nils-herrmann/clean_scirepeval huggingface dataset:
Sentence Selection and Evaluation Metrics

Human-quality text summarization systems are di cult to design, and even more di cult to evaluate, in part because documents can di er along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted combination of statistical and linguistic features. The statistical features were adapted from standard IR methods. The potential linguistic ones were derived from an analysis of news-wire summaries. To evaluate these features we use a normalized version of precision-recall curves, with a baseline of random sentence selection, as well as analyze the properties of such a baseline. We illustrate our discussions with empirical results showing the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries.
Towards lifelong visual maps

The typical SLAM mapping system assumes a static environment and constructs a map that is then used without regard for ongoing changes. Most SLAM systems, such as FastSLAM, also require a single connected run to create a map. In this paper we present a system of visual mapping, using only input from a stereo camera, that continually updates an optimized metric map in large indoor spaces with movable objects: people, furniture, partitions, etc. The system can be stopped and restarted at arbitrary disconnected points, is robust to occlusion and localization failures, and efficiently maintains alternative views of a dynamic environment. It operates completely online at a 30 Hz frame rate.
Supervised Word Mover's Distance

Recently, a new document metric called the word mover’s distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely unsupervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient technique to learn a supervised metric, which we call the Supervised-WMD (S-WMD) metric. The supervised training minimizes the stochastic leave-one-out nearest neighbor classification error on a perdocument level by updating an affine transformation of the underlying word embedding space and a word-imporance weight vector. As the gradient of the original WMD distance would result in an inefficient nested optimization problem, we provide an arbitrarily close approximation that results in a practical and efficient update rule. We evaluate S-WMD on eight real-world text classification tasks on which it consistently outperforms almost all of our 26 competitive baselines.
Incorporating Rich Features into Deep Knowledge Tracing

Knowledge Tracing aims to model student knowledge by predicting the correctness of each next item as students work through an assignment. Through recent developments in deep learning, Deep Knowledge Tracing (DKT) was explored as a method to improve upon traditional methods. Thus far, the DKT model has only considered the knowledge components and correctness as input, neglecting the other important features collected by computer-based learning platforms. This paper seeks to further improve upon DKT by incorporating more problem-level features. With this higher dimensional input, an adaption to the original DKT model structure is also proposed to convert the input into a low dimensional feature vector. Our results show that this adapted DKT model can effectively improve accuracy.
Intensifying Remote Healthcare Monitoring System using Internet of Things : A Survey

Internet of Things (IoT) is an indispensable paradigm to assimilate several computing and communication technologies. IoT network connects different smart objects together via Internet and providing more data exchange from anyplace, anytime, connectivity for anyone. The perpetual rise of chronic diseases leads to an expanding demand for constant monitoring and also created a major incentive to emerging accessible remote healthcare monitoring system. IoT based remote monitoring enables unified monitoring and control of all the actions taking place in healthcare supervisions. This system has potential to provide error free treatment by identifying the right medical data, tracking hospitalized patients, create alerting system in emergency situations, and deliver a convenient healthcare service to both caregivers and patients without visiting the hospital or clinic. This survey inspects the potential applications of the IoT based remote monitoring framework, integration of different technologies, and computing system that enables the remote healthcare monitoring in IoT conception are discussed. Finally, the open areas for future research challenges and deployment of secure remote monitoring system isexplored.
Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching

Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.
A single-phase grid-connected PV converter with minimal DC-link capacitor and low-frequency ripple-free maximum power point tracking

This paper presents a novel single-phase grid-connected photovoltaic (PV) converter based on current-fed dual active bridge (CF-DAB) dc-dc converter. The proposed converter allows a minimal DC-link capacitor with large voltage variation without low-frequency effect on maximum power point tracking (MPPT). An advanced control system is developed based on the converter operation principles. Minimized transformer peak current is capable to be achieved through a symmetrical dc voltage control. Simulation results are presented to validate the performance of the proposed PV converter. A 5kW laboratorial prototype is also implemented and the results will be presented later.
Image-based ghostings for single layer occlusions in augmented reality

In augmented reality displays, X-Ray visualization techniques make hidden objects visible through combining the physical view with an artificial rendering of the hidden information. An important step in X-Ray visualization is to decide which parts of the physical scene should be kept and which should be replaced by overlays. The combination should provide users with essential perceptual cues to understand the relationship of depth between hidden information and the physical scene. In this paper we present an approach that addresses this decision in unknown environments by analyzing camera images of the physical scene and using the extracted information for occlusion management. Pixels are grouped into perceptually coherent image regions and a set of parameters is determined for each region. The parameters change the X-Ray visualization for either preserving existing structures or generating synthetic structures. Finally, users can customize the overall opacity of foreground regions to adapt the visualization.
Capital Investments and Stock Returns

Firms that substantially increase capital investments subsequently achieve negative benchmark-adjusted returns. The negative abnormal capital investment/return relation is shown to be stronger for firms that have greater investment discretion, i.e., firms with higher cash flows and lower debt ratios, and is shown to be significant only in time periods when hostile takeovers were less prevalent. These observations are consistent with the hypothesis that investors tend to underreact to the empire building implications of increased investment expenditures. Although firms that increase capital investments tend to have high past returns and often issue equity, the negative abnormal capital investment/return relation is independent of the previously documented long-term return reversal and secondary equity issue anomalies.
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing in a wide range of wireless networking applications. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. On the other hand, UAVs can operate as flying mobile terminals within the cellular networks. Such cellular-connected UAVs can enable a wide range of key applications expanding from real-time video streaming to item delivery. Despite the several benefits and practical applications of using UAVs as aerial wireless devices, one must address many technical challenges. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional (3D) deployment, performance analysis, air-to-ground channel modeling, and energy efficiency are explored along with representative results. Then, fundamental open problems and potential research directions pertaining to wireless communications and networking with UAVs are introduced. To cope with the open research problems, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.
Hybrid Dialog State Tracker

This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker. The machine learning in our tracker is realized by a Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker sets a new state-of-the-art result for the Dialog State Tracking Challenge (DSTC) 2 dataset when the system uses only live SLU as its input.
Scalable Semantics-Based Detection of Similar Android Applications

The popularity and utility of smartphones rely on their vibrant application markets; however, plagiarism threatens the long-term health of these markets. In this paper, we present a scalable approach to detecting similar Android apps based on semantic information. We implement our approach in a tool called AnDarwin and evaluate it on 265,359 apps collected from 17 markets including Google Play and numerous third-party markets. In contrast with earlier approaches, AnDarwin does not compare apps pairwise, thus greatly increasing its scalability. Additionally, AnDarwin does not rely on information other than the app code — such as the app’s market, signature, or description — thus greatly increasing its reliability. AnDarwin can automatically detect library code and remove it from the similarity analysis. We present two use cases for AnDarwin: finding similar apps by different developers (“clones”) and similar apps from the same developer (“rebranded”). In ten hours, AnDarwin detected at least 4,295 apps which have been the victims of cloning and 36,106 apps that are rebranded. By analyzing the clusters found by AnDarwin, we found 88 new variants of malware and identified 169 malicious apps based on differences in the requested permissions. In contrast to earlier approaches, AnDarwin can detect both full and partial app similarity. Additionally, AnDarwin can automatically detect similar code that is injected into many apps, which may indicate the spread of malware. Our evaluation demonstrates AnDarwin’s ability to accurately detect similar apps on a large scale.
Exploring multicriteria decision strategies in GIS with linguistic quantifiers: A case study of residential quality evaluation

Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decision support systems. We propose to extend a previous implementation of OWA with linguistic quantifiers to simplify the definition of decision strategies and to facilitate an exploratory analysis of multiple criteria. The linguistic quantifier-guided OWA procedure is illustrated using a dataset for evaluating residential quality of neighborhoods in London, Ontario.
An Adaptive Distributed Simulator for Cloud and MapReduce Algorithms and Architectures

Scalability and performance are crucial for simulations as much as accuracy is. Due to the limited availability and access to the variety of resources, cloud and MapReduce solutions are often evaluated on simulator platforms. As the complexity of the architectures and algorithms keep increasing, simulations themselves become large and resource-hungry. Simulators can be designed to be adaptive, exploiting the clusters and data-grid platforms. This paper describes the research for the design, development, and evaluation of a complete fully parallel and distributed cloud and MapReduce simulator (Cloud2Sim), leveraging the Java in-memory data grid platforms. Cloud2Sim provides a concurrent and distributed cloud simulator, by extending Cloud Sim cloud simulator, using Hazel cast in-memory key-value store. It also provides an assessment of the MapReduce implementations of Hazel cast and Infinispan, with means of simulating MapReduce executions. Cloud2Sim scales out the cloud and MapReduce simulations to multiple nodes running Hazel cast and Infinispan, based on load. The distributed execution model and adaptive scaling solution could further be leveraged as a general purpose auto-scaler middleware for a multi-tenanted deployment.
A simple and efficient method to align very long speech signals to acoustically imperfect transcriptions

In the framework of a contract with the Basque Parliament for subtitling the videos of bilingual plenary sessions, which basically consisted of aligning very long (around 3 hours long) audio tracks with syntactically correct but acoustically inaccurate text transcriptions (since all the disfluencies, mistakes, etc. were edited), a very simple and efficient procedure (avoiding the need for language nor lexical models, which was key because of the mix of languages) was developed as a first approach, before trying more complex schemes found in the literature. Since it worked pretty well and the output was quite satisfactory for the intended application, that simple approach was finally chosen. In this paper, we describe the approach in detail and apply it to a widely known annotated dataset (specifically, to the 1997 Hub4 task), to allow the comparison to a reference approach. Results demonstrate that our approach provides only slightly worse segmentations at a much lower computational cost and requiring much fewer resources. Moreover, if the resource to be segmented includes speech in two or more languages and speakers conmute between them at any time, applying a speech recognizer becomes unfeasible in practice, whereas our approach can be still applied with no additional cost.
Personalized Recommendation Combining User Interest and Social Circle

With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on three rating datasets: Yelp ,MovieLens, and Douban Movie. Experimental results show the proposed approach outperforms the existing RS approaches
LDS-OFDM an Efficient Multiple Access Technique

In this paper LDS-OFDM is introduced as an uplink multicarrier multiple access scheme. LDS-OFDM uses Low Density Signature (LDS) structure for spreading the symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams up to 400% more than the number of subcarriers (overloaded condition). The performance of LDS-OFDM is evaluated and compared with conventional OFDMA systems over multipath fading channel. Monte Carlo based simulations for various loading conditions indicate significant performance improvement over OFDMA system.
Fast Variational Inference in the Conjugate Exponential Family

We present a general method for deriving collapsed variational inference algorithms for probabilistic models in the conjugate exponential family. Our method unifies many existing approaches to collapsed variational inference. Our collapsed variational inference leads to a new lower bound on the marginal likelihood. We exploit the information geometry of the bound to derive much faster optimization methods based on conjugate gradients for these models. Our approach is very general and is easily applied to any model where the mean field update equations have been derived. Empirically we show significant speed-ups for probabilistic inference using our bound.
The circulatory system of amphioxus (Branchiostoma floridae)

The endothelial cell of Amphioxus has an endocytotic capability since both ferritin and horseradish peroxidase, when injected into the endostylar artery, became localized within endothelial cell cytoplasmic vesicles. The endothelial cells also contain vesicles within which the lysosomal enzyme acid phosphatase has been localized. Because of the presence of an endocytotic capability and lysosomes, these cells possess both the means and the method for the acquisition and disposal of exogenous proteins.
Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO

Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood detector has the high complexity, whereas state-of-the-art low-complexity detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM signals, we propose a low-complexity signal detector based on structured compressive sensing (SCS) to improve the signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM signals by leveraging the structured sparsity. In addition, we also propose the SM signal interleaving to permute SM signals in the same transmission group, whereby the channel diversity can be exploited to further improve signal detection performance. Theoretical analysis quantifies the gain from SM signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme.
ZeroTouch: a zero-thickness optical multi-touch force field

We present zero-thickness optical multi-touch sensing, a technique that simplifies sensordisplay integration, and enables new forms of interaction not previously possible with other multi-touch sensing techniques. Using low-cost modulated infrared sensors to quickly determine the visual hull of an interactive area, we enable robust real-time sensing of fingers and hands, even in the presence of strong ambient lighting. Our technology allows for 20+ fingers to be detected, many more than through prior visual hull techniques, and our use of wide-angle optoelectonics allows for excellent touch resolution, even in the corners of the sensor. With the ability to track objects in free space, as well as its use as a traditional multi-touch sensor, ZeroTouch opens up a new world of interaction possibilities.
Defects Detection Based on Deep Learning and Transfer Learning

Defect detection is an important step in the field of industrial production. Through the study of deep learning and transfer learning, this paper proposes a method of defect detection based on deep learning and transfer learning. Our method firstly establishes Deep Belief Networks and trains it according to the source domain sample feature, in order to obtain the weights of the network according to source domain samples. Then, the structure and parameters of the source domain DBN is transferred to the target domain and target domain samples are used to fine-tune the network parameters to get the mapping relationship between the target domain training sample and defect-free template. Finally, the defects of testing samples will be detected by compared with the reconstruction image. This method not only can make full use of the advantages of DBN, also can solve over-fitting in DBN network training through parameters transfer learning. These experiments show that DBN is a successful approach in the high-dimensional-feature-space information extraction task, which can perfectly establishes the mapping relationship, and can quickly detect defects with a high accuracy.