nils-herrmann/clean_scirepeval huggingface dataset:
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

Preference-based recommendation systems have transformed how we consume media. By analyzing usage data, these methods uncover our latent preferences for items (such as articles or movies) and form recommendations based on the behavior of others with similar tastes. But traditional preference-based recommendations do not account for the social aspect of consumption, where a trusted friend might point us to an interesting item that does not match our typical preferences. In this work, we aim to bridge the gap between preference- and social-based recommendations. We develop social Poisson factorization (SPF), a probabilistic model that incorporates social network information into a traditional factorization method; SPF introduces the social aspect to algorithmic recommendation. We develop a scalable algorithm for analyzing data with SPF, and demonstrate that it outperforms competing methods on six real-world datasets; data sources include a social reader and Etsy.
ITS Data Fusion

A road interpretation module is presented! which is part of a real-time vehicle guidance system for autonomous driving. Based on bifocal computer vision, the complete system is able to drive a vehicle on marked or unmarked roads, to detect obstacles, and to react appropriately. The hardware is a network of 23 transputers, organized in modular clusters. Parallel modules performing image analysis, feature extraction, object modelling, sensor data integration and vehicle control, are organized in hierarchical levels. The road interpretation module is based on the principle of recursive state estimation by Kalman filter techniques. Internal 4-D models of the road, vehicle position, and orientation are updated using data produced by the image-processing module. The system has been implemented on two vehicles (VITA and VaMoRs) and demonstrated in the framework of PROMETHEUS, where the ability of autonomous driving through narrow curves and of lane changing were demonstrated. Meanwhile, the system has been tested on public roads in real traffic situations, including travel on a German Autobahn autonomously at speeds up to 85 km/h. Belcastro, C.M., Fischl, R., and M. Kam. “Fusion Techniques Using Distributed Kalman Filtering for Detecting Changes in Systems.”  Boston, MA. American Autom. Control Council, 1991.  (2296-2298).
Softassign and EM-ICP on GPU

In this paper we propose CUDA-based implementations of two 3D point sets registration algorithms: Soft assign and EM-ICP. Both algorithms are known for being time demanding, even on modern multi-core CPUs. Our GPUbased implementations vastly outperform CPU ones. For instance, our CUDA EM-ICP aligns 5000 points in less than 7 seconds on a GeForce 8800GT, while the same implementation in OpenMP on an Intel Core 2 Quad would take 7 minutes.
Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art

State-of-the-art approaches for unsupervised keyphrase extraction are typically evaluated on a single dataset with a single parameter setting. Consequently, it is unclear how effective these approaches are on a new dataset from a different domain, and how sensitive they are to changes in parameter settings. To gain a better understanding of state-of-the-art unsupervised keyphrase extraction algorithms, we conduct a systematic evaluation and analysis of these algorithms on a variety of standard evaluation datasets.
MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction

Clothing retrieval and clothing style recognition are important and practical problems. They have drawn a lot of attention in recent years. However, the clothing photos collected in existing datasets are mostly of front- or near-front view. There are no datasets designed to study the influences of different viewing angles on clothing retrieval performance. To address view-invariant clothing retrieval problem properly, we construct a challenge clothing dataset, called Multi-View Clothing dataset. This dataset not only has four different views for each clothing item, but also provides 264 attributes for describing clothing appearance. We adopt a state-of-the-art deep learning method to present baseline results for the attribute prediction and clothing retrieval performance. We also evaluate the method on a more difficult setting, cross-view exact clothing item retrieval. Our dataset will be made publicly available for further studies towards view-invariant clothing retrieval.
Pregel Algorithms for Graph Connectivity Problems with Performance Guarantees

Graphs in real life applications are often huge, such as the Web graph and various social networks. These massive graphs are often stored and processed in distributed sites. In this paper, we study graph algorithms that adopt Google’s Pregel, an iterative vertexcentric framework for graph processing in the Cloud. We first identify a set of desirable properties of an efficient Pregel algorithm, such as linear space, communication and computation cost per iteration, and logarithmic number of iterations. We define such an algorithm as a practical Pregel algorithm (PPA). We then propose PPAs for computing connected components (CCs), biconnected components (BCCs) and strongly connected components (SCCs). The PPAs for computing BCCs and SCCs use the PPAs of many fundamental graph problems as building blocks, which are of interest by themselves. Extensive experiments over large real graphs verified the efficiency of our algorithms.
Example-dependent cost-sensitive decision trees

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. State-of-the-art example-dependent cost-sensitive techniques only introduce the cost to the algorithm, either before or after training, therefore, leaving opportunities to investigate the potential impact of algorithms that take into account the real financial example-dependent costs during an algorithm training. In this paper, we propose an example-dependent cost-sensitive decision tree algorithm, by incorporating the different example-dependent costs into a new cost-based impurity measure and a new cost-based pruning criteria. Then, using three different databases, from three real-world applications: credit card fraud detection, credit scoring and direct marketing, we evaluate the proposed method. The results show that the proposed algorithm is the best performing method for all databases. Furthermore, when compared against a standard decision tree, our method builds significantly smaller trees in only a fifth of the time, while having a superior performance measured by cost savings, leading to a method that not only has more business-oriented results, but also a method that creates simpler models that are easier to analyze.
Millimeter-Wave Planar Broadband Circularly Polarized Antenna Array Using Stacked Curl Elements

Using aperture-fed stacked curl elements, a full corporate-fed millimeter-wave planar broadband circularly polarized antenna array is proposed. First, the stacked curl element is aperture-fed, using the substrate-integrated waveguide (SIW) feeding network. This element is designed under a specific boundary condition, which leads to an excellent agreement of impedance bandwidth and axial ratio (AR) bandwidth between the designed element and the corresponding antenna array. Simulation results show that the antenna element achieves 35.8% impedance bandwidth and 37.8% 3 dB AR bandwidth with a maximum gain of 8.2 dBic. Then, a <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> subarray is designed to achieve 33.1% impedance bandwidth. Next, the full-corporate SIW feeding scheme is utilized to construct the <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> broadband antenna array. Finally, the <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> antenna array is fabricated and its performance is evaluated. Measurement results show that its impedance bandwidth is 35.4%, 3 dB AR bandwidth is 33.8%, and 3 dB gain bandwidth is 32.2% with a peak gain of 23.5 dBic. Considering these three types of bandwidth together, its overall bandwidth is 30.6%. Good efficiency of about 70% is obtained because of the low-loss full-corporate SIW feeding scheme. A <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> multibeam array is also simulated to verify the multibeam potential of the proposed antenna array.
Coverage-Directed Test Generation Automated by Machine Learning - A Review

The increasing complexity and size of digital designs, in conjunction with the lack of a potent verification methodology that can effectively cope with this trend, continue to inspire engineers and academics in seeking ways to further automate design verification. In an effort to increase performance and to decrease engineering effort, research has turned to artificial intelligence (AI) techniques for effective solutions. The generation of tests for simulation-based verification can be guided by machine-learning techniques. In fact, recent advances demonstrate that embedding machine-learning (ML) techniques into a coverage-directed test generation (CDG) framework can effectively automate the test generation process, making it more effective and less error-prone. This article reviews some of the most promising approaches in this field, aiming to evaluate the approaches and to further stimulate more directed research in this area.
LabelRankT: incremental community detection in dynamic networks via label propagation

An increasingly important challenge in network analysis is efficient detection and tracking of communities in dynamic networks for which changes arrive as a stream. There is a need for algorithms that can incrementally update and monitor communities whose evolution generates huge real-time data streams, such as the Internet or on-line social networks. In this paper, we propose LabelRankT, an on-line distributed algorithm for detection of communities in large-scale dynamic networks through stabilized label propagation. Results of tests on real-world networks demonstrate that LabelRankT has much lower computational costs than other algorithms. It also improves the quality of the detected communities compared to dynamic detection methods and matches the quality achieved by static detection approaches. Unlike most of other algorithms which apply only to binary networks, LabelRankT works on weighted and directed networks, which provides a flexible and promising solution for real-world applications.
Stochastic Dual Ascent for Solving Linear Systems

We develop a new randomized iterative algorithm—stochastic dual ascent (SDA)—for finding the projection of a given vector onto the solution space of a linear system. The method is dual in nature: with the dual being a non-strongly concave quadratic maximization problem without constraints. In each iteration of SDA, a dual variable is updated by a carefully chosen point in a subspace spanned by the columns of a random matrix drawn independently from a fixed distribution. The distribution plays the role of a parameter of the method. Our complexity results hold for a wide family of distributions of random matrices, which opens the possibility to fine-tune the stochasticity of the method to particular applications. We prove that primal iterates associated with the dual process converge to the projection exponentially fast in expectation, and give a formula and an insightful lower bound for the convergence rate. We also prove that the same rate applies to dual function values, primal function values and the duality gap. Unlike traditional iterative methods, SDA converges under no additional assumptions on the system (e.g., rank, diagonal dominance) beyond consistency. In fact, our lower bound improves as the rank of the system matrix drops. Many existing randomized methods for linear systems arise as special cases of SDA, including randomized Kaczmarz, randomized Newton, randomized coordinate descent, Gaussian descent, and their variants. In special cases where our method specializes to a known algorithm, we either recover the best known rates, or improve upon them. Finally, we show that the framework can be applied to the distributed average consensus problem to obtain an array of new algorithms. The randomized gossip algorithm arises as a special case.
Geometric Range Searching and Its Relatives

About ten years ago, the eld of range searching, especially simplex range searching, was wide open. At that time, neither e cient algorithms nor nontrivial lower bounds were known for most range-searching problems. A series of papers by Haussler and Welzl [161], Clarkson [88, 89], and Clarkson and Shor [92] not only marked the beginning of a new chapter in geometric searching, but also revitalized computational geometry as a whole. Led by these and a number of subsequent papers, tremendous progress has been made in geometric range searching, both in terms of developing e cient data structures and proving nontrivial lower bounds. From a theoretical point of view, range searching is now almost completely solved. The impact of general techniques developed for geometric range searching | "-nets, 1=rcuttings, partition trees, multi-level data structures, to name a few | is evident throughout computational geometry. This volume provides an excellent opportunity to recapitulate the current status of geometric range searching and to summarize the recent progress in this area. Range searching arises in a wide range of applications, including geographic information systems, computer graphics, spatial databases, and time-series databases. Furthermore, a variety of geometric problems can be formulated as a range-searching problem. A typical range-searching problem has the following form. Let S be a set of n points in R , and let
Localizing Handle-Like Grasp Affordances in 3D Point Clouds

We propose a new approach to localizing handle-like grasp affordances in 3-D point clouds. The main idea is to identify a set of sufficient geometric conditions for the existence of a grasp affordance and to search the point cloud for neighborhoods that satisfy these conditions. Our goal is not to find all possible grasp affordances, but instead to develop a method of localizing important types of grasp affordances quickly and reliably. The strength of this method relative to other current approaches is that it is very practical: it can have good precision/recall for the types of affordances under consideration, it runs in real-time, and it is easy to adapt to different robots and operating scenarios. We validate with a set of experiments where the approach is used to enable the Rethink Baxter robot to localize and grasp unmodelled objects.
M-net: A Convolutional Neural Network for deep brain structure segmentation

In this paper, we propose an end-to-end trainable Convolutional Neural Network (CNN) architecture called the M-net, for segmenting deep (human) brain structures from Magnetic Resonance Images (MRI). A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data, which makes M-net memory efficient. The segmentation method is evaluated on two publicly available datasets and is compared against publicly available model based segmentation algorithms as well as other classification based algorithms such as Random Forrest and 2D CNN based approaches. Experiment results show that the M-net outperforms all these methods in terms of dice coefficient and is at least 3 times faster than other methods in segmenting a new volume which is attractive for clinical use.
A User Study of Visualization Effectiveness Using EEG and Cognitive Load

Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer’s cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.
Mining the Web of Linked Data with RapidMiner

Lots of data from different domains is published as Linked Open Data (LOD). While there are quite a few browsers for such data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this system paper, we introduce the RapidMiner Linked Open Data extension. The extension hooks into the powerful data mining and analysis platform RapidMiner, and offers operators for accessing Linked Open Data in RapidMiner, allowing for using it in sophisticated data analysis workflows without the need for expert knowledge in SPARQL or RDF. The extension allows for autonomously exploring the Web of Data by following links, thereby discovering relevant datasets on the fly, as well as for integrating overlapping data found in different datasets. As an example, we show how statistical data from the World Bank on scientific publications, published as an RDF data cube, can be automatically linked to further datasets and analyzed using additional background knowledge from ten different LOD datasets.
Efficient Byzantine Fault-Tolerance

We present two asynchronous Byzantine fault-tolerant state machine replication (BFT) algorithms, which improve previous algorithms in terms of several metrics. First, they require only 2f+1 replicas, instead of the usual 3f+1. Second, the trusted service in which this reduction of replicas is based is quite simple, making a verified implementation straightforward (and even feasible using commercial trusted hardware). Third, in nice executions the two algorithms run in the minimum number of communication steps for nonspeculative and speculative algorithms, respectively, four and three steps. Besides the obvious benefits in terms of cost, resilience and management complexity-fewer replicas to tolerate a certain number of faults-our algorithms are simpler than previous ones, being closer to crash fault-tolerant replication algorithms. The performance evaluation shows that, even with the trusted component access overhead, they can have better throughput than Castro and Liskov's PBFT, and better latency in networks with nonnegligible communication delays.
Identification of multiple intelligences with the Multiple Intelligence Profiling Questionnaire III

In this study, we present the latest version of the Multiple Intelligences Profiling Questionnaire (MIPQ III) that is based on Howard Gardner’s (e.g., 1983, 1999) MI theory. The operationalization of nine MI scales is tested with an empirical sample of Finnish preadolescents and adults (n = 410). Results of the internal consistency analysis show that the nine MIPQ III dimensions have satisfactory reliability coefficients with the sample. Results of the inter-scale correlation analysis show that (1) Logical-mathematical intelligence correlates positively with Spatial intelligence; (2) Linguistic intelligence correlates positively with Intrapersonal intelligence; (3) Linguistic and Intrapersonal scales correlate positively with the Spiritual and Environmental intelligences. Results of the correlation analysis between the gender, age and the MI scales show that (1) Males in both samples have higher self-rated Logical-mathematical intelligence than females; (2) Females rate their linguistic abilities higher than the males. The results of CFA show good generalizability characteristics of the MIPQ III scales. Our findings give important information to teachers and educators on how gender influences the self-perception of students’ abilities.
Spectre Attacks: Exploiting Speculative Execution

Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim’s memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim’s confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim’s process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.