nils-herrmann/clean_scirepeval huggingface dataset:
Building a database on S3

There has been a great deal of hype about Amazon's simple storage service (S3). S3 provides infinite scalability and high availability at low cost. Currently, S3 is used mostly to store multi-media documents (videos, photos, audio) which are shared by a community of people and rarely updated. The purpose of this paper is to demonstrate the opportunities and limitations of using S3 as a storage system for general-purpose database applications which involve small objects and frequent updates. Read, write, and commit protocols are presented. Furthermore, the cost ($), performance, and consistency properties of such a storage system are studied.
Faster Secure Two-Party Computation Using Garbled Circuits

Secure two-party computation enables two parties to evaluate a function cooperatively without revealing to either party anything beyond the function’s output. The garbled-circuit technique, a generic approach to secure two-party computation for semi-honest participants, was developed by Yao in the 1980s, but has been viewed as being of limited practical significance due to its inefficiency. We demonstrate several techniques for improving the running time and memory requirements of the garbled-circuit technique, resulting in an implementation of generic secure two-party computation that is significantly faster than any previously reported while also scaling to arbitrarily large circuits. We validate our approach by demonstrating secure computation of circuits with over 109 gates at a rate of roughly 10 μs per garbled gate, and showing order-of-magnitude improvements over the best previous privacy-preserving protocols for computing Hamming distance, Levenshtein distance, Smith-Waterman genome alignment, and AES.
Structural Analysis of User Choices for Mobile App Recommendation

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. Although there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this article, we provide a systematic study for addressing these challenges. Specifically, we develop a structural user choice model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption dataset collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art Top-N recommendation methods by a significant margin.
Cache cookies for browser authentication

Like conventional cookies, cache cookies are data objects that servers store in Web browsers. Cache cookies, however, are unintentional byproducts of protocol design for browser caches. They do not enjoy any explicit interface support or security policies. In this paper, we show that despite limitations, cache cookies can play a useful role in the identification and authentication of users. Many users today block conventional cookies in their browsers as a privacy measure. The cache-cookie tools we propose can help restore lost usability and convenience to such users while maintaining good privacy. As we show, our techniques can also help combat online security threats such as phishing and pharming that ordinary cookies cannot. The ideas we introduce for cache-cookie management can strengthen ordinary cookies as well.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning

We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective. Depending on the specific setting, the outer variables take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We provide sufficient conditions under which solutions of the approximate problem converge to those of the exact problem. We instantiate our approach for meta-learning in the case of deep learning where representation layers are treated as hyperparameters shared across a set of training episodes. In experiments, we confirm our theoretical findings, present encouraging results for few-shot learning and contrast the bilevel approach against classical approaches for learning-to-learn.
Microscopic evolution of social networks

We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at such a large scale, we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks. Using a methodology based on the maximum-likelihood principle, we investigate a wide variety of network formation strategies, and show that edge locality plays a critical role in evolution of networks. Our findings supplement earlier network models based on the inherently non-local preferential attachment.
 Based on our observations, we develop a complete model of network evolution, where nodes arrive at a prespecified rate and select their lifetimes. Each node then independently initiates edges according to a "gap" process, selecting a destination for each edge according to a simple triangle-closing model free of any parameters. We show analytically that the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases. Finally, we give model parameter settings that allow automatic evolution and generation of realistic synthetic networks of arbitrary scale.
Multi-Language Online Handwriting Recognition

We describe Google's online handwriting recognition system that currently supports 22 scripts and 97 languages. The system's focus is on fast, high-accuracy text entry for mobile, touch-enabled devices. We use a combination of state-of-the-art components and combine them with novel additions in a flexible framework. This architecture allows us to easily transfer improvements between languages and scripts. This made it possible to build recognizers for languages that, to the best of our knowledge, are not handled by any other online handwriting recognition system. The approach also enabled us to use the same architecture both on very powerful machines for recognition in the cloud as well as on mobile devices with more limited computational power by changing some of the settings of the system. In this paper we give a general overview of the system architecture and the novel components, such as unified time- and position-based input interpretation, trainable segmentation, minimum-error rate training for feature combination, and a cascade of pruning strategies. We present experimental results for different setups. The system is currently publicly available in several Google products, for example in Google Translate and as an input method for Android devices.
Evolutionary reinforcement learning of artificial neural networks

In this article we describe EANT2,  The structure of the networks is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES, Covariance Matrix Adaptation Evolution Strategy , a derandomised variant of evolution strategies. EANT2 can create neural networks that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, NeuroEvolution of Augmenting Topologies , to create networks that control a robot in a visual servoing scenario.
Context based object categorization: A critical survey

