nils-herrmann/clean_scirepeval huggingface dataset:
Spatio-temporal determinants of mental health and well-being: advances in geographically-explicit ecological momentary assessment (GEMA)

Overview of geographically explicit momentary assessment research, applied to the study of mental health and well-being, which allows for cross-validation, extension, and enrichment of research on place and health. Building on the historical foundations of both ecological momentary assessment and geographic momentary assessment research, this review explores their emerging synergy into a more generalized and powerful research framework. Geographically explicit momentary assessment methods are rapidly advancing across a number of complimentary literatures that intersect but have not yet converged. Key contributions from these areas reveal tremendous potential for transdisciplinary and translational science. Mobile communication devices are revolutionizing research on mental health and well-being by physically linking momentary experience sampling to objective measures of socio-ecological context in time and place. Methodological standards are not well-established and will be required for transdisciplinary collaboration and scientific inference moving forward.
Code Design for Short Blocks: A Survey

The design of block codes for short information blocks (e.g., a thousand or less information bits) is an open research problem which is gaining relevance thanks to emerg ing applications in wireless communication networks. In this work, we review some of the most recent code constructions targeti ng the short block regime, and we compare then with both finitelength performance bounds and classical error correction c oding schemes. We will see how it is possible to effectively approa ch the theoretical bounds, with different performance vs. dec oding complexity trade-offs.
60 GHz antennas in HTCC and glass technology

This paper presents a 60 GHz antenna structure built on glass and flip-chipped on a ceramic module. A single antenna and a two antenna array have been fabricated and demonstrated good performances. The single antenna shows a return loss below −10 dB and a gain of 6–7 dBi over a 7 GHz bandwidth. The array shows a gain of 7–8 dBi over a 3 GHz bandwidth.
Automated Pothole Detection Using Wireless Sensor Motes

Poorly maintained roads are a fact of life in most developing countries; it is certainly the case in Sri Lanka. The lack of funding to maintain the road surfaces is a main reason for the dilapidated condition of roads in these countries, but i t is not the only reason. The absence of an effective monitoring system is one reason for this problem. A monitoring system can help to take remedial actions and that can lower the cost of maintenance. However, the cost of an effective monitoring system itself can be quite costly and hardy affordable fo r a developing country. We are developing a road surface condition monitoring system that uses acceleration sensors mounted on public transport buses to monitor the road surface condition with only a few sensors. These bus mounted sensors gather vertical and horizontal acceleration data on its route togethe r with the GPS coordinates of the data collection points. This road surface condition monitoring system is in fact piggy backed on an environmental pollution monitoring system called BusNet that uses the sensors mounted on the public transport buses to collect data on environmental pollution . In this paper we present a technique that we developed to identify ”potholes” on roads by analyzing the acceleration data.
Security analysis and enhancements of 3GPP authentication and key agreement protocol

This paper analyzes the authentication and key agreement protocol adopted by Universal Mobile Telecommunication System (UMTS), an emerging standard for third-generation (3G) wireless communications. The protocol, known as 3GPP AKA, is based on the security framework in GSM and provides significant enhancement to address and correct real and perceived weaknesses in GSM and other wireless communication systems. In this paper, we first show that the 3GPP AKA protocol is vulnerable to a variant of the so-called false base station attack. The vulnerability allows an adversary to redirect user traffic from one network to another. It also allows an adversary to use authentication vectors corrupted from one network to impersonate all other networks. Moreover, we demonstrate that the use of synchronization between a mobile station and its home network incurs considerable difficulty for the normal operation of 3GPP AKA. To address such security problems in the current 3GPP AKA, we then present a new authentication and key agreement protocol which defeats redirection attack and drastically lowers the impact of network corruption. The protocol, called AP-AKA, also eliminates the need of synchronization between a mobile station and its home network. AP-AKA specifies a sequence of six flows. Dependent on the execution environment, entities in the protocol have the flexibility of adaptively selecting flows for execution, which helps to optimize the efficiency of AP-AKA both in the home network and in foreign networks.
Protecting Poorly Chosen Secrets from Guessing Attacks

