nils-herrmann/clean_scirepeval huggingface dataset:
Additive manufacturing and mechanical characterization of graded porosity scaffolds designed based on triply periodic minimal surface architectures.

Since the advent of additive manufacturing techniques, triply periodic minimal surfaces have emerged as a novel tool for designing porous scaffolds. Whereas scaffolds are expected to provide multifunctional performance, spatially changing pore patterns have been a promising approach to integrate mechanical characteristics of different architectures into a unique scaffold. Smooth morphological variations are also frequently seen in nature particularly in bone and cartilage structures and can be inspiring for designing of artificial tissues. In this study, we carried out experimental and numerical procedures to uncover the mechanical properties and deformation mechanisms of linearly graded porosity scaffolds for two different mathematically defined pore structures. Among TPMS-based scaffolds, P and D surfaces were subjected to gradient modeling to explore the mechanical responses for stretching and bending dominated deformations, respectively. Moreover, the results were compared to their corresponding uniform porosity structures. Mechanical properties were found to be by far greater for the stretching dominated structure (P-Surface). For bending dominated architecture (D-Surface), although there was no global fracture for uniform structures, graded structure showed a brittle fracture at 0.08 strain. A layer by layer deformation mechanism for stretching dominated structure was observed. For bending dominated scaffolds, deformation was accompanied by development of 45° shearing bands. Finite element simulations were also performed and the results showed a good agreement with the experimental observations.
A Real-Time Malaysian Automatic License Plate Recognition ( M-ALPR ) using Hybrid Fuzzy

The Road Transport Department of Malaysia has endorsed a specification for car plates that includes the font and size of characters that must be followed by car owners. However, there are cases where this specification is not followed. This paper proposes a new methodology to segment and recognize Malaysian car license plates automatically. The proposed methodology solves the problem of segmenting different length licenses such as license with different number of character and number. There are two main objectives for this paper: first is to develop fuzzy rules to recognize the segmented characters and numbers from the same input-sets, which is the same size without overlapping between the characters and numbers sets. Secondly, this paper proposes a method to recognize non-standard plates by Template Matching theorem. Finally, the hybrid method of Fuzzy and Template matching is tested on 300 samples of car images captured in outdoor environment. The results yield 90.4% recognition accuracy, the Fuzzy based required 1.7 seconds and Template matching based took 0.75 seconds to perform the recognition. The adaptability factor of the hybrid method is also discussed.
To have or not to have Internet at home: Implications for online shopping

This paper analyzes the individual decision of online shopping, in terms of socioeconomic characteristics, internet related variables and location factors. Since online shopping is only observed for internet users, we use the two-step Heckmans model to correct sample selection. We argue that one of the relevant variables to explain online shopping, the existence of a home internet connection, can be endogenous. To account for this potential endogeneity, we jointly estimate the probability of online shopping and the probability of having internet at home. The dataset used in this paper comes from the Household Survey of ICT Equipment and Usage, conducted by the Spanish Statistical O¢ ce on an annual basis. Our analysis covers the period 2004-2009. Our results show that not accounting for the endogeneity of having internet at home, yields to overestimate its e¤ect on the probability of buying online. This nding can be important in the design of public policies aimed at enhancing e-commerce through providing households with internet connection at home. We also show that, compared to other variables that are also relevant for online shopping, the e¤ect of internet at home is quite small.
A Probabilistic Geocoding System based on a National Address File

It is estimated that between 80% and 90% of governmental and business data collections contain address information. Geocoding – the process of assigning geographic coordinates to addresses – is becoming increasingly important in many application areas that involve the analysis and mining of such data. In many cases, address records are captured and/or stored in a free-form or inconsistent manner. This fact complicates the task of robustly matching such addresses to spatiallyannotated reference data. In this paper we describe a geocoding system that is based on a comprehensive high-quality geocoded national address database. It uses a learning address parser based on hidden Markov models to separate free-form addresses into components, and a rule-based matching engine to determine the best set of candidate matches to a reference file. The geocoding software modules are implemented (as part of the Febrl open source data linkage system) in the object-oriented language Python, which allows rapid prototype development and testing.
Automatic music video generation based on temporal pattern analysis

