nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle type classification via adaptive feature clustering for traffic surveillance video

Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.
Affective style, psychopathology, and resilience: brain mechanisms and plasticity.

The brain circuitry underlying emotion includes several territories of the prefrontal cortex (PFC), the amygdala, hippocampus, anterior cingulate, and related structures. In general, the PFC represents emotion in the absence of immediately present incentives and thus plays a crucial role in the anticipation of the future affective consequences of action, as well as in the persistence of emotion following the offset of an elicitor. The functions of the other structures in this circuit are also considered. Individual differences in this circuitry are reviewed, with an emphasis on asymmetries within the PFC and activation of the amygdala as 2 key components of affective style. These individual differences are related to both behavioral and biological variables associated with affective style and emotion regulation. Plasticity in this circuitry and its implications for transforming emotion and cultivating positive affect and resilience are considered.
Mendeley: Recommendations for Researchers

For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.
Wide-band microstrip-to-coplanar stripline/slotline transitions

Wide-band microstrip line to coplanar stripline (CPS) transitions are proposed. The transition consists of a multisection matching transformer and a quarter-wavelength radial stub for the impedance matching and field matching between the microstrip line and CPS, respectively. The proposed planar transition has the advantages of compact size, wide bandwidth, and straightforward design procedure. Several parameters are studied through simulations and experiments to derive some design guidelines. With the return loss of better than 14 dB, the 1- and 3-dB back-to-back insertion loss bandwidth can cover from 1.4 to 6.6 GHz (1 : 4.7) and from 1.1 to 10.5 GHz (1 : 9.6), respectively. In addition, the microstrip-to-CPS transition is extended to design a microstrip-to-slotline transition by tapering the CPS into a slotline. From 2.7 to 10.4 GHz (1 : 3.85), the back-to-back return loss is better than 15 dB and the insertion loss is less than 3 dB.
Sentiment Adaptive End-to-End Dialog Systems

v This work focuses on incorporating sentiment information into task-oriented dialogue systems. v Current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. v But the ultimate evaluator of dialog systems is the end-users and their sentiment is a direct reflection of user satisfaction and should be taken into consideration. v Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. v We incorporated user sentiment information in both supervised and reinforcement learning settings. v In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. Multimodal Sentiment Detector v We manually annotated 50 dialogs with 517 conversation turns to train this sentiment detector. The annotated set is open to public. v Prediction made by the detector will be used in the supervised learning and reinforcement learning. v Three sets of features: 1) Acoustic features; 2) Dialogic features; 3) Textual features. v Dialogic features include: 1) Interruption; 2) Button usage; 3) Repetitions; 4) Start over. These four categories of dialog features are chosen based on the previous literature and the observed statistics in the dataset.
iCROSS: toward a scalable infrastructure for cross-domain context management

With the development of pervasive computing techniques, the world will be filled with interconnected context-aware domains (e.g., homes, offices, hospitals, etc.). While the previous studies focused solely on the management of contexts produced in a single domain, in this paper we discuss the challenges to be addressed for cross-domain context management. By analyzing the requirements from several scenarios, we identify two context producer–consumer patterns in multi-domain environments. Furthermore, to deal with the mobile entity problem raised in cross-domain context sharing, a transparent query mechanism that enables applications to obtain context information about mobile entities from remote domains is proposed. Two prototype applications—smart home and community services in a smart campus—have been developed to demonstrate the key features and usefulness of cross-domain context management. Initial experiments have also been conducted to evaluate the performance of our system.
M-invariance: towards privacy preserving re-publication of dynamic datasets

The previous literature of privacy preserving data publication has focused on performing "one-time" releases. Specifically, none of the existing solutions supports re-publication of the microdata, after it has been updated with insertions <u>and</u> deletions. This is a serious drawback, because currently a publisher cannot provide researchers with the most recent dataset continuously.
 This paper remedies the drawback. First, we reveal the characteristics of the re-publication problem that invalidate the conventional approaches leveraging k-anonymity and l-diversity. Based on rigorous theoretical analysis, we develop a new generalization principle m-invariance that effectively limits the risk of privacy disclosure in re-publication. We accompany the principle with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata. Our theoretical results are confirmed by extensive experiments with real data.
Development of ambient environmental monitoring system through wireless sensor network (WSN) using NodeMCU and “WSN monitoring”

In this paper we have developed a system for web based environment monitoring using the WSN Technology. WSN sensor nodes transmit data to the cloud-based database via Web API request. Measured data can be monitored by the user anywhere from internet by using the Web Application which one is also compatible for mobile phones. If the data measured by sensor node exceeds the configured value range in Web Application, Web Application sends a warning e-mail to users for improving environmental conditions.
A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services

The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation

Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. However, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Examples of these approaches include an approach that uses a polarity dictionary that cannot handle unknown or newly invented words and another approach that uses a complex model with 13 types of feature templates. We propose a novel high performance sentiment classification method with stacked denoising auto-encoders that uses distributed word representation instead of building dictionaries or utilizing engineering features. The results of experiments conducted indicate that our model achieves state-of-the-art performance in Japanese sentiment classification tasks.
Persian-English Machine Translation: An Overview of the Shiraz Project

This report describes the Shiraz project MT prototype for a Persian to English machine translation system using typed feature structures and unification. An overview of the linguistic properties of Persian is presented and the morphological and syntactic grammars developed within the Shiraz project are discussed. The underlying model for the system is a layered chart, capable of representing heterogeneous types of hypotheses in an integrated way. .. 2 ...... 2 ..... 4 ... 5 ..... 5 ... .. 9 ... 9 .... 9 .. 11 ... 11 ... 11 ... 11 13 ... 13 ..... 15 ..... 15 ... .... ... 16 ... 17 .... 17 ... 18 ... 18 ... 18 .... 19 ... 19 ... 19 .... 19 .... 20
Real-time scheme for 3-dimensional localizing and tracking of people in multiple camera settings

This paper proposes a real-time multi-camera system that solves three-dimensional localizing and tracking of people. Unlike the existing multi-camera system, the proposed system is designed to achieve real-time performance and operate in an online manner. The proposed system adopts computationally efficient features to detect people and performs data association of the detections for real-time tracking. The online multi-camera data association problem is formulated as multi-dimensional assignment problem and solved by the proposed approximated algorithm using random splitting and re-merging operations for real-time operation. Through evaluation of the challenging multi-camera datasets, the proposed multi-camera system achieves a competitive performance with five times faster speed than the state-of-the-art methods.
Parasitic-Aware Common-Centroid Binary-Weighted Capacitor Layout Generation Integrating Placement, Routing, and Unit Capacitor Sizing

Capacitor sizing is a crucial step when designing charge-scaling digital-to-analog converters (DACs). Larger capacitor size can achieve better circuit accuracy and performance due to less impact from process gradient, parasitic mismatch, and local variation. However, it also results in larger chip area and higher power consumption. The size of binary-weighted capacitors in charge-scaling DACs is highly sensitive to the routing parasitics. Unmatched routing parasitics among binary-weighted capacitors will lead to large capacitor size for satisfying circuit accuracy and performance. Previous work focuses on the study of generating high-quality common-centroid placement of unit capacitor arrays while ignoring routing parasitics. None of them address the sizing of binary-weighed capacitors. This paper presents the first problem formulation in the literature which simultaneously considers capacitor sizing and parasitic matching during common-centroid capacitor layout generation such that the power consumption is minimized while the circuit accuracy/performance is also satisfied. Experimental results show that the proposed approach can achieve very significant chip area and power reductions compared with the state-of-the-art approaches.
Self-compassion as a moderator of the relationship between academic burn-out and psychological health in Korean cyber university students

The moderating effect of self-compassion was examined in the relationship between academic burden and psychological health. The participants were students (N = 350) from a cyber university in Korea. At the beginning of the fall 2011 semester, the participants were surveyed using items from the academic burn-out, self-compassion, depression, and psychological well-being measures. Multiple regression analysis showed that self-compassion moderated the relationship between academic burn-out and psychological well-being. And self-compassion also moderated the relationship between academic burn-out and depression. This study presents an empirical framework for the research through investigating the relationship among academic burden, self-compassion, and psychological health for cyber university students.
Mobile Virtual Network Admission Control and Resource Allocation for Wireless Network Virtualization: A Robust Optimization Approach

