nils-herrmann/clean_scirepeval huggingface dataset:
Interaction Forces of Soft Fiber Reinforced Bending Actuators

Soft-bending actuators are inherently compliant, compact, and lightweight. They are preferable candidates over rigid actuators for robotic applications ranging from physical human interaction to delicate object manipulation. However, characterizing and predicting their behaviors are challenging due to the material nonlinearities and the complex motions they can produce. This paper investigates a soft-bending actuator design that uses a single air chamber and fiber reinforcements. Additionally, the actuator design incorporates a sensing layer to enable real-time bending angle measurement for analysis and control. In order to study the bending and force exertion characteristics when interacting with the environment, a quasi-static analytical model is developed based on the bending moments generated from the applied internal pressure and stretches of the soft materials. Comparatively, a finite-element method model is created for the same actuator design. Both the analytical model and the finite-element model are used in the fiber reinforcement analysis and the validation experiments with fabricated actuators. The experimental results demonstrate that the analytical model captures the relationships of supplied air pressure, actuator bending angle, and interaction force at the actuator tip. Moreover, it is shown that an off-the-shelf bend angle sensor integrated to the actuator in this study could provide real-time force estimation, thus eliminating the need for a force sensor.
Conformal double exponentially tapered slot antenna (DETSA) on LCP for UWB applications

We discuss the use of a double exponentially tapered slot antenna (DETSA) fabricated on flexible liquid crystal polymer (LCP) as a candidate for ultrawideband (UWB) communications systems. The features of the antenna and the effect of the antenna on a transmitted pulse are investigated. Return loss and E and H plane radiation pattern measurements are presented in several frequencies covering the whole ultra wide band. The return loss remains below -10 dB and the shape of the radiation pattern remains fairly constant in the whole UWB range (3.1 to 10.6 GHz). The main lobe characteristic of the radiation pattern remains stable even when the antenna is significantly conformed. The major effect of the conformation is an increase in the cross polarization component amplitude. The system: transmitter DETSA-channel receiver DETSA is measured in frequency domain and shows that the antenna adds very little distortion on a transmitted pulse. The distortion remains small even when both transmitter and receiver antennas are folded, although it increases slightly.
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets

For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. A frequent itemset P is maximal if P is included in no other frequent itemset, and closed if P is included in no other itemset included in the exactly same transactions as P . The problems of finding these frequent itemsets are fundamental in data mining, and from the applications, fast implementations for solving the problems are needed. In this paper, we propose efficient algorithms LCM (Linear time Closed itemset Miner), LCMfreq and LCMmax for these problems. We show the efficiency of our algorithms by computational experiments compared with existing algorithms.
The dark side of information: overload, anxiety and other paradoxes and pathologies

This review article identifies and discusses some of main issues and potential problems – paradoxes and pathologies – around the communication of recorded information, and points to some possible solutions. The article considers the changing contexts of information communication, with some caveats about the identification of ‘pathologies of information’, and analyses the changes over time in the way in which issues of the quantity and quality of information available have been regarded. Two main classes of problems and issues are discussed. The first comprises issues relating to the quantity and diversity of information available: information overload, information anxiety, etc. The second comprises issues relating to the changing information environment with the advent of Web 2.0: loss of identity and authority, emphasis on micro-chunking and shallow novelty, and the impermanence of information. A final section proposes some means of solution of problems and of improvements to the situation.
802 . 11 i WLAN Security Protocol – A Software Engineer ’ s Model

Wireless local area networks (WLANs) based on the IEEE 802.11 standards are one of today’s fastest growing technologies in businesses, schools, and homes, for good reasons. As WLAN deployments increase, so does the challenge to provide these networks with security. Security risks can originate either due to technical lapse in the security mechanisms or due to defects in software implementations. Standard Bodies and researchers have mainly used UML state machines to address the implementation issues. In this paper we propose the use of GSE methodology to analyse the incompleteness and uncertainties in specifications. The IEEE 802.11i security protocol is used as an example to compare the effectiveness of the GSE and UML models. The GSE methodology was found to be more effective in identifying ambiguities in specifications and inconsistencies between the specification and the state machines. Resolving all issues, we represent the robust security network (RSN) proposed in the IEEE 802.11i standard using different GSE models.
Recommender Systems Handbook

Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd recommender systems handbook as the choice of reading, you can find here.
Marketplace or Reseller?

Intermediaries can choose between functioning as a marketplace (on which suppliers sell their products directly to buyers) or as a reseller (purchasing products from suppliers and selling them to buyers). We model this as a decision between whether control rights over a non-contractible decision variable (the choice of some marketing activity) are better held by suppliers (the marketplacemode) or by the intermediary (the reseller-mode). Whether the marketplace or the reseller mode is preferred depends on whether independent suppliers or the intermediary have more important information relevant to the optimal tailoring of marketing activities for each specific product. We show that this tradeoff is shifted towards the reseller-mode when marketing activities create spillovers across products and when network effects lead to unfavorable expectations about supplier participation. If the reseller has a variable cost advantage (respectively, disadvantage) relative to the marketplace then the tradeoff is shifted towards the marketplace for long-tail (respectively, shorttail) products. We thus provide a theory of which products an intermediary should offer in each mode. We also provide some empirical evidence that supports our main results.
An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential Evolution

In this paper we have developed an algorithm for many-objective optimization problems, which will work more quickly than existing ones, while offering competitive performance. The algorithm periodically reorders the objectives based on their conflict status and selects a subset of conflicting objectives for further processing. We have taken differential evolution multiobjective optimization (DEMO) as the underlying metaheuristic evolutionary algorithm, and implemented the technique of selecting a subset of conflicting objectives using a correlation-based ordering of objectives. The resultant method is called α-DEMO, where α is a parameter determining the number of conflicting objectives to be selected. We have also proposed a new form of elitism so as to restrict the number of higher ranked solutions that are selected in the next population. The α-DEMO with the revised elitism is referred to as α-DEMO-revised. Extensive results of the five DTLZ functions show that the number of objective computations required in the proposed algorithm is much less compared to the existing algorithms, while the convergence measures are competitive or often better. Statistical significance testing is also performed. A real-life application on structural optimization of factory shed truss is demonstrated.
Towards Improving Data Quality

This paper is based on the notion of data quality. It includes correctness, completeness and minimality for which a notational framework is shown. In long living databases the maintenance of data quality is a rst order issue. This paper shows that even well designed and implemented information systems cannot guarantee correct data in any circumstances. It is shown that in any such system data quality tends to decrease and therefore some data correction procedure should be applied from time to time. One aspect of increasing data quality is the correction of data values. Characteristics of a software tool which supports this data value correction process are presented and discussed.
Active Data Selection for Motor Imagery EEG Classification

Rejecting or selecting data from multiple trials of electroencephalography (EEG) recordings is crucial. We propose a sparsity-aware method to data selection from a set of multiple EEG recordings during motor-imagery tasks, aiming at brain machine interfaces (BMIs). Instead of empirical averaging over sample covariance matrices for multiple trials including low-quality data, which can lead to poor performance in BMI classification, we introduce weighted averaging with weight coefficients that can reject such trials. The weight coefficients are determined by the ℓ1-minimization problem that lead to sparse weights such that almost zero-values are allocated to low-quality trials. The proposed method was successfully applied for estimating covariance matrices for the so-called common spatial pattern (CSP) method, which is widely used for feature extraction from EEG in the two-class classification. Classification of EEG signals during motor imagery was examined to support the proposed method. It should be noted that the proposed data selection method can be applied to a number of variants of the original CSP method.
Surface electromyography: Detection and recording

When detecting and recording the EMG signal, there are two main issues of concern that influence the fidelity of the signal. The first is the signal to noise ratio. That is, the ratio of the energy in the EMG signal to the energy in the noise signal. In general, noise is defined as electrical signals that are not part of the wanted EMG signal. The other is the distortion of the signal, meaning that the relative contribution of any frequency component in the EMG signal should not be altered. It is well established that the amplitude of the EMG signal is stochastic (random) in nature and can be reasonably represented by a Gausian distribution function. The amplitude of the signal can range from 0 to 10 mV (peak-to-peak) or 0 to 1.5 mV (rms). The usable energy of the signal is limited to the 0 to 500 Hz frequency range, with the dominant energy being in the 50-150 Hz range. Usable signals are those with energy above the electrical noise level. An example of the frequency spectrum of the EMG signal is presented in  Frequency spectrum of the EMG signal detected from the Tibialis Anterior muscle during a constant force isometric contraction at 50% of voluntary maximum.
Mobile augmented reality for cultural heritage: A technology acceptance study

We have developed a mobile augmented reality application with historical photographs and information about a historical street. We follow a design science research methodology and use an extended version of the technology acceptance model (TAM) to study the acceptance of this application. A prototype has been developed in accordance with general principles for usability design, and two surveys have been conducted. A web survey with 200 participants that watched a short video demonstration of the application to validate the adapted acceptance model, and a street survey, where 42 participants got the opportunity to try the application in a live setting before answering a similar questionnaire and provide more concrete feedback. The results show that both perceived usefulness and perceived enjoyment has a direct impact on the intention to use mobile augmented reality applications with historical pictures and information. Further a number of practical recommendations for the development and deployment of such systems are provided.
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrentlyexploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non-observable, making these approaches inapplicable. This paper formalizes and addresses the problem of multi-task multi-agent reinforcement learning under partial observability. We introduce a decentralized single-task learning approach that is robust to concurrent interactions of teammates, and present an approach for distilling single-task policies into a unified policy that performs well across multiple related tasks, without explicit provision of task identity.
A Circularly-Polarized Array Antenna Using Linearly-Polarized Sub Grid Arrays for Highly-Integrated 60-GHz Radio

In this communication, we present a new circularly-polarized array antenna using 2×2 linearly-polarized sub grid arrays in a low temperature co-fired ceramic technology for highly-integrated 60-GHz radio. The sub grid arrays are sequentially rotated and excited with a 90°-phase increment to radiate circularly-polarized waves. The feeding network of the array antenna is based on stripline quarter-wave matched T-junctions. The array antenna has a size of 15×15×0.9 mm3. Simulated and measured results confirm wide impedance, axial ratio, pattern, and gain bandwidths.
Glasses detection on real images based on robust alignment

Automatic glasses detection on real face images is a challenging problem due to different appearance variations. Nevertheless, glasses detection on face images has not been thoroughly investigated. In this paper, an innovative algorithm for automatic glasses detection based on Robust Local Binary Pattern and robust alignment is proposed. Firstly, images are preprocessed and normalized in order to deal with scale and rotation. Secondly, eye glasses region is detected considering that the nosepiece of the glasses is usually placed at the same level as the center of the eyes in both height and width. Thirdly, Robust Local Binary Pattern is built to describe the eyes region, and finally, support vector machine is used to classify the LBP features. This algorithm can be applied as the first step of a glasses removal algorithm due to its robustness and speed. The proposed algorithm has been tested over the Labeled Faces in the Wild database showing a 98.65 % recognition rate. Influences of the resolution, the alignment of the normalized images and the number of divisions in the LBP operator are also investigated.
Snake-like robots [Tutorial]

We have introduced the biomechanical research on snakes and developmental research on snake-like robots that we have been working on. We could not introduce everything we developed. There were also a smaller snake-like active endoscope; a large-sized snake-like inspection robot for nuclear reactor related facility, Koryu, 1 m in height, 3.5 m in length, and 350 kg in weight; and several other snake-like robots. Development of snake-like robots is still one of our latest research topics. We feel that the technical difficulties in putting snake-like robots into practice have almost been overcome by past research, so we believe that such practical use of snake-like robots can be realized soon.
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies

We use single-agent and multi-agent Reinforcement Learning (RL) for learning dialogue policies in a resource allocation negotiation scenario. Two agents learn concurrently by interacting with each other without any need for simulated users (SUs) to train against or corpora to learn from. In particular, we compare the Qlearning, Policy Hill-Climbing (PHC) and Win or Learn Fast Policy Hill-Climbing (PHC-WoLF) algorithms, varying the scenario complexity (state space size), the number of training episodes, the learning rate, and the exploration rate. Our results show that generally Q-learning fails to converge whereas PHC and PHC-WoLF always converge and perform similarly. We also show that very high gradually decreasing exploration rates are required for convergence. We conclude that multiagent RL of dialogue policies is a promising alternative to using single-agent RL and SUs or learning directly from corpora.
ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016

We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org.
Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis

Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequence labeling problem, so as to detect Chinese grammatical errors, to identify the error types and to locate the error positions. In the corpora of this year’s shared task, there can be multiple errors in a single offset of a sentence, to address which, we simutaneously train three Bi-LSTM models sharing word embeddings which label Missing, Redundant and Selection errors respectively. We regard word ordering error as a special kind of word selection error which is longer during training phase, and then separate them by length during testing phase. In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our system achieved relatively high F1 for all the three levels in the traditional Chinese track and for the detection level in the Simpified Chinese track.
A isolated bidirectional interleaved flyback converter for battery backup system application

This paper proposes a novel isolated bidirectional converter, which can efficiently transfer energy between 400 V DC micro grid and 48 V DC batteries. The proposed structure includes primary windings of two flyback transformers, which are connected in series and sharing the high DC micro grid voltage equally, and secondary windings, which are connected in parallel to batteries. Few decoupling diodes are added into the proposed circuit on both sides, which can let the leakage inductance energy of flyback transformers be recycled easily and reduce the voltage stress as well as power losses during bidirectional power transfer. Therefore, low voltage rating and low conduction resistance switches can be selected to improve system efficiency. A laboratory prototype of the proposed converter with an input/output nominal voltage of 400 V/48 V and the maximum capacity of 500 W is implemented. The highest power conversion efficiency is 93.1 % in step-down function, and near 93 % in step-up function.
Neural Processes

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rules of probabilistic inference. GPs are probabilistic, data-efficient and flexible, however they are also computationally intensive and thus limited in their applicability. We introduce a class of neural latent variable models which we call Neural Processes (NPs), combining the best of both worlds. Like GPs, NPs define distributions over functions, are capable of rapid adaptation to new observations, and can estimate the uncertainty in their predictions. Like NNs, NPs are computationally efficient during training and evaluation but also learn to adapt their priors to data. We demonstrate the performance of NPs on a range of learning tasks, including regression and optimisation, and compare and contrast with related models in the literature.
Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach

This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables. The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffusion of updated beliefs is accomplished in a single pass and complies with the tenets of Bayes calculus.
Detecting Change in Categorical Data: Mining Contrast Sets

A fundamental task in data analysis is understanding the differences between several contrasting groups. These groups can represent different classes of objects, such as male or female students, or the same group over time, e.g. freshman students in 1993 versus 1998. We present the problem of mining contrast-sets: conjunctions of attributes and values that differ meaningfully in their distribution across groups. We provide an algorithm for mining contrast-sets as well as several pruning rules to reduce the computational complexity. Once the deviations are found, we post-process the results to present a subset that are surprising to the user given what we have already shown. We explicitly control the probability of Type I error (false positives) and guarantee a maximum error rate for the entire analysis by using Bonferroni corrections.