nils-herrmann/clean_scirepeval huggingface dataset:
Ransomware Analysis and Defense-WannaCry and the Win32 environment

Ransomware is a specific type of malware that threatens the victim’s access to her data unless a ransom is paid. It is also known as a cryptovirus due to its method of operation. Typically, ransomware encrypts the contents of the victim’s hard drive thereby rendering it inaccessible to the victim. Upon payment of the ransom, the decryption key is released to the victim. This means of attack is therefore also sometimes aptly called cryptoviral extortion. The ransomware itself is delivered to the victim using several channels. The most common channel of delivery is by masquerading the malware as a Trojan horse via an email attachment. In this work, we study a high-profile example of a ransomware called the WannaCry worm. This ransomware is particularly malicious since it has the ability to traverse computing equipment on a network without any human intervention. Since this worm has had a large scale impact, we find it imperative and instructive to better understand the inner workings of this high-profile ransomware. To this end, we obtain a sample of WannaCry and dissect it completely using advanced static and dynamic malware analysis techniques. This effort, we hope, will shed light on the inner workings of the malware and will enable cyber security experts to better thwart similar attacks in the future by: a) generating appropriate signatures and b) developing stronger defense solutions. Our analysis is conducted in a Win32 environment and we present our detailed analysis so as to enable reproduction of our work by other malware analysts. This, we hope, will further advancement in generating appropriate signatures to detect the worm. Secondly, we present a prototype software that will enable a user to prevent this malware from unleashing its payload and protect the user on a Win32 environment in an effort to advance the development of efficient software defense mechanisms to protect users from such a worm attack in the future. Ransomware, cryptovirus, extortion, static and dynamic analysis, malware analysis, cyber security.
Reconciling id, ego, and superego within interleukin-23.

The study of interleukin-23 (IL-23) over the past 8 years has led to the realization that cellular immunity is far more complex than previously appreciated, because it is controlled by additional newly identified players. From the analysis of seemingly straightforward cytokine regulation of autoimmune diseases, many limitations of the established paradigms emerged that required reevaluation of the 'rules' that govern the initiation and maintenance of immune responses. This information led to a major revision of the T-helper 1 (Th1)/Th2 hypothesis and discovery of an unexpected link between transforming growth factor-beta-dependent Th17 and inducible regulatory T cells. The aim of this review is to explore the multiple characteristics of IL-23 with respect to its 'id' in autoimmunity, 'ego' in T-cell help, and 'superego' in defense against mucosal pathogens.
Data Driven Reduced Order Modeling of Fluid Dynamics Using Linear Multistep Network

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams-Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a twodimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projectionbased approaches.
Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil.

To assess time trends in the contribution of processed foods to food purchases made by Brazilian households and to explore the potential impact on the overall quality of the diet.



Application of a new classification of foodstuffs based on extent and purpose of food processing to data collected by comparable probabilistic household budget surveys. The classification assigns foodstuffs to the following groups: unprocessed/minimally processed foods (Group 1); processed culinary ingredients (Group 2); or ultra-processed ready-to-eat or ready-to-heat food products (Group 3).



Eleven metropolitan areas of Brazil.



Households; n 13,611 in 1987-8, n 16,014 in 1995-5 and n 13,848 in 2002-3.



Over the last three decades, the household consumption of Group 1 and Group 2 foods has been steadily replaced by consumption of Group 3 ultra-processed food products, both overall and in lower- and upper-income groups. In the 2002-3 survey, Group 3 items represented more than one-quarter of total energy (more than one-third for higher-income households). The overall nutrient profile of Group 3 items, compared with that of Group 1 and Group 2 items, revealed more added sugar, more saturated fat, more sodium, less fibre and much higher energy density.



