nils-herrmann/clean_scirepeval huggingface dataset:
A One-Time Password Scheme with QR-Code Based on Mobile Phone

User authentication is one of the fundamental procedures to ensure secure communications and share system resources over an insecure public network channel. Especially, the purpose of the one-time password is to make it more difficult to gain unauthorized access to restricted resources. Instead of using the password file as conventional authentication systems, many researchers have devoted to implement various one-time password schemes using smart cards, time-synchronized token or short message service in order to reduce the risk of tampering and maintenance cost. However, these schemes are impractical because of the far from ubiquitous hardware devices or the infrastructure requirements. To remedy these weaknesses, the attraction of the QR code technique can be introduced into our one-time password authentication protocol. Not the same as before, the proposed scheme based on QR code not only eliminates the usage of the password verification table, but also is a cost effective solution since most internet users already have mobile phones.
Zero moment point manipulability ellipsoid

In this paper, we propose zero moment point (ZMP) manipulability ellipsoid as an extension to the existing ZMP balance criterion. The ZMP manipulability ellipsoid was developed by combining the ZMP balance criterion, the humanoid robot dynamics and the manipulability of robotic manipulators. The ZMP manipulability ellipsoid represents the ability of a humanoid robot to instantly move the ZMP from its current ZMP location within the balance criterion. The size and shape of the ZMP manipulability ellipsoid are a function of the joint-torque limitation of a humanoid robot. Thus, the ellipsoid forms an area in which the ZMP can be manipulated instantly, and the larger the ellipsoid, the better the propensity of a humanoid robot will be to recover from an unbalanced situation. We also show that the gravity force of a humanoid robot affects neither the shape nor the translation of the ellipsoid. Furthermore, we show that the ZMP manipulability ellipsoid can aid the humanoid-robot design process by choosing appropriate actuators. Finally, a four degree-of-freedom walking robot was used to illustrate the proposed ZMP manipulability ellipsoid
Model-based Adversarial Imitation Learning

Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle D that discriminates between the expert’s data distribution and that of the generative model G. The generative model is trained to capture the expert’s distribution by maximizing the probability of D misclassifying the data it generates. Overall, the system is differentiable end-toend and is trained using basic backpropagation. This type of learning was successfully applied to the problem of policy imitation in a model-free setup. However, a model-free approach does not allow the system to be differentiable, which requires the use of high-variance gradient estimations. In this paper we introduce the Model based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach for the problem of adversarial imitation learning. We show how to use a forward model to make the system fully differentiable, which enables us to train policies using the (stochastic) gradient of D. Moreover, our approach requires relatively few environment interactions, and fewer hyper-parameters to tune. We test our method on the MuJoCo physics simulator and report initial results that surpass the current state-of-the-art.
Activity Based Metadata for Semantic Desktop Search

With increasing storage capacities on current PCs, searching the World Wide Web has ironically become more efficient than searching one’s own personal computer. The recently introduced desktop search engines are a first step towards coping with this problem, but not yet a satisfying solution. The reason for that is that desktop search is actually quite different from its web counterpart. Documents on the desktop are not linked to each other in a way comparable to the web, which means that result ranking is poor or even inexistent, because algorithms like PageRank cannot be used for desktop search. On the other hand, desktop search could potentially profit from a lot of implicit and explicit semantic information available in emails, folder hierarchies, browser cache contexts and others. This paper investigates how to extract and store these activity based context information explicitly as RDF metadata and how to use them, as well as additional background information and ontologies, to enhance desktop search.
The role of social capital and community belongingness for exercise adherence: An exploratory study of the CrossFit gym model.

This is the first study to measure the 'sense of community' reportedly offered by the CrossFit gym model. A cross-sectional study adapted Social Capital and General Belongingness scales to compare perceptions of a CrossFit gym and a traditional gym. CrossFit gym members reported significantly higher levels of social capital (both bridging and bonding) and community belongingness compared with traditional gym members. However, regression analysis showed neither social capital, community belongingness, nor gym type was an independent predictor of gym attendance. Exercise and health professionals may benefit from evaluating further the 'sense of community' offered by gym-based exercise programmes.
A Branch-Cut-and-Price Algorithm for the Energy Minimization Vehicle Routing Problem

