nils-herrmann/clean_scirepeval huggingface dataset:
Public Key Compression and Modulus Switching for Fully Homomorphic Encryption over the Integers

We describe a compression technique that reduces the public key size of van Dijk, Gentry, Halevi and Vaikuntanathan’s (DGHV) fully homomorphic scheme over the integers from Õ(λ) to Õ(λ). Our variant remains semantically secure, but in the random oracle model. We obtain an implementation of the full scheme with a 10.1 MB public key instead of 802 MB using similar parameters as in . Additionally we show how to extend the quadratic encryption technique of [7] to higher degrees, to obtain a shorter public-key for the basic scheme. This paper also describes a new modulus switching technique for the DGHV scheme that enables to use the new FHE framework without bootstrapping from Brakerski, Gentry and Vaikuntanathan with the DGHV scheme. Finally we describe an improved attack against the Approximate GCD Problem on which the DGHV scheme is based, with complexity Õ(2) instead of Õ(2).
Research paper recommender system evaluation: a quantitative literature survey

Over 80 approaches for academic literature recommendation exist today. The approaches were introduced and evaluated in more than 170 research articles, as well as patents, presentations and blogs. We reviewed these approaches and found most evaluations to contain major shortcomings. Of the approaches proposed, 21% were not evaluated. Among the evaluated approaches, 19% were not evaluated against a baseline. Of the user studies performed, 60% had 15 or fewer participants or did not report on the number of participants. Information on runtime and coverage was rarely provided. Due to these and several other shortcomings described in this paper, we conclude that it is currently not possible to determine which recommendation approaches for academic literature are the most promising. However, there is little value in the existence of more than 80 approaches if the best performing approaches are unknown.
Differential privacy for location pattern mining

One main concern for individuals to participate in the data collection of personal location history records is the disclosure of their location and related information when a user queries for statistical or pattern mining results derived from these records. In this paper, we investigate how the privacy goal that the inclusion of one's location history in a statistical database with location pattern mining capabilities does not substantially increase one's privacy risk. In particular, we propose a differentially private pattern mining algorithm for interesting geographic location discovery using a region quadtree spatial decomposition to preprocess the location points followed by applying a density-based clustering algorithm. A differentially private region quadtree is used for both de-noising the spatial domain and identifying the likely geographic regions containing the interesting locations. Then, a differential privacy mechanism is applied to the algorithm outputs, namely: the interesting regions and their corresponding stay point counts. The quadtree spatial decomposition enables one to obtain a localized reduced sensitivity to achieve the differential privacy goal and accurate outputs. Experimental results on synthetic datasets are used to show the feasibility of the proposed privacy preserving location pattern mining algorithm.
Aggregate G-Buffer Anti-Aliasing -Extended Version-

We present Aggregate G-Buffer Anti-Aliasing (AGAA), a new technique for efficient anti-aliased deferred rendering of complex geometry using modern graphics hardware. In geometrically complex situations where many surfaces intersect a pixel, current rendering systems shade each contributing surface <italic>at least</italic> once per pixel. As the sample density and geometric complexity increase, the shading cost becomes prohibitive for real-time rendering. Under deferred shading, so does the required framebuffer memory. Our goal is to make high per-pixel sampling rates practical for real-time applications by substantially reducing shading costs and per-pixel storage compared to traditional deferred shading. AGAA uses the rasterization pipeline to generate a compact, pre-filtered geometric representation inside each pixel. We shade this representation at a fixed rate, independent of geometric complexity. By decoupling shading rate from geometric sampling rate, the algorithm reduces the storage and bandwidth costs of a geometry buffer, and allows scaling to high visibility sampling rates for anti-aliasing. AGAA with two aggregates per-pixel generates results comparable to 32<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq1-2586073.gif"/></alternatives></inline-formula> MSAA, but requires 54 percent less memory and is up to 2.6<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq2-2586073.gif"/></alternatives></inline-formula> faster (<inline-formula> <tex-math notation="LaTeX">$-30$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq3-2586073.gif"/></alternatives></inline-formula> percent memory and 1.7 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq4-2586073.gif"/></alternatives></inline-formula> faster for 8<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq5-2586073.gif"/></alternatives></inline-formula> MSAA).
Smart Ideas for Smart Cities: Investigating Crowdsourcing for Generating and Selecting Ideas for ICT Innovation in a City Context

Within this article, the strengths and weaknesses of crowdsourcing for idea generation and idea selection in the context of smart city innovation are investigated. First, smart cities are defined next to similar but different concepts such as digital cities, intelligent cities or ubiquitous cities. It is argued that the smart city-concept is in fact a more user-centered evolution of the other city-concepts which seem to be more technological deterministic in nature. The principles of crowdsourcing are explained and the different manifestations are demonstrated. By means of a case study, the generation of ideas for innovative uses of ICT for city innovation by citizens through an online platform is studied, as well as the selection process. For this selection, a crowdsourcing solution is compared to a selection made by external experts. The comparison of both indicates that using the crowd as gatekeeper and selector of innovative ideas yields a long list with high user benefits. However, the generation of ideas in itself appeared not to deliver extremely innovative ideas. Crowdsourcing thus appears to be a useful and effective tool in the context of smart city innovation, but should be thoughtfully used and combined with other user involvement approaches and within broader frameworks such as Living Labs.
"Now, i have a body": uses and social norms for mobile remote presence in the workplace

