nils-herrmann/clean_scirepeval huggingface dataset:
How to Combine Homomorphic Encryption and Garbled Circuits Improved Circuits and Computing the Minimum Distance Efficiently

We show how two existing paradigms for two-party secure function evaluation (SFE) in the semi-honest model can be combined securely and efficiently – those based on additively homomorphic encryption (HE) with those based on garbled circuits (GC) and vice versa. Additionally, we propose new GC constructions for addition, subtraction, multiplication, and comparison functions. Our circuits are approximately two times smaller (in terms of garbled tables) than previous constructions. This implies corresponding computation and communication improvements in SFE of functions using the above building blocks (including the protocols for combining HE and GC). Based on these results, we present efficient constant-round protocols for secure integer comparison, and the related problems of minimum selection and minimum distance, which are crucial building blocks of many cryptographic schemes such as privacy preserving biometric authentication (e.g., face recognition, fingerprint matching, etc).
Knowledge management enablers: a case study

To analyze the crucial role that enablers play in carrying out knowledge management within the enterprise.  This research uses the method of a case study and has directed the survey on Advanced Semiconductor Engineering, Inc. (ASE) and VIA Technologies, Inc. (VIA). It is anticipated through the case study of these two companies that it will be possible to verify the finding of enablers concluded by other papers, thus showing the inter-relationship between theory and business.  It is found that among the enablers: on the part of strategy and leadership; obtaining top managements’ support is most important; among organization culture enablers is the forming of an atmosphere and culture of sharing is most important but needs to be supplemented by informational technology; among people enablers, other than training courses and channels that provide learning, employee incentive program is one of the executing key factor; and among informational technology enablers, other than the digitization of documents, the function of speedy search of information for its re-use is becoming more and more important.  The result of this study not just validates theory with reality; it also provides a reference for the academia as well as the business field.  This paper has discovered that establishing a dedicated unit for implementing knowledge management is also one of the key enablers. Its role does not just stop at managing knowledge, but instead it plays the role of furthering knowledge management by taking on the duty of assisting and coordinating different departments in their communication.
Design patterns in FPS levels

Level designers create gameplay through geometry, AI scripting, and item placement. There is little formal understanding of this process, but rather a large body of design lore and rules of thumb. As a result, there is no accepted common language for describing the building blocks of level design and the gameplay they create. This paper presents level design patterns for first-person shooter (FPS) games, providing cause-effect relationships between level design elements and gameplay. These patterns allow designers to create more interesting and varied levels.
Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems

We propose a simplified model of attention which is applicable to feed-forward neural networks and demonstrate that the resulting model can solve the synthetic “addition” and “multiplication” long-term memory problems for sequence lengths which are both longer and more widely varying than the best published results for these tasks. 1 MODELS FOR SEQUENTIAL DATA Many problems in machine learning are best formulated using sequential data and appropriate models for these tasks must be able to capture temporal dependencies in sequences, potentially of arbitrary length. One such class of models are recurrent neural networks (RNNs), which can be considered a learnable function f whose output ht = f(xt, ht−1) at time t depends on input xt and the model’s previous state ht−1. Training of RNNs with backpropagation through time (Werbos, 1990) is hindered by the vanishing and exploding gradient problem (Pascanu et al., 2012; Hochreiter & Schmidhuber, 1997; Bengio et al., 1994), and as a result RNNs are in practice typically only applied in tasks where sequential dependencies span at most hundreds of time steps. Very long sequences can also make training computationally inefficient due to the fact that RNNs must be evaluated sequentially and cannot be fully parallelized.
Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media

Crowdturfing has recently been identified as a sinister counterpart to the enormous positive opportunities of crowdsourcing. Crowdturfers leverage human-powered crowdsourcing platforms to spread malicious URLs in social media, form “astroturf” campaigns, and manipulate search engines, ultimately degrading the quality of online information and threatening the usefulness of these systems. In this paper we present a framework for “pulling back the curtain” on crowdturfers to reveal their underlying ecosystem. Concretely, we analyze the types of malicious tasks and the properties of requesters and workers in crowdsourcing sites such as Microworkers.com, ShortTask.com and Rapidworkers.com, and link these tasks (and their associated workers) on crowdsourcing sites to social media, by monitoring the activities of social media participants. Based on this linkage, we identify the relationship structure connecting these workers in social media, which can reveal the implicit power structure of crowdturfers identified on crowdsourcing sites. We identify three classes of crowdturfers – professional workers, casual workers, and middlemen – and we develop statistical user models to automatically differentiate these workers and regular social media users.
A Situated Ontology for Practical NLP

A situated ontology is a world model used as a computational resource for solving a particular set of problems. It is treated as neither a \natural" entity waiting to be discovered nor a purely theoretical construct. This paper describes how a semantico-pragmatic analyzer, Mikrokosmos, uses knowledge from a situated ontology as well as from language-speciic knowledge sources (lexicons and microtheory rules). Also presented are some guidelines for acquiring ontological concepts and an overview of the technology developed in the Mikrokosmos project for large-scale acquisition and maintenance of ontological databases. Tools for acquiring, maintaining, and browsing ontologies can be shared more readily than ontologies themselves. Ontological knowledge bases can be shared as computational resources if such tools provide translators between diierent representation formats. 1 A Situated Ontology World models (ontologies) in computational applications are artiicially constructed entities. They are created, not discovered. This is why so many diierent world models were suggested. Many ontologies are developed for purely theoretical purposes or without the context of a practical situation (e. Many practical knowledge-based systems, on the other hand, employ world or domain models without recognizing them as a separate knowledge source (e.g., Farwell, et al. 1993). In the eld of natural language processing (NLP) there is now a consensus that all NLP systems that seek to represent and manipulate meanings of texts need an ontology (e. In our continued eeorts to build a multilingual knowledge-based machine translation (KBMT) system using an interlingual meaning representation (e.g., Onyshkevych and Nirenburg, 1994), we have developed an ontology to facilitate natural language interpretation and generation. The central goal of the Mikrokosmos project is to develop a system that produces a comprehensive Text Meaning Representation (TMR) for an input text in any of a set of source languages. 1 Knowledge that supports this process is stored both in language-speciic knowledge sources and in an independently motivated, language-neutral ontology (e. An ontology for NLP purposes is a body of knowledge about the world (or a domain) that a) is a repository of primitive symbols used in meaning representation; b) organizes these symbols in a tangled subsumption hierarchy; and c) further interconnects these symbols using a rich system of semantic and discourse-pragmatic relations deened among the concepts. In order for such an ontology to become a computational resource for solving problems such as ambiguity and reference resolution, it must be actually constructed, not merely deened formally, as is the …
Community Evolution Mining in Dynamic Social Networks

Data that encompasses relationships is represented by a graph of interconnected nodes. Social network analysis is the study of such graphs which examines questions related to structures and patterns that can lead to the understanding of the data and predicting the trends of social networks. Static analysis, where the time of interaction is not considered (i.e., the network is frozen in time), misses the opportunity to capture the evolutionary patterns in dynamic networks. Specifically, detecting the community evolutions, the community structures that changes in time, provides insight into the underlying behaviour of the network. Recently, a number of researchers have started focusing on identifying critical events that characterize the evolution of communities in dynamic scenarios. In this paper, we present a framework for modeling and detecting community evolution in social networks, where a series of significant events is defined for each community. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. We also define the concept of meta community which is a series of similar communities captured in different timeframes and detected by our matching algorithm. We illustrate the capabilities and potential of our framework by applying it to two real datasets. Furthermore, the events detected by the framework is supplemented by extraction and investigation of the topics discovered for each community.
Big data, open government and e-government: Issues, policies and recommendations

