nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing performance by improving core stability and core strength.

Core stability and core strength have been subject to research since the early 1980s. Research has highlighted benefits of training these processes for people with back pain and for carrying out everyday activities. However, less research has been performed on the benefits of core training for elite athletes and how this training should be carried out to optimize sporting performance. Many elite athletes undertake core stability and core strength training as part of their training programme, despite contradictory findings and conclusions as to their efficacy. This is mainly due to the lack of a gold standard method for measuring core stability and strength when performing everyday tasks and sporting movements. A further confounding factor is that because of the differing demands on the core musculature during everyday activities (low load, slow movements) and sporting activities (high load, resisted, dynamic movements), research performed in the rehabilitation sector cannot be applied to the sporting environment and, subsequently, data regarding core training programmes and their effectiveness on sporting performance are lacking. There are many articles in the literature that promote core training programmes and exercises for performance enhancement without providing a strong scientific rationale of their effectiveness, especially in the sporting sector. In the rehabilitation sector, improvements in lower back injuries have been reported by improving core stability. Few studies have observed any performance enhancement in sporting activities despite observing improvements in core stability and core strength following a core training programme. A clearer understanding of the roles that specific muscles have during core stability and core strength exercises would enable more functional training programmes to be implemented, which may result in a more effective transfer of these skills to actual sporting activities.
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, “reinforced self-attention (ReSA)”, for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called “reinforced sequence sampling (RSS)”, selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, “reinforced self-attention network (ReSAN)”, solely based on ReSA. It achieves state-of-the-art performance on both Stanford Natural Language Inference (SNLI) and Sentences Involving Compositional Knowledge (SICK) datasets.
Disease tolerance as a defense strategy.

The immune system protects from infections primarily by detecting and eliminating the invading pathogens; however, the host organism can also protect itself from infectious diseases by reducing the negative impact of infections on host fitness. This ability to tolerate a pathogen's presence is a distinct host defense strategy, which has been largely overlooked in animal and human studies. Introduction of the notion of "disease tolerance" into the conceptual tool kit of immunology will expand our understanding of infectious diseases and host pathogen interactions. Analysis of disease tolerance mechanisms should provide new approaches for the treatment of infections and other diseases.
Neural Segmental Hypergraphs for Overlapping Mention Recognition

In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping
Blockchain Security in Cloud Computing: Use Cases, Challenges, and Solutions

Blockchain has drawn attention as the next-generation financial technology due to its security that suits the informatization era. In particular, it provides security through the authentication of peers that share virtual cash, encryption, and the generation of hash value. According to the global financial industry, the market for security-based blockchain technology is expected to grow to about USD 20 billion by 2020. In addition, blockchain can be applied beyond the Internet of Things (IoT) environment; its applications are expected to expand. Cloud computing has been dramatically adopted in all IT environments for its efficiency and availability. In this paper, we discuss the concept of blockchain technology and its hot research trends. In addition, we will study how to adapt blockchain security to cloud computing and its secure solutions in detail.
A Fully Integrated 60-GHz CMOS Direct-Conversion Doppler Radar RF Sensor With Clutter Canceller for Single-Antenna Noncontact Human Vital-Signs Detection

This paper presents a 60-GHz CMOS direct-conversion Doppler radar RF sensor with a clutter canceller for single-antenna noncontact human vital-signs detection. A high isolation quasi-circulator (QC) is designed to reduce the transmitting (Tx) power leakage (to the receiver). The clutter canceller performs cancellation for the Tx leakage power (from the QC) and the stationary background reflection clutter to enhance the detection sensitivity of weak vital signals. The integration of the 60-GHz RF sensor consists of the voltage-controlled oscillator, divided-by-2 frequency divider, power amplifier, QC, clutter canceller (consisting of variable-gain amplifier and 360 ° phase shifter), low-noise amplifier, in-phase/quadrature-phase sub-harmonic mixer, and three couplers. In the human vital-signs detection experimental measurement, at a distance of 75 cm, the detected heartbeat (1-1.3 Hz) and respiratory (0.35-0.45 Hz) signals can be clearly observed with a 60-GHz 17-dBi patch-array antenna. The RF sensor is fabricated in 90-nm CMOS technology with a chip size of 2 mm×2 mm and a consuming power of 217 mW.
Natural demodulation of two-dimensional fringe patterns. I. General background of the spiral phase quadrature transform.

It is widely believed, in the areas of optics, image analysis, and visual perception, that the Hilbert transform does not extend naturally and isotropically beyond one dimension. In some areas of image analysis, this belief has restricted the application of the analytic signal concept to multiple dimensions. We show that, contrary to this view, there is a natural, isotropic, and elegant extension. We develop a novel two-dimensional transform in terms of two multiplicative operators: a spiral phase spectral (Fourier) operator and an orientational phase spatial operator. Combining the two operators results in a meaningful two-dimensional quadrature (or Hilbert) transform. The new transform is applied to the problem of closed fringe pattern demodulation in two dimensions, resulting in a direct solution. The new transform has connections with the Riesz transform of classical harmonic analysis. We consider these connections, as well as others such as the propagation of optical phase singularities and the reconstruction of geomagnetic fields.
Fingerprint recognition performance with WSQ, CAWDR, and JPEG2000 compression

In this paper, fingerprint images compressed with WSQ, CAWDR and JPEG2000 are evaluated for fingerprint recognition performance. With high compression ratio between 40:1 to 160:1, fingerprint images which lost a lot of their details are used to find fingercodes, or core point. Euclidean Distance is the method used to find the matched fingerprint. We also proposed the subband-reduced CAWDR method which results in comparative recognition performance to the conventional CAWDR. The results of recognition performance of all coders are summarized in the paper.
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling

We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models.
Organizing conceptual knowledge in humans with a gridlike code

It has been hypothesized that the brain organizes concepts into a mental map, allowing conceptual relationships to be navigated in a manner similar to that of space. Grid cells use a hexagonally symmetric code to organize spatial representations and are the likely source of a precise hexagonal symmetry in the functional magnetic resonance imaging signal. Humans navigating conceptual two-dimensional knowledge showed the same hexagonal signal in a set of brain regions markedly similar to those activated during spatial navigation. This gridlike signal is consistent across sessions acquired within an hour and more than a week apart. Our findings suggest that global relational codes may be used to organize nonspatial conceptual representations and that these codes may have a hexagonal gridlike pattern when conceptual knowledge is laid out in two continuous dimensions.
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes

In this paper, we introduce a novel framework for combining scientific knowledge within physicsbased models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks. We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimension and time dimension. By using scientific knowledge to guide the construction and learning the data-driven model, we demonstrate that this method can achieve better prediction accuracy as well as scientific consistency of results.
Periodization paradigms in the 21st century: evidence-led or tradition-driven?

The planning and organization of athletic training have historically been much discussed and debated in the coaching and sports science literature. Various influential periodization theorists have devised, promoted, and substantiated particular training-planning models based on interpretation of the scientific evidence and individual beliefs and experiences. Superficially, these proposed planning models appear to differ substantially. However, at a deeper level, it can be suggested that such models share a deep-rooted cultural heritage underpinned by a common set of historically pervasive planning beliefs and assumptions. A concern with certain of these formative assumptions is that, although no longer scientifically justifiable, their shaping influence remains deeply embedded. In recent years substantial evidence has emerged demonstrating that training responses vary extensively, depending upon multiple underlying factors. Such findings challenge the appropriateness of applying generic methodologies, founded in overly simplistic rule-based decision making, to the planning problems posed by inherently complex biological systems. The purpose of this review is not to suggest a whole-scale rejection of periodization theories but to promote a refined awareness of their various strengths and weaknesses. Eminent periodization theorists-and their variously proposed periodization models-have contributed substantially to the evolution of training-planning practice. However, there is a logical line of reasoning suggesting an urgent need for periodization theories to be realigned with contemporary elite practice and modern scientific conceptual models. In concluding, it is recommended that increased emphasis be placed on the design and implementation of sensitive and responsive training systems that facilitate the guided emergence of customized context-specific training-planning solutions.
PADS: a domain-specific language for processing ad hoc data

PADS is a declarative data description language that allows data analysts to describe both the physical layout of ad hoc data sources and semantic properties of that data. From such descriptions, the PADS compiler generates libraries and tools for manipulating the data, including parsing routines, statistical profiling tools, translation programs to produce well-behaved formats such as Xml or those required for loading relational databases, and tools for running XQueries over raw PADS data sources. The descriptions are concise enough to serve as "living" documentation while flexible enough to describe most of the ASCII, binary, and Cobol formats that we have seen in practice. The generated parsing library provides for robust, application-specific error handling.
Learning Multiple Layers of Features from Tiny Images

Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images.
Tall Buildings and Elevators : A Review of Recent Technological Advances

