nils-herrmann/clean_scirepeval huggingface dataset:
Developing an explanatory model for the process of online radicalisation and terrorism

While the use of the internet and social media as a tool for extremists and terrorists has been well documented, understanding the mechanisms at work has been much more elusive. This paper begins with a grounded theory approach guided by a new theoretical approach to power that utilizes both terrorism cases and extremist social media groups to develop an explanatory model of radicalization. Preliminary hypotheses are developed, explored and refined in order to develop a comprehensive model which is then presented. This model utilizes and applies concepts from social theorist Michel Foucault, including the use of discourse and networked power relations in order to normalize and modify thoughts and behaviors. The internet is conceptualized as a type of institution in which this framework of power operates and seeks to recruit and radicalize. Overall, findings suggest that the explanatory model presented is a well suited, yet still incomplete in explaining the process of online radicalization.
Face recognition with radial basis function (RBF) neural networks

A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.
Big data storage technologies: a survey

There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ‘big data’. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer’s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.
Warpage Prediction and Experiments of Fan-Out Waferlevel Package During Encapsulation Process

A wafer level package (WLP) that has a flip chip form and uses thin-film redistribution with solder bumps to connect the package to the printed wiring board directly is discussed in this paper. A liquid molding compound is used for the encapsulation process. Since the thickness of the fan-out WLP is smaller than that in a traditional integrated circuit (IC) package, the fan-out WLP induces more serious warpage. Warpage plays an important role during the IC encapsulation process, and too large a warpage would not let the package proceed to the next manufacturing process. This paper uses an approach that considers both cure- and thermal-induced shrinkages during the encapsulation process to predict the amount of warpage. Cure-induced shrinkage is described by the pressure-volume-temperature-cure (PVTC) equation of the liquid compound. The thermally induced shrinkage is described by the coefficients of thermal expansion of the component materials. The liquid compound properties are obtained by various techniques, such as cure kinetics by differential scanning calorimetry- and cure-induced shrinkage by a PVTC testing machine. These experimental data are used to formulate the PVTC equation. A fan-out WLP is first simulated, and the simulation results are verified with experiments. It is shown that an approach that considers both thermal and cure/compressibility effects can better predict the amount of warpage for the fan-out WLP. The PVTC equation is successfully implemented, and it is verified that warpage is governed by both thermal and cure shrinkages. The amount of warpage after molding could be accurately predicted with this methodology. Simulation results show that cure shrinkage of the liquid compound is the dominant factor responsible for package warpage after encapsulation.
A 3D-stacked logic-in-memory accelerator for application-specific data intensive computing

This paper introduces a 3D-stacked logic-in-memory (LiM) system that integrates the 3D die-stacked DRAM architecture with the application-specific LiM IC to accelerate important data-intensive computing. The proposed system comprises a fine-grained rank-level 3D die-stacked DRAM device and extra LiM layers implementing logic-enhanced SRAM blocks that are dedicated to a particular application. Through silicon vias (TSVs) are used for vertical interconnections providing the required bandwidth to support the high performance LiM computing. We performed a comprehensive 3D DRAM design space exploration and exploit the efficient architectures to accelerate the computing that can balance the performance and power. Our experiments demonstrate orders of magnitude of performance and power efficiency improvements compared with the traditional multithreaded software implementation on modern CPU.
High-dimensional sequence transduction

We investigate the problem of transforming an input sequence into a high-dimensional output sequence in order to transcribe polyphonic audio music into symbolic notation. We introduce a probabilistic model based on a recurrent neural network that is able to learn realistic output distributions given the input and we devise an efficient algorithm to search for the global mode of that distribution. The resulting method produces musically plausible transcriptions even under high levels of noise and drastically outperforms previous state-of- the-art approaches on five datasets of synthesized sounds and real recordings, approximately halving the test error rate.
Single neurons in prefrontal cortex encode abstract rules

The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the ‘rules’ for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. They were required to indicate whether two successively presented pictures were the same or different depending on which rule was currently in effect. The monkeys performed this task with new pictures, thus showing that they had learned two general principles that could be applied to stimuli that they had not yet experienced. The most prevalent neuronal activity observed in the PFC reflected the coding of these abstract rules.
Code Flows: Visualizing Structural Evolution of Source Code

Understanding detailed changes done to source code is of great importance in software maintenance. We present Code Flows, a method to visualize the evolution of source code geared to the understanding of fine and mid-level scale changes across several file versions. We enhance an existing visual metaphor to depict software structure changes with techniques that emphasize both following unchanged code as well as detecting and highlighting important events such as code drift, splits, merges, insertions and deletions. The method is illustrated with the analysis of a real-world C++ code system.
Multi-Generator Generative Adversarial Nets

We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture Generative Adversarial Nets (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators.
New Single-Input, Multiple-Output Converter Topologies: Combining Single-Switch Nonisolated dc-dc Converters for Single-Input, Multiple-Output Applications

This article presents a methodology that allows the development of new converter topologies for single-input, multiple-output (SIMO) from different basic configurations of single-input, single-output dc-dc converters. These typologies have in common the use of only one power-switching device, and they are all nonisolated converters. Sixteen different topologies are highlighted, and their main features are explained. The 16 typologies include nine twooutput-type, five three-output-type, one four-output-type, and one six-output-type dc-dc converter configurations. In addition, an experimental prototype of a three-output-type configuration with six different output voltages based on a single-ended primary inductance (SEPIC)-Cuk-boost combination converter was developed, and the proposed design methodology for a basic converter combination was experimentally verified.
Universal Correspondence Network

We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations. In contrast to previous CNN-based approaches that optimize a surrogate patch similarity objective, we use deep metric learning to directly learn a feature space that preserves either geometric or semantic similarity. Our fully convolutional architecture, along with a novel correspondence contrastive loss allows faster training by effective reuse of computations, accurate gradient computation through the use of thousands of examples per image pair and faster testing with O(n) feedforward passes for n keypoints, instead of O(n) for typical patch similarity methods. We propose a convolutional spatial transformer to mimic patch normalization in traditional features like SIFT, which is shown to dramatically boost accuracy for semantic correspondences across intra-class shape variations. Extensive experiments on KITTI, PASCAL and CUB-2011 datasets demonstrate the significant advantages of our features over prior works that use either hand-constructed or learned features.
Information security culture - from analysis to change

Information Security Culture includes all socio-cultural measures that support technical security methods, so that information security becomes a natural aspect in the daily activity of every employee. To apply these socio-cultural measures in an effective and efficient way, certain management models and tools are needed. In our research we developed a framework analyzing the security culture of an organization which we then applied in a pre-evaluation survey. This paper is based on the results of this survey. We will develop a management model for creating, changing and maintaining Information Security Culture. This model will then be used to define explicit sociocultural measures, based on the concept of internal marketing.
A dual-feed dual-band L-probe patch antenna

By embedding shorting vias, a dual-feed and dual-band L-probe patch antenna, with flexible frequency ratio and relatively small lateral size, is proposed. Dual resonant frequency bands are produced by two radiating patches located in different layers, with the lower patch supported by shorting vias. The measured impedance bandwidths, determined by 10 dB return loss, of the two operating bands reach 26.6% and 42.2%, respectively. Also the radiation patterns are stable over both operating bands. Simulation results are compared well with experiments. This antenna is highly suitable to be used as a base station antenna for multiband operation.
New text indexing functionalities of the compressed suffix arrays

New text indexing functionalities of the compressed suffix arrays are proposed. The comp suffix array proposed by Grossi and Vitter is a space-efficient data structure for text index occupies onlyO(n) bits for a text of lengthn; however it also uses the text itself that occup n log2 |A| bits for the alphabetA. In this paper we modify the data structure so that pat matching can be done without any access to the text. In addition to the original functions compressed suffix array, we add new operations search, decompressand inverseto the compresse suffix arrays. We show that the new index can find occoccurrences of any substring P of the text in O(|P | logn+ occlog n) time for any fixed 1 > 0 without access to the text. The index also c decompress a part of the text of length m inO(m+ log n) time. For a text of lengthn on an alphabe A such that|A| = polylog(n), our new index occupies only O(nH0 + n log log|A|) bits where H0 log |A| is the order-0 entropy of the text. Especially for = 1 the size isnH0+O(n log log|A|) bits. Therefore the index will be smaller than the text, which means we can perform fast querie compressed texts.
Performance debugging for distributed systems of black boxes

Many interesting large-scale systems are distributed systems of multiple communicating components. Such systems can be very hard to debug, especially when they exhibit poor performance. The problem becomes much harder when systems are composed of "black-box" components: software from many different (perhaps competing) vendors, usually without source code available. Typical solutions-provider employees are not always skilled or experienced enough to debug these systems efficiently. Our goal is to design tools that enable modestly-skilled programmers (and experts, too) to isolate performance bottlenecks in distributed systems composed of black-box nodes.We approach this problem by obtaining message-level traces of system activity, as passively as possible and without any knowledge of node internals or message semantics. We have developed two very different algorithms for inferring the dominant causal paths through a distributed system from these traces. One uses timing information from RPC messages to infer inter-call causality; the other uses signal-processing techniques. Our algorithms can ascribe delay to specific nodes on specific causal paths. Unlike previous approaches to similar problems, our approach requires no modifications to applications, middleware, or messages.
Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.
Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems

0045-7949/$ see front matter 2012  A http://dx.doi.org/10.1016/j.compstruc.2012.07.010 ⇑ Corresponding author.  +60 379675266; fax: E-mail addresses: hadi.eskandar@yahoo.com (H. hoo.com (A. Sadollah), bahreininejad@um.edu.my (A m.edu.my (M. Hamdi). This paper presents a new optimization technique called water cycle algorithm (WCA) which is applied to a number of constrained optimization and engineering design problems. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. A comparative study has been carried out to show the effectiveness of the WCA over other well-known optimizers in terms of computational effort (measures as number of function evaluations) and function value (accuracy) in this paper.
High efficiency two-stage 48V VRM with PCB winding matrix transformer

High efficiency power supply solutions for data centers are gaining more attention, in order to minimize the fast growing power demands of such loads, the 48V Voltage Regulator Module (VRM) for powering CPU is a promising solution replacing the legacy 12V VRM by which the bus distribution loss, cost and size can be dramatically minimized. In this paper, a two-stage 48V/12V/1.8V–250W VRM is proposed, the first stage is a high efficiency, high power density isolated — unregulated DC/DC converter (DCX) based on LLC resonant converter stepping the input voltage from 48V to 12V. The Matrix transformer concept was utilized for designing the high frequency transformer of the first stage, an enhanced termination loop for the synchronous rectifiers and a non-uniform winding structure is proposed resulting in significant increase in both power density and efficiency of the first stage converter. The second stage is a 4-phases buck converter stepping the voltage from 12V to 1.8V to the CPU. Since the CPU runs in the sleep mode most of the time a light load efficiency improvement method by changing the bus voltage from 12V to 6 V during light load operation is proposed showing more than 8% light load efficiency enhancement than fixed bus voltage. Experimental results demonstrate the high efficiency of the proposed solution reaching peak of 91% with a significant light load efficiency improvement.
Evaluation of Dependency Parsers on Unbounded Dependencies

We evaluate two dependency parsers, MSTParser and MaltParser, with respect to their capacity to recover unbounded dependencies in English, a type of evaluation that has been applied to grammarbased parsers and statistical phrase structure parsers but not to dependency parsers. The evaluation shows that when combined with simple post-processing heuristics, the parsers correctly recall unbounded dependencies roughly 50% of the time, which is only slightly worse than two grammar-based parsers specifically designed to cope with such dependencies.
Performance Studies of Underwater Wireless Optical Communication Systems With Spatial Diversity: MIMO Scheme

In this paper, we analytically study the performance of multiple-input multiple-output underwater wireless optical communication (UWOC) systems with ON–OFF keying modulation. To mitigate turbulence-induced fading, which is amongst the major degrading effects of underwater channels on the propagating optical signal, we use spatial diversity over UWOC links. Furthermore, the effects of absorption and scattering are considered in our analysis. We analytically obtain the exact and an upper bound bit error rate (BER) expressions for both optimal and equal gain combining. In order to more effectively calculate the system BER, we apply Gauss-Hermite quadrature formula as well as approximation to the sum of lognormal random variables. We also apply the photon-counting method to evaluate the system BER in the presence of shot noise. Our numerical results indicate an excellent match between the exact and upper bound BER curves. Also, a good match between the analytical results and numerical simulations confirms the accuracy of our derived expressions. Moreover, our results show that spatial diversity can considerably improve the system performance, especially for channels with higher turbulence, e.g., a  $3\times 1$  multiple-input single-output transmission in a 25 m coastal water link with a log-amplitude variance of 0.16 can introduce 8 dB performance improvement at the BER of 10−9.