nils-herrmann/clean_scirepeval huggingface dataset:
TRUSTED CODE EXECUTION ON UNTRUSTED PLATFORMS USING INTEL SGX

Today, isolated trusted computation and code execution is of paramount importance to protect sensitive information and workfl ows from other malicious privileged or unprivileged software. Intel Software Guard Extensions (SGX) is a set of security architecture extensions fi rst introduced in the Skylake microarchitecture that enables a Trusted Execution Environment (TEE) It provides an 'inverse sandbox' for sensitive programs, and guarantees the integrity and confi dentiality of secure computations, even from the most privileged malicious software (e.g. OS, hypervisor) SGX capable CPUs only became available in production systems in Q3 2015, and they are not yet fully supported and adopted in systems. Besides the capability in the CPU, the BIOS also needs to provide support for the enclaves, and not many vendors have released the required updates for the system support. This has led to many wrong assumptions being made about the capabilities, features, and ultimately dangers of secure enclaves. By having access to resources and publications such as white papers, patents and the actual SGX capable hardware and software development environment, we are in a privileged position to be able to investigate and demystify SGX. In this paper, we fi rst review the previous trusted execution technologies, such as ARM Trust Zone and Intel TXT, to better understand and appreciate the new innovations of SGX. Then, we look at the details of SGX technology, cryptographic primitives and the underlying concepts that power it, namely the sealing, attestation, and the Memory Encryption Engine (MEE) We also consider use cases such as trusted and secure code execution on an untrusted cloud platform, and digital rights management (DRM) This is followed by an overview of the software development environment and the available libraries.
Online Learning to Rank for Information Retrieval: SIGIR 2016 Tutorial
Venue: SIGIR

During the past 10 15 years offline learning to rank has had a tremendous influence on information retrieval, both scientifically and in practice. Recently, as the limitations of offline learning to rank for information retrieval have become apparent, there is increased attention for online learning to rank methods for information retrieval in the community. Such methods learn from user interactions rather than from a set of labeled data that is fully available for training up front. Below we describe why we believe that the time is right for an intermediate level tutorial on online learning to rank, the objectives of the proposed tutorial, its relevance, as well as more practical details, such as format, schedule and support materials.
Boosting video popularity through keyword suggestion and recommendation systems
Venue: Neurocomputing

YouTube offers a great opportunity for people to entertain, advertise, gain popularity, and generate revenue. How to increase views for a video has become the key question for anyone who wish to be famous or gain more revenue. Recognizing that a recommendation system is a major view source for videos, our goal in this paper is to increase video views in YouTube by leveraging on the recommendation system. We first perform measurements to understand how views are propagated among videos through recommendation links and identify factors that influence the recommendation produced by the system. Our measurement results show that similarity in video meta data is a crucial ingredient in connecting videos. We then propose a keyword suggestion method for a video with the aim to raise video views through the recommendation system. The keyword suggestion method utilizes video clusters on a referrer video graph to obtain relevant keywords and ranks keywords based on both their relevance and their potential to attract video views. The effectiveness of the keyword suggestion method is demonstrated through a case study, showing that using the keywords suggested by our method leads to a larger number of video views and higher average watching time per video playback compared to initial user given keywords.
Dynamic survey of wind turbine vibrations
Venue: SPIE Smart Structures and Materials Nondestructive Evaluation and Health Monitoring

Six wind turbines were blown to the ground by the wind gust during the attack of Typhoon Soudelor in August 2015. Survey using unmanned aerial vehicle, UAV, found the collapsed wind turbines had been broken at the lower section of the supporting towers. The dynamic behavior of wind turbine systems is thus in need of attention. The vibration of rotor blades and supporting towers of two wind turbine systems have been measured remotely using IBIS, a microwave interferometer. However the frequency of the rotor blade can be analyzed only if the microwave measurements are taken as the wind turbine is parked and secured. Time frequency analyses such as continuous wavelet transform and reassigned spectrograms are applied to the displacement signals obtained. A frequency of 0.44Hz exists in both turbines B and C at various operating conditions. Possible links between dynamic characteristics and structural integrity of wind turbine tower systems is discussed.
Human disorders of peroxisome metabolism and biogenesis.
Venue: Biochimica et biophysica acta

Peroxisomes are dynamic organelles that play an essential role in a variety of cellular catabolic and anabolic metabolic pathways, including fatty acid alpha and beta oxidation, and plasmalogen and bile acid synthesis. Defects in genes encoding peroxisomal proteins can result in a large variety of peroxisomal disorders either affecting specific metabolic pathways, i.e. the single peroxisomal enzyme deficiencies, or causing a generalized defect in function and assembly of peroxisomes, i.e. peroxisome biogenesis disorders. In this review, we discuss the clinical, biochemical, and genetic aspects of all human peroxisomal disorders currently known.
USING RENEWABLE ENERGY FOR SEAWATER DESALINATION AND ELECTRICITY PRODUCTION IN THE SITE OCP MOROCCO

OCP is the world's largest exporter and producer of phosphate rock, In this context, OCP has adopted a new improvement plan which proposes to develop production of phosphate rock from 28 to 47 million tons in 2020. The horizon industrial advancement program of OCP excepted to increase water consumption of 73 to 181 million m3 year for its various operations. In this paper we will do a feasibility study to achieve a positive energy balance due to the energy collected generated by an exothermic system for sulfuric acid production in two chemical processing sites (Jorf Lasfar and Asfi) and renewable energy integration in two mining operation sites: Khouribga, Ben Guerir for meet the growing needs on energy. The principal purpose of this article is to develop an innovative idea of a desalination plant to produce fresh water from seawater by using renewable marine energy, principally hydrokinetic and wave energy. The main achievements will be connected with the environment protection, low maintenance and its accessibility to small communities.
Contribution of Seawater Desalination to Cope with Water Scarcity in Souss Massa Region in Southern Morocco

Souss Massa region is characterized by a high value in terms of horticultural production, mainly in greenhouse crops, which contributes to the socioeconomic development of the region. Since the early 2000s, the region is subject to increasing pressure on water resources, resulting in exploitation of groundwater beyond its renewable potential leading to a continuous decline in its piezometric level and threatening the agricultural development as well as all groundwater dependent ecosystems. In order to cope with water scarcity, the government decided to setup a project for groundwater safeguarding by implementing a seawater desalination plant for greenhouse crops irrigation in Chtouka zone with final capacity of 167,000 m3/day feeding irrigation network covering about 13,600 ha. The project aims as well to substitute some of the groundwater uptake and further more contributes to groundwater recharge by irrigation water leaching to the aquifer. The Government has established an agreement with representative agricultural organizations and stakeholders focusing on the implementation of collective management of water resources in Chtouka region particularly through the introduction of a quota system of groundwater withdrawals to limit the groundwater overexploitation and commitment of high cash crops producers to use a desalinated water volume of 3,600 4,000 m3 per hectare per year. The project will have a positive impact on the environment as it will contribute significantly in the reduction of the aquifer overexploitation and consequently will achieve an equilibrium in terms of groundwater balance as well as sustainable use of the aquifer in the Chtouka zone. Through this project the region will avoid a loss of nearly 9 billion DH as added value and 3 billion DH as capital in the horizon of 2035. The project will allow to increase the agricultural added value by 21.9% and preserve more than 2,830 permanent jobs.
Got milk? Workplace factors related to breastfeeding among working mothers

Because maternal employment during the first year of the child's life has been identified as a reason for breastfeeding cessation, we develop and test a role theory based framework to explain women's continuation of breastfeeding after return to work (Study 1) and report results of an exploratory study linking breastfeeding at work with job attitudes and well being (Study 2) Applying survival analysis to a longitudinal dataset gathered by the Centers for Disease Control (Study 1) we identify pregnant women's perceived employer support for breastfeeding as a predictor of women's breastfeeding goal intentions. Supervisors' negative workplace remarks about breastfeeding related to an eightfold increase of women's likelihood to discontinue exclusive breastfeeding and perceived support for breastfeeding after return to work predicted exclusive breastfeeding continuation. Results of Study 2 suggest that women who return to work and continue breastfeeding experience more family to work conflict and overload than women who do not reconcile work and breastfeeding. Further, results of Study 2 provide preliminary evidence suggesting that perceptions of supervisor and coworker support for breastfeeding relate positively to general perceptions of organizational support and negatively to depressive symptoms.
Cross cultural Generalizability of the Alternative Five factor Model Using the Zuckerman Kuhlman Aluja Personality Questionnaire

Several personality models are known for being replicable across cultures, such as the Five Factor Model (FFM) or Eysenck's Psychoticism Extraversion Neuroticism (PEN) model, and are for this reason considered universal. The aim of the current study was to evaluate the cross cultural replicability of the recently revised Alternative FFM (AFFM) A total of 15 048 participants from 23 cultures completed the Zuckerman Kuhlman Aluja Personality Questionnaire (ZKA PQ) aimed at assessing personality according to this revised AFFM. Internal consistencies, gender differences and correlations with age were similar across cultures for all five factors and facet scales. The AFFM structure was very similar across samples and can be considered as highly replicable with total congruence coefficients ranging from .94 to .99. Measurement invariance across cultures was assessed using multi group confirmatory factor analyses, and each higher order personality factor did reach configural and metric invariance. Scalar invariance was never reached, which implies that culture specific norms should be considered. The underlying structure of the ZKA PQ replicates well across cultures, suggesting that this questionnaire can be used in a large diversity of cultures and that the AFFM might be as universal as the FFM or the PEN model. This suggests that more research is needed to identify and define an integrative framework underlying these personality models.
Cross cultural examination of the five factor model of drinking motives in Spanish and Canadian undergraduates.
Venue: Adicciones

This study aims to test the cross cultural suitability of Modified Drinking Motives Questionnaire Revised (M DMQ R) (Grant, Stewart, O'Connor, Blackwell, Conrod, 2007) The sample included 571 Spanish and 571 Canadian undergraduates between the ages of 18 and 22 (65.8% women) The confirmatory factor analysis demonstrated factorial invariance between samples. The regression analysis showed that social, enhancement and low conformity motives were related to drinking frequency and drinking quantity in the total sample. No moderation effect of country on predicting alcohol consumption was found. The results suggest that M DMQ R is a suitable instrument for comparing drinking motives across Spanish and Canadian undergraduates, and that motives focused prevention and treatment programmes developed in one country could be generalised to another.
Rotational contour signatures for robust local surface description
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

This paper presents a novel local surface descriptor called rotational contour signatures (RCS) for 3D rigid objects. RCS comprises several signatures that characterize the 2D contour information derived from 3D to 2D projection of the local surface. The inspiration of our encoding technique comes from that, viewing towards an object, its contour is an effective and robust cue for representing its shape. In order to achieve a comprehensive geometry encoding, the local surface is continually rotated in a predefined local reference frame (LRF) so that multi view information is obtained. Experiments on two publicly available datasets demonstrate the effectiveness and robustness of the proposed descriptor. Further, comparisons with five state of the art descriptors show the superiority of our RCS descriptor.
Using Task Features for Zero Shot Knowledge Transfer in Lifelong Learning
Venue: IJCAI

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter task relationships to identify the relevant knowledge to transfer. These inter task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong reinforcement learning method based on coupled dictionary learning that incorporates high level task descriptors to model the intertask relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of dynamical control problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict the task policy through zero shot learning using the coupled dictionary, eliminating the need to pause to gather training data before addressing the task.
Heterogeneous Cellular Network With Energy Harvesting Based D2D Communication
Venue: IEEE Transactions on Wireless Communications

The concept of mobile user equipment (UE) relay (UER) has been introduced to support device to device (D2D) communications for enhancing communication reliability. However, as the UER needs to use its own power for other UE's data transmission, relaying information in D2D communication may be undesirable for the UER. To overcome this issue, motivated by the recent advances in energy harvesting (EH) techniques, we propose a D2D communication provided EH heterogeneous cellular network (D2D EHHN) where UERs harvest energy from an access point (AP) and use the harvested energy for D2D communication. We develop a framework for the design and analysis of D2D EHHN by introducing the EH region (EHR) and modeling the status of harvested energy using Markov chain. The UER distribution is derived, and a transmission mode selection scheme including the efficient UER selection method is proposed. The network outage probability is derived in close form to measure the performance of D2D EHHN. Based on our analysis results, we explore the effects of network parameters on the outage probability and the optimal offloading bias in terms of the outage probability. Particularly, we show that having a high EH efficiency enhances the performance of D2D EHHN, but can also degrade, especially for dense network.
Social aware energy harvesting device to device communications in 5G networks
Venue: IEEE Wireless Communications

With ever increasing demands for local area services in 5G cellular networks, solutions are necessary to deliver local area data in a spectrum and energy efficient manner. In this article we propose a new cellular communication architecture that integrates energy harvesting technologies and social networking characteristics into D2D communications for local data dissemination. The proposed architecture includes three domains: the physical domain, the energy domain, and the social domain. Specifically, in the physical domain, D2D communications enable two nearby users to communicate with each other directly. In the energy domain, devices harvest energy from renewable energy sources. In the social domain, D2D users form social networks exhibiting stable social structures and relations. Then we mainly focus on the efficient local data dissemination issues in the proposed architecture and propose two social aware energy harvesting D2D communication schemes (device relay and device multicast) Illustrative results demonstrate significant spectrum and energy efficiency enhancement for local data dissemination in 5G cellular networks.
Anteseden Keterikatan Merek dalam Membangun Hubungan Merek pada Konsumen (Kepercayaan, Kepuasan, dan Komitmen) terhadap Loyalitas

Keterikatan merk merupakan konsep yang penting bagi perusahaan jika mereka ingin tet a p kompetitif dan berkembang. Pelanggan yang memiliki keterikatan yang kuat dengan merk akan menyukai merek tertentu dan mempertahankan hubungan merek dengan cara melakukan pembelian ulang. Dalam penelitian ini bertujuan untuk mengetahui pengaruh keterikatan merk dalam hubungan antara merk d engan konsumen dan pengaruh kausalitasnya dengan variabe l lain seperti kepercayaan, kepuasan komitmen dan loyalitas. Berdasarkan keterbatasan penelitian terdahulu, maka dalam penelitian ini menambahkan variabel keakraban merek dan kepribadian merk sebagai anteseden kepuasan pelanggan. Penelitian dilakukan pada mahasiswa Fakultas Hukum Universitas Swadaya Gunung Djati Cirebon. Jumlah responden 255 orang. Pengambilan sampel dilakukan secara simple random sampling Caranya yaitu dengan melakukan randomisasi dengan menggunakan program MS. Excel. Alat analisis menggunakan st r uctural equation modeling (SEM) dengan program AMOS.18. Dari hasil analisis, dapat disimpulkan bahwa kepribadian merk berpengaruh terhadap keterikatan merk, keakraban merk berpengaruh terhadap keterikatan merk, keterikatan merek berpengaruh terhadap kepercayaan merk, keterikatan merk berpengaruh terhadap kepuasan, keterikatan merk tidak berpengaruh terhadap komitmen, keterikatan merek tidak berpengaruh terhadap loyalitas, komitmen berpengaruh terhadap loyalitas, kepuasan tidak berpengaruh terhadap loyalitas, kepercayaan merk berpengaruh terhadap loyalitas, dan kepercayaan merk tidak berpengaruh terhadap kepuasan.
Learning Laplacian Matrix in Smooth Graph Signal Representations
Venue: IEEE Transactions on Signal Processing

The construction of a meaningful graph plays a crucial role in the success of many graph based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not always readily available from the data, nor easy to define depending on the application domain. In particular, it is often desirable in graph signal processing applications that a graph is chosen such that the data admit certain regularity or smoothness on the graph. In this paper, we address the problem of learning graph Laplacians, which is equivalent to learning graph topologies, such that the input data form graph signals with smooth variations on the resulting topology. To this end, we adopt a factor analysis model for the graph signals and impose a Gaussian probabilistic prior on the latent variables that control these signals. We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals. We then propose an algorithm for learning graphs that enforces such property and is based on minimizing the variations of the signals on the learned graph. Experiments on both synthetic and real world data demonstrate that the proposed graph learning framework can efficiently infer meaningful graph topologies from signal observations under the smoothness prior.
Dual Graph Regularized Dictionary Learning
Venue: IEEE Transactions on Signal and Information Processing over Networks

Dictionary learning (DL) techniques aim to find sparse signal representations that capture prominent characteristics in a given data. Such methods operate on a data matrix Y RNxM, where each of its columns yi RN constitutes a training sample, and these columns together represent a sampling from the data manifold. For signals y RN residing on weighted graphs, an additional challenge is incorporating the underlying geometric structure of the data domain into the learning process. In such cases, the topological graph structure may provide a crucial interpretation for the columns, while the data manifold itself may also possess a low dimensional intrinsic structure that should be taken into account. In this work, we propose a novel dictionary learning algorithm for graph signals that simultaneously takes into account the underlying structure in both the signal and the manifold domains. Specifically, we require that the dictionary atoms are smooth with respect to the graph topology, as encapsulated by the graph Laplacian matrix. Furthermore, we propose to learn this graph Laplacian within the dictionary learning process, adapting it to promote the desired smoothness. Utilizing the manifold structure, we propose to encourage the smoothness of the sparse representations on the data manifold in a similar manner. Both these smoothness forces implicitly enhance the learned dictionary. The efficiency of the proposed approach is demonstrated on synthetic examples as well as on real data, showing that it outperforms other dictionary learning methods in typical problems such as resistance to noise and data completion.
Robust laplacian matrix learning for smooth graph signals
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

We propose a new method for robust learning Laplacian matrices from observed smooth graph signals in the presence of both Gaussian noise and random valued impulse noise (i.e. outliers) Using the recently developed factor analysis model for representing smooth graph signals, we formulate our learning process as a constrained optimization problem, and adopt the PSi norm for measuring the data fidelity in order to improve robustness. Computational results on three types of synthetic graphs demonstrate that the proposed method outperforms the state of the art methods in terms of commonly used information retrieval metrics, such as F measure, precision, recall and normalized mutual information. In particular, we observed that F measure is improved by up to 16%