nils-herrmann/clean_scirepeval huggingface dataset:
Topics over time: a non-Markov continuous-time model of topical trends

This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work that relies on Markov assumptions or discretization of time, here each topic is associated with a continuous distribution over timestamps, and for each generated document, the mixture distribution over topics is influenced by both word co-occurrences and the document's timestamp. Thus, the meaning of a particular topic can be relied upon as constant, but the topics' occurrence and correlations change significantly over time. We present results on nine months of personal email, 17 years of NIPS research papers and over 200 years of presidential state-of-the-union addresses, showing improved topics, better timestamp prediction, and interpretable trends.
ALPS: A Bluetooth and Ultrasound Platform for Mapping and Localization

The proliferation of Bluetooth Low-Energy (BLE) chipsets on mobile devices has lead to a wide variety of user-installable tags and beacons designed for location-aware applications. In this paper, we present the Acoustic Location Processing System (ALPS), a platform that augments BLE transmitters with ultrasound in a manner that improves ranging accuracy and can help users configure indoor localization systems with minimal effort. A user places three or more beacons in an environment and then walks through a calibration sequence with their mobile device where they touch key points in the environment like the floor and the corners of the room. This process automatically computes the room geometry as well as the precise beacon locations without needing auxiliary measurements. Once configured, the system can track a user's location referenced to a map.
 The platform consists of time-synchronized ultrasonic transmitters that utilize the bandwidth just above the human hearing limit, where mobile devices are still sensitive and can detect ranging signals. To aid in the mapping process, the beacons perform inter-beacon ranging during setup. Each beacon includes a BLE radio that can identify and trigger the ultrasonic signals. By using differences in propagation characteristics between ultrasound and radio, the system can classify if beacons are within Line-Of-Sight (LOS) to the mobile phone. In cases where beacons are blocked, we show how the phone's inertial measurement sensors can be used to supplement localization data. We experimentally evaluate that our system can estimate three-dimensional beacon location with a Euclidean distance error of 16.1cm, and can generate maps with room measurements with a two-dimensional Euclidean distance error of 19.8cm. When tested in six different environments, we saw that the system can identify Non-Line-Of-Sight (NLOS) signals with over 80% accuracy and track a user's location to within less than 100cm.
It is Not Just a Picture: Revealing Some User Practices in Instagram

In this work we investigate the user practices in Instagram, a social photo sharing service. Some interesting conclusions emerge from our analysis. For instance, users tend to concentrate their posts during the weekend and at the end of the day. Furthermore, people tend to endorse photos with many likes and comments, inducing the rich get richer phenomenon. Our findings can support future research on sociology and cultural analytics research areas, such as on the proposal of new clustering algorithms based on the user practices in different social media networks.
Generating Long and Diverse Responses with Neural Conversation Models

Building general-purpose conversation agents is a very challenging task, but necessary on the road toward intelligent agents that can interact with humans in natural language. Neural conversation models – purely data-driven systems trained end-to-end on dialogue corpora – have shown great promise recently, yet they often produce short and generic responses. This work presents new training and decoding methods that improve the quality, coherence, and diversity of long responses generated using sequence-to-sequence models. Our approach adds selfattention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A backoff strategy produces better responses overall, in the full spectrum of lengths.
DimpVis: Exploring Time-varying Information Visualizations by Direct Manipulation

We introduce a new direct manipulation technique, DimpVis, for interacting with visual items in information visualizations to enable exploration of the time dimension. DimpVis is guided by visual hint paths which indicate how a selected data item changes through the time dimension in a visualization. Temporal navigation is controlled by manipulating any data item along its hint path. All other items are updated to reflect the new time. We demonstrate how the DimpVis technique can be designed to directly manipulate position, colour, and size in familiar visualizations such as bar charts and scatter plots, as a means for temporal navigation. We present results from a comparative evaluation, showing that the DimpVis technique was subjectively preferred and quantitatively competitive with the traditional time slider, and significantly faster than small multiples for a variety of tasks.
Acronym Disambiguation Using Word Embedding

According to the website AcronymFinder.com which is one of the world's largest and most comprehensive dictionaries of acronyms, an average of 37 new human-edited acronym definitions are added every day. There are 379,918 acronyms with 4,766,899 definitions on that site up to now, and each acronym has 12.5 definitions on average. It is a very important research topic to identify what exactly an acronym means in a given context for document comprehension as well as for document retrieval. In this paper, we propose two word embedding based models for acronym disambiguation. Word embedding is to represent words in a continuous and multidimensional vector space, so that it is easy to calculate the semantic similarity between words by calculating the vector distance. We evaluate the models on MSH Dataset and ScienceWISE Dataset, and both models outperform the state-of-art methods on accuracy. The experimental results show that word embedding helps to improve acronym disambiguation.
Intersecting Parallel-Plate Waveguide Loaded Cavities for Dual-Mode and Dual-Band Filters

