nils-herrmann/clean_scirepeval huggingface dataset:
Robust Multi View Subspace Learning through Dual Low Rank Decompositions
Venue: AAAI

Multi view data is highly common nowadays, since various view points and different sensors tend to facilitate better data representation. However, data from different views show a large divergence. Specifically, one sample lies in two kinds of structures, one is class structure and the other is view structure, which are intertwined with one another in the original feature space. To address this, we develop a Robust Multi view Subspace Learning algorithm (RMSL) through dual low rank decompositions, which desires to seek a low dimensional view invariant subspace for multi view data. Through dual low rank decompositions, RMSL aims to disassemble two intertwined structures from each other in the low dimensional subspace. Furthermore, we develop two novel graph regularizers to guide dual low rank decompositions in a supervised fashion. In this way, the semantic gap across different views would be mitigated so that RMSL can preserve more within class information and reduce the influence of view variance to seek a more robust low dimensional subspace. Extensive experiments on two multi view benchmarks, e.g. face and object images, have witnessed the superiority of our proposed algorithm, by comparing it with the state of the art algorithms.
vDNN: Virtualized deep neural networks for scalable, memory efficient neural network design
Venue: 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory usage of AlexNet by up to 89% OverFeat by 91% and GoogLeNet by 95% a significant reduction in memory requirements of DNNs. Similar experiments on VGG 16, one of the deepest and memory hungry DNNs to date, demonstrate the memory efficiency of our proposal. vDNN enables VGG 16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card containing 12 GB of memory, with 18% performance loss compared to a hypothetical, oracular GPU with enough memory to hold the entire DNN.
Virtualizing Deep Neural Networks for Memory Efficient Neural Network Design
Venue: ArXiv

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average memory usage of AlexNet by 61% and OverFeat by 83% a significant reduction in memory requirements of DNNs. Similar experiments on VGG 16, one of the deepest and memory hungry DNNs to date, demonstrate the memory efficiency of our proposal. vDNN enables VGG 16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA K40 GPU card containing 12 GB of memory, with 22% performance loss compared to a hypothetical GPU with enough memory to hold the entire DNN.
High performance GPU based optimized feature matching for computer vision applications

In this paper, a graphics processing unit (GPU) based matching technique is proposed to perform fast feature matching between different images under various image conditions with viewpoint changes. Most recently, general purpose graphics processing units (GPGPUs or GPUs) have become commonplace within high performance supercomputers. GPUs allow developers to effectively exploit the computational power for high performance computing. This paper focuses on improving the performance of feature matching based on self organizing map by porting it onto the GPUs. GPU optimization has been applied for the fast computation of keypoints to make the system fast and efficient. This scheme has enhanced the overall performance and is much more efficient compared to other methods without degradation of detection results. The proposed matching algorithm is partitioned between the CPU and GPU in a way that best exploits the parallelism and perform matching between the different images. Experimental results demonstrate that fast feature matching is achieved using the graphics processing units, and its computational efficiency is checked by comparing its results and run times with those of some standard software (MATLAB) run on central processing unit (CPU)
Accelerating a Computer Vision Algorithm on a Mobile SoC Using CPU GPU Co processing A Case Study on Face Detection
Venue: 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)

Recently, mobile devices have become equipped with sophisticated hardware components such as a heterogeneous multi core SoC that consists of a CPU, GPU, and DSP. This provides opportunities to realize computationally intensive computer vision applications using General Purpose GPU (GPGPU) programming tools such as Open Graphics Library for Embedded System (OpenGL ES) and Open Computing Language (OpenCL) As a case study, the aim of this research was to accelerate the Viola Jones face detection algorithm which is computationally expensive and limited in use on mobile devices due to irregular memory access and imbalanced workloads resulting in low performance regarding the processing time. To solve the above challenges, the proposed method of this study adapted CPU GPU task parallelism, sliding window parallelism, scale image parallelism, dynamic allocation of threads, and local memory optimization to improve the computational time. The experimental results show that the proposed method achieved a 3.3~6.29 times increased computational time compared to the well optimized OpenCV implementation on a CPU. The proposed method can be adapted to other applications using mobile GPUs and CPUs.
Biomedical Uses for 2D Materials Beyond Graphene: Current Advances and Challenges Ahead.
Venue: Advanced materials

