nils-herrmann/clean_scirepeval huggingface dataset:
An Analysis on the Correlation and Gender Difference between College Students' Internet Addiction and Mobile Phone Addiction in Taiwan

This study is aimed at constructing a correlative model between Internet addiction and mobile phone addiction; the aim is to analyse the correlation (if any) between the two traits and to discuss the influence confirming that the gender has difference on this fascinating topic; taking gender into account opens a new world of scientific study to us. The study collected 448 college students on an island as study subjects, with 61.2% males and 38.8% females. Moreover, this study issued Mobile Phone Addiction Scale and Internet Addiction Scale to conduct surveys on the participants and adopts the structural equation model (SEM) to process the collected data. According to the study result, (1) mobile phone addiction and Internet addiction are positively related; (2) female college students score higher than male ones in the aspect of mobile addiction. Lastly, this study proposes relevant suggestions to serve as a reference for schools, college students, and future studies based on the study results.
Wigner distributions and how they relate to the light field

In wave optics, the Wigner distribution and its Fourier dual, the ambiguity function, are important tools in optical system simulation and analysis. The light field fulfills a similar role in the computer graphics community. In this paper, we establish that the light field as it is used in computer graphics is equivalent to a smoothed Wigner distribution and that these are equivalent to the raw Wigner distribution under a geometric optics approximation. Using this insight, we then explore two recent contributions: Fourier slice photography in computer graphics and wavefront coding in optics, and we examine the similarity between explanations of them using Wigner distributions and explanations of them using light fields. Understanding this long-suspected equivalence may lead to additional insights and the productive exchange of ideas between the two fields.
Intrusion detection in SCADA systems using machine learning techniques

In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automate SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF (Intrusion Detection Message Exchange Format) messages that carry information about the source of the incident, the time and a classification of the alarm.
Privacy-aware location privacy preference recommendations

Location-Based Services have become increasingly popular due to the prevalence of smart devices and location-sharing applications such as Facebook and Foursquare. The protection of people’s sensitive location data in such applications is an important requirement. Conventional location privacy protection methods, however, such as manually defining privacy rules or asking users to make decisions each time they enter a new location may be overly complex, intrusive or unwieldy. An alternative is to use machine learning to predict people’s privacy preferences and automatically configure settings. Model-based machine learning classifiers may be too computationally complex to be used in real-world applications, or suffer from poor performance when training data are insufficient. In this paper we propose a location-privacy recommender that can provide people with recommendations of appropriate location privacy settings through user-user collaborative filtering. Using a realworld location-sharing dataset, we show that the prediction accuracy of our scheme (73.08%) is similar to the best performance of model-based classifiers (75.30%), and at the same time causes fewer privacy leaks (11.75% vs 12.70%). Our scheme further outperforms model-based classifiers when there are insufficient training data. Since privacy preferences are innately private, we make our recommender privacy-aware by obfuscating people’s preferences. Our results show that obfuscation leads to a minimal loss of prediction accuracy (0.76%).
Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

We describe the winning entry to the Amazon Picking Challenge. From the experience of building this system and competing in the Amazon Picking Challenge, we derive several conclusions: 1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions—modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback. 2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. To achieve this, our community should agree on key aspects that characterize the solution space of robotic systems. 3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra.
Discovering and understanding android sensor usage behaviors with data flow analysis

Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.
Personalization of Context-Dependent Applications Through Trigger-Action Rules

Our life is characterized by the presence of a multitude of interactive devices and smart objects exploited for disparate goals in different contexts of use. Thus, it is impossible for application developers to predict at design time the devices and objects users will exploit, how they will be arranged, and in which situations and for which objectives they will be used. For such reasons, it is important to make end users able to easily and autonomously personalize the behaviour of their Internet of Things applications, so that they can better comply with their specific expectations. In this paper, we present a method and a set of tools that allow end users without programming experience to customize the context-dependent behaviour of their Web applications through the specification of trigger-action rules. The environment is able to support end-user specification of more flexible behaviour than what can be done with existing commercial tools, and it also includes an underlying infrastructure able to detect the possible contextual changes in order to achieve the desired behaviour. The resulting set of tools is able to support the dynamic creation and execution of personalized application versions more suitable for users’ needs in specific contexts of use. Thus, it represents a contribution to obtaining low threshold/high ceiling environments. We also report on an example application in the home automation domain, and a user study that has provided useful positive feedback.
How Teachers Use Data to Help Students Learn: Contextual Inquiry for the Design of a Dashboard

