Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Two Types of 9-Month Adapted Physical Activity Program on Muscle Mass, Muscle Strength, and Balance in Moderate Sarcopenic Older Women

The present study aimed to evaluate the effects of two types of 9-month adapted physical activity (APA) program, based on a muscle reinforcement training and a postural training, respectively, on muscle mass, muscle strength, and static balance in moderate sarcopenic older women. The diagnosis of sarcopenia was done in accordance with measurable variables and cut-off points suggested by the European Working Group on Sarcopenia in Older People (EWGSOP). Seventy-two participants were randomly assigned to two groups: the muscle reinforcement training group (RESISTANCE) (n=35; 69.9 ± 2.7 years) and the postural training group (POSTURAL) (n=37; 70.0±2.8 years). Body composition, muscle mass, skeletal muscle mass index (SMI), and handgrip strength (HGS) were evaluated for sarcopenia assessment, whereas Sway Path, Sway Area, Stay Time, and Spatial Distance were evaluated for static balance assessment. Sixty-six participants completed the study (RESISTANCE group: n=33; POSTURAL group: n=33). Significant increases of muscle mass, SMI, and handgrip strength values were found in the RESISTANCE group, after muscle reinforcement program. No significant differences appeared in the POSTURAL group, after postural training. Furthermore, RESISTANCE group showed significant improvements in static balance parameters, whereas no significant differences appeared in the POSTURAL group. On the whole, the results of this study suggest that the APA program based on muscle reinforcement applied on moderate sarcopenic older women was able to significantly improve muscle mass and muscle strength, and it was also more effective than the applied postural protocol in determining positive effects on static balance.
A Complexity Dichotomy for Critical Values of the b-Chromatic Number of Graphs

A $b$-coloring of a graph $G$ is a proper coloring of its vertices such that each color class contains a vertex that has at least one neighbor in all the other color classes. The b-Coloring problem asks whether a graph $G$ has a $b$-coloring with $k$ colors. The $b$-chromatic number of a graph $G$, denoted by $\chi_b(G)$, is the maximum number $k$ such that $G$ admits a $b$-coloring with $k$ colors. We consider the complexity of the b-Coloring problem, whenever the value of $k$ is close to one of two upper bounds on $\chi_b(G)$: The maximum degree $\Delta(G)$ plus one, and the $m$-degree, denoted by $m(G)$, which is defined as the maximum number $i$ such that $G$ has $i$ vertices of degree at least $i-1$. We obtain a dichotomy result stating that for fixed $k \in \{\Delta(G) + 1 - p, m(G) - p\}$, the problem is polynomial-time solvable whenever $p \in \{0, 1\}$ and, even when $k = 3$, it is NP-complete whenever $p \ge 2$. We furthermore consider parameterizations of the b-Coloring problem that involve the maximum degree $\Delta(G)$ of the input graph $G$ and give two FPT-algorithms. First, we show that deciding whether a graph $G$ has a $b$-coloring with $m(G)$ colors is FPT parameterized by $\Delta(G)$. Second, we show that b-Coloring is FPT parameterized by $\Delta(G) + \ell_k(G)$, where $\ell_k(G)$ denotes the number of vertices of degree at least $k$.
EASYFLOW: Keep Ethereum Away from Overflow

While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum, mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EASYFLOW, an overflow detector at Ethereum Virtual Machine level. The key insight behind EASYFLOW is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EASYFLOW can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EASYFLOW managed to find potentially vulnerable Ethereum contracts with little runtime overhead. A demo video of EASYFLOW is at https://youtu.be/QbUJkQI0L6o.
Formal Semantics for Probabilistic Verification of Stochastic Regular Expressions

Modelling and verification of software systems is an effective phase of system development, as it can uncover failures in design early in the development process. There is an increasing need for languages and processes that allow for the specification of uncertainty that allow, for example, the modelling of the unknown behaviour of a user, or the stochastic failure rate of hardware components. In this paper we introduce a formal semantics on Stochastic Regular Expressions (SREs) over probabilistic action logics for quantitative verification. We provide the recursive calculation of the language generated by an SRE, enhanced to reuse local results for global verification of system specifications. Furthermore, we demonstrate how to model systems with SREs and how to perform reachability analysis with Probabilistic Action-based Computational Tree Logic (PACTL*).
Provably Secure Steganography on Generative Media

