Miracle-dz/UniFAR-FTUs huggingface dataset:
ORLICZ SPACES AND ENDPOINT SOBOLEV-POINCARÉ INEQUALITIES FOR DIFFERENTIAL FORMS IN HEISENBERG GROUPS ANNALISA BALDI-BRUNO FRANCHI-PIERRE PANSU

In this paper we prove Poincaré and Sobolev inequalities for differential forms in the Rumin’s contact complex on Heisenberg groups. In particular, we deal with endpoint values of the exponents, obtaining finally estimates akin to exponential Trudinger inequalities for scalar function. These results complete previous results obtained by the authors away from the exponential case. From the geometric point of view, Poincaré and Sobolev inequalities for differential forms provide a quantitative formulation of the vanishing of the cohomology. They have also applications to regularity issues for partial differential equations.
Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer

—Motion sensors on current smartphones have been exploited for audio eavesdropping due to their sensitivity to vibrations. However, this threat is considered low-risk because of two widely acknowledged limitations: First, unlike microphones, motion sensors can only pick up speech signals traveling through a solid medium. Thus, the only feasible setup reported previously is to use a smartphone gyroscope to eavesdrop on a loudspeaker placed on the same table. The second limitation comes from a common sense that these sensors can only pick up a narrow band (85-100Hz) of speech signals due to a sampling ceiling of 200Hz. In this paper, we revisit the threat of motion sensors to speech privacy and propose AccelEve, a new side-channel attack that employs a smartphone’s accelerometer to eavesdrop on the speaker in the same smartphone. Speciﬁcally, it utilizes the accelerometer measurements to recognize the speech emitted by the speaker and to reconstruct the corresponding audio signals. In contrast to previous works, our setup allows the speech signals to always produce strong responses in accelerometer measurements through the shared motherboard, which successfully addresses the ﬁrst limitation and allows this kind of attacks to penetrate into real-life scenarios. Regarding the sampling rate limitation, contrary to the widely-held belief, we observe up to 500Hz sampling rates in recent smartphones, which almost covers the entire fundamental frequency band (85-255Hz) of adult speech. On top of these pivotal observations, we propose a novel deep learning based system that learns to recognize and reconstruct speech information from the spectrogram representation of acceleration signals. This system employs adaptive optimization on deep neural networks with skip connections using robust and generalizable losses to achieve robust recognition and reconstruction performance. Extensive evaluations demonstrate the effectiveness and high accuracy of our attack under various settings.
DETERMINATION AND PREDIC- TION OF THE ULTIMATE BEAR- ING CAPACITY OF A STRIP

In the present study, a footing resting on clayey slopes is analysed using finite element analysis, and the results are further used for predicting the bearing capacity of the footing using statistical analyses. The absolute bearing capacity increases with an increase in the embedment depth of the footing, the edge distance and the soil undrained strength. However, the rate of increase of the bearing capacity with the footing depth on the slope is, relatively, less than the level ground. The critical edge distance is found to increase with an increase in the embedment depth of the footing and the slope inclination. In contrast to cohesionless soil slopes, the critical edge distance decreases and the bearing-capacity ratio increases with an increase in the soil strength for a given slope geometry and footing depth. The magnitude of the critical edge distance is found to vary from 0.5 to 5 times the footing width in cohesive soil, which is relatively very small when compared to cohesionless soil. A number of differences have been observed between the failure mechanism of a footing resting on a cohesive soil slope and a cohesionless soil slope, which contributes to the differences in their behaviour. The soil strength and the slope inclination are found to be the two most important factors affecting the bearing capacity of a footing on slopes. The predicted values of the bearing capacity are compared with the existing experimental and theoretical values. Rajesh Prasad Shukla Adani Institute of Infrastructure Engineering, Department of Civil and Infrastructure Engineering
Aggression and Learning Behavior during the Transition from Preschool to School. Relations among Parent, Preschool Teacher and School Teacher Ratings

