nils-herrmann/clean_scirepeval huggingface dataset:
Context-aware query classification

Understanding users'search intent expressed through their search queries is crucial to Web search and online advertisement. Web query classification (QC) has been widely studied for this purpose. Most previous QC algorithms classify individual queries without considering their context information. However, as exemplified by the well-known example on query "jaguar", many Web queries are short and ambiguous, whose real meanings are uncertain without the context information. In this paper, we incorporate context information into the problem of query classification by using conditional random field (CRF) models. In our approach, we use neighboring queries and their corresponding clicked URLs (Web pages) in search sessions as the context information. We perform extensive experiments on real world search logs and validate the effectiveness and effciency of our approach. We show that we can improve the F1 score by 52% as compared to other state-of-the-art baselines.
A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes

We investigate semisupervised learning (SL) and pool-based active learning (AL) of a classifier for domains with label-scarce (LS) and unknown categories, i.e., defined categories for which there are initially no labeled examples. This scenario manifests, e.g., when a category is rare, or expensive to label. There are several learning issues when there are unknown categories: 1) it is a priori unknown which subset of (possibly many) measured features are needed to discriminate unknown from common classes and 2) label scarcity suggests that overtraining is a concern. Our classifier exploits the inductive bias that an unknown class consists of the subset of the unlabeled pool’s samples that are atypical (relative to the common classes) with respect to certain key (albeit a priori unknown) features and feature interactions. Accordingly, we treat negative log- $p$ -values on raw features as nonnegatively weighted derived feature inputs to our class posterior, with zero weights identifying irrelevant features. Through a hierarchical class posterior, our model accommodates multiple common classes, multiple LS classes, and unknown classes. For learning, we propose a novel semisupervised objective customized for the LS/unknown category scenarios. While several works minimize class decision uncertainty on unlabeled samples, we instead preserve this uncertainty [maximum entropy (maxEnt)] to avoid overtraining. Our experiments on a variety of UCI Machine learning (ML) domains show: 1) the use of  $p$ -value features coupled with weight constraints leads to sparse solutions and gives significant improvement over the use of raw features and 2) for LS SL and AL, unlabeled samples are helpful, and should be used to preserve decision uncertainty (maxEnt), rather than to minimize it, especially during the early stages of AL. Our AL system, leveraging a novel sample-selection scheme, discovers unknown classes and discriminates LS classes from common ones, with sparing use of oracle labeling.
Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality

The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections. We examine current practices and show that GEC’s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human judgments. In light of this, we propose an alternate approach that jettisons costly error coding in favor of unannotated, whole-sentence rewrites. We compare the performance of existing metrics over different gold-standard annotations, and show that automatic evaluation with our new annotation scheme has very strong correlation with expert rankings (ρ = 0.82). As a result, we advocate for a fundamental and necessary shift in the goal of GEC, from correcting small, labeled error types, to producing text that has native fluency.
Segmenting Consumers Based on Luxury Value Perceptions

This study seeks to discover consumer segments by using a multidimensional concept of luxury by encompassing functional, individual and social components in the luxury market. Survey data was collected from 1097 consumers in Iran. Eight luxury factors were indentified through an exploratory factor analysis. These factors are used for segmenting these consumers with the K-means method. Cluster analysis of the data resulted in four different behavioral style segments namely: non-luxury consumers, rational consumers, social seeker consumers and materialistic consumers. Each segment shows the importance of luxury value dimensions differently. This study sheds light on the differences between consumers’ perception about luxury value, which helps marketers to choose their marketing strategies more consistently with the consumers’ viewpoint.
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine

We describe a new Bayesian click-through rate (CTR) prediction algorithm used for Sponsored Search in Microsoft’s Bing search engine. The algorithm is based on a probit regression model that maps discrete or real-valued input features to probabilities. It maintains Gaussian beliefs over weights of the model and performs Gaussian online updates derived from approximate message passing. Scalability of the algorithm is ensured through a principled weight pruning procedure and an approximate parallel implementation. We discuss the challenges arising from evaluating and tuning the predictor as part of the complex system of sponsored search where the predictions made by the algorithm decide about future training sample composition. Finally, we show experimental results from the production system and compare to a calibrated Naïve Bayes algorithm.
Syntax errors just aren't natural: improving error reporting with language models

A frustrating aspect of software development is that compiler error messages often fail to locate the actual cause of a syntax error. An errant semicolon or brace can result in many errors reported throughout the file. We seek to find the actual source of these syntax errors by relying on the consistency of software: valid source code is usually repetitive and unsurprising. We exploit this consistency by constructing a simple N-gram language model of lexed source code tokens. We implemented an automatic Java syntax-error locator using the corpus of the project itself and evaluated its performance on mutated source code from several projects. Our tool, trained on the past versions of a project, can effectively augment the syntax error locations produced by the native compiler. Thus we provide a methodology and tool that exploits the naturalness of software source code to detect syntax errors alongside the parser.
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is over a computer network and the other is on a shared memory system. We establish an ergodic convergence rate O(1/ √ K) for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by √ K (K is the total number of iterations). Our results generalize and improve existing analysis for convex minimization.
Real-Time Editing , Synthesis , and Rendering of Infinite Landscapes on GPUs

Recent advances in algorithms and graphics hardware have opened the possibility to render large terrain fields at interactive rates on commodity PCs. Due to these advances it is possible today to interactively synthesize artificial terrains using procedural descriptions. Our paper extends on this work by presenting a new GPU method for real-time editing, synthesis, and rendering of infinite landscapes exhibiting a wide range of geological structures. Our method builds upon the concept of projected grids to achieve near-optimal sampling of the landscape. We describe the integration of procedural shaders for multifractals into this approach, and we propose intuitive options to edit the shape of the resulting terrain. The method is multi-scale and adaptive in nature, and it has been extended towards infinite and spherical domains. In combination with geo-typical textures that automatically adapt to the shape being synthesized, a powerful method for the creation and rendering of realistic landscapes is presented.
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering

Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional space, which is due to the so-called "curse of dimensionality". In addition, the high-dimensional data often contains a signi cant amount of noise which causes additional e ectiveness problems. In this paper, we review and compare the existing algorithms for clustering highdimensional data and show the impact of the curse of dimensionality on their e ectiveness and e ciency. The comparison reveals that condensation-based approaches (such as BIRCH or STING) are the most promising candidates for achieving the necessary e ciency, but it also shows that basically all condensation-based approaches have severe weaknesses with respect to their e ectiveness in highdimensional space. To overcome these problems, we develop a new clustering technique called OptiGrid which is based on constructing an optimal grid-partitioning of the data. The optimal grid-partitioning is determined by calculating the best partitioning hyperplanes for each dimension (if such a partitioning exists) using certain projections of the data. The advantages of our new approach are (1) it has a rm mathematical basis (2) it is by far more e ective than existing clustering algorithms for highdimensional data (3) it is very e cient even for large data sets of high dimensionality. To demonstrate the e ectiveness and e ciency of our new approach, we perform a series of experiments on a number of di erent data sets including real data sets from CAD and molecular biology. A comparison with one of the best known algorithms (BIRCH) shows the superiority of our new approach. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.
Cognitive load in hypertext reading: A review

A process model of hypertext reading was used to generate predictions about the effects of hypertext features on cognitive processing during text navigation and comprehension. We evaluated the predictions of the model with respect to the extant literature, focusing on studies in which versions of hypertexts were compared. Consistent with our predictions, the increased demands of decisionmaking and visual processing in hypertext impaired reading performance. Individual differences in readers, such as working memory capacity and prior knowledge, mediated the impact of hypertext features. For example, readers with low working memory and low prior knowledge were usually disadvantaged in hypertext. Some benefits were observed for learners with low prior knowledge, however, if the hypertext structure was hierarchical and consistent with that of the knowledge domain. We also surveyed the effectiveness of structural features designed to reduce cognitive load, including graphical overviews, restricted access to links, and visible link types. Complex graphical overviews did not reliably enable learning and navigation, whereas navigational support from restricted access and visible link types were helpful. We identified gaps in the empirical literature and suggested future studies to investigate cognitive processes in hypertext reading.
Color map optimization for 3D reconstruction with consumer depth cameras

We present a global optimization approach for mapping color images onto geometric reconstructions. Range and color videos produced by consumer-grade RGB-D cameras suffer from noise and optical distortions, which impede accurate mapping of the acquired color data to the reconstructed geometry. Our approach addresses these sources of error by optimizing camera poses in tandem with non-rigid correction functions for all images. All parameters are optimized jointly to maximize the photometric consistency of the reconstructed mapping. We show that this optimization can be performed efficiently by an alternating optimization algorithm that interleaves analytical updates of the color map with decoupled parameter updates for all images. Experimental results demonstrate that our approach substantially improves color mapping fidelity.
Light-weight bounds checking

Memory errors in C and C++ programs continue to be one of the dominant sources of security problems, accounting for over a third of the high severity vulnerabilities reported in 2011. Wide-spread deployment of defenses such as address-space layout randomization (ASLR) have made memory exploit development more difficult, but recent trends indicate that attacks are evolving to overcome this defense. Techniques for systematic detection and blocking of memory errors can provide more comprehensive protection that can stand up to skilled adversaries, but unfortunately, these techniques introduce much higher overheads and provide significantly less compatibility than ASLR. We propose a new memory error detection technique that explores a part of the design space that trades off some ability to detect bounds errors in order to obtain good performance and excellent backwards compatibility. On the SPECINT 2000 benchmark, the runtime overheads of our technique is about half of that reported by the fastest previous bounds-checking technique. On the compatibility front, our technique has been tested on over 7 million lines of code, which is much larger than that reported for previous bounds-checking techniques.
Energy-Information Transmission Tradeoff in Green Cloud Computing

With the rise of Internet-scale systems and cloud computing services, there is an increasing trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid and potentially the amount of greenhouse gas emissions and carbon footprint. In this regard, the locations that are selected to build future data centers as well as the service load to be routed to each data center after it is built need to be carefully studied given various environmental, cost, and quality-of-service considerations. To gain insights into these problems, we develop an optimizationbased framework, where the objective functions range from minimizing the energy cost to minimizing the carbon footprint subject to essential quality-of-service constraints. We show that in multiple scenarios, these objectives can be conflicting leading to an energy-information tradeoff in green cloud computing.
VLSI implementation of deep neural networks using integral stochastic computing

The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention since many applications require high-speed operations. However, numerous processing elements and complex interconnections are usually required, leading to a large area occupation and a high power consumption. Stochastic computing has shown promising results for area-efficient hardware implementations, even though existing stochastic algorithms require long streams that exhibit long latency. In this paper, we propose an integer form of stochastic computation and introduce some elementary circuits. We then propose an efficient implementation of a DNN based on integral stochastic computing. The proposed architecture uses integer stochastic streams and a modified Finite State Machine-based tanh function to improve the performance and reduce the latency compared to existing stochastic architectures for DNN. The simulation results show the negligible performance loss of the proposed integer stochastic DNN for different network sizes compared to their floating point versions.
More than bin packing: Dynamic resource allocation strategies in cloud data centers

Resource allocation strategies in virtualized data centers have received considerable attention recently as they can have substantial impact on the energy efficiency of a data center. This led to new decision and control strategies with significant managerial impact for IT service providers. We focus on dynamic environments where virtual machines need been analyzed and used to place virtual machines upon arrival. However, these placement heuristics can lead to suboptimal server utilization, because they cannot consider virtual machines, which arrive in the future. We ran extensive lab experiments and simulations with different controllers and different workloads to understand which control strategies achieve high levels of energy efficiency in different workload environments. We found that combinations of placement controllers and periodic reallocations achieve the highest energy efficiency subject to predefined service levels. While the type of placement heuristic had little impact on the average server demand, the type of virtual machine resource demand estimator used for the placement decisions had a significant impact on the overall energy efficiency. & 2015
Digital Construction through BIM Systems will Drive the Re-engineering of Construction Business Practices

The construction industry has been facing a paradigm shift to (i) increase; productivity, efficiency, infrastructure value, quality and sustainability, (ii) reduce; lifecycle costs, lead times and duplications, via effective collaboration and communication of stakeholders in construction projects. Digital construction is a political initiative to address low productivity in the sector. This seeks to integrate processes throughout the entire lifecycle by utilising building information modelling (BIM) systems. The focus is to create and reuse consistent digital information by the stakeholders throughout the lifecycle. However, implementation and use of BIM systems requires dramatic changes in the current business practices, bring new challenges for stakeholders e.g., the emerging knowledge and skill gap. This paper reviews and discusses the status of implementation of the BIM systems around the globe and their implications to the industry. Moreover, based on the lessons learnt, it will provide a guide to tackle these challenges and to facilitate successful transition towards utilizing BIM systems in construction projects.
Plastic Analysis and Design of Steel Plate Shear Walls

plate is ntation of by plastic plex, wall ection of procedure  A revised procedure for the design of steel plate shear walls is proposed. In this procedure the thickness of the infill found using equations that are derived from the plastic analysis of the strip model, which is an accepted model for the represe steel plate shear walls. Comparisons of experimentally obtained ultimate strengths of steel plate shear walls and those predicted analysis are given and reasonable agreement is observed. Fundamental plastic collapse mechanisms for several, more com configurations are also given. Additionally, an existing codified procedure for the design of steel plate walls is reviewed and a s this procedure which could lead to designs with less-than-expected ultimate strength is identified. It is shown that the proposed eliminates this possibility without changing the other valid sections of the current procedure.
One-shot learning of manipulation skills with online dynamics adaptation and neural network priors

One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand. Model-based reinforcement learning can achieve good sample efficiency, but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy. In this work, we develop a model-based reinforcement learning algorithm that combines prior knowledge from previous tasks with online adaptation of the dynamics model. These two ingredients enable highly sample-efficient learning even in regimes where estimating the true dynamics is very difficult, since the online model adaptation allows the method to locally compensate for unmodeled variation in the dynamics. We encode the prior experience into a neural network dynamics model, adapt it online by progressively refitting a local linear model of the dynamics, and use model predictive control to plan under these dynamics. Our experimental results show that this approach can be used to solve a variety of complex robotic manipulation tasks in just a single attempt, using prior data from other manipulation behaviors.
CacheKit: Evading Memory Introspection Using Cache Incoherence

With the growing importance of networked embedded devices in the upcoming Internet of Things, new attacks targeting embedded OSes are emerging. ARM processors, which power over 60% of embedded devices, introduce a hardware security extension called TrustZone to protect secure applications in an isolated secure world that cannot be manipulated by a compromised OS in the normal world. Leveraging TrustZone technology, a number of memory integrity checking schemes have been proposed in the secure world to introspect malicious memory modification of the normal world. In this paper, we first discover and verify an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure and non-secure, potentially being different even when they are mapped to the same physical address. Furthermore, code in one TrustZone world cannot access the cache content in the other world. Based on this observation, we develop a new rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. We implement a CacheKit prototype on Cortex-A8 processors after solving a number of challenges. First, we employ the Cache-as-RAM technique to ensure that the malicious code is only loaded into the CPU cache and not RAM. Thus, the secure world cannot detect the existence of the malicious code by examining the RAM. Second, we use the ARM processor's hardware support on cache settings to keep the malicious code persistent in the cache. Third, to evade introspection that flushes cache content back into RAM, we utilize physical addresses from the I/O address range that is not backed by any real I/O devices or RAM. The experimental results show that CacheKit can successfully evade memory introspection from the secure world and has small performance impacts on the rich OS. We discuss potential countermeasures to detect this type of rootkit attack.