nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Domain Learning: When Do Domains Matter?

We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multidomain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multidomain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multidomain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.
On the role of file system metadata in digital forensics

Most of the effort in today’s digital forensics community lies in the retrieval and analysis of existing information from computing systems. Little is being done to increase the quantity and quality of the forensic information on today’s computing systems. In this paper we pose the question of what kind of information is desired on a system by a forensic investigator. We give an overview of the information that exists on current systems and discuss its shortcomings. We then examine the role that file system metadata plays in digital forensics and analyze what kind of information is desirable for different types of forensic investigations, how feasible it is to obtain it, and discuss issues about storing the information.
Comprehensive lower eyelid rejuvenation.

Historically, lower eyelid blepharoplasty has been a challenging surgery fraught with many potential complications, ranging from ocular irritation to full-blown lower eyelid malposition and a poor cosmetic outcome. The prevention of these complications requires a detailed knowledge of lower eyelid anatomy and a focused examination of the factors that may predispose to poor outcome. A thorough preoperative evaluation of lower eyelid skin, muscle, tone, laxity, fat prominence, tear trough deformity, and eyelid vector are critical for surgical planning. When these factors are analyzed appropriately, a natural and aesthetically pleasing outcome is more likely to occur. I have found that performing lower eyelid blepharoplasty in a bilamellar fashion (transconjunctivally to address fat prominence and transcutaneously for skin excision only), along with integrating contemporary concepts of volume preservation/augmentation, canthal eyelid support, and eyelid vector analysis, has been an integral part of successful surgery. In addition, this approach has significantly increased my confidence in attaining more consistent and reproducible results.
Cellular texture generation

We propose an approach for modeling surface details such as scales, feathers, or thorns. These types of cellular textures require a representation with more detail than texture-mapping but are inconvenient to model with hand-crafted geometry. We generate patterns of geometric elements using a biologically-motivated cellular development simulation together with a constraint to keep the cells on a surface. The surface may be defined by an implicit function, a volume dataset, or a polygonal mesh. Our simulation combines and extends previous work in developmental models and constrained particle systems.
Current trends on ICT technologies for enterprise information systems

As business conditions change rapidly, the need for integrating business and technical systems calls for novel ICT frameworks and solutions to remain concurrent in highly competitive markets. A number of problems and issues arise in this regard. In this paper, four big challenges of enterprise information systems (EIS) are defined and discussed: (1) data value chain management; (2) context awareness; (3) usability, interaction and visualization; and (4) human learning and continuous education. Major contributions and research orientations of ICT technologies are elaborated based on selected key issues and lessons learned. First, the semantic mediator is proposed as a key enabler for dealing with semantic interoperability. Second, the context-aware infrastructures are proposed as a main solution for making efficient use of EIS to offer a high level of customization of delivered services and data. Third, the product avatar is proposed as a contribution to an evolutionary social, collaborative and product-centric and interaction metaphor with EIS. Fourth, human learning solutions are considered to develop individual competences in order to cope with new technological advances. The paper ends with a discussion on the impact of the proposed solutions on the economic and social landscape and proposes a set of recommendations as a perspective towards next generation of information systems.
A Hardware Gaussian Noise Generator for Channel Code Evaluation

Hardware simulation of channel codes offers the potential of improving code evaluation speed by orders of magnitude over workstationor PC-based simulation. We describe a hardware-based Gaussian noise generator used as a key component in a hardware simulation system, for exploring channel code behavior at very low bit error rates (BERs) in the range of 10−9 to 10−10. The main novelty is the design and use of non-uniform piecewise linear approximations in computing trigonometric and logarithmic functions. The parameters of the approximation are chosen carefully to enable rapid computation of coefficients from the inputs, while still retaining extremely high fidelity to the modelled functions. The output of the noise generator accurately models a true Gaussian PDF even at very high σ values. Its properties are explored using: (a) several different statistical tests, including the chi-square test and the Kolmogorov-Smirnov test, and (b) an application for decoding of low density parity check (LDPC) codes. An implementation at 133MHz on a Xilinx Virtex-II XC2V4000-6 FPGA produces 133 million samples per second, which is 40 times faster than a 2.13GHz PC; another implementation on a Xilinx Spartan-IIE XC2S300E-7 FPGA at 62MHz is capable of a 20 times speedup. The performance can be improved by exploiting parallelism: an XC2V4000-6 FPGA with three parallel instances of the noise generator at 126MHz can run 100 times faster than a 2.13GHz PC. We illustrate the deterioration of clock speed with the increase in the number of instances.
Robust Deep Auto-encoder for Occluded Face Recognition

Occlusions by sunglasses, scarf, hats, beard, shadow etc, can significantly reduce the performance of face recognition systems. Although there exists a rich literature of researches focusing on face recognition with illuminations, poses and facial expression variations, there is very limited work reported for occlusion robust face recognition. In this paper, we present a method to restore occluded facial regions using deep learning technique to improve face recognition performance. Inspired by SSDA for facial occlusion removal with known occlusion type and explicit occlusion location detection from a preprocessing step, this paper further introduces Double Channel SSDA (DC-SSDA) which requires no prior knowledge of the types and the locations of occlusions. Experimental results based on CMU-PIE face database have showed that, the proposed method is robust to a variety of occlusion types and locations, and the restored faces could yield significant recognition performance improvements over occluded ones.
Critical Success Factors for Implementing Business Intelligence Systems in Small and Medium Enterprises on the Example of Upper Silesia , Poland

The main objective of this article is to identify the critical success factors (CSFs) for Business Intelligence (BI) systems implementation in small and medium enterprises (SMEs).The structure of the article is subordinated to this objective. The paper identifies the term Business Intelligence, characteristics of Business Intelligence systems, and various perspectives of their development. Then, the existing CSFs of IT projects and BI projects proposed by various authors in literature are reviewed. Next, on the basis of statistical data and literature, the role of SMEs in the economy and the barriers to their development are assessed. The obtained results allowed us to determine that one of the barriers to the SMEs development is the implementation and use of IT. Subsequently, using in-depth interviews with SMEs, the SMEs need for BI systems as well as the determinants and barriers to their implementation are recognized. Based on the findings, using critical thinking and inductive reasoning, the authors of the article along with the researched enterprises have defined CSFs that are crucial for implementing BI systems in SMEs. The results obtained may be useful for managers, policy makers, business analysts, and IT specialists in dealing with planning and implementation of BI systems in SMEs.
Multi-attribute decision making: A simulation comparison of select methods

Several methods have been proposed for solving multi-attribute decision making problems (MADM). A major criticism of MADM is that different techniques may yield different results when applied to the same problem. The problem considered in this study consists of a decision matrix input of N criteria weights and ratings of L alternatives on each criterion. The comparative performance of some methods has been investigated in a few, mostly field, studies. In this simulation experiment we investigate the performance of eight methods: ELECTRE, TOPSIS, Multiplicative Exponential Weighting (MEW), Simple Additive Weighting (SAW), and four versions of AHP (original vs. geometric scale and right eigenvector vs. mean transformation solution). Simulation parameters are the number of alternatives, criteria and their distribution. The solutions are analyzed using twelve measures of similarity of performance. Similarities and differences in the behavior of these methods are investigated. Dissimilarities in weights produced by these methods become stronger in problems with few alternatives; however, the corresponding final rankings of the alternatives vary across methods more in problems with many alternatives. Although less significant, the distribution of criterion weights affects the methods differently. In general, all AHP versions behave similarly and closer to SAW than the other methods. ELECTRE is the least similar to SAW (except for closer matching the top-ranked alternative), followed by MEW. TOPSIS behaves closer to AHP and differently from ELECTRE and MEW, except for problems with few criteria. A similar rank-reversal experiment produced the following performance order of methods: SAW and MEW (best), followed by TOPSIS, AHPs and ELECTRE. It should be noted that the ELECTRE version used was adapted to the common MADM problem and therefore it did not take advantage of the method’s capabilities in handling problems with ordinal or imprecise information.
Prefuse: a Toolkit for Interactive Information Visualization

Although information visualization (infovis) technologies have proven indispensable tools for making sense of complex data, wide-spread deployment has yet to take hold, as successful infovis applications are often difficult to author and require domain-specific customization. To address these issues, we have created prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data. prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations. To evaluate prefuse we have built both existing and novel visualizations testing the toolkit's flexibility and performance, and have run usability studies and usage surveys finding that programmers find the toolkit usable and effective.
Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid

Advanced metering infrastructure (AMI) is an important component for a smart grid system to measure, collect, store, analyze, and operate users consumption data. The need of communication and data transmission between consumers (smart meters) and utilities make AMI vulnerable to various attacks. In this paper, we focus on distributed denial of service attack in the AMI network. We introduce honeypots into the AMI network as a decoy system to detect and gather attack information. We analyze the interactions between the attackers and the defenders, and derive optimal strategies for both sides. We further prove the existence of several Bayesian-Nash equilibriums in the honeypot game. Finally, we evaluate our proposals on an AMI testbed in the smart grid, and the results show that our proposed strategy is effective in improving the efficiency of defense with the deployment of honeypots.
Dual-Band Circularly Polarized Annular-Ring Microstrip Antenna for GNSS Applications

A dual-band circularly polarized antenna fed by four apertures that covers the bands of GPS (L1, L2, L5), Galileo (E5a, E5b, E1, E2, L1), and GLONASS (L1, L3) is introduced. A lotus-shaped aperture is added to optimize the coupling between the microstrip lines and the rings. Three wideband planar baluns are used to achieve good axial ratio (lower than 2.1 dB in both bands) and VSWR (41.2%). The measured results of the annular-ring microstrip antenna show good performance of a dual-band operation, and they confirm the validity of this design, which meets the requirement of Global Navigation Satellite System (GNSS) applications.
Towards SDN-based fog computing: MQTT broker virtualization for effective and reliable delivery

Performance of data analytics in Internet of Things (IoTs) depends on effective transport services offered by the underlying network. Fog computing enables independent data-plane computational features at the edge-switches, which serves as a platform for performing certain critical analytics required at the IoT source. To this end, in this paper, we implement a working prototype of Fog computing node based on Software-Defined Networking (SDN). Message Queuing Telemetry Transport (MQTT) is chosen as the candidate IoT protocol that transports data generated from IoT devices (a:k:a: MQTT publishers) to a remote host (called MQTT broker). We implement the MQTT broker functionalities integrated at the edge-switches, that serves as a platform to perform simple message-based analytics at the switches, and also deliver messages in a reliable manner to the end-host for post-delivery analytics. We mathematically validate the improved delivery performance as offered by the proposed switch-embedded brokers.
Planning, Scheduling and Monitoring for Airport Surface Operations

This paper explores the problem of managing movements of aircraft along the surface of busy airports. Airport surface management is a complex logistics problem involving the coordination of humans and machines. The work described here arose from the idea that autonomous towing vehicles for taxiing aircraft could offer a solution to the ’capacity problem’ for busy airports, the problem of getting more efficient use of existing surface area to meet increasing demand. Supporting autonomous surface operations requires continuous planning, scheduling and monitoring of operations, as well as systems for optimizing complex human-machine interaction. We identify a set of computational subproblems of the surface management problem that would benefit from recent advances in multi-agent planning and scheduling and probabilistic predictive modeling, and discuss preliminary work at integrating these components into a prototype of a surface
Dense Visual SLAM with Probabilistic Surfel Map

Visual SLAM is one of the key technologies to align the virtual and real world together in Augmented Reality applications. RGBD dense Visual SLAM approaches have shown their advantages in robustness and accuracy in recent years. However, there are still several challenges such as the inconsistencies in RGBD measurements across multiple frames that could jeopardize the accuracy of both camera trajectory and scene reconstruction. In this paper, we propose a novel map representation called Probabilistic Surfel Map (PSM) for dense visual SLAM. The main idea is to maintain a globally consistent map with both photometric and geometric uncertainties encoded in order to address the inconsistency issue. The key of our PSM is proper modeling and updating of sensor measurement uncertainties, as well as the strategies to apply them for improving both the front-end pose estimation and the back-end optimization. Experimental results on publicly available datasets demonstrate major improvements with our approach over the state-of-the-art methods. Specifically, comparing with <inline-formula><tex-math notation="LaTeX">$\sigma$</tex-math><alternatives><inline-graphic xlink:href="23tvcg11-yan-2734458-ieq-1-source.gif"/></alternatives></inline-formula>-DVO, we achieve a 40% reduction in absolute trajectory error and an 18% reduction in relative pose error in visual odometry, as well as an 8.5% reduction in absolute trajectory error in complete SLAM. Moreover, our PSM enables generation of a high quality dense point cloud with comparable accuracy as the state-of-the-art approach.
A New Reconstruction Filter for Undersampled Light Fields

This paper builds on previous research in the light field area of image-based rendering. We present a new reconstruction filter that significantly reduces the “ghosting” artifacts seen in undersampled light fields, while preserving important high-fidelity features such as sharp object boundaries and view-dependent reflectance. By improving the rendering quality achievable from undersampled light fields, our method allows acceptable images to be generated from smaller image sets. We present both frequency and spatial domain justifications for our techniques. We also present a practical framework for implementing the reconstruction filter in multiple rendering passes. CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation ― Viewing algorithms; I.3.6 [Computer Graphics]: Methodologies and Techniques ― Graphics data structures and data types; I.4.1 [Image Processing and Computer  Digitization and Image Capture ― Sampling
Semantic and Verbatim Word Spotting Using Deep Neural Networks

In the last few years, deep convolutional neural networks have become ubiquitous in computer vision, achieving state-of-the-art results on problems like object detection, semantic segmentation, and image captioning. However, they have not yet been widely investigated in the document analysis community. In this paper, we present a word spotting system based on convolutional neural networks. We train a network to extract a powerful image representation, which we then embed into a word embedding space. This allows us to perform word spotting using both query-by-string and query-by-example in a variety of word embedding spaces, both learned and handcrafted, for verbatim as well as semantic word spotting. Our novel approach is versatile and the evaluation shows that it outperforms the previous state-of-the-art for word spotting on standard datasets.
Design and Implementation of Indian Paper Currency Authentication System Based on Feature Extraction by Edge Based Segmentation Using

–Automated paper currency recognition system can be a very good utility in banking systems and other field of commerce. Since many years counterfeiting of paper currency challenges the financial system of every country in different sectors, India is also one of them. In this article, recognition of paper currency with the help of digital image processing techniques is described. Three characteristics of Indian paper currency is selected for counterfeit detection included identification mark, security thread and watermark. The characteristics extraction is performed on the image of the currency and it is compared with the characteristics of the genuine currency. The sobel operator with gradient magnitude is used for characteristic extraction. Paper currency recognition with good accuracy and high processing speed has great importance for banking system. The proposed method has advantages of simplicity and high speed. The experimental results show that this approach is effective and efficient and clearly meet the system requirements. Keywords––Paper currency recognition, Characteristics extraction, Digital image processing, Counterfeit detection, Sobel operator with gradient magnitude.
Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.
Simple Realizations of Current-Mode and Voltage-Mode PID, PI and PD Controllers

This paper presents new circuits for realizing both current-mode and voltage-mode proportional-integralderivative (PID), proportional-derivative (PD) and proportional-integral (PI) controllers employing secondgeneration current conveyors (CCIIs) as active elements. All of the proposed PID, PI and PD controllers have grounded passive elements and adjustable parameters. The controllers employ reduced number of active and passive components with respect to the traditional op-amp-based PID, PI and PD controllers. A closed loop control system using the proposed PID controller is designed and simulated with SPICE.
Crystallize: An Immersive, Collaborative Game for Second Language Learning

Learning a second language is challenging. Becoming fluent requires learning contextual information about how language should be used as well as word meanings and grammar. The majority of existing language learning applications provide only thin context around content. In this paper, we present Crystallize, a collaborative 3D game that provides rich context along with scaffolded learning and engaging gameplay mechanics. Players collaborate through joint tasks, or quests. We present a user study with 42 participants that examined the impact of low and high levels of task interdependence on language learning experience and outcomes. We found that requiring players to help each other led to improved collaborative partner interactions, learning outcomes, and gameplay. A detailed analysis of the chat-logs further revealed that changes in task interdependence affected learning behaviors.