nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge-based Query Expansion in Real-Time Microblog Search

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect users’ search intent. Besides, in order to further satisfy users’ real-time information need, we incorporate temporal evidences into the expansion method, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.
DESIGN AND CALIBRATION OF AN UNDERWATER STEREO-VIDEO SYSTEM FOR THE MONITORING OF MARINE FAUNA POPULATIONS

Assessment of age and size structure of marine populations is often used to detect and determine the effect of natural and anthropogenic factors, such as commercial fishing, upon marine communities. A primary tool in the characterisation of population structure is the distribution of the lengths or biomass of a large sample of individual specimens of a particular species. Rather than use relatively unreliable visual estimates by divers, an underwater stereo-video system has been developed to improve the accuracy of the measurement of lengths of highly indicative species such as reef fish. In common with any system used for accurate measurements, the design and calibration of the underwater stereo-video system are of paramount importance to realise the maximum possible accuracy from the system. Aspects of the design of the system, the calibration procedure and algorithm, the determination of the relative orientation of the two cameras, stereo-measurement and stereo-matching, and the tracking of individual specimens are discussed. Also addressed is the stability of the calibrations and relative orientation of the cameras during dives to capture video sequences of marine life.
Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets : Theory and Performance

The chapter introduces the latest developments and results of Iterative Single Data Algorithm (ISDA) for solving large-scale support vector machines (SVMs) problems. First, the equality of a Kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and the Sequential Minimal Optimization (SMO) learning algorithm (based on an analytic quadratic programming step for a model without bias term b) in designing SVMs with positive definite kernels is shown for both the nonlinear classification and the nonlinear regression tasks. The chapter also introduces the classic Gauss-Seidel procedure and its derivative known as the successive over-relaxation algorithm as viable (and usually faster) training algorithms. The convergence theorem for these related iterative algorithms is proven. The second part of the chapter presents the effects and the methods of incorporating explicit bias term b into the ISDA. The algorithms shown here implement the single training data based iteration routine (a.k.a. per-pattern learning). This makes the proposed ISDAs remarkably quick. The final solution in a dual domain is not an approximate one, but it is the optimal set of dual variables which would have been obtained by using any of existing and proven QP problem solvers if they only could deal with huge data sets.
BoostClean: Automated Error Detection and Repair for Machine Learning

Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined from a variety of different sources, each susceptible to different types of inconsistencies, and as new data stream in during prediction time, the model may encounter previously unseen inconsistencies. An important class of such inconsistencies are domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy. We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting. BoostClean selects this ensemble from an extensible library that is pre-populated general detection functions, including a novel detector based on the Word2Vec deep learning model, which detects errors across a diverse set of domains. Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, realworld data analyses, and production datasets that show that BoostClean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives. Our optimizations including parallelism, materialization, and indexing techniques show a 22.2× end-to-end speedup on a 16-core machine.
A Prolegomenon on OLTP Database Systems for Non-Volatile Memory

The design of a database management system’s (DBMS) architecture is predicated on the target storage hierarchy. Traditional diskoriented systems use a two-level hierarchy, with fast volatile memory used for caching, and slower, durable device used for primary storage. As such, these systems use a buffer pool and complex concurrency control schemes to mask disk latencies. Compare this to main memory DBMSs that assume all data can reside in DRAM, and thus do not need these components. But emerging non-volatile memory (NVM) technologies require us to rethink this dichotomy. Such memory devices are slightly slower than DRAM, but all writes are persistent, even after power loss. We explore two possible use cases of NVM for on-line transaction processing (OLTP) DBMSs. The first is where NVM completely replaces DRAM and the other is where NVM and DRAM coexist in the system. For each case, we compare the performance of a disk-oriented DBMS with a memory-oriented DBMS using two OLTP benchmarks. We also evaluate the performance of different recovery algorithms on these NVM devices. Our evaluation shows that in both storage hierarchies, memory-oriented systems are able to outperform their disk-oriented counterparts. However, as skew decreases the performance of the two architectures converge, showing that neither architecture is ideally suited for an NVM-based storage hierarchy.
A new collaborative filtering algorithm using K-means clustering and neighbors' voting

The Collaborative Filtering is the most successful algorithm in the recommender systems' field. A recommender system is an intelligent system can help users to come across interesting items. It uses data mining and information filtering techniques. The collaborative filtering creates suggestions for users based on their neighbors' preferences. But it suffers from its poor accuracy and scalability. This paper considers the users are m (m is the number of users) points in n dimensional space (n is the number of items) and represents an approach based on user clustering to produce a recommendation for active user by a new method. It uses k-means clustering algorithm to categorize users based on their interests. Then it uses a new method called voting algorithm to develop a recommendation. We evaluate the traditional collaborative filtering and the new one to compare them. Our results show the proposed algorithm is more accurate than the traditional one, besides it is less time consuming than it.
To Groupon or Not to Groupon : The Profitability of Deep Discounts

We examine the profitability and implications of online discount vouchers, a new marketing tool that offers consumers large discounts when they prepay for participating merchants’ goods and services. Within a model of repeat experience good purchase, we examine two mechanisms by which a discount voucher service can benefit affiliated merchants: price discrimination and advertising. For vouchers to provide successful price discrimination, the valuations of consumers who have access to vouchers must systematically differ from—and typically be lower than—those of consumers who do not have access to vouchers. Offering vouchers is more profitable for merchants which are patient or relatively unknown, and for merchants with low marginal costs. Extensions to our model accommodate the possibilities of multiple voucher purchases and merchant price re-optimization.
Counterfactual Multi-Agent Policy Gradients

Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents’ policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent’s action, while keeping the other agents’ actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actorcritic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.
Speed Control of Induction Motor using Scalar Control Technique

This paper presents design and implementation of scalar control of induction motor. This method leads to be able to adjust the speed of the motor by control the frequency and amplitude of the stator voltage of induction motor, the ratio of stator voltage to frequency should be kept constant, which is called as V/F or scalar control of induction motor drive. This paper presents a comparative study of open loop and close loop V/F control induction motor. The V/F
PROBING THE FEED LINE PARAMETERS IN VIVALDI NOTCH ANTENNAS

Some new parameters in Vivaldi Notch antennas are debated over in this paper. They can be availed for the bandwidth application amelioration. The aforementioned limiting factors comprise two parameters for the radial stub dislocation, one parameter for the stub opening angle, and one parameter for the stub’s offset angle. The aforementioned parameters are rectified by means of the optimization algorithm to accomplish a better frequency application. The results obtained in this article will eventually be collated with those of the other similar antennas. The best achieved bandwidth in this article is 17.1 GHz.
An AMR parser for English, French, German, Spanish and Japanese and a new AMR-annotated corpus

In this demonstration, we will present our online parser that allows users to submit any sentence and obtain an analysis following the specification of AMR (Banarescu et al., 2014) to a large extent. This AMR analysis is generated by a small set of rules that convert a native Logical Form analysis provided by a preexisting parser (see Vanderwende, 2015) into the AMR format. While we demonstrate the performance of our AMR parser on data sets annotated by the LDC, we will focus attention in the demo on the following two areas: 1) we will make available AMR annotations for the data sets that were used to develop our parser, to serve as a supplement to the LDC data sets, and 2) we will demonstrate AMR parsers for German, French, Spanish and Japanese that make use of the same small set of LF-to-AMR conversion rules.
Capturing the human figure through a wall

We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subject's body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.
Steering symbolic execution to less traveled paths

Symbolic execution is a promising testing and analysis methodology. It systematically explores a program's execution space and can generate test cases with high coverage. One significant practical challenge for symbolic execution is how to effectively explore the enormous number of program paths in real-world programs. Various heuristics have been proposed for guiding symbolic execution, but they are generally inefficient and ad-hoc. In this paper, we introduce a novel, unified strategy to guide symbolic execution to less explored parts of a program. Our key idea is to exploit a specific type of path spectra, namely the length-n subpath program spectra, to systematically approximate full path information for guiding path exploration. In particular, we use frequency distributions of explored length-n subpaths to prioritize "less traveled" parts of the program to improve test coverage and error detection. We have implemented our general strategy in KLEE, a state-of-the-art symbolic execution engine. Evaluation results on the GNU Coreutils programs show that (1) varying the length n captures program-specific information and exhibits different degrees of effectiveness, and (2) our general approach outperforms traditional strategies in both coverage and error detection.
Spectrum and energy efficiency maximization in UAV-enabled mobile relaying

Wireless communication by leveraging the use of low-altitude unmanned aerial vehicles (UAVs) has received significant interests recently due to its low-cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. This paper studies a UAV-enabled mobile relaying system, where a high-mobility UAV is deployed to assist in the information transmission from a ground source to a ground destination with their direct link blocked. By assuming that the UAV adopts the energy-efficient circular trajectory and employs time-division duplexing (TDD) based decode-and-forward (DF) relaying, we maximize the spectrum efficiency (SE) in bits/second/Hz as well as energy efficiency (EE) in bits/Joule of the considered system by jointly optimizing the time allocations for the UAV's relaying together with its flying speed and trajectory. It is revealed that for UAV-enabled mobile relaying with the UAV propulsion energy consumption taken into account, there exists a trade-off between the maximum achievable SE and EE by exploiting the new degree of freedom of UAV trajectory design.
NSH: Normality Sensitive Hashing for Anomaly Detection

Locality sensitive hashing (LSH) is a computationally efficient alternative to the distance based anomaly detection. The main advantages of LSH lie in constant detection time, low memory requirement, and simple implementation. However, since the metric of distance in LSHs does not consider the property of normal training data, a naive use of existing LSHs would not perform well. In this paper, we propose a new hashing scheme so that hash functions are selected dependently on the properties of the normal training data for reliable anomaly detection. The distance metric of the proposed method, called NSH (Normality Sensitive Hashing) is theoretically interpreted in terms of the region of normal training data and its effectiveness is demonstrated through experiments on real-world data. Our results are favorably comparable to state-of-the arts with the low-level features.
EFFICIENT ZONE BASED FEATURE EXTRATION ALGORITHM FOR HANDWRITTEN NUMERAL RECOGNITION OF FOUR POPULAR SOUTH INDIAN SCRIPTS

Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose Zone centroid and Image centroid based Distance metric feature extraction system. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. Similarly zone centroid is computed and average distance from the zone centroid to each pixel present in the zone is computed. We repeated this procedure for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 2*n such features are extracted. Nearest neighbor and Feed forward back propagation neural network classifiers are used for subsequent classification and recognition purpose. We obtained 99 %, 99%, 96% and 95 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.
Optic disc localization in retinal images using histogram matching

In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9%, respectively.
Generative Models for Cold-Start Recommendations

Systems for automati ally re ommending items (e.g., movies, produ ts, or information) to users are be oming in reasingly important in eommer e appli ations, digital libraries, and other domains where mass personalization is highly valued. Su h re ommender systems typi ally base their suggestions on (1) ollaborative data en oding whi h users like whi h items, and/or (2) ontent data des ribing item features and user demographi s. Systems that rely solely on ollaborative data fail when operating from a old start|that is, when re ommending items (e.g., rst-run movies) that no member of the ommunity has yet seen. We develop several generative probabilisti models that ir umvent the old-start problem by mixing ontent data with ollaborative data in a sound statisti al manner. We evaluate the algorithms using MovieLens movie ratings data, augmented with a tor and dire tor information from the Internet Movie Database. We nd that maximum likelihood learning with the expe tation maximization (EM) algorithm and variants tends to over t omplex models that are initialized randomly. However, by seeding parameters of the omplex models with parameters learned in simpler models, we obtain greatly improved performan e. We explore both methods that exploit a single type of ontent data (e.g., a tors only) and methods that leverage multiple types of ontent data (e.g., both a tors and dire tors) simultaneously.
Efficient and reliable low-power backscatter networks

There is a long-standing vision of embedding backscatter nodes like RFIDs into everyday objects to build ultra-low power ubiquitous networks. A major problem that has challenged this vision is that backscatter communication is neither reliable nor efficient. Backscatter nodes cannot sense each other, and hence tend to suffer from colliding transmissions. Further, they are ineffective at adapting the bit rate to channel conditions, and thus miss opportunities to increase throughput, or transmit above capacity causing errors.
 This paper introduces a new approach to backscatter communication. The key idea is to treat all nodes as if they were a single virtual sender. One can then view collisions as a code across the bits transmitted by the nodes. By ensuring only a few nodes collide at any time, we make collisions act as a sparse code and decode them using a new customized compressive sensing algorithm. Further, we can make these collisions act as a rateless code to automatically adapt the bit rate to channel quality --i.e., nodes can keep colliding until the base station has collected enough collisions to decode. Results from a network of backscatter nodes communicating with a USRP backscatter base station demonstrate that the new design produces a 3.5× throughput gain, and due to its rateless code, reduces message loss rate in challenging scenarios from 50% to zero.
A new standardized method for objectively measuring video quality

The National Telecommunications and Information Administration (NTIA) General Model for estimating video quality and its associated calibration techniques were independently evaluated by the Video Quality Experts Group (VQEG) in their Phase II Full Reference Television (FR-TV) test. The NTIA General Model was the only video quality estimator that was in the top performing group for both the 525-line and 625-line video tests. As a result, the American National Standards Institute (ANSI) adopted the NTIA General Model and its associated calibration techniques as a North American Standard in 2003. The International Telecommunication Union (ITU) has also included the NTIA General Model as a normative method in two Draft Recommendations. This paper presents a description of the NTIA General Model and its associated calibration techniques. The independent test results from the VQEG FR-TV Phase II tests are summarized, as well as results from eleven other subjective data sets that were used to develop the method.
Novel concept for mains voltage proportional input current shaping of a VIENNA rectifier eliminating controller multipliers

This paper proposes a novel mains voltage proportional input current control concept eliminating the multiplication of the output voltage controller output and the mains ac phase voltages for the derivation of mains phase current reference values of a three-phase/level/switch pulsewidth-modulated (VIENNA) rectifier system. Furthermore, the concept features low input current ripple amplitude as, e.g., achieved for space-vector modulation, a low amplitude of the third harmonic of the current flowing into the output voltage center point, and a wide range of modulation. The practical realization of the analog control concept as well as experimental results for application with a 5-kW prototype of the pulsewidth-modulated rectifier are presented. Furthermore, a control scheme which relies only on the absolute values of the input phase currents and a modified control scheme which does not require information about the mains phase voltages are presented.