nils-herrmann/clean_scirepeval huggingface dataset:
Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague’s task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.
Density Based k-Nearest Neighbors Clustering Algorithm for Trajectory Data

With widespread availability of low cost GPS, cellular phones, satellite imagery, robotics, Web traffic monitoring devices, it is becoming possible to record and store data about the movement of people and objects at a large amount. While these data hide important knowledge for the enhancement of location and mobility oriented infrastructures and services, by themselves, they demand the necessary semantic embedding which would make fully automatic algorithmic analysis possible. Clustering algorithm is an important task in data mining. Clustering algorithms for these moving objects provide new and helpful information, such as Jam detection and significant Location identification. In this paper we present augmentation of relative density-based clustering algorithm for movement data or trajectory data. It provides a k-nearest neighbors clustering algorithm based on relative density, which efficiently resolves the problem of being very sensitive to the user-defined parameters in DBSCAN. In this paper we consider two real datasets of moving vehicles in Milan (Italy) and Athens (Greece) and extensive experiments were conducted.
Foreground Segmentation for Anomaly Detection in Surveillance Videos Using Deep Residual Networks

Efficient anomaly detection in surveillance videos across diverse environments represents a major challenge in Computer Vision. This paper proposes a background subtraction approach based on the recent deep learning framework of residual neural networks that is capable of detecting multiple objects of different sizes by pixel-wise foreground segmentation. The proposed algorithm takes as input a reference (anomalyfree) and a target frame, both temporally aligned, and outputs a segmentation map of same spatial resolution where the highlighted pixels denoting the detected anomalies, which should be all the elements not present in the reference frame. Furthermore, we analyze the benefits of different reconstruction methods to the restore original image resolution and demonstrate the improvement of residual architectures over the smaller and simpler models proposed by previous similar works. Experiments show competitive performance in the tested dataset, as well as real-time processing capability.  Deep learning, convolutional neural networks, ResNet, residual networks, background subtraction, foreground segmentation, anomaly detection, surveillance, real-time.
The design synthesis of multiband artificial magnetic conductors using high impedance frequency selective surfaces

This paper introduces several different design methodologies for multiband artificial magnetic conducting (AMC) surfaces. The paper begins by investigating the multiband properties exhibited by a conventional electromagnetic bandgap (EBG) AMC that consists of a frequency selective surface (FSS) on top of a thin dielectric substrate with a PEC back plane. The higher-order resonances associated with these surfaces have not been discussed in detail to date, as previous research has been concerned only with exploiting the primary resonant frequency. However, it will be shown that by understanding and making appropriate use of these higher order resonances, it is possible to design multiband AMC surfaces that work for nearly any desired combination of operating frequencies. The first multiband AMC design approach that will be considered is based on the introduction of FSS screens that have fractal or nearly fractal unit cell geometries. This is followed by a more general and robust genetic algorithm (GA) technique for the synthesis of optimal multiband AMC surfaces. In this case, a GA is used to evolve multiband AMC surface designs by simultaneously optimizing the geometry and size of the FSS unit cell as well as the thickness and dielectric constant of the substrate material. Finally, several examples of multiband AMC surfaces are presented, including some practical dual-band and tri-band designs genetically evolved for operation at GPS and cellular frequencies, as well as an example illustrating the success in creating a multiband AMC surface with angular stability.
Pattern recognition of human arm movement using deep reinforcement learning

Hand gesture recognition is one of the major research areas in the field of Human computer interaction (HCl). This paper proposes a deep reinforcement learning algorithm to recognize the human arm movement patterns using an IoT sensor device. Recent studies have explored supervised learning based methods, such as CNN and RNN to implement the HCl device. On the other hand, the deep reinforcement learning approach has also been investigated. Algorithms using this approach, learn the patterns from sensors using only the reward feedback with no class labels. This allows users to control the IoT device and produce the desired arm movement patterns without creating any labels. In this paper, the performance of convolutional neural network (CNN) with the DQN model was compared with that of long short-term memory (LSTM) models with DQN. Results show that the CNN based DQN model was more stable compared to the LSTM based model, and yielded a high classification accuracy of 98.33% to predict the arm movement patterns.
One-shot deep neural network for pose and illumination normalization face recognition

Pose and illumination are considered as two main challenges that face recognition system encounters. In this paper, we consider face recognition problem across pose and illumination variations, given small amount of training samples and single sample per gallery (a.k.a., one shot classification). We combine the strength of 3D models in generating multiviews and various illumination samples and the ability of deep learning in learning non-linear transformation, which is very suitable for pose and illumination normalization, by using a multi-task deep neural network. By the pose and illumination augmentation strategy, we train a pose and illumination normalization neural network with much less training data compared to other methods. Experiments on MultiPIE database achieve competitive recognition results, demonstrating the effectiveness of proposed method.
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.
A complex activity based emotion recognition algorithm for affect aware systems

The usefulness of affect aware systems can be summarized as optimizing the system's services to improve the user experience. A means to achieve this objective is to make the system aware of the real-world situations and enable intelligent emotion analysis of its users. The ability of smart systems to automatically recognize human affective behavior through their movements and to respond are the key challenges in this area of study. This paper thus proposes an algorithm that can effectively categorize the underlining emotion of an activity as positive or negative and then classify it amongst the six basic emotions — sadness, fear, happiness, anger, disgust and surprise . To portray the efficacy of this proposed model, typical scenes in the context of a smart home have been analyzed and the results have been presented. The results presented show that the proposed model would help developers to understand user experiences in the form of user's emotions in any scenario, which would help them to improve or modify the existing technologies to enhance computerhuman interaction.
Combining perspiration- and morphology-based static features for fingerprint liveness detection

0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.01.009 ⇑ Corresponding author at: Dipartimento di Informa of Naples Federico II, via Claudio 21, 80125 Naples, It E-mail addresses: emanuela.marasco@unina.it (E. (C. Sansone). It has been showed that, by employing fake fingers, the existing fingerprint recognition systems may be easily deceived. So, there is an urgent need for improving their security. Software-based liveness detection algorithms typically exploit morphological and perspiration-based characteristics separately to measure the vitality. Both such features provide discriminant information about live and fake fingers, then, it is reasonable to investigate also their joint contribution. In this paper, we combine a set of the most robust morphological and perspiration-based measures. The effectiveness of the proposed approach has been assessed through a comparison with several state-ofthe-art techniques for liveness detection. Experiments have been carried out, for the first time, by adopting standard databases. They have been taken from the Liveness Detection Competition 2009 whose data have been acquired by using three different optical sensors. Further, we have analyzed how the performance of our algorithm changes when the material employed for the spoof attack is not available during the training of the system.
2.1 28Gb/s 560mW multi-standard SerDes with single-stage analog front-end and 14-tap decision-feedback equalizer in 28nm CMOS

A high-speed SerDes must meet multiple challenges including high-speed operation, intensive equalization technique, low power consumption, small area and robustness. In order to meet new standards, such a OIF CEI-25G-LR, CEI-28G-MR/SR/VSR, IEEE802.3bj and 32G-FC, data-rates are increased to 25 to 28Gb/s, which is more than 75% higher than the previous generation of SerDes. For SerDes applications with several hundreds of lanes integrated in single chip, power consumption is very important factor while maintaining high performance. There are several previous works at 28Gb/s or higher data-rate [1-2]. They use an unrolled DFE to meet the critical timing margin, but the unrolled DFE structure increases the number of DFE slicers, increasing the overall power and die area. In order to tackle these challenges, we introduce several circuits and architectural techniques. The analog front-end (AFE) uses a single-stage architecture and a compact on-chip passive inductor in the transimpedance amplifier (TIA), providing 15dB boost. The boost is adaptive and its adaptation loop is decoupled from the decision-feedback equalizer (DFE) adaptation loop by the use of a group-delay adaptation (GDA) algorithm. DFE has a half-rate 1-tap unrolled structure with 2 total error latches for power and area reduction. A two-stage sense-amplifier-based slicer achieves a sensitivity of 15mV and DFE timing closure. We also develop a high-speed clock buffer that uses a new active-inductor circuit. This active-inductor circuit has the capability to control output-common-mode voltage to optimize circuit operating points.
Approximating the Solution of Optimal Control Problems by Fuzzy Systems

In this paper, the ability of fuzzy systems is used to estimate the solution of crisp optimal control problems. To solve an optimal control problem, first the well-known Euler–Lagrange conditions are obtained and then, the solution of these conditions is approximated by defining a trial solution based on fuzzy systems. The parameters of fuzzy systems are adjusted by an optimization algorithm. Numerical examples and comparisons with exact solutions reveal the capability and accuracy of proposed method.
Improvements to Deep Convolutional Neural Networks for LVCSR

Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce spectral variation in the input signal. This has also been confirmed experimentally, with CNNs showing improvements in word error rate (WER) between 4-12% relative compared to DNNs across a variety of LVCSR tasks. In this paper, we describe different methods to further improve CNN performance. First, we conduct a deep analysis comparing limited weight sharing and full weight sharing with state-of-the-art features. Second, we apply various pooling strategies that have shown improvements in computer vision to an LVCSR speech task. Third, we introduce a method to effectively incorporate speaker adaptation, namely fMLLR, into log-mel features. Fourth, we introduce an effective strategy to use dropout during Hessian-free sequence training. We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5% relative improvement over our previous best CNN baseline.
Optogenetics in Mice Performing a Visual Discrimination Task: Measurement and Suppression of Retinal Activation and the Resulting Behavioral Artifact

