Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial Resistance and Virulence-Associated Traits of Campylobacter jejuni Isolated From Poultry Food Chain and Humans With Diarrhea

The objective of this study was to test the prevalence of virulence-associated markers and antimicrobial resistance in 624 C. jejuni isolated from poultry food chain, i. e. , chicken feces (n = 160), poultry carcasses (n = 157), poultry meat (n = 152) and from humans (n = 155). All human strains were positive for 9 out of 13 putative virulence genes responsible for expression of pathogenic factors involved in different stages of the infection. The presence of all markers was also high in strains from chicken feces, carcasses and meat although not all of them were identified in 100% of the isolates. On the other hand, the virB11, wlaN, and iam putative pathogenic genes were detected in only 1.9, 15.2, and 20.5% of strains, respectively. C. jejuni isolates, irrespective of the origin, were highly resistant to ciprofloxacin (92.5% isolates), followed by nalidixic acid (88.9%) and tetracycline (68.4%). In case of ciprofloxacin, significantly more isolates from poultry feces, carcasses and meat were resistant than those obtained from humans and the same relationship was observed for tetracycline where the isolates from chicken feces were more often resistant than C. jejuni of carcasses and meat origin. A low number of strains was resistant to streptomycin (18.4% isolates) and only 5 strains (0.8%) displayed resistance to erythromycin. A relationship between resistance to fluoroquinolones and presence of selected pathogenic markers was observed, e.g., from 83.3% strains with the virB11 to 93.4% with the docA genes were resistant to ciprofloxacin. The isolates that did not possess any of the pathogenic traits were also mainly resistant to this antimicrobial, although the number of such strains was usually low, except virB11 (612 isolates), wlaN (529 strains), and iam (496 isolates). Furthermore, resistance to tetracycline was somehow associated with the presence of the virulence associated genes wlaN and virB11 (56.8 and 75.0% isolates, respectively). The present study shows a high antimicrobial resistance to quinolones and tetracycline of C. jejuni isolated along poultry food chain and from patients with diarrhea, which was closely correlated with the presence of several virulence genes playing a role in the pathogenesis of Campylobacter infection.
Exploiting linkedin to predict employee resignation likelihood

Turnover is the organizational movement of hiring and dismissing employees motivated by different reasons, such as termination, retirement, and resignations. High turnover rates can be harmful to organizational productivity, potentially disrupting investments in human resources, generating loss of tacit knowledge and non-scheduled costs with staff replacement. Usually, organizations are unprepared for premature employee resignation, and the problems arising from turnover are even more harmful in this case. Estimating the employees inclination to resignation is paramount to reduce turnover, thereby reducing its negative impact on organizational performance. In this article, we exploit LinkedIn to predict employee resignation likelihood. Particularly, we introduce the ERP approach, which collect professional profiles from LinkedIn and use them as a source of features about employees inclination to resignation. Additionally, we evaluate different algorithms used by ERP to classify employees, considering their resignation likelihood. Experimental results show that the decision tree is the most effective algorithm, classifying correctly more than 88% of the employees. Furthermore the kappa measure show a substantial agreement between the decision tree and an optimal classifier.
Dominance, epsilon, and hypervolume local optimal sets in multi-objective optimization, and how to tell the difference

Local search algorithms have shown good performance for several multi-objective combinatorial optimization problems. These approaches naturally stop at a local optimal set (LO-set) under given definitions of neighborhood and preference relation among subsets of solutions, such as set-based dominance relation, hypervolume or epsilon indicator. It is an open question how LO-sets under different set preference relations relate to each other. This paper reports an in-depth experimental analysis on multi-objective nk-landscapes. Our results reveal that, whatever the preference relation, the number of LO-sets typically increases with the problem non-linearity, and decreases with the number of objectives. We observe that strict LO-sets of bounded cardinality under set-dominance are LO-sets under both epsilon and hypervolume, and that LO-sets under hyper-volume are LO-sets under set-dominance, whereas LO-sets under epsilon are not. Nonetheless, LO-sets under set-dominance are more similar to LO-sets under epsilon than under hypervolume. These findings have important implications for multi-objective local search. For instance, a dominance-based approach with bounded archive gets more easily trapped and might experience difficulty to identify an LO-set under epsilon or hypervolume. On the contrary, a hypervolume-based approach is expected to perform more steps before converging to better approximations.
Expected improvement of constraint violation for expensive constrained optimization

For computationally expensive constrained optimization problems, one crucial issue is that the existing expected improvement (EI) criteria are no longer applicable when a feasible point is not initially provided. To address this challenge, this paper uses the expected improvement of constraint violation to reach feasible region. A new constrained expected improvement criterion is proposed to select sample solutions for the update of Gaussian process (GP) surrogate models. The validity of the proposed constrained expected improvement criterion is proved theoretically. It is also verified by experimental studies and results show that it performs better than or competitive to compared criteria.
Sentiment phrase generation using statistical methods

In this paper, we describe a new algorithm designed to generate lexical resources in the field of sentiment analysis. For this approach, based on corpora of customer reviews, we determine words and phrases as candidates for our sentiment lexicon solely by calculating a word co-occurrence measure and by considering word frequencies. The sentiment values of every single word or phrase are derived automatically from the review titles and the associated given ratings. We consciously renounce the use of natural language processing methods in order to ensure language independency of our algorithm. Furthermore, by using exclusively statistical methods, we are able to identify rather unusual word combinations, such as idiomatic expressions. This differentiates our work from most prior approaches which concentrate on single words or word-modifier combinations. An example lexicon is generated by the use of a corpus of 1.5 million German Amazon customer reviews.
An enhancement algorithm for mobile document image

With the popularity of mobile and mobile camera function more and more powerful, the use of mobile snapshot documents become simple, convenient and economical. But the document image produced by mobile, generally more blurring, change illumination, noise, and its quality is relatively poor. A new method is presented for a local adaptive document image enhancement based on meshing. The problem caused by the grid division are addressed and two new algorithms are applied to solve those problems. The results show that the method adapts and performs well in each case qualitatively and quantitatively.
Leaking data from enterprise networks using a compromised smartwatch device

The recent proliferation of the Internet of Things (IoT) technology poses major security and privacy concerns. Specifically, the use of personal IoT devices, such as tablets, smartphones, and even smartwatches, as part of the Bring Your Own Device (BYOD) trend, may result in severe network security breaches in enterprise environments. Such devices increase the attack surface by weakening the digital perimeter of the enterprise network and opening new points of entry for malicious activities. In this paper we demonstrate a novel attack scenario in an enterprise environment by exploiting the smartwatch device of an innocent employee. The attack scenario establishes a rogue wireless access point using a malicious application running on a capable smartwatch device that imitates a real Wi-Fi direct printer service in the network. Using this scenario, supported by a practical proof of concept, we illustrate how an advanced attacker located outside of the organization can exploit the compromised smartwatch device of the victim user to intercept print jobs sent to a legitimate Wi-Fi direct printer deployed in the network in order to leak/steal sensitive data from the organization.
Toward Code-Aware Robotic Simulation

This vision paper explores the potential to dramatically enrich robotic simulations with insights gleaned from program analysis, and promises to be a key tool for future robot system developers to reduce effort and find tricky corner cases. Robotic simulations are a critical, cost-effective tool for developing, testing, and validating robotic software. However, most robotics simulations are intentionally unaware of how the code works. Our approach leverages two recent developments: 1) automatic program analysis that can semantically ground program variables and predicates in physical quantities like distance, velocity, or force; and 2) standardized simulation specifications that identify both what elements are simulated and also how they are simulated. Code-aware robotic simulation could enable robot system developers who increasingly rely on simulation to lower the cost and risk of system development by having access to richer simulation scenarios. We describe the approach using a detailed, step-by-step illustration for C++ using the Robot Operating System (ROS) and the Simulation Description Format (SDFormat), and identify key challenges to realizing this vision.
Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization

A promising idea for evolutionary constrained optimization is to efficiently utilize not only feasible solutions (feasible individuals) but also infeasible ones. In this paper, we propose a simple implementation of this idea in MOEA/D. In the proposed method, MOEA/D has two grids of weight vectors. One is used for maintaining the main population as in the standard MOEA/D. In the main population, feasible solutions always have higher fitness than infeasible ones. Among infeasible solutions, solutions with smaller constraint violations have higher fitness. The other grid is for maintaining a secondary population where non-dominated solutions with respect to scalarizing function values and constraint violations are stored. More specifically, a single non-dominated solution with respect to the scalarizing function and the total constraint violation is stored for each weight vector. A new solution is generated from a pair of neighboring solutions in the two grids. That is, there exist three possible combinations of two parents: both from the main population, both from the secondary population, and each from each population. The proposed MOEA/D variant is compared with the standard MOEA/D and other evolutionary algorithms for constrained multiobjective optimization through computational experiments.
Sequential sampling for noisy optimisation with CMA-ES

This paper proposes a novel sequential sampling scheme to allocate samples to individuals in order to maximally inform the selection step in Covariance Matrix Adaptation Evolution Strategies (CMA-ES) for noisy function optimisation. More specifically we adopt the well-known Knowledge Gradient (KG) method to minimise the Kullback-Leibler divergence (relative entropy) between the distribution used for generating the next offspring population based on the μ selected individuals, and the distribution based on the true μ best individuals that would have been chosen in the absence of noise. Empirical tests demonstrate the benefit of integrating sequential sampling into CMA-ES, and that the proposed KG technique specifically adapted to the needs of CMA-ES indeed outperforms a more straightforward application of KG.
MediaEval 2018: Predicting Media Memorability

In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. Participants are expected to design systems that automatically predict memorability scores for videos, which reflect the probability of a video being remembered. In contrast to previous work in image memorability prediction, where memorability was measured a few minutes after memorization, the proposed dataset comes with short-term and long-term memorability annotations. All task characteristics are described, namely: the task's challenges and breakthrough, the released data set and ground truth, the required participant runs and the evaluation metrics.
Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. Interestingly, coherency of optical flow is a source of supervision that does not require manual labeling, and can be leveraged during detector training. For example, we can enforce in the training loss function that a detected landmark at framet-1 followed by optical flow tracking from framet-1 to framet should coincide with the location of the detection at framet. Essentially, supervision-by-registration augments the training loss function with a registration loss, thus training the detector to have output that is not only close to the annotations in labeled images, but also consistent with registration on large amounts of unlabeled videos. End-to-end training with the registration loss is made possible by a differentiable Lucas-Kanade operation, which computes optical flow registration in the forward pass, and back-propagates gradients that encourage temporal coherency in the detector. The output of our method is a more precise image-based facial landmark detector, which can be applied to single images or video. With supervision-by-registration, we demonstrate (1) improvements in facial landmark detection on both images (300W, ALFW) and video (300VW, Youtube-Celebrities), and (2) significant reduction of jittering in video detections.
Precious Property or Magnificent Money? How Money Salience but Not Temperature Priming Affects First-Offer Anchors in Economic Transactions

The present study aims for a better understanding of how individuals’ behavior in monetary price negotiations differs from their behavior in bartering situations. Two contrasting hypotheses were derived from endowment theory and current negotiation research to examine whether negotiators are more susceptible to anchoring in price negotiations versus in bartering transactions. In addition, past research found that cues of coldness enhance cognitive control and reduce anchoring effects. We attempted to replicate these coldness findings for price anchors in a distributive negotiations scenario and to illuminate the potential interplay of coldness priming with a price versus bartering manipulation. Participants (N = 219) were recruited for a 2 × 2 between-subjects negotiation experiment manipulating (1) monetary focus and (2) temperature priming. Our data show a higher anchoring susceptibility in price negotiations than in bartering transactions. Despite a successful priming manipulation check, coldness priming did not affect participants’ anchoring susceptibility (nor interact with the price/bartering manipulation). Our findings improve our theoretical understanding of how the focus on negotiation resources frames economic transactions as either unidirectional or bidirectional, and how this focus shapes parties’ susceptibility to the anchoring bias and negotiation behavior. Implications for theory and practice are discussed.
Spatio-Temporal Routine Mining on Mobile Phone Data

Mining human behaviors has always been an important subarea of Data Mining. While it provides empirical evidences to psychological/behavioral studies, it also builds the foundation of various big-data systems, which rely heavily on the prediction of human behaviors. In recent years, the ubiquitous spreading of mobile phones and the massive amount of spatio-temporal data collected from them make it possible to keep track of the daily commute behaviors of mobile subscribers and further conduct routine mining on them. In this article, we propose to model mobile subscribers’ daily commute behaviors by three levels: location trajectory, one-day pattern, and routine pattern. We develop the model Spatio-Temporal Routine Mining Model (STRMM) to characterize the generative process between these three levels. From daily trajectories, the STRMM model unsupervisedly extracts spatio-temporal routine patterns that contain two aspects of information: (1) How people’s typical commute patterns are. (2) How much their commute behaviors vary from day to day. Compared to traditional methods, STRMM takes into account the different degrees of behavioral uncertainty in different timespans of a day, yielding more realistic and intuitive results. To learn model parameters, we adopt Stochastic Expectation Maximization algorithm. Experiments are conducted on two real world datasets, and the empirical results show that the STRMM model can effectively discover hidden routine patterns of human commute behaviors and yields higher accuracy results in trajectory prediction task.
Detecting smartphone state changes through a Bluetooth based timing attack

