nils-herrmann/clean_scirepeval huggingface dataset:
Signed networks in social media

Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive interpretations of links between people, we study how the interplay between positive and negative relationships affects the structure of on-line social networks. We connect our analyses to theories of signed networks from social psychology. We find that the classical theory of structural balance tends to capture certain common patterns of interaction, but that it is also at odds with some of the fundamental phenomena we observe --- particularly related to the evolving, directed nature of these on-line networks. We then develop an alternate theory of status that better explains the observed edge signs and provides insights into the underlying social mechanisms. Our work provides one of the first large-scale evaluations of theories of signed networks using on-line datasets, as well as providing a perspective for reasoning about social media sites.
Predicting Markov-Switching Vector Autoregressive Processes

While there has been a great deal of interest in the modelling of non-linearities and regime shifts in economic time series, there is no clear consensus regarding the forecasting abilities of these models. In this paper we develop a general approach to predict multiple time series subject to Markovian shifts in the regime. The feasibility of the proposed forecasting techniques in empirical research is demonstrated and their forecast accuracy is evaluated.
Gamification and Education : A Literature

The term " gamification " is generally used to denote the application of game mechanisms in non‐gaming environments with the aim of enhancing the processes enacted and the experience of those involved. In recent years, gamification has become a catchword throughout the fields of education and training, thanks to its perceived potential to make learning more motivating and engaging. This paper is an attempt to shed light on the emergence and consolidation of gamification in education/training. It reports the results of a literature review that collected and analysed around 120 papers on the topic published between 2011 and 2014. These originate from different countries and deal with gamification both in training contexts and in formal educational, from primary school to higher education. The collected papers were analysed and classified according to various criteria, including target population, type of research (theoretical vs experimental), kind of educational contents delivered, and the tools deployed. The results that emerge from this study point to the increasing popularity of gamification techniques applied in a wide range of educational settings. At the same time, it appears that over the last few years the concept of gamification has become more clearly defined in the minds of researchers and practitioners. Indeed, until fairly recently the term was used by many to denote the adoption of game artefacts (especially digital ones) as educational tools for learning a specific subject such as algebra. In other words, it was used as a synonym of Game Based Learning (GBL) rather than to identify an educational strategy informing the overall learning process, which is treated globally as a game or competition. However, this terminological confusion appears only in a few isolated cases in this literature review, suggesting that a certain level of taxonomic and epistemological convergence is underway.
Hardware-security technologies for industrial IoT: TrustZone and security controller

The transition from product-centric to service-centric business models presents a major challenge to industrial automation and manufacturing systems. This transition increases Machine-to-Machine connectivity among industrial devices, industrial controls systems, and factory floor devices. While initiatives like Industry 4.0 or the Industrial Internet Consortium motivate this transition, the emergence of the Internet of Things and Cyber Physical Systems are key enablers. However, automated and autonomous processes require trust in the communication entities and transferred data. Therefore, we study how to secure a smart service use case for industrial maintenance scenarios. In this use case, equipment needs to securely transmit its status information to local and remote recipients. We investigate and compare two security technologies that provide isolation and a secured execution environment: ARM TrustZone and a Security Controller. To compare these technologies we design and implement a device snapshot authentication system. Our results indicate that the TrustZone based approach promises greater flexibility and performance, but only the Security Controller strongly protects against physical attacks. We argue that the best technology actually depends on the use case and propose a hybrid approach that maximizes security for high-security industrial applications. We believe that the insights we gained will help introducing advanced security mechanisms into the future Industrial Internet of Things.
Extreme learning machine: a new learning scheme of feedforward neural networks

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: 1) the slow gradient-based learning algorithms are extensively used to train neural networks, and 2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these traditional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses the input weights and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. The experimental results based on real-world benchmarking function approximation and classification problems including large complex applications show that the new algorithm can produce best generalization performance in some cases and can learn much faster than traditional popular learning algorithms for feedforward neural networks.
Catch Me If You Can: A Cloud-Enabled DDoS Defense

We introduce a cloud-enabled defense mechanism for Internet services against network and computational Distributed Denial-of-Service (DDoS) attacks. Our approach performs selective server replication and intelligent client re-assignment, turning victim servers into moving targets for attack isolation. We introduce a novel system architecture that leverages a "shuffling" mechanism to compute the optimal re-assignment strategy for clients on attacked servers, effectively separating benign clients from even sophisticated adversaries that persistently follow the moving targets. We introduce a family of algorithms to optimize the runtime client-to-server re-assignment plans and minimize the number of shuffles to achieve attack mitigation. The proposed shuffling-based moving target mechanism enables effective attack containment using fewer resources than attack dilution strategies using pure server expansion. Our simulations and proof-of-concept prototype using Amazon EC2  demonstrate that we can successfully mitigate large-scale DDoS attacks in a small number of shuffles, each of which incurs a few seconds of user-perceived latency.
A Transition-based Model for Joint Segmentation, POS-tagging and Normalization

We propose a transition-based model for joint word segmentation, POS tagging and text normalization. Different from previous methods, the model can be trained on standard text corpora, overcoming the lack of annotated microblog corpora. To evaluate our model, we develop an annotated corpus based on microblogs. Experimental results show that our joint model can help improve the performance of word segmentation on microblogs, giving an error reduction in segmentation accuracy of 12.02%, compared to the traditional approach.
Algorithms for Characterization and Trend Detection in Spatial Databases

The number and the size of spatial databases, e.g. for geomarketing, traffic control or environmental studies, are rapidly growing which results in an increasing need for spatial data mining. In this paper, we present new algorithms for spatial characterization and spatial trend analysis. For spatial characterization it is important that class membership of a database object is not only determined by its non-spatial attributes but also by the attributes of objects in its neighborhood. In spatial trend analysis, patterns of change of some non-spatial attributes in the neighborhood of a database object are determined. We present several algorithms for these tasks. These algorithms were implemented within a general framework for spatial data mining providing a small set of database primitives on top of a commercial spatial database management system. A performance evaluation using a real geographic database demonstrates the effectiveness of the proposed algorithms. Furthermore, we show how the algorithms can be combined to discover even more interesting spatial knowledge.
Autonomously Generating Hints by Inferring Problem Solving Policies

Exploring the whole sequence of steps a student takes to produce work, and the patterns that emerge from thousands of such sequences is fertile ground for a richer understanding of learning. In this paper we autonomously generate hints for the Code.org `Hour of Code,' (which is to the best of our knowledge the largest online course to date) using historical student data. We first develop a family of algorithms that can predict the way an expert teacher would encourage a student to make forward progress. Such predictions can form the basis for effective hint generation systems. The algorithms are more accurate than current state-of-the-art methods at recreating expert suggestions, are easy to implement and scale well. We then show that the same framework which motivated the hint generating algorithms suggests a sequence-based statistic that can be measured for each learner. We discover that this statistic is highly predictive of a student's future success.
A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. The users or service providers with the key have exclusive rights on the data. Especially with popular cloud services, control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. Therefore, this survey focuses on HE and FHE schemes. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars for achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes, are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, and extending the state-of-the-art HE, PHE, SWHE, and FHE systems.
Error propagation analysis for file systems

Unchecked errors are especially pernicious in operating system file management code. Transient or permanent hardware failures are inevitable, and error-management bugs at the file system layer can cause silent, unrecoverable data corruption. We propose an interprocedural static analysis that tracks errors as they propagate through file system code. Our implementation detects overwritten, out-of-scope, and unsaved unchecked errors. Analysis of four widely-used Linux file system implementations (CIFS, ext3, IBM JFS and ReiserFS), a relatively new file system implementation (ext4), and shared virtual file system (VFS) code uncovers 312 error propagation bugs. Our flow- and context-sensitive approach produces more precise results than related techniques while providing better diagnostic information, including possible execution paths that demonstrate each bug found.
Bohatei: Flexible and Elastic DDoS Defense

DDoS defense today relies on expensive and proprietary hardware appliances deployed at fixed locations. This introduces key limitations with respect to flexibility (e.g., complex routing to get traffic to these “chokepoints”) and elasticity in handling changing attack patterns. We observe an opportunity to address these limitations using new networking paradigms such as softwaredefined networking (SDN) and network functions virtualization (NFV). Based on this observation, we design and implement Bohatei, a flexible and elastic DDoS defense system. In designing Bohatei, we address key challenges with respect to scalability, responsiveness, and adversary-resilience. We have implemented defenses for several DDoS attacks using Bohatei. Our evaluations show that Bohatei is scalable (handling 500 Gbps attacks), responsive (mitigating attacks within one minute), and resilient to dynamic adversaries.
Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.

Quantitative-diffusion-tensor MRI consists of deriving and displaying parameters that resemble histological or physiological stains, i.e., that characterize intrinsic features of tissue microstructure and microdynamics. Specifically, these parameters are objective, and insensitive to the choice of laboratory coordinate system. Here, these two properties are used to derive intravoxel measures of diffusion isotropy and the degree of diffusion anisotropy, as well as intervoxel measures of structural similarity, and fiber-tract organization from the effective diffusion tensor, D, which is estimated in each voxel. First, D is decomposed into its isotropic and anisotropic parts, [D] I and D - [D] I, respectively (where [D] = Trace(D)/3 is the mean diffusivity, and I is the identity tensor). Then, the tensor (dot) product operator is used to generate a family of new rotationally and translationally invariant quantities. Finally, maps of these quantitative parameters are produced from high-resolution diffusion tensor images (in which D is estimated in each voxel from a series of 2D-FT spin-echo diffusion-weighted images) in living cat brain. Due to the high inherent sensitivity of these parameters to changes in tissue architecture (i.e., macromolecular, cellular, tissue, and organ structure) and in its physiologic state, their potential applications include monitoring structural changes in development, aging, and disease.
Automatic Soccer Player Tracking in Single Camera with Robust Occlusion Handling Using Attribute Matching

This paper presents an automatic method to track soccer players in soccer video recorded from a single camera where the occurrence of pan-tilt-zoom can take place. The automatic object tracking is intended to support texture extraction in a free viewpoint video authoring application for soccer video. To ensure that the identity of the tracked object can be correctly obtained, background segmentation is performed and automatically removes commercial billboards whenever it overlaps with the soccer player. Next, object tracking is performed by an attribute matching algorithm for all objects in the temporal domain to find and maintain the correlation of the detected objects. The attribute matching process finds the best match between two objects in different frames according to their pre-determined attributes: position, size, dominant color and motion information. Utilizing these attributes, the experimental results show that the tracking process can handle occlusion problems such as occlusion involving more than three objects and occluded objects with similar color and moving direction, as well as correctly identify objects in the presence of camera movements. key words: free viewpoint, attribute matching, automatic object tracking, soccer video
Chapter 5 Musical Virtuosity and Creativity

Virtuosos are human beings who exhibit exceptional performance in their field of activity. In particular, virtuosos are interesting for creativity studies because they are exceptional problem solvers. However, virtuosity is an under-studied field of human behaviour. Little is known about the processes involved to become a virtuoso, and in how they distinguish themselves from normal performers. Virtuosos exist in virtually all domains of human activities, and we focus in this chapter on the specific case of virtuosity in jazz improvisation. We first introduce some facts about virtuosos coming from physiology, and then focus on the case of jazz. Automatic generation of improvisation has long been a subject of study for computer science, and many techniques have been proposed to generate music improvisation in various genres. The jazz style in particular abounds with programs that create improvisations of a reasonable level. However, no approach so far exhibits virtuosolevel performance. We describe an architecture for the generation of virtuoso bebop phrases which integrates novel music generation mechanisms in a principled way. We argue that modelling such outstanding phenomena can contribute substantially to the understanding of creativity in humans and machines. 5.1 Virtuosos as Exceptional Humans 5.1.1 Virtuosity in Art There is no precise definition of virtuosity, but only a commonly accepted view that virtuosos are human beings that excel in their practice to the point of exhibiting exceptional performance. Virtuosity exists in virtually all forms of human activity. In painting, several artists use virtuosity as a means to attract the attention of their audience. Felice Varini paints on urban spaces in such a way that there is a unique viewpoint from which a spectator sees the painting as a perfect geometrical figure. The F. Pachet ( ) Sony CSL-Paris, 6, rue Amyot, 75005 Paris, France e-mail: pachet@csl.sony.fr J. McCormack, M. d’Inverno (eds.), Computers and Creativity, DOI 10.1007/978-3-642-31727-9_5,
Pathway Graphical Lasso

Graphical models provide a rich framework for summarizing the dependencies among variables. The graphical lasso approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an l1 penalty on the elements of the inverse co-variance matrix. Most algorithms for solving the graphical lasso problem do not scale to a very large number of variables. Furthermore, the learned network structure is hard to interpret. To overcome these challenges, we propose a novel GGM structure learning method that exploits the fact that for many real-world problems we have prior knowledge that certain edges are unlikely to be present. For example, in gene regulatory networks, a pair of genes that does not participate together in any of the cellular processes, typically referred to as pathways, is less likely to be connected. In computer vision applications in which each variable corresponds to a pixel, each variable is likely to be connected to the nearby variables. In this paper, we propose the pathway graphical lasso, which learns the structure of a GGM subject to pathway-based constraints. In order to solve this problem, we decompose the network into smaller parts, and use a message-passing algorithm in order to communicate among the subnetworks. Our algorithm has orders of magnitude improvement in run time compared to the state-of-the-art optimization methods for the graphical lasso problem that were modified to handle pathway-based constraints.
Automation of attendance system using RFID, biometrics, GSM Modem with .Net framework

The purpose of this paper is to develop a wireless system to detect and maintain the attendance of a student and locate a student. For, this the students ID (identification) card is tagged with an Radio-frequency identification (RFID) passive tag which is matched against the database and only finalized once his fingerprint is verified using the biometric fingerprint scanner. The guardian is intimated by a sms (short message service) sent using the GSM (Global System for Mobile Communications) Modem of the same that is the student has reached the university or not on a daily basis, in the present day every guardian is worried whether his child has reached safely or not. In every classroom, laboratory, libraries, staffrooms etc. a RFID transponder is installed through which we will be detecting the location of the student and staff. There will be a website through which the student, teacher and the guardians can view the status of attendance and location of a student at present in the campus. A person needs to be located can be done by two means that is via the website or by sending the roll number of the student as an sms to the GSM modem which will reply by taking the last location stored of the student in the database.
Substrate-Integrated Waveguide Vertical Interconnects for 3-D Integrated Circuits

This paper presents and demonstrates a class of 3-D integration platforms of substrate-integrated waveguide (SIW). The proposed right angle E-plane corner based on SIW technology enables the implementation of various 3-D architectures of planar circuits with the printed circuit board and other similar processes. This design scheme brings up attractive advantages in terms of cost, flexibility, and integration. Two circuit prototypes with both 0- and 45° vertical rotated arms are demonstrated. The straight version of the prototypes shows 0.5 dB of insertion loss from 30 to 40 GHz, while the rotated version gives 0.7 dB over the same frequency range. With this H-to-E-plane interconnect, a T-junction is studied and designed. Simulated results show 20-dB return loss over 19.25% of bandwidth. Measured results suggest an excellent performance within the experimental frequency range of 32-37.4 GHz, with 10-dB return loss and less than ±4° phase imbalance. An optimized wideband magic-T structure is demonstrated and fabricated. Both simulated and measured results show a very promising performance with very good isolation and power equality. With two 45° vertical rotated arm bends, two antennas are used to build up a dual polarization system. An isolation of 20 dB is shown over 32-40 GHz and the radiation patterns of the antenna are also given.