nils-herrmann/clean_scirepeval huggingface dataset:
Turn-on Performance of Reverse Blocking IGBT (RB IGBT) and Optimization Using Advanced Gate Driver

Turn-on performance of a reverse blocking insulated gate bipolar transistor (RB IGBT) is discussed in this paper. The RB IGBT is a specially designed IGBT having ability to sustain blocking voltage of both the polarities. Such a switch shows superior conduction but much worst switching (turn- on) performances compared to a combination of an ordinary IGBT and blocking diode. Because of that, optimization of the switching performance is a key issue that makes the RB IGB not well accepted in the real applications. In this paper, the RB IGBT turn-on losses and reverse recovery current are analyzed for different gate driver techniques, and a new gate driver is proposed. Commonly used conventional gate drivers do not have capability for the switching dynamics optimization. In contrast to this, the new proposed gate driver provides robust and simple way to control and optimize the reverse recovery current and turn-on losses. The collector current slope and reverse recovery current are controlled by the means of the gate emitter voltage control in feedforward manner. In addition, the collector emitter voltage slope is controlled during the voltage falling phase by the means of inherent increase of the gate current. Therefore, the collector emitter voltage tail and the total turn- on losses are reduced, independently on the reverse recovery current. The proposed gate driver was experimentally verified and the results presented and discussed.
Alpha-band oscillations, attention, and controlled access to stored information

Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
Predicting the Parts Weight in Plastic Injection Molding Using Least Squares Support Vector Regression

To achieve the desired quality in plastic injection molding, advanced monitoring techniques are often recommended in the workshop. Unfortunately, the signal in plastic injection modeling process such as nozzle pressure that is relevant to part quality is not easy to obtain because of the cost of sensors. The sensor-based modeling idea is therefore adopted. In this paper, a new method for predicting the parts weight in plastic injection molding using least squares support vector regression (LS-SVR) is proposed, which is composed of two steps. The first step is to estimate the nozzle pressure with the hydraulic system pressure using an LS-SVR model. The second step is to predict product weight using the estimated nozzle pressure, which is done using another LS-SVR model. The experimental results show that the new method is very effective.
Emotion-Preserving Representation Learning via Generative Adversarial Network for Multi-View Facial Expression Recognition

Face frontalization is one way to overcome the pose variation problem, which simplifies multi-view recognition into one canonical-view recognition. This paper presents a multi-task learning approach based on the generative adversarial network (GAN) that learns the emotion-preserving representations in the face frontalization framework. Taking advantage of adversarial relationship between the generator and the discriminator in GAN, the generator can frontalize input non-frontal face images into frontal face images while preserving the identity and expression characteristics; in the meantime, it can employ the learnt emotion-preserving representations to predict the expression class label from the input face. The proposed network is optimized by combining both synthesis and classification objective functions to make the learnt representations generative and discriminative simultaneously. Experimental results demonstrate that the proposed face frontalization system is very effective for expression recognition with large head pose variations.
An Access Control scheme for Big Data processing

Access Control (AC) systems are among the most critical of network security components. A system's privacy and security controls are more likely to be compromised due to the misconfiguration of access control policies rather than the failure of cryptographic primitives or protocols. This problem becomes increasingly severe as software systems become more and more complex, such as Big Data (BD) processing systems, which are deployed to manage a large amount of sensitive information and resources organized into a sophisticated BD processing cluster. Basically, BD access control requires the collaboration among cooperating processing domains to be protected as computing environments that consist of computing units under distributed AC managements. Many BD architecture designs were proposed to address BD challenges; however, most of them were focused on the processing capabilities of the “three Vs” (Velocity, Volume, and Variety). Considerations for security in protecting BD are mostly ad hoc and patch efforts. Even with some inclusion of security in recent BD systems, a critical security component, AC (Authorization), for protecting BD processing components and their users from the insider attacks, remains elusive. This paper proposes a general purpose AC scheme for distributed BD processing clusters.
Energy-based anomaly detection for mixed data

Anomalies are those deviating significantly from the norm. Thus anomaly detection amount to finding data points located far away from their neighbors, i.e., those lying in low density regions. Classic anomaly detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Mixed data poses multiple challenges including (a) capturing the inter-type correlation structures and (b) measuring deviation from the norm under multiple types. These challenges are exaggerated under (c) high-dimensional regimes. In this paper, we propose a new scalable unsupervised anomaly detection method for mixed data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that estimates density of mixed data. We propose to use free-energy derived from Mv.RBM as anomaly score as it is identical to data negative log-density up-to an additive constant. We then extend this method to detect anomalies across multiple levels of data abstraction, an effective approach to deal with high-dimensional settings. The extension is dubbed MIXMAD, which stands for MIXed data Multilevel Anomaly Detection. In MIXMAD, we sequentially constructs an ensemble of mixeddata Deep Belief Nets (DBNs) with varying depths. Each DBN is an energy-based detector at a predefined abstraction level. Predictions across the ensemble are finally combined via a simple rank aggregation method. The proposed methods are evaluated on a comprehensive suit of synthetic and real high-dimensional datasets. The results demonstrate that for anomaly detection, (a) a proper handling mixed-types is necessary, (b) free-energy is a powerful anomaly scoring method, (c) multilevel abstraction of data is important for high-dimensional data, and (d) empirically Mv.RBM and MIXMAD are superior to popular unsupervised detection methods for both homogeneous and mixed data.
Incremental Translation Utilizing Constituent Boundary Patterns

We have proposed an incremental translation method in Transfer-Driven Machine Translation (TDMT). In this method, constituent boundary patterns are applied to an input in a bottom-up fashion. Also, by dealing with best-only substructures, the explosion of structural ambiguity is constrained and an efficient translation of a lengthy input can be achieved. Through preliminary experimentation our new TDMT has been shown to be more efficient while main-tMning translation quality.
Improving Retrieval Performance for Verbose Queries via Axiomatic Analysis of Term Discrimination Heuristic

Number of terms in a query is a query-specific constant that is typically ignored in retrieval functions. However, previous studies have shown that the performance of retrieval models varies for different query lengths, and it usually degrades when query length increases. A possible reason for this issue can be the extraneous terms in longer queries that makes it a challenge for the retrieval models to distinguish between the key and complementary concepts of the query. As a signal to understand the importance of a term, inverse document frequency (IDF) can be used to discriminate query terms. In this paper, we propose a constraint to model the interaction between query length and IDF. Our theoretical analysis shows that current state-of-the-art retrieval models, such as BM25, do not satisfy the proposed constraint. We further analyze the BM25 model and suggest a modification to adapt BM25 so that it adheres to the new constraint. Our experiments on three TREC collections demonstrate that the proposed modification outperforms the baselines, especially for verbose queries.
A Low Rank Weighted Graph Convolutional Approach to Weather Prediction

Weather forecasting is an important but challenging problem as one must contend with the inherent non-linearities and spatiotemporal autocorrelation present in the data. This paper presents a novel deep learning approach based on a coupled weighted graph convolutional LSTM (WGC-LSTM) to address these challenges. Specifically, our proposed approach uses an LSTM to capture the inherent temporal autocorrelation of the data and a graph convolution to model its spatial relationships. As the weather condition can be influenced by various spatial factors besides the distance between locations, e.g., topography, prevailing winds and jet streams, imposing a fixed graph structure based on the proximity between locations is insufficient to train a robust deep learning model. Instead, our proposed approach treats the adjacency matrix of the graph as a model parameter that can be learned from the training data. However, this introduces an additional O(|V|^2) parameters to be estimated, where V is the number of locations. With large graphs this may also lead to slower performance as well as susceptibility to overfitting. We propose a modified version of our approach that can address this difficulty by assuming that the adjacency matrix is either sparse or low rank. Experimental results using two real-world weather datasets show that WGC-LSTM outperforms all other baseline methods for the majority of the evaluated locations.
Novel Control Method for Multimodule PV Microinverter With Multiple Functions

