nils-herrmann/clean_scirepeval huggingface dataset:
A case study on using neural networks to perform technical forecasting of forex

This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying `rulesa of the movement in currency exchange rates. The exchange rates between American Dollar and "ve other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar are forecast by the trained neural networks. The traditional rescaled range analysis is used to test the `e$ciencya of each market before using historical data to train the neural networks. The results presented here show that without the use of extensive market data or knowledge, useful prediction can be made and signi"cant paper pro"ts can be achieved for out-of-sample data with simple technical indicators. A further research on exchange rates between Swiss Franc and American Dollar is also conducted. However, the experiments show that with e$cient market it is not easy to make pro"ts using technical indicators or time series input neural networks. This article also discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model's predictive power. After presenting the experimental results, a discussion on future research concludes the paper.
Scene Text Extraction with Edge Constraint and Text Collinearity

In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two functions: it generates text region candidates, and it verifies of the label of the candidates (text or non-text). The text region candidates are generated through a modified K-means clustering algorithm, which references texture features, edge information and color information. The candidate labels are then verified in a global sense by the Markov Random Field model where collinearity weight is added as long as most texts are aligned. The proposed method achieves reasonable accuracy for text extraction from moderately difficult examples from the ICDAR 2003 database.
Compact Wideband Circularly Polarized Patch Antenna for CNSS Applications

A compact wideband circularly polarized (CP) patch antenna utilizing a quad-feed network (QFN) and quadruple semi-fan-annulus (QSFA) patches is proposed. For using a coupled-line dual-band power divider (PD) and improved phase shifter (PS) with stepped-impedance-open-stub (SIOS), the QFN has a compact size and exhibits the electromagnetic (EM) simulated bandwidth over 96% for the power distribution -6.5±0.5 dB, return loss (RL) > 14 dB, and a consistent 90° (±9°) phase deviation. Moreover, the QSFA patches can expand the CP bandwidth and reduce the size of the antenna effectively. Measurement results show that the proposed antenna achieves CP bandwidth of 72% from 1.15 to 2.45 GHz for RL > 10 dB, axial ratio dB, and 3-dB gain variation (gain > 4.4 dBi). Therefore, the proposed antenna is a good candidate for Compass Navigation Satellite System (CNSS) applications.
Computational thinking in educational activities: an evaluation of the educational game light-bot

Computational thinking is gaining recognition as an important skill set for students, both in computer science and other disciplines. Although there has been much focus on this field in recent years, it is rarely taught as a formal course within the curriculum, and there is little consensus on what exactly computational thinking entails and how to teach and evaluate it. To address these concerns, we have developed a computational thinking framework to be used as a planning and evaluative tool. Within this framework, we aim to unify the differing opinions about what computational thinking should involve. As a case study, we have applied the framework to Light-Bot, an educational game with a strong focus on programming, and found that the framework provides us with insight into the usefulness of the game to reinforce computer science concepts.
Recognition of Emotion from Speech: A Review

Emotional speech recognition is an area of great interest for human-computer interaction. The system must be able to recognize the user’s emotion and perform the actions accordingly. It is essential to have a framework that includes various modules performing actions like speech to text conversion, feature extraction, feature selection and classification of those features to identify the emotions. The classifications of features involve the training of various emotional models to perform the classification appropriately. Another important aspect to be considered in emotional speech recognition is the database used for training the models. Then the features selected to be classified must be salient to identify the emotions correctly. The integration of all the above modules provides us with an application that can recognize the emotions of the user and give it as input to the system to respond appropriately.
Learning styles: where's the evidence?

Armitage G. Coping with medical error: a systematic review of papers to assess the effects of involvement in medical errors on healthcare professionals’ psychological wellbeing. Qual Saf Health Care 2010;19 (6):43. 10 Fischer MA, Mazor KM, Baril J, Alper E, DeMarco D, Pugnaire M. Learning from mistakes. Factors that influence how students and residents learn from medical errors. J Gen Intern Med 2006;21 (5):419–23.
On the Feasibility of Distributed Beamforming in Wireless Networks

Energy efficient communication is a fundamental problem in wireless ad-hoc and sensor networks. In this paper, we explore the feasibility of a distributed beamforming approach to this problem, with a cluster of distributed transmitters emulating a centralized antenna array so as to transmit a common message signal coherently to a distant base station. The potential SNR gains from beamforming are well-known. However, realizing these gains requires synchronization of the individual carrier signals in phase and frequency. In this paper we show that a large fraction of the beamforming gains can be realised even with imperfect synchronization corresponding to phase errors with moderately large variance. We present a master-slave architecture where a designated master transmitter coordinates the synchronization of other (slave) transmitters for beamforming. We observe that the transmitters can achieve distributed beamforming with minimal coordination with the base station using channel reciprocity. Thus, inexpensive local coordination with a master transmitter makes the expensive communication with a distant base station receiver more efficient. However, the duplexing constraints of the wireless channel place a fundamental limitation on the achievable accuracy of synchronization. We present a stochastic analysis that demonstrates the robustness of beamforming gains with imperfect synchronization, and demonstrate a tradeoff between synchronization overhead and beamforming gains. We also present simulation results for the phase errors that validate the analysis
Augmented Implicitly Restarted Lanczos Bidiagonalization Methods

New restarted Lanczos bidiagonalization methods for the computation of a few of the largest or smallest singular values of a large matrix are presented. Restarting is carried out by augmentation of Krylov subspaces that arise naturally in the standard Lanczos bidiagonalization method. The augmenting vectors are associated with certain Ritz or harmonic Ritz vectors. Computed examples show the new methods to be competitive with available schemes.
Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda

We combine data from a field survey with transaction log data from a mobile phone operator to provide new insight into daily patterns of mobile phone use in Rwanda. The analysis is divided into three parts. First, we present a statistical comparison of the general Rwandan population to the population of mobile phone owners in Rwanda. We find that phone owners are considerably wealthier, better educated, and more predominantly male than the general population. Second, we analyze patterns of phone use and access, based on self-reported survey data. We note statistically significant differences by gender; for instance, women are more likely to use shared phones than men. Third, we perform a quantitative analysis of calling patterns and social network structure using mobile operator billing logs. By these measures, the differences between men and women are more modest, but we observe vast differences in utilization between the relatively rich and the relatively poor. Taken together, the evidence in this paper suggests that phones are disproportionately owned and used by the privileged strata of Rwandan society.
LZfuzz: a fast compression-based fuzzer for poorly documented protocols

Computers make very fast, very accurate mistakes. From a refrigerator magnet. Real-world infrastructure offers many scenarios where protocols (and other details) are not released due to being considered too sensitive or for other reasons. This situation makes it hard to apply fuzzing techniques to test their security and reliability, since their full documentation is only available to their developers, and domain developer expertise does not necessarily intersect with fuzz-testing expertise (nor deployment responsibility). State-of-the-art fuzzing techniques, however, work best when protocol specifications are available. Still, operators whose networks include equipment communicating via proprietary protocols should be able to reap the benefits of fuzz-testing them. In particular, administrators should be able to test proprietary protocols in the absence of end-to-end application-level encryption to understand whether they can withstand injection of bad traffic, and thus be able to plan adequate network protection measures. Such protocols can be observed in action prior to fuzzing, and packet captures can be used to learn enough about the structure of the protocol to make fuzzing more efficient. Various machine learning approaches, e.g. bioinformatics methods, have been proposed for learning models of the targeted protocols. The problem with most of these approaches to date is that, although sometimes quite successful, they are very computationally heavy and thus are hardly practical for application by network administrators and equipment owners who cannot easily dedicate a compute cluster to such tasks. We propose a simple method that, despite its roughness, allowed us to learn facts useful for fuzzing from protocol traces at much smaller CPU and time costs. Our fuzzing approach proved itself empirically in testing actual proprietary SCADA protocols in an isolated control network test environment, and was also successful in triggering flaws in implementations of several popular commodity Internet protocols. Our fuzzer, LZfuzz (pronounced “lazy-fuzz”) relies on a variant of Lempel–Ziv compression algorithm to guess boundaries between the structural units of the protocol, and builds on the well-known free software GPF fuzzer.
Teaching Effectiveness Research in the Past Decade : The Role of Theory and Research Design in Disentangling Meta-Analysis Results

This meta-analysis summarizes teaching effectiveness studies of the past decade and investigates the role of theory and research design in disentangling results. Compared to past analyses based on the process–product model, a framework based on cognitive models of teaching and learning proved useful in analyzing studies and accounting for variations in effect sizes. Although the effects of teaching on student learning were diverse and complex, they were fairly systematic. The authors found the largest effects for domainspecific components of teaching—teaching most proximal to executive processes of learning. By taking into account research design, the authors further disentangled meta-analytic findings. For example, domain-specific teaching components were mainly studied with quasi-experimental or experimental designs. Finally, correlational survey studies dominated teaching effectiveness studies in the past decade but proved to be more distal from the teaching–learning process.
In Code We Trust? - Measuring the Control Flow Immutability of All Smart Contracts Deployed on Ethereum

Program code stored on the Ethereum blockchain is considered immutable, but this does not imply that its control flow cannot be modified. This bears the risk of loopholes whenever parties encode binding agreements in smart contracts. In order to quantify the issue, we define a heuristic indicator of control flow immutability, evaluate it based on a call graph of all smart contracts deployed on Ethereum, and find that two out of five smart contracts require trust in at least one third party. Besides, the analysis reveals that significant parts of the Ethereum blockchain are interspersed with debris from past attacks against the platform. We leverage the call graph to develop a method for data cleanup, which allows for less biased statistics of Ethereum use in practice.
An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

Visible videos captured under different weather conditions may exhibit different characteristics, and thermal infrared videos are easily affected by ambient temperature variations; this sensitivity to environmental conditions makes the fusion of visible and thermal infrared videos a challenge. This paper proposes an adaptive fusion algorithm for visible and infrared videos, and uses cumulative distribution of gray levels and the entropy to adaptively retain infrared-hot targets and visible textures. The original visible and infrared frames are decomposed into two layers, namely, the base layer and the detail layer. The guided filter is employed to decompose frames due to its high efficiency. Two weight maps, one for the infrared base layer and one for the visible base layer, are adaptively generated based on the cumulative distribution of gray levels and the entropy, respectively. The visible base layer and the infrared base layer are fused based on their weight maps. The final fusion result is obtained by combining the fused base layer with the visible detail layer. Experimental results demonstrate that the proposed algorithm can achieve better fusion results compared with state-of-the-art methods.
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets (for both training and evaluation) focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios that are rarely observed, such as children playing in the street and people using bicycles/skateboards in unexpected ways. Such in-the-tail data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a dedicated collection effort, it is relatively small by contemporary standards (&#x2248; 1000 images). To explore large-scale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right priors or parameters for synthesis: we would like realistic data with realistic poses and object configurations. Inspired by Generative Adversarial Networks, we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset (that fools the classifier), which we deem Synthetic Imposters. We demonstrate that this pipeline allows one to generate realistic (or adverserial) training data by making use of rendering/animation engines. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Synthetic Imposters can also be used for in-the-tail validation at test-time, a notoriously difficult challenge for real-world deployment.
How Many Folders Do You Really Need?: Classifying Email into a Handful of Categories

Email classification is still a mostly manual task. Consequently, most Web mail users never define a single folder. Recently however, automatic classification offering the same categories to all users has started to appear in some Web mail clients, such as AOL or Gmail. We adopt this approach, rather than previous (unsuccessful) personalized approaches because of the change in the nature of consumer email traffic, which is now dominated by (non-spam) machine-generated email. We propose here a novel approach for (1) automatically distinguishing between personal and machine-generated email and (2) classifying messages into latent categories, without requiring users to have defined any folder. We report how we have discovered that a set of 6 "latent" categories (one for human- and the others for machine-generated messages) can explain a significant portion of email traffic. We describe in details the steps involved in building a Web-scale email categorization system, from the collection of ground-truth labels, the selection of features to the training of models. Experimental evaluation was performed on more than 500 billion messages received during a period of six months by users of Yahoo mail service, who elected to be part of such research studies. Our system achieved precision and recall rates close to 90% and the latent categories we discovered were shown to cover 70% of both email traffic and email search queries. We believe that these results pave the way for a change of approach in the Web mail industry, and could support the invention of new large-scale email discovery paradigms that had not been possible before.
Software-defined wireless network architectures for the Internet-of-Things

The Internet-of-Things (IoT) envisions a world where billions of everyday objects and mobile devices communicate using a large number of interconnected wired and wireless networks. Maximizing the utilization of this paradigm requires fine-grained QoS support for differentiated application requirements, context-aware semantic information retrieval, and quick and easy deployment of resources, among many other objectives. These objectives can only be achieved if components of the IoT can be dynamically managed end-to-end across heterogeneous objects, transmission technologies, and networking architectures. Software-defined Networking (SDN) is a new paradigm that provides powerful tools for addressing some of these challenges. Using a software-based control plane, SDNs introduce significant flexibility for resource management and adaptation of network functions. In this article, we study some promising solutions for the IoT based on SDN architectures. Particularly, we analyze the application of SDN in managing resources of different types of networks such as Wireless Sensor Networks (WSN) and mobile networks, the utilization of SDN for information-centric networking, and how SDN can leverage Sensing-as-a-Service (SaaS) as a key cloud application in the IoT.
Implicit self-esteem compensation: automatic threat defense.

Four experiments demonstrated implicit self-esteem compensation (ISEC) in response to threats involving gender identity (Experiment 1), implicit racism (Experiment 2), and social rejection (Experiments 3-4). Under conditions in which people might be expected to suffer a blow to self-worth, they instead showed high scores on 2 implicit self-esteem measures. There was no comparable effect on explicit self-esteem. However, ISEC was eliminated following self-affirmation (Experiment 3). Furthermore, threat manipulations increased automatic intergroup bias, but ISEC mediated these relationships (Experiments 2-3). Thus, a process that serves as damage control for the self may have negative social consequences. Finally, pretest anxiety mediated the relationship between threat and ISEC (Experiment 3), whereas ISEC negatively predicted anxiety among high-threat participants (Experiment 4), suggesting that ISEC may function to regulate anxiety. The implications of these findings for automatic emotion regulation, intergroup bias, and implicit self-esteem measures are discussed.
The state-of-the-art of silicon-on-sapphire CMOS RF switches

Silicon-on-Sapphire (SOS) CMOS FETs have many properties which are desirable for RF switch applications. By being manufactured on an insulating sapphire substrate, the bulk parasitic capacitances typical of CMOS FETs are eliminated. The SOS FET has a very low Ron-Coff product, allowing for low insertion loss and high isolation in high frequency applications. Despite the low breakdown voltage intrinsic to Si, SOS FETs can be stacked in series to withstand high voltages when biased in subthreshold. This work studies the tradeoffs of SOS RF switch design and compares SOS against other technologies such as GaAs and Si-based SOI. Also presented is a high power SP6T switch with insertion loss of 0.6 dB at 2 GHz and isolation of 40 dB at 2 GHz. The presented switch has the highest linearity reported to date of any SP6T switch with a P1dB of 20 W and OIP3 of <+70 dBm.
A review of channel selection algorithms for EEG signal processing

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.