nils-herrmann/clean_scirepeval huggingface dataset:
Identifying hot and cold data in main-memory databases

Main memories are becoming sufficiently large that most OLTP databases can be stored entirely in main memory, but this may not be the best solution. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). It is more economical to store the coldest records on secondary storage such as flash. As a first step towards managing cold data in databases optimized for main memory we investigate how to efficiently identify hot and cold data. We propose to log record accesses - possibly only a sample to reduce overhead - and perform offline analysis to estimate record access frequencies. We present four estimation algorithms based on exponential smoothing and experimentally evaluate their efficiency and accuracy. We find that exponential smoothing produces very accurate estimates, leading to higher hit rates than the best caching techniques. Our most efficient algorithm is able to analyze a log of 1B accesses in sub-second time on a workstation-class machine.
CD SLAM - continuous localization and mapping in a dynamic world

When performing large-scale perpetual localization and mapping one faces problems like memory consumption or repetitive and dynamic scene elements requiring robust data association. We propose a visual SLAM method which handles short- and long-term scene dynamics in large environments using a single camera only. Through visibility-dependent map filtering and efficient keyframe organization we reach a considerable performance gain only through incorporation of a slightly more complex map representation. Experiments on a large, mixed indoor/outdoor dataset over a time period of two weeks demonstrate the scalability and robustness of our approach.
STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving

We describe a new problem solver called STRIPS that attempts to f ind a sequence of operators in a space of world models to transform a given i n i t i a l world model into a model in which a given goal formula can be proven to be true. STRIPS represents a world model as an arbi trary col lect ion of f i rs t -order predicate calculus formulas and is designed to work with models consisting of 1arge numbers of formulas. 1t employs a resolut ion theorem p rover to answer (jues t ions of par t icular models and uses means-ends analysis to guide it to the desired goal-satisfying model .
Optimizing Multiway Joins in a Map-Reduce Environment

Implementations of map-reduce are being used to perform many operations on very large data. We examine strategies for joining several relations in the map-reduce environment. Our new approach begins by identifying the “map-key,” the set of attributes that identify the Reduce process to which a Map process must send a particular tuple. Each attribute of the map-key gets a “share,” which is the number of buckets into which its values are hashed, to form a component of the identifier of a Reduce process. Relations have their tuples replicated in limited fashion, the degree of replication depending on the shares for those map-key attributes that are missing from their schema. We study the problem of optimizing the shares, given a fixed number of Reduce processes. An algorithm for detecting and fixing problems where a variable is mistakenly included in the map-key is given. Then, we consider two important special cases: chain joins and star joins. In each case, we are able to determine the map-key and determine the shares that yield the least replication. While the method we propose is not always superior to the conventional way of using map-reduce to implement joins, there are some important cases involving large-scale data where our method wins, including: 1) analytic queries in which a very large fact table is joined with smaller dimension tables, and 2) queries involving paths through graphs with high out-degree, such as the Web or a social network.
ccg2lambda: A Compositional Semantics System

We demonstrate a simple and easy-to-use system to produce logical semantic representations of sentences. Our software operates by composing semantic formulas bottom-up given a CCG parse tree. It uses flexible semantic templates to specify semantic patterns. Templates for English and Japanese accompany our software, and they are easy to understand, use and extend to cover other linguistic phenomena or languages. We also provide scripts to use our semantic representations in a textual entailment task, and a visualization tool to display semantically augmented CCG trees in HTML.
MicroRNAs Genomics, Biogenesis, Mechanism, and Function

MicroRNAs (miRNAs) are endogenous approximately 22 nt RNAs that can play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.
Particle Markov Chain Monte Carlo Methods

Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions.Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Lévy-driven stochastic volatility model.
A direct method for locating the focus of expansion

We address the problem of recovering the motion of a monocular observer relative to a rigid scene. We do not make any assumptions about the shapes of the surfaces in the scene, nor do we use estimates of the optical flow or point correspondences. Instead, we exploit the spatial gradient and the time rate of change of brightness over the whole image and explicitly impose the constraint that the surface of an object in the scene must be in front of the camera for it to
Ensemble classification of colon biopsy images based on information rich hybrid features

In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the performance of CBIC on original and reduced feature sets.
The Value of Semantic Parse Labeling for Knowledge Base Question Answering

We demonstrate the value of collecting semantic parse labels for knowledge base question answering. In particular, (1) unlike previous studies on small-scale datasets, we show that learning from labeled semantic parses significantly improves overall performance, resulting in absolute 5 point gain compared to learning from answers, (2) we show that with an appropriate user interface, one can obtain semantic parses with high accuracy and at a cost comparable or lower than obtaining just answers, and (3) we have created and shared the largest semantic-parse labeled dataset to date in order to advance research in question answering.
A Compact Gysel Power Divider With Unequal Power-Dividing Ratio Using One Resistor

A novel compact Gysel power divider is proposed and investigated in this paper. The proposed power divider consists of two transmission lines, a pair of coupled lines, and only one grounded resistor for unequal power-dividing ratio. Comparing to the conventional Gysel power divider, the new power divider saves its space by eliminating the 180 ° electrical length transmission line and reduces complicity by using one grounded resistor. The flexibility in choosing the value of the resistor provides favorable freedom in circuit realization. In addition, analytic design formulas of the proposed power divider, for both equal and unequal ratio cases, are given. For the equal radio case, two grounded resistors are required. Two prototype power dividers are simulated, fabricated, and measured. Both prototypes operate at 2 GHz, but with different power divisions, one for power division ratio k = 1 and the other for k = 2. There are good correlation between the measured results and those of the theoretically designed, justifying the circuit configuration and the design theory.
Deep Direct Regression for Multi-oriented Scene Text Detection

In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. In the context of multioriented scene text detection, we analyze the drawbacks of indirect regression, which covers the state-of-the-art detection structures Faster-RCNN and SSD as instances, and point out the potential superiority of direct regression. To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection. Our detection framework is simple and effective with a fully convolutional network and one-step post processing. The fully convolutional network is optimized in an end-to-end way and has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. The proposed method is particularly beneficial to localize incidental scene texts. On the ICDAR2015 Incidental Scene Text benchmark, our method achieves the F-measure of 81%, which is a new state-ofthe-art and significantly outperforms previous approaches. On other standard datasets with focused scene texts, our method also reaches the state-of-the-art performance.
Automated Monitoring Suspicious Discussions on Online Forums Using Data Mining Statistical Corpus Based Approach

M.Suruthi Murugesan, R. Pavitha Devi, S. Deepthi, V.Sri Lavanya & Dr. Annie Princy Ph.D Department of information technology (B-tech), Panimalar Engineering College. Chennai, India .  The Forum is a huge virtual space where to express and share individual opinions, influencing any aspect of life, with implications for marketing and communication alike. Forums are influencing users preferences by shaping their attitudes and behaviours. Monitoring the suspicious activities is a good way to measure users loyalty, keeping a track on their sentiment towards their posts. The exponential advancement in information and communication technology has fostered the creation of new online forums for much online discussion and has also reduced distances between people. Unfortunately, malicious people use these online forums for illegal purposes. In online forums, the users produce several and various formats of suspicious posts (text, image, video...) and exchange them online with other people .The law enforcement agencies are looking for solutions to monitor these discussion forums for possible illegal activities and download suspected postings that are in text formats as evidence for investigation .The data in most online forums are stored in text format, so this system will focus only on text posts.
Diverse Image-to-Image Translation via Disentangled Representations

Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for producing diverse outputs without paired training images. To achieve diversity, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time. To handle unpaired training data, we introduce a novel cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative comparisons, we measure realism with user study and diversity with a perceptual distance metric. We apply the proposed model to domain adaptation and show competitive performance when compared to the state-of-the-art on the MNIST-M and the LineMod datasets. ? equal contribution ar X iv :1 80 8. 00 94 8v 1 [ cs .C V ] 2 A ug 2 01 8 2 Authors Suppressed Due to Excessive Length
A Paradigm for Situated and Goal-Driven Language Learning

A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research in natural language dialogue should focus on designing communicative agents which can integrate themselves into these contexts and productively collaborate with humans. In this abstract, we propose a general situated language learning paradigm which is designed to bring about robust language agents able to cooperate productively with humans. This dialogue paradigm is built on a utilitarian definition of language understanding. Language is one of multiple tools which an agent may use to accomplish goals in its environment. We say an agent “understands” language only when it is able to use language productively to accomplish these goals. Under this definition, an agent’s communication success reduces to its success on tasks within its environment. This setup contrasts with many conventional natural language tasks, which maximize linguistic objectives derived from static datasets. Such applications often make the mistake of reifying language as an end in itself. The tasks prioritize an isolated measure of linguistic intelligence (often one of linguistic competence, in the sense of Chomsky (1965)), rather than measuring a model’s effectiveness in real-world scenarios. Our utilitarian definition is motivated by recent successes in reinforcement learning methods. In a reinforcement learning setting, agents maximize success metrics on real-world tasks, without requiring direct supervision of linguistic behavior.
Moodplay: Interactive Mood-based Music Discovery and Recommendation

A large body of research in recommender systems focuses on optimizing prediction and ranking. However, recent work has highlighted the importance of other aspects of the recommendations, including transparency, control and user experience in general. Building on these aspects, we introduce MoodPlay, a hybrid recommender system music which integrates content and mood-based filtering in an interactive interface. We show how MoodPlay allows the user to explore a music collection by latent affective dimensions, and we explain how to integrate user input at recommendation time with predictions based on a pre-existing user profile. Results of a user study (N=240) are discussed, with four conditions being evaluated with varying degrees of visualization, interaction and control. Results show that visualization and interaction in a latent space improve acceptance and understanding of both metadata and item recommendations. However, too much of either can result in cognitive overload and a negative impact on user experience.
On Security Analysis of Proof-of-Elapsed-Time (PoET)

As more applications are built on top of blockchain and public ledger, different approaches are developed to improve the performance of blockchain construction. Recently Intel proposed a new concept of proof-of-elapsed-time (PoET), which leverages trusted computing to enforce random waiting times for block construction. However, trusted computing component may not be perfect and 100% reliable. It is not clear, to what extent, blockchain systems based on PoET can tolerate failures of trusted computing component. The current design of PoET lacks rigorous security analysis and a theoretical foundation for assessing its strength against such attacks. To fulfill this gap, we develop a theoretical framework for evaluating a PoET based blockchain system, and show that the current design is vulnerable in the sense that adversary can jeopardize the blockchain system by only compromising Θ(log log n/ log n) fraction of the participating nodes, which is very small when n is relatively large. Based on our theoretical analysis, we also propose methods to mitigate these vulnerabilities.
Simulation and Implementation of Grid-connected Inverters

Solar, wind and hydro are renewable energy sources that are seen as reliable alternatives to conventional energy sources such as oil or natural gas. However, the efficiency and the performance of renewable energy systems are still under development. Consequently, the control structures of the grid-connected inverter as an important section for energy conversion and transmission should be improved to meet the requirements for grid interconnection. In this paper, a comprehensive simulation and implementation of a three-phase grid-connected inverter is presented. The control structure of the grid-side inverter is firstly discussed. Secondly, the space vector modulation SVM is presented. Thirdly, the synchronization for grid-connected inverters is discussed. Finally, the simulation of the grid-connected inverter system using PSIM simulation package and the system implementation are presented to illustrate concepts and compare their results.
SVM Based Decision Support System for Heart Disease Classification with Integer-Coded Genetic Algorithm to Select Critical Features

The paper presents a decision support system for heart diseases classification based on support vector machines (SVM) and integer-coded genetic algorithm (GA). Simple Support Vector Machine (SSVM) algorithm has been used to determine the support vectors in a fast, iterative manner. For selecting the important and relevant features and discarding those irrelevant and redundant ones, integer-coded genetic algorithm is used which also maximizes SVM‘s classification accuracy. The heart disease database used in this study includes 303 cases, and 13 diagnostic features were used for each case. The results of the 5-class classification problem indicate an increase in the overall accuracy using the optimal feature subset, accuracy achieved being 72.55% indicating the potential of the system to be used as a practical decision support system. As a two class problem, the proposed method gives an accuracy of 90.57% which is better than the existing methods.
A Study of Time Management: The Correlation between Video Game Usage and Academic Performance Markers

This study analyzes the correlation between video game usage and academic performance. Scholastic Aptitude Test (SAT) and grade-point average (GPA) scores were used to gauge academic performance. The amount of time a student spends playing video games has a negative correlation with students' GPA and SAT scores. As video game usage increases, GPA and SAT scores decrease. A chi-squared analysis found a p value for video game usage and GPA was greater than a 95% confidence level (0.005 < p < 0.01). This finding suggests that dependence exists. SAT score and video game usage also returned a p value that was significant (0.01 < p < 0.05). Chi-squared results were not significant when comparing time spent studying and an individual's SAT score. This research suggests that video games may have a detrimental effect on an individual's GPA and possibly on SAT scores. Although these results show statistical dependence, proving cause and effect remains difficult, since SAT scores represent a single test on a given day. The effects of video games maybe be cumulative; however, drawing a conclusion is difficult because SAT scores represent a measure of general knowledge. GPA versus video games is more reliable because both involve a continuous measurement of engaged activity and performance. The connection remains difficult because of the complex nature of student life and academic performance. Also, video game usage may simply be a function of specific personality types and characteristics.
From MAP to Marginals: Variational Inference in Bayesian Submodular Models

Submodular optimization has found many applications in machine learning and beyond. We carry out the first systematic investigation of inference in probabilistic models defined through submodular functions, generalizing regular pairwise MRFs and Determinantal Point Processes. In particular, we present L-FIELD, a variational approach to general log-submodular and log-supermodular distributions based on suband supergradients. We obtain both lower and upper bounds on the log-partition function, which enables us to compute probability intervals for marginals, conditionals and marginal likelihoods. We also obtain fully factorized approximate posteriors, at the same computational cost as ordinary submodular optimization. Our framework results in convex problems for optimizing over differentials of submodular functions, which we show how to optimally solve. We provide theoretical guarantees of the approximation quality with respect to the curvature of the function. We further establish natural relations between our variational approach and the classical mean-field method. Lastly, we empirically demonstrate the accuracy of our inference scheme on several submodular models.