nils-herrmann/clean_scirepeval huggingface dataset:
Reconfiguring the Imaging Pipeline for Computer Vision

Advancements in deep learning have ignited an explosion of research on efficient hardware for embedded computer vision. Hardware vision acceleration, however, does not address the cost of capturing and processing the image data that feeds these algorithms. We examine the role of the image signal processing (ISP) pipeline in computer vision to identify opportunities to reduce computation and save energy. The key insight is that imaging pipelines should be be configurable: to switch between a traditional photography mode and a low-power vision mode that produces lower-quality image data suitable only for computer vision. We use eight computer vision algorithms and a reversible pipeline simulation tool to study the imaging system's impact on vision performance. For both CNN-based and classical vision algorithms, we observe that only two ISP stages, demosaicing and gamma compression, are critical for task performance. We propose a new image sensor design that can compensate for these stages. The sensor design features an adjustable resolution and tunable analog-to-digital converters (ADCs). Our proposed imaging system's vision mode disables the ISP entirely and configures the sensor to produce subsampled, lower-precision image data. This vision mode can save ~75% of the average energy of a baseline photography mode with only a small impact on vision task accuracy.
Participatory design and "democratizing innovation"

Participatory design has become increasingly engaged in public spheres and everyday life and is no longer solely concerned with the workplace. This is not only a shift from work oriented productive activities to leisure and pleasurable engagements, but also a new milieu for production and innovation and entails a reorientation from "democracy at work" to "democratic innovation". What democratic innovation entails is currently defined by management and innovation research, which claims that innovation has been democratized through easy access to production tools and lead-users as the new experts driving innovation. We sketch an alternative "democratizing innovation" practice more in line with the original visions of participatory design based on our experience of running Malmö Living Labs - an open innovation milieu where new constellations, issues and ideas evolve from bottom-up long-term collaborations amongst diverse stakeholders. Two cases and controversial matters of concern are discussed. The fruitfulness of the concepts "Things" (as opposed to objects), "infrastructuring" (as opposed to projects) and "agonistic public spaces" (as opposed to consensual decision-making) are explored in relation to participatory innovation practices and democracy.
Face alignment by coarse-to-fine shape searching

We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-the-art results on various benchmarks including the challenging 300-W dataset.
Endoscopic Surgery: The History, the Pioneers

The introduction of endoscopy into surgical practice is one of the biggest success stories in the history of medicine. Endoscopy has its roots in the nineteenth century and was initially developed by urologists and internists. During the 1960s and 1970s gynecologists took the lead in the development of endoscopic surgery while most of the surgical community continued to ignore the possibilities of the new technique. This was due in part to the introduction of ever more sophisticated drugs, the impressive results of intensive care medicine, and advances in anesthesia, which led to the development of more radical and extensive operations, or “major surgery.” The idea that large problems require large incisions so deeply dominated surgical thinking that there was little room to appreciate the advances of “key-hole” surgery. Working against this current, some general surgeons took up the challenge. In 1976 the Surgical Study Group on Endoscopy and Ultrasound (CAES) was formed in Hamburg. Five years later, on the other side of the Atlantic, the Society of American Gastrointestinal Endoscopic Surgeons (SAGES) was called into being. In 1987 the first issue of the journal Surgical Endoscopy was published, and the following year the First World Congress on Surgical Endoscopy took place in Berlin. The sweeping success of the “laparoscopic revolution” (1989–1990) marked the end of traditional open surgery and encouraged surgeons to consider new perspectives. By the 1990s the breakthrough had been accomplished: endoscopy was incorporated into surgical thinking.
Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.
Localizing anomalous changes in time-evolving graphs

Given a time-evolving sequence of undirected, weighted graphs, we address the problem of localizing anomalous changes in graph structure over time. In this paper, we use the term `localization' to refer to the problem of identifying abnormal changes in node relationships (edges) that cause anomalous changes in graph structure. While there already exist several methods that can detect whether a graph transition is anomalous or not, these methods are not well suited for localizing the edges which are responsible for a transition being marked as an anomaly. This is a limitation in applications such as insider threat detection, where identifying the anomalous graph transitions is not sufficient, but rather, identifying the anomalous node relationships and associated nodes is key. To this end, we propose a novel, fast method based on commute time distance called CAD (Commute-time based Anomaly detection in Dynamic graphs) that detects node relationships responsible for abnormal changes in graph structure. In particular, CAD localizes anomalous edges by tracking a measure that combines information regarding changes in graph structure (in terms of commute time distance) as well as changes in edge weights. For large, sparse graphs, CAD returns a list of these anomalous edges and associated nodes in O(n\log n) time per graph instance in the sequence, where $n$ is the number of nodes. We analyze the performance of CAD on several synthetic and real-world data sets such as the Enron email network, the DBLP co-authorship network and a worldwide precipitation network data. Based on experiments conducted, we conclude that CAD consistently and efficiently identifies anomalous changes in relationships between nodes over time.
A real-coded genetic algorithm for training recurrent neural networks

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.
Smart Meters Big Data: Game Theoretic Model for Fair Data Sharing in Deregulated Smart Grids

Aggregating fine-granular data measurements from smart meters presents an opportunity for utility companies to learn about consumers' power consumption patterns. Several research studies have shown that power consumption patterns can reveal a range of information about consumers, such as how many people are in the home, the types of appliances they use, their eating and sleeping routines, and even the TV programs they watch. As we move toward liberalized energy markets, many different parties are interested in gaining access to such data, which has enormous economical, societal, and environmental benefits. However, the main concern is that many such beneficial uses of smart meter big data would be severely curtailed if the data were excessively protected due to individuals' privacy. In this paper, we propose a game theoretic mechanism that balances between beneficial uses of data and individuals' privacy in deregulated smart grids. Our mechanism solves the problem of access control by fairly compensating consumers for their participation in the data market based on the concept of differential privacy. The results of our experiments show the importance of taking consumers' attitudes toward privacy as a crucial element in designing balanced markets for fair data sharing. Furthermore, the experiments provide a principled way to choose reasonable values for privacy levels that are more relevant to real-world scenarios.
Deep Gaussian Processes

In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples.
Virtualized Congestion Control

New congestion control algorithms are rapidly improving datacenters by reducing latency, overcoming incast, increasing throughput and improving fairness. Ideally, the operating system in every server and virtual machine is updated to support new congestion control algorithms. However, legacy applications often cannot be upgraded to a new operating system version, which means the advances are off-limits to them. Worse, as we show, legacy applications can be squeezed out, which in the worst case prevents the entire network from adopting new algorithms.
 Our goal is to make it easy to deploy new and improved congestion control algorithms into multitenant datacenters, without having to worry about TCP-friendliness with non-participating virtual machines. This paper presents a solution we call virtualized congestion control. The datacenter owner may introduce a new congestion control algorithm in the hypervisors. Internally, the hypervisors translate between the new congestion control algorithm and the old legacy congestion control, allowing legacy applications to enjoy the benefits of the new algorithm. We have implemented proof-of-concept systems for virtualized congestion control in the Linux kernel and in VMware’s ESXi hypervisor, achieving improved fairness, performance, and control over guest bandwidth allocations.
Multi-scale CNNs for 3D model retrieval

Recent advancements in low-cost 3D sensors and mobile devices of virtual 3D models have additionally facilitated the accessibility of 3D data. 3D model retrieval is becoming an indispensable function for modern search engines. An effective retrieval model is at the core of computer vision. With the continuous improvement of 3D data, there are large number of methods to solve this problem. Existing works proposed numerous works to deal with feature extraction and object matching. Most of them are unable to fully exploit the information of 3D representations. To address this problem, we propose a novel multi-layer deep network in this paper. First, multiple rendered images are extracted from a 3D object, and combined into one representative view, which is the actual input of the network. Then, the novel multi-layer network structure is trained and tested on these representative views, generating the feature leaning model, which owns the local and global information of a 3D object. Finally, simple Euclidean metric is used to compute the similarity between two different 3D models to complete the retrieval problem. Extensive experiments and corresponding experimental results have demonstrated the superiority of our approach.
Small-signal modeling of pulse-width modulated switched-mode power converters

A power processing system is required to convert electrical energy from one voltage, current, or frequency to another with, ideally, 100-percent efficiency, together with adjustability of the conversion ratio. The allowable elements are switches, capacitors, and magnetic devices. Essential to the design is a knowledge of the transfer functions, and since the switching converter is nonlinear, a suitable modeling approach is needed. This paper discusses the state-space averaging method, which is an almost ideal compromise between accuracy and simplicity. The treatment here emphasizes the motivation and objectives of modeling by equivalent circuits based upon physical interpretation of state-space averaging, and is limited to pulse-width modulated dc-to-dc converters.
Javalanche: efficient mutation testing for Java

To assess the quality of a test suite, one can use mutation testing - seeding artificial defects (mutations) into the program and checking whether the test suite finds them. Javalanche is an open source framework for mutation testing Java programs with a special focus on automation, efficiency, and effectiveness. In particular, Javalanche assesses the impact of individual mutations to effectively weed out equivalent mutants; it has been demonstrated to work on programs with up to 100,000 lines of code.
Maximum likelihood analysis of conflicting observations in social sensing

This article addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown trustworthiness together with their sensory measurements, we pose the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. In our previous conference paper, we offered the first maximum likelihood solution to the aforesaid truth discovery problem for corroborating observations only. In contrast, this article extends the conference paper and provides the first maximum likelihood solution to handle the cases where measurements from different participants may be conflicting. The article focuses on binary measurements. The approach is shown to outperform our previous work used for corroborating observations, the state-of-the-art fact-finding baselines, as well as simple heuristics such as majority voting.
Analysis and Predictions on Students' Behavior Using Decision Trees in Weka Environment

