nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

Given an image of a street scene in a city, this paper develops a new method that can quickly and precisely pinpoint at which location (as well as viewing direction) the image was taken, against a pre-stored large-scale 3D point-cloud map of the city. We adopt the recently developed 2D-3D direct feature matching framework for this task [23,31,32,42–44]. This is a challenging task especially for large-scale problems. As the map size grows bigger, many 3D points in the wider geographical area can be visually very similar–or even identical–causing severe ambiguities in 2D-3D feature matching. The key is to quickly and unambiguously find the correct matches between a query image and the large 3D map. Existing methods solve this problem mainly via comparing individual features’ visual similarities in a local and per feature manner, thus only local solutions can be found, inadequate for large-scale applications. In this paper, we introduce a global method which harnesses global contextual information exhibited both within the query image and among all the 3D points in the map. This is achieved by a novel global ranking algorithm, applied to a Markov network built upon the 3D map, which takes account of not only visual similarities between individual 2D-3D matches, but also their global compatibilities (as measured by co-visibility) among all matching pairs found in the scene. Tests on standard benchmark datasets show that our method achieved both higher precision and comparable recall, compared with the state-of-the-art.
A Mobile Cloud-Based Parkinson’s Disease Assessment System for Home-Based Monitoring

Parkinson's disease (PD) is the most prevalent movement disorder of the central nervous system, and affects more than 6.3 million people in the world. The characteristic motor features include tremor, bradykinesia, rigidity, and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients' motor performance but often leads to levodopa-induced adverse effects, such as dyskinesia and motor fluctuation. Clinicians must regularly monitor patients in order to identify these effects and other declines in motor function as soon as possible. Current clinical assessment for Parkinson's is subjective and mostly conducted by brief observations made during patient visits. Changes in patients' motor function between visits are hard to track and clinicians are not able to make the most informed decisions about the course of therapy without frequent visits. Frequent clinic visits increase the physical and economic burden on patients and their families.



In this project, we sought to design, develop, and evaluate a prototype mobile cloud-based mHealth app, "PD Dr", which collects quantitative and objective information about PD and would enable home-based assessment and monitoring of major PD symptoms.



We designed and developed a mobile app on the Android platform to collect PD-related motion data using the smartphone 3D accelerometer and to send the data to a cloud service for storage, data processing, and PD symptoms severity estimation. To evaluate this system, data from the system were collected from 40 patients with PD and compared with experts' rating on standardized rating scales.



The evaluation showed that PD Dr could effectively capture important motion features that differentiate PD severity and identify critical symptoms. For hand resting tremor detection, the sensitivity was .77 and accuracy was .82. For gait difficulty detection, the sensitivity was .89 and accuracy was .81. In PD severity estimation, the captured motion features also demonstrated strong correlation with PD severity stage, hand resting tremor severity, and gait difficulty. The system is simple to use, user friendly, and economically affordable.



The key contribution of this study was building a mobile PD assessment and monitoring system to extend current PD assessment based in the clinic setting to the home-based environment. The results of this study proved feasibility and a promising future for utilizing mobile technology in PD management.
The ATR Multilingual Speech-to-Speech Translation System

In this paper, we describe the ATR multilingual speech-to-speech translation (S2ST) system, which is mainly focused on translation between English and Asian languages (Japanese and Chinese). There are three main modules of our S2ST system: large-vocabulary continuous speech recognition, machine text-to-text (T2T) translation, and text-to-speech synthesis. All of them are multilingual and are designed using state-of-the-art technologies developed at ATR. A corpus-based statistical machine learning framework forms the basis of our system design. We use a parallel multilingual database consisting of over 600 000 sentences that cover a broad range of travel-related conversations. Recent evaluation of the overall system showed that speech-to-speech translation quality is high, being at the level of a person having a Test of English for International Communication (TOEIC) score of 750 out of the perfect score of 990.
SF-LRU cache replacement algorithm

In this paper we propose a replacement algorithm, SF-LRU (second chance-frequency - least recently used) that combines the LRU (least recently used) and the LFU (least frequently used) using the second chance concept. A comprehensive comparison is made between our algorithm and both LRU and LFU algorithms. Experimental results show that the SF-LRU significantly reduces the number of cache misses compared the other two algorithms. Simulation results show that our algorithm can provide a maximum value of approximately 6.3% improvement in the miss ratio over the LRU algorithm in data cache and approximately 9.3% improvement in miss ratio in instruction cache. This performance improvement is attributed to the fact that our algorithm provides a second chance to the block that may be deleted according to LRU's rules. This is done by comparing the frequency of the block with the block next to it in the set.
USER PERCEPTION OF THE QUALITY , VALUE , AND UTILITY OF USER-GENERATED CONTENT

