nils-herrmann/clean_scirepeval huggingface dataset:
Towards a Generic and Flexible Citation Classifier Based on a Faceted Classification Scheme

Citations are a valuable resource for characterizing scientific publications that has already been used in applications such as summarization and information retrieval. These applications could be even better served by expanding citation information. We aim to achieve this by extracting and classifying citation information from the text, so that subsequent applications may make use of it. We make three contributions to the advancement of fine-grained citation classification. First, our work uses a standard classification scheme for citations that was developed independently of automatic classification and therefore is not bound to any particular citation application. Second, to address the lack of available annotated corpora and reproducible results for citation classification, we are making available a manually-annotated corpus as a benchmark for further citation classification research. Third, we introduce new features designed for citation classification and compare them experimentally with previously proposed citation features, showing that these new features improve classification accuracy.
AI Grand Challenges for Education

Artificial intelligence impacts growth and productivity in many industries (e.g., transportation, communication, commerce, and finance). However, one painful exception is education; today, very few AI-based learning systems are consistently used in classrooms or homes. Yet the potential for an impact on education is great: today’s instructional software now routinely tailors learning to individual needs, connects learners together, provides access to digital materials, supports decentralized learning tools and engages students in meaningful ways. As a society we have great expectations from the educational establishment (train employees, support scientific and artistic development, transmit culture, etc.) and yet, no matter how much is achieved, society continues to expect even more from education. The current educational environment (fixed classrooms, repeated lectures and static printed textbooks) is clearly not capable of either serving society nor of flexibly changing for the future. Classrooms and printed textbooks are especially inappropriate for people who use technology on a daily basis. For example, digital natives learn and work at twitch speed, through parallel processing, with graphics and connected to others (vs. stand alone) (Beavis, 2010). For these digital natives, information is instantly available, change is constant, distance and time do not matter, and multimedia entertainment is omnipresent. No wonder schools and classrooms are boring!
Reinforcement Learning: A Survey

This paper surveys the eld of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the eld and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but di ers considerably in the details and in the use of the word \reinforcement." The paper discusses central issues of reinforcement learning, including trading o exploration and exploitation, establishing the foundations of the eld via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles

This paper reports the formulation and evaluation of a centralized extended Kalman filter designed for a novel navigation system for underwater vehicles. The navigation system employs Doppler sonar, depth sensors, synchronous clocks, and acoustic modems to achieve simultaneous acoustic communication and navigation. The use of a single moving reference beacon eliminates the requirement for the underwater vehicle to remain in a bounded navigable area; the use of underwater modems and synchronous clocks enables range measurements based on one-way time-of-flight information from acoustic data-packet broadcasts. The acoustic data packets are broadcast from a single, moving reference beacon and can be received simultaneously by multiple vehicles within acoustic range. We report results from a simulated deep-water survey and real field data collected from an autonomous underwater vehicle survey in 4000 m of water on the southern MidAtlantic Ridge with an independent long-baseline navigation system for ground truth.
"Dave...I can assure you ...that it's going to be all right ..." A Definition, Case for, and Survey of Algorithmic Assurances in Human-Autonomy Trust Relationships

People who design, use, and are affected by autonomous artificially intelligent agents want to be able to trust such agents—that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately. Many techniques have been devised to assess and influence human trust in artificially intelligent agents. However, these approaches are typically ad hoc and have not been formally related to each other or to formal trust models. This article presents a survey of algorithmic assurances, i.e., programmed components of agent operation that are expressly designed to calibrate user trust in artificially intelligent agents. Algorithmic assurances are first formally defined and classified from the perspective of formally modeled human-artificially intelligent agent trust relationships. Building on these definitions, a synthesis of research across communities such as machine learning, human-computer interaction, robotics, e-commerce, and others reveals that assurance algorithms naturally fall along a spectrum in terms of their impact on an agent’s core functionality, with seven notable classes ranging from integral assurances (which impact an agent’s core functionality) to supplemental assurances (which have no direct effect on agent performance). Common approaches within each of these classes are identified and discussed; benefits and drawbacks of different approaches are also investigated.
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

DBSCAN is a well-known density based clustering algorithm capable of discovering arbitrary shaped clusters and eliminating noise data. However, parallelization of Dbscan is challenging as it exhibits an inherent sequential data access order. Moreover, existing parallel implementations adopt a master-slave strategy which can easily cause an unbalanced workload and hence result in low parallel efficiency.
 We present a new parallel Dbscan algorithm (Pdsdbscan) using graph algorithmic concepts. More specifically, we employ the disjoint-set data structure to break the access sequentiality of Dbscan. In addition, we use a tree-based bottom-up approach to construct the clusters. This yields a better-balanced workload distribution. We implement the algorithm both for shared and for distributed memory.
 Using data sets containing up to several hundred million high-dimensional points, we show that Pdsdbscan significantly outperforms the master-slave approach, achieving speedups up to 25.97 using 40 cores on shared memory architecture, and speedups up to 5,765 using 8,192 cores on distributed memory architecture.
Security Requirements Engineering: A Framework for Representation and Analysis

