nils-herrmann/clean_scirepeval huggingface dataset:
Self-organizing neural integration of pose-motion features for human action recognition

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions.
Black cattle body shape and temperature measurement using thermography and KINECT sensor

Black cattle body shape and temperature measurement system is introduced. It is important to evaluate the quality of Japanese black cattle periodically during their growth process. Not only the weight and size of cattle, but also the posture, shape, and temperature need to be tracked as primary evaluation criteria. In the present study, KINECT sensor and thermal camera obtains the body shape and its temperature. The whole system is calibrated to operate in a common coordinate system. Point cloud data are obtained from different angles and reconstructed in a computer. The thermal data are captured too. Both point cloud data and thermal information are combined by considering the orientation of the cow. The collected information is used to evaluate and estimate cattle conditions.
Understanding user privacy in Internet of Things environments

During the past decade, user privacy has become an important issue in networked computing environments. For instance, mobile applications and devices are increasingly asking users to provide personal information, as well as monitoring users through behavioral tracking. This privacy-invasive practice is likely to increase with the proliferation of sensor devices in the upcoming era of Internet of Things (IoT). However, there has been comparatively little research so far aimed at understanding people's notion of privacy in connection with IoT. In earlier work, we unveiled five contextual parameters that characterize IoT service scenarios, and five reaction parameters that describe people's attitudes toward the scenarios. In this paper, we aim to understand how these contextual parameters impact people's privacy perceptions of IoT scenarios. To this end, we conducted a survey with 200 respondents on 2800 hypothetical IoT scenarios (mostly about information monitoring activities), and analyzed them using a K-modes clustering algorithm. We identified four clusters of scenarios, with clearly distinctive associated user reactions. By comparing the different clusters, we can identify contextual parameters that are associated with higher or lower acceptance of sensor tracking in IoT environments.
Supervised Matrix Factorization Hashing for Cross-Modal Retrieval

The target of cross-modal hashing is to embed heterogeneous multimedia data into a common low-dimensional Hamming space, which plays a pivotal part in multimedia retrieval due to the emergence of big multimodal data. Recently, matrix factorization has achieved great success in cross-modal hashing. However, how to effectively use label information and local geometric structure is still a challenging problem for these approaches. To address this issue, we propose a cross-modal hashing method based on collective matrix factorization, which considers both the label consistency across different modalities and the local geometric consistency in each modality. These two elements are formulated as a graph Laplacian term in the objective function, leading to a substantial improvement on the discriminative power of latent semantic features obtained by collective matrix factorization. Moreover, the proposed method learns unified hash codes for different modalities of an instance to facilitate cross-modal search, and the objective function is solved using an iterative strategy. The experimental results on two benchmark data sets show the effectiveness of the proposed method and its superiority over state-of-the-art cross-modal hashing methods.
Tabu Search - Part II

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Multiband Printed Monopole Antennas Loaded With OCSRRs for PANs and WLANs

Multiband printed monopole antennas loaded with open complementary split-ring resonators (OCSRRs) are presented. The OCSRRs, modeled as parallel LC resonant tanks, act as high-impedance elements at their resonance frequencies, and different effective λ/4 sections can be achieved in the monopole by placing them at proper locations. Thus, the first working frequency is related to the length of the monopole, while the additional bands are controlled by the resonance frequencies of the OCSRRs. Moreover, the proposed antennas present monopolar radiation characteristics at all the operation bands. Two prototypes are designed, manufactured and measured: 1) a single-loaded OCSRR dual-band printed monopole antenna covering the Bluetooth and IEEE 802.11a/b/g/n bands (2.40-2.48 and 5.15-5.80 GHz, respectively); and 2) a triband prototype based on the same design, but with an additional OCSRR designed to also cover the IEEE 802.11y frequency band (3.65-3.70 GHz). Both antennas are printed on a single-layer of a low-cost substrate, resulting in very compact designs.
Action and object words are differentially anchored in the sensory motor system - A perspective on cognitive embodiment

Embodied and grounded cognition theories have assumed that the sensorimotor system is causally involved in processing motor-related language content. Although a causal proof on a single-cell basis is ethically not possible today, the present fMRI study provides confirmation of this longstanding speculation, as far as it is possible with recent methods, employing a new computational approach. More specifically, we were looking for common activation of nouns and objects, and actions and verbs, representing the canonical and mirror neuron system, respectively. Using multivariate pattern analysis, a resulting linear classifier indeed successfully generalized from distinguishing actions from objects in pictures to distinguishing the respective verbs from nouns in written words. Further, these action-related pattern responses were detailed by recently introduced predictive pattern decomposition into the constituent activity atoms and their relative contributions. The findings support the concept of canonical neurons and mirror neurons implementing embodied processes with separate roles in distinguishing objects from actions, and nouns from verbs, respectively. This example of neuronal recycling processing algorithms is consistent with a multimodal brain signature of human action and object concepts. Embodied language theory is thus merged with actual neurobiological implementation.
Estimation of variable-speed-drive power consumption from harmonic content

Nonintrusive load monitoring can be used to identify the operating schedule of individual loads strictly from measurements of an aggregate power signal. Unfortunately, certain classes of loads present a continuously varying power demand. The power demand of these loads can be difficult to separate from an aggregate measurement. Variable-speed drives (VSDs) are industrially important variable-demand loads that are difficult to track non-intrusively. This paper proposes a VSD power estimation method based on observed correlations between fundamental and higher harmonic spectral content in current. The technique can be generalized to any load with signature correlations in harmonic content, including many power electronic and electromechanical loads. The approach presented here expands the applicability and field reliability of nonintrusive load monitoring.
A Development and Execution Environment for Early Warning Systems for Natural Disasters

Early Warning Systems (EWS) may become a powerful tool for mitigating the negative impact of natural disasters, especially when combined with advanced IT solutions - such as on-demand scenario simulations, semi-automatic impact assessment, or real-time analysis of measurements from in-situ sensors. However, such complex systems require a proper computing environment supporting their development and operation. We propose the Common Information Space (CIS), a software framework facilitating design, deployment and execution of early warning systems based on real-time monitoring of natural phenomena and computationally intensive, time-critical computations. CIS provides a service-oriented technology stack which helps design 'blueprints' for early warning application scenarios and deploy these blueprints as services - system factories enabling users to rapidly deploy new EWSs in new settings. CIS also provides advanced runtime services and resource orchestration capabilities in order to address the specific requirements of EWSs: continuous operation, highly variable resource demands and mission-critical computations. The CIS concept is validated through the Flood Early Warning System whose goal is to monitor embankments in urban areas and assist in rapid decision making whenever a dike failure results in a flooding threat.
A differential inverter-based switched-capacitor oscillator in 65 nm CMOS technology

This paper presents a differential inverter-based switched-capacitor (SC) oscillator in 65 nm CMOS technology. The proposed SC oscillator is based on the continuous-time two-integrator oscillator, and the differential version is formed by the inverter-based pseudo-differential operational trans-conductance amplifier (OTA). By employing the inverter-based OTA in the SC oscillator, the power consumption can be reduced because inverters operate at the class-AB mode compared with traditional OTAs which operate at the class-A mode. The oscillating frequency can be tunable by changing the capacitor ratios. The SC oscillator can be applied as a frequency divider for the clock frequency, while it can be adopted as an oscillator at the lower frequency band which requires huge values of the inductor and the capacitor if the LC oscillator is employed. The oscillating frequency in this work is 4.2 MHz, the power consumption is 0.14 mW at a 0.7 V supply voltage, and the clock frequency for the switches is 100 MHz. Arrays of such oscillators may find use in analog image processing applications.
Planning and spatial working memory following frontal lobe lesions in man

Twenty-six patients with unilateral or bilateral frontal lobe excisions were compared with age and IQ matched controls on a computerized battery of tests of spatial working memory and planning. A computerized test of spatial short term memory capacity revealed no significant impairment in the patients' ability to execute a given sequence of visuo-spatial moves. In contrast, a paradigm designed to assess spatial working memory capacity, revealed significant impairments in the patient group in both possible types of search errors. Furthermore, additional analysis showed that the frontal lobe patients were less efficient than controls in their usage of a strategy for improving performance on this test. Higher level planning was also investigated using a test based on the "Tower of London" problem [SHALLICE, T. Phil.  298, 199-209, 1982]. Patients with frontal lobe damage required more moves to complete the problems and a yoked motor control condition revealed that movement times were significantly increased in this group. Taking both of these factors into consideration, initial thinking (planning) time was unimpaired in the patient group although the thinking time subsequent to the first move was significantly prolonged. These data are compared to previous findings from patients with idiopathic Parkinson's disease and are discussed in terms of an impairment of higher cognitive functioning following frontal lobe damage.
Temporal Data Mining: An Overview

— To classify data mining problems and algorithms we used two dimensions: data type and type of mining operations. One of the main issue that arise during the data mining process is treating data that contains temporal information. The area of temporal data mining has very much attention in the last decade because from the time related feature of the data, one can extract much significant information which can not be extracted by the general methods of data mining. Many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as databases, statistics and machine learning the literature is scattered among many different sources. In this paper, we present a survey on techniques of temporal data mining.
Time-Agnostic Prediction: Predicting Predictable Video Frames

Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks—while we cannot predict the precise trajectory of a robot arm between being at rest and holding an object up, we can be certain that it must have picked the object up. To exploit this, we decouple visual prediction from a rigid notion of time. While conventional approaches predict frames at regularly spaced temporal intervals, our time-agnostic predictors (TAP) are not tied to specific times so that they may instead discover predictable “bottleneck” frames no matter when they occur. We evaluate our approach for future and intermediate frame prediction across three robotic manipulation tasks. Our predictions are not only of higher visual quality, but also correspond to coherent semantic subgoals in temporally extended tasks.
A 1 MHz Half-Bridge Resonant DC/DC Converter Based on GaN FETs and Planar Magnetics

A 1 MHz half-bridge resonant dc/dc converter based on GaN FETs and planar magnetics is proposed in this paper, which improves the system efficiency and power density. The resonant network can achieve satisfactory soft-switching characteristics based on a small impedance angle, which greatly reduces the losses of switches and diodes. The losses characteristics during the turn-on and turn-off transitions are analyzed in detail. The calculation results show that the GaN FETs with low output capacitance and on resistance can achieve fast switching speed and low losses in high-frequency conditions. To reduce the profile and increase the power density of the system, planar magnetics are used in this paper. The response surface methodology (RSM) and modular layer model (MLM) are adopted to help design the planar inductor and transformer, respectively. Both of the methods offer clearer and more effective ways to design the planar magnetics. A 25-W prototype is built to verify the feasibility of the proposed high-frequency converter.
Striving for the moral self: the effects of recalling past moral actions on future moral behavior.

People's desires to see themselves as moral actors can contribute to their striving for and achievement of a sense of self-completeness. The authors use self-completion theory to predict (and show) that recalling one's own (im)moral behavior leads to compensatory rather than consistent moral action as a way of completing the moral self. In three studies, people who recalled their immoral behavior reported greater participation in moral activities (Study 1), reported stronger prosocial intentions (Study 2), and showed less cheating (Study 3) than people who recalled their moral behavior. These compensatory effects were related to the moral magnitude of the recalled event, but they did not emerge when people recalled their own positive or negative nonmoral behavior (Study 2) or others' (im)moral behavior (Study 3). Thus, the authors extend self-completion theory to the moral domain and use it to integrate the research on moral cleansing (remunerative moral strivings) and moral licensing (relaxed moral strivings).
Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor

The use of the commercial video games as rehabilitation tools, such as the Nintendo WiiFit, has recently gained much interest in the physical therapy arena. Motion tracking controllers such as the Nintendo Wiimote are not sensitive enough to accurately measure performance in all components of balance. Additionally, users can figure out how to "cheat" inaccurate trackers by performing minimal movement (e.g. wrist twisting a Wiimote instead of a full arm swing). Physical rehabilitation requires accurate and appropriate tracking and feedback of performance. To this end, we are developing applications that leverage recent advances in commercial video game technology to provide full-body control of animated virtual characters. A key component of our approach is the use of newly available low cost depth sensing camera technology that provides markerless full-body tracking on a conventional PC. The aim of this research was to develop and assess an interactive game-based rehabilitation tool for balance training of adults with neurological injury.
Short Text Clustering via Convolutional Neural Networks

Short text clustering has become an increasing important task with the popularity of social media, and it is a challenging problem due to its sparseness of text representation. In this paper, we propose a Short Text Clustering via Convolutional neural networks (abbr. to STCC), which is more beneficial for clustering by considering one constraint on learned features through a self-taught learning framework without using any external tags/labels. First, we embed the original keyword features into compact binary codes with a localitypreserving constraint. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, with the output units fitting the pre-trained binary code in the training process. After obtaining the learned representations, we use K-means to cluster them. Our extensive experimental study on two public short text datasets shows that the deep feature representation learned by our approach can achieve a significantly better performance than some other existing features, such as term frequency-inverse document frequency, Laplacian eigenvectors and average embedding, for clustering.
Context-Aware Data Loss Prevention for Cloud Storage Services

With the wide adoption of hybrid cloud, there are many potential risks that need to be mitigated to ensure that the utilizations of services are at their optimal levels. One of the major risks that has garnered much attention is maintaining maximum security and confidentiality for sensitive information. Detecting sensitive content at near real-time and at cloud scale has become a critical first step for organizations to prevent data loss and comply with data privacy laws and regulations. Proactive detection raises security awareness at the early stage and thus can be used to govern how the information should be managed, protected, and utilized in the hybrid cloud. In contrast to traditional dictionary or policy-based approaches, we introduce a system that detects sensitive content by leveraging its semantic contextual information through various machine learning and deep learning techniques at different levels of granularity within the document, and is the first of its kind.
Image set for deep learning: field images of maize annotated with disease symptoms

Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data. This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.
Movie Review Classification Based on a Multiple Classifier

In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.