nils-herrmann/clean_scirepeval huggingface dataset:
3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow

In this paper, we propose a 3D HMM (Three-dimensional Hidden Markov Models) approach to recognizing human facial expressions and associated emotions. Human emotion is usually classified by psychologists into six categories: Happiness, Sadness, Anger, Fear, Disgust and Surprise. Further, psychologists categorize facial movements based on the muscles that produce those movements using a Facial Action Coding System (FACS). We look beyond pure muscle movements and investigate facial features – brow, mouth, nose, eye height and facial shape – as a means of determining associated emotions. Histogram of Optical Flow is used as the descriptor for extracting and describing the key features, while training and testing are performed on 3D Hidden Markov Models. Experiments on datasets show our approach is promising and robust.
A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming Over HTTP

With companies such as Netflix and YouTube accounting for more than 50% of the peak download traffic on North American fixed networks in 2015, video streaming represents a significant source of Internet traffic. Multimedia delivery over the Internet has evolved rapidly over the past few years. The last decade has seen video streaming transitioning from User Datagram Protocol to Transmission Control Protocol-based technologies. Dynamic adaptive streaming over HTTP (DASH) has recently emerged as a standard for Internet video streaming. A range of rate adaptation mechanisms are proposed for DASH systems in order to deliver video quality that matches the throughput of dynamic network conditions for a richer user experience. This survey paper looks at emerging research into the application of client-side, server-side, and in-network rate adaptation techniques to support DASH-based content delivery. We provide context and motivation for the application of these techniques and review significant works in the literature from the past decade. These works are categorized according to the feedback signals used and the end-node that performs or assists with the adaptation. We also provide a review of several notable video traffic measurement and characterization studies and outline open research questions in the field.
PMLB: a large benchmark suite for machine learning evaluation and comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.
Human exploration of Mars, Design Reference Architecture 5.0

This paper provides a summary of the 2007 Mars Design Reference Architecture 5.0 (DRA 5.0) , which is the latest in a series of NASA Mars reference missions. It provides a vision of one potential approach to human Mars exploration, including how Constellation systems could be used. The strategy and example implementation concepts that are described here should not be viewed as constituting a formal plan for the human exploration of Mars, but rather provide a common framework for future planning of systems concepts, technology development, and operational testing as well as potential Mars robotic missions, research that is conducted on the International Space Station, and future potential lunar exploration missions. This summary of the Mars DRA 5.0 provides an overview of the overall mission approach, surface strategy and exploration goals, as well as the key systems and challenges for the first three concepts for human missions to Mars.1,2
Support Vector Echo-State Machine for Chaotic Time-Series Prediction

A novel chaotic time-series prediction method based on support vector machines (SVMs) and echo-state mechanisms is proposed. The basic idea is replacing "kernel trick" with "reservoir trick" in dealing with nonlinearity, that is, performing linear support vector regression (SVR) in the high-dimension "reservoir" state space, and the solution benefits from the advantages from structural risk minimization principle, and we call it support vector echo-state machines (SVESMs). SVESMs belong to a special kind of recurrent neural networks (RNNs) with convex objective function, and their solution is global, optimal, and unique. SVESMs are especially efficient in dealing with real life nonlinear time series, and its generalization ability and robustness are obtained by regularization operator and robust loss function. The method is tested on the benchmark prediction problem of Mackey-Glass time series and applied to some real life time series such as monthly sunspots time series and runoff time series of the Yellow River, and the prediction results are promising
The mechanisms of muscle hypertrophy and their application to resistance training.

