nils-herrmann/clean_scirepeval huggingface dataset:
Acquiring reflectance and shape from continuous spherical harmonic illumination

We present a novel technique for acquiring the geometry and spatially-varying reflectance properties of 3D objects by observing them under continuous spherical harmonic illumination conditions. The technique is general enough to characterize either entirely specular or entirely diffuse materials, or any varying combination across the surface of the object. We employ a novel computational illumination setup consisting of a rotating arc of controllable LEDs which sweep out programmable spheres of incident illumination during 1-second exposures. We illuminate the object with a succession of spherical harmonic illumination conditions, as well as photographed environmental lighting for validation. From the response of the object to the harmonics, we can separate diffuse and specular reflections, estimate world-space diffuse and specular normals, and compute anisotropic roughness parameters for each view of the object. We then use the maps of both diffuse and specular reflectance to form correspondences in a multiview stereo algorithm, which allows even highly specular surfaces to be corresponded across views. The algorithm yields a complete 3D model and a set of merged reflectance maps. We use this technique to digitize the shape and reflectance of a variety of objects difficult to acquire with other techniques and present validation renderings which match well to photographs in similar lighting.
Offline handwritten signature verification system using a supervised neural network approach

Signatures are imperative biometric attributes of humans that have long been used for authorization purposes. Most organizations primarily focus on the visual appearance of the signature for verification purposes. Many documents, such as forms, contracts, bank cheques, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures accurately, effortlessly, and in a timely manner. In this work, an artificial neural network based on the well-known Back-propagation algorithm is used for recognition and verification. To test the performance of the system, the False Reject Rate, the False Accept Rate, and the Equal Error Rate (EER) are calculated. The system was tested with 400 test signature samples, which include genuine and forged signatures of twenty individuals. The aim of this work is to limit the computer singularity in deciding whether the signature is forged or not, and to allow the signature verification personnel to participate in the deciding process through adding a label which indicates the amount of similarity between the signature which we want to recognize and the original signature. This approach allows judging the signature accuracy, and achieving more effective results.
High-performance NiO/Ag/NiO transparent electrodes for flexible organic photovoltaic cells.

Transparent electrodes with a dielectric-metal-dielectric (DMD) structure can be implemented in a simple manufacturing process and have good optical and electrical properties. In this study, nickel oxide (NiO) is introduced into the DMD structure as a more appropriate dielectric material that has a high conduction band for electron blocking and a low valence band for efficient hole transport. The indium-free NiO/Ag/NiO (NAN) transparent electrode exhibits an adjustable high transmittance of ∼82% combined with a low sheet resistance of ∼7.6 Ω·s·q(-1) and a work function of 5.3 eV after UVO treatment. The NAN electrode shows excellent surface morphology and good thermal, humidity, and environmental stabilities. Only a small change in sheet resistance can be found after NAN electrode is preserved in air for 1 year. The power conversion efficiencies of organic photovoltaic cells with NAN electrodes deposited on glass and polyethylene terephthalate (PET) substrates are 6.07 and 5.55%, respectively, which are competitive with those of indium tin oxide (ITO)-based devices. Good photoelectric properties, the low-cost material, and the room-temperature deposition process imply that NAN electrode is a striking candidate for low-cost and flexible transparent electrode for efficient flexible optoelectronic devices.
— Technical Report — Analyzing and Detecting Flash-based Malware using Lightweight MultiPath Exploration

Adobe Flash is a popular platform for providing dynamic and multimedia content on web pages. Despite being declared dead for years, Flash is still deployed on millions of devices. Unfortunately, the Adobe Flash Player increasingly suffers from vulnerabilities and attacks using Flash-based malware regularly put users at risk of being remotely attacked—most prominently highlighted by numerous exploits made public earlier this year. As a remedy, we present Gordon, a method for the comprehensive analysis and detection of Flash-based malware. By analyzing Flash animations at different levels during the interpreter’s loading and execution process, our method is able to spot attacks against the Flash Player as well as malicious functionality embedded in ActionScript code. To achieve this goal, Gordon combines a structural analysis of the container format with guided execution of the contained code—a novel analysis strategy that manipulates the control flow to maximize the coverage of indicative code regions. In an empirical evaluation with 26,600 Flash samples collected over 12 consecutive weeks, Gordon significantly outperforms related approaches when applied to samples shortly after their first occurrence in the wild, demonstrating its ability to provide timely protection for end users.
Single- and Two-Stage Inverter-Based Grid-Connected Photovoltaic Power Plants With Ride-Through Capability Under Grid Faults

Grid-connected distributed generation sources interfaced with voltage source inverters (VSIs) need to be disconnected from the grid under: 1)excessive dc-link voltage; 2)excessive ac currents; and 3)loss of grid-voltage synchronization. In this paper, the control of single- and two-stage grid-connected VSIs in photovoltaic (PV) power plants is developed to address the issue of inverter disconnecting under various grid faults. Inverter control incorporates reactive power support in the case of voltage sags based on the grid codes (GCs) requirements to ride-through the faults and support the grid voltages. A case study of a 1-MW system simulated in MATLAB/Simulink software is used to illustrate the proposed control. Problems that may occur during grid faults along with associated remedies are discussed. The results presented illustrate the capability of the system to ride-through different types of grid faults.
Compact, omni-directional, circularly-polarized mm-Wave antenna for device-to-device (D2D) communications in future 5G cellular systems

A simple, compact, omni-directional, circularly-polarized (CP) millimeter-wave antenna for Device-to-Device (D2D) communications in the next generation (5G) cellular systems is reported. It is a CP omni-directional antenna operating at 28 GHz for mobile terminals. The antenna combines a vertical electric monopole element with four magnetic elements to coherently excite parallel electric and magnetic dipoles. This combination generates the omni-directional CP radiation. The overlapping −10-dB impedance and 3-dB axial ratio (AR) bandwidth is from 27 to 28.5 GHz, which covers the 28 GHz frequency band proposed for 5G mobile cellular networks (i.e., from 27.5 to 28.35 GHz). The antenna has an omni-directional radiation pattern at 28 GHz whose peak realized RHCP gain is 2.08 dBic and whose 3-dB AR beamwidth is wide, from elevation angles 3° to 136°. Mass production of the antenna is possible by PCB manufacturing technologies. The overall size is 3.44 mm × 3.44 mm × 1 mm (ka = 1.1017). Consequently, it could be embedded in many current popular, smart wireless devices such as cell phones, laptops, digital watches, and smart glasses as well as their future versions for operation in 5G cellular networks.
Combining Logic and Probability: P-log Perspective

My research focuses on investigation and improvement of knowledge representation (KR) language P-log which was designed to reason about both logical and probabilistic knowledge. In particular, I aim to extend P-log with new constructs, clarify its semantics, develop a new efficient inference engine for it and establish its relationship with other related formalisms. Successful completion of this work will greatly increase the scope of practical applications of the language.
FRVT 2006 and ICE 2006 Large-Scale Experimental Results

This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces.
Usefulness of modified Pulmonary Index Score (mPIS) as a quantitative tool for the evaluation of severe acute exacerbation in asthmatic children.

Acute exacerbation of asthma is divided qualitatively into mild, moderate, and severe attacks and respiratory failure. This system is, however, not suitable for estimating small changes in respiratory condition with time and for determining the efficacy of treatments, because it has a qualitative, but not quantitative nature.



To evaluate the usefulness of quantitative estimation of asthma exacerbation, modified Pulmonary Index Score (mPIS) values were measured in 87 asthmatic children (mean age, 5.0 ± 0.4 years) during hospitalization. mPIS was calculated by adding the sum of scores for 6 items (scores of 0-3 were given for each item). These consisted of heart rate, respiratory rate, accessory muscle use, inspiratory-to-expiratory flow ratio, degree of wheezing, and oxygen saturation in room air. Measurements were made at visits and at hospitalization and were then made twice a day until discharge.



mPIS values were highly correlated among raters. mPIS values at visits were 9.1 ± 0.1 and 12.6 ± 0.4 in subjects with moderate and severe attacks, respectively (p < 0.001). mPIS values of subjects requiring continuous inhalation therapy (CIT) with isoproterenol in addition to systemic steroids were significantly higher than the values of those without CIT (12.0 ± 0.5 and 9.3 ± 0.2, respectively, p < 0.001). A score of 10 was suggested to be the optimal cutoff for distinguishing between subjects requiring and not requiring CIT, from the perspectives of both sensitivity and specificity. mPIS at hospitalization correlated well with the period until discharge, suggesting that this score was a useful predictor for the clinical course after hospitalization.



mPIS could be a useful tool for several aspects during acute asthma attacks, including the determination of a treatment plan, and prediction of the period of hospitalization in admitted patients, although prospective studies would be required to establish our hypothesis.
Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance

One approach to secure systems is through the analysis of audit trails. An audit trail is a record of all events that take place in a system and across a network, i.e., it provides a trace of user/system actions so that security events can be related to the actions of a specific individual or system component. Audit trails can be inspected for the presence or absence of certain patterns. This paper advocates the use of process mining techniques to analyze audit trails for security violations. It is shown how a specific algorithm, called the α-algorithm, can be used to support security efforts at various levels ranging from low-level intrusion detection to high-level fraud prevention.
An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Sentiment analysis is a recent advance in text mining applications for analyzing textual data according to orientation of human comments to determine whether they are positive, negative, or neutral. Different data mining techniques and algorithms such as support vector machine, naïve Bayes, decision tree, k-nearest neighbor and other techniques are used for analyzing textual data. These techniques are evaluated based on Arabic language due to its richness and diversity that can lead to difficulties in analyzing and mining large number of morphological and linguistic words that can lead to different meaning. This research provides sophisticated categorization of most or all recent articles according to the algorithms used in analyzing sentiment data. A comparison table for the proposed algorithms is presented that explains each algorithm and its use in mining and analysis of Arabic textual data and provides different evaluation for each sentiment analysis and classification algorithm according to different categories such as sentiment type, feature selection, sentiment polarity, domain oriented , data scope and data source, algorithm used in the sentiment analysis or classification, and the best algorithm result during the analysis and mining process. The experimental results explain that
Task Clustering and Gating for Bayesian Multitask Learning

Modeling a collection of similar regression or classification tasks can be improved by making the tasks ‘learn from each other’. In machine learning, this subject is approached through ‘multitask learning’, where parallel tasks are modeled as multiple outputs of the same network. In multilevel analysis this is generally implemented through the mixed-effects linear model where a distinction is made between ‘fixed effects’, which are the same for all tasks, and ‘random effects’, which may vary between tasks. In the present article we will adopt a Bayesian approach in which some of the model parameters are shared (the same for all tasks) and others more loosely connected through a joint prior distribution that can be learned from the data. We seek in this way to combine the best parts of both the statistical multilevel approach and the neural network machinery. The standard assumption expressed in both approaches is that each task can learn equally well from any other task. In this article we extend the model by allowing more differentiation in the similarities between tasks. One such extension is to make the prior mean depend on higher-level task characteristics. More unsupervised clustering of tasks is obtained if we go from a single Gaussian prior to a mixture of Gaussians. This can be further generalized to a mixture of experts architecture with the gates depending on task characteristics. All three extensions are demonstrated through application both on an artificial data set and on two realworld problems, one a school problem and the other involving single-copy newspaper sales.
Extracting Influential Nodes for Information Diffusion on a Social Network

We consider the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models. It was shown that a natural greedy strategy can give a good approximate solution to this optimization problem. However, a conventional method under the greedy algorithm needs a large amount of computation, since it estimates the marginal gains for the expected number of nodes influenced by a set of nodes by simulating the random process of each model many times. In this paper, we propose a method of efficiently estimating all those quantities on the basis of bond percolation and graph theory, and apply it to approximately solving the optimization problem under the greedy algorithm. Using real-world large-scale networks including blog networks, we experimentally demonstrate that the proposed method can outperform the conventional method, and achieve a large reduction in computational cost.
Bringing Deep Causality to Multimedia Data Streams

We live in a data abundance era. Availability of large volume of diverse multimedia data streams (ranging from video, to tweets, to activity, and to PM2.5) can now be used to solve many critical societal problems. Causal modeling across multimedia data streams is essential to reap the potential of this data. However, effective frameworks combining formal abstract approaches with practical computational algorithms for causal inference from such data are needed to utilize available data from diverse sensors. We propose a causal modeling framework that builds on data-driven techniques while emphasizing and including the appropriate human knowledge in causal inference. We show that this formal framework can help in designing a causal model with a systematic approach that facilitates framing sharper scientific questions, incorporating expert's knowledge as causal assumptions, and evaluating the plausibility of these assumptions. We show the applicability of the framework in a an important Asthma management application using meteorological and pollution data streams.
Action unit selective feature maps in deep networks for facial expression recognition

Facial expression recognizers based on handcrafted features have achieved satisfactory performance on many databases. Recently, deep neural networks, e. g. deep convolutional neural networks (CNNs) have been shown to boost performance on vision tasks. However, the mechanisms exploited by CNNs are not well established. In this paper, we establish the existence and utility of feature maps selective to action units in a deep CNN trained by transfer learning. We transfer a network pre-trained on the Image-Net dataset to the facial expression recognition task using the Karolinska Directed Emotional Faces (KDEF), Radboud Faces Database(RaFD) and extended Cohn-Kanade (CK+) database. We demonstrate that higher convolutional layers of the deep CNN trained on generic images are selective to facial action units. We also show that feature selection is critical in achieving robustness, with action unit selective feature maps being more critical in the facial expression recognition task. These results support the hypothesis that both human and deeply learned CNNs use similar mechanisms for recognizing facial expressions.
A Real-Time and Energy-Efficient Implementation of Difference-of-Gaussian with Flexible Thin-Film Transistors

With many advantageous features such as softness and better biocompatibility, flexible electronic device is a promising technology that can enable many emerging applications. However, most of the existing applications with flexible devices are sensors and drivers, while there is nearly no utilization aiming at complex computation, because the flexible devices have lower electron mobility, simple structure, and large process variation. In this paper, we propose an innovative method that enabled flexible devices to implement real-time and energy-efficient Difference-of-Gaussian, which illustrate feasibility and potentials for the flexible devices to achieve complicated real-time computation in future generation products.
A Decade Bandwidth 90 W GaN HEMT Push-Pull Power Amplifier for VHF / UHF Applications

A decade bandwidth 90 W, GaN HEMT push-pull power amplifier has been demonstrated. The power amplifier exhibits 18 dB small-signal gain with 20-1100 MHz 3-dB bandwidth and obtains 82.2-107.5 W CW output power with 51.9-73.8 % drain efficiency and 15.2-16.3 dB power gain over the 100-1000 MHz band. The push-pull power amplifier occupies a 2 x 2 inch PCB area and uses a novel compact broadband low loss coaxial coiled 1:1 balun to combine two 45 W packaged broadband lossy matched GaN HEMT amplifiers matched to 25 U. The packaged amplifiers contain a GaN on SiC HEMT operating at 50 V drain voltage with integrated passive matching circuitry on GaAs substrate. These amplifiers are targeted for use in multi-band multi-standard communication systems and for instrumentation applications.
Adopting Electronic Business in Ghana: Story of the Hospitality Industry

Electronic business (E-business) is a process of conducting business through the use of Information and Communication Technology (ICT), specifically the Internet. As a result of the current proliferation of ICT, e-business as well as other electronic applications and activities have globally sprung up and many companies and organizations in Ghana are realizing their importance and necessity. Over the past few years, the hospitality standards for hotels, restaurants and tourism operations in Ghana have improved. This has enabled the harmonization of hotel and restaurant licensing standards in West Africa, which is part of the Revised Treaty of the Economic Community of West African States (ECOWAS). Using relevant literature, this paper describes the importance of adopting e-business in the hospitality industry of Ghana with a focus on its benefits and challenges in such an industry. Additionally, the paper
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features

This paper focuses on subword-based Neural Machine Translation (NMT). We hypothesize that in the NMT model, the appropriate subword units for the following three modules (layers) can differ: (1) the encoder embedding layer, (2) the decoder embedding layer, and (3) the decoder output layer. We find the subword based on Sennrich et al. (2016) has a feature that a large vocabulary is a superset of a small vocabulary and modify the NMT model enables the incorporation of several different subword units in a single embedding layer. We refer these small subword features as hierarchical subword features. To empirically investigate our assumption, we compare the performance of several different subword units and hierarchical subword features for both the encoder and decoder embedding layers. We confirmed that incorporating hierarchical subword features in the encoder consistently improves BLEU scores on the IWSLT evaluation datasets. Title and Abstract in Japanese 階層的部分単語特徴を用いたニューラル機械翻訳 本稿では、部分単語 (subword) を用いたニューラル機械翻訳 (Neural Machine Translation, NMT)に着目する。NMTモデルでは、エンコーダの埋め込み層、デコーダの埋め込み層お よびデコーダの出力層の 3箇所で部分単語が用いられるが、それぞれの層で適切な部分単 語単位は異なるという仮説を立てた。我々は、Sennrich et al. (2016)に基づく部分単語は、 大きな語彙集合が小さい語彙集合を必ず包含するという特徴を利用して、複数の異なる部 分単語列を同時に一つの埋め込み層として扱えるよう NMTモデルを改良する。以降、こ の小さな語彙集合特徴を階層的部分単語特徴と呼ぶ。本仮説を検証するために、様々な部 分単語単位や階層的部分単語特徴をエンコーダ・デコーダの埋め込み層に適用して、その 精度の変化を確認する。IWSLT評価セットを用いた実験により、エンコーダ側で階層的な 部分単語を用いたモデルは BLEUスコアが一貫して向上することが確認できた。