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The increasing market share of electric vehicles (EVs) makes charging facilities indispensable infrastructure for integrating EVs into the future intelligent transportation systems and smart grid. One promising facility called fast charging reservation(FCR) system was recently proposed. It allows people to reserve fast chargers ahead of time. In this system, fast chargers are the most scarce resource instead of electricity. Thus how to allocate these charging points requires careful designing. A good allocation policy should 1) ensure charging points to be allocated to EV users who really value them, and 2) prevent users' private information, e.g., identity, personal agenda, residing area and etc., from being inferred. A simple combination of classic multi-item auction and user identity anonymization cannot satisfy both criteria simultaneously. To find such an allocation, in this paper we investigate the design of privacy-preserving auctions in FCR systems. Traditional privacy-preserving strategies such as cryptography could incur high computation and communication overhead and hence jeopardize the efficiency of allocation. To this end, we propose Auc2Reserve, a differentially private randomized auction. Auc2Reserve applies an improved approximate sampler and the belief propagation (BP) technique to accelerate the resource allocation and pricing process. As a result, it is much more computationally efficient than generic exponential differentially private mechanisms and other theoretical approximate implementations. Through theoretical analysis, we show that Auc2Reserve is ?-incentive compatible, individual rational and ?-differentially private. And it provides a close-form approximation ratio in social welfare of FCR systems. In addition, we also demonstrate the efficacy of Auc2Reserve in terms of social welfare and privacy leakage via numerical simulation.

Personal data storages (PDSs) give individuals the ability to store their personal data in a data unified repository and control release of their data to data consumers. Being able to gather personal data from different data sources (e.g., banks, hospitals), PDSs will play strategic role in individual privacy management. As such, PDS demands for new privacy models for protecting personal data. In this paper, we propose a new technical approach that empowers individuals to better control data in PDS. Particularly, we present a privacy-aware PDS architecture by focusing on two logical data zones based on the categories of personal data. Moreover, we propose a strategy for regulating personal data release that takes in consideration both user preferences and possible risks and benefits of the data release.

Advanced map-matching algorithms use location and heading of GPS points along with geometrical and topological features of digital road networks to find the road segment on which the vehicle is moving. However, GPS errors sometimes impede map-matching algorithms in finding the correct segment, especially in dense and complicated parts of the network, such as near intersections with acute angles or on close parallel roads. In this paper an artificial neural network (ANN) approach is explored to improve the segment identification accuracy of map-matching algorithms. The proposed ANN is continuously trained by using the horizontal shift imposed on GPS points and once it is trained, it will be used to correct raw GPS points before inputting them into the map-matching algorithm. Integrating the proposed ANN enabled an existing map-matching algorithm to find the correct segments for some of the GPS points where the original map-matching algorithm had failed to do so.

We consider cluster analysis task on web pages based on various techniques to group the pages. While grouping the web pages based on the semantic meaning expressed in the content is required for some applications, we focus on clustering based on the web page structure and style for applications like categorization, cleaning, schema detection and automatic extractions. This paper describes some of the applications of similarity measures and a clustering technique to group the web pages into clusters. The structural similarity of HTML pages is measured by using Tree Edit Distance measure on DOM trees. The stylistic similarity is measured by using Jaccard similarity on CSS class names. An aggregated similarity measure is computed by combining structural and stylistic measures. A clustering method is then applied to this aggregated similarity measure to group the documents.

Numerous security incidents caused by malwaresand hackers have recently utilized anti-forensic techniquesto bypass analysis and detection. It is critical to build aknowledge base that would help understand such anti-forensictechniques. In this paper, we present a forensic analysis methodto detect an anti-forensic technique which leverages timestampmanipulation in NTFS file system. Our approach analyzeshow timestamp manipulation occurs in NTFS file system andalso extracts some features to detect timestamp manipulationbehaviors. We also evaluate our approach with several usecases and describe how our approach helps detect timestampmanipulation behaviors.

Elastic systems utilize both human and machine working units to accomplish tasks that are eligible for crowdsourcing. The quality in the results of work completed by either type of computing unit is tantamount on the characteristics they bear. In this paper we draw parallels from our previous work into looking at the suitability of working units in completing viable tasks in crowdsourcing. We seek to understand characteristics for modeling tasks and workers within these types of systems. Based on our experiments and lessons learned in related literature, we propose a dynamic worker-task information meta-model with a corresponding operational workflow model that can be used in a variety of problem domains involving crowdsourced tasks to provide support in making this decision.

Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for transfer learning algorithms. With this in mind, a test framework is proposed, comprising of distortion profiles which define a comprehensive test suite. The unique contribution of this paper is the definition of a test framework that measures a more complete profile of a transfer learning algorithm's capability, facilitating the identification of relative poor and good performance areas. As a proof of concept, the test framework is used to test a homogeneous transfer learning algorithm. The test framework will be the basis for a number of future applications.

The effective bandwidth management in multi-service computer networks such as university networks has become a challenge in recent years. The growth of internet traffic and limitation of bandwidth resources persuade the information technology (IT) managers to focus on effective bandwidth allocation policies. One of the important issues discussed in this domain is how to assign the bandwidth fairly based on the priority levels. In this paper, focusing on the "priority-based bandwidth allocation", a hybrid data mining method is developed to manage the limited bandwidth in a university network more effectively. This method is composed of two main steps and uses the clustering and classification techniques. The main purpose is to detect, analyze and predict students' behavioral patterns in a university network and identify the main factors that affect their tendency in using internet. The proposed method is applied on a real data of a network university. The results indicate that "degree level" and "age" are the most important factors that influence students' tendency to use internet. The results would be also useful for prediction purposes. It helps the IT managers to predict a new student's tendency to use internet given his/her characteristics. By analyzing the results, the IT managers can make better decisions to optimize the allocation of bandwidth resources.

This paper describes algorithmic decision support that facilitates recommendation of course schedules personal- ized to the background and interests of a given student. More specifically, recommendations are made with pri- oritized consideration of four categories of information: (1) degree requirements, (2) student interests, (3) student performance, and (4) time-to-degree. All four categories of information may not be available for a given student. The algorithm generates personalized recommendations by constructing a graph from degree requirements, identifying critical paths in the graph, and placing such paths within a course schedule. We describe the implementation of the algorithm in the context of PERCEPOLIS, a Perva- sive Cyberinfrastructure for Personalized Learning and Instructional Support, a framework constructed in our earlier work on personalized learning.

We design and study ExamParser, an innovative intelligent semantic automatic indexing method, for orchestrating today's programming classes. ExamParser automatically processes paper-based exams by associating sets of concepts to the exam questions, which provide graders semantic grading guidelines and leave personalized semantic feedback. Results showed that the ExamPraser significantly extract more and diverse concepts from exams. It also achieves high coherence within exam, indicating the automatic concept extraction from exams is promising and could be a potential technological solution to provide personalized feedback for large-size programming classes.

The Florida Public Hurricane Loss Model (FPHLM) is a public catastrophe model that integrates and regulates all key components, such as meteorology, engineering, and actuarial components, by following a certain workflow in the execution phase. The validation phase governed by an Automatic Data Validation (ADV) program simulates each modeled execution component with a large number of historical insurance data of specific hurricane events. The differences between the actual losses and the modeled losses of the insurance portfolios are evaluated to validate the model. The original validation process is time-consuming and error-prone when handling large data sets. This paper presents how the automated computer program efficiently and correctly incorporates the key components and produces useful reports for the validation purposes. By considering sixty-six combinations (i.e., the combination of one company and one hurricane) of the claim data, the FPHLM model adopts the largest set of portfolios comparing to the other four private models, which makes the validation process more challenging.

SIPTA — Informatic System for Working Papers in Auditing is a CAATT (Computer Assisted Audit Tools and Techniques) running in a web environment, technically developed by statutory auditors and other professionals of auditing in Portugal. It was developed according to the need of these professionals by using an appropriate software at work, and can be classified in different categories of CAATTs, because includes multiple features. Allows for documenting and performing of all audit process, including combining several operations, from audit planning (with identified risks consideration), data preparation, using statistical techniques for the analysis of balances and transactions and collection of evidence, to automated circularization process, until reporting phase and monitoring. Working online with uniformly and well-structured processes allows for the interaction of multiple auditors on the tool, regardless of their location, in real time, optimizing the entire process.

