nils-herrmann/clean_scirepeval huggingface dataset:
The Delphi Method for Graduate Research

The Delphi method is an attractive method for graduate students completing masters and PhD level research. It is a flexible research technique that has been successfully used in our program at the University of Calgary to explore new concepts within and outside of the information systems body of knowledge. The Delphi method is an iterative process to collect and distill the anonymous judgments of experts using a series of data collection and analysis techniques interspersed with feedback. The Delphi method is well suited as a research instrument when there is incomplete knowledge about a problem or phenomenon; however it is not a method for all types of IS research questions. The Delphi method works especially well when the goal is to improve our understanding of problems, opportunities, solutions, or to develop forecasts. In this paper, we provide a brief background of the Classical Delphi followed by a presentation of how it has evolved into a flexible research method appropriate for a wide variety of IS research projects, such as determining the criteria for IS prototyping decisions, ranking technology management issues in new product development projects, and developing a descriptive framework of knowledge manipulation activities. To illustrate the method's flexibility, we summarize distinctive non-IS, IS, and graduate studies Delphi research projects. We end by discussing what we have learned from using the Delphi method in our own research regarding this method's design factors and how it may be applied to those conducting graduate studies research: i) methodological choices such as a qualitative , quantitative or mixed methods approach; ii) initial question degree of focus whether it be broad or narrowly focused; iii) expertise criteria such as technical knowledge and experience, capacity and willingness to participate, sufficient time, and communication skills; vi) number of participants in the heterogeneous or homogeneous sample, v) number of Delphi rounds varying from one to 6, vi) mode of interaction such as through email, online surveys or groupware, vii) methodological rigor and a research audit trail, viii) results analysis, ix) further verification through triangulation or with another sample, and x) publishing of the results. We include an extensive bibliography and an appendix with a wide-ranging list of dissertations that have used the Delphi method (including brief research description, number of rounds and sample size). The Delphi method is a flexible , effective and efficient research method that can be successful used by IS graduate students …
Situation Assessment in Tactical Lane Change Behavior Planning for Automated Vehicles

Automated driving within a lane is a fascinating experience already. However, more exiting but also technically more challenging is to dare the next step of automating tactical behavior decisions for lane changes, as well. In this paper, we present our approach for situation assessment in tactical behavior planning for lane changes, whether lane changes are beneficial and/or possible. We present a way to tackle perception uncertainties and how to monitor the system's abilities and current skills. This is achieved by a dynamic Bayesian network and an unscented variance transform. Our approach is evaluated not only in a simulation, but also in real traffic. Our implementation has recently been demonstrated to the public in the Audi A7 piloted driving concept vehicle, driving 550 miles from Stanford to Las Vegas to the Consumer Electronics Show (CES) 2015.
Extending the JADE Agent Behaviour Model with JBehaviourTrees Framework

Creating modular behaviours in JADE using a traditional method such as the FSM (Finite State Machine) can be a difficult task to achieve. The first issue with FSMs is that they do not allow reusability of logic in different contexts. Secondly, the FSMs do not lend themselves well to concurrency within the execution thread and thus eliminating the possibility for parallel behaviours. Lastly, as the number of states in FSMs becomes increasing large, it becomes difficult to manage them. In this paper we introduce our JBehaviourTrees Framework that extends JADE Behaviours with BTs (Behaviour Trees) model. BTs are built via the composition of basic tasks increasing the possibility for modularity and code reuse. The proposed approach is verified through a case study concerning a FIPA-Request Interaction Protocol.
Unsupervised Representation Learning by Predicting Image Rotations

Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: https://github.com/gidariss/FeatureLearningRotNet.
Coordinate Friendly Structures, Algorithms and Applications

This paper focuses on coordinate update methods, which are useful for solving problems involving large or high-dimensional datasets. They decompose a problem into simple subproblems, where each updates one, or a small block of, variables while fixing others. These methods can deal with linear and nonlinear mappings, smooth and nonsmooth functions, as well as convex and nonconvex problems. In addition, they are easy to parallelize. The great performance of coordinate update methods depends on solving simple subproblems. To derive simple subproblems for several new classes of applications, this paper systematically studies coordinate friendly operators that perform low-cost coordinate updates. Based on the discovered coordinate friendly operators, as well as operator splitting techniques, we obtain new coordinate update algorithms for a variety of problems in machine learning, image processing, as well as sub-areas of optimization. Several problems are treated with coordinate update for the first time in history. The obtained algorithms are scalable to large instances through parallel and even asynchronous computing. We present numerical examples to illustrate how effective these algorithms are.
Inferring relevant social networks from interpersonal communication

Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected to j) from observed communication events (e.g. i emails j). Often overlooked, however, is the impact of tie definition on the corresponding network, and in turn the relevance of the inferred network to the research question of interest. Here we study the problem of network inference and relevance for two email data sets of different size and origin. In each case, we generate a family of networks parameterized by a threshold condition on the frequency of emails exchanged between pairs of individuals. After demonstrating that different choices of the threshold correspond to dramatically different network structures, we then formulate the relevance of these networks in terms of a series of prediction tasks that depend on various network features. In general, we find: a) that prediction accuracy is maximized over a non-trivial range of thresholds corresponding to 5-10 reciprocated emails per year; b) that for any prediction task, choosing the optimal value of the threshold yields a sizable (~30%) boost in accuracy over naive choices; and c) that the optimal threshold value appears to be (somewhat surprisingly) consistent across data sets and prediction tasks. We emphasize the practical utility in defining ties via their relevance to the prediction task(s) at hand and discuss implications of our empirical results.
Cloud storage forensics: ownCloud as a case study

The storage as a service (StaaS) cloud computing architecture is showing significant growth as users adopt the capability to store data in the cloud environment across a range of devices. Cloud (storage) forensics has recently emerged as a salient area of inquiry. Using a widely used open source cloud StaaS application – ownCloud – as a case study, we document a series of digital forensic experiments with the aim of providing forensic researchers and practitioners with an in-depth understanding of the artefacts required to undertake cloud storage forensics. Our experiments focus upon client and server artefacts, which are categories of potential evidential data specified before commencement of the experiments. A number of digital forensic artefacts are found as part of these experiments and are used to support the selection of artefact categories and provide a technical summary to practitioners of artefact types. Finally we provide some general guidelines for future forensic analysis on open source StaaS products and recommendations for future work.
Wearable Button Antenna for Dual-Band WLAN Applications With Combined on and off-Body Radiation Patterns

A new type of wearable button antenna for wireless local area network (WLAN) applications is proposed. The antenna is composed of a button with a diameter of circa 16 mm incorporating a patch on top of a dielectric disc. The button is located on top of a textile substrate and a conductive textile ground that are to be incorporated in clothing. The main characteristic feature of this antenna is that it shows two different types of radiation patterns, a monopole type pattern in the 2.4 GHz band for on-body communications and a broadside type pattern in the 5 GHz band for off-body communications. A very high efficiency of about 90% is obtained, which is much higher than similar full textile solutions in the literature. A prototype has been fabricated and measured. The effect of several real-life situations such as a tilted button and bending of the textile ground have been studied. Measurements agree very well with simulations.
Topic level expertise search over heterogeneous networks

In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.
High-level synthesis of dynamic data structures: A case study using Vivado HLS

High-level synthesis promises a significant shortening of the FPGA design cycle when compared with design entry using register transfer level (RTL) languages. Recent evaluations report that C-to-RTL flows can produce results with a quality close to hand-crafted designs . Algorithms which use dynamic, pointer-based data structures, which are common in software, remain difficult to implement well. In this paper, we describe a comparative case study using Xilinx Vivado HLS as an exemplary state-of-the-art high-level synthesis tool. Our test cases are two alternative algorithms for the same compute-intensive machine learning technique (clustering) with significantly different computational properties. We compare a data-flow centric implementation to a recursive tree traversal implementation which incorporates complex data-dependent control flow and makes use of pointer-linked data structures and dynamic memory allocation. The outcome of this case study is twofold: We confirm similar performance between the hand-written and automatically generated RTL designs for the first test case. The second case reveals a degradation in latency by a factor greater than 30× if the source code is not altered prior to high-level synthesis. We identify the reasons for this shortcoming and present code transformations that narrow the performance gap to a factor of four. We generalise our source-to-source transformations whose automation motivates research directions to improve high-level synthesis of dynamic data structures in the future.
Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models

3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions. A new approach directly extracts valley-ridge features from point-cloud-based 3D fingerprint models. It first applies the moving least-squares method to fit a local paraboloid surface and represent the local point cloud area. It then computes the local surface's curvatures and curvature tensors to facilitate detection of the potential valley and ridge points. The approach projects those points to the most likely valley-ridge lines, using statistical means such as covariance analysis and cross correlation. To finally extract the valley-ridge lines, it grows the polylines that approximate the projected feature points and removes the perturbations between the sampled points. Experiments with different 3D fingerprint models demonstrate this approach's feasibility and performance.
World survey of artificial brains, Part II: Biologically inspired cognitive architectures

A number of leading cognitive architectures that are inspired by the human brain, at various levels of granularity, are reviewed and compared, with special attention paid to the way their internal structures and dynamics map onto neural processes. Four categories of Biologically Inspired Cognitive Architectures (BICAs) are considered, with multiple examples of each category briefly reviewed, and selected examples discussed in more depth: primarily symbolic architectures (e.g. ACT-R), emergentist architectures (e.g. DeSTIN), developmental robotics architectures (e.g. IM-CLEVER), and our central focus, hybrid architectures (e.g. LIDA, CLARION, 4D/RCS, DUAL, MicroPsi, and OpenCog). Given the state of the art in BICA, it is not yet possible to tell whether emulating the brain on the architectural level is going to be enough to allow rough emulation of brain function; and given the state of the art in neuroscience, it is not yet possible to connect BICAs with large-scale brain simulations in a thoroughgoing way. However, it is nonetheless possible to draw reasonably close function connections between various components of various BICAs and various brain regions and dynamics, and as both BICAs and brain simulations mature, these connections should become richer and may extend further into the domain of internal dynamics as well as overall behavior.
Learning a Mahalanobis Metric from Equivalence Constraints

Many learning algorithms use a metric defined over the input s ace as a principal tool, and their performance critically depends on the quality of this metric. We address the problem of learning metrics using side-information in the form of equi valence constraints. Unlike labels, we demonstrate that this type of side-information can sometim es be automatically obtained without the need of human intervention. We show how such side-inform ation can be used to modify the representation of the data, leading to improved clustering and classification. Specifically, we present the Relevant Component Analysis (R CA) algorithm, which is a simple and efficient algorithm for learning a Mahalanobis metric. W e show that RCA is the solution of an interesting optimization problem, founded on an informa tion theoretic basis. If dimensionality reduction is allowed within RCA, we show that it is optimally accomplished by a version of Fisher’s linear discriminant that uses constraints. Moreover, unde r certain Gaussian assumptions, RCA can be viewed as a Maximum Likelihood estimation of the within cl ass covariance matrix. We conclude with extensive empirical evaluations of RCA, showing its ad v ntage over alternative methods.
Auction-Based Spectrum Sharing

We study auction mechanisms for sharing spectrum among a group of users, subject to a constraint on the interference temperature at a measurement point. The users access the channel using spread spectrum signaling and so interfere with each other. Each user receives a utility that is a function of the received signal-to-interference plus noise ratio. We propose two auction mechanisms for allocating the received power. The first is an auction in which users are charged for received SINR, which, when combined with logarithmic utilities, leads to a weighted max-min fair SINR allocation. The second is an auction in which users are charged for power, which maximizes the total utility when the bandwidth is large enough and the receivers are co-located. Both auction mechanisms are shown to be socially optimal for a limiting “large system” with co-located receivers, where bandwidth, power and the number of users are increased in fixed proportion. We also formulate an iterative and distributed bid updating algorithm, and specify conditions under which this algorithm converges globally to the Nash equilibrium of the auction.
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation

Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including speech separation, singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30-4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30-2.48 dB GNSDR gain and 4.32-5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.
sRoute: Treating the Storage Stack Like a Network

In a data center, an IO from an application to distributed storage traverses not only the network, but also several software stages with diverse functionality. This set of ordered stages is known as the storage or IO stack. Stages include caches, hypervisors, IO schedulers, file systems, and device drivers. Indeed, in a typical data center, the number of these stages is often larger than the number of network hops to the destination. Yet, while packet routing is fundamental to networks, no notion of IO routing exists on the storage stack. The path of an IO to an endpoint is predetermined and hard-coded. This forces IO with different needs (e.g., requiring different caching or replica selection) to flow through a one-size-fits-all IO stack structure, resulting in an ossified IO stack. This paper proposes sRoute, an architecture that provides a routing abstraction for the storage stack. sRoute comprises a centralized control plane and “sSwitches” on the data plane. The control plane sets the forwarding rules in each sSwitch to route IO requests at runtime based on application-specific policies. A key strength of our architecture is that it works with unmodified applications and VMs. This paper shows significant benefits of customized IO routing to data center tenants (e.g., a factor of ten for tail IO latency, more than 60% better throughput for a customized replication protocol and a factor of two in throughput for customized caching).
Does stress damage the brain?

Studies in animals showed that stress results in damage to the hippocampus, a brain area involved in learning and memory, with associated memory deficits. The mechanism involves glucocorticoids and possibly serotonin acting through excitatory amino acids to mediate hippocampal atrophy. Patients with posttraumatic stress disorder (PTSD) from Vietnam combat and childhood abuse had deficits on neuropsychological measures that have been validated as probes of hippocampal function. In addition, magnetic resonance imaging (MRI) showed reduction in volume of the hippocampus in both combat veterans and victims of childhood abuse. In combat veterans, hippocampal volume reduction was correlated with deficits in verbal memory on neuropsychological testing. These studies introduce the possibility that experiences in the form of traumatic stressors can have long-term effects on the structure and function of the brain.
Faster and smaller inverted indices with treaps

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that our index uses about 20% less space, and performs queries up to three times faster, than state-of-the-art compact representations.