nils-herrmann/clean_scirepeval huggingface dataset:
Driver fatigue monitoring system using Support Vector Machines

Driver fatigue is one of the leading causes of traffic accidents. This paper presents a real-time non-intrusive fatigue monitoring system which exploits the driver's facial expression to detect and alert fatigued drivers. The presented approach adopts the Viola-Jones classifier to detect the driver's facial features. The correlation coefficient template matching method is then applied to derive the state of each feature on a frame by frame basis. A Support Vector Machine (SVM) is finally integrated within the system to classify the facial appearance as either fatigued or otherwise. Using this simple and cheap implementation, the overall system achieved an accuracy of 95.2%, outperforming other developed systems employing expensive hardware to reach the same objective.
A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions

The derivatives of differential equation solutions are commonly used as model diagnostics and as part of parameter estimation routines. In this manuscript we investigate an implementation of Discrete local Sensitivity Analysis via Automatic Differentiation (DSAAD). A non-stiff Lotka-Volterra model, a discretization of the two dimensional (N × N) Brusselator stiff reaction-diffusion PDE, a stiff non-linear air pollution and a non-stiff pharmacokinetic/pharmacodynamic (PK/PD) model were used as prototype models for this investigation. Our benchmarks show that on sufficiently small (<100 parameters) stiff and non-stiff systems of ODEs, forward-mode DSAAD is more efficient than both reverse-mode DSAAD and continuous forward/adjoint sensitivity analysis. The scalability of continuous adjoint methods is shown to result in better efficiency for larger ODE systems such as PDE discretizations. In addition to testing efficiency, results on test equations demonstrate the applicability of DSAAD to differential-algebraic equations, delay differential equations, and hybrid differential equation systems where the event timing and effects are dependent on model parameters. Together, these results show that language-level automatic differentiation is an efficient method for calculating local sensitivities of a wide range of differential equation models.
The contribution of sleep to hippocampus-dependent memory consolidation

There is now compelling evidence that sleep promotes the long-term consolidation of declarative and procedural memories. Behavioral studies suggest that sleep preferentially consolidates explicit aspects of these memories, which during encoding are possibly associated with activation in prefrontal-hippocampal circuitry. Hippocampus-dependent declarative memory benefits particularly from slow-wave sleep (SWS), whereas rapid-eye-movement (REM) sleep seems to benefit procedural aspects of memory. Consolidation of hippocampus-dependent memories relies on a dialog between the neocortex and hippocampus. Crucial features of this dialog are the neuronal reactivation of new memories in the hippocampus during SWS, which stimulates the redistribution of memory representations to neocortical networks; and the neocortical slow (<1Hz) oscillation that synchronizes hippocampal-to-neocortical information transfer to activity in other brain structures.
The Greeks in the West: genetic signatures of the Hellenic colonisation in southern Italy and Sicily

Greek colonisation of South Italy and Sicily (Magna Graecia) was a defining event in European cultural history, although the demographic processes and genetic impacts involved have not been systematically investigated. Here, we combine high-resolution surveys of the variability at the uni-parentally inherited Y chromosome and mitochondrial DNA in selected samples of putative source and recipient populations with forward-in-time simulations of alternative demographic models to detect signatures of that impact. Using a subset of haplotypes chosen to represent historical sources, we recover a clear signature of Greek ancestry in East Sicily compatible with the settlement from Euboea during the Archaic Period (eighth to fifth century BCE). We inferred moderate sex-bias in the numbers of individuals involved in the colonisation: a few thousand breeding men and a few hundred breeding women were the estimated number of migrants. Last, we demonstrate that studies aimed at quantifying Hellenic genetic flow by the proportion of specific lineages surviving in present-day populations may be misleading.
Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization

Recommender Systems present a high-level of sparsity in their ratings matrices. The collaborative filtering sparse data makes it difficult to: 1) compare elements using memory-based solutions; 2) obtain precise models using model-based solutions; 3) get accurate predictions; and 4) properly cluster elements. We propose the use of a Bayesian non-negative matrix factorization (BNMF) method to improve the current clustering results in the collaborative filtering area. We also provide an original pre-clustering algorithm adapted to the proposed probabilistic method. Results obtained using several open data sets show: 1) a conclusive clustering quality improvement when BNMF is used, compared with the classical matrix factorization or to the improved KMeans results; 2) a higher predictions accuracy using matrix factorization-based methods than using improved KMeans; and 3) better BNMF execution times compared with those of the classic matrix factorization, and an additional improvement when using the proposed pre-clustering algorithm.
Online Tracker Optimization for Multi-Pedestrian Tracking Using a Moving Vehicle Camera

Multi-pedestrian tracking (MPT) on the road is closely related to a reduction in the possibility of pedestrian–vehicle collisions when using advanced driver assistance systems. Therefore, this paper focuses on MPT on real roads using a moving camera. Although convolutional neural network (CNN)-based single object tracking methods have recently been proposed, the online learning of CNN for MPT is a significant burden in terms of real-time processing. However, because the features extracted from CNN are a good representation and achieve a high generalization capability, to reduce the learning time, a shallow pre-trained CNN was applied in the present study as a feature extractor instead of an object tracker As an online tracker, this study uses random ferns (RF) with pre-trained CNN output feature, which can make the problem computationally tractable and robustness. However, a large RF requires significant amount of memory and computational complexity, and is still a burden in terms of online learning in cases of multi-pedestrian tracking. Therefore, we introduce a teacher–student model compression algorithm for selecting a few optimal ferns for each tracker, thereby, reducing the online learning time. The online learning of a student-RF tracker has an advantage in that it can adaptively update the tracker to achieve robustness against various changes, such as in pose and illumination, and partial occlusions within a limited period of time. The proposed algorithm was successfully applied to benchmark video sequences captured from a moving camera that include multiple pedestrians in various poses. Specifically, the proposed algorithm yields a more accurate tracking performance than the other state-of-the-art methods.
E-LOYALTY IS NOT ALL ABOUT TRUST , PRICE ALSO MATTERS : EXTENDING EXPECTATION-CONFIRMATION THEORY IN BOOKSELLING WEBSITES

Identifying factors that influence customers’ e-loyalty is paramount for practitioners and academics to develop successful marketing strategies and behavioral models. Online bookselling is a rapid growing industry in the UK, where e-loyalty models have yet to reach a conclusive argument. This paper aims to explore factors influencing customers’ e-loyalty to five online bookselling websites in the UK by testing a theoretical model, based on expectation-confirmation theory. A quantitative approach was employed using questionnaires; the sample consisted of 290 respondents (50% males, age range: 18 to over 54). The questionnaire was pretested and confirmatory factor analysis was performed to assess the measurement model. Structural equation modeling and MANOVA were employed to examine the association between latent constructs. Eleven hypotheses were formulated examining different independent variables in the theoretical model; results showed a significant direct and positive association between satisfaction and e-loyalty. Web design affected e-loyalty significantly on all bookselling websites, perceived value was a significant predictor of satisfaction while price notably influenced e-trust development. Etrust was not associated with e-loyalty. This study introduces new variables that affect e-loyalty as well as illuminates new associations between existing factors – perceived value, price, and trust are new aspects which practitioners and academics should take into account for marketing strategies and behavioral models, respectively. Hence, managers will likely increase customer satisfaction and loyalty by improving web design and balancing quality with price, predisposing positively customer’s attitudes towards the website.
A Double-layered Timber Plate Shell-Computational Methods for Assembly , Prefabrication and Structural Design

This paper presents a new lightweight construction system for doublycurved shells, built from two interconnected layers of structural wood veneer plates. The system uses integral through-tenon joints for a fast, precise, and simple assembly, allowing for the construction of a series of differently shaped shells without a costly mould or support structure. Instead, inclined joints cut with a 5-axis CNC milling machine embed the correct location and angle between plates into the shape of the parts. This constrains the relative motions between joined parts to one assembly path. To take advantage of the benefits of such connectors, the constrained assembly paths must be considered in the fundamental design of the system, allowing for the insertion of each plate. This imposes additional constraints in the segmentation process of doubly-curved shells. In order to meet the requirements and resolve the multi-constraint system, we use a global, nonlinear optimization approach. Developed as a close collaboration between architects, computer scientists and structural engineers, the paper includes an experimental analysis of the influence of parametric modifications in the shape of connectors on their load-bearing performance.
Co-designing accelerators and SoC interfaces using gem5-Aladdin

Increasing demand for power-efficient, high-performance computing has spurred a growing number and diversity of hardware accelerators in mobile and server Systems on Chip (SoCs). This paper makes the case that the co-design of the accelerator microarchitecture with the system in which it belongs is critical to balanced, efficient accelerator microarchitectures. We find that data movement and coherence management for accelerators are significant yet often unaccounted components of total accelerator runtime, resulting in misleading performance predictions and inefficient accelerator designs. To explore the design space of accelerator-system co-design, we develop gem5-Aladdin, an SoC simulator that captures dynamic interactions between accelerators and the SoC platform, and validate it to within 6% against real hardware. Our co-design studies show that the optimal energy-delay-product (EDP) of an accelerator microarchitecture can improve by up to 7.4× when system-level effects are considered compared to optimizing accelerators in isolation.
Physical , Spectroscopic and Thermal Characterization of Biofield Treated Fish Peptone

The by-products of industrially processed fish are enzymatically converted into fish protein isolates and hydrolysates having a wide biological activity and nutritional properties. However, the heat processing may cause their thermal denaturation thereby causing the conformational changes in them. The present study utilized the strategy of biofield energy treatment and analysed its impact on various properties of the fish peptone as compared to the untreated (control) sample. The fish peptone sample was divided into two parts; one part was subjected to Mr. Trivedi’s biofield treatment, coded as the treated sample and another part was coded as the control. The impact of biofield treatment was analysed through various analytical techniques and results were compared with the control sample. The particle size data revealed 4.61% increase in the average particle size (d50) along with 2.66% reduction in the surface area of the treated sample as compared to the control. The X-ray diffraction studies revealed the amorphous nature of the fish peptone sample; however no alteration was found in the diffractogram of the treated sample with respect to the control. The Fourier transform infrared studies showed the alterations in the frequency of peaks corresponding to N-H, C-H, C=O, C-N, and C-OH, functional groups in the treated sample as compared to the control. The differential scanning calorimetry data revealed the increase in transition enthalpy (∆H) from -71.14 J/g (control) to -105.32 J/g in the treated sample. The thermal gravimetric analysis data showed the increase in maximum thermal degradation temperature (Tmax) from 213.31°C (control) to 221.38°C along with a reduction in the percent weight loss of the treated sample during the thermal degradation event. These data revealed the increase in thermal stability of the treated fish peptone and suggested that the biofield energy treatment may be used to improve the thermal stability of the heat sensitive compounds.
Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case

This paper presents a new approach to the multi-robot map-alignment problem that enables teams of robots to build joint maps without initial knowledge of their relative poses. The key contribution of this work is an optimal algorithm for merging (not necessarily overlapping) maps that are created by different robots independently. Relative pose measurements between pairs of robots are processed to compute the coordinate transformation between any two maps. Noise in the robot-to-robot observations, propagated through the map-alignment process, increases the error in the position estimates of the transformed landmarks, and reduces the overall accuracy of the merged map. When there is overlap between the two maps, landmarks that appear twice provide additional information, in the form of constraints, which increases the alignment accuracy. Landmark duplicates are identified through a fast nearest-neighbor matching algorithm. In order to reduce the computational complexity of this search process, a kd-tree is used to represent the landmarks in the original map. The criterion employed for matching any two landmarks is the Mahalanobis distance. As a means of validation, we present experimental results obtained from two robots mapping an area of 4,800 m2
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model outperforms the state-ofthe-art results on two different datasets for sequential sentence classification in medical abstracts.
Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing

We propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. We aim to provide a unified model where TSG rules and symbol refinement are learned from training data in a fully automatic and consistent fashion. We present a novel probabilistic SR-TSG model based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, and develop an efficient training method based on Markov Chain Monte Carlo (MCMC) sampling. Our SR-TSG parser achieves an F1 score of 92.4% in the Wall Street Journal (WSJ) English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and better than state-of-the-art discriminative reranking parsers.
Translation, rotation, and scale-invariant object recognition

A method for object recognition invariant under translation, rotation and scaling is addressed. The rst step of the method (preprocessing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a Holographic Nearest Neighbor algorithm (HNN), where vectors obtained in the preprocessing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper case letters of the alphabet. Only four di erent orientations and one size (for each letter) were used for training. Recognition was tested with 17 di erent sizes and 14 rotations. The results are encouraging since we achieved 98% correct recognition. Tolerance to manual and random noise was tested. Results for character recognition in real images of car plates are also presented.
Contemporary reconstruction of the mandible.

Reconstruction of the mandible has evolved significantly over the last 40years. Early attempts were often disfiguring and wrought with complications but with the introduction of free tissue transfer of well vascularized bone in the 1970's there was a significant improvement in outcomes. In recent years the harvest, inset, and microvascular anatomosis have been refined to the point that success rates are reported as high as 99% throughout the literature. Focus has now shifted to optimizing functional and aesthetic outcomes after mandible reconstruction. This paper will be a review defect classification, goals of reconstruction, the various donor sites, dental rehabilitation, new advances, and persistent problems. Reconstruction of segmental mandibular defects after ablative surgery is best accomplished using free tissue transfer to restore mandibular continuity and function. Reestablishing occlusion and optimizing tongue mobility are important to post-operative oral function. Persistent problems in oro-mandibular reconstruction relate to the effects of radiation treatment on the native tissue and include xerostomia, dysgeusia, osteoradionecrosis and trismus. These problems continue to plague the oral cancer patient despite the significant advances that allow a far more complete functional restoration than could be accomplished a mere two decades ago.
The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation

Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once? We evaluate on several challenging single-frame data sets containing a wide variety of body poses, shapes, torso rotations, and image cropping. Our experiments demonstrate that one-shot pose estimation achieves state of the art results and runs in real-time.
Towards a Neural Conversation Model With Diversity Net Using Determinantal Point Processes

Typically, neural conversation systems generate replies based on the sequence-to-sequence (seq2seq) model. seq2seq tends to produce safe and universal replies, which suffers from the lack of diversity and information. Determinantal Point Processes (DPPs) is a probabilistic model defined on item sets, which can select the items with good diversity and quality. In this paper, we investigate the diversity issue in two different aspects, namely query-level and system-level diversity. We propose a novel framework which organically combines seq2seq model with Determinantal Point Processes (DPPs). The new framework achieves high quality in generated reply and significantly improves the diversity among them. Experiments show that our model achieves the best performance among various baselines in terms of both quality and diversity.
Unsupervised Learning of Narrative Schemas and their Participants

We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE,SUSPECT), convicted( JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (JUDGE = {judge, jury, court}, POLICE = {police, agent, authorities}). Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles. Our unsupervised learning algorithm uses coreferring arguments in chains of verbs to learn both rich narrative event structure and argument roles. By jointly addressing both tasks, we improve on previous results in narrative/frame learning and induce rich frame-specific semantic roles.
Understanding experience in interactive systems

Understanding experience is a critical issue for a variety of professions, especially design. To understand experience and the user experience that results from interacting with products, designers conduct situated research activities focused on the interactions between people and products, and the experience that results. This paper attempts to clarify experience in interactive systems. We characterize current approaches to experience from a number of disciplines, and present a framework for designing experience for interactive system. We show how the framework can be applied by members of a multidisciplinary team to understand and generate the kinds of interactions and experiences new product and system designs might offer.
Liquid crystal polymer (LCP) for microwave/millimeter wave multilayer packaging

This paper presents characterization and analysis of liquid crystal polymer materials for microwave and millimeter multi-layer packaging. Processing techniques have been developed to fabricate interconnects on this new LCP material. The experimental results demonstrate that an interconnect on LCP achieves a measured insertion loss of less than 0.1 dB/mm up to 50 GHz. This material is highly suitable for microwave and millimeter wave packaging.
Network game traffic modelling

A significant share of today's Internet traffic is generated by network gaming. This kind of traffic is interesting in regard to it's market potential as well as to it's real time requirements on the network. For the consideration of game traffic in network dimensioning, traffic models are required that allow to generate a characteristic load for analytical or simulative performance evaluation of networks. In this paper we evaluate the fast action multiplayer game ,,Counter Strike" from a 36 hour LAN party measurement and present traffic models for client and server. The paper concludes with remarks on the use of game traffic models in simulations and on QoS metrics for an adequate evaluation of simulation results.