nils-herrmann/clean_scirepeval huggingface dataset:
CaffePresso: An optimized library for Deep Learning on embedded accelerator-based platforms

Off-the-shelf accelerator-based embedded platforms offer a competitive energy-efficient solution for lightweight deep learning computations over CPU-based systems. Low-complexity classifiers used in power-constrained and performance-limited scenarios are characterized by operations on small image maps with 2-3 deep layers and few class labels. For these use cases, we consider a range of embedded systems with 5--20 W power budgets such as the Xilinx ZC706 board (with MXP soft vector processor), NVIDIA Jetson TX1 (GPU), TI Keystone II (DSP) as well as the Adapteva Parallella board (custom multi-core with NoC). Deep Learning computations push the capabilities of these platforms to the limit through compute-intensive evaluations of multiple 2D convolution filters per layer, and high communication requirements arising from the movement of intermediate maps across layers. We present CaffePresso, a Caffe-compatible framework for generating optimized mappings of user-supplied ConvNet specifications to target various accelerators such as FPGAs, DSPs, GPUs, RISC-multicores. We use an automated code generation and auto-tuning approach based on knowledge of the ConvNet requirements, as well as platform-specific constraints such as on-chip memory capacity, bandwidth and ALU potential. While one may expect the Jetson TX1 + cuDNN to deliver high performance for ConvNet configurations, (1) we observe a flipped result with slower GPU processing compared to most other systems for smaller embedded-friendly datasets such as MNIST and CIFAR10, and (2) faster and more energy efficient implementation on the older 28nm TI Keystone II DSP over the newer 20nm NVIDIA TX1 SoC in all cases.
Convolution Kernels on Discrete Structures

We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on a innnite set from kernels involving generators of the set. The family of kernels generated generalizes the family of radial basis kernels. It can also be used to deene kernels in the form of joint Gibbs probability distributions. Kernels can be built from hidden Markov random elds, generalized regular expressions, pair-HMMs, or ANOVA de-compositions. Uses of the method lead to open problems involving the theory of innnitely divisible positive deenite functions. Fundamentals of this theory and the theory of reproducing kernel Hilbert spaces are reviewed and applied in establishing the validity of the method.
Engaging with massive online courses

The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood.
 In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course.
 We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.
A ROBUST VARIABLE STEP-SIZE FREQUENCY-DOMAIN ADAPTIVE FILTER ALGORITHM FOR ACOUSTIC ECHO CANCELLATION

In acoustic echo cancellation (AEC), frequency-domain adaptive filter (FDAF) algorithm is commonly used to improve the computational efficiency and the convergence rate. However, the choice of step size has to compromise between the convergence rate and the steady-state performance. A variable step-size algorithm was presented by Nitsch to tackle this problem, where the spectral convergence state is estimated to control the step size. In this paper, a new approach for the estimation of the optimal step size is proposed. The relationship between the time-domain system mismatch and the frequency-domain spectral convergence state is derived using Parsevalś theorem. Moreover, an artificial delay is inserted into the signal path to calculate the time-domain system mismatch. The proposed algorithm achieves both fast convergence rate and low steady-state misadjustment. Simulation results demonstrate the effectiveness and the robustness of the proposed method.
Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks

We are presenting a novel approach for the task of driving behavior classification based on stacked LSTM Recurrent Neural Networks. Given a nine different sensor data captured using a smart phone internal sensors during a naturalistic driving sessions, we formulated the driving behavior classification problem as time-series classification task. Whereas, given a window sequence of fused feature vectors of sensor data at any time step of a driving trip, we can accurately classify the driving behavior during that window sequence from a three distinctive driving behavior classes, namely normal, aggressive or drowsy driving. We evaluated our proposed Stacked-LSTM model on one of the recent naturalistic driving behavior analysis and classification dataset, UAH-DriveSet. Our proposed Stacked-LSTM model has achieved state-of-the-art results on the UAH-DriveSet with much higher true positive rate as well as lower false positive rate in comparison to the baseline approach. We have also compared the performance of our proposed Stacked-LSTM model against a number of the common classification algorithms used in the driving behavior classification and analysis studies and we achieved F1-measure score of 91% with an improvement of more than 10% over the closest compared approaches.
Repeatable Reverse Engineering with PANDA

We present PANDA, an open-source tool that has been purpose-built to support whole system reverse engineering. It is built upon the QEMU whole system emulator, and so analyses have access to all code executing in the guest and all data. PANDA adds the ability to record and replay executions, enabling iterative, deep, whole system analyses. Further, the replay log files are compact and shareable, allowing for repeatable experiments. A nine billion instruction boot of FreeBSD, e.g., is represented by only a few hundred MB. PANDA leverages QEMU's support of thirteen different CPU architectures to make analyses of those diverse instruction sets possible within the LLVM IR. In this way, PANDA can have a single dynamic taint analysis, for example, that precisely supports many CPUs. PANDA analyses are written in a simple plugin architecture which includes a mechanism to share functionality between plugins, increasing analysis code re-use and simplifying complex analysis development. We demonstrate PANDA's effectiveness via a number of use cases, including enabling an old but legitimately purchased game to run despite a lost CD key, in-depth diagnosis of an Internet Explorer crash, and uncovering the censorship activities and mechanisms of an IM client.
Root coverage in molar teeth: a comparative controlled randomized clinical trial.

To compare the clinical outcomes of laterally moved, coronally advanced flap (LMCAF) versus Bilaminar technique (BT) in the treatment of single gingival recession on molar teeth.



Fifty patients showing Miller I and II gingival recessions at first molar teeth were treated: 25 were randomly assigned to the BT group and 25 belonged to the LMCAF group. Patient's post-operative morbidity was assessed 1 week after the surgery, while aesthetic evaluation and the clinical evaluation were made 1 year later.



No statistically significant difference was demonstrated in terms of recession and PPD reduction. Statistically greater probability of complete root coverage (CRC, Odds Ratio 22.1) and greater increase in gingival thickness were observed in the BT group. Greater increase in keratinized tissue was obtained in the LMCAF. Patient satisfaction with aesthetics was very high in both treatment groups. Better post-operative course was observed in the LMCAF, while better post-operative sensitivity and root coverage evaluation were demonstrated in patients treated with BT.



Gingival recession at first molar teeth can be successfully treated with LMCAF and BT. Better CRC was achieved with BT, while more comfortable post-operative course was associated with the LMCAF.
Principled Detection of Out-of-Distribution Examples in Neural Networks

We consider the problem of detecting out-of-distribution examples in neural networks. We propose ODIN, a simple and effective out-of-distribution detector for neural networks, that does not require any change to a pre-trained model. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions of inand out-of-distribution samples, allowing for more effective detection. We show in a series of experiments that our approach is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach  by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%. We theoretically analyze the method and prove that performance improvement is guaranteed under mild conditions on the image distributions.
The Economics of State Fragmentation-Assessing the Economic Impact of Secession

This paper provides empirical evidence that declaring independence significantly lowers per capita GDP based on a large panel of countries covering the period 1950-2016. To do so, we rely on a semi-parametric identification strategy that controls for the confounding effects of past GDP dynamics, anticipation effects, unobserved heterogeneity, model uncertainty and effect heterogeneity. Our baseline results indicate that declaring independence reduces per capita GDP by around 20% in the long run. We subsequently propose a quadruple-difference procedure to demonstrate that the results are not driven by simulation and matching inaccuracies or spillover effects. A second methodological novelty consists of the development of a two-step estimator that relies on the control function approach to control for the potential endogeneity of the estimated independence payoffs and their potential determinants, to shed some light on the primary channels driving our results. We find tentative evidence that the adverse effects of independence decrease in territorial size, pointing to the presence of economies of scale, but that they are mitigated when newly independent states liberalize their trade regime or use their new-found political autonomy to democratize.
Predictive analytics with surveillance big data

In this paper, we describe a novel analytics system that enables query processing and predictive analytics over streams of aviation data. As part of an Internal Research and Development project, Boeing Research and Technology (BR&T) Advanced Air Traffic Management (AATM) built a system that makes predictions based upon descriptive patterns of archived aviation data. Boeing AATM has been receiving live Aircraft Situation Display to Industry (ASDI) data and archiving it for over two years. At the present time, there is not an easy mechanism to perform analytics on the data. The incoming ASDI data is large, compressed, and requires correlation with other flight data before it can be analyzed.
 The service exposes this data once it has been uncompressed, correlated, and stored in a data warehouse for further analysis using a variety of descriptive, predictive, and possibly prescriptive analytics tools. The service is being built partially in response to requests from Boeing Commercial Aviation (BCA) for analysis of capacity and flow in the US National Airspace System (NAS). The service utilizes a custom tool for correlating the raw ASDI feed, IBM Warehouse with DB2 for data management, WebSphere Message Broker for real-time message brokering, SPSS Modeler for statistical analysis, and Cognos BI for front-end business intelligence (BI) visualization. This paper describes a scalable service architecture, implementation and the value it adds to the aviation domain.
Development of a Dynamic Simulation Tool for the ExoMars Rover

Future planetary missions, including the 2011 European Space Agency (ESA) ExoMars mission, will require rovers to travel further, faster, and over more demanding terrain than has been encountered to date. To improve overall mobility, advances need to be made in autonomous navigation, power collection, and locomotion. In this paper we focus on the locomotion problem and discuss the development of a planetary rover chassis simulation tool that allows us to study key locomotion test cases such as slope climbing in loose soil. We have also constructed rover wheels and obtained experimental data to validate the wheel-soil interaction module. The main conclusion is that to fully validate such a complex simulation, experimental data from a full rover chassis is required. This is a first step in an on-going effort to validate the simulation with experimental data obtained from a full rover prototype.
Older adults' views of "successful aging"--how do they compare with researchers' definitions?

To determine whether older adults have thought about aging and aging successfully and to compare their perceptions of successful aging with attributes of successful aging identified in the published literature.



A cross-sectional, mailed survey.



King County, Washington.



Nondemented participants from two cohorts. The first cohort, referred to as Kame, which means turtle, a symbol of longevity for Japanese, enrolled 1,985 Japanese Americans aged 65 and older and was established in 1992-94. The second cohort, Adult Changes in Thought, enrolled 2,581 white men and women aged 65 and older from a health maintenance organization and was established in 1994-96.



Respondents were asked whether they had ever thought about aging and aging successfully and whether these thoughts had changed over the previous 20 years and about how important specific attributes, originating from the published literature, were in characterizing successful aging.



Overall, 90% had previously thought about aging and aging successfully, and approximately 60% said their thoughts had changed over the previous 20 years. The Japanese-American group rated 13 attributes as important to successful aging; the white group rated the same 13 as important and added one additional attribute, learning new things.



Older adults' definition of successful aging is multidimensional, encompassing physical, functional, psychological, and social health. In contrast, none of the published work describing attributes of successful aging includes all four dimensions. Future work would benefit from an expanded definition to adequately reflect the perceptions of older adults.
On the Perturb-and-Observe and Incremental Conductance MPPT Methods for PV Systems

This paper presents a detailed analysis of the two most well-known hill-climbing maximum power point tracking (MPPT) algorithms: the perturb-and-observe (P&O) and incremental conductance (INC). The purpose of the analysis is to clarify some common misconceptions in the literature regarding these two trackers, therefore helping the selection process for a suitable MPPT for both researchers and industry. The two methods are thoroughly analyzed both from a mathematical and practical implementation point of view. Their mathematical analysis reveals that there is no difference between the two. This has been confirmed by experimental tests according to the EN 50530 standard, resulting in a deviation between their efficiencies of 0.13% in dynamic and as low as 0.02% under static conditions. The results show that despite the common opinion in the literature, the P&O and INC are equivalent.
Can Active Memory Replace Attention?

Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention has improved image classification, image captioning, speech recognition, generative models, and learning algorithmic tasks, but it had probably the largest impact on neural machine translation. Recently, similar improvements have been obtained using alternative mechanisms that do not focus on a single part of a memory but operate on all of it in parallel, in a uniform way. Such mechanism, which we call active memory, improved over attention in algorithmic tasks, image processing, and in generative modelling. So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.
A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.
Efficiency of voluntary closing hand and hook prostheses.

The Delft Institute of Prosthetics and Orthotics has started a research program to develop an improved voluntary closing, body-powered hand prosthesis. Five commercially available voluntary closing terminal devices were mechanically tested: three hands [Hosmer APRL VC hand, Hosmer Soft VC Male hand, Otto Bock 8K24] and two hooks [Hosmer APRL VC hook, TRS Grip 2S]. The test results serve as a design guideline for future prostheses. A test bench was used to measure activation cable forces and displacements, and the produced pinch forces. The measurements show that the hands require higher activation forces than the hooks and 1.5-8 times more mechanical work. The TRS hook requires the smallest activation force (33 N for a 15 N pinch force) and has the lowest energy dissipation (52 Nmm). The Hosmer Soft hand requires the largest activation force (131 N for a 15 N pinch force) and has the highest energy dissipation (1409 Nmm). The main recommendations for future prostheses are the following: (1) Required activation forces should be below the critical muscle force (∼18% of maximum), to enable continuous activation without muscle fatigue; and (2) hysteresis of mechanism and glove should be lowered, to increase efficiency and controllability.
Polygraph: automatically generating signatures for polymorphic worms

It is widely believed that content-signature-based intrusion detection systems (IDS) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives.
Usability Engineering for Augmented Reality: Employing User-Based Studies to Inform Design

A major challenge, and thus opportunity, in the field of human-computer interaction and specifically usability engineering (UE) is designing effective user interfaces for emerging technologies that have no established design guidelines or interaction metaphors or introduce completely new ways for users to perceive and interact with technology and the world around them. Clearly, augmented reality (AR) is one such emerging technology. We propose a UE approach that employs user-based studies to inform design by iteratively inserting a series of user-based studies into a traditional usability-engineering life cycle to better inform initial user interface designs. We present an exemplar user-based study conducted to gain insight into how users perceive text in outdoor AR settings and to derive implications for design in outdoor AR. We also describe "lessons learned" from our experiences, conducting user-based studies as part of the design process.
On-demand multicast routing protocol

This paper presents a novel multicast routing protocol for mobile ad hoc wireless networks. The protocol, termed ODMRP (On-De mand Multicast Routing Protocol), is a mesh-based, rather than a con ve tional treebased, multicast scheme and uses a forwarding group concept (only a subset of nodes forwards the multicast packets via scoped flood ing). It applies on-demand procedures to dynamically build routes and maint i multicast group membership. ODMRP is well suited for ad hoc wireless ne tworks with mobile hosts where bandwidth is limited, topology changes f requently, and power is constrained. We evaluate ODMRP’s scalability and p erformance via simulation.