The high energy density and the unfavourable nutrition profiling of Group 3 food products, and also their potential harmful effects on eating and drinking behaviours, indicate that governments and health authorities should use all possible methods, including legislation and statutory regulation, to halt and reverse the replacement of minimally processed foods and processed culinary ingredients by ultra-processed food products.
2 The Role of Quantitative Models in Science

Models in science may be used for various purposes: organizing data, synthesizing information, and making predictions. However, the value of model predictions is undermined by their uncertainty, which arises primarily from the fact that our models of complex natural systems are always open. Models can never fully specify the systems that they describe, and therefore their predictions are always subject to uncertainties that we cannot fully specify. Moreover, the attempt to make models capture the complexities of natural systems leads to a paradox: the more we strive for realism by incorporating as many as possible of the different processes and parameters that we believe to be operating in the system, the more difficult it is for us to know if our tests of the model are meaningful. A complex model may be more realistic, yet it is ironic that as we add more factors to a model, the certainty of its predictions may decrease even as our intuitive faith in the model increases. For this and other reasons, model output should not be viewed as an accurate prediction of the future state of the system. Short timeframe model output can and should be used to evaluate models and suggest avenues for future study. Model output can also generate “what if” scenarios that can help to evaluate alternative courses of action (or inaction), including worst-case and best-case outcomes. But scientists should eschew long-range deterministic predictions, which are likely to be erroneous and may damage the credibility of the communities that generate them.
THE IMPORTANCE OF SUBWORD EMBEDDINGS IN SENTENCE PAIR MODELING

Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretrained word embedding and compose sentence-level semantics in varied ways; however, few works have attempted to verify whether we really need pretrained embeddings in these tasks. In this paper, we study how effective subwordlevel (character and character n-gram) representations are in sentence pair modeling. Though it is well-known that subword models are effective in tasks with single sentence input, including language modeling and machine translation, they have not been systematically studied in sentence pair modeling tasks where the semantic and string similarities between texts matter. Our experiments show that subword models without any pretrained word embedding can achieve new state-of-the-art results on two social media datasets and competitive results on news data for paraphrase identification.
Deutsch ’ s Universal Quantum Turing Machine ( Revisited )

Deutsch, Feynman, and Manin viewed quantum computing as a kind of universal physical simulation procedure. Much of the writing about quantum Turing machines has shown how these machines can simulate an arbitrary unitary transformation on a finite number of qubits. This interesting problem has been addressed most famously in a paper by Deutsch, and later by Bernstein and Vazirani. Quantum Turing machines form a class closely related to deterministic and probabilistic Turing machines and one might hope to find a universal machine in this class. A universal machine is the basis of a notion of programmability. The extent to which universality has in fact been established by the pioneers in the field is examined and a key notion in theoretical computer science (universality) is scrutinised. In a forthcoming paper, the authors will also consider universality in the quantum gate model.
Malware Detection through Low-level Features and Stacked Denoising Autoencoders

In recent years, the diffusion of malicious software through various channels has gained the request for intelligent techniques capable of timely detecting new malware spread. In this work, we focus on the application of Deep Learning methods for malware detection, by evaluating their effectiveness when malware are represented by high-level, and lowlevel features respectively. Experimental results show that, when using high-level features, deep neural networks do not significantly improve the overall detection accuracy. On the other hand, when low-level features, i.e., small pieces of information extracted through a light processing, are chosen, they allow to increase the capability of correctly classifying malware.
Relationship between endurance field tests and match performance in young soccer players.

