nils-herrmann/clean_scirepeval huggingface dataset:
Hardware secured and transparent multi stakeholder data exchange for industrial IoT
Venue: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN)

Authentic and confidential, but at the same time traceable and transparent, data exchange among multiple stakeholders is a key challenge in Industrial Internet of Things (IIoT) applications. Specifically, smart service connectivity requires the secure and transparent acquisition of equipment status information, which we call snapshots, from globally distributed equipment instances at customer sites by the equipment vendor. Related work has proposed to use a Message Queue Telemetry Transport (MQTT) Broker and hardware secured Transport Layer Security (TLS) with client authentication. However, this approach lacks strong cryptographic end to end protection of snapshots. Here we show a hardware rooted snapshot protection system that utilizes a Broker based messaging infrastructure, hybrid encryption and a single pass Elliptic Curve Menezes Qu Vanstone (ECMQV) scheme. We evaluate our concept by means of a prototype implementation and discuss security and performance implications. Our approach provides strong end to end data protection, while at the same time enabling customers to trace what data has been transferred off their equipment. We believe that our concept can serve as a template for a multitude of Industrial Internet of Things applications, which by their very nature call for strong security.
FDI Impact on Employment Generation and GDP Growth in India

In the era of homogenization of development all over the globe, capital has become crucial aspect and major concern for countries, especially for the developing ones. Foreign funds inflow has become one of the major resources for this. Countries are in a constant race to attract more of foreign fund inflows or foreign direct investment (FDI).The objective of this paper is to study the impact of FDI in India on the employment generation capacity and GDP growth, it will go through sector wise inflow of FDI in India and analyze its ability to generate employment and productivity in India.
Data science and big data analytics in financial services:a case study

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of observe, orient, decide and act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.
A Mapping Between Structural and Functional Brain Networks
Venue: Brain Connect.

The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure function mapping are found for different functional modalities, our results indicate that the structure function relationship is modality dependent.
Possibilistic modeling palmprint and fingerprint based multimodal biometric recognition system
Venue: 2016 International Image Processing, Applications and Systems (IPAS)

Unimodal biometric recognition systems have random performances which can be efficient for some contexts and not for others. Fusing multiple modalities improves recognition performance and reduces some limitations of biometric systems based on a single modality, such as intentional fraud or universality problem (inability to acquire data from certain persons for some modes) Furthermore, in real world applications, biometric data imperfections affect the performance of the recognition algorithms which may be caused by several factors such as the poor image quality, noise and adverse illumination and/or contrast. To handle data imperfections or redundancy and cover data variability, possibilistic modeling is a powerful tool. In this paper, we propose a robust multimodal biometric recognition system integrating fingerprint and palmprint based on possibilistic modeling approach. The proposed approach relies on possibility theory concepts for modeling biometric features. Accordingly, a set of relevant biometric features extracted from image samples is statistically analyzed and represented by a possibility distribution. The biometric templates from the palmprint and fingerprint are used for decision making by applying a score level data fusion process. Validation of the proposed approach is done on the public palmprint image database, CASIA (Chinese Academy of Sciences Institution of Automation) and the public fingerprint database, FVC (Fingerprint Verification Competition) As performance metric, we adopt the area under the ROC curve. Each both modalities give an AUC (0,8694) and (0,9531) respectively for palmprint and fingerprint when used alone. A typique multimodal system using both modalities gives an AUC of (0,9531) The proposed multimodal system has an AUC very close to unity (0,9997)
Output driven EAP Teaching for Chinese College Students Based on Blended Learning

This paper intends to discuss how to make EAP teaching design in an output driven approach based on blended learning. It firstly made a literature review about the research on EAP both in China and abroad, and then proposed the possible EAP course arrangement according to students' current learning situation. Then it summarized the main points of output driven approach and proposed the possible teaching methods of outputdriven approach. Finally, it pointed out the important issues when output driven EAP teaching was applied in blended learning environment. There should be enough output activities both online and in the classroom and evaluation system software should be designed to supervise and evaluate students' online learning after class. Keywords blended learning; EAP; output driven approach; college English teaching
Implementation of Blended Learning in Higher Learning Institutions: A Review of Literature

While many educational premises including higher learning institutions favor blended learning over traditional approach and merely online learning, some academicians are still apprehensive about teaching in blended learning. The aim of this review is to synthesize the available evidence in the literature on challenges faced in implementing blended learning as well as the recommendations or lessons learnt from the experience. Eight articles published between January 2010 and December 2013 were appraised. This review revealed that among the challenges faced by the instructors are increased workload and time devotion, lack of pedagogical and technical skills to conduct the program and difficulty in finding the right blend between face to face and online learning. The review also discovered the importance of staff training, support and networking as strategies to help instructors deal with such issues.
The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model
Venue: Environmental Science and Pollution Research

In this paper, the relationship between carbon dioxide and agriculture in Ghana was investigated by comparing a Vector Error Correction Model (VECM) and Autoregressive Distributed Lag (ARDL) Model. Ten study variables spanning from 1961 to 2012 were employed from the Food Agricultural Organization. Results from the study show that carbon dioxide emissions affect the percentage annual change of agricultural area, coarse grain production, cocoa bean production, fruit production, vegetable production, and the total livestock per hectare of the agricultural area. The vector error correction model and the autoregressive distributed lag model show evidence of a causal relationship between carbon dioxide emissions and agriculture; however, the relationship decreases periodically which may die over time. All the endogenous variables except total primary vegetable production lead to carbon dioxide emissions, which may be due to poor agricultural practices to meet the growing food demand in Ghana. The autoregressive distributed lag bounds test shows evidence of a long run equilibrium relationship between the percentage annual change of agricultural area, cocoa bean production, total livestock per hectare of agricultural area, total pulses production, total primary vegetable production, and carbon dioxide emissions. It is important to end hunger and ensure people have access to safe and nutritious food, especially the poor, orphans, pregnant women, and children under 5 years in order to reduce maternal and infant mortalities. Nevertheless, it is also important that the Government of Ghana institutes agricultural policies that focus on promoting a sustainable agriculture using environmental friendly agricultural practices. The study recommends an integration of climate change measures into Ghana's national strategies, policies and planning in order to strengthen the country's effort to achieving a sustainable environment.
Deep Reinforcement Learning for Mention Ranking Coreference Models
Venue: EMNLP

Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning. In this paper we instead apply reinforcement learning to directly optimize a neural mention ranking model for coreference evaluation metrics. We experiment with two approaches: the REINFORCE policy gradient algorithm and a reward rescaled max margin objective. We find the latter to be more effective, resulting in significant improvements over the current state of the art on the English and Chinese portions of the CoNLL 2012 Shared Task.
Clay/Polyaniline Hybrid through Diazonium Chemistry: Conductive Nanofiller with Unusual Effects on Interfacial Properties of Epoxy Nanocomposites.
Venue: Langmuir the ACS journal of surfaces and colloids

The concept of conductive network structure in thermoset matrix without sacrificing the inherent mechanical properties of thermoset polymer (e.g. epoxy) is investigated here using "hairy" bentonite fillers. The latter were prepared through the in situ polymerization of aniline in the presence of 4 diphenylamine diazonium (DPA) modified bentonite (B DPA) resulting in a highly exfoliated bentonite DPA/polyaniline (B DPA/PANI) The nanocomposite filler was mixed with diglycidyl ether of bisphenol A (DGEBA) and the curing agent (4,4' diaminodiphenylsulfone) (DDS) at high temperature in order to obtain nanocomposites through the conventional melt mixing technique. The role of B DPA in the modification of the interface between epoxy and B DPA/polyaniline (B DPA/PANI) is investigated and compared with the filler B/PANI prepared without any diazonium modification of the bentonite. Synergistic improvement in dielectric properties and mechanical properties points to the fact that the DPA aryl groups from the diazonium precursor significantly modify the interface by acting as an efficient stress transfer medium. In DPA containing nanocomposites, unique fibril formation was observed on the fracture surface. Moreover, dramatic improvement (210 220% in fracture toughness of epoxy composite was obtained with B DPA/PANI filler as compared to the weak improvement of 20 30% noted in the case of the B/PANI filler. This work shows that the DPA diazonium salt has an important effect on the improvement of the interfacial properties and adhesion of DGEBA and clay/PANI nanofillers.
Unifying Representations and Large Scale Whole Body Motion Databases for Studying Human Motion
Venue: IEEE Transactions on Robotics

Large scale human motion databases are key for research questions ranging from human motion analysis and synthesis, biomechanics of human motion, data driven learning of motion primitives, and rehabilitation robotics to the design of humanoid robots and wearable robots such as exoskeletons. In this paper we present a large scale database of whole body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject specific motions to robots with different embodiments. To this end, captured subject specific motion is normalized regarding the subject's height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM) In contrast with previous approaches and human motion databases, the motion data in our database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, we present procedures and techniques for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject object relations. To allow efficient search for certain motion types in the database, motion recordings are manually annotated with motion description tags organized in a tree structure. We demonstrate the transfer of human motion to humanoid robots and provide several examples of motion analysis using the database.
Human activity recognition using deep belief networks
Venue: 2016 24th Signal Processing and Communication Application Conference (SIU)

Human activity recognition using new generation depth sensors are particularly important for application that require human activity recognition. In this paper, a deep learning based algorithm is developed human activity recognition using RGB D video sequences. Based on the assumption that every human activity is composed of many smaller actions, a temporal structure is being learnt in order to improve the classification of human activities. Since our approach is an attempt to develop a deep learning structure to the problem, it can be considered as a deep structural arhitecture. A deep neural network is obtained manipulating the activitation functions which yield hidden variables at every hidden layer. Our approach outperforms the methods that are constructed upon engineered features, since it uses the skeleton coordinates extracted from depth images. Tested on a new dataset, it is observed that our appproach outputs better recognition rates compared to those of other state of art methods.
Development of the Tactical Human Optimization, Rapid Rehabilitation, and Reconditioning Program Military Operator Readiness Assessment for the Special Forces Operator

A NOVEL MILITARY UNIQUE OPERATOR READINESS ASSESSMENT (ORA) THE ORA WAS DESIGNED BY TACTICAL HUMAN OPTIMIZATION, RAPID REHABILITATION, AND RECONDITIONING PROFESSIONALS AS PART OF A RETURN TO DUTY PROTOCOL TO SPECIFICALLY EVALUATE THE MUSCULOSKELETAL READINESS AND PHYSIOLOGICAL PREPAREDNESS OF PREVIOUSLY INJURED OPERATORS. THE ORA COMPRISES 11 TACTICALLY RELEVANT AND PHYSICALLY TAXING EVENTS COMPLETED IN ORDER WITH A 2 MINUTE REST BETWEEN EVENTS.  A COMPREHENSIVE RETURN TO DUTY PROTOCOL OF THIS NATURE IS A NECESSARY FIRST STEP FOR TACTICAL STRENGTH COACHES TO REDUCE RISK OF REINJURY TO PREVIOUSLY INJURED PERSONNEL.
Readiness Assessment of Tele ICU implementation in Technical and Human Aspects in Teaching Hospitals with ICUs Affiliated to Isfahan University of Medical Sciences in 2015

Tele ICU is a solution to shortage of intensive care specialists providing simultaneously health care services in several ICUs. The study aimed to determine readiness of Tele ICU implementation in technical and human aspects in teaching hospitals affiliated to Isfahan University of Medical Sciences (IUMS) This research was a descriptive analytical study. Samples included 16 anesthesiologists and 120 nurses having experience in ICU, that were chosen through convenient sampling. This method was not conducted for information technology (IT) officials due to their society limitations, and census was performed, instead. For data gathering, questionnaires and checklists were used. Content and face validity were confirmed by IUMS health information technology (HIT) and computer professors. The average correlation reliability coefficient for physicians nurses and IT officers questionnaires were 0.93 and 0.96, respectively. Technical checklists were completed through researcher's direct observation. Collected data were analyzed using descriptive statistics (frequency, mean, percentage) and inferential statistics methods (t test, ANOVA) by SPSS software. Given infrastructure hardware facilities, technical readiness were assessed as moderate (mean positive responses was 54.7% The level of knowledge was measured with seven questions, and third quartile (above 75% of correct answers was considered as desired knowledge that included 56.6 of samples. The problems and barriers of implementation of this project from the perspective of samples were found to be "high cost to set up and resources constraints" as main obstacle to set up tele ICU with 65.2% Improving technical infrastructures, developing comprehensive strategic plan and the deployment of tele ICU standards and creating electronic record, organizing training courses to raise knowledge and promote culture of applying tele technologies are recommended.
Cholinergic Signaling Controls Conditioned Fear Behaviors and Enhances Plasticity of Cortical Amygdala Circuits
Venue: Neuron

We examined the contribution of endogenous cholinergic signaling to the acquisition and extinction of fear related memory by optogenetic regulation of cholinergic input to the basal lateral amygdala (BLA) Stimulation of cholinergic terminal fields within the BLA in awake behaving mice during training in a cued fear conditioning paradigm slowed the extinction of learned fear as assayed by multi day retention of extinction learning. Inhibition of cholinergic activity during training reduced the acquisition of learned fear behaviors. Circuit mechanisms underlying the behavioral effects of cholinergic signaling in the BLA were assessed by in vivo and ex vivo electrophysiological recording. Photostimulation of endogenous cholinergic input (1) enhances firing of putative BLA principal neurons through activation of acetylcholine receptors (AChRs) (2) enhances glutamatergic synaptic transmission in the BLA, and (3) induces LTP of cortical amygdala circuits. These studies support an essential role of cholinergic modulation of BLA circuits in the inscription and retention of fear memories.
Social buffering ameliorates conditioned fear responses in female rats
Venue: Hormones and Behavior

The stress experienced by an animal is ameliorated when the animal is exposed to distressing stimuli along with a conspecific animal(s) This is known as social buffering. Previously, we found that the presence of an unfamiliar male rat induced social buffering and ameliorated conditioned fear responses of a male rat subjected to an auditory conditioned stimulus (CS) However, because our knowledge of social buffering is highly biased towards findings in male subjects, analyses using female subjects are crucial for comprehensively understanding the social buffering phenomenon. In the present studies, we assessed social buffering of conditioned fear responses in female rats. We found that the estrus cycle did not affect the intensity of the rats' fear responses to the CS or their degree of vigilance due to the presence of a conspecific animal. Based on these findings, we then assessed whether social buffering ameliorated conditioned fear responses in female rats without taking into account their estrus cycles. When fear conditioned female rats were exposed to the CS without the presence of a conspecific, they exhibited behavioral responses, including freezing, and elevated corticosterone levels. By contrast, the presence of an unfamiliar female rat suppressed these responses. Based on these findings, we conclude that social buffering can ameliorate conditioned fear responses in female rats.
Development of an Orthotropic Elasto Plastic Generalized Composite Material Model Suitable for Impact Problems

The need for accurate material models to simulate the deformation, damage, and failure of polymer matrix composites under impact conditions is becoming critical as these materials are gaining increased usage in the aerospace and automotive industries. There are a variety of material models currently available within commercial transient dynamic finite element codes to analyze the response of composite materials under impact conditions. However, there are several features that are lacking in the currently available models that could improve the predictive capability of the impact simulations. To address these needs, a combined elasto plastic model with damage suitable for implementation within transient dynamic finite element codes has been developed. A key feature of the improved material model is the use of tabulated stress strain data in a variety of coordinate directions to fully define the stress strain response of the material. Currently, the model development efforts have focused on creating the plasticity portion of the model. A commonly used composite failure model has been generalized and extended to a strain hardening based orthotropic yield function with a non associative flow rule. The coefficients of the yield function are computed based on the input stress strain curves using the effective plastic strain as the tracking variable. The coefficients of the flow rule are determined in a systematic manner based on the available stress strain data for the material. The evolution of the yield surface is examined, in detail, for a sample composite. A numerical algorithm based on the classic radial return method is employed to compute the evolution of the effective plastic strain. A specific laminated composite is examined to demonstrate the process of characterizing and analyzing the response of a composite using the developed model. The developed material model is suitable for use within commercial transient dynamic finite element codes for use in analyzing the nonlinear response of polymer composites.
Development, validation and test of optical based algorithms for autonomous planetary landing

In this paper, a suite of tools and algorithms devoted to optical navigation for autonomous landing on planets and small bodies, currently under development at PoliMi DAER, is presented. An hazard detection system based on a single camera and artificial neural networks selects the most suitable landing site for the lander. Autonomous guidance on board computes the trajectory to reach for the designated landing site, taking into account the lander dynamics and path constraints. The two systems are linked by vision based navigation, which reconstructs the relative spacecraft states with respect to the ground, in particular by means of a camera through feature tracking and other typical sensors, such as Inertial Measurement Unit and altimeter. The ultimate goal is to create a whole Adaptive Guidance, Navigation and Control chain for autonomous landings. An experimental facility is currently under construction at PoliMi premises to verify, validate and test the aforementioned optical landing tools.