nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Augmented Reality Survey: From Where We Are to Where We Go

The boom in the capabilities and features of mobile devices, like smartphones, tablets, and wearables, combined with the ubiquitous and affordable Internet access and the advances in the areas of cooperative networking, computer vision, and mobile cloud computing transformed mobile augmented reality (MAR) from science fiction to a reality. Although mobile devices are more constrained computationalwise from traditional computers, they have a multitude of sensors that can be used to the development of more sophisticated MAR applications and can be assisted from remote servers for the execution of their intensive parts. In this paper, after introducing the reader to the basics of MAR, we present a categorization of the application fields together with some representative examples. Next, we introduce the reader to the user interface and experience in MAR applications and continue with the core system components of the MAR systems. After that, we discuss advances in tracking and registration, since their functionality is crucial to any MAR application and the network connectivity of the devices that run MAR applications together with its importance to the performance of the application. We continue with the importance of data management in MAR systems and the systems performance and sustainability, and before we conclude this survey, we present existing challenging problems.
[New drugs of abuse on the Web: the role of the Psychonaut Web Mapping Project].

In the rapid change of drug scenarios, as the powerful development in the drug market, particularly in the number and the kind of the compound available, Internet plays a dominant role to become one of the major "drug market". The European Commission funded the Psychonaut Web Mapping Project (carried out in the time-frame January 2008-December 2009), with the aim to start/implement an Early Warning System (through the data/information collected from the Web virtual market), to identify and categorise novel recreational drugs/psychoactive compounds (synthetical/herbal drugs), and new trends in drug use to provide information for immediate and prevention intervention. The Psychonaut is a multi-site research project involving 8 research centres (De Sleutel, Belgium; University of Hertfordshire School of Pharmacy, St George's University of London, England; A-klinikkasäätiö, Finlandia; Klinik für Psychiatrie und Psychotherapie, Germany; Assessorato Salute Regione Marche, Italy; Drug Abuse Unit, Spain; Centre of Competence Bergen Clinics Foundation, Norway) based in 7 European Countries (England, Italy, Belgium, Finland, Germany, Spain, Norway).
Bidirectional PWM Converter Integrating Cell Voltage Equalizer Using Series-Resonant Voltage Multiplier for Series-Connected Energy Storage Cells

In conventional energy storage systems using series-connected energy storage cells such as lithium-ion battery cells and supercapacitors (SCs), an interface bidirectional converter and cell voltage equalizer are separately required to manage charging/discharging and ensure years of safe operation. In this paper, a bidirectional PWM converter integrating cell voltage equalizer is proposed. This proposed integrated converter can be derived by combining a traditional bidirectional PWM converter and series-resonant voltage multiplier (SRVM) that functionally operates as an equalizer and is driven by asymmetric square wave voltage generated at the switching node of the converter. The converter and equalizer can be integrated into a single unit without increasing the switch count, achieving not only system-level but also circuit-level simplifications. Open-loop control is feasible for the SRVM when operated in discontinuous conduction mode, meaning the proposed integrated converter can operate similarly to conventional bidirectional converters. An experimental charge-discharge cycling test for six SCs connected in series was performed using the proposed integrated converter. The cell voltage imbalance was gradually eliminated by the SRVM while series-connected SCs were cycled by the bidirectional converter. All the cell voltages were eventually unified, demonstrating the integrated functions of the proposed converter.
A peek into the future: predicting the evolution of popularity in user generated content

Content popularity prediction finds application in many areas, including media advertising, content caching, movie revenue estimation, traffic management and macro-economic trends forecasting, to name a few. However, predicting this popularity is difficult due to, among others, the effects of external phenomena, the influence of context such as locality and relevance to users,and the difficulty of forecasting information cascades.
 In this paper we identify patterns of temporal evolution that are generalisable to distinct types of data, and show that we can (1) accurately classify content based on the evolution of its popularity over time and (2) predict the value of the content's future popularity. We verify the generality of our method by testing it on YouTube, Digg and Vimeo data sets and find our results to outperform the K-Means baseline when classifying the behaviour of content and the linear regression baseline when predicting its popularity.
Artificial Neural Networks: A Tutorial

Numerous eeorts have been made in developing \intelligent" programs based on the Von Neumann's centralized architecture. However, these eeorts have not been very successful in building general-purpose intelligent systems. Inspired by biological neural networks, researchers in a number of scientiic disciplines are designing artiicial neural networks (ANNs) to solve a variety of problems in decision making, optimization , prediction, and control. Artiicial neural networks can be viewed as parallel and distributed processing systems which consist of a huge number of simple and massively connected processors. There has been a resurgence of interest in the eld of ANNs for several years. This article intends to serve as a tutorial for those readers with little or no knowledge about ANNs to enable them to understand the remaining articles of this special issue. We discuss the motivations behind developing ANNs, basic network models, and two main issues in designing ANNs: network architecture and learning process. We also present one of the most successful application of ANNs, namely automatic character recognition.
A novel CNTFET-based ternary logic gate design

This paper presents a novel design of ternary logic inverters using carbon nanotube FETs (CNTFETs). Multiple-valued logic (MVL) circuits have attracted substantial interest due to the capability of increasing information content per unit area. In the past extensive design techniques for MVL circuits (especially ternary logic inverters) have been proposed for implementation in CMOS technology. In CNTFET device, the threshold voltage of the transistor can be controlled by controlling the chirality vector (i.e. the diameter); in this paper this feature is exploited to design ternary logic inverters. New designs are proposed and compared with existing CNTFET-based designs. Extensive simulation results using SPICE demonstrate that power delay product is improved by 300% comparing to the conventional ternary gate design.
Echo Chamber: A Persuasive Game on Climate Change Rhetoric

Echo Chamber is a game that persuades players to re-examine their argumentation style and adopt new rhetorical techniques procedurally delivered through gameplay. Several games have been made addressing the environmental impacts of climate change; none have examined the gap between scientific and public discourse over climate change, and our goal was to teach players more effective communication techniques for conveying climate change in public venues. Our game provides other developers insight into persuasion through game mechanics with good design practices for similar persuasive games.
Single-image super-resolution of brain MR images using overcomplete dictionaries

Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.
A Compact Wideband SIW-Fed Dielectric Antenna With End-Fire Radiation Pattern

In this communication, a triangular-shaped dielectric antenna fed by substrate-integrated waveguide (SIW) is proposed and researched. The effect of the extended substrate's length on performance of the proposed antenna is first researched. In order to reduce sidelobe level (SLL) at high frequency as well as increase the adjustability of the proposed antenna while maintaining its planar structure, air vias are periodically perforated into the loaded substrate to modify the permittivity. The variation trend of modifying permittivity with changing performance of the proposed antenna is studied, followed by analyzing function of the transition stage(s) of the extended substrate. Through optimizing the dielectric length and modifying the diameters of air vias to change the permittivity, the proposed antenna with wide operating bandwidth and low SLL can be realized. Measured results indicate that the proposed antenna works from 17.6 to 26.7 GHz, which almost covers the whole K band. Besides, stable end-fire radiation patterns in the whole operating band are obtained. Moreover, at least 8.3-dBi peak gains with low SLL are achieved as well.
The need for multivocal literature reviews in software engineering: complementing systematic literature reviews with grey literature

Systematic Literature Reviews (SLR) may not provide insight into the "state of the practice" in SE, as they do not typically include the "grey" (non-published) literature. A Multivocal Literature Review (MLR) is a form of a SLR which includes grey literature in addition to the published (formal) literature. Only a few MLRs have been published in SE so far. We aim at raising the awareness for MLRs in SE by addressing two research questions (RQs): (1) What types of knowledge are missed when a SLR does not include the multivocal literature in a SE field? and (2) What do we, as a community, gain when we include the multivocal literature and conduct MLRs? To answer these RQs, we sample a few example SLRs and MLRs and identify the missing and the gained knowledge due to excluding or including the grey literature. We find that (1) grey literature can give substantial benefits in certain areas of SE, and that (2) the inclusion of grey literature brings forward certain challenges as evidence in them is often experience and opinion based. Given these conflicting viewpoints, the authors are planning to prepare systematic guidelines for performing MLRs in SE.
Health dialog systems for patients and consumers

There is a growing need for automated systems that can interview patients and consumers about their health and provide health education and behavior change interventions using natural language dialog. A number of these health dialog systems have been developed over the last two decades, many of which have been formally evaluated in clinical trials and shown to be effective. This article provides an overview of the theories, technologies and methodologies that are used in the construction and evaluation of these systems, along with a description of many of the systems developed and tested to date. The strengths and weaknesses of these approaches are also discussed, and the needs for future work in the field are delineated.
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Deep learning models, which learn high-level feature representations from raw data, have become popular for machine learning and artificial intelligence tasks that involve images, audio, and other forms of complex data. A number of software “frameworks” have been developed to expedite the process of designing and training deep neural networks, such as Caffe [11], Torch [4], and Theano . Currently, these frameworks can harness multiple GPUs on the same machine, but are unable to use GPUs that are distributed across multiple machines; because even average-sized deep networks can take days to train on a single GPU when faced with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up deep learning. However, the limited inter-machine bandwidth available on commodity Ethernet networks presents a bottleneck to distributed GPU training, and prevents its trivial realization. To investigate how existing software frameworks can be adapted to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing deep learning frameworks. In order to assess Poseidon’s effectiveness, we integrate Poseidon into the Caffe [11] framework and evaluate its performance at training convolutional neural networks for object recognition in images. Poseidon features three key contributions that improve the training speed of deep neural networks on clusters: (i) a three-level hybrid architecture that allows Poseidon to support both CPU-only clusters as well as GPU-equipped clusters, (ii) a distributed wait-free backpropagation (DWBP) algorithm to improve GPU utilization and to balance communication, and (iii) a dedicated structure-aware communication protocol (SACP) to minimize communication overheads. We empirically show that Poseidon converges to the same objective value as a single machine, and achieves state-of-the-art training speedup across multiple models and well-established datasets, using a commodity GPU cluster of 8 nodes (e.g. 4.5× speedup on AlexNet, 4× on GoogLeNet, 4× on CIFAR-10). On the much larger ImageNet 22K dataset, Poseidon with 8 nodes achieves better speedup and competitive accuracy to recent CPU-based distributed deep learning systems such as Adam  and Le et al. , which use 10s to 1000s of nodes.
An Empirical Study of Online Word of Mouth as a Predictor for Multi-product Category e-Commerce Sales

The ability to exchange opinions and experiences online is known as online word of mouth (WOM) and has been shown in the literature to have the potential to impact e-commerce sales. The purpose of this paper is to expand previous findings by empirically evaluating the impact of online WOM attributes and other related factors (e.g. product views, promotion, and category) on e-commerce sales using real data from a multi-product retail e-commerce firm. Research has previously shown that the introduction of online WOM on a retail e-commerce site can positively impact product sales. We propose and validate a conceptual model of online WOM and its impact on product sales and the impact of moderator variables such as promotion, product category and product views. It is our conclusion that previous research on online WOM has been limited as our research empirically demonstrates the conclusion that it is the interaction of product category, volume and product views, and the interaction of product views and product category which are statistically significant in explaining changes in unit product sales. Pure increase in volume or number of reviewer comments has no significant effect on sales. These conclusions have critical implications for the practical use of online WOM in e-commerce and for internet marketing.
Hierarchical Clustering for Software Architecture Recovery

Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main contributions of this paper are given as follows. First, we review hierarchical clustering research in the context of software architecture recovery and modularization. Second, to employ clustering meaningfully, it is necessary to understand the peculiarities of the software domain, as well as the behavior of clustering measures and algorithms in this domain. To this end, we provide a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering. Third, we analyze the clustering process of various well-known clustering algorithms by using multiple criteria, and we show how arbitrary decisions taken by these algorithms during clustering affect the quality of their results. Finally, we present an analysis of two recently proposed clustering algorithms, revealing close similarities in their apparently different clustering approaches. Experiments on four legacy software systems provide insight into the behavior of well-known clustering algorithms and their characteristics in the software domain.
Gesture Recognition

