nils-herrmann/clean_scirepeval huggingface dataset:
Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system’s ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter’s price, without a need for predicting target prices.
Face detection using generic eye template matching

Face identification and detection has become very popular, interesting and wide field of current research area. As there are several algorithms for face detection exist but none of the algorithms globally detect all sorts of human faces among the different colors and intensities in a given picture. In this paper, a novel method for face detection technique has been described. Here, the centers of both the eyes are detected using generic eye template matching method. After detecting the center of both the eyes, the corresponding face bounding box is determined. The experimental results have shown that the proposed algorithm is able to accomplish successfully proper detection and to mark the exact face and eye region in the given image.
Compact Broadband Microstrip-Line-Fed Sleeve Monopole Antenna for DTV Application and Ground Plane Effect

This letter presents a compact broadband microstrip-line-fed sleeve monopole antenna for application in the DTV system. The design of meandering the monopole into a compact structure is applied for size reduction. By properly selecting the length and spacing of the sleeve, the broadband operation for the proposed design can be achieved, and the obtained impedance bandwidth covers the whole DTV (470862 MHz) band. Most importantly, the matching condition over a wide frequency range can be performed well even when a small ground-plane length is used; meanwhile, a small variation in the impedance bandwidth is observed for the ground-plane length varied in a great range.
Power Efficient Resource Allocation for Clouds Using Ant Colony Framework

Cloud computing is one of the rapidly improving technologies. It provides scalable resources needed for the applications hosted on it. As cloud-based services become more dynamic, resource provisioning becomes more challenging. The QoS constrained resource allocation problem is considered in this paper, in which customers are willing to host their applications on the provider’s cloud with a given SLA requirements for performance such as throughput and response time. Since, the data centers hosting the applications consume huge amounts of energy and cause huge operational costs, solutions that reduce energy consumption as well as operational costs are gaining importance. In this work, we propose an energy efficient mechanism that allocates the cloud resources to the applications without violating the given service level agreements(SLA) using Ant colony framework.
Causal video object segmentation from persistence of occlusions

Occlusion relations inform the partition of the image domain into “objects” but are difficult to determine from a single image or short-baseline video. We show how long-term occlusion relations can be robustly inferred from video, and used within a convex optimization framework to segment the image domain into regions. We highlight the challenges in determining these occluder/occluded relations and ensuring regions remain temporally consistent, propose strategies to overcome them, and introduce an efficient numerical scheme to perform the partition directly on the pixel grid, without the need for superpixelization or other preprocessing steps.
Embedded Wafer Level Packaging for 77-GHz Automotive Radar Front-End With Through Silicon Via and its 3-D Integration

In this paper, a 77-GHz automotive radar sensor transceiver front-end module is packaged with a novel embedded wafer level packaging (EMWLP) technology. The bare transceiver die and the pre-fabricated through silicon via (TSV) chip are reconfigured to form a molded wafer through a compression molding process. The TSVs built on a high resistivity wafer serve as vertical interconnects, carrying radio-frequency (RF) signals up to 77 GHz. The RF path transitions are carefully designed to minimize the insertion loss in the frequency band of concern. The proposed EMWLP module also provides a platform to design integrated passive components. A substrate-integrated waveguide resonator is implemented with TSVs as the via fences, and it is later used to design a second-order 77-GHz high performance bandpass filter. Both the resonator and the bandpass filter are fabricated and measured, and the measurement results match with the simulation results very well.
Representing the existence and the location of hidden objects: Object permanence in 6- and 8-month-old infants

The present experiment investigated two facets of object permanence in young infants: the ability to represent the existence and the location of a hidden stationary object, and the ability to ‘represent the existence and the trajectory of a hidden moving object. Sixand B-month-old infants sat in front of a screen; to the left of the screen was an inclined ramp. The infants watched the following event: the screen was raised and lowered, and a toy car rolled down the ramp, passed behind the screen, and exited the apparatus to the right. After the infants habituated to this event, they saw two test events. These were identical to the habituation event, except that a box was placed behind the screen. In one event (possible event), the box stood in back of the car’s tracks; in the other (impossible event), it stood on top of the tracks, blocking the car’s path. Infants looked longer at the impossible than at the possible event, indicating that they were surprised to see the car reappear from behind the screen when the box stood in its path. A control experiment in which the box was placed in front (possible event) or on top (impossible event) of the car’s tracks yielded similar results. Together, the results of these experiments suggest that infants understood that (I) the box continued to exist, in its same location, after it was occluded by the screen; (2) the car continued to exist, and pursued its trajectory, after it disappeared behind the screen; and (3) the car could not roll through the space occupied by the box. These results have implications for theory and research on the development of infants’ knowledge about objects and infants’ reasoning abilities. The research reported in this manuscript was supported by a grant from the University Research Institute of the University of Texas at Austin. I thank Judy Deloache and Gwen Gustafson, for their careful reading of the paper; Marty Banks, Kathy Cain, Carol Dweck, Marcia Graber, and Liz Spelke, for helpful comments on different versions of the paper; and Stanley Wasserman and Dawn Iaccobucci for their help with the statistical analyses. I also thank the undergraduates who served as observers and experimenters, and the parents who kindly allowed their infants to participate in the studies. Reprint requests should be sent to Renee Baillargeon, Psychology Department, University of Illinois at Urbana/Champaign 603 E Daniel Street, Champaign, IL. 61820, U.S.A.
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain

Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features for making robust prediction while mimicking the performance of deep learning models. Our framework uses Gradient Boosting Trees to learn interpretable features from deep learning models such as Stacked Denoising Autoencoder and Long Short-Term Memory. Exhaustive experiments on a real-world clinical time-series dataset show that our method obtains similar or better performance than the deep learning models, and it provides interpretable phenotypes for clinical decision making.
A survey on self-healing systems: approaches and systems

Present large-scale information technology environments are complex, heterogeneous compositions often affected by unpredictable behavior and poor manageability. This fostered substantial research on designs and techniques that enhance these systems with an autonomous behavior. In this survey, we focus on the self-healing branch of the research and give an overview of the current existing approaches. The survey is introduced by an outline of the origins of self-healing. Based on the principles of autonomic computing and self-adapting system research, we identify self-healing systems’ fundamental principles. The extracted principles support our analysis of the collected approaches. In a final discussion, we summarize the approaches’ common and individual characteristics. A comprehensive tabular overview of the researched material concludes the survey.
Closed Timelike Curves Make Quantum and Classical Computing Equivalent

While closed timelike curves (CTCs) are not known to exist, studying their consequences has led to nontrivial insights in general relativity, quantum information, and other areas. In this paper we show that if CTCs existed, then quantum computers would be no more powerful than classical computers: both would have the (extremely large) power of the complexity class PSPACE, consisting of all problems solvable by a conventional computer using a polynomial amount of memory. This solves an open problem proposed by one of us in 2005, and gives an essentially complete understanding of computational complexity in the presence of CTCs. Following the work of Deutsch, we treat a CTC as simply a region of spacetime where a “causal consistency” condition is imposed, meaning that Nature has to produce a (probabilistic or quantum) fixed-point of some evolution operator. Our conclusion is then a consequence of the following theorem: given any quantum circuit (not necessarily unitary), a fixed-point of the circuit can be (implicitly) computed in polynomial space. This theorem might have independent applications in quantum information.
Bias-Variance Error Bounds for Temporal Difference Updates

We give the first rigorous upper bounds on the error of tempora l difference (td) algorithms for policy evaluation as a function of the amount of experience. These upper bounds prove exponentially fast convergence, with bo the rate of convergence and the asymptote strongly dependent on the length of t he backupsk or the parameter . Our bounds give formal verification to the long-standing in tuition that td methods are subject to a “bias-variance” trade-off, and the y lead to schedules for k and that are predicted to be better than any fixed values for these parameters. We give preliminary experimental confirmation of our t heory for a version of the random walk problem.
Haptic Output in Multimodal User Interfaces

This paper presents an intelligent adaptive system for the integration of haptic output in graphical user interfaces. The system observes the user’s actions, extracts meaningful features, and generates a user and application specific model. When the model is sufficiently detailled, it is used to predict the widget which is most likely to be used next by the user. Upon entering this widget, two magnets in a specialized mouse are activated to stop the movement, so target acquisition becomes easier and more comfortable. Besides the intelligent control system, we will present several methods to generate haptic cues which might be integrated in multimodal user interfaces in the future.
Information transfer in social media

Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information--theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine--grain notions of influence, and admits a natural, predictive interpretation. Networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real-world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups.
The CHEMDNER corpus of chemicals and drugs and its annotation principles

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.
Four Principles of Memory Improvement : A Guide to Improving Learning Efficiency

Recent advances in memory research suggest methods that can be applied to enhance educational practices. We outline four principles of memory improvement that have emerged from research: 1) process material actively, 2) practice retrieval, 3) use distributed practice, and 4) use metamemory. Our discussion of each principle describes current experimental research underlying the principle and explains how people can take advantage of the principle to improve their learning. The techniques that we suggest are designed to increase efficiency—that is, to allow a person to learn more, in the same unit of study time, than someone using less efficient memory strategies. A common thread uniting all four principles is that people learn best when they are active participants in their own learning.
Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks

There has been an increased interest in combining fuzzy systems with neural networks because fuzzy neural systems merge the advantages of both paradigms. On the one hand, parameters in fuzzy systems have clear physical meanings and rule-based and linguistic information can be incorporated into adaptive fuzzy systems in a systematic way. On the other hand, there exist powerful algorithms for training various neural network models. However, most of the proposed combined architectures are only able to process static input-output relationships, i.e. they are not able to process temporal input sequences of arbitrary length. Fuzzy nite-state automata (FFAs) can model dynamical processes whose current state depends on the current input and previous states. Unlike in the case of deterministic nite-state automata (DFAs), FFAs are not in one particular state, rather each state is occupied to some degree deened by a membership function. Based on previous work on encoding DFAs in discrete-time, second-order recurrent neural networks, we propose an algorithm that constructs an augmented recurrent neural network that encodes a FFA and recognizes a given fuzzy regular language with arbitrary accuracy. We then empirically verify the encoding methodology by measuring string recognition performance of recurrent neural networks which encode large randomly generated FFAs. In particular, we examine how the networks' performance varies as a function of synaptic weight strength.
Profiling mobile malware behaviour through hybrid malware analysis approach

Nowadays, the usage of mobile device among the community worldwide has been tremendously increased. With this proliferation of mobile devices, more users are able to access the internet for variety of online application and services. As the use of mobile devices and applications grows, the rate of vulnerabilities exploitation and sophistication of attack towards the mobile user are increasing as well. To date, Google's Android Operating System (OS) are among the widely used OS for the mobile devices, the openness design and ease of use have made them popular among developer and user. Despite the advantages the android-based mobile devices have, it also invited the malware author to exploit the mobile application on the market. Prior to this matter, this research focused on investigating the behaviour of mobile malware through hybrid approach. The hybrid approach correlates and reconstructs the result from the static and dynamic malware analysis in producing a trace of malicious event. Based on the finding, this research proposed a general mobile malware behaviour model that can contribute in identifying the key features in detecting mobile malware on an Android Platform device.
A traffic sign detection algorithm based on deep convolutional neural network

Traffic sign detection plays an important role in driving assistance systems and traffic safety. But the existing detection methods are usually limited to a predefined set of traffic signs. Therefore we propose a traffic sign detection algorithm based on deep Convolutional Neural Network (CNN) using Region Proposal Network(RPN) to detect all Chinese traffic sign. Firstly, a Chinese traffic sign dataset is obtained by collecting seven main categories of traffic signs and their subclasses. Then a traffic sign detection CNN model is trained and evaluated by fine-tuning technology using the collected dataset. Finally, the model is tested by 33 video sequences with the size of 640×480. The result shows that the proposed method has towards real-time detection speed and above 99% detection precision. The trained model can be used to capture the traffic sign from videos by on-board camera or driving recorder and construct a complete traffic sign dataset.
GUESS: a language and interface for graph exploration

As graph models are applied to more widely varying fields, researchers struggle with tools for exploring and analyzing these structures. We describe GUESS, a novel system for graph exploration that combines an interpreted language with a graphical front end that allows researchers to rapidly prototype and deploy new visualizations. GUESS also contains a novel, interactive interpreter that connects the language and interface in a way that facilities exploratory visualization tasks. Our language, Gython, is a domain-specific embedded language which provides all the advantages of Python with new, graph specific operators, primitives, and shortcuts. We highlight key aspects of the system in the context of a large user survey and specific, real-world, case studies ranging from social and knowledge networks to distributed computer network analysis.
ADAPTIVE LASSO FOR SPARSE HIGH-DIMENSIONAL REGRESSION MODELS

We study the asymptotic properties of the adaptive Lasso estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase with the sample size. We consider variable selection using the adaptive Lasso, where the L1 norms in the penalty are re-weighted by data-dependent weights. We show that, if a reasonable initial estimator is available, under appropriate conditions, the adaptive Lasso correctly selects covariates with nonzero coefficients with probability converging to one, and that the estimators of nonzero coefficients have the same asymptotic distribution they would have if the zero coefficients were known in advance. Thus, the adaptive Lasso has an oracle property in the sense of Fan and Li (2001) and Fan and Peng (2004). In addition, under a partial orthogonality condition in which the covariates with zero coefficients are weakly correlated with the covariates with nonzero coefficients, marginal regression can be used to obtain the initial estimator. With this initial estimator, the adaptive Lasso has the oracle property even when the number of covariates is much larger than the sample size.
Algorithms for Scheduling Runway Operations Under Constrained Position Shifting

The efficient operation of airports, and runways in particular, is critical to the throughput of the air transportation system as a whole. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations of efficiency, safety, and equity among airlines. One approach to runway scheduling that arises from operational and fairness considerations is that of constrained position shifting (CPS), which requires that an aircraft’s position in the optimized sequence not deviate significantly from its position in the first-come-first-served sequence. This paper presents a class of scalable dynamic programming algorithms for runway scheduling under constrained position shifting and other system constraints. The results from a prototype implementation, which is fast enough to be used in real-time, are also presented.