nils-herrmann/clean_scirepeval huggingface dataset:
DenseNet : Implementing Efficient ConvNet Descriptor Pyramids Technical Report

Convolutional Neural Networks (CNNs) can provide accurate object classification. They can be extended to perform object detection by iterating over dense or selected proposed object regions. However, the runtime of such detectors scales as the total number and/or area of regions to examine per image, and training such detectors may be prohibitively slow. However, for some CNN classifier topologies, it is possible to share significant work among overlapping regions to be classified. This paper presents DenseNet, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier. Future work will involve training efficient object detectors with DenseNet feature descriptors.
Offline Strangers, Online Friends: Bridging Classroom Gender Segregation with WhatsApp

Mobile Instant Messaging (MIM) apps such as WhatsApp are heralding new communication behaviors amongst students in peri-urban India. From an ethnographic study of a co-ed engineering college, we describe and analyze the role of WhatsApp in both engendering and balancing the ripples caused by cross gender communication. Through in-depth interviews and immersive participant observations in both physical as well as digital spaces, we show how WhatsApp emerges as the backbone of student interactions in a gender segregated academic environment. In analyzing social conditions and identifying structural features of WhatsApp that led to its bottoms up appropriation, our study presents possibilities for educators and designers aiming to create technology enabled collaborative spaces between people otherwise hemmed in by social norms from reaching out to one another.
Autonomous repositioning and localization of an in situ fabricator

Despite the prevalent use of robotic technologies in industrial manufacturing, their use on building construction sites is still very limited. This is mainly due to the unstructured nature of construction sites and the fact that the structures built must be larger than the machines which build them. This paper addresses these difficulties by presenting a repositioning and localization system which, using purely on-board sensing, allows a mobile robot to maintain a high degree of end-effector positioning accuracy while moving among numerous building positions during a building task. We introduce a newly developed machine, called the In situ Fabricator (IF), whose goal is to bring digital fabrication to the construction site. The capabilities of the repositioning and localization system is demonstrated through the construction of a vertical stack of bricks, in which the IF repositions itself after placing each brick. With this experiment, we demonstrate that the IF can build with sub-centimeter accuracy over long building sequences.
Impacts of Phase Noise on Digital Self-Interference Cancellation in Full-Duplex Communications

In full-duplex (FD) radios, phase noise leads to random phase mismatch between the self-interference (SI) and the reconstructed cancellation signal, resulting in possible performance degradation during SI cancellation. To explicitly analyze its impacts on the digital SI cancellation, an orthogonal frequency division multiplexing (OFDM)-modulated FD radio is considered with phase noises at both the transmitter and receiver. The closed-form expressions for both the digital cancellation capability and its limit for the large interference-to-noise ratio (INR) case are derived in terms of the power of the common phase error, INR, desired signal-to-noise ratio (SNR), channel estimation error and transmission delay. Based on the obtained digital cancellation capability, the achievable rate region of a two-way FD OFDM system with phase noise is characterized. Then, with a limited SI cancellation capability, the maximum outer bound of the rate region is proved to exist for sufficiently large transmission power. Furthermore, a minimum transmission power is obtained to achieve $\beta$ -portion of the cancellation capability limit and to ensure that the outer bound of the rate region is close to its maximum.
Data-Driven Composition for Service-Oriented Situational Web Applications

The convergence of Services Computing and Web 2.0 gains a large space of opportunities to compose “situational” web applications from web-delivered services. However, the large number of services and the complexity of composition constraints make manual composition difficult to application developers, who might be non-professional programmers or even end-users. This paper presents a systematic data-driven approach to assisting situational application development. We first propose a technique to extract useful information from multiple sources to abstract service capabilities with a set tags. This supports intuitive expression of user's desired composition goals by simple queries, without having to know underlying technical details. A planning technique then exploits composition solutions which can constitute the desired goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A series of experiments demonstrate the efficiency and effectiveness of our approach.
Emotion-based music recommendation using supervised learning

Music recommendation systems are well explored and commonly used but are normally based on manually tagged parameters and simple similarity calculation. Our project proposes a recommendation system based on emotional computing, automatic classification and feature extraction, which recommends music based on the emotion expressed by the song.
 To achieve this goal a set of features is extracted from the song, including the MFCC (mel-frequency cepstral coefficients) following the works of McKinney et al.  and a machine learning system is trained on a set of 424 songs, which are categorized by emotion. The categorization of the song is performed manually by multiple persons to avoid error. The emotional categorization is performed using a modified version of the Tellegen-Watson-Clark emotion model [7], as proposed by Trohidis et al. . The System is intended as desktop application that can reliably determine similarities between the main emotion in multiple pieces of music, allowing the user to choose music by emotion. We report our findings below.
Skeleton-based action recognition using LSTM and CNN

Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep learning, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have achieved promising performance for action recognition. However, for CNN-based methods, it is inevitable to loss temporal information when a sequence is encoded into images. In order to capture as much spatial-temporal information as possible, LSTM and CNN are adopted to conduct effective recognition with later score fusion. In addition, experimental results show that the score fusion between CNN and LSTM performs better than that between LSTM and LSTM for the same feature. Our method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis. The proposed method achieved 87.40% in terms of accuracy and ranked 1st place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.
Analysis of Three-Phase Rectifiers With Constant-Voltage Loads

This brief presents a quantitative analysis of the operating characteristics of three-phase diode bridge rectifiers with ac-side reactance and constant-voltage loads. We focus on the case where the ac-side currents vary continuously (continuous ac-side conduction mode). This operating mode is of particular importance in alternators and generators, for example. Simple approximate expressions are derived for the line and output current characteristics as well as the input power factor. Expressions describing the necessary operating conditions for continuous ac-side conduction are also developed. The derived analytical expressions are applied to practical examples and both simulations and experimental results are utilized to validate the analytical results. It is shown that the derived expressions are far more accurate than calculations based on traditional constant-current models.
Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks

Adverse reactions to drugs are among the most common causes of death in industrialized nations. Expensive clinical trials are not sufficient to uncover all of the adverse reactions a drug may cause, necessitating systems for post-marketing surveillance, or pharmacovigilance. These systems have typically relied on voluntary reporting by health care professionals. However, self-reported patient data has become an increasingly important resource, with efforts such as MedWatch from the FDA allowing reports directly from the consumer. In this paper, we propose mining the relationships between drugs and adverse reactions as reported by the patients themselves in user comments to health-related websites. We evaluate our system on a manually annotated set of user comments, with promising performance. We also report encouraging correlations between the frequency of adverse drug reactions found by our system in unlabeled data and the frequency of documented adverse drug reactions. We conclude that user comments pose a significant natural language processing challenge, but do contain useful extractable information which merits further exploration.
Wide-Beam SIW-Slot Antenna for Wide-Angle Scanning Phased Array

In order to enhance the scanning range of planar phased arrays, a planar substrate integrated waveguide slot (SIWslot) antenna with wide beamwidth is proposed in this letter. The proposed antenna is fabricated on a single-layer substrate, which is fully covered with a metal ground on the back. The SIW works like a dielectric-filled rectangular waveguide working in the TE10 mode. There are four inclined slots etched on the top metal layer following the rules of rectangular waveguide slot antenna. The electric fields in the slots work as equivalent magnetic currents. As opposed to normal microstrip antennas, the equivalent magnetic currents from the slots over a larger metal ground can radiate with a wide beamwidth. Its operating bandwidth is from 5.4 to 6.45 GHz with a relative bandwidth of 17.7%. Meanwhile, the 3-dB beamwidth in the xz-plane is between 130° and 148° in the whole operating band. Furthermore, the SIW-slot element is employed in a 1 × 8 planar phased array. The measured results show that the main lobe of phased array can obtain a wide-angle scanning from -71° to 73° in the whole operating band.
Attention-Aware Deep Reinforcement Learning for Video Face Recognition

In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attentions in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov decision process and train the attention model through a deep reinforcement learning framework without using extra labels. Unlike existing attention models, our method takes information from both the image space and the feature space as the input to make better use of face information that is discarded in the feature learning process. Besides, our approach is attention-aware, which seeks different attentions of videos for the recognition of different pairs of videos. Our approach achieves very competitive video face recognition performance on three widely used video face datasets.
Evaluating and Optimizing OpenCL Kernels for High Performance Computing with FPGAs

We evaluate the power and performance of the Rodinia benchmark suite using the Altera SDK for OpenCL targeting a Stratix V FPGA against a modern CPU and GPU. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined kernels specifically optimized for FPGAs. Based on our results, we find that even though OpenCL is functionally portable across devices, direct ports of GPU-optimized code do not perform well compared to kernels optimized with FPGA-specific techniques such as sliding windows. However, by exploiting FPGA-specific optimizations, it is possible to achieve up to 3.4x better power efficiency using an Altera Stratix V FPGA in comparison to an NVIDIA K20c GPU, and better run time and power efficiency in comparison to CPU. We also present preliminary results for Arria 10, which, due to hardened FPUs, exhibits noticeably better performance compared to Stratix V in floating-point-intensive benchmarks.
Peer-to-Peer Rental Markets in the Sharing Economy ∗

We develop a new dynamic model of peer-to-peer Internet-enabled rental markets for durable goods in which consumers may also trade their durable assets in (traditional) secondary markets, transaction costs and depreciation rates may vary with usage intensity, and consumers are heterogeneous in their price sensitivity and asset utilization rates. We characterize the stationary equilibrium of the model. We analyze the welfare and distributional effects of introducing these rental markets by calibrating our model with US automobile industry data and 2 years of transaction-level data we have obtained from Getaround, a large peer-to-peer car rental marketplace. Our counterfactual analyses vary marketplace access levels and matching frictions, showing that peer-to-peer rental markets change the allocation of goods significantly, substituting rental for ownership and lowering used-good prices while increasing consumer surplus. Consumption shifts are significantly more pronounced for below-median income users, who also provide a majority of rental supply. Our results also suggest that these below-median income consumers will enjoy a disproportionate fraction of eventual welfare gains from this kind of ’sharing economy’ through broader inclusion, higher quality rental-based consumption, and new ownership facilitated by rental supply revenues.  We thank the executive team at Getaround (and especially Padden Murphy, Jessica Scorpio, Sam Zaid and Ranjit Chacko) for numerous helpful conversations and for providing access to their anonymized data. We thank Anmol Bandhari, Andrew Caplin, Natalie Foster, Lisa Gansky, Shane Greenstein, Anindya Ghose, John Horton, John Lazarev, Alessandro Lizzeri, Romain Ranciere, Justin Rao, David Rothschild, Shachar Reichman, Marshall Van Alstyne, and seminar participants at Carnegie-Mellon University, New York University and the 2014 MIT/BU Platform Strategy Research Symposium for helpful discussions on preliminary versions of this work. Fraiberger and Sundararajan have no current or prior commercial relationship with Getaround.
Personalized Research Paper Recommendation System using Keyword Extraction Based on UserProfile

In this paper, we proposed PRPRS (Personalized Research Paper Recommendation System) that designed expansively and implemented a UserProfile-based algorithm for extracting keyword by keyword extraction and keyword inference. If the papers don't have keyword section, we consider the title and text as an argument of keyword and execute the algorithm. Then, we create the possible combination from each word of title. We extract the combinations presented in the main text among the longest word combinations which include the same words. If the number of extracted combinations is more than the standard number, we used that combination as keyword. Otherwise, we refer the main text and extract combination as much as standard in order of high Term-Frequency. Whenever collected research papers by topic are selected, a renewal of UserProfile increases the frequency of each Domain, Topic and keyword. Each ratio of occurrence is recalculated and reflected on UserProfile. PRPRS calculates the similarity between given topic and collected papers by using Cosine Similarity which is used to recommend initial paper for each topic in Information retrieval. We measured satisfaction and accuracy for each system-recommended paper to test and evaluated performances of the suggested system. Finally PRPRS represents high level of satisfaction and accuracy.
Determining Both Surface Position and Orientation in Structured-Light-Based Sensing

Position and orientation profiles are two principal descriptions of shape in space. We describe how a structured light system, coupled with the illumination of a pseudorandom pattern and a suitable choice of feature points, can allow not only the position but also the orientation of individual surface elements to be determined independently. Unlike traditional designs which use the centroids of the illuminated pattern elements as the feature points, the proposed design uses the grid points between the pattern elements instead. The grid points have the essences that their positions in the image data are inert to the effect of perspective distortion, their individual extractions are not directly dependent on one another, and the grid points possess strong symmetry that can be exploited for their precise localization in the image data. Most importantly, the grid lines of the illuminated pattern that form the grid points can aid in determining surface normals. In this paper, we describe how each of the grid points can be labeled with a unique color code, what symmetry they possess and how the symmetry can be exploited for their precise localization at subpixel accuracy in the image data, and how 3D orientation in addition to 3D position can be determined at each of them. Both the position and orientation profiles can be determined with only a single pattern illumination and a single image capture.
Optical Metasurfaces and Prospect of Their Applications Including Fiber Optics

Metasurfaces have emerged in the recent years as a platform to design subwavelength-thick optical components (“flat optics”), which can be used to implement any optical function (beam deflection, focusing, waveplates, etc). These flat optical components can be fabricated using a single lithographic step. The approach is particularly suited for patterning nonconventional substrates, such as semiconductor laser facets and optical fiber facets. In this paper, we review recent applications of metasurfaces to flat optical devices, including their use in semiconductor lasers and fiber optics. Metasurfaces make it possible to design all properties of light (amplitude, phase, and polarization), which enable us to build a large variety of flat optical components, including planar lenses, quarter-wave plates, optical vortex plates, holograms for vector beam generation, and ultrathin perfect absorbers and color coatings. We also review flat collimating lenses integrated on the facets of mid-infrared and far-infrared (terahertz) quantum cascade lasers, and novel techniques to create large arrays of nanostructures on fiber facets.
Arbitrary Importance Functions for Metropolis Light Transport

We present a generalization of the scalar importance function employed by Metropolis Light Transport (MLT) and related Markov chain rendering algorithms. Although MLT is known for its user-designable mutation rules, we demonstrate that its scalar contribution function is similarly programmable in an unbiased manner. Normally, MLT samples light paths with a tendency proportional to their brightness. For a range of scenes, we demonstrate that this importance function is undesirable and leads to poor sampling behaviour. Instead, we argue that simple user-designable importance functions can concentrate work in transport effects of interest and increase estimator efficiency. Unlike mutation rules, these functions are not encumbered with the calculation of transitional probabilities. We introduce alternative importance functions, which encourage the Markov chain to aggressively pursue sampling goals of interest to the user. In addition, we prove that these importance functions may adapt over the course of a render in an unbiased fashion. To that end, we introduce multi-stage MLT, a general rendering setting for creating such adaptive functions. This allows us to create a noise-sensitive MLT renderer whose importance function explicitly targets noise. Finally, we demonstrate that our techniques are compatible with existing Markov chain rendering algorithms and significantly improve their visual efficiency.
Adding Semantic Web Knowledge to Intelligent Personal Assistant Agents

Intelligent Personal Assistant (IPA) agents are software agents which assist users in performing specific tasks. They should be able to communicate, cooperate, discuss, and guide people. This paper presents a proposal to add Semantic Web Knowledge to IPA agents. In our solution, the IPA agent has a modular knowledge organization composed by four differentiated areas: (i) the rational area, which adds semantic web knowledge, (ii) the association area, which simplifies building appropriate responses, (iii) the commonsense area, which provides commonsense responses, and (iv) the behavioral area, which allows IPA agents to show empathy. Our main objective is to create more intelligent and more human alike IPA agents, enhancing the current abilities that these software agents provide.
Operating frequency selection for low-power magnetic induction-based wireless underground sensor networks

The possibility of employing sensor nodes that wireless communicate under the ground, through concrete, or under-the-debris (disaster scenario) has been recently highlighted at the Wireless Underground Sensor Networks (WUSN) literature. Nonetheless, the best operating frequency for such systems is still an open research aspect. In this work, we address this question for mid-range distances (e.g., 15..30m) by proposing a soil path attenuation model for an underground magnetic induction (MI)-based system involving a pair of nodes. The model is empirically validated and based on simulation results it is possible to conclude that for mid-range MI systems it is strategic to adopt a dynamic frequency selection scheme where audio frequencies are chosen whenever high soil moisture levels are detected.
Forensic Triage for Mobile Phones with DEC0DE

We present DEC0DE, a system for recovering information from phones with unknown storage formats, a critical problem for forensic triage. Because phones have myriad custom hardware and software, we examine only the stored data. Via flexible descriptions of typical data structures, and using a classic dynamic programming algorithm, we are able to identify call logs and address book entries in phones across varied models and manufacturers. We designed DEC0DE by examining the formats of one set of phone models, and we evaluate its performance on other models. Overall, we are able to obtain high performance for these unexamined models: an average recall of 97% and precision of 80% for call logs; and average recall of 93% and precision of 52% for address books. Moreover, at the expense of recall dropping to 14%, we can increase precision of address book recovery to 94% by culling results that don’t match between call logs and address book entries on the same phone.