User Generated Content (UGC) is a rapidly emerging growth engine of many Internet businesses and an important component of the new knowledge society. However, little research has been done on the mechanisms inherent to UGC. This research explores the relationships among the quality, value, and benefits of UGC. The main objective is to identify and evaluate the quality factors that affect UGC value, which ultimately influences the utility of UGC. We identify the three quality dimensions of UGC: content, design, and technology. We classify UGC value into three categories: functional value, emotional value, and social value. We attempt to characterize the mechanism underlying UGC value by evaluating the relationships between the quality and value of UGC and investigating what types of UGC value affect UGC utility. Our results show that all three factors of UGC quality are strongly associated with increases in the functional, emotional, and social values of UGC. Our findings also demonstrate that the functional and emotional values of UGC are critically important factors for UGC utility. Based on these findings, we discuss theoretical implications for future research and practical implications for UGC services.
Modeling confirmation bias and polarization

Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.
Interactive development of cross-device user interfaces

Current GUI builders provide a design environment for user interfaces that target either a single type or fixed set of devices, and provide little support for scenarios in which the user interface, or parts of it, are distributed over multiple devices. Distributed user interfaces have received increasing attention over the past years. There are different, often model-based, approaches that focus on technical issues. This paper presents XDStudio--a new GUI builder designed to support interactive development of cross-device web interfaces. XDStudio implements two complementary authoring modes with a focus on the design process of distributed user interfaces. First, simulated authoring allows designing for a multi-device environment on a single device by simulating other target devices. Second, on-device authoring allows the design process itself to be distributed over multiple devices, as design and development take place on the target devices themselves. To support interactive development for multi-device environments, where not all devices may be present at design and run-time, XDStudio supports switching between the two authoring modes, as well as between design and use modes, as required. This paper focuses on the design of XDStudio, and evaluates its support for two distribution scenarios.
Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding

The modern data compression is mainly based on two approaches to entropy coding: Huffman (HC) and arithmetic/range coding (AC). The former is much faster, but approximates probabilities with powers of 2, usually leading to relatively low compression rates. The latter uses nearly exact probabilities easily approaching theoretical compression rate limit (Shannon entropy), but at cost of much larger computational cost. Asymmetric numeral systems (ANS) is a new approach to accurate entropy coding, which allows to end this tradeoff between speed and rate: the recent implementation  provides about 50% faster decoding than HC for 256 size alphabet, with compression rate similar to provided by AC. This advantage is due to being simpler than AC: using single natural number as the state, instead of two to represent a range. Beside simplifying renormalization, it allows to put the entire behavior for given probability distribution into a relatively small table: defining entropy coding automaton. The memory cost of such table for 256 size alphabet is a few kilobytes. There is a large freedom while choosing a specific table using pseudorandom number generator initialized with cryptographic key for this purpose allows to simultaneously encrypt the data. This article also introduces and discusses many other variants of this new entropy coding approach, which can provide direct alternatives for standard AC, for large alphabet range coding, or for approximated quasi arithmetic coding.
Enhancements to Bluetooth baseband security

Bluetooth system has been developed by Bluetooth Special Interest Group (Bluetooth SIG) as a cable replacement for short-range connectivity. In Bluetooth, special effort has been taken to develop and standardise adequate security mechanisms and procedures for protecting the wireless radio link. This set of mechanisms is defined in the Bluetooth Baseband specification  and is referred to as Bluetooth Baseband security. It is based on strong cryptographic algorithms and well-established security principles. Still, more work is required to integrate Bluetooth Baseband security into various applications that may have very different link layer security requirements. Bluetooth Baseband security is implemented in the Bluetooth module and is common to all Bluetooth units. Also the application specific security functionality may need to be standardised for interoperability. Recently, Jakobsson and Wetzel identified some potential trap holes in Bluetooth security in [11]. Their main concerns were certain weak options included in the Bluetooth security standard. They also criticised the way Bluetooth units make themselves discoverable by other units just by broadcasting messages that include their unique identities in clear. The purpose of this paper is to introduce some recent work in the area of Bluetooth Baseband security. Specifically, we address the problems discovered by Jakobsson and Wetzel, and develop possible counter measures. First, a brief introduction to Bluetooth Baseband security is given. Then major security shortcomings are identified. These include usage of the unit key and the short Bluetooth PIN value in the initialisation procedure, on the one hand, and the privacy problem created by location tracking, on the other hand. In section 4, we discuss passkey-based methods of exchanging the Bluetooth link key. Using public key cryptography adequate security can be provided while keeping the passkey short for the user's convenience. Furthermore, in section 5, an application to LAN access is developed enabling access point roaming. Finally, in section 6, we describe a technique that offers reasonable protection against location tracking. Identities of Bluetooth units can be efficiently hidden from unauthorised units using temporary Bluetooth device addresses.
Web-Based Keystroke Dynamics Identity Verification Using Neural Network

Password typing is the most widely used identity verification method in World Wide Web based Electronic Commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owner's keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct password can be detected with very high accuracy using the proposed approach. This approach can be effectively implemented by a Java applet and used in the World Wide Web.
Managing a Mobile Agricultural Robot Swarm for a seeding task