Bluetooth is a popular wireless communication technology that is available on most mobile devices. Although Bluetooth includes security and privacy preserving mechanisms, we show that a Bluetooth harmless inherent request-response mechanism can taint users privacy. More specifically, we introduce a timing attack that can be triggered by a remote attacker in order to infer information about a Bluetooth device state. By observing the L2CAP layer ping mechanism timing variations, it is possible to detect device state changes, for instance when the device goes in or out of the locked state. Our experimental results show that change point detection analysis of the timing allows to detect device state changes with a high accuracy. Finally, we discuss applications and countermeasures.
A novel fitness function for automated program repair based on source code checkpoints

Software maintenance, especially bug fixing, is one of the most expensive problems in software practice. Bugs have global impact in terms of cost and time, and they also reflect negatively on a company's brand. GenProg is a method for Automated Program Repair based on an evolutionary approach. It aims to generate bug repairs without human intervention or a need for special instrumentation or source code annotations. Its canonical fitness function evaluates each variant as the weighted sum of the test cases that a modified program passes. However, it evaluates distinct individuals with the same fitness score (plateaus). We propose a fitness function that minimizes these plateaus using dynamic analysis to increase the granularity of the fitness information that can be gleaned from test case execution, increasing the diversity of the population, the number of repairs found (expressiveness), and the efficiency of the search. We evaluate the proposed fitness functions on two standard benchmarks for Automated Program Repair: IntroClass and ManyBugs. We find that our proposed fitness function minimizes plateaus, increases expressiveness, and the efficiency of the search.
Better Late ( r ) than Never : Increasing CyberSecurity Compliance by Reducing Present Bias

Despite recent advances in increasing computer security by eliminating human involvement and error, there are still situations in which humans must manually perform computer security tasks, such as enabling automatic updates, rebooting machines to apply some of those updates, or enrolling in two-factor authentication. We argue that present bias—the tendency to discount future risks and gains in favor of immediate gratifications—could be the root cause explaining why many users fail to take such actions. Thus, we systematically explore the application of commitment devices, a technique from behavioral economics, to mitigate the effects of present bias on the adoption of end-user security measures. Offering users the option to be reminded or to schedule such tasks in the future could be effective in increasing their proclivity. While some current systems have begun incorporating such commitment nudges into software update messaging, we are unaware of rigorous scientific research that demonstrates how effective these techniques are, how they may be improved, and how they may be applied to other security behaviors. Using two online experiments, with over 1,000 participants total, we find that both reminders and commitment nudges can be effective at reducing the intentions to ignore the request to enable automatic updates (Study 1), and to install security updates and enable two-factor authentication, but not to configure automatic backups (Study 2). We also find that intentions of Mac OS users are generally more affected by these nudges.
Styling with Attention to Details

Fashion as characterized by its nature, is driven by style. In this paper, we propose a method that takes into account the style information to complete a given set of selected fashion items with a complementary fashion item. Complementary items are those items that can be worn along with the selected items according to the style. Addressing this problem facilitates in automatically generating stylish fashion ensembles leading to a richer shopping experience for users. Recently, there has been a surge of online social websites where fashion enthusiasts post the outfit of the day and other users can like and comment on them. These posts contain a gold-mine of information about style. In this paper, we exploit these posts to train a deep neural network which captures style in an automated manner. We pose the problem of predicting complementary fashion items as a sequence to sequence problem where the input is the selected set of fashion items and the output is a complementary fashion item based on the style information learned by the model. We use the encoder decoder architecture to solve this problem of completing the set of fashion items. We evaluate the goodness of the proposed model through a variety of experiments. We empirically observe that our proposed model outperforms competitive baseline like apriori algorithm by ~28 in terms of accuracy for top-1 recommendation to complete the fashion ensemble. We also perform retrieval based experiments to understand the ability of the model to learn style and rank the complementary fashion items and find that using attention in our encoder decoder model helps in improving the mean reciprocal rank by ~24. Qualitatively we find the complementary fashion items generated by our proposed model are richer than the apriori algorithm.