nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning Advances on Different 3D Data Representations: A Survey

3D data is a valuable asset in the field of computer vision as it provides rich information about the full geometry of sensed objects and scenes. With the recent availability of large 3D datasets and the increase in computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition and correspondence. Depending on the considered 3D data representation, different challenges may be foreseen in using existent deep learning architectures. In this paper, we provide a comprehensive overview of various 3D data representations highlighting the difference between Euclidean and non-Euclidean ones. We also discuss how deep learning methods are applied on each representation, analyzing the challenges to
An EEG artifact identification embedded system using ICA and multi-instance learning

Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However these approaches are artifact specific. In this paper, we present a novel software-hardware system that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. The EEG data is decomposed into independent components using ICA, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. We also performed extensive hyperparameter optimization for the model with the goal of improving accuracy without increasing execution time. This resulted the execution time to be reduced from 282 s to 8.8 s when running the model on an embedded ARM CPU processor at 1.6 GHz clock frequency. In this paper, we present the overall system which includes ICA, SAX and MIL, along with preliminary results for software and hardware implementation when using real EEG data from 64 electrodes. The proposed system consumes 909 mW power during processing above a baseline of 2.32 W idle, while achieving 91.2% artifact identification accuracy.
Stair case cleaning robot: Design considerations and a case study

Using robots for cleaning surface, glass, rooms etc. are not new and are in the field of research for more than a decade. But when it comes to stair case cleaning, we see that the research area is only few years old. The main reason is the enormous difference is design mechanism when it comes to vertical climbing robots compared to the robots that move over surface. These robots function in 3-D space where as surface cleaning robots function in 2-D space which is comparatively easier to design and implement compared to the 3-D space robots. In this work, we present various design considerations for vertical climbing robots to be used in stair case cleaning. We also present a case study with the design of one such robot that functions in 3-D space, the implementation, the testing, and the performance. The test results are very encouraging for a possible good quality design outcome which can serve the intended purpose.
A NEW COLOR CORRECTION METHOD FOR UNDERWATER IMAGING

Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.
A Neurocognitive Framework for Human Creative Thought

We are an intensely creative species. Creativity is the fountainhead of our civilizations and a defining characteristic of what makes us human. But for all its prominence at the apex of human mental faculties, we know next to nothing about how brains generate creative ideas. With all previous attempts to tighten the screws on this vexed problem unsuccessful - right brains, divergent thinking, defocused attention, default mode network, alpha enhancement, prefrontal activation, etc. (Dietrich and Kanso, 2010) - the neuroscientific study of creativity finds itself in a theoretical arid zone that has perhaps no equal in psychology. We propose here a general framework for a fresh attack on the problem and set it out under 10 foundational concepts. Most of the ideas we favor are part and parcel of the standard conceptual toolbox of cognitive neuroscience but their combination and significance to creativity are original. By outlining, even in such broad strokes, the theoretical landscape of cognitive neuroscience as it relates to creative insights, we hope to bring into clear focus the key enabling factors that are likely to have a hand in computing ideational combinations in the brain.
A 20-mV Input DC/DC Converter for Energy Harvesting Applications

A low input step-up DC/DC converter and power manager in 0.18m CMOS process is presented. The proposed converter can work with the input voltage as low as 20mV. The extremely low input voltage makes it suitable for energy harvesting and power management. Four logic controlled outputs provide the best voltage for various applications to accommodate low power design requirements. A low current low dropout regulator (LDO) is utilized to provide a regulated 2.2V output for powering low power processors or other low power integrated circuit (ICs). Reserve energy on the storage capacitor CSTORE provides power when the input voltage source is unavailable, thus prolongs the life of the system and expands the application range. Extremely low quiescent current (6 A) and high e±ciency design (64%@300 A load current) ensure the fastest possible charge times of the output reservoir capacitor. This work provides a complete power management solution for wireless sensing and data acquisition.
Integrated CMOS transmit-receive switch using LC-tuned substrate bias for 2.4-GHz and 5.2-GHz applications

CMOS transmit-receive (T/R) switches have been integrated in a 0.18-/spl mu/m standard CMOS technology for wireless applications at 2.4 and 5.2 GHz. This switch design achieves low loss and high linearity by increasing the substrate impedance of a MOSFET at the frequency of operation using a properly tuned LC tank. The switch design is asymmetric to accommodate the different linearity and isolation requirements in the transmit and receive modes. In the transmit mode, the switch exhibits 1.5-dB insertion loss, 28-dBm power, 1-dB compression point (P/sub 1dB/), and 30-dB isolation, at 2.4 and 5.2 GHz. In the receive mode, the switch achieves 1.6-dB insertion loss, 11.5-dBm P/sub 1dB/, and 15-dB isolation, at 2.4 and 5.2 GHz. The linearity obtained in the transmit mode is the highest reported to date in a standard CMOS process. The switch passes the 4-kV Human Body Model electrostatic discharge test. These results show that the switch design is suitable for narrow-band applications requiring a moderate-high transmitter power level (<1 W).
Deep learning and process understanding for data-driven Earth system science

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. Complex Earth system challenges can be addressed by incorporating spatial and temporal context into machine learning, especially via deep learning, and further by combining with physical models into hybrid models.
Duty-cycling buildings aggressively: The next frontier in HVAC control

Buildings are known to be the largest consumers of electricity in the United States, and often times the dominant energy consumer is the HVAC system. Despite this fact, in most buildings the HVAC system is run using primitive static control algorithms based on fixed work schedules causing wasted energy during periods of low occupancy. In this paper we present a novel control architecture that uses occupancy sensing to guide the operation of a building HVAC system. We show how we can enable aggressive duty-cycling of building HVAC systems — that is, turn them ON or OFF — to save energy while meeting building performance requirements using inexpensive sensing and control methods. We have deployed our occupancy sensor network across an entire floor of a university building and our data shows several periods of low occupancy with significant opportunities to save energy over normal HVAC schedules. Furthermore, by interfacing with the building Energy Management System (EMS) directly and using real-time occupancy data collected by our occupancy nodes, we measure electrical energy savings of 9.54% to 15.73% and thermal energy savings of 7.59% to 12.85% for the HVAC system by controlling just one floor of our four floor building.
New Developments for Case Conceptualization in Emotion-Focused Therapy.

Emotion-focused therapy (EFT) has increasingly made use of case conceptualization. The current paper presents a development in the case conceptualization approach of EFT. It takes inspiration from recent research on emotion transformation in EFT. The case conceptualization presented here can guide the therapist in listening to the client's narrative and in observing the client's emotional presentation in sessions. Through observing regularities, the therapist can tentatively determine core emotion schemes' organizations, triggers that bring about the emotional pain, the client's self-treatment that contributes to the pain, the fear of emotional pain that drives avoidance and emotional interruption strategies. The framework recognizes global distress, into which the client falls, as a result of his or her inability to process the underlying pain, the underlying core pain and the unmet needs embedded in it. This conceptual framework then informs therapists as to which self-organizations (compassion and protective anger based) have to be facilitated to respond to the pain and unmet needs, so that they might transform it. The conceptual framework can guide the therapist's thinking/perceptions and actions in the session.



Therapists can better facilitate emotional transformation when they understand the dynamics involved in the client's distress. Emotion transformation is facilitated by first helping the client to access the core underlying painful feelings and unmet needs embedded in them and then by helping the client to generate adaptive emotional responses to those unmet needs.
Exploring the behaviour of base classifiers in credit scoring ensembles

Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be more accurate than single prediction models. However, it is still a question what base classifiers should be employed in each ensemble in order to achieve the highest performance. Accordingly, the present paper evaluates the performance of seven individual prediction techniques when used as members of five different ensemble methods. The ultimate aim of this study is to suggest appropriate classifiers for each ensemble approach in the context of credit scoring. The experimental results and statistical tests show that the C4.5 decision tree constitutes the best solution for most ensemble methods, closely followed by the multilayer perceptron neural network and logistic regression, whereas the nearest neighbour and the naive Bayes classifiers appear to be significantly the worst.
The multilayer perceptron as an approximation to a Bayes optimal discriminant function

The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear.
Cross-lingual Semantic Generalization for the Detection of Metaphor

In this work, we describe a supervised cross-lingual methodology for detecting novel and conventionalized metaphors that derives generalized semantic patterns from a collection of metaphor annotations. For this purpose, we model each metaphor annotation as an abstract tuple – (source, target, relation, metaphoricity) – that packages a metaphoricity judgement with a relational grounding of the source and target lexical units in text. From these annotations, we derive a set of semantic patterns using a three-step process. First, we employ several generalized representations of the target using a variety of WordNet information and representative domain terms. Then, we generalize relations using a rule-based, pseudo-semantic role labeling. Finally, we generalize the source by partitioning a semantic hierarchy (defined by the target and the relation) into metaphoric and non-metaphoric regions so as to optimally account for the evidence in the annotated data. Experiments show that by varying the generality of the source, target, and relation representations in our derived patterns, we are able to significantly extend the impact of our annotations, detecting metaphors in a variety of domains at an F-measure of between 0.88 and 0.92 for English, Spanish, Russian, and Farsi. This generalization process both enhances our ability to jointly detect novel and conventionalized metaphors and enables us to transfer the knowledge encoded in metaphoricity annotations to novel languages.
Volume of signaling traffic reaching cellular networks from mobile phones

Cellular networks are facing a big challenge emerging from excessive increase in signaling traffic due to background activities on smartphones. The excessive signaling if not properly managed contributes to signaling storms causing outages in mobile networks and poor quality of service, especially during peak times. For smartphone users, device-specific excessive signaling traffic caused by mobile apps can lead to poor device performance. Additionally, the cost of supporting this traffic is increasing and becoming unsustainable compared to the revenues generated. This paper aims to provide an understanding of the magnitude, scale, and the sources of signaling traffic, which is important for researchers and network operators if they are to devise effective solutions to manage the excessive signaling. We provide an analysis of characteristics of traffic resulting from background activity on smartphones and cellular networks based on datasets collected using passive (traces from a leading mobile operator) and active experiments (over 29 million logs from selected mobile apps). From the findings we deduce that the volume of signaling traffic is determined by the number of mobile devices, the kind of technologies supported by the devices, and roaming of mobile devices between different networks. Apps on the mobile devices generate background traffic without the active intervention of the device user(s). The traffic is generated in small quantities as the devices connect and tear down connections to the network periodically. Though, the average signaling traffic is tolerable, high peaks of this traffic can cause signaling floods, when some elements in the network become unbearable; this can render the network unavailable. Our findings provide network operators with a quantified understanding of the magnitude of the signaling challenge. For smartphone owners, our results provide an awareness of and insights into the impact of signaling traffic caused by individual mobile apps.
Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability. This paper puts forth attributes of industrial big data processing and actively explores industrial big data processing-based predictive maintenance. A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era. The effectiveness of the proposed scheme was verified by analyzing multisource heterogeneous industrial data for the remaining life prediction of key components of machining equipment.
Analytical Design Methodology for Litz-Wired High-Frequency Power Transformers

In the last quarter of a century, high-frequency (HF) transformer design has been one of the major concerns to power electronics designers in order to increase converter power densities and efficiencies. Conventional design methodologies are based on iterative processes and rules of thumb founded more on expertise than on theoretical developments. This paper presents an analytical design methodology for litz-wired HF power transformers that provides a deep insight into the transformer design problem making it a powerful tool for converter designers. The most suitable models for the calculation of core and winding losses and the transformer thermal resistance are first selected and then validated with a 5-kW 50-kHz commercial transformer for a photovoltaic application. Based on these models, the design methodology is finally proposed, reducing the design issue to directly solve a five-variable nonlinear optimization problem. The methodology is illustrated with a detailed design in terms of magnetic material, core geometry, and primary and secondary litz-wire sizing. The optimal design achieves a 46.5% power density increase and a higher efficiency of 99.70% when compared with the commercial one.
FORECASTING IONOSPHERIC TOTAL ELECTRON CONTENT MAPS WITH DEEP NEURAL NETWORKS

Satellite telecommunications and Global Navigation Satellite Systems (GNSS) would benefit from an early prediction of the ionospheric activity. The Total Electron Content (TEC) values of the ionosphere are already locally predicted by models from previous studies, but no model exists to our knowledge for worldwide prediction. A large amount of data for world TEC maps is available from the Center for Orbit Determination in Europe (CODE). With Deep Neural Networks (DNN), we propose a method to forecast a sequence of global TEC maps following past given TEC maps, without introducing any prior knowledge. By combining several state-of-theart architectures, the proposed approach is competitive with previous works on TEC forecast, while predicting global TEC maps.
Is a metaphor ( like ) a simile ? Differences in meaning , effect and processing *

Current theoretical work in metaphor and simile stu dies is preoccupied with the precise nature of the difference between them, and whether metaphors and similes are essentially interchangeable (as comparison theories hold) or altogether different in meaning and effect (as categorization theories woul d suggest). Adhering to the categorization line, I illustrate differences in ef fect by close examination of examples, and show that the fundamental difference in referen ce, and the intrinsic focus of the simile form on precise points of similarity between two concepts, is responsible for a range of distinctive effects. Contrary to popular t hought, simile can sometimes be more powerful than metaphor.
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.