nils-herrmann/clean_scirepeval huggingface dataset:
The twisted string actuation system: Modeling and control

This paper describes a novel actuation system for very compact and light-weight robotic devices, like artificial hands. The actuation concept presented here allows the implementation of powerful tendon-based driving systems, using as actuators small-size DC motors characterized by high speed and low torque. After the presentation of the basic concept of this novel actuation system, the constitutive equations of the system are given, validated by means of laboratory tests. Moreover, the problem of tracking a desired actuation force profile is taken into account, considering as load a mass-spring-damper system. A control algorithm based on a second-order sliding manifold has been firstly evaluated by means of simulations, and then validated by experiments. This output-feedback controller has been chosen to guarantee a high level of robustness against disturbances, parameter variations and uncertainties while maintaining a low computational burden.
Superfaces: A Super-Resolution Model for 3D Faces

Face recognition based on the analysis of 3D scans has been an active research subject over the last few years. However, the impact of the resolution of 3D scans on the recognition process has not been addressed explicitly yet being of primal importance after the introduction of a new generation of low cost 4D scanning devices. These devices are capable of combined depth/rgb acquisition over time with a low resolution compared to the 3D scanners typically used in 3D face recognition benchmarks. In this paper, we define a super-resolution model for 3D faces by which a sequence of low-resolution 3D scans can be processed to extract a higher resolution 3D face model, namely the superface model. The proposed solution relies on the Scaled ICP procedure to align the low-resolution 3D models with each other and estimate the value of the high-resolution 3D model based on the statistics of values of the lowresolution scans in corresponding points. The approach is validated on a data set that includes, for each subject, one sequence of low-resolution 3D face scans and one ground-truth high-resolution 3D face model acquired through a high-resolution 3D scanner. In this way, results of the super-resolution process are evaluated qualitatively and quantitatively by measuring the error between the superface and the ground-truth.
Device-to-device communication as an underlay to LTE-advanced networks

In this article device-to-device (D2D) communication underlaying a 3GPP LTE-Advanced cellular network is studied as an enabler of local services with limited interference impact on the primary cellular network. The approach of the study is a tight integration of D2D communication into an LTE-Advanced network. In particular, we propose mechanisms for D2D communication session setup and management involving procedures in the LTE System Architecture Evolution. Moreover, we present numerical results based on system simulations in an interference limited local area scenario. Our results show that D2D communication can increase the total throughput observed in the cell area.
Graph-Based Visual Saliency

L. Itti, C. Koch, PAMI 1998  N. Bruce, J. Tsotsos, NIPS 2005  Einhaeuser, et al.  2006  Tatler, et al.  In biology, individual “nodes” (neurons) exist in a connected, retinotopically organized, network (the visual cortex), and communicate with each other (synaptic firing) in a way which gives rise to emergent behavior, viz., rapid scene analysis for salient locations.
Multiresolution Models for Object Detection

Most current approaches to recognition aim to be scaleinvariant. However, the cues available for recognizing a 300 pixel tall object are qualitatively different from those for recognizing a 3 pixel tall object. We argue that for sensors with finite resolution, one should instead use scale-variant, or multiresolution representations that adapt in complexity to the size of a putative detection window. We describe a multiresolution model that acts as a deformable part-based model when scoring large instances and a rigid template with scoring small instances. We also examine the interplay of resolution and context, and demonstrate that context is most helpful for detecting low-resolution instances when local models are limited in discriminative power. We demonstrate impressive results on the Caltech Pedestrian benchmark, which contains object instances at a wide range of scales. Whereas recent state-of-theart methods demonstrate missed detection rates of 86%-37% at 1 falsepositive-per-image, our multiresolution model reduces the rate to 29%.
Glove Based User Interaction Techniques for Augmented Reality in an Outdoor Environment

This paper presents a set of pinch glove-based user interface tools for an outdoor wearable augmented reality computer system. The main form of user interaction is the use of hand and head gestures. We have developed a set of augmented reality information presentation techniques. To support direct manipulation, the following three selection techniques have been implemented: two-handed framing, line of sight and laser beam. A new glove-based text entry mechanism has been developed to support symbolic manipulation. A scenario for a military logistics task is described to illustrate the functionality of this form of interaction.
Learning Automata as a Basis for Multi Agent Reinforcement Learning

Learning Automata (LA) are adaptive decision making devices suited for operation in unknown environments . Originally they were developed in the area of mathematical psychology and used for modeling observed behavior. In its current form, LA are closely related to Reinforcement Learning (RL) approaches and most popular in the area of engineering. LA combine fast and accurate convergence with low computational complexity, and have been applied to a broad range of modeling and control problems. However, the intuitive, yet analytically tractable concept of learning automata makes them also very suitable as a theoretical framework for Multi agent Reinforcement Learning (MARL). Reinforcement Learning (RL) is already an established and profound theoretical framework for learning in stand-alone or single-agent systems. Yet, extending RL to multi-agent systems (MAS) does not guarantee the same theoretical grounding. As long as the environment an agent is experiencing is Markov, and the agent can experiment sufficiently, RL guarantees convergence to the optimal strategy. In a MAS however, the reinforcement an agent receives, may depend on the actions taken by the other agents acting in the same environment. Hence, the Markov property no longer holds. And as such, guarantees of convergence are lost. In the light of the above problem it is important to fully understand the dynamics of multi-agent reinforcement learning. Although, they are not fully recognized as such, LA are valuable tools for current MARL research. LA are updated strictly on the basis of the response of the environment, and not on the basis of any knowledge regarding other automata, i.e. nor their strategies, nor their feedback. As such LA agents are very simple. Moreover, LA can be treated analytically. Convergence proofs do exist for a variety of settings ranging from a single automaton model acting in a simple stationary random environment to a distributed automata model interacting in a complex environment. In this paper we argue that LA are very interesting building blocks for learning in multi agent systems. The LA can be viewed as policy iterators, who update their action probabilities based on private information only. This is a very attractive property in applications where communication is expensive. LA are in particular appealing in games with stochastic payoffs. Then we move to collections of learning automata, that can independently converge to interesting solution concepts. We study the single stage setting, including the analytical results. Then we generalize to interconnected learning automata, that can deal with multi agent multi-stage problems. We also show how Ant Colony Optimization can be mapped to the interconnected Learning
Contextual Equivalence for Probabilistic Programs with Continuous Random Variables and Scoring

We present a logical relation for proving contextual equivalence in a probabilistic programming language (PPL) with continuous random variables and with a scoring operation for expressing observations and soft constraints. Our PPL model is based on a big-step operational semantics that represents an idealized sampler with likelihood weighting. The semantics treats probabilistic non-determinism as a deterministic process guided by a source of entropy. We derive a measure on result values by aggregating (that is, integrating) the behavior of the operational semantics over the entropy space. Contextual equivalence is defined in terms of these measures, taking real events as observable behavior. We define a logical relation and prove it sound with respect to contextual equivalence. We demonstrate the utility of the logical relation by using it to prove several useful examples of equivalences, including the equivalence of a βv-redex and its contractum and a general form of expression re-ordering. The latter equivalence is sound for the sampling and scoring effects of probabilistic programming but not for effects like mutation or control.
Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

Matching pedestrians across multiple camera views, known as human re-identification, is a challenging research problem that has numerous applications in visual surveillance. With the resurgence of Convolutional Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have been proposed for human re-identification with the objective of projecting the images of similar pairs (i.e. same identity) to be closer to each other and those of dissimilar pairs to be distant from each other. However, current networks extract fixed representations for each image regardless of other images which are paired with it and the comparison with other images is done only at the final level. In this setting, the network is at risk of failing to extract finer local patterns that may be essential to distinguish positive pairs from hard negative pairs. In this paper, we propose a gating function to selectively emphasize such fine common local patterns by comparing the mid-level features across pairs of images. This produces flexible representations for the same image according to the images they are paired with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and demonstrate improved performance compared to a baseline Siamese CNN architecture.
Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities

In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.
Why Is Facebook So Successful? Psychophysiological Measures Describe a Core Flow State While Using Facebook

People are more and more using social networking sites (SNSs) like Facebook and MySpace to engage with others. The use of SNSs can have both positive and negative effect on the individual; however, the increasing use of SNSs might reveal that people look for SNSs because they have a positive experience when they use them. Few studies have tried to identify which particular aspects of the social networking experience make SNSs so successful. In this study we focus on the affective experience evoked by SNSs. In particular, we explore whether the use of SNSs elicits a specific psychophysiological pattern. Specifically, we recorded skin conductance, blood volume pulse, electroencephalogram, electromyography, respiratory activity, and pupil dilation in 30 healthy subjects during a 3-minute exposure to (a) a slide show of natural panoramas (relaxation condition), (b) the subject's personal Facebook account, and (c) a Stroop and mathematical task (stress condition). Statistical analysis of the psychophysiological data and pupil dilation indicates that the Facebook experience was significantly different from stress and relaxation on many linear and spectral indices of somatic activity. Moreover, the biological signals revealed that Facebook use can evoke a psychophysiological state characterized by high positive valence and high arousal (Core Flow State). These findings support the hypothesis that the successful spread of SNSs might be associated with a specific positive affective state experienced by users when they use their SNSs account.
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys

This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema with low generalization error have a large proportion of almost-zero eigenvalues in the Hessian with very few positive or negative eigenvalues. We leverage upon this observation to construct a local-entropy-based objective function that favors well-generalizable solutions lying in large flat regions of the energy landscape, while avoiding poorly-generalizable solutions located in the sharp valleys. Conceptually, our algorithm resembles two nested loops of SGD where we use Langevin dynamics in the inner loop to compute the gradient of the local entropy before each update of the weights. We show that the new objective has a smoother energy landscape and show improved generalization over SGD using uniform stability, under certain assumptions. Our experiments on convolutional and recurrent neural networks demonstrate that Entropy-SGD compares favorably to state-of-the-art techniques in terms of generalization error and training time.
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset

In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR  STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.
Application of Feature Selection Methods in Educational Data Mining

In the recent years, web based learning has emerged as a new field of research due to growth of network and communication technology. These learning systems generate a large volume of student data. Data mining algorithms may be applied on this data set to study interesting patterns. As an example, student enrollment data and his past examination records could be used to predict his grades in the term end examination. However this prediction could mean examining a lot of features of the student data resulting in creation of a model with high computational complexity. In this context this work first defines a student data set with 309 records and 14 features collected by a survey from various graduation level students majoring in Computer Science under University of Calcutta. Different feature selection algorithms are applied on this data set. The best results are obtained by Correlation Based Feature Selection algorithm with 8 features. Subsequently classification algorithms may be applied on this feature subset for predicting student grades.
The Impact of Flow on Online Consumer Behavior

Previous research has acknowledged flow as a useful construct for explaining online consumer behavior. However, there is dearth of knowledge about what dimensions of flow and how they actually influence online consumer behavior as flow is difficult to conceptualize and measure. This research examines flow and its effects on online consumer behavior in a unified model which draws upon theory of planned behavior (TPB). The four important dimensions of flow (concentration, enjoyment, time distortion, telepresence) are explored in terms of their antecedent effects on online consumer behavior. Results of this empirical study show that flow influences online consumer behavior through several important latent constructs. Findings of this research not only extend the existing knowledge of flow and its antecedent effects on online consumer behavior but also provide new insights into how flow can be conceptualized and studied in the e-commerce setting.
Biofeedback-based behavioral treatment for chronic tinnitus: results of a randomized controlled trial.

Many tinnitus sufferers believe that their tinnitus has an organic basis and thus seek medical rather than psychological treatments. Tinnitus has been found to be associated with negative appraisal, dysfunctional attention shift, and heightened psychophysiological arousal, so cognitive-behavioral interventions and biofeedback are commonly suggested as treatments. This study developed and investigated the efficacy of a biofeedback-based cognitive-behavioral treatment for tinnitus. In total, 130 tinnitus patients were randomly assigned to an intervention or a wait-list control group. Treatment consisted of 12 sessions of a biofeedback-based behavioral intervention over a 3-month period. Patients in the wait-list group participated in the treatment after the intervention group had completed the treatment. Results showed clear improvements regarding tinnitus annoyance, diary ratings of loudness, and feelings of controllability. Furthermore, changes in coping cognitions as well as changes in depressive symptoms were found. Improvements were maintained over a 6-month follow-up period in which medium-to-large effect sizes were observed. The treatment developed and investigated in this study is well accepted and leads to clear and stable improvements. Through demonstration of psychophysiological interrelationships, the treatment enables patients to change their somatic illness perceptions to a more psychosomatic point of view.
Cognitive radio network security: A survey

Recent advancements in wireless communication are creating a spectrum shortage problem on a daily basis. Recently, Cognitive Radio (CR), a novel technology, has attempted to minimize this problem by dynamically using the free spectrum in wireless communications and mobile computing. Cognitive radio networks (CRNs) can be formed using cognitive radios by extending the radio link features to network layer functions. The objective of CRN architecture is to improve the whole network operation to fulfil the user’s demands anytime and anywhere, through accessing CRNs in a more efficient way, rather than by just linking spectral efficiency. CRNs are more flexible and exposed to wireless networks compared with other traditional radio networks. Hence, there are many security threats to CRNs, more so than other traditional radio environments. The unique characteristics of CRNs make security more challenging. Several crucial issues have not yet been investigated in the area of security for CRNs. A typical public key infrastructure (PKI) scheme which achieves secure routing and other purposes in typical ad hoc networks is not enough to guarantee the security of CRNs under limited communication and computation resources. However, there has been increasing research attention on security threats caused specifically by CR techniques and special characteristics of CR in CRNs. Therefore, in this research, a survey of CRNs and their architectures and security issues has been carried out in a broad
AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.
Quantum Programming Languages: Survey and Bibliography

The emphasis is made on the juxtaposition of (quantum theorem) proving versus quantum (theorem proving). The logical contents of verification of the statements concerning quantum systems is outlined. The Zittereingang (trembling input) principle is introduced to enhance the resolution of predicate satisfiability problem provided the processor is in a position to perform operations with continuous input. A realization of Zittereingang machine by a quantum system is suggested.  What is the desirable way to broaden the facilities of processors in order to have progress in solving NP-hard problems? The principle of trembling input I am going to put forth in this paper will require a flexibility of processor. That means that it will be assumed that the processor is able to deal with ”intermediate outputs”, or, in other words, that it will be possible to pass continuously from one input to another. We shall also assume that all possible inputs form a linear space, and that the result depends contiuously on input preserving linear combinations. To elucidate the idea of the principle I suggest to consider the SAT problem. Suppose that, solving some problem, you succeded to reduce it to the following one: given a propositional form P (x1, . . . , xn) of,