We study a variant of the capacitated vehicle routing problem where the cost over each arc is defined as the product of the arc length and the weight of the vehicle when it traverses that arc. We propose two new mixed integer linear programming formulations for the problem: an arc-load formulation and a set partitioning formulation based on q-routes with additional constraints. A family of cycle elimination constraints are derived for the arc-load formulation. We then compare the linear programming (LP) relaxations of these formulations with the twoindex one-commodity flow formulation proposed in the literature. In particular, we show that the arc-load formulation with the new cycle elimination constraints gives the same LP bound as the set partitioning formulation based on 2-cycle-free q-routes, which is stronger than the LP bound given by the two-index one-commodity flow formulation. We propose a branchand-cut algorithm for the arc-load formulation, and a branch-cut-and-price algorithm for the set partitioning formulation strengthened by additional constraints. Computational results on instances from the literature demonstrate that a significant improvement can be achieved by the branch-cut-and-price algorithm over other methods.
Implementation and stability analysis of prioritized whole-body compliant controllers on a wheeled humanoid robot in uneven terrains

In this work, we implement the floating base prioritized whole-body compliant control framework described in Sentis et al. ( 2010) on a wheeled humanoid robot maneuvering in sloped terrains. We then test it for a variety of compliant whole-body behaviors including balance and kinesthetic mobility on irregular terrain, and Cartesian hand position tracking using the co-actuated (i.e. two joints are simultaneously actuated with one motor) robot’s upper body. The implementation serves as a hardware proof for a variety of whole-body control concepts that had previously been developed and tested in simulation. First, behaviors of two and three priority tasks are implemented and successfully executed on the humanoid hardware. In particular, first and second priority tasks are linearized in the task space through model feedback and then controlled through task accelerations. Postures, on the other hand, are shown to be asymptotically stable when using prioritized whole-body control structures and then successfully tested in the real hardware. To cope with irregular terrains, the base is modeled as a six degree of freedom floating system and the wheels are characterized through contact and rolling constraints. Finally, center of mass balance capabilities using whole-body compliElectronic supplementary material The online version of this article (doi:10.1007/s10514-013-9358-8) contains supplementary material, which is available to authorized users. L. Sentis (B) · J. Petersen The University of Texas at Austin, Austin, TX, USA e-mail: lsentis@austin.utexas.edu J. Petersen e-mail: joshpetersen1231@gmail.com R. Philippsen Halmstad University, Halmstad, Sweden e-mail: roland.philippsen@gmx.net ant control and kinesthetic mobility are implemented and tested in the humanoid hardware to climb terrains with various slopes.
Stereophonic Acoustic Echo Suppression Based on Wiener Filter in the Short-Time Fourier Transform Domain

An open-loop stereophonic acoustic echo suppression (SAES) method without preprocessing is presented for teleconferencing systems, where the Wiener filter in the short-time Fourier transform (STFT) domain is employed. Instead of identifying the echo path impulse responses with adaptive filters, the proposed algorithm estimates the echo spectra from the stereo signals using two weighting functions. The spectral modification technique originally proposed for noise reduction is adopted to remove the echo from the microphone signal. Moreover, a priori signal-to-echo ratio (SER) based Wiener filter is used as the gain function to achieve a trade-off between musical noise reduction and computational load for real-time operations. Computer simulation shows the effectiveness and the robustness of the proposed method in several different scenarios.
Persistent Single-Neuron Activity during Working Memory in the Human Medial Temporal Lobe

Working memory is an essential component of human cognition. Persistent activity related to working memory has been reported in many brain areas, including the inferior temporal and prefrontal cortex [1-8]. The medial temporal lobe (MTL) contains "concept cells" that respond invariantly to specific individuals or places whether presented as images, text, or speech [9, 10]. It is unknown, however, whether the MTL also participates in working memory processes. We thus sought to determine whether human MTL neurons respond to images held in working memory. We recorded from patients with chronically intractable epilepsy as they performed a task that required them to remember three or four sequentially presented pictures across a brief delay. 48% of visually selective neurons continued to carry image-specific information after image offset, but most ceased to encode previously presented images after a subsequent presentation of a different image. However, 8% of visually selective neurons encoded previously presented images during a final maintenance period, despite presentation of further images in the intervening interval. Population activity of stimulus-selective neurons predicted behavioral outcome in terms of correct and incorrect responses. These findings indicate that the MTL is part of a brain-wide network for working memory.
Invariant characterization of DOVID security features using a photometric descriptor

Diffractive Optically Variable Image Devices (DOVIDs) are popular security features used to protect security documents such as banknotes, ID cards, passports, etc. Checking authenticity of these security features on both user as well as forensic level remains a challenging task, requiring sophisticated hardware tools and expert knowledge. Recently, we proposed a technique exploiting a large-scale photometric behavior of DOVIDs in order to discriminate denominations and detect counterfeits. Here we investigate invariance properties of the proposed method and demonstrate its robustness against various common perturbations, which may have negative impact on the acquisition quality in practice. Presented results show a great potential of this approach primarily for security and forensic purposes, but also for other applications, where automated inspection of DOVIDs is of interest.
Studies on tissue culture of Chinese medicinal plant resources in Taiwan and their sustainable utilization

Medicinal plants are sources of important therapeutic aid for alleviating human ailments. With increasing realization of the health hazards and toxicity associated with the indiscriminate use of synthetic drugs and antibiotics, interest in the use of plants and plant-based drugs has revived throughout the world. However, a large number of medicinal plants remain to be investigated for their possible pharmacological value. Most of the pharmaceutical industry is highly dependent on wild populations for the supply of raw materials for extraction of medicinally important compounds. Due to a lack of proper cultivation practices, destruction of plant habitats, and the illegal and indiscriminate collection of plants from these habitats, many medicinal plants are severely threatened. Advanced biotechnological methods of culturing plant cells and tissues should provide new means of conserving and rapidly propagating valuable, rare, and endangered medicinal plants. This paper describes the work carried out at the Taiwan Agricultural Research Institute and Chaoyang University of Technology on in vitro propagation of some important medicinal plants.
Automatic Hand-Over Animation using Principle Component Analysis

This paper introduces a method for producing high quality hand motion using a small number of markers. The proposed "handover" animation technique constructs joint angle trajectories with the help of a reference database. Utilizing principle component analysis (PCA) applied to the database, the system automatically determines the sparse marker set to record. Further, to produce hand animation, PCA is used along with a locally weighted regression (LWR) model to reconstruct joint angles. The resulting animation is a full-resolution hand which reflects the original motion without the need for capturing a full marker set. Comparing the technique to other methods reveals improvement over the state of the art in terms of the marker set selection. In addition, the results highlight the ability to generalize the motion synthesized, both by extending the use of a single reference database to new motions, and from distinct reference datasets, over a variety of freehand motions.
2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.

Endorsed by the American Association of Cardiovascular and Pulmonary Rehabilitation, American Pharmacists Association, American Society for Nutrition, American Society for Preventive Cardiology, American Society of Hypertension, Association of Black Cardiologists, National Lipid Association, Preventive Cardiovascular Nurses Association, and WomenHeart: The National Coalition for Women With Heart Disease
Natural solution to antibiotic resistance: bacteriophages ‘The Living Drugs’

Antibiotics have been a panacea in animal husbandry as well as in human therapy for decades. The huge amount of antibiotics used to induce the growth and protect the health of farm animals has lead to the evolution of bacteria that are resistant to the drug's effects. Today, many researchers are working with bacteriophages (phages) as an alternative to antibiotics in the control of pathogens for human therapy as well as prevention, biocontrol, and therapy in animal agriculture. Phage therapy and biocontrol have yet to fulfill their promise or potential, largely due to several key obstacles to their performance. Several suggestions are shared in order to point a direction for overcoming common obstacles in applied phage technology. The key to successful use of phages in modern scientific, farm, food processing and clinical applications is to understand the common obstacles as well as best practices and to develop answers that work in harmony with nature.
Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition

One of the serious obstacles to the applications of speech emotion recognition systems in real-life settings is the lack of generalization of the emotion classifiers. Many recognition systems often present a dramatic drop in performance when tested on speech data obtained from different speakers, acoustic environments, linguistic content, and domain conditions. In this letter, we propose a novel unsupervised domain adaptation model, called Universum autoencoders, to improve the performance of the systems evaluated in mismatched training and test conditions. To address the mismatch, our proposed model not only learns discriminative information from labeled data, but also learns to incorporate the prior knowledge from unlabeled data into the learning. Experimental results on the labeled Geneva Whispered Emotion Corpus database plus other three unlabeled databases demonstrate the effectiveness of the proposed method when compared to other domain adaptation methods.
Pathogenesis of liver fibrosis.

Liver fibrosis is a major cause of morbidity and mortality worldwide due to chronic viral hepatitis and, more recently, from fatty liver disease associated with obesity. Hepatic stellate cell activation represents a critical event in fibrosis because these cells become the primary source of extracellular matrix in liver upon injury. Use of cell-culture and animal models has expanded our understanding of the mechanisms underlying stellate cell activation and has shed new light on genetic regulation, the contribution of immune signaling, and the potential reversibility of the disease. As pathways of fibrogenesis are increasingly clarified, the key challenge will be translating new advances into the development of antifibrotic therapies for patients with chronic liver disease.
Multiple Human Tracking in RGB-D Data: A Survey

Multiple human tracking (MHT) is a fundamental task in many computer vision applications. Appearance-based approaches, primarily formulated on RGB data, are constrained and affected by problems arising from occlusions and/or illumination variations. In recent years, the arrival of cheap RGB-Depth (RGB-D) devices has led to many new approaches to MHT, and many of these integrate color and depth cues to improve each and every stage of the process. In this survey, we present the common processing pipeline of these methods and review their methodology based (a) on how they implement this pipeline and (b) on what role depth plays within each stage of it. We identify and introduce existing, publicly available, benchmark datasets and software resources that fuse color and depth data for MHT. Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.
Generating Test Adapters for ModelJunit

Concretization is one of the most labor-intensive phases of the modelbased testing process. This study concentrates on concretization of the abstract tests generated from the test models. The purpose of the study is to design and implement a structure to automate this phase which can reduce the required effort specially in every system update. The structure is completed and discussed as an extension on a modelbased testing tool named ModelJUnit using adaptation approach. In this structure, the focus is mainly on bridging the gap in data-level between the SUT and the model.
A global map of dominant malaria vectors

Global maps, in particular those based on vector distributions, have long been used to help visualise the global extent of malaria. Few, however, have been created with the support of a comprehensive and extensive evidence-based approach. Here we describe the generation of a global map of the dominant vector species (DVS) of malaria that makes use of predicted distribution maps for individual species or species complexes. Our global map highlights the spatial variability in the complexity of the vector situation. In Africa, An. gambiae, An. arabiensis and An. funestus are co-dominant across much of the continent, whereas in the Asian-Pacific region there is a highly complex situation with multi-species coexistence and variable species dominance. The competence of the mapping methodology to accurately portray DVS distributions is discussed. The comprehensive and contemporary database of species-specific spatial occurrence (currently available on request) will be made directly available via the Malaria Atlas Project (MAP) website from early 2012.
Reinforcement Learning and Neural Networks for Tetris

This paper presents the results of experiments carried out with the goal of applying the machine learning techniques of reinforcement learning and neural networks with reinforcement learning to the game of Tetris. Tetris is a well-known computer game that can be played either by a single player or competitively with slight variations, toward the end of accumulating a high score or defeating the opponent. The fundamental hypothesis of this paper is that if the points earned in Tetris are used as the reward function for a machine learning agent, then that agent should be able to learn to play Tetris without other supervision. Toward this end, a state-space that summarizes the essential feature of the Tetris board is designed, high-level actions are developed to interact with the game, and agents are trained using Q-Learning and neural networks. As a result of these efforts, agents learn to play Tetris and to compete with other players. While the learning agents fail to accumulate as many points as the most advanced AI agents, they do learn to play more efficiently.
Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.
DNA repair dysregulation from cancer driver to therapeutic target

Dysregulation of DNA damage repair and signalling to cell cycle checkpoints, known as the DNA damage response (DDR), is associated with a predisposition to cancer and affects responses to DNA-damaging anticancer therapy. Dysfunction of one DNA repair pathway may be compensated for by the function of another compensatory DDR pathway, which may be increased and contribute to resistance to DNA-damaging chemotherapy and radiotherapy. Therefore, DDR pathways make an ideal target for therapeutic intervention; first, to prevent or reverse therapy resistance; and second, using a synthetic lethal approach to specifically kill cancer cells that are dependent on a compensatory DNA repair pathway for survival in the context of cancer-associated oxidative and replicative stress. These hypotheses are currently being tested in the laboratory and are being translated into clinical studies