nils-herrmann/clean_scirepeval huggingface dataset:
Controlled mixed culture fermentation: a new perspective on the use of non-Saccharomyces yeasts in winemaking.

Mixed fermentations using controlled inoculation of Saccharomyces cerevisiae starter cultures and non-Saccharomyces yeasts represent a feasible way towards improving the complexity and enhancing the particular and specific characteristics of wines. The profusion of selected starter cultures has allowed the more widespread use of inoculated fermentations, with consequent improvements to the control of the fermentation process, and the use of new biotechnological processes in winemaking. Over the last few years, as a consequence of the re-evaluation of the role of non-Saccharomyces yeasts in winemaking, there have been several studies that have evaluated the use of controlled mixed fermentations using Saccharomyces and different non-Saccharomyces yeast species from the wine environment. The combined use of different species often results in unpredictable compounds and/or different levels of fermentation products being produced, which can affect both the chemical and the aromatic composition of wines. Moreover, possible synergistic interactions between different yeasts might provide a tool for the implementation of new fermentation technologies. Thus, knowledge of the Saccharomyces and non-Saccharomyces wine yeast interactions during wine fermentation needs to be improved. To reach this goal, further investigations into the genetic and physiological background of such non-Saccharomyces wine yeasts are needed, so as to apply '-omics' approaches to mixed culture fermentations.
Geometric tracking control of thrust vectoring UAVs

In this paper a geometric approach to the trajectory tracking control of Unmanned Aerial Vehicles with thrust vectoring capabilities is proposed. The control design is suitable for aerial systems that allow to effectively decouple position and orientation tracking tasks. The control problem is developed within the framework of geometric control theory on the group of rigid displacements SE (3), yielding a control law that is independent of any parametrization of the configuration space. The proposed design works seamlessy when the thrust vectoring capability is limited, by prioritizing position over orientation tracking. A characterization of the region of attraction and of the convergence properties is explicitly derived. Finally, a numerical example is presented to test the proposed control law. The generality of the control scheme can be exploited for a broad class of aerial vehicles.
Hinfinity filtering for fuzzy systems with immeasurable premise variables: An uncertain system approach

This paper discusses a design of stable filters withH∞ disturbance attenuation of Takagi–Sugeno fuzzy systemswith immeasurable premise variables. When we consider the filter design of Takagi–Sugeno fuzzy systems, the selection of premise variables plays an important role. If the premise variable is the state of the system, then a fuzzy system describes a wide class of nonlinear systems. In this case, however, a filter design of fuzzy systems based on parallel distributed compensator idea is infeasible. To avoid such a difficulty, we consider the premise variables uncertainties. Then we consider a robust H∞ filtering problem for such an uncertain system. A solution of the problem is given in terms of linear matrix inequalities (LMIs). Some numerical examples are given to illustrate our theory.
Towards Cognitive Automation of Data Science

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e.g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.
Implementing a Serial ATA Controller Base on FPGA

In order to store massive image data in real-time system, a high performance Serial Advanced Technology Attachment[1] (SATA) controller is proposed in this paper. RocketIO GTX transceiver[2] realizes physical layer of SATA protocol. Link layer and transport layers are implemented in VHDL with programmable logic resources. Application layer is developed on POWERPC440 embedded in Xilinx Virtex-5 FPGA. The whole SATA protocol implement in a platform FPGA has better features in expansibility, scalability, improvability and in-system programmability comparing with realizing it using Application Specific Integrated Circuit (ASIC). The experiment results shown that the controller works accurately and stably and the maximal sustained orderly data transfer rate up to 110 MB/s when connect to SATA hard disk. The high performance of the host SATA controller makes it possible that cheap SATA hard disk instead expensive Small Computer System Interface (SCSI) hard disk in some application. The controller is very suited for high speed mass data storage in embedded system.
How to use attribute-based encryption to implement role-based access control in the cloud

This paper addresses how to construct a RBAC-compatible attribute-based encryption (ABE) for secure cloud storage, which provides a user-friendly and easy-to-manage security mechanism without user intervention. Similar to role hierarchy in RBAC, attribute lattice introduced into ABE is used to define a seniority relation among all values of an attribute, whereby a user holding the senior attribute values acquires permissions of their juniors. Based on these notations, we present a new ABE scheme called Attribute-Based Encryption with Attribute Lattice (ABE-AL) that provides an efficient approach to implement comparison operations between attribute values on a poset derived from attribute lattice. By using bilinear groups of composite order, we propose a practical construction of ABE-AL based on forward and backward derivation functions. Compared with prior solutions, our scheme offers a compact policy representation solution, which can significantly reduce the size of privatekeys and ciphertexts. Furthermore, our solution provides a richer expressive power of access policies to facilitate flexible access control for ABE scheme.
Optimising Rule Order for a Packet Filtering Firewall

A heuristic approximation algorithm that can optimise the order of firewall rules to minimise packet matching is presented. It has been noted that firewall operators tend to make use of the fact that some firewall rules match most of the traffic, and conversely that others match little of the traffic. Consequently, ordering the rules such that the highest matched rules are as high in the table as possible reduces the processing load in the firewall. Due to dependencies between rules in the rule set this problem, optimising the cost of the packet matching process, has been shown to be NP-hard. This paper proposes an algorithm that is designed to give good performance in terms of minimising the packet matching cost of the firewall. The performance of the algorithm is related to complexity of the firewall rule set and is compared to an alternative algorithm demonstrating that the algorithm here has improved the packet matching cost in all cases.
Corrigendum: Antibacterial activities of bacteriocins: application in foods and pharmaceuticals

Shih-Chun Yang , Chih-Hung Lin , Calvin T. Sung and Jia-You Fang* 1 Research Center for Industry of Human Ecology, Chang Gung University of Science and Technology, Taoyuan, Taiwan 2 Pharmaceutics Laboratory, Graduate Institute of Natural Products, Chang Gung University, Taoyuan, Taiwan 3 Chang Gung Memorial Hospital, Taoyuan, Taiwan 4 Center for General Education, Chang Gung University of Science and Technology, Taoyuan, Taiwan 5 Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Taoyuan, Taiwan 6 Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA 7 Chinese Herbal Medicine Research Team, Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan *Correspondence: fajy@mail.cgu.edu.tw †These authors have contributed equally to this work.
Radial basis function neural network-based face recognition using firefly algorithm

This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma (γ) of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition.
The Rational Analysis Of Mind And Behavior

Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning leads to a radicalreinterpretation of empirical results which are typically viewed asdemonstrating human irrationality.
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering

In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
Exemplar Component Analysis: A Fast Band Selection Method for Hyperspectral Imagery

How to find the representative bands is a key issue in band selection for hyperspectral data. Very often, unsupervised band selection is associated with data clustering, and the cluster centers (or exemplars) are considered ideal representatives. However, partitioning the bands into clusters may be very time-consuming and affected by the distribution of the data points. In this letter, we propose a new band selection method, i.e., exemplar component analysis (ECA), aiming at selecting the exemplars of bands. Interestingly, ECA does not involve actual clustering. Instead, it prioritizes the bands according to their exemplar score, which is an easy-to-compute indicator defined in this letter measuring the possibility of bands to be exemplars. As a result, ECA is of high efficiency and immune to distribution structures of the data. The experiments on real hyperspectral data set demonstrate that ECA is an effective and efficient band selection method.
Monoidal Cut Strengthening and Generalized Mixed-Integer Rounding for Disjunctive Programs ∗

This article investigates cutting planes for mixed-integer disjunctive programs. In the early 1980s, Balas and Jeroslow presented monoidal disjunctive cuts exploiting the integrality of variables. For disjunctions arising from binary variables, it is known that these cutting planes are essentially the same as Gomory mixed-integer and mixed-integer rounding cuts. In this article, we investigate the relation of monoidal cut strengthening to other classes of cutting planes for general twoterm disjunctions. In this context, we introduce a generalization of mixed-integer rounding cuts. We also demonstrate the effectiveness of monoidal disjunctive cuts via computational experiments on instances involving complementarity constraints.
Cognitive gains from video game use in older age : a review of the literature corroborating them

In this review article, the authors have summarized the published literature supporting the value of video game use on the following topics: improvement of cognitive functioning in older individuals, potential reasons for the positive effects of video game use in older age, and psychological factors related to using video games in older age. It is important for geriatric researchers and practitioners to identify approaches and interventions that minimize the negative effects of the various changes that occur within the aging body. Generally speaking, biological aging results in a decline of both physical and cognitive functioning.1–3 However, a growing body of literature indicates that taking part in physically and/or mentally stimulating activities may contribute to the maintenance of cognitive abilities and even lead to acquiring cognitive gains.4 It is important to identify ways to induce cognitive improvements in older age, especially considering that the population of the United States (U.S.) is aging rapidly, with the number of people age 65 and older expected to increase to almost 84 million by 2050.5 This suggests that there will likely be a rapid escalation in the number of older individuals living with age-related cognitive impairment. It is currently estimated that there are 5.5 million people in the U.S. who have been diagnosed with Alzheimer’s disease,6 which is one of the most common forms of dementia.7 Thus, research aimed at helping older adults maintain good cognitive functioning is highly needed. Due to space limitations, this article is not meant to include all of the available research in this area; it contains mainly supporting evidence on the effects of video game use among older adults. Some opposing evidence is briefly mentioned when covering whether the skills acquired during video game training transfer to non-practiced tasks (which is a particularly controversial topic with ample mixed evidence).
Video Stabilization and Face Saliency-based Retargeting

Technology revolution has brought great convenience of daily life recording using cellphones and wearable devices nowadays. However, hand shake and human body movement is likely to happen during the capture period, which significantly degrades the video quality. In this work, we study and implement an algorithm that automatically stabilizes the shaky videos. We first calculate the video motion path using feature matching and then smooth out high frequency undesired jitters with L1 optimization. The method ensures that the smoothed paths only compose of constant, linear and parabolic segments, mimicking the camera motions employed by professional cinematographers. Since the human face are of broad interest and appear in large amount of videos, we further incorporated face feature detection module for video retargeting purposes. The detected faces in the video also enables many potential applications, and we add decoration features in this work, e.g., glasses and hats on the faces.
Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review

Humidity measurement is one of the most significant issues in various areas of applications such as instrumentation, automated systems, agriculture, climatology and GIS. Numerous sorts of humidity sensors fabricated and developed for industrial and laboratory applications are reviewed and presented in this article. The survey frequently concentrates on the RH sensors based upon their organic and inorganic functional materials, e.g., porous ceramics (semiconductors), polymers, ceramic/polymer and electrolytes, as well as conduction mechanism and fabrication technologies. A significant aim of this review is to provide a distinct categorization pursuant to state of the art humidity sensor types, principles of work, sensing substances, transduction mechanisms, and production technologies. Furthermore, performance characteristics of the different humidity sensors such as electrical and statistical data will be detailed and gives an added value to the report. By comparison of overall prospects of the sensors it was revealed that there are still drawbacks as to efficiency of sensing elements and conduction values. The flexibility offered by thick film and thin film processes either in the preparation of materials or in the choice of shape and size of the sensor structure provides advantages over other technologies. These ceramic sensors show faster response than other types.
Polycythemia from mast cell activation syndrome: lessons learned.

A middle-aged woman presented with fatigue and mild increases in hematocrit and red cell mass. Polycythemia vera was diagnosed. She underwent therapeutic phlebotomy but clinically worsened. On reevaluation, other problems were noted including episodic malaise, nausea, rash and vasomotor issues. The JAK2V617F mutation was absent; paraneoplastic erythrocytosis was investigated. Serum tryptase and urinary N-methylhistamine were normal, but urinary prostaglandin D2 was elevated. Skin and marrow biopsies showed no mast cell abnormalities. Extensive other evaluation was negative. Gastrointestinal tract biopsies were histologically normal but revealed increased, aberrant mast cells on immunohistochemistry; the KITD816V mutation was absent. Mast cell activation syndrome, recently identified as a clonal disorder involving assorted KIT mutations, was diagnosed. Imatinib 200 mg/d rapidly effected complete, sustained response. Diagnosis of mast cell activation syndrome is hindered by multiple factors, but existing therapies for mast cell disease are usually achieve significant benefit, highlighting the importance of early diagnosis. Multiple important aspects of clinical reasoning are illustrated by the case.
Virtual network functions placement and routing optimization

Network Functions Virtualization (NFV) is incrementally deployed by Internet Service Providers (ISPs) in their carrier networks, by means of Virtual Network Function (VNF) chains, to address customers' demands. The motivation is the increasing manageability, reliability and performance of NFV systems, the gains in energy and space granted by virtualization, at a cost that becomes competitive with respect to legacy physical network function nodes. From a network optimization perspective, the routing of VNF chains across a carrier network implies key novelties making the VNF chain routing problem unique with respect to the state of the art: the bitrate of each demand flow can change along a VNF chain, the VNF processing latency and computing load can be a function of the demands traffic, VNFs can be shared among demands, etc. In this paper, we provide an NFV network model suitable for ISP operations. We define the generic VNF chain routing optimization problem and devise a mixed integer linear programming formulation. By extensive simulation on realistic ISP topologies, we draw conclusions on the trade-offs achievable between legacy Traffic Engineering (TE) ISP goals and novel combined TE-NFV goals.
Identifying Human Mobility via Trajectory Embeddings

Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semisupervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.
Quantitative Study of Music Listening Behavior in a Social and Affective Context

A scientific understanding of emotion experience requires information on the contexts in which the emotion is induced. Moreover, as one of the primary functions of music is to regulate the listener's mood, the individual's short-term music preference may reveal the emotional state of the individual. In light of these observations, this paper presents the first scientific study that exploits the online repository of social data to investigate the connections between a blogger's emotional state, user context manifested in the blog articles, and the content of the music titles the blogger attached to the post. A number of computational models are developed to evaluate the accuracy of different content or context cues in predicting emotional state, using 40,000 pieces of music listening records collected from the social blogging website LiveJournal. Our study shows that it is feasible to computationally model the latent structure underlying music listening and mood regulation. The average area under the receiver operating characteristic curve (AUC) for the content-based and context-based models attains 0.5462 and 0.6851, respectively. The association among user mood, music emotion, and individual's personality is also identified.
An Overview of the Theory of Computational Complexity

The purpose of this paper is to outline the theory of computational complexity which has emerged as a comprehensive theory during the last decade. This theory is concerned with the quantitative aspects of computations and its central theme is the measuring of the difficulty of computing functions. The paper concentrates on the study of computational complexity measures defined for all computable functions and makes no attempt to survey the whole field exhaustively nor to present the material in historical order. Rather it presents the basic concepts, results, and techniques of computational complexity from a new point of view from which the ideas are more easily understood and fit together as a coherent whole. K E Y W O R D S A N D P H R A S E S : computational complexity, complexity axioms, complexity measures, computation speed, time-bounds, tape-bounds, speed-up, Turing machines, diagonalization, length of programs CR C A T E G O R I E S : 5.20, 5.22, 5.23, 5.24