nils-herrmann/clean_scirepeval huggingface dataset:
Interpreting traffic dynamics using ubiquitous urban data

Given a large collection of urban datasets, how can we find their hidden correlations? For example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 with about half million taxi trips generated per day. In the meantime, we have a rich set of urban data in NYC including points-of-interest (POIs), geo-tagged tweets, weather, vehicle collisions, etc. Is it possible that these ubiquitous datasets can be used to explain the city traffic? Understanding the hidden correlation between external data and traffic data would allow us to answer many important questions in urban computing such as: If we observe a high traffic volume at Madison Square Garden (MSG) in NYC, is it because of the regular peak hour or a big event being held at MSG? If a disaster weather such as a hurricane or a snow storm hits the city, how would the traffic be affected?
 Most of existing studies on traffic dynamics focus only on traffic data itself and do not seek for external datasets to explain traffic. In this paper, we present our results in attempts to understand taxi traffic dynamics in NYC from multiple external data sources. We use four real-world ubiquitous urban datasets, including POIs, weather, geo-tagged tweets, and collision records. To address the heterogeneity of ubiquitous urban data, we present carefully-designed feature representations for these datasets. Our analysis suggests that POIs can well describe the regular traffic patterns. In addition, geo-tagged tweets can be used to explain irregular traffic caused by big events, and weather may account for abnormal traffic drops.
The Effects of Using Mobile Devices on Student Achievement in Language Learning : A Meta-Analysis

The use of mobile technologies has recently received great attention in language learning. Most research evaluates the effects of employing mobile devices in language learning and explores the design of mobile-learning interventions that can maximize the benefits of new technologies. However, it is still unclear whether the use of mobile devices in language learning is more effective than other instructional approaches. It is also not clear whether the effects of mobile-device use vary in different settings. Our meta-analysis will explore these questions about mobile technology use in language learning. Based on the specific inclusion and exclusion criteria, 22 d-type effect sizes from 20 studies were calculated for the meta-analysis. We adopted the random-effects model, and the estimated average effect was 0.51 (se = 0.10). This is a moderate positive overall effect of using mobile devices on language acquisition and language-learning achievement. Moderator analyses under the mixed-effects model examined six features; effects varied significantly only by test type and source of the study. The overall effect and the effects of these moderators of mobile-device use on achievement in language learning are discussed.
Unsupervised Multi-Class Co-Segmentation via Joint-Cut Over $L_{1}$ -Manifold Hyper-Graph of Discriminative Image Regions

This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. It can combat certain image co-segmentation difficulties due to viewpoint change, partial occlusion, complex background, transient illumination, and cluttering texture patterns. Our key idea is to construct a powerful hyper-graph joint-cut framework, which incorporates mid-level image regions-based intra-image feature representation and  $L_{1}$ -manifold graph-based inter-image coherency exploration. For local image region generation, we propose a bi-harmonic distance distribution difference metric to govern the super-pixel clustering in a bottom-up way. It not only affords drastic data reduction but also gives rise to discriminative and structure meaningful feature representation. As for the inter-image coherency, we leverage multi-type features involved  $L_{1}$ -graph to detect the underlying local manifold from cross-image regions. As a result, the implicit supervising information could be encoded into the unsupervised hyper-graph joint-cut framework. We conduct extensive experiments and make comprehensive evaluations with other state-of-the-art methods over various benchmarks, including iCoseg, MSRC, and Oxford flower. All the results demonstrate the superiorities of our method in terms of accuracy, robustness, efficiency, and versatility.
A feature-based approach for monocular camera tracking in unknown environments

Camera tracking is an important issue in many computer vision and robotics applications, such as, augmented reality and Simultaneous Localization And Mapping (SLAM). In this paper, a feature-based technique for monocular camera tracking is proposed. The proposed approach is based on tracking a set of sparse features, which are successively tracked in a stream of video frames. In the developed system, camera initially views a chessboard with known cell size for few frames to be enabled to construct initial map of the environment. Thereafter, Camera pose estimation for each new incoming frame is carried out in a framework that is merely working with a set of visible natural landmarks. Estimation of 6-DOF camera pose parameters is performed using a particle filter. Moreover, recovering depth of newly detected landmarks, a linear triangulation method is used. The proposed method is applied on real world videos and positioning error of the camera pose is less than 3 cm in average that indicates effectiveness and accuracy of the proposed method.
Au th or ' s pe rs on al co py Facial symmetry detection ability changes across the menstrual cycle

The effects of menstrual cycle phase and hormones on women’s visual ability to detect symmetry and visual preference for symmetry were examined. Participants completed tests of symmetry detection and preference for male facial symmetry at two of three menstrual cycle phases (menses, periovulatory, and luteal). Women were better at detecting facial symmetry during the menses than luteal phase of their cycle. A trend indicated the opposite pattern for dot symmetry detection. Similarly, change in salivary progesterone levels across the cycle was negatively related to change in facial symmetry detection scores. However, there was no clear evidence of a greater preference for facial symmetry at any cycle phase, despite an overall preference for facial symmetry across phases. These findings suggest a menses phase advantage and a low progesterone advantage in women’s ability to detect facial symmetry. The results are discussed in the context of hormonal, evolutionary mate selection, and functional neurocognitive theories.
Navicular drop as a composite measure of excessive pronation.

The measure of navicular drop has been used as an indicator of pronation at the foot. It is defined as the distance the navicular tuberosity moves in standing, as the subtalar joint is allowed to move from its neutral position to a relaxed position. The purposes of this study were to test the reliability of a method to measure navicular drop and to assess the relationships among measures of forefoot to rearfoot position, subtalar joint neutral position, and navicular drop. The results support traditional biomechanical theory but indicate that other factors contribute significantly to navicular drop.
Fleet: defending SDNs from malicious administrators

We present the malicious administrator problem, in which one or more network administrators attempt to damage routing, forwarding, or network availability by misconfiguring controllers. While this threat vector has been acknowledged in previous work, most solutions have focused on enforcing specific policies for forwarding rules. We present a definition of this problem and a controller design called Fleet that makes a first step towards addressing this problem. We present two protocols that can be used with the Fleet controller, and argue that its lower layer deployed on top of switches eliminates many problems of using multiple controllers in SDNs. We then present a prototype simulation and show that as long as a majority of non-malicious administrators exists, we can usually recover from link failures within several seconds (a time dominated by failure detection speed and inter-administrator latency).
Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.
Defect cluster recognition system for fabricated semiconductor wafers

The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the highvolume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry’s needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).
Complications of circumcision

Circumcision remains the most common operation performed on males. Although, not technically difficult, it is accompanied by a rate of morbidity and can result in complications ranging from trivial to tragic. The reported incidence of complications varies from 0.1% to 35% the most common being infection, bleeding and failure to remove the appropriate amount of foreskin. Forty patients suffering from different degrees of circumcision complications and their treatment are presented. In all patients satisfactory functional and cosmetic results were achieved. Whether it is done for ritualistic, religious or medical reasons circumcision should be performed by a fully trained surgeon using a proper technique as follows 1) adequate use of antiseptic agents; 2) complete separation of inner preputial epithelium from the glans; 3) marking the skin to be removed at the beginning of operation; 4) careful attention to the baby’s voiding within the first 6 to 8 h after circumcision; 5) removal or replacement of the dressings on the day following circumcision.
Room-Temperature Wafer Bonding Using Al/Ti/Au Layers for Integrated Reflectors in the Ultraviolet Spectral Region

A room-temperature wafer bonding process using Al/Ti/Au multi-layers has been demonstrated for integrated reflectors in the ultraviolet (UV) spectral region. A glass wafer with Al (35 nm)/Ti (3 nm)/Au (15 nm) layers and the wafer with Ti (3 nm)/Au (15 nm) layers were successfully bonded in ambient air at room temperature after argon radio-frequency plasma activation process. A thin native aluminum oxide (Al2O3) layer was formed on the Al layer and was found to be effective in avoiding solid-state reactive diffusion between Au and Al. Interface reflectance of more than 80 % was obtained in the UV spectral region (200-400 nm).
SHILLING ATTACK DETECTION IN RECOMMENDER SYSTEMS USING CLASSIFICATION TECHNIQUES

Collaborative filtering based recommender system is prone to shilling attacks because of its open nature. Shillers inject pseudonomous profiles in the system’s database with the intent of manipulating the recommendations to their benefits. Prior study has shown that the system’s behavior can be easily influenced by even a less number of shilling profiles. In this paper, we simulated various attack models on Movie-Lens 1 dataset and used machine learning techniques to detect the attacks. We compared five classification algorithms and proposed a new model by integrating two models with high performances. In our experiments, we investigated and proved that the combination of random forest and adaptive boosting algorithm is more accurate than simple random forest model.  Collaborative filtering, recommender system, shilling attack, prediction shift, precision, recall, fmeasure, classification
An empirical study of electronic reverse auction project outcomes

We extend the conceptual model developed by Amelinckx et al. (2008) by relating electronic reverse auction (ERA) project outcomes to ERA project satisfaction. We formulate hypotheses about the relationships among organizational and project antecedents, a set of financial, operational, and strategic ERA project outcomes, and ERA project satisfaction. We empirically test the extended model with a sample of 180 buying professionals from ERA project teams at large global companies. Our results show that operational and strategic outcomes are positively related to ERA project satisfaction, while price savings are not. We also find positive relationships between financial outcomes and project team expertise; operational outcomes and organizational commitment, cross-functional project team composition, and procedural fairness ; and strategic outcomes and top management support, organizational commitment, and procedural fairness. An electronic reverse auction (ERA) is ''an online, real-time dynamic auction between a buying organization and a group of pre-qualified suppliers who compete against each other to win the business to supply goods or services that have clearly defined specifications for design, quantity, quality, delivery, and related terms and conditions. These suppliers compete by bidding against each other online over the Internet using specialized software by submitting successively lower priced bids during a scheduled time period'' (Beall et al. 2003). Over the past two decades, ERAs have been used in various industries, (Beall et al. 2003, Ray et al. 2011, Wang et al. 2013). ERAs are increasingly popular among buying organizations, although their use sparks controversy and ethical concerns in the sourcing world (Charki et al. 2010). Indeed, the one-sided focus on price savings in ERAs is considered to be at odds with the benefits of long-term cooperative buyer–supplier relationships (Beall et al. 2003, Hunt et al. 2006). However, several researchers have declared that ERAs are here to stay, as they are relatively easy to install and use and have resulted in positive outcomes across a range of offerings and contexts (Beall et al. 2003, Hur et al. 2006). In prior research work on ERAs, Amelinckx et al. (2008) developed a conceptual model based on an extensive review of the electronic sourcing literature and exploratory research involving multiple case studies. The authors identified operational and strategic outcomes that buying organizations can obtain in ERAs, in addition to financial gains. Furthermore, the authors asserted that the different outcomes can be obtained jointly, through the implementation of important organizational and project antecedents, and as such alleviate …
The Digital Asthma Patient: The History and Future of Inhaler Based Health Monitoring Devices.

The wave of digital health is continuously growing and promises to transform healthcare and optimize the patients' experience. Asthma is in the center of these digital developments, as it is a chronic disease that requires the continuous attention of both health care professionals and patients themselves. The accurate and timely assessment of the state of asthma is the fundamental basis of digital health approaches and is also the most significant factor toward the preventive and efficient management of the disease. Furthermore, the necessity of inhaled medication offers a basic platform upon which modern technologies can be integrated, namely the inhaler device itself. Inhaler-based monitoring devices were introduced in the beginning of the 1980s and have been evolving but mainly for the assessment of medication adherence. As technology progresses and novel sensing components are becoming available, the enhancement of inhalers with a wider range of monitoring capabilities holds the promise to further support and optimize asthma self-management. The current article aims to take a step for the mapping of this territory and start the discussion among healthcare professionals and engineers for the identification and the development of technologies that can offer personalized asthma self-management with clinical significance. In this direction, a technical review of inhaler based monitoring devices is presented, together with an overview of their use in clinical research. The aggregated results are then summarized and discussed for the identification of key drivers that can lead the future of inhalers.
Reynolds-Averaged Turbulence Modeling Using Type I and Type II Machine Learning Frameworks with Deep Learning

