nils-herrmann/clean_scirepeval huggingface dataset:
The architecture of complex weighted networks.

Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.
How to build an optimal broadband channel sounder

Spatio-temporal channel information obtained via channel sounding is invaluable for implementing equalizers, multi-antenna systems, and dynamic modulation schemes in next-generation wireless systems. The most straightforward means of performing channel measurements is in the frequency domain using a vector network analyzer (VNA). However, the high cost of VNAs often leads engineers to seek more economical solutions by measuring the wireless channel in the time domain. The bandwidth compression of the sliding correlator channel sounder makes it the preferred means of performing time-domain channel measurements.
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep CNN architecture for environmental sound classification. Second, we propose the use of audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture. Combined with data augmentation, the proposed model produces state-of-the-art results for environmental sound classification. We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a “shallow” dictionary learning model with augmentation. Finally, we examine the influence of each augmentation on the model's classification accuracy for each class, and observe that the accuracy for each class is influenced differently by each augmentation, suggesting that the performance of the model could be improved further by applying class-conditional data augmentation.
What Do Mobile App Users Complain About?

Mobile-app quality is becoming an increasingly important issue. These apps are generally delivered through app stores that let users post reviews. These reviews provide a rich data source you can leverage to understand user-reported issues. Researchers qualitatively studied 6,390 low-rated user reviews for 20 free-to-download iOS apps. They uncovered 12 types of user complaints. The most frequent complaints were functional errors, feature requests, and app crashes. Complaints about privacy and ethical issues and hidden app costs most negatively affected ratings. In 11 percent of the reviews, users attributed their complaints to a recent app update. This study provides insight into the user-reported issues of iOS apps, along with their frequency and impact, which can help developers better prioritize their limited quality assurance resources.
HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network

With explosive growth of Android malware and due to the severity of its damages to smart phone users, the detection of Android malware has become increasingly important in cybersecurity. The increasing sophistication of Android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect Android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more effort for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for Android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.
A Novel Remote Center-of Motion Parallel manipulator for Minimally Invasive Celiac Surgery

In order to improve the security and expand the work space of minimally invasive celiac surgical robot, a new remote center-of motion (RCM) mechanism is designed. The remote center-of motion (RCM) mechanism can be used to hold surgical instrument instead of doctors, and then help doctors complete surgical operation (such as holding、 stapling or resecting the patient's diseased tissue and organs). In this paper, the function of the remote center-of motion (RCM) mechanism of minimally invasive surgical ( MIS) robot in the process of surgery are analyzed in detail. the design requirements of the remote center-of motion (RCM) are described. And then, a detailed analysis of the kinematic principle and motion decouplability of the circular tracking arc and double parallelogram structure are gave. On these foundations, a novel parallel robotic remote center-of motion (RCM) mechanism which owing a circular track structure and some series bar is proposed. Through analyzing the degree of freedom of this institution, the kinematics principle and decoupling characteristics, we find that the new institution has a good performance on “Fixed point motion” and the three rotational degrees of freedom of this mechanism can be fully decoupled. with respect to the remote center-of motion (RCM) mechanism which was made by the circular tracking arc and double parallelogram structure, the novel remote center-of motion (RCM) mechanism can realize the robot’s lightweight, improve the structural rigidity, reduce motion inertia, and also expand the working space. As a result, the new remote center-of motion (RCM) mechanism would be a good choice for a minimally invasive surgical robot.
Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV)

Deployment of PHEV will initiate an integration of transportation and power systems. Intuitively, the PHEVs will constitute an additional demand to the electricity grid, potentially violating converter or line capacities when recharging. Smart management schemes can alleviate possible congestions in power systems, intelligently distributing available energy. As PHEV are inherently independent entities, an agent based approach is expedient. Nonlinear pricing will be adapted to model and manage recharging behavior of large numbers of autonomous PHEV agents connecting in one urban area modelled as an energy hub. The scheme will incorporate price dependability. An aggregation entity, with no private information about its customers, will manage the PHEV agents whose individual parameters will be based on technical constraints and individual objectives. Analysis of the management scheme will give implications for PHEV modelling and integration schemes as well as tentative ideas of possible repercussions on power systems.
The MOND limit from space-time scale invariance

The MOND limit is shown to follow from a requirement of space-time scale invariance of the equations of motion for nonrelativistic, purely gravitational systems; i.e., invariance of the equations of motion under (t, r) → (λt, λr) in the limit a 0 → ∞. It is suggested that this should replace the definition of the MOND limit based on the asymptotic behavior of a Newtonian-MOND interpolating function. In this way, the salient, deep-MOND results–asymptotically flat rotation curves, the mass-rotational-speed relation (baryonic Tully-Fisher relation), the Faber-Jackson relation, etc.–follow from a symmetry principle. For example, asymptotic flatness of rotation curves reflects the fact that radii change under scaling, while velocities do not. I then comment on the interpretation of the deep-MOND limit as one of " zero mass " : Rest masses, whose presence obstructs scaling symmetry, become negligible compared to the " phantom " , dynamical masses–those that some would attribute to dark matter. Unlike the former masses, the latter transform in a way that is consistent with the symmetry. Finally, I discuss the putative MOND-cosmology connection, in particular the possibility that MOND-especially the deep-MOND limit– is related to the asymptotic de Sitter geometry of our universe. I point out, in this connection, the possible relevance of a (classical) de Sitter-conformal-field-theory (dS/CFT) correspondence.
The Barriers to the Use of ICT in Teaching in Saudi Arabia : A Review of Literature

The paper reports some of the reasons behind the low use of Information and Communication Technology (ICT) by teachers. The paper has reviewed a number or studies from different parts of the world and paid greater attention to Saudi Arabia. The literature reveals a number of factors that hinder teachers’ use of ICT. This paper will focus on lack of access to technology, lack of training and lack of time.
Developed a Mathematical Model for Solving the Two-Objective Dynamic Facility Layout Problem with Budget Constraints and Optimal Allocation of Buffer Size 1

Optimal facility layout is of the factors that can affect the efficiency of any organization and annually millions of dollars costs are created for it or profits are saved. Studies done in the field of facility layout can be classified into two general categories: Facility layout issues in static position and facility layout issues in dynamic position. Because that facilities dynamic layout problem is realistic this paper investigates it and tries to consider all necessary aspects of this issue to become it more practical. In this regard, this research has developed three objectives model and tries to simultaneously minimize total operating costs and also production time. Since the calculation of production time using analytical relations is impossible, this research using simulation and regression analysis of a statistical correlation tries to measure production time. So the developed model is a combination of analytical and statistical relationships. The proposed model is of NP-HARD issues, so that even finding an optimal solution for its small scale is very difficult and time consuming. Multi-objective meta-heuristic NSGAII and NRGA algorithms are used to solve the problem. Since the outputs of meta-heuristic algorithms are highly dependent on the algorithms input parameters, Taguchi experimental design method is also used to set parameters. Alsoin order to assess the efficiency of provided procedures, the proposed method has been analyzed on generated pilot issues with various aspects. The results of the comparing algorithms on several criteria, consistently show the superiority of the NSGA-II than NRGA in problem solving.
Building decision tree classifier on private data

This paper studies how to build a decision tree classifier under the following scenario: a database is vertically partitioned into two pieces, with one piece owned by Alice and the other piece owned by Bob. Alice and Bob want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces to the other party or to any third party. We present a protocol that allows Alice and Bob to conduct such a classifier building without having to compromise their privacy. Our protocol uses an untrusted third-party server, and is built upon a useful building block, the scalar product protocol. Our solution to the scalar product protocol is more efficient than any existing solutions.
Agile Methods and CMMI: Compatibility or Conflict?

During the last years, agile methods like eXtreme Programming have become increasingly popular. Parallel to this, more and more organizations rely on process maturity models to assess and improve their own processes or those of suppliers, since it has been getting clear that most project failures can be imputed to inconsistent, undisciplined processes. Many organizations demand CMMI compliance of projects where agile methods are employed. In this situation it is necessary to analyze the interrelations and mutual restrictions between agile methods and approaches for software process analysis and improvement. This paper analyzes to what extent the CMMI process areas can be covered by XP and where adjustments of XP have to be made. Based on this, we describe the limitations of CMMI in an agile environment and show that level 4 or 5 are not feasible under the current specifications of CMMI and XP.
Diagnostic accuracy of dermoscopy.

The accuracy of the dinical diagnosis of cutaneous melanoma with the unaided eye is only about 60%. Dermoscopy, a non-invasive, in vivo technique for the microscopic examination of pigmented skin lesions, has the potential to improve the diagnostic accuracy. Our objectives were to review previous publications, to compare the accuracy of melanoma diagnosis with and without dermoscopy, and to assess the influence of study characteristics on the diagnostic accuracy. We searched for publications between 1987 and 2000 and identified 27 studies eligible for meta-analysis. The diagnostic accuracy for melanoma was significantly higher with dermoscopy than without this technique (log odds ratio 4.0 [95% CI 3.0 to 5.1] versus 2.7 [1.9 to 3.4]; an improvement of 49%, p = 0.001). The diagnostic accuracy of dermoscopy significantly depended on the degree of experience of the examiners. Dermoscopy by untrained or less experienced examiners was no better than clinical inspection without dermoscopy. The diagnostic performance of dermoscopy improved when the diagnosis was made by a group of examiners in consensus and diminished as the prevalence of melanoma increased. A comparison of various diagnostic algorithms for dermoscopy showed no significant differences in their diagnostic performance. A thorough appraisal of the study characteristics showed that most of the studies were potentially influenced by verification bias. In conclusion, dermoscopy improves the diagnostic accuracy for melanoma in comparison with inspection by the unaided eye, but only for experienced examiners.
Distributed Scrum: Agile Project Management with Outsourced Development Teams

Agile project management with Scrum derives from best business practices in companies like Fuji-Xerox, Honda, Canon, and Toyota. Toyota routinely achieves four times the productivity and 12 times the quality of competitors. Can Scrum do the same for globally distributed teams? Two agile companies, SirsiDynix and StarSoft development laboratories achieved comparable performance developing a Java application with over 1,000,000 lines of code. During 2005, a distributed team of 56 Scrum developers working from Provo, Utah; Waterloo, Canada; and St. Petersburg, Russia, delivered 671,688 lines of production Java code. At 15.3 function points per developer/month, this is the most productive Java project ever documented. SirsiDynix best practices are similar to those observed on distributed Scrum teams at IDX Systems, radically different than those promoted by PMBOK, and counterintuitive to practices advocated by the Scrum Alliance. This paper analyzes and recommends best practices for globally distributed agile teams
Data Generation as Sequential Decision Making

We connect a broad class of generative models through their shared reliance on sequential decision making. Motivated by this view, we develop extensions to an existing model, and then explore the idea further in the context of data imputation – perhaps the simplest setting in which to investigate the relation between unconditional and conditional generative modelling. We formulate data imputation as an MDP and develop models capable of representing effective policies for it. We construct the models using neural networks and train them using a form of guided policy search [11]. Our models generate predictions through an iterative process of feedback and refinement. We show that this approach can learn effective policies for imputation problems of varying difficulty and across multiple datasets.
Wideband Radar Cross-Section Reduction of a Stacked Patch Array Antenna Using Metasurface

In this letter, a two-layer metasurface is proposed to achieve radar cross-section (RCS) reduction of a stacked patch antenna at a broadband. The lower layer metasurface is composed of four square patches loaded with four resistors, which is utilized to reduce RCS in the operation band (2.75-3.4 GHz) of the patch antenna. The periodic square loops with four resistors mounted on each side are adopted to construct the upper layer metasurface for absorbing the incoming wave out of band. We first investigate the effectiveness of the proposed metasurface on the RCS reduction of the single stacked patch and then apply this strategy to the 1 ×4 stacked patch array. The proposed low RCS stacked patch array antenna is fabricated and measured. The experimental results show that the designed metasurface makes the antenna RCS dramatically reduced in a broadband covering the operation band and out-of-band from 5.5-16 GHz. Moreover, the introduction of metasurface is demonstrated to have little influence on the antenna performance.
Semi-supervised Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data

The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product. In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention. Generally, in those studies, only normal images are used for training to model the distribution of normal images. The model measures the anomalies in the target images by reproducing the most similar images and scoring image patches indicating their fit to the learned distribution. This approach is based on a strong presumption; the trained model should not be able to generate abnormal images. However, in reality, the model can generate abnormal images mainly due to noisy normal data which include small abnormal pixels, and such noise severely affects the accuracy of the model. Therefore, we propose a novel anomaly detection method to distort the distribution of the model with existing abnormal images. The proposed method detects pixel-level micro anomalies with a high accuracy from 1024×1024 high resolution images which are actually used in an industrial scene. In this paper, we share experimental results on open datasets, due to the confidentiality of the data.
Draft : Deep Learning in Neural Networks : An Overview

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. PDF of earlier draft (v1): http://www.idsia.ch/∼juergen/DeepLearning30April2014.pdf LATEX source: http://www.idsia.ch/∼juergen/DeepLearning30April2014.tex Complete BIBTEX file: http://www.idsia.ch/∼juergen/bib.bib Preface This is the draft of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using “local search” to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present draft mostly consists of references (about 800 entries so far). Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, the present draft should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.
Improved Techniques for Training GANs

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.