nils-herrmann/clean_scirepeval huggingface dataset:
An Automated Approach for Digital Forensic Analysis of Heterogeneous Big Data

The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous big data. The framework mainly focuses upon the investigations of three core issues: data volume, heterogeneous data and the investigators cognitive load in understanding the relationships between artefacts. The proposed approach focuses upon the use of metadata to solve the data volume problem, semantic web ontologies to solve the heterogeneous data sources and artificial intelligence models to support the automated identification and correlation of artefacts to reduce the burden placed upon the investigator to understand the nature and relationship of the artefacts.
Motivation in online learning: Testing a model of self-determination theory

As high attrition rates becomes a pressing issue of online learning and a major concern of online educators, it is important to investigate online learner motivation, including its antecedents and outcomes. Drawing on Deci and Ryan’s self-determination theory, this study proposed and tested a model for online learner motivation in two online certificate programs (N = 262). Results from structural equation modeling provided evidence for the mediating effect of need satisfaction between contextual support and motivation/self-determination; however, motivation/self-determination failed to predict learning outcomes. Additionally, this study supported SDT’s main theorizing that intrinsic motivation, extrinsic motivation, and amotivation are distinctive constructs, and found that the direct effect and indirect effects of contextual support exerted opposite impacts on learning outcomes. Implications for online learner support were discussed.
An OGSA-Based Quality of Service Framework

Grid computing provides a robust paradigm to aggregate disparate resources in a secure and controlled environment. Grid architectures require an underpinning Quality of Service (QoS) support in order to manage complex data and computation intensive applications. However, QoS guarantees in the Grid context have not been given the attention they merit. In order to enhance the functionality offered by computational Grids, we overlay the Grid framework with an advanced QoS architecture, called G-QoSM. The G-QoSM framework provides a new service-oriented QoS management model that leverages the Open Grid Service Architecture (OGSA) and has a number of interesting features: (1) Grid service discovery based on QoS attributes, (2) policybased admission control for advance reservation support, and (3) Grid service execution with QoS constraints. This paper discusses the different components of the G-QoSM framework, in the context of OGSA architectures.
System Diagnosis of Coronary Heart Disease Using a Combination of Dimensional Reduction and Data Mining Techniques : A Review

Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of b iomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.
Towards Effective Log Summarization

Database access logs are the canonical go-to resource for tasks ranging from performance tuning to security auditing. Unfortunately, they are also large, unwieldy, and it can be difficult for a human analyst to divine the intent behind typical queries in the log. With an eye towards creating tools for ad-hoc exploration of queries by intent, we analyze techniques for clustering queries by intent. Although numerous techniques have already been developed for log summarization, they target specific goals like query recommendation or storage layout optimization rather than the more fuzzy notion of query intent. In this paper, we first survey a variety of log summarization techniques, focusing on a class of approaches that use query similarity metrics. We then propose DCABench, a benchmark that evaluates how well query similarity metrics capture query intent, and use it to evaluate three similarity metrics. DCABench uses student answers to query construction assignments to capture a wide range of distinct SQL queries that all have the same intent. Next, we propose and evaluate a query regularization process that standardizes query representations, significantly improving the effectiveness of the three similarity metrics tested. Finally, we propose an entirely new similarity metric based on the Weisfeiler-Lehman (WL) approximate graph isomorphism algorithm, which identifies salient features of a graph — or in our case, of the abstract syntax tree of a query. We show experimentally that distances in WL-feature space capture a meaningful notion of similarity, while still retaining competitive performance.
Unsupervised Learning of the Morphology of a Natural Language

This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.
Lean thinking in the supply chain operations and its integration with customer order decoupling point and bottlenecks

The thesis study reveals that the position of bottleneck is a significant importance in supply chain process. The modern supply chain is characterized as having diverse products due to mass customization, dynamic production technology and ever changing customer demand. Usually customized supply chain process consists of an assemble to order (ATO) or make-to-order (MTO) type of operation. By controlling the supply constraints at upstream, a smooth material flow achieved at downstream. Effective management on operational constraint will results in speed up customer delivery. A throughout evaluation of demand constraints is necessary to gain the competitive benefits over the entire market. The study is based on a conceptual model, comprises of engineer to order (ETO), MTO, ATO and make to stock (MTS) separated by decoupling point. All these represent the particular process in supply chain. The important perspective of this study is that the constraints are allocated in particular part of supply chain, which will become the supply chain more versatile. This thesis study explains the use of detail capacity and material planning techniques in upstream, midstream and downstream of the customer order decoupling supply chain. A shifting bottleneck concept has been studied in a better way, in order to keep relative stability and reduce the complexity in production management. This thesis study tries to find out some possible factors that reduce the bottleneck shifting in supply chain. This research provides the guideline about the implementation of lean, leagile and agility in customized supply chain. Also key factors are discussed which are necessary to achieve these paradigms. Basically leagile is the mixture of both lean & agile strategies. Leagile is inclined towards sustaining the flow for mixed-model production. Its intent is to achieve the efficiencies in mass production, while producing a medium variety of products. The focus in leagile strategy is to gain effective control at shop floor planning, including capacity and material planning systems. Different relevant aspects of standardization and customization are considered through the production processes. In addition to this rate base, hybrid and time phase material planning techniques are sorted in different supply chain parts through multiple level of bill of material. Acknowledgement The completion of this master thesis has been a rewarding experience for me. The thesis was carried out at IEI department in Linköping University Sweden, this department is renowned for management and excellent research center in engineering. I would like to express enormous gratitude and …
Eye Tracking and Head Movement Detection: A State-of-Art Survey

Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers' attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated.
Agilla: A mobile agent middleware for self-adaptive wireless sensor networks

This article presents Agilla, a mobile agent middleware designed to support self-adaptive applications in wireless sensor networks. Agilla provides a programming model in which applications consist of evolving communities of agents that share a wireless sensor network. Coordination among the agents and access to physical resources are supported by a tuple space abstraction. Agents can dynamically enter and exit a network and can autonomously clone and migrate themselves in response to environmental changes. Agilla's ability to support self-adaptive applications in wireless sensor networks has been demonstrated in the context of several applications, including fire detection and tracking, monitoring cargo containers, and robot navigation. Agilla, the first mobile agent system to operate in resource-constrained wireless sensor platforms, was implemented on top of TinyOS. Agilla's feasibility and efficiency was demonstrated by experimental evaluation on two physical testbeds consisting of Mica2 and TelosB nodes.
Design of a wall-climbing robot with passive suction cups

This paper presents a wall-climbing robot which adopts passive suction cups as the attaching components. Using only one motor, this robot can not only move on a wall but also attach suction cups to the wall and remove them from the wall. Passive suction cups do not consume additional energy to keep adhesion. Therefore, the proposed robot can realize the climbing motion on a wall with relatively low energy consumption. The prototype has been designed, fabricated and tested. The experiments showed that the proposed robot could attach and remove suction cups passively. However, the robot could not move up the wall well and fell down often. In order to solve this problem, the load of each suction cup when attached to a vertical wall is analyzed. As a result, it is shown that a moment generated by both of the gravity and the attaching force of suction cups turns the robot down from the wall. Then a new model which improves the falling problems is thus designed.
How to Predict Mood? Delving into Features of Smartphone-Based Data

Smartphones are increasingly utilized in society and enable scientists to record a wide range of behavioral and environmental information. These information, referred to as Unobtrusive Ecological Momentary Assessment Data, might support prediction procedures regarding the mood level of users and simultaneously contribute to an enhancement of therapy strategies. In this paper, we analyze how the mood level of healthy clients is affected by unobtrusive measures and how this kind of data contributes to the prediction performance of various statistical models (Bayesian methods, Lasso procedures, etc.). We conduct analyses on a non-user and a user level. We then compare the models by utilizing introduced performance measures. Our findings indicate that the prediction performance increases when considering individual users. However, the implemented models only perform slightly better than the introduced mean model. Indicated by feature selection methods, we assume that more meaningful variables regarding the outcome can potentially increase prediction performance.
Targeted Employee Retention : Performance-Based and Job-Related Differences in Reported Reasons for Staying

A content model of 12 retention factors is developed in the context of previous theory and research. Coding of open-ended responses from 24,829 employees in the leisure and hospitality industry lends support to the identified framework and reveals that job satisfaction, extrinsic rewards, constituent attachments, organizational commitment, and organizational prestige were the most frequently mentioned reasons for staying. Advancement opportunities and organizational prestige were more common reasons for staying among high performers and non-hourly workers, and extrinsic rewards was more common among low performers and hourly employees, providing support for ease/desirability of movement and psychological contract rationales. The findings highlight the importance of differentiating human resource management practices when the goal is to retain those employees valued most by the organization.
Mobile Forensic Data Analysis: Suspicious Pattern Detection in Mobile Evidence

Culprits’ identification by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of the mobile forensic data analysis. When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behavior is a rather achievable task. However, when human behavior is involved, such as in cases of traditional crimes, prediction and detection become more compelling. This paper introduces a combined criminal profiling and suspicious pattern detection methodology for two criminal activities with moderate to the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset for additional testing and verification.
Electromyography sensor based control for a hand exoskeleton

This paper presents a electromyography (EMG) control for a hand exoskeleton. The device was developed with focus on support of the rehabilitation process after hand injuries or strokes. As the device is designed for the later use on patients, which have limited hand mobility, fast undesired movements have to be averted. Safety precautions in the hardware and software design of the system must be taken to ensure this. The construction allows controlling the motion of finger joints. However, due to friction in gears and mechanical construction it is not possible to move finger joints within the construction without help of actuators. Therefore force sensors are integrated into the construction to measure force exchanged between human and exoskeleton. These allow the human to control the movements of the hand exoskeleton which is useful to teach new trajectories, for muscle training, or for diagnostic purposes. The control method using electromyography (EMG) sensor presented in this paper uses the EMG sensor values to generate a trajectory, which is executed by a position control loop based on sliding mode control.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng Google Research∗ Abstract
CAPTCHAs: An Artificial Intelligence Application to Web Security

Nowadays, it is hard to find a popular Web site with a registration form that is not protected by an automated human proof test which displays a sequence of characters in an image, and requests the user to enter the sequence into an input field. This security mechanism is based on the Turing Test—one of the oldest concepts in Artificial Intelligence—and it is most often called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). This kind of test has been conceived to prevent the automated access to an important Web resource, for example, a Web mail service or a Social Network. There are currently hundreds of these tests, which are served millions of times a day, thus involving a huge amount of human work. On the other side, a number of these tests have been broken, that is, automated programs designed by researchers, hackers, and spammers have been able to automatically serve the correct answer. In this chapter, we present the history and the concept of CAPTCHAs, along with their applications and a wide review of their instantiations. We also discuss their evaluation, both from the user and the security perspectives, including usability, attacks, and countermeasures. We expect this chapter provides to the reader a good overview of this interesting field. CES IN COMPUTERS, VOL. 83 109  Turing Test and the Origin of CAPTCHAs .  G eneral Description of CAPTCHAs .  D esirable Properties of CAPTCHAs .  A pplications and the Rise of the Robots . . . . . . . . . . . . . . . . . . . 121 3. T ypes of CAPTCHAs .  135 3 .3. A udio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 3 .4. C ognitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 4. E valuation of CAPTCHAs .  E fficiency .  A ccessibility Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 4 .3. P ractical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5. S ecurity and Attacks on CAPTCHAs .  A ttacks on CAPTCHAs .  158 5 .2. S ecurity Requirements on CAPTCHAs .  A lternatives to CAPTCHAs .  C onclusions and Future Trends . . . . . . . . . . . . . . . . . . . . . . . 173 R eferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid, and electromagnetic systems) are commonly modeled by PDEs, which historically have been a reliable way to enable engineering applications in these domains. However, it is known that the control of these PDE models is notoriously difficult. We show how neural network-based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of mutual weight regularization (MWR), which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in. A discretized PDE, such as the scalar Lighthill–Whitham–Richards PDE can indeed be considered as a macroscopic freeway traffic simulator and which presents the most salient challenges for learning to control large cyberphysical system with multiple agents. We consider two different discretization procedures and show the opportunities offered by applying deep reinforcement for continuous control on both. Using a neural RL PDE controller on a traffic flow simulation based on a Godunov discretization of the San Francisco Bay Bridge, we are able to achieve precise adaptive metering without model calibration thereby beating the state of the art in traffic metering. Furthermore, with the more accurate BeATS simulator, we manage to achieve a control performance on par with ALINEA, a state-of-the-art parametric control scheme, and show how using MWR improves the learning procedure.
Entropy based optimal multilevel thresholding using cuckoo optimization algorithm

Image thresholding considered as a popular method for image segmentation. So far, many approaches have been proposed for image thresholding. Maximum entropy thresholding has been widely applied in the literature. This paper proposes a multilevel image thresholding (MECOAT) using cuckoo optimization algorithm (COA). COA is a new nature- based optimization algorithm which is inspired by a bird named cuckoo. This algorithm is based unusual egg laying and breeding of cuckoos. MECOAT tries to maximize entropy criterion. Three different algorithms are compared with MECOAT algorithm: particle swarm optimization, genetic algorithm, and bat algorithm. Experimental results indicate that MECOAT presents better results in terms of fitness value, peak signal to noise ratio (PSNR) and robustness in most cases.