nils-herrmann/clean_scirepeval huggingface dataset:
Gemini: A Computation-Centric Distributed Graph Processing System

Traditionally distributed graph processing systems have largely focused on scalability through the optimizations of inter-node communication and load balance. However, they often deliver unsatisfactory overall processing efficiency compared with shared-memory graph computing frameworks. We analyze the behavior of several graph-parallel systems and find that the added overhead for achieving scalability becomes a major limiting factor for efficiency, especially with modern multi-core processors and high-speed interconnection networks. Based on our observations, we present Gemini, a distributed graph processing system that applies multiple optimizations targeting computation performance to build scalability on top of efficiency. Gemini adopts (1) a sparse-dense signal-slot abstraction to extend the hybrid push-pull computation model from shared-memory to distributed scenarios, (2) a chunk-based partitioning scheme enabling low-overhead scaling out designs and locality-preserving vertex accesses, (3) a dual representation scheme to compress accesses to vertex indices, (4) NUMA-aware sub-partitioning for efficient intra-node memory accesses, plus (5) locality-aware chunking and fine-grained work-stealing for improving both inter-node and intra-node load balance, respectively. Our evaluation on an 8-node high-performance cluster (using five widely used graph applications and five real-world graphs) shows that Gemini significantly outperforms all well-known existing distributed graph processing systems, delivering up to 39.8× (from 8.91×) improvement over the fastest among them.
MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees

Multi-agent path planning is a challenging problem with numerous real-life applications. Running a centralized search such as A* in the combined state space of all units is complete and cost-optimal, but scales poorly, as the state space size is exponential in the number of mobile units. Traditional decentralized approaches, such as FAR and WHCA*, are faster and more scalable, being based on problem decomposition. However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality. They are not necessarily able to tell in a reasonable time whether they would succeed in finding a solution to a given instance. We introduce MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. We present a basic version and several extensions. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Even though all algorithmic versions are incomplete in the general case, each provides formal guarantees on problems it can solve. For each version, we discuss the algorithm’s completeness with respect to clearly defined subclasses of instances. Experiments were run on realistic game grid maps. MAPP solved 99.86% of all mobile units, which is 18–22% better than the percentage of FAR and WHCA*. MAPP marked 98.82% of all units as provably solvable during the first stage of plan computation. Parts of MAPP’s computation can be re-used across instances on the same map. Speed-wise, MAPP is competitive or significantly faster than WHCA*, depending on whether MAPP performs all computations from scratch. When data that MAPP can re-use are preprocessed offline and readily available, MAPP is slower than the very fast FAR algorithm by a factor of 2.18 on average. MAPP’s solutions are on average 20% longer than FAR’s solutions and 7–31% longer than WHCA*’s solutions.
Practical privacy: the SuLQ framework

We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is ΣiεS f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise -- much less than the sampling error -- provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].
CoRAD: Visual Analytics for Cohort Analysis

In this paper, we introduce a novel dynamic visual analytic tool called the Cohort Relative Aligned Dashboard (CoRAD). We present the design components of CoRAD, along with alternatives that lead to the final instantiation. We also present an evaluation involving expert clinical researchers, comparing CoRAD against an existing analytics method. The results of the evaluation show CoRAD to be more usable and useful for the target user. The relative alignment of physiologic data to clinical events were found to be a highlight of the tool. Clinical experts also found the interactive selection and filter functions to be useful in reducing information overload. Moreover, CoRAD was also found to allow clinical researchers to generate alternative hypotheses and test them in vivo.
Distance Visualization for Interactive 3D Implant Planning

An instant and quantitative assessment of spatial distances between two objects plays an important role in interactive applications such as virtual model assembly, medical operation planning, or computational steering. While some research has been done on the development of distance-based measures between two objects, only very few attempts have been reported to visualize such measures in interactive scenarios. In this paper we present two different approaches for this purpose, and we investigate the effectiveness of these approaches for intuitive 3D implant positioning in a medical operation planning system. The first approach uses cylindrical glyphs to depict distances, which smoothly adapt their shape and color to changing distances when the objects are moved. This approach computes distances directly on the polygonal object representations by means of ray/triangle mesh intersection. The second approach introduces a set of slices as additional geometric structures, and uses color coding on surfaces to indicate distances. This approach obtains distances from a precomputed distance field of each object. The major findings of the performed user study indicate that a visualization that can facilitate an instant and quantitative analysis of distances between two objects in interactive 3D scenarios is demanding, yet can be achieved by including additional monocular cues into the visualization.
Dynamic 2D/3D registration for the Kinect

Image and geometry registration algorithms are essential components of many computer graphics and computer vision systems. With recent technological advances in RGB-D sensors, robust algorithms that combine 2D image and 3D geometry registration have become an active area of research.
 This course introduces the basics of 2D/3D registration algorithms and provides theoretical explanations and practical tools for designing computer vision and computer graphics systems based on RGB-D devices such as the Microsoft Kinect or Asus Xtion Live. To illustrate the theory and demonstrate practical relevance, the course briefly discusses three applications: rigid scanning, non-rigid modeling, and real-time face tracking.
When Does More Money Work? Examining the Role of Perceived Fairness in Pay on the Performance Quality of Crowdworkers

This research adds to the rich discussion on whether increases in payment to crowdworkers lead to increases in performance quality by introducing the concept of perceived fairness in pay (PFP). PFP refers to the belief that one is fairly compensated for their work. We examine whether PFP mediates the impact of payment amount on the performance quality of crowdworkers. We conducted a field experiment with 152 crowdworkers performing a buttonclicking (BC) task and an instructional manipulation check (IMC) task. PFP mediated the impact of payment amount on performance quality in the BC task but not in the IMC task. PFP also mediated the impact of payment amount on satisfaction and task time. Results suggest that PFP can help us better understand the relationship between payment and performance quality in crowdsourcing.
Reduction of relative centrifugation force within injectable platelet-rich-fibrin (PRF) concentrates advances patients’ own inflammatory cells, platelets and growth factors: the first introduction to the low speed centrifugation concept

The aim of this study was to analyze systematically the influence of the relative centrifugation force (RCF) on leukocytes, platelets and growth factor release within fluid platelet-rich fibrin matrices (PRF). Systematically using peripheral blood from six healthy volunteers, the RCF was reduced four times for each of the three experimental protocols (I–III) within the spectrum (710–44 g), while maintaining a constant centrifugation time. Flow cytometry was applied to determine the platelets and leukocyte number. The growth factor concentration was quantified 1 and 24 h after clotting using ELISA. Reducing RCF in accordance with protocol-II (177 g) led to a significantly higher platelets and leukocytes numbers compared to protocol-I (710 g). Protocol-III (44 g) showed a highly significant increase of leukocytes and platelets number in comparison to -I and -II. The growth factors’ concentration of VEGF and TGF-β1 was significantly higher in protocol-II compared to -I, whereas protocol-III exhibited significantly higher growth factor concentration compared to protocols-I and -II. These findings were observed among 1 and 24 h after clotting, as well as the accumulated growth factor concentration over 24 h. Based on the results, it has been demonstrated that it is possible to enrich PRF-based fluid matrices with leukocytes, platelets and growth factors by means of a single alteration of the centrifugation settings within the clinical routine. We postulate that the so-called low speed centrifugation concept (LSCC) selectively enriches leukocytes, platelets and growth factors within fluid PRF-based matrices. Further studies are needed to evaluate the effect of cell and growth factor enrichment on wound healing and tissue regeneration while comparing blood concentrates gained by high and low RCF.
ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning

Databases can be corrupted with various errors such as missing, incorrect, or inconsistent values. Increasingly, modern data analysis pipelines involve Machine Learning, and the effects of dirty data can be difficult to debug.Dirty data is often sparse, and naive sampling solutions are not suited for high-dimensional models. We propose ActiveClean, a progressive framework for training Machine Learning models with data cleaning. Our framework updates a model iteratively as the analyst cleans small batches of data, and includes numerous optimizations such as importance weighting and dirty data detection. We designed a visual interface to wrap around this framework and demonstrate ActiveClean for a video classification problem and a topic modeling problem.
Exploring the Benefits of Augmented Reality Documentation for Maintenance and Repair

We explore the development of an experimental augmented reality application that provides benefits to professional mechanics performing maintenance and repair tasks in a field setting. We developed a prototype that supports military mechanics conducting routine maintenance tasks inside an armored vehicle turret, and evaluated it with a user study. Our prototype uses a tracked headworn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, localization, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: the same headworn display providing untracked text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed that mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.
THREE S Assessment for Learning in Preservice Teacher Education Performance-Based Assessments

Teacher education programs have long used a customized set of curriculumembedded assessments to support teacher candidate learning. High-stakes summative assessments have been left up to the states, which have usually tested basic skills, content knowledge, and, increasingly, pedagogical knowledge. However, recent changes in national and state accreditation processes have put program outcomes under the microscope, and the policy environment increasingly demands that teacher education programs provide evidence that their graduates have learned to teach. The quest for more valid licensing examinations has led some states and teacher education programs to look toward the use of performance-based assessments that measure teachers’ competencies with more authentic instruments as the basis for licensure and professional development.1 Preservice teacher credential programs across the country have independently created and implemented their own assessment systems that include performance-based approaches, focusing not only on teaching knowledge but on the application of this knowledge in practice. (For descriptions of a wide variety of assessment approaches used in teacher education programs, see Castle & Shaklee, 2006; Wise, Ehrenberg, & Leibbrand, 2008; Coggshall, Max, & Bassett, 2008.) The strength of university-based approaches to assessment is that they are often used in formative ways to support candidate learning, while current licensure examinations are only summative in nature and do not generate detailed information about specific strengths and weaknesses in candidate performance
Explorations in engagement for humans and robots

This paper explores the concept of engagement, the process by which individuals in an interaction start, maintain and end their perceived connection one another. The paper reports on one aspect of engagement among human interactors the effect of tracking faces during an interaction. It also describes the architecture of a robot that can particpate in conversational, collaborative interactions with engagement gestures. Finally, the paper reports on findings of experiments with human participants who interacted with a robot when it either performed or did not perform engagement gestures. Results of the human-robot studeis indicate that people become engaged with robots: they direct their attention to the robot more often in interactions where engagement gestures are present, and they find interactions more appropriate when engagement gestures are present than when they are not Journal Artificial Intelligence This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Connecting Big Scholarly Data With Science of Science Policy: An Ontology-Based-Data- Management (Obdm) Approach

The OBDM approach in a nutshell The key idea of OBDM is to resort to a three-level architecture, constituted by the ontology, the sources, and the mapping between the two. The ontology is a conceptual, formal description of the domain of interest to a given organization (or, a community of users), expressed in terms of relevant concepts, attributes of concepts, relationships between concepts, and logical assertions characterizing the domain knowledge. The data sources are the repositories accessible by the organization where data concerning the domain are stored. In the general case, such repositories are numerous, heterogeneous, each one managed and maintained independently from the others. The mapping is a precise specification of the correspondence between the data contained in the data sources and the elements of the ontology. The main purpose of an OBDM system is to allow information consumers to query the data using the elements in the ontology as predicates. In this sense, OBDM is a form of information integration, where the conceptual model of the application domain, formulated as an ontology expressed in a logic-based language, replaces the usual global schema. The integrated view that the system provides to information consumers is not merely a data structure accommodating the various data at the sources, but becomes a semantically rich description of the relevant concepts in the domain of interest, as well as the relationships between such concepts.
An attack scenario and mitigation mechanism for enterprise BYOD environments

The recent proliferation of the Internet of Things (IoT) technology poses major security and privacy concerns. Specifically, the use of personal IoT devices, such as tablets, smartphones, and even smartwatches, as part of the Bring Your Own Device (BYOD) trend, may result in severe network security breaches in enterprise environments. Such devices increase the attack surface by weakening the digital perimeter of the enterprise network and opening new points of entry for malicious activities. In this paper we demonstrate a novel attack scenario in an enterprise environment by exploiting the smartwatch device of an innocent employee. Using a malicious application running on a suitable smartwatch, the device imitates a real Wi-Fi direct printer service in the network. Using this attack scenario, we illustrate how an advanced attacker located outside of the organization can leak/steal sensitive information from the organization by utilizing the compromised smartwatch as a means of attack. An attack mitigation process and countermeasures are suggested in order to limit the capability of the remote attacker to execute the attack on the network, thus minimizing the data leakage by the smartwatch.
A background subtraction algorithm for detecting and tracking vehicles

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.07.083 * Corresponding author.  This system involves locating moving objects present in complex road scenes by implementing an advanced background subtraction methodology. The innovation concerns a histogram-based filtering procedure, which collects scatter background information carried in a series of frames, at pixel level, generating reliable instances of the actual background. The proposed algorithm reconstructs a background instance on demand under any traffic conditions. The background reconstruction algorithm demonstrated a rather robust performance in various operating conditions including unstable lighting, different view-angles and congestion.
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradient in parallel, aggregates all gradients in a single server to obtain the average, and update each worker’s local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead.  Consider the distributed training of deep neural networks over multiple workers (Dean et al. 2012), where all workers can access all or partial training data and aim to find a common model that yields the minimum training loss. Such a scenario can be modeled as the following distributed parallel non-convex optimization min x∈Rm f(x) ∆ = 1 N N ∑
Online anomaly detection for sensor systems: A simple and efficient approach

Wireless sensor systems aid scientific studies by instrumen ting the real world and collecting measurements. Given the large volume of measurements colle cted by sensor systems, one problem arises – an automated approach to identifying the “inter esting” parts of these data sets, or anomaly detection . A good anomaly detection methodology should be able to accu r tely identify many types of anomalies, be robust, require relatively little resources, and perform detection in (near) real-time. Thus, in this paper we focus on an approa ch to online anomaly detection in measurements collected by sensor systems, where our eval ation, using real-world datasets, shows that our approach is accurate (it detects over 90% of th e anomalies with few false positives), works well over a range of parameter choices, and has a small (CPU, memory) footprint.
Collaborative detection of cyberbullying behavior in Twitter data

As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.