Decision trees classifiers are simple and prompt data classifiers as supervised learning means with the potential of generating comprehensible output, usually used in data mining to study the data and generate the tree and its rules that will he used to formulate predictions. One of the major challenges for knowledge discovery and data mining systems stands in developing their data analysis capability to discover out of the ordinary models in data. The excellence of a university is specified among other concerns by its adapting competence to the constant changing needs of the socio-economic background, the quality of the managerial system based on a high level of professionalism and on applying the latest technologies. This article represents an implementation of a J48 algorithm analysis tool on data collected from surveys on different specialization students of my faculty, with the purpose of differentiating and predicting their choice in continuing their education with post university studies (master degree, Ph.D. studies) through decision trees.
High Performance MPI Library for Container-Based HPC Cloud on InfiniBand Clusters

Virtualization technology has grown rapidly over the past few decades. As a lightweight solution, container-based virtualization provides a promising approach to efficiently build HPC clouds. However, our study shows clear performance bottleneck when running MPI jobs on multi-container environments. This motivates us to first analyze the performance bottleneck for MPI jobs running in different container deployment scenarios. To eliminate performance bottleneck, we propose a high performance locality-aware MPI library, which is able to dynamically detect co-resident containers at runtime. Through this design, the MPI processes in co-resident containers can communicate to each other by shared memory and Cross Memory Attach (CMA) channels instead of the network channel. A comprehensive performance study indicates that compared with the default case, our proposed design can significantly improve the communication performance by up to 9X and 86% in terms of MPI point-to-point and collective operations, respectively. The results for applications demonstrate that the locality-aware design can reduce up to 16% of execution time. The evaluation results also show that by the help of locality-aware design, we can achieve near-native performance in container-based HPC cloud with minor overhead. The proposed locality-aware MPI design reveals significant potential to be utilized to efficiently build large scale container-based HPC clouds.
Object Proposal Generation With Fully Convolutional Networks

Object proposal generation, as a preprocessing technique, has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images. Current object proposals are mostly based on low-level image cues, such as edges and saliency. However, objectness is possibly a high-level semantic concept showing whether one region contains objects. This paper presents a framework utilizing fully convolutional networks (FCNs) to produce object proposal positions and bounding box location refinement with Support Vector Machine (SVM) to further improve proposal localization. Experiments on the PASCAL VOC 2007 show that using high-level semantic object proposals obtained by FCN, the object recall can be improved. An improvement in detection mean average precision is also seen when using our proposals in the Fast R-convolutional neural network framework. In addition, we also demonstrate that our method shows stronger robustness when introduced to image perturbations, e.g., blurring, JPEG compression, and salt and pepper noise. Finally, the generalization capability of our model (trained on the PASCAL VOC 2007) is evaluated and validated by testing on PASCAL VOC 2012 validation set, ILSVRC 2013 validation set, and MS COCO 2014 validation set.
Curbing Android Permission Creep Encouraging Least Privilege in development

The Android platform has about 130 application level permissions that govern access to resources. The determination of which permissions to request is left solely to the application developer. Users are prompted to approve all application permissions at install time, and permissions are silently enforced at execution time. Although many applications make use of a wide range of permissions, we have observed that some applications request permissions that are not required for the application to execute, and that existing developer APIs make it difficult for developers to align their permission requests with application functionality. In this paper we describe a tool we developed to assist developers in utilizing least privilege.
Performance of an Electrically Small Aperture, Axially Displaced Ellipse Reflector Antenna

This work reports the performance of an axially displaced ellipse (ADE) antenna with an electrically small aperture. The diameter of the antenna is about 20¿0, which is considerably smaller than the ADE antennas already reported. This antenna provides a measured peak gain of 33.3 dBi at 8.484 GHz, which corresponds to an overall efficiency of 55%.
Language Problems and ADHD Symptoms: How Specific Are the Links?

Symptoms of inattention and hyperactivity frequently co-occur with language difficulties in both clinical and community samples. We explore the specificity and strength of these associations in a heterogeneous sample of 254 children aged 5 to 15 years identified by education and health professionals as having problems with attention, learning and/or memory. Parents/carers rated pragmatic and structural communication skills and behaviour, and children completed standardised assessments of reading, spelling, vocabulary, and phonological awareness. A single dimension of behavioural difficulties including both hyperactivity and inattention captured behaviour problems. This was strongly and negatively associated with pragmatic communication skills. There was less evidence for a relationship between behaviour and language structure: behaviour ratings were more weakly associated with the use of structural language in communication, and there were no links with direct measures of literacy. These behaviour problems and pragmatic communication difficulties co-occur in this sample, but impairments in the more formal use of language that impact on literacy and structural communication skills are tied less strongly to behavioural difficulties. One interpretation is that impairments in executive function give rise to both behavioural and social communication problems, and additional or alternative deficits in other cognitive abilities impact on the development of structural language skills.