Mobile Agricultural Robot Swarms (MARS) is an approach for autonomous farming operations by a coordinated group of robots. One key aspect of the MARS concept is the low individual intelligence, meaning that each robot is equipped with only a minimum of sensor technology in order to achieve a low cost and energy efficient system that provides scalability and reliability for field tasks. The robot swarms are coordinated by a centralized entity (OptiVisor) which is responsible for path planning, optimization and supervision. It also serves as a mediator between the robots and different cloud services responsible for the documentation of the procedure. This paper focuses on the architecture and function of OptiVisor within the overall MARS system. An OptiVisor in combination with a simulation environment for a robot swarm is presented and shows the feasibility of the general concept and the current state of the algorithms. Furthermore, the paper shows results about the current progress of OptiVisor integration using real robots.
Finding Opinion Strength Using Rule-Based Parsing for Arabic Sentiment Analysis

With increasing interest in sentiment analysis research and opinionated web content always on the rise, focus on analysis of text in various domains and different languages is a relevant and important task. This paper explores the problems of sentiment analysis and opinion strength measurement using a rule-based approach tailored to the Arabic language. The approach takes into account language-specific traits that are valuable to syntactically segment a text, and allow for closer analysis of opinion-bearing language queues. By using an adapted sentiment lexicon along with sets of opinion indicators, a rule-based methodology for opinion-phrase extraction is introduced, followed by a method to rate the parsed opinions and offer a measure of opinion strength for the text under analysis. The proposed method, even with a small set of rules, shows potential for a simple and scalable opinion-rating system, which is of particular interest for morphologically-rich languages such as Arabic.
Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks

The IMT 2020 requirements of 20 Gb/s peak data rate and 1 ms latency present significant engineering challenges for the design of 5G cellular systems. Systems that make use of the mmWave bands above 10 GHz ---where large regions of spectrum are available --- are a promising 5G candidate that may be able to rise to the occasion. However, although the mmWave bands can support massive peak data rates, delivering these data rates for end-to-end services while maintaining reliability and ultra-low-latency performance to support emerging applications and use cases will require rethinking all layers of the protocol stack. This article surveys some of the challenges and possible solutions for delivering end-to-end, reliable, ultra-low-latency services in mmWave cellular systems in terms of the MAC layer, congestion control, and core network architecture.
Ground Target Tracking Control System for Unmanned Aerial Vehicles

The work here presented contributes to the development of ground target tracking control systems for fixed wing unmanned aerial vehicles (UAVs). The control laws are derived at the kinematic level, relying on a commercial inner loop controller onboard that accepts commands in indicated air speed and bank, and appropriately sets the control surface deflections and thrust in order to follow those references in the presence of unknown wind. Position and velocity of the target on the ground is assumed to be known. The algorithm proposed derives from a path following control law that enables the UAV to converge to a circumference centered at the target and moving with it, thus keeping the UAV in the vicinity of the target even if the target moves at a velocity lower than the UAV stall speed. If the target speed is close to the UAV speed, the control law behaves similar to a controller that tracks a particular T. Oliveira Science Laboratory, Portuguese Air Force Academy, Sintra, 2715-021, Portugal e-mail: tmoliveira@academiafa.edu.pt P. Encarnação (B) Faculty of Engineering, Catholic University of Portugal, Rio de Mouro, 2635-631, Portugal e-mail: pme@fe.lisboa.ucp.pt point on the circumference centered at the target position. Real flight tests results show the good performance of the control scheme presented.
Exploiting the deep learning paradigm for recognizing human actions

In this paper we propose a novel method for recognizing human actions by exploiting a multi-layer representation based on a deep learning based architecture. A first level feature vector is extracted and then a high level representation is obtained by taking advantage of a Deep Belief Network trained using a Restricted Boltzmann Machine. The classification is finally performed by a feed-forward neural network. The main advantage behind the proposed approach lies in the fact that the high level representation is automatically built by the system exploiting the regularities in the dataset; given a suitably large dataset, it can be expected that such a representation can outperform a hand-design description scheme. The proposed approach has been tested on two standard datasets and the achieved results, compared with state of the art algorithms, confirm its effectiveness.
Nonparametric regression using deep neural networks with ReLU activation function

Consider the multivariate nonparametric regression model. It is shown that estimators based on sparsely connected deep neural networks with ReLU activation function and properly chosen network architecture achieve the minimax rates of convergence (up to log n-factors) under a general composition assumption on the regression function. The framework includes many well-studied structural constraints such as (generalized) additive models. While there is a lot of flexibility in the network architecture, the tuning parameter is the sparsity of the network. Specifically, we consider large networks with number of potential network parameters exceeding the sample size. The analysis gives some insights why multilayer feedforward neural networks perform well in practice. Interestingly, the depth (number of layers) of the neural network architectures plays an important role and our theory suggests that for nonparametric regression scaling the network depth with the logarithm of the sample size is natural. It is also shown that under the composition assumption wavelet estimators can only achieve suboptimal rates.
Socially Aware Networking: A Survey

The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g., opportunistic networks, mobile social networks, delay-tolerant networks, ad hoc networks, etc.) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber-physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms, and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness, and scalability are discussed.
A prescription fraud detection model

Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs.