nils-herrmann/clean_scirepeval huggingface dataset:
A Pairwise Document Analysis Approach for Monolingual Plagiarism Detection

The task of plagiarism detection entails two main steps, suspicious candidate retrieval and pairwise document similarity analysis also called detailed analysis. In this paper we focus on the second subtask. We will report our monolingual plagiarism detection system which is used to process the Persian plagiarism corpus for the task of pairwise document similarity. To retrieve plagiarised passages a plagiarism detection method based on vector space model, insensitive to context reordering, is presented. We evaluate the performance in terms of precision, recall, granularity and plagdet metrics.
Metal object detection circuit with non-overlapped coils for wireless EV chargers

In the inductive power transfer (IPT) system operation, overheating of metal objects caused by eddy current may cause ignition, resulting in fire or damages to the equipment. Therefore, a metal object detection (MOD) circuit with non-overlapped coils to ensure safe operation of a wireless EV chargers is proposed in this paper. The proposed MOD circuit is composed of a variable resistor, a low pass filter, and a precision rectifier with amplification for accurate data acquisition. Influence of metal object under alternating magnetic field is described with respect to various thickness and types of metal objects. The proposed MOD circuit was experimentally verified using three non-overlapped coils and a 4cm × 4cm metal object, where its output voltage was changed from 200 mV to 1V. The MOD circuit can detect multiple metal objects at the same time and is suitable for high power wireless EV chargers.
A knowledge-based system for breast cancer classification using fuzzy logic method

Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.
Blockchain Technology in Business Organizations: A Scoping Review

The scientific literature on blockchain technology is emerging but increasing rapidly. This review paper aims to provide a deeper understanding of the nature and scope of the extant literature on blockchain technology in the particular context of business organizations. To achieve our main objective, we searched five databases and screened 320 papers for inclusion. As a result of the search and screen process, we identified 39 relevant articles. Data coding was first pilot tested and then performed independently by two teams of researchers. All disagreements were reconciled by a third coder. Our findings reveal that most of the extant literature focuses on “how” blockchain technology works and, to a lesser extent, on the “what”, i.e. its potential applications and usages in business organizations. For its part, the “why” question, which focuses on the organizational motivations for adopting blockchain technology, was scarcely discussed in prior literature. In short, our findings reveal that many issues and questions remain to be investigated. Based on a gap analysis, we propose a few promising avenues that shall guide future research efforts in this important topic.
A competition on generalized software-based face presentation attack detection in mobile scenarios

In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.
Unifying Color and Texture Transfer for Predictive Appearance Manipulation

Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from “sunny” to “overcast”. However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season – e.g., leaves on bare trees or piles of snow on a street – and flooding.
A Survey on Deep Transfer Learning

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.
Modeling User Rating Profiles For Collaborative Filtering

In this paper we present a generative latent variable model for rating-based collaborative filtering called the User Rating Profile model (URP). The generative process which underlies URP is designed to produce complete user rating profiles, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable. The rating for each item is generated by selecting a user attitude for the item, and then selecting a rating according to the preference pattern associated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model [7], and LDA , but has clear advantages over each.
Frequency-reconfigurable antennas for multiradio wireless platforms

Looking to increase the functionality of current wireless platforms and to improve their quality of service, we have explored the merits of using frequency-reconfigurable antennas as an alternative for multiband antennas. Our study included an analysis of various reconfigurable and multiband structures such as patches, wires, and combinations. Switches, such as radio-frequency microelectromechanical systems (RFMEMS) and p-i-n diodes, were also studied and directly incorporated onto antenna structures to successfully form frequency-reconfigurable antennas.
SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips

We present a new interactive and online approach to 3D scene understanding. Our system, SemanticPaint, allows users to simultaneously scan their environment whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the environment. Unlike offline systems where capture, labeling, and batch learning often take hours or even days to perform, our approach is fully online. This provides users with continuous live feedback of the recognition during capture, allowing to immediately correct errors in the segmentation and/or learning—a feature that has so far been unavailable to batch and offline methods. This leads to models that are tailored or personalized specifically to the user's environments and object classes of interest, opening up the potential for new applications in augmented reality, interior design, and human/robot navigation. It also provides the ability to capture substantial labeled 3D datasets for training large-scale visual recognition systems.
Multigrid Neural Architectures

We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. They consume multigrid inputs and produce multigrid outputs, convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple multiscale designs, which only process the input at different scales. Viewed in terms of information flow, a multigrid network passes messages across a spatial pyramid. As a consequence, receptive field size grows exponentially with depth, facilitating rapid integration of context. Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail. Experiments demonstrate wide-ranging performance advantages of multigrid. On CIFAR and ImageNet classification tasks, flipping from a single grid to multigrid within the standard CNN paradigm improves accuracy, while being compute and parameter efficient. Multigrid is independent of other architectural choices, we show synergy in combination with residual connections. Multigrid yields dramatic improvement on a synthetic semantic segmentation dataset. Most strikingly, relatively shallow multigrid networks can learn to directly perform spatial transformation tasks, where, in contrast, current CNNs fail. Together, our results suggest that continuous evolution of features on a multigrid pyramid is a more powerful alternative to existing CNN designs on a flat grid.
A hand-held AR magic lens with user-perspective rendering

In this paper we present a user study evaluating the benefits of geometrically correct user-perspective rendering using an Augmented Reality (AR) magic lens. In simulation we compared a user-perspective magic lens against the common device-perspective magic lens on both phone-sized and tablet-sized displays. Our results indicate that a tablet-sized display allows for significantly faster performance of a selection task and that a user-perspective lens has benefits over a device-perspective lens for a selection task. Based on these promising results, we created a proof-of-concept prototype, engineered with current off-the-shelf devices and software. To our knowledge, this is the first geometrically correct user-perspective magic lens.
Particle RRT for Path Planning with Uncertainty

This paper describes a new extension to the rapidly-exploring random tree (RRT) path planning algorithm. The particle RRT algorithm explicitly considers uncertainty in its domain, similar to the operation of a particle filter. Each extension to the search tree is treated as a stochastic process and is simulated multiple times. The behavior of the robot can be characterized based on the specified uncertainty in the environment, and guarantees can be made as to the performance under this uncertainty. Extensions to the search tree, and therefore entire paths, may be chosen based on the expected probability of successful execution. The benefit of this algorithm is demonstrated in the simulation of a rover operating in rough terrain with unknown coefficients of friction
SmartFactory - Towards a factory-of-things

In 1991, Mark Weiser described the vision of a future world under the name of Ubiquitous Computing. Since then, many details of the described vision have become reality: Our mobile phones are powerful multimedia systems, our cars computer systems on wheels, and our homes are turning into smart living environments. All these advances must be turned into products for very cost-sensitive world markets in shorter cycles than ever before. Today, the resulting requirements for design, setup, and operation of our factories become crucial for success. In the past, we often increased the complexity in structures and control systems, resulting in inflexible monolithic production systems. But the future must become ‘‘lean’’—not only in organization, but also in planning and technology! We must develop technologies which allow us to speed up planning and setup, to adapt to rapid product changes during operation, and to reduce the planning effort. To meet these challenges we should also make use of the smart technologies of our daily lives. But for industrial use, there are many open questions to be answered. The existing technologies may be acceptable for consumer use but not yet for industrial applications with high safety and security requirements. Therefore, the SmartFactory initiative was founded by industrial and academic partners to create and operate a demonstration and research test bed for future factory technologies. Many projects develop, test, and evaluate new solutions. This presentation describes changes and challenges, and it summarizes the experience gained to date in the SmartFactory.
Language access and theory of mind reasoning: evidence from deaf children in bilingual and oralist environments.

This investigation examined whether access to sign language as a medium for instruction influences theory of mind (ToM) reasoning in deaf children with similar home language environments. Experiment 1 involved 97 deaf Italian children ages 4-12 years: 56 were from deaf families and had LIS (Italian Sign Language) as their native language, and 41 had acquired LIS as late signers following contact with signers outside their hearing families. Children receiving bimodal/bilingual instruction in LIS together with Sign-Supported and spoken Italian significantly outperformed children in oralist schools in which communication was in Italian and often relied on lipreading. Experiment 2 involved 61 deaf children in Estonia and Sweden ages 6-16 years. On a wide variety of ToM tasks, bilingually instructed native signers in Estonian Sign Language and spoken Estonian succeeded at a level similar to age-matched hearing children. They outperformed bilingually instructed late signers and native signers attending oralist schools. Particularly for native signers, access to sign language in a bilingual environment may facilitate conversational exchanges that promote the expression of ToM by enabling children to monitor others' mental states effectively.
Efficient inference and learning in a large knowledge base

One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information extraction systems. One scalability problem shared by many probabilistic logics is that answering queries involves “grounding” the query—i.e., mapping it to a propositional representation—and the size of a “grounding” grows with database size. To address this bottleneck, we present a first-order probabilistic language called ProPPR in which approximate “local groundings” can be constructed in time independent of database size. Technically, ProPPR is an extension to stochastic logic programs that is biased towards short derivations; it is also closely related to an earlier relational learning algorithm called the path ranking algorithm. We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank on a linearized version of the proof space, and based on this connection, we develop a provably-correct approximate grounding scheme, based on the PageRank–Nibble algorithm. Building on this, we develop a fast and easily-parallelized weight-learning algorithm for ProPPR. In our experiments, we show that learning for ProPPR is orders of magnitude faster than learning for Markov logic networks; that allowing mutual recursion (joint learning) in KB inference leads to improvements in performance; and that ProPPR can learn weights for a mutually recursive program with hundreds of clauses defining scores of interrelated predicates over a KB containing one million entities.
Exploring the longitudinal relationships between the use of grammar in text messaging and performance on grammatical tasks

Research has demonstrated that use of texting slang (textisms) when text messaging does not appear to impact negatively on children's literacy outcomes and may even benefit children's spelling attainment. However, less attention has been paid to the impact of text messaging on the development of children's and young people's understanding of grammar. This study therefore examined the interrelationships between children's and young adults' tendency to make grammatical violations when texting and their performance on formal assessments of spoken and written grammatical understanding, orthographic processing and spelling ability over the course of 1 year. Zero-order correlations showed patterns consistent with previous research on textism use and spelling, and there was no evidence of any negative associations between the development of the children's performance on the grammar tasks and their use of grammatical violations when texting. Adults' tendency to use ungrammatical word forms ('does you') was positively related to performance on the test of written grammar. Grammatical violations were found to be positively associated with growth in spelling for secondary school children. However, not all forms of violation were observed to be consistently used in samples of text messages taken 12 months apart or were characteristic of typical text messages. The need to differentiate between genuine errors and deliberate violation of rules is discussed, as are the educational implications of these findings.
A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance

We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.
SAFER: System-level Architecture for Failure Evasion in Real-time Applications

Recent trends towards increasing complexity in distributed embedded real-time systems pose challenges in designing and implementing a reliable system such as a self-driving car. The conventional way of improving reliability is to use redundant hardware to replicate the whole (sub)system. Although hardware replication has been widely deployed in hard real-time systems such as avionics, space shuttles and nuclear power plants, it is significantly less attractive to many applications because the amount of necessary hardware multiplies as the size of the system increases. The growing needs of flexible system design are also not consistent with hardware replication techniques. To address the needs of dependability through redundancy operating in real-time, we propose a layer called SAFER(System-level Architecture for Failure Evasion in Real-time applications) to incorporate configurable task-level fault-tolerance features to tolerate fail-stop processor and task failures for distributed embedded real-time systems. To detect such failures, SAFER monitors the health status and state information of each task and broadcasts the information. When a failure is detected using either time-based failure detection or event-based failure detection, SAFER reconfigures the system to retain the functionality of the whole system. We provide a formal analysis of the worst-case timing behaviors of SAFER features. We also describe the modeling of a system equipped with SAFER to analyze timing characteristics through a model-based design tool called SysWeaver. SAFER has been implemented on Ubuntu 10.04 LTS and deployed on Boss, an award-winning autonomous vehicle developed at Carnegie Mellon University. We show various measurements using simulation scenarios used during the 2007 DARPA Urban Challenge. Finally, we present a case study of failure recovery by SAFER when node failures are injected.