The Internet of Things (IoT) is a vision for interconnecting all of the world's "things" -- from vehicles to diet scales, smart homes and electrical grids -- through a common set of networking technologies. Realizing this vision using a host-to-host communication paradigm, such as that of the Internet Protocol (IP), is challenging in the context of highly heterogeneous, constrained devices that connect intermittently to one or more networks, often using multiple interfaces, communicate within various security regimes, and require both local and global communication capability. Using IP and similar protocols as the narrow waist of interoperability for IoT requires managing data exchange and security in terms that are largely orthogonal to application semantics, while simultaneously needing to minimize resource usage. This paper explores how Named Data Networking (NDN), a proposed future Internet architecture, addresses the root causes of these challenges and can help achieve the IoT vision in a more secure, straightforward, and innovation-friendly manner. NDN's data-centric communication model aligns network and application semantics, enabling developers to work with "things" and their data directly, and for IoT networks to be deployed and configured easily. To substantiate the high-level discussion, we give examples of ongoing design and implementation work in IoT over NDN and compare the architecture to well-known existing protocols and frameworks. Finally, we discuss short-and long-term scenarios for employing NDN to enable the Internet of Things.

MANET finds many advantages over the wired networks, however it still has some security problems which affects network in many ways. Till now a lot of work has been performed in identifying and solving these issues. In this paper we will be discussing attacks on the network layer of the protocol stack and the existing solutions to these issues.

Security and trust are major prerequisites for the deployment of Internet of Things (IoT) infrastructures. In this paper we present secure elements providing TLS and DTLS stacks, and running the CoAP protocol dedicated to IoT systems. These secure elements are used in an electronic lock context, in which the lock is a CoAP server and the key a CoAP client. We define two main security domains one for key interaction with the lock, and the other for key generation. These two domains are associated to applications running in SIM cards managed by mobile applications. Key generators are hosted in secure elements plugged in key servers, built over the RACS protocol. We introduce an innovative double TLS concept that avoids application tampering by cloud provider. Finally we discuss scalability issues of this approach.

Keylogging threats have been reasonably studied in computer systems but poorly covered in the domain of mobile operating systems. Android took the lead among the other mobile operating systems in allowing developers to build custom third-party keyboards to replace the stock Android on-screen keyboard. This opened the door for malicious developers to create keyloggers for the purpose of spying and/or phishing for users' sensitive data. A malicious developer may build a keylogger from scratch or utilize an existing keyboard. Furthermore, users may unknowingly install keyloggers from the online app markets or may use a keylogger that a malicious user with physical access has installed on their devices. In this paper, we discuss users' and keyboard developers' roles in increasing/decreasing the chance of successful keylogger attacks. We developed an Android app, KBsChecker, and asked participants to install it on their devices. The app collects data from participants' devices and prompts them to complete a survey. We also asked a number of developers to answer a few questions with regard to their experiences in building third-party keyboards. Our study showed that keylogging threat is of high probability due to the current security configurations and the choices of users and developers. Moreover, the study showed that the risk can be mitigated by educating users and by adopting new development approaches.

This paper compares the differences of man-machine interaction between digital and paper note-taking. We set two tests including questionnaire test and NASA-TLX test. We analyzed statistical data on the ratio, reasons of users who use digital or paper note-taking, and the comparisons for NASA-TLX workload weight of different ways of note-taking as well as 6 items of each way. By determining the optimal target for the future digital note-taking means, we can take the advantages of paper note-taking like the comfortable man-machine interaction and high performance of reading comprehension combined with the conveniences of digital note-taking.

This paper presents VEGAS - an online system that can illustrate the influence of one scientific paper on citation networks via the influence graph summarization and visualization. The system is built over an algorithm pipeline that maximizes the rate of influence flows in the final summarization. Both visualization and interaction designs are described with respect to a real usage scenario of the VEGAS system.

We address the problem of the access to the results of scientific publications in the agri-food domain. We focus on the description of main contributions of the papers treating them as accepted or rejected beliefs of their authors expressed in the form of scientific laws. We define the structure of different kinds of scientific laws present in the domain in the form of an ontology. The main concern of the paper is a process in which we proceed from the abstracts of papers to the ontological representation of laws. Moreover, we present examples of SPARQL queries which show how the resulting knowledge base can be used. Among the uses we point out discovering new scientific hypotheses and incoherencies among scientific laws.

This paper argues the need for considering mitigating circumstances in cybercrime. Mitigating circumstances are conditions which moderate the culpability of an offender of a committed offence. Our argument is based on several observations. The cyberspace introduces a new family of communication and interaction styles and designs which could facilitate, make available, deceive, and in some cases persuade, a user to commit an offence. User’s lack of awareness could be a valid mitigation when using software features introduced without a proper management of change and enough precautionary mechanisms, e.g. warning messages. The cyber behaviour of users may not be necessarily a reflection of their real character and intention. Their irrational and unconscious actions may result from their immersed and prolonged presence in a particular cyber context. Hence, the consideration of the cyberspace design, the “cyber psychological” status of an offender and their inter-relation could form a new family of mitigating circumstances inherent and unique to cybercrime. This paper elaborates on this initial argument from different perspectives including software engineering, cyber psychology, digital forensics, social responsibility and law.

Data visualization traditionally is the most powerful tool for demonstration and analysis of scientific results and mathematical models in particular. In this paper we introduce the graphical framework for citation graph clustering. Furthermore, we discuss ways to detect factors responsible for scientific groups formation. Two datasets of scientific papers related to different fields were used in this work. Firstly we applied scientometric analysis to our data with the view to determine the most influential keywords. After that, we used two different ways for data clustering - graphic clustering method comprising N-body communication graph and a keyword-based hierarchical clustering. As a result of our studies we propose method for dynamic visualization of scientific papers clusters, built using open-access data.

This paper presents a novel method for automated manipulation of individual paper fiber bonds using a microrobotic platform, a computer vision algorithm and a robotic software framework. This is a challenging task due to the three-dimensional, heterogeneous and complex morphology of the fiber bonds. The goal is to automatically grasp the fiber bond, and break it by pulling apart the fibers it consists of. We present the components of the microrobotic platform, and the different rules utilized in detecting suitable grasp points from a 3D reconstruction of the bond generated from an image pair. We demonstrate the functionality of the approach with bond breaking experiments of seven fiber bonds. The time required for grasping and breaking of a bond is 10 – 15 seconds making the approach much faster than the current state-of-the-art testing, which is based on manual manipulation. The success rate of the tests is as high as 80 %. acceptedVersion

Sentence ranking is one of the most important research issues in text analysis. It can be used in text summarization and information retrieval. Graph-based methods are a common way of ranking and extracting sentences. In graph based methods, sentences are nodes of graph and edges are built based on the sentence similarities or on sentence co-occurrence relationship. PageRank style algorithms can be applied to get sentence ranks. In this paper, we focus on how to rank sentences in a single scientific paper. A scientific literature has more structural information than general texts and this structural information has not been fully explored yet in graph based ranking models. We investigated several different methods that used the is-part-of link on paragraph and section and similar link and co-occurrence link to construct a heterogeneous graph for ranking sentences. We conducted experiments on these methods to compare the results on sentence ranking. It shows that structural information can help identify more representative sentences.

People often need help when filling out paper forms, because they do not fully understand the meaning of form fields. Commonly adopted solutions are referring to the form filling instructions or consulting other people. However, they are either inefficient or inconvenient. In this paper, we propose a situation-aware and interactive system, named Interact Form, to help people fill out paper forms. First, it provides an easy-to-use tool to build the knowledge offline about any given form. The knowledge includes the instruction and examples of each form field, and the constraints between fields. Then, when people are filling out a paper form, with the equipped video camera, the system can determine the field-grained position of the pen and provide text or audio instructions based on the user's form filling situations. When a pre-defined constraint is possibly going to be violated, the provided pen will send out an alert by vibration, and an explanation of the alert will also be given at the same time. We evaluate Interact Form with 60 paper form filling activities on 10 real-world paper forms, and the results show the usability of the system.

The task of citation counts prediction is to predict the citation counts of a paper after a given time period. Future citation counts of papers are an important metric to estimate potential influences of published papers, and will be helpful for researchers to choose representative literatures. This task can be treated as a regression problem. This paper proposes two types of predictive features to represent fundamental characteristics of papers and authors: six content features and ten author features. We introduce the IBM Model 1 to calculate the association probabilities between paper topics which are employed to extract content features, and use the bipartite network projection to obtain the author collaboration network which is utilized to extract author features. Further, we introduce the Gradient Boosted Regression Trees to predict citation counts of papers. Our approach combines contents and topics of papers and multi-dimensional measures of author collaborations in one learning process. Experimental results on the KDD CUP dataset demonstrate that our predicting features and models are effective to solve the problem of citation counts prediction of papers.

Location Based Service (LBS) is gaining popularity on smart phones. One fundamental LBS is range search, which returns all Point of Interests (POIs) within a user-specified range. However, people also leave their location privacy at risks when using LBS like range search. How can a user invoke such service without revealing his location is an interesting, yet challenging problem to solve. Most existing approaches blur a user's location into a cloaked region, so that LBS cannot figure out the exact location of the requesting user. However, this would make the returning results inaccurate, containing some out-of-range POIs. To this end, we propose PAPERS, a new method to provide location privacy for users of range search. PAPERS leverage homomorphic encryption to let the user encrypt her location, and the LBS server can compute distances on ciphertext. In this way, the returning results by LBS are exactly the POIs within the specified range, while LBS learns nothing about user's real location. We implement a prototype of PAPERS, and evaluate it with real POI set of a large-scale production LBS. Experimental results show that PAPERS can achieve the goal of privacy protection, with reasonable overhead in response time and communication cost.

It's an effective method to protect the intangible cultural heritage that by constructing the database of traditional folk paper cutting design. Constructing the pattern database of nanjing folk paper cutting depends on the scientific description of resources and classification system. It completely reflects the wide range of applicable objects, the interactivity of category system, concise layer architecture, the accuracy of resource naming and the convenience for the users. The database will be incorporated into a higher level database system as a sub database. It is not only a useful way to supplement the research of the traditional Nanjing folk paper cutting, but also a highly operational platform for the protection, inheritance, research and utilization of cultural heritage.

This paper presents a demonstration of our SECON 2015 paper using Twitter based case studies for social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals volunteer (or are recruited) to share certain observations or measurements about the physical world. A key challenge in social sensing applications lies in ascertaining the correctness of reported observations from unvetted data sources with unknown reliability. We refer to this problem as truth estimation. In this paper, we showed a demo of a new confidence-aware truth estimation scheme that explicitly considers different degrees of confidence that sources express on the reported data. In the demo session: the participants will have a chance to (i) play with the tool on some historic datasets we have collected from Twitter; (ii) send live queries to Twitter and perform real-time truth estimation analysis in the events of their interests.

International audience; This paper presents our recent research on augmented maps where unprepared paper maps are used and Geographic Information System serves both as reference database and augmentation data. We use road intersections to register paper map and GIS so several paper maps of the same city whatever their styles and colors can be augmented using the same reference database. We present a new road extraction method to get better road intersection for a quick initialization and a faster method to register maps and GIS. Different maps are used to demonstrate the effectiveness of our method.

This paper proposes a method for exploring technical phrase frames by extracting word n-grams that match our information needs and interests from research paper titles. Technical phrase frames, the outcome of our method, are phrases with wildcards that may be substituted for any technical term. Our method, first of all, extracts word trigrams from research paper titles and constructs a co-occurrence graph of the trigrams. Even by simply applying Page Rank algorithm to the co-occurrence graph, we obtain the trigrams that can be regarded as technical key phrases at the higher ranks in terms of Page Rank score. In contrast, our method assigns weights to the edges of the co-occurrence graph based on Jaccard similarity between trigrams and then apply weighted Page Rank algorithm. Consequently, we obtain widely different but more interesting results. While the top-ranked trigrams obtained by unweighted Page Rank have just a self-contained meaning, those obtained by our method are technical phrase frames, i.e., A word sequence that forms a complete technical phrase only after putting a technical word (or words) before or/and after it. We claim that our method is a useful tool for discovering important phrase logical patterns, which can expand query keywords for improving information retrieval performance and can also work as candidate phrasings in technical writing to make our research papers attractive., 29th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2015; Gwangju; South Korea; 25 March 2015 through 27 March 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 7096236; 2015

The final quality of paper depends on many quality and process variables. It is very difficult to find theoretical rules of the behavior of paper properties when variables depend from each other and when the interdependencies are not linear. In this paper we present a neural network based system for estimating the final quality of paper from process measurements. Inverse computation of the network model is used to find a control action that will produce the desired quality. A separate self-organizing map is used to monitor the movement of the operating point of the process and to give a hint of the estimation error of the network. >

The recent advances with respect to the costs, size, and power consumption of electronic components paved the way for System of Systems (SoS), Cyber-Physical Systems (CPS), or the Internet of Things (IoT). As a next stage, these developments currently motivate the consideration of Complex Swarm Systems (CSS), i.e., continuously running systems that will dynamically change after deployment and are connected by heterogeneous components which can join and leave the system at any time. Due to this dynamic nature and the constant reconfigurations, it is not possible to completely verify those systems with conventional verification methods anymore. Therefore, we propose a new methodology which follows a different scheme: Instead of trying to verify all possible behavior of a CSS (which, due to the vast number of possible instantiations or connections of the heterogeneous components, becomes an impracticable task anyway), we aim for verifying that, at least, no scenario which violates certain (safety-critical) forbidden actions is possible. To this end, solutions for model-based verification are employed. By means of a case study, the feasibility and promises of the proposed methodology are illustrated.

This technical demo presents PreSense, an AssistivePresentation Self-Quantification System. Oral presentation istraditionally evaluated by a human instructor, which is cost-ineffective and time-consuming. PreSense allows individuals toself-evaluate their presentation skills. The system is designedto receive multimodal inputs from multiple sources, includingwebcam, Kinect sensor and Google Glass. The multimodal datais processed by a deep assessment framework, which outputsthe evaluation results based on a carefully designed assessmentrubric. In order to systematically visualize the individual as-sessment results, we develop an interactive graphical interfaceand demonstrate its efficacy using presentation data from theNUS Multi-Sensor Presentation (NUSMAP) dataset.

Cobham Gaisler presents the SpaceFibre Port IP Core implementation GRSPFI. A fully validated VHDL implementation is readily available.

The common DPU platform for ESA JUICE mission instruments is a hardware and software platform developed by Cobham Gaisler for the scientific instrument payloads of the European Space Agency Jupiter Icy Moons spacecraft. The hardware is based around the GR712RC dual-core LEON3-FT processor with GRSPW2 SpaceWire interfaces. To accompany the JUICE instrument hardware, a flight quality SpaceWire software package has been developed, compliant with ESA ECSS standards for Space Software engineering. The software includes SpaceWire device drivers and protocol support for the SpaceWire CCSDS Packet Transfer Protocol, the Packet Utilization Standard and the SpaceWire Time Distribution protocol.

Pairs of agents, called dyads or ego-alter pairs, meet, develop and dissolve within the confines and provisions of the built environment. Within the environment, dyads transfer and receive information from one another and the external world and use this information to (re) evaluate their inclination toward the relationship. Thus, any active dyadic relationship can be represented by three states: colocating (COL), telecommunicating (TEL), or evaluating the cost/benefit of sustaining the relationship, i.e. inclination (INC). At any given time, relationships can transition between states. These states/events are manifested within large datasets in the digital era. The numeric cost of colocating and telecommunicating can be computed easily with context from the geographic environment. Evaluating INC cost is more difficult, but relies on social network alternatives and personal values. When any costs change, agents reevaluate the cost and benefit of relinquishing some relationships for more attractive alternatives. We experiment with this framework using an example action script and collected data from 27 agents.

Healthcare has and continues to be an integral component in people's lives, especially for the rising elderly population. One such healthcare program that provides for the needs of the elderly is Medicare. It is important that any such program be affordable but, unfortunately, this is not always the case. Out of the many possible factors for the rising cost of healthcare, fraud is a major contributor, but its impacts can be lessened through the use of fraud detection methods. We assess possible fraudulent activities by looking at the amounts paid to providers for services rendered to patients. In this study, we propose a novel methodology and framework towards identifying potential sources of fraud. We model these Medicare payments in order to create baseline values that reflect what the payments should be for a provider's specialty. We use these baseline expected payments and compare them to what was actually paid by Medicare for distinct specialties and healthcare services. Any deviations from the expected payments are flagged for further investigation. Our overall approach is consistent with related works, in healthcare, using anomaly-based detection methods to detect fraudulent activities, but we focus on an implementable and generalizable framework for initial fraud detection. Our results demonstrate the detection of possible fraudulent activities, with one specialty, Cardiology, demonstrating the detection of a known, real-world fraud case.

There is currently a big demand for automating big data analysis. In the data analysis field, data abstraction or summarization playes an important role in the extraction of generalized information from large scale data. We developped an artificial intelligence computer system with the aim of automating big data analysis and came up with a method that can abstract numerical type data (age, height, time, etc.). However, it could not abstract or summarize label type data (customer ID, product code, name, etc.). In the present work, we have developed a label abstraction method based on information entropy. Experiments using open real data showed that the proposed method achieved an extraction accuracy of 80% evaluated by f measure. We intended to apply the proposed method to our artificial intelligence and perform further evaluations.

Language endangerment is one of the most urgent problems facing humanities, with roughly one language disappearing every two weeks. Currently most of the linguistics tools used for endangered language documentation and analysis are desktop-based standalone software systems that do not expose libraries or services to be integrated by user applications, nor do they sufficiently support data sharing among different users for collaborative research efforts. To address this issue, we present a general architecture to design and develop an integrative computational platform for endangered language documentation and analysis (called PELDA) using RESTful web services and cloud computing techniques. The web services developed and deployed on the cloud platform can be easily integrated into any applications that support HTTP communications, while general users can access the linguistics tools and manage data files via web browsers. Then using Praat, an open source linguistics tool for acoustic analysis, as an example, we discuss the unique challenges and basic procedure to migrate linguistics tools to the cloud platform and demonstrate the feasibility of the proposed approach.

A conventional lamp may tend to be of high power consumption because aging and the lamp cover discolors. According to the study, after the conventional lamp is replaced by the proposed lamp, the proposed lamp can save about 65% of the power consumption. On the other hand, the study finds out the best luminous efficiency of the main body by the addressable lamp control method, and we calculate the possible illumination intensity via the lighting simulation software so as to check whether the proposed lamp conforms to the illumination standards and the safety illumination standards. After the whole simulation is finished, we actually replace the conventional lamp by the proposed lamp, and then practically test the total power consumption efficiency during two weeks. Afterward, we find that the power saving rate can be up to 77.5%. In other words, in addition to the original saved efficiency by using proposed lamps, the control system can further create 12.5% power saving rate, and provide an environment with good light quality and safe usage site. The study adopts the currently available T5 lamp with high luminous efficiency for users to conveniently replace the lamp by themselves, which can solve the matching problem that the lighting control systems have. Therefore, the study provides a best solution for public constructions of lamp replacement.

Cloud computing is the growing technology. Each organization wants to connect to the cloud computing environment. But some of them resist connecting due to security issues. Various security factors had been raised, some also lead to include third party auditor for solving security issues. Another major factor in concern is the Trust on the cloud service provider's i.e., the level to which an organization can trust the cloud service provider for handling their company's data.

In the last decade, the increasing plurality of materials, media types and software tools within the internet has established first steps towards more individualized learning approaches. However, the development of utilizing Big Data-based algorithms of the next internet generation, the so-called ‘Internet of Things’, leads to a comprehensive approach of personalized learning for very different target groups. Understanding the learner's profile and interests, learning goals and learning difficulties, ‘intelligent agents’ accompany and guide the learner during the learning process in the future. In particular, for students with learning disabilities such as dyslexia and dyscalculia, the new development of ‘learning analytics’ based on Big Data algorithms has a large potential. The goal is to overcome the severe gap between their existing intellectual potentials and their often unsuccessful learning biographies in schools and universities. The changes of daily environments towards a 4.0-Society, in particular through fully networked Smart Cities, result in an increasing number of data from very heterogeneous sources. Due to technologies such as, e.g., smart wearables combined with data generated through traffic and mobility or logistics, smart geographical places will be enabled to act as a basis for intelligent learning and evaluation mechanisms.

Crowdsourcing offers a cost-effective approach to distributed problem solving and data collection by soliciting contributions (solutions, ideas, data, etc.) from a large group of people. Recently, due to the burgeoning smartphone industry and the surging demand for sensing data, a new mobile computing and sensing paradigm called mobile crowdsensing has emerged and has created significant momentum in both industry and academia. Pivotal to the viability of all such crowdsourcing systems, is whether there is enough incentive to attract sufficient participation. Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers types (e.g., abilities and costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this talk, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions for each potential winner (worker). We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In the second part of the talk, we propose the use of Tullock contests as an alternative framework to design incentive mechanisms for crowdsourcing. We explore a new dimension of optimal Tullock contents design by provisioning the prize as a function. We are inspired by the conduciveness of Tullock contests to attracting user entry in other domains. In this talk, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize-which is fixed in conventional Tullock contests-with a prize function that is dependent on the (unknown) winners contribution, in order to maximize the crowdsourcers utility. We show that this approach leads to several attractive practical advantages.

Pencil Code, a web-based program, utilizes blocks to aid students in creating code. This paper presents work creating a debugging tool to further improve Pencil Code and create an environment where students can better learn to understand code by having a way to visually trace its operations.

Semantic annotation of Web Services has emerged as an efficient solution to automatic service discovery, composition, invocation and interoperation tasks. In this paper a three-level automated semantic classification mechanism of SOAP and Restful Web Services is examined by taking into account the textual description of Web Services. The proposed approach provides a mechanism for categorizing any Web Service into application domains, prototype operations and prototype i/o into the predefined ontology. The effectiveness of the proposed methods is validated through extensive experiments.

Mastering academic English is crucial for both academicians and students functioning in English speaking environment. However, getting the command of the language at the advanced level might be a long and a painful process. Creating software providing analysis of academic English can play an important role in addressing the issues of linguistic analysis of academic discourse as well as in teaching English for academic purposes. The paper describes the results of development and implementation of the special software to analyse academic texts. Categories are suggested for identifying most important features of academic discourse.

Internet of Things (IoT) with Cloud Computing (CC) is a new paradigm incorporating a pervasive presence of a wide range of things/objects which can interact with each other and cooperate, creating new services and reaching common goals. This will lead to more intelligent smart environments in a wide range of applications. In this context, protecting the Internet of Things with Cloud Computing (IoTwCC) against interference, including service attacks and viruses, becomes paramount. In this paper, we introduce a transition system representation to capture the information flow in IoTwCCs, and then investigate the opacity of the information flow model. In addition, we introduce a threat model to describe the actions of the system, and propose entropy as a security metrics to quantify the amount of information related to a service that might be exposed to other users or adversaries. It turns out that the opacity of the system is affected by the availability of the services. As a result, the trade-off between opacity and service availability can be analyzed.

Assessing student knowledge of programming concepts is a long studied, but many would say unsolved, problem. From the initial Empirical Studies of Programmers series [1] to McCracken's study [2] to Elliott-Tew's validated assessment of fundamental CS knowledge [3] many have attempted to precisely measure the knowledge and skills of students learning to program. All of the previous efforts have been centered on text based languages. Now we have another element to add to the mix — block-based programming languages. Can any of the existing assessment instruments work for both text and block based programming languages? Do we believe they will measure the same knowledge? Can existing assessments be modified and tailored for block-based languages? Are the two approaches equivalent, and would we even want to assess the same knowledge outcomes for both? These are but a few of the questions I will address in this position paper.

We report a simple low-cost paper-based electronic circuit that it is stable electric characteristics comparable to copper pattering by Gerber format-based etching processing onto FR-4 substrate. This electrode was designed to catch the electrical signals through electrodes during biological assay. Therefore, the combination of colorimetric bioassay and electrical signal are likely to lead to more precise analysis in point-of-care applications.

Web usage mining refers to the process of discovering user access patterns from the log of website. Usually the web log contains unstructured, noisy and irrelevant data. To make this data suitable for pattern mining and pattern analysis it has to be passed through data preprocessing phase. Data preprocessing not only improves the quality of data but it also reduces the size of web log file. Data preprocessing involves several steps including data collection, data cleaning, session identification, user identification and path completion. This paper presents several data preprocessing techniques in order to prepare raw data suitable for mining and analysis tasks.