This paper presents a framework for security requirements elicitation and analysis. The framework is based on constructing a context for the system, representing security requirements as constraints, and developing satisfaction arguments for the security requirements. The system context is described using a problem-oriented notation, then is validated against the security requirements through construction of a satisfaction argument. The satisfaction argument consists of two parts: a formal argument that the system can meet its security requirements and a structured informal argument supporting the assumptions expressed in the formal argument. The construction of the satisfaction argument may fail, revealing either that the security requirement cannot be satisfied in the context or that the context does not contain sufficient information to develop the argument. In this case, designers and architects are asked to provide additional design information to resolve the problems. We evaluate the framework by applying it to a security requirements analysis within an air traffic control technology evaluation project.
Linearizing Oscillometric Blood-Pressure Measurements: (Non)Sense?

This paper proposes a simplified method to compute the systolic and diastolic blood pressures from measured oscillometric blood-pressure waveforms. Therefore, the oscillometric waveform is analyzed in the frequency domain, which reveals that the measured blood-pressure signals are heavily disturbed by nonlinear contributions. The proposed approach will linearize the measured oscillometric waveform in order to obtain a more accurate and transparent estimation of the systolic and diastolic pressure based on a robust preprocessing technique. This new approach will be compared with the Korotkoff method and a commercially available noninvasive blood-pressure meter. This allows verification if the linearized approach contains as much information as the Korotkoff method in order to calculate a correct systolic and diastolic blood pressure.
Current-Only Directional Overcurrent Relay

Overcurrent relays are widely used for protection of power systems, directional ones for transmission side, and nondirectional ones for distribution side. The fault direction may be forward (between relay and grid), or reverse (between relay and source), the normal power flow being from source to the grid. Known directional overcurrent relays rely on a reference voltage phasor for estimating the direction of the fault, requiring both current and voltage sensors. This increases the cost of the relays, prohibiting the utilization of such relays in the distribution side protection and automation, which is going to be a key part in the smart grid initiative. In this paper, a novel current-only directional detection possibility is highlighted.
Discovering regions of different functions in a city using human mobility and POIs

The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility.
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension

Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence. In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task. In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer. We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information. Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.
3D Convolutional Neural Networks for landing zone detection from LiDAR

We present a system for the detection of small and potentially obscured obstacles in vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy map with a 3D Convolutional Neural Network (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to train an extremely efficient and highly accurate system for detection tasks from raw occupancy data. We apply this method to the problem of detecting safe landing zones for autonomous helicopters from LiDAR point clouds. Current methods for this problem rely on heuristic rules and use simple geometric features. These heuristics break down in the presence of low vegetation, as they do not distinguish between vegetation that may be landed on and solid objects that should be avoided. We evaluate the system with a combination of real and synthetic range data. We show our system outperforms various benchmarks, including a system integrating various hand-crafted point cloud features from the literature.
Charagram: Embedding Words and Sentences via Character n-grams

We present CHARAGRAM embeddings, a simple approach for learning character-based compositional models to embed textual sequences. A word or sentence is represented using a character n-gram count vector, followed by a single nonlinear transformation to yield a low-dimensional embedding. We use three tasks for evaluation: word similarity, sentence similarity, and part-of-speech tagging. We demonstrate that CHARAGRAM embeddings outperform more complex architectures based on character-level recurrent and convolutional neural networks, achieving new state-of-the-art performance on several similarity tasks.
Advertising , Learning , and Consumer Choice in Experience Good Markets

This paper empirically analyzes di erent e ects of advertising in a nondurable, experience good market. A dynamic learning model of consumer behavior is presented in which we allow both \informative" e ects of advertising and \prestige" or \image" e ects of advertising. This learning model is estimated using consumer level panel data tracking grocery purchases and advertising exposures over time. Empirical results suggest that in this data, advertising's primary e ect was that of informing consumers. The estimates are used to quantify the value of this information to consumers and evaluate welfare implications of an alternative advertising regulatory regime.  This paper is a revised version of the second and third chapters of my doctoral dissertation at Yale University. Many thanks to my advisors: Steve Berry and Ariel Pakes, as well as Lanier Benkard, Russell Cooper, Gautam Gowrisankaran, Sam Kortum, Mike Riordan, John Rust, Roni Shachar, and many seminar participants, including most recently those at the NBER 1997Winter IO meetings, for advice and comments. I thank the Yale School of Management for gratefully providing the data used in this study. Financial support from the Cowles Foundation in the form of the Arvid Anderson Dissertation Fellowship is acknowledged and appreciated. All remaining errors in this paper are my own.
Traffic Road Sign Detection and Recognition for Automotive Vehicles

