nils-herrmann/clean_scirepeval huggingface dataset:
Sinusoidally Modulated Leaky-Wave Antenna for Millimeter-Wave Application

A millimeter-wave sinusoidally modulated (SM) leaky-wave antenna (LWA) based on inset dielectric waveguide (IDW) is presented in this paper. The proposed antenna, radiating at 10° from broadside at 60 GHz, consists of a SM IDW, a rectangular waveguide for excitation and a transition for impedance matching. Fundamental TE01 mode is excited by the IDW with the leaky wave generated by the SM inset groove depth. The electric field is normal to the metallic waveguide wall and thus reduces the conductor loss. As a proof of concept, the modulated dielectric inset as well as the dielectric transition are conveniently fabricated by 3-D printing (tan δ = 0.02). Measurements of the antenna prototype show that the main beam can be scanned from -9° to 40° in a frequency range from 50 to 85 GHz within a gain variation between 9.1 and 14.2 dBi. Meanwhile, the reflection coefficient |S11| is kept below -13.4 dB over the whole frequency band. The measured results agree reasonably well with simulations. Furthermore, the gain of the proposed antenna can be enhanced by extending its length and using low-loss dielectric materials such as Teflon (tan δ <; 0.002).
Statistical similarity of binaries

We address the problem of finding similar procedures in stripped binaries. We present a new statistical approach for measuring the similarity between two procedures. Our notion of similarity allows us to find similar code even when it has been compiled using different compilers, or has been modified. The main idea is to use similarity by composition: decompose the code into smaller comparable fragments, define semantic similarity between fragments, and use statistical reasoning to lift fragment similarity into similarity between procedures. We have implemented our approach in a tool called Esh, and applied it to find various prominent vulnerabilities across compilers and versions, including Heartbleed, Shellshock and Venom. We show that Esh produces high accuracy results, with few to no false positives -- a crucial factor in the scenario of vulnerability search in stripped binaries.
Persuasive Robotics: The influence of robot gender on human behavior

Persuasive Robotics is the study of persuasion as it applies to human-robot interaction (HRI). Persuasion can be generally defined as an attempt to change another's beliefs or behavior. The act of influencing others is fundamental to nearly every type of social interaction. Any agent desiring to seamlessly operate in a social manner will need to incorporate this type of core human behavior. As in human interaction, myriad aspects of a humanoid robot's appearance and behavior can significantly alter its persuasiveness - this work will focus on one particular factor: gender. In the current study, run at the Museum of Science in Boston, subjects interacted with a humanoid robot whose gender was varied. After a short interaction and persuasive appeal, subjects responded to a donation request made by the robot, and subsequently completed a post-study questionnaire. Findings showed that men were more likely to donate money to the female robot, while women showed little preference. Subjects also tended to rate the robot of the opposite sex as more credible, trustworthy, and engaging. In the case of trust and engagement the effect was much stronger between male subjects and the female robot. These results demonstrate the importance of considering robot and human gender in the design of HRI.
End-To-End Memory Networks

We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network [23] but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable in realistic settings. It can also be seen as an extension of RNNsearch  to the case where multiple computational steps (hops) are performed per output symbol. The flexibility of the model allows us to apply it to tasks as diverse as (synthetic) question answering [22] and to language modeling. For the former our approach is competitive with Memory Networks, but with less supervision. For the latter, on the Penn TreeBank and Text8 datasets our approach demonstrates comparable performance to RNNs and LSTMs. In both cases we show that the key concept of multiple computational hops yields improved results.
Heart rate monitoring from wrist-type photoplethysmographic (PPG) signals during intensive physical exercise

Heart rate monitoring from wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the PPG signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. In this work, we formulate the heart rate estimation problem as a sparse signal recovery problem, and use a sparse signal recovery algorithm to calculate high-resolution power spectra of PPG signals, from which heart rates are estimated by selecting corresponding spectrum peaks. To facilitate the use of sparse signal recovery, we propose using bandpass filtering, singular spectrum analysis, and temporal difference operation to partially remove motion artifacts and sparsify PPG spectra. The proposed method was tested on PPG recordings from 10 subjects who were fast running at the peak speed of 15km/hour. The results showed that the averaged absolute estimation error was only 2.56 Beats/Minute, or 1.94% error compared to ground-truth heart rates from simultaneously recorded ECG.
The SMAPH system for query entity recognition and disambiguation

The SMAPH system implements a pipeline of four main steps: (1) Fetching -- it fetches the search results returned by a search engine given the query to be annotated; (2) Spotting -- search result snippets are parsed to identify candidate mentions for the entities to be annotated. This is done in a novel way by detecting the keywords-in-context by looking at the bold parts of the search snippets; (3) Candidate generation -- candidate entities are generated in two ways: from the Wikipedia pages occurring in the search results, and from an existing annotator, using the mentions identified in the spotting step as input; (4) Pruning -- a binary SVM classifier is used to decide which entities to keep/discard in order to generate the final annotation set for the query. The SMAPH system ranked third on the development set and first on the final blind test of the 2014 ERD Challenge short text track.
Training CNNs with Low-Rank Filters for Efficient Image Classification

I We train CNNs with composite layers of oriented low-rank filters, of which the network learns the most effective linear combination I In effect our networks learn a basis space for filters, based on simpler low-rank filters I We propose an initialization for composite layers of heterogeneous filters, to train such networks from scratch I Our models are faster and use less parameters I With a small number of full filters, our models also generalize better
Folktale Classification Using Learning to Rank

We present a learning to rank approach to classify folktales, such as fairy tales and urban legends, according to their story type, a concept that is widely used by folktale researchers to organize and classify folktales. A story type represents a collection of similar stories often with recurring plot and themes. Our work is guided by two frequently used story type classification schemes. Contrary to most information retrieval problems, the text similarity in this problem goes beyond topical similarity. We experiment with approaches inspired by distributed information retrieval and features that compare subject-verb-object triplets. Our system was found to be highly effective compared with a baseline system.
Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis

This paper presents a remarkably simple, yet powerful, algorithm termed coherence pursuit (CoP) to robust principal component analysis (PCA). As inliers lie in a low-dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points. By contrast, outliers either do not admit low-dimensional structures or form small clusters. In either case, an outlier is unlikely to bear strong resemblance to a large number of data points. Given that, CoP sets an outlier apart from an inlier by comparing their coherence with the rest of the data points. The mutual coherences are computed by forming the Gram matrix of the normalized data points. Subsequently, the sought subspace is recovered from the span of the subset of the data points that exhibit strong coherence with the rest of the data. As CoP only involves one simple matrix multiplication, it is significantly faster than the state-of-the-art robust PCA algorithms. We derive analytical performance guarantees for CoP under different models for the distributions of inliers and outliers in both noise-free and noisy settings. CoP is the first robust PCA algorithm that is simultaneously non-iterative, provably robust to both unstructured and structured outliers, and can tolerate a large number of unstructured outliers.
Activity recognition based on inertial sensors for Ambient Assisted Living

Ambient Assisted Living (AAL) aims to create innovative technical solutions and services to support independent living among older adults, improve their quality of life and reduce the costs associated with health and social care. AAL systems provide health monitoring through sensor based technologies to preserve health and functional ability and facilitate social support for the ageing population. Human activity recognition (HAR) is an enabler for the development of robust AAL solutions, especially in safety critical environments. Therefore, HAR models applied within this domain (e.g. for fall detection or for providing contextual information to caregivers) need to be accurate to assist in developing reliable support systems. In this paper, we evaluate three machine learning algorithms, namely Support Vector Machine (SVM), a hybrid of Hidden Markov Models (HMM) and SVM (SVM-HMM) and Artificial Neural Networks (ANNs) applied on a dataset collected between the elderly and their caregiver counterparts. Detected activities will later serve as inputs to a bidirectional activity awareness system for increasing social connectedness. Results show high classification performances for all three algorithms. Specifically, the SVM-HMM hybrid demonstrates the best classification performance. In addition to this, we make our dataset publicly available for use by the machine learning community.
Reinforcement Learning for Trading Systems and Portfolios

We propose to train trading systems by optimizing financial objective functions via reinforcement learning. The performance functions that we consider as value functions are profit or wealth, the Sharpe ratio and our recently proposed ifferential Sharpe ratio for online learning. In Moody & Wu (1997), we presented empirical results in controlled experiments that demonstrated the advantages of reinforcement learning relative to supervised learning. Here we extend our previous work to compare Q-Learning to a reinforcement learning technique based on real-time recurrent learning (RTRL) that maximizes immediate reward. Our simulation results include a spectacular demonstration of the presence of predictability in the monthly Standard and Poors 500 stock index for the 25 year period 1970 through 1994. Our reinforcement trader achieves a simulated out-of-sample profit of over 4000% for this period, compared to the return for a buy and hold strategy of about 1300% (with dividends reinvested). This superior result is achieved with substantially lower isk.
An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures

We explore dierent approaches to integrating a simple convolutional neural network (CNN) with the Lucene search engine in a multi-stage ranking architecture. Our models are trained using the PyTorch deep learning toolkit, which is implemented in C/C++ with a Python frontend. One obvious integration strategy is to expose the neural network directly as a service. For this, we use Apache ri, a soware framework for building scalable cross-language services. In exploring alternative architectures, we observe that once trained, the feedforward evaluation of neural networks is quite straightforward. erefore, we can extract the parameters of a trained CNN from PyTorch and import the model into Java, taking advantage of the Java Deeplearning4J library for feedforward evaluation. is has the advantage that the entire end-to-end system can be implemented in Java. As a third approach, we can extract the neural network from PyTorch and “compile” it into a C++ program that exposes a ri service. We evaluate these alternatives in terms of performance (latency and throughput) as well as ease of integration. Experiments show that feedforward evaluation of the convolutional neural network is signicantly slower in Java, while the performance of the compiled C++ network does not consistently beat the PyTorch implementation.
Ant system: optimization by a colony of cooperating agents

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Evolving Neural Networks through Augmenting Topologies

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is signicantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
Using accumulated degree-days to estimate the postmortem interval from decomposed human remains.

Forensic anthropologists often rely on the state of decomposition to estimate the postmortem interval (PMI) in a human remains case. The state of decomposition can provide much information about the PMI, especially when decomposition is treated as a semi-continuous variable and used in conjunction with accumulated-degree-days (ADD). This preliminary study demonstrates a supplemental method of determining the PMI based on scoring decomposition using a point-based system and taking into account temperatures in which the remains were exposed. This project was designed to examine the ways that forensic anthropologists could improve their PMI estimates based on decomposition by using a more quantitative approach. A total of 68 human remains cases with a known date of death were scored for decomposition and a regression equation was calculated to predict ADD from decomposition score. ADD accounts for approximately 80% of the variation in decomposition. This study indicates that decomposition is best modeled as dependent on accumulated temperature, not just time.
Optimized Wind Energy Harvesting System Using Resistance Emulator and Active Rectifier for Wireless Sensor Nodes

This paper presents an optimized wind energy harvesting (WEH) system that uses a specially designed ultra-low-power-management circuit for sustaining the operation of a wireless sensor node. The proposed power management circuit has two distinct features: 1) an active rectifier using MOSFETs for rectifying the low amplitude ac voltage generated by the wind turbine generator under low wind speed condition efficiently and 2) a dc-dc boost converter with resistor emulation algorithm to perform maximum power point tracking (MPPT) under varying wind-speed conditions. As compared to the conventional diode-bridge rectifier, it is shown that the efficiency of the active rectifier with a low input voltage of 1.2 V has been increased from 40% to 70% due to the significant reduction in the ON-state voltage drop (from 0.6 to 0.15 V) across each pair of MOSFETs used. The proposed robust low-power microcontroller-based resistance emulator is implemented with closed-loop resistance feedback control to ensure close impedance matching between the source and the load, resulting in an efficient power conversion. From the experimental test results obtained, an average electrical power of 7.86 mW is harvested by the optimized WEH system at an average wind speed of 3.62 m/s, which is almost four times higher than the conventional energy harvesting method without using the MPPT.
MegaPipe: A New Programming Interface for Scalable Network I/O

We present MegaPipe, a new API for efficient, scalable network I/O for message-oriented workloads. The design of MegaPipe centers around the abstraction of a channel – a per-core, bidirectional pipe between the kernel and user space, used to exchange both I/O requests and event notifications. On top of the channel abstraction, we introduce three key concepts of MegaPipe: partitioning, lightweight socket (lwsocket), and batching. We implement MegaPipe in Linux and adapt memcached and nginx. Our results show that, by embracing a clean-slate design approach, MegaPipe is able to exploit new opportunities for improved performance and ease of programmability. In microbenchmarks on an 8-core server with 64 B messages, MegaPipe outperforms baseline Linux between 29% (for long connections) and 582% (for short connections). MegaPipe improves the performance of a modified version of memcached between 15% and 320%. For a workload based on real-world HTTP traces, MegaPipe boosts the throughput of nginx by 75%.
Trends in biorobotic autonomous undersea vehicles

The emergence of biorobotic autonomous undersea vehicle (AUV) as a focus for discipline-integrated research in the context of underwater propulsion and maneuvering is considered within the confines of the Biorobotics Program in the Office of Naval Research. The significant advances in three disciplines, namely the biology-inspired high-lift unsteady hydrodynamics, artificial muscle technology and neuroscience-based control, are discussed in an effort to integrate them into viable products. The understanding of the mechanisms of delayed stall, molecular design of artificial muscles and the neural approaches to the actuation of control surfaces is reviewed in the context of devices based on the pectoral fins of fish, while remaining focused on their integrated implementation in biorobotic AUVs. A mechanistic understanding of the balance between cruising and maneuvering in swimming animals and undersea vehicles is given. All aquatic platforms, in both nature and engineering, except during short duration burst speeds that are observed in a few species, appear to lie within the condition where their natural period of oscillation equals the time taken by them to travel the distance of their own lengths. Progress in the development of small underwater experimental biorobotic vehicles is considered where the three aforementioned disciplines are integrated into one novel maneuvering device or propulsor. The potential in maneuvering and silencing is discussed.
Multi-Document Summarization Based on Two-Level Sparse Representation Model

Multi-document summarization is of great value to many real world applications since it can help people get the main ideas within a short time. In this paper, we tackle the problem of extracting summary sentences from multi-document sets by applying sparse coding techniques and present a novel framework to this challenging problem. Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization. Three requisite properties is proposed to form an ideal reconstructable summary: Coverage, Sparsity and Diversity. We then formalize the task of multi-document summarization as an optimization problem according to the above properties, and use simulated annealing algorithm to solve it. Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms.
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing availability of 3D models, we propose a framework to address both issues by combining render-based image synthesis and CNNs (Convolutional Neural Networks). We believe that 3D models have the potential in generating a large number of images of high variation, which can be well exploited by deep CNN with a high learning capacity. Towards this goal, we propose a scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task. Experimentally, we show that the viewpoint estimation from our pipeline can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.
View management for virtual and augmented reality

We describe a view-management component for interactive 3D user interfaces. By view management, we mean maintaining visual constraints on the projections of objects on the view plane, such as locating related objects near each other, or preventing objects from occluding each other. Our view-management component accomplishes this by modifying selected object properties, including position, size, and transparency, which are tagged to indicate their constraints. For example, some objects may have geometric properties that are determined entirely by a physical simulation and which cannot be modified, while other objects may be annotations whose position and size are flexible.We introduce algorithms that use upright rectangular extents to represent on the view plane a dynamic and efficient approximation of the occupied space containing the projections of visible portions of 3D objects, as well as the unoccupied space in which objects can be placed to avoid occlusion. Layout decisions from previous frames are taken into account to reduce visual discontinuities. We present augmented reality and virtual reality examples to which we have applied our approach, including a dynamically labeled and annotated environment.