title: From WannaCry to WannaDie: Security trade-offs and design for implantable medical devices

abstract: Healthcare sectors are increasingly facing cyber security challenges and threats from adversaries due to numerous security flaws and the lack of security safeguards in medical devices. Among these medical devices and systems, security issues that concern implantable medical devices (IMDs) have attracted attention from both academia and the industry. In this paper, we discuss security vulnerabilities in current IMD products by presenting security tests and demonstrations performed by researchers. Based on this, three critical trade-offs in the IMD security design are analyzed, namely security vs. accessibility in medical emergencies, emergency access vs. checkup access and strong security requirements vs. limited IMD resources. Biometrics based security solutions can provide support for emergency access and thus are surveyed, including those using electrocardiogram signals, iris and fingerprints. During the design, we propose to adopt the concept of decoupled design and usable security in order to develop a viable security solution for the IMDs.


title: Negotiation and cooperation in multi-agent environments

abstract: Abstract Automated intelligent agents inhabiting a shared environment must coordinate their activities. Cooperation—not merely coordination—may improve the performance of the individual agents or the overall behavior of the system they form. Research in Distributed Artificial Intelligence (DAI) addresses the problem of designing automated intelligent systems which interact effectively. DAI is not the only field to take on the challenge of understanding cooperation and coordination. There are a variety of other multi-entity environments in which the entities coordinate their activity and cooperate. Among them are groups of people, animals, particles, and computers. We argue that in order to address the challenge of building coordinated and collaborated intelligent agents, it is beneficial to combine AI techniques with methods and techniques from a range of multi-entity fields, such as game theory, operations research, physics and philosophy. To support this claim, we describe some of our projects, where we have successfully taken an interdisciplinary approach. We demonstrate the benefits in applying multi-entity methodologies and show the adaptations, modifications and extensions necessary for solving the DAI problems


title: Plasma Fetuin-A Levels and the Risk of Type 2 Diabetes

abstract: OBJECTIVE— The liver-secreted protein fetuin-A induces insulin resistance in animals, and circulating fetuin-A is elevated in insulin resistance and fatty liver in humans. We investigated whether plasma fetuin-A levels predict the incidence of type 2 diabetes in a large prospective, population-based study. ::: ::: RESEARCH DESIGN AND METHODS— A case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study comprising 27,548 subjects was designed. We randomly selected a subcohort of 2,500 individuals of whom 2,164 were diabetes free at baseline and had anamnestic, anthropometrical, and metabolic data for analysis. Of the 849 incident diabetic case subjects identified in the full cohort during 7 years of follow-up, 703 remained for analyses after similar exclusions. ::: ::: RESULTS— Plasma fetuin-A levels were positively associated with diabetes risk after adjustment for age (relative risk [RR] for extreme quintiles 1.75 [95% CI 1.32–2.31]; RR for 10 μg/ml 1.04 [1.03–1.06]). The association remained significant after adjustment for sex, BMI, waist circumference, and lifestyle risk factors (RR for 10 μg/ml 1.03 [1.01–1.06]). Adjustment for glucose, triglycerides, HDL cholesterol, A1C, γ-glutamyltransferase, or high-sensitivity C-reactive protein or mutual adjustment for these biomarkers did not appreciably change this result (RR for 10 μg/ml full adjusted model 1.05 [1.02–1.07]). Furthermore, fetuin-A was associated with increased diabetes risk particularly in individuals with elevated plasma glucose. ::: ::: CONCLUSIONS— Our data suggest that fetuin-A is an independent risk factor of type 2 diabetes.


title: Feasibility study of micro hydro power plant for rural electrification in Thailand by using axial flux permanent magnet

abstract: This paper presents the study to investigate the possibility of the stand-alone micro hydro for low-cost electricity production which can satisfy the energy load requirements of a typical remote and isolated rural area. In this framework, the feasibility study in term of the technical and economical performances of the micro hydro system are determined according to the rural electrification concept. The proposed axial flux permanent magnet (AFPM) generator will be designed for micro hydro under sustainable development to optimize between cost and efficiency by using the local materials and basic engineering knowledge. First of all, the simple simulation of micro hydro model for lighting system is developed by considering the optimal size of AFPM generator. The simulation results show that the optimal micro hydro power plant with 70 W can supply the 9 W compact fluorescent up to 20 set for 8 hours by using pressure of water with 6 meters and 0.141 m 3 /min of flow rate. Lastly, a proposed micro hydro power plant can supply lighting system for rural electrification up to 525.6 kWh/year or 1,839.60 Baht/year and reduce 0.33 ton/year of CO2 emission.


On Maximum Clique Problems In Very Large Graphs


title: MOBILITY OF LEGGED ROBOT LOCOMOTION WITH ELASTICALLY-SUSPENDED LOADS OVER ROUGH TERRAIN *

abstract: Elastically-suspended loads can reduce the energetic cost of legged robot locomotion over flat ground. In this paper, we studied the effect of elastically-suspended loads on the mobility of legged robot locomotion over rough terrain. The power input and speed of a simple hexapod robot running over rough terrain with an elastically-suspended load and a rigidly-attached load was compared. On average, the robot ran 16% faster and consumed 8.9% less power while carrying an elastically-suspended load versus a rigidly-attached load over rough terrain. Therefore, the experiments suggest that elastically-suspended loads may increase the mobility of legged robot locomotion over rough terrain.


title: Deep Learning for Classification Tasks on Geospatial Vector Polygons

abstract: In this paper, we evaluate the accuracy of deep learning approaches on geospatial vector geometry classification tasks. The purpose of this evaluation is to investigate the ability of deep learning models to learn from geometry coordinates directly. Previous machine learning research applied to geospatial polygon data did not use geometries directly, but derived properties thereof. These are produced by way of extracting geometry properties such as Fourier descriptors. Instead, our introduced deep neural net architectures are able to learn on sequences of coordinates mapped directly from polygons. In three classification tasks we show that the deep learning architectures are competitive with common learning algorithms that require extracted features.


title: Deep Network Embedding with Aggregated Proximity Preserving

abstract: Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.


title: Small planar monopole antenna with a shorted parasitic inverted-L wire for wireless communications in the 2.4-, 5.2-, and 5.8-GHz bands

abstract: A low-profile planar monopole antenna with a shorted parasitic inverted-L wire fed using a microstrip feedline for wireless communications in the wireless local-area network (WLAN) bands is studied. The driven monopole element and shorted parasitic wire can separately control the operating frequencies of two excited resonant modes, which cover the 2.4-, 5.2-, and 5.8-GHz WLAN bands. This antenna design is not only suitable as a monopole antenna but also as a diversity antenna for 2.4-, 5.2-, and 5.8-GHz band operations. The lower mode of the proposed antenna has an impedance bandwidth (2:1 VSWR) of about 188 MHz (2313-2501 MHz), which covers the required bandwidth for 2.4 GHz WLAN band (2400-2484 MHz); on the other band, the upper mode has a bandwidth of about 2843 MHz (3930-6773 MHz) covering the HIPERLAN band (5150-5350 MHz) and 5.8-GHz WLAN band (5725-5852 MHz). For frequencies across the three operating bands, the proposed antenna shows similar monopole-like radiation patterns, and good antenna gain across the operating bands is obtained. Details of the design considerations for the proposed antenna are described, and the experimental results of the antenna performances obtained are presented and discussed.


title: Indoor localization system benchmark based on wireless local network technologies

abstract: Localization need is appeared with the evolution of mobile communication devices. Nowadays, smart phones are equipped with many sensors that allow position estimation. Based on localisation, many applications and values added services are developed to meet the increasing users need. In order to design localization system, it requires first to understand the fundamental localization principles and to identify existing methods. A compromise between cost, complexity and performance should be taken into account while choosing the best solution. This paper makes benchmark of existing solutions for RF indoor localization with a detailed presentation of performance and limitations related to the most promising solutions. It presents also the latest researches and stimulates new ways to develop new research in this field in particular for fingperpinting based solution and CSI used metric for WLAN network.


