nils-herrmann/clean_scirepeval huggingface dataset:
Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.
Real-time trending topics detection and description from Twitter content

Twitter has become, over the last years, a major source of information. Twitter enables its users to send and read short text-based messages called tweets. Users are busy reporting news about what’s going around and within their personal. Numerous researchers from various disciplines have examined Twitter, due to the heterogeneity and immense scale of the data. One of the challenging problems is to automatically identify trending topics in real time on Twitter. Trending topics detection in real time is, thus, of high value to journalists, news reporters, analysts, e-marketing specialists, real-time application developers, and social media researchers to understand what is happening, what emergent trending topics are exchanged between people. In this paper, we propose a new approach that discovers many different trending topics from tweets in real time. Our trending topics are detected for a specific geographic town and compared with the top trending topics shown on Twitter. Contrary to Twitter, our proposed approach distinguishes between different terms corresponding to the same trending topic. We exploit the semantic similarity between keywords composing tweets, by unifying them using a tweets thesaurus former created. Each trending topic has a description presented by keywords of ten tweets that are more representative.
A Regulated Charge Pump for Tunneling Floating-Gate Transistors

Flash memory is an important component in many embedded system-on-a-chip applications, which drives the need to generate high write/erase voltages in generic CMOS processes. In this paper, we present a regulated, high-voltage charge pump for erasing Flash memory and floating-gate transistors. This 0.069-mm<sup>2</sup> charge pump was fabricated in a 0.35 <inline-formula> <tex-math notation="LaTeX">$\mu \text {m}$ </tex-math></inline-formula> standard CMOS process. While operating from a 2.5 V supply, the charge pump generates regulated voltages up to 16 V with a PSRR of 52 dB and an output impedance of 6.8 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula>. To reduce power consumption for use in battery-powered applications, this charge pump uses a variable-frequency regulation technique and a new circuit for minimizing short-circuit current in the clock-generation circuitry; the resulting charge pump is able to erase the charge on floating-gate transistors using only <inline-formula> <tex-math notation="LaTeX">$1.45 \mu J$ </tex-math></inline-formula>.
A C3D-Based Convolutional Neural Network for Frame Dropping Detection in a Single Video Shot

Frame dropping is a type of video manipulation where consecutive frames are deleted to omit content from the original video. Automatically detecting dropped frames across a large archive of videos while maintaining a low false alarm rate is a challenging task in digital video forensics. We propose a new approach for forensic analysis by exploiting the local spatio-temporal relationships within a portion of a video to robustly detect frame removals. In this paper, we propose to adapt the Convolutional 3D Neural Network (C3D) for frame drop detection. In order to further suppress the errors due by the network, we produce a refined video-level confidence score and demonstrate that it is superior to the raw output scores from the network. We conduct experiments on two challenging video datasets containing rapid camera motion and zoom changes. The experimental results clearly demonstrate the efficacy of the proposed approach.
SustData: A Public Dataset for ICT4S Electric Energy Research

Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy ecofeedback and demand side management research.
Adolescent Internet Addiction: Testing the Association Between Self-Esteem, the Perception of Internet Attributes, and Preference for Online Social Interactions

There is a general consensus that Internet addiction (IA) is mainly related to social aspects of the Web, especially among adolescents. The empirical link between poor social skills and IA is well documented; however, theoretical explanations for this relationship are less developed. One possibility is that people with poor social skills are especially prone to develop a preference for online social interaction (POSI), which, in turn, predicts problematic usage. This hypothesis has been tested for loneliness and social anxiety, but not for self-esteem (SE; one of the main antecedents of IA). Furthermore, the mediating role of the perceived relevance of some Internet features (e.g., anonymity) in the relationship between SE and POSI has never been investigated. A cross-sectional study was conducted with 257 adolescents. Using mediation analyses, we found evidence among females for the mediating role of (a) POSI in the relationship between SE and IA, and (b) the subjective relevance of some Internet features in the association between SE and POSI. No significant effects were found for males.
Differentiation of optic disc edema from optic nerve head drusen with spectral-domain optical coherence tomography.

To assess the efficacy of quantitative analysis of the optic nerve head and peripapillary retinal nerve fiber layer (RNFL) with the spectral-domain optical coherence tomography (SD-OCT) in differentiating optic disc edema (ODE) from optic nerve head drusen (ONHD).



Prospective clinical study. Twenty-five eyes of 25 ODE patients (group 1), 25 eyes of 25 ONHD patients (group 2), and 25 eyes of 25 healthy subjects were included. The thickness of the peripapillary RNFL, the thickness of the subretinal hyporeflective space (SHYPS), the area of the SHYPS, the horizontal length of the optic nerve head, and the angle between the temporal RNFL and the optic nerve head (α-angle) were evaluated with SD-OCT.



The mean RNFL thickness was significantly greater in group 1 when compared with group 2 and control group (P < 0.001). The receiver operating characteristic curve areas for temporal and nasal RNFL thicknesses in differentiating group 1 and group 2 were 0.819 and 0.851, respectively (for temporal RNFL thickness >101.5 μm: sensitivity 92%, specificity 65%; for nasal RNFL thickness >74.5 μm: sensitivity 92%, specificity 47%). The mean SHYPS thickness, SHYPS area, and degree of the α-angle were greater in group 1 when compared with group 2 (P < 0.05). For the SHYPS thickness >464 μm: 85% sensitivity and 60% specificity; for the SHYPS area >811 μm: 85% sensitivity and 89% specificity; and for the α-angle >141°: 77% sensitivity and 95% specificity were obtained.



The quantitative analysis of the optic nerve head and peripapillary RNFL with SD-OCT can provide useful data in differentiating ODE from ONHD.
Cognitive sophistication does not attenuate the bias blind spot.

The so-called bias blind spot arises when people report that thinking biases are more prevalent in others than in themselves. Bias turns out to be relatively easy to recognize in the behaviors of others, but often difficult to detect in one's own judgments. Most previous research on the bias blind spot has focused on bias in the social domain. In 2 studies, we found replicable bias blind spots with respect to many of the classic cognitive biases studied in the heuristics and biases literature (e.g., Tversky & Kahneman, 1974). Further, we found that none of these bias blind spots were attenuated by measures of cognitive sophistication such as cognitive ability or thinking dispositions related to bias. If anything, a larger bias blind spot was associated with higher cognitive ability. Additional analyses indicated that being free of the bias blind spot does not help a person avoid the actual classic cognitive biases. We discuss these findings in terms of a generic dual-process theory of cognition.
Combining segmentation and attention: a new foveal attention model

Artificial vision systems cannot process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. Inspired by biological perception systems, artificial attention models pursuit to select only the relevant part of the scene. On human vision, it is also well established that these units of attention are not merely spatial but closely related to perceptual objects (proto-objects). This implies a strong bidirectional relationship between segmentation and attention processes. While the segmentation process is the responsible to extract the proto-objects from the scene, attention can guide segmentation, arising the concept of foveal attention. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. In this paper, a bottom-up foveal attention model is presented. In this model the input image is a foveal image represented using a Cartesian Foveal Geometry (CFG), which encodes the field of view of the sensor as a fovea (placed in the focus of attention) surrounded by a set of concentric rings with decreasing resolution. Then multi-resolution perceptual segmentation is performed by building a foveal polygon using the Bounded Irregular Pyramid (BIP). Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image proto-object. Saliency is computed as a linear combination of multiple low level features such as color and intensity contrast, symmetry, orientation and roundness. Obtained results from natural images show that the performance of the combination of hierarchical foveal segmentation and saliency estimation is good in terms of accuracy and speed.
Action recognition in depth videos using hierarchical gaussian descriptor

In this paper, we propose a new approach based on distribution descriptors for action recognition in depth videos. Our local features are computed from binary patterns which incorporate the shape and motion cues for effective action recognition. Given pixel-level features, our approach estimates video local statistics in a hierarchical manner, where the distribution of pixel-level features and that of frame-level descriptors are modeled using single Gaussians. In this way, our approach constructs video descriptors directly from low-level features without resorting to codebook learning required by Bag-of-features (BoF) based approaches. In order to capture the spatial geometry and temporal order of a video, we use a spatio-temporal pyramid representation for each video. Our approach is validated on six benchmark datasets, i.e. MSRAction3D, MSRGesture3D, DHA, SKIG, UTD-MHAD and CAD-120. The experimental results show that our approach gives good performance on all the datasets. In particular, it achieves state-of-the-art accuracies on DHA, SKIG and UTD-MHAD datasets.
Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks

This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.
A Model Selection Criterion for Classification: Application to HMM Topology Optimization

This paper proposes a model selection criterion for classification problems. The criterion focuses on selecting models that are discriminant instead of models based on the Occam’s razor principle of parsimony between accurate modeling and complexity. The criterion, dubbed Discriminative Information Criterion (DIC), is applied to the optimization of Hidden Markov Model topology aimed at the recognition of cursively-handwritten digits. The results show that DICgenerated models achieve 18% relative improvement in performance from a baseline system generated by the Bayesian Information Criterion (BIC).
Overview: narcissistic personality disorder.

The authors trace the evolution of narcissistic personality disorder as a nosological entity in a critical survey of the literature, considering and comparing differing theoretical viewpoints regarding the genesis of this disorder. They review its various descriptions, including the one in DSM-III, and develop a composite picture of the syndrome. The disorder consists of characteristic deficits in six broad areas of functioning: 1) self-concept, 2) interpersonal relationships, 3) social adaptation, 4) ethics, standards, and ideals, 5) love and sexuality, and 6) cognitive style. The authors identify guidelines for distinguishing the narcissistic personality from other personality disorders as well as areas needing continued research.
Adult norms for a commercially available Nine Hole Peg Test for finger dexterity.

The Nine Hole Peg Test is commonly used by occupational therapists as a simple, quick assessment for finger dexterity. The purpose of this study was to evaluate the interrater and test-retest reliability of the commercially available Smith & Nephew Rehabilitation Division version of the Nine Hole Peg Test, and to establish new adult norms for the Nine Hole Peg Test for finger dexterity utilizing this particular version. Two of the researchers established interrater and test-retest reliability by evaluating 25 occupational therapy student volunteers. Seven hundred and three subjects, ranging in age from 21 to 71+ years, were tested to establish norms, using the standard protocol. Results showed high interrater reliability and only moderate test-retest reliability. Scores obtained by using the commercially available version were not statistically different from previously published norms (Mathiowetz, Weber, Kashman, & Volland, 1985). This study supports the original norms and further assists occupational therapists to evaluate dexterity accurately.
Local-global ranking for facial expression intensity estimation

Facial action units provide an objective characterization of facial muscle movements. Automatic estimation of facial action unit intensities is a challenging problem given individual differences in neutral face appearances and the need to generalize across different pose, illumination and datasets. In this paper, we introduce the Local-Global Ranking method as a novel alternative to direct prediction of facial action unit intensities. Our method takes advantage of the additional information present in videos and image collections of the same person (e.g. a photo album). Instead of trying to estimate facial expression intensities independently for each image, our proposed method performs a two-stage ranking: a local pair-wise ranking followed by a global ranking. The local ranking is designed to be accurate and robust by making a simple 3-class comparison (higher, equal, or lower) between randomly sampled pairs of images. We use a Bayesian model to integrate all these pair-wise rankings and construct a global ranking. Our Local-Global Ranking method shows state-of-the-art performance on two publicly-available datasets. Our cross-dataset experiments also show better generalizability.
The Whole Body 11.7 T MRI Magnet for Iseult/INUMAC Project

Neurospin, a neuroscience research centre with very high field MRI equipments, just opened in November 2006 at Saclay by the CEA life science division. One of the imaging systems will require an 11.7 T magnet with a 900 mm warm bore. This magnet is currently under development at CEA Saclay, in collaboration with Siemens Medical Solutions and Alstom Magnets and Superconductors, within the framework of the French-German consortium Iseult/INUMAC (Imaging of Neuro disease Using high field. MAgnetic resonance and Contrastophores). The main aim of the consortium is to promote magnetic resonance and molecular imaging within high magnetic fields. The proposed magnet design is based on conservative options, but definitely unusual construction methods, for an MRI magnet (pancake winding, liquefier, stabilized power supply). These key design points therefore need to be assessed with several prototypes, integrated within a 5 years projected development plan, ending in 2011. The paper will present the objectives of the project as well as the main characteristics of the magnet and its development plan.
Generating entangled photon pairs in a parallel crystal geometry

We present recent findings towards developing brighter entangled photon sources in critically phase matched (CPM) nonlinear crystals. Specifically, we use type-I collinear phase matching at non-degenerate wavelengths in parallel β-Barium Borate (BBO) crystals to generate pairs of polarization entangled photons for free-space quantum key distribution (QKD). We first review the entangled source configuration and then discuss ways to further improve the source brightness by means of tailoring the pump and collection modes. We present preliminary results that may lead to brighter entangled photon sources that are intrinsically robust to changes in their environment.
Estimating Key Parameters in the EKV MOST Model for Analogue Desgin and Simulation

Recent availability of the EKV (Enz-Krummenacher-Vittoz) MOST model  from EPFL in a number of circuit simulators facilitates the intuitive design, analysis and simulation of analogue and mixed-mode circuits and systems exploring the numerous modes of operation of the MOST , particularly at low-voltage (LV) and low-current (LC). A practical approach for either extracting the most critical parameters and/or adapting those already available from widely used SPICE models (levels 2 and 3) for use with the EKV model is presented and opportunities for engineering education are considered. The effectiveness of the approach is illustrated by measured results from CMOS and BiCMOS technologies.
Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids

Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.
Broadcast Encryption with Traitor Tracing

In this thesis, we look at definitions and black-box constructions with efficient instantiations for broadcast encryption and traitor tracing. We begin by looking at the security notions for broadcast encryption found in the literature. Since there is no easy way to compare these existing notions, we propose a framework of security notions for which we establish relationships. We then show where existing notions fit within this framework. Second, we present a black-box construction of a decentralized dynamic broadcast encryption scheme. This scheme does not rely on any trusted authorities, and new users can join at any time. It achieves the strongest security notion based on the security of its components and has an efficient instantiation that is fully secure under the DDH assumption in the standard model. Finally, we give a black-box construction of a message-based traitor tracing scheme, which allows tracing not only based on pirate decoders but also based on watermarks contained in a message. Our scheme is the first one to obtain the optimal ciphertext rate of 1 asymptotically. We then show that at today’s data rates, the scheme is already practical for standard choices of values.