nils-herrmann/clean_scirepeval huggingface dataset:
A Versatile Resonant Tank Identification Methodology for Induction Heating Systems

Induction heating has become the most advance heating process due to benefits, such as efficiency, performance, cleanness, and safety. In this process, the electromagnetic coupling between the induction coil and the induction target is a key, determining the heating performance as well as the resonant power converter operation. This letter proposes an accurate and cost-effective resonant tank identification method applied to induction heating systems. It is based on monitoring the resonant capacitor voltage in order to calculate the resonant tank quality factor. The proposed methodology has been tested using a versatile power electronics test bench applied to domestic induction heating, proving the feasibility of this proposal.
Performance-Oriented Coordinated Adaptive Robust Control for Four-Wheel Independently Driven Skid Steer Mobile Robot

Four-wheel independently driven skid-steered mobile robots are widely used in the fields of industrial automation and outdoor exploration. In most of existing controllers of skid-steered mobile robots, the wheel velocities are controlled independently to track the desired velocities from the high-level kinematic controller. However, this kind of control method may lead to chattering phenomenon of skid-steered mobile robots in practice, when the desired velocity commands of four wheels are not matched under different ground conditions. In this paper, the coordinated control problem is investigated for the four-wheel independently driven skid steer mobile robots, so as to solve the chattering phenomenon and also achieve good control performance under different ground conditions. Since the mobile robots are over-actuated and lack of suspension systems, a coordinated adaptive robust control scheme integrated with torque allocation technique is proposed. First, an adaptive robust control law is developed to attenuate the negative effects of load variations and uncertainties. Second, instead of directly giving the desired velocity commands, a torque control and allocation algorithm is developed to regulate the driving torque of each wheel motor. A coordinated control law with considering the wheel slip compensation is also proposed. Comparative experiments are carried out, and the results show the proposed scheme can avoid the chattering problem and achieve the excellent performance under different ground conditions.
A smart parking system based on NB-IoT and third-party payment platform

In recent years, with the increasing number of vehicles and insufficient parking spaces, the urban traffic congestion has become a great challenge that cannot be neglected. In order to mitigate problems such as high power consumption of sensor node and high deployment costs of wireless network, a smart parking system is proposed in this paper. In the proposed system, the data of the sensor node is transmitted by Narrowband Internet of Things (NB-IoT) module, which is a new cellular technology introduced for Low-Power Wide-Area (LPWA) applications. Basic information management, charge management, sensor node surveillance, task management and business intelligence modules are implemented on the cloud server. With integrated third-party payment platform and parking guide service, the mobile application developed for drivers is easy and convenient to use. Currently, the proposed system has been deployed in two cities to improve the utilization of existing parking facilities effectively.
Mechanical Compliance Control System for a pneumatic robot arm

The design and control of robots from the perspective of human safety is desired. We propose a mechanical compliance control system as a new pneumatic arm control system. However, safety against collisions with obstacles in an unpredictable environment is difficult to insure in previous system. The main feature of the proposed system is that the two desired pressure values are calculated by using two other desired values, the end compliance of the arm and the end position and posture of the arm.
Investigating Content Selection for Language Generation using Machine Learning

The content selection component of a natural language generation system decides which information should be communicated in its output. We use information from reports on the game of cricket. We first describe a simple factoid-to-text alignment algorithm then treat content selection as a collective classification problem and demonstrate that simple ‘grouping’ of statistics at various levels of granularity yields substantially improved results over a probabilistic baseline. We additionally show that holding back of specific types of input data, and linking database structures with commonality further increase performance.
Parietal lobe: from action organization to intention understanding.

Inferior parietal lobule (IPL) neurons were studied when monkeys performed motor acts embedded in different actions and when they observed similar acts done by an experimenter. Most motor IPL neurons coding a specific act (e.g., grasping) showed markedly different activations when this act was part of different actions (e.g., for eating or for placing). Many motor IPL neurons also discharged during the observation of acts done by others. Most responded differentially when the same observed act was embedded in a specific action. These neurons fired during the observation of an act, before the beginning of the subsequent acts specifying the action. Thus, these neurons not only code the observed motor act but also allow the observer to understand the agent's intentions.
Factors influencing the ownership and utilization of long-lasting insecticidal nets for malaria prevention in Ethiopia