Wireless network virtualization is a promising technology in next generation wireless networks. In this paper, motivated by the experience of user equipment (UE) admission control in traditional wireless networks, we propose a novel concept of mobile virtual network (MVN) admission control for wireless virtualization. By limiting the number of MVNs embedded in the physical network, MVN admission control can effectively guarantee quality of service (QoS) experienced by users of MVNs and maximize the utilization of the physical networks at the same time. Specifically, we propose a two-stage MVN embedding mechanism that can decouple short-term physical resource allocation from long-term admission control and resource leasing. With recent advances in robust optimization, we formulate the MVN admission control problem as a robust optimization problem. Both the long-term admission control and short- term resource allocation problems are transformed to convex problems, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.
A Capacitive Fringing Field Sensor Design for Moisture Measurement Based on Printed Circuit Board Technology

Interdigitated electrode capacitive fringing field sensors have been utilized in numerous applications. Although various technologies are used to realize these types of sensors, printed circuit board technology is particularly advantageous for realizing this type of sensor through fabricating the interdigitated electrode structures in the patterned Cu foil. Additionally, the solder mask coating can insulate the electrodes to prevent shorting in the presence of water. Using this approach, prototype sensors were designed, simulated, fabricated, and successfully evaluated. Applications include water detection and quantity measurement and soil moisture content measurement.
Discriminative Label Consistent Domain Adaptation

Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target. We present in this paper a novel unsupervised DA method for cross-domain visual recognition which simultaneously optimizes the three terms of a theoretically established error bound. Specifically, the proposed DA method iteratively searches a latent shared feature subspace where not only the divergence of data distributions between the source domain and the target domain is decreased as most state-of-the-art DA methods do, but also the inter-class distances are increased to facilitate discriminative learning. Moreover, the proposed DA method sparsely regresses class labels from the features achieved in the shared subspace while minimizing the prediction errors on the source data and ensuring label consistency between source and target. Data outliers are also accounted for to further avoid negative knowledge transfer. Comprehensive experiments and in-depth analysis verify the effectiveness of the proposed DA method which consistently outperforms the state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain image classification tasks.
Action Anticipation with RBF Kernelized Feature Mapping RNN

We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN . Our novel RNN architecture builds upon three effective principles of machine learning, namely parameter sharing, Radial Basis Function kernels and adversarial training. Using only some of the earliest frames of a video, the feature mapping RNN is able to generate future features with a fraction of the parameters needed in traditional RNN. By feeding these future features into a simple multilayer perceptron facilitated with an RBF kernel layer, we are able to accurately predict the action in the video. In our experiments, we obtain 18% improvement on JHMDB-21 dataset, 6% on UCF101-24 and 13% improvement on UT-Interaction datasets over prior stateof-the-art for action anticipation.
Towards String-to-Tree Neural Machine Translation

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. Experiments on the WMT16 German-English news translation task shown improved BLEU scores when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A smallscale human evaluation also showed an advantage to the syntax-aware system. ”accepted...” header
Next Basket Recommendation with Neural Networks

One crucial task in recommendation is to predict what a user will buy next given her shopping history. In this paper, we propose a novel neural network to complete this task. The model consists of an embedding layer, a hidden layer and an output layer. Firstly, the distributed representations of the user and the items bought before are obtained and used to form a feature vector by the embedding layer. Then the hidden layer transforms the feature vector to another space by a non-linear operator. Finally, the softmax operator is adopted to output the probabilities of next items. We can see that the model elegantly involves both the user’s general interest and the sequential dependencies between items for prediction. Experimental results on two real datasets prove the effectiveness of our model.
Blind PSF estimation and methods of deconvolution optimization

We have shown that the left side null space of the autoregression (AR) matrix operator is the lexicographical presentation of the point spread function (PSF) on condition the AR parameters are common for original and blurred images. The method of inverse PSF evaluation with regularization functional as the function of surface area is offered. The inverse PSF was used for primary image estimation. Two methods of original image estimate optimization were designed basing on maximum entropy generalization of sought and blurred images conditional probability density and regularization. The first method uses balanced variations of convolution and deconvolution transforms to obtaining iterative schema of image optimization. The variations balance was defined by dynamic regularization basing on condition of iteration process convergence. The regularization has dynamic character because depends on current and previous image estimate variations. The second method implements the regularization of deconvolution optimization in curved space with metric defined on image estimate surface. It is basing on target functional invariance to fluctuations of optimal argument value. The given iterative schemas have faster convergence in comparison with known ones, so they can be used for reconstruction of high resolution images series in real time.