nils-herrmann/clean_scirepeval huggingface dataset:
LTE-advanced in 3GPP Rel -13/14: an evolution toward 5G

As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.
A Novel Phase-Shift Control of Semibridgeless Active Rectifier for Wireless Power Transfer

A novel phase-shift control of a semibridgeless active rectifier (S-BAR) is investigated in order to utilize the S-BAR in wireless energy transfer applications. The standard receiver-side rectifier topology is developed by replacing rectifier lower diodes with synchronous switches controlled by a phase-shifted PWM signal. Theoretical and simulation results show that with the proposed control technique, the output quantities can be regulated without communication between the receiver and transmitter. To confirm the performance of the proposed converter and control, experimental results are provided using 8-, 15-, and 23-cm air gap coreless transformer which has dimension of 76 cm × 76 cm, with 120-V input and the output power range of 0 to 1kW with a maximum efficiency of 94.4%.
Experimental and Numerical Analysis of DC Induction Heating of Aluminum Billets

We have analyzed the characteristics of dc-induction heating of aluminium billets both numerically and experimentally and developed a numerical model for the calculation of the power and temperature distributions inside the billet during the heating. We validated the model by comparison with the experimental results from a laboratory-scale prototype using permanent magnets. We studied the heating process of a real scale system by means of the model, taking into account the weakening of the mechanical properties during the heating. We provide an example of design of an industrial scale dc induction heater.
Texture Mapping for 3D Reconstruction with RGB-D Sensor

Acquiring realistic texture details for 3D models is important in 3D reconstruction. However, the existence of geometric errors, caused by noisy RGB-D sensor data, always makes the color images cannot be accurately aligned onto reconstructed 3D models. In this paper, we propose a global-to-local correction strategy to obtain more desired texture mapping results. Our algorithm first adaptively selects an optimal image for each face of the 3D model, which can effectively remove blurring and ghost artifacts produced by multiple image blending. We then adopt a non-rigid global-to-local correction step to reduce the seaming effect between textures. This can effectively compensate for the texture and the geometric misalignment caused by camera pose drift and geometric errors. We evaluate the proposed algorithm in a range of complex scenes and demonstrate its effective performance in generating seamless high fidelity textures for 3D models.
Development of an efficient fractal based texture analysis technique for improved classification of dermoscopic images

Dermoscopy, a non-invasive imaging technique has been significantly used by the doctors and radiologists for the early diagnosis of the various skin disorders. The characteristically similar nature of the melanocytic skin lesions specifically melanoma and dysplastic nevi make the diagnosis more subjective and time consuming, even for expert clinicians. Computer aided diagnostic system has a great impact on the notable discrimination of two closely similar classes of skin diseases by extracting a large number of effective features. In this reported work fractal geometry has been used for both skin lesion border irregularity measurement and texture features extraction. Here, in fractal based texture analysis technique the dermoscopic images have been decomposed into a set of binary images to extract more effective texture features from different grey regions of the image. From each of the image grey region, some statistical features have been extracted along with the fractal dimension measurement to quantify the intensity variation in that region. In this paper it has been established that the extraction of texture information from different small sub regions of the original image using fractal texture analysis increases the classification performance for both the classes. An analysis of the dependence of the performance of classifier using fractal based texture analysis, on the number of decomposed binary images, has been discussed. The highest classification sensitivity of 93.75% and 91.66% have been achieved for melanoma and dysplastic nevi respectively, using support vector machine (SVM) classifier by extracting fractal based texture features from forty grey scale regions of the dermoscopic images.
Encoder-Decoder Shift-Reduce Syntactic Parsing

Starting from NMT, encoder-decoder neural networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the encoder components achieve state-of-the-art performance on dependency parsing and constituent parsing, respectively. However, there has not been work empirically studying the encoder-decoder neural networks for transition-based parsing. We apply a simple encoder-decoder to this end, achieving comparable results to the parser of Dyer et al. (2015) on standard dependency parsing, and outperforming the parser of Vinyals et al. (2015) on constituent parsing.
Tool for 3D Gazebo Map Construction from Arbitrary Images and Laser Scans

Algorithms for mobile robots, such as navigation, mapping, and SLAM, require proper modelling of environment. Our paper presents an automatic tool that allows creating a realistic 3D-landscape in Gazebo simulation, which is based on the real sensor-based experimental results. The tool provides an occupancy grid map automatic filtering and import to Gazebo framework as a heightmap and enables to configure the settings for created simulation environment. In addition, the tool is capable to create a 3D Gazebo map from any arbitrary image.
Semantic trajectory-based high utility item recommendation system

http://dx.doi.org/10.1016/j.eswa.2014.01.042 0957-4174/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author.  E-mail addresses: jashying@gmail.com (J.-C. Ying), spide0606@gmail.com (H.-S. Chen), linwc@kuas.edu.tw (K.W. Lin), eric@idb.csie.ncku.edu.tw (E.H.-C. Lu), tsengsm@mail.ncku.edu.tw (V.S. Tseng), hwtsai@itri.org.tw (H.-W. Tsai), itriA00432@ itri.org.tw (K.H. Cheng), jason.lin@itri.org.tw (S.-C. Lin). Jia-Ching Ying , Huan-Sheng Chen , Kawuu W. Lin , Eric Hsueh-Chan Lu , Vincent S. Tseng a,⇑, Huan-Wen Tsai , Kuang Hung Cheng , Shun-Chieh Lin
Low-frequency meandering piezoelectric vibration energy harvester

The design, fabrication, and characterization of a novel low-frequency meandering piezoelectric vibration energy harvester is presented. The energy harvester is designed for sensor node applications where the node targets a width-tolength aspect ratio close to 1:1 while simultaneously achieving a low resonant frequency. The measured power output and normalized power density are 118 μW and 5.02 μW/mm3/g2, respectively, when excited by an acceleration magnitude of 0.2 g at 49.7 Hz. The energy harvester consists of a laser-machined meandering PZT bimorph. Two methods, strain-matched electrode (SME) and strain-matched polarization (SMP), are utilized to mitigate the voltage cancellation caused by having both positive and negative strains in the piezoelectric layer during operation at the meander's first resonant frequency. We have performed finite element analysis and experimentally demonstrated a prototype harvester with a footprint of 27 x 23 mm and a height of 6.5 mm including the tip mass. The device achieves a low resonant frequency while maintaining a form factor suitable for sensor node applications. The meandering design enables energy harvesters to harvest energy from vibration sources with frequencies less than 100 Hz within a compact footprint.
From Online Behaviors to Offline Retailing

To combat the ease of online shopping in pajamas, offline mall owners focus increasingly on driving satisfaction and improving retention by identifying customers' preferences. However, most of these studies are based on customers' offline consuming history only. Benefiting from the internet, we can also get customers' online behaviors, such as the search logs, web browsing logs, online shopping logs, and so on. Might these seemingly irrelevant information from two different modalities (i.e. online and offline) be somehow interrelated? How can we make use of the online behaviors and offline actions jointly to promote recommendation for offline retailing? In this study, we formulate this task as a cross-modality recommendation problem, and present its solution via a proposed probabilistic graphical model, called Online-to-Offline Topic Modeling (O2OTM). Specifically, this method explicitly models the relationships between online and offline topics so that the likelihood of both online and offline behaviors is maximized. Then, the recommendation is made only based on the pairs of online and offline topics, denoted by (t,l), with high values of lift, such that the existence of the online topic $t$ greatly increases the response on the corresponding offline topic $l$ compared with the average response for the population without the online topic t. Furthermore, we evaluate this solution in both live and retrospect experiments. The real-world deployment of this model for the anniversary promotion campaign of a famous shopping mall in Beijing shows that our approach increases the occurred customer purchases per promotion message by 29.75\% compared with the baseline. Also, our model finds some interesting interpretable relationships between the online search topics and offline brand topics.
Language Guided Fashion Image Manipulation with Feature-wise Transformations

Developing techniques for editing an outfit image through natural sentences and accordingly generating new outfits has promising applications for art, fashion and design. However, it is considered as a certainly challenging task since image manipulation should be carried out only on the relevant parts of the image while keeping the remaining sections untouched. Moreover, this manipulation process should generate an image that is as realistic as possible. In this work, we propose FiLMedGAN, which leverages feature-wise linear modulation (FiLM) to relate and transform visual features with natural language representations without using extra spatial information. Our experiments demonstrate that this approach, when combined with skip connections and total variation regularization, produces more plausible results than the baseline work, and has a better localization capability when generating new outfits consistent with the target description.
Mining and Analyzing the Future Works in Scientific Articles