Children’s aggressive and learning behavior, academic skills and cognitive development before school and in school, and children’s adjustment in school is studied, using parent, preschool teacher and school teacher reports. Data were collected twice – first, in spring in preschool and, second, in fall in school. The participants were 151 6–7-year-old children (77 boys and 74 girls), their parents, preschool and school teachers. Preschool aggression and learning behaviors were related to school behaviors, skills and victimization. In addition to preschool teachers’ reports, parents’ reports had an additional predictive value for aggression. The concordance between preschool teachers’ and parents’ evaluations was not high. Aggression and Learning Behaviour 3 Aggression and Learning Behavior during the Transition from Preschool to School. Relations among Parent, Preschool Teacher and School Teacher
Harnessing the power of advocacy to improve seasonal influenza vaccination coverage in the Eastern Mediterranean Region.

1Department of Experimental Pathology, Immunology & Microbiology, Faculty of Medicine, American University of Beirut, Beirut, Lebanon. 2Center for Infectious Diseases Research, Faculty of Medicine, American University of Beirut, Beirut, Lebanon. 3Infectious Hazard Management, Department of Health Emergencies, World Health Organization, Regional Office for the Eastern Mediterranean, Cairo, Egypt. (Correspondence to: Abdinasir Abubakar: abubakara@who.int)
Indigenous Development of Carbon Aerogel Farad Super-capacitors and Applications in Electronic circuits

Indigenous R&D efforts are taken to develop carbon aerogel based super-capacitors also known as electric double layer capacitors (EDLC). They possess wide application potentials in many electronics industry including applications in power electronics, UPS, high speed communication systems requiring ‘burst currents’, powering electronics in airborne defence strike systems, powering sensors in remote boarder areas, electric vehicles, pulsed power devices and Micro-Grid power systems, solar-energy to electrical energy conversion systems (for improving store/load performance and reducing the size of battery based systems), which are important R&D programmes of our immediate interest toward the national needs. Here we report the major achievements on the development of aerogel super-capacitors and applications in electronic circuits. Carbon aerogel supercapacitors of cell capacitance in the range of 1-35 F@/2.5V were developed using carbon aerogel as the active electrode materials, and some demonstration circuits were made.
Not All Coverage Measurements Are Equal: Fuzzing by Coverage Accounting for Input Prioritization

—Coverage-based fuzzing has been actively studied and widely adopted for ﬁnding vulnerabilities in real-world software applications. With coverage information, such as statement coverage and transition coverage, as the guidance of input mutation, coverage-based fuzzing can generate inputs that cover more code and thus ﬁnd more vulnerabilities without prerequisite information such as input format. Current coverage-based fuzzing tools treat covered code equally. All inputs that contribute to new statements or transitions are kept for future mutation no matter what the statements or transitions are and how much they impact security. Although this design is reasonable from the perspective of software testing that aims at full code coverage, it is inefﬁcient for vulnerability discovery since that 1) current techniques are still inadequate to reach full coverage within a reasonable amount of time, and that 2) we always want to discover vulnerabilities early so that it can be ﬁxed promptly. Even worse, due to the non-discriminative code coverage treatment, current fuzzing tools suffer from recent anti-fuzzing techniques and become much less effective in ﬁnding vulnerabilities from programs enabled with anti-fuzzing schemes. To address the limitation caused by equal coverage, we propose coverage accounting , a novel approach that evaluates coverage by security impacts. Coverage accounting attributes edges by three metrics based on three different levels: function, loop and basic block. Based on the proposed metrics, we
CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Cloud-based Machine Learning as a Service (MLaaS) is gradually gaining acceptance as a reliable solution to various real-life scenarios. These services typically utilize Deep Neural Networks (DNNs) to perform classification and detection tasks and are accessed through Application Programming Interfaces (APIs). Unfortunately, it is possible for an adversary to steal models from cloud-based platforms, even with black-box constraints, by repeatedly querying the public prediction API with malicious inputs. In this paper, we introduce an effective and efficient black-box attack methodology that extracts largescale DNN models from cloud-based platforms with near-perfect performance. In comparison to existing attack methods, we significantly reduce the number of queries required to steal the target model by incorporating several novel algorithms, including active learning, transfer learning, and adversarial attacks. During our experimental evaluations, we validate our proposed model for conducting theft attacks on various commercialized MLaaS platforms hosted by Microsoft, Face++, IBM, Google and Clarifai. Our results demonstrate that the proposed method can easily reveal/steal large-scale DNN models from these cloud platforms. The proposed attack method can also be used to accurately evaluates the robustness of DNN based MLaaS classifiers against theft attacks.
USING THE ALTERNATIVE MINIMUM TAX TO ESTIMATE THE ELASTICITY OF TAXABLE INCOME FOR HIGHER- INCOME TAXPAYERS

