nils-herrmann/clean_scirepeval huggingface dataset:
Synthetic data augmentation using GAN for improved liver lesion classification

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.
Stable and Efficient Representation Learning with Nonnegativity Constraints

Orthogonal matching pursuit (OMP) is an efficient approximation algorithm for computing sparse representations. However, prior research has shown that the representations computed by OMP may be of inferior quality, as they deliver suboptimal classification accuracy on several image datasets. We have found that this problem is caused by OMP’s relatively weak stability under data variations, which leads to unreliability in supervised classifier training. We show that by imposing a simple nonnegativity constraint, this nonnegative variant of OMP (NOMP) can mitigate OMP’s stability issue and is resistant to noise overfitting. In this work, we provide extensive analysis and experimental results to examine and validate the stability advantage of NOMP. In our experiments, we use a multi-layer deep architecture for representation learning, where we use K-means for feature learning and NOMP for representation encoding. The resulting learning framework is not only efficient and scalable to large feature dictionaries, but also is robust against input noise. This framework achieves the state-of-the-art accuracy on the STL-10 dataset.
Functional imaging of human crossmodal identification and object recognition

The perception of objects is a cognitive function of prime importance. In everyday life, object perception benefits from the coordinated interplay of vision, audition, and touch. The different sensory modalities provide both complementary and redundant information about objects, which may improve recognition speed and accuracy in many circumstances. We review crossmodal studies of object recognition in humans that mainly employed functional magnetic resonance imaging (fMRI). These studies show that visual, tactile, and auditory information about objects can activate cortical association areas that were once believed to be modality-specific. Processing converges either in multisensory zones or via direct crossmodal interaction of modality-specific cortices without relay through multisensory regions. We integrate these findings with existing theories about semantic processing and propose a general mechanism for crossmodal object recognition: The recruitment and location of multisensory convergence zones varies depending on the information content and the dominant modality.
Hybrid speech recognition with Deep Bidirectional LSTM

Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.
Age differences in the neural systems supporting human allocentric spatial navigation

Age-related declines in spatial navigation are well-known in human and non-human species. Studies in non-human species suggest that alteration in hippocampal and other neural circuitry may underlie behavioral deficits associated with aging but little is known about the neural mechanisms of human age-related decline in spatial navigation. The purpose of the present study was to examine age differences in functional brain activation during virtual environment navigation. Voxel-based analysis of activation patterns in young subjects identified activation in the hippocampus and parahippocampal gyrus, retrosplenial cortex, right and left lateral parietal cortex, medial parietal lobe and cerebellum. In comparison to younger subjects, elderly participants showed reduced activation in the hippocampus and parahippocampal gyrus, medial parietal lobe and retrosplenial cortex. Relative to younger participants elderly subjects showed increased activation in anterior cingulate gyrus and medial frontal lobe. These results provide evidence of age specific neural networks supporting spatial navigation and identify a putative neural substrate for age-related differences in spatial memory and navigational skill.
A Complete Recipe for Stochastic Gradient MCMC

Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic simulations. However, such stochastic gradient MCMC samplers have lagged behind their full-data counterparts in terms of the complexity of dynamics considered since proving convergence in the presence of the stochastic gradient noise is nontrivial. Even with simple dynamics, significant physical intuition is often required to modify the dynamical system to account for the stochastic gradient noise. In this paper, we provide a general recipe for constructing MCMC samplers—including stochastic gradient versions—based on continuous Markov processes specified via two matrices. We constructively prove that the framework is complete. That is, any continuous Markov process that provides samples from the target distribution can be written in our framework. We show how previous continuous-dynamic samplers can be trivially “reinvented” in our framework, avoiding the complicated sampler-specific proofs. We likewise use our recipe to straightforwardly propose a new state-adaptive sampler: stochastic gradient Riemann Hamiltonian Monte Carlo (SGRHMC). Our experiments on simulated data and a streaming Wikipedia analysis demonstrate that the proposed SGRHMC sampler inherits the benefits of Riemann HMC, with the scalability of stochastic gradient methods.
Design of Permanent Multipole Magnets with Oriented Rare Earth Cobalt Materials

By taking advantage of both the magnetic strength and the astounding simplicity of the magnetic properties of oriented rare earth cobalt material, new designs have been developed for a number of devices. In this article on multi pole magnets, special emphasis is put on quad rupoles because of their frequent use and because the aperture fields achievable 1.2 1.4 T are rather large. This paper also lays the foundation for future papers on:
Trust in leadership : A multi-level review and integration

Leaders have been argued to play a key role in determining organizational effectiveness across all levels (e.g., individual, team, unit) that exist within organizations. A key component in a leader's ability to be effective within such environments is the degree to which subordinates and co-workers trust him/her. Therefore, it is not surprising that researchers and practitioners alike are interested in identifying the mechanisms through which trust in leadership can be developed as well as those factors which moderate this relationship [e.g., Gillespie, N. A., Mann, L. (2004). Transformational leadership and shared values: The building blocks of trust. Journal of Managerial Psychology, 19, 588–607; Kouzes and Posner, 1995; Roberts, K. H., O'Reilly, C. A. (1974). Failures in upward communication in organizations: Three possible culprits. Academy of Management Journal, 17, 205– 215; Whitener, E. M. (1997). The impact of human resource activities on employee trust. Human Resource Management Review, 7, 389–404]. Despite this, research that has addressed the factors that foster trust in leaders and the outcomes of this trust has been disjointed and, as yet, no comprehensive model has been presented to systematically examine these factors. Therefore, the purpose of this article will be to present an integrative model of trust in leadership.
Vacuum-assisted thermal annealing of CH3NH3PbI3 for highly stable and efficient perovskite solar cells.

Solar cells incorporating lead halide-based perovskite absorbers can exhibit impressive power conversion efficiencies (PCEs), recently surpassing 15%. Despite rapid developments, achieving precise control over the morphologies of the perovskite films (minimizing pore formation) and enhanced stability and reproducibility of the devices remain challenging, both of which are necessary for further advancements. Here we demonstrate vacuum-assisted thermal annealing as an effective means for controlling the composition and morphology of the CH(3)NH(3)PbI(3) films formed from the precursors of PbCl(2) and CH(3)NH(3)I. We identify the critical role played by the byproduct of CH(3)NH(3)Cl on the formation and the photovoltaic performance of the perovskite film. By completely removing the byproduct through our vacuum-assisted thermal annealing approach, we are able to produce pure, pore-free planar CH(3)NH(3)PbI(3) films with high PCE reaching 14.5% in solar cell device. Importantly, the removal of CH(3)NH(3)Cl significantly improves the device stability and reproducibility with a standard deviation of only 0.92% in PCE as well as strongly reducing the photocurrent hysteresis.
SKEE: A lightweight Secure Kernel-level Execution Environment for ARM

Previous research on kernel monitoring and protection widely relies on higher privileged system components, such as hardware virtualization extensions, to isolate security tools from potential kernel attacks. These approaches increase both the maintenance effort and the code base size of privileged system components, which consequently increases the risk of having security vulnerabilities. SKEE, which stands for Secure Kernellevel Execution Environment, solves this fundamental problem. SKEE is a novel system that provides an isolated lightweight execution environment at the same privilege level of the kernel. SKEE is designed for commodity ARM platforms. Its main goal is to allow secure monitoring and protection of the kernel without active involvement of higher privileged software. SKEE provides a set of novel techniques to guarantee isolation. It creates a protected address space that is not accessible to the kernel, which is challenging to achieve when both the kernel and the isolated environment share the same privilege level. SKEE solves this challenge by preventing the kernel from managing its own memory translation tables. Hence, the kernel is forced to switch to SKEE to modify the system’s memory layout. In turn, SKEE verifies that the requested modification does not compromise the isolation of the protected address space. Switching from the OS kernel to SKEE exclusively passes through a well-controlled switch gate. This switch gate is carefully designed so that its execution sequence is atomic and deterministic. These properties combined guarantee that a potentially compromised kernel cannot exploit the switching sequence to compromise the isolation. If the kernel attempts to violate these properties, it will only cause the system to fail without exposing the protected address space. SKEE exclusively controls access permissions of the entire OS memory. Hence, it prevents attacks that attempt to inject unverified code into the kernel. Moreover, it can be easily extended to intercept other system events in order to support various intrusion detection and integrity verification tools. This paper presents a SKEE prototype that runs on both 32-bit ARMv7 and 64-bit ARMv8 architectures. Performance evaluation results demonstrate that SKEE is a practical solution for real world systems. 1These authors contributed equally to this work
Relating Newcomer Personality to Survival and Activity in Recommender Systems

In this work, we explore the degree to which personality information can be used to model newcomer retention, investment, intensity of engagement, and distribution of activity in a recommender community. Prior work shows that Big-Five Personality traits can explain variation in user behavior in other contexts. Building on this, we carry out and report on an analysis of 1008 MovieLens users with identified personality profiles. We find that Introverts and low Agreeableness users are more likely to survive into the second and subsequent sessions compared to their respective counterparts; Introverts and low Conscientiousness users are a significantly more active population compared to their respective counterparts; High Openness and High Neuroticism users contribute (tag) significantly more compared to their counterparts, but their counterparts consume (browse and bookmark) more; and low Agreeableness users are more likely to rate whereas high Agreeableness users are more likely to tag. These results show how modeling newcomer behavior from user personality can be useful for recommender systems designers as they customize the system to guide people towards tasks that need to be done or tasks the users will find rewarding and also decide which users to invest retention efforts in.
Predicting Online Extremism, Content Adopters, and Interaction Reciprocity

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.
EnergyViz: an interactive system for visualization of energy systems

Energy systems are under pressure to transform to address concerns about climate change. The modeling and visualization of energy systems can play an important role in communicating the costs, benefits and trade-offs of energy systems choices. We introduce EnergyViz, a visualization system that provides an interface for exploring time-varying, multi-attribute and spatial properties of a particular energy system. EnergyViz integrates several visualization techniques to facilitate exploration of a particular energy system. These techniques include flow diagram representation to show energy flow, 3D interaction with flow diagrams for expanding viewable data attributes such as emissions and an interactive map integrated with flow diagrams for simultaneous exploration of spatial and abstract information. We also perform level-of-detail exploration on flow diagrams and use smooth animation across the visualizations to represent time-varying data. Finally, we include evaluation results of EnergyViz collected from expert and inexperienced participants.
Methods and Technologies for the Implementation of Large-Scale Robot Tactile Sensors

Even though the sense of touch is crucial for humans, most humanoid robots lack tactile sensing. While a large number of sensing technologies exist, it is not trivial to incorporate them into a robot. We have developed a compliant “skin” for humanoids that integrates a distributed pressure sensor based on capacitive technology. The skin is modular and can be deployed on nonflat surfaces. Each module scans locally a limited number of tactile-sensing elements and sends the data through a serial bus. This is a critical advantage as it reduces the number of wires. The resulting system is compact and has been successfully integrated into three different humanoid robots. We have performed tests that show that the sensor has favorable characteristics and implemented algorithms to compensate the hysteresis and drift of the sensor. Experiments with the humanoid robot iCub prove that the sensors can be used to grasp unmodeled, fragile objects.
Evaluation of cross-language voice conversion based on GMM and straight

Voice conversion is a technique for producing utterances using any target speakers’ voice from a single source speaker’s utterance. In this paper, we apply cross-language voice conversion between Japanese and English to a system based on a Gaussian Mixture Model (GMM) method and STRAIGHT, a high quality vocoder. To investigate the effects of this conversion system across different languages, we recorded two sets of bilingual utterances and performed voice conversion experiments using a mapping function which converts parameters of acoustic features for a source speaker to those of a target speaker. The mapping functions were trained using bilingual databases of both Japanese and English speech. In an objective evaluation using Mel cepstrum distortion (Mel CD), it was confirmed that the system can perform cross-language voice conversion with the same performance as that within a single-language.
Elephant herding optimization algorithm for support vector machine parameters tuning

Classification is part of various applications and it is an important problem that represents active research topic. Support vector machine is one of the widely used and very powerful classifier. The accuracy of support vector machine highly depends on learning parameters. Optimal parameters can be efficiently determined by using swarm intelligence algorithms. In this paper, we proposed recent elephant herding optimization algorithm for support vector machine parameter tuning. The proposed approach is tested on standard datasets and it was compared to other approaches from literature. The results of computational experiments show that our proposed algorithm outperformed genetic algorithms and grid search considering accuracy of classification.
An ultra-low power capacitor-less LDO for always-on domain in NB-IoT applications

This paper presents an ultra-low power 55 nm CMOS capacitor-less Low-Dropout (LDO) voltage regulator for power management of an Always-On domain module for implementation of NB-IoT SoC applications. Compared with traditional IoT SoC, the power consumption specification of NB-IoT SoC is lower. One effective way to achieve low power application is to reduce the power of the Always-On domain module in the SoC, where the LDO plays a critical role. According to the tape-out, results are validated with a 0.89V output voltage from 1.8V to 3.3V supply voltage, delivering a load current of 100uA. The peak-peak variance of output voltage is less than 100mV from 1.8V to 3.3V supply voltage. The quiescent current is lower than 61.86nA, including a 2-Transistor reference voltage, which can implement NB-IoT SoC application, with yield as high as 87.88%.
A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media

Named entity recognition (NER) in Chinese social media is important but difficult because of its informality and strong noise. Previous methods only focus on in-domain supervised learning which is limited by the rare annotated data. However, there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts. The unified model contains two major functions. One is for cross-domain learning and another for semi-supervised learning. Cross-domain learning function can learn out-of-domain information based on domain similarity. Semi-Supervised learning function can learn in-domain unannotated information by self-training. Both learning functions outperform existing methods for NER in Chinese social media. Finally, our unified model yields nearly 11% absolute improvement over previously published results.
A Low Voltage Delta-Sigma Fractional Frequency Divider for Multi-band WSN Frequency Synthesizers

A 1 V low voltage delta-sigma fractional-N frequency divider for multi-band (780/868/915 MHz and 2.4 GHz) WSN frequency synthesizers is presented. The frequency divider consists of a dual-modulus prescaler, a pulse-swallow counter and a delta-sigma modulator. The high-speed and low-voltage phase-switching dualmodulus prescaler is used in the frequency divider. Low threshold voltage transistors are applied to overcome low voltage supply and forward phase-switching technique is adopted to prevent glitches. The modified deltasigma modulator with long output sequence length and less spurs is adopted to minimize the fractional spurs. The frequency divider is designed in 0.18 μm TSMC RF CMOS technology under 1 V supply instead of the standard 1.8 V supply. The total chip area is 1190 μm × 485 μm including I/O pads. The post simulation results show the frequency divider operates normally over a wide range of 1.3-5.0 GHz and the core circuit (without test buffers) consumes 2.3 mW.
Data Mining Techniques: To Predict and Resolve Breast Cancer Survivability

Breast cancer is one of the deadliest disease, is the most common of all cancers and is the leading cause of cancer deaths in women worldwide, accounting for >1.6% of deaths and case fatality rates are highest in low-resource countries. The breast cancer risks are broadly classified into modifiable and non –modifiable factors. The non modifiable risk factors are age, gender, number of first degree relatives suffering from breast cancer, menstrual history, age at menarche and age at menopause. While the modifiable risk factors are BMI, age at first child birth, number of children, duration of breast feeding, alcohol, diet and number of abortions. This paper presents a diagnosis system for detecting breast cancer based on RepTree, RBF Network and Simple Logistic. In test stage, 10-fold cross validation method was applied to the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia database to evaluate the proposed system performances. The correct classification rate of proposed system is 74.5%. This research demonstrated that the Simple Logistic can be used for reducing the dimension of feature space and proposed Rep Tree and RBF Network model can be used to obtain fast automatic diagnostic systems for other diseases.  Breast cancer survivability; Rep Tree; RBF Network; Simple Logistic; Diagnostic systems