nils-herrmann/clean_scirepeval huggingface dataset:
Efficient OFDMA for LiFi Downlink

Light-fidelity (LiFi) relies on visible light communication to provide multiuser access in a small cell. For higher throughput, this paper studies the resource allocation problem for an orthogonal frequency-division multiplexing access scheme for LiFi downlink transmissions and presents a joint design of the bias level, power and subcarrier allocation. Decomposing the original optimization into subproblems, we first develop an efficient algorithm for the bias optimization, along with its performance analysis in terms of convergence properties and the global optimality. We then propose several power and subcarrier allocation strategies, including an optimal algorithm and a near-optimal simplified method, to satisfy different complexity and accuracy requirements. Finally, we present two algorithms to jointly optimize the bias, power, and subcarrier, and analyze the convergence properties in terms of both the objective and the solution. Comprehensive simulation results under practical LiFi system setups illustrate the effectiveness of the proposed algorithms.
Predicting breast cancer survivability: a comparison of three data mining methods

The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Therefore, the main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability.



We used two popular data mining algorithms (artificial neural networks and decision trees) along with a most commonly used statistical method (logistic regression) to develop the prediction models using a large dataset (more than 200,000 cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes.



The results indicated that the decision tree (C5) is the best predictor with 93.6% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), artificial neural networks came out to be the second with 91.2% accuracy and the logistic regression models came out to be the worst of the three with 89.2% accuracy.



The comparative study of multiple prediction models for breast cancer survivability using a large dataset along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data mining methods. Using sensitivity analysis on neural network models provided us with the prioritized importance of the prognostic factors used in the study.
Architecture and Implementation of a Java Package for Multiple Input Devices ( MID )

A major difficulty in writing Single Display Groupware (co-present collaborative) applications is getting input from multiple devices. We introduce MID, a Java package that addresses this problem and offers an architecture to access advanced events through Java. In this paper, we describe the features, architecture and limitations of MID. We also briefly describe an application that uses MID to get input from multiple mice: KidPad.
2D shape morphing via automatic feature matching and hierarchical interpolation

The paper presents a new method to interpolate a pair of 2D shapes that are represented by piecewise linear curves. The method addresses two key problems in 2D shape morphing process: feature correspondence and path interpolation. First, a robust feature metric is defined to measure the similarity of a pair of 2D shapes in terms of visual appearance, orientation and relative size. Based on the metric, an optimal problem is defined and solved to associate the features on the source shape with the corresponding ones on the target shape. Then, a two-level hierarchical approach is proposed to solve the corresponding features interpolation trajectory problem. The algorithm decomposes the input shapes into a pair of corresponding coarse polygons and several pairs of corresponding features. Then the corresponding coarse polygons are interpolated in an as-rigid-as-possible plausible way; meanwhile the corresponding features are interpolated using the intrinsic method. Thus interior distortions of the intermediate shapes could be avoided and the feature details on the input shapes could be well preserved. Experimental results show that the method can generate smooth, natural and visually pleasing 2D shape morphing effects. & 2009 Elsevier Ltd. All rights reserved.
Grammatical Error Correction

Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Earlier attempts to grammatical error correction involve rule-based and classifier approaches which are limited to correcting only some particular type of errors in a sentence. As sentences may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this report, we investigate GEC as a translation task from incorrect to correct English and explore some machine translation approaches for developing end-to-end GEC systems for all error types. We apply Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) approaches to GEC and show that they can correct multiple errors of different types in a sentence when compared to the earlier methods which focus on individual errors. We also discuss some of the weakness of machine translation approaches. Finally, we also experiment on a candidate re-ranking technique to re-rank the hypotheses generated by machine translation systems. With regression models, we try to predict a grammaticality score for each candidate hypothesis and re-rank them according to the score.
Soft robotic glove for combined assistance and at-home rehabilitation

This paper presents a portable, assistive, soft robotic glove designed to augment hand rehabilitation for individuals with functional grasp pathologies. The robotic glove utilizes soft actuators consisting of molded elastomeric chambers with fiber reinforcements that induce specific bending, twisting and extending trajectories under fluid pressurization. These soft actuators were mechanically programmed tomatch and support the range of motion of individual fingers. They demonstrated the ability to generate significant force when pressurized and exhibited low impedance when un-actuated. To operate the soft robotic glove, a control hardware system was designed and included fluidic pressure sensors in line with the hydraulic actuators and a closed-loop controller to regulate the pressure. Demonstrations with the complete system were performed to evaluate the ability of the soft robotic glove to carry out gross and precise functional grasping. Compared to existing devices, the soft robotic glove has the potential to increase user freedom and independence through its portable waist belt pack and open palm design.
CiteSeer: An Automatic Citation Indexing System

We presentCiteSeer : an autonomous citation indexing system which indexes academic literature in electronic format (e.g. Postscript files on the Web). CiteSeer understands how to parse citations, identify citations to the same paper in d ifferent formats, and identify the context of citations in the body of articles. CiteSeer provides most of the advantages o f traditional (manually constructed) citation indexes (e.g . the ISI citation indexes), including: literature retrieval by following citation links (e.g. by providing a list of papers tha cite a given paper), the evaluation and ranking of papers, au thors, journals, etc. based on the number of citations, and t he identification of research trends. CiteSeer has many advantages over traditional citation indexes, including the abi lity to create more up-to-date databases which are not limited to a preselected set of journals or restricted by journal public ation delays, completely autonomous operation with a correspond ing reduction in cost, and powerful interactive browsing of the literature using the context of citations. Given a parti cular paper of interest, CiteSeer can display the context of ho w the paper is cited in subsequent publications. This context may contain a brief summary of the paper, another author’s response to the paper, or subsequent work which builds upon the original article. CiteSeer allows the location of paper s by keyword search or by citation links. Papers related to a give n paper can be located using common citation information or word vector similarity. CiteSeer will soon be available for public use.
Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School

Research shows that middle school is an important juncture for a student where he or she starts to be conscious about academic achievement and thinks about college attendance. It is already known that access to financial resources, family background, career aspirations and academic ability are indicative of a student’s choice to attend college; though these variables are interesting, they do not necessarily give sufficient actionable information to instructors or guidance counselors to intervene for individual students. However, increasing numbers of students are using educational software at this phase of their education, and detectors of specific aspects of student learning and engagement have been developed for these types of learning environments. If these types of models can be used to predict college attendance, it may provide more actionable information than the previous generation of predictive models. In this paper, we predict college attendance from these types of detectors, in the context of 3,747 students using the ASSISTment system in New England, producing detection that is both successful and potentially more actionable than previous approaches; we can distinguish between a student who will attend college and a student who will not attend college 68.6% of the time.
P300 brain computer interface: current challenges and emerging trends

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.
LAST: locality-aware sector translation for NAND flash memory-based storage systems

As flash memory technologies quickly improve, NAND flash memory-based storage devices are becoming a viable alternative as a secondary storage solution for general-purpose computing systems such as personal computers and enterprise server systems. Most existing flash translation layer (FTL) schemes are, however, ill-suited for such systems because they were optimized for storage write patterns of embedded systems only. In this paper, we propose a new flash management technique called LAST which is optimized for access characteristics of general-purpose computing systems. By exploiting the locality of storage access patterns, LAST reduces the garbage collection overhead significantly, thus increasing the I/O performance of flash-based storage devices. Our experimental results show that the proposed technique reduces the garbage collection overhead by 54% over the existing flash memory management techniques.
Minimum cost solar power systems for LTE macro base stations

This paper proposes an algorithm for the identification of the minimum cost solution over a 10 year time horizon to power an LTE (Long-Term Evolution) macro base station, using a photovoltaic solar panel, a set of batteries, and optionally also a secondary power source, which can be a connection to a (possibly unreliable) power grid, or a small Diesel generator. The optimization is formalised as an Mixed Integer Programming (MIP) problem, which, after linearization, can be solved with CPLEX. A heuristic algorithm is also proposed, with the objective of decreasing the computational complexity of the optimization. Numerical results show that a hybrid solar-grid (or solar-diesel) power system saves a significant fraction of the total cost, compared to a pure solar system, and to the traditional power-grid system, over the investigated 10-year period, in a south European city, like Torino in Italy, as well as in a location close to the tropic, like Aswan in Egypt. Our proposed heuristic algorithm can be used to obtain a solution within 10–20% of the optimum, at a computational speed 200 times faster than the MIP solution.
Improving Vector Space Word Representations Via Kernel Canonical Correlation Analysis

Cross-lingual word embeddings are representations for vocabularies of two or more languages in one common continuous vector space and are widely used in various natural language processing tasks. A state-of-the-art way to generate cross-lingual word embeddings is to learn a linear mapping, with an assumption that the vector representations of similar words in different languages are related by a linear relationship. However, this assumption does not always hold true, especially for substantially different languages. We therefore propose to use kernel canonical correlation analysis to capture a non-linear relationship between word embeddings of two languages. By extensively evaluating the learned word embeddings on three tasks (word similarity, cross-lingual dictionary induction, and cross-lingual document classification) across five language pairs, we demonstrate that our proposed approach achieves essentially better performances than previous linear methods on all of the three tasks, especially for language pairs with substantial typological difference.
Beyond Cybersecurity Awareness: Antecedents and Satisfaction

Organizations develop technical and procedural measures to protect information systems. Relying only on technical based security solutions is not enough. Organizations must consider technical security solutions along with social, human, and organizational factors. The human element represents the employees (insiders) who use the information systems and other technology resources in their day-to-day operations. ISP awareness is essential to protect organizational information systems. This study adapts the Innovation Diffusion Theory to examine the antecedents of ISP awareness and its impact on the satisfaction with ISP and security practices. A sample of 236 employees in universities in the United States is collected to evaluate the research model. Results indicated that ISP quality, self-efficacy, and technology security awareness significantly impact ISP awareness. The current study presents significant contributions toward understanding the antecedents of ISP awareness and provides a starting point toward including satisfaction aspect in information security behavioral domain.
IRIS performer: a high performance multiprocessing toolkit for real-time 3D graphics

This paper describes the design and implementation of IRIS Performer, a toolkit for visual simulation, virtual reality, and other real-time 3D graphics applications. The principal design goal is to allow application developers to more easily obtain maximal performance from 3D graphics workstations which feature multiple CPUs and support an immediate-mode rendering library. To this end, the toolkit combines a low-level library for high-performance rendering with a high-level library that implements pipelined, parallel traversals of a hierarchical scene graph. While discussing the toolkit architecture, the paper illuminates and addresses performance issues fundamental to immediate-mode graphics and coarse-grained, pipelined multiprocessing. Graphics optimizations focus on efficient data transfer to the graphics subsystem, reduction of mode settings, and restricting state inheritance. The toolkit's multiprocessing features solve the problems of how to partition work among multiple processes, how to synchronize these processes, and how to manage data in a pipelined, multiprocessing environment. The paper also discusses support for intersection detection, fixed-frame rates, run-time profiling and special effects such as geometric morphing.
A Single-Chip CMOS-Based Parallel Optical Transceiver Capable of 240-Gb/s Bidirectional Data Rates

We report here on the design, fabrication, and high-speed performance of a parallel optical transceiver based on a single CMOS amplifier chip incorporating 16 transmitter and 16 receiver channels. The optical interfaces to the chip are provided by 16-channel photodiode (PD) and VCSEL arrays that are directly flip-chip soldered to the CMOS IC. The substrate emitting/illuminated VCSEL/PD arrays operate at 985 nm and include integrated lenses. The complete transceivers are low-cost, low-profile, highly integrated assemblies that are compatible with conventional chip packaging technology such as direct flip-chip soldering to organic circuit boards. In addition, the packaging approach, dense hybrid integration, readily scales to higher channel counts, supporting future massively parallel optical data buses. All transmitter and receiver channels operate at speeds up to 15 Gb/s for an aggregate bidirectional data rate of 240 Gb/s. Interchannel crosstalk was extensively characterized and the dominant source was found to be between receiver channels, with a maximum sensitivity penalty of 1 dB measured at 10 Gb/s for a victim channel completely surrounded by active aggressor channels. The transceiver measures 3.25times5.25 mm and consumes 2.15 W of power with all channels fully operational. The per-bit power consumption is as low as 9 mW/Gb/s, and this is the first single-chip optical transceiver capable of channel rates in excess of 10 Gb/s. The area efficiency of 14 Gb/s/mm2 per link is the highest ever reported for any parallel optical transmitter, receiver, or transceiver reported to-date.
The dark side of food addiction

In drug addiction, the transition from casual drug use to dependence has been linked to a shift away from positive reinforcement and toward negative reinforcement. That is, drugs ultimately are relied on to prevent or relieve negative states that otherwise result from abstinence (e.g., withdrawal) or from adverse environmental circumstances (e.g., stress). Recent work has suggested that this "dark side" shift also is a key in the development of food addiction. Initially, palatable food consumption has both positively reinforcing, pleasurable effects and negatively reinforcing, "comforting" effects that can acutely normalize organism responses to stress. Repeated, intermittent intake of palatable food may instead amplify brain stress circuitry and downregulate brain reward pathways such that continued intake becomes obligatory to prevent negative emotional states via negative reinforcement. Stress, anxiety and depressed mood have shown high comorbidity with and the potential to trigger bouts of addiction-like eating behavior in humans. Animal models indicate that repeated, intermittent access to palatable foods can lead to emotional and somatic signs of withdrawal when the food is no longer available, tolerance and dampening of brain reward circuitry, compulsive seeking of palatable food despite potentially aversive consequences, and relapse to palatable food-seeking in response to anxiogenic-like stimuli. The neurocircuitry identified to date in the "dark" side of food addiction qualitatively resembles that associated with drug and alcohol dependence. The present review summarizes Bart Hoebel's groundbreaking conceptual and empirical contributions to understanding the role of the "dark side" in food addiction along with related work of those that have followed him.
On Managing Very Large Sensor-Network Data Using Bigtable

Recent advances and innovations in smart sensor technologies, energy storage, data communications, and distributed computing paradigms are enabling technological breakthroughs in very large sensor networks. There is an emerging surge of next-generation sensor-rich computers in consumer mobile devices as well as tailor-made field platforms wirelessly connected to the Internet. Billions of such sensor computers are posing both challenges and opportunities in relation to scalable and reliable management of the peta- and exa-scale time series being generated over time. This paper presents a Cloud-computing approach to this issue based on the two well-known data storage and processing paradigms: Bigtable and MapReduce.
An examination of the relationship among structure, trust, and conflict management styles in virtual teams

In today’s competitive environment, successful organizations that are able to utilize advanced information technology to establish a dynamic form to adapt to the ever-changing landscape and customer requirements always gain a competitive advantage in global competition (Porter & Lilly, 1996). The virtual team has become one of the building blocks of a successful organization. In this article, a virtual team is defined as ‘‘a group of people with complementary competencies executing simultaneous, collaborative work processes through electronic media without regard to geographic location’’ (Chinowsky & Rojas, 2003, p. 98). Virtual teams can offer a range of benefits to organizations, among them encouraging constructive dialogue and knowledge, nurturing a community of workers, triggering deeper processing of content through interaction, and offering flexible adaptation to complex tasks (Duarte & Snyder, 1999; Conrad & Donaldson, 2004; Palloff & Pratt, 2005; Lee, Bonk, Magjuka, Su, & Liu, 2006). However, a virtual team can also face challenges and issues that must be addressed for a successful team operation. From existing telework research, Workman, Kahnweiler, and Bommer (2001) summarize three attributes of virtual environments that are barriers to successful virtual collaboration: elevated ambiguity, increased isolation, and a less-structured environment. For example, in virtual environments lack of informal interactions, the constraints of dispersed asynchronous communication context, and limited capabilities of transmitting social