This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push–pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.
Demand-Side Management via Distributed Energy Generation and Storage Optimization

Demand-side management, together with the integration of distributed energy generation and storage, are considered increasingly essential elements for implementing the smart grid concept and balancing massive energy production from renewable sources. We focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy sources and/or energy storage devices. By means of a day-ahead optimization process regulated by an independent central unit, the latter users intend to reduce their monetary energy expense by producing or storing energy rather than just purchasing their energy needs from the grid. In this paper, we formulate the resulting grid optimization problem as a noncooperative game and analyze the existence of optimal strategies. Furthermore, we present a distributed algorithm to be run on the users' smart meters, which provides the optimal production and/or storage strategies, while preserving the privacy of the users and minimizing the required signaling with the central unit. Finally, the proposed day-ahead optimization is tested in a realistic situation.
An Effective High Threating Alarm Mining Method for Cloud Security Management

Security equipment such as intrusion prevention system is an important supplementary for security management. They reduce the difficulty of network management by giving alarms corresponding to different attacks instead of raw traffic packet inspection. But there are many false alarms due to their running mechanism, which greatly reduces its usability. In this paper, we develop a hierarchical framework to mine high threating alarms from the massive alarm logs, and aim to provide fundamental and useful information for administrators to design efficient management policy. First, the alarms are divided into two parts based on their attributes, the first part mainly includes several kinds of famous attacks which are critical for security management, we proposed a similar alarm mining method based on Choquet integral to cluster and rank the frequently occurred attacks. The rest alarms constitute the second part, which are caused by the potential threats attacks, also include many false alarms. To reduce the effect of false alarms and rank the potential threats, we employ the frequent pattern mining algorithm to mine correlation rules and then filter false alarms. Following, we proposed a self-adapting threat degree calculation method to qualify the threat degree of these alarms after filtering. To verity the methods developed, an experimental platform is constructed in the campus network of Xi’an Jiaotong University. Experimental results based on the data collected verify the efficiency of the developed methods. For the first kind of alarms, the similar alarms mining accuracy is higher than 97% and the alarms are ranked with different processing urgencies. For the rest alarms, the proposed methods have filtering accuracy above 80% and can rank the potential threats. Based on the ranking results, administrators can deal with the high threats with their limited time and energy, in turn, keep the network under control.
A new paradigm of human gait analysis with Kinect

Analysis of human gait helps to find an intrinsic gait signature through which ubiquitous human identification and medical disorder problems can be investigated in a broad spectrum. The gait biometric provides an unobtrusive feature by which video gait data can be captured at a larger distance without prior awareness of the subject. In this paper, a new technique has been addressed to study the human gait analysis with Kinect Xbox device. It ensures us to minimize the segmentation errors with automated background subtraction technique. The closely similar human skeleton model can be generated from background subtracted gait images, altered by covariate conditions, such as change in walking speed and variations in clothing type. The gait signatures are captured from joint angle trajectories of left hip, left knee, right hip and right knee of subject's skeleton model. The experimental verification on Kinect gait data has been compared with our in-house development of sensor based biometric suit, Intelligent Gait Oscillation Detector (IGOD). An endeavor has been taken to investigate whether this sensor based biometric suit can be altered with a Kinect device for the proliferation of robust gait identification system. The Fisher discriminant analysis has been applied on training gait signature to look into the discriminatory power of feature vector. The Naïve Bayesian classifier demonstrates an encouraging classification result with estimation of errors on limited dataset captured by Kinect sensor.
Blockchain inefficiency in the Bitcoin peers network

We investigate Bitcoin network observing transactions broadcasted into the network during a week from 04/05/2016 and then monitoring their inclusion into the blockchain during the following seven months.We unveil that 42% of the transactions are still not included in the Blockchain after 1 h from their appearance and 20% of the transactions are still not included in the Blockchain after 30 days, therefore revealing a great inefficiency in the Bitcoin system. However, we observe that most of these “forgotten” transactions have low values and in terms of transferred value the system is less inefficient with 93% of the transactions value being included into the Blockchain within 3 h and 98.8% within a day. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes. It also calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording
Webcam-Based Accurate Eye-Central Localization

This paper contains experimental procedure of webcam-based eye-tracker specially for low power devices. This paper consists of five processes. First one is background suppression for reducing average processing requirement. Second one is Haar-cascade feature-based face detection algorithm. Third one is geometrically eye-position determination. The fourth part is to detect and track eye-ball center using mean of gradient vector. The fifth and last step is to detect where the user looking. We simply calculate percentage of movement of eye to detect either it looking left, right, up or down. This procedure is highly effective with satisfactory accuracy. It also requires less processing power.
Risk and maintenance factors for eating pathology: a meta-analytic review.

This meta-analytic review of prospective and experimental studies reveals that several accepted risk factors for eating pathology have not received empirical support (e.g., sexual abuse) or have received contradictory support (e.g.. dieting). There was consistent support for less-accepted risk factors(e.g., thin-ideal internalization) as well as emerging evidence for variables that potentiate and mitigate the effects of risk factors(e.g., social support) and factors that predict eating pathology maintenance(e.g., negative affect). In addition, certain multivariate etiologic and maintenance models received preliminary support. However, the predictive power of individual risk and maintenance factors was limited, suggesting it will be important to search for additional risk and maintenance factors, develop more comprehensive multivariate models, and address methodological limitations that attenuate effects.
Fairness in Package-to-Group Recommendations

Recommending packages of items to groups of users has several applications, including recommending vacation packages to groups of tourists, entertainment packages to groups of friends, or sets of courses to groups of students. In this paper, we focus on a novel aspect of package-to-group recommendations, that of fairness. Specifically, when we recommend a package to a group of people, we ask that this recommendation is fair in the sense that every group member is satisfied by a sufficient number of items in the package. We explore two definitions of fairness and show that for either definition the problem of finding the most fair package is NP-hard. We exploit the fact that our problem can be modeled as a coverage problem, and we propose greedy algorithms that find approximate solutions within reasonable time. In addition, we study two extensions of the problem, where we impose category or spatial constraints on the items to be included in the recommended packages. We evaluate the appropriateness of the fairness models and the performance of the proposed algorithms using real data from Yelp, and a user study.
Immune attack: the role of inflammation in Alzheimer disease

The past two decades of research into the pathogenesis of Alzheimer disease (AD) have been driven largely by the amyloid hypothesis; the neuroinflammation that is associated with AD has been assumed to be merely a response to pathophysiological events. However, new data from preclinical and clinical studies have established that immune system-mediated actions in fact contribute to and drive AD pathogenesis. These insights have suggested both novel and well-defined potential therapeutic targets for AD, including microglia and several cytokines. In addition, as inflammation in AD primarily concerns the innate immune system — unlike in 'typical' neuroinflammatory diseases such as multiple sclerosis and encephalitides — the concept of neuroinflammation in AD may need refinement.
Social Interactions and Social Preferences in Social Networks

We extend the utility specification in Ballester et al. (2006) to study social interactions when individuals hold altruistic preferences in social networks. We show that more enriched network features can be captured in the best response function derived from the maximization of the extended utility which incorporates altruism, providing microfoundation for studies which investigate how network features mediate peer effects or other important features in social interactions. We demonstrate that often ignored altruism is another serious confounding factor of peer effects. Our results show that the estimates of peer affects are about 36% smaller under altruistic preferences. Furthermore, we are able to separate the two different types of effects generated by peers: the (usually positive) spillover effects from peers and the (negative or positive) externality effects directly generated from peers’ outcomes, which is not possible in conventional social interactions model based on self-interest hypothesis.
The SLAM problem: a survey

This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive when the scenario becomes larger.