In a security system that allows people to choose their own passwords those people tend to choose passwords that can be easily guessed This weakness exists in practically all widely used systems Instead of forcing users to choose well chosen secrets which are likely to be di cult to remember we propose solutions that maintain both user convenience and a high level of security at the same time The basic idea is to ensure that data available to the attacker is su ciently unpredictable to prevent an o line veri cation of whether a guess is successful or not We examine common forms of guessing attacks develop examples of cryptographic protocols that are immune to such attacks and suggest a systematic way to examine protocols to detect vulnerabilities to such attacks
Preference Elicitation For Single Crossing Domain

Eliciting the preferences of a set of agents over a set of alternatives is a problem of fundamental importance in social choice theory. Prior work on this problem has studied the query complexity of preference elicitation for the unrestricted domain and for the domain of single peaked preferences. In this paper, we consider the domain of single crossing preference profiles and study the query complexity of preference elicitation under various settings. We consider two distinct situations: when an ordering of the voters with respect to which the profile is single crossing is known versus when it is unknown. We also consider different access models: when the votes can be accessed at random, as opposed to when they are coming in a pre-defined sequence. In the sequential access model, we distinguish two cases when the ordering is known: the first is that sequence in which the votes appear is also a single-crossing order, versus when it is not. The main contribution of our work is to provide polynomial time algorithms with low query complexity for preference elicitation in all the above six cases. Further, we show that the query complexities of our algorithms are optimal up to constant factors for all but one of the above six cases. We then present preference elicitation algorithms for profiles which are close to being single crossing under various notions of closeness, for example, single crossing width, minimum number of candidates|voters whose deletion makes a profile single crossing.
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting

We present the Mim-Solution's approach to the RecSys Challenge 2016, which ranked 2nd. The goal of the competition was to prepare job recommendations for the users of the website Xing.com.
 Our two phase algorithm consists of candidate selection followed by the candidate ranking. We ranked the candidates by the predicted probability that the user will positively interact with the job offer. We have used Gradient Boosting Decision Trees as the regression tool.
Prevention of system deadlocks

A well-known problem in the design of operating systems is the selection of a resource allocation policy that will prevent deadlock. Deadlock is the situation in which resources have been allocated to various tasks in such a way that none of the tasks can continue. The various published solutions have been somewhat restrictive: either they do not handle the problem in sufficient generality or they suggest policies which will on occasion refuse a request which could have been safely granted. Algorithms are presented which examine a request in the light of the current allocation of resources and determi.~e whether or not the granting of the request will introduce the possibility of a deadlock. Proofs given in the appendixes show that the conditions imposed by the algorithms are both necessary and sufficient to prevent deadlock. The algorithms have been successfully used in the THE system.
Selective attention gates visual processing in the extrastriate cortex.

Single cells were recorded in the visual cortex of monkeys trained to attend to stimuli at one location in the visual field and ignore stimuli at another. When both locations were within the receptive field of a cell in prestriate area V4 or the inferior temporal cortex, the response to the unattended stimulus was dramatically reduced. Cells in the striate cortex were unaffected by attention. The filtering of irrelevant information from the receptive fields of extrastriate neurons may underlie the ability to identify and remember the properties of a particular object out of the many that may be represented on the retina.
Gait recognition using acceleration from MEMS

This paper presents an approach on recognising individuals based on 3D acceleration data from walking, which are collected using MEMS. Unlike most other gait recognition methods, which are based on video source, our approach uses walking acceleration in three directions: vertical, backward-forward and sideways. Using gait samples from 21 individuals and applying two methods, histogram similarity and cycle length, the equal error rates of 5% and 9% are achieved, respectively.
SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces

Motion planning in uncertain and dynamic environments is an essential capability for autonomous robots. Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for solving such problems, but they are often avoided in robotics due to high computational complexity. Our goal is to create practical POMDP algorithms and software for common robotic tasks. To this end, we have developed a new point-based POMDP algorithm that exploits the notion of optimally reachable belief spaces to improve computational efficiency. In simulation, we successfully applied the algorithm to a set of common robotic tasks, including instances of coastal navigation, grasping, mobile robot exploration, and target tracking, all modeled as POMDPs with a large number of states. In most of the instances studied, our algorithm substantially outperformed one of the fastest existing point-based algorithms. A software package implementing our algorithm will soon be released at http://motion.comp.nus.edu.sg/ projects/pomdp/pomdp.html.
Personalization of image enhancement