Although learning with Intelligent Tutoring Systems (ITS) has been well studied, little research has investigated what role teachers can play, if empowered with data. Many ITSs provide student performance reports, but they may not be designed to serve teachers’ needs well, which is important for a well-designed dashboard. We investigated what student data is most helpful to teachers and how they use data to adjust and individualize instruction. Specifically, we conducted Contextual Inquiry interviews with teachers and used Interpretation Sessions and Affinity Diagramming to analyze the data. We found that teachers generate data on students’ concept mastery, misconceptions and errors, and utilize data provided by ITSs and other software. Teachers use this data to drive instruction and remediate issues on an individual and class level. Our study uncovers how data can support teachers in helping students learn and provides a solid foundation and recommendations for designing a teacher’s dashboard.
High Fidelity Data Reduction for Big Data Security Dependency Analyses

Intrusive multi-step attacks, such as Advanced Persistent Threat (APT) attacks, have plagued enterprises with significant financial losses and are the top reason for enterprises to increase their security budgets. Since these attacks are sophisticated and stealthy, they can remain undetected for years if individual steps are buried in background "noise." Thus, enterprises are seeking solutions to "connect the suspicious dots" across multiple activities. This requires ubiquitous system auditing for long periods of time, which in turn causes overwhelmingly large amount of system audit events. Given a limited system budget, how to efficiently handle ever-increasing system audit logs is a great challenge. This paper proposes a new approach that exploits the dependency among system events to reduce the number of log entries while still supporting high-quality forensic analysis. In particular, we first propose an aggregation algorithm that preserves the dependency of events during data reduction to ensure the high quality of forensic analysis. Then we propose an aggressive reduction algorithm and exploit domain knowledge for further data reduction. To validate the efficacy of our proposed approach, we conduct a comprehensive evaluation on real-world auditing systems using log traces of more than one month. Our evaluation results demonstrate that our approach can significantly reduce the size of system logs and improve the efficiency of forensic analysis without losing accuracy.
Keynote paper: Unlocking the learning value of wireless mobile devices

Many researchers see the potential of wireless mobile learning devices to achieve large-scale impact on learning because of portability, low cost, and communications features. This enthusiasm is shared but the lessons drawn from three well-documented uses of connected handheld devices in education lead towards challenges ahead. First, ‘wireless, mobile learning’ is an imprecise description of what it takes to connect learners and their devices together in a productive manner. Research needs to arrive at a more precise understanding of the attributes of wireless networking that meet acclaimed pedagogical requirements and desires. Second, ‘pedagogical applications’ are often led down the wrong road by complex views of technology and simplistic views of social practices. Further research is needed that tells the story of rich pedagogical practice arising out of simple wireless and mobile technologies. Third, ‘large scale’ impact depends on the extent to which a common platform, that meets the requirements of pedagogically rich applications, becomes available. At the moment ‘wireless mobile technologies for education’ are incredibly diverse and incompatible; to achieve scale, a strong vision will be needed to lead to standardisation, overcoming the tendency to marketplace fragmentation.
Robust clustering methods: a unified view

Clustering methods need to be robust if they are to be useful in practice. In this paper, we analyze several popular robust clustering methods and show that they have much in common. We also establish a connection between fuzzy set theory and robust statistics and point out the similarities between robust clustering methods and statistical methods such as the weighted least-squares (LS) technique, the M estimator, the minimum volume ellipsoid (MVE) algorithm, cooperative robust estimation (CRE), minimization of probability of randomness (MINPRAN), and the epsilon contamination model. By gleaning the common principles upon which the methods proposed in the literature are based, we arrive at a unified view of robust clustering methods. We define several general concepts that are useful in robust clustering, state the robust clustering problem in terms of the defined concepts, and propose generic algorithms and guidelines for clustering noisy data. We also discuss why the generalized Hough transform is a suboptimal solution to the robust clustering problem.
Understanding the Role and Methods of Meta-Analysis in IS Research

Four methods for reviewing a body of research literature – narrative review, descriptive review, vote-counting, and meta-analysis – are compared. Meta-analysis as a formalized, systematic review method is discussed in detail in terms of its history, current status, advantages, common analytic methods, and recent developments. Meta-analysis is found to be underutilized in IS. Suggestions on encouraging the use of metaanalysis in IS research and procedures recommended for meta-analysis are also provided.
Quadrotor Using Minimal Sensing For Autonomous Indoor Flight

This paper presents a Miniature Aerial Vehicle (MAV) capable of handsoff autonomous operation within indoor environments. Our prototype is a Quadrotor weighing approximately 600g, with a diameter of 550mm, which carries the necessary electronics for stability control, altitude control, collision avoidance and anti-drift control. This MAV is equipped with three rate gyroscopes, three accelerometers, one ultrasonic sensor, four infrared sensors, a high-speed motor controller and a flight computer. Autonomous flight tests have been carried out in a 7x6-m room.
LaRED: a large RGB-D extensible hand gesture dataset

We present the LaRED, a Large RGB-D Extensible hand gesture Dataset, recorded with an Intel's newly-developed short range depth camera. This dataset is unique and differs from the existing ones in several aspects. Firstly, the large volume of data recorded: 243, 000 tuples where each tuple is composed of a color image, a depth image, and a mask of the hand region. Secondly, the number of different classes provided: a total of 81 classes (27 gestures in 3 different rotations). Thirdly, the extensibility of dataset: the software used to record and inspect the dataset is also available, giving the possibility for future users to increase the number of data as well as the number of gestures. Finally, in this paper, some experiments are presented to characterize the dataset and establish a baseline as the start point to develop more complex recognition algorithms. The LaRED dataset is publicly available at: http://mclab.citi.sinica.edu.tw/dataset/lared/lared.html.
Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator

The goal of this work is to develop a soft robotic manipulation system that is capable of autonomous, dynamic, and safe interactions with humans and its environment. First, we develop a dynamic model for a multi-body fluidic elastomer manipulator that is composed entirely from soft rubber and subject to the self-loading effects of gravity. Then, we present a strategy for independently identifying all unknown components of the system: the soft manipulator, its distributed fluidic elastomer actuators, as well as drive cylinders that supply fluid energy. Next, using this model and trajectory optimization techniques we find locally optimal open-loop policies that allow the system to perform dynamic maneuvers we call grabs. In 37 experimental trials with a physical prototype, we successfully perform a grab 92% of the time. By studying such an extreme example of a soft robot, we can begin to solve hard problems inhibiting the mainstream use of soft machines.
Fast shortest path distance estimation in large networks

In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landmarks and computing offline the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, it can be estimated quickly by combining the precomputed distances. We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. We therefore explore theoretical insights to devise a variety of simple methods that scale well in very large networks. The efficiency of the suggested techniques is tested experimentally using five real-world graphs having millions of edges. While theoretical bounds support the claim that random landmarks work well in practice, our extensive experimentation shows that smart landmark selection can yield dramatically more accurate results: for a given target accuracy, our methods require as much as 250 times less space than selecting landmarks at random. In addition, we demonstrate that at a very small accuracy loss our techniques are several orders of magnitude faster than the state-of-the-art exact methods. Finally, we study an application of our methods to the task of social search in large graphs.
Design of a CMOS Potentiostat Circuit for Electrochemical Detector Arrays

High-throughput electrode arrays are required for advancing devices for testing the effect of drugs on cellular function. In this paper, we present design criteria for a potentiostat circuit that is capable of measuring transient amperometric oxidation currents at the surface of an electrode with submillisecond time resolution and picoampere current resolution. The potentiostat is a regulated cascode stage in which a high-gain amplifier maintains the electrode voltage through a negative feedback loop. The potentiostat uses a new shared amplifier structure in which all of the amplifiers in a given row of detectors share a common half circuit permitting us to use fewer transistors per detector. We also present measurements from a test chip that was fabricated in a 0.5-mum, 5-V CMOS process through MOSIS. Each detector occupied a layout area of 35 mumtimes15 mum and contained eight transistors and a 50-fF integrating capacitor. The rms current noise at 2-kHz bandwidth is ap110 fA. The maximum charge storage capacity at 2 kHz is 1.26times106 electrons
DBIQS — An intelligent system for querying and mining databases using NLP

Facts play a critical role in our lives. We have got radical ways to accumulate and store these facts as meaningful information. A database is an organized collection of facts and information stored in the form of data. A good organization has to maintain lots and lots of data. There are ultimate tools which can collect and store the huge data in its proper format in the main repository but still the users doesn't get benefited from it up to its full potential. Retrieving meaningful information from such repositories requires an extensive knowledge of database languages like SQL. However, not everybody is able to write the complex SQL queries to extract the needful information. This intricate problem can be solved to a great extent by providing a Natural Language Interface to the users using which they can query a database in their own language. In this paper, we propose a Database Intelligent Querying System (DBIQS) which portrays completely automatic, simple, fast and reliable way to query a database.
Planning formalisms and authoring in interactive storytelling

Interacting Storytelling systems integrate AI techniques such as planning with narrative representations to generate stories. In this paper, we discuss the use of planning formalisms in Interactive Storytelling from the perspective of story generation and authoring. We compare two different planning formalisms, Hierarchical Task Network (HTN) planning and Heuristic Search Planning (HSP). While HTN provide a strong basis for narrative coherence in the context of interactivity, HSP offer additional flexibility and the generation of stories and the mechanisms for generating comic situations.
Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis

Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled data. Thus, CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this paper. To reduce the redundant information and extract the most distinct features, ROI and PCA operations are performed for learned features of convolutional layer or pooling layer. After that, the extracted features are fed into SVM classifier. Experimental results based on the LivDet (2013) and the LivDet (2011) datasets, which are captured by using different fingerprint materials, indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.