nils-herrmann/clean_scirepeval huggingface dataset:
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search

This paper introduces NSGA-Net, an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a NAS procedure for multiple, possibly conflicting, objectives, (2) efficient exploration and exploitation of the space of potential neural network architectures, and (3) output of a diverse set of network architectures spanning a trade-off frontier of the objectives in a single run. NSGA-Net is a population-based search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures and finally an exploitation step that applies the entire history of evaluated neural architectures in the form of a Bayesian Network prior. Experimental results suggest that combining the objectives of minimizing both an error metric and computational complexity, as measured by FLOPS, allows NSGA-Net to find competitive neural architectures near the Pareto front of both objectives on two different tasks, object classification and object alignment. NSGA-Net obtains networks that achieve 3.72% (at 4.5 million FLOP) error on CIFAR-10 classification and 8.64% (at 26.6 million FLOP) error on the CMU-Car alignment task. Code available at: https://github.com/ianwhale/nsga-net.
A Unified Model for Word Sense Representation and Disambiguation

Most word representation methods assume that each word owns a single semantic vector. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be resolved by word sense disambiguation. In this paper, we present a unified model for joint word sense representation and disambiguation, which will assign distinct representations for each word  The basic idea is that both word sense representation (WSR) and word sense disambiguation (WSD) will benefit from each other: (1) highquality WSR will capture rich information about words and senses, which should be helpful for WSD, and (2) high-quality WSD will provide reliable disambiguated corpora for learning better sense representations. Experimental results show that, our model improves the performance of contextual word similarity compared to existing WSR methods, outperforms stateof-the-art supervised methods on domainspecific WSD, and achieves competitive performance on coarse-grained all-words WSD.
Affective Neural Response Generation

Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding. Experiments show that these techniques improve the open-domain conversational prowess of encoderdecoder networks by enabling them to produce more natural and emotionally rich responses.
Explainable Social Contextual Image Recommendation with Hierarchical Attention

Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users’ preferences to the user-generated images and recommending them to users have become an urgent need. However, this is a challenging task. On one hand, we have to overcome the extremely data sparsity issue in image recommendation. On the other hand, we have to model the complex aspects that influence users’ preferences to these highly subjective content from the heterogeneous data. In this paper, we develop an explainable social contextual image recommendation model to simultaneously explain and predict users’ preferences to images. Specifically, in addition to user interest modeling in the standard recommendation, we identify three key aspects that affect each user’s preference on the social platform, where each aspect summarizes a contextual representation from the complex relationships between users and images. We design a hierarchical attention model in recommendation process given the three contextual aspects. Particularly, the bottom layered attention networks learn to select informative elements of each aspect from heterogeneous data, and the top layered attention network learns to score the aspect importance of the three identified aspects for each user. In this way, we could overcome the data sparsity issue by leveraging the social contextual aspects from heterogeneous data, and explain the underlying reasons for each user’s behavior with the learned hierarchial attention scores. Extensive experimental results on realworld datasets clearly show the superiority of our proposed model.
Oculomotor responses during partial and total sleep deprivation.

Oculomotor responses related to the pupil light reflex (PLR) and saccadic velocity may be sensitive to the effects of sleepiness and therefore could be used to evaluate an individual's fitness for duty.



There were 12 normal subjects who completed an 8-d study. They were allowed 8 h in bed on the first three nights, 4 h in bed on the fourth night, and then were sleep deprived for the following 64 h. Approximately every 3 h, subjects performed a battery of tests which included a 45-s automated oculomotor test and a 40-min PC-based driving simulator task. Sleepiness was evaluated with a self-assessment instrument. Subjects were allowed 10 h of recovery sleep following sleep deprivation.



Oculomotor results for nine subjects showed a significant increase in latency to pupil constriction and a significant decrease in saccadic velocity with total, but not partial, sleep deprivation. The most robust changes during sleep deprivation occurred for saccadic velocity. A night of recovery sleep reversed the effects of total sleep deprivation on latency to pupil constriction and saccadic velocity. Subjective sleepiness and off-road accidents were found to significantly increase over the sleep deprivation period. A significant positive correlation between increasing latency to pupil constriction and increasing sleepiness and driving accidents, and a significant negative correlation between decreasing saccadic velocity and increasing sleepiness and driving accidents during sleep deprivation were found.



These findings suggest that oculomotor functions, particularly saccadic velocity, are feasible for assessing neurophysiological changes associated with and predictive of sleep deprivation-induced operational performance degradation.
IoT-cloud based framework for patient's data collection in smart healthcare system using raspberry-pi

The Internet of things(IoT) has brought the vision of the smarter world into reality and including healthcare it has a many application domains. The convergence of IOT-cloud can play a significant role in the smart healthcare by offering better insight of healthcare content to support affordable and quality patient care. In this paper, we proposed a model that allows the sensor to monitor the patient's symptom. The collected monitored data transmitted to the gateway via Bluetooth and then to the cloud server through docker container using the internet. Thus enabling the physician to diagnose and monitor health problems wherever the patient is. Also, we address the several challenges related to health monitoring and management using IoT.
High-degree cubature Kalman filter

The cubature Kalman filter (CKF), which is based on the third degree spherical–radial cubature rule, is numericallymore stable than the unscented Kalman filter (UKF) but less accurate than theGauss–Hermite quadrature filter (GHQF). To improve the performance of the CKF, a new class of CKFs with arbitrary degrees of accuracy in computing the spherical and radial integrals is proposed. The third-degree CKF is a special case of the class. The high-degree CKFs of the class can achieve the accuracy and stability performances close to those of the GHQF but at lower computational cost. A numerical integration problem and a target tracking problem are utilized to demonstrate the necessity of using the high-degree cubature rules to improve the performance. The target tracking simulation shows that the fifth-degree CKF can achieve higher accuracy than the extended Kalman filter, the UKF, the third-degree CKF, and the particle filter, and is computationally much more efficient than the GHQF.
Targeted use of LEDs in improvement of production efficiency through phytochemical enrichment.

Based on available literature, ecology and economy of light emitting diode (LED) lights in plant foods production were assessed and compared to high pressure sodium (HPS) and compact fluorescent light (CFL) lamps. The assessment summarises that LEDs are superior compared to other lamp types. LEDs are ideal in luminous efficiency, life span and electricity usage. Mercury, carbon dioxide and heat emissions are also lowest in comparison to HPS and CFL lamps. This indicates that LEDs are indeed economic and eco-friendly lighting devices. The present review indicates also that LEDs have many practical benefits compared to other lamp types. In addition, they are applicable in many purposes in plant foods production. The main focus of the review is the targeted use of LEDs in order to enrich phytochemicals in plants. This is an expedient to massive improvement in production efficiency, since it diminishes the number of plants per phytochemical unit. Consequently, any other production costs (e.g. growing space, water, nutrient and transport) may be reduced markedly. Finally, 24 research articles published between 2013 and 2017 were reviewed for targeted use of LEDs in the specific, i.e. blue range (400-500 nm) of spectrum. The articles indicate that blue light is efficient in enhancing the accumulation of health beneficial phytochemicals in various species. The finding is important for global food production.
Biomechanics of the ankle-foot system during stair ambulation: implications for design of advanced ankle-foot prostheses.

Unilateral lower limb prosthesis users display temporal, kinematic, and kinetic asymmetries between limbs while ascending and descending stairs. These asymmetries are due, in part, to the inability of current prosthetic devices to effectively mimic normal ankle function. The purpose of this study was to provide a comprehensive set of biomechanical data for able-bodied and unilateral transtibial amputee (TTA) ankle-foot systems for level-ground (LG), stair ascent (SA), and stair descent (SD), and to characterize deviations from normal performance associated with prosthesis use. Ankle joint kinematics, kinetics, torque-angle curves, and effective shapes were calculated for twelve able-bodied individuals and twelve individuals with TTA. The data from this study demonstrated the prosthetic limb can more effectively mimic the range of motion and power output of a normal ankle-foot during LG compared to SA and SD. There were larger differences between the prosthetic and able-bodied limbs during SA and SD, most evident in the torque-angle curves and effective shapes. These data can be used by persons designing ankle-foot prostheses and provide comparative data for assessment of future ankle-foot prosthesis designs.
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization

As it requires a huge number of parameters when exposed to high dimensional inputs in video detection and classification, there is a grand challenge to develop a compact yet accurate video comprehension at terminal devices. Current works focus on optimizations of video detection and classification in a separated fashion. In this paper, we introduce a video comprehension (object detection and action recognition) system for terminal devices, namely DEEPEYE. Based on You Only Look Once (YOLO), we have developed an 8-bit quantization method when training YOLO; and also developed a tensorized-compression method of Recurrent Neural Network (RNN) composed of features extracted from YOLO. The developed quantization and tensorization can significantly compress the original network model yet with maintained accuracy. Using the challenging video datasets: MOMENTS and UCF11 as benchmarks, the results show that the proposed DEEPEYE achieves 3.994× model compression rate with only 0.47% mAP decreased; and 15, 047× parameter reduction and 2.87× speed-up with 16.58% accuracy improvement.
COOLCAT: an entropy-based algorithm for categorical clustering

In this paper we explore the connection between clustering categorical data and entropy: clusters of similar poi lower entropy than those of dissimilar ones. We use this connection to design an incremental heuristic algorithm, COOLCAT, which is capable of efficiently clustering large data sets of records with categorical attributes, and data streams. In contrast with other categorical clustering algorithms published in the past, COOLCAT's clustering results are very stable for different sample sizes and parameter settings. Also, the criteria for clustering is a very intuitive one, since it is deeply rooted on the well-known notion of entropy. Most importantly, COOLCAT is well equipped to deal with clustering of data streams(continuously arriving streams of data point) since it is an incremental algorithm capable of clustering new points without having to look at every point that has been clustered so far. We demonstrate the efficiency and scalability of COOLCAT by a series of experiments on real and synthetic data sets.
Lay dispositionism and implicit theories of personality.

Lay dispositionism refers to lay people's tendency to use traits as the basic unit of analysis in social perception (L. Ross & R. E. Nisbett, 1991). Five studies explored the relation between the practices indicative of lay dispositionism and people's implicit theories about the nature of personal attributes. As predicted, compared with those who believed that personal attributes are malleable (incremental theorists), those who believed in fixed traits (entity theorists) used traits or trait-relevant information to make stronger future behavioral predictions (Studies 1 and 2) and made stronger trait inferences from behavior (Study 3). Moreover, the relation between implicit theories and lay dispositionism was found in both the United States (a more individualistic culture) and Hong Kong (a more collectivistic culture), suggesting this relation to be generalizable across cultures (Study 4). Finally, an experiment in which implicit theories were manipulated provided preliminary evidence for the possible causal role of implicit theories in lay dispositionism (Study 5).
Semantic topic models for source code analysis

Topic modeling techniques have been recently applied to analyze and model source code. Such techniques exploit the textual content of source code to provide automated support for several basic software engineering activities. Despite these advances, applications of topic modeling in software engineering are frequently suboptimal. This can be attributed to the fact that current state-of-the-art topic modeling techniques tend to be data intensive. However, the textual content of source code, embedded in its identifiers, comments, and string literals, tends to be sparse in nature. This prevents classical topic modeling techniques, typically used to model natural language texts, to generate proper models when applied to source code. Furthermore, the operational complexity and multi-parameter calibration often associated with conventional topic modeling techniques raise important concerns about their feasibility as data analysis models in software engineering. Motivated by these observations, in this paper we propose a novel approach for topic modeling designed for source code. The proposed approach exploits the basic assumptions of the cluster hypothesis and information theory to discover semantically coherent topics in software systems. Ten software systems from different application domains are used to empirically calibrate and configure the proposed approach. The usefulness of generated topics is empirically validated using human judgment. Furthermore, a case study that demonstrates thet operation of the proposed approach in analyzing code evolution is reported. The results show that our approach produces stable, more interpretable, and more expressive topics than classical topic modeling techniques without the necessity for extensive parameter calibration.
Threat evaluation of enemy air fighters via neural network-based Markov chain modeling

Threat evaluation (TE) is a process used to assess the threat values (TVs) of air-breathing threats (ABTs), such as air fighters, that are approaching defended assets (DAs). This study proposes an automatic method for conducting TE using radar information when ABTs infiltrate into territory where DAs are located. The method consists of target asset (TA) prediction and TE. We divide a friendly territory into discrete cells based on the effective range of anti-aircraft missiles. The TA prediction identifies the TA of each ABT by predicting the ABT’s movement through cells in the territory via a Markov chain, and the cell transition is modeled by neural networks. We calculate the TVs of the ABTs based on the TA prediction results. A simulation-based experiment revealed that the proposed method outperformed TE based on the closest point of approach or the radial speed vector methods.
Deep convolutional networks for pancreas segmentation in CT imaging

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for “deep learning” methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve average Dice scores of 68% ± 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.
Lonely People Are No Longer Lonely on Social Networking Sites: The Mediating Role of Self-Disclosure and Social Support

Most previous studies assert the negative effect of loneliness on social life and an individual's well-being when individuals use the Internet. To expand this previous research tradition, the current study proposes a model to test whether loneliness has a direct or indirect effect on well-being when mediated by self-disclosure and social support. The results show that loneliness has a direct negative impact on well-being but a positive effect on self-disclosure. While self-disclosure positively influences social support, self-disclosure has no impact on well-being, and social support positively influences well-being. The results also show a full mediation effect of social support in the self-disclosure to well-being link. The results imply that even if lonely people's well-being is poor, their well-being can be enhanced through the use of SNSs, including self-presentation and social support from their friends.
Construction and validation of a prognostic index for patients with metastatic pancreatic adenocarcinoma.

To identify prognostic factors in patients with metastatic pancreatic adenocarcinoma.



The relationship between patient characteristics and outcome was examined by multivariate regression analyses of data from 409 consecutive patients with metastatic pancreatic adenocarcinoma who had been treated with a gemcitabine-containing regimen, and we stratified the patients into 3 risk groups according to the number of prognostic factors they had for a poor outcome. A validation data set obtained from 145 patients who had been treated with agents other than gemcitabine was analyzed. The prognostic index was applied the each of the patients.



The multivariate regression analyses revealed that the presence of pain, peritoneal dissemination, liver metastasis, and an elevated serum C-reactive protein value significantly contributed to a shorter survival time. The patients were stratified into 3 groups according to their number of risk factors, and their outcomes of the 3 groups were significantly different. When the prognostic index was applied to the validation data set, the respective outcomes of the 3 groups were found to be significantly differed from each other.



Pain, peritoneal dissemination, liver metastasis, and an elevated serum C-reactive protein value are important prognostic factors for patients with metastatic pancreatic adenocarcinoma.
Intercrossed Access Controls for Secure Financial Services on Multimedia Big Data in Cloud Systems

The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm. We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.