nils-herrmann/clean_scirepeval huggingface dataset:
Service Migration Patterns -- Decision Support and Best Practices for the Migration of Existing Service-Based Applications to Cloud Environments

In many ways cloud computing is an extension of the service-oriented computing (SOC) approach to create resilient and elastic hosting environments and applications. Service-oriented Architectures (SOA), thus, share many architectural properties with cloud environments and cloud applications, such as the distribution of application functionality among multiple application components (services) and their loosely coupled integration to form a distributed application. Existing service-based applications are, therefore, ideal candidates to be moved to cloud environments in order to benefit from the cloud properties, such as elasticity or pay-per-use pricing models. In order for such an application migration and the overall restructuring of an IT application landscape to be successful, decisions have to be made regarding (i) the portion of the application stack to be migrated and (ii) the process to follow during the migration in order to guarantee an acceptable service level to application users. In this paper, we present best practices how we addressed these challenges in form of service migration patterns as well as a methodology how these patterns should be applied during the migration of a service-based application or multiples thereof. Also, we present an implementation of the approach, which has been used to migrate a web-application stack from Amazon Web Services to the T-Systems cloud offering Dynamic Services for Infrastructure (DSI).
On the applicability of Two-Ray path loss models for vehicular network simulation

We discuss the applicability of simplified Two-Ray Ground path loss models to simulation-based performance evaluation studies of Inter-Vehicle Communication (IVC) protocols. We contrast this with the applicability of a more exact Two-Ray Interference model. A key result is that, in most cases, the commonly used simplified Two-Ray Ground models add no additional value compared to the most simple Free-space model - in particular in highway and suburban environments. We further argue that replacing a simplified with a fully featured Two-Ray Interference model can not only substantially improve the accuracy of simulation results but also allow capturing one notable artifact that becomes immediately visible in field tests, namely strong signal attenuation at short and medium ranges. We implemented the Two-ray Interference model within the Veins simulation framework and validated it using analytical predictions and field measurements. We show the impact of the more accurate Two-Ray Interference model, which only comes with negligible additional computational cost for simulation experiments.
Towards Machine Learning-Based Auto-tuning of MapReduce

MapReduce, which is the de facto programming model for large-scale distributed data processing, and its most popular implementation Hadoop have enjoyed widespread adoption in industry during the past few years. Unfortunately, from a performance point of view getting the most out of Hadoop is still a big challenge due to the large number of configuration parameters. Currently these parameters are tuned manually by trial and error, which is ineffective due to the large parameter space and the complex interactions among the parameters. Even worse, the parameters have to be re-tuned for different MapReduce applications and clusters. To make the parameter tuning process more effective, in this paper we explore machine learning-based performance models that we use to auto-tune the configuration parameters. To this end, we first evaluate several machine learning models with diverse MapReduce applications and cluster configurations, and we show that support vector regression model (SVR) has good accuracy and is also computationally efficient. We further assess our auto-tuning approach, which uses the SVR performance model, against the Starfish auto tuner, which uses a cost-based performance model. Our findings reveal that our auto-tuning approach can provide comparable or in some cases better performance improvements than Starfish with a smaller number of parameters. Finally, we propose and discuss a complete and practical end-to-end auto-tuning flow that combines our machine learning-based performance models with smart search algorithms for the effective training of the models and the effective exploration of the parameter space.
Multimodal Measurement of Depression Using Deep Learning Models

