nils-herrmann/clean_scirepeval huggingface dataset:
How (not) to Use Welch's T-test in Side-Channel Security Evaluations

The Test Vector Leakage Assessment (TVLA) methodology is a qualitative tool relying on Welch’s T-test to assess the security of cryptographic implementations against side-channel attacks. Despite known limitations (e.g., risks of false negatives and positives), it is sometimes considered as a pass-fail test to determine whether such implementations are “safe” or not (without clear definition of what is “safe”). In this note, we clarify the limited quantitative meaning of this test when used as a standalone tool. For this purpose, we first show that the straightforward application of this approach to assess the security of a masked implementation is not sufficient. More precisely, we show that even in a simple (more precisely, univariate) case study that seems best suited for the TVLA methodology, detection (or lack thereof) with Welch’s T-test can be totally disconnected from the actual security level of an implementation. For this purpose, we put forward the case of a realistic masking scheme that looks very safe from the TVLA pointof-view and is nevertheless easy to break. We then discuss this result in more general terms and argue that this limitation is shared by all “moment-based” security evaluations. We conclude the note positively, by describing how to use moment-based analyses as a useful ingredient of side-channel security evaluations, to determine a “security order”.
An analysis for a just cyber warfare

This article focuses on the ethical analysis of cyber warfare, the warfare characterised by the deployment of information and communication technologies. It addresses the vacuum of ethical principles surrounding this phenomenon by providing an ethical framework for the definition of such principles. The article is divided in three parts. The first one considers cyber warfare in relation to the so-called information revolution and provides a conceptual analysis of this kind of warfare. The second part focuses on the ethical problems posed by cyber warfare and describes the issues that arise when Just War Theory is endorsed to address them. The final part introduces Information Ethics as a suitable ethical framework for the analysis of cyber warfare, and argues that the vacuum of ethical principles for this kind warfare is overcome when Just War Theory and Information Ethics are merged together.
Robust dual-band MWIR/LWIR infrared target tracking

We introduce an SIR particle filter for tracking civilian targets including vehicles and pedestrians in dual-band midwave/longwave infrared imagery as well as a novel dual-band track consistency check for triggering appearance model updates. Because of the paucity of available dual-band data, we constructed a custom sensor to acquire the test sequences. The proposed algorithm is robust against magnification changes, aspect changes, and clutter and successfully tracked all 17 cases tested, including two partial occlusions. Future work is needed to comprehensively evaluate performance of the algorithm against state-of-the-art video trackers, especially considering the relatively small number of previous dual-band tracking results that have appeared.
Finding all bessel type solutions for linear differential equations with rational function coefficients

A linear differential equation with rational function coefficients has a Bessel type solution when it is solvable in terms of <i>B</i><sub><i>v</i></sub>(<i>f</i>), <i>B</i><sub><i>v</i>+1</sub>(<i>f</i>). For second order equations, with rational function coefficients, <i>f</i> must be a rational function or the square root of a rational function. An algorithm was given by Debeerst, van Hoeij, and Koepf, that can compute Bessel type solutions if and only if <i>f</i> is a rational function. In this paper we extend this work to the square root case, resulting in a complete algorithm to find all Bessel type solutions.
Multi-task , Multi-Kernel Learning for Estimating Individual Wellbeing

We apply a recently proposed technique – Multi-task Multi-Kernel Learning (MTMKL) – to the problem of modeling students’ wellbeing. Because wellbeing is a complex internal state consisting of several related dimensions, Multi-task learning can be used to classify them simultaneously. Multiple Kernel Learning is used to efficiently combine data from multiple modalities. MTMKL combines these approaches using an optimization function similar to a support vector machine (SVM). We show that MTMKL successfully classifies five dimensions of wellbeing, and provides performance benefits above both SVM and MKL.
Biosynthesis of plant volatiles: nature's diversity and ingenuity.

