Miracle-dz/UniFAR-FTUs huggingface dataset:
From Dependability to Resilience

Definition of resilience Resilience (from the Latin etymology resilire, to rebound) is literally the act or action of springing back. As a property, two strands can historically be identified: a) in social psychology [Claudel 1936], where it is about elasticity, spirit, resource and good mood, and b) and in material science, where it is about robustness and elasticity. The notion of resilience has then been elaborated: • in child psychology and psychiatry [Engle et al. 1996], referring to living and developing successfully when facing adversity; • in ecology [Holling 1973], referring to moving from a stability domain to another one under the influence of disturbances; • in business [Hamel & Välikangas 2003], referring to the capacity to reinvent a business model before circumstances force to; • in industrial safety [Hollnagel et al. 2006], referring to anticipating risk changes before damage occurrence. A common point to the above senses of the notion of resilience is the ability to successfully accommodate unforeseen environmental perturbations or disturbances. A careful examination of [Holling 1973] leads to draw interesting parallels between ecological systems and computing systems, due to: a) the emphasis on the notion of persistence of a property: resilience is said to " determine the persistence of relationships within a system and is a measure of the ability of these systems to absorb changes of state variables, driving variables, and parameters, and still persist " ; b) the dissociation between resilience and stability: it is noted that " a system can be very resilient and still fluctuate greatly, i.e., have low stability " and that " low stability seems to introduce high resilience " ; c) the mention that diversity is of significant influence on both stability (decreasing it) and resilience (increasing it). The adjective resilient has been in use for decades in the field of dependable computing systems, e.g. [Alsberg & Day 1976], and is more and more in use, however essentially as a synonym of fault-tolerant, thus generally ignoring the unexpected aspect of the phenomena the systems may have to face. A noteworthy exception is the preface of [Anderson 1985], which says " The two key attributes here are dependability and robustness. […] A computing system can be said to be robust if it retains its ability to deliver service in conditions which are beyond its normal domain of operation ". Fault-tolerant computing systems are known for exhibiting some robustness with respect …
Portable, layer-wise task performance monitoring for NLP models

There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns.
Women College Presidents: Interviews about Journeys and Adaptations

While the progress of women into higher education presidencies is dramatic, it is also, in the words of oneexperienced woman president, "not exactly a lot! " In the 15 years from 1986 to 2001, the American Councilon Education (ACE) reports that the percentage of women presidents increased from 9.5 to 21.1 (TheAmerican college president, 2002). Of these women, 26.8% led two-year institutions; 18.7% ledbaccalaureate schools; 20.3% led comprehensive universities; and only 13.3% headed doctorate-grantingschools; with the remaining 14.8% guiding specialized schools.
Stochastic Theater: Stochastic Datapath Generation Framework for Fault-Tolerant IoT Sensors

Stochastic Computing has emerged as a competitive computing paradigm that produces fast and simple implementations of arithmetic operations, while offering high levels of parallelism, and graceful degradation of the results when in the presence of errors. IoT devices are often operate under limited power and area constraints and subjected to harsh environments, for which, traditional computing paradigms struggle to provide high availability and fault-tolerance. Stochastic Computing is based on the computation of pseudo-random sequences of bits, hence requiring only a single bit per signal, rather than a data-bus. Notwithstanding, we haven’t witnessed its inclusion in custom computing systems. In this direction, this work presents Stochastic Theater, a framework to specify, simulate, and test Stochastic Datapaths to perform computations using stochastic bitstreams targeting IoT systems. In virtue of the granularity of the bitstreams, the bit-level specification of circuits, high-performance characteristics and reconfigurable capabilities, FPGAs were adopted to implement and test such systems. The proposed framework creates Stochastic Machines from a set of user defined arithmetic expressions, and then tests them with the corresponding input values and specific fault injection patterns. Besides the support to create autonomous Stochastic Computing systems, the presented framework also provides generation of stochastic units, being able to produce estimates on performance, resources and power. A demonstration is presented targeting KLT, typical method for data compression in IoT applications.
Autonomous Mobile Robot Visual SLAM Based on Improved CNN Method

Although the research on autonomous mobile robot SLAM has received extensive research, the current mobile robot still exists in practical applications: it may move under the condition of disordered and irregular obstacle placement; the shape of the obstacle and the position of the obstacle change; and indoor and outdoor scene switching occurs at different times and other issues. Autonomous mobile robots need to continuously optimize SLAM during motion and obtain real-time information from multiple sensors for real-time identification and rapid response to the surrounding environment. We have improved the CNN-based VSLAM system by replacing the original single convolutional layer with parallelism and reducing the number of model parameters. This approach can reduce the problem of system gradient disappearance and continue to train deeper networks. Finally, a global map is generated with the fully connected layer and passed to the robot’s navigation. The experimental results based on RGB-D SLAM Dataset and Benchmark database dataset show that the proposed VSLAM system based on CNN is superior to the traditional CNN VSLAM system in both ATE and RPE indicators.
Molecular Characterization of Pandemic Influenza A Virus A(H1N1)pdm09 in Southern Palestine

Background: A pandemic influenza has been announced in 2009 caused by a novel influenza A virus A(H1N1)pdm09 which spreads globally and has a high transmission rate among human by the WHO. Waterfowl is the source of all influenza A subtypes; A(H1N1)pdm09 occurred due to antigenic shift when the avian and the swine influenza A subtypes combine in the swine cells and result in genetic reassortment that leads to an unprecedented combination of haemagglutinin and neuraminidase subtypes creating a virus which is able to transmit efficiently to which human has a naive immunity. The human viruses bind to the epithelial cells located at the upper respiratory tract at their sialyoligosaccharides by SA α2,6Gal whereas avian viruses attach to SA α2,3Gal. The swine respiratory cells contain both receptors proposing that the swine serves as a “mixing vessel” by providing a place for the reassortment to take place. Hemagglutinin (HA) and neuraminidase (NA) proteins are expressed at the surface of the virion and are responsible for the viral identification, replication and transmission processes. Besides, they evolved rapidly to escape the immune system via the genetic drift rendering the vaccines of low efficacy. A(H1N1)pdm09 is associated with high mortality rates and led to the death of millions worldwide. Objectives: The aim of this study was to investigate the types of influenza A viruses which were circulating among the Palestinian population detected at Caritas Baby Hospital during the period 2008-2016, to investigate the influenza A(H1N1)pdm09 virus HA gene evolution by detecting the antigenic
Performance Analysis of Grid Integrated Doubly Fed Induction Generator for Small Hydropower Plant

The motive of this work is to present the modeling and simulation of the hydraulic turbine which is driven by the doubly-fed induction generator and the generated ac power given to the grid. For feeding the ac power to the grid, we are using two PWM voltage source converters which are connected back to back between the utility grid and rotor terminals with the help of common dc-link. Machine side converter is used for pertinent excitation. The power flow between AC side and DC bus is controlled by grid side converter (GSC) as well as it allows the system to be operated in less than and greater than synchronous speed mode of operation. MATLAB/SIMULINK is used for Time domain simulations. The system is stable under different load conditions and it is investigated with the help of model and simulations result. To preserve the system stability whenever the power demand is precipitously increasing in the utility grid side, an appropriate system and controller parameter is used.
PSYCHOLOGICAL DISTRESS AMONG CANCER PATIENTS ON CHEMOTHERAPY

Distress has become a major issue in cancer population. Patients may suffer from either physical, psychological distress or both. Cancer patients who are undergoing chemotherapy are more likely to experience psychological distress. This could be due to the negative effects of chemotherapy agents, the uncertainty of post-treatment, and the occurrence of psychosocial problems. As a result, the patient may experience a normal reaction such as sadness or may develop common psychiatric disorders such as depression and anxiety. (JUMMEC 2010; 13(1): 12-18)
Certified Roundoff Error Bounds Using Bernstein Expansions and Sparse Krivine-Stengle Representations

Floating point error is a drawback of embedded systems implementation that is difficult to avoid. Computing rigorous upper bounds of roundoff errors is absolutely necessary for the validation of critical software. This problem of computing rigorous upper bounds is even more challenging when addressing non-linear programs. In this paper, we propose and compare two new algorithms based on Bernstein expansions and sparse Krivine-Stengle representations, adapted from the field of the global optimization, to compute upper bounds of roundoff errors for programs implementing polynomial and rational functions. We also provide the convergence rate of these two algorithms. We release two related software package $\mathtt {FPBern}$FPBern and $\mathtt {FPKriSten}$FPKriSten, and compare them with the state-of-the-art tools. We show that these two methods achieve competitive performance, while providing accurate upper bounds by comparison with the other tools.
Profitable Bayesian implementation in one-shot mechanism settings

In the mechanism design theory, a designer would like to implement a desired social choice function which specifies her favorite outcome for each possible profile of all agents' types. Traditionally, the designer may be in a dilemma in the sense that even if she is not satisfied with some outcome with low profit, she has to announce it because she must obey the mechanism designed by herself. In this paper, we investigate a case where the designer can induce each agent to adjust his type in a one-shot mechanism. We propose that for a profitable Bayesian implementable social choice function, the designer may escape from the above-mentioned dilemma by spending the optimal adjustment cost and obtain a higher profit. Finally, we construct an example to show that the designer can breakthrough the limit of expected profit which she can obtain at most in the traditional optimal auction model.
Triangle-degrees in graphs and tetrahedron coverings in 3-graphs

Abstract We investigate a covering problem in 3-uniform hypergraphs (3-graphs): Given a 3-graph F, what is c 1(n, F), the least integer d such that if G is an n-vertex 3-graph with minimum vertex-degree $\delta_1(G)>d$ then every vertex of G is contained in a copy of F in G? We asymptotically determine c 1(n, F) when F is the generalized triangle $K_4^{(3)-}$ , and we give close to optimal bounds in the case where F is the tetrahedron $K_4^{(3)}$ (the complete 3-graph on 4 vertices). This latter problem turns out to be a special instance of the following problem for graphs: Given an n-vertex graph G with $m> n^2/4$ edges, what is the largest t such that some vertex in G must be contained in t triangles? We give upper bound constructions for this problem that we conjecture are asymptotically tight. We prove our conjecture for tripartite graphs, and use flag algebra computations to give some evidence of its truth in the general case.
Simple Surveys: Response Retrieval Inspired by Recommendation Systems

In the last decade, the use of simple rating and comparison surveys has proliferated on social and digital media platforms to fuel recommendations. These simple surveys and their extrapolation with machine learning algorithms such as matrix factorization shed light on user preferences over large and growing pools of items such as movies, songs, and ads. Social scientists also have a long history of measuring perceptions, preferences, and opinions, typically often over smaller, discrete item sets with exhaustive rating or ranking surveys. This article introduces simple surveys for social science application. We ran experiments to compare the predictive accuracy of both individual and aggregate comparative assessments using four types of simple surveys—pairwise comparisons (PCs) and ratings on 2, 5, and continuous point scales in three contexts—perceived safety of Google Street View images, likability of artwork, and hilarity of animal GIFs. Across contexts, we find that continuous scale ratings best predict individual assessments but consume the most time and cognitive effort. Binary choice surveys are quick and best predict aggregate assessments, useful for collective decision tasks, but poorly predict personalized preferences, for which they are currently used by Netflix to recommend movies. PCs, by contrast, successfully predict personal assessments but poorly predict aggregate assessments despite being widely used to crowdsource ideas and collective preferences. We also demonstrate how findings from these surveys can be visualized in a low-dimensional space to reveal distinct respondent interpretations of questions asked in each context. We conclude by reflecting on differences between sparse, incomplete “simple surveys” and their traditional survey counterparts in terms of efficiency, information elicited, and settings in which knowing less about more may be critical for social science.
Modeling and Simulation of Robotic Finger Powered by Nylon Artificial Muscles- Equations with Simulink model

This paper shows a detailed modeling of three-link robotic finger that is actuated by nylon artificial muscles and a simulink model that can be used for numerical study of a robotic finger. The robotic hand prototype was recently demonstrated in recent publication Wu, L. , Jung de Andrade, M. , Saharan, L. ,Rome, R. , Baughman, R. , and Tadesse, Y. , 2017, Compact and Low-cost Humanoid Hand Powered by Nylon Artificial Muscles, Bioinspiration & Biomimetics, 12 (2). The robotic hand is a 3D printed, lightweight and compact hand actuated by silver-coated nylon muscles, often called Twisted and coiled Polymer (TCP) muscles. TCP muscles are thermal actuators that contract when they are heated and they are getting attention for application in robotics. The purpose of this paper is to demonstrate the modeling equations that were derived based on Euler Lagrangian approach that is suitable for implementation in simulink model.
EXIT Analysis for Community Detection

This paper employs the extrinsic information transfer (EXIT) method, a technique imported from the analysis of the iterative decoding of error control codes, to study the performance of belief propagation in community detection in the presence of side information. We consider both the detection of a single (hidden) community, as well as the problem of identifying two symmetric communities. For single community detection, this paper demonstrates the suitability of EXIT to predict the asymptotic phase transition for weak recovery. More importantly, EXIT analysis is leveraged to produce useful insights such as the performance of belief propagation near the threshold. For two symmetric communities, the asymptotic residual error for belief propagation is calculated under finite-alphabet side information, generalizing a previous result with noisy labels. EXIT analysis is used to illuminate the effect of side information on community detection, its relative importance depending on the correlation of the graphical information with node labels, as well as the effect of side information on residual errors.
Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided. For a monotone and continuous DR-submodular function, and subject to a bounded convex body constraint, we propose Black-box Continuous Greedy, a derivative-free algorithm that provably achieves the tight $[(1-1/e)OPT-\epsilon]$ approximation guarantee with $O(d/\epsilon^3)$ function evaluations. We then extend our result to the stochastic setting where function values are subject to stochastic zero-mean noise. It is through this stochastic generalization that we revisit the discrete submodular maximization problem and use the multi-linear extension as a bridge between discrete and continuous settings. Finally, we extensively evaluate the performance of our algorithm on continuous and discrete submodular objective functions using both synthetic and real data.
Competitive Online Optimization under Inventory Constraints

This paper studies online optimization under inventory (budget) constraints. While online optimization is a well-studied topic, versions with inventory constraints have proven difficult. We consider a formulation of inventory-constrained optimization that is a generalization of the classic one-way trading problem and has a wide range of applications. We present a new algorithmic framework, \textsfCR-Pursuit, and prove that it achieves the minimal competitive ratio among all deterministic algorithms (up to a problem-dependent constant factor) for inventory-constrained online optimization. Our algorithm and its analysis not only simplify and unify the state-of-the-art results for the standard one-way trading problem, but they also establish novel bounds for generalizations including concave revenue functions. For example, for one-way trading with price elasticity, the \textsfCR-Pursuit algorithm achieves a competitive ratio that is within a small additive constant (i.e., 1/3) to the lower bound of ln 0+1, where 0 is the ratio between the maximum and minimum base prices.
CURE: Curvature Regularization For Missing Data Recovery

Missing data recovery is an important and yet challenging problem in imaging and data science. Successful models often adopt certain carefully chosen regularization. Recently, the low dimension manifold model (LDMM) was introduced by S.Osher et al. and shown effective in image inpainting. They observed that enforcing low dimensionality on image patch manifold serves as a good image regularizer. In this paper, we observe that having only the low dimension manifold regularization is not enough sometimes, and we need smoothness as well. For that, we introduce a new regularization by combining the low dimension manifold regularization with a higher order Curvature Regularization, and we call this new regularization CURE for short. The key step of solving CURE is to solve a biharmonic equation on a manifold. We further introduce a weighted version of CURE, called WeCURE, in a similar manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments for image inpainting and semi-supervised learning show that the proposed CURE and WeCURE significantly outperform LDMM and WNLL respectively.
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction

We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features. Our method is unsupervised, and is based on using a concrete selector layer as the encoder and using a standard neural network as the decoder. During the training phase, the temperature of the concrete selector layer is gradually decreased, which encourages a user-specified number of discrete features to be learned. During test time, the selected features can be used with the decoder network to reconstruct the remaining input features. We evaluate concrete autoencoders on a variety of datasets, where they significantly outperform state-of-the-art methods for feature selection and data reconstruction. In particular, on a large-scale gene expression dataset, the concrete autoencoder selects a small subset of genes whose expression levels can be use to impute the expression levels of the remaining genes. In doing so, it improves on the current widely-used expert-curated L1000 landmark genes, potentially reducing measurement costs by 20%. The concrete autoencoder can be implemented by adding just a few lines of code to a standard autoencoder.
Evaluating word embedding models: methods and experimental results

Abstract Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a representation independent of specific natural language processing tasks while extrinsic evaluators use word embeddings as input features to a downstream task and measure changes in performance metrics specific to that task. We report experimental results of intrinsic and extrinsic evaluators on six word embedding models. It is shown that different evaluators focus on different aspects of word models, and some are more correlated with natural language processing tasks. Finally, we adopt correlation analysis to study performance consistency of extrinsic and intrinsic evaluators.