Efficient vertical mobility is a critical component of tall building development and construction. This paper investigates recent advances in elevator technology and examines their impact on tall building development. It maps out, organizes, and collates complex and scattered information on multiple aspects of elevator design, and presents them in an accessible and non-technical discourse. Importantly, the paper contextualizes recent technological innovations by examining their implementations in recent major projects including One World Trade Center in New York;  Burj Khalifa in Dubai; Kingdom Tower in Jeddah, Saudi Arabia; and the green retrofit project of the Empire State Building in New York. Further, the paper discusses future vertical transportation models including a vertical subway concept, a space lift, and electromagnetic levitation technology. As these new technological advancements in elevator design empower architects to create new forms and shapes of large-scale, mixed-use developments, this paper concludes by highlighting the need for interdisciplinary research in incorporating elevators in skyscrapers.
ERP SYSTEMS AND INTERNAL AUDIT

From an internal audit perspective, enterprise systems have created new opportunities and challenges in managing internal as well as external risks. In this work, we report results of a survey that examines internal auditors’ ability to identify and manage operational, financial, technological, compliance and other risks as the organization migrates to an ERP environment. Our findings show that the internal auditors perceive a reduction in financial and operational risk and an increase in technical risks. These effects are somewhat mitigated by their ability to assess and manage these risks. We also find that internal audit departments satisfied their needs for ERP skills not by outsourcing but by providing staff with in-house training.
Power management strategy for a parallel hybrid electric truck

Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dualpower-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we will present a procedure for the design of an approximately optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic Programming (DP) is then utilized to find the optimal control actions. Through analysis of the behavior of the DP control actions, approximately optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of the power management control strategy is verified by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A trade-off study between fuel economy and emissions was performed. It was found that significant emission reduction can be achieved at the expense of a small increase in fuel consumption. Power Management Strategy for a Parallel Hybrid Electric Truck
Modelling of ball and plate system based on first principle model and optimal control

This paper presents modelling of ball and plate systems based on first principles by considering balance of forces and torques. A non-linear model is derived considering the dynamics of motors, gears, ball and plate. The non-linear model is linearized near the operating region to obtain a standard state space model. This linear model is used for discrete optimal control of the ball and plate system — the trajectory of the ball is controlled by control voltages to the motor.
Assessing the effects of consumers' product evaluations and trust on repurchase intention in e-commerce environments

This study enhances the existing literature on online trust by integrating the consumers’ product evaluations model and technology adoption model in e-commerce environments. In this study, we investigate how perceived value influences the perceptions of online trust among online buyers and their willingness to repurchase from the same website. This study proposes a research model that compares the relative importance of perceived value and online trust to perceived usefulness in influencing consumers’ repurchase intention. The proposed model is tested using data collected from online consumers of e-commerce. The findings show that although trust and ecommerce adoption components are critical in influencing repurchase intention, product evaluation factors are also important in determining repurchase intention. Perceived quality is influenced by the perceptions of competitive price and website reputation, which in turn influences perceived value; and perceived value, website reputation, and perceived risk influence online trust, which in turn influence repurchase intention. The findings also indicate that the effect of perceived usefulness on repurchase intention is not significant whereas perceived value and online trust are the major determinants of repurchase intention. Major theoretical contributions and practical implications are discussed.
Developing a Leadership Identity : A Grounded Theory

This grounded theory study on developing a leadership identity revealed a 6-stage developmental process. The thirteen diverse students in this study described their leadership identity as moving from a leader-centric view to one that embraced leadership as a collaborative, relational process. Developing a leadership identity was connected to the categories of developmental influences, developing self, group influences, students’ changing view of self with others, and students’ broadening view of leadership. A conceptual model illustrating the grounded theory of developing a leadership identity is presented.
Development and simulation on V-REP of an algorithm for the RoboCup@Work BNT

Recently the RoboCup@Work league emerged in the world's largest robotics competition, intended for competitors wishing to compete in the field of mobile robotics for manipulation tasks in industrial environments. This competition consists of several tasks with one reflected in this work (Basic Navigation Test). This project involves the simulation in Virtual Robot Experimentation Platform (V-REP) of the behavior of a KUKA youBot. The goal is to verify that the robots can navigate in their environment, in a standalone mode, in a robust and secure way. To achieve the proposed objectives, it was necessary to create a program in Lua and test it in simulation. This involved the study of robot kinematics and mechanics, Simultaneous Localization And Mapping (SLAM) and perception from sensors. In this work is introduced an algorithm developed for a KUKA youBot platform to perform the SLAM while reaching for the goal position, which works according to the requirements of this competition BNT. This algorithm also minimizes the errors in the built map and in the path travelled by the robot.