nils-herrmann/clean_scirepeval huggingface dataset:
Designing the digital workplace of the future - what scholars recommend to practitioners

The digital workplace is widely acknowledged as an important organizational asset for optimizing knowledge worker productivity. While there is no particular research stream on the digital workplace, scholars have conducted intensive research on related topics. This study aims to summarize the practical implications of the current academic body of knowledge on the digital workplace. For this purpose, a screening of academic-practitioner literature was conducted, followed by a systematic review of academic top journal literature. The screening revealed four main research topics on the digital workplace that are present in academic-practitioner literature: 1) Collaboration, 2) Compliance, 3) Mobility, and 4) Stress and overload. Based on the four topics, this study categorizes practical implications on the digital workplace into 15 concepts. Thereby, it provides two main contributions. First, the study delivers condensed information for practitioners about digital workplace design. Second, the results shed light on the relevance of IS research.
Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead

This article is an update of an article five of us published in 1992. The areas of Multiple Criteria Decision Making (MCDM) and Multiattribute Utility Theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields. as well as two anonymous reviewers for valuable comments.
CONTEXTUALIST RESEARCH AND THE STUDY OF ORGANIZATIONAL CHANGE PROCESSES1

On theoretical, methodological, and practical grounds, this paper argues the case for conducting processual studies of organizational change. Such process studies may be conducted through a research approach which is not only longitudinal but also seeks to analyze processes in their intra-organizational and social, economic, political and business context. This paper outlines some of the epistemological and craft features of contextualist research and ends by posing questions about the evaluation of research conducted in a contextualist manner. Contrary to the way the practice of research is often taught and written up, the activity of research is clearly a social process and not merely a rationally contrived act. Furthermore it is a social process descriptively more easily characterized in the language of muddling through, incrementalism, and political process than it is as rational, foresightful, goal directed activity. Indeed it seems naive and two-faced of us to recognize, on the one hand, the now familiar notions that problem solving and decision making processes in organizations have elements of political process (Pettigrew 1973a), incrementalism
Implementation of RANSAC Algorithm for Feature-Based Image Registration

This paper describes the hardware implementation of the RANdom Sample Consensus (RANSAC) algorithm for featured-based image registration applications. The Multiple-Input Signature Register (MISR) and the index register are used to achieve the random sampling effect. The systolic array architecture is adopted to implement the forward elimination step in the Gaussian elimination. The computational complexity in the forward elimination is reduced by sharing the coefficient matrix. As a result, the area of the hardware cost is reduced by more than 50%. The proposed architecture is realized using Verilog and achieves real-time calculation on 30 fps 1024 * 1024 video stream on 100 MHz clock.
The Meditation Depth Index ( MEDI ) and the Meditation Depth Questionnaire ( MEDEQ ) by

Is there a universal dimension of “Meditation Depth” valid to every individual and way of meditation? Or is there only a very subjective feeling of depth, that differs from person to person and from tradition to tradition? 45 authorized meditation teachers were asked about the depth of different experiences verbalized in 30 items. The agreement is highly significant. Five clusters, interpreted as depth structures, could be found through cluster analysis: “hindrances”, “relaxation”, “personal self”, “transpersonal qualities” and “transpersonal self”. An itemand factor-analysis supports a uni-dimensional Depth – factor in a group of 122 meditators. On this base the Meditation Depth Questionnaire was constructed. First investigations support the reliability and validity of the instrument.
Automatic Generation of Crossword Puzzles

Crossword puzzles are used everyday by millions of people for entertainment, but have applications also in educational and rehabilitation contexts. Unfortunately, the generation of ad-hoc puzzles, especially on specific subjects, typically requires a great deal of human expert work. This paper presents the architecture of WebCrow-generation, a system that is able to generate crosswords with no human intervention, including clue generation and crossword compilation. In particular, the proposed system crawls information sources on the Web, extracts definitions from the downloaded pages using state-of-the-art natural language processing techniques and, finally, compiles the crossword schema with the extracted definitions by constraint satisfaction programming. The system has been tested on the creation of Italian crosswords, but the extensive use of machine learning makes the system easily portable to other languages.
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,”  2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.
Vocabulary Manipulation for Neural Machine Translation

In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage. In this paper, we alleviate this issue by introducing a sentence-level or batch-level vocabulary, which is only a very small sub-set of the full output vocabulary. For each sentence or batch, we only predict the target words in its sentencelevel or batch-level vocabulary. Thus, we reduce both the computing time and the memory usage. Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a wordto-word translation model or a bilingual phrase library learned from a traditional machine translation model. Experimental results on the large-scale English-toFrench task show that our method achieves better translation performance by 1 BLEU point over the large vocabulary neural machine translation system of Jean et al. (2015).
Temporal Opinion Spam Detection by Multivariate Indicative Signals

Online consumer reviews reflect the testimonials of real people, unlike advertisements. As such, they have critical impact on potential consumers, and indirectly on businesses. According to a Harvard study (Luca 2011), +1 rise in star-rating increases revenue by 5–9%. Problematically, such financial incentives have created a market for spammers to fabricate reviews, to unjustly promote or demote businesses, activities known as opinion spam (Jindal and Liu 2008). A vast majority of existing work on this problem have formulations based on static review data, with respective techniques operating in an offline fashion. Spam campaigns, however, are intended to make most impact during their course. Abnormal events triggered by spammers’ activities could be masked in the load of future events, which static analysis would fail to identify. In this work, we approach the opinion spam problem with a temporal formulation. Specifically, we monitor a list of carefully selected indicative signals of opinion spam over time and design efficient techniques to both detect and characterize abnormal events in real-time. Experiments on datasets from two different review sites show that our approach is fast, effective, and practical to be deployed in real-world systems.
DoubleFusion: Real-Time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor

We propose DoubleFusion, a new real-time system that combines volumetric dynamic reconstruction with data-driven template fitting to simultaneously reconstruct detailed geometry, non-rigid motion and the inner human body shape from a single depth camera. One of the key contributions of this method is a double layer representation consisting of a complete parametric body shape inside, and a gradually fused outer surface layer. A pre-defined node graph on the body surface parameterizes the non-rigid deformations near the body, and a free-form dynamically changing graph parameterizes the outer surface layer far from the body, which allows more general reconstruction. We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance. Moreover, the inner body shape is optimized online and forced to fit inside the outer surface layer. Overall, our method enables increasingly denoised, detailed and complete surface reconstructions, fast motion tracking performance and plausible inner body shape reconstruction in real-time. In particular, experiments show improved fast motion tracking and loop closure performance on more challenging scenarios.
Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data

In the recent years there has been growing interest in exploiting multibaseline (MB) SAR interferometry in a tomographic framework, to produce full 3D imaging e.g. of forest layers. However, Fourier-based MB SAR tomography is generally affected by unsatisfactory imaging quality due to a typically low number of baselines and their irregular distribution. In this work, we apply the more modern adaptive Capon spectral estimator to the vertical image reconstruction problem, using real airborne MB data. A first demonstration of possible imaging enhancement in real-world conditions is given. Keywordssynthetic aperture radar interferometry, electromagnetic tomography, forestry, spectral analysis.
Simple, Robust Autonomous Grasping in Unstructured Environments

The inherent uncertainty associated with unstructured grasping tasks makes establishing a successful grasp difficult. Traditional approaches to this problem involve hands that are complex, fragile, require elaborate sensor suites, and are difficult to control. In this paper, we demonstrate a novel autonomous grasping system that is both simple and robust. The four-fingered hand is driven by a single actuator, yet can grasp objects spanning a wide range of size, shape, and mass. The hand is constructed using polymer-based shape deposition manufacturing, with joints formed by elastomeric flexures and actuator and sensor components embedded in tough rigid polymers. The hand has superior robustness properties, able to withstand large impacts without damage and capable of grasping objects in the presence of large positioning errors. We present experimental results showing that the hand mounted on a three degree of freedom manipulator arm can reliably grasp 5 cm-scale objects in the presence of positioning error of up to 100% of the object size and 10 cm-scale objects in the presence of positioning error of up to 33% of the object size, while keeping acquisition contact forces low.
Quantifying over play: Constraining undesirable solutions in puzzle design

Motivated by our ongoing efforts in the development of Refraction 2, a puzzle game targeting mathematics education, we realized that the quality of a puzzle is critically sensitive to the presence of alternative solutions with undesirable properties. Where, in our game, we seek a way to automatically synthesize puzzles that can only be solved if the player demonstrates specific concepts, concern for the possibility of undesirable play touches other interactive design domains. To frame this problem (and our solution to it) in a general context, we formalize the problem of generating solvable puzzles that admit no undesirable solutions as an NPcomplete search problem. By making two design-oriented extensions to answer set programming (a technology that has been recently applied to constrained game content generation problems) we offer a general way to declaratively pose and automatically solve the high-complexity problems coming from this formulation. Applying this technique to Refraction, we demonstrate a qualitative leap in the kind of puzzles we can reliably generate. This work opens up new possibilities for quality-focused content generators that guarantee properties over their entire combinatorial space of play.
Torque production capabilities of electrical machines with planar windings

This paper describes the torque production capabilities of electrical machines with planar windings and presents an automated procedure for coils conductors' arrangement. The procedure has been applied on an ironless axial flux slotless permanent magnet machines having stator windings realized using printed circuit board (PCB) coils. An optimization algorithm has been implemented to find a proper arrangement of PCB traces in order to find the best compromise between the maximization of average torque and the minimization of torque ripple. A time-efficient numerical model has been developed to reduce computational load and thus make the optimization based design feasible.
Increasing performance in byzantine fault-tolerant systems with on-demand replica consistency

Traditional agreement-based Byzantine fault-tolerant (BFT) systems process all requests on all replicas to ensure consistency. In addition to the overhead for BFT protocol and state-machine replication, this practice degrades performance and prevents throughput scalability. In this paper, we propose an extension to existing BFT architectures that increases performance for the default number of replicas by optimizing the resource utilization of their execution stages.
 Our approach executes a request on only a selected subset of replicas, using a selector component co-located with each replica. As this leads to divergent replica states, a selector on-demand updates outdated objects on the local replica prior to processing a request. Our evaluation shows that with each replica executing only a part of all requests, the overall performance of a Byzantine fault-tolerant NFS can be almost doubled; our prototype even outperforms unreplicated NFS.
Transformation of a Mamdani FIS to First Order Sugeno FIS

In many decision support applications, it is important to guarantee the expressive power, easy formalization and interpretability of Mamdani-type fuzzy inference systems (FIS), while ensuring the computational efficiency and accuracy of Sugeno-type FIS. Hence, in this paper we present an approach to transform a Mamdani-type FIS into a Sugeno-type FIS. We consider the problem of mapping Mamdani FIS to Sugeno FIS as an optimization problem and by determining the first order Sugeno parameters, the transformation is achieved. To solve this optimization problem we compare three methods: least squares, genetic algorithms and an adaptive neuro-fuzzy inference system. An illustrative example is presented to discuss the approaches.
On the Foundations of Relaxation Labeling Processes