Music video (MV) is a short film meant to present a visual representation of a popular music song. In this paper, we present a system that automatically generates MV-like videos from personal home videos based on observations that generally there are obvious repetitive visual and aural patterns in MVs. Based on a set of video and music analysis algorithms, the automatic music video (AMV) generation system automatically extracts temporal structures of the video and music, as well as repetitive patterns in the music. And then, according to the structure and patterns, a set of highlight segments from the raw home video footage are selected, aiming at matching the visual content with the aural structure and pattern. And last, the output music video is rendered by connecting the selected highlight video segments with appropriate transition effects, accompanied with the music. Experiments show that the results are compelling and promising.
Reflection Removal Using Low-Rank Matrix Completion

The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose a low-rank matrix completion algorithm to remove reflection artifacts automatically from multiple glass images taken at slightly different camera locations. We assume that the transmitted scenes are more dominant than the reflected scenes in typical glass images. We first warp the multiple glass images to a reference image, where the gradients are consistent in the transmission images while the gradients are varying across the reflection images. Based on this observation, we compute a gradient reliability such that the pixels belonging to the salient edges of the transmission image are assigned high reliability. Then we suppress the gradients of the reflection images and recover the gradients of the transmission images only, by solving a low-rank matrix completion problem in gradient domain. We reconstruct an original transmission image using the resulting optimal gradient map. Experimental results show that the proposed algorithm removes the reflection artifacts from glass images faithfully and outperforms the existing algorithms on typical glass images.
Multi-agent systems for protecting critical infrastructures: A survey

Multi-agent systems have emerged as a very significant platform in provisioning distributed and collaborative services to critical applications. Such applications require ubiquitous agent presence in the environment for monitoring, collecting data, communication, and subsequent data analysis, where the sensitivity of the application’s nature cannot be understated. Recent advances in the field of autonomous, ubiquitous, intelligent and distributed computing have led to corresponding developments in the use of collaborating multi-agents to protect critical infrastructures. Such systems have witnessed crucial demand for deployment in diverse application scenarios such as E-commerce, E-health, Network Intrusion Detection, Telematics and Transport Systems, Environmental Monitoring, as well as for distributed information processing in general. Critical infrastructures have longed for a distributed system in place for their uninterrupted and accurate operations. Multi-agents have provided one such approach towards addressing the issue of protecting such infrastructures through collaborative and distributed information processing. In this paper, a state-of-the-art on the use of multi-agent based systems for protecting five most common critical infrastructures, is presented. & 2012 Elsevier Ltd. All rights reserved.
RT-Mover: a rough terrain mobile robot with a simple leg-wheel hybrid mechanism

There is a strong demand in many fields for practical robots, such as a porter robot and a personal mobility robot, that can move over rough terrain while carrying a load horizontally. We have developed a robot, called RT-Mover, which shows adequate mobility performance on targeted types of rough terrain. It has four drivable wheels and two leg-like axles but only five active shafts. A strength of this robot is that it realizes both a leg mode and a wheel mode in a simple mechanism. In this paper, the mechanical design concept is discussed. With an emphasis on minimizing the number of drive shafts, a mechanism is designed for a four-wheeled mobile body that is widely used in practical locomotive machinery. Also, strategies for moving on rough terrain are proposed. The kinematics, stability, and control of RT-Mover are also described in detail. Some typical cases of rough terrain for wheel mode and leg mode are selected, and the robot’s ability of locomotion is assessed through simulations and experiments. In each case, the robot is able to move over rough terrain while maintaining the horizontal orientation of its platform.
Case Study On SYSML and VHDL-AMS for Designing and Validating Systems

An approach combining SysML and VHDL-AMS is proposed in this paper. The design is modeled with SysML and then we derive some intuitive rules to obtain the VHDLAMS model of the lower level blocks built in SysML. The work is at the level of the tentative approach that is being carried out on real industrial application for onboard systems. The paper goes beyond the models issues and carries out the simulation procedure that are available on tools to validate the design for the intended blocks.
Unemployment alters the set point for life satisfaction.

According to set-point theories of subjective well-being, people react to events but then return to baseline levels of happiness and satisfaction over time. We tested this idea by examining reaction and adaptation to unemployment in a 15-year longitudinal study of more than 24,000 individuals living in Germany. In accordance with set-point theories, individuals reacted strongly to unemployment and then shifted back toward their baseline levels of life satisfaction. However, on average, individuals did not completely return to their former levels of satisfaction, even after they became reemployed. Furthermore, contrary to expectations from adaptation theories, people who had experienced unemployment in the past did not react any less negatively to a new bout of unemployment than did people who had not been previously unemployed. These results suggest that although life satisfaction is moderately stable over time, life events can have a strong influence on long-term levels of subjective well-being.
Application of linear matrix inequalities for load frequency control with communication delays

Load frequency control has been used for decades in power systems. Traditionally, this has been a centralized control by area with communication over a dedicated and closed network. New regulatory guidelines allow for competitive markets to supply this load frequency control. In order to allow an effective market operation, an open communication infrastructure is needed to support an increasing complex system of controls. While such a system has great advantage in terms of cost and reliability, the possibility of communication signal delays and other problems must be carefully analyzed. This paper presents a load frequency control method based on linear matrix inequalities. The primary aim is to find a robust controller that can ensure good performance despite indeterminate delays and other problems in the communication network.
Virtual reality exposure therapy.

It has been proposed that virtual reality (VR) exposure may be an alternative to standard in vivo exposure. Virtual reality integrates real-time computer graphics, body tracking devices, visual displays, and other sensory input devices to immerse a participant in a computer-generated virtual environment. Virtual reality exposure is potentially an efficient and cost-effective treatment of anxiety disorders. VR exposure therapy reduced the fear of heights in the first controlled study of virtual reality in treatment of a psychiatric disorder. A case study supported the efficacy of VR exposure therapy for the fear of flying. The potential for virtual reality exposure treatment for these and other disorders is explored, and therapeutic issues surrounding the delivery of VR exposure are discussed.
Applications of SAT Solvers to AES Key Recovery from Decayed Key Schedule Images

Cold boot attack is a side channel attack which exploits the data remanence property of random access memory (RAM) to retrieve its contents which remain readable shortly after its power has been removed. Given the nature of the cold boot attack, only a corrupted image of the memory contents will be available to the attacker. In this paper, we investigate the use of an off-the-shelf SAT solver, CryptoMinSat, to improve the key recovery of the AES-128 key schedules from its corresponding decayed memory images. By exploiting the asymmetric decay of the memory images and the redundancy of key material inherent in the AES key schedule, rectifying the faults in the corrupted memory images of the AES-128 key schedule is formulated as a Boolean satisfiability problem which can be solved efficiently for relatively very large decay factors. Our experimental results show that this approach improves upon the previously known results.
Hyperspectral image unmixing using autoencoder cascade

Hyperspectral image unmixing is the process of estimating pure source signals (endmemebers) and their proportions (abundances) from highly mixed spectroscopic images. Due to model inaccuracies and observation noise, unmixing has been a very challenging problem. In this paper, we exploit the potential of using autoencoder to tackle the unmixing challenges. Two important facts are considered in the algorithm: first, the observation noise in the hyperspectral image generally exists and largely affects the unmixing results; second, the mixing process contains sparsity priori which should be considered to assist the endmember extraction. The proposed autoencoder cascade concatenates a marginalized denoising autoencoder and a non-negative sparse autoencoder to solve the unmixing problem which implicitly denoises the observation data and employs the self-adaptive sparsity constraint. The algorithm is tested on a set of synthetic mixtures and a real hyperspectral image. The experimental results demonstrate the proposed algorithm outperforms several advanced unmixing approaches in highly noisy environment.
Expanded identification and characterization of mammalian circular RNAs

The recent reports of two circular RNAs (circRNAs) with strong potential to act as microRNA (miRNA) sponges suggest that circRNAs might play important roles in regulating gene expression. However, the global properties of circRNAs are not well understood. We developed a computational pipeline to identify circRNAs and quantify their relative abundance from RNA-seq data. Applying this pipeline to a large set of non-poly(A)-selected RNA-seq data from the ENCODE project, we annotated 7,112 human circRNAs that were estimated to comprise at least 10% of the transcripts accumulating from their loci. Most circRNAs are expressed in only a few cell types and at low abundance, but they are no more cell-type-specific than are mRNAs with similar overall expression levels. Although most circRNAs overlap protein-coding sequences, ribosome profiling provides no evidence for their translation. We also annotated 635 mouse circRNAs, and although 20% of them are orthologous to human circRNAs, the sequence conservation of these circRNA orthologs is no higher than that of their neighboring linear exons. The previously proposed miR-7 sponge, CDR1as, is one of only two circRNAs with more miRNA sites than expected by chance, with the next best miRNA-sponge candidate deriving from a gene encoding a primate-specific zinc-finger protein, ZNF91. Our results provide a new framework for future investigation of this intriguing topological isoform while raising doubts regarding a biological function of most circRNAs.
The evolutionary impact of invasive species.

