nils-herrmann/clean_scirepeval huggingface dataset:
Generation Like: Comparative Characteristics in Instagram

The emergence of social media has had a significant impact on how people communicate and socialize. Teens use social media to make and maintain social connections with friends and build their reputation. However, the way of analyzing the characteristics of teens in social media has mostly relied on ethnographic accounts or quantitative analyses with small datasets. This paper shows the possibility of detecting age information in user profiles by using a combination of textual and facial recognition methods and presents a comparative study of 27K teens and adults in Instagram. Our analysis highlights that (1) teens tend to post fewer photos but highly engage in adding more tags to their own photos and receiving more Likes and comments about their photos from others, and (2) to post more selfies and express themselves more than adults, showing a higher sense of self-representation. We demonstrate the application of our novel method that shows clear trends of age differences as well as substantiates previous insights in social media.
Runtime Neural Pruning

In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime. Unlike existing neural pruning methods which produce a fixed pruned model for deployment, our method preserves the full ability of the original network and conducts pruning according to the input image and current feature maps adaptively. The pruning is performed in a bottom-up, layer-by-layer manner, which we model as a Markov decision process and use reinforcement learning for training. The agent judges the importance of each convolutional kernel and conducts channel-wise pruning conditioned on different samples, where the network is pruned more when the image is easier for the task. Since the ability of network is fully preserved, the balance point is easily adjustable according to the available resources. Our method can be applied to off-the-shelf network structures and reach a better tradeoff between speed and accuracy, especially with a large pruning rate.
Learning filter banks within a deep neural network framework

Mel-filter banks are commonly used in speech recognition, as they are motivated from theory related to speech production and perception. While features derived from mel-filter banks are quite popular, we argue that this filter bank is not really an appropriate choice as it is not learned for the objective at hand, i.e. speech recognition. In this paper, we explore replacing the filter bank with a filter bank layer that is learned jointly with the rest of a deep neural network. Thus, the filter bank is learned to minimize cross-entropy, which is more closely tied to the speech recognition objective. On a 50-hour English Broadcast News task, we show that we can achieve a 5% relative improvement in word error rate (WER) using the filter bank learning approach, compared to having a fixed set of filters.
Modelling Intelligent Phishing Detection System for E-banking Using Fuzzy Data Mining

Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the ‘fuzziness’ in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria’s with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria’s (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.
Hooked on Facebook: The Role of Social Anxiety and Need for Social Assurance in Problematic Use of Facebook

There is a growing concern that excessive and uncontrolled use of Facebook not only interferes with performance at school or work but also poses threats to physical and psychological well-being. The present research investigated how two individual difference variables--social anxiety and need for social assurance--affect problematic use of Facebook. Drawing on the basic premises of the social skill model of problematic Internet use, we hypothesized that social anxiety and need for social assurance would be positively correlated with problematic use of Facebook. Furthermore, it was predicted that need for social assurance would moderate the relationship between social anxiety and problematic use. A cross-sectional online survey was conducted with a college student sample in the United States (N=243) to test the proposed hypotheses. Results showed that both social anxiety and need for social assurance had a significant positive association with problematic use of Facebook. More importantly, the data demonstrated that need for social assurance served as a significant moderator of the relationship between social anxiety and problematic Facebook use. The positive association between social anxiety and problematic Facebook use was significant only for Facebook users with medium to high levels of need for social assurance but not for those with a low level of need for social assurance. Theoretical and practical implications of these findings were discussed.
Cr-N alloy thin-film based torque sensors and joint torque servo systems for compliant robot control

This paper proposes a new torque sensing method, Cr-N alloy strain sensitive thin-film based torque sensors and distributed joint torque servo systems that enable human support robots to have capabilities to make physical interaction in adaptive and safe operation tasks. Stiffer torque sensing with stable and high-resolution sensing to meet practical level of the developed torque sensors have been achieved. We have developed the joint torque control based 4-DOF arm models in order to verify practical effectiveness of the proposed joint torque servo systems. We also demonstrated the joint torque control based bilateral master slave system exploring future applications.
The Big Five Personality Traits and the Life Course : A 45-Year Longitudinal Study

One hundred sixty-three men who have been followed prospectively for over 45 years were rated on a set of 25 personality traits at the end of their college careers and took the NEO-PI at approximately ages 67–68. The college traits were transformed, via a rating procedure, to scales assessing each of the Big Five dimensions and related to the NEO-PI. Three traits—Neuroticism, Extraversion, and Openness—exhibited significant correlations across the 45-year interval. Furthermore, the trait profiles remained relatively stable over that interval. Both sets of personality traits were related to a wide variety of life course variables representing the domains of global adult adjustment, career functioning/success, creativity, social relations, mental health, substance abuse, childhood characteristics, familial history of pathology, maturity of defenses, and political attitudes. Conscientiousness in college was the best predictor of what happened to the men in the future, whereas Neuroticism in late midlife was the best correlate of life course functioning across a variety of domains.  1999
STREAMCUBE: Hierarchical spatio-temporal hashtag clustering for event exploration over the Twitter stream

What is happening around the world? When and where? Mining the geo-tagged Twitter stream makes it possible to answer the above questions in real-time. Although a single tweet can be short and noisy, proper aggregations of tweets can provide meaningful results. In this paper, we focus on hierarchical spatio-temporal hashtag clustering techniques. Our system has the following features: (1) Exploring events (hashtag clusters) with different space granularity. Users can zoom in and out on maps to find out what is happening in a particular area. (2) Exploring events with different time granularity. Users can choose to see what is happening today or in the past week. (3) Efficient single-pass algorithm for event identification, which provides human-readable hashtag clusters. (4) Efficient event ranking which aims to find burst events and localized events given a particular region and time frame. To support aggregation with different space and time granularity, we propose a data structure called STREAMCUBE, which is an extension of the data cube structure from the database community with spatial and temporal hierarchy. To achieve high scalability, we propose a divide-and-conquer method to construct the STREAMCUBE. To support flexible event ranking with different weights, we proposed a top-k based index. Different efficient methods are used to speed up event similarity computations. Finally, we have conducted extensive experiments on a real twitter data. Experimental results show that our framework can provide meaningful results with high scalability.
Functional requirements of future automotive radar systems

Automotive radar systems are starting to divide into two groups: Highly specialized stand alone low-cost sensors targeting high volume markets and high-performance multi purpose sensors used in sophisticated data fusion architectures. While the first group focuses on ultimate cost reduction, the latter provides the basis for future high-performance driver assistance functions which might be deployed in middle- to luxury-class cars. This paper addresses both sensor groups and lists OEM requirements resulting from the specifications of future driver assistance functions and from upcoming perception system architectures. The second part of this contribution addresses recent trends in automotive radar sensing technology.
Emotions in Text: Dimensional and Categorical Models