title: Doppler Resilient Golay Complementary Waveforms

abstract: We describe a method of constructing a sequence (pulse train) of phase-coded waveforms, for which the ambiguity function is free of range sidelobes along modest Doppler shifts. The constituent waveforms are Golay complementary waveforms which have ideal ambiguity along the zero Doppler axis but are sensitive to nonzero Doppler shifts. We extend this construction to multiple dimensions, in particular to radar polarimetry, where the two dimensions are realized by orthogonal polarizations. Here we determine a sequence of two-by-two Alamouti matrices where the entries involve Golay pairs and for which the range sidelobes associated with a matrix-valued ambiguity function vanish at modest Doppler shifts. The Prouhet-Thue-Morse sequence plays a key role in the construction of Doppler resilient sequences of Golay complementary waveforms.


title: Automatic Detection of Video Shot Boundary in Social Media Using a Hybrid Approach of HLFPN and Keypoint Matching

abstract: Shot boundary detection (SBD) is an important and fundamental step in video content analysis such as content-based video indexing, browsing, and retrieval. In this paper, a hybrid SBD method is presented by integrating a high-level fuzzy Petri net (HLFPN) model with keypoint matching. The HLFPN model with histogram difference is executed as a predetection. Next, the speeded-up robust features (SURF) algorithm that is reliably robust to image affine transformation and illumination variation is used to figure out all possible false shots and the gradual transition based on the assumption from the HLFPN model. The top-down design can effectively lower down the computational complexity of SURF algorithm. The proposed approach has increased the precision of SBD and can be applied in different types of videos.


title: X-ray In-Depth Decomposition: Can Deep Learning Reveal The Latent Structures?

abstract: X-ray is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation of the hidden and obscured anatomy in X-ray images remains a challenge and often requires high radiation dose and imaging from several perspectives. In this work, we aim at decomposing the conventional X-ray image into d X-ray components of independent, non-overlapped, clipped sub-volume, that separate rigid structures into distinct layers, leaving all deformable organs in one layer, such that the sum resembles the original input. Our proposed model is validaed on 6 clinical datasets (\(\sim \)7200 X-ray images) in addition to 615 real chest X-ray images. Despite the challenging aspects of modeling such a highly ill-posed problem, exciting and encouraging results are obtained paving the path for further contributions in this direction.


title: Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments

abstract: With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.


title: Truth Inference in Crowdsourcing: Is the Problem Solved?

abstract: Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates addressing problems that are hard for computers, e.g., entity resolution and sentiment analysis. However, due to the openness of crowdsourcing, workers may yield low-quality answers, and a redundancy-based method is widely employed, which first assigns each task to multiple workers and then infers the correct answer (called truth) for the task based on the answers of the assigned workers. A fundamental problem in this method is Truth Inference, which decides how to effectively infer the truth. Recently, the database community and data mining community independently study this problem and propose various algorithms. However, these algorithms are not compared extensively under the same framework and it is hard for practitioners to select appropriate algorithms. To alleviate this problem, we provide a detailed survey on 17 existing algorithms and perform a comprehensive evaluation using 5 real datasets. We make all codes and datasets public for future research. Through experiments we find that existing algorithms are not stable across different datasets and there is no algorithm that outperforms others consistently. We believe that the truth inference problem is not fully solved, and identify the limitations of existing algorithms and point out promising research directions.


title: Simulation of the DNP3 protocol over TCP/IP on a network IEEE 802.11g ad-hoc with smart meter

abstract: With the growing demand for electricity, there was the need to develop an efficient system load demand management for power grids. For implementation of a load demand management system was proposed smart grid architecture. The smart grid is a highly power system complex system involving a large number of information and communication technology devices integrated with the purpose of fault detection and control and integration with power grid technology with energy generation technologies renewable. Already the installation of smart meters in residential consumers helps themselves to use the electric power more conscious way and cost saving in load periods. Therefore, this paper proposes an evaluation of the DNP3 performance through a network IEEE 802.11g wireless ad hoc, encapsulated in TCP/IP using the Network Simulator Version 2.35 (NS-2). The aim is to investigate the feasibility of using DNP3 through an ad hoc network, in residences with smart meters for electric power saving purposes. With the simulation results, you can tell if a 802.11g wireless ad hoc network with DNP3 is a communication protocol and viable for low cost smart grid applications for monitoring and tracking of electric power consumption in real time.


title: Integrated technologies for solid waste bin monitoring system.

abstract: The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer's inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as in truck, GPRS/GSM as web server, and GIS as map server, database server, and control server. The tracking devices mounted in the trucks collect location information in real time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.


title: Pose-guided human parsing by an and/or graph using pose-context features

abstract: Parsing human into semantic parts is crucial to human-centric analysis. In this paper, we propose a human parsing pipeline that uses pose cues, i.e., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. These segment proposals are ranked using standard appearance cues, deep-learned semantic feature, and a novel pose feature called pose-context. Then these proposals are selected and assembled using an And-Or graph to output a parse of the person. The And-Or graph is able to deal with large human appearance variability due to pose, choice of clothes, etc. We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, showing that it significantly outperforms the state-of-the-arts, and perform diagnostics to demonstrate the effectiveness of different stages of our pipeline.


title: Industry report: Amazon.com recommendations: Item-to-item collaborative filtering

abstract: Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.


title: Work and Play: An Experiment in Enterprise Gamification

abstract: In recent years, gamification, “the use of game design elements in non-game contexts”, has drawn the attention of an increasing number of scientists. Although several studies highlighted the benefits of gamification in many applications, its potential in the \emph{enterprise} environment still needs to be fully understood. This work contributes to the studies in enterprise gamification with an experiment performed at a large multinational company. The experiment involved 206 employees for a period of 2 months. We describe a modular and extensible framework for enterprise gamification, designed to seamlessly integrate with existing enterprise-class Web systems. We studied how a gamified tool can help to foster employees' engagement with such systems, by making day-to-day tasks more stimulating. We show how different game mechanics can help to achieve two business needs, namely social interaction and learning. To this end, we exploited the gamification framework to develop a Q&A Web application combined with learning, news sharing, and social connections capabilities. Results provide strong evidence of how a gamified experience can foster learning and social behaviour in employees, and provide new insights about the effectiveness of several game mechanics in an enterprise context.


title: Monitoring and depth of strategy use in computer‐based learning environments for science and history

abstract: Background ::: Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance. ::: ::: Aims ::: In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history. ::: ::: Sample ::: This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area. ::: ::: Methods ::: We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions. ::: ::: Results ::: Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations. ::: ::: Conclusions ::: These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.


title: Subliminal Speech Priming

abstract: We present a novel subliminal priming technique that operates in the auditory modality. Masking is achieved by hiding a spoken word within a stream of time-compressed speechlike sounds with similar spectral characteristics. Participants were unable to consciously identify the hidden words, yet reliable repetition priming was found. This effect was unaffected by a change in the speaker's voice and remained restricted to lexical processing. The results show that the speech modality, like the written modality, involves the automatic extraction of abstract word-form representations that do not include non-linguistic details. In both cases, priming operates at the level of discrete and abstract lexical entries and is little influenced by overlap in form or semantics.


title: Study for renewable energy telemetry using a decentralized cloud M2M system

abstract: Renewable energy sources are increasingly used for providing green alternatives to existing energy producers and need to be constantly monitored for optimizing the power grid. Unfortunately, such micro power plants are located in difficult to access remote locations where often only satellite or sparse GSM radio signals are available. In this paper we study the way how to use a decentralized cloud system, based on general systems and RTU (Remote Telemetry Units), for tele-monitoring of such objectives. In our proposed cloud M2M system, each RTU communicates by radio with a telemetry data gateway connected to a cloud computing infrastructure equipped with appropriate software that delivers processed data. Finally, we present the measurement results for a hydro-energy case study and discuss the applicability for other renewable sources such as solar or wind energy.


title: Joint Image Clustering and Labeling by Matrix Factorization

abstract: We propose a novel algorithm to cluster and annotate a set of input images jointly, where the images are clustered into several discriminative groups and each group is identified with representative labels automatically. For these purposes, each input image is first represented by a distribution of candidate labels based on its similarity to images in a labeled reference image database. A set of these label-based representations are then refined collectively through a non-negative matrix factorization with sparsity and orthogonality constraints; the refined representations are employed to cluster and annotate the input images jointly. The proposed approach demonstrates performance improvements in image clustering over existing techniques, and illustrates competitive image labeling accuracy in both quantitative and qualitative evaluation. In addition, we extend our joint clustering and labeling framework to solving the weakly-supervised image classification problem and obtain promising results.


title: Hydration reminding smart bottle: IoT experimentation

abstract: This paper describes the experimentation architecture of Internet of Things (IoT) in real life. Objective of this work is to design water bottles which intimate a person to intake water on time to keep body temperature in stable. With busy schedules of humans and endless distractions, it's hard to remember to drink enough water by the humans. One of the health issues is not drinking sufficient amount of water properly in a human's daily life. Here the water float sensor can be used to sense the level of the water and it not only helps the users to measure the level of water, but also indicates the human to drink water when it's time to top up their body. This is a user-friendly project, that water bottle or flask can speaks with the person via passing commands as a message to drink sufficient water. The IoT experiment facility describes in this paper is conceived to provide a suitable platform for a large scale experimentation and evaluation of IoT concept under real life conditions.


title: Circadian rhythms in cognitive performance: Methodological constraints, protocols, theoretical underpinnings

abstract: The investigation of time-of-day effects on cognitive performance began in the early days of psychophysiological performance assessments. Since then, standardised, highly controlled protocols (constant routine and forced desynchrony) and a standard performance task (psychomotor vigilance task) have been developed to quantify sleep–wake homeostatic and internal circadian time-dependent effects on human cognitive performance. However, performance assessment in this field depends on a plethora of factors. The roles of task difficulty, task duration and complexity, the performance measure per se, practice effects, inter-individual differences, and ageing are all relevant aspects. Therefore, welldefined theoretical approaches and standard procedures are needed for tasks pinpointing higher cortical functions along with more information about time-dependent changes in the neural basis of task performance. This promises a fascinating challenge for future research on sleep–wake related and circadian aspects of different cognitive domains.


title: Privacy and authentication: An introduction to cryptography

abstract: This paper presents a tutorial introduction to contemporary cryptography. The basic information theoretic and computational properties of classical and modern cryptographic systems are presented, followed by cryptanalytic examination of several important systems and an examination of the application of cryptography to the security of timesharing systems and computer networks. The paper concludes with a guide to the cryptographic literature.


title: A new intrusion detection system using support vector machines and hierarchical clustering

abstract: Whenever an intrusion occurs, the security and value of a computer system is compromised. Network-based attacks make it difficult for legitimate users to access various network services by purposely occupying or sabotaging network resources and services. This can be done by sending large amounts of network traffic, exploiting well-known faults in networking services, and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems. Anomaly detection is an attempt to search for malicious behavior that deviates from established normal patterns. Misuse detection is used to identify intrusions that match known attack scenarios. Our interest here is in anomaly detection and our proposed method is a scalable solution for detecting network-based anomalies. We use Support Vector Machines (SVM) for classification. The SVM is one of the most successful classification algorithms in the data mining area, but its long training time limits its use. This paper presents a study for enhancing the training time of SVM, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps find the boundary points, which are the most qualified data points to train SVM, between two classes. We present a new approach of combination of SVM and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique and random selection in terms of accuracy loss and training time gain using a single benchmark real data set. We show that our proposed variations contribute significantly in improving the training process of SVM with high generalization accuracy and outperform the Rocchio Bundling technique.


title: Light Ears: Information Leakage via Smart Lights

abstract: Modern Internet-enabled smart lights promise energy efficiency and many additional capabilities over traditional lamps. However, these connected lights also create a new attack surface, which can be maliciously used to violate users' privacy and security. In this paper, we design and evaluate novel attacks that take advantage of light emitted by modern smart bulbs, in order to infer users' private data and preferences. The first two attacks are designed to infer users' audio and video playback by a systematic observation and analysis of the multimedia-visualization functionality of smart light bulbs. The third attack utilizes the infrared capabilities of such smart light bulbs to create a covert-channel, which can be used as a gateway to exfiltrate user's private data out of their secured home or office network. A comprehensive evaluation of these attacks in various real-life settings confirms their feasibility and affirms the need for new privacy protection mechanisms.


title: Personalization in distributed e-learning environments

abstract: Personalized support for learners becomes even more important, when e-Learning takes place in open and dynamic learning and information networks. This paper shows how to realize personalized learning support in distributed learning environments based on Semantic Web technologies. Our approach fills the existing gap between current adaptive educational systems with well-established personalization functionality, and open, dynamic learning repository networks. We propose a service-based architecture for establishing personalized e-Learning, where personalization functionality is provided by various web-services. A Personal Learning Assistant integrates personalization services and other supporting services, and provides the personalized access to learning resources in an e-Learning network.


title: Fine-grained entity recognition

abstract: Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents the FIGER implementation. Experiments show that the system accurately predicts the tags for entities. Moreover, it provides useful information for a relation extraction system, increasing the F1 score by 93%.We make FIGER and its data available as a resource for future work.


title: Soft Biometric Traits for Personal Recognition Systems

abstract: Many existing biometric systems collect ancillary information like gender, age, height, and eye color from the users during enrollment. However, only the primary biometric identifier (fingerprint, face, hand-geometry, etc.) is used for recognition and the ancillary information is rarely utilized. We propose the utilization of “soft” biometric traits like gender, height, weight, age, and ethnicity to complement the identity information provided by the primary biometric identifiers. Although soft biometric characteristics lack the distinctiveness and permanence to identify an individual uniquely and reliably, they provide some evidence about the user identity that could be beneficial. This paper presents a framework for integrating the ancillary information with the output of a primary biometric system. Experiments conducted on a database of 263 users show that the recognition performance of a fingerprint system can be improved significantly (≈ 5%) by using additional user information like gender, ethnicity, and height.


title: A heads-up no-limit Texas Hold ’ em poker player : Discretized betting models and automatically generated equilibrium-finding programs

abstract: We present Tartanian, a game theory-based player for heads-up no-limit Texas Hold'em poker. Tartanian is built from three components. First, to deal with the virtually infinite strategy space of no-limit poker, we develop a discretized betting model designed to capture the most important strategic choices in the game. Second, we employ potential-aware automated abstraction algorithms for identifying strategically similar situations in order to decrease the size of the game tree. Third, we develop a new technique for automatically generating the source code of an equilibrium-finding algorithm from an XML-based description of a game. This automatically generated program is more efficient than what would be possible with a general-purpose equilibrium-finding program. Finally, we present results from the AAAI-07 Computer Poker Competition, in which Tartanian placed second out of ten entries.


title: Using Word Embeddings for Automatic Query Expansion

abstract: In this paper a framework for Automatic Query Expansion (AQE) is proposed using distributed neural language model word2vec. Using semantic and contextual relation in a distributed and unsupervised framework, word2vec learns a low dimensional embedding for each vocabulary entry. Using such a framework, we devise a query expansion technique, where related terms to a query are obtained by K-nearest neighbor approach. We explore the performance of the AQE methods, with and without feedback query expansion, and a variant of simple K-nearest neighbor in the proposed framework. Experiments on standard TREC ad-hoc data (Disk 4, 5 with query sets 301-450, 601-700) and web data (WT10G data with query set 451-550) shows significant improvement over standard term-overlapping based retrieval methods. However the proposed method fails to achieve comparable performance with statistical co-occurrence based feedback method such as RM3. We have also found that the word2vec based query expansion methods perform similarly with and without any feedback information.


title: Modeling of electronic energy converter for induction heating using LabVIEW

abstract: The aim of this paper to present the modeling of electronic energy converter in the popular development system LabVIEW. Using the various extensions and hardware capabilities it allows an intuitive, easy and comfortable way to examine, study and prototype these kind of devices. For example is reviewed an induction heating energy converter — parallel current source thyristor inverter. In the formalization and analysis of the power circuit are used interconnected parameters with which are described the behavior and operating regimes. The model is verified using the specialized simulation software LT Spice® and data taken from real electrothermic plants for induction heating.


title: Internet of Things Security Analysis

abstract: Internet of Things is an up-and-coming information and technology industry, and in such an environment of the concept and the content and the extension of Internet of Things are not very distinctive, the project of Internet of Things which is a piece of small area with small-scale and self-system obtain gratifying achievement and bright future, it can promote the development of Internet of Things in some extent. But there are some serious hidden danger and potential crisis problems. The paper focuses on the application of Internet of Things in the nation and even in the global in the future, analysing the existed security risks of the Internet of Things 's network points, transmission, finally we propose some suggestive solutions due to these problems.


