nils-herrmann/clean_scirepeval huggingface dataset:
Control design of coordinated droop control for hybrid AC/DC microgrid considering distributed generation characteristics

Recently, interest in distributed generation (DG) including renewable energy sources (RES) and energy storage systems (ESS) has been increased due to the depletion of fossil fuels, environmental issues and the requirement of electrical energy reliability. The power electronic based DC distribution system makes easy integration and has a potential for improved system efficiency and reliability. As the multiple sources and storage are interacting, critical issues in the system integration such as control strategies and subsystem interaction must be addressed. This paper is based on the preliminary research on the configurations for multiple sources and the control of the system using various adaptive droop controls. Design of the local controllers is explained in detail regarding the operating characteristics of the DG sources. The algorithm design and implementation of the energy management system (EMS) is delivered. A laboratory-level dc microgrid system has been implemented for verification of the proposed control strategies.
Word Meaning in Context: A Simple and Effective Vector Model

We present a model that represents word meaning in context by vectors which are modified according to the words in the target’s syntactic context. Contextualization of a vector is realized by reweighting its components, based on distributional information about the context words. Evaluation on a paraphrase ranking task derived from the SemEval 2007 Lexical Substitution Task shows that our model outperforms all previous models on this task. We show that our model supports a wider range of applications by evaluating it on a word sense disambiguation task. Results show that our model achieves state-of-the-art performance.
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generativeadversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f -divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models.
Phonological systems in bilinguals: age of learning effects on the stop consonant systems of Korean-English bilinguals.

Interaction of Korean and English stop systems in Korean-English bilinguals as a function of age of acquisition (AOA) of English was investigated. It was hypothesized that early bilinguals (mean AOA=3.8 years) would more likely be native-like in production of English and Korean stops and maintain greater independence between Korean and English stop systems than late bilinguals (mean AOA=21.4 years). Production of Korean and English stops was analyzed in terms of three acoustic-phonetic properties: voice-onset time, amplitude difference between the first two harmonics, and fundamental frequency. Late bilinguals were different from English monolinguals for English voiceless and voiced stops in all three properties. As for Korean stops, late bilinguals were different from Korean monolinguals for fortis stops in voice-onset time. Early bilinguals were not different from the monolinguals of either language. Considering the independence of the two stop systems, late bilinguals seem to have merged English voiceless and Korean aspirated stops and produced English voiced stops with similarities to both Korean fortis and lenis stops, whereas early bilinguals produced five distinct stop types. Thus, the early bilinguals seem to have two independent stop systems, whereas the late bilinguals likely have a merged Korean-English system.
Emotion Cause Events: Corpus Construction and Analysis

Emotion processing has always been a great challenge. Given the fact that an emotion is triggered by cause events and that cause events are an integral part of emotion, this paper constructs a Chinese emotion cause corpus as a first step towards automatic inference of cause-emotion correlation. The corpus focuses on five primary emotions, namely happiness, sadness, fear, anger, and surprise. It is annotated with emotion cause events based on our proposed annotation scheme. Corpus data shows that most emotions are expressed with causes, and that causes mostly occur before the corresponding emotion verbs. We also examine the correlations between emotions and cause events in terms of linguistic cues: causative verbs, perception verbs, epistemic markers, conjunctions, prepositions, and others. Results show that each group of linguistic cues serves as an indicator marking the cause events in different structures of emotional constructions. We believe that the emotion cause corpus will be the useful resource for automatic emotion cause detection as well as emotion detection and classification.
Frequency-Modulated Lorentz Force Magnetometer With Enhanced Sensitivity via Mechanical Amplification

This letter presents a micromachined silicon Lorentz force magnetometer, which consists of a flexural beam resonator coupled to current-carrying silicon beams via a microleverage mechanism. The flexural beam resonator is a force sensor, which measures the magnetic field through resonant frequency shift induced by the Lorentz force, which acts as an axial load. Previous frequency-modulated Lorentz force magnetometers suffer from low sensitivity, limited by both fabrication restrictions and lack of a force amplification mechanism. In this letter, the microleverage mechanism amplifies the Lorentz force, thereby enhancing the sensitivity of the magnetometer by a factor of 42. The device has a measured sensitivity of 6687 ppm/(mA · T), which is two orders of magnitude larger than the prior state-of-the-art. The measured results agree with an analytical model and finite-element analysis. The frequency stability of the sensor is limited by the quality factor (Q) of 540, which can be increased through improved vacuum packaging.
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies

Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.
Enabled Generalized Vector Space Model to Improve Document Retrieval

This paper presents two approaches to semantic search by incorporating Linked Data annotations of documents into a Generalized Vector Space Model. One model exploits taxonomic relationships among entities in documents and queries, while the other model computes term weights based on semantic relationships within a document. We publish an evaluation dataset with annotated documents and queries as well as user-rated relevance assessments. The evaluation on this dataset shows significant improvements of both models over traditional keyword based search.
On the design of individual and group recommender systems for tourism

This paper presents a recommender system for tourism based on the tastes of the users, their demographic classification and the places they have visited in former trips. The system is able to offer recommendations for a single user or a group of users. The group recommendation is elicited out of the individual personal recommendations through the application of mechanisms such as aggregation and intersection. The elicitation mechanism is implemented as an extension of e-Tourism, a user-adapted tourism and leisure application whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven recommender system.
Recognition and retrieval of mathematical expressions

Document recognition and retrieval technologies complement one another, providing improved access to increasingly large document collections. While recognition and retrieval of textual information is fairly mature, with wide-spread availability of optical character recognition and text-based search engines, recognition and retrieval of graphics such as images, figures, tables, diagrams, and mathematical expressions are in comparatively early stages of research. This paper surveys the state of the art in recognition and retrieval of mathematical expressions, organized around four key problems in math retrieval (query construction, normalization, indexing, and relevance feedback), and four key problems in math recognition (detecting expressions, detecting and classifying symbols, analyzing symbol layout, and constructing a representation of meaning). Of special interest is the machine learning problem of jointly optimizing the component algorithms in a math recognition system, and developing effective indexing, retrieval and relevance feedback algorithms for math retrieval. Another important open problem is developing user interfaces that seamlessly integrate recognition and retrieval. Activity in these important research areas is increasing, in part because math notation provides an excellent domain for studying problems common to many document and graphics recognition and retrieval applications, and also because mature applications will likely provide substantial benefits for education, research, and mathematical literacy.
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.
Distributed e-voting using the Smart Card Web Server

Voting in elections is the basis of democracy, but citizens may not be able or willing to go to polling stations to vote on election days. Remote e-voting via the Internet provides the convenience of voting on the voter's own computer or mobile device, but Internet voting systems are vulnerable to many common attacks, affecting the integrity of an election. Distributing the processing of votes over many web servers installed in tamper-resistant, secure environments can improve security: this is possible by using the Smart Card Web Server (SCWS) on a mobile phone Subscriber Identity Module (SIM). This paper proposes a generic model for a voting application installed in the SIM/SCWS, which uses standardised Mobile Network Operator (MNO) management procedures to communicate (via HTTPs) with a voting authority to vote. The generic SCWS voting model is then used with the e-voting system Prêt à Voter. A preliminary security analysis of the proposal is carried out, and further research areas are identified. As the SCWS voting application is used in a distributed processing architecture, e-voting security is enhanced because to compromise an election, an attacker must target many individual mobile devices rather than a centralised web server.
Wide-band modified printed bow-tie antenna with single and dual polarization for C - and X-band applications

A modified printed bow-tie antenna is designed to simultaneously cover the operations in the C and X-bands from 5.5 to 12.5 GHz. The presented antenna has an end fire radiation pattern that makes it suitable for integration in single and dual polarized phased array systems. The antenna exhibits small size and wide bandwidth of 91%. The radiation characteristics are presented for a single element and a linear array of this antenna.
Vectorized Bloom filters for advanced SIMD processors

Analytics are at the core of many business intelligence tasks. Efficient query execution is facilitated by advanced hardware features, such as multi-core parallelism, shared-nothing low-latency caches, and SIMD vector instructions. Only recently, the SIMD capabilities of mainstream hardware have been augmented with wider vectors and non-contiguous loads termed gathers. While analytical DBMSs minimize the use of indexes in favor of scans based on sequential memory accesses, some data structures remain crucial. The Bloom filter, one such example, is the most efficient structure for filtering tuples based on their existence in a set and its performance is critical when joining tables with vastly different cardinalities. We introduce a vectorized implementation for probing Bloom filters based on gathers that eliminates conditional control flow and is independent of the SIMD length. Our techniques are generic and can be reused for accelerating other database operations. Our evaluation indicates a significant performance improvement over scalar code that can exceed 3X when the Bloom filter is cache-resident.
I-Vectors for Timbre-Based Music Similarity and Music Artist Classification

In this paper, we present a novel approach to extract songlevel descriptors built from frame-level timbral features such as Mel-frequency cepstral coefficient (MFCC). These descriptors are called identity vectors or i-vectors and are the results of a factor analysis procedure applied on framelevel features. The i-vectors provide a low-dimensional and fixed-length representation for each song and can be used in a supervised and unsupervised manner. First, we use the i-vectors for an unsupervised music similarity estimation, where we calculate the distance between i-vectors in order to predict the genre of songs. Second, for a supervised artist classification task we report the performance measures using multiple classifiers trained on the i-vectors. Standard datasets for each task are used to evaluate our method and the results are compared with the state of the art. By only using timbral information, we already achieved the state of the art performance in music similarity (which uses extra information such as rhythm). In artist classification using timbre descriptors, our method outperformed the state of the art.
Supervised and Projected Sparse Coding for Image Classification

Classic sparse representation for classification (SRC) method fails to incorporate the label information of training images, and meanwhile has a poor scalability due to the expensive computation for `1 norm. In this paper, we propose a novel subspace sparse coding method with utilizing label information to effectively classify the images in the subspace. Our new approach unifies the tasks of dimension reduction and supervised sparse vector learning, by simultaneously preserving the data sparse structure and meanwhile seeking the optimal projection direction in the training stage, therefore accelerates the classification process in the test stage. Our method achieves both flat and structured sparsity for the vector representations, therefore making our framework more discriminative during the subspace learning and subsequent classification. The empirical results on 4 benchmark data sets demonstrate the effectiveness of our method.
Localization In Wireless Sensor Networks Based on Support Vector Machines

We consider the problem of estimating the geographic locations of nodes in a wireless sensor network where most sensors are without an effective self-positioning functionality. We propose LSVM-a novel solution with the following merits. First, LSVM localizes the network based on mere connectivity information (that is, hop counts only) and therefore is simple and does not require specialized ranging hardware or assisting mobile devices as in most existing techniques. Second, LSVM is based on Support Vector Machine (SVM) learning. Although SVM is a classification method, we show its applicability to the localization problem and prove that the localization error can be upper bounded by any small threshold given an appropriate training data size. Third, LSVM addresses the border and coverage-hole problems effectively. Last but not least, LSVM offers fast localization in a distributed manner with efficient use of processing and communication resources. We also propose a modified version of mass-spring optimization to further improve the location estimation in LSVM. The promising performance of LSVM is exhibited by our simulation study.
Analysis of Trust-based Access Control Using Game Theory

Access control is an important security mechanism that is used to limit user access to information and resources and to prevent malicious users from making unauthorized access. Traditional access control models are well suited for centralized and relatively static environments in which information about subjects and objects are known in priori, but they can hardly meet the needs of open and dynamic network environments. Access control in open network environments must therefore adapt to dynamic addition and deletion of subjects and objects. In this paper, we use game theory to analyze trust-based access control to help compute trust values that involve several factors. By viewing access control as a game played between the requester and the provider entities, we can develop strategies that would motivate subjects to make honest access to objects continuously to get the most payoffs.
A 300-mV 220-nW Event-Driven ADC With Real-Time QRS Detection for Wearable ECG Sensors

This paper presents an ultra-low-power event-driven analog-to-digital converter (ADC) with real-time QRS detection for wearable electrocardiogram (ECG) sensors in wireless body sensor network (WBSN) applications. Two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. The PUT detector achieves 97.63% sensitivity and 97.33% positive prediction in simulation on the MIT-BIH Arrhythmia Database. The t-PUT improves the sensitivity and positive prediction to 97.76% and 98.59% respectively. Fabricated in 0.13 μm CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector.
ZVS phase shift full bridge converter with separated primary winding

Generally additional leakage inductance and two clamp diodes are adopted into the conventional phase shift full bridge (PSFB) converter for reducing the voltage stress of secondary rectifier diodes and extending the range of zero voltage switching (ZVS) operation. However, the core and copper loss caused by additional leakage inductor can be high enough to decrease the whole efficiency of DC/DC converter. Therefore, a new ZVS PSFB converter with separated primary winding (SPW) is proposed. The proposed converter makes both the transformer and additional leakage inductor with same ferrite core by separating the primary winding method. Using this method, leakage inductance is controlled by the winding ratio of SPW. Moreover, by using this integrated magnetic component with single core, size and core loss can be greatly reduced and it results in the improvement of efficiency and power density of DC/DC converter. The operational principle and analysis of proposed converter are presented and verified by the 1.2kW prototype.
Heuristic Search Value Iteration for POMDPs

We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI’s soundness and convergence have been proven. On some benchmark problems from the literature, HSVI displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.
Bidirectional DC-DC converter modeling and unified controller with digital implementation

In this paper, a unified current controller is introduced for a bidirectional dc-dc converter which employs complementary switching between upper and lower switches. The unified current controller is to use one controller for both buck and boost modes. Such a controller may be designed with analog implementation that adopts current injection control method, which is difficult to be implemented in high power applications due to parasitic noises. The averaged current mode is thus proposed in this paper to avoid the current sensing related issues. Additional advantage with the unified digital controller is also found in smooth mode transition between battery charging and discharging modes where conventional analog controller tends to saturate and take a long delay to get out of saturation. The unified controller has been designed based on a proposed novel third- order bidirectional charging/discharging model and implemented with a TMS320F2808 based digital controller. The complete system has been simulated and verified with a high-power hardware prototype testing.
Speed improvement of object recognition using Boundary-Bitmap of histogram of oriented Gradients

Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means that significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. Moreover, some areas of the foreground also can be eliminated from the processing to improve the algorithm speed and memory wise. In Boundary-Bitmap HoG approach proposed in this paper, the boundary of irregular shape of the object is represented by a bitmap to avoid processing of extra background and (partially) foreground pixels. Bitmap, derived from the training dataset, encodes those portions of an image to be used to train a classifier. Experimental results show that not only the proposed algorithm decreases the workload associated with HoG/SVM classifiers by 92.5% compared to the state-of-the-art, but also it shows an average increase about 6% in recall and a decrease about 3% in precision in comparison with standard HoG.