nils-herrmann/clean_scirepeval huggingface dataset:
Smartwatch in vivo

In recent years, the smartwatch has returned as a form factor for mobile computing with some success. Yet it is not clear how smartwatches are used and integrated into everyday life differently from mobile phones. For this paper, we used wearable cameras to record twelve participants' daily use of smartwatches, collecting and analysing incidents where watches were used from over 34 days of user recording. This allows us to analyse in detail 1009 watch uses. Using the watch as a timepiece was the most common use, making up 50% of interactions, but only 14% of total watch usage time. The videos also let us examine why and how smartwatches are used for activity tracking, notifications, and in combination with smartphones. In discussion, we return to a key question in the study of mobile devices: how are smartwatches integrated into everyday life, in both the actions that we take and the social interactions we are part of?
A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks

Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.
Blockchain-based sharing services : What blockchain technology can contribute to smart cities

The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word “smart” and benefits from innovative applications of new kinds of information and communications technology to support communal sharing.  By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective.  Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services.  This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area.
Joint Camera Pose Estimation and 3D Human Pose Estimation in a Multi-camera Setup

In this paper we propose an approach to jointly perform camera pose estimation and human pose estimation from videos recorded by a set of cameras separated by wide baselines. Multi-camera pose estimation is very challenging in case of wide baselines or in general when patch-based feature correspondences are difficult to establish across images. For this reason, we propose to exploit the motion of an articulated structure in the scene, such as a human, to relate these cameras. More precisely, we first run a part-based human pose estimation for each camera and each frame independently. Correctly detected joints are then used to compute an initial estimate of the epipolar geometry between pairs of cameras. In a combined optimization over all the recorded sequences, the multi-camera configuration and the 3D motion of the kinematic structure in the scene are inferred. The optimization accounts for time continuity, part-based detection scores, optical flow, and body part visibility. Our approach was evaluated on 4 publicly available datasets, evaluating the accuracy of the camera poses and the human poses.
Interactive surface design with interlocking elements

We present an interactive tool for designing physical surfaces made from flexible interlocking quadrilateral elements of a single size and shape. With the element shape fixed, the design task becomes one of finding a discrete structure---i.e., element connectivity and binary orientations---that leads to a desired geometry. In order to address this challenging problem of combinatorial geometry, we propose a forward modeling tool that allows the user to interactively explore the space of feasible designs. Paralleling principles from conventional modeling software, our approach leverages a library of base shapes that can be instantiated, combined, and extended using two fundamental operations: merging and extrusion. In order to assist the user in building the designs, we furthermore propose a method to automatically generate assembly instructions. We demonstrate the versatility of our method by creating a diverse set of digital and physical examples that can serve as personalized lamps or decorative items.
Research review: Attention bias modification (ABM): a novel treatment for anxiety disorders.

Attention bias modification (ABM) is a newly emerging therapy for anxiety disorders that is rooted in current cognitive models of anxiety and in established experimental data on threat-related attentional biases in anxiety. This review describes the evidence indicating that ABM has the potential to become an enhancing tool for current psychological and pharmacological treatments for anxiety or even a novel standalone treatment. The review also outlines the gaps in need of bridging before ABM techniques could be routinely applied and incorporated into standard treatment protocols.
A Double-Auction Mechanism for Mobile Data-Offloading Markets

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through third-party Wi-Fi or femtocell access points (APs) can significantly alleviate the cellular congestion and enhance user quality of service (QoS), without requiring costly and time-consuming infrastructure investments. This solution has substantial benefits both for the mobile network operators (MNOs) and the mobile users, but comes with unique technical and economic challenges that must be jointly addressed. In this paper, we consider a market where MNOs lease APs that are already deployed by residential users for the offloading purpose. We assume that each MNO can employ multiple APs, and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double-auction mechanism that ensures the efficient operation of the market by maximizing the differences between the MNOs' offloading benefits and APs' offloading costs. The proposed scheme takes into account the particular characteristics of the wireless network, such as the coupling of MNOs' offloading decisions and APs' capacity constraints. Additionally, it does not require full information about the MNOs and APs and creates non-negative revenue for the market broker.
Development of attentional networks in childhood

Recent research in attention has involved three networks of anatomical areas that carry out the functions of orienting, alerting and executive control (including conflict monitoring). There have been extensive cognitive and neuroimaging studies of these networks in adults. We developed an integrated Attention Network Test (ANT) to measure the efficiency of the three networks with adults. We have now adapted this test to study the development of these networks during childhood. The test is a child-friendly version of the flanker task with alerting and orienting cues. We studied the development of the attentional networks in a cross-sectional experiment with four age groups ranging from 6 through 9 (Experiment 1). In a second experiment, we compared children (age 10 years) and adult performance in both child and adults versions of the ANT. Reaction time and accuracy improved at each age interval and positive values were found for the average efficiency of each of the networks. Alertness showed evidence of change up to and beyond age 10, while conflict scores appear stable after age seven and orienting scores do not change in the age range studied. A final experiment with forty 7-year-old children suggested that children like adults showed independence between the three networks under some conditions.
Joint Optimization of Constellation With Mapping Matrix for SCMA Codebook Design

Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.
The Effect of Extremist Violence on Hateful Speech Online