Deep learning (DL)-based Reynolds stress with its capability to leverage values of large data can be used to close Reynolds-averaged Navier-Stoke (RANS) equations. Type I and Type II machine learning (ML) frameworks are studied to investigate data and flow feature requirements while training DL-based Reynolds stress. The paper presents a method, flow features coverage mapping (FFCM), to quantify the physics coverage of DL-based closures that can be used to examine the sufficiency of training data points as well as input flow features for data-driven turbulence models. Three case studies are formulated to demonstrate the properties of Type I and Type II ML. The first case indicates that errors of RANS equations with DL-based Reynolds stress by Type I ML are accumulated along with the simulation time when training data do not sufficiently cover transient details. The second case uses Type I ML to show that DL can figure out time history of flow transients from data sampled at various times. The case study also shows that the necessary and sufficient flow features of DL-based closures are first-order spatial derivatives of velocity fields. The last case demonstrates the limitation of Type II ML for unsteady flow simulation. Type II ML requires initial conditions to be sufficiently close to reference data. Then reference data can be used to improve RANS simulation.
Chapter 2 Heterogeneous Wireless Networks

In this chapter, we will review the underlying mechanisms for the evolution of wireless communication networks. We will first discuss macro-cellular technologies used in traditional telecommunication systems, and then introduce some micro-cellular technologies as a recent advance in the telecommunications industry. Finally, we will describe existing interworking techniques available in literature and in standardization, including loosely and tightly coupled, I-WLAN and IEEE 802.21. The term macro-cell is used to describe cells with larger sizes. A macro-cell is a cell in mobile phone networks that provide radio coverage served by a high power cellular base station. The antennas for macro-cells are mounted on ground-based masts and other existing structures, at a height that provides a clear view over the surrounding buildings and terrain. Macro-cell base stations have power outputs of typically tens of watts [18]. Most wireless communication systems maintained by traditional mobile network operators are powered by macro-cellular technologies. In the 1980s, the 1G wireless communication system came to the mobile communication environment, which provided a data speed of 2.4 Kbps to support data communication with mobile phones. An example is Nordic Mobile Telephone (NMT). However, this generation still worked in analog system and there were tight limitations in terms of the system capacity and data rate.
Embodied Conversational Agents in Clinical Psychology: A Scoping Review

Embodied conversational agents (ECAs) are computer-generated characters that simulate key properties of human face-to-face conversation, such as verbal and nonverbal behavior. In Internet-based eHealth interventions, ECAs may be used for the delivery of automated human support factors.



We aim to provide an overview of the technological and clinical possibilities, as well as the evidence base for ECA applications in clinical psychology, to inform health professionals about the activity in this field of research.



Given the large variety of applied methodologies, types of applications, and scientific disciplines involved in ECA research, we conducted a systematic scoping review. Scoping reviews aim to map key concepts and types of evidence underlying an area of research, and answer less-specific questions than traditional systematic reviews. Systematic searches for ECA applications in the treatment of mood, anxiety, psychotic, autism spectrum, and substance use disorders were conducted in databases in the fields of psychology and computer science, as well as in interdisciplinary databases. Studies were included if they conveyed primary research findings on an ECA application that targeted one of the disorders. We mapped each study's background information, how the different disorders were addressed, how ECAs and users could interact with one another, methodological aspects, and the study's aims and outcomes.



This study included N=54 publications (N=49 studies). More than half of the studies (n=26) focused on autism treatment, and ECAs were used most often for social skills training (n=23). Applications ranged from simple reinforcement of social behaviors through emotional expressions to sophisticated multimodal conversational systems. Most applications (n=43) were still in the development and piloting phase, that is, not yet ready for routine practice evaluation or application. Few studies conducted controlled research into clinical effects of ECAs, such as a reduction in symptom severity.



ECAs for mental disorders are emerging. State-of-the-art techniques, involving, for example, communication through natural language or nonverbal behavior, are increasingly being considered and adopted for psychotherapeutic interventions in ECA research with promising results. However, evidence on their clinical application remains scarce. At present, their value to clinical practice lies mostly in the experimental determination of critical human support factors. In the context of using ECAs as an adjunct to existing interventions with the aim of supporting users, important questions remain with regard to the personalization of ECAs' interaction with users, and the optimal timing and manner of providing support. To increase the evidence base with regard to Internet interventions, we propose an additional focus on low-tech ECA solutions that can be rapidly developed, tested, and applied in routine practice.