Please cite this article in press as: C. Galleguillos doi:10.1016/j.cviu.2010.02.004 The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the same scene. Several models for object categorization use appearance and context information from objects to improve recognition accuracy. Appearance information, based on visual cues, can successfully identify object classes up to a certain extent. Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. In this work we address the problem of incorporating different types of contextual information for robust object categorization in computer vision. We review different ways of using contextual information in the field of object categorization, considering the most common levels of extraction of context and the different levels of contextual interactions. We also examine common machine learning models that integrate context information into object recognition frameworks and discuss scalability, optimizations and possible future approaches.
Hierarchical Indexing for Out-of-Core Access to Multi-Resolution Data

In this paper we introduce a new indexing scheme for progressive traversal and visualization of large regular grids. We demonstrate the potential of our approach by providing a tool that displays at interactive rates planar slices of scalar field data with very modest computing resources. We obtain unprecedented results both in terms of absolute performance and, more importantly, in terms of scalability. On a laptop computer we provide real time interaction with a 2048 grid (8 Giga-nodes) using only 20MB of memory. On an SGI Onyx we slice interactively an 8192 grid (1⁄2 tera-nodes) using only 60MB of memory. The scheme relies simply on the determination of an appropriate reordering of the rectilinear grid data and a progressive construction of the output slice. The reordering minimizes the amount of I/O performed during the out-of-core computation. The progressive and asynchronous computation of the output provides flexible quality/speed tradeoffs and a timecritical and interruptible user interface.
Cluster-wise assessment of cluster stability

Stability in cluster analysis is strongly dependent on the data set, especially on how well separated and how homogeneous the clusters are. In the same clustering, some clusters may be very stable and others may be extremely unstable. The Jaccard coefficient, a similarity measure between sets, is used as a clusterwise measure of cluster stability, which is assessed by the bootstrap distribution of the Jaccard coefficient for every single cluster of a clustering compared to the most similar cluster in the bootstrapped data sets. This can be applied to very general cluster analysis methods. Some alternative resampling methods are investigated as well, namely subsetting, jittering the data points and replacing some data points by artificial noise points. The different methods are compared by means of a simulation study. A data example illustrates the use of the cluster-wise stability assessment to distinguish between meaningful stable and spurious clusters, but it is also shown that clusters are sometimes only stable because of the inflexibility of certain clustering methods.
Networks, Crowds, and Markets - Reasoning About a Highly Connected World

Over the past decade there has been a growing public fascination with the complex connectedness of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet, in the ease with which global communication takes place, and in the ability of news and information as well as epidemics and financial crises to spread with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which our decisions can have subtle consequences for others. This introductory undergraduate textbook takes an interdisciplinary look at economics, sociology, computing and information science, and applied mathematics to understand networks and behavior. It describes the emerging field of study that is growing at the interface of these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
Automatic Stress Classification With Pupil Diameter Analysis

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neu-ral network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection. 1.  Stress detection and measurement are important issues in several human–computer interaction domains such as Affective Computing, Adaptive Automation, and Ambient Intelligence. In general, researchers and system designers seek to estimate the psychological state of operators in order to adapt or redesign the working environment accordingly (Sauter, 1991). The primary goal of such adaptation is to enhance overall system performance, trying to reduce workers' psychophysi-cal detriment (e. One key aspect of stress measurement concerns the recording of physiological parameters, which are known to be modulated by the autonomic nervous system (ANS). However, despite
Imitation Learning of Dual-Arm Manipulation Tasks in Humanoid Robots

In this paper, we deal with imitation learning of arm movements in humanoid robots. Hidden Markov models (HMM) are used to generalize movements demonstrated to a robot multiple times. They are trained with the characteristic features (key points) of each demonstration. Using the same HMM, key points that are common to all demonstrations are identified; only those are considered when reproducing a movement. We also show how HMM can be used to detect temporal dependencies between both arms in dual-arm tasks. We created a model of the human upper body to simulate the reproduction of dual-arm movements and generate natural-looking joint configurations from tracked hand paths. Results are presented and discussed
Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data

Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.
RELICS: In-network realization of incentives to combat selfishness in DTNs

In this paper, we develop a cooperative mechanism, RELICS, to combat selfishness in DTNs. In DTNs, nodes belong to self-interested individuals. A node may be selfish in expending resources, such as energy, on forwarding messages from others, unless offered incentives. We devise a rewarding scheme that provides incentives to nodes in a physically realizable way in that the rewards are reflected into network operation. We call it in-network realization of incentives. We introduce explicit ranking of nodes depending on their transit behavior, and translate those ranks into message priority. Selfishness drives each node to set its energy depletion rate as low as possible while maintaining its own delivery ratio above some threshold. We show that our cooperative mechanism compels nodes to cooperate and also achieves higher energy-economy compared to other previous results.
Combinatorial Optimization: Algorithms and Complexity

y p. 27, line 12: the \i" in the rst subscript of a is not dotted: for a j read aij p. 32, in the last displayed equation: replace x̂ij by x̂i in the summation on the right-hand side z p. 38, in eqs. (2.8') and (2.9): change subscripts i's on b and a to i n+m z p. 38, after (2.8'): replace \last m rows of A" by \last m columns of A" p. 49, Fig. 2-3, lines 6 and 8: for cj read cj p. 61, Theorem 2.10, line 2: for fy1;  ; yn mg read (y1 ; yn m) p. 83, last equation: for ci read cj p. 123, line 6 of Fig. 6-6: append after second comma: \set L2[s] := 1;" z p. 142, proof of Theorem 7.2: insert before \Replace...": \Suppose rst that this arc is the only one that P and C have in common." insert at end of proof: \If P and C have more than one arc in common, the construction produces a path plus cycles. The cycles, however, must have existed in N 0(f1) and hence have nonnegative cost. The same contradiction is therefore reached." p. 171, line 4 from bottom: for \Example 8.5" read \Example 8.4" p. 173, line 7 from bottom: for xi read x1 p. 175, Lemma 8.10: for \0; : : : 0" read \0;  ; 0" y p. 178, Lemma 8.12, line 2: for \0 : : : ; 0" read \0;  ; 0" p. 251, Fig. 11-2, line 10: for \i :=" read \s :=" p. 251, Fig. 11-2, line 13: insert after \exposed[v]:=0" \,label[v]:=0" p. 251, Fig. 11-2, line 17: insert after else \if vi 6= mate[uj] then"
Automated News Item Categorization

We present a system for automatic categorization of news items into a standard set of categories. The system has been built specifically for news stories written in Croatian language. It uses the standard set of news categories established by the International Press Telecommunications Council (IPTC). The algorithm used for categorization transforms each document into a vector of weights corresponding to an automatically chosen set of keywords. This process is performed on a large training set of news items, forming the multi-dimensional space populated by news items of known categories. An unknown news item is also transformed into a vector of keyword weights and then categorized using the k-NN method in this space. The system has been trained on the collection of approx. 2700 manually categorized news items provided by the Croatian News Agency and tested on a different set of approx. 500 randomly chosen news items from the same source. The automatic categorization gave a correct result for 85% of tested news items. Subjective evaluation by news professionals concluded that the system is useful enough to be used in news production process.
Robust Bilayer Segmentation and Motion/Depth Estimation with a Handheld Camera

Extracting high-quality dynamic foreground layers from a video sequence is a challenging problem due to the coupling of color, motion, and occlusion. Many approaches assume that the background scene is static or undergoes the planar perspective transformation. In this paper, we relax these restrictions and present a comprehensive system for accurately computing object motion, layer, and depth information. A novel algorithm that combines different clues to extract the foreground layer is proposed, where a voting-like scheme robust to outliers is employed in optimization. The system is capable of handling difficult examples in which the background is nonplanar and the camera freely moves during video capturing. Our work finds several applications, such as high-quality view interpolation and video editing.
Canonical Time Warping for Alignment of Human Behavior

Alignment of time series is an important problem to solve in many scientific disciplines. In particular, temporal alignment of two or more subjects performing similar activities is a challenging problem due to the large temporal scale difference between human actions as well as the inter/intra subject variability. In this paper we present canonical time warping (CTW), an extension of canonical correlation analysis (CCA) for spatio-temporal alignment of human motion between two subjects. CTW extends previous work on CCA in two ways: (i) it combines CCA with dynamic time warping (DTW), and (ii) it extends CCA by allowing local spatial deformations. We show CTW’s effectiveness in three experiments: alignment of synthetic data, alignment of motion capture data of two subjects performing similar actions, and alignment of similar facial expressions made by two people. Our results demonstrate that CTW provides both visually and qualitatively better alignment than state-of-the-art techniques based on DTW.
Conceptual-Model-Based Data Extraction from Multiple-Record Web Pages

Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured, which makes searching hard and traditional database querying impossible. Many Web documents, however, contain an abundance of recognizable constants that together describe the essence of a document’s content. For these kinds of data-rich, multiple-record documents (e.g. advertisements, movie reviews, weather reports, travel information, sports summaries, financial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extract and structure data automatically. The approach is based on an ontology—a conceptual model instance—that describes the data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we can automatically produce a database scheme and recognizers for constants and keywords, and then invoke routines to recognize and extract data from unstructured documents and structure it according to the generated database scheme. Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in recognizable constants and narrow in ontological breadth. Our approach is less labor-intensive than other approaches that manually or semiautomatically generate wrappers, and it is generally insensitive to changes in Web-page format.