User-generated content online is shaped by many factors, including endogenous elements such as platform affordances and norms, as well as exogenous elements, in particular significant events. These impact what users say, how they say it, and when they say it. In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence. We anchor this study in a series of attacks involving Arabs and Muslims as perpetrators or victims, occurring in Western countries, that have been covered extensively by news media. These attacks have fueled intense policy debates around immigration in various fora, including online media, which have been marred by racist prejudice and hateful speech. The focus of our research is to model the effect of the attacks on the volume and type of hateful speech on two social media platforms, Twitter and Reddit. Among other findings, we observe that extremist violence tends to lead to an increase in online hate speech, particularly on messages directly advocating violence. Our research has implications for the way in which hate speech online is monitored and suggests ways in which it could be fought.
Concept Detection on Medical Images using Deep Residual Learning Network

Medical images are often used in clinical diagnosis. However, interpreting the insights gained from them is often a time-consuming task even for experts. For this reason, there is a need for methods that can automatically approximate the mapping from medical images to condensed textual descriptions. For identifying the presence of relevant biomedical concepts in medical images for the ImageCLEF 2017 Caption concept detection subtask we propose the use of a pretrained residual deep neural network. Specifically, a 50-layered resNet was used and retrained on the medical images. The proposed method achieved F1 Score 0.1583 on the test data.
Deep Q learning for traffic simulation in autonomous driving at a highway junction

Trafïîc congestion is a serious global problem. Enhanced or deep Q learning algorithms were applied to a traffic simulation study. The enhanced Q learning was based on a repeated local search algorithm, and it was able to find optimal pathways under a multi-agent system. Deep Q learning was also capable of learning a suitable strategy, considering dynamic changes in traffic circumstances at a highway junction. In particular, the target network of the Q learning realized a stable regulation of the loss function. This intelligent method based on deep reinforcement learning could become an effective tool to optimize car pathways including an autonomous driving system.
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset

We investigate the problem of readeraware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset. The annotated dataset for RA-MDS is available online1.
Dataset Augmentation in Feature Space

Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.
An eye-tracking study of website complexity from cognitive load perspective

a r t i c l e i n f o Keywords: HCI Eye-tracking Website complexity Cognitive load Task complexity Online shopping Online shopping is becoming one of the most popular applications on the Internet. Websites are the important interfaces in HCI (Human–Computer Interaction). Website design significantly affects online shopping behavior. This research used eye-tracker to track the eye-movement process for 42 college students when they were surfing websites with different levels of complexity and completing simple and complex tasks respectively. The study examines how website complexity and task complexity jointly affect users' visual attention and behavior due to different cognitive loads. The study fills a research gap by examining this phenomenon from the cognitive load perspective and taking the moderate effect of task complexity into consideration. The results show that task complexity can moderate the effect of website complexity on users' visual attention and behavior. Specifically, when users conducted a simple task, fixation count and task completion time were at the highest level on the website with high complexity, while fixation duration was not significantly different on the websites with different complexity. However, when users conducted a complex task on a website with medium complexity, task completion time, fixation count, and fixation duration were all at their highest level. The load theory of attention was used to provide the explanation for the results. The findings provide guidelines for website managers and designers to maximize users' visual attention.
Understanding Citizen ’ s Continuance Intention to Use e-Government Website : a Composite View of Technology Acceptance Model and Computer Self-Efficacy

This study aims to understand the fundamental factors influencing the citizen’s continuance intention to use eGovernment websites by using the Technology Acceptance Model (TAM) as a based theoretical model. Computer selfefficacy is adopted as an additional factor that influences the citizen’s continuance intention to use e-Government websites. To empirically test the proposed research model, the web-based survey was employed. The participants consisted of 614 country-wide citizens with at least a bachelor’s degree and an experience with e-Government websites. Regression analysis was conducted to test the model. The results revealed that perceived usefulness and perceived ease of use of e-Government websites and citizen’s computer self-efficacy directly enhanced citizen’s continuance intention to use e-Government websites. In addition, perceived ease of use of e-Government websites indirectly enhanced citizen’s continuance intention through perceived usefulness.
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., “if X pays Y a compliment, then Y will likely return the compliment”). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.  Given a snapshot observation of an event, people can easily anticipate and reason about unobserved causes and effects in relation to the observed event: what might have happened just before, what might happen next as a result, and how different events are chained through causes and effects. For instance, if we observe an event “X repels Y’s attack” , we can immediately infer various plausible facts surrounding that event. In terms of the plausible motivations behind the event, X probably wants to protect herself. As for the plausible pre-conditions prior to the event, X may have been trained in self-defense to successfully fend off Y’s attack. We can also infer the plausible characteristics of X; she might be strong, skilled, and brave. As a result of the event, X probably feels angry and might want to file a police report. Y, on the other hand, might feel scared of getting caught and want to run away. The examples above illustrate how day-to-day commonsense reasoning can be operationalized through a densely connected collection of inferential knowledge. It is through this knowledge that we can watch a two-hour movie and understand a story that spans over several months, as we can reason about a great number of events, causes, and effects,  X repels Y's attack X wanted to save themselves X wanted to protect others Y wants to run home X wants to file a police report X wants to leave the scene X needs to know self-defense X is skilled as a result, X
Cortical dynamics of contextually cued attentive visual learning and search: spatial and object evidence accumulation.

How do humans use target-predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, humans can learn that a certain combination of objects may define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. The ARTSCENE Search model is developed to illustrate the neural mechanisms of such memory-based context learning and guidance and to explain challenging behavioral data on positive-negative, spatial-object, and local-distant cueing effects during visual search, as well as related neuroanatomical, neurophysiological, and neuroimaging data. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined as a scene is scanned with saccadic eye movements. The model simulates the interactive dynamics of object and spatial contextual cueing and attention in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortex (area 46) primes possible target locations in posterior parietal cortex based on goal-modulated percepts of spatial scene gist that are represented in parahippocampal cortex. Model ventral prefrontal cortex (area 47/12) primes possible target identities in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex.