nils-herrmann/clean_scirepeval huggingface dataset:
Three-Phase Cascaded Delayed Signal Cancellation PLL for Fast Selective Harmonic Detection

Fast and accurate selective harmonic detection has crucial value for many power system harmonic compensation systems. The existing detection methods are known to have a few drawbacks, such as long delay time, sensitivity to grid frequency variation, and difficulty to achieve zero steady-state error. In order to overcome these drawbacks, this paper proposes a selective harmonic detection system based on the three-phase cascaded delayed signal cancellation phase-locked loop. The system can be flexibly configured to detect any individual harmonic from the source with various background harmonics. It also features very short transient and excellent adaptability under small and considerable frequency variations, as verified by comprehensive experimental results. Finally, this paper provides guidance on how to tailor the detection system for different applications and a solution to address the practical implementation issues.
Developing an artificial intelligence bot for Othello

In this paper, I describe the Othello AI project completed during the 2014 summer Leap program at Carnegie Mellon University. I will first look at the basic components of two-player game AI. I will then look at basic properties of Othello AI design in the context of the project. Then I will examine the development of the Othello bot from the earliest to the latest versions. The performance of the bot relevant to humans and other bots will be analyzed and discussed. Finally I will look at future possible improvements to MyPlayer, the Othello Artificial Intelligence bot.
Analysis and design of monolithic, high PSR, linear regulators for SoC applications

Linear regulators are critical analog blocks that shield a system from fluctuations in supply rails and the importance of determining their power supply rejection (PSR) performance is magnified in SoC systems, given their inherently noisy environments. In this work, a simple, intuitive, voltage divider model is introduced to analyze the PSR of linear regulators, from which design guidelines for obtaining high PSR performance are derived. The PSR of regulators that use PMOS output stages for low drop-out (LDO), crucial for modern low-voltage systems, is enhanced by error amplifiers which present a supply-correlated ripple at the gate of the PMOS pass device. On the other hand, amplifiers that suppress the supply ripple at their output are optimal for NMOS output stages since the source is now free from output ripple. A better PSR bandwidth, at the cost of dc PSR, can be obtained by interchanging the amplifiers in the two cases. It has also been proved that the dc PSR, its dominant frequency breakpoint (where performance starts to degrade), and three subsequent breakpoints are determined by the dc open-loop gain, error amplifier bandwidth, unity-gain frequency (UGF) of the system, output pole, and ESR zero, respectively. These results were verified with SPICE simulations using BSIM3 models for the TSMC 0.35 /spl mu/m CMOS process from MOSIS.
Impact of the variation of horse racing odds on the outcome of horse races at the Champ de Mars

Horse Racing is the favourite sports of Mauritians. This can be demonstrated by the presence of huge crowds at the Champ de Mars on racing days. Many people wait for the last moment to bet as they feel that the variation of odds has an influence on the winner of a race at the Champ de Mars. This is the motivation for this research. Thus, in this work, we have used artificial neural networks to predict the winner of a race based on the variation of the odds. The odds were collected at eight different intervals. The training has been done on 232 races and the testing on 27 races. An overall percentage of 7.4% was obtained for the prediction of winners. This shows that the variation in horse racing odds does not have any impact on the outcome of horse races at the Champs de Mars. To our knowledge, this is the first study which studies the relationship between the variation in odds and the rank of horses.
Joint Network Coding and Superposition Coding for Multi-User Information Exchange in Wireless Relaying Networks

A joint network coding and superposition coding (JNSC) scheme is proposed for information exchange between more than two users in a wireless relaying network. In this paper we consider two scenarios in a relaying network with four nodes: single and multiple information exchange loops between three source nodes, and two alternative transmission schemes, i.e. pure time division (PTD) and pure network coding (PNC), are also considered in order to compare with JNSC. The achievable rate regions of the PTD, PNC and JNSC schemes are all characterized, indicating the JNSC scheme is not always superior to the other two schemes. The sum rate optimization problem with a certain traffic pattern is also solved. We showed that the maximum coding gains of the JNSC and PNC schemes compared with the PTD scheme is achieved as the transmission rate of each node is one third of the sum rate in the network. Simulation results also reveal this phenomenon.
Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review

Difficulties are often an unavoidable but important part of the learning process. This seems particularly so for complex conceptual learning. Challenges in the learning process are however, particularly difficult to detect and respond to in educational environments where growing class sizes and the increased use of digital technologies mean that teachers are unable to provide nuanced and personalized feedback and support to help students overcome their difficulties. Individual differences, the specifics of the learning activity, and the difficulty of giving individual feedback in large classes and digital environments all add to the challenge of responding to student difficulties and confusion. In this integrative review, we aim to explore difficulties and resulting emotional responses in learning. We will review the primary principles of cognitive disequilibrium and contrast these principles with work on desirable difficulties, productive failure, impasse driven learning, and pure discovery-based learning. We conclude with a theoretical model of the zones of optimal and sub-optimal confusion as a way of conceptualizing the parameters of productive and non-productive difficulties experienced by students while they learn.
Military position source challenges for worldwide ADS-B out compliance

Military aircraft program offices face numerous challenges over the next decade as they equip their aircraft to meet Automatic Dependent Surveillance-Broadcast (ADS-B) Out mandates. However, a primary challenge is satisfying the position source performance requirements established in various regulatory documents. This paper describes position source challenges and approaches to dealing with those challenges that can result in satisfying the necessary requirements as well as providing specific advantages to military aircraft programs. This paper covers the work being conducted to create position source standards and approvals for use of Precise Positioning Service (PPS) Global Positioning System (GPS) to provide civil and military navigation and surveillance functions. This includes the creation of a new Military Standards Order (MSO) that will communicate these standards to industry as well as military aircraft programs. Military aircraft need to equip with ADS-B Out equipment in order to participate in civil airspace after 2020 in the United States, Europe, Canada, and Australia. Additionally, many aircraft programs desire to equip aircraft with military Mode 5 Level 2 (M5L2) Out technology, which like ADS-B, periodically transmits position and identity information, except M5L2 Out data is protected using encryption. This paper also describes the ADS-B and M5L2 position source regulations and standards. Most of the ADS-B Out regulations are performance based; however, civil aviation authorities often assume compliance will be achieved through equipment meeting Technical Standard Orders (TSOs) utilizing standard civilian data bus architectures. Military aircraft often operate equipment without a TSO and with a military data bus, such as MIL-STD-1553B. Therefore, civil ADS-B Out standards must be adapted for use by the military. The challenges of selecting and integrating a position source for civil ADS-B Out, as well as for military M5L2, include uncertain position and velocity latency from the GPS equipment. In addition, other challenges exist in the availability of ADS-B parameters, utilizing blended inertial and GPS sources, and ensuring proper design assurance for certification. ADS-B Out and M5L2 Out position source requirements do not conflict, allowing a single position source to be leveraged for both capabilities. Single and dual position source architectures are described along with their comparative advantages and disadvantages. An optimal architecture and certification approach is presented which uses MSOd PPS GPS equipment for ADS-B Out and M5L2 Out. This architecture, if implemented using common standards across multiple aircraft types, could greatly reduce ADS-B and M5L2 development, integration, and procurement costs to military aircraft program offices. In addition, common standards could enhance industry's effectiveness in developing dualpurpose PPS GPS equipment.
Modeling attention to salient proto-objects

Selective visual attention is believed to be responsible for serializing visual information for recognizing one object at a time in a complex scene. But how can we attend to objects before they are recognized? In coherence theory of visual cognition, so-called proto-objects form volatile units of visual information that can be accessed by selective attention and subsequently validated as actual objects. We propose a biologically plausible model of forming and attending to proto-objects in natural scenes. We demonstrate that the suggested model can enable a model of object recognition in cortex to expand from recognizing individual objects in isolation to sequentially recognizing all objects in a more complex scene.
Efficient FPGA acceleration of Convolutional Neural Networks using logical-3D compute array

Convolutional Deep Neural Networks (DNNs) are reported to show outstanding recognition performance in many image-related machine learning tasks. DNNs have a very high computational requirement, making accelerators a very attractive option. These DNNs have many convolutional layers with different parameters in terms of input/output/kernel sizes as well as input stride. Design constraints usually require a single design for all layers of a given DNN. Thus a key challenge is how to design a common architecture that can perform well for all convolutional layers of a DNN, which can be quite diverse and complex. In this paper we present a flexible yet highly efficient 3D neuron array architecture that is a natural fit for convolutional layers. We also present our technique to optimize its parameters including on-chip buffer sizes for a given set of resource constraint for modern FPGAs. Our experimental results targeting a Virtex-7 FPGA demonstrate that our proposed technique can generate DNN accelerators that can outperform the state-of-the-art solutions, by 22% for 32-bit floating-point MAC implementations, and are far more scalable in terms of compute resources and DNN size.
Efficient 3D LIDAR based loop closing using deep neural network

Loop closure detection in 3D LIDAR data is an essential but challenging problem in SLAM system. It is important to reduce global inconsistency or re-localize the robot that loses the localization, while is difficult for the lack of prior information. We present a semi-handcrafted representation learning method for LIDAR point cloud using siamese convolution neural network, which states the loop closure detection to a similarity modeling problem. With the learned representation, the similarity between two LIDAR scans is transformed as the Euclidean distance between the representations respectively. Based on it, we furthermore establish kd-tree to accelerate the searching of similar scans. To demonstrate the performance and effectiveness of the proposed method, the KITTI dataset is employed for comparison with other LIDAR loop closure detection methods. The result shows that our method can achieve both higher accuracy and efficiency.
A Ground-Based Near Infrared Camera Array System for UAV Auto-Landing in GPS-Denied Environment

This paper proposes a novel infrared camera array guidance system with capability to track and provide real time position and speed of a fixed-wing Unmanned air vehicle (UAV) during a landing process. The system mainly include three novel parts: (1) Infrared camera array and near infrared laser lamp based cooperative long range optical imaging module; (2) Large scale outdoor camera array calibration module; and (3) Laser marker detection and 3D tracking module. Extensive automatic landing experiments with fixed-wing flight demonstrate that our infrared camera array system has the unique ability to guide the UAV landing safely and accurately in real time. Moreover, the measurement and control distance of our system is more than 1000 m. The experimental results also demonstrate that our system can be used for UAV automatic accurate landing in Global Position System (GPS)-denied environments.
An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
Culture and the distinctiveness motive: constructing identity in individualistic and collectivistic contexts.

The motive to attain a distinctive identity is sometimes thought to be stronger in, or even specific to, those socialized into individualistic cultures. Using data from 4,751 participants in 21 cultural groups (18 nations and 3 regions), we tested this prediction against our alternative view that culture would moderate the ways in which people achieve feelings of distinctiveness, rather than influence the strength of their motivation to do so. We measured the distinctiveness motive using an indirect technique to avoid cultural response biases. Analyses showed that the distinctiveness motive was not weaker-and, if anything, was stronger-in more collectivistic nations. However, individualism-collectivism was found to moderate the ways in which feelings of distinctiveness were constructed: Distinctiveness was associated more closely with difference and separateness in more individualistic cultures and was associated more closely with social position in more collectivistic cultures. Multilevel analysis confirmed that it is the prevailing beliefs and values in an individual's context, rather than the individual's own beliefs and values, that account for these differences.
Bricks, Blocks, Boxes, Cubes, and Dice: On the Role of Cubic Shapes for the Design of Tangible Interactive Devices

Cubic shapes play an astonishing role in the design of tangible interactive devices. Due to our curiosity for this widespread design preference lasting over thirty years, we constituted a literature survey of papers, books and products since the late 1970s. Out of a corpus of fourty-seven papers, books and products that propose cubic shapes for tangible interactive devices we trace the origins of cubicle tangibles and highlight the rationale for their application. Through a comparative study, we analyze the properties of this shape for tangible interaction design and classify these along the themes of: Manipulation as Input, Placement in Space as Input, Arrangement, Multifunctionality, Randomness, Togetherness & Variations, Physical Qualities, Container, and Pedestal for Output. We propose a taxonomy for cubic shaped tangible interactive devices based on the reviewed contributions, in order to support researchers and designers in their future work of designing cubic shaped tangible interactive devices.
NTRU Prime: Reducing Attack Surface at Low Cost

Several ideal-lattice-based cryptosystems have been broken by recent attacks that exploit special structures of the rings used in those cryptosystems. The same structures are also used in the leading proposals for post-quantum lattice-based cryptography, including the classic NTRU cryptosystem and typical Ring-LWE-based cryptosystems. This paper (1) proposes NTRU Prime, which tweaks NTRU to use rings without these structures; (2) proposes Streamlined NTRU Prime, a public-key cryptosystem optimized from an implementation perspective, subject to the standard design goal of IND-CCA2 security; (3) finds high-security post-quantum parameters for Streamlined NTRU Prime; and (4) optimizes a constant-time implementation of those parameters. The resulting sizes and speeds show that reducing the attack surface has very low cost.
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

This paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets were manually curated for the five languages with high inter-annotator agreements (consistently in the 0.9 ballpark). These were used for semi-automatic construction of ten cross-lingual datasets. 17 teams participated in the task, submitting 24 systems in subtask 1 and 14 systems in subtask 2. Results show that systems that combine statistical knowledge from text corpora, in the form of word embeddings, and external knowledge from lexical resources are best performers in both subtasks. More information can be found on the task website: http://alt.qcri. org/semeval2017/task2/ .
Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context

The most effective superresolution methods proposed in the literature require precise knowledge of the so-called point spread function of the imager, while in practice its accurate estimation is nearly impossible. This paper presents a new superresolution method, whose main feature is its ability to account for the scant knowledge of the imager point spread function. This ability is based on representing this imprecise knowledge via a non-additive neighborhood function. The superresolution reconstruction algorithm transfers this imprecise knowledge to output by producing an imprecise (interval-valued) high-resolution image. We propose some experiments illustrating the robustness of the proposed method with respect to the imager point spread function. These experiments also highlight its high performance compared with very competitive earlier approaches. Finally, we show that the imprecision of the high-resolution interval-valued reconstructed image is a reconstruction error marker.
DeeperBind: Enhancing prediction of sequence specificities of DNA binding proteins

Transcription factors (TFs) are macromolecules that bind to cis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several in vivo and in vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.
A survey of software refactoring

We provide an extensive overview of existing research in the field of software refactoring. This research is compared and discussed based on a number of different criteria: the refactoring activities that are supported, the specific techniques and formalisms that are used for supporting these activities, the types of software artifacts that are being refactored, the important issues that need to be taken into account when building refactoring tool support, and the effect of refactoring on the software process. A running example is used to explain and illustrate the main concepts.
The role of brand logos in fi rm performance

a r t i c l e i n f o Keywords: Brand logos Brand management Aesthetics Commitment Brand extensions Firm performance This research demonstrates that the positive effects of brand logos on customer brand commitment and firm performance derive not from enabling brand identification, as is currently understood, but primarily from facilitating customer self-identity/expressiveness, representing a brand's functional benefits, and offering aesthetic appeal. This study examines whether brand names or visual symbols as logos are more effective at creating these benefits and whether or not the impact of the three aforementioned brand logo benefits on customer brand commitment and firm performance is contingent on the extent to which a firm leverages its brand (i.e., employs brand extensions to different product categories).
How Blockchain Could Empower eHealth: An Application for Radiation Oncology - (Extended Abstract)

Electronic medical records (EMRs) contain critical, highly sensitive private healthcare information, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we discuss our perspectives on blockchain based healthcare data management and present a prototype of a framework for managing and sharing EMR data for cancer patient care.