The transformative promises and potential of Big and Open Data are substantial for e-government services, openness and transparency, governments, and the interaction between governments, citizens, and the business sector. From “smart” government to transformational government, Big and Open Data can foster collaboration; create real-time solutions to challenges in agriculture, health, transportation, and more; promote greater openness; and usher in a new era of policyand decision-making. There are, however, a range of policy challenges to address regarding Big and Open Data, including access and dissemination; digital asset management, archiving and preservation; privacy; and security. After presenting a discussion of the open data policies that serve as a foundation for Big Data initiatives, this paper examines the ways in which the current information policy framework fails to address a number of these policy challenges. It then offers recommendations intended to serve as a beginning point for a revised policy framework to address significant issues raised by the U.S. government’s engagement in Big Data efforts.
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network

The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied in the wild, their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. The 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems.
A linguistic consensus model for Web 2.0 communities

Web 2.0 communities are a quite recent phenomenon which involve large numbers of users and where communication between members is carried out in real time. Despite of those good characteristics, there is still a necessity of developing tools to help users to reach decisions with a high level of consensus in those new virtual environments. In this contribution a new consensus reaching model is presented which uses linguistic preferences and is designed to minimize the main problems that this kind of organization
Real World Performance of Approximate String Comparators for use in Patient Matching

Medical record linkage is becoming increasingly important as clinical data is distributed across independent sources. To improve linkage accuracy we studied different name comparison methods that establish agreement or disagreement between corresponding names. In addition to exact raw name matching and exact phonetic name matching, we tested three approximate string comparators. The approximate comparators included the modified Jaro-Winkler method, the longest common substring, and the Levenshtein edit distance. We also calculated the combined root-mean square of all three. We tested each name comparison method using a deterministic record linkage algorithm. Results were consistent across both hospitals. At a threshold comparator score of 0.8, the Jaro-Winkler comparator achieved the highest linkage sensitivities of 97.4% and 97.7%. The combined root-mean square method achieved sensitivities higher than the Levenshtein edit distance or long-est common substring while sustaining high linkage specificity. Approximate string comparators increase deterministic linkage sensitivity by up to 10% compared to exact match comparisons and represent an accurate method of linking to vital statistics data.
Disentangled Sequential Autoencoder

We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression.
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification

Attributes, or semantic features, have gained popularity in the past few years in domains ranging from activity recognition in video to face verification. Improving the accuracy of attribute classifiers is an important first step in any application which uses these attributes. In most works to date, attributes have been considered to be independent. However, we know this not to be the case. Many attributes are very strongly related, such as heavy makeup and wearing lipstick. We propose to take advantage of attribute relationships in three ways: by using a multi-task deep convolutional neural network (MCNN) sharing the lowest layers amongst all attributes, sharing the higher layers for related attributes, and by building an auxiliary network on top of the MCNN which utilizes the scores from all attributes to improve the final classification of each attribute. We demonstrate the effectiveness of our method by producing results on two challenging publicly available datasets.
Manifold-ranking based image retrieval

In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance. In relevance feedback, if only positive examples are available, they are added to the query set to improve the retrieval result; if examples of both labels can be obtained, MRBIR discriminately spreads the ranking scores of positive and negative examples, considering the asymmetry between these two types of images. Furthermore, three active learning methods are incorporated into MRBIR, which select images in each round of relevance feedback according to different principles, aiming to maximally improve the ranking result. Experimental results on a general-purpose image database show that MRBIR attains a significant improvement over existing systems from all aspects.
Structure and Motion from Images of Smooth Textureless Objects

This paper addresses the problem of estimating the 3D shape of a smooth textureless solid from multiple images acquired under orthographic projection from unknown and unconstrained viewpoints. In this setting, the only reliable image features are the object’s silhouettes, and the only true stereo correspondence between pairs of silhouettes are the frontier points where two viewing rays intersect in the tangent plane of the surface. An algorithm for identifying geometrically-consistent frontier points candidates while estimating the cameras’ projection matrices is presented. This algorithm uses the signature representation of the dual of image silhouettes to identify promising correspondences, and it exploits the redundancy of multiple epipolar geometries to retain the consistent ones. The visual hull of the observed solid is finally reconstructed from the recovered viewpoints. The proposed approach has been implemented, and experiments with six real image sequences are presented, including a comparison between ground-truth and recovered camera configurations, and sample visual hulls computed by the algorithm.
MMSE reception and successive interference cancellation for MIMO systems with high spectral efficiency

In this paper, we investigate the performance in terms of symbol error probability (SEP) of multiple-input-multiple-output (MIMO) systems with high spectral efficiency. In particular, we consider the coherent detection of M-PSK signals in a flat Rayleigh-fading environment. We focus on spectrally efficient MIMO systems where, after serial-to-parallel conversion, several substreams of symbols are simultaneously transmitted by using an antenna array, thereby increasing the spectral efficiency. The reception is based on linear minimum mean-square-error (MMSE) combining, eventually followed by successive interference cancellation. Exact and approximate expressions are derived for an arbitrary number of transmitting and receiving antenna elements. Simulation results confirm the validity of our analytical methodology.
Bluetooth telemedicine Processor for multichannel biomedical signal transmission via mobile cellular networks

One of the emerging issues in m-Health is how best to exploit the mobile communications technologies that are now almost globally available. The challenge is to produce a system to transmit a patient's biomedical signals directly to a hospital for monitoring or diagnosis, using an unmodified mobile telephone. The paper focuses on the design of a processor, which samples signals from sensors on the patient. It then transmits digital data over a Bluetooth link to a mobile telephone that uses the General Packet Radio Service. The modular design adopted is intended to provide a "future-proofed" system, whose functionality may be upgraded by modifying the software.
Calibrated Fairness in Bandits

We study fairness within the stochastic,multi-armed bandit (MAB) decision making framework. We adapt the fairness framework of “treating similar individuals similarly”  to this seing. Here, an ‘individual’ corresponds to an arm and two arms are ‘similar’ if they have a similar quality distribution. First, we adopt a smoothness constraint that if two arms have a similar quality distribution then the probability of selecting each arm should be similar. In addition, we dene the fairness regret, which corresponds to the degree to which an algorithm is not calibrated, where perfect calibration requires that the probability of selecting an arm is equal to the probability with which the arm has the best quality realization. We show that a variation on ompson sampling satises smooth fairness for total variation distance, and give an Õ((kT )2/3) bound on fairness regret. is complements prior work , which protects an on-average beer arm from being less favored. We also explain how to extend our algorithm to the dueling bandit seing.  Yang Liu, Goran Radanovic, Christos Dimitrakakis, DebmalyaMandal, andDavid C. Parkes.
Dynamic Reconfiguration of Primary/Backup Clusters

Dynamically changing (reconfiguring) the membership of a replicated distributed system while preserving data consistency and system availability is a challenging problem. In this paper, we show that reconfiguration can be simplified by taking advantage of certain properties commonly provided by Primary/Backup systems. We describe a new reconfiguration protocol, recently implemented in Apache Zookeeper. It fully automates configuration changes and minimizes any interruption in service to clients while maintaining data consistency. By leveraging the properties already provided by Zookeeper our protocol is considerably simpler than state of the art.
TDOA-based optical wireless indoor localization using LED ceiling lamps

We propose an optical wireless indoor localization using light emitting diodes (LEDs) and demonstrate it via simulation. Unique frequency addresses are assigned to each LED lamp, and transmitted through the light radiated by the LED. Using the phase difference, time difference of arrival (TDOA) localization algorithm is employed. Because the proposed localization method used pre-installed LED ceiling lamps, no additional infrastructure for localization is required to install and therefore, inexpensive system can be realized. The performance of the proposed localization method is evaluated by computer simulation, and the indoor location accuracy is less than 1 cm in the space of 5m x 5 m x 3 m.