Plant volatiles (PVs) are lipophilic molecules with high vapor pressure that serve various ecological roles. The synthesis of PVs involves the removal of hydrophilic moieties and oxidation/hydroxylation, reduction, methylation, and acylation reactions. Some PV biosynthetic enzymes produce multiple products from a single substrate or act on multiple substrates. Genes for PV biosynthesis evolve by duplication of genes that direct other aspects of plant metabolism; these duplicated genes then diverge from each other over time. Changes in the preferred substrate or resultant product of PV enzymes may occur through minimal changes of critical residues. Convergent evolution is often responsible for the ability of distally related species to synthesize the same volatile.
The Java Memory Model: Operationally, Denotationally, Axiomatically

A semantics to a small fragment of Java capturing the new memory model (JMM) described in the Language Specification is given by combining operational, denotational and axiomatic techniques in a novel semantic framework. The operational steps (specified in the form of SOS) construct denotational models (configuration structures) and are constrained by the axioms of a configuration theory. The semantics is proven correct with respect to the Language Specification and shown to capture many common examples in the JMM literature.
Personality Profiles, Values and Empathy: Differences between Lacto-Ovo-Vegetarians and Vegans.

Vegetarian nutrition is gaining increasing public attention worldwide. While some studies have examined differences in motivations and personality traits between vegetarians and omnivores, only few studies have considered differences in motivations and personality traits between the 2 largest vegetarian subgroups: lacto-ovo-vegetarians and vegans.



To examine differences between lacto-ovo-vegetarians and vegans in the distribution patterns of motives, values, empathy, and personality profiles.



An anonymous online survey was performed in January 2014. Group differences between vegetarians and vegans in their initial motives for the choice of nutritional approaches, health-related quality of life (World Health Organization Quality of Life-BREF (WHOQOL-BREF)), personality traits (Big Five Inventory-SOEP (BFI-S)), values (Portraits Value Questionnaire (PVQ)), and empathy (Empathizing Scale) were analyzed by univariate analyses of covariance; P values were adjusted for multiple testing.



10,184 individuals completed the survey; 4,427 (43.5%) were vegetarians and 4,822 (47.3%) were vegans. Regarding the initial motives for the choice of nutritional approaches, vegans rated food taste, love of animals, and global/humanitarian reasons as more important, and the influence of their social environment as less important than did vegetarians. Compared to vegetarians, vegans had higher values on physical, psychological, and social quality of life on the WHOQOL-BREF, and scored lower on neuroticism and higher on openness on the BFI-S. In the PVQ, vegans scored lower than vegetarians on power/might, achievement, safety, conformity, and tradition and higher on self-determination and universalism. Vegans had higher empathy than vegetarians (all p < 0.001).



This survey suggests that vegans have more open and compatible personality traits, are more universalistic, empathic, and ethically oriented, and have a slightly higher quality of life when compared to vegetarians. Given the small absolute size of these differences, further research is needed to evaluate whether these group differences are relevant in everyday life and can be confirmed in other populations.
900 MHz and 2.45 GHz compact dual-band circularly-polarized patch antenna for RFID application

A compact design of a circularly-polarized microstrip antenna in order to achieve dual-band behavior for Radio Frequency Identification (RFID) applications is presented, defected ground structure (DGS) technique is used to miniaturize and get a dual-band antenna, the entire size is 38×40×1.58 mm3. This antenna was designed to cover both ultra-height frequency (740MHz ~ 1GHz) and slow height frequency (2.35 GHz ~ 2.51GHz), return loss <; -10 dB, the 3-dB axial ratio bandwidths are about 110 MHz at the lower band (900 MHz).
The Use of Autoencoders for Discovering Patient Phenotypes

We use autoencoders to create low-dimensional embeddings of underlying patient phenotypes that we hypothesize are a governing factor in determining how different patients will react to different interventions. We compare the performance of autoencoders that take fixed length sequences of concatenated timesteps as input with a recurrent sequence-to-sequence autoencoder. We evaluate our methods on around 35,500 patients from the latest MIMIC III dataset from Beth Israel Deaconess Hospital.
A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

