nils-herrmann/clean_scirepeval huggingface dataset:
Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.
Personalized point-of-interest recommendation by mining users' preference transition

Location-based social networks (LBSNs) offer researchers rich data to study people's online activities and mobility patterns. One important application of such studies is to provide personalized point-of-interest (POI) recommendations to enhance user experience in LBSNs. Previous solutions directly predict users' preference on locations but fail to provide insights about users' preference transitions among locations. In this work, we propose a novel category-aware POI recommendation model, which exploits the transition patterns of users' preference over location categories to improve location recommendation accuracy. Our approach consists of two stages: (1) preference transition (over location categories) prediction, and (2) category-aware POI recommendation. Matrix factorization is employed to predict a user's preference transitions over categories and then her preference on locations in the corresponding categories. Real data based experiments demonstrate that our approach outperforms the state-of-the-art POI recommendation models by at least 39.75% in terms of recall.
Comparative Study of Air-Cored Axial-Flux Permanent-Magnet Machines With Different Stator Winding Configurations

This paper presents a comprehensive analysis and comparison of air-cored axial-flux permanent-magnet machines with different types of coil configurations. Although coil factor is particularly more sensitive to coil-band width and coil pitch in air-cored machines than conventional slotted machines, remarkably no comprehensive analytical equations exist. Here, new formulas are derived to compare the coil factor of two common concentrated-coil stator winding types. Then, respective coil factors for the winding types are used to determine the torque characteristics and, from that, the optimized coil configurations. Three-dimensional finite-element analysis (FEA) models are built to verify the analytical models. Furthermore, overlapping and wave windings are investigated and compared with the concentrated-coil types. Finally, a prototype machine is designed and built for experimental validations. The results show that the concentrated-coil type with constant coil pitch is superior to all other coil types under study.
Robust Predictive Control for semi-autonomous vehicles with an uncertain driver model

A robust control design is proposed for the lane-keeping and obstacle avoidance of semiautonomous ground vehicles. A robust Model Predictive Controller (MPC) is used in order to enforce safety constraints with minimal control intervention. An uncertain driver model is used to obtain sets of predicted vehicle trajectories in closed-loop with the predicted driver's behavior. The robust MPC computes the smallest corrective steering action needed to keep the driver safe for all predicted trajectories in the set. Simulations of a driver approaching multiple obstacles, with uncertainty obtained from measured data, show the effect of the proposed framework.
Convolutional 2D Knowledge Graph Embeddings

Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models – which potentially limits performance. In this work we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree – which are common in highlyconnected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set – however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets – deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models, and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across all datasets.
A Multi-linear Polarization Reconfigurable Unidirectional Patch Antenna

A novel multi-linear polarization (multi-LP) reconfigurable unidirectional circular patch antenna is proposed, which can switch among four LPs at every 45° rotation. The patch antenna is fed in the center and shorting posts are used to excite TM<sub>11</sub> mode with LP. By controlling the connections between the four shorting posts and the ground plane using p-i-n diodes, four reconfigurable polarization states at <inline-formula> <tex-math notation="LaTeX">$\phi =0 {^{\circ }}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\phi =45 {^{\circ }}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\phi =90 {^{\circ }}$ </tex-math></inline-formula>, or <inline-formula> <tex-math notation="LaTeX">$\phi =135 {^{\circ }}$ </tex-math></inline-formula> can be realized. The size of this antenna is about <inline-formula> <tex-math notation="LaTeX">$0.57\lambda \times 0.57\lambda \times 0.07\lambda $ </tex-math></inline-formula> at 2.45 GHz. It can be easily fabricated and has a simple biasing network. The measured overlapping impedance bandwidth for different polarizations under the condition <inline-formula> <tex-math notation="LaTeX">$|\text{S}11| \leq -10$ </tex-math></inline-formula> dB is from 2.33 to 2.50 GHz, which agrees well with the simulated one. Moreover, the antenna maintains stable radiation patterns and the measured realized gains range from 5.3 to 5.9 dBi.
Interaction primitives for human-robot cooperation tasks

To engage in cooperative activities with human partners, robots have to possess basic interactive abilities and skills. However, programming such interactive skills is a challenging task, as each interaction partner can have different timing or an alternative way of executing movements. In this paper, we propose to learn interaction skills by observing how two humans engage in a similar task. To this end, we introduce a new representation called Interaction Primitives. Interaction primitives build on the framework of dynamic motor primitives (DMPs) by maintaining a distribution over the parameters of the DMP. With this distribution, we can learn the inherent correlations of cooperative activities which allow us to infer the behavior of the partner and to participate in the cooperation. We will provide algorithms for synchronizing and adapting the behavior of humans and robots during joint physical activities.
Virtual View Generation with A Hybrid Camera Array

Virtual view synthesis from an array of cameras has been an essential element of three-dimensional video broadcasting/conferencing. In this paper, we propose a scheme based on a hybrid camera array consisting of four regular video cameras and one time-of-flight depth camera. During rendering, we use the depth image from the depth camera as initialization, and compute a view-dependent scene geometry using constrained plane sweeping from the regular cameras. View-dependent texture mapping is then deployed to render the scene at the desired virtual viewpoint. Experimental results show that the addition of the time-of-flight depth camera greatly improves the rendering quality compared with an array of regular cameras with similar sparsity. In the application of 3D video boardcasting/conferencing, our hybrid camera system demonstrates great potential in reducing the amount of data for compression/streaming while maintaining high rendering quality.
Revisiting IS business value research: what we already know, what we still need to know, and how we can get there

24 September 2009  22 March 2010 2nd  6 December 2010 3rd Revision: 29 July 2011 4th Revision: 5 February 2012  April 2012 6th Revision: 10 June 2012  29 August 2012  The business value of investments in Information Systems (IS) has been, and is predicted to remain, one of the major research topics for IS researchers. While the vast majority of research papers on IS business value find empirical evidence in favour of both the operational and strategic relevance of IS, the fundamental question of the causal relationship between IS investments and business value remains partly unexplained. Three research tasks are essential requisites on the path towards addressing this epistemological question: the synthesis of existing knowledge, the identification of a lack of knowledge and the proposition of paths for closing the knowledge gaps. This paper considers each of these tasks. Research findings include that correlations between IS investments and productivity vary widely among companies and that the mismeasurement of IS investment impact may be rooted in delayed effects. Key limitations of current research are based on the ambiguity and fuzziness of IS business value, the neglected disaggregation of IS investments, and the unexplained process of creating internal and competitive value. Addressing the limitations we suggest research paths, such as the identification of synergy opportunities of IS assets, and the explanation of relationships between IS innovation and change in IS capabilities.  (2013) 22, 139–169. doi:10.1057/ejis.2012.45; published online 13 November 2012
A Low-Profile Wideband Circularly Polarized Crossed-Dipole Antenna With Wide Axial-Ratio and Gain Beamwidths

A low-profile wideband circularly polarized (CP) crossed-dipole antenna that has both wide axial-ratio beamwidth (ARBW) and half-power beamwidth (HPBW) is presented in this paper. The crossed dipoles are composed of four trapezoidal patch arms, and they are fed by a pair of vacant-quarter printed rings to generate CP radiation. Four identical parasitic elements that consist of a horizontal triangle patch and a vertical metallic plate are symmetrically intervened between the crossed dipoles and the ground plane. It has been found that the parasitic elements can effectively decrease the antenna profile, increase the operating bandwidth, and simultaneously enhance the ARBW as well as the HPBW. A prototype was fabricated and measured to verify the design. The measured results show that the prototype has a low profile of  $0.1\lambda _{0}$ , a −10 dB impedance bandwidth of 78.3% and a 3 dB AR bandwidth of 63.4%. Moreover, a 3 dB ARBW of more than 120° and an HPBW of more than 110° are achieved simultaneously within a wide passband of 50.7%.
Facial Expression Recognition in Video with Multiple Feature Fusion

Video based facial expression recognition has been a long standing problem and attracted growing attention recently. The key to a successful facial expression recognition system is to exploit the potentials of audiovisual modalities and design robust features to effectively characterize the facial appearance and configuration changes caused by facial motions. We propose an effective framework to address this issue in this paper. In our study, both visual modalities (face images) and audio modalities (speech) are utilized. A new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG-TOP) is proposed to extract dynamic textures from video sequences to characterize facial appearance changes. And a new effective geometric feature derived from the warp transformation of facial landmarks is proposed to capture facial configuration changes. Moreover, the role of audio modalities on recognition is also explored in our study. We applied the multiple feature fusion to tackle the video-based facial expression recognition problems under lab-controlled environment and in the wild, respectively. Experiments conducted on the extended Cohn-Kanade (CK+) database and the Acted Facial Expression in Wild (AFEW) 4.0 database show that our approach is robust in dealing with video-based facial expression recognition problems under lab-controlled environment and in the wild compared with the other state-of-the-art methods.
Deep Heuristic-learning in the Rubik ’ s Cube Domain : an Experimental Evaluation

Recent successes of neural networks in solving combinatorial problems and games like Go, Poker and others inspire further attempts to use deep learning approaches in discrete domains. In the field of automated planning, the most popular approach is informed forward search driven by a heuristic function which estimates the quality of encountered states. Designing a powerful and easily-computable heuristics however is still a challenging problem on many domains. In this paper, we use machine learning to construct such heuristic automatically. We train a neural network to predict a minimal number of moves required to solve a given instance of Rubik’s cube. We then use the trained network as a heuristic distance estimator with a standard forward-search algorithm and compare the results with other heuristics. Our experiments show that the learning approach is competitive with state-of-the-art and might be the best choice in some use-case scenarios.
WikiCoref: An English Coreference-annotated Corpus of Wikipedia Articles

This paper presents WikiCoref, an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia. Our annotation scheme follows the one of OntoNotes with a few disparities. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Since most similar annotation efforts concentrate on very specific types of written text, mainly newswire, there is a lack of resources for otherwise over-used Wikipedia texts. The corpus described in this paper addresses this issue. We present a freely available resource we initially devised for improving coreference resolution algorithms dedicated to Wikipedia texts. Our corpus has no restriction on the topics of the documents being annotated, and documents of various sizes have been considered for annotation.
A Novel Broadband Circularly Polarized Monopole Antenna Based on C-Shaped Radiator

The design and implementation of a compact monopole antenna with broadband circular polarization is presented in this letter. The proposed antenna consists of a simple C-shaped patch and a modified ground plane with the overall size of 0.33 λ × 0.37 λ. By properly embedding a slit in the C-shaped patch and improving the ground plane with two triangular stubs, the measured broadband 3-dB axial-ratio bandwidth of 104.7% (2.05–6.55 GHz) is obtained, while the measured impedance bandwidth of 106.3% (2.25–7.35 GHz), defined by –10-dB return loss, is achieved. The performance for different parameters is analyzed. The proposed antenna is a good candidate for the application of various wireless communication systems.
Automatic detection of rumor on Sina Weibo

The problem of gauging information credibility on social networks has received considerable attention in recent years. Most previous work has chosen Twitter, the world's largest micro-blogging platform, as the premise of research. In this work, we shift the premise and study the problem of information credibility on Sina Weibo, China's leading micro-blogging service provider. With eight times more users than Twitter, Sina Weibo is more of a Facebook-Twitter hybrid than a pure Twitter clone, and exhibits several important characteristics that distinguish it from Twitter. We collect an extensive set of microblogs which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Sina Weibo. Unlike previous studies on Twitter where the labeling of rumors is done manually by the participants of the experiments, the official nature of this service ensures the high quality of the dataset. We then examine an extensive set of features that can be extracted from the microblogs, and train a classifier to automatically detect the rumors from a mixed set of true information and false information. The experiments show that some of the new features we propose are indeed effective in the classification, and even the features considered in previous studies have different implications with Sina Weibo than with Twitter. To the best of our knowledge, this is the first study on rumor analysis and detection on Sina Weibo.
A survey of RDB to RDF translation approaches and tools

Relational databases scattered over the web are generally opaque to regular web crawling tools. To address this concern, many RDB-to-RDF approaches have been proposed over the last years. In this paper, we propose a detailed review of seventeen RDB-to-RDF initiatives, considering end-to-end projects that delivered operational tools. The different tools are classified along three major axes: mapping description language, mapping implementation and data retrieval method. We analyse the motivations, commonalities and differences between existing approaches. The expressiveness of existing mapping languages is not always sufficient to produce semantically rich data and make it usable, interoperable and linkable. We therefore briefly present various strategies investigated in the literature to produce additional knowledge. Finally, we show that R2RML, the W3C recommendation for describing RDB to RDF mappings, may not apply to all needs in the wide scope of RDB to RDF translation applications, leaving space for future extensions.
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.
An extensive review of research in swarm robotics

Swarm robotics is a new approach to the coordination of multi-robot systems which consist of large numbers of relatively simple robots which takes its inspiration from social insects. The most remarkable characteristic of swarm robots are the ability to work cooperatively to achieve a common goal. In this paper, classification of existing researches, problems and algorithms aroused in the study of swarm robotics are presented. The existing studies are classified into major areas and relevant sub-categories in the major areas.
Sparse coding with memristor networks.

Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.
A 160 $\mu{\rm A}$ Biopotential Acquisition IC With Fully Integrated IA and Motion Artifact Suppression

This paper proposes a 3-channel biopotential monitoring ASIC with simultaneous electrode-tissue impedance measurements which allows real-time estimation of motion artifacts on each channel using an an external μC. The ASIC features a high performance instrumentation amplifier with fully integrated sub-Hz HPF rejecting rail-to-rail electrode-offset voltages. Each readout channel further has a programmable gain amplifier and programmable 4th order low-pass filter. Time-multiplexed 12 b SAR-ADCs are used to convert all the analog data to digital. The ASIC achieves >; 115 dB of CMRR (at 50/60 Hz), a high input impedance of >; 1 GΩ and low noise (1.3 μVrms in 100 Hz). Unlike traditional methods, the ASIC is capable of actual motion artifact suppression in the analog domain before final amplification. The complete ASIC core operates from 1.2 V with 2 V digital IOs and consumes 200 μW when all 3 channels are active.
Using Network Knowledge to Improve Workload Performance in Virtualized Data Centers

The scale and expense of modern data centers motivates running them as efficiently as possible. This paper explores how virtualized data center performance can be improved when network traffic and topology data informs VM placement. Our practical heuristics, tested on network-heavy, scale-out workloads in an 80 server cluster, improve overall performance by up to 70% compared to random placement in a multi-tenant configuration.