nils-herrmann/clean_scirepeval huggingface dataset:
Learning model-based planning from scratch

Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the “Imagination-based Planner”, the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans. Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a “plan context” which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex “imagination tree” by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination. We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.
Mechanisms governing empowerment effects: a self-efficacy analysis.

This experiment tested the hypotheses that perceived coping and cognitive control self-efficacy govern the effects of personal empowerment over physical threats. Women participated in a mastery modeling program in which they mastered the physical skills to defend themselves successfully against unarmed sexual assailants. Multifaceted measures of theoretically relevant variables were administered within a staggered intragroup control design to test the immediate and long-term effects of the empowerment program and the mechanisms through which it produced its effects. Mastery modeling enhanced perceived coping and cognitive control efficacy, decreased perceived vulnerability to assault, and reduced the incidence of intrusive negative thinking and anxiety arousal. These changes were accompanied by increased freedom of action and decreased avoidant behavior. Path analyses of causal structures revealed a dual path of regulation of behavior by perceived coping self-efficacy, one mediated through perceived vulnerability and risk discernment and the other through perceived cognitive control self-efficacy and intrusive negative thinking.
Octree-based fusion for realtime 3D reconstruction

This paper proposes an octree-based surface representatio n for KinectFusion, a realtime reconstruction technique of in-door scenes using a low-cost moving depth camera and a com modity graphics hardware. In KinectFusion, the scene is represented as a signed distance function (SDF) and store d as an uniform grid of voxels. Though the grid-based SDF is suitable for parallel computation in graphics hardwa re, most of the storage are wasted, because the geometry is very sparse in the scene volume. In order to reduce the memo ry cost and save the computation time, we represent the SDF in an octree, and developed several octree-based alg orithms for reconstruction update and surface prediction that are suitable for parallel computation in graphics hard w re. In the reconstruction update step, the octree nodes ar e adaptively split in breath-first order. To handle scenes wit h moving objects, the corresponding nodes are automaticall y detected and removed to avoid storage overflow. In the surfac e prediction step, an octree-based ray tracing method is adopted and parallelized for graphic hardware. To further r educe the computation time, the octree is organized into four layers, called top layer, branch layer, middle layer an d data layer. The experiments showed that, the proposed method consumes only less than 10% memory of original Kinect Fusion method, and achieves faster performance. Consequently, it can reconstruct scenes with more than 10 ti mes larger size than the original KinectFusion on the same hardware setup.
DESIGN AND DEVELOPMENT OF 3 D PRINTED MYOELECTRIC ROBOTIC EXOSKELETON FOR HAND REHABILITATION

The development of dynamic rehabilitation devices can be evaluated as a research fastgrowing field. Indeed, robot-assisted therapy is an advanced new technology mainly in stroke rehabilitation. Although patients benefit from this enormous development of technology, including the presence of rehabilitation robots, the therapeutic field still suffering a lack in hand robotic rehabilitation devices. In this context, this work proposes a new design of a 3D printed hand exoskeleton for the stroke rehabilitation. Based on the EMG signals measured from the muscles responsible for the hand motion, the designed mechatronic system detects the intention of hand opening or hand closing from the stroked subject. Based on an embedded controller and five servomotors, the low cost robotic system is able to drive in real time three degrees of freedom (DOFs) for each finger. The real tests with stroked subjects showed that the designed hand exoskeleton architecture has a positive effect on the motion finger range and mainly in the hand ability to perform some simple tasks. The case studies showed a good recovery of the motor functions and consequently the developed system efficiency.
The SAGE project: a storage centric approach for exascale computing: invited paper

SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme Computing (BDEC) capable infrastructure with associated software stack. The SAGE system follows a storage centric approach as it is capable of storing and processing large data volumes at the Exascale regime.
 SAGE addresses the convergence of Big Data Analysis and HPC in an era of next-generation data centric computing. This convergence is driven by the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors where data needs to be processed, analyzed and integrated into simulations to derive scientific and innovative insights. A first prototype of the SAGE system has been been implemented and installed at the Jülich Supercomputing Center. The SAGE storage system consists of multiple types of storage device technologies in a multi-tier I/O hierarchy, including flash, disk, and non-volatile memory technologies. The main SAGE software component is the Seagate Mero Object Storage that is accessible via the Clovis API and higher level interfaces. The SAGE project also includes scientific applications for the validation of the SAGE concepts.
 The objective of this paper is to present the SAGE project concepts, the prototype of the SAGE platform and discuss the software architecture of the SAGE system.
Chipless RFID Tag Localization

A novel localization technique for frequency-domain chipless RF identification (RFID) tag is proposed. A short-duration ultra-wideband impulse radio signal interrogates the tags, and multiple receivers in the interrogation zone capture the backscattered signal from the tags. The received signals from the chipless tags are analyzed for the structural mode radar cross section to determine the relative ranges. Using the range information, the linear least square (LLS) method is employed for accurate localization of tagged items. The accuracy of the localization method is analyzed by moving the chipless tag within a fixed interrogation zone. The whole system is modeled in CST Microwave Studio Suite 2012 to comply with a realistic scenario. The post-processing for range and tag position estimation through LLS is done in MATLAB. The method is also verified in laboratory environment with fabricated chipless RFID tags and multiple receiving units. The range and angular resolution are 2.1 cm and 3.5 °, respectively. The analysis and results create a strong foundation for chipless RFID tags to be used in tracking and localization.
Non-interactive Zero-Knowledge Arguments for Voting

In voting based on homomorphic threshold encryption, the voter encrypts his vote and sends it in to the authorities that tally the votes. If voters can send in arbitrary plaintexts then they can cheat. It is therefore important that they attach an argument of knowledge of the plaintext being a correctly formed vote. Typically, these arguments are honest verifier zero-knowledge arguments that are made non-interactive using the Fiat-Shamir heuristic. Security is argued in the random oracle model. The simplest case is where each voter has a single vote to cast. Practical solutions have already been suggested for the single vote case. However, as we shall see homomorphic threshold encryption can be used for a variety of elections, in particular there are many cases where voters can cast multiple votes at once. In these cases, it remains important to bring down the cost of the NIZK argument. We improve on state of the art in the case of limited votes, where each voter can vote a small number of times. We also improve on the state of the art in shareholder elections, where each voter may have a large number of votes to spend. Moreover, we improve on the state of the art in Borda voting. Finally, we suggest a NIZK argument for correctness of an approval vote. To the best of our knowledge, approval voting has not been considered before in the cryptographic literature.
Smart vehicle accident detection and alarming system using a smartphone

Vehicle accident is the paramount thread for the people's life which causes a serious wound or even dead. The automotive companies have made lots of progress in alleviating this thread, but still the probability of detrimental effect due to an accident is not reduced. Infringement of spFieed is one of the elementary reasons for a vehicle accident. Therewithal, external pressure and change of tilt angle with road surface blameworthy for this mishap. As soon as the emergency service could divulge about an accident, the more the effect would be mitigated. For this purpose, we developed an Android based application that detects an accidental situation and sends emergency alert message to the nearest police station and health care center. This application is integrated with an external pressure sensor to extract the outward force of the vehicle body. It measures speed and change of tilt angle with GPS and accelerometer sensors respectively on Android phone. By checking conditions, this application also capable of reducing the rate of false alarm.
Adaptation of Word Vectors using Tree Structure for Visual Semantics

We propose a framework of word-vector adaptation, which makes vectors of visually similar concepts close to each other. Here, word vectors are real-valued vector representation of words, e.g., word2vec representation. Our basic idea is to assume that each concept has some hypernyms that are important to determine its visual features. For example, for a concept Swallow with hypernyms Bird, Animal and Entity, we believe Bird is the most important since birds have common visual features with their feathers etc. Adapted word vectors are obtained for each word by taking a weighted sum of a given original word vector and its hypernym word vectors. Our weight optimization makes vectors of visually similar concepts close to each other, by giving a large weight for such important hypernyms. We apply the adapted word vectors to zero-shot learning on the TRECVID 2014 semantic indexing dataset. We achieved 0.083 of Mean Average Precision, which is the best performance without using TRECVID training data to the best of our knowledge.
A Different Approach to the Management of Osteoarthritis in the Knee: Ultrasound Guided Genicular Nerve Block.

A 61-year-old female was referred to our tertiary physical medicine and rehabilitation outpatient clinic with a 7-year history of severe left knee pain. The patient was a nonsmoker and obese, with no comorbidities, and reported that the left knee pain worsened when walking and descending stairs and particularly when climbing stairs. Radiography of the left knee revealed KellgrenLawrence grade 3 osteoarthritis (OA) with multiple osteophytes and definite joint space narrowing.
Attribute-based People Search: Lessons Learnt from a Practical Surveillance System

We address the problem of attribute-based people search in real surveillance environments. The system we developed is capable of answering user queries such as "show me all people with a beard and sunglasses, wearing a white hat and a patterned blue shirt, from all metro cameras in the downtown area, from 2pm to 4pm last Saturday". In this paper, we describe the lessons we learned from practical deployments of our system, and how we made our algorithms achieve the accuracy and efficiency required by many police departments around the world. In particular, we show that a novel set of multimodal integral filters and proper normalization of attribute scores are critical to obtain good performance. We conduct a comprehensive experimental analysis on video footage captured from a large set of surveillance cameras monitoring metro chokepoints, in both crowded and normal activity periods. Moreover, we show impressive results using images from the recent Boston marathon bombing event, where our system can rapidly retrieve the two suspects based on their attributes from a database containing more than one thousand people present at the event.
Insights into Alzheimer disease pathogenesis from studies in transgenic animal models

Alzheimer disease is the most common cause of dementia among the elderly, accounting for ~60-70% of all cases of dementia. The neuropathological hallmarks of Alzheimer disease are senile plaques (mainly containing p-amyloid peptide derived from amyloid precursor protein) and neurofibrillary tangles (containing hyperphosphorylated Tau protein), along with neuronal loss. At present there is no effective treatment for Alzheimer disease. Given the prevalence and poor prognosis of the disease, the development of animal models has been a research priority to understand pathogenic mechanisms and to test therapeutic strategies. Most cases of Alzheimer disease occur sporadically in people over 65 years old, and are not genetically inherited. Roughly 5% of patients with Alzheimer disease have familial Alzheimer disease--that is, related to a genetic predisposition, including mutations in the amyloid precursor protein, presenilin 1, and presenilin 2 genes. The discovery of genes for familial Alzheimer disease has allowed transgenic models to be generated through the overexpression of the amyloid precursor protein and/or presenilins harboring one or several mutations found in familial Alzheimer disease. Although none of these models fully replicates the human disease, they have provided valuable insights into disease mechanisms as well as opportunities to test therapeutic approaches. This review describes the main transgenic mouse models of Alzheimer disease which have been adopted in Alzheimer disease research, and discusses the insights into Alzheimer disease pathogenesis from studies in such models. In summary, the Alzheimer disease mouse models have been the key to understanding the roles of soluble b-amyloid oligomers in disease pathogenesis, as well as of the relationship between p-amyloid and Tau pathologies.
A High-Performance HOG Extractor on FPGA

Pedestrian detection is one of the key problems in emerging selfdriving car industry. And HOG algorithm has proven to provide good accuracy for pedestrian detection. There are plenty of research works have been done in accelerating HOG algorithm on FPGA because of its low-power and high-throughput characteristics. In this paper, we present a high-performance HOG architecture for pedestrian detection on a low-cost FPGA platform. It achieves a maximum throughput of 526 FPS with 640x480 input images, which is 3.25 times faster than the state of the art design. The accelerator is integrated with SVM-based prediction in realizing a pedestrian detection system. And the power consumption of the whole system is comparable with the best existing implementations.
Automated extraction of non-functional requirements in available documentation

While all systems have non-functional requirements (NFRs), they may not be explicitly stated in a formal requirements specification. Furthermore, NFRs may also be externally imposed via government regulations or industry standards. As some NFRs represent emergent system proprieties, those NFRs require appropriate analysis and design efforts to ensure they are met. When the specified NFRs are not met, projects incur costly re-work to correct the issues. The goal of our research is to aid analysts in more effectively extracting relevant non-functional requirements in available unconstrained natural language documents through automated natural language processing. Specifically, we examine which document types (data use agreements, install manuals, regulations, request for proposals, requirements specifications, and user manuals) contain NFRs categorized to 14 NFR categories (e.g. capacity, reliability, and security). We measure how effectively we can identify and classify NFR statements within these documents. In each of the documents evaluated, we found NFRs present. Using a word vector representation of the NFRs, a support vector machine algorithm performed twice as effectively compared to the same input to a multinomial naïve Bayes classifier. Our k-nearest neighbor classifier with a unique distance metric had an F1 measure of 0.54, outperforming in our experiments the optimal naïve Bayes classifier which had a F1 measure of 0.32. We also found that stop word lists beyond common determiners had no minimal performance effect.
Portable and Efficient Auto-vectorized Bytecode: a Look at the Interaction between Static and JIT Compilers

Heterogeneity is a confirmed trend of computing systems. Bytecode formats and just-in-time compilers have been proposed to deal with the diversity of the platforms. By hiding architectural details and giving software developers a unified view of the machine, they help improve portability and manage the complexity of large software. Independently, careful exploitation of SIMD instructions has become crucial for the performance of many applications. However, auto-vectorizing compilers need detailed information about the architectural features of the processor to generate efficient code. We propose to reconcile the use of architecture neutral bytecode formats with the need to generate highly efficient vectorized native code. We make three contributions. 1) We show that vectorized bytecode is a viable approach that can deliver portable performance in the presence of SIMD extensions, while incurring only minor penalty when SIMD is not supported. In other words, the information that a loop can be vectorized is the vectorized loop itself. 2) We analyze the interaction between the static and just-in-time compilers and we derive conditions to deliver performance. 3) We add vectorization capabilities to the CLI port of the GCC compiler.
Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store

Mobile app stores provide a unique platform for developers to rapidly deploy new updates of their apps. We studied the frequency of updates of 10,713 mobile apps (the top free 400 apps at the start of 2014 in each of the 30 categories in the Google Play store). We find that a small subset of these apps (98 apps representing ˜1 % of the studied apps) are updated at a very frequent rate — more than one update per week and 14 % of the studied apps are updated on a bi-weekly basis (or more frequently). We observed that 45 % of the frequently-updated apps do not provide the users with any information about the rationale for the new updates and updates exhibit a median growth in size of 6 %. This paper provides information regarding the update strategies employed by the top mobile apps. The results of our study show that 1) developers should not shy away from updating their apps very frequently, however the frequency varies across store categories. 2) Developers do not need to be too concerned about detailing the content of new updates. It appears that users are not too concerned about such information. 3) Users highly rank frequently-updated apps instead of being annoyed about the high update frequency.
Video games do affect social outcomes: a meta-analytic review of the effects of violent and prosocial video game play.

Whether video game play affects social behavior is a topic of debate. Many argue that aggression and helping are affected by video game play, whereas this stance is disputed by others. The present research provides a meta-analytical test of the idea that depending on their content, video games do affect social outcomes. Data from 98 independent studies with 36,965 participants revealed that for both violent video games and prosocial video games, there was a significant association with social outcomes. Whereas violent video games increase aggression and aggression-related variables and decrease prosocial outcomes, prosocial video games have the opposite effects. These effects were reliable across experimental, correlational, and longitudinal studies, indicating that video game exposure causally affects social outcomes and that there are both short- and long-term effects.
A cortical–hippocampal system for declarative memory

Recent neurobiological studies have begun to reveal the cognitive and neural coding mechanisms that underlie declarative memory — our ability to recollect everyday events and factual knowledge. These studies indicate that the critical circuitry involves bidirectional connections between the neocortex, the parahippocampal region and the hippocampus. Each of these areas makes a unique contribution to memory processing. Widespread high-order neocortical areas provide dedicated processors for perceptual, motor or cognitive information that is influenced by other components of the system. The parahippocampal region mediates convergence of this information and extends the persistence of neocortical memory representations. The hippocampus encodes the sequences of places and events that compose episodic memories, and links them together through their common elements. Here I describe how these mechanisms work together to create and re-create fully networked representations of previous experiences and knowledge about the world.
Online purchase intentions: A multi-channel store image perspective

The advantages of the bricks-and-clicks retail format in the battle for the online customer has been widely discussed but empirical research on it has been limited. We applied a multi-channel store image perspective to assess its influence on online purchase intentions. Drawing on a sample of 630 customers of a large music retail store in the Netherlands, the results demonstrated that offline and online store perceptions directly influenced online purchase intention. In addition, our findings confirmed that offline store impressions were used as references for their online store counterparts. Synergy and reference effects are discussed. ! 2008 Elsevier B.V. All rights reserved.  Corresponding author.  E-mail address: tverhagen@feweb.vu.nl (T. Verhagen).