Emergent heterogeneous systems must be optimized for both power and performance at exascale. Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases orders of magnitude and power consumption can easily double. Models have been proposed to isolate power and performance bottlenecks and identify their root causes. However, no current models combine simplicity, accuracy, and support for emergent GPU architectures (e.g. NVIDIA Fermi). We combine hardware performance counter data with machine learning and advanced analytics to model power-performance efficiency for modern GPU-based systems. Our performance counter based approach is simpler than previous approaches and does not require detailed understanding of the underlying architecture. The resulting model is accurate for predicting power (within 2.1%) and performance (within 6.7%) for application kernels on modern GPUs. Our model can identify power-performance bottlenecks and their root causes for various complex computation and memory access patterns (e.g. global, shared, texture). We measure the accuracy of our power and performance models on a NVIDIA Fermi C2075 GPU for more than a dozen CUDA applications. We show our power model is more accurate and robust than the best available GPU power models - multiple linear regression models MLR and MLR+. We demonstrate how to use our models to identify power-performance bottlenecks and suggest optimization strategies for high-performance codes such as GEM, a biomolecular electrostatic analysis application. We verify our power-performance model is accurate on clusters of NVIDIA Fermi M2090s and useful for suggesting optimal runtime configurations on the Keeneland supercomputer at Georgia Tech.
Cross-language Citation Recommendation via Hierarchical Representation Learning on Heterogeneous Graph

While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task.
Cyclodextrins: application in different routes of drug administration.

The objective of this review article is to explain the use of cyclodextrin in the different routes of drug administration. The article gives the chemistry of cyclodextrins and addresses the issue of the mechanism of drug release from cyclodextrin complexes. Dilution, competitive displacement, protein binding, change in ionic strength and temperature and drug uptake by tissues are the different release mechanisms of the drug from the drug-cyclodextrin complex discussed here. Use and its limitations in the different drug delivery systems like nasal, ophthalmic, transdermal and rectal drug delivery are explained. The application of the cyclodextrins in the oral drug delivery is detailed in this review. Many studies have shown that cyclodextrins are useful additives in the different routes of drug administration because of increased aqueous solubility, stability, bioavailability and reduced drug irritation.
A meta-analysis and review of unmanned aircraft system ( UAS ) imagery for terrestrial applications

Over the past decade, the remote-sensing community has eagerly adopted unmanned aircraft systems (UAS) as a costeffective means to capture imagery at spatial and temporal resolutions not typically feasible with manned aircraft and satellites. The rapid adoption has outpaced our understanding of the relationships between data collection methods and data quality, causing uncertainties in data and products derived from UAS and necessitating exploration into how researchers are using UAS for terrestrial applications. We synthesize these procedures through a meta-analysis of UAS applications alongside a review of recent, basic science research surrounding theory and method development. We performed a search of the Web of Science (WoS) database on 17 May 2017 using UAS-related keywords to identify all peer-reviewed studies indexed by WoS. We manually filtered the results to retain only terrestrial studies (n 1⁄4 412) and further categorized results into basic theoretical studies (n 1⁄4 63), method development (n 1⁄4 63), and applications (n 1⁄4 286). After randomly selecting a subset of applications (n 1⁄4 108), we performed an in-depth content analysis to examine platforms, sensors, data capture parameters (e.g. flight altitude, spatial resolution, imagery overlap, etc.), preprocessing procedures (e.g. radiometric and geometric corrections), and analysis techniques. Our findings show considerable variation in UAS practices, suggesting a need for establishing standardized image collection and processing procedures. We reviewed basic research and methodological developments to assess how data quality and uncertainty issues are being addressed and found those findings are not necessarily being considered in application studies. ARTICLE HISTORY Received 30 September 2017 Accepted 5 December 2017
Analysis of atrium's architectural aspects in office buildings under tropical sky conditions

This paper discusses atrium building typology by analysing the architectural aspects of existing atrium buildings. One hundred sixty commercial office buildings in Klang Valley were identified as the chosen building type for the initial selection process. Thirteen out of 160 office buildings surveyed were further analyzed based on the following architectural aspects: i) atrium spaces that include the description of atrium type, form and shape, physical dimensions, number of floors and height; ii) skylight design and roof fenestration system; and iii) atrium usage/activity and indoor environmental conditions. The atrium designs in these tropical office buildings are briefly described. The results show the most common atrium form is the enclosed central rectangular atrium with 4-storey average height. This study could lead to further research on design considerations for innovative applications to improve daylight performance in tropical office building atrium.
Residue-Specific Peptide Modification: A Chemist's Guide.

Advances in bioconjugation and native protein modification are appearing at a blistering pace, making it increasingly time consuming for practitioners to identify the best chemical method for modifying a specific amino acid residue in a complex setting. The purpose of this perspective is to provide an informative, graphically rich manual highlighting significant advances in the field over the past decade. This guide will help triage candidate methods for peptide alteration and will serve as a starting point for those seeking to solve long-standing challenges.
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification

Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on crossdomain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.
ABCD3-I score and the risk of early or 3-month stroke recurrence in tissue- and time-based definitions of TIA and minor stroke

Changing definition of TIA from time to a tissue basis questions the validity of the well-established ABCD3-I risk score for recurrent ischemic cerebrovascular events. We analyzed patients with ischemic stroke with mild neurological symptoms arriving < 24 h after symptom onset in a phase where it is unclear, if the event turns out to be a TIA or minor stroke, in the prospective multi-center Austrian Stroke Unit Registry. Patients were retrospectively categorized according to a time-based (symptom duration below/above 24 h) and tissue-based (without/with corresponding brain lesion on CT or MRI) definition of TIA or minor stroke. Outcome parameters were early stroke during stroke unit stay and 3-month ischemic stroke. Of the 5237 TIA and minor stroke patients with prospectively documented ABCD3-I score, 2755 (52.6%) had a TIA by the time-based and 2183 (41.7%) by the tissue-based definition. Of the 2457 (46.9%) patients with complete 3-month followup, corresponding numbers were 1195 (48.3%) for the time- and 971 (39.5%) for the tissue-based definition of TIA. Early and 3-month ischemic stroke occurred in 1.1 and 2.5% of time-based TIA, 3.8 and 5.9% of time-based minor stroke, 1.2 and 2.3% of tissue-based TIA as well as in 3.1 and 5.5% of tissue-based minor stroke patients. Irrespective of the definition of TIA and minor stroke, the risk of early and 3-month ischemic stroke steadily increased with increasing ABCD3-I score points. The ABCD3-I score performs equally in TIA patients in tissue- as well as time-based definition and the same is true for minor stroke patients.
Music segmentation and summarization based on self-similarity matrix

In this paper, we propose a new method for segmenting and summarizing music based on its structure analysis. To do that, we first extract timbre feature from acoustic music signal and construct a self-similarity matrix that shows similarities among the features within music clip. We then determine candidate boundaries for music segmentation by tracking standard deviation in the matrix. Similar segments such as repetition in music clip are clustered and merged. In this way, each music clip can be represented by a sequence of states where each state represents a music segment with similar feature. We assume that the longest segment of a music clip represents the music and hence use it as a summary of the music clip. We show the performance of our proposed method through experiments.
Deep Neural Factorization for Speech Recognition

Conventional speech recognition system is constructed by unfolding the spectral-temporal input matrices into one-way vectors and using these vectors to estimate the affine parameters of neural network according to the vector-based error backpropagation algorithm. System performance is constrained because the contextual correlations in frequency and time horizons are disregarded and the spectral and temporal factors are excluded. This paper proposes a spectral-temporal factorized neural network (STFNN) to tackle this weakness. The spectral-temporal structure is preserved and factorized in hidden layers through two ways of factor matrices which are trained by using the factorized error backpropagation. Affine transformation in standard neural network is generalized to the spectro-temporal factorization in STFNN. The structural features or patterns are extracted and forwarded towards the softmax outputs. A deep neural factorization is built by cascading a number of factorization layers with fully-connected layers for speech recognition. An orthogonal constraint is imposed in factor matrices for redundancy reduction. Experimental results show the merit of integrating the factorized features in deep feedforward and recurrent neural networks for speech recognition.
Learning moods and emotions from color combinations

In this paper, we tackle the problem of associating combinations of colors to abstract categories (e.g. capricious, classic, cool, delicate, etc.). It is evident that such concepts would be difficult to distinguish using single colors, therefore we consider combinations of colors or color palettes. We leverage two novel databases for color palettes and we learn categorization models using low and high level descriptors. Preliminary results show that Fisher representation based on GMMs is the most rewarding strategy in terms of classification performance over a baseline model. We also suggest a process for cleaning weakly annotated data, whilst preserving the visual coherence of categories. Finally, we demonstrate how learning abstract categories on color palettes can be used in the application of color transfer, personalization and image re-ranking.