nils-herrmann/clean_scirepeval huggingface dataset:
Mood and Emotions in Small Groups and Work Teams

Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individuallevel affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group’s affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group’s particular emotional history, may serve to constrain or amplify group members’ emotions. The outcome, group emotion, results from the combination of the group’s affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research.
60-GHz Two-Dimensionally Scanning Array Employing Wideband Planar Switched Beam Network

A two-dimensionally (2-D) scanning array employing a planar switched beam network (SBN) is proposed for 60-GHz high data rate wireless communication. SBNs offer a number of advantages including low cost, multibeam operation, simple direction finding, and minimal power consumption. Unlike existing switched beam arrays operating at 60 GHz, the proposed array scans in both planes. Also, in contrast to 2-D scanning stacks of Butler matrices and Rotman lenses, the proposed SBN is uniplanar and, hence, leads to low-cost manufacturing and integration with 60-GHz radios. The network produces eight 2-D beam states and demonstrates beamforming capability across the entire band of interest (57-64 GHz) with the aid of dummy crossover structures. It is printed on a Rogers Duroid 5880 substrate along with a 2 × 4 patch array. This letter describes the design, operation, and realization of the beamforming array. Simulated and measured performance results are presented to verify the design.
Frequency Domain Acceleration of Convolutional Neural Networks on CPU-FPGA Shared Memory System

We present a novel mechanism to accelerate state-of-art Convolutional Neural Networks (CNNs) on CPU-FPGA platform with coherent shared memory. First, we exploit Fast Fourier Transform (FFT) and Overlap-and-Add (OaA) to reduce the computational requirements of the convolutional layer. We map the frequency domain algorithms onto a highly-parallel OaA-based 2D convolver design on the FPGA. Then, we propose a novel data layout in shared memory for efficient data communication between the CPU and the FPGA. To reduce the memory access latency and sustain peak performance of the FPGA, our design employs double buffering. To reduce the inter-layer data remapping latency, we exploit concurrent processing on the CPU and the FPGA. Our approach can be applied to any kernel size less than the chosen FFT size with appropriate zero-padding leading to acceleration of a wide range of CNN models. We exploit the data parallelism of OaA-based 2D convolver and task parallelism to scale the overall system performance. By using OaA, the number of floating point operations is reduced by 39.14% ~54.10% for the state-of-art CNNs. We implement VGG16, AlexNet and GoogLeNet on Intel QuickAssist QPI FPGA Platform. These designs sustain 123.48 GFLOPs/sec, 83.00 GFLOPs/sec and 96.60 GFLOPs/sec, respectively. Compared with the state-of-the-art AlexNet implementation, our design achieves 1.35x GFLOPs/sec improvement using 3.33x less multipliers and 1.1x less memory. Compared with the state-of-art VGG16 implementation, our design has 0.66x GFLOPs/sec using 3.48x less multipliers without impacting the classification accuracy. For GoogLeNet implementation, our design achieves 5.56x improvement in performance compared with 16 threads running on a 10 Core Intel Xeon Processor at 2.8 GHz.
Nonlinear control of VTOL UAVs incorporating flapping dynamics

This paper presents the design and evaluation of a nonlinear control scheme for multirotor helicopters that takes first-order drag effects into account explicitly. A dynamic model including the blade flapping and induced drag forces is presented. Based on this model, a hierarchical nonlinear controller is designed to actively compensates for the nonlinear effects these drag forces. Reported simulation and experimental results indicate the significant performance improvement of the proposed drag-augmented control scheme with respect to a conventional nonlinear controller. For completeness, an offline procedure allowing for efficiently identifying the drag parameters is proposed.
Capacitive coupling for power and data telemetry to implantable biomedical microsystems

This paper proposes a new method for power telemetry and wireless data transfer to biomedical implants. Confinement of the transferred energy within a predefined area, hence not influencing the neighboring sensitive circuitry on the microsystem, and also allowing for more than one telemetry link on the same platform and even operating at the same carrier frequency are among the advantages of the proposed method over the traditional approaches for power and data telemetry.
Medicare Fraud Detection Using Machine Learning Methods

Healthcare is an integral component in people’s lives, especially for the rising elderly population, and must be affordable. Medicare is one such healthcare program. Claims fraud is a major contributor to increased healthcare costs, but its impact can be lessened through fraud detection. In this paper, we compare several machine learning methods to detect Medicare fraud. We perform a comparative study with supervised, unsupervised, and hybrid machine learning approaches using four performance metrics and class imbalance reduction via oversampling and an 80-20 undersampling method. We group the 2015 Medicare data into provider types, with fraud labels from the List of Excluded Individuals/Entities database. Our results show that the successful detection of fraudulent providers is possible, with the 80-20 sampling method demonstrating the best performance across the learners. Furthermore, supervised methods performed better than unsupervised or hybrid methods, but these results varied based on the class imbalance sampling technique and provider type.
A Study On Unmanned Vehicles and Cyber Security

During the past years Unmanned Aerial Vehicle (UAV) usage has grown in military and civilian fields. Every year, emergency response operations are more and more dependent of these unmanned vehicles. Lacking direct human interference, the correction of onboard errors and security breaches is a growing concern in unmanned vehicles. One of the concerns raised by first responders using unmanned vehicles is the security and privacy of the victims they are rescuing. Video channels used for the control of unmanned vehicles are of particular importance, having great susceptibility to hijack, jamming and spoofing attacks. It is assumed the video feeds are not protected by any type of encryption and are therefore vulnerable to hacking and/or spoofing. This summer we have conducted a preliminary survey of the security vulnerabilities and their implications on the operation of unmanned vehicles from a network security perspective.
Active Imitation Learning via Reduction to I.I.D. Active Learning

In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of reducing this effort by querying the expert about the desired action at individual states, which are selected based on answers to past queries and the learner’s interactions with an environment simulator. We introduce a new approach based on reducing active imitation learning to i.i.d. active learning, which can leverage progress in the i.i.d. setting. Our first contribution, is to analyze reductions for both non-stationary and stationary policies, showing that the label complexity (number of queries) of active imitation learning can be substantially less than passive learning. Our second contribution, is to introduce a practical algorithm inspired by the reductions, which is shown to be highly effective in four test domains compared to a number of alternatives.
A fast nearest neighbor search algorithm by nonlinear embedding

We propose an efficient algorithm to find the exact nearest neighbor based on the Euclidean distance for large-scale computer vision problems. We embed data points nonlinearly onto a low-dimensional space by simple computations and prove that the distance between two points in the embedded space is bounded by the distance in the original space. Instead of computing the distances in the high-dimensional original space to find the nearest neighbor, a lot of candidates are to be rejected based on the distances in the low-dimensional embedded space; due to this property, our algorithm is well-suited for high-dimensional and large-scale problems. We also show that our algorithm is improved further by partitioning input vectors recursively. Contrary to most of existing fast nearest neighbor search algorithms, our technique reports the exact nearest neighbor - not an approximate one - and requires a very simple preprocessing with no sophisticated data structures. We provide the theoretical analysis of our algorithm and evaluate its performance in synthetic and real data.
Vocal cues of ovulation in human females.

Recent research has documented a variety of ovulatory cues in humans, and in many nonhuman species, the vocal channel provides cues of reproductive state. We collected two sets of vocal samples from 69 normally ovulating women: one set during the follicular (high-fertility) phase of the cycle and one set during the luteal (low-fertility) phase, with ovulation confirmed by luteinizing hormone tests. In these samples we measured fundamental frequency (pitch), formant dispersion, jitter, shimmer, harmonics-to-noise ratio and speech rate. When speaking a simple introductory sentence, women's pitch increased during high- as compared with low-fertility, and this difference was the greatest for women whose voices were recorded on the two highest fertility days within the fertile window (the 2 days just before ovulation). This pattern did not occur when the same women produced vowels. The high- versus low-fertility difference in pitch was associated with the approach of ovulation and not menstrual onset, thus representing, to our knowledge, the first research to show a specific cyclic fertility cue in the human voice. We interpret this finding as evidence of a fertility-related enhancement of femininity consistent with other research documenting attractiveness-related changes associated with ovulation.
Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many realworld applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.
Practice Makes Perfect ? When Does Massed Learning Improve Product Usage Proficiency ?

Previous research has shown that spacing of information (over time) leads to better learning of product information. We develop a theoretical framework to describe how massed or spaced learning schedules interact with different learning styles to influence product usage proficiency. The core finding is that with experiential learning, proficiency in a product usage task is better under massed conditions, whereas with verbal learning, spacing works better. This effect is demonstrated for usage proficiency assessed via speed as well as quality of use. Further, massed learning also results in better usage proficiency on transfer tasks, for both experiential and verbal learning. We also find that massed learning in experiential learning conditions leads not only to better usage proficiency but also to positive perceptions of the product. Overall, the pattern of results is consistent with a conceptual mapping account, with massed experiences leading to a superior mental model of usage and thus to better usage proficiency.
A DC-90 GHz 4-Vpp differential linear driver in a 0.13 μm SiGe:C BiCMOS technology for optical modulators

In this paper, a linear driver for optical modulators in a 0.13 μm SiGe:C BiCMOS technology with fT/fmax of 300/500 GHz is presented. The driver is implemented following a distributed amplifier topology in a differential manner. In a 50-Ω environment, the circuit delivers a maximum differential output amplitude of 4 Vpp, featuring a small-signal gain of 13 dB and 3-dB bandwidth of 90 GHz. Time-domain measurements using OOK (up to 56 Gb/s) and PAM-4 (at 30Gbaud) are performed, demonstrating the maximum output swing and linearity of the driver. The output power to power dissipation ratio is 3.6%. To the best knowledge of the authors, this is the first time a linear driver for optical modulators demonstrates such bandwidth.
Evaluating radial, axial and transverse flux topologies for 'in-wheel' motor

Three different motor topologies, (1) radial, (2) axial and (3) transverse flux, are investigated for 'in-wheel' electric propulsion system. The design constraints are: 350mm/spl times/100mm package limitation, 48 V DC bus restriction and per phase inverter current limited to 500 A/sup pk/. In addition, magnet pole pairs are fixed to 9 due to limitation in 'position sensor' capacity. The paper summarizes results obtained from analytical design followed by 2D and 3D static and transient finite element analysis accompanied with experimental verification for the radial flux topology.
One-shot learning for fine-grained relation extraction via convolutional siamese neural network

Extracting fine-grained relations between entities of interest is of great importance to information extraction and large-scale knowledge graph construction. Conventional approaches on relation extraction require an existing knowledge graph to start with or sufficient observed samples from each relation type in the training process. However, such resources are not always available, and fine-grained manual labeling is extremely time-consuming and requires extensive expertise for specific domains such as healthcare and bioinformatics. Additionally, the distribution of fine-grained relations is often highly imbalanced in practice. We tackle this label scarcity and distribution imbalance issue from a one-shot classification perspective via a convolutional siamese neural network which extracts discriminative semantic-aware features to verify the relations between a pair of input samples. The proposed siamese network effectively extracts uncommon relations with only limited observed samples on the tasks of 1-shot and few-shot classification, demonstrating significant benefits to domain-specific information extraction in practical applications.
Deep Voice 2 : Multi-Speaker Neural Text-to-Speech

We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.
Power grids reliability appraisal with intrusion tolerant capability in SCADA systems

By intruding into the substations and control center of the supervisory control and data acquisition (SCADA) system, trip commands can be sent to intelligent electronic devices (IEDs) which control the system breakers. Reliability of the power system can be impacted through the cyber attacks. In this paper, a modified semi-Markov process (SMP) model is used to describe the procedures of normal and penetration attacks against the intrusion tolerant system. By modeling the transition probabilities between the SMP states and the sojourn time of each SMP state, the mean times to compromise (MTTCs) of the normal and penetration attacks are calculated. With increased probabilities of breaker trips resulted from the cyber attacks, the loss of load probabilities (LOLP) are evaluated based on IEEE reliability test system 79 (RTS79). When the level of attack increases or the level of defense in the system decreases, the simulation results demonstrate that the power system becomes less reliable.
Cyberbullying : A Review of the Literature

The article is a literature review on cyberbullying from 2007-2013. Topics covered in the review have been categorized starting with definition of cyberbullying; roles of persons involved and statistics of who is being targeted; reasons for cyberbullying; differences between traditional bullying and cyberbullying; and gender comparisons related to cyberbullying. This introduction to cyberbullying will provide a foundation for developing a cyberbullying intervention/prevention program.
Development of an FPGA-Based SPWM Generator for High Switching Frequency DC/AC Inverters

The digital implementations of Sinusoidal Pulse Width Modulation (SPWM) generators have dominated over their counterparts based on analog circuits. In this paper, an FPGA-based SPWM generator is presented, which is capable to operate at switching frequencies up to 1 MHz (requiring FPGA operation at 100-160 MHz), thus it is capable to support the high switching frequency requirements of modern single-phase dc/ac power converters. The proposed design occupies a small fraction of a medium-sized FPGA and, thus, can be incorporated in larger designs. Additionally, it has a flexible architecture that can be tuned to a variety of single-phase dc/ac inverter applications. The post-layout simulation and experimental results confirm that compared to the past-proposed SPWM generation designs, the SPWM generator presented in this paper exhibits much faster switching frequency, lower power consumption, and higher accuracy of generating the desired SPWM waveform.
IoT Security Framework for Smart Cyber Infrastructures

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being corrupted into another. We further show how one can estimate these probabilities, adapting a recent technique for noise estimation to the multi-class setting, and thus providing an end-to-end framework. Extensive experiments on MNIST, IMDB, CIFAR-10, CIFAR-100 and a large scale dataset of clothing images employing a diversity of architectures &#x2014; stacking dense, convolutional, pooling, dropout, batch normalization, word embedding, LSTM and residual layers &#x2014; demonstrate the noise robustness of our proposals. Incidentally, we also prove that, when ReLU is the only non-linearity, the loss curvature is immune to class-dependent label noise.
MapReduce-based deep learning with handwritten digit recognition case study

Faced with the continuously increasing scale of data and expectation on response time, complex deep learning technologies, though highly accurate, present two non-rival challenges: a large amount of training data makes a model impossible to be built in short time and intolerable time-cost prohibits acceptable real-time responses. In this research we focus on improving the accuracy and efficiency of the handwritten digit recognition problem. We chose this problem because it is regarded as the prototype of a lot of complex recognition and classification problems. The success of classification of the handwritten digit dataset can be extended further to other advanced areas. The Convolutional Neural Network (CNN) is implemented to do the recognition. We further improvd the accuracy by adding elastic distortion to the input data, which helps the model better select the features. In addition we implement distributed computing to reduce the time cost. The training process is divided and a final model is formulated by the combination of each trained model. The results shows two facts: the elastic distortion helped the CNN model to improve the accuracy by 7–10%; and the distributed computing method reduced the training time consumption by about 50%.