Text often expresses the writer's emotional state or evokes emotions in the reader. The nature of emotional phenomena like reading and writing can be interpreted in different ways and represented with different computational models. Affective computing (AC) researchers often use a categorical model in which text data is associated with emotional labels. We introduce a new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches. The approach is evaluated using four data sets of texts reflecting different emotional phenomena. An emotional thesaurus and a bag-­‐of-­‐words model are used to generate vectors for each pseudo-­‐ document, then for the categorical models three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-­‐negative Matrix Factorization (NMF). For the dimensional model a normative database is used to produce three-­‐dimensional vectors (valence, arousal, dominance) for each pseudo-­‐document. This 3-­‐dimensional model can be used to generate psychologically driven visualizations. Both models can be used for affect detection based on distances amongst categories and pseudo-­‐documents. Experiments show that the categorical model using NMF and the dimensional model tend to perform best. 1.  Emotions and affective states are pervasive in all forms of communication, including text based, and increasingly recognized as important to understanding the full meaning that a message conveys, or the impact it will have on readers. Given the increasing amounts of textual communication being produced (e.g. emails, user created content, published content) researchers are seeking automated language processing techniques that include models of emotions. Emotions and other affective states (e.g. moods) have been studied by many disciplines. Affect scientists have studied emotions since Darwin (Darwin, 1872), and different schools within psychology have produced different theories representing different ways of interpreting affective phenomena (comprehensively reviewed in Davidson, Scherer and Goldsmith, 2003). In the last decade technologists have also started contributing to this research. Affective Computing (AC) in particular is contributing new ways to improve communication between the sensitive human and the unemotionally computer. AC researchers have developed computational systems that recognize and respond to the affective states of the user (Calvo and D'Mello, 2010). Affect-­‐sensitive user interfaces are being developed in a number of domains including gaming, mental health, and learning technologies. The basic tenet behind most AC systems is that automatically recognizing and responding to a user's affective states during interactions with a computer, …
Optimal Airline Ticket Purchasing Using Automated User-Guided Feature Selection

Airline ticket purchase timing is a strategic problem that requires both historical data and domain knowledge to solve consistently. Even with some historical information (often a feature of modern travel reservation web sites), it is difficult for consumers to make true cost-minimizing decisions. To address this problem, we introduce an automated agent which is able to optimize purchase timing on behalf of customers and provide performance estimates of its computed action policy based on past performance. We apply machine learning to recent ticket price quotes from many competing airlines for the target flight route. Our novelty lies in extending this using a systematic feature extraction technique incorporating elementary user-provided domain knowledge that greatly enhances the performance of machine learning algorithms. Using this technique, our agent achieves much closer to the optimal purchase policy than other proposed decision theoretic approaches for this domain.
Torque characteristics of IPMSM with spoke and axial type magnets

This paper describes torque characteristics of the interior permanent magnet synchronous motor (IPMSM) using the inexpensive ferrite magnets. IPMSM model used in this study has the spoke and the axial type magnets in the rotor, and torque characteristics are analyzed by the three-dimensional finite element method (3D-FEM). As a result, torque characteristics can be improved by using both the spoke type magnets and the axial type magnets in the rotor.
Remote-Controllable Power Outlet System for Home Power Management

In this paper we describe the Wireless Power-Controlled Outlet Module (WPCOM) with a scalable mechanism for home power management which we have developed. The WPCOM integrates the multiple AC power sockets and a simple low-power microcontroller into a power outlet to switch the power of the sockets ON/OFF and to measure the power consumption of plugged electric home appliances. Our WPCOM consists of six scalable modules, that is, the Essential Control Module, the Bluetooth Module, the GSM Module, the Ethernet Module, the SD Card Module and the Power Measuring Module, which together provide an indoor wireless, and an outdoor remote control and monitor of electric home appliances. We have designed a PDA control software and remote control software which support the Graphic User Interface, thus allowing the user to easily monitor the electric home appliances through the PDA and the Internet individually. In addition, we use a Short Message Service to achieve control and monitoring through a GSM cellular mobile phone for remote use anytime and anywhere.
OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs

The rise of Uber as the global alternative taxi operator has attracted a lot of interest recently. Aside from the media headlines which discuss the new phenomenon, e.g. on how it has disrupted the traditional transportation industry, policy makers, economists, citizens and scientists have engaged in a discussion that is centred around the means to integrate the new generation of the sharing economy services in urban ecosystems. In this work, we aim to shed new light on the discussion, by taking advantage of a publicly available longitudinal dataset that describes the mobility of yellow taxis in New York City. In addition to movement, this data contains information on the fares paid by the taxi customers for each trip. As a result we are given the opportunity to provide a first head to head comparison between the iconic yellow taxi and its modern competitor, Uber, in one of the world’s largest metropolitan centres. We identify situations when Uber X, the cheapest version of the Uber taxi service, tends to be more expensive than yellow taxis for the same journey. We also demonstrate how Uber’s economic model effectively takes advantage of well known patterns in human movement. Finally, we take our analysis a step further by proposing a new mobile application that compares taxi prices in the city to facilitate traveller’s taxi choices, hoping to ultimately to lead to a reduction of commuter costs. Our study provides a case on how big datasets that become public can improve urban services for consumers by offering the opportunity for transparency in economic sectors that lack up to date regulations.
A Unified Building Model for 3D Urban GIS

Several tasks in urban and architectural design are today undertaken in a geospatial context. Building Information Models (BIM) and geospatial technologies offer 3D data models that provide information about buildings and the surrounding environment. The Industry Foundation Classes (IFC) and CityGML are today the two most prominent semantic models for representation of BIM and geospatial models respectively. CityGML has emerged as a standard for modeling city models while IFC has been developed as a reference model for building objects and sites. Current CAD and geospatial software provide tools that allow the conversion of information from one format to the other. These tools are however fairly limited in their capabilities, often resulting in data and information losses in the transformations. This paper describes a new approach for data integration based on a unified building model (UBM) which encapsulates both the CityGML and IFC models, thus avoiding translations between the models and loss of information. To build the UBM, all classes and related concepts were initially collected from both models, overlapping concepts were merged, new objects were created to ensure the capturing of both indoor and outdoor objects, and finally, spatial relationships between the objects were redefined. Unified Modeling Language (UML) notations were used for representing its objects and relationships between them. There are two use-case scenarios, both set in a hospital: “evacuation” and “allocating spaces for patient wards” were developed to validate and test the proposed UBM data model. Based on these two scenarios, four validation queries OPEN ACCESS ISPRS Int.  2012, 1 121 were defined in order to validate the appropriateness of the proposed unified building model. It has been validated, through the case scenarios and four queries, that the UBM being developed is able to integrate CityGML data as well as IFC data in an apparently seamless way. Constraints and enrichment functions are used for populating empty database tables and fields. The motivation scenarios also show the needs and benefits of having an integrated approach to the modeling of indoor and outdoor spatial features.
Privilege Escalation Attacks on Android

Android is a modern and popular software platform for smartphones. Among its predominant features is an advanced security model which is based on application-oriented mandatory access control and sandboxing. This allows developers and users to restrict the execution of an application to the privileges it has (mandatorily) assigned at installation time. The exploitation of vulnerabilities in program code is hence believed to be confined within the privilege boundaries of an application’s sandbox. However, in this paper we show that a privilege escalation attack is possible. We show that a genuine application exploited at runtime or a malicious application can escalate granted permissions. Our results immediately imply that Android’s security model cannot deal with a transitive permission usage attack and Android’s sandbox model fails as a last resort against malware and sophisticated runtime attacks.
Chi-Square Test Based Decision Trees Induction in Distributed Environment

The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one distributed learning algorithm which extends the original(centralized) CHAID algorithm to its distributed version. This distributed algorithm generates exactly the same results as its centralized counterpart. For completeness, a distributed quantization method is proposed so that continuous data can be processed by our algorithm. Experimental results for several well known data sets are presented and compared with decision trees generated using CHAID with centrally stored data.
Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening

The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.
Optical Versus Video See-Through Head-Mounted Displays in Medical Visualization

We compare two technological approaches to augmented reality for 3-D medical visualization: optical and video see-through devices. We provide a context to discuss the technology by reviewing several medical applications of augmented-reality re search efforts driven by real needs in the medical field, both in the United States and in Europe. We then discuss the issues for each approach, optical versus video, from both a technology and human-factor point of view. Finally, we point to potentially promising future developments of such devices including eye tracking and multifocus planes capabilities, as well as hybrid optical/video technology.