Traffic road sign detection and recognition is important to transport system with a robotic eyes or camera while driving in the road. This paper presents and overview the traffic road sign detection and recognition, we developed and implemented the procedure to extract the road sign from a natural complex image. The main objective of this paper is to design and construct a computer based system which can automatically detect the direction of the road sign. This paper is based upon a major approach to detect the direction. In this paper, we will demonstrate the basic idea of how detect the area and extract it. This system will play an important role for the detection purpose of specific domains like island, schools, traffic sign, universities, hospitals, offices etc.
Change Detection In Synthetic Aperture Radar Images Based On Image Fusion And Fuzzy Clustering

Change detection is the process of finding out difference between two images taken at two different times. With the help of remote sensing the . Here we will try to find out the difference of the same image taken at different times. here we use mean ratio and log ratio to find out the difference in the images. Log is use to find background image and fore ground detected by mean ratio. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.
Look away: the anti-saccade task and the voluntary control of eye movement

The anti-saccade task has emerged as an important task for investigating the flexible control that we have over behaviour. In this task, participants must suppress the reflexive urge to look at a visual target that appears suddenly in the peripheral visual field and must instead look away from the target in the opposite direction. A crucial step involved in performing this task is the top-down inhibition of a reflexive, automatic saccade. Here, we describe recent neurophysiological evidence demonstrating the presence of this inhibitory function in single-cell activity in the frontal eye fields and superior colliculus. Patients diagnosed with various neurological and/or psychiatric disorders that affect the frontal lobes or basal ganglia find it difficult to suppress the automatic pro-saccade, revealing a deficit in top-down inhibition.
Programming with personalized pagerank: a locally groundable first-order probabilistic logic

Many information-management tasks (including classification, retrieval, information extraction, and information integration) can be formalized as inference in an appropriate probabilistic first-order logic. However, most probabilistic first-order logics are not efficient enough for realistically-sized instances of these tasks. One key problem is that queries are typically answered by "grounding" the query---i.e., mapping it to a propositional representation, and then performing propositional inference---and with a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: in particular, every query $Q$ can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well on an entity resolution task, a classification task, and a joint inference task; that the cost of inference is independent of database size; and that speedup in learning is possible by multi-threading.
A measurement study of available bandwidth estimation tools

Available bandwidth estimation is useful for route selection in overlay networks, QoS verification, and traffic engineering. Recent years have seen a surge in interest in available bandwidth estimation. A few tools have been proposed and evaluated in simulation and over a limited number of Internet paths, but there is still great uncertainty in the performance of these tools over the Internet at large.This paper introduces Spruce, a simple, light-weight tool for measuring available bandwidth, and compares it with two existing tools, IGI and Pathload, over 400 different Internet paths. The comparison focuses on accuracy, failure patterns, probe overhead, and implementation issues. The paper verifies the measured available bandwidth by comparing it to Multi-Router Traffic Grapher (MRTG) data and by measuring how each tool responds to induced changes in available bandwidth.The measurements show that Spruce is more accurate than Pathload and IGI. Pathload tends to overestimate the available bandwidth whereas IGI becomes insensitive when the bottleneck utilization is large.
Fast malware classification by automated behavioral graph matching

Malicious software (malware) is a serious problem in the Internet. Malware classification is useful for detection and analysis of new threats for which signatures are not available, or possible (due to polymorphism). This paper proposes a new malware classification method based on maximal common subgraph detection. A behavior graph is obtained by capturing system calls during the execution (in a sandboxed environment) of the suspicious software. The method has been implemented and tested on a set of 300 malware instances in 6 families. Results demonstrate the method effectively groups the malware instances, compared with previous methods of classification, is fast, and has a low false positive rate when presented with benign software.
1 € Filter: a Simple Speed-based Low-pass Filter for Noisy Input in Interactive Systems

The 1 € filter ("one Euro filter") is a simple algorithm to filter noisy signals for high precision and responsiveness. It uses a first order low-pass filter with an adaptive cutoff frequency: at low speeds, a low cutoff stabilizes the signal by reducing jitter, but as speed increases, the cutoff is increased to reduce lag. The algorithm is easy to implement, uses very few resources, and with two easily understood parameters, it is easy to tune. In a comparison with other filters, the 1 € filter has less lag using a reference amount of jitter reduction.