Future works in scientific articles are valuable for researchers and they can guide researchers to new research directions or ideas. In this paper, we mine the future works in scientific articles in order to 1) provide an insight for future work analysis and 2) facilitate researchers to search and browse future works in a research area. First, we study the problem of future work extraction and propose a regular expression based method to address the problem. Second, we define four different categories for the future works by observing the data and investigate the multi-class future work classification problem. Third, we apply the extraction method and the classification model to a paper dataset in the computer science field and conduct a further analysis of the future works. Finally, we design a prototype system to search and demonstrate the future works mined from the scientific papers. Our evaluation results show that our extraction method can get high precision and recall values and our classification model can also get good results and it outperforms several baseline models. Further analysis of the future work sentences also indicates interesting results.
On the cognitive process of human problem solving

One of the fundamental human cognitive processes is problem solving. As a higher-layer cognitive process, problem solving interacts with many other cognitive processes such as abstraction, searching, learning, decision making, inference, analysis, and synthesis on the basis of internal knowledge representation by the object–attribute-relation (OAR) model. Problem solving is a cognitive process of the brain that searches a solution for a given problem or finds a path to reach a given goal. When a problem object is identified, problem solving can be perceived as a search process in the memory space for finding a relationship between a set of solution goals and a set of alternative paths. This paper presents both a cognitive model and a mathematical model of the problem solving process. The cognitive structures of the brain and the mechanisms of internal knowledge representation behind the cognitive process of problem solving are explained. The cognitive process is formally described using real-time process algebra (RTPA) and concept algebra. This work is a part of the cognitive computing project that designed to reveal and simulate the fundamental mechanisms and processes of the brain according to Wang’s layered reference model of the brain (LRMB), which is expected to lead to the development of future generation methodologies for cognitive computing and novel cognitive computers that are capable of think, learn, and perceive.
Chapter 18 Building Intelligent Tutoring Systems : An Overview

This chapter addresses the challenge of building or authoring an Intelligent Tutoring System (ITS), along with the problems that have arisen and been dealt with, and the solutions that have been tested. We begin by clarifying what building an ITS entails, and then position today's systems in the overall historical context of ITS research. The chapter concludes with a series of open questions and an introduction to the other chapters in this part of the book.
Machine Translation Evaluation Meets Community Question Answering

We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a pairwise neural network (NN) architecture, which incorporates MTE features, as well as rich syntactic and semantic embeddings, and which efficiently models complex non-linear interactions. The evaluation results show state-of-the-art performance, with sizeable contribution from both the MTE features and from the pairwise NN architecture.
DroNet: Learning to Fly by Driving

Civilian drones are soon expected to be used in a wide variety of tasks, such as aerial surveillance, delivery, or monitoring of existing architectures. Nevertheless, their deployment in urban environments has so far been limited. Indeed, in unstructured and highly dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way. In contrast to traditional “map-localize-plan” methods, this letter explores a data-driven approach to cope with the above challenges. To accomplish this, we propose DroNet: a convolutional neural network that can safely drive a drone through the streets of a city. Designed as a fast eight-layers residual network, DroNet produces two outputs for each single input image: A steering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the UAV recognize dangerous situations and promptly react to them. The challenge is however to collect enough data in an unstructured outdoor environment such as a city. Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for other vehicles or pedestrians moving in the streets. Therefore, we propose to train a UAV from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians. Although trained on city streets from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable. Indeed, it allows a UAV to successfully fly at relative high altitudes and even in indoor environments, such as parking lots and corridors. To share our findings with the robotics community, we publicly release all our datasets, code, and trained networks.
Cannabidiol and (-)Delta9-tetrahydrocannabinol are neuroprotective antioxidants.

