torsional testing. Furthermore, bone formation is also impacted during bone regeneration in terms of a diminished healing capacity observed in young animals who have an experienced human immune system. We show the impact of an experienced immune system compared to a naïve immune system, demonstrating the substantial differences in the healing capacity and bone homeostasis due to the immune composition. We further showed that in vivo mechanical stimulation changed the immune system phenotype in young mice toward a more naïve composition. While this rescue was found to be significant in young individuals, aged mice only showed a trend toward the reconstitution of a more naïve immune phenotype. Considering the immune system's experience level in an individual, will likely allow one to differentiate (stratify) and treat (immune-modulate) patients more effectively. This work illustrates the relevance of including immune diagnostics when discussing immunomodulatory therapeutic strategies for the progressively aging population of the industrial countries.
Dynamic Office Environments Improve Brain Activity and Attentional Performance Mediated by Increased Motor Activity

Current research demonstrates beneficial effects of physical activity on brain functions and cognitive performance. To date, less is known on the effects of gross motor movements that do not fall into the category of sports-related aerobic or anaerobic exercise. In previous studies, we found beneficial effects of dynamic working environments, i.e., environments that encourage movements during cognitive task performance, on cognitive performance and corresponding brain activity. Aim of the present study was to examine the effects of working in a dynamic and a static office environment on attentional and vigilance performance, and on the corresponding electroencephalographic (EEG) brain oscillatory patterns. In a 2-week intervention study, participants worked either in a dynamic or a static office. In each intervention group, 12 subjects performed attentional and vigilance tasks. Spontaneous EEG was measured from 19 electrodes continuosly before, during, and immediately after each experimental condition at the first, and at the last intervention session. Results showed differences in EEG brain activity in the dynamic compared to the static office at the beginning as well as at the end of the intervention. EEG theta power increased in the vigilance task in anterior regions, alpha power in central and parietal regions in the dynamic compared to the static office. Further, increases in beta activity in the attention and vigilance task were shown in frontal and central regions in the dynamic office. Gamma power increased in the attention task in frontal and central regions. After 2 weeks, effects on brain activity increased in the attentional and vigilance task in the dynamic office. Increased theta and alpha oscillations were obtained in anterior areas with higher activity in the beta band in anterior and central areas in the dynamic compared to the static office. EEG oscillatory patterns indicate beneficial effects of dynamic office environments on attentional and vigilance performance that are mediated by increased motor activity. We discuss the obtained patterns of EEG oscillations in terms of the close interrelations between the attentional and the motor system.
Association of Vasculitis and Familial Mediterranean Fever

Certain types of vasculitis occur more frequently and present differently in patients with familial Mediterranean fever (FMF). We assessed the characteristics of patients with FMF and systemic vasculitis through a systematic review of the literature. Medline was searched by two independent investigators until December 2017. We screened 310 articles and selected 58 of them (IgA vasculitis n = 12, polyarteritis nodosa (PAN) n = 25, Behçet's disease (BD) n = 7, other vasculitis n = 14). Clinical case reports were available for 167 patients (IgA vasculitis n = 46, PAN n = 61, BD n = 46, other vasculitis n = 14), and unavailable for 45 patients (IgA vasculitis n = 38, PAN n = 7). IgA vasculitis was the most common vasculitis in FMF patients with a prevalence of 2.7–7%, followed by PAN with a prevalence of 0.9–1.4%. Characteristics of FMF did not differ between patients with and without vasculitis. Patients with FMF and IgA vasculitis displayed more intussusception (8.7%) and possibly less IgA deposits on histological analysis than patients with IgA vasculitis alone. Patients with FMF and PAN had a younger age at vasculitis onset (mean age = 17.9 years), as well as more perirenal hematomas (49%) and CNS involvement (31%) than patients with PAN alone. Glomerular involvement was noted in 33% of patients diagnosed with PAN, suggesting an alternative diagnosis. Sequencing of the MEFV gene confirmed the presence of two pathogenic variants in 73% of FMF patients with IgA vasculitis or PAN. The majority of patients with BD were from one case series, and presented more skin, gastrointestinal, and CNS involvement than patients with isolated BD. In conclusion, FMF, particularly when supported by two pathogenic MEFV mutations, could predispose to IgA vasculitis, or a PAN-like vasculitis with more perirenal bleeding and CNS involvement.
Decrease in Sightings of Sixgill Sharks, Hexanchus griseus, in Elliott Bay, Seattle, WA, United States, a Comparison Between 2003–2005 and 2008–2015

The Bluntnose Sixgill Shark, Hexanchus griseus, is a large predatory shark, has a worldwide distribution and is listed as near-threatened by the International Union of Conservation of Nature (IUCN). The Seattle Aquarium collected observations of free-swimming Sixgill Sharks in Elliott Bay, Washington, under the aquarium’s pier in 20 m of water from 2003 to 2005 and again from 2008 to 2015 using the same methodology. Compared to total Sixgill sightings between 2003-2005 (273) fewer total Sixgills were sighted at the aquarium’s research station between 2008-2015 (33). The reason for the observed decline in sightings in unknown but based on data from other studies on Sixgills in Puget Sound during the same timeperiod the authors hypothesize the decrease may be due to natural variability of juvenile Sixgill recruitment to Elliott Bay.
Electrical characteristics for triangular resistors–capacitors–inductors network designed on two coats

This work deals with a theoretical study of a triangular electrical lattice built on two layers. First, the auxiliary source notion is introduced for characterizing the potential difference over each electrical element, then the mathematical formalism of the Wave Concept Iterative Process (WCIP) method is developed and adapted to the studied circuit. The method is based on the concept of the incident and reflected waves which are defined from the current and voltage at each branch of the circuit. Two relations connecting the waves are established into two definition domains: a spectral domain using the Kirchhoff laws and the auxiliary source connections and another spatial domain defining the boundary conditions and the circuit design. Hence, a system of two equations is obtained, and it is resolved by an iterative process; the transition between the two domains is ensured by the fast Fourier transform and its inverse. Moreover, the equivalent impedance between the feeding source and the nodes of the bottom layer has been calculated. Among the numerical simulation methods, this method has demonstrated its performance for analyzing various designs of the networks including resistors–inductors (RL), resistors–capacitors (RC), and resistors–capacitors–inductors (RLC) circuits excited by a lumped voltage source. The effect of the circuit parameters on the electrical currents and equivalent impedance has been studied.