This paper presents a method for recognizing human-hand gestures using a model-based approach. A nite state machine is used to model four qualitatively distinct phases of a generic gesture. Fingertips are tracked in multiple frames to compute motion trajectories. The trajectories are then used for nding the start and stop position of the gesture. Gestures are represented as a list of vectors and are then matched to stored gesture vector models using table lookup based on vector displacements. Results are presented showing recognition of seven gestures using images sampled at 4Hz on a SPARC-1 without any special hardware. The seven gestures are representatives for
Neural Networks Incorporating Dictionaries for Chinese Word Segmentation

In recent years, deep neural networks have achieved significant success in Chinese word segmentation and many other natural language processing tasks. Most of these algorithms are end-to-end trainable systems and can effectively process and learn from large scale labeled datasets. However, these methods typically lack the capability of processing rare words and data whose domains are different from training data. Previous statistical methods have demonstrated that human knowledge can provide valuable information for handling rare cases and domain shifting problems. In this paper, we seek to address the problem of incorporating dictionaries into neural networks for the Chinese word segmentation task. Two different methods that extend the bi-directional long short-term memory neural network are proposed to perform the task. To evaluate the performance of the proposed methods, state-of-the-art supervised models based methods and domain adaptation approaches are compared with our methods on nine datasets from different domains. The experimental results demonstrate that the proposed methods can achieve better performance than other state-of-the-art neural network methods and domain adaptation approaches in most cases.
An integrated low-voltage class AB CMOS OTA

We present a new circuit topology for a low-voltage class AB amplifier. The circuit shows superior current efficiency in the use of the supply current to charge and discharge the output load. It uses negative feedback rather than component matching to optimize current efficiency and performance, resulting in a current boost ratio exactly equal to one. Measurement results for an example circuit fabricated in a 2m CMOS process are given. The circuit uses a quiescent supply current of 0.2 A and is able to settle to a 1% error in 1.1 ms for a 0.4-V input step and a load capacitance of 35 pF. The circuit design is straightforward and modular, and the core circuit can be used to replace the differential pair of other op-amp topologies.
App Store Mining Is Not Enough

App store reviews are currently the main source of information for analyzing different aspects of app development and evolution. However, app users' feedback do not only occur on the app store. In fact, a large quantity of posts about apps are made daily on social media. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analysing a total of 70 apps over a period of six weeks, we show that 22.4% more feature requests and 12.89% more bug reports could be found on Twitter.
User-Defined Game Input for Smart Glasses in Public Space

Smart glasses, such as Google Glass, provide always-available displays not offered by console and mobile gaming devices, and could potentially offer a pervasive gaming experience. However, research on input for games on smart glasses has been constrained by the available sensors to date. To help inform design directions, this paper explores user-defined game input for smart glasses beyond the capabilities of current sensors, and focuses on the interaction in public settings. We conducted a user-defined input study with 24 participants, each performing 17 common game control tasks using 3 classes of interaction and 2 form factors of smart glasses, for a total of 2448 trials. Results show that users significantly preferred non-touch and non-handheld interaction over using handheld input devices, such as in-air gestures. Also, for touch input without handheld devices, users preferred interacting with their palms over wearable devices (51% vs 20%). In addition, users preferred interactions that are less noticeable due to concerns with social acceptance, and preferred in-air gestures in front of the torso rather than in front of the face (63% vs 37%).
Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm

Lane detection can provide important information for safety driving. In this paper, a real time vision-based lane detection method is presented to find the position and type of lanes in each video frame. In the proposed lane detection method, lane hypothesis is generated and verified based on an effective combination of lane-mark edge-link features. First, lane-mark candidates are searched inside region of interest (ROI). During this searching process, an extended edge-linking algorithm with directional edge-gap closing is used to produce more complete edge-links, and features like lane-mark edge orientation and lane-mark width are used to select candidate lane-mark edge-link pairs. For the verification of lane-mark candidates, color is checked inside the region enclosed by candidate edge-link pairs in YUV color space. Additionally, the continuity of the lane is estimated employing a Bayesian probability model based on lane-mark color and edge-link length ratio. Finally, a simple lane departure model is built to detect lane departures based on lane locations in the image Experiment results show that the proposed lane detection method can work robustly in real-time, and can achieve an average speed of 30~50ms per frame for 180x120 image size, with a correct detection rate over 92%.