nils-herrmann/clean_scirepeval huggingface dataset:
Fuel-Free Synthetic Micro-/Nanomachines.

Inspired by the swimming of natural microorganisms, synthetic micro-/nanomachines, which convert energy into movement, are able to mimic the function of these amazing natural systems and help humanity by completing environmental and biological tasks. While offering autonomous propulsion, conventional micro-/nanomachines usually rely on the decomposition of external chemical fuels (e.g., H2 O2 ), which greatly hinders their applications in biologically relevant media. Recent developments have resulted in various micro-/nanomotors that can be powered by biocompatible fuels. Fuel-free synthetic micro-/nanomotors, which can move without external chemical fuels, represent another attractive solution for practical applications owing to their biocompatibility and sustainability. Here, recent developments on fuel-free micro-/nanomotors (powered by various external stimuli such as light, magnetic, electric, or ultrasonic fields) are summarized, ranging from fabrication to propulsion mechanisms. The applications of these fuel-free micro-/nanomotors are also discussed, including nanopatterning, targeted drug/gene delivery, cell manipulation, and precision nanosurgery. With continuous innovation, future autonomous, intelligent and multifunctional fuel-free micro-/nanomachines are expected to have a profound impact upon diverse biomedical applications, providing unlimited opportunities beyond one's imagination.
MuJoCo: A physics engine for model-based control

We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available.
The chatbot feels you - a counseling service using emotional response generation

Early study tries to use chatbot for counseling services. They changed drinking habit of who being consulted by leading them via intervene chatbot. However, the application did not concerned about psychiatric status through continuous conversation with user monitoring. Furthermore, they had no ethical judgment method that about the intervention of the chatbot. We argue that more reasonable and continuous emotion recognition will make better mental healthcare experiment. It will be more proper clinical psychiatric consolation in ethical view as well. This paper suggests a introduce a novel chatbot system for psychiatric counseling service. Our system understands content of conversation based on recent natural language processing (NLP) methods with emotion recognition. It senses emotional flow through the continuous observation of conversation. Also, we generate personalized counseling response from user input, to do this, we use additional constrains to generation model for the proper response generation which can detect conversational context, user emotion and expected reaction.
Security and Privacy in Cloud Computing: A Survey

Cloud computing can be defined as management and provision of different resources, such as, software, applications and information as services over the cloud (internet) on demand. Cloud computing is based on the assumption that the information can be quickly and easily accessed via the net. With its ability to provide dynamically scalable access for users, and the ability to share resources over the Internet, cloud computing has recently emerged as a promising hosting platform that performs an intelligent usage of a collection of services, applications, information and infrastructure comprised of pools of computers, networks, information and storage resources. Cloud computing is a multi-tenant resource sharing platform, which allows different service providers to deliver software as services in an economical way. Cloud computing is the latest technology revolution in terms of usage and management of IT resources and services driven largely by marketing and service offerings from the largest IT vendors including Google [26], IBM [19], Microsoft, and HP along with Amazon [17, 20, 24] and VMWare. However along with these advantages, storing a large amount of data including critical information on the cloud motivates highly skilled hackers, thus creating a need for the security is considered as one of the top issues while considering Cloud Computing. In this paper, we first explain the security model of cloud computing, and then analyze the feasibility, threats, and security in cloud computing in terms of extensive existing methods to control them along with their pros and cons. After that, the related open research problems and challenges are explored to promote the development of cloud computing. KeywordsCloud Computing; Fault Tolerance; Security And Privacy; Services
Container-Based Cloud Platform for Mobile Computation Offloading

With the explosive growth of smartphones and cloud computing, mobile cloud, which leverages cloud resource to boost the performance of mobile applications, becomes attrac- tive. Many efforts have been made to improve the performance and reduce energy consumption of mobile devices by offloading computational codes to the cloud. However, the offloading cost caused by the cloud platform has been ignored for many years. In this paper, we propose Rattrap, a lightweight cloud platform which improves the offloading performance from cloud side. To achieve such goals, we analyze the characteristics of typical of- floading workloads and design our platform solution accordingly. Rattrap develops a new runtime environment, Cloud Android Container, for mobile computation offloading, replacing heavy- weight virtual machines (VMs). Our design exploits the idea of running operating systems with differential kernel features inside containers with driver extensions, which partially breaks the limitation of OS-level virtualization. With proposed resource sharing and code cache mechanism, Rattrap fundamentally improves the offloading performance. Our evaluation shows that Rattrap not only reduces the startup time of runtime environments and shows an average speedup of 16x, but also saves a large amount of system resources such as 75% memory footprint and at least 79% disk capacity. Moreover, Rattrap improves offloading response by as high as 63% over the cloud platform based on VM, and thus saving the battery life.
Potential effect of population and climate changes on global distribution of dengue fever: an empirical model

Existing theoretical models of the potential effects of climate change on vector-borne diseases do not account for social factors such as population increase, or interactions between climate variables. Our aim was to investigate the potential effects of global climate change on human health, and in particular, on the transmission of vector-borne diseases.



We modelled the reported global distribution of dengue fever on the basis of vapour pressure, which is a measure of humidity. We assessed changes in the geographical limits of dengue fever transmission, and in the number of people at risk of dengue by incorporating future climate change and human population projections into our model.



We showed that the current geographical limits of dengue fever transmission can be modelled with 89% accuracy on the basis of long-term average vapour pressure. In 1990, almost 30% of the world population, 1.5 billion people, lived in regions where the estimated risk of dengue transmission was greater than 50%. With population and climate change projections for 2085, we estimate that about 5-6 billion people (50-60% of the projected global population) would be at risk of dengue transmission, compared with 3.5 billion people, or 35% of the population, if climate change did not happen.



We conclude that climate change is likely to increase the area of land with a climate suitable for dengue fever transmission, and that if no other contributing factors were to change, a large proportion of the human population would then be put at risk.
The anaerobic digestion of solid organic waste.

The accumulation of solid organic waste is thought to be reaching critical levels in almost all regions of the world. These organic wastes require to be managed in a sustainable way to avoid depletion of natural resources, minimize risk to human health, reduce environmental burdens and maintain an overall balance in the ecosystem. A number of methods are currently applied to the treatment and management of solid organic waste. This review focuses on the process of anaerobic digestion which is considered to be one of the most viable options for recycling the organic fraction of solid waste. This manuscript provides a broad overview of the digestibility and energy production (biogas) yield of a range of substrates and the digester configurations that achieve these yields. The involvement of a diverse array of microorganisms and effects of co-substrates and environmental factors on the efficiency of the process has been comprehensively addressed. The recent literature indicates that anaerobic digestion could be an appealing option for converting raw solid organic wastes into useful products such as biogas and other energy-rich compounds, which may play a critical role in meeting the world's ever-increasing energy requirements in the future.
Topic Modeling for Short Texts with Auxiliary Word Embeddings

Many applications require semantic understanding of short texts, and inferring discriminative and coherent latent topics is a critical and fundamental task in these applications. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short texts are much more sparse in terms of word co-occurrences. Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic. However, DMM has two main limitations. First, even though it seems reasonable to assume that each short text has only one topic because of its shortness, the definition of “shortness” is subjective and the length of the short texts is dataset dependent. That is, the single-topic assumption may be too strong for some datasets. To address this limitation, we propose to model the topic number as a Poisson distribution, allowing each short text to be associated with a small number of topics (e.g., one to three topics). This model is named PDMM. Second, DMM (and also PDMM) does not have access to background knowledge (e.g., semantic relations between words) when modeling short texts. When a human being interprets a piece of short text, the understanding is not solely based on its content words, but also their semantic relations. Recent advances in word embeddings offer effective learning of word semantic relations from a large corpus. Such auxiliary word embeddings enable us to address the second limitation. To this end, we propose to promote the semantically related words under the same topic during the sampling process, by using the generalized Pólya urn (GPU) model. Through the GPU model, background knowledge about word semantic relations learned from millions of external documents can be easily exploited to improve topic modeling for short texts. By directly extending the PDMM model with the GPU model, we propose two more effective topic models for short texts, named GPU-DMM and GPU-PDMM. Through extensive experiments on two real-world short text collections in two languages, we demonstrate that PDMM achieves better topic representations than state-of-the-art models, measured by topic coherence. The learned topic representation leads to better accuracy in a text classification task, as an indirect evaluation. Both GPU-DMM and GPU-PDMM further improve topic coherence and text classification accuracy. GPU-PDMM outperforms GPU-DMM at the price of higher computational costs.
Accuracy and precision of a novel non-invasive core thermometer.

Accurate measurement of core temperature is a standard component of perioperative and intensive care patient management. However, core temperature measurements are difficult to obtain in awake patients. A new non-invasive thermometer has been developed, combining two sensors separated by a known thermal resistance ('double-sensor' thermometer). We thus evaluated the accuracy of the double-sensor thermometer compared with a distal oesophageal thermometer to determine if the double-sensor thermometer is a suitable substitute.



In perioperative and intensive care patient populations (n=68 total), double-sensor measurements were compared with measurements from a distal oesophageal thermometer using Bland-Altman analysis and Lin's concordance correlation coefficient (CCC).



Overall, 1287 measurement pairs were obtained at 5 min intervals. Ninety-eight per cent of all double-sensor values were within +/-0.5 degrees C of oesophageal temperature. The mean bias between the methods was -0.08 degrees C; the limits of agreement were -0.66 degrees C to 0.50 degrees C. Sensitivity and specificity for detection of fever were 0.86 and 0.97, respectively. Sensitivity and specificity for detection of hypothermia were 0.77 and 0.93, respectively. Lin's CCC was 0.93.



The new double-sensor thermometer is sufficiently accurate to be considered an alternative to distal oesophageal core temperature measurement, and may be particularly useful in patients undergoing regional anaesthesia.
Automatic differentiation in PyTorch

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd , and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features.
Computational Anatomy in Theano

To model deformation of anatomical shapes, non-linear statistics are required to take into account the non-linear structure of the data space. Computer implementations of non-linear statistics and differential geometry algorithms often lead to long and complex code sequences. The aim of the paper is to show how the Theano framework can be used for simple and concise implementation of complex differential geometry algorithms while being able to handle complex and high-dimensional data structures. We show how the Theano framework meets both of these requirements. The framework provides a symbolic language that allows mathematical equations to be directly translated into Theano code, and it is able to perform both fast CPU and GPU computations on highdimensional data. We show how different concepts from non-linear statistics and differential geometry can be implemented in Theano, and give examples of the implemented theory visualized on landmark representations of Corpus Callosum shapes.
Unified Forensic Methodology for the Analysis of Embedded Systems

Embedded systems are ubiquitous in society and can contain information that could be used in criminal cases for example in a serious road traffic accident where the car management systems could provide vital forensic information concerning the engine speed etc. A critical review of a number of methods and procedures for the analysis of embedded systems were compared against a ‘standard’ methodology for use in a Forensic Computing Investigation. A Unified Forensic Methodology (UFM) has been developed that is forensically sound and capable of dealing with the analysis of a wide variety of Embedded Systems.
The cyber decade: Cyber defence at a X-ing point

As a consequence of the numerous cyber attacks over the last decade, both the consideration and use of cyberspace has fundamentally changed, and will continue to evolve. Military forces all over the world have come to value the new role of cyberspace in warfare, building up cyber commands, and establishing new capabilities. Integral to such capabilities is that military forces fundamentally depend on the rapid exchange of information in order for their decision-making processes to gain superiority on the battlefield; this compounds the need to develop network- enabled capabilities to realize network-centric warfare. This triangle of cyber offense, cyber defence, and cyber dependence creates a challenging and complex system of interdependencies. Alongside, while numerous technologies have not improved cyber security significantly, this may change with upcoming new concepts and systems, like decentralized ledger technologies (Blockchains) or quantum-secured communication. Following these thoughts, the paper analyses the development of both cyber threats and defence capabilities during the past 10 years, evaluates the current situation and gives recommendations for improvements. To this end, the paper is structured as follows: first, general conditions for military forces with respect to "cyber" are described, including an analysis of the most likely courses of action of the West and their seemingly traditional adversary in the East, Russia. The overview includes a discussion of the usefulness of the measures and an overview of upcoming technologies critical for cyber security. Finally, requirements and recommendations for the further development of cyber defence are briefly covered.
Deep automatic license plate recognition system

Automatic License Plate Recognition (ALPR) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. The difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. This work aims to address ALPR using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. These proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.
Fake News as We Feel It: Perception and Conceptualization of the Term "Fake News" in the Media

In this article, we quantitatively analyze how the term “fake news” is being shaped in news media in recent years. We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries. Our results not only corroborate previous indications of a high increase in the usage of the expression “fake news”, but also show contextual changes around this expression after the United States presidential election of 2016. Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term “fake news”, suggesting that this expression underwent a change in perception and conceptualization after 2016. These outcomes expand the understandings on the usage of the term “fake news”, helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.
Gödel ' s Theorem and Information

Gödel's theorem may be demonstrated using arguments having an information-theoretic flavor. In such an approach it is possible to argue that if a theorem contains more information than a given set of axioms, then it is impossible for the theorem to be derived from the axioms. In contrast with the traditional proof based on the paradox of the liar, this new viewpoint suggests that the incompleteness phenomenon discovered by Gödel is natural and widespread rather than pathological and unusual.
A survey of fault localization techniques in computer networks

Fault localization, a central aspect of network fault management, is a process of deducing the exact source of a failure from a set of observed failure indications. It has been a focus of research activity since the advent of modern comm unication systems, which produced numerous fault localization techniques. Howeve r, ascommunication systems evolved becoming more complex and offering new capabilities, the requirements imposed on fault localization techniques have changed as well. It is fair to say that despite this research effort, fault localization in complex communication systems remains an open research problem. This paper discusses the challenges of fault localization in complex communication systems and presents an overview of solutions proposed in the course of the last t en years, while discussing their advantages and shortcomings. The survey is followed by the presenta tion of potential directions for future research in this area.
DOES THE BALANCED SCORECARD WORK : AN EMPIRICAL

Commentators suggest that between 30 and 60% of large US firms have adopted the Balanced Scorecard, first described by Bob Kaplan and David Norton in their seminal Harvard Business Review paper of 1992 (Kaplan and Norton, 1992; Neely and Marr, 2003). Empirical evidence that explores the performance impact of the balanced scorecard, however, is extremely rare and much that is available is anecdotal at best. This paper reports a study that set out to explore the performance impact of the balanced scorecard by employing a quasiexperimental design. Up to three years worth of financial data were collected from two sister divisions of an electrical wholesale chain based in the UK, one of which had implemented the balanced scorecard and one of which had not. The relative performance improvements of matched pairs of branches were compared to establish what, if any, performance differentials existed between the branches that had implemented the balanced scorecard and those that had not. The key findings of the study include: (i) when analyzing just the data from Electrical – the business that implemented the balanced scorecard it appears that implementation of the balanced scorecard might have had a positive impact on sales, gross profit and net profit; but (ii) when comparing Electrical’s performance with its sister company these findings can be questioned. Clearly further work on this important topic is required in similar settings where natural experiments occur.