We address the problem of incorporating user preference in automatic image enhancement. Unlike generic tools for automatically enhancing images, we seek to develop methods that can first observe user preferences on a training set, and then learn a model of these preferences to personalize enhancement of unseen images. The challenge of designing such system lies at intersection of computer vision, learning, and usability; we use techniques such as active sensor selection and distance metric learning in order to solve the problem. The experimental evaluation based on user studies indicates that different users do have different preferences in image enhancement, which suggests that personalization can further help improve the subjective quality of generic image enhancements.
Point cloud segmentation towards urban ground modeling

This paper presents a new method for segmentation and interpretation of 3D point clouds from mobile LIDAR data. The main contribution of this work is the automatic detection and classification of artifacts located at the ground level. The detection is based on Top-Hat of hole filling algorithm of range images. Then, several features are extracted from the detected connected components (CCs). Afterward, a stepwise forward variable selection by using Wilk's Lambda criterion is performed. Finally, CCs are classified in four categories (lampposts, pedestrians, cars, the others) by using a SVM machine learning method.
Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM)

A challenging and daunting task for financial investors is determining stock market timing - when to buy, sell and the future price of a stock. This challenge is due to the complexity of the stock market. New methods have emerged that increase the accuracy of stock prediction. Examples of these methods are fuzzy logic, neural network and hybridized methods such as hybrid Kohonen self organizing map (SOM), adaptive neuro-fuzzy inference system (ANFIS) etc. This paper presents a number of methods used to predict the stock price of the day. These methods are backpropagation, Kohonen SOM, and a hybrid Kohonen SOM. The results show that the difference in error of the hybrid Kohonen SOM is significantly reduced compared to the other methods used. Hence, the results suggest that the hybrid Kohonen SOM is a better predictor compared to Kohonen SOM and backpropagation
A method for taxonomy development and its application in information systems

24 November 2010  15 April 2012  17 April 2012  A fundamental problem in many disciplines is the classification of objects in a domain of interest into a taxonomy. Developing a taxonomy, however, is a complex process that has not been adequately addressed in the information systems (IS) literature. The purpose of this paper is to present a method for taxonomy development that can be used in IS. First, this paper demonstrates through a comprehensive literature survey that taxonomy development in IS has largely been ad hoc. Then the paper defines the problem of taxonomy development. Next, the paper presents a method for taxonomy development that is based on taxonomy development literature in other disciplines and shows that the method has certain desirable qualities. Finally, the paper demonstrates the efficacy of the method by developing a taxonomy in a domain in IS. European Journal of Information Systems advance online publication, 19 June 2012;
5G fronthaul-latency and jitter studies of CPRI over ethernet

Common Public Radio Interface (CPRI) is a successful industry cooperation defining the publicly available specification for the key internal interface of radio base stations between the radio equipment control (REC) and the radio equipment (RE) in the fronthaul of mobile networks. However, CPRI is expensive to deploy, consumes large bandwidth, and currently is statically configured. On the other hand, an Ethernet-based mobile fronthaul will be cost-efficient and more easily reconfigurable. Encapsulating CPRI over Ethernet (CoE) is an attractive solution, but stringent CPRI requirements such as delay and jitter are major challenges that need to be met to make CoE a reality. This study investigates whether CoE can meet delay and jitter requirements by performing FPGA-based Verilog experiments and simulations. Verilog experiments show that CoE encapsulation with fixed Ethernet frame size requires about tens of microseconds. Numerical experiments show that the proposed scheduling policy of CoE flows on Ethernet can reduce jitter when redundant Ethernet capacity is provided. The reduction in jitter can be as large as 1 μs, hence making Ethernet-based mobile fronthaul a credible technology.
The DLR-KUKA success story: robotics research improves industrial robots

This paper discusses the joint venture between the German Aerospace Center (DLR) Institute of Robotics and Mechatronics and the German robot manufacturer, KUKA Robotics GmbH, for the successful transfer of the knowledge in robot dynamics and control, human robot interaction, and sensory feedback as originally developed for the remote control of space robots to industrial robotics. This technology transfer venture aims to promote more industrial sensory feedback applications with particular focus on the fusion of vision and force, while merging service and industrial robots into the future. As such, the aim is to transfer new robonaut technologies and ultra-lightweight, torque-controlled, kinematically redundant arms with multifingered articulated hands into the terrestrial robot industry.
Creating Summaries from User Videos

