nils-herrmann/clean_scirepeval huggingface dataset:
Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data

Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.
Feature selection for ranking using boosted trees

Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues to grow, making feature computation a latency bottleneck. As a result, not all available features can be used for ranking, and in fact, much of the time, only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. To this end, we explore different feature selection methods using boosted regression trees, including both greedy approaches (selecting the features with highest relative importance as computed by boosted trees; discounting importance by feature similarity and a randomized approach. We evaluate and compare these approaches using data from a commercial search engine. The experimental results show that the proposed randomized feature selection with feature-importance-based backward elimination outperforms greedy approaches and achieves a comparable relevance with 30 features to a full-feature model trained with 419 features and the same modeling parameters.
Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios.
Modular control-flow integrity

Control-Flow Integrity (CFI) is a software-hardening technique. It inlines checks into a program so that its execution always follows a predetermined Control-Flow Graph (CFG). As a result, CFI is effective at preventing control-flow hijacking attacks. However, past fine-grained CFI implementations do not support separate compilation, which hinders its adoption.
 We present Modular Control-Flow Integrity (MCFI), a new CFI technique that supports separate compilation. MCFI allows modules to be independently instrumented and linked statically or dynamically. The combined module enforces a CFG that is a combination of the individual modules' CFGs. One challenge in supporting dynamic linking in multithreaded code is how to ensure a safe transition from the old CFG to the new CFG when libraries are dynamically linked. The key technique we use is to have the CFG represented in a runtime data structure and have reads and updates of the data structure wrapped in transactions to ensure thread safety. Our evaluation on SPECCPU2006 benchmarks shows that MCFI supports separate compilation, incurs low overhead of around 5%, and enhances security.
Fully integrated automotive radar sensor with versatile resolution

A planar radar sensor for automotive application is presented. The design comprises a fully integrated transceiver multi-chip module (MCM) and an electronically steerable microstrip patch array. The antenna feed network is based on a modified Rotman-lens. An extended angular coverage together with an adapted resolution allows for the integration of automatic cruise control (ACC), precrash sensing and cut-in detection within a single 77 GHz frontend. For ease of manufacturing the interconnects between antenna and MCM rely on a mixed wire bond and flip-chip approach. The concept is validated by laboratory radar measurements.
Thinking about global warming

Attitudes toward global warming are influenced by various heuristics, which may distort policy away from what is optimal for the well-being of people. These possible distortions, or biases, include: a focus on harms that we cause, as opposed to those that we can remedy more easily; a feeling that those who cause a problem should fix it; a desire to undo a problem rather than compensate for its presence; parochial concern with one’s own group (nation); and neglect of risks that are not available. Although most of these biases tend to make us attend relatively too much to global warming, other biases, such as wishful thinking, cause us to attend too little. I discuss these possible effects and illustrate some of them with an experiment conducted on the World Wide Web.
Trisomy 8 Mosaicism in Two Filipino Children

We describe two Filipino patients with Trisomy 8 mosaicism syndrome. Both had global developmental delay, mental deficiency, facial dysmorphism, skeletal deformities and the characteristic deep plantar and palmar creases. Their phenotype and radiologic features were consistent with the previously reported cases. Hematologic malignancies have recently been found to be associated with this chromosomal abnormality, emphasizing the importance of monitoring
Cloudward bound: planning for beneficial migration of enterprise applications to the cloud

In this paper, we tackle challenges in migrating enterprise services into hybrid cloud-based deployments, where enterprise operations are partly hosted on-premise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloud-based architectures, while honoring application performance requirements, and privacy restrictions on what services may be migrated to the cloud. We make several contributions. First, we highlight the complexity inherent in enterprise applications today in terms of their multi-tiered nature, large number of application components, and interdependencies. Second, we have developed a model to explore the benefits of a hybrid migration approach. Our model takes into account enterprise-specific constraints, cost savings, and increased transaction delays and wide-area communication costs that may result from the migration. Evaluations based on real enterprise applications and Azure-based cloud deployments show the benefits of a hybrid migration approach, and the importance of planning which components to migrate. Third, we shed insight on security policies associated with enterprise applications in data centers. We articulate the importance of ensuring assurable reconfiguration of security policies as enterprise applications are migrated to the cloud. We present algorithms to achieve this goal, and demonstrate their efficacy on realistic migration scenarios.
A Source-to-Source Compiler for Generating Dependable Software

Over the last years, an increasing number of safetycritical tasks have been demanded to computer systems. In particular, safety-critical computer-based applications are hitting market area where cost is a major issue, and thus solutions are required which conjugate fault tolerance with low costs. In this paper, a source-to-source compiler supporting a Software-Implemented Hardware Fault Tolerance approach is proposed, based on a set of source code transformation rules. The proposed approach hardens a program against transient memory errors by introducing software redundancy: every computation is performed twice and results are compared, and controlflow invariants are checked explicitly. By exploiting the tool’s capabilities, several benchmark applications have been hardened against transient errors. Fault Injection campaigns have been performed to evaluate the fault detection capability of the hardened applications. In addition we analyzed the proposed approach in terms of space and time overheads.
Video game training to improve selective visual attention in older adults

The current study investigated the effect of video game training on older adult's useful field of view performance (the UFOV® test). Fifty-eight older adult participants were randomized to receive practice with the target action game (Medal of Honor), a placebo control arcade game (Tetris), a clinically validated UFOV training program, or into a no contact control group. Examining pretest-posttest change in selective visual attention, the UFOV improved significantly more than the game groups; all three intervention groups improved significantly more than no-contact controls. There was a lack of difference between the two game conditions, differing from findings with younger adults. Discussion considers whether games posing less challenge might still be effective interventions for elders, and whether optimal training dosages should be higher.
Enabling improved power management in multicore processors through clustered DVFS

In recent years, chip multiprocessors (CMP) have emerged as a solution for high-speed computing demands. However, power dissipation in CMPs can be high if numerous cores are simultaneously active. Dynamic voltage and frequency scaling (DVFS) is widely used to reduce the active power, but its effectiveness and cost depends on the granularity at which it is applied. Per-core DVFS allows the greatest flexibility in controlling power, but incurs the expense of an unrealistically large number of on-chip voltage regulators. Per-chip DVFS, where all cores are controlled by a single regulator overcomes this problem at the expense of greatly reduced flexibility. This work considers the problem of building an intermediate solution, clustering the cores of a multicore processor into DVFS domains and implementing DVFS on a per-cluster basis. Based on a typical workload, we propose a scheme to find similarity among the cores and cluster them based on this similarity. We also provide an algorithm to implement DVFS for the clusters, and evaluate the effectiveness of per-cluster DVFS in power reduction.
Automated segmentation of gallstones in ultrasound images

Gallstones are solid particles formed from bile in the gall bladder. In this paper, we propose a technique to automatically detect Gallstones in ultrasound images, christened as, Automated Gallstone Segmentation (AGS) Technique. Speckle Noise in the ultrasound image is first suppressed using Anisotropic Diffusion Technique. The edges are then enhanced using Unsharp Filtering. NCUT Segmentation Technique is then put to use to segment the image. Afterwards, edges are detected using Sobel Edge Detection. Further, Edge Thickening Process is used to smoothen the edges and probability maps are generated using Floodfill Technique. Then, the image is scribbled using Automatic Scribbling Technique. Finally, we get the segmented gallstone within the gallbladder using the Closed Form Matting Technique.
Design of a new independently-mobile reconfigurable modular robot

A new self-reconfigurable robot is presented. The robot is a hybrid chain/lattice design with several novel features. An active mechanical docking mechanism provides inter-module connection, along with optical and electrical interface. The docking mechanisms function additionally as driven wheels. Internal slip rings provide unlimited rotary motion to the wheels, allowing the modules to move independently by driving on flat surfaces, or in assemblies negotiating more complex terrain. Modules in the system are mechanically homogeneous, with three identical docking mechanisms within a module. Each mechanical dock is driven by a high torque actuator to enable movement of large segments within a multi-module structure, as well as low-speed driving. Preliminary experimental results demonstrate locomotion, mechanical docking, and lifting of a single module.
Women-led firms and the gender gap in top executive jobs

Women-Led Firms and the Gender Gap in Top Executive Jobs Using data on Executive Compensation from Standard and Poor's ExecuComp, this paper explores the gender gap in top executive jobs and the effect of women CEOs, Chairs, and Directors on the pay of other women executives. The results show a narrowing of the uncorrected gender pay gap from the mid-1990s. Women top executives earn between 8% to 25% less than male executives after controlling for differences in company size, occupational title, and industry. The magnitude of the gender pay gap is statistically related to the gender of the Chief Executive and Corporate Board Chair. Women CEO and Board Chairs bring more top women and at higher pay than is found in non-women-led firms. Specifically, female executives in women-led firms earn between 10-20% more than comparable executive women in male-led firms and are between 3-18% more likely to be among the highest five paid executives in these firms as well. The paper thereby provides strong empirical evidence that women leaders are associated with positive outcomes for women executives in substantive and important ways.
Maintenance of Chronic Fatigue Syndrome (CFS) in Young CFS Patients Is Associated with the 5-HTTLPR and SNP rs25531 A > G Genotype

Earlier studies have shown that genetic variability in the SLC6A4 gene encoding the serotonin transporter (5-HTT) may be important for the re-uptake of serotonin (5-HT) in the central nervous system. In the present study we investigated how the 5-HTT genotype i.e. the short (S) versus long (L) 5-HTTLPR allele and the SNP rs25531 A > G affect the physical and psychosocial functioning in patients with chronic fatigue syndrome (CFS). All 120 patients were recruited from The Department of Paediatrics at Oslo University Hospital, Norway, a national referral center for young CFS patients (12-18 years). Main outcomes were number of steps per day obtained by an accelerometer and disability scored by the Functional Disability Inventory (FDI). Patients with the 5-HTT SS or SLG genotype had a significantly lower number of steps per day than patients with the 5-HTT LALG, SLA or LALA genotype. Patients with the 5-HTT SS or SLG genotype also had a significantly higher FDI score than patients with the 5-HTT LALG, SLA or LALA genotype. Thus, CFS patients with the 5-HTT SS or SLG genotype had worse 30 weeks outcome than CFS patients with the 5-HTT LALG, SLA or LALA genotype. The present study suggests that the 5-HTT genotype may be a factor that contributes to maintenance of CFS.
Using Channel State Information for Tamper Detection in the Internet of Things

The Internet of Things (IoT) is increasingly used for critical applications and securing the IoT has become a major concern. Among other issues it is important to ensure that tampering with IoT devices is detected. Many IoT devices use WiFi for communication and Channel State Information (CSI) based tamper detection is a valid option. Each 802.11n WiFi frame contains a preamble which allows a receiver to estimate the impact of the wireless channel, the transmitter and the receiver on the signal. The estimation result - the CSI - is used by a receiver to extract the transmitted information. However, as the CSI depends on the communication environment and the transmitter hardware, it can be used as well for security purposes. If an attacker tampers with a transmitter it will have an effect on the CSI measured at a receiver. Unfortunately not only tamper events lead to CSI fluctuations; movement of people in the communication environment has an impact too. We propose to analyse CSI values of a transmission simultaneously at multiple receivers to improve distinction of tamper and movement events. A moving person is expected to have an impact on some but not all communication links between transmitter and the receivers. A tamper event impacts on all links between transmitter and the receivers. The paper describes the necessary algorithms for the proposed tamper detection method. In particular we analyse the tamper detection capability in practical deployments with varying intensity of people movement. In our experiments the proposed system deployed in a busy office environment was capable to detect 53% of tamper events (TPR = 53%) while creating zero false alarms (FPR = 0%).
PET/MR in children. Initial clinical experience in paediatric oncology using an integrated PET/MR scanner

Use of PET/MR in children has not previously been reported, to the best of our knowledge. Children with systemic malignancies may benefit from the reduced radiation exposure offered by PET/MR. We report our initial experience with PET/MR hybrid imaging and our current established sequence protocol after 21 PET/MR studies in 15 children with multifocal malignant diseases. The effective dose of a PET/MR scan was only about 20% that of the equivalent PET/CT examination. Simultaneous acquisition of PET and MR data combines the advantages of the two previously separate modalities. Furthermore, the technique also enables whole-body diffusion-weighted imaging (DWI) and statements to be made about the biological cellularity and nuclear/cytoplasmic ratio of tumours. Combined PET/MR saves time and resources. One disadvantage of PET/MR is that in order to have an effect, a significantly longer examination time is needed than with PET/CT. In our initial experience, PET/MR has turned out to be an unexpectedly stable and reliable hybrid imaging modality, which generates a complementary diagnostic study of great additional value.
Concentric Polycyclic Regrowth Pattern in Alopecia Areata

Alopecia areata (AA) is a common form of autoimmune nonscarring hair loss of scalp and/or body. Atypical hair regrowth in AA is considered a rare phenomenon. It includes atypical pattern of hair growth (sudden graying, perinevoid alopecia, Renbok phenomenon, castling phenomenon, and concentric or targetoid regrowth) and atypical dark color hair regrowth. We report a case of AA that resulted in a concentric targetoid hair regrowth and discuss the possible related theories regarding the significance of this phenomenon.
Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.
Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis and Argyros 2008). The proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. Three different methods have been used to qualitatively assess the improvements of the camera parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).
Mixed Membership Stochastic Blockmodels

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.
Decomposing Petri nets for process mining: A generic approach

The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.