Dual-mode and/or dual-band microwave filters often employ high quality factor (Q ), physically large, and frequency static cavity resonators or low Q, compact, and tunable planar resonators. While each resonator type has advantages, choosing a dual-mode and/or dual-band resonator type is often limited by these extremes. In this paper, a new dual-mode and/or dual-band resonator is shown with Q (360-400) that is higher than that of planar resonators while being frequency tunable (6.7% tuning range) and compact relative to standard cavity resonators. In addition, both degenerate modes of the resonator are tunable using a single actuator. The resonator is used in a single-resonator two-pole filter design and a double-resonator dual-band filter design. An analytical model is developed and design techniques are given for both designs. Measured results confirm that the proposed resonator fits between the design spaces of established dual-mode and/or dual-band resonator types and could find application in systems that require a combination of relatively high Q, tuning capability, and ease of integration.
Taming the elephants: New TCP slow start

Standard slow start does not work well under large bandwidthdelay product (BDP) networks. We find two causes of this problem in existing three popular operating systems, Linux, FreeBSD and Windows XP. The first cause is that because of the exponential increase of cwnd during standard slow start, heavy packet losses occur. Recovering from heavy packet losses puts extremely high load on end systems which renders the end systems completely unresponsive for a long time, resulting in a long blackout period of no transmission. This problem commonly occurs with the three operating systems. The second cause is that some of proprietary protocol optimizations applied for slow start by these operating systems to relieve the system load happen to slow down the loss recovery followed by slow start. To remedy this problem, we propose a new slow start algorithm, called Hybrid Start (HyStart) that finds a “safe” exit point of slow start at which slow start can finish and safely move to congestion avoidance without causing any heavy packet losses. HyStart uses ACK trains and RTT delay samples to detect whether (1) the forward path is congested or (2) the current size of congestion window has reached the available capacity of the forward path. HyStart is a plug-in to the TCP sender and does not require any change in TCP receivers. We implemented HyStart for TCP-NewReno and TCP-SACK in Linux and compare its performance with five different slow start schemes with the TCP receivers of the three different operating systems in the Internet and also in the lab testbeds. Our results indicate that HyStart works consistently well under diverse network environments including asymmetric links and high and low BDP networks. Especially with different operating system receivers (Windows XP and FreeBSD), HyStart improves the start-up throughput of TCP more than 2 to 3 times.
Neural network based language models for highly inflective languages

Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. Several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. This paper describes improvements obtained in recognition of spoken Czech lectures using language models based on neural networks. Relative reductions in word error rate are more than 15% over baseline obtained with adapted 4-gram backoff language model using modified Kneser-Ney smoothing.
Secure and Authenticated Data Communication in Wireless Sensor Networks

Securing communications in wireless sensor networks is increasingly important as the diversity of applications increases. However, even today, it is equally important for the measures employed to be energy efficient. For this reason, this publication analyzes the suitability of various cryptographic primitives for use in WSNs according to various criteria and, finally, describes a modular, PKI-based framework for confidential, authenticated, secure communications in which most suitable primitives can be employed. Due to the limited capabilities of common WSN motes, criteria for the selection of primitives are security, power efficiency and memory requirements. The implementation of the framework and the singular components have been tested and benchmarked in our testbed of IRISmotes.
Building organizational commitment : A multifirm study

Although much research has been conducted in the area of organizational commitment, few studies have explicitly examined how organizations facilitate commitment among members. Using a sample of 291 respondents from 45 firms, the results of this study show that rigorous recruitment and selection procedures and a strong, clear organizational value system are associated with higher levels of employee commitment based on internalization and identification. Strong organizational career and reward systems are related to higher levels of instrumental or compliance-based commitment.
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training

While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. This is primarily due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, humans – rightfully so – generate multiple, diverse captions, due to the inherent ambiguity in the captioning task which is not explicitly considered in today's systems. To address these challenges, we change the training objective of the caption generator from reproducing ground-truth captions to generating a set of captions that is indistinguishable from human written captions. Instead of handcrafting such a learning target, we employ adversarial training in combination with an approximate Gumbel sampler to implicitly match the generated distribution to the human one. While our method achieves comparable performance to the state-of-the-art in terms of the correctness of the captions, we generate a set of diverse captions that are significantly less biased and better match the global uni-, bi- and tri-gram distributions of the human captions.
Speech Processing Algorithm For Detection Of Parkinson’s Disease

The brief survey on detection of Parkinson’s Disease (PD) by speech impairment gives an overview of the major technological perspective and appreciated the fundamental progress of PD detection .Here we compute the dysphonia measures from sustained vowels and the feature selection algorithms are used to select the features and then classify the PD by Back-Propagation learning algorithm based on Levenberg-Marquardt Back-propagation algorithm and have used an existing database consisting of 195 samples computing 132 dysphonia measures and these measures can perform and give out the best-art results, reaching the maximum of overall classification accuracy using only 22 features, where the selected measures represent the PD in male and
Event Processing in Sensor Streams

Sensors including RFID tags have been widely deployed for measuring environmental parameters such as temperature, humidity, oxygen concentration, monitoring the location and velocity of moving objects, tracking tagged objects, and many others. To support effective, efficient, and near real-time phenomena probing and objects monitoring, streaming sensor data have to be gracefully managed in an event processing manner. Different from the traditional events, sensor events come with temporal or spatio-temporal constraints and can be non-spontaneous. Meanwhile, like general event streams, sensor event streams can be generated with very high volumes and rates. Primitive sensor events need to be filtered, aggregated and correlated to generate more semantically rich complex events to facilitate the requirements of up-streaming applications. Motivated by such challenges, many new methods have been proposed in the past to support event processing in sensor event streams. In this chapter, we survey state-of-the-art research on event processing in sensor networks, and provide a broad overview of major topics in Springer Science+Business Media New York 2013  DOI 10.1007/978-1-4614-6309-2_4, C.C. Aggarwal (ed.), 77 78 MANAGING AND MINING SENSOR DATA complex RFID event processing, including event specification languages, event detection models, event processing methods and their optimizations. Additionally, we have presented an open discussion on advanced issues such as processing uncertain and out-of-order sensor events.
A Focused Backpropagation Algorithm for Temporal Pattern Recognition

Time is at th e heart of many pat tern recognition t asks, e.g., speech recognit ion . However, connectionis t learning algorithms to date are not well suited for dealing with tim e-varying input patterns. This paper introduces a specialized connectionist architecture and corre sponding specialization of the backpropagation learnin g algori thm th at opera tes efficiently on temporal sequences . The key feature of t he archit ecture is a layer of self-connecte d hidden units that integrate their curre nt value with th e new input at each time ste p to construct a static represent ation of the temporal input sequence . Thi s architecture avoids two deficiencies found in other models of sequence recognition: first , i t reduces the difficulty of temporal credit assignm ent by focusing th e backpropagated err or signal; second, it eliminates the need for a buffer to hold th e input sequence and/or intermediat e activity levels. The lat ter prop erty is due to the fact th at during th e forward (activation) phase, incremental activity tra ces can be locally compute d that hold all information necessar y for backpropagation in time . It is argued tha t thi s architecture should scale better t han conventional recurrent architectures wit h respect to sequenc e length . The architecture has been used to implement a temporal version of Rumelhart and McClelland's verb past-tense model . The hidden units learn to beh ave something like Rumelhart and McClelland 's "Wickelphones," a rich and flexibl e representation of temporal information.
Shadow Detection and Removal from a Single Image Using LAB Color Space

A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.
Partial Functional Correspondence

In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes. We use perturbation analysis to show how removal of shape parts changes the Laplace-Beltrami eigenfunctions, and exploit it as a prior on the spectral representation of the correspondence. Corresponding parts are optimization variables in our problem and are used to weight the functional correspondence; we are looking for the largest and most regular (in the Mumford-Shah sense) parts that minimize correspondence distortion. We show that our approach can cope with very challenging correspondence settings.
Understanding and improving the realism of image composites

Compositing is one of the most commonly performed operations in computer graphics. A realistic composite requires adjusting the appearance of the foreground and background so that they appear compatible; unfortunately, this task is challenging and poorly understood. We use statistical and visual perception experiments to study the realism of image composites. First, we evaluate a number of standard 2D image statistical measures, and identify those that are most significant in determining the realism of a composite. Then, we perform a human subjects experiment to determine how the changes in these key statistics influence human judgements of composite realism. Finally, we describe a data-driven algorithm that automatically adjusts these statistical measures in a foreground to make it more compatible with its background in a composite. We show a number of compositing results, and evaluate the performance of both our algorithm and previous work with a human subjects study.
Attribute-based Decision Graphs: A framework for multiclass data classification

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph-AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes.
Conditional Affordance Learning for Driving in Urban Environments

Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation.