nils-herrmann/clean_scirepeval huggingface dataset:
Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media

a r t i c l e i n f o The purpose of this research was to explore teaching and learning when mobile computing devices, such as cellphones and smartphones, were implemented in higher education. This paper presents a portion of the findings on students' perceptions of learning with mobile computing devices and the roles social media played. This qualitative research study focused on students from three universities across the US. The students' teachers had been integrating mobile computing devices, such as cellphones and smartphones, into their courses for at least two semesters. Data were collected through student focus group interviews. Two specific themes emerged from the interview data: (a) advantages of mobile computing devices for student learning and (b) frustrations from learning with mobile computing devices. Mobile computing devices and the use of social media created opportunities for interaction, provided opportunities for collaboration, as well as allowed students to engage in content creation and communication using social media and Web 2.0 tools with the assistance of constant connectivity.
Neurologic Effects of Caffeine

Classic drugs of abuse lead to specific increases in cerebral functional activity and dopamine release in the shell of the nucleus accumbens (the key neural structure for reward, motivation, and addiction). In contrast, caffeine at doses reflecting daily human consumption does not induce a release of dopamine in the shell of the nucleus accumbens but leads to a release of dopamine in the prefrontal cortex, which is consistent with its reinforcing properties.
Association between commercial television exposure and fast-food consumption among adults.

To examine the association between television advertising exposure and adults' consumption of fast foods.



Cross-sectional telephone survey. Questions included measures of frequency of fast-food consumption at different meal times and average daily hours spent watching commercial television.



Subjects comprised 1495 adults (41 % response rate) aged >or=18 years from Victoria, Australia.



Twenty-three per cent of respondents usually ate fast food for dinner at least once weekly, while 17 % consumed fast food for lunch on a weekly basis. The majority of respondents reported never eating fast food for breakfast (73 %) or snacks (65 %). Forty-one per cent of respondents estimated watching commercial television for <or=1 h/d (low viewers); 29 % watched for 2 h/d (moderate viewers); 30 % watched for >or=3 h/d (high viewers). After adjusting for demographic variables, high viewers were more likely to eat fast food for dinner at least once weekly compared with low viewers (OR = 1.45; 95 % CI 1.04, 2.03). Both moderate viewers (OR = 1.53; 95 % CI 1.01, 2.31) and high viewers (OR = 1.81; 95 % CI 1.20, 2.72) were more likely to eat fast food for snacks at least once weekly compared with low viewers. Commercial television viewing was not significantly related (P > 0.05) to fast-food consumption at breakfast or lunch.



The results of the present study provide evidence to suggest that cumulative exposure to television food advertising is linked to adults' fast-food consumption. Additional research that systematically assesses adults' behavioural responses to fast-food advertisements is needed to gain a greater understanding of the mechanisms driving this association.
Where Does EEG Come From and What Does It Mean?

Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations.
Vision Processing for Realtime 3-D Data Acquisition Based on Coded Structured Light

Structured light vision systems have been successfully used for accurate measurement of 3D surfaces in computer vision. However, their applications are mainly limited to scanning stationary objects so far since tens of images have to be captured for recovering one 3D scene. This paper presents an idea for real-time acquisition of 3D surface data by a specially coded vision system. To achieve 3D measurement for a dynamic scene, the data acquisition must be performed with only a single image. A principle of uniquely color-encoded pattern projection is proposed to design a color matrix for improving the reconstruction efficiency. The matrix is produced by a special code sequence and a number of state transitions. A color projector is controlled by a computer to generate the desired color patterns in the scene. The unique indexing of the light codes is crucial here for color projection since it is essential that each light grid be uniquely identified by incorporating local neighborhoods so that 3D reconstruction can be performed with only local analysis of a single image. A scheme is presented to describe such a vision processing method for fast 3D data acquisition. Practical experimental performance is provided to analyze the efficiency of the proposed methods.
Evaluation of a wireless infrared thermometer with a narrow field of view

Many agricultural studies rely on infrared sensors for remote measurement of surface temperatures for crop status monitoring and estimating sensible and latent heat fluxes. Historically, applications for these non-contact thermometers employed the use of hand-held or stationary industrial infrared thermometers (IRTs) wired to data loggers. Wireless sensors in agricultural applications are a practical alternative, but the availability of low cost wireless IRTs is limited. In this study, we designed prototype narrow (10◦) field of view wireless infrared sensor modules and evaluated the performance of the IRT sensor by comparing temperature readings of an object (Tobj) against a blackbody calibrator in a controlled temperature room at ambient temperatures of 15 ◦C, 25 ◦C, 35 ◦C, and 45 ◦C. Additional comparative readings were taken over plant and soil samples alongside a hand-held IRT and over an isothermal target in the outdoors next to a wired IRT. The average root mean square error (RMSE) and mean absolute error (MAE) between the collected IRT object temperature readings and the blackbody target ranged between 0.10 and 0.79 ◦C. The wireless IRT readings also compared well with the hand-held IRT and wired industrial IRT. Additional tests performed to investigate the influence of direct radiation on IRT measurements indicated that housing the sensor in white polyvinyl chloride provided ample shielding for the self-compensating circuitry of the IR detector. The relatively low cost of the wireless IRT modules and repeatable measurements against a blackbody calibrator and commercial IR thermometers demonstrated that these wireless prototypes have the potential to provide accurate surface radiometric temperature readings in outdoor applications. Further studies are needed to thoroughly test radio frequency communication and power consumption characteristics in an outdoor setting.
Building the gist of a scene: the role of global image features in recognition.

Humans can recognize the gist of a novel image in a single glance, independent of its complexity. How is this remarkable feat accomplished? On the basis of behavioral and computational evidence, this paper describes a formal approach to the representation and the mechanism of scene gist understanding, based on scene-centered, rather than object-centered primitives. We show that the structure of a scene image can be estimated by the mean of global image features, providing a statistical summary of the spatial layout properties (Spatial Envelope representation) of the scene. Global features are based on configurations of spatial scales and are estimated without invoking segmentation or grouping operations. The scene-centered approach is not an alternative to local image analysis but would serve as a feed-forward and parallel pathway of visual processing, able to quickly constrain local feature analysis and enhance object recognition in cluttered natural scenes.
Optimization of ReLU Neural Networks using Quotient Stochastic Gradient Descent

It has been well known that neural networks with rectified linear hidden units (ReLU) as activation functions are positively scale invariant, which results in severe redundancy in their weight space (i.e., many ReLU networks with different weights are actually equivalent). In this paper, we formally characterize this redundancy/equivalence using the language of quotient space and discuss its negative impact on the optimization of ReLU neural networks. Specifically, we show that all equivalent ReLU networks correspond to the same vector in the quotient space, and each such vector can be characterized by the so-called skeleton paths in the ReLU networks. With this, we prove that the dimensionality of the quotient space is #weight−#(hidden nodes), indicating that the redundancy of the weight space is huge. In this paper, we propose to optimize ReLU neural networks directly in the quotient space, instead of the original weight space. We represent the loss function in the quotient space and design a new stochastic gradient descent algorithm to iteratively learn the model, which we call Quotient stochastic gradient descent (abbreviated as Quotient SGD). We also develop efficient tricks to ensure that the implementation of Quotient SGD almost requires no extra computations as compared to standard SGD. According to the experiments on benchmark datasets, our proposed Quotient SGD can significantly improve the accuracy of the learned model.
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer and max-min pooling for classifying common thoracic diseases as well as localizing suspicious lesion regions on chest X-rays. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of proposed model and its better performance against the state-of-the-art pipelines.
Ice-Bound: Combining richly-realized story with expressive gameplay

This paper describes the design and implementation of IceBound, an indie game that uses dynamic story techniques. Storygames with high narrative process intensity (where story is generated or recombined in algorithmically interesting ways, rather than being simply pre-authored) are still rare, in part because story creators are hesitant to cede control over output quality to a system. As a result, most readers still encounter digital fiction in the form of linear e-books or simple branching-path models of interactive story. Getting more dynamic models into the public consciousness requires exploring new frontiers of design space driven by the twin concerns of fiction authors (for high-quality story realization) and game designers (for frequent, high-impact player decisions). Our design for Ice-Bound rejects both branching-path models of interactive story as well as overly simulationist approaches, targeting a middle-road aesthetic of sculptural construction that marries a focus on quality output with the player’s exploration of both an emergent expressive space and an AR-enabled art book. Through implementing Ice-Bound, we developed design strategies and practical lessons useful to other interactive narrative designers, including three high-level lessons. First, quantifiable metrics and tools for content authoring for a combinatorial system are essential to maintain control over such a system. Second, authors should consider embedding highly dynamic but less narratively cohesive mechanics within layers of less dynamic but more tightly authored storytelling, so each part of the story can be told within a framework to which it is best suited. Finally, iterating the design of all story layers at once leads to tighter coupling between ludic and narrative levels and a stronger narrative experience overall.
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.
Extending BM25 with multiple query operators

Traditional probabilistic relevance frameworks for informational retrieval refrain from taking positional information into account, due to the hurdles of developing a sound model while avoiding an explosion in the number of parameters. Nonetheless, the well-known BM25F extension of the successful Okapi ranking function can be seen as an embryonic attempt in that direction. In this paper, we proceed along the same line, defining the notion of virtual region: a virtual region is a part of the document that, like a BM25F-field, can provide a (larger or smaller, depending on a tunable weighting parameter) evidence of relevance of the document; differently from BM25F fields, though, virtual regions are generated implicitly by applying suitable (usually, but not necessarily, positional-aware) operators to the query. This technique fits nicely in the eliteness model behind BM25 and provides a principled explanation to BM25F; it specializes to BM25(F) for some trivial operators, but has a much more general appeal. Our experiments (both on standard collections, such as TREC, and on Web-like repertoires) show that the use of virtual regions is beneficial for retrieval effectiveness.
Gain Enhancement of Beam Scanning Substrate Integrated Waveguide Slot Array Antennas Using a Phase-Correcting Grating Cover

In this paper, planar beam scanning substrate integrated waveguide (SIW) slot leaky-wave antennas (LWAs) are proposed for gain enhancement using a metallic phase correction grating cover. Unlike conventional Fabry-Pérot (FP) cavity antennas, the proposed antenna is fed by the SIW beam scanning LWA instead of a feeding antenna. The beam scanning angle range is enlarged by meandering the entire feeding structure and the gain is enhanced by a metallic grating cover acting as a 1-D lens. Two kinds of the gratings with different metal strip parameters are designed and analyzed. The proposed antennas operating at the center frequency of 25.45 GHz are designed and experimentally verified for an automotive collision avoidance radar with a gain enhancement of about 4 ~ 6 dB. The proposed SIW LWAs have the advantages of high gain, low profile, easy fabrication and beam-scanning capability good for millimeter wave radar applications.
Fast vehicle detection in UAV images

Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) images remains a challenge, due to its very high spatial resolution and very few annotations. Although numerous vehicle detection methods exist, most of them cannot achieve real-time detection for different scenes. Recently, deep learning algorithms has achieved fantastic detection performance in computer vision, especially regression based convolutional neural networks YOLOv2. It's good both at accuracy and speed, outperforming other state-of-the-art detection methods. This paper for the first time aims to investigate the use of YOLOv2 for vehicle detection in UAV images, as well as to explore the new method for data annotation. Our method starts with image annotation and data augmentation. CSK tracking method is used to help annotate vehicles in images captured from simple scenes. Subsequently, a regression based single convolutional neural network YOLOv2 is used to detect vehicles in UAV images. To evaluate our method, UAV video images were taken over several urban areas, and experiments were conducted on this dataset and Stanford Drone dataset. The experimental results have proven that our data preparation strategy is useful, and YOLOv2 is effective for real-time vehicle detection of UAV video images.
Visual Navigation for Mobile Robots: A Survey

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.
Near-infrared optical absorption enhanced in black silicon via Ag nanoparticle-induced localized surface plasmon

Due to the localized surface plasmon (LSP) effect induced by Ag nanoparticles inside black silicon, the optical absorption of black silicon is enhanced dramatically in near-infrared range (1,100 to 2,500 nm). The black silicon with Ag nanoparticles shows much higher absorption than black silicon fabricated by chemical etching or reactive ion etching over ultraviolet to near-infrared (UV-VIS-NIR, 250 to 2,500 nm). The maximum absorption even increased up to 93.6% in the NIR range (820 to 2,500 nm). The high absorption in NIR range makes LSP-enhanced black silicon a potential material used for NIR-sensitive optoelectronic device.


PACS
78.67.Bf; 78.30.Fs; 78.40.-q; 42.70.Gi.
Online Deduplication for Databases

dbDedup is a similarity-based deduplication scheme for on-line database management systems (DBMSs). Beyond block-level compression of individual database pages or operation log (oplog) messages, as used in today's DBMSs, dbDedup uses byte-level delta encoding of individual records within the database to achieve greater savings. dbDedup's single-pass encoding method can be integrated into the storage and logging components of a DBMS to provide two benefits: (1) reduced size of data stored on disk beyond what traditional compression schemes provide, and (2) reduced amount of data transmitted over the network for replication services. To evaluate our work, we implemented dbDedup in a distributed NoSQL DBMS and analyzed its properties using four real datasets. Our results show that dbDedup achieves up to 37x reduction in the storage size and replication traffic of the database on its own and up to 61x reduction when paired with the DBMS's block-level compression. dbDedup provides both benefits with negligible effect on DBMS throughput or client latency (average and tail).
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets. However, unless the regularization parameters are tuned carefully, this approach is prone to overfitting because it finds a single point estimate of the parameters. In this paper we present a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters. We show that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset, which consists of over 100 million movie ratings. The resulting models achieve significantly higher prediction accuracy than PMF models trained using MAP estimation.
Cicada: Dependably Fast Multi-Core In-Memory Transactions

Multi-core in-memory databases promise high-speed online transaction processing. However, the performance of individual designs suffers when the workload characteristics miss their small sweet spot of a desired contention level, read-write ratio, record size, processing rate, and so forth.
 Cicada is a single-node multi-core in-memory transactional database with serializability. To provide high performance under diverse workloads, Cicada reduces overhead and contention at several levels of the system by leveraging optimistic and multi-version concurrency control schemes and multiple loosely synchronized clocks while mitigating their drawbacks. On the TPC-C and YCSB benchmarks, Cicada outperforms Silo, TicToc, FOEDUS, MOCC, two-phase locking, Hekaton, and ERMIA in most scenarios, achieving up to 3X higher throughput than the next fastest design. It handles up to 2.07 M TPC-C transactions per second and 56.5 M YCSB transactions per second, and scans up to 356 M records per second on a single 28-core machine.
HiText: Text Reading with Dynamic Salience Marking

The staggering amounts of content readily available to us via digital channels can often appear overwhelming. While much research has focused on aiding people at selecting relevant articles to read, only few approaches have been developed to assist readers in more efficiently reading an individual text. In this paper, we present HiText, a simple yet effective way of dynamically marking parts of a document in accordance with their salience. Rather than skimming a text by focusing on randomly chosen sentences, students and other readers can direct their attention to sentences determined to be important by our system. For this, we rely on a deep learning-based sentence ranking method. Our experiments show that this results in marked increases in user satisfaction and reading efficiency, as assessed using TOEFL-style reading comprehension tests.