This paper exploits large kinks generated by the intersection of the regular income tax and the alternative minimum tax (AMT) schedules to provide fresh estimates of the elasticity of taxable income with respect to the net-of-tax rate. Such cross-schedule interactions can confound the location of analyzable kinks if either of the two schedules are considered independently of each other. Further, the magnitude of the kink resulting from this interaction is greater than for kinks found separately on the two tax schedules, potentially generating more substantial behavioral responses. I use publicly available Statistics of Income (SOI) individual tax return data from 1993-2011 to exploit bunching around the “intersection kink” point and generate estimates of the elasticity of reported income for higher-income individuals in the United States. Estimated elasticity of taxable income around the intersection kink is 0.08, an order of magnitude higher than earlier estimates using only the regular tax schedule, and eight times as high as in other countries such as Denmark. Preferred estimates are higher at 0.15.
Parameter Space Noise for Exploration in Deep Reinforcement Learning

The success of any reinforcement learning algorithm hinges on its ability to explore efficiently. While many sophisticated approaches have been proposed in recent years, today’s state-of-the-art algorithms still rely on traditional action space noise due to its simplicity. In this thesis, we propose a novel method: parameter space noise. Parameter space noise is conceptually simple yet allows state-of-the-art algorithms to learn in environments in which traditional action space noise exploration fails. We theoretically justify why we expect this approach to outperform traditional action space noise, analyze potential pitfalls when applying it to policies realized by deep neural networks, and propose approaches that mitigate these problems. Next, we evaluate our proposed approach on a large variety of different environments where we show that parameter space noise indeed outperforms action space noise in the majority of cases. We also demonstrate that parameter space noise can be combined with other exploration methods and show that this combination is capable of learning successful strategies on a set of complex and realistic robot manipulation tasks.
STRATIFIED SAMPLING FOR RISK MANAGEMENT

The authors discuss the approximation of Value at Risk (VaR) and other quantities relevant to risk management. One of the core problems in this context is the approximation of the distribution of quadratic forms of Gaussian vectors. It appears as an intermediate problem in the variance reduction techniques proposed by Glasserman et. al. as well as in so-called ∆-Γ-normal approaches. The purpose of this paper is to show that sampling methods are faster than Fourier inversion for a range of practical problems. In fact, the asymptotic cost to achieve a given accuracy is O(d) for Monte-Carlo and O(d) for Fourier inversion in a specific setting. The theoretical results are supported by a case study based on real-life problems, showing that sampling methods are faster than Fourier inversion for all but the smallest problems. Thus the use of randomized methods is recommended and the issue of variance reduction becomes important. Stratification methods – especially randomized orthogonal arrays – turn out to lead to the most effective methods in terms of accuracy per computational cost. 1. Problem formulation Given a portfolio, the typical task of a bank’s risk controller is to model the change in the portfolio’s value over a certain time horizon h in the future. If S t denotes the value of asset i at time t and w i t the number of units of assets of type i in the portfolio, then
Cytogenetic Characterization of the Lower-Attine Mycocepurus goeldii (Formicidae: Myrmicinae: Attini)

The objective of this study was the cytogenetic characterization number, morphology and banding pattern of Mycocepurus goeldii Forel, 1893 as a contribution for a better comprehension of the evolution of the Attini tribe, a particular group of ants that obligately depend on the cultivation of fungus for food. The chromosome number observed was 2n=8 chromosomes (2K=8M). C-banding technique indicated positive markings in the centromeric regions of three pairs of chromosomes, and the second-largest pair had a pericentromeric band on its long arm. Also, C-banding markings were coincident with CMA3 fluorochrome markings. The genus Mycocepurus is characterized by the lowest chromosome number in the Attini tribe, suggesting the karyotype to be relict, based on Minimum Interaction Theory. This plesiomorphy confirms this genus in a basal condition relative to the remaining Attini.
Degradation Characteristics of Pentachlorophenol in Soil under Different Plasmas Using Pulsed Electrical Discharge

Pentachlorophenol (PCP) is a wood preserving agent that is commonly found in contaminated soils at wood treatment sites. The degradation characteristics of PCP under different plasmas (such as O2 plasma, air plasma and N2 plasma) were studied using pulsed discharge plasma. PCP degradation efficiencies in soil were greatly influenced by different plasmas. The removal efficiency ranked in the following order: O2 plasma> air plasma> N2 plasma. Almost 90%, 65% and 20% of PCP removal could be achieved within 15 min of discharge treatment under O2, air and N2 plasmas with the energy yields of 2.69, 1.95 and 0.60 g/kWh, respectively. The FT-IR results under diverse plasmas revealed that partial C-Cl bonds in PCP were cleaved during pulsed discharge plasma treatment. The mineralization of PCP was confirmed by the regular decrease in quantity of total organic carbon (TOC) and the quantitative release of chloride ions. Dechlorination corresponded to about 78%, 57% and 12% of total conversion of PCP under O2, air and N2 plasmas, respectively. The trend of the amount of organic chlorine in organic products indicated that the chlorinated intermediates were produced during pulsed discharge plasma treatment, and then they were undergone the dechlorination reactions as the process continued. Ozone played a decisive role for the removal process. However, in the case of N2 plasma, hydroxylation of PCP and atomic N, N2 and N attack might be the degradation pathways for PCP degradation. Keywords—Contaminated soil, pulsed corona discharge, different plasmas, pentachlorophenol Corresponding author: Jie Li e-mail address: lijie@dlut.edu. cn Presented at the Seventh International Symposium on NonThermal/Thermal Plasma Pollution Control Technology & Sustainable Energy, ISNTP-7, in June 2010 101 International Journal of Plasma Environmental Science & Technology, Vol.4, No.2, SEPTEMBER 2010
Does touching information on a surface tablet affect how it is evaluated?

Actual as well as virtual touch have been shown to increase object valuation. The current study investigated its impact on information evaluation, perceived ownership of information, and information recognition. In a preregistered experimental study 69 participants controlled the presentation of information items by touching them on the screen (ntouch = 36) vs. via keyboard (nno-touch = 33). We tested our hypotheses using both a within(touched vs. untouched items) and a between-participant approach (touch vs. no-touch condition). Analyses did not support any of our hypotheses; bootstrapped confidence intervals for the statistical parameters narrowly enclosed zero. These results suggest that potential effects of touch-based interaction on judgments of verbal content are likely very small.
SurfingAttack: Interactive Hidden Attack on Voice Assistants Using Ultrasonic Guided Waves

—With recent advances in artiﬁcial intelligence and natural language processing, voice has become a primary method for human-computer interaction. It has enabled game-changing new technologies in both commercial sectors and military sectors, such as Siri, Alexa, Google Assistant, and voice-controlled naval warships. Recently, researchers have demonstrated that these voice assistant systems are susceptible to signal injection at the inaudible frequencies. To date, most of the existing works focus primarily on delivering a single command via line-of-sight ultrasound speaker or extending the range of this attack via speaker array. However, besides air, sound waves also propagate through other materials where vibration is possible. In this work, we aim to understand the characteristics of this new genre of attack in the context of different transmission media. Furthermore, by leveraging the unique properties of acoustic transmission in solid materials, we design a new attack called SurﬁngAttack that would enable multiple rounds of interactions between the voice-controlled device and the attacker over a longer distance and without the need to be in line-of-sight. By completing the interaction loop of inaudible sound attack, SurﬁngAttack enables new attack scenarios, such as hijacking a mobile Short Message Service (SMS) passcode, making ghost fraud calls without owners’ knowledge, etc. To accomplish SurﬁngAttack , we have solved several major challenges. First, the signal has been specially designed to allow omni-directional transmission for performing effective attacks over a solid medium. Second, the new attack enables multi-round interaction without alerting the legitimate user at the scene, which is challenging since the device is designed to interact with users in physical proximity rather than sensors. To mitigate this newly discovered threat,
Who Bears the Burden of Local Taxes?

We study the incidence of local taxes on the welfare of heterogeneous residents. A structural model of imperfectly mobile households who differ in terms of income and family status allows us to back out type-specific preferences for local public goods. We calibrate the model with plausibly causal tax-base and housing-price elasticity estimates. Based on municipality-level data for Switzerland, we find that households with children perceive locally provided public services as a normal good, whereas households without children perceive them as an inferior good. This in turn implies that the burden of local taxes is mainly borne – linearity of taxes and capitalization into lower housing prices notwithstanding – by top-25% income households without children. JEL Classification: H24, H71, R21, R31
Metamorph: Injecting Inaudible Commands into Over-the-air Voice Controlled Systems

—This paper presents Metamorph, a system that generates imperceptible audio that can survive over-the-air transmission to attack the neural network of a speech recognition system. The key challenge stems from how to ensure the added perturbation of the original audio in advance at the sender side is immune to unknown signal distortions during the transmission process. Our empirical study reveals that signal distortion is mainly due to device and channel frequency selectivity but with different characteristics. This brings a chance to capture and further pre-code this impact to generate adversarial examples that are robust to the over-the-air transmission. We leverage this opportunity in Metamorph and obtain an initial perturbation that captures the core distortion’s impact from only a small set of prior measurements, and then take advantage of a domain adaptation algorithm to reﬁne the perturbation to further improve the attack distance and reliability. Moreover, we consider also reducing human perceptibility of the added perturbation. Evaluation achieves a high attack success rate (90%) over the attack distance of up to 6 m. Within a moderate distance, e.g. , 3 m, Metamorph maintains this high success rate, yet can be further adapted to largely improve the audio quality, conﬁrmed by a human perceptibility study.
Improving Student Motivation and Attitudes in Learning English as a Second Language; Literature as Pleasurable Reading: Applying Garner’s Theory of Multiple Intelligences and Krashen’s Filter Hypothesis

Literature is an essential tool for English as a Foreign Language (EFL) teaching that provides students with an opportunity to practice language skills. Literature further helps students to explore the various facets of language, such as grammar, vocabulary, spelling, intonation, stress, and pronunciation. This article applies Garner’s Theory of Multiple Intelligences and Krashen’s Filter Hypothesis to clarify how motivation and other attitudinal factors affect a learner’s ability to learn English as a Second Language while proposing an alternative perspective for English language learning. The concepts discussed in this article, therefore, address the attitudinal factors that affect a learner’s understanding of English by utilizing four elements: 1) motivation, 2) attitude, 3) anxiety, and 4) self-confidence as a way to demonstrate how, rather than through vocabulary overload, the literary experience in English as a Second Language teaching can be improved for students through pleasurable reading.
THE IWASAWA MAIN CONJECTURES FOR GL2 AND DERIVATIVES OF p-ADIC L-FUNCTIONS

We prove under mild hypotheses the three-variable Iwasawa Main Conjecture for p-ordinary modular forms base changed to an imaginary quadratic field K in which p splits in the indefinite setting (in the definite setting this is a result due to Skinner–Urban). Being in a setting encompassing Heegner points and their variation in p-adic families, our main result has new applications to Greenberg’s nonvanishing conjecture for central derivatives of p-adic L-functions of Hida families with root number −1.