This paper proposes a novel approach and a new benchmark for video summarization. Thereby we focus on user videos, which are raw videos containing a set of interesting events. Our method starts by segmenting the video by using a novel “superframe” segmentation, tailored to raw videos. Then, we estimate visual interestingness per superframe using a set of low-, midand high-level features. Based on this scoring, we select an optimal subset of superframes to create an informative and interesting summary. The introduced benchmark comes with multiple human created summaries, which were acquired in a controlled psychological experiment. This data paves the way to evaluate summarization methods objectively and to get new insights in video summarization. When evaluating our method, we find that it generates high-quality results, comparable to manual, human-created summaries.
Ontological User Profile Modeling for Context-Aware Application Personalization

Existing context-aware adaptation techniques are limited in their support for user personalization. There is relatively less developed research involving adaptive user modeling for user applications in the emerging areas of mobile and pervasive computing. This paper describes the creation of a User Profile Ontology for context-aware application personalization within mobile environments. We analyze users’ behavior and characterize users’ needs for context-aware applications. Special emphasis is placed in the ontological modeling of dynamic components for use in adaptable applications. We illustrate the use of the model in the context of a case study, focusing on providing personalized services to older people via smart-device technologies.
OpinionSeer: Interactive Visualization of Hotel Customer Feedback

The rapid development of Web technology has resulted in an increasing number of hotel customers sharing their opinions on the hotel services. Effective visual analysis of online customer opinions is needed, as it has a significant impact on building a successful business. In this paper, we present OpinionSeer, an interactive visualization system that could visually analyze a large collection of online hotel customer reviews. The system is built on a new visualization-centric opinion mining technique that considers uncertainty for faithfully modeling and analyzing customer opinions. A new visual representation is developed to convey customer opinions by augmenting well-established scatterplots and radial visualization. To provide multiple-level exploration, we introduce subjective logic to handle and organize subjective opinions with degrees of uncertainty. Several case studies illustrate the effectiveness and usefulness of OpinionSeer on analyzing relationships among multiple data dimensions and comparing opinions of different groups. Aside from data on hotel customer feedback, OpinionSeer could also be applied to visually analyze customer opinions on other products or services.
FingerPad: private and subtle interaction using fingertips

We present FingerPad, a nail-mounted device that turns the tip of the index finger into a touchpad, allowing private and subtle interaction while on the move. FingerPad enables touch input using magnetic tracking, by adding a Hall sensor grid on the index fingernail, and a magnet on the thumbnail. Since it permits input through the pinch gesture, FingerPad is suitable for private use because the movements of the fingers in a pinch are subtle and are naturally hidden by the hand. Functionally, FingerPad resembles a touchpad, and also allows for eyes-free use. Additionally, since the necessary devices are attached to the nails, FingerPad preserves natural haptic feedback without affecting the native function of the fingertips. Through user study, we analyze the three design factors, namely posture, commitment method and target size, to assess the design of the FingerPad. Though the results show some trade-off among the factors, generally participants achieve 93% accuracy for very small targets (1.2mm-width) in the seated condition, and 92% accuracy for 2.5mm-width targets in the walking condition.
Road Crack Detection Using Visual Features Extracted by Gabor Filters

Pavement management systems require detailed information of the current state of the roads to take appropriate actions to optimize expenditure on maintenance and rehabilitation. In particular, the presence of cracks is a cardinal aspect to be considered. This article presents a solution based on an instrumented vehicle equipped with an imaging system, two Inertial Profilers, a Differential Global Positioning System, and a webcam. Information about the state of the road is acquired at normal road speed. A method based on the use of Gabor filters is used to detect the longitudinal and transverse cracks. The methodologies used to create Gabor filter banks and the use of the filtered images as descriptors for subsequent classifiers are discussed in detail. Three different methodologies for setting the threshold of the classifiers are also evaluated. Finally, an AdaBoost algorithm is used for selecting and combining the classifiers, thus improving the results provided by a single classifier. A large database has been acquired and used to train and test the proposed system and methods, and suitable results have been obtained in comparison with other refer-
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning

We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences—a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually.