nils-herrmann/clean_scirepeval huggingface dataset:
Factor-Based Asset Allocation vs . Asset-Class-Based Asset Allocation

This article addresses the issue of the alleged superiority of risk-factor-based asset allocations over the more traditional asset-class-based asset allocation. The authors used both an idealized model, capable of precise mathematical treatment, and optimizations based on different periods of historical data to show that neither approach is inherently superior to the other. Although the authors appreciate the role of risk models in portfolio management, they urge caution with respect to unwarranted claims of their dominance.
CrimeNet explorer: a framework for criminal network knowledge discovery

Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminal-justice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we proposed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling. Results from controlled experiments involving student subjects demonstrated that our system could achieve higher clustering recall and precision than did untrained subjects when detecting subgroups from criminal networks. Moreover, subjects identified central members and interaction patterns between groups significantly faster with the help of structural analysis functionality than with only visualization functionality. No significant gain in effectiveness was present, however. Our domain experts also reported that they believed CrimeNet Explorer could be very useful in crime investigation.
Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.
Effects and mechanisms of working memory training: a review.

Can cognitive abilities such as reasoning be improved through working memory training? This question is still highly controversial, with prior studies providing contradictory findings. The lack of theory-driven, systematic approaches and (occasionally serious) methodological shortcomings complicates this debate even more. This review suggests two general mechanisms mediating transfer effects that are (or are not) observed after working memory training: enhanced working memory capacity, enabling people to hold more items in working memory than before training, or enhanced efficiency using the working memory capacity available (e.g., using chunking strategies to remember more items correctly). We then highlight multiple factors that could influence these mechanisms of transfer and thus the success of training interventions. These factors include (1) the nature of the training regime (i.e., intensity, duration, and adaptivity of the training tasks) and, with it, the magnitude of improvements during training, and (2) individual differences in age, cognitive abilities, biological factors, and motivational and personality factors. Finally, we summarize the findings revealed by existing training studies for each of these factors, and thereby present a roadmap for accumulating further empirical evidence regarding the efficacy of working memory training in a systematic way.
A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches

Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajökull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events.  Hooper, A., (2008), A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches,  L16302, doi:10.1029/ 2008GL034654.
Homotopic path validation using non-holonomic differential drive robot model

In this paper the tracking of collision-free path obtained by using Homotopic Path Planning Method (HPPM) with automatic value assignation of repulsion parameter is presented. This work is center on show the utility of this method to obtain a collision free path for maps with narrow corridors on indoor environments. First, the basis of homotopic path planning method with automatic value assignation of repulsion parameter are explained. Second, the Pure-Pursuit controller is brief explained. Third, the capability of the HPPM is shown by using two experiments that which contain narrow passages and several circular obstacles. Finally, in order to validate the solution paths, a tracking on Robot Operating System by using 3P-DX robot model and on MATLAB using a Pure-Pursuit controller from the Robotics Toolbox is performed.
Are augmented reality picture books magic or real for preschool children aged five to six?

The aim of this study is to determine preschool children’s attitudes towards augmented reality picture books (ARPB), their story comprehension performance (SCP) and the relationships between these variables. The sample consisted of 92 fiveand six-year-olds (49 boys, 43 girls). An attitude form, story comprehension test and interview form were used as data collection tools. Most of the children reported feeling “very happy” with the activity and enjoyed using the ARPB, which they found interesting and fun. The children also delivered strong story SCP. Their happiness was found to affect their SCP, while their enjoyment did not. ARPB are attractive and evocative for children, who perceive them as magic and more enjoyable than conventional books. This study demonstrates how they can be used as effective educational tools to improve preschool children’s cognition and listening skills.  Technology is developing at a rapid pace that has dramatically affected education. With one new technology used in education, augmented reality (AR), students interact with virtual and real worlds in real time (Azuma, 1997, 2004). As the educational potential of AR technology is beginning to be researched, numerous applications are being designed. One of the most wellknown AR educational applications is the MagicBook (Billinghurst, Kato, & Poupyrev, 2001). The MagicBook looks like a normal book, but AR markers are included in the interface. Users look at pictures, turn pages and read text without any external technology. With AR displays, however, users can view multimedia materials such as three-dimensional (3D) virtual models or videos and manipulate virtual objects using real physical markers and natural motions (Billinghurst et al., 2001). This interaction is an interesting way to bridge real and virtual worlds. Physical objects provide natural interactions compared with traditional computer input devices (Bujak et al., 2013). Further, the appearance of 3D objects on the pages creates a magical feeling that captures and keeps the attention of students during learning. The creation of these books is cost-effective and simple, requiring only proper AR software and a webcam-enabled PC VC 2016 British Educational Research Association British Journal of Educational Technology Vol 48 No 3 2017 824–841 doi:10.1111/bjet.12452
Transaction-Cost Economics: The Governance of Contractual Relations

Transaction- The Governance of Contractual Relations Author(s): Oliver E. Williamson Reviewed work(s): Source: Journal of Law and Economics, Vol. 22, No. 2 (Oct., 1979), pp. 233-261  The University of Chicago Press for The Booth School of Business of the University of Chicago and The University of Chicago Law School Stable URL: http://www.jstor.org/stable/725118 . Accessed: 07/01/2013 16:49
Mobile Location Prediction in Spatio-Temporal Context

The increasing use of mobile devices and popular mobile services has led to massive availability of mobile data. Location prediction is a specific topic in mobile data mining, with its potential application in traffic planning, location-base advertisement, and user oriented coupon dispersion. Traditional location prediction methods often separately consider spatial or temporal approach. Although there have been some attempts to integrate both spatial and temporal information for location prediction, most of them suffer from the overfitting problem due to the large number of spatio-temporal trajectory patterns. Therefore, smoothing techniques are indispensable for proper training of spatio-temporal models. In this paper, we propose a novel location prediction model to capture the spatio-temporal context of user visits. It considers not only the spatial historical trajectories, but also the temporal periodic patterns. By applying smoothing techniques on both patterns, our model obtains significant improvement compared to the state-of-the-art approaches.
Agile software development: Impact on productivity and quality

Agile means swift, active and responsive and this is what agile software development methodology refers to. Agile software development methodologies are widely accepted these days. It is an iterative approach to keep pace with dynamic development environments. Traditional software development methods are not efficient enough to convene with the rapid change in requirements and short iterations that are required for efficient product delivery. This study contains a quantitative analysis of the agile methodologies in practice, and the benefits associated with them. Analyses are done on a defined set of attributes that result in increased productivity and improved quality through the application of these methodologies. A development model has also been derived from empirical research conducted in this paper.
Unexpected failure in power-rail ESD clamp circuits of CMOS integrated circuits in microelectronics systems during electrical fast transient (EFT) test and the re-design solution

Four different on-chip power-rail electrostatic discharge (ESD) clamp circuits have been designed to investigate their susceptibility to electrical fast transient (EFT) test. From the experimental results, the feedback loop in two kinds of on-chip power-rail ESD clamp circuits provides the lock function to perform a latchup-like failure after the EFT test. The re-design solution will be developed to overcome this issue to meet the regulation of EFT/EMC test.
A Blockchain-Based Approach to Health Information Exchange Networks

Sharing healthcare data between institutions is challenging. Heterogeneous data structures may preclude compatibility, while disparate use of healthcare terminology limits data comprehension. Even if structure and semantics could be agreed upon, both security and data consistency concerns abound. Centralized data stores and authority providers are attractive targets for cyber attack, and establishing a consistent view of the patient record across a data sharing network is problematic. In this work we present a Blockchain-based approach to sharing patient data. This approach trades a single centralized source of trust in favor of network consensus, and predicates consensus on proof of structural and semantic interoperability.
Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network

This paper presents a new fusion scheme for the CT and MR medical images that utilizes both the features of the nonsubsampled shearlet transform (NSST) and spiking neural network. As a new image representation with the different features, the NSST is utilized to provide an effective representation of the image coefficients. Firstly, the source CT and MR images are decomposed by the NSST into several subimages. The regional energy is used to fuse the low frequency coefficients. High frequency coefficients are also fused using a pulse coupled neural network model that is used as a biologically inspired type neural network. It also retains the edges and detail information from the source images. Finally, the inverse NSST is agnetic resonance (MR) onsubsampled shearlet transform (NSST) ulse coupled neural network used to produce the fused image. The performance of the proposed fusion method is evaluated by conducting several experiments on the different CT and MR medical image datasets. Experimental results demonstrate that the proposed method does not only produce better results by successfully fusing the different CT and MR images, but also ensures an improvement in the various quantitative parameters as compared to other existing methods.
Automated Trajectory Planner of Industrial Robot for Pick-and-Place Task

Industrial  robots,  due  to  their  great  speed, precision  and  cost‐effectiveness  in  repetitive  tasks,  now tend  to be used  in place of human workers  in automated manufacturing  systems.  In  particular,  they  perform  the pick‐and‐place  operation,  a  non‐value‐added  activity which  at  the  same  time  cannot  be  eliminated.  Hence, minimum time is an important consideration for economic reasons  in  the  trajectory  planning  system  of  the manipulator.  The  trajectory  should  also  be  smooth  to handle  parts  precisely  in  applications  such  as semiconductor manufacturing, processing and handling of chemicals and medicines, and fluid and aerosol deposition. In this paper, an automated trajectory planner is proposed to determine  a  smooth, minimum‐time  and  collision‐free trajectory  for  the  pick‐and‐place  operations  of  a  6‐DOF robotic  manipulator  in  the  presence  of  an  obstacle. Subsequently,  it  also  proposes  an  algorithm  for  the  jerk‐ bounded  Synchronized  Trigonometric  S‐curve  Trajectory (STST)  and  the  ‘forbidden‐sphere’  technique  to  avoid  the obstacle.  The  proposed  planner  is  demonstrated  with suitable examples and comparisons. The experiments show that  the  proposed  planner  is  capable  of  providing  a smoother trajectory than the cubic spline based trajectory.
Automated Verification of Electrum Wallet

We introduce a formal modeling in ASLan++ of the twofactor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify i) new intruder deduction rules with clauses and ii) non-deducibility constraints.
Exploration of adaptive gait patterns with a reconfigurable linkage mechanism

Legged robots are able to move across irregular terrains and some can be energy efficient, but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This paper reports a reconfigurable design approach to robotic legged locomotion that produces a wide variety of gait cycles, opening new possibilities for innovative applications. In this paper, we present a distance-based formulation and its application to solve the position analysis problem of a standard Theo Jansen mechanism. By changing the configuration of a linkage, our objective in this study is to identify novel gait patterns of interest for a walking platform. The exemplary gait variations presented in this work demonstrate the feasibility of our approach, and considerably extend the capabilities of the original design to not only produce novel cum useful gait patterns but also to realize behaviors beyond locomotion.
A 65 nm CMOS 4-Element Sub-34 mW/Element 60 GHz Phased-Array Transceiver

This paper describes a low power and element-scalable 60 GHz 4-element phased array transceiver implemented in a standard 65 nm CMOS process. Using a 1.2 V supply, the array consumes <;34 mW/element including LO synthesis and distribution. Energy and area efficiency are achieved by utilizing a baseband phase shifting architecture, holistic impedance optimization, and lumped-element based design. Each receiver (RX) element provides 24 dB of gain with an average noise figure (NF) of 6.8 dB while the total saturated output power of the transmitter (TX) is 4.5 dBm. The array achieves 360° of phase shifting range with a worst-case measured phase resolution of 6 bits (TX)/ 5 bits (RX) while maintaining amplitude variations less than ±0.5 dB.
A Highly Efficient Runtime and Graph Library for Large-Scale Graph Analytics

Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing systems have emerged targeting various perspectives of a graph application such as in memory and on disk representations, persistent storage, database capability, runtimes and execution models for exploiting parallelism, etc.
 In this paper we discuss a novel graph processing system called System G Native Store which allows for efficient graph data organization and processing on modern computing architectures. In particular we describe a runtime designed to exploit multiple levels of parallelism and a generic infrastructure that allows users to express graphs with various in memory and persistent storage properties. We experimentally show the efficiency of System G Native Store for processing graph queries on state-of-the-art platforms.
Semantics Extraction from Images

An overview of the state-of-the-art on semantics extraction from images is presented. In this survey, we present the relevant approaches in terms of content representation as well as in terms of knowledge representation. Knowledge can be represented in either implicit or explicit fashion while the image is represented in different levels, namely, low-level, intermediate and semantic level. For each combination of knowledge and image representation, a detailed discussion is addressed that leads to fruitful conclusions for the impact of each approach. 1 Semantics extraction basic pipeline Semantics extraction refers to digital data interpretation from a human point of view. In the case that the digital data correspond to images, this usually entails an appearance-based inference using color, texture and/or shape information along with a type of context inference (or representation) that can combine and transform these machine-extracted evidence into what we call a scene description. Following Biederman et al.  definitions of context in a visual scene, we can derive three types of context for real-world scene annotation problems: (i) semantic context which encodes the probability of a certain category to be present in a scene (e.g category “streets” has high probability to coexist with category “building” in the same scene ); (ii) spatial context which encodes the spatial relations of categories (e.g sky is usually above grass in a scene) and (iii) scale context which encodes the relative object size (category “human” is expected to occupy a small region in a scene which includes the“building” category). The research goals in semantics extraction are mostly a function of the granularity of the semantics in question. The goal could be the extraction of a single or multiple semantics of the entire image (e.g. indoor/outdoor setting), or the extraction of the semantics for different objects in an image. In the latter case, the semantics could be generic (e.g. a vehicle) or specific (e.g. a motorbike). Those goals make it clear that semantics extraction is not a new research area. Depending on the goal, the task of semantics extraction can be considered as a categorization, classification, recognition and understanding task that all share in common the effort for solving the semantic gap. As stated in , “Despite intensive recent research, the automatic establishment of a correspondence between
E-mail Header Injection Vulnerabilities

E-mail Header Injection vulnerability is a class of vulnerability that can occur in web applications that use user input to construct e-mail messages. E-mail Header Injection is possible when the mailing script fails to check for the presence of e-mail headers in user input (either form fields or URL parameters). The vulnerability exists in the reference implementation of the built-in mail functionality in popular languages such as PHP, Java, Python, and Ruby. With the proper injection string, this vulnerability can be exploited to inject additional headers, modify existing headers, and alter the content of the e-mail.  Security and privacy → Software and application security → Web application security
Metaphoric structuring: understanding time through spatial metaphors

The present paper evaluates the claim that abstract conceptual domains are structured through metaphorical mappings from domains grounded directly in experience. In particular, the paper asks whether the abstract domain of time gets its relational structure from the more concrete domain of space. Relational similarities between space and time are outlined along with several explanations of how these similarities may have arisen. Three experiments designed to distinguish between these explanations are described. The results indicate that (1) the domains of space and time do share conceptual structure, (2) spatial relational information is just as useful for thinking about time as temporal information, and (3) with frequent use, mappings between space and time come to be stored in the domain of time and so thinking about time does not necessarily require access to spatial schemas. These findings provide some of the first empirical evidence for Metaphoric Structuring. It appears that abstract domains such as time are indeed shaped by metaphorical mappings from more concrete and experiential domains such as space.