As geographically distributed teams become increasingly common, there are more pressing demands for communication work practices and technologies that support distributed collaboration. One set of technologies that are emerging on the commercial market is mobile remote presence (MRP) systems, physically embodied videoconferencing systems that remote workers use to drive through a workplace, communicating with locals there. Our interviews, observations, and survey results from people, who had 2-18 months of MRP use, showed how remotely-controlled mobility enabled remote workers to live and work with local coworkers almost as if they were physically there. The MRP supported informal communications and connections between distributed coworkers. We also found that the mobile embodiment of the remote worker evoked orientations toward the MRP both as a person and as a machine, leading to formation of new usage norms among remote and local coworkers.
Algorithmic Trading Using Intelligent Agents

Trading in financial markets is undergoing a radical transformation, one in which algorithmic methods are becoming increasingly more important. The development of intelligent agents that can act as autonomous traders seems like a logical step forward in this “algorithms arms race”. In this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade financial instruments without requiring human supervision. We used this infrastructure, which relies heavily on artificial intelligence models and problem solving methodologies, to implement two currency trading agents. So far these agents have been able to profit from inefficiencies in the currency market, which lead us to believe our infrastructure can be of practical interest to the investment industry.
Understanding and improving recurrent networks for human activity recognition by continuous attention

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean Fl score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.
Sparse Deep Transfer Learning for Convolutional Neural Network

Extensive studies have demonstrated that the representations of convolutional neural networks (CNN), which are learned from a large-scale data set in the source domain, can be effectively transferred to a new target domain. However, compared to the source domain, the target domain often has limited data in practice. In this case, overfitting may significantly depress transferability, due to the model redundancy of the intensive CNN structures. To deal with this difficulty, we propose a novel sparse deep transfer learning approach for CNN. There are three main contributions in this work. First, we introduce a Sparse-SourceNet to reduce the redundancy in the source domain. Second, we introduce a Hybrid-TransferNet to improve the generalization ability and the prediction accuracy of transfer learning, by taking advantage of both model sparsity and implicit knowledge. Third, we introduce a Sparse-TargetNet, where we prune our Hybrid-TransferNet to obtain a highlycompact, source-knowledge-integrated CNN in the target domain. To examine the effectiveness of our methods, we perform our sparse deep transfer learning approach on a number of benchmark transfer learning tasks. The results show that, compared to the standard fine-tuning approach, our proposed approach achieves a significant pruning rate on CNN while improves the accuracy of transfer learning.
Manipulating Attributes of Natural Scenes via Hallucination

Fig. 1. Given a natural image, the proposed approach can hallucinate different versions of the same scene in a wide range of conditions, e.g.night, sunset, winter, spring, rain, fog or even a combination of those. First, our method utilizes a single generator network to imagine the scene with respect to its semantic layout and the desired set of attributes. Then, it directly transfers the look from the hallucinated output to the input image, without a need to have access to a reference style image.
Mapping science through bibliometric triangulation: An experimental approach applied to water research

The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question what can be learned from a combination – triangulation – of these different science maps. In this paper, we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method does individually. The outcomes from the three different approaches are associated with each other and can be systematically interpreted, and provide insights into the complex multidisciplinary structure of the field of water
Deep Temporal Architecture for Audiovisual Speech Recognition

The Audiovisual Speech Recognition (AVSR) is one of the applications of multimodal machine learning related to speech recognition, lipreading systems and video classification. In recent and related work, increasing efforts are made in Deep Neural Network (DNN) for AVSR, moreover some DNN models including Multimodal Deep Autoencoder, Multimodal Deep Belief Network and Multimodal Deep Boltzmann Machine perform well in experiments owing to the better generalization and nonlinear transformation. However, these DNN models have several disadvantages: (1) They mainly deal with modal fusion while ignoring temporal fusion. (2) Traditional methods fail to consider the connection among frames in the modal fusion. (3) These models aren’t end-to-end structure. We propose a deep temporal architecture, which has not only classical modal fusion, but temporal modal fusion and temporal fusion. Furthermore, the overfitting and learning with small size samples in the AVSR are also studied, so that we propose a set of useful training strategies. The experiments show the superiority of our model and necessity of the training strategies in three datasets: AVLetters, AVLetters2, AVDigits. In the end, we conclude the work.
Maps of random walks on complex networks reveal community structure.

To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
Mop: an efficient and generic runtime verification framework

Monitoring-Oriented Programming (MOP1) [21, 18, 22, 19] is a formal framework for software development and analysis, in which the developer specifies desired properties using definable specification formalisms, along with code to execute when properties are violated or validated. The MOP framework automatically generates monitors from the specified properties and then integrates them together with the user-defined code into the original system.
 The previous design of MOP only allowed specifications without parameters, so it could not be used to state and monitor safety properties referring to two or more related objects. In this paper we propose a parametric specification formalism-independent extension of MOP, together with an implementation of JavaMOP that supports parameters. In our current implementation, parametric specifications are translated into AspectJ code and then weaved into the application using off-the-shelf AspectJ compilers; hence, MOP specifications can be seen as formal or logical aspects.
 Our JavaMOP implementation was extensively evaluated on two benchmarks, Dacapo [14] and Tracematches , showing that runtime verification in general and MOP in particular are feasible. In some of the examples, millions of monitor instances are generated, each observing a set of related objects. To keep the runtime overhead of monitoring and event observation low, we devised and implemented a decentralized indexing optimization. Less than 8% of the experiments showed more than 10% runtime overhead; in most cases our tool generates monitoring code as efficient as the hand-optimized code. Despite its genericity, JavaMOP is empirically shown to be more efficient than runtime verification systems specialized and optimized for particular specification formalisms. Many property violations were detected during our experiments; some of them are benign, others indicate defects in programs. Many of these are subtle and hard to find by ordinary testing.
Generation Y, web design, and eye tracking

Generation Y (age 18-31) is a very large and economically powerful generation, containing eighty-two million people and spending $200 billion annually. It is not surprising that companies are interested in gaining the patronage of this group, particularly via the web. Surprisingly, very little research into making web pages appealing to this important demographic has been done. This paper addresses this need through two separate studies. The first, an online survey, provides evidence that our proposed score for predicting the visual appeal of web pages reflects the self-report measure of what pages Generation Y likes. To refine these findings, an eye tracking study is conducted using the pages that were most and least liked in Study I. Participants’ eye-movement is tracked while browsing these pages, providing evidence of what attracts their attention. The results of these two studies suggest that Generation Y may prefer pages that include a main large image, images of celebrities, little text, and a search feature. This research has both important theoretical and practical implications.
A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines

Road safety is one of the most vital and crucial part of every countries daily economic growth. It gives so much impact in public health specially in the Philippines. Securing its safety will be a big help to a particular country's economic growth. The main objective of this paper is to analyze road accident's data to discover hidden pattern that can be used as precautionary measure to at least lessen the accident that occur yearly in Los Baños, Laguna, Philippines. Predicting algorithms such as Decision Tree, Naïve Bayes and Rule induction were used to identify factors affecting accident in Los Baños, Laguna. Using these three classifier the following are the results obtained by the researchers; for Decision Tree 92.84% accuracy occurred with 0.797 kappa while in Naïve Bayes 91.50% accuracy was generated with 0.741 kappa and 92.50% accuracy for Rule Induction and 0.783 kappa was produced. The researchers discovered that the place where an accident happened don't have significant correlation on the fatality of the victim. On the other hand, the researchers also found that the time and day play a vital role on the fatality or severity of victims of road accident particularly car collision.
Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.
Human Sensing Using Visible Light Communication

We present LiSense, the first-of-its-kind system that enables both data communication and fine-grained, real-time human skeleton reconstruction using Visible Light Communication (VLC). LiSense uses shadows created by the human body from blocked light and reconstructs 3D human skeleton postures in real time. We overcome two key challenges to realize shadow-based human sensing. First, multiple lights on the ceiling lead to diminished and complex shadow patterns on the floor. We design light beacons enabled by VLC to separate light rays from different light sources and recover the shadow pattern cast by each individual light. Second, we design an efficient inference algorithm to reconstruct user postures using 2D shadow information with a limited resolution collected by photodiodes embedded in the floor. We build a 3 m x 3 m LiSense testbed using off-the-shelf LEDs and photodiodes. Experiments show that LiSense reconstructs the 3D user skeleton at 60 Hz in real time with 10 degrees mean angular error for five body joints.
The use of pit and fissure sealants.

This paper reviews key issues of sealant use and methodology and poses recommendations to inform the discussion toward a consensus statement by participants. A comprehensive review of sealant literature, including policy recommendations from previous conferences that reviewed best practices for sealant use, was completed. Building on the review paper by Simonsen and on previous policy statements by dental and public health groups, this paper discusses key questions about sealant use in light of contemporary caries data and cost-benefit analyses. In addition, newest material advancements are reviewed to establish the next step in sealant improvement for young patients.
Sensor scheduling algorithms requiring limited computation

In this paper, we consider the scenario where many sensors co-operate to estimate a process. Only one sensor can take a measurement at any time step. We wish to come up with optimal sensor scheduling algorithms. The problem is motivated by the use of sonar range-finders used by the vehicles on the Caltech Multi-Vehicle Wireless Testbed. We see that this problem involves searching a tree in general and propose and analyze two strategies for pruning the tree to keep the computation limited. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. We also study a technique that employs choosing the sensors randomly from a probability distribution which can then be optimized. The performance of the algorithms are illustrated with the help of numerical examples.