Optogenetic techniques are used widely to perturb and interrogate neural circuits in behaving animals, but illumination can have additional effects, such as the activation of endogenous opsins in the retina. We found that illumination, delivered deep into the brain via an optical fiber, evoked a behavioral artifact in mice performing a visually guided discrimination task. Compared with blue (473 nm) and yellow (589 nm) illumination, red (640 nm) illumination evoked a greater behavioral artifact and more activity in the retina, the latter measured with electrical recordings. In the mouse, the sensitivity of retinal opsins declines steeply with wavelength across the visible spectrum, but propagation of light through brain tissue increases with wavelength. Our results suggest that poor retinal sensitivity to red light was overcome by relatively robust propagation of red light through brain tissue and stronger illumination of the retina by red than by blue or yellow light. Light adaptation of the retina, via an external source of illumination, suppressed retinal activation and the behavioral artifact without otherwise impacting behavioral performance. In summary, long wavelength optogenetic stimuli are particularly prone to evoke behavioral artifacts via activation of retinal opsins in the mouse, but light adaptation of the retina can provide a simple and effective mitigation of
Finding traces of self-regulated learning in activity streams

This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.
Learning Multilevel Distributed Representations for High-Dimensional Sequences

We describe a new family of non-linear sequence models that are substantially more powerful than hidden Markov models or linear dynamical systems. Our models have simple approximate inference and learning procedures that work well in practice. Multilevel representations of sequential data can be learned one hidden layer at a time, and adding extra hidden layers improves the resulting generative models. The models can be trained with very high-dimensional, very non-linear data such as raw pixel sequences. Their performance is demonstrated using synthetic video sequences of two balls bouncing in a box.
Software Defined Networking Meets Information Centric Networking: A Survey

Information centric networking (ICN) and software-defined networking (SDN) are two emerging networking paradigms that promise to solve different aspects of networking problems. ICN is a clean-slate design for accommodating the ever increasing growth of the Internet traffic by regarding content as the network primitive, adopting in-network caching, and name-based routing, while SDN focuses on agile and flexible network management by decoupling network control logic from data forwarding. ICN and SDN have gained significant research attention separately in the most of the previous work. However, the features of ICN have profound impacts on the design and operation of SDN, such as in-network caching and data-centric security. Conversely, SDN provides a powerful tool for experimenting and deploying ICN architectures and can greatly facilitate ICN functionality and management. In this paper, we point out the necessity of surveying the scattered works on integrating SDN and ICN (SD-ICN) for improving operational networks. Specifically, we analyze the points of SD-ICN strengths and opportunities, and discuss the SDN enablers for deploying ICN architectures. In addition, we review and classify the recent work on improving the network management by SD-ICN and discuss the potential security benefits of SD-ICN. Finally, a number of open issues and future trends are highlighted.
Joint Learning of Sentence Embeddings for Relevance and Entailment

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Polyglot: Distributed Word Representations for Multilingual NLP

Distributed word representations (word embeddings) have recently contributed to competitive performance in language modeling and several NLP tasks. In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias. We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages. We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish. Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings. We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.
Hybrid supervised clustering based ensemble scheme for text classification

PurposeThe immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. Providing diversity among the classification algorithms is a key issue in the ensemble design.  ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, Bagging and Random Subspace) using eleven text benchmarks. FindingThe experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification.  presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification
Matching RGB Images to CAD Models for Object Pose Estimation

We propose a novel method for 3D object pose estimation in RGB images, which does not require pose annotations of objects in images in the training stage. We tackle the pose estimation problem by learning how to establish correspondences between RGB images and rendered depth images of CAD models. During training, our approach only requires textureless CAD models and aligned RGB-D frames of a subset of object instances, without explicitly requiring pose annotations for the RGB images. We employ a deep quadruplet convolutional neural network for joint learning of suitable keypoints and their associated descriptors in pairs of rendered depth images which can be matched across modalities with aligned RGB-D views. During testing, keypoints are extracted from a query RGB image and matched to keypoints extracted from rendered depth images, followed by establishing 2D-3D correspondences. The object’s pose is then estimated using the RANSAC and PnP algorithms. We conduct experiments on the recently introduced Pix3D [33] dataset and demonstrate the efficacy of our proposed approach in object pose estimation as well as generalization to object instances not seen during training.