The neuroprotective actions of cannabidiol and other cannabinoids were examined in rat cortical neuron cultures exposed to toxic levels of the excitatory neurotransmitter glutamate. Glutamate toxicity was reduced by both cannabidiol, a nonpsychoactive constituent of marijuana, and the psychotropic cannabinoid (-)Delta9-tetrahydrocannabinol (THC). Cannabinoids protected equally well against neurotoxicity mediated by N-methyl-D-aspartate receptors, 2-amino-3-(4-butyl-3-hydroxyisoxazol-5-yl)propionic acid receptors, or kainate receptors. N-methyl-D-aspartate receptor-induced toxicity has been shown to be calcium dependent; this study demonstrates that 2-amino-3-(4-butyl-3-hydroxyisoxazol-5-yl)propionic acid/kainate receptor-type neurotoxicity is also calcium-dependent, partly mediated by voltage sensitive calcium channels. The neuroprotection observed with cannabidiol and THC was unaffected by cannabinoid receptor antagonist, indicating it to be cannabinoid receptor independent. Previous studies have shown that glutamate toxicity may be prevented by antioxidants. Cannabidiol, THC and several synthetic cannabinoids all were demonstrated to be antioxidants by cyclic voltametry. Cannabidiol and THC also were shown to prevent hydroperoxide-induced oxidative damage as well as or better than other antioxidants in a chemical (Fenton reaction) system and neuronal cultures. Cannabidiol was more protective against glutamate neurotoxicity than either ascorbate or alpha-tocopherol, indicating it to be a potent antioxidant. These data also suggest that the naturally occurring, nonpsychotropic cannabinoid, cannabidiol, may be a potentially useful therapeutic agent for the treatment of oxidative neurological disorders such as cerebral ischemia.
Triple-H: A Hybrid Approach to Accelerate HDFS on HPC Clusters with Heterogeneous Storage Architecture

HDFS (Hadoop Distributed File System) is the primary storage of Hadoop. Even though data locality offered by HDFS is important for Big Data applications, HDFS suffers from huge I/O bottlenecks due to the tri-replicated data blocks and cannot efficiently utilize the available storage devices in an HPC (High Performance Computing) cluster. Moreover, due to the limitation of local storage space, it is challenging to deploy HDFS in HPC environments. In this paper, we present a hybrid design (Triple-H) that can minimize the I/O bottlenecks in HDFS and ensure efficient utilization of the heterogeneous storage devices (e.g. RAM, SSD, and HDD) available on HPC clusters. We also propose effective data placement policies to speed up Triple-H. Our design integrated with parallel file system (e.g. Lustre) can lead to significant storage space savings and guarantee fault-tolerance. Performance evaluations show that Triple-H can improve the write and read throughputs of HDFS by up to 7x and 2x, respectively. The execution times of data generation benchmarks are reduced by up to 3x. Our design also improves the execution time of the Sort benchmark by up to 40% over default HDFS and 54% over Lustre. The alignment phase of the Cloudburst application is accelerated by 19%. Triple-H also benefits the performance of SequenceCount and Grep in PUMA [15] over both default HDFS and Lustre.
Mining Social Science Publications for Survey Variables

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding a significant increase in performance over the random baseline.
Assessment of healthcare claims rejection risk using machine learning

Modern healthcare service records, called Claims, record the medical treatments by a Provider (Doctor/Clinic), medication advised etc., along with the charges, and payments to be made by the patient and the Payer (insurance provider). Denial and rejection of healthcare claims is a significant administrative burden and source of loss to various healthcare providers and payers as well. Automating the identification of Claims prone to denial by reason, source, cause and other deciding factors is critical to lowering this burden of rework. We present classification methods based on Machine Learning (ML) to fully automate identification of such claims prone to rejection or denial with high accuracy, investigate the reasons for claims denial and recommend methods to engineer features using Claim Adjustment Reason Codes (CARC) as features with high Information Gain. The ML engine reported is first of its kind in Claims risk identification and represents a novel, significant enhancement to the state of practice of using ML for automating and containing claims denial risks.
Use Cases, Requirements, and Design Considerations for 5G V2X

Ultimate goal of next generation Vehicle-to-everything (V2X) communication systems is enabling accident-free cooperative automated driving that uses the available roadway efficiently. To achieve this goal, the communication system will need to enable a diverse set of use cases, each with a specific set of requirements. We discuss the main use case categories, analyze their requirements, and compare them against the capabilities of currently available communication technologies. Based on the analysis, we identify a gap and point out towards possible system design for 5G V2X that could close the gap. Furthermore, we discuss an architecture of the 5G V2X radio access network that incorporates diverse communication technologies, including current and cellular systems in centimeter wave and millimeter wave, IEEE 802.11p and vehicular visible light communications. Finally, we discuss the role of future 5G V2X systems in enabling more efficient vehicular transportation: from improved traffic flow through reduced inter-vehicle spacing on highways and coordinated intersections in cities (the cheapest way to increasing the road capacity), to automated smart parking (no more visits to the parking!), ultimately enabling seamless end-to-end personal mobility.