Since the Age of Exploration began, there has been a drastic breaching of biogeographic barriers that previously had isolated the continental biotas for millions of years. We explore the nature of these recent biotic exchanges and their consequences on evolutionary processes. The direct evidence of evolutionary consequences of the biotic rearrangements is of variable quality, but the results of trajectories are becoming clear as the number of studies increases. There are examples of invasive species altering the evolutionary pathway of native species by competitive exclusion, niche displacement, hybridization, introgression, predation, and ultimately extinction. Invaders themselves evolve in response to their interactions with natives, as well as in response to the new abiotic environment. Flexibility in behavior, and mutualistic interactions, can aid in the success of invaders in their new environment.
Design equations for the Quasi Yagi-Uda antenna operating in the UHF band

Design equations for the standard Quasi Yagi-Uda antenna built on 60 mil FR4 are presented to be used on fixed wireless telecommunication systems, Wi-Fi, WiMAX and LTE. In this work, the design equations are systematically proposed for the band between 1 GHz and 3 GHz, where relative bandwidth is better than 39%, the cross-polarization is lower than - 15dB for every angle and frequency, the cross-polarization is lower than -25dB in the main lobe and a front to back ratio better than 12dB in all the bandwidth. Three antennas where designed, built and measured with the proposed equations at 1.5GHz, 2GHz and 3 GHz, showing good agreement with the simulation results.
From goals to components: a combined approach to self-management

Autonomous or semi-autonomous systems are deployed in environments where contact with programmers or technicians is infrequent or undesirable. To operate reliably, such systems should be able to adapt to new circumstances on their own. This paper describes our combined approach for adaptable software architecture and task synthesis from high-level goals, which is based on a three-layer model. In the uppermost layer, reactive plans are generated from goals expressed in a temporal logic. The middle layer is responsible for plan execution and assembling a configuration of domain-specific software components, which reside in the lowest layer. Moreover, the middle layer is responsible for selecting alternative components when the current configuration is no longer viable for the circumstances that have arisen. The implementation demonstrates that the approach enables us to handle non-determinism in the environment and unexpected failures in software components.
Identification of MCI individuals using structural and functional connectivity networks

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.
Perfect Diffusion Primitives for Block Ciphers – Building Efficient MDS Matrices

Although linear perfect diffusion primitives, i.e. MDS matrices, are widely used in block ciphers, e.g. AES, very little systematic work has been done on how to find “efficient” ones. In this paper we attempt to do so by considering software implementations on various platforms. These considerations lead to interesting combinatorial problems: how to maximize the number of occurrences of 1 in those matrices, and how to minimize the number of pairwise different entries. We investigate these problems and construct efficient 4×4 and 8×8 MDS matrices to be used e.g. in block ciphers.
Attacking an AES-Enabled NFC Tag: Implications from Design to a Real-World Scenario

Radio-frequency identification (RFID) technology is the enabler for applications like the future internet of things (IoT), where security plays an important role. When integrating security to RFID tags, not only the cryptographic algorithms need to be secure but also their implementation. In this work we present differential power analysis (DPA) and differential electromagnetic analysis (DEMA) attacks on a securityenabled RFID tag. The attacks are conducted on both an ASIC-chip version and on an FPGA-prototype version of the tag. The design of the ASIC version equals that of commercial RFID tags and has analog and digital part integrated on a single chip. Target of the attacks is an implementation of the Advanced Encryption Standard (AES) with 128-bit key length and DPA countermeasures. The countermeasures are shuffling of operations and insertion of dummy rounds. Our results illustrate that the effort for successfully attacking the ASIC chip in a real-world scenario is only 4.5 times higher than for the FPGA prototype in a laboratory environment. This let us come to the conclusion that the effort for attacking contactless devices like RFID tags is only slightly higher than that for contact-based devices. The results further underline that the design of countermeasures like the insertion of dummy rounds has to be done with great care, since the detection of patterns in power or electromagnetic traces can be used to significantly lower the attacking effort.