nils-herrmann/clean_scirepeval huggingface dataset:
A systematic mapping study of mobile application testing techniques

The importance of mobile application specific testing techniques and methods has been attracting much attention of software engineers over the past few years. This is due to the fact that mobile applications are different than traditional web and desktop applications, and more and more they are moving to being used in critical domains. Mobile applications require a different approach to application quality and dependability and require an effective testing approach to build high quality and more reliable software. We performed a systematic mapping study to categorize and to structure the research evidence that has been published in the area of mobile application testing techniques and challenges that they have reported. Seventy nine (79) empirical studies are mapped to a classification schema. Several research gaps are identified and specific key testing issues for practitioners are identified: there is a need for eliciting testing requirements early during development process; the need to conduct research in real-world development environments; specific testing techniques targeting application life-cycle conformance and mobile services testing; and comparative studies for security and usability testing.
Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools

In this paper, we quantify the effectiveness of third-party tracker blockers on a large scale. First, we analyze the architecture of various state-of-the-art blocking solutions and discuss the advantages and disadvantages of each method. Second, we perform a two-part measurement study on the effectiveness of popular tracker-blocking tools. Our analysis quantifies the protection offered against trackers present on more than 100,000 popular websites and 10,000 popular Android applications. We provide novel insights into the ongoing arms race between trackers and developers of blocking tools as well as which tools achieve the best results under what circumstances. Among others, we discover that rule-based browser extensions outperform learning-based ones, trackers with smaller footprints are more successful at avoiding being blocked, and CDNs pose a major threat towards the future of tracker-blocking tools. Overall, the contributions of this paper advance the field of web privacy by providing not only the largest study to date on the effectiveness of tracker-blocking tools, but also by highlighting the most pressing challenges and privacy issues of third-party tracking.
Using Dynamic Time Warping to Find Patterns in Time Series

Knowledge discovery in databases presents many interesting challenges within the ¢onte~t of providing computer tools for exploring large data archives. Electronic data .repositories are growing qulckiy and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detect£ug patterns in such data streams or time series is an important knowledge discovery task. This paper describes some pr~|~m;~,ry experiments with a dynamic prograrnm~,~g approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.
IoT interoperability: A hub-based approach

Interoperability in the Internet of Things is critical for emerging services and applications. In this paper we advocate the use of IoT `hubs' to aggregate things using web protocols, and suggest a staged approach to interoperability. In the context of a UK government funded project involving 8 IoT sub-projects to address cross-domain IoT interoperability, we introduce the HyperCat IoT catalogue specification. We then describe the tools and techniques we developed to adapt an existing data portal and IoT platform to this specification, and provide an IoT hub focused on the highways industry called `Smart Streets'. Based on our experience developing this large scale IoT hub, we outline lessons learned which we hope will contribute to ongoing efforts to create an interoperable global IoT ecosystem.
Cloud computing for the architecture, engineering & construction sector: requirements, prototype & experience

The Architecture, Engineering & Construction (AEC) sector is a highly fragmented, data intensive, project based industry, involving a number of very different professions and organisations. Projects carried out within this sector involve collaboration between various people, using a variety of different systems. This, along with the industry’s strong data sharing and processing requirements, means that the management of building data is complex and challenging. This paper presents a solution to data sharing requirements of the AEC sector by utilising Cloud Computing. Our solution presents two key contributions, first a governance model for building data, based on extensive research and industry consultation. Second, a prototype implementation of this governance model, utilising the CometCloud autonomic Cloud Computing engine based on the Master/Worker paradigm. We have integrated our prototype with the 3D modelling software Google Sketchup. The approach and prototype presented has applicability in a number of other eScience related applications involving multi-disciplinary, collaborative working using Cloud Computing infrastructure.
Territoriality in collaborative tabletop workspaces

Researchers seeking alternatives to traditional desktop computers have begun exploring the potential collaborative benefits of digital tabletop displays. However, there are still many open issues related to the design of collaborative tabletop interfaces, such as whether these systems should automatically orient workspace items or enforce ownership of workspace content. Understanding the natural interaction practices that people use during tabletop collaboration with traditional media (e.g., pen and paper) can help to address these issues. Interfaces that are modeled on these practices will have the additional advantage of supporting the interaction skills people have developed over years of collaborating at traditional tables. To gain a deeper understanding of these interaction practices we conducted two observational studies of traditional tabletop collaboration in both casual and formal settings. Our results reveal that collaborators use three types of tabletop territories to help coordinate their interactions within the shared tabletop workspace: <i>personal, group</i>, and <i>storage</i> territories. Findings from a spatial analysis of collaborators' tabletop interactions reveal important properties of these tabletop territories. In order to provide a comprehensive picture of the role of tabletop territoriality in collaboration, we conclude with a synthesis of our findings and previous research findings and with several relevant design implications.
Image Classification and Retrieval are ONE

In this paper, we demonstrate that the essentials of image classification and retrieval are the same, since both tasks could be tackled by measuring the similarity between images. To this end, we propose ONE (Online Nearest-neighbor Estimation), a unified algorithm for both image classification and retrieval. ONE is surprisingly simple, which only involves manual object definition, regional description and nearest-neighbor search. We take advantage of PCA and PQ approximation and GPU parallelization to scale our algorithm up to large-scale image search. Experimental results verify that ONE achieves state-of-the-art accuracy in a wide range of image classification and retrieval benchmarks.
Comparison of frameworks for high-performance packet IO

Network stacks currently implemented in operating systems can no longer cope with the packet rates offered by 10 Gbit Ethernet. Thus, frameworks were developed claiming to offer a faster alternative for this demand. These frameworks enable arbitrary packet processing systems to be built from commodity hardware handling a traffic rate of several 10 Gbit interfaces, entering a domain previously only available to custom-built hardware. In this paper, we survey various frameworks for high-performance packet IO. We analyze the performance of the most prominent frameworks based on representative measurements in packet forwarding scenarios. Therefore, we quantify the effects of caching and look at the tradeoff between throughput and latency. Moreover, we introduce a model to estimate and assess the performance of these packet processing frameworks.
Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems

Pedestrian detection is one of the most important components in driver-assistance systems. In this paper, we propose a monocular vision system for real-time pedestrian detection and tracking during nighttime driving with a near-infrared (NIR) camera. Three modules (region-of-interest (ROI) generation, object classification, and tracking) are integrated in a cascade, and each utilizes complementary visual features to distinguish the objects from the cluttered background in the range of 20-80 m. Based on the common fact that the objects appear brighter than the nearby background in nighttime NIR images, efficient ROI generation is done based on the dual-threshold segmentation algorithm. As there is large intraclass variability in the pedestrian class, a tree-structured, two-stage detector is proposed to tackle the problem through training separate classifiers on disjoint subsets of different image sizes and arranging the classifiers based on Haar-like and histogram-of-oriented-gradients (HOG) features in a coarse-to-fine manner. To suppress the false alarms and fill the detection gaps, template-matching-based tracking is adopted, and multiframe validation is used to obtain the final results. Results from extensive tests on both urban and suburban videos indicate that the algorithm can produce a detection rate of more than 90% at the cost of about 10 false alarms/h and perform as fast as the frame rate (30 frames/s) on a Pentium IV 3.0-GHz personal computer, which also demonstrates that the proposed system is feasible for practical applications and enjoys the advantage of low implementation cost.
Lexicosyntactic inference in neural models

We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.
Framing Public Opinion in Competitive Democracies

What is the effect of democratic competition on the power of elites to frame public opinion? We address this issue first by defining the range of competitive contexts that might surround any debate over a policy issue. We then offer a theory that predicts how audiences, messages, and competitive environments interact to influence the magnitude of framing effects. These hypotheses are tested using experimental data gathered on the opinions of adults and college students toward two policy issues—–the management of urban growth and the right of an extremist group to conduct a rally. Our results indicate that framing effects depend more heavily on the qualities of frames than on their frequency of dissemination and that competition alters but does not eliminate the influence of framing. We conclude by discussing the implications of these results for the study of public opinion and democratic political debate.
Adolescent storm and stress, reconsidered.

G. S. Hall's (1904) view that adolescence is a period of heightened "storm and stress" is reconsidered in light of contemporary research. The author provides a brief history of the storm-and-stress view and examines 3 key aspects of this view: conflict with parents, mood disruptions, and risk behavior. In all 3 areas, evidence supports a modified storm-and-stress view that takes into account individual differences and cultural variations. Not all adolescents experience storm and stress, but storm and stress is more likely during adolescence than at other ages. Adolescent storm and stress tends to be lower in traditional cultures than in the West but may increase as globalization increases individualism. Similar issues apply to minority cultures in American society. Finally, although the general public is sometimes portrayed by scholars as having a stereotypical view of adolescent storm and stress, both scholars and the general public appear to support a modified storm-and-stress view.
Retrieving Attitudes: Sentiment Analysis from Clinical Narratives

Physicians and nurses express their judgments and observations towards a patient’s health status in clinical narratives. Thus, their judgments are explicitly or implicitly included in patient records. To get impressions on the current health situation of a patient or on changes in the status, analysis and retrieval of this subjective content is crucial. In this paper, we approach this question as sentiment analysis problem and analyze the feasibility of assessing these judgments in clinical text by means of general sentiment analysis methods. Specifically, the word usage in clinical narratives and in a general text corpus is compared. The linguistic characteristics of judgments in clinical narratives are collected. Besides, the requirements for sentiment analysis and retrieval from clinical narratives are derived.
A Survey of Current Methods in Medical Image Segmentation

Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. We present herein a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications. The use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality. We conclude with a discussion on the future of image segmentation methods in biomedical research.
Periodicity detection in time series databases

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.
Visual closed-loop control for pouring liquids

Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.
Review of Artificial intelligence and literary creativity: inside the mind of BRUTUS, a storytelling machine by Selmer Bringsjord and David A. Ferrucci. Lawrence Erlbaum Associates 2000.

BRUTUS is a program that tells stories. The stories are intriguing, they hold a hint of mystery, and—not least impressive—they are written in correct English prose. An example (p. 124) is shown in Figure 1. This remarkable feat is grounded in a complex architecture making use of a number of levels, each of which is parameterized so as to become a locus of possible variation. The specific BRUTUS1 implementation that illustrates the program’s prowess exploits the theme of betrayal, which receives an elaborate analysis, culminating in a set
Distributed aggregation for data-parallel computing: interfaces and implementations

Data-intensive applications are increasingly designed to execute on large computing clusters. Grouped aggregation is a core primitive of many distributed programming models, and it is often the most efficient available mechanism for computations such as matrix multiplication and graph traversal. Such algorithms typically require non-standard aggregations that are more sophisticated than traditional built-in database functions such as Sum and Max. As a result, the ease of programming user-defined aggregations, and the efficiency of their implementation, is of great current interest.
 This paper evaluates the interfaces and implementations for user-defined aggregation in several state of the art distributed computing systems: Hadoop, databases such as Oracle Parallel Server, and DryadLINQ. We show that: the degree of language integration between user-defined functions and the high-level query language has an impact on code legibility and simplicity; the choice of programming interface has a material effect on the performance of computations; some execution plans perform better than others on average; and that in order to get good performance on a variety of workloads a system must be able to select between execution plans depending on the computation. The interface and execution plan described in the MapReduce paper, and implemented by Hadoop, are found to be among the worst-performing choices.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a large fraction of labels have very few positive instances in the data distribution. Most state-of-the-art approaches for extreme multi-label classification attempt to capture correlation among labels by embedding the label matrix to a low-dimensional linear sub-space. However, in the presence of power-law distributed extremely large and diverse label spaces, structural assumptions such as low rank can be easily violated.
 In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size. Unlike most state-of-the-art methods, DiSMEC does not make any low rank assumptions on the label matrix. Using double layer of parallelization, DiSMEC can learn classifiers for datasets consisting hundreds of thousands labels within few hours. The explicit capacity control mechanism filters out spurious parameters which keep the model compact in size, without losing prediction accuracy. We conduct extensive empirical evaluation on publicly available real-world datasets consisting upto 670,000 labels. We compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which is a leading approach for learning sparse local embeddings, and FastXML which is a tree-based approach optimizing ranking based loss function. On some of the datasets, DiSMEC can significantly boost prediction accuracies - 10% better compared to SLECC and 15% better compared to FastXML, in absolute terms.
Financial incentives and the "performance of crowds"

The relationship between financial incentives and performance, long of interest to social scientists, has gained new relevance with the advent of web-based "crowd-sourcing" models of production. Here we investigate the effect of compensation on performance in the context of two experiments, conducted on Amazon's Mechanical Turk (AMT). We find that increased financial incentives increase the quantity, but not the quality, of work performed by participants, where the difference appears to be due to an "anchoring" effect: workers who were paid more also perceived the value of their work to be greater, and thus were no more motivated than workers paid less. In contrast with compensation levels, we find the details of the compensation scheme do matter--specifically, a "quota" system results in better work for less pay than an equivalent "piece rate" system. Although counterintuitive, these findings are consistent with previous laboratory studies, and may have real-world analogs as well.
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core

We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an order of magnitude while retaining similar accuracy. Furthermore, we show how the method can be made more robust in the presence of depth data and consequently formulate a new joint contour and ICP tracking energy. We present better results than the state-of-the-art while being much faster then most other methods and achieving all of the above on a single CPU core.
Maintaining Sensing Coverage and Connectivity in Large Sensor Networks

In this paper, we address the issues of maintaining sensing coverage and connectivity by keeping a minimum number of sensor nodes in the active mode in wireless sensor networks. We investigate the relationship between coverage and connectivity by solving the following two sub-problems. First, we prove that if the radio range is at least twice the sensing range, complete coverage of a convex area implies connectivity among the working set of nodes. Second, we derive, under the ideal case in which node density is sufficiently high, a set of optimality conditions under which a subset of working sensor nodes can be chosen for complete coverage. Based on the optimality conditions, we then devise a decentralized density control algorithm, Optimal Geographical Density Control (OGDC), for density control in large scale sensor networks. The OGDC algorithm is fully localized and can maintain coverage as well as connectivity, regardless of the relationship between the radio range and the sensing range. Ns-2 simulations show that OGDC outperforms existing density control algorithms [25, 26, 29] with respect to the number of working nodes needed and network lifetime (with up to 50% improvement), and achieves almost the same coverage as the algorithm with the best result.
3D blob based brain tumor detection and segmentation in MR images

Automatic detection and segmentation of brain tumors in 3D MR neuroimages can significantly aid early diagnosis, surgical planning, and follow-up assessment. However, due to diverse location and varying size, primary and metastatic tumors present substantial challenges for detection. We present a fully automatic, unsupervised algorithm that can detect single and multiple tumors from 3 to 28,079 mm3 in volume. Using 20 clinical 3D MR scans containing from 1 to 15 tumors per scan, the proposed approach achieves between 87.84% and 95.30% detection rate and an average end-to-end running time of under 3 minutes. In addition, 5 normal clinical 3D MR scans are evaluated quantitatively to demonstrate that the approach has the potential to discriminate between abnormal and normal brains.