nils-herrmann/clean_scirepeval huggingface dataset:
Utility preserving query log anonymization via semantic microaggregation

Query logs are of great interest for scientists and companies for research, statistical and commercial purposes. However, the availability of query logs for secondary uses raises privacy issues since they allow the identification and/or revelation of sensitive information about individual users. Hence, query anonymization is crucial to avoid identity disclosure. To enable the publication of privacy-preserved -but still usefulquery logs, in this paper, we present an anonymization method based on semantic microaggregation. Our proposal aims at minimizing the disclosure risk of anonymized query logs while retaining their semantics as much as possible. First, a method to map queries to their formal semantics extracted from the structured categories of the Open Directory Project is presented. Then, a microaggregation method is adapted to perform a semantically-grounded anonymization of query logs. To do so, appropriate semantic similarity and semantic aggregation functions are proposed. Experiments performed using real AOL query logs show that our proposal better retains the utility of anonymized query logs than other related works, while also minimizing the disclosure risk.
EventMiner: Mining Events from Annotated Documents

Events are central in human history and thus also in Web queries, in particular if they relate to history or news. However, ambiguity issues arise as queries may refer to ambiguous events differing in time, geography, or participating entities. Thus, users would greatly benefit if search results were presented along different events. In this paper, we present EventMiner, an algorithm that mines events from top-k pseudo-relevant documents for a given query. It is a probabilistic framework that leverages semantic annotations in the form of temporal expressions, geographic locations, and named entities to analyze natural language text and determine important events. Using a large news corpus, we show that using semantic annotations, EventMiner detects important events and presents documents covering the identified events in the order of their importance.
Summarizing Answers in Non-Factoid Community Question-Answering

We aim at summarizing answers in community question-answering (CQA). While most previous work focuses on factoid question-answering, we focus on the non-factoid question-answering. Unlike factoid CQA, non-factoid question-answering usually requires passages as answers. The shortness, sparsity and diversity of answers form interesting challenges for summarization. To tackle these challenges, we propose a sparse coding-based summarization strategy that includes three core ingredients: short document expansion, sentence vectorization, and a sparse-coding optimization framework. Specifically, we extend each answer in a question-answering thread to a more comprehensive representation via entity linking and sentence ranking strategies. From answers extended in this manner, each sentence is represented as a feature vector trained from a short text convolutional neural network model. We then use these sentence representations to estimate the saliency of candidate sentences via a sparse-coding framework that jointly considers candidate sentences and Wikipedia sentences as reconstruction items. Given the saliency vectors for all candidate sentences, we extract sentences to generate an answer summary based on a maximal marginal relevance algorithm. Experimental results on a benchmark data collection confirm the effectiveness of our proposed method in answer summarization of non-factoid CQA, and moreover, its significant improvement compared to state-of-the-art baselines in terms of ROUGE metrics.
Accurate Junction Detection and Characterization in Natural Images

Accurate junction detection and characterization are of primary importance for several aspects of scene analysis, including depth recovery and motion analysis. In this work, we introduce a generic junction analysis scheme. The first asset of the proposed procedure is an automatic criterion for the detection of junctions, permitting to deal with textured parts in which no detection is expected. Second, the method yields a characterization of L-, Y- and X- junctions, including a precise computation of their type, localization and scale. Contrary to classical approaches, scale characterization does not rely on the linear scale-space. First, an a contrario approach is used to compute the meaningfulness of a junction. This approach relies on a statistical modeling of suitably normalized gray level gradients. Then, exclusion principles between junctions permit their precise characterization. We give implementation details for this procedure and evaluate its efficiency through various experiments.
ARRIVE: Algorithm for Robust Routing in Volatile Environments

Wireless sensor networks for environmental monitoring and distributed control will be deployed on a large scale in the near future. Due to the low per-node cost, these networks are expected to be both large and dense. However, because of the limited computation, storage, and power available to each node, conventional ad-hoc routing techniques are not feasible in this domain, and more novel routing algorithms are required. Despite the need for a simpler approach, routing in sensor networks still needs to be both robust to failures and secure against compromised and malicious nodes. We propose ARRIVE, a probabilistic algorithm that leverages the high node density and the inherent broadcast medium found in sensor networks to achieve routing robust to both link failures and patterned node failures without resorting to periodic flooding of the network. Our algorithm is based on a tree-like topology rooted at the sink of the network, and nodes use localized observed behavior of the surrounding nodes to make probabilistic decisions for forwarding packets. We have found that ARRIVE adapts to large patterned failures within a relatively short period of time at the cost of only moderate increases in overall power consumption and source-to-sink latency.
Efficient Stereo Matching Leveraging Deep Local and Context Information

Stereo matching is a challenging problem with respect to weak texture, discontinuities, illumination difference and occlusions. Therefore, a deep learning framework is presented in this paper, which focuses on the first and last stage of typical stereo methods: the matching cost computation and the disparity refinement. For matching cost computation, two patch-based network architectures are exploited to allow the trade-off between speed and accuracy, both of which leverage multi-size and multi-layer pooling unit with no strides to learn cross-scale feature representations. For disparity refinement, unlike traditional handcrafted refinement algorithms, we incorporate the initial optimal and sub-optimal disparity maps before outlier detection. Furthermore, diverse base learners are encouraged to focus on specific replacement tasks, corresponding to the smooth regions and details. Experiments on different datasets demonstrate the effectiveness of our approach, which is able to obtain sub-pixel accuracy and restore occlusions to a great extent. Specifically, our accurate framework attains near-peak accuracy both in non-occluded and occluded region and our fast framework achieves competitive performance against the fast algorithms on Middlebury benchmark.
Carmen : A Twitter Geolocation System with Applications to Public Health

Public health applications using social media often require accurate, broad-coverage location information. However, the standard information provided by social media APIs, such as Twitter, cover a limited number of messages. This paper presents Carmen, a geolocation system that can determine structured location information for messages provided by the Twitter API. Our system utilizes geocoding tools and a combination of automatic and manual alias resolution methods to infer location structures from GPS positions and user-provided profile data. We show that our system is accurate and covers many locations, and we demonstrate its utility for improving influenza surveillance.
HYPERSHELL: A Practical Hypervisor Layer Guest OS Shell for Automated In-VM Management

To direct the operation of a computer, we often use a shell, a user interface that provides accesses to the OS kernel services. Traditionally, shells are designed atop an OS kernel. In this paper, we show that a shell can also be designed below an OS. More specifically, we present HYPERSHELL, a practical hypervisor layer guest OS shell that has all of the functionality of a traditional shell, but offers better automation, uniformity and centralized management. This will be particularly useful for cloud and data center providers to manage the running VMs in a large scale. To overcome the semantic gap challenge, we introduce a reverse system call abstraction, and we show that this abstraction can significantly relieve the painful process of developing software below an OS. More importantly, we also show that this abstraction can be implemented transparently. As such, many of the legacy guest OS management utilities can be directly reused in HYPERSHELL without any modification. Our evaluation with over one hundred management utilities demonstrates that HYPERSHELL has 2.73X slowdown on average compared to their native in-VM execution, and has less than 5% overhead to the guest OS kernel.
Accelerated image processing on FPGAs

The Cameron project has developed a language called single assignment C (SA-C), and a compiler for mapping image-based applications written in SA-C to field programmable gate arrays (FPGAs). The paper tests this technology by implementing several applications in SA-C and compiling them to an Annapolis Microsystems (AMS) WildStar board with a Xilinx XV2000E FPGA. The performance of these applications on the FPGA is compared to the performance of the same applications written in assembly code or C for an 800 MHz Pentium III. (Although no comparison across processors is perfect, these chips were the first of their respective classes fabricated at 0.18 microns, and are therefore of comparable ages.) We find that applications written in SA-C and compiled to FPGAs are between 8 and 800 times faster than the equivalent program run on the Pentium III.
IKE - An Interactive Tool for Knowledge Extraction

Recent work on information extraction has suggested that fast, interactive tools can be highly effective; however, creating a usable system is challenging, and few publically available tools exist. In this paper we present IKE, a new extraction tool that performs fast, interactive bootstrapping to develop high-quality extraction patterns for targeted relations. Central to IKE is the notion that an extraction pattern can be treated as a search query over a corpus. To operationalize this, IKE uses a novel query language that is expressive, easy to understand, and fast to execute essential requirements for a practical system. It is also the first interactive extraction tool to seamlessly integrate symbolic (boolean) and distributional (similarity-based) methods for search. An initial evaluation suggests that relation tables can be populated substantially faster than by manual pattern authoring while retaining accuracy, and more reliably than fully automated tools, an important step towards practical KB construction. We are making IKE publically available (http://allenai.org/ software/interactive-knowledge-extraction).
A Multi-Path Gated Ring Oscillator TDC With First-Order Noise Shaping

An 11-bit, 50-MS/s time-to-digital converter (TDC) using a multipath gated ring oscillator with 6 ps of effective delay per stage demonstrates 1st-order noise shaping. At frequencies below 1 MHz, the TDC error integrates to 80 fs (rms) for a dynamic range of 95 dB with no calibration required. The 157 times 258 mum TDC is realized in 0.13 mum CMOS and, depending on the time difference between input edges, consumes 2.2 to 21 mA from a 1.5 V supply.
Structures of Cas9 Endonucleases Reveal RNA-Mediated Conformational Activation

Type II CRISPR (clustered regularly interspaced short palindromic repeats)–Cas (CRISPR-associated) systems use an RNA-guided DNA endonuclease, Cas9, to generate double-strand breaks in invasive DNA during an adaptive bacterial immune response. Cas9 has been harnessed as a powerful tool for genome editing and gene regulation in many eukaryotic organisms. We report 2.6 and 2.2 angstrom resolution crystal structures of two major Cas9 enzyme subtypes, revealing the structural core shared by all Cas9 family members. The architectures of Cas9 enzymes define nucleic acid binding clefts, and single-particle electron microscopy reconstructions show that the two structural lobes harboring these clefts undergo guide RNA–induced reorientation to form a central channel where DNA substrates are bound. The observation that extensive structural rearrangements occur before target DNA duplex binding implicates guide RNA loading as a key step in Cas9 activation.
GaN MMIC based T/R-Module Front-End for X-Band Applications

Amplifiers for a next generation of T/R-modules in future active array antennas are realized as monolithically integrated circuits (MMIC) on the bases of novel AlGaN/GaN HEMT structures. Both, low noise and power amplifiers are designed for X-band frequencies. The MMICs are designed, simulated and fabricated using a novel via-hole microstrip technology. Output power levels of 6.8 W (38 dBm) for the driver amplifier (DA) and 20 W (43 dBm) for the high power amplifier (HPA) are measured. The measured noise figure of the low noise amplifier (LNA) is in the range of 1.5 dB. A T/R-module front-end with mounted GaN MMICs is designed based on a multilayer LTCC technology.
Face Detection with End-to-End Integration of a ConvNet and a 3D Model

This paper presents a method for face detection in the wild, which integrates a ConvNet and a 3D mean face model in an end-to-end multi-task discriminative learning framework. There are two components: i) The face proposal component computes face proposals via estimating facial key-points and the 3D transformation parameters for each predicted keypoint w.r.t. the 3D mean face model. ii) The face verification component computes detection results by refining proposals based on configuration pooling.
Morph Deterction from Single Face Image: a Multi-Algorithm Fusion Approach

The vulnerability of face, fingerprint and iris recognition systems to attacks based on morphed biometric samples has been established in the recent past. However, so far a reliable detection of morphed biometric samples has remained an unsolved research challenge. In this work, we propose the first multi-algorithm fusion approach to detect morphed facial images. The FRGCv2 face database is used to create a set of 4,808 morphed and 2,210 bona fide face images which are divided into a training and test set. From a single cropped facial image features are extracted using four types of complementary feature extraction algorithms, including texture descriptors, keypoint extractors, gradient estimators and a deep learning-based method. By performing a score-level fusion of comparison scores obtained by four different types of feature extractors, a detection equal error rate (D-EER) of 2.8% is achieved. Compared to the best single algorithm approach achieving a D-EER of 5.5%, the D-EER of the proposed multi-algorithm fusion system is al- most twice as low, confirming the soundness of the presented approach.
Mass spectrometry-based proteomics

Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein–protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.
Toward terabyte pattern mining: an architecture-conscious solution

We present a strategy for mining frequent item sets from terabyte-scale data sets on cluster systems. The algorithm embraces the holistic notion of architecture-conscious datamining, taking into account the capabilities of the processor, the memory hierarchy and the available network interconnects. Optimizations have been designed for lowering communication costs using compressed data structures and a succinct encoding. Optimizations for improving cache, memory and I/O utilization using pruningand tiling techniques, and smart data placement strategies are also employed. We leverage the extended memory spaceand computational resources of a distributed message-passing clusterto design a scalable solution, where each node can extend its metastructures beyond main memory by leveraging 64-bit architecture support. Our solution strategy is presented in the context of FPGrowth, a well-studied and rather efficient frequent pattern mining algorithm. Results demonstrate that the proposed strategy result in near-linearscaleup on up to 48 nodes.
An optical cloak made of dielectrics.

Invisibility devices have captured the human imagination for many years. Recent theories have proposed schemes for cloaking devices using transformation optics and conformal mapping. Metamaterials, with spatially tailored properties, have provided the necessary medium by enabling precise control over the flow of electromagnetic waves. Using metamaterials, the first microwave cloaking has been achieved but the realization of cloaking at optical frequencies, a key step towards achieving actual invisibility, has remained elusive. Here, we report the first experimental demonstration of optical cloaking. The optical 'carpet' cloak is designed using quasi-conformal mapping to conceal an object that is placed under a curved reflecting surface by imitating the reflection of a flat surface. The cloak consists only of isotropic dielectric materials, which enables broadband and low-loss invisibility at a wavelength range of 1,400-1,800 nm.
Occupy the cloud: distributed computing for the 99%

Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation frameworks have moved beyond a simple map-reduce model, many users are still left to struggle with complex cluster management and configuration tools, even for running simple embarrassingly parallel jobs. We argue that stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. Furthermore, using our prototype implementation, PyWren, we show that this model is general enough to implement a number of distributed computing models, such as BSP, efficiently. Extrapolating from recent trends in network bandwidth and the advent of disaggregated storage, we suggest that stateless functions are a natural fit for data processing in future computing environments.
Wireless sensor networks localization algorithms: a comprehensive survey

Wireless sensor networks (WSNs) have recently gained a lot of attention by scientific community. Small and inexpensive devices with low energy consumption and limited computing resources are increasingly being adopted in different application scenarios including environmental monitoring, target tracking and biomedical health monitoring. In many such applications, node localization is inherently one of the system parameters. Localization process is necessary to report the origin of events, routing and to answer questions on the network coverage ,assist group querying of sensors. In general, localization schemes are classified into two broad categories: range-based and range-free. However, it is difficult to classify hybrid solutions as range-based or range-free. In this paper we make this classification easy, where range-based schemes and range-free schemes are divided into two types: fully schemes and hybrid schemes. Moreover, we compare the most relevant localization algorithms and discuss the future research directions for wireless sensor networks localization schemes.
Optimal Thresholding of Classifiers to Maximize F1 Measure

This paper provides new insight into maximizing F1 measures in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, the F1 measure is widely used to evaluate the success of a binary classifier when one class is rare. Micro average, macro average, and per instance average F1 measures are used in multilabel classification. For any classifier that produces a real-valued output, we derive the relationship between the best achievable F1 value and the decision-making threshold that achieves this optimum. As a special case, if the classifier outputs are well-calibrated conditional probabilities, then the optimal threshold is half the optimal F1 value. As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive. When the actual prevalence of positive examples is low, this behavior can be undesirable. As a case study, we discuss the results, which can be surprising, of maximizing F1 when predicting 26,853 labels for Medline documents.
Bootstrapping Ontologies for Web Services

Ontologies have become the de-facto modeling tool of choice, employed in many applications and prominently in the semantic web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping, which aims at automatically generating concepts and their relations in a given domain, is a promising technique for ontology construction. Bootstrapping an ontology based on a set of predefined textual sources, such as web services, must address the problem of multiple, largely unrelated concepts. In this paper, we propose an ontology bootstrapping process for web services. We exploit the advantage that web services usually consist of both WSDL and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. Our proposed ontology bootstrapping process integrates the results of both methods and applies a third method to validate the concepts using the service free text descriptor, thereby offering a more accurate definition of ontologies. We extensively validated our bootstrapping method using a large repository of real-world web services and verified the results against existing ontologies. The experimental results indicate high precision. Furthermore, the recall versus precision comparison of the results when each method is separately implemented presents the advantage of our integrated bootstrapping approach.
Computer Vision for Head Pose Estimation: Review of a Competition

This paper studies the prediction of head pose from still images, and summarizes the outcome of a recently organized competition, where the task was to predict the yaw and pitch angles of an image dataset with 2790 samples with known angles. The competition received 292 entries from 52 participants, the best ones clearly exceeding the state-of-the-art accuracy. In this paper, we present the key methodologies behind selected top methods, summarize their prediction accuracy and compare with the current state of the art.