nils-herrmann/clean_scirepeval huggingface dataset:
Analysis Matrix for Smart Cities

The current digital revolution has ignited the evolution of communications grids and the development of new schemes for productive systems. Traditional technologic scenarios have been challenged, and Smart Cities have become the basis for urban competitiveness. The citizen is the one who has the power to set new scenarios, and that is why a definition of the way people interact with their cities is needed, as is commented in the first part of the article. At the same time, a lack of clarity has been detected in the way of describing what Smart Cities are, and the second part will try to set the basis for that. For all before, the information and communication technologies that manage and transform 21st century cities must be reviewed, analyzing their impact on new social behaviors that shape the spaces and means of communication, as is posed in the experimental section, setting the basis for an analysis matrix to score the different elements that affect a Smart City environment. So, as the better way to evaluate what a Smart City is, there is a need for a tool to score the different technologies on the basis of their usefulness and consequences, considering the impact of each application. For all of that, the final section describes the main objective of this article in practical scenarios, considering how the technologies are used by citizens, who must be the main concern of all urban development.
Design of Low-Cost Chipless System Using Printable Chipless Tag With Electromagnetic Code

In this letter, a printable chipless tag with electromagnetic code using split ring resonators is proposed. A 4 b chipless tag that can be applied to paper/plastic-based items such as ID cards, tickets, banknotes and security documents is designed. The chipless tag generates distinct electromagnetic characteristics by various combinations of a split ring resonator. Furthermore, a reader system is proposed to digitize electromagnetic characteristics and convert chipless tag to electromagnetic code.
Design Principles for Energy-Efficient Legged Locomotion and Implementation on the MIT Cheetah Robot

This paper presents the design principles for highly efficient legged robots, the implementation of the principles in the design of the MIT Cheetah, and the analysis of the high-speed trotting experimental results. The design principles were derived by analyzing three major energy-loss mechanisms in locomotion: heat losses from the actuators, friction losses in transmission, and the interaction losses caused by the interface between the system and the environment. Four design principles that minimize these losses are discussed: employment of high torque-density motors, energy regenerative electronic system, low loss transmission, and a low leg inertia. These principles were implemented in the design of the MIT Cheetah; the major design features are large gap diameter motors, regenerative electric motor drivers, single-stage low gear transmission, dual coaxial motors with composite legs, and the differential actuated spine. The experimental results of fast trotting are presented; the 33-kg robot runs at 22 km/h (6 m/s). The total power consumption from the battery pack was 973 W and resulted in a total cost of transport of 0.5, which rivals running animals' at the same scale. 76% of the total energy consumption is attributed to heat loss from the motor, and the remaining 24% is used in mechanical work, which is dissipated as interaction loss as well as friction losses at the joint and transmission.
Mining Fuzzy Association Rules

In this paper, we introduce a novel technique, called F-APACS, for mining fuzzy association rules. Existing algorithms involve discretizing the domains of quantitative attributes into intervals so as to discover quantitative association rules. These intervals may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from those rules discovered. Instead of using intervals, F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The linguistic representation is especially useful when those rules discovered are presented to human experts for examination. The definition of linguistic terms is based on fuzzy set theory and hence we call the rules having these terms fuzzy association rules. The use of fuzzy techniques makes F-APACS resilient to noises such as inaccuracies in physical measurements of real-life entities and missing values in the databases. Furthermore, F-APACS employs adjusted difference analysis which has the advantage that it does not require any user-supplied thresholds which are often hard to determine. The fact that F-APACS is able to mine fuzzy association rules which utilize linguistic representation and that it uses an objective yet meaningful confidence measure to determine the interestingness of a rule makes it very effective at the discovery of rules from a real-life transactional database of a PBX system provided by a telecommunication corporation.
Efficient K-Shot Learning With Regularized Deep Networks

Feature representations from pre-trained deep neural networks have been known to exhibit excellent generalization and utility across a variety of related tasks. Fine-tuning is by far the simplest and most widely used approach that seeks to exploit and adapt these feature representations to novel tasks with limited data. Despite the effectiveness of fine-tuning, it is often sub-optimal and requires very careful optimization to prevent severe over-fitting to small datasets. The problem of sub-optimality and over-fitting, is due in part to the large number of parameters (≈ 10) used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning. To prevent overfitting, our key strategy is to cluster the model parameters while ensuring intra-cluster similarity and intercluster diversity of the parameters, effectively regularizing the dimensionality of the parameter search space. In particular, we identify groups of neurons within each layer of a deep network that share similar activation patterns. When the network is to be fine-tuned for a classification task using only k examples, we propagate a single gradient to all of the neuron parameters that belong to the same group. The grouping of neurons is non-trivial as neuron activations depend on the distribution of the input data. To efficiently search for optimal groupings conditioned on the input data, we propose a reinforcement learning search strategy using recurrent networks to learn the optimal group assignments for each network layer. Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than 10% of accuracy.
Dichotomizing continuous predictors in multiple regression: a bad idea.

In medical research, continuous variables are often converted into categorical variables by grouping values into two or more categories. We consider in detail issues pertaining to creating just two groups, a common approach in clinical research. We argue that the simplicity achieved is gained at a cost; dichotomization may create rather than avoid problems, notably a considerable loss of power and residual confounding. In addition, the use of a data-derived 'optimal' cutpoint leads to serious bias. We illustrate the impact of dichotomization of continuous predictor variables using as a detailed case study a randomized trial in primary biliary cirrhosis. Dichotomization of continuous data is unnecessary for statistical analysis and in particular should not be applied to explanatory variables in regression models.
Short-circuit behavior of the three-level advanced-active-neutral-point-clamped converter

The Advanced-ANPC converter is designed to withstand almost every possible short-circuit failure of the semiconductors in comparison to state-of-the-art (A)NPC converters. The idea behind this concept is an additional short-circuit inductor to improve the robustness against faults. The presented investigations within this paper examine all the possible short-circuit situations and their effects for an (A)ANPC converter with high-voltage IGBTs.
Cultural Confusions Show that Facial Expressions Are Not Universal

Central to all human interaction is the mutual understanding of emotions, achieved primarily by a set of biologically rooted social signals evolved for this purpose-facial expressions of emotion. Although facial expressions are widely considered to be the universal language of emotion, some negative facial expressions consistently elicit lower recognition levels among Eastern compared to Western groups (see  for a meta-analysis and [5, 6] for review). Here, focusing on the decoding of facial expression signals, we merge behavioral and computational analyses with novel spatiotemporal analyses of eye movements, showing that Eastern observers use a culture-specific decoding strategy that is inadequate to reliably distinguish universal facial expressions of "fear" and "disgust." Rather than distributing their fixations evenly across the face as Westerners do, Eastern observers persistently fixate the eye region. Using a model information sampler, we demonstrate that by persistently fixating the eyes, Eastern observers sample ambiguous information, thus causing significant confusion. Our results question the universality of human facial expressions of emotion, highlighting their true complexity, with critical consequences for cross-cultural communication and globalization.
A Neural Local Coherence Model

Coherence evaluation results on Discrimination and Insertion tasks. † indicates a neural model is significantly superior to its non-neural counterpart with p-value < 0.01. Discr. Ins. Acc F1 Random 50.00 50.00 12.60 Graph-based (G&S) 64.23 65.01 11.93 Dist. sentence (L&H) 77.54 77.54 19.32 Grid-all nouns (E&C) 81.58 81.60 22.13 Extended Grid (E&C) 84.95 84.95 23.28 Grid-CNN 85.57† 85.57† 23.12 Extended Grid-CNN 88.69†  25.95†
Finding others online: reputation systems for social online spaces

In this paper, we examine what types of reputation information users find valuable when selecting someone to interact with in online environments. In an online experiment, we asked users to imagine that they were looking for a partner for a social chat. We found that similarity to the user and ratings from the user's friends were the most valuable pieces of reputation information when selecting chat partners. The context in which reputations were used (social chat, game or newsgroup) affected the self-reported utility of the pieces of reputation information
Using MotSaRT to Support On-Line Teachers in Student Motivation

Motivation to learn is affected by a student’s self-efficacy, goal orientation, locus of control and perceived task difficulty. In the classroom, teachers know how to motivate their students and how to exploit this knowledge to adapt or optimize their instruction when a student shows signs of demotivation. In on-line learning environments it is much more difficult to assess the level of motivation of the student and to have adaptive intervention strategies and rules of application to help prevent attrition. We have developed MotSaRT – a motivational strategies recommender tool to support on-line teachers in motivating learners. The design is informed by the Social Cognitive Theory constructs outlined above and a survey on motivation intervention strategies carried out with sixty on-line teachers. The survey results were analysed using a data mining algorithm (J48 decision trees) which resulted in a set of decision rules for recommending motivational strategies. The recommender tool, MotSaRT, has been developed based on these decision rules. Its functionality enables the teacher to specify the learner’s motivation profile. MotSaRT then recommends the most likely intervention strategies to increase motivation. A pilot study is currently being carried out using the MotSaRT tool.
Toward a Useful Concept of Causality for Lexical Semantics

We do things in the world by exploiting our knowledge of what causes what. But in trying to reason formally about causality, there is a difficulty: to reason with certainty we need complete knowledge of all the relevant events and circumstances, whereas in everyday reasoning tasks we need a more serviceable but looser notion that does not make such demands on our knowledge. In this work the notion of “causal complex” is introduced for a complete set of events and conditions necessary for the causal consequent to occur, and the term “cause” is used for the makeshift, nonmonotonic notion we require for everyday tasks such as planning and language understanding. Like all interesting concepts, neither of these can be defined with necessary and sufficient conditions, but they can be more or less tightly constrained by necessary conditions or sufficient conditions. The issue of how to distinguish between what is in a causal complex from what is outside it is discussed, and within a causal complex, how to distinugish the eventualities that deserve to be called “causes” from those that do not, in particular circumstances. One particular modal, the word “would”, is examined from the standpoint of its underlying causal content, as a linguistic motivation for this enterprise.
Initial quantitative comparison of 940nm and 950nm infrared sensor performance for measuring glucose non-invasively

This project uses a non-invasive method for measuring the blood glucose concentration levels. By implementing two infrared light with different wavelength; 940nm and 950nm based on the use of light emitting diodes and measure transmittance through solution of distilled water and d-glucose of concentration from 0mg/dL to 200mg/dL by using a 1000nm photodiode. It is observed that the output voltage from the photodiode increased proportionally to the increased of concentration levels. The relation observed was linear. Nine subjects with the same age but different body weight have been used to observe the glucose level during fasting and non-fasting. During fasting the voltage is about 0.13096V to 0.236V and during non-fasting the voltage range is about 0.12V to 0.256V. This method of measuring blood glucose level may become a preferably choice for diabetics because of the non-invasive and may extend to the general public. For having a large majority people able to monitor their blood glucose levels, it may prevent hypoglycemia, hyperglycemia and perhaps the onset of diabetes.
Human-Robot Collaboration: a Survey

As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. They do not only have to operate efficiently and safely in natural, populated environments, but also be able to achieve higher levels of cooperation and communication with humans. Human-robot collaboration (HRC) is a research field with a wide range of applications, future scenarios, and potentially a high economic impact. HRC is an interdisciplinary research area comprising classical robotics, cognitive sciences, and psychology. This article gives a survey of the state of the art of human-robot collaboration. Established methods for intention estimation, action planning, joint action, and machine learning are presented together with existing guidelines to hardware design. This article is meant to provide the reader with a good overview of technologies and methods for HRC.
System-level throughput of NOMA using intra-beam superposition coding and SIC in MIMO downlink when channel estimation error exists

We investigate the influence of channel estimation error on the achievable system-level throughput performance of our previous non-orthogonal multiple access (NOMA) scheme in a multiple-input multiple-output (MIMO) downlink. The NOMA scheme employs intra-beam superposition coding of a multiuser signal at the transmitter and spatial filtering of inter-beam interference followed by an intra-beam successive interference canceller (SIC) at the user terminal receiver. The intra-beam SIC cancels the inter-user interference within a beam. This configuration achieves reduced overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal user multiplexing and enables the SIC receiver to be applied to the MIMO downlink. The channel estimation error in the NOMA scheme causes residual interference in the SIC process, which decreases the achievable user throughput. Furthermore, the channel estimation error causes error In the transmission rate control for the respective users, which may result in decoding error at not only the destination user terminal but also at other user terminals for the SIC process. However, we show that by using a simple transmission rate back-off algorithm, the impact of the channel estimation error is effectively abated and the NOMA scheme achieves clear average and cell-edge user throughput gains relative to orthogonal multiple access (OMA) similar to the case with perfect channel estimation.
Big data, bigger dilemmas: A critical review

The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate amongst its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with an eye on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socio-economic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that need to receive more attention — autonomy, opacity, and generativity, disparity, and futurity — leading to questions and ideas for moving beyond dilemmas.
Current-Pumped Battery Charger

A novel current-pumped battery charger (CPBC) is proposed in this paper to increase the Li-ion battery charging performance. A complete charging process, consisting of three subprocesses, namely: 1) the bulk current charging process; 2) the pulsed current charging process; and 3) the pulsed float charging process, can be automatically implemented by using the inherent characteristics of current-pumped phase-locked loop (CPLL). A design example for a 700-mA ldr h Li-ion battery is built to assess the CPBC's performance. In comparison with the conventional phase-locked battery charger, the battery available capacity and charging efficiency of the proposed CPBC are improved by about 6.9% and 1.5%, respectively. The results of the experiment show that a CPLL is really suitable for carrying out a Li-ion battery pulse charger.
Towards a Self-Deploying and Gliding Robot

Strategies for hybrid locomotion such as jumping and gliding are used in nature by many different animals for traveling over rough terrain. This combination of locomotion modes also allows small robots to overcome relatively large obstacles at a minimal energetic cost compared to wheeled or flying robots. In this chapter we describe the development of a novel palm sized robot of 10 g that is able to autonomously deploy itself from ground or walls, open its wings, recover in midair and subsequently perform goal-directed gliding. In particular, we focus on the subsystems that will in the future be integrated such as a 1.5 g microglider that can perform phototaxis; a 4.5 g, bat-inspired, wing folding mechanism that can unfold in only 50 ms; and a locust-inspired, 7 g robot that can jump more than 27 times its own height. We also review the relevance of jumping and gliding for living and robotic systems and we highlight future directions for the realization of a fully integrated robot.
On the application of VTOL UAVs to the inspection of power utility assets

This paper presents a review of the state of the art and a discussion on vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) applied to the inspection of power utility assets and other similar civil applications. The first part of the paper presents the authors' view on specific benefits and operation constraints associated with the use of UAVs in power industry applications. The second part cites more than 70 recent publications related to this field of application. Among them, some present complete technologies while others deal with specific subsystems relevant to the application of such mobile platforms to power line inspection. The authors close with a discussion of key factors for successful application of VTOL UAVs to power industry infrastructure inspection.
Indoor Intelligent Mobile Robot Localization Using Fuzzy Compensation and Kalman Filter to Fuse the Data of Gyroscope and Magnetometer

This paper develops an indoor intelligent service mobile robot that has multiple functions, can recognize and grip the target object, avoid obstacles, and accurately localize via relative position. The locating method of the robot uses the output values of the sensor module, which includes data from a gyroscope and a magnetometer, to correct the current rotation direction angle of the robot. An angle correction method can be divided into three parts. The first part calculates the angle values obtained from the gyroscope and the magnetometer that are installed on the robot. The second part explores the error characteristics between the sensor module and the actual rotation direction angle of the robot. The third part uses the error characteristic data to design the fuzzy rule base and the Kalman filter to eliminate errors and to get a more accurate orientation angle. These errors can be described as either regular or irregular. The former can be eliminated by fuzzy algorithm compensation, and the latter can be eliminated by the Kalman filter. The contribution of this paper is to propose an error correction method between the calculus rotation angle determined by the sensor and the actual rotation angle of the robot such that the three moving paths, i.e., specified, actual, and calculus paths, have more accurate approximation. The experimental results demonstrate that the combination of fuzzy compensation and the Kalman filter is an accurate correction method.
Autonomous UAV path planning and estimation

Unmanned aerial vehicles (UAVs) have shown promise in recent years for autonomous sensing. UAVs systems have been proposed for a wide range of applications such as mapping, surveillance, search, and tracking operations. The recent availability of low-cost UAVs suggests the use of teams of vehicles to perform sensing tasks. To leverage the capabilities of a team of vehicles, efficient methods of decentralized sensing and cooperative path planning are necessary. The goal of this work is to examine practical control strategies for a team of fixed-wing vehicles performing cooperative sensing. We seek to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions. Sensing goals are posed from an information theoretic standpoint to design strategies that explicitly minimize uncertainty. This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning.