nils-herrmann/clean_scirepeval huggingface dataset:
Universal Shape Formation for Programmable Matter

We envision programmable matter consisting of systems of computationally limited devices (which we call particles) that are able to self-organize in order to achieve a desired collective goal without the need for central control or external intervention. Central problems for these particle systems are shape formation and coating problems. In this paper, we present a universal shape formation algorithm which takes an arbitrary shape composed of a constant number of equilateral triangles of unit size and lets the particles build that shape at a scale depending on the number of particles in the system. Our algorithm runs in O(√n) asynchronous execution rounds, where $n$ is the number of particles in the system, provided we start from a well-initialized configuration of the particles. This is optimal in a sense that for any shape deviating from the initial configuration, any movement strategy would require Ω(√n) rounds in the worst case (over all asynchronous activations of the particles). Our algorithm relies only on local information (e.g., particles do not have ids, nor do they know n, or have any sort of global coordinate system), and requires only a constant-size memory per particle.
Tracking-assisted Weakly Supervised Online Visual Object Segmentation in Unconstrained Videos

This paper tackles the task of online video object segmentation with weak supervision, i.e., labeling the target object and background with pixel-level accuracy in unconstrained videos, given only one bounding box information in the first frame. We present a novel tracking-assisted visual object segmentation framework to achieve this. On the one hand, initialized with a given bounding box in the first frame, the auxiliary object tracking module guides the segmentation module frame by frame by providing motion and region information, which is usually missing in semi-supervised methods. Moreover, compared with the unsupervised approach, our approach with such minimum supervision can focus on the target object without bringing unrelated objects into the final results. On the other hand, the video object segmentation module also improves the robustness of the visual object tracking module by pixel-level localization and objectness information. Thus, segmentation and tracking in our framework can mutually help each other in an online manner. To verify the generality and effectiveness of the proposed framework, we evaluate our weakly supervised method on two cross-domain datasets, i.e., the DAVIS and VOT2016 datasets, with the same configuration and parameter setting. Experimental results show the top performance of our method, which is even better than the leading semi-supervised methods. Furthermore, we conduct the extensive ablation study on our approach to investigate the influence of each component and main parameters.
The accuracy of prognostic scoring systems for post-operative morbidity and mortality in patients with perforated peptic ulcer -

In urgent surgical procedures for perforated peptic ulcer (PPU), there is considerable postoperative morbidity and mortality. The overall mortality rate is about 9-27%. 1-3 A large number of prognostic factors for morbidity and mortality in patients with perforated peptic ulcer have been reported. 2-7 Several clinical scoring system have been proposed for prognostic prediction. The most wellknown predicted scoring system is Boey score 3, which predicted mortality in PPU patients base on the time from perforation to admission, pre-operative systolic blood pressure and comorbid conditions of patients. Then Boey score was classified in three groups, score 0, 1 and 2 and mortality rate were 0%, 10% and 100% respectively. Lohsiriwat et al was found that a higher Boey score was associated with increasing rates of both morbidity and mortality and could be considered as a simple and appropriate prognostic marker in the management of PPU.
Relative Entropy Inverse Reinforcement Learning

We consider the problem of imitation learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP). Most of the past work on IRL requires that a (near)optimal policy can be computed for different reward functions. However, this requirement can hardly be satisfied in systems with a large, or continuous, state space. In this paper, we propose a model-free IRL algorithm, where the relative entropy between the empirical distribution of the state-action trajectories under a uniform policy and their distribution under the learned policy is minimized by stochastic gradient descent. We compare this new approach to well-known IRL algorithms using approximate MDP models. Empirical results on simulated car racing, gridworld and ball-in-a-cup problems show that our approach is able to learn good policies from a small number of demonstrations.
Fast Screening Algorithm for Rotation and Scale Invariant Template Matching

This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast prescreening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively. Extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match.
Blended learning in higher education: Students' perceptions and their relation to outcomes

New information and communication technologies (ICTs) provide educators and learners with an innovative learning environment to stimulate and enhance the teaching and learning process. In this context, novel educational concepts such as blended learning are being developed. In the present paper, we present the results obtained from a blended learning experience carried out at the University of Granada. A total of 17 groups took part, with 1431 students registered for the 2009–2010 academic year. In this study, we use objective outcomes and the students’ perceptions regarding the blended learning activities performed. The study shows that the use of blended learning has a positive effect in reducing dropout rates and in improving exam marks. Moreover, the students’ perceptions on blended learning are interrelated, with their final marks depending on the blended learning activities, and on the students’ age, background and class attendance rate.
Toward Characterizing the Productivity Benefits of Very Large Displays

Larger display surfaces are becoming increasingly available due to multi-monitor capability built into many systems, in addition to the rapid decrease in their costs. However, little is known about the performance benefits of using these larger surfaces compared to traditional single-monitor displays. In addition, it is not clear that current software designs and interaction techniques have been properly tuned for these larger surfaces. A preliminary user study was carried out to provide some initial evidence about the benefits of large versus small display surfaces for complex, multi-application office work. Significant benefits were observed in the use of a prototype, larger display, in addition to significant positive user preference and satisfaction with its use over a small display. In addition, design guidelines for enhancing user interaction across large display surfaces were identified. User productivity could be significantly enhanced in future graphical user interface designs if developed with these findings in mind.
The Market for " Lemons " : Quality Uncertainty and the Market Mechanism

This paper examines the allocation of credit in a market in which borrowers have greater information concerning their own riskiness than do lenders. It illustrates that (1)the allocation of credit is inefficient and at times can be improved by government intervention, and (2) small changes in the exogenous risk-free interest rate can cause large (discontinuous) changes in the allocation of credit and the efficiency of the market equilibrium. These conclusions suggests a role for government as the lender of last resort.
Channel selection based on multichannel cross-correlation coefficients for distant speech recognition

In theory, beamforming performance can be improved by using as many microphones as possible, but in practice it has been shown that using all possible channels does not always improve speech recognition performance [1, 2, 3, 4, 5]. In this work, we present a new channel selection method in order to increase the computational efficiency of beamforming for distant speech recognition (DSR) without sacrficing performance.
On Parallelizing the Multiprocessor Scheduling Problem

Existing heuristics for scheduling a node and edge weighted directed task graph to multiple processors can produce satisfactory solutions but incur high time complexities which tend to exacerbate in more realistic environments with relaxed assumptions. Consequently, these heuristics do not scale well and cannot handle problems of moderate sizes. A natural approach to reducing complexity while aiming for a similar or potentially better solution is to parallelize the scheduling algorithm. This can be done by partitioning the task graphs and concurrently generating partial schedules for the partitioned parts, which are then concatenated to obtain the final schedule. The problem, however, is nontrivial as there exists dependencies among the nodes of a task graph which must be preserved for generating a valid schedule. Moreover, the time clock for scheduling is global for all the processors (that are executing the parallel scheduling algorithm), making the inherent parallelism invisible. In this paper, we introduce a parallel algorithm that is guided by a systematic partitioning of the task graph to perform scheduling using multiple processors. The algorithm schedules both the tasks and messages, and is suitable for graphs with arbitrary computation and communication costs, and is applicable to systems with arbitrary network topologies using homogeneous or heterogeneous processors. We have implemented the algorithm on the Intel Paragon and compared it with three closely related algorithms. The experimental results indicate that our algorithm yields higher quality solutions while using an order of magnitude smaller scheduling times. The algorithm also exhibits an interesting trade-off between the solution quality and speedup while scaling well with the problem size.
Off-line flux injection test probe for screening defective rotors in squirrel cage induction machines

The recent trend in squirrel cage induction motor manufacturing is to replace fabricated copper rotors with aluminum die-cast rotors to reduce manufacturing cost to stay competitive in the global market. Porosity in aluminum die-cast squirrel cage rotors is inevitably introduced during the die cast process. Porosity can cause degradation in motor performance and can lead to a forced outage causing irreversible damage if it is extreme. Many off-line and on-line quality assurance test methods have been developed and applied for assessment of rotor quality. However, years of experience with the existing test methods revealed that they are not suitable for quality testing or capable of providing a quantitative assessment of rotor condition with sufficient sensitivity. In this paper, a new off-line test method capable of providing sensitive assessment of rotor porosity is proposed. It is shown that rotors with minor and distributed porosity that are difficult to detect with other tests can be screened out during manufacturing. The proposed method is verified through a 3 dimensional finite element analysis and experimental testing on closed and semi-open slot aluminum die cast rotors of 5.5 kW induction motors with porosity.
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Generative Adversarial Examples

Adversarial examples are typically constructed by perturbing an existing data point, and current defense methods are focused on guarding against this type of attack. In this paper, we propose a new class of adversarial examples that are synthesized entirely from scratch using a conditional generative model. We first train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to model the class-conditional distribution over inputs. Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier. We demonstrate through human evaluation that this new kind of adversarial inputs, which we call Generative Adversarial Examples, are legitimate and belong to the desired class. Our empirical results on the MNIST, SVHN, and CelebA datasets show that generative adversarial examples can easily bypass strong adversarial training and certified defense methods which can foil existing adversarial attacks.
Information Quality in Social Media: A Conceptual Model

Social Media (SM) is increasingly being integrated with business information in decision making. Unique characteristics of social media (e.g. wide accessibility, permanence, global audience, recentness, and ease of use) raise new issues with information quality (IQ); quite different from traditional considerations of IQ in information systems (IS) evaluation. This paper presents a preliminary conceptual model of information quality in social media (IQnSM) derived through directed content analysis and employing characteristics of analytic theory in the study protocol. Based in the notion of ‘fitness for use’, IQnSM is highly use and user centric and is defined as “the degree to which information is suitable for doing a specified task by a specific user, in a certain context”. IQnSM is operationalised as hierarchical, formed by the three dimensions (18 measures): intrinsic quality, contextual quality and representational quality. A research plan for empirically validating the model is proposed.
Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution

Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.
Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework?

Internet of things (IoT) is fascinating; its future architecture is still under construction. Based on the analysis on the basic and essential characters of IoT, this paper deals with Future IoT architecture in two aspects: Unit IoT and Ubiquitous IoT. Focusing on a special application, the architecture of the Unit IoT is built from man like neural network (MLN) model and its modified model. Ubiquitous IoT refers to the global IoT or the integration of multiple Unit IoTs with "ubiquitous" characters, and its architecture employs social organization framework (SOF) model. The models for Future IoT are not only helpful to interpret the relationship between IoT and reality world, but also beneficial to the implementation of IoT in its current development milieu.
Unexpected Ramifications of Knot Theory Mx

A well known mathematical joke goes as follows: “a topologist is someone who doesn’t see the difference between a doughnut and a coffee mug”. Aside from capturing one of the main ideas of topology, the concept of genus as an invariant under diffeomorphism, it happens to capture the essence of mathematics itself; coffee. But is this all there is to topologists? Topology,a which arose in the 20th century is one of the most recent branches of mathematics. Due to its dependence on the notion of continuity, it lacks any precedent from antiquity. The fact that it exists independently of number systems, and appears to have little care for real world limitation of how surfaces can be deformed, might lead some to think that it cannot have any applications. In fact, for decades after the idea of topology was defined, under the name analysis situs, there hardly were any problems that mathematicians could associate with this type of analysis. This all changed with Vandermonde. Industrialisation, cloth production, shoelaces, etc. Nobody can go a day in life without benefitting from non-trivial embedding of codimension two manifolds. That is to say: knots, tangles, and braids. Moreover, knot theory has been related to numerous research topics in fundamental sciences such as quantum physics, molecular biology and even theoretical psychology [16]. This paper is in three core sections. The first describes the inspiration and motivation for knot theory, the second concentrates on the role of knots in the development of topology, and the last talks about applications and repercussions.
VisualBackProp: efficient visualization of CNNs

This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. it was designed to require less computations than a forward propagation. This makes the presented visualization method a valuable debugging tool which can be easily used during both training and inference. We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. Our theoretical findings stand in agreement with the experimental results. The empirical evaluation shows the plausibility of the proposed approach on the road video data as well as in other applications and reveals that it compares favorably to the layer-wise relevance propagation approach, i.e. it obtains similar visualization results and simultaneously achieves order of magnitude speed-ups.
Determinants of Successful Knowledge Management Programs

The main objective of this paper is to investigate and identify the main determinants of successful knowledge management (KM) programs. We draw upon the institutional theory and the theory of technology assimilation to develop an integrative model of KM success that clarifies the role of information technology (IT) in relation to other important KM infrastructural capabilities and to KM process capabilities. We argue that the role of IT cannot be studied in isolation and that the effect of IT on KM success is fully mediated by KM process capabilities. The research model is tested with a survey study involving 191 KM practitioners. The empirical results provided strong support for the model. In addition to its theoretical contributions, this study also presents important practical implications through the identification of specific infrastructural capabilities leading to KM success.
Predicting SMEs' adoption of enterprise systems

The purpose of this paper is to develop a model that can be used to predict which small to medium-sized enterprises (SMEs) are more likely to become adopters of enterprise systems (ERP, CRM, SCM and e-procurement).  Direct interviews were used to collect data from a random sample of SMEs located in the Northwest of England. Using logistic regression, 102 responses were analysed.  The results reveal that the factors influencing SMEs’ adoption of enterprise systems are different from the factors influencing SMEs’ adoption of other previously studied information systems (IS) innovations. SMEs were found to be more influenced by technological and organisational factors than environmental factors. Moreover, the results indicate that firms with a greater perceived relative advantage, a greater ability to experiment with these systems before adoption, greater top management support, greater organisational readiness and a larger size are predicted to become adopters of enterprise systems.  Although this study focused on the factors that influence SMEs’ adoption of a set of enterprise systems (i.e. ERP, CRM, SCM and e-procurement), it fails to differentiate between factors that influence each of these systems.  The model can be used to assist software vendors not only to develop marketing strategies that can target potential adopters, but also to develop strategies to increase the adoption of ES among SMEs.  The paper contributes to the continuing research in IS innovations adoption/diffusion in the small business context.