Utilization of long-lasting insecticidal nets (LLINs) is regarded as key malaria prevention and control strategy. However, studies have reported a large gap in terms of both ownership and utilization particularly in the sub-Saharan Africa (SSA). With continual efforts to improve the use of LLIN and to progress malaria elimination, examining the factors influencing the ownership and usage of LLIN is of high importance. Therefore, the current study was conducted to examine the level of ownership and use of LLIN along with identification of associated factors at household level. A cross-sectional study was conducted in Mirab Abaya District, Southern Ethiopia in June and July 2014. A total of 540 households, with an estimated 2690 members, were selected in four kebeles of the district known to have high incidence of malaria. Trained data collectors interviewed household heads to collect information on the knowledge, ownership and utilization of LLINs, which was complemented by direct observation on the conditions and use of the nets through house-to-house visit. Bivariate and multivariable logistic regression analyses were used to determine factors associated to LLIN use. Of 540 households intended to be included in the survey, 507 responded to the study (94.24% response rate), covering the homes of 2759 people. More than 58% of the households had family size >5 (the regional average), and 60.2% of them had at least one child below the age of 5 years. The ownership of at least one LLIN among households surveyed was 89.9%, and using at least one LLIN during the night prior to the survey among net owners was 85.1% (n = 456). Only 36.7% (186) mentioned at least as the mean of correct scores of all participants for 14 possible malaria symptoms and 32.7% (166) knew at least as the mean of correct scores of all participants for possible preventive methods. Over 30% of nets owned by the households were out of use. After controlling for confounding factors, having two or more sleeping places (adjusted odds ratio [aOR] = 2.58, 95% CI 1.17, 5.73), knowledge that LLIN prevents malaria (aOR = 2.51, 95% CI 1.17, 5.37), the presence of hanging bed nets (aOR = 19.24, 95% CI 9.24, 40.07) and walls of the house plastered or painted >12 months ago (aOR = 0.09, 95% CI 0.01, 0.71) were important predictors of LLIN utilization. This study found a higher proportion of LLIN ownership and utilization by households than had previously been found in similar studies in Ethiopia, and in many studies in SSA. However, poor knowledge of the transmission mechanisms and the symptoms of malaria, and vector control measures to prevent malaria were evident. Moderate proportions of nets were found to be out of use or in poor repair. Efforts should be in place to maintain the current rate of utilization of LLIN in the district and improve on the identified gaps in order to support the elimination of malaria.
Random number generation with multiple streams for sequential and parallel computing

We provide a review of the state of the art on the design and implementation of random number generators (RNGs) for simulation, on both sequential and parallel computing environments. We focus on the need for multiple independent streams and substreams of random numbers, explain how they can be constructed and managed, review software libraries that offer them, and illustrate their usefulness via examples. We also review the basic quality criteria for good random number generators and their theoretical and empirical testing.
Ink dating using thermal desorption and gas chromatography/mass spectrometry: comparison of results obtained in two laboratories.

An ink dating method based on solvent analysis was recently developed using thermal desorption followed by gas chromatography/mass spectrometry (GC/MS) and is currently implemented in several forensic laboratories. The main aims of this work were to implement this method in a new laboratory to evaluate whether results were comparable at three levels: (i) validation criteria, (ii) aging curves, and (iii) results interpretation. While the results were indeed comparable in terms of validation, the method proved to be very sensitive to maintenances. Moreover, the aging curves were influenced by ink composition, as well as storage conditions (particularly when the samples were not stored in "normal" room conditions). Finally, as current interpretation models showed limitations, an alternative model based on slope calculation was proposed. However, in the future, a probabilistic approach may represent a better solution to deal with ink sample inhomogeneity.
Psychological factors and delayed healing in chronic wounds.

Studies have shown that stress can delay the healing of experimental punch biopsy wounds. This study examined the relationship between the healing of natural wounds and anxiety and depression.



Fifty-three subjects (31 women and 22 men) were studied. Wound healing was rated using a five-point Likert scale. Anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HAD), a well-validated psychometric questionnaire. Psychological and clinical wound assessments were each conducted with raters and subjects blinded to the results of the other assessment.



Delayed healing was associated with a higher mean HAD score (p = .0348). Higher HAD anxiety and depression scores (indicating "caseness") were also associated with delayed healing (p = .0476 and p = .0311, respectively). Patients scoring in the top 50% of total HAD scores were four times more likely to have delayed healing than those scoring in the bottom 50% (confidence interval = 1.06-15.08).



The relationship between healing of chronic wounds and anxiety and depression as measured by the HAD was statistically significant. Further research in the form of a longitudinal study and/or an interventional study is proposed.
A survey on facilities for experimental internet of things research

The initial vision of the Internet of Things was of a world in which all physical objects are tagged and uniquely identified by RFID transponders. However, the concept has grown into multiple dimensions, encompassing sensor networks able to provide real-world intelligence and goal-oriented collaboration of distributed smart objects via local networks or global interconnections such as the Internet. Despite significant technological advances, difficulties associated with the evaluation of IoT solutions under realistic conditions in real-world experimental deployments still hamper their maturation and significant rollout. In this article we identify requirements for the next generation of IoT experimental facilities. While providing a taxonomy, we also survey currently available research testbeds, identify existing gaps, and suggest new directions based on experience from recent efforts in this field.
The infrapatellar fat pad: anatomy and clinical correlations

This study examined the anatomy of the infrapatellar fat pad (IFP) in relation to knee pathology and surgical approaches. Eight embalmed knees were dissected via semicircular parapatellar incisions and each IFP was examined. Their volume, shape and constituent features were recorded. They were found in all knees and were constant in shape, consisting of a central body with medial and lateral extensions. The ligamentum mucosum was found inferior to the central body in all eight knees, while a fat tag was located superior to the central body in seven cases. Two clefts were consistently found on the posterior aspect of the IFP, a horizontal cleft below the ligamentum mucosum in six knees and a vertical cleft above, in seven cases. Our study found that the IFP is a constant structure in the knee joint, which may play a number of roles in knee joint function and pathology. Its significance in knee surgery is discussed.
Asynchronous-Logic QDI Quad-Rail Sense-Amplifier Half-Buffer Approach for NoC Router Design

We propose a low area overhead and power-efficient asynchronous-logic quasi-delay-insensitive (QDI) sense-amplifier half-buffer (SAHB) approach with quad-rail (i.e., 1-of-4) data encoding. The proposed quad-rail SAHB approach is targeted for area- and energy-efficient asynchronous network-on-chip (ANoC) router designs. There are three main features in the proposed quad-rail SAHB approach. First, the quad-rail SAHB is designed to use four wires for selecting four ANoC router directions, hence reducing the number of transistors and area overhead. Second, the quad-rail SAHB switches only one out of four wires for 2-bit data propagation, hence reducing the number of transistor switchings and dynamic power dissipation. Third, the quad-rail SAHB abides by QDI rules, hence the designed ANoC router features high operational robustness toward process-voltage-temperature (PVT) variations. Based on the 65-nm CMOS process, we use the proposed quad-rail SAHB to implement and prototype an 18-bit ANoC router design. When benchmarked against the dual-rail counterpart, the proposed quad-rail SAHB ANoC router features 32% smaller area and dissipates 50% lower energy under the same excellent operational robustness toward PVT variations. When compared to the other reported ANoC routers, our proposed quad-rail SAHB ANoC router is one of the high operational robustness, smallest area, and most energy-efficient designs.
Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data

Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.
A Robot-Partner for Preschool Children Learning English Using Socio-Cognitive Conflict

This paper presents an exploratory study in which a humanoid robot (MecWilly) acted as a partner to preschool children, helping them to learn English words. In order to use the Socio-Cognitive Conflict paradigm to induce the knowledge acquisition process, we designed a playful activity in which children worked in pairs with another child or with the humanoid robot on a word-picture association task involving fruit and vegetables. The analysis of the two experimental conditions (child-child and child-robot) demonstrates the effectiveness of Socio-Cognitive Conflict in improving the children’s learning of English. Furthermore, the analysis of children's performances as reported in this study appears to highlight the potential use of humanoid robots in the acquisition of English by young children.
Convolutional Decision Trees for Feature Learning and Segmentation

Most computer vision and especially segmentation tasks require to extract features that represent local appearance of patches. Relevant features can be further processed by learning algorithms to infer posterior probabilities that pixels belong to an object of interest. Deep Convolutional Neural Networks (CNN) define a particularly successful class of learning algorithms for semantic segmentation, although they proved to be very slow to train even when employing special purpose hardware. We propose, for the first time, a general purpose segmentation algorithm to extract the most informative and interpretable features as convolution kernels while simultaneously building a multivariate decision tree. The algorithm trains several orders of magnitude faster than regular CNNs and achieves state of the art results in processing quality on benchmark datasets.
A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. "Large-scale" means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed.
Automation Bias in Intelligent Time Critical Decision Support Systems

Various levels of automation can be introduced by intelligent decision support systems, from fully automated, where the operator is completely left out of the decision process, to minimal levels of automation, where the automation only makes recommendations and the operator has the final say. For rigid tasks that require no flexibility in decision-making and with a low probability of system failure, higher levels of automation often provide the best solution. However, in time critical environments with many external and changing constraints such as air traffic control and military command and control operations, higher levels of automation are not advisable because of the risks and the complexity of both the system and the inability of the automated decision aid to be perfectly reliable. Human-inthe-loop designs, which employ automation for redundant, manual, and monotonous tasks and allow operators active participation, provide not only safety benefits, but also allow a human operator and a system to respond more flexibly to uncertain and unexpected events. However, there can be measurable costs to human performance when automation is used, such as loss of situational awareness, complacency, skill degradation, and automation bias. This paper will discuss the influence of automation bias in intelligent decision support systems, particularly those in aviation domains. Automation bias occurs in decision-making because humans have a tendency to disregard or not search for contradictory information in light of a computer-generated solution that is accepted as correct and can be exacerbated in time critical domains. Automated decision aids are designed to reduce human error but actually can cause new errors in the operation of a system if not designed with human cognitive limitations in mind.
Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.