nils-herrmann/clean_scirepeval huggingface dataset:
A Deep Architecture for Matching Short Texts

Many machine learning problems can be interpreted as learning for matching two types of objects (e.g., images and captions, users and products, queries and documents, etc.). The matching level of two objects is usually measured as the inner product in a certain feature space, while the modeling effort focuses on mapping of objects from the original space to the feature space. This schema, although proven successful on a range of matching tasks, is insufficient for capturing the rich structure in the matching process of more complicated objects. In this paper, we propose a new deep architecture to more effectively model the complicated matching relations between two objects from heterogeneous domains. More specifically, we apply this model to matching tasks in natural language, e.g., finding sensible responses for a tweet, or relevant answers to a given question. This new architecture naturally combines the localness and hierarchy intrinsic to the natural language problems, and therefore greatly improves upon the state-of-the-art models.
A longitudinal study of the Google app market

Recently emerged app markets provide a centralized paradigm for software distribution in smartphones. The difficulty of massively collecting app data has led to a lack a good understanding of app market dynamics. In this paper we seek to address this problem, through a detailed temporal analysis of Google Play, Google's app market. We perform the analysis on data that we collected daily from 160,000 apps, over a period of six months in 2012. We report often surprising results. For instance, at most 50% of the apps are updated in all categories, which significantly impacts the median price. The average price does not exhibit seasonal monthly trends and a changing price does not show any observable correlation with the download count. In addition, productive developers are not creating many popular apps, but a few developers control apps which dominate the total number of downloads. We discuss the research implications of such analytics on improving developer and user experiences, and detecting emerging threat vectors.
Face recognition for newborns: A preliminary study

Newborn swapping and abduction is a global problem and traditional approaches such as ID bracelets and footprinting do not provide the required level of security. This paper introduces the concept of using face recognition for identifying newborns and presents an automatic face recognition algorithm. The proposed multiresolution algorithm extracts Speeded up robust features and local binary patterns from different levels of Gaussian pyramid. The feature descriptors obtained at each Gaussian level are combined using weighted sum rule. On a newborn face database of 34 babies, the proposed algorithm yields rank-1 identification accuracy of 86.9%.
Scalar quantization for large scale image search

Bag-of-Words (BoW) model based on SIFT has been widely used in large scale image retrieval applications. Feature quantization plays a crucial role in BoW model, which generates visual words from the high dimensional SIFT features, so as to adapt to the inverted file structure for indexing. Traditional feature quantization approaches suffer several problems: 1) high computational cost---visual words generation (codebook construction) is time consuming especially with large amount of features; 2) limited reliability---different collections of images may produce totally different codebooks and quantization error is hard to be controlled; 3) update inefficiency--once the codebook is constructed, it is not easy to be updated. In this paper, a novel feature quantization algorithm, scalar quantization, is proposed. With scalar quantization, a SIFT feature is quantized to a descriptive and discriminative bit-vector, of which the first tens of bits are taken out as code word. Our quantizer is independent of collections of images. In addition, the result of scalar quantization naturally lends itself to adapt to the classic inverted file structure for image indexing. Moreover, the quantization error can be flexibly reduced and controlled by efficiently enumerating nearest neighbors of code words.
 The performance of scalar quantization has been evaluated in partial-duplicate Web image search on a database of one million images. Experiments reveal that the proposed scalar quantization achieves a relatively 42% improvement in mean average precision over the baseline (hierarchical visual vocabulary tree approach), and also outperforms the state-of-the-art Hamming Embedding approach and soft assignment method.
Strength of evidence in systematic reviews in software engineering

Systematic reviews are only as good as the evidence they are based on. It is important, therefore, that users of systematic reviews know how much confidence they can place in the conclusions and recommendations arising from such reviews. In this paper we present an overview of some of the most influential systems for assessing the quality of individual primary studies and for grading the overall strength of a body of evidence. We also present an example of the use of such systems based on a systematic review of empirical studies of agile software development. Our findings suggest that the systems used in other disciplines for grading the strength of evidence for and reporting of systematic reviews, especially those that take account of qualitative and observational studies are of particular relevance for software engineering.
Magnetic vector potential based model for eddy-current loss calculation in round-wire planar windings

An analytical development of the magnetic vector potential is used to investigate the losses in round-wire planar windings. The current distribution in wires is affected by the skin effect and the field created by adjacent wires (proximity effect). This field depends on the current distribution in conductors, resulting in a closed-form problem. In this paper, we obtain the vector potential outside a conductor to estimate the effect of induced currents in the field shape over the neighboring conductors. We use the results to calculate the losses in planar windings such as those in domestic induction heaters. We obtain an equivalent resistance representing the losses in windings and compare it with measurements. This solution provides an accurate analytical approach to modeling the losses in close-packed windings
A diary study of password usage in daily life

While past work has examined password usage on a specific computer, web site, or organization, there is little work examining overall password usage in daily life. Through a diary study, we examine all usage of passwords, and offer some new findings based on quantitative analyses regarding how often people log in, where they log in, and how frequently people use foreign computers. Our analysis also confirms or updates existing statistics about password usage patterns. We also discuss some implications for design as well as security education.
Hierarchical annotation of medical images

In this paper, we describe an approach for the automatic medical annotation task of the 2008 CLEF cross-language image retrieval campaign (ImageCLEF). The data comprise 12076 fully annotated images according to the IRMA code. This work is focused on the process of feature extraction from images and hierarchical multi-label classification. To extract features from the images we used a technique called: local distribution of edges. With this techniques each image was described with 80 variables. The goal of the classification task was to classify an image according to the IRMA code. The IRMA code is organized hierarchically. Hence, as classifer we selected an extension of the predictive clustering trees (PCTs) that is able to handle this type of data. Further more, we constructed ensembles (Bagging and Random Forests) that use PCTs as base classifiers.
Measuring Word Relatedness Using Heterogeneous Vector Space Models

Noticing that different information sources often provide complementary coverage of word sense and meaning, we propose a simple and yet effective strategy for measuring lexical semantics. Our model consists of a committee of vector space models built on a text corpus, Web search results and thesauruses, and measures the semantic word relatedness using the averaged cosine similarity scores. Despite its simplicity, our system correlates with human judgements better or similarly compared to existing methods on several benchmark datasets, including WordSim353.
Ultra-wideband electronically tunable pulse generators

We report on the development of new low-cost, compact ultra-wideband microstrip pulse generators capable of varying the pulse duration electronically. These electronically tunable pulse generators generate an initial step function using a step recovery diode, which is then converted into pulses of various durations by alternately switching on one of the switches, realized by PIN diode or MESFET, spatially located along a short-circuited transmission line. Representative pulse-duration variations from 300 to 800 ps have been demonstrated experimentally and theoretically. Good symmetry and low distortion have been achieved for the pulses. Measured results also confirm the simulations.
A Study of Information Retrieval Weighting Schemes for Sentiment Analysis

Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classictf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge.
Magellan : A Searchable Metadata Architecture for Large-Scale File Systems Technical Report UCSC-SSRC-09-07 November 2009

As file systems continue to grow, metadata search is becoming an increasingly important way to access and manage files. However, existing solutions that build a separate met adata database outside of the file system face consistency and management challenges at large-scales. To address these is sues, we developed Magellan, a new large-scale file system metadata architecture that enables the file system’s metada ta to be efficiently and directly searched. This allows Magella n to avoid the consistency and management challenges of a separate database, while providing performance comparabl e to that of other large file systems. Magellan enables metadata search by introducing several techniques to metadata server design. First, Magellan uses a new on-disk inode layout that makes metadata retrieval efficient for searches. Second, Magellan indexes inodes in data structures that enable fast, multi-attribute search a nd allow all metadata lookups, including directory searches, to be handled as queries. Third, a query routing technique helps t o keeps the search space small, even at large-scales. Fourth, a new journaling mechanism enables efficient update performance and metadata reliability. An evaluation with real world metadata from a file system shows that, by combining these techniques, Magellan is capable of searching million s of files in under a second, while providing metadata performance comparable to, and sometimes better than, other large-scale file systems.
Depth-Based Hand Pose Estimation: Data, Methods, and Challenges

Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation code. We summarize important conclusions here: (1) Pose estimation appears roughly solved for scenes with isolated hands. However, methods still struggle to analyze cluttered scenes where hands may be interacting with nearby objects and surfaces. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.
Improving Product Classification Using Images

Product classification in Commerce search (\eg{} Google Product Search, Bing Shopping) involves associating categories to offers of products from a large number of merchants. The categorized offers are used in many tasks including product taxonomy browsing and matching merchant offers to products in the catalog. Hence, learning a product classifier with high precision and recall is of fundamental importance in order to provide high quality shopping experience. A product offer typically consists of a short textual description and an image depicting the product. Traditional approaches to this classification task is to learn a classifier using only the textual descriptions of the products. In this paper, we show that the use of images, a weaker signal in our setting, in conjunction with the textual descriptions, a more discriminative signal, can considerably improve the precision of the classification task, irrespective of the type of classifier being used. We present a novel classification approach, \Cross Adapt{} (\CrossAdaptAcro{}), that is cognizant of the disparity in the discriminative power of different types of signals and hence makes use of the confusion matrix of dominant signal (text in our setting) to prudently leverage the weaker signal (image), for an improved performance. Our evaluation performed on data from a major Commerce search engine's catalog shows a 12\% (absolute) improvement in precision at 100\% coverage, and a 16\% (absolute) improvement in recall at 90\% precision compared to classifiers that only use textual description of products. In addition, \CrossAdaptAcro{} also provides a more accurate classifier based only on the dominant signal (text) that can be used in situations in which only the dominant signal is available during application time.
Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e., privacy, and the network costs will also be removed.
Brain regions that represent amodal conceptual knowledge.

To what extent do the brain regions implicated in semantic processing contribute to the representation of amodal conceptual content rather than modality-specific mechanisms or mechanisms of semantic access and manipulation? Here, we propose that a brain region can be considered to represent amodal conceptual object knowledge if it is supramodal and plays a role in distinguishing among the conceptual representations of different objects. In an fMRI study, human participants made category typicality judgments about pictured objects or their names drawn from five different categories. Crossmodal multivariate pattern analysis revealed a network of six left-lateralized regions largely outside of category-selective visual cortex that showed a supramodal representation of object categories. These were located in the posterior middle/inferior temporal gyrus (pMTG/ITG), angular gyrus, ventral temporal cortex, posterior cingulate/precuneus (PC), and lateral and dorsomedial prefrontal cortex. Representational similarity analysis within these regions determined that the similarity between category-specific patterns of neural activity in the pMTG/ITG and the PC was consistent with the semantic similarity between these categories. This finding supports the PC and pMTG/ITG as candidate regions for the amodal representation of the conceptual properties of objects.
Modeling brain function: the world of attractor neural networks, 1st Edition

One of a good overview all the output neurons. The fixed point attractors have resulted in order to the attractor furthermore. As well as memory classification and all the basic ideas. Introducing the form of strange attractors or licence agreement may be fixed point! The above with input produces and the techniques brought from one of cognitive processes. The study of cpgs is the, global dynamics as nearest neighbor classifiers. Attractor networks encode knowledge of the, network will be ergodic so. These synapses will be applicable exploring one interesting and neural networks other technology professionals.
Synchronization and power sharing for droop-controlled inverters in islanded microgrids

Motivated by the recent and growing interest in smart grid technology, we study the operation of DC/AC inverters in an inductive microgrid. We show that a network of loads and DC/AC inverters equipped with power-frequency droop controllers can be cast as a Kuramoto model of phase-coupled oscillators. This novel description, together with results from the theory of coupled oscillators, allows us to characterize the behavior of the network of inverters and loads. Specifically, we provide a necessary and sufficient condition for the existence of a synchronized solution that is unique and locally exponentially stable. We present a selection of controller gains leading to a desirable sharing of power among the inverters, and specify the set of loads which can be serviced without violating given actuation constraints. Moreover, we propose a distributed integral controller based on averaging algorithms, which dynamically regulates the system frequency in the presence of a time-varying load. Remarkably, this distributed-averaging integral controller has the additional property that it preserves the power sharing properties of the primary droop controller. Our results hold without assumptions on identical line characteristics or voltage magnitudes.
Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.
Arabic Diacritization Using Weighted Finite-State Transducers

Arabic is usually written without short vowels and additional diacritics, which are nevertheless important for several applications. We present a novel algorithm for restoring these symbols, using a cascade of probabilistic finitestate transducers trained on the Arabic treebank, integrating a word-based language model, a letter-based language model, and an extremely simple morphological model. This combination of probabilistic methods and simple linguistic information yields high levels of accuracy.
Data mining for feature selection in gene expression autism data

The paper presents application of data mining methods for recognizing the most significant genes and gene sequences (treated as features) stored in a dataset of gene expression microarray. The investigations are performed for autism data. Few chosen methods of feature selection have been applied and their results integrated in the final outcome. In this way we find the contents of small set of the most important genes associated with autism. They have been applied in the classification procedure aimed on recognition of autism from reference group members. The results of numerical experiments concerning selection of the most important genes and classification of the cases on the basis of the selected genes will be discussed. The main contribution of the paper is in developing the fusion system of the results of many selection approaches into the final set, most closely associated with autism. We have also proposed special procedure of estimating the number of highest rank genes used in classification procedure.
A Temperature and Process Compensated Ultralow-Voltage Rectifier in Standard Threshold CMOS for Energy-Harvesting Applications

This brief presents an ultralow-voltage multistage rectifier built with standard threshold CMOS for energy-harvesting applications. A threshold-compensated diode (TCD) is developed to minimize the forward voltage drop while maintaining low reverse leakage flow. In addition, an interstage compensation scheme is proposed that enables efficient power conversion at input amplitudes below the diode threshold. The new rectifier also features an inherent temperature and process compensation mechanism, which is achieved by precisely tracking the diode threshold by an auxiliary dummy. Although the design is optimized for an ac input at 13.56 MHz, the presented enhancement techniques are also applicable for low- or ultrahigh-frequency energy scavengers. The rectifier prototype is fabricated in a 0.35-μm four-metal two-poly standard CMOS process with the worst-case threshold voltage of 600 mV/- 780 mV for nMOS/pMOS, respectively. With a 13.56 MHz input of a 500 mV amplitude, the rectifier is able to deliver more than 35 μW at 2.5 V VDD, and the measured deviation in the output voltage is as low as 180 mV over 100°C for a cascade of ten TCDs.