nils-herrmann/clean_scirepeval huggingface dataset:
A smart parking system using WiFi and wireless sensor network

In the last few years, smart parking has been focused due to the development of indoor position technology. There are several smart parking systems for garage. But there are two major problems in these systems. The first one is the detection of parking space occupation, and the second one is space navigation. To solve these two problems, we propose a smart parking system combining WiFi and wireless sensor network. In this system, geomagnetic sensors are used to detect the occupation of parking spaces, and WiFi is used for navigation. A prototype system has been developed.
Modeling Coverage for Neural Machine Translation

Attention mechanism has enhanced stateof-the-art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, however, which often leads to over-translation and under-translation. To address this problem, we propose coverage-based NMT in this paper. We maintain a coverage vector to keep track of the attention history. The coverage vector is fed to the attention model to help adjust future attention, which lets NMT system to consider more about untranslated source words. Experiments show that the proposed approach significantly improves both translation quality and alignment quality over standard attention-based NMT.1
Algorithms for rendering depth of field effects in computer graphics

Computer generated images by default render the entire scene in perfect focus. Both camera optics and the human visual system have limited depth of field, due to the finite aperture or pupil of the optical system. For more realistic computer graphics as well as to enable artistic control over what is and what is not in focus, it is desirable to add depth of field blurring. Starting with the work of Potmesil and Chakravarty[33][34], there have been numerous approaches to adding depth of field effects to computer graphics. Published work in depth of field for computer graphics has been previously surveyed by Barsky [2][3]. Later, interactive depth of field techniques were surveyed by Demers . Subsequent to these surveys, however, there have been important developments. This paper surveys depth of field approaches in computer graphics, from its introduction to the current state of the art.
Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data

Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.
Data-swapping --a Technique for Disclosure Control

In recent years there has been increasing concern about the confidentiality of computerized databases. This has led to a fast growing interest in the development of techniques for controlling the disclosure of information from such databases about individuals, both natural and legal persons. 3 Much of this interest has been focused on the release of statistical tabulations and microdata files. In this paper, we introduce a new technique, data-swapping, that addresses these interests. This technique can be used to both produce microdata and release statistical tabulations so that confidentiality is not violated. The ideas presented in this report are tentative and have not been tested on real-life data. The illustrations provided in the text are based on small-scale numerical experiments, and serve to illustrate the use of data-swapping for disclosure control. Before the proposed techniques can be used on a large scale, more research will be needed. Directions for this research are pointed out throughout the report. This report is organized in two parts. In Part I, we present the underlying idea of the technique. In Part II, we present some theory and methods.
Face Recognition Using Active Near-IR Illumination

A new approach to overcome the problem caused by illumination variation in face recognition is proposed in this paper. Active Near-Infrared (Near-IR) illumination projected by a Light Emitting Diode (LED) light source is used to provide a constant illumination. The difference between two face images captured when the LED light is on and off respectively, is the image of a face under just the LED illumination, and is independent of ambient illumination. In preliminary experiments with various ambient illuminations, significantly better results are achieved for both automatic and semi-automatic face recognition experiments on LED illuminated faces than on face images under ambient illuminations.
High-power GaN MMIC PA Over 40–4000MHz

We report a high-performance GaN MMIC power amplifier operating from 40MHz to 4,000MHz. The MMIC achieved 80W pulsed (100us pulse width and 10% duty cycle) output power (P5dB) with 54% efficiency at 40MHz, 50W with about 30% efficiency across most of the mid band, and gradually decreases to 30W with 22% efficiency at 4000MHz. Power gain is 25dB across the 40-4000MHz band. This ultra wideband performance is achieved by both tailoring the device output impedance, and using a unique wide-band, circuit-matching topology. Detailed design techniques of both the device and the matching circuit will be presented.
Inferring Gender from the Content of Tweets: A Region Specific Example

There is growing interest in using social networking sites such as Twitter to gather real-time data on the reactions and opinions of a region's population, including locations in the developing world where social media has played an important role in recent events, such as the 2011 Arab Spring. However, many interesting and important opinions and reactions may differ significantly within a given region depending on the demographics of the subpopulation, including such categories as gender and ethnicity. This information may not be explicitly available in user content or metadata, however, and automated methods are required to infer such hidden attributes. In this paper we describe a method to infer the gender of Twitter users from only the content of their tweets. Looking at Twitter users from the West African nation of Nigeria, we applied supervised machine learning using features derived from the content of user tweets to train a classifier. Using unigram features alone, we obtained an accuracy of 80% for predicting gender, suggesting that content alone can be a good predictor of gender. An analysis of the highest weighted features shows some interesting distinctions between men and women both topically and emotionally. We argue that approaches such as the one described here can give us a clearer picture of who is utilizing social media when certain user attributes are unreliable or not available.
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric statespace models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once an approximation of the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.
Are web applications ready for parallelism?

In recent years, web applications have become pervasive. Their backbone is JavaScript, the only programming language supported by all major web browsers. Most browsers run on desktop or mobile devices with parallel hardware. However, JavaScript is by design sequential, and current web applications make little use of hardware parallelism. Are web applications ready to exploit parallel hardware? We answer the question in two steps: First, we survey 174 web developers about the potential and challenges of using parallelism. Then, we study the performance and computation shape of a set of web applications that are representative for the emerging web. Our findings indicate that emerging web applications do have latent data parallelism, and JavaScript developers' programming style is not a significant impediment to exploiting this parallelism.
Financial Frictions and the Persistence of History : A Quantitative Exploration

The transitional dynamics of the neoclassical growth model is at odds with the growth experiences of many developing economies. For example, consider the post-communist transition of Eastern Europe: Following liberalization, output grew slowly (and even fell), while interest rates and investment rates remained low, contrary to the predictions of the standard growth theory. We incorporate financial frictions and resource misallocation into an otherwise-standard growth model, and account for the observed growth dynamics. We discipline the model by calibrating its stationary equilibrium to the US data on standard macroeconomic aggregates, wealth distribution, and firms’ internal v. external financing. Our model economy converges slowly to the steady state, with the interest rate and the investment rate starting low and rising over time. We also obtain a new result on heterogeneity and macroeconomic dynamics: Even though strict approximate aggregation—that relies only on unconditional first moments—does not hold in our economy, the effect of heterogeneity on aggregate dynamics is almost completely captured by conditional first moments. Department of Economics, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208, USA (e-mail: f-buera@northwestern.edu). Department of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI 53706, USA (email: yshin@ssc.wisc.edu). The transitional dynamics of the neoclassical growth model is characterized by swift convergence to the steady state, staggeringly high real interest rates in the early stages of development, and decreasing investment-to-output ratio over time. However, most economies’ growth experiences are at odds with these neoclassical predictions. One such example is the “miracle” economies of East Asia.  Even after going through an exhaustive list of modified neoclassical growth models, King and Rebelo (1993) conclude that neoclassical transitional dynamics can only play a minor role in explaining observed growth experiences. This is a fundamental challenge for the neoclassical growth theory. Output Per Capita Relative to the US + + + + + + + + + + + JPN KOR SGP bc bc bc bc bc bc bc bc bc bc TWN rs rs rs rs rs rs rs rs rs rs rs THA 195
Show and tell: A neural image caption generator

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.
EPBC: Efficient Public Blockchain Client for lightweight users

Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for a user to track transactions, a simple method is that every user keeps a local copy of the entire public ledger. Since the size of a ledger keeps growing, this method becomes increasingly less practical, especially for lightweight users such as IoT devices and smartphones. In order to deal with this problem, there have been some proposals. However, existing solutions either achieve a limited storage reduction (e.g., simple payment verification), or rely on some strong security assumption (e.g., the use of trusted server). We propose EPBC, a novel and efficient transaction verification scheme for public ledgers, which only requires lightweight users to store a small amount of data that is independent of the size of the blockchain. We analyze EPBC's performance and security, and discuss its integration with existing public ledger systems. Experimental results confirm that EPBC is practical for lightweight users.
A 65-nm CMOS Constant Current Source With Reduced PVT Variation

This paper presents a new nanometer-based low-power constant current reference that attains a small value in the total process–voltage–temperature variation. The circuit architecture is based on the embodiment of a process-tolerant bias current circuit and a scaled process-tracking bias voltage source for the dedicated temperature-compensated voltage-to-current conversion in a preregulator loop. Fabricated in a UMC 65-nm CMOS process, it consumes <inline-formula> <tex-math notation="LaTeX">$7.18~\mu \text{W}$ </tex-math></inline-formula> with a 1.4 V supply. The measured results indicate that the current reference achieves an average temperature coefficient of 119 ppm/°C over 12 samples in a temperature range from −30 °C to 90 °C without any calibration. Besides, a low line sensitivity of 180 ppm/V is obtained. This paper offers a better sensitivity figure of merit with respect to the reported representative counterparts.
WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES

The many motivations for employing agent-based computation in the social sciences are reviewed. It is argued that there exist three distinct uses of agent modeling techniques. One such use — the simplest — is conceptually quite close to traditional simulation in operations research. This use arises when equations can be formulated that completely describe a social process, and these equations are explicitly soluble, either analytically or numerically. In the former case, the agent model is merely a tool for presenting results, while in the latter it is a novel kind of Monte Carlo analysis. A second, more commonplace usage of computational agent models arises when mathematical models can be written down but not completely solved. In this case the agent-based model can shed significant light on the solution structure, illustrate dynamical properties of the model, serve to test the dependence of results on parameters and assumptions, and be a source of counter-examples. Finally, there are important classes of problems for which writing down equations is not a useful activity. In such circumstances, resort to agent-based computational models may be the only way available to explore such processes systematically, and constitute a third distinct usage of such models. ∗ Corresponding author address: Robert Axtell, Center on Social and Economic Dynamics, The Brookings Institution, 1775 Massachusetts Ave. NW, Washington, DC 20036; .
Labeled Memory Networks for Online Model Adaptation

Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time. We present the design of Labeled Memory Network (LMN), a new memory augmented neural network (MANN) for fast online model adaptation. We highlight three key features of LMNs. First, LMNs treat memory as a second boosted stage following the trained network thereby allowing the memory and network to play complementary roles. Unlike all existing MANNs that write to memory at every cycle, LMNs provide better memory utilization by writing only labeled data with non-zero loss. Second, LMNs organize the memory with the discrete class label as the primary key unlike existing MANNs where key is a real vector derived from the input. This simple, yet surprisingly unexplored alternative organization, safeguards against catastrophic forgetting of rare labels that current LRU based MANNs are subject to. Finally, LMNs model the evolving expertise of memory and network using a RNN, to determine online their respective weights We evaluate online model adaptation strategies on five sequence prediction tasks, an image classification task, and two language modeling tasks. We show that LMNs are better than other MANNs designed for meta-learning. We also found them to be more accurate and faster than state-of-theart methods of retuning model parameters for adapting to domain-specific labeled data.
The application of virtual reality to (chemical engineering) education

Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This poster presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual chemical plants, virtual laboratory accidents, and a virtual UIC campus. The first area provides guided exploration of domains otherwise inaccessible, such as the interior of operating reactors and microscopic reaction mechanisms. The second promotes safety by demonstrating the consequences of not following proper lab safety procedures. And the third provides valuable guidance for (foreign) visitors. All programs developed are available on the Web, for free download to any interested parties.
Assessing wine quality using a decision tree

Even though wine-drinkers generally agree that wines may be ranked by quality, wine-tasting is famously subjective. There have been many attempts to construct a more methodical approach to the assessment of wines. We propose a method of assessing wine quality using a decision tree, and test it against the wine-quality dataset from the UC Irvine Machine Learning Repository. Results are 60% in agreement with traditional assessment techniques.
Magnetic Metamaterial Superlens for Increased Range Wireless Power Transfer

The ability to wirelessly power electrical devices is becoming of greater urgency as a component of energy conservation and sustainability efforts. Due to health and safety concerns, most wireless power transfer (WPT) schemes utilize very low frequency, quasi-static, magnetic fields; power transfer occurs via magneto-inductive (MI) coupling between conducting loops serving as transmitter and receiver. At the "long range" regime - referring to distances larger than the diameter of the largest loop - WPT efficiency in free space falls off as (1/d)(6); power loss quickly approaches 100% and limits practical implementations of WPT to relatively tight distances between power source and device. A "superlens", however, can concentrate the magnetic near fields of a source. Here, we demonstrate the impact of a magnetic metamaterial (MM) superlens on long-range near-field WPT, quantitatively confirming in simulation and measurement at 13-16 MHz the conditions under which the superlens can enhance power transfer efficiency compared to the lens-less free-space system.
Discretization: An Enabling Technique

Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers which are more concise to represent and specify, easier to use and comprehend as they are closer to a knowledge-level representation than continuous values. Many studies show induction tasks can benefit from discretization: rules with discrete values are normally shorter and more understandable and discretization can lead to improved predictive accuracy. Furthermore, many induction algorithms found in the literature require discrete features. All these prompt researchers and practitioners to discretize continuous features before or during a machine learning or data mining task. There are numerous discretization methods available in the literature. It is time for us to examine these seemingly different methods for discretization and find out how different they really are, what are the key components of a discretization process, how we can improve the current level of research for new development as well as the use of existing methods. This paper aims at a systematic study of discretization methods with their history of development, effect on classification, and trade-off between speed and accuracy. Contributions of this paper are an abstract description summarizing existing discretization methods, a hierarchical framework to categorize the existing methods and pave the way for further development, concise discussions of representative discretization methods, extensive experiments and their analysis, and some guidelines as to how to choose a discretization method under various circumstances. We also identify some issues yet to solve and future research for discretization.