The quest to increase lean body mass is widely pursued by those who lift weights. Research is lacking, however, as to the best approach for maximizing exercise-induced muscle growth. Bodybuilders generally train with moderate loads and fairly short rest intervals that induce high amounts of metabolic stress. Powerlifters, on the other hand, routinely train with high-intensity loads and lengthy rest periods between sets. Although both groups are known to display impressive muscularity, it is not clear which method is superior for hypertrophic gains. It has been shown that many factors mediate the hypertrophic process and that mechanical tension, muscle damage, and metabolic stress all can play a role in exercise-induced muscle growth. Therefore, the purpose of this paper is twofold: (a) to extensively review the literature as to the mechanisms of muscle hypertrophy and their application to exercise training and (b) to draw conclusions from the research as to the optimal protocol for maximizing muscle growth.
A Hierarchical Probabilistic Model for Novelty Detection in Text

Topic Detection and Tracking (TDT) is a variant of classiication in which the classes are not known or xed in advance. Consider for example an incoming stream of news articles or email messages that are to be classiied by topic; new classes must be created as new topics arise. The problem is a challenging one for machine learning. Instances of new topics must be recognized as not belonging to any of the existing classes (detection), and instances of old topics must be correctly classiied (tracking)|often with extremely little training data per class. This paper proposes a new approach to TDT based on probabilis-tic, generative models. Strong statistical techniques are used to address the many challenges: hierarchical shrinkage for sparse data, statistical \garbage collection" for new event detection, clustering in time to separate the diierent events of a common topic, and deterministic anneal-ing for creating the hierarchy. Preliminary experimental results show promise.
Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards

In just a few years, crowdsourcing markets like Mechanical Turk have become the dominant mechanism for for building "gold standard" datasets in areas of computer science ranging from natural language processing to audio transcription. The assumption behind this sea change - an assumption that is central to the approaches taken in hundreds of research projects - is that crowdsourced markets can accurately replicate the judgments of the general population for knowledge-oriented tasks. Focusing on the important domain of semantic relatedness algorithms and leveraging Clark's theory of common ground as a framework, we demonstrate that this assumption can be highly problematic. Using 7,921 semantic relatedness judgements from 72 scholars and 39 crowdworkers, we show that crowdworkers on Mechanical Turk produce significantly different semantic relatedness gold standard judgements than people from other communities. We also show that algorithms that perform well against Mechanical Turk gold standard datasets do significantly worse when evaluated against other communities' gold standards. Our results call into question the broad use of Mechanical Turk for the development of gold standard datasets and demonstrate the importance of understanding these datasets from a human-centered point-of-view. More generally, our findings problematize the notion that a universal gold standard dataset exists for all knowledge tasks.
Impact of inadequate adherence on response to subcutaneously administered anti-tumour necrosis factor drugs: results from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort

Non-adherence to DMARDs is common, but little is known about adherence to biologic therapies and its relationship to treatment response. The purpose of this study was to investigate the association between self-reported non-adherence to s.c. anti-TNF therapy and response in individuals with RA.



Participants about to start s.c. anti-TNF therapy were recruited to a large UK multicentre prospective observational cohort study. Demographic information and disease characteristics were assessed at baseline. Self-reported non-adherence, defined as whether the previous due dose of biologic therapy was reported as not taken on the day agreed with the health care professional, was recorded at 3 and 6 months following the start of therapy. The 28-joint DAS (DAS28) was recorded at baseline and following 3 and 6 months of therapy. Multivariate linear regression was used to examine these relationships.



Three hundred and ninety-two patients with a median disease duration of 7 years [interquartile range (IQR) 3-15] were recruited. Adherence data were available in 286 patients. Of these, 27% reported non-adherence to biologic therapy according to the defined criteria at least once within the first 6-month period. In multivariate linear regression analysis, older age, lower baseline DAS28 and ever non-adherence at either 3 or 6 months from baseline were significantly associated with a poorer DAS28 response at 6 months to anti-TNF therapy.



Patients with RA who reported not taking their biologic on the day agreed with their health care professional showed poorer clinical outcomes than their counterparts, emphasizing the need to investigate causes of non-adherence to biologics.
NORTH-HOLLAND GENERATING OPTIMAL TOPOLOGIES IN STRUCTURAL DESIGN USING A HOMOGENIZATION METHOD