This paper addresses multi-modal depression analysis. We propose a multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models. Our framework considers audio, video and text streams. For each modality, handcrafted feature descriptors are input into a DCNN to learn high-level global features with compact dynamic information, then the learned features are fed to a DNN to predict the PHQ-8 scores. For multi-modal fusion, the estimated PHQ-8 scores from the three modalities are integrated in a DNN to obtain the final PHQ-8 score. Moreover, in this work, we propose new feature descriptors for text and video. For the text descriptors, we select the participant»s answers to the questions associated with psychoanalytic aspects of depression, such as sleep disorder, and make use of the Paragraph Vector (PV) to learn the distributed representations of these sentences. For the video descriptors, we propose a new global descriptor, the Histogram of Displacement Range (HDR), calculated directly from the facial landmarks to measure their displacements and speed. Experiments have been carried out on the AVEC2017 depression sub-challenge dataset. The obtained results show that the proposed depression recognition framework obtains very promising accuracy, with the root mean square error (RMSE) as 4.653, mean absolute error (MAE) as 3.980 on the development set, and RMSE as 5.974, MAE as 5.163 on the test set.
Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort

Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation.



Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs.


AVAILABILITY
Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.
Efficient Object Localization and Pose Estimation with 3D Wireframe Models

We propose a new and efficient method for 3D object localization and fine-grained 3D pose estimation from a single 2D image. Our approach follows the classical paradigm of matching a 3D model to the 2D observations. Our first contribution is a 3D object model composed of a set of 3D edge primitives learned from 2D object blueprints, which can be viewed as a 3D generalization of HOG features. This model is used to define a matching cost obtained by applying a rigid-body transformation to the 3D object model, projecting it onto the image plane, and matching the projected model to HOG features extracted from the input image. Our second contribution is a very efficient branch-and-bound algorithm for finding the 3D pose that maximizes the matching score. For this, 3D integral images of quantized HOGs are employed to evaluate in constant time the maximum attainable matching scores of individual model primitives. We applied our method to three different datasets of cars and achieved promising results with testing times as low as less than half a second.
Synthesizing API usage examples

Key program interfaces are sometimes documented with usage examples: concrete code snippets that characterize common use cases for a particular data type. While such documentation is known to be of great utility, it is burdensome to create and can be incomplete, out of date, or not representative of actual practice. We present an automatic technique for mining and synthesizing succinct and representative human-readable documentation of program interfaces. Our algorithm is based on a combination of path sensitive dataflow analysis, clustering, and pattern abstraction. It produces output in the form of well-typed program snippets which document initialization, method calls, assignments, looping constructs, and exception handling. In a human study involving over 150 participants, 82% of our generated examples were found to be at least as good at human-written instances and 94% were strictly preferred to state of the art code search.
Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system

Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naïve Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-λ to improve the initial naïve Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.
IM2GPS: estimating geographic information from a single image

Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally - on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earthpsilas surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.
Google this!: using Google apps for collaboration and productivity

In 2009, Colorado State University migrated to Google Apps for Education as an e-mail hosting solution for its students from an internal on-premise e-mail system. The additional capabilities of Google Apps, originally seen as a nonessential add-on to the e-mail solution, have boosted the collaboration and communication among CSU's students beyond our expectations. Once the faculty and staff saw the potential for collaboration the requests to opt-in increased. This allowed collaboration between faculty and students on a scale not previously witnessed at CSU. Faculty who have made the switch to Google Apps are satisfied and enthusiastic with the service. The Google Apps for Education suite comprises Google Mail, Calendar, Talk, Docs, Sites and Video. In this paper I will provide an overview of each App as well as specific techniques on using each App. We will discuss the potential of teamwork and idea exchanges made possible by Google Apps and how they can be implemented in the academic environment. We will discuss and demonstrate interoperability between Google Apps and external applications, IT team collaboration, student employee management integration, migration techniques and more.
Enhancing Physical Education with Exergames and Wearable Technology

Increases in the numbers of obese and overweight children are a major issue in post-industrial societies because obesity can lead to severe health-related problems. In addition, many challenges affect the quantity and quality of physical education (PE) provided by schools. Exergames that combine exercise with gaming have been recognized as a possible method for motivating children to become physically active and to make PE more fun. Furthermore, exergames that utilize wearable sensors devices allow players' movements to be tracked for estimating the efficiency of exercise. In this study, we developed the Running Othello 2 (RO2) exergame, where players wear a smartphone and a smart wrist band to compete in a board game enhanced with physical and pedagogical missions. In physical missions, the game uses inertial sensors and a heart rate meter to detect the physical activities of players. The pedagogical part of the game is based on the South Korean PE curriculum. We evaluated RO2 with 61 South Korean third grade elementary school students, 32 of whom learned curriculum topics by playing the game. The other 29 students comprised a control group who studied the pedagogical content using handouts. The results indicated that learning with RO2 was more efficient, the players were engaged, and their heart rates increased. Based on the evaluation, we identified several issues to be addressed in future research. Finally, we discussed how RO2 supports the educational affordances of wearables and we explained how exergames using wearables can overcome some of the challenges faced by PE.
On the Reliability of Profile Matching Across Large Online Social Networks

Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has showed that it is possible to accurately match profiles, but their evaluation focused only on sampled datasets. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. Today's social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes--Availability, Consistency, non-Impersonability, and Discriminability (ACID)--that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios.
Anchors: High-Precision Model-Agnostic Explanations

We introduce a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, “sufficient” conditions for predictions. We propose an algorithm to efficiently compute these explanations for any black-box model with high-probability guarantees. We demonstrate the flexibility of anchors by explaining a myriad of different models for different domains and tasks. In a user study, we show that anchors enable users to predict how a model would behave on unseen instances with less effort and higher precision, as compared to existing linear explanations or no explanations.
The UJI librarian robot

This paper describes the UJI Librarian Robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.
Detecting Semantic Parts on Partially Occluded Objects

In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. WANG ET.AL.: DETECTING SEMANTIC PARTS ON PARTIALLY OCCLUDED OBJECTS 1 Detecting Semantic Parts on Partially Occluded Objects Jianyu Wang*1 wjyouch@gmail.com Cihang Xie*2 cihangxie306@gmail.com Zhishuai Zhang*2 zhshuai.zhang@gmail.com Jun Zhu1 zhujun.sjtu@gmail.com Lingxi Xie( )2 198808xc@gmail.com Alan L. Yuille2 alan.l.yuille@gmail.com 1 Baidu Research (USA), Sunnyvale, CA 94089 USA 2 Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA . . . .  This work was done when the first author was a Ph.D. student at UCLA. The first three authors contributed equally to this work.
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.
Secure multi-party computation in large networks

We describe scalable protocols for solving the secure multi-party computation (MPC) problem among a significant number of parties. We consider both the synchronous and the asynchronous communication models. In the synchronous setting, our protocol is secure against a static malicious adversary corrupting less than a 1/3 fraction of the parties. In the asynchronous environment, we allow the adversary to corrupt less than a 1/8 fraction of parties. For any deterministic function that can be computed by an arithmetic circuit with m gates, both of our protocols require each party to send a number of messages and perform an amount of computation that is $$\tilde{O}(m/n + \sqrt{n})$$ O ~ ( m / n + n ) . We also show that our protocols provide statistical and universally-composable security. To achieve our asynchronous MPC result, we define the threshold counting problem and present a distributed protocol to solve it in the asynchronous setting. This protocol is load balanced, with computation, communication and latency complexity of $$O(\log {n})$$ O ( log n ) , and can also be used for designing other load-balanced applications in the asynchronous communication model.
The dark side of personality at work

Although there has been increasing interest in dark personality traits in the organizational sciences, these characteristics remain relatively understudied and somewhat misunderstood. The present manuscript aims to clarify some of the issues surrounding dark personality traits by discussing the history of dark personality traits, how they relate to normal personality traits, their relative importance as determinants of organizational outcomes, and measurement issues surrounding the assessment of these characteristics. We will then discuss potential future directions for research investigating the causes and consequences of these traits as well as providing guidance on the implementation of dark personality assessment in the workplace for selection and training.