Currently, a broad interdisciplinary research effort is pursued on biomedical applications of 2D materials (2DMs) beyond graphene, due to their unique physicochemical and electronic properties. The discovery of new 2DMs is driven by the diverse chemical compositions and tuneable characteristics offered. Researchers are increasingly attracted to exploit those as drug delivery systems, highly efficient photothermal modalities, multimodal therapeutics with non invasive diagnostic capabilities, biosensing, and tissue engineering. A crucial limitation of some of the 2DMs is their moderate colloidal stability in aqueous media. In addition, the lack of suitable functionalisation strategies should encourage the exploration of novel chemical methodologies with that purpose. Moreover, the clinical translation of these emerging materials will require undertaking of fundamental research on biocompatibility, toxicology and biopersistence in the living body as well as in the environment. Here, a thorough account of the biomedical applications using 2DMs explored today is given.
Biomedical Uses for 2 D Materials Beyond Graphene Current Advances and Challenges Ahead

Dr. R. Kurapati, Dr. A. Bianco CNRS Institut de Biologie Moleculaire et Cellulaire Laboratoire d'Immunopathologie et Chimie Therapeutique 67000 Strasbourg France E mail: a.bianco@ibmc cnrs.unistra.fr Prof. K. Kostarelos Nanomedicine Laboratory School of Medicine and National Graphene Institute University of Manchester AV Hill Building Manchester M13 9PT United Kingdom Prof. M. Prato Dipartimento di Scienze Chimiche e Farmaceutiche Universita di Trieste, 34127 Trieste Italy Prof. M. Prato Carbon Nanobiotechnology Laboratory CIC biomaGUNE Donostia San Sebastian Paseo de Miramon 182, 20009 Spain Prof. M. Prato Basque Foundation for Science (IKERBASQUE) Bilbao 48013 Spain
Effects of income redistribution on the evolution of cooperation in spatial public goods games

Income redistribution is the transfer of income from some individuals to others directly or indirectly by means of social mechanisms, such as taxation, public services and so on. Employing a spatial public goods game, we study the influence of income redistribution on the evolution of cooperation. Two kinds of evolutionary models are constructed, which describe local and global redistribution of income respectively. In the local model, players have to pay part of their income after each PGG and the accumulated income is redistributed to the members. While in the global model, all the players pay part of their income after engaging in all the local PGGs, which are centered on himself and his nearest neighbours, and the accumulated income is redistributed to the whole population. We show that the cooperation prospers significantly with increasing income expenditure proportion in the local redistribution of income, while in the global model the situation is opposite. Furthermore, the cooperation drops dramatically from the maximum curvature point of income expenditure proportion. In particular, the intermediate critical points are closely related to the renormalized enhancement factors.
Evolution of cooperation in spatial iterated Prisoner's Dilemma games under localized extremal dynamics

Prisoner's Dilemma game has been widely studied in order to explain the evolution of cooperation. Considering the large strategy space size and infinite interaction times, it is unrealistic to adopt the common imitate best updating rule, which assumes that the human players have much stronger abilities to recognize their neighbors' strategies than they do in the one shot game. In this paper, a novel localized extremal dynamic system is proposed, in which each player only needs to recognize the payoff of his neighbors and changes his strategy randomly when he receives the lowest payoff in his neighborhood. The evolution of cooperation is here explored under this updating rule for neighborhoods of different sizes, which are characterized by their corresponding radiuses r The results show that when r 1 the system is trapped in a checkerboard like state, where half of the players consistently use AllD like strategies and the other half constantly change their strategies. When r 2 the system first enters an AllD like state, from which it escapes, and finally evolves to a TFT like state. When r is larger, the system locks in a situation with similar low average fitness as r 1 The number of active players and the ability to form clusters jointly distinguish the evolutionary processes for different values of r from each other. The current findings further provide some insight into the evolution of cooperation and collective behavior in biological and social systems.
A divide and conquer method for the synthesis of liveness enforcing supervisors for flexible manufacturing systems
Venue: J. Intell. Manuf.

In this paper a divide and conquer method for the synthesis of liveness enforcing supervisors (LES) for flexible manufacturing systems (FMS) is proposed. Given the Petri net model (PNM) of an FMS prone to deadlocks, it aims to synthesize a live controlled Petri net system. For complex systems, the use of reachability graph (RG) based deadlock prevention methods is a challenging problem, as the RG of a PNM easily becomes unmanageable. To obtain the LESs from a large PNM is usually intractable. In this paper, to ease this problem the PNM of a system is divided into small connected subnets. Each connected subnet prone to deadlocks is then used to compute the LES for the original PNM. Starting from the simplest subnet prone to deadlocks to make the subnet live, monitors (control places) are computed. The RG of each subnet is considered and split into a dead zone (DZ) and a live zone. All states in the DZ are prevented from being reached by means of a well established invariant based control method. Next, the computation of monitors is followed for bigger subnets. Previously computed monitors are included within the bigger subnets based on a criterion. This process keeps the DZ of the bigger subnets smaller compared with the original uncontrolled subnets. When all subnets are live we obtain a set of monitors that are included within the PNM to obtain a partially controlled PNM (pCPNM) A new set of monitors is also computed for the pCPNM. Finally, a live controlled Petri net system is obtained. The proposed method is generally applicable, easy to use, effective and straightforward although its off line computation is of exponential complexity in theory. Its use for FMS control guarantees deadlock free operation and high performance in terms of resource utilization and system throughput. Two FMS deadlock problems from the literature are used to illustrate the applicability and the effectiveness of the proposed method.
Developing a General Extended Technology Acceptance Model for E Learning (GETAMEL) by analysing commonly used external factors
Venue: Comput. Hum. Behav.

To identify the most commonly used external factors of Technology Acceptance Model (TAM) in the context of e learning adoption, a quantitative meta analysis of 107 papers covering the last ten years was performed. The results show that Self Efficacy, Subjective Norm, Enjoyment, Computer Anxiety and Experience are the most commonly used external factors of TAM. The effects of these commonly used external factors on TAM's two main constructs, Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) have been studied across a range of e learning technology types and e learning user types. The results show that the best predictor of student's PEOU of e learning systems is Self Efficacy (b??0.352) followed by Enjoyment (b??0.341) Experience (b??0.221) Computer Anxiety (b? 0.199) and Subjective Norm (b??0.195) The best predictor of student's PU of e learning systems is Enjoyment (b??0.452) followed by Subjective Norm (b??0.301) Self Efficacy (b??0.174) and Experience (b??0.169) Using these external factors and their effect sizes on PEOU and PU, this study proposes a General Extended Technology Acceptance Model for E Learning (GETAMEL) We performed a meta analysis of 107 recent e learning acceptance studies.We identified the most commonly used external factors of Technology Acceptance Model.They are Self Efficacy, Subjective Norm, Enjoyment, Computer Anxiety and Experience.We identified the effects of these factors on students' perceptions of e learning.As a result the study proposes an extended technology acceptance model for e learning.
SOCIAL, HISTORICAL AND CULTURAL DIMENSIONS OF TUBERCULOSIS.
Venue: Journal of biosocial science

Tuberculosis (TB) researchers and clinicians, by virtue of the social disease they study, are drawn into an engagement with ways of understanding illness that extend beyond the strictly biomedical model. Primers on social science concepts directly relevant to TB, however, are lacking. The particularities of TB disease mean that certain social science concepts are more relevant than others. Concepts such as structural violence can seem complicated and off putting. Other concepts, such as gender, can seem so familiar that they are left relatively unexplored. An intimate familiarity with the social dimensions of disease is valuable, particularly for infectious diseases, because the social model is an important complement to the biomedical model. This review article offers an important introduction to a selection of concepts directly relevant to TB from health sociology, medical anthropology and social cognitive theory. The article has pedagogical utility and also serves as a useful refresher for those researchers already engaged in this genre of work. The conceptual tools of health sociology, medical anthropology and social cognitive theory offer insightful ways to examine the social, historical and cultural dimensions of public health. By recognizing cultural experience as a central force shaping human interactions with the world, TB researchers and clinicians develop a more nuanced consideration of how health, illness and medical treatment are understood, interpreted and confronted.
Parochial prosocial religions: Historical and contemporary evidence for a cultural evolutionary process
Venue: Behavioral and Brain Sciences

In our response to the 27 commentaries, we refine the theoretical claims, clarify several misconceptions of our framework, and explore substantial disagreements. In doing so, we (1) show that our framework accommodates multiple historical scenarios; (2) debate the historical evidence, particularly about "pre Axial" religions; (3) offer important details about cultural evolutionary theory; (4) clarify the term prosociality; and (4) discuss proximal mechanisms. We review many interesting extensions, amplifications, and qualifications of our approach made by the commentators.
THE INFLUENCE OF JOB SATISFACTION AND ORGANIZATIONAL COMMITMENT TO EMPLOYEE TURNOVER INTENTION (Study at PT. Kawanua Dasa Pratama/Freshmart Superstore Manado)

Turnover is commonly problem faced in PT. Kawanua Dasa Pratama (Freshmart Superstore) Manado. Turnover intention comes up as a plan to quit from company. Most of the turnover brought an adverse influence on the organization, both in terms of cost and in loss of time and chance to take advantage of opportunities. High job satisfaction and organizational commitment will reduce employee turnover intention rates from company. This research aims to find out how job satisfaction and organizational commitment can affect the turnover intention of employee in PT. Kawanua Dasa Pratama (Freshmart Superstore) Manado. Multiple regressions model is used to answer the research problem. The total sample for this research is 100 employees, and questionnaire had been using to obtain the information. The research findings show that the independent variable job satisfaction and organizational commitment had a negative significant influence to employee turnover intention simultaneously and partially. The management of Freshmart Superstore Manado may consider about the satisfaction of employees, such as the salary, promotion, and also appreciation for their work.  job satisfaction, organizational commitment, employee turnover intention
Aggressive behavior and hair cortisol levels in captive Dorcas gazelles (Gazella dorcas) as animal based welfare indicators.
Venue: Zoo biology

Ensuring welfare in captive wild animal populations is important not only for ethical and legal reasons, but also to maintain healthy individuals and populations. An increased level of social behaviors such as aggression can reduce welfare by causing physical damage and chronic stress to animals. Recently, cortisol in hair has been advanced as a non invasive indicator to quantify long lasting stress in many species. The sensitivity of social behavior and hair cortisol concentration was evaluated in several groups of dorcas gazelles (Gazella dorcas) Four different groups of gazelles from three different zoos were observed and the expression of intra specific affiliative and negative social behaviors was assessed across the different groups. Hair samples were taken from sub groups of animals and analyzed for cortisol concentrations. Significant differences between groups of dorcas gazelles were found in frequency of negative social behavior and hair cortisol concentration. Despite the low sample size, these two parameters had a positive Spearman correlation coefficient (rs +0.80, P 0.20) These results suggest that hair cortisol levels are sensitive to differences in the social structure of dorcas gazelles. Zoo Biol. 35:467 473, 2016.
Catch me in the dark: Effective privacy preserving outsourcing of feature extractions over image data
Venue: IEEE INFOCOM 2016 The 35th Annual IEEE International Conference on Computer Communications

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the semi trusted cloud by leveraging its abundant resources for cost saving and flexibility. From the privacy perspective, however, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy preserving computations over outsourced multimedia data. In this paper, we propose an effective privacy preserving computation outsourcing protocol for the prevailing scale invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We for the first time present a new privacy preserving outsourcing protocol for SIFT with the preservation of its key characteristics, by randomly splitting the original image data, carefully distributing the feature extraction computations to two independent cloud servers and further leveraging the garbled circuit for secure keypoints comparisons. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state of the art and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion and change in 3D viewpoint.
Securing SIFT: Privacy Preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.
Venue: IEEE transactions on image processing a publication of the IEEE Signal Processing Society

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations, or even financial profiles. This observation has recently aroused new research interest on privacy preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy preserving computation outsourcing protocol for the prevailing scale invariant feature transform (SIFT) over massive encrypted image data. We first show that the previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state of the art, and performs comparably with the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, and addition of noise and change in 3D viewpoint and illumination.