Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to classify human object sketches

We present ongoing work on object category recognition from binary human outline sketches. We first define a novel set of 187 "sketchable" object categories by extracting the labels of the most frequent objects in the LabelMe dataset. In a large-scale experiment, we then gather a dataset of over 5,500 human sketches, evenly distributed over all categories. We show that by training multi-class support vector machines on this dataset, we can classify novel sketches with high accuracy. We demonstrate this in an inter-active sketching application that progressively updates its category prediction as users add more strokes to a sketch.
Nonembeddability theorems via Fourier analysis

Various new nonembeddability results (mainly into L1) are proved via Fourier analysis. In particular, it is shown that the Edit Distance on {0,1}d has L1 distortion We also give new lower bounds on the L1 distortion of flat tori, quotients of the discrete hypercube under group actions, and the transportation cost (Earthmover) metric.
Protection of consumer data in the smart grid compliant with the German smart metering guideline

Smart metering systems obtain fine-grained consumption data of their users. This allows for effective load balancing, but at the same time threatens consumers' privacy. Since the electricity provider only needs the characteristics of a region, not individuals, approaches like one by Mármol et al. suggest to aggregate data to protect consumer privacy. However, an implementation of such an approach also has to consider the legal and regulatory situation. In Germany, the technical guideline TR-03109 issued by the Federal Office for Information Security specifies demands which have to be fulfilled so that a smart meter gateway can be certified for use. These specifications imply limitations to the protocol design. Within this paper, we discuss the applicability of the method presented by Mármol et al. under consideration of the German Smart Metering guideline. Where conformity is not given, we offer a solution to overcome these restrictions by adapting their method and introduce a third party aggregator who does not have to be trusted. Our method comes with additional communication effort but behaves well in terms of memory and computational overhead. The achieved privacy level outreaches a purely pseudonymous value transmission. Also it does not contradict the postulations of TR-03109, making it an applicable choice for privacy protection in real-world smart metering systems.
Secure data hiding using steganographic technique with Visual cryptography and watermarking scheme

The Visual cryptography scheme (VCS) is a secure method that encrypts a secret image into shares. The key idea behind the proposed approach deals with information hiding in image using Zig-Zag scanning pattern which is more complex algorithm in Steganography again encrypted as shares by VC technique for embedding in separate host images to provide authentication for the VC shares which makes these secret shares invisible by embedding them into host images. The secret shares generated from VC encryption are watermarked into some host images using digital watermarking. Digital watermarking is used for providing the double security of image shares. The share is embedded into the host image using Least Significant Bit Insertion Technique (LSB). The scheme provides more secure and meaningful secret shares that are robust against a number of attacks. The performance of the proposed system is evaluated using peak signal to noise ratio (PSNR), histogram analysis and also numerical experimentation suggests that embedding time varies linearly with message length. The simulation results show that, the proposed system provides high level of security.
Englishisation at a global space: students and staff making sense of language choices

ABSTRACT This study starts from the premise that academic mobility contributes to the development of students’ plurilingual identities and that study abroad contexts aiming at becoming global spaces are particularly interesting sites to explore the individuals’ discursive work to (re-)construct their plurilingual identities by reconciling their language ideology with specific practical and institutional constraints. Two focus-group discussions with students from a Catalan/Spanish bilingual institution selected to take part in an Erasmus exchange in a Danish university, and one with academic and administrative staff of this host university, have been analysed using Membership Categorisation Analysis. The analysis reveals the discursive ‘methods’ that students and staff, as plurilingual speakers whose repertoire includes a lesser-spoken language such as Danish and Catalan, display in order to ‘make sense’ of their language choice, which places the local (Danish) language in a clearly subsidiary position in the life of the university.
Text classification with sparse composite document vectors

In this work, we present a modified feature formation technique - graded-weighted Bag of Word Vectors (gwBoWV) by Vivek Gupta, 2016 for faster and better composite document feature representation. We propose a very simple feature construction algorithm that potentially overcomes many weaknesses in current distributional vector representations and other composite document representation methods widely used for text representation. Through extensive experiments on multi-class classification on 20newsgroup dataset and multi-label text classification on Reuters-21578, we achieve better performance results and also a significant reduction in training and prediction time compared to composite document representation methods gwBoWV and TWE Liu et al., 2015b.
Absorption enhancement in hexagonal plasmonic solar cell

In this paper, a novel design of hexagonal cylindrical plasmonic solar cell (HC-PSC) is investigated and analysed by using 3D finite difference time domain method. The aim of our work is to get maximum absorption of thin film solar cell by scattering the light from metal nanoparticles. Therefore the effects of the structure geometrical parameters on the absorption are investigated. The numerical results show around 35% absorption improvement compared to the conventional thin film solar cell without metal nanoparticles.
Women’s Preference for a Male Acquaintance Enhances Social Reward Processing of Material Goods in the Anterior Cingulate Cortex

Men, like the male of many animal species, use gifts to build satisfactory relationships with a desired woman. From the woman’s perspective, all gifts are not always equally rewarding; the reward value of a gift depends on two factors: (1) the giver and (2) the type of the gift (the gift’s social meaning). In this study, we investigated how these two factors interactively determine the reward value of a gift. Specifically, we examined how the neural processing for understanding a gift’s social meaning is modulated by preferences for the giver. We performed a functional magnetic resonance imaging (fMRI) study in which a female participant was asked to judge a gift from a male she was acquainted with in real life. We examined the interactive effects between (1) the female participant’s attitude toward the male acquaintance (liked vs. uninteresting) and (2) the type of the gift (romantic [e.g., bouquet, earrings, and perfumes] vs. non-romantic [e.g., pencils, memo pad, and moneybox]). We found that preference for an acquaintance selectively modulated activity in the anterior cingulate cortex (ACC) in response to romantic gifts, compared to non-romantic gifts. In contrast, if the woman was indifferent toward an acquaintance, no activity modulation was observed in this area for the same gifts. In addition, the ACC showed functional connectivity with the supplementary motor area/dorsal ACC (SMA/dACC), an area within the dorsal mediofrontal cortex, suggesting that it integrates action monitoring and emotional and cognitive processing in decision-making. These results suggest that attitude toward an opposite sex member has a modulatory role in recognizing the social meaning of material goods—preference for the member is a powerful modulator of social reward processing.
Comparative Performance Analysis of Filtered-xLMS and Feedback ANC Adaptive Algorithms to Control the Attenuation of Acoustic Noise

This paper presents the comparative performance analysis of two adaptive algorithms to control the attenuation of acoustic noise. Active noise control (ANC) is one of the most popular applications for adaptive filters. The basic idea behind the active control of acoustic noise is to inject a secondary sound (anti-noise) into an environment so as to cancel the primary sound using destructive interference. So, ANC is a method of reducing the unwanted noise by actively generating an anti-noise, cancelling out the noise. To achieve this objective, throughout this paper, I describe two well-known filtered-x least Mean Square (filtered-x LMS) and feedback active noise control algorithms which provide a new structure for improving acoustic noise reduction. Finally, from the simulation results, it is obvious that the proposed adaptive algorithms can effectively control the acoustic narrowband noise from the corrupted speech signal.
Software for detecting suspected plagiarism: comparing structure and attribute-counting systems

The early automated systems for detecting plagiarism in student programs employed attribute-counting techniques in their comparisons of programs. It has been claimed that the more recent, automated systems, which encode structural information, are more effective in their detection of plagiarisms than systems based on attribute-counting. To explore the validity of these claims, a comparison is presented of two systems based on attribute counting and a structure-metric system. The major result of this study is that the systems based on structural information consistently equal or better the performance of systems based on attribute-counting metrics. A distinction is also made between plagiarisms due to novice programmers and those of more experienced programmers.
A Patient with Posterior Cortical Atrophy Possesses a Novel Mutation in the Presenilin 1 Gene

Posterior cortical atrophy is a dementia syndrome with symptoms of cortical visual dysfunction, associated with amyloid plaques and neurofibrillary tangles predominantly affecting visual association cortex. Most patients diagnosed with posterior cortical atrophy will finally develop a typical Alzheimer's disease. However, there are a variety of neuropathological processes, which could lead towards a clinical presentation of posterior cortical atrophy. Mutations in the presenilin 1 gene, affecting the function of γ-secretase, are the most common genetic cause of familial, early-onset Alzheimer's disease. Here we present a patient with a clinical diagnosis of posterior cortical atrophy who harbors a novel Presenilin 1 mutation (I211M). In silico analysis predicts that the mutation could influence the interaction between presenilin 1 and presenilin1 enhancer-2 protein, a protein partner within the γ-secretase complex. These findings along with published literature support the inclusion of posterior cortical atrophy on the Alzheimer's disease spectrum.
A Lower Bound by One-against-all Decomposition for Potts Model Energy Minimization

