nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing LTE-Advanced network performance using active antenna systems

This work exploits the benefits of adaptive downtilt and vertical sectorization schemes for Long Term Evolution Advanced (LTE-A) networks equipped with active antenna systems (AAS). We highlight how the additional control in the elevation domain (via AAS) enables use of adaptive downtilt and vertical sectorization techniques, thereby improving system spectrum efficiency. Our results, based on a full 3 dimensional (3D) channel, demonstrate that adaptive downtilt achieves up to 11% cell edge and 5% cell average spectrum efficiency gains when compared to a baseline system utilizing fixed downtilt, without the need for complex coordination among cells. In addition, vertical sectorization, especially high-order vertical sectorization utilizing multiple vertical beams, which increases spatial reuse of time and frequency resources, is shown to provide even higher performance gains.
3D printed compact dual-polarized wideband antenna

A novel compact dual-polarized unidirectional wideband antenna based on two crossed magneto-electric dipoles is proposed. The proposed miniaturization method consist in transforming the electrical filled square dipoles into vertical folded square loops. The surface of the radiating element is reduced to 0.23λ0∗0.23λ0, where λ0 is the wavelength at the lowest operation frequency for a standing wave ratio (SWR) <2.5, which corresponds to a reduction factor of 48%. The antenna has been prototyped using 3D printing technology. The measured input impedance bandwidth is 51.2% from 1.7 GHz to 2.9 GHz with a Standing wave ratio (SWR) <2.
CNN Based Transfer Learning for Historical Chinese Character Recognition

Historical Chinese character recognition has been suffering from the problem of lacking sufficient labeled training samples. A transfer learning method based on Convolutional Neural Network (CNN) for historical Chinese character recognition is proposed in this paper. A CNN model L is trained by printed Chinese character samples in the source domain. The network structure and weights of model L are used to initialize another CNN model T, which is regarded as the feature extractor and classifier in the target domain. The model T is then fine-tuned by a few labeled historical or handwritten Chinese character samples, and used for final evaluation in the target domain. Several experiments regarding essential factors of the CNNbased transfer learning method are conducted, showing that the proposed method is effective.
Scaper: A library for soundscape synthesis and augmentation

Sound event detection (SED) in environmental recordings is a key topic of research in machine listening, with applications in noise monitoring for smart cities, self-driving cars, surveillance, bioa-coustic monitoring, and indexing of large multimedia collections. Developing new solutions for SED often relies on the availability of strongly labeled audio recordings, where the annotation includes the onset, offset and source of every event. Generating such precise annotations manually is very time consuming, and as a result existing datasets for SED with strong labels are scarce and limited in size. To address this issue, we present Scaper, an open-source library for soundscape synthesis and augmentation. Given a collection of iso-lated sound events, Scaper acts as a high-level sequencer that can generate multiple soundscapes from a single, probabilistically defined, “specification”. To increase the variability of the output, Scaper supports the application of audio transformations such as pitch shifting and time stretching individually to every event. To illustrate the potential of the library, we generate a dataset of 10,000 sound-scapes and use it to compare the performance of two state-of-the-art algorithms, including a breakdown by soundscape characteristics. We also describe how Scaper was used to generate audio stimuli for an audio labeling crowdsourcing experiment, and conclude with a discussion of Scaper's limitations and potential applications.
Page As You Go: Piecewise Columnar Access In SAP HANA

In-memory columnar databases such as SAP HANA achieve extreme performance by means of vector processing over logical units of main memory resident columns. The core in-memory algorithms can be challenged when the working set of an application does not fit into main memory. To deal with memory pressure, most in-memory columnar databases evict candidate columns (or tables) using a set of heuristics gleaned from recent workload. As an alternative approach, we propose to reduce the unit of load and eviction from column to a contiguous portion of the in-memory columnar representation, which we call a page. In this paper, we adapt the core algorithms to be able to operate with partially loaded columns while preserving the performance benefits of vector processing. Our approach has two key advantages. First, partial column loading reduces the mandatory memory footprint for each column, making more memory available for other purposes. Second, partial eviction extends the in-memory lifetime of partially loaded column. We present a new in-memory columnar implementation for our approach, that we term page loadable column. We design a new persistency layout and access algorithms for the encoded data vector of the column, the order-preserving dictionary, and the inverted index. We compare the performance attributes of page loadable columns with those of regular in-memory columns and present a use-case for page loadable columns for cold data in data aging scenarios. Page loadable columns are completely integrated in SAP HANA, and we present extensive experimental results that quantify the performance overhead and the resource consumption when these columns are deployed.
Grid Array Antennas With Subarrays and Multiple Feeds for 60-GHz Radios

A grid array antenna is presented in this paper with sub grid arrays and multiple feed points, showing enhanced radiation characteristics and sufficient design flexibility. For instance, the grid array antenna can be easily designed as a linearly- or circularly-polarized, unbalanced or balanced antenna. A design example is given for a linearly-polarized unbalanced grid array antenna in Ferro A6M low temperature co-fired ceramic technology for 60-GHz radios to operate from 57 to 66 GHz (≈ 14.6% at 61.5 GHz ). It consists of 4 sub grid arrays and 4 feed points that are connected to a single-ended 50-Ω source by a quarter-wave matched T-junction network. The simulated results indicate that the grid array antenna has the maximum gain of 17.7 dBi at 59 GHz , an impedance bandwidth (|S11| ≤ -10&nbsp;dB) nearly from 56 to 67.5 GHz (or 18.7%), a 3-dB gain bandwidth from 55.4 to 66 GHz (or 17.2%), and a vertical beam bandwidth in the broadside direction from 57 to 66 GHz (14.6%). The measured results are compared with the simulated ones. Discrepancies and their causes are identified with a tolerance analysis on the fabrication process.
Empirical Study of Artificial Fish Swarm Algorithm

Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In standard AFSA, these two parameters remain constant until the algorithm termination. Large values of these parameters increase the capability of algorithm in global search, while small values improve the local search ability of the algorithm. In this paper, we empirically study the performance of the AFSA and different approaches to balance between local and global exploration have been tested based on the adaptive modification of visual and step during algorithm execution. The proposed approaches have been evaluated based on the four well-known benchmark functions. Experimental results show considerable positive impact on the performance of AFSA.
Online passive learning of timed automata for cyber-physical production systems

Model-based approaches are very often used for diagnosis in production systems. And since the manual creation of behavior models is a tough task, many learning algorithms have been constructed for the automatic model identification. Most of them are tested and evaluated on artificial datasets on personal computers only. However, the implementation on cyber-physical production systems puts additional requirements on learning algorithms, for instance the real-time aspect or the usage of memory space. This paper analyzes the requirements on learning algorithms for cyber-physical production systems and presents an appropriate online learning algorithm, the Online Timed Automaton Learning Algorithm, OTALA. It is the first online passive learning algorithm for timed automata which in addition copes without negative learning examples. An analysis of the algorithm and comparison with offline learning algorithms completes this contribution.
Emotion Distribution Recognition from Facial Expressions

Most existing facial expression recognition methods assume the availability of a single emotion for each expression in the training set. However, in practical applications, an expression rarely expresses pure emotion, but often a mixture of different emotions. To address this problem, this paper deals with a more common case where multiple emotions are associated to each expression. The key idea is to learn the specific description degrees of all basic emotions for each expression and the mapping from the expression images to the emotion distributions by the proposed emotion distribution learning (EDL) method.The databases used in the experiments are the s-JAFFE database and the s-BU\_3DFE database as they are the databases with explicit scores for each emotion on each expression image. Experimental results show that EDL can effectively deal with the emotion distribution recognition problem and perform remarkably better than the state-of-the-art multi-label learning methods.
3D object classification using salient point patterns with application to craniofacial research

This paper presents a new 3D shape representation and classification methodology developed for use in craniofacial dysmorphology studies. The methodology computes low-level features at each point of a 3D mesh representation, aggregates the features into histograms over mesh neighborhoods, learns the characteristics of salient point histograms for each particular application, and represents the points in a 2D spatial map based on a longitude–latitude transformation. Experimental results on the medical classification tasks show that our methodology achieves higher classification accuracy compared to medical experts and existing state-of-the-art 3D descriptors. Additional experimental results highlight the strength and advantage of the flexible framework that allows the methodology to generalize from specific medical classification tasks to general 3D object classification tasks. & 2009 Elsevier Ltd. All rights reserved.
A novel way to Soccer Match Prediction

Our hypothesis is that the video game industry, in the attempt to simulate a realistic experience, has inadvertently collected very accurate data which can be used to solve problems in the real world. In this paper we describe a novel approach to soccer match prediction that makes use of only virtual data collected from a video game(FIFA 2015). Our results were comparable and in some places better than results achieved by predictors that used real data. We also use the data provided for each player and the players present in the squad, to analyze the team strategy. Based on our analysis, we were able to suggest better strategies for weak teams
Tourist Trip Planning Functionalities: State-of-the-Art and Future

When tourists visit a city or region, they cannot visit every point of interest available, as they are constrained in time and budget. Tourist recommender applications help tourists by presenting a personal selection. Providing adequate tour scheduling support for these kinds of applications is a daunting task for the application developer. The objective of this paper is to demonstrate how existing models from the field of Operations Research (OR) fit this scheduling problem, and enable a wide range of tourist trip planning functionalities. Using the Orienteering Problem (OP) and its extensions to model the tourist trip planning problem, allows to deal with a vast number of practical planning problems.
Iconoscope: Designing a Game for Fostering Creativity

This paper introduces Iconoscope, a game aiming to foster the creativity of a young target audience in formal or informal educational settings. At the core of the Iconoscope design is the creative, playful interpretation of word-concepts via the construction of visual icons. In addition to that, the game rewards ambiguity via a scoring system which favors icons that dichotomize public opinion. The game is played by a group of players, with each player attempting to guess which of the concepts provided by the system is represented by each opponent’s created icon. Through the social interaction that emerges, Iconoscope prompts co-creativity within a group of players; in addition, the game offers the potential of human-machine co-creativity via computer-generated suggestions to the player’s icon. Experiments with early prototypes, described in this paper, provide insight into the design process and motivate certain decisions taken for the current version of Iconoscope which, at the time of writing, is being evaluated in selected schools in Greece, Austria and the United Kingdom.
Neurobiology of Decision Making: A Selective Review from a Neurocognitive and Clinical Perspective

We present a temporal map of key processes that occur during decision making, which consists of three stages: 1) formation of preferences among options, 2) selection and execution of an action, and 3) experience or evaluation of an outcome. This framework can be used to integrate findings of traditional choice psychology, neuropsychology, brain lesion studies, and functional neuroimaging. Decision making is distributed across various brain centers, which are differentially active across these stages of decision making. This approach can be used to follow developmental trajectories of the different stages of decision making and to identify unique deficits associated with distinct psychiatric disorders.
Robust Hash for Detecting and Localizing Image Tampering

An image hash should be (1) robust to allowable operations and (2) sensitive to illegal manipulations and distinct queries. Some applications also require the hash to be able to localize image tampering. This requires the hash to contain both robust content and alignment information to meet the above criterion. Fulfilling this is difficult because of two contradictory requirements. First, the hash should be small and second, to verify authenticity and then localize tampering, the amount of information in the hash about the original required would be large. Hence a tradeoff between these requirements needs to be found. This paper presents an image hashing method that addresses this concern, to not only detect but also localize tampering using a small signature (< 1kB). Illustrative experiments bring out the efficacy of the proposed method compared to existing methods.
Goal commitment and the goal-setting process: conceptual clarification and empirical synthesis.

Goals are central to current treatments of work motivation, and goal commitment is a critical construct in understanding the relationship between goals and task performance. Despite this importance, there is confusion about the role of goal commitment and only recently has this key construct received the empirical attention it warrants. This meta-analysis, based on 83 independent samples, updates the goal commitment literature by summarizing the accumulated evidence on the antecedents and consequences of goal commitment. Using this aggregate empirical evidence, the role of goal commitment in the goal-setting process is clarified and key areas for future research are identified.
Attachment security in couple relationships: a systemic model and its implications for family dynamics.

Theory and research on adult attachment style emphasize the crucial role that the sense of attachment security plays in the formation and maintenance of couple relationships. In the present article, we review studies that have examined the contribution of adult attachment style to relational cognitions, emotions, and behaviors as well as to the formation, quality, and stability of dating and marital relationships. We discuss some of the measurement and design issues raised by this research. Based on the reviewed findings, we provide an integrative, systemic theoretical model delineating how the links between partners' attachment security and the quality of their couple relationship occurs. Finally, we discuss the implications of this model for the understanding of how attachment style and couple relationships combine to affect the family system in general, and parent-child relationships and children's developmental outcomes, in particular.
Compact Printed Quadrifilar Helical Antenna With Iso-Flux-Shaped Pattern and High Cross-Polarization Discrimination

In this letter, a technique for the miniaturization of conventional printed quadrifilar helical antennas using simple sinusoidal profiles is described and supported by experimental results. The challenge is to reduce the size of the antenna while maintaining iso-flux radiation patterns and high cross-polarization discrimination. The miniaturization design approach simulation and measurement results are given in the case of a specific design of L-band telemetry link antenna for meteorological stratospheric balloon payloads, but the achieved results can be extended to any quadrifilar helical antenna with shaped pattern. Size reduction close to 50% is reached while preserving major radiation pattern characteristics and good axial-ratio performance over a wide angular range.
MMD GAN: Towards Deeper Understanding of Moment Matching Network

Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performance of GMMN is still not as competitive as that of GAN on challenging and large benchmark datasets. The computational efficiency of GMMN is also less desirable in comparison with GAN, partially due to its requirement for a rather large batch size during the training. In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN. The new approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN. The new distance measure in MMD GAN is a meaningful loss that enjoys the advantage of weak⇤ topology and can be optimized via gradient descent with relatively small batch sizes. In our evaluation on multiple benchmark datasets, including MNIST, CIFAR-10, CelebA and LSUN, the performance of MMD GAN significantly outperforms GMMN, and is competitive with other representative GAN works.
ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture

The inversion of remote sensing images is crucial for soil moisture mapping in precision agriculture. However, the large size of remote sensing images complicates their management. Therefore, this study proposes a remote sensing observation sharing method based on cloud computing (ROSCC) to enhance remote sensing observation storage, processing, and service capability. The ROSCC framework consists of a cloud computing-enabled sensor observation service, web processing service tier, and a distributed database tier. Using MongoDB as the distributed database and Apache Hadoop as the cloud computing service, this study achieves a high-throughput method for remote sensing observation storage and distribution. The map, reduced algorithms and the table structure design in distributed databases are then explained. Along the Yangtze River, the longest river in China, Hubei Province was selected as the study area to test the proposed framework. Using GF-1 as a data source, an experiment was performed to enhance earth observation data (EOD) storage and achieve large-scale soil moisture mapping. The proposed ROSCC can be applied to enhance EOD sharing in cloud computing context, so as to achieve soil moisture mapping via the modified perpendicular drought index in an efficient way to better serve precision agriculture.
Improving Language Understanding by Generative Pre-Training

Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant, labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures specifically crafted for each task, significantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI).
Display supersampling

Supersampling is widely used by graphics hardware to render anti-aliased images. In conventional supersampling, multiple scene samples are computationally combined to produce a single screen pixel. We consider a novel imaging paradigm that we call display supersampling, where multiple display samples are physically combined via the superimposition of multiple image subframes. Conventional anti-aliasing and texture mapping techniques are shown inadequate for the task of rendering high-quality images on supersampled displays. Instead of requiring anti-aliasing filters, supersampled displays actually require alias generation filters to cancel the aliasing introduced by nonuniform sampling. We present fundamental theory and efficient algorithms for the real-time rendering of high-resolution anti-aliased images on supersampled displays. We show that significant image quality gains are achievable by taking advantage of display supersampling. We prove that alias-free resolution beyond the Nyquist limits of a single subframe may be achieved by designing a bank of alias-canceling rendering filters. In addition, we derive a practical noniterative filter bank approach to real-time rendering and discuss implementations on commodity graphics hardware.