A large class of problems can be formulated in terms of the assignment of labels to objects. Frequently, processes are needed which reduce ambiguity and noise, and select the best label among several possible choices. Relaxation labeling processes are just such a class of algorithms. They are based on the parallel use of local constraints between labels. This paper develops a theory to characterize the goal of relaxation labeling. The theory is founded on a definition of con-sistency in labelings, extending the notion of constraint satisfaction. In certain restricted circumstances, an explicit functional exists that can be maximized to guide the search for consistent labelings. This functional is used to derive a new relaxation labeling operator. When the restrictions are not satisfied, the theory relies on variational cal-culus. It is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality. A procedure nearly identical to the relaxation operator derived under restricted circum-stances serves in the more general setting. Further, a local convergence result is established for this operator. The standard relaxation labeling formulas are shown to approximate our new operator, which leads us to conjecture that successful applications of the standard methods are explainable by the theory developed here. Observations about con-vergence and generalizations to higher order compatibility relations are described.
ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we propose ShapeNet, a generalization of the popular convolutional neural networks (CNN) paradigm to non-Euclidean manifolds. Our construction is based on a local geodesic system of polar coordinates to extract “patches”, which are then passed through a cascade of filters and linear and non-linear operators. The coefficients of the filters and linear combination weights are optimization variables that are learned to minimize a task-specific cost function. We use ShapeNet to learn invariant shape feature descriptors that significantly outperform recent state-of-the-art methods, and show that previous approaches such as heat and wave kernel signatures, optimal spectral descriptors, and intrinsic shape contexts can be obtained as particular configurations of ShapeNet. CR Categories: I.2.6 [Artificial Intelligence]: Learning— Connectionism and neural nets
Nourishing the ground for sustainable HCI: considerations from ecologically engaged art

Sustainable HCI is now a recognized area of human-computer interaction drawing from a variety of disciplinary approaches, including the arts. How might HCI researchers working on sustainability productively understand the discourses and practices of ecologically engaged art as a means of enriching their own activities? We argue that an understanding of both the history of ecologically engaged art, and the art-historical and critical discourses surrounding it, provide a fruitful entry-point into a more critically aware sustainable HCI. We illustrate this through a consideration of frameworks from the arts, looking specifically at how these frameworks act more as generative devices than prescriptive recipes. Taking artistic influences seriously will require a concomitant rethinking of sustainable HCI standpoints - a potentially useful exercise for HCI research in general.
An Analysis of Implicit Social Networks in Multiplayer Online Games

For many networked games, such as the Defense of the Ancients and StarCraft series, the unofficial leagues created by players themselves greatly enhance user-experience, and extend the success of each game. Understanding the social structure that players of these game s implicitly form helps to create innovative gaming services to the benefit of both players and game operators. But how to extract and analyse the implicit social structure? We address this question by first proposing a formalism consisting of various ways to map interaction to social structure, and apply this to real-world data collected from three different game genres. We analyse the implications of these mappings for in-game and gaming-related services, ranging from network and socially-aware matchmaking of players, to an investigation of social network robustnes against player departure.
A security enforcement kernel for OpenFlow networks

Software-defined networks facilitate rapid and open innovation at the network control layer by providing a programmable network infrastructure for computing flow policies on demand. However, the dynamism of programmable networks also introduces new security challenges that demand innovative solutions. A critical challenge is efficient detection and reconciliation of potentially conflicting flow rules imposed by dynamic OpenFlow (OF) applications. To that end, we introduce FortNOX, a software extension that provides role-based authorization and security constraint enforcement for the NOX OpenFlow controller. FortNOX enables NOX to check flow rule contradictions in real time, and implements a novel analysis algorithm that is robust even in cases where an adversarial OF application attempts to strategically insert flow rules that would otherwise circumvent flow rules imposed by OF security applications. We demonstrate the utility of FortNOX through a prototype implementation and use it to examine performance and efficiency aspects of the proposed framework.
Note 65 10 January 2014 Measurement of Complex Permittivity of Large Concrete Samples with an Open-Ended Coaxial Line

A 70 mm-open-ended coaxial line probe was developed to perform measurements of the dielectric properties of large concrete samples. The complex permittivity was measured in the frequency range 50 MHz – 1.5 GHz during the hardening process of the concrete. As expected, strong dependence of water content was observed.
Automatic music genre classification using ensemble of classifiers

This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted from each music segment. At the classification, the outputs provided by each individual classifier are combined through simple combination rules such as majority vote, max, sum and product rules, with the aim of improving music genre classification accuracy. Experiments carried out on a large dataset containing more than 3,000 music samples from ten different Latin music genres have shown that for the task of automatic music genre classification, the features extracted from the middle part of the music provide better results than using the segments from the beginning or end part of the music. Furthermore, the proposed ensemble approach, which combines the multiple feature vectors, provides better accuracy than using single classifiers and any individual music segment.