nils-herrmann/clean_scirepeval huggingface dataset:
Hypervisor Memory Forensics

Memory forensics is the branch of computer forensics that aims at extracting artifacts from memory snapshots taken from a running system. Even though it is a relatively recent field, it is rapidly growing and it is attracting considerable attention from both industrial and academic researchers. In this paper, we present a set of techniques to extend the field of memory forensics toward the analysis of hypervisors and virtual machines. With the increasing adoption of virtualization techniques (both as part of the cloud and in normal desktop environments), we believe that memory forensics will soon play a very important role in many investigations that involve virtual environments. Our approach, implemented in an open source tool as an extension of the Volatility framework, is designed to detect both the existence and the characteristics of any hypervisor that uses the Intel VT-x technology. It also supports the analysis of nested virtualization and it is able to infer the hierarchy of multiple hypervisors and virtual machines. Finally, by exploiting the techniques presented in this paper, our tool can reconstruct the address space of a virtual machine in order to transparently support any existing Volatility plugin allowing analysts to reuse their code for the analysis of virtual environments.
Flexible dry surface-electrodes for ECG long-term monitoring

In this paper a new generation of dry electrodes for ECG-recording purposes is presented. These electrodes are flexible, biocompatible, biostable and perform a dry signal acquisition without the use of any electrolyte gel or adhesive. Thereby no skin preparation is needed. The material properties allow integration into textiles to form intelligent clothes for long-term monitoring duties. The electrodes can be washed together with the carrier textiles and provide a very good wearing comfort resulting in wide acceptance by the patients. The Department Medical Engineering and Neuroprosthetics of the Fraunhofer Institute for Biomedical Engineering developed these electrodes mainly to process the Electrocardiogram, however, in first tests they proved their capability even for other biosignal requirements like surface-EMG. In either case the signal quality of the dry electrodes is comparable to commercial Ag/AgCl gel-electrodes.
Surveys on attitudes towards legalisation of euthanasia: importance of question phrasing.

To explore whether the phrasing of the questions and the response alternatives would influence the answers to questions about legalisation of euthanasia.



Results were compared from two different surveys in populations with similar characteristics. The alternatives "positive", "negative", and "don't know" (first questionnaire) were replaced with an explanatory text, "no legal sanction", four types of legal sanctions, and no possibility to answer "don't know" (second questionnaire). Four undergraduate student groups (engineering, law, medicine, and nursing) answered.



In the first questionnaire (n = 684) 43% accepted euthanasia (range 28-50%), 14% (8-33%) did not, and 43% (39-59%) answered "don't know". Two per cent of the respondents declined to answer. In comparison with previous surveys on attitudes to euthanasia the proportion of "don't know" was large. The results of the second questionnaire (n = 639), showed that 38% favoured "no legal prosecution" (26-50%). However, 62% (50-74%) opted for different kinds of legal sanctions, and two of four groups expressed significantly different views in the two surveys. A proportion of 10% declined to answer the second questionnaire.



An introduction of an explanatory text and a wider range of response alternatives produced differences between the results of the two surveys conducted.
Domain Adaptation for Automatic Detection of Speculative Sentences

The use of speculative, uncertain or vague sentences is common in both spoken and written language. Automatic detection of such sentences plays significant role in natural language processing and social sciences as it could enhance information extraction systems and lead to faster and more reliable analyses in social sciences. However, this problem has only been addressed in biomedical and encyclopedic domains. In this paper, we address automatic speculation detection (as a binary sentence classification task) in monetary policy domain, and for the first time, on the transcripts of spoken language. We build two new speculation detection datasets and a dictionary of speculation triggers using expert annotations, and benchmark the performance of automatic speculation detection systems in this new domain.
Evaluation of biomechanical properties of human skin.

Measurement of the physical properties of skin may seem "esoteric" and of little relevance to clinical science; however, the noninvasive nature of available techniques provides unique opportunities for monitoring the effects of disease, drugs, or cosmetics over time on exactly the same area of skin. In vitro testing gives repeatable standardized methods that can supply basic elastic and viscoelastic moduli for skin, which for low strain are comparable to results obtained from in vivo tests. Interpretation of in vivo tests can be difficult, as no analytical model has been developed that can relate measurements from these tests directly to basic skin properties. Each method and each implementation of that method has subtle differences from every other method so that results between studies are difficult to compare; however, the alternative to the bioengineering tests is the hand and eye, which provide subjective (and often biased), nonlinear, and notoriously variable data between individuals. Judging the severity of involvement, the changes due to treatment or progress of disease, and the efficacy of competing treatments is very difficult, even for an experienced clinician. Objective, numerical information on the effects of different active compounds and their formulations is essential if new preparations are to be optimized. Measurement of mechanical properties is one aspect of this important field of endeavor.
Wavelet Neural Networks and their application in the study of dynamical systems

The main aim of this dissertation is to study the topic of wavelet neural networks and see how they are useful for dynamical systems applications such as predicting chaotic time series and nonlinear noise reduction. To do this, the theory of wavelets has been studied in the first chapter, with the emphasis being on discrete wavelets. The theory of neural networks and its current applications in the modelling of dynamical systems has been shown in the second chapter. This provides sufficient background theory to be able to model and study wavelet neural networks. In the final chapter a wavelet neural network is implemented and shown to accurately estimate the dynamics of a chaotic system, enabling prediction and enhancing methods already available in nonlinear noise reduction.
Modélisation thématique à l'aide des plongements lexicaux issus de Word2Vec(Topic modeling with word embeddings)

This paper discusses approaches for static topic modeling, in particular an improved method based on topic parametrization from a continuous distribution over the space of word embeddings. Word embeddings corpora proves to reflect semantic interdependences. Thus, we incorporate vectorized word representations trained with Word2Vec neural network in a generative process of topic modeling. The alternative approach with beta approximation of mutual information distribution over embeddings is proposed and compared with vanilla LDA and Gaussian LDA methods.
Computer analysis of computed tomography scans of the lung: a survey

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.
Study designs for PDSA quality improvement research.

The purpose of this article is to discuss strengths and weaknesses of quasi-experimental designs used in health care quality improvement research. The target groups for this article are investigators in plan-do-study-act (PDSA) quality improvement initiatives who wish to improve the rigor of their methodology and publish their work and reviewers who evaluate the quality of research proposals or published work.



A primary purpose of PDSA quality improvement research is to establish a functional relationship between process changes in systems of health care and variation in outcomes. The time series design is the fundamental paradigm for demonstrating such functional relationships. The rigor of a PDSA quality improvement study design is strengthened using replication schemes and research methodology to address extraneous factors that weaken validity of observational studies.



The design of PDSA quality improvement research should follow from the purpose and context of the project. Improving the rigor of the quality improvement literature will build a stronger foundation and more convincing justification for the study and practice of quality improvement in health care.
Type shifting in construction grammar : An integrated approach to aspectual coercion *

Implicit type shifting, or coercion, appears to indicate a modular grammatical architecture, in which the process of semantic composition may add meanings absent from the syntax in order to ensure that certain operators, e.g., the progressive, receive suitable arguments (Jackendo¤ 1997; De Swart 1998). I will argue that coercion phenomena actually provide strong support for a sign-based model of grammar, in which rules of morphosyntactic combination can shift the designations of content words with which they combine. On this account, enriched composition is a by-product of the ordinary referring behavior of constructions. Thus, for example, the constraint which requires semantic concord between the syntactic sisters in the string a bottle is also what underlies the coerced interpretation found in a beer. If this concord constraint is stated for a rule of morphosyntactic combination, we capture an important generalization: a single combinatory mechanism, the construction, is responsible for both coerced and compositional meanings. Since both type-selecting constructions (e.g., the French Imparfait) and type-shifting constructions (e.g., English progressive aspect) require semantic concord between syntactic sisters, we account for the fact that constructions of both types perform coercion. Coercion data suggest that aspectual sensitivity is not merely a property of formally di¤erentiated past tenses, as in French and Latin, but a general property of tense constructions, including the English present and past tenses.
Unseen Noise Estimation Using Separable Deep Auto Encoder for Speech Enhancement

Unseen noise estimation is a key yet challenging step to make a speech enhancement algorithm work in adverse environments. At worst, the only prior knowledge we know about the encountered noise is that it is different from the involved speech. Therefore, by subtracting the components which cannot be adequately represented by a well defined speech model, the noises can be estimated and removed. Given the good performance of deep learning in signal representation, a deep auto encoder (DAE) is employed in this work for accurately modeling the clean speech spectrum. In the subsequent stage of speech enhancement, an extra DAE is introduced to represent the residual part obtained by subtracting the estimated clean speech spectrum (by using the pre-trained DAE) from the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown parameters of the noise DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. The enhanced speech signal is thus obtained by transforming the estimated clean speech spectrum back into time domain. The above proposed technique is called separable deep auto encoder (SDAE). Given the under-determined nature of the above optimization problem, the clean speech reconstruction is confined in the convex hull spanned by a pre-trained speech dictionary. New learning algorithms are investigated to respect the non-negativity of the parameters in the SDAE. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the conventional baselines.
Using Social Network Analysis as a Strategy for E-Commerce Recommendation

Recommender agents are being widely used by E-commerce business to help customers make decisions from a large amount of choices. To improve the performance of recommendation agents, three main approaches (content-based approaches, collaborative approaches and hybrid approaches) have been proposed to address recommendation problem whose basic idea is to discover similarity of items 1 and users and predicate users’ preference toward a set of items. This provides potential for using social network analysis to make recommendations since social network analysis can be used to investigate the relationships of customers. In this research, we illustrate the concepts of social network analysis and how it can be employed to make better recommendations in E-commerce context. Application and research opportunities are presented.
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intuition about how human generates the equations given the problem texts, this paper presents a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. This paper views the process of generating equation as a bridge between the semantic world and the symbolic world, where the proposed neural math solver is based on an encoder-decoder framework. In the proposed model, the encoder is designed to understand the semantics of problems, and the decoder focuses on tracking semantic meanings of the generated symbols and then deciding which symbol to generate next. The preliminary experiments are conducted in a dataset Math23K, and our model significantly outperforms both the state-of-the-art single model and the best non-retrieval-based model over about 10% accuracy, demonstrating the effectiveness of bridging the symbolic and semantic worlds from math word problems.
Design of reconfigurable slot antenna using varactor diode

In this paper, reconfigurable slot antenna for LTE (2.6GHz), AMT-fixed service (4.5GHz), and WLAN (5.4GHz) applications is presented. There are two L-shaped slots and a U-shaped slot inserted in the ground plane. The size of the antenna is reduced as a result of these slots. There are three varactor diodes inserted in these slots. This is done for the purpose of frequency, directivity, and polarization reconfiguration. The directivities and polarization states of the antenna are changed at different frequencies i.e. at 2.6GHz, 5.4GHz, and at 4.5 GHz. These three different frequencies correspond to different states of the antenna. The states of the antenna are changed by switching the varactor diodes on and off. The varactor diodes are used in this paper as they have the property of fast frequency tuning.
Managing Employee Compliance with Information Security Policies: The Critical Role of Top Management and Organizational Culture

We develop an individual behavioral model that integrates the role of top management and organizational culture into the theory of planned behavior in an attempt to better understand how top management can influence security compliance behavior of employees. Using survey data and structural equation modeling, we test hypotheses on the relationships among top management participation, organizational culture, and key determinants of employee compliance with information security policies. We find that top management participation in information security initiatives has significant direct and indirect influences on employees’ attitudes towards, subjective norm of, and perceived behavioral control over compliance with information security policies. We also find that the top management participation strongly influences organizational culture which in turn impacts employees’ attitudes towards and perceived behavioral control over compliance with information security policies. Furthermore, we find that the effects of top management participation and organizational culture on employee behavioral intentions are fully mediated by employee cognitive beliefs about compliance with information security policies. Our findings extend information security research literature by showing how top management can play a proactive role in shaping employee compliance behavior in addition to the deterrence oriented remedies advocated in the extant literature. Our findings also refine the theories about the role of organizational culture in shaping employee compliance behavior. Significant theoretical and practical implications of ∗This project was partially funded by a grant to the authors from the Defense Information Systems Agency (DISA) of the Department of Defense (DoD). The authors express their thanks to the editor, senior editor, associate editor, and two anonymous reviewers for their detailed and constructive comments and suggestions throughout the review process. †Corresponding author.
This is how we play it: what a mega-LAN can teach us about games

Using data gathered through our participant observation and informal interviews at DreamHack Winter 2005 and 2009 we explore a number of themes that not only provide insight into aspects of face-to-face real-time play at LAN parties but also highlight considerations for game studies more generally. In particular, we focus on the heterogeneity of play and experience, the role of spectatorship in computer gaming, the public performance of leisure and gamer identity, and the growing presence of women in game culture. We conclude by suggesting that researchers should begin to consider the much larger trend in which this form of leisure activity is integrating itself into mainstream pop/youth/network culture.
Highly Efficient Doherty Amplifier Based on Class-E Topology for WCDMA Applications

This letter reports a high-efficiency gallium nitride (GaN) high-electron mobility transistor (HEMT) Doherty power amplifier (DPA) based on the class-E topology for wideband code-division multiple-access (WCDMA) applications. The class-E topology is employed as the carrier and peaking cells of the Doherty configuration. For validations, the proposed DPA is designed and implemented with 25-W GaN HEMTs at 2.14 GHz. For the proposed DPA, the power-added efficiency (PAE) and drain efficiency of 56.1% and 61.2% are achieved at 40 dBm (6-dB backoff power from Psat) for a continuous wave. For a 1-carrier WCDMA signal, the PAE of 44.8% is obtained with an adjacent channel leakage ratio (ACLR) of -31 dBc at 37 dBm, which is an 8.9% improvement over the conventional DPA with an ACLR of -36.4 dBc.
Automatic code generation of convolutional neural networks in FPGA implementation

Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, state-of-the-art CNN models are computation-intensive and hence are mainly processed on high performance processors like server CPUs and GPUs. Owing to the advantages of high performance, energy efficiency and reconfigurability, Field-Programmable Gate Arrays (FPGAs) have been widely explored as CNN accelerators. In this paper, we propose parallel structures to exploit the inherent parallelism and efficient computation units to perform operations in convolutional and fully-connected layers. Further, an automatic generator is proposed to generate Verilog HDL source code automatically according to high-level hardware description language. Execution time, DSP consumption and performance are analytically modeled based on some critical design variables. We demonstrate the automatic methodology by implementing two representative CNNs (LeNet and AlexNet) and evaluate the execution time models by comparing estimated and measured values. Our results show that the proposed automatic methodology yields hardware design with good performance and saves much developing round time.
Driver fatigue detection based on saccadic eye movements

The correct determination of driver's level of fatigue has been of vital importance for the safety of driving. There are various methods, such as analyzing facial expression, eyelid activity, and head movements to assess the fatigue level of drivers. This paper describes the design and prototype implementation of a driver fatigue level determination system based on detection of saccadic eye movements. Driver's eye movement speed is used to assess driver's fatigue level. The information about eyes is obtained via infrared led camera device. Movements of pupils were recorded in two driving scenarios with different traffic density. In the first scenario, the traffic density was set to low while the second scenario was based on high density and aggressive traffic. Based on the movements of pupils, the data on saccadic eye movement was analyzed to determine fatigue level of the driver. Acceleration, speed, and size of pupils at both traffic scenarios were compared with data mining techniques, such as segmentation adaptive peak, entropy, and data distribution analyses. Significantly different levels of fatigue were found between the tired and vigorous driver for the different types of scenarios.
Actor-Critic Fictitious Play in Simultaneous Move Multistage Games

Fictitious play is a game theoretic iterative procedure meant to learn an equilibrium in normal form games. However, this algorithm requires that each player has full knowledge of other players’ strategies. Using an architecture inspired by actor-critic algorithms, we build a stochastic approximation of the fictitious play process. This procedure is online, decentralized (an agent has no information of others’ strategies and rewards) and applies to multistage games (a generalization of normal form games). In addition, we prove convergence of our method towards a Nash equilibrium in both the cases of zero-sum two-player multistage games and cooperative multistage games. We also provide empirical evidence of the soundness of our approach on the game of Alesia with and without function approximation.