The purpose of this study was to examine the relationship between popular endurance field tests and physical match performance in elite male youth soccer players. Eighteen young male soccer players (age 14.4 ± 0.1 years, height 1.67 ± 4.8 cm, body mass 53.6 ± 1.8 kg) were randomly chosen among a population of elite-level soccer players. Players were observed during international championship games of the corresponding age categories and randomly submitted to the level 1 of the Yo-Yo intermittent recovery test (Yo-Yo IR1), the Multistage Fitness Test (MSFT), and the Hoff test on separate occasions. Physical and physiological match demands were assessed using Global Positioning System technology and short-range telemetry (GPS Elite, Canberra, Australia), respectively. Players covered 6,087 ± 582 m (5,098-7,019 m) of which 15% (930 ± 362 m; 442-1,513) were performed as a high-intensity activity. During the first and second halves, players attained 86.8 ± 6.5 and 85.8 ± 5.8% of maximum heart rate (HRmax; p = 0.17) with peak HRs of 100 ± 2 and 99.4 ± 3.2% of HRmax, respectively. Players' Yo-Yo IR1 and MSFT performance were significantly related (r = 0.62-0.76) to a number of match physical activities. However, the Hoff test was only significantly related with sprint distance (r = 0.70, p = 0.04). The Yo-Yo IR1 showed a very large association with MSFT performance (r = 0.89, p < 0.0001). The results of this study showed that the Yo-Yo IR1 and MSFT may be regarded as valuable tests to assess match fitness and subsequently guide training prescription in youth soccer players. The very strong relationship between Yo-Yo IR1 and MSFT suggests their use according to the period of the season and the aerobic fitness level of the players. Because of the association of the Yo-Yo IR1 and MSFT with match physical performances, these tests should be considered in talent selection and development of players.
Reconstructing detailed dynamic face geometry from monocular video

Detailed facial performance geometry can be reconstructed using dense camera and light setups in controlled studios. However, a wide range of important applications cannot employ these approaches, including all movie productions shot from a single principal camera. For post-production, these require dynamic monocular face capture for appearance modification. We present a new method for capturing face geometry from monocular video. Our approach captures detailed, dynamic, spatio-temporally coherent 3D face geometry without the need for markers. It works under uncontrolled lighting, and it successfully reconstructs expressive motion including high-frequency face detail such as folds and laugh lines. After simple manual initialization, the capturing process is fully automatic, which makes it versatile, lightweight and easy-to-deploy. Our approach tracks accurate sparse 2D features between automatically selected key frames to animate a parametric blend shape model, which is further refined in pose, expression and shape by temporally coherent optical flow and photometric stereo. We demonstrate performance capture results for long and complex face sequences captured indoors and outdoors, and we exemplify the relevance of our approach as an enabling technology for model-based face editing in movies and video, such as adding new facial textures, as well as a step towards enabling everyone to do facial performance capture with a single affordable camera.
Trust as a Process : A Three-Dimensional Approach

What is trust? Should trust be used as a variable or as a process? Is trust in people similar in nature to trust in institutions? These are three main questions I address in this article. I argue that trust is a complex and multi-dimensional phenomenon, which consists of a mix of trust in strong ties, weak ties, and institutions. I explain the need for a new approach to trust, using the Soviet Union as an example. I argue that rigid distinctions in social capital theory between high-trust and lowtrust societies fail to account for the complexity of trust. In this article, I view trust as a process. I also suggest a new definition of trust based on the notion of agency, which underscores the idea of temporality and incorporates the roles of the past, present, and future.
REWIEW OF LITERATURE 3.1. URINARY TRACT INFECTION

3.1.  Urinary tract infection is one of the important causes of morbidity and mortality in Indian population, affecting all age groups across the life span. Anatomically, urinary tract is divided into an upper portion composed of kidneys, renal pelvis, and ureters and a lower portion made up of urinary bladder and urethra. UTI is an inflammatory response of the urothelium to bacterial invasion that is usually associated with bacteriuria and pyuria. UTI may involve only the lower urinary tract or both the upper and lower tract .
A Comparison of Cultural Connotations between Chinese and English Animal Words

Throughout the history of the social evolution, man and animals come into frequent contact that forms an interdependent relationship between man and animals. The images of animals root in the everyday life of all nations, forming unique animal culture of each nation. Therefore, Chinese and English, as the two languages which spoken by the most people in the world, naturally contain a lot of words relating to animals, and because of different history and culture, the connotations of animal words in one language do not coincide with those in another. The clever use of animal words is by no means scarce in everyday communication or literary works, which helps make English and Chinese vivid and lively in image, plain and expressive in character, and rich and strong in flavor. In this study, many animal words are collected for the analysis of the similarities and the differences between the cultural connotations carried by animal words in Chinese and English, find out the causes of differences, and then discuss some methods and techniques for translating these animal words.
Age and gender recognition in the wild with deep attention

Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show Preprint submitted to Pattern Recognition June 30, 2017
Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding

Evaluating the clinical similarities between pairwisepatients is a fundamental problem in healthcare informatics. Aproper patient similarity measure enables various downstreamapplications, such as cohort study and treatment comparative effectiveness research. One major carrier for conductingpatient similarity research is the Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, andsparse. Though existing studies on learning patient similarityfrom EHRs have shown being useful in solving real clinicalproblems, their applicability is limited due to the lack of medicalinterpretations. Moreover, most previous methods assume avector based representation for patients, which typically requiresaggregation of medical events over a certain time period. As aconsequence, the temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based ontemporal matching of longitudinal patient EHRs. Two efficientmethods are presented, unsupervised and supervised, both ofwhich preserve the temporal properties in EHRs. The supervisedscheme takes a convolutional neural network architecture, andlearns an optimal representation of patient clinical recordswith medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement overthe baselines.
A Data-Centric Approach for Modeling and Estimating Efficiency of Dataflows for Accelerator Design.

The mechanisms used by DNN accelerators to leverage datareuse and perform data staging are known as dataflow, and they directly impact the performance and energy efficiency of DNN accelerator designs. Co-optimizing the accelerator microarchitecture and its internal dataflow is crucial for accelerator designers, but there is a severe lack of tools and methodologies to help them explore the co-optimization design space. In this work, we first introduce a set of datacentric directives to concisely specify DNN dataflows in a compiler-friendly form. Next, we present an analytical model, MAESTRO, that estimates various cost-benefit tradeoffs of a dataflow including execution time and energy efficiency for a DNN model and hardware configuration. Finally, we demonstrate the use of MAESTRO to drive a hardware design space exploration (DSE) engine. The DSE engine searched 480M designs and identified 2.5M valid designs at an average rate of 0.17M designs per second, and also identified throughputand energy-optimized designs among this set.
AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control

In this paper, a new adaptive multi-batch experience replay scheme is proposed for proximal policy optimization (PPO) for continuous action control. On the contrary to original PPO, the proposed scheme uses the batch samples of past policies as well as the current policy for the update for the next policy, where the number of the used past batches is adaptively determined based on the oldness of the past batches measured by the average importance sampling (IS) weight. The new algorithm constructed by combining PPO with the proposed multi-batch experience replay scheme maintains the advantages of original PPO such as random minibatch sampling and small bias due to low IS weights by storing the pre-computed advantages and values and adaptively determining the mini-batch size. Numerical results show that the proposed method significantly increases the speed and stability of convergence on various continuous control tasks compared to original PPO.
Gender nonconformity as a target of prejudice, discrimination, and violence against LGB individuals.

Research into antigay violence has been limited by a lack of attention to issues of gender presentation. Understanding gender nonconformity is important for addressing antigay prejudice and hate crimes. We assessed experiences of gender-nonconformity-related prejudice among 396 Black, Latino, and White lesbian, gay, and bisexual individuals recruited from diverse community venues in New York City. We assessed the prevalence and contexts of prejudice-related life events and everyday discrimination using both quantitative and qualitative approaches. Gender nonconformity had precipitated major prejudice events for 9% of the respondents and discrimination instances for 19%. Women were more likely than men to report gender-nonconformity-related discrimination but there were no differences by other demographic characteristics. In analysis of events narratives, we show that gender nonconformity prejudice is often intertwined with antigay prejudice. Our results demonstrate that both constructs should be included when addressing prejudice and hate crimes targeting lesbian, gay, bisexual, and transgender individuals and communities.