nils-herrmann/clean_scirepeval huggingface dataset:
Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions

Every year, traffic accidents due to human errors cause increasing amounts of deaths and injuries globally. To help reduce the amount of fatalities, in the paper presented here, a new module for Advanced Driver Assistance System (ADAS) which deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index, where this real-time system works under varying light conditions (diurnal and nocturnal driving). Examples of different images of drivers taken in a real vehicle are shown to validate the algorithms used.
PointCNN: Convolution On $\mathcal{X}$-Transformed Points

We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point clouds are irregular and unordered, thus directly convolving kernels against features associated with the points will result in desertion of shape information and variance to point ordering. To address these problems, we propose to learn an X -transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order. Element-wise product and sum operations of the typical convolution operator are subsequently applied on the X -transformed features. The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.
A framework for Arabic sentiment analysis using supervised classification

Sentiment analysis aims to determine the polarity that is embedded in people comments and reviews. Sentiment analysis is important for companies and organisations which are interested in evaluating their products or services. The current paper deals with sentiment analysis in Arabic reviews. Three classifiers were applied on an in-house developed dataset of tweets/comments. In particular, the Naïve Bayes, SVM and K-nearest neighbour classifiers were employed. This paper also addresses the effects of term weighting schemes on the accuracy of the results. The binary model, term frequency and term frequency inverse document frequency were used to assign weights to the tokens of tweets/comments. The results show that alternating between the three weighting schemes slightly affects the accuracies. The results also clarify that the classifiers were able to remove false examples (high precision) but were not that successful in identifying all correct examples (low recall).
Low self-esteem during adolescence predicts poor health, criminal behavior, and limited economic prospects during adulthood.

Using prospective data from the Dunedin Multidisciplinary Health and Development Study birth cohort, the authors found that adolescents with low self-esteem had poorer mental and physical health, worse economic prospects, and higher levels of criminal behavior during adulthood, compared with adolescents with high self-esteem. The long-term consequences of self-esteem could not be explained by adolescent depression, gender, or socioeconomic status. Moreover, the findings held when the outcome variables were assessed using objective measures and informant reports; therefore, the findings cannot be explained by shared method variance in self-report data. The findings suggest that low self-esteem during adolescence predicts negative real-world consequences during adulthood.
Design of an airline coax radial power combiner with enhanced isolation

An X-band radial power combiner based on an airline coax with 0.15 dB measured insertion loss, 8 dB minimum port-to-port isolation, 33% bandwidth, and the ability to handle kilowatt power levels is presented in this work. The design can readily be scaled to arbitrary frequencies or any number of ports. The methods used to select the parameters and optimize the design are presented. The models are validated by a 9-port X-band proof-of-concept combiner.
Deep neural network ensemble architecture for eye movements classification

Up to now, eye tracking technologies have been used for different purposes in various industries, from medical to gaming. Eye tracking methods could include predicting fixations, gaze mapping or movement classification. Recent advances in deep learning techniques provide possibilities for solving many computer vision tasks with high accuracy. Authors of this paper propose a novel deep learning based architecture for eye movement classification task. Proposed architecture is an ensemble approach which employs deep convolutional neural networks that run in parallel, for both eyes separately, for visual feature extractions along with recurrent layers for temporal information gathering. Dataset images for training and validation were gathered from standard web camera and pre-processed automatically using dedicated tools. Overall accuracy of developed classifier on the validation set was 92%. Proposed architecture uses relatively small networks which brings the possibility of real time usage (successfully tested on 15–20fps) on regular CPU. Classifier achieved overall accuracy of 88% on the real-time test, using standard laptop and web camera.
Conceptualizing IT Resilience : An Explorative Approach

Modern technologies such as mobile phones and wearables are increasingly embedded in our daily life which makes detachment almost impossible. Therefore, understanding personal characteristics that allow individuals to buffer negative effects is an important tool to reduce negative consequences of technology use. Extant literature on technostress provides initial insights into how individuals are able to handle stressors. However, important constructs have not yet been investigated. We contribute to existing literature on technostress by proposing IT resilience as a new construct that can be considered a coping mechanism for technostress. We present the results of an explorative factor analysis (n=80), which suggest that IT resilience is a multi-dimensional construct with three sub dimensions: bounce back, self-efficacy, and coping. We conclude with a discussion on how to include IT resilience in theory development and human centric design.
Hedonic and instrumental motives in anger regulation.

What motivates individuals to regulate their emotions? One answer, which has been highlighted in emotion-regulation research, is that individuals are motivated by short-term hedonic goals (e.g., the motivation to feel pleasure). Another answer, however, is that individuals are motivated by instrumental goals (e.g., the motivation to perform certain behaviors). We suggest that both answers have merit. To demonstrate the role instrumental goals may play in emotion regulation, we pitted short-term hedonic motives and instrumental motives against each other, by testing whether individuals were motivated to experience a potentially useful, albeit unpleasant, emotion. We found that (a) individuals preferred activities that would increase their level of anger (but not their level of excitement) when they were anticipating confrontational, but not nonconfrontational, tasks and that (b) anger improved performance in a confrontational, but not a nonconfrontational, task. These findings support a functional view of emotion regulation, and demonstrate that in certain contexts, individuals may choose to experience emotions that are instrumental, despite short-term hedonic costs.
Gibson Env: Real-World Perception for Embodied Agents

Developing visual perception models for active agents and sensorimotor control in the physical world are cumbersome as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we investigate developing real-world perception for active agents, propose Gibson Environment for this purpose, and showcase a set of perceptual tasks learned therein. Gibson is based upon virtualizing real spaces, rather than artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism "Goggles" enabling deploying the trained models in real-world without needing domain adaptation, III. embodiment of agents and making them subject to constraints of physics and space.
Multiple templates auto exposure control based on luminance histogram for onboard camera

In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.
Character-based movie summarization

A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding.
Estimation of safe sensor measurements of autonomous system under attack

The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.
Using the k-Nearest Neighbor Graph for Proximity Searching in Metric Spaces

Proximity searching consists in retrieving from a database, objects that are close to a query. For this type of searching problem, the most general model is the metric space, where proximity is defined in terms of a distance function. A solution for this problem consists in building an offline index to quickly satisfy online queries. The ultimate goal is to use as few distance computations as possible to satisfy queries, since the distance is considered expensive to compute. Proximity searching is central to several applications, ranging from multimedia indexing and querying to data compression and clustering. In this paper we present a new approach to solve the proximity searching problem. Our solution is based on indexing the database with the knearest neighbor graph (knng), which is a directed graph connecting each element to its k closest neighbors. We present two search algorithms for both range and nearest neighbor queries which use navigational and metrical features of the knng graph. We show that our approach is competitive against current ones. For instance, in the document metric space our nearest neighbor search algorithms perform 30% more distance evaluations than AESA using only a 0.25% of its space requirement. In the same space, the pivot-based technique is completely useless.
Developmental Changes in the Relationship Between Grammar and the Lexicon

How does abstract structure emerge during language learning? On some accounts, children’s early syntax emerges from direct generalizations from particular lexical items, while on others, syntactic structure is acquired independently and follows its own timetable. Progress on differentiating these views requires detailed developmental data. Using parental reports of vocabulary and grammar abilities, previous analyses have shown that early syntactic abstraction strongly depends on the growth of the lexicon, providing support for lexicalist and emergentist theories. Leveraging a large cross-linguistic dataset, we replicate and extend these findings, demonstrating similar patterns in each of four languages. Moreover, the power of our dataset reveals that there are measurable effects of age over and above those attributable to vocabulary size, and that these effects are greater for aspects of language ability more closely tied to syntax than morphology. These findings suggest non-lexical contributions to the growth of syntactic abstraction that all theories must address.
Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.
Automatic Text Summarization

This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 English religious articles. The results of the proposed approach are promising. Automatic Summarization, Genetic Algorithm, Mathematical Regression, Text Features.
A Precoding-Based Multicarrier Non-Orthogonal Multiple Access Scheme for 5G Cellular Networks

Non-orthogonal multiple access (NOMA) has become one of the desirable schemes for the 5G cellular network standard due to its better cell coverage capability, higher data rate, and massive connectivity. Orthogonal frequency division multiplexing (OFDM) can be combined with NOMA to get the higher spectral efficiency. The main drawback of OFDM-based NOMA (OFDM-NOMA) scheme is high peak-to-average power ratio (PAPR). Therefore, this paper presents a new discrete-cosine transform matrix precoding-based uplink OFDM-NOMA scheme for PAPR reduction. Additionally, the proposed precoding-based uplink multicarrier NOMA scheme takes advantage of information spreading over the entire signal spectrum; thus, bit-error rate is also reduced. Simulation results show that the proposed precoding-based NOMA scheme outperforms as compared with the non-precoding-based NOMA schemes available in the literature.
Effective Bug Triage Based on Historical Bug-Fix Information

For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called Bug Fixer, which recommends developers for a new bug report based on historical bug-fix information. Bug Fixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship between the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, Bug Fixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate Bug Fixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.
MARTE: Technology development and lessons learned from a Mars drilling mission simulation

The NASA Mars Astrobiology Research and Technology Experiment (MARTE) performed a field test simulating a robotic drilling mission on Mars in September, 2005. The experiment took place in Minas de Riotinto in southwestern Spain, a highly relevant Mars analog site. The experiment utilized a 10 meter class dry auger coring drill, a robotic core sample handling system, onboard science and life detection instruments, and a borehole inspection probe, all of which were mounted to a simulated lander platform. Much of the operation of the system was automated, and the resulting data was transmitted via satellite to remote science teams for analysis. The science team used the data to characterize the subsurface geology and to search for signs of life. Based on the data being received and operational constraints, the science team also directed the daily operation of the equipment. This experiment represents an important first step in understanding the technology and operational requirements for a future Mars drilling mission. In the past there have been numerous rover field tests that have helped guide the design and implementation of the highly successful rover missions to Mars. However a drilling mission potentially adds a new level of complexity, and it is important to understand the associated challenges. This paper documents the design of the experimental system, highlighting some of the more important design criteria and design trades. It also discusses the results of the field testing, and lists some of the key technological lessons learned.
A Rotationally-Invariant Convolution Module by Feature Map Back-Rotation

Despite breakthroughs in image classification due to the evolution of deep learning and, in particular, convolutional neural networks (CNNs), state-of-the-art models only possess a very limited amount of rotational invariance. Known workarounds include artificial rotations of the training data or ensemble approaches, where several models are evaluated. These approaches either increase the workload of the training or inflate the number of parameters. Further approaches add rotational invariance by globally pooling over rotationally equivariant features. Instead, we propose to incorporate rotational invariance into the feature-extraction part of the CNN directly. This allows to train on unrotated data and perform well on a rotated test set. We use rotational convolutions and introduce a rotational pooling layer that performs a pooling over the back-rotated output feature maps. We show that when training on the original, unrotated MNIST training dataset, but evaluating on rotations of the MNIST test dataset, the error rate can be reduced substantially from 58.20% to 12.20%. Similar results are shown for the CIFAR-10 and CIFAR-100 datasets.
S-Sensors: Integrating physical world inputs with social networks using wireless sensor networks

Real-world monitoring and surveillance applications are rapidly increasing with the growth in the demand for awareness of the environment and surrounding. Sensor-to-web integration models are envisaged to improve the utilisation of sensory infrastructures and data via advanced coordination and collaboration of sensor nodes and web applications. This paper proposes to employ micro-blogging to publish and share sensory data and resources. S-Sensors provides a framework to globally share locally measured sensory readings. Short messages depicting the status of the environment are used to convey sensory data of the physical world. Moreover, data accessibility and resource manageability are facilitated by using social networks paradigms to form communities of sensor networks.