Optimal shape design of structural elements based on boundary variations results in final designs that are topologically equivalent to the initial choice of design, and general, stable computational schemes for this approach often require some kind of remeshing of the finite element approximation of the analysis problem. This paper presents a methodology for optimal shape design where both these drawbacks can be avoided. The method is related to modern production techniques and consists of computing the optimal distribution in space of an anisotropic material that is constructed by introducing an infimum of periodically distributed small holes in a given homogeneous, i~otropic material, with the requirement that the resulting structure can carry the given loads as well as satisfy other design requirements. The computation of effective material properties for the anisotropic material is carried out using the method of homogenization. Computational results are presented and compared with results obtained by boundary variations.
Mixed criticality systems with weakly-hard constraints

Current adaptive mixed criticality scheduling policies assume a high criticality mode in which all low criticality tasks are descheduled to ensure that high criticality tasks can meet timing constraints derived from certification approved methods. In this paper we present a new scheduling policy, Adaptive Mixed Criticality - Weakly Hard, which provides a guaranteed minimum quality of service for low criticality tasks in the event of a criticality mode change. We derive response time based schedulability tests for this model. Empirical evaluations are then used to assess the relative performance against previously published policies and their schedulability tests.
Adaptive authentication based on analysis of user behavior

Authentication is a mechanism to verify identity of users. Those who can present valid credential are considered as authenticated identities. In this paper, we introduce an adaptive authentication system called Unified Authentication Platform (UAP) which incorporates adaptive control to identify high-risk and suspicious illegitimate login attempts. The system evaluates comprehensive set of known information about the users from the past login history to define their normal behavior profile. The system leverages this information that has been previously stored to determine the security risk and level of assurance of current login attempt.
ZeroG: overground gait and balance training system.

A new overground body-weight support system called ZeroG has been developed that allows patients with severe gait impairments to practice gait and balance activities in a safe, controlled manner. The unloading system is capable of providing up to 300 lb of static support and 150 lb of dynamic (or constant force) support using a custom-series elastic actuator. The unloading system is mounted to a driven trolley, which rides along an overhead rail. We evaluated the performance of ZeroG's unloading system, as well as the trolley tracking system, using benchtop and human-subject testing. Average root-mean-square and peak errors in unloading were 2.2 and 7.2 percent, respectively, over the range of forces tested while trolley tracking errors were less than 3 degrees, indicating the system was able to maintain its position above the subject. We believe training with ZeroG will allow patients to practice activities that are critical to achieving functional independence at home and in the community.
Cramer-Wold AutoEncoder

Assessing distance betweeen the true and the sample distribution is a key component of many state of the art generative models, such as Wasserstein Autoencoder (WAE). Inspired by prior work on Sliced-Wasserstein Autoencoders (SWAE) and kernel smoothing we construct a new generative model – Cramer-Wold AutoEncoder (CWAE). CWAE cost function, based on introduced Cramer-Wold distance between samples, has a simple closed-form in the case of normal prior. As a consequence, while simplifying the optimization procedure (no need of sampling necessary to evaluate the distance function in the training loop), CWAE performance matches quantitatively and qualitatively that of WAE-MMD (WAE using maximum mean discrepancy based distance function) and often improves upon SWAE.
Multihop Free-Space Optical Communications Over Turbulence Channels with Pointing Errors using Heterodyne Detection

This paper proposes and analyzes the performance of the multihop free-space optical (FSO) communication links using a heterodyne differential phase-shift keying modulation scheme operating over a turbulence induced fading channel. A novel statistical fading channel model for multihop FSO systems using channel-state-information-assisted and fixed-gain relays is developed incorporating the atmospheric turbulence, pointing errors, and path-loss effects. The closed-form expressions for the moment generating function, probability density function, and cumulative distribution function of the multihop FSO channel are derived using Meijer's G-function. They are then used to derive the fundamental limits of the outage probability and average symbol error rate. Results confirm the performance loss as a function of the number of hops. Effects of the turbulence strength varying from weak-to-moderate and moderate-to-strong turbulence, geometric loss, and pointing errors are studied. The pointing errors can be mitigated by widening the beam at the expense of the received power level, whereas narrowing the beam can reduce the geometric loss at the cost of increased misalignment effects.
Persuasive Design for Behaviour Change Apps: Issues for Designers

The main aim of this position paper is to identify and briefly discuss design-related issues commonly encountered with the implementation of both behaviour change techniques and persuasive design principles in physical activity smartphone applications. These overlapping issues highlight a disconnect in the perspectives held between health scientists' focus on the application of behaviour change theories and components of interventions, and the information systems designers' focus on the application of persuasive design principles as software design features intended to motivate, facilitate and support individuals through the behaviour change process. A review of the current status and some examples of these different perspectives is presented, leading to the identification of the main issues associated with this disconnection. The main behaviour change technique issues identified are concerned with: the fragmented integration of techniques, hindrances in successful use, diversity of user needs and preferences, and the informational flow and presentation. The main persuasive design issues identified are associated with: the fragmented application of persuasive design principles, hindrances in successful usage, diversity of user needs and preferences, informational flow and presentation, the lack of pragmatic guidance for application designers, and the maintenance of immersive user interactions and engagements. Given the common overlap across four of the identified issues, it is concluded that a methodological approach for integrating these two perspectives, and their associated issues, into a consolidated framework is necessary to address the apparent disconnect between these two independently-established, yet complementary fields.
Leveraging Entrepreneurial Orientation to Enhance SME Export Performance An

*This contract was given to the United States Association for Small Business and Entrepreneurship (USASBE) for a best doctoral student paper award, presented to the awardees at the USASBE annual meeting. The opinions and recommendations of the authors of this study do not necessarily reflect official policies of the SBA or other agencies of the U.S. government.  Export strategy has become increasingly important for SMEs in recent years. To realize the full potential of export strategy, SMEs must be able to address challenges in export markets successfully. A firm must have adequate capabilities to meet unique challenges in such efforts. However, SMEs are limited by their access to resources and capabilities. While prior studies have looked at the importance of organizational learning in export strategy, they have overlooked the firm capabilities that facilitate the use of the learning. As firms that partake in export activity are entrepreneurial in nature, these firms would benefit by proactively seeking new markets, engaging in innovative action to meet local market needs, and be able and willing to take risks by venturing into previously unknown markets. The authors of this paper propose that SMEs make use of capabilities such as entrepreneurial orientation in an attempt to reduce impediments to exporting, which in turn could lead to enhanced export performance. This study finds that proactivity and risk-taking play a role in enhancing export performance of SMEs. However, it did not find support for innovation as a factor that enhances export performance. These findings could mean that firms that are proactive in nature are better at reducing export impediments. This is because these firms are able to bring new products quickly into the marketplace, and are better able to anticipate future demand, creating a first mover advantage. The results of the study also suggest that risk-taking firms might choose strategies that move away from the status quo, thereby increasing the firm's engagement in process enhancements, new product services, innovative marketing techniques , and the like. The data for this report were collected for the National Federation of Independent Business by the executive interviewing group of The Gallup Organization. The survey focused on international trade efforts of small manufacturers …
Switching Mechanisms Triggered by a Collector Voltage Ramp in Avalanche Transistors With Short-Connected Base and Emitter

Although Marx-bank connection of avalanche transistors is widely used in applications requiring high-voltage nanosecond and subnanosecond pulses, the physical mechanisms responsible for the voltage-ramp-initiated switching of a single transistor in the Marx chain remain unclear. It is shown here by detailed comparison of experiments with physical modeling that picosecond switching determined by double avalanche injection in the collector-base diode gives way to formation and shrinkage of the collector field domain typical of avalanche transistors under the second breakdown. The latter regime, characterized by a lower residual voltage, becomes possible despite a short-connected emitter and base, thanks to the 2-D effects.
A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms

Load Balancing is essential for efficient operations indistributed environments. As Cloud Computing is growingrapidly and clients are demanding more services and betterresults, load balancing for the Cloud has become a veryinteresting and important research area. Many algorithms weresuggested to provide efficient mechanisms and algorithms forassigning the client's requests to available Cloud nodes. Theseapproaches aim to enhance the overall performance of the Cloudand provide the user more satisfying and efficient services. Inthis paper, we investigate the different algorithms proposed toresolve the issue of load balancing and task scheduling in CloudComputing. We discuss and compare these algorithms to providean overview of the latest approaches in the field.