Energy minimization in computer vision can be formulated as minimizing a separable function of discrete variables. Such formulation arises, in particular, in Gibbs probabilistic models, which are suitable for many low-level image processing tasks. A special type of this probabilistic model, the Potts model, was successfully applied in segmen- tation, stereo and image denoising. Importantly, the Potts model is general enough to incorporate difficult problems: the energy minimization remains NP-hard. In this paper, we investigate a new linear relaxation for the Potts model. The relaxation is obtained by considering a family of decompositions of the energy function in the fash- ion of one label vs. all other. The tightest lower bound in the family constitutes a linear maximization program, for which we propose a coordinate ascent method. We show that our bound is dual to the known LP-relaxation of energy mini- mization, for which several dedicated solvers are available. Our new formulation could lead to more efficient algorithms (this was not confirmed yet) and to new per-variable-label bounds. The letter should allow to reject some selections which are not optimal. These bounds may be used efficiently, e.g., in branch and bound algorithm.
Yes, But

Reflecting the wide range of consensual unwanted sexual experiences, researchers often have contrasting views of the impact of these incidents on young women. Some scholars support a normalizing view of these as fairly harmless and ordinary aspects of relationships, akin to other forms of willing compromises between partners. Other researchers problematize unwanted sexual experiences, framing them in terms of gender inequalities and detrimental effects. In the current study, we were interested in how young women themselves characterized their unwanted sexual experiences and whether these accounts varied according to a woman’s social location. We interviewed 41 young women (18–22 years old) from three groups: affluent undergraduates, low-income undergraduates, and low-income nonstudents. Almost all of the affluent undergraduates framed their unwanted sexual experiences in normalizing terms, representing such events as relatively harmless incidents and outgrowths of developmental experimentation. In contrast, the low-income students and nonstudents both articulated more ambivalent positions and were more inclined to link their experience to sources of vulnerability, including personal adversity (e.g., trauma, social, and material insecurity) and social norms and stigma. Participants’ sexual histories, life circumstances, and standpoints at the intersection of gender and class were reflected in their experiences of unwanted sex, reinforcing that contextualized analyses and interventions are essential to advancing women’s sexual rights and well-being. Online slides for instructors who want to use this article for teaching are available on PWQ's website at http://pwq.sagepub. com/supplemental
English Digits Recognition using MFCC, LPC and Pearson's Correlation

Automatic speech recognition is an important topic of speech processing. This paper presents the use of a Pearson's correlation for English digits recognition. The Pre- processing is done and voiced speech is detected based on energy and zero crossing rates (ZCR). The proposed approach used in speech recognition is Mel Frequency Cepstral Coefficients (MFCC) and combine features of both MFCC and Linear Predictive Coding (LPC). The Pearson's correlation is used as a classifier. The recognition accuracy is increased when combine features of both LPC and MFCC are used as compared to only MFCC approach using Pearson's correlation as a classifier.
Limits to and prospects of entrepreneurship education in the academic context

Abstract Process philosophy has drawn attention to the world as ambiguous and ever changing, however also enactable. This makes entrepreneurship a processual phenomenon, rightly addressed as ‘entrepreneuring’. Recognizing not only their cognitive, yet also affective and conative capabilities, makes it possible for human actors to mobilize forces that bring the world to a standstill long enough to create a venture for value creation. This, however, calls for the insight that is different to universal scientific knowledge – episteme and techne – namely, the situated insights that Aristotle addressed as mētis and phronesis. Mētis then concerns alertness and shrewdness and phronesis is about prudence in the context of action. Academic education can only provide these competencies needed to train for entrepreneuring by letting the students travelling across the boundaries of the university. In addition, the dominance of management as an ideology must be proactively dealt with. Three cases in academic training for entrepreneuring, all in the Swedish context, which show radically different ways of dealing with these challenges, are presented in a comparative analysis. The lessons are summarized in general conditions for providing training that advances entrepreneurship students’ situated and actionable insights.
Modeling segmentation cuts using support vector machines

In this paper we propose a method to evaluate segmentation cuts for handwritten touching digits. The idea of this method is to work as a filter in segmentation-based recognition systems. These types of systems usually rely on over-segmentation methods, where several segmentation hypotheses are created for each touching group of digits and then assessed by a general-purpose classifier. Through the use of the proposed method, unnecessary segmentation cuts can be identified without any attempt of classification by a general-purpose classifier, reducing the number of paths in a segmentation graph, what can consequently lead to a reduction in computational cost. Concavity analysis is performed in each digit before and after segmentation. The difference of those concavities is used to model the segmentation cuts. SVM is used to classify those segmentation cuts. The preliminary results obtained are very promising as for the segmentation algorithm tested, 67.9% of the unnecessary segmentation cuts were eliminated. Moreover, it was possible to achieve a significant increase in the recognition rate for the generalpurpose classifier.
How Neutral is a CDN? An economic approach

The growing importance of Content Delivery Network (CDN) in the value chain of content delivery raises concerns about the “neutrality” of these players. We propose in this paper a model to analyze the impact of revenue-oriented CDN management policies on the fairness of the competition among two content providers that use CDN services to deliver contents. We show that there exists a unique optimal revenue-maximizing policy for a CDN actor-the dimensioning and allocation of its storage capacity-that depends on prices for service/transport/storage, and on the distribution of content popularity. Using data from the analysis of traces from two major content providers (YouTube Live and justin.tv), we remark that a CDN remains a relatively neutral actor even when one of the content providers it serves tries to monopolize the CDN storage space by implementing an aggressive policy to harm its competitors.
Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.
Towards multiscale mesh denoising

Recently developed methods address the problem of denoising polygonal meshes derived from automatic scanning procedures. Nonlinear filtering is applied iteratively based on the estimated statistical properties of errors associated with the measured vertex locations. This procedure requires a rather uniform sampling interval across the surface and cannot remove noise below a certain frequency due to the local support of the filter kernel. We consider an approach to remedy these problems by interleaving mesh denoising and mesh simplification based on the quadric error metric. Instead of directly operating on vertex locations, the error quadrics (which are evaluated to determine vertex locations) are manipulated. Therefore denoising is applied at different scales according to the progress of simplification, being less sensitive to varying resolution and low-frequency noise. This is work in progress, which reveals some interesting properties in the context of denoising and simplification. However, the desired results have not yet been achieved.
Bevacizumab increases the risk of arterial ischemia: a large study in cancer patients with a focus on different subgroup outcomes.

BACKGROUND Bevacizumab, a humanized monoclonal antibody targeting the vascular endothelial growth factor, is a therapeutic agent used in a variety of neoplasms. We did a meta-analysis of randomized controlled trials to fully characterize the arterial thromboembolic events (ATEs) risk with bevacizumab in certain patients' subgroups. MATERIALS AND METHODS We carried out a literature search on Medline for randomized trial reported from January 1966 to December 2009. Abstracts presented at the American Society of Clinical Oncology held between 2004 and 2009 were also searched for relevant clinical trials. Summary incidence, relative risks (RRs) and 95% confidence intervals (CIs) were calculated using random-effects or fixed-effects models based on the heterogeneity of included studies. RESULTS A total of 13,026 patients from 20 randomized trials were included in the meta-analysis. Overall RR for ATE with bevacizumab-based therapy versus controls was 1.46 (95% CI 1.11-1.93, P = 0.007). On subgroup analysis, no significant risk differences were found based on the type of malignancy, type of clinical trial (phase II or III trials), type of publication (full papers versus presentations), high- versus low-dose bevacizumab and early versus advanced disease trials. When stratified by concomitant therapies, we found that gemcitabine-based regimens had a significant lower ATE risk compared with non-gemcitabine regimens (P = 0.01). CONCLUSIONS Bevacizumab treatment is associated with a significant increase in the risk of arterial thrombosis. Our results seem to be generalizable to the vast majority of patients receiving bevacizumab in multiple settings.
Asymptotic hitting time for a simple evolutionary model of protein folding

We consider two versions of a simple evolutionary algorithm (EA) model for protein folding at zero temperature, namely the (1 + 1)-EA on the LeadingOnes problem. In this schematic model, the structure of the protein, which is encoded as a bit-string of length n, is evolved to its native conformation through a stochastic pathway of sequential contact bindings. We study the asymptotic behavior of the hitting time, in the mean case scenario, under two different mutations: the one-flip, which flips a unique bit chosen uniformly at random in the bit-string, and the Bernoulli-flip, which flips each bit in the bit-string independently with probability c/n, for some c ∈ R + (0 ≤ c ≤ n). For each algorithm, we prove a law of large numbers, a central limit theorem, and compare the performance of the two models.