In this paper, we propose provably secure steganography on generative media. Firstly, we discuss the essence of the steganographic security, which is identical to behavioral security. The behavioral security implies that the generative media are suitable for information hiding as well. Based on the duality of source coding and generating discrete distribution from fair coins and the explicit probability distribution yielded by generative model, perfectly secure steganography on generative media is proposed. Instead of random sampling from the probability distribution as ordinary generative models do, we combine the source decoding into the process of generation, which can implement the sampling according to the probability distribution as well as embed the encrypted message. Adaptive Arithmetic Coding is selected as the source coding method, and it is proved theoretically that the proposed generative steganography framework using adaptive Arithmetic Coding is asymptotically perfect secure. Taking text-to-speech system based on WaveNet as an instance, we describe the process of embedding and extracting message in detail, and the experimental results show that the proposed method is nearly perfectly secure when resists state-of-the-art steganalysis.
Validating Hyperspectral Image Segmentation

Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, and hence, automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to overoptimistic experimental insights. We introduce a new routine for generating segmentation benchmarks and use it to elaborate ready-to-use hyperspectral training–test data partitions. They can be utilized for fair validation of new and existing algorithms without any training–test data leakage.
SPALEX: A Spanish Lexical Decision Database From a Massive Online Data Collection

“Megastudy” is the term coined to refer to large-scale experiments completed by thousands (or even hundred-thousands) of participants (Chetail et al., 2015; Gimenes et al., 2016). These types of studies have exponentially increased in the last decade, from around 130 publications referencing the term in 2007 to more than 300 in 2017 (Kuperman, 2015). Their uses span different fields, such as cognitive science, medicine, education, and psychology research. Particularly for the field of psycholinguistics, the increasing demand for massive and diverse databases upon which non-trivial hypotheses and models can be tested, has led to a dramatic increase in large-scale lexical megastudies (Keuleers and Balota, 2015). Lexical decision megastudies have been carried out in multiple languages, including American and British English ((Balota et al., 2007; Keuleers et al., 2012); respectively), French (Ferrand et al., 2010), and Dutch (Keuleers et al., 2010; Brysbaert et al., 2016). In Spanish, previous laboratory studies have explored the effects of psycholinguistic variables on a large amount of words, but with a relatively small number of participants (Davies et al., 2013; González-Nosti et al., 2014). Although these are laboratory studies that involve a large number of participants, or a large number of words, other approaches, such as crowdsourced megastudies, distributed through online platforms, allow the collection of information with large numbers of participants and words at a reduced cost (Keuleers et al., 2015). The shift in view from the laboratory to this type of crowdsourced research makes novel technologies like smartphones or tablets powerful research tools that allow for large-scale studies (Dufau et al., 2011). Likewise, large-scale studies have the benefit of studying and quantifying phenomena of interest across a varied and a vast number of participants. Another essential advantage of megastudies is the ability to conduct virtual experiments with randomly selected samples within the same database to test multiple hypotheses of different nature (Kuperman, 2015). They are becoming essential for psycholinguistic studies, as the study of language has been traditionally focalized in small and homogeneous groups of participants, not allowing the evaluation of important factors, such as previous linguistic experience, degree of second-language proficiency, or age, to cite a few (Keuleers et al., 2015).
A Case Of Recurrent Helicobacter cinaedi Prosthetic Joint Infection In An HIV-Infected Man

We describe the case of an HIV-infected man who developed twice a Helicobacter cinaedi prosthetic joint infection. In our knowledge, it is the first case to date. Furthermore, it illustrates the fact that this bacterium is difficult to isolate and that recurrences can occur even after apparently successful treatment.
NEMGAN: Noise Engineered Mode-matching GAN

Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data. This can be achieved by augmenting the generative model with the desired semantic labels, albeit it is not straightforward in an unsupervised setting where the semantic label of every data sample is unknown. In this paper, we address this issue by proposing a method that can generate samples conditioned on the properties of a latent distribution engineered in accordance with a certain data prior. In particular, a latent space inversion network is trained in tandem with a generative adversarial network such that the modal properties of the latent space distribution are induced in the data generating distribution. We demonstrate that our model, despite being fully unsupervised, is effective in learning meaningful representations through its mode matching property. We validate our method on multiple unsupervised tasks such as conditional generation, dataset attribute discovery and inference using three real world image datasets namely MNIST, CIFAR-10 and CelebA and show that the results are comparable to the state-of-the-art methods.
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling

This technical report describes a new feature of the CleverHans library called "attack bundling". Many papers about adversarial examples present lists of error rates corresponding to different attack algorithms. A common approach is to take the maximum across this list and compare defenses against that error rate. We argue that a better approach is to use attack bundling: the max should be taken across many examples at the level of individual examples, then the error rate should be calculated by averaging after this maximization operation. Reporting the bundled attacker error rate provides a lower bound on the true worst-case error rate. The traditional approach of reporting the maximum error rate across attacks can underestimate the true worst-case error rate by an amount approaching 100\% as the number of attacks approaches infinity. Attack bundling can be used with different prioritization schemes to optimize quantities such as error rate on adversarial examples, perturbation size needed to cause misclassification, or failure rate when using a specific confidence threshold.
On Conditional Correlations

The Pearson correlation, correlation ratio, and maximal correlation have been well-studied in the literature. In this paper, we study the conditional versions of these quantities. We extend the most important properties of the unconditional versions to the conditional versions, and also derive some new properties. Based on the conditional maximal correlation, we define an information-correlation function of two arbitrary random variables, and use it to derive an impossibility result for the problem of the non-interactive simulation of random variables.
Walking in a Patient’s Shoes: An Evaluation Study of Immersive Learning Using a Digital Training Intervention

Objectives: Evidence suggests that immersive learning increases empathy and understanding of the patient experience of illness. This study evaluated a digital training intervention ‘In Their Shoes’ which immerses participants in the experience of living with inflammatory bowel disease (IBD), highlighting the biopsychosocial impact. The simulation program uses a mobile application to deliver time-based tasks and challenges over 36 h, supplemented with telephone role-play and ‘kit’ items to open and use. This study investigated changes in IBD understanding and connection to patients, empathy and perception of job value in a group of pharmaceutical employees. Additionally, it explored experiences and impact of taking part in the intervention. Methods: A mixed methods pre–post design was utilized, with an opportunity sample of employees taking part in the training. 104 participants from sites in 12 countries completed measures at baseline and 97 post-intervention. Measures included the Toronto Empathy Questionnaire, Prosocial Job Characteristics Scale, and structured questions around IBD understanding and connection to patients. Two focus groups (N = 14) were conducted regarding participants experiences of the intervention to complement an open-response question in the questionnaire (N = 75). Qualitative data was analyzed using thematic analysis. Results: Following the intervention, there were statistically significant increases in IBD understanding and connection to patients (p < 0.00025), evaluation of organizational innovation (p < 0.00025), empathy (d = 0.45) and prosocial job perceptions (d = 0.28). Qualitative analysis revealed more fully the transformative personal journey undertaken by participants which provided ‘eye opening’ insight into the psychosocial impact of living and working with IBD. This insight encouraged patient perspective-taking and a strong desire to promote patient advocacy and reduce stigma around chronic illness. Finally, greater organizational pride and connectivity was evident for some participants. Conclusions: An immersive training program, focussing on the lived experience of illness, led to significant increases in disease understanding and empathy. These findings align with other literature evaluating immersive learning and the potential for increasing knowledge, empathy and motivation. The present study offers opportunities to extend this outside of the body of work focussing on healthcare practitioners and explores the benefits of using this type of learning experience within an organizational setting.
Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network

Objectives To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC). Materials and Methods In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists. Results The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies. Conclusions We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted.
Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.
Ion Selectivity and Stability Enhancement of SPEEK/Lignin Membrane for Vanadium Redox Flow Battery: The Degree of Sulfonation Effect

A membrane of high ion selectivity, high stability, and low cost is desirable for vanadium redox flow battery (VRB). In this study, a composite membrane is formed by blending the sulfonated poly (ether ether ketone) with lignin (SPEEK/lignin), and optimized by tailoring the degree of sulfonation. The incorporation of lignin into the SPEEK matrix provides more proton transport pathway and meanwhile adjusts the water channel to repulse vanadium ions. The VRB cells assembled with the composite membranes exhibit high coulombic efficiency (~99.27%) and impressive energy efficiency (~82.75%). The cells maintain a discharge capacity of ~95% after 100 cycles and ~85% after 200 cycles at 120 mA cm−2, much higher than the commercial Nafion 212. The SPEEK/lignin composite membranes are promising for application in VRB system.
A Comparison of Lattice-free Discriminative Training Criteria for Purely Sequence-trained Neural Network Acoustic Models

In this work, three lattice-free (LF) discriminative training criteria for purely sequence-trained neural network acoustic models are compared on LVCSR tasks, namely maximum mutual information (MMI), boosted maximum mutual information (bMMI) and state-level minimum Bayes risk (sMBR). We demonstrate that, analogous to LF-MMI, a neural network acoustic model can also be trained from scratch using LF-bMMI or LF-sMBR criteria respectively without the need of cross-entropy pre-training. Furthermore, experimental results on Switchboard-300hrs and Switchboard+Fisher-2100hrs datasets show that models trained with LF-bMMI consistently outperform those trained with plain LF-MMI and achieve a relative word error rate (WER) reduction of ∼5% over competitive temporal convolution projected LSTM (TDNN-LSTMP) LF-MMI baselines.
Plug-In Stochastic Gradient Method

Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm. In this paper, we discuss our recent online variant of PnP that uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We additionally present novel convergence results for both batch and online PnP algorithms.
Securing Behavior-based Opinion Spam Detection

Reviews spams are prevalent in e-commerce to manipulate product ranking and customers decisions maliciously. While spams generated based on simple spamming strategy can be detected effectively, hardened spammers can evade regular detectors via more advanced spamming strategies. Previous work gave more attention to evasion against text and graph-based detectors, but evasions against behavior-based detectors are largely ignored, leading to vulnerabilities in spam detection systems. Since real evasion data are scarce, we first propose EMERAL (Evasion via Maximum Entropy and Rating sAmpLing) to generate evasive spams to certain existing detectors. EMERAL can simulate spammers with different goals and levels of knowledge about the detectors, targeting at different stages of the life cycle of target products. We show that in the evasion-defense dynamic, only a few evasion types are meaningful to the spammers, and any spammer will not be able to evade too many detection signals at the same time. We reveal that some evasions are quite insidious and can fail all detection signals. We then propose DETER (Defense via Evasion generaTion using EmeRal), based on model re-training on diverse evasive samples generated by EMERAL. Experiments confirm that DETER is more accurate in detecting both suspicious time window and individual spamming reviews. In terms of security, DETER is versatile enough to be vaccinated against diverse and unexpected evasions, is agnostic about evasion strategy and can be released without privacy concern.
The Hepatitis C Virus-Induced Membranous Web in Liver Tissue

Host cell membrane rearrangements induced by the hepatitis C virus (HCV) have been exclusively studied in vitro. These studies have shown that HCV induces double-membrane vesicles (DMVs), which probably serve to separate replication sites from the cytoplasmic sensors of the innate immune response. We report for the first time the observation of HCV-induced membrane rearrangements in liver biopsy specimens from patients chronically infected with HCV. Unlike observations performed in vitro, the membranous web detected in liver tissue seems essentially made of clusters of single-membrane vesicles derived from the endoplasmic reticulum and close to lipid droplets. This suggests that the DMVs could be a hallmark of laboratory-adapted HCV strains, possibly due to their ability to achieve a high level of replication. Alternatively, the concealment of viral RNA in DMVs may be part of innate immune response mechanisms particularly developed in hepatoma cell lines cultured in vitro. In any case, this constitutes the first report showing the differences in the membranous web established by HCV in vitro and in vivo.