title: Road detection from aerial imagery

abstract: We present a fast, robust road detection algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert and urban images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 97% of the images. We experimentally validated our algorithm on over ten thousand (10,000) aerial images obtained using our UAV. These images consist of intersecting roads, bifurcating roads and roundabouts in various conditions with significant changes in lighting and intensity. Our algorithm is able to successfully detect single roads effectively in almost all the images. It is also able to detect at least one road in over 95% of the images containing bifurcating or intersecting roads.


title: API recommendation system for software development

abstract: Nowadays, software developers often utilize existing third party libraries and make use of Application Programming Interface (API) to develop a software. However, it is not always obvious which library to use or whether the chosen library will play well with other libraries in the system. Furthermore, developers need to spend some time to understand the API to the point that they can freely use the API methods and putting the right parameters inside them. In this work, I plan to automatically recommend relevant APIs to developers. This API recommendation can be divided into multiple stages. First, we can recommend relevant libraries provided a given task to complete. Second, we can recommend relevant API methods that developer can use to program the required task. Third, we can recommend correct parameters for a given method according to its context. Last but not least, we can recommend how different API methods can be combined to achieve a given task. In effort to realize this API recommendation system, I have published two related papers. The first one deals with recommending additional relevant API libraries given known useful API libraries for the target program. This system can achieve recall rate@5 of 0.852 and recall rate@10 of 0.894 in recommending additional relevant libraries. The second one deals with recommending relevant API methods a given target API and a textual description of the task. This system can achieve recall-rate@5 of 0.690 and recallrate@10 of 0.779. The results for both system indicate that the systems are useful and capable in recommending the right API/library reasonably well. Currently, I am working on another system which can recommend web APIs (i.e., libraries) given a description of the task. I am also working on a system that recommends correct parameters given an API method. In the future, I also plan to realize API composition recommendation for the given task


title: EMI Noise Prediction for Electronic Ballasts

abstract: The design of electromagnetic (EM) interference filters for converter systems is usually based on measurements with a prototype during the final stages of the design process. Predicting the conducted EM noise spectrum of a converter by simulation in an early stage has the potential to save time/cost and to investigate different noise reduction methods, which could, for example, influence the layout or the design of the control integrated circuit. Therefore, the main sources of conducted differential-mode (DM) and common-mode (CM) noise of electronic ballasts for fluorescent lamps are identified in this paper. For each source, the noise spectrum is calculated and a noise propagation model is presented. The influence of the line impedance stabilizing network (LISN) and the test receiver is also included. Based on the presented models, noise spectrums are calculated and validated by measurements.


EYE BLINK DETECTION USING INTENSITY VERTICAL PROJECTION


Information Security Awareness: Literature Review and Integrative Framework


title: Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

abstract: Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.


title: Deformable GANs for Pose-Based Human Image Generation

abstract: In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. We test our approach using photos of persons in different poses and we compare our method with previous work in this area showing state-of-the-art results in two benchmarks. Our method can be applied to the wider field of deformable object generation, provided that the pose of the articulated object can be extracted using a keypoint detector.


title: Interactive focus+context visualization with linked 2D/3D scatterplots

abstract: Scatterplots in 2D and 3D are very useful tools, but also suffer from a number of problems. Overplotting hides the true number of points that are displayed, and showing point clouds in 3D is problematic both in terms of perception and interaction. We propose a combination of 2D and 3D scatterplots, together with some extensions to overcome these problems. By linking 2D and 3D views, it is possible to interact in 2D and get feedback in 3D. That feedback is also enhanced by depth cues (color and point size) such that the user gets a better depth impression. Histograms in 2D and 3D show additional information about point densities and additional context can be displayed. An example application demonstrates the usefulness of the technique.


title: The exact bound in the Erdös-Ko-Rado theorem

abstract: This paper contains a proof of the following result: ifn≧(t+1)(k−t−1), then any family ofk-subsets of ann-set with the property that any two of the subsets meet in at leastt points contains at most\(\left( {\begin{array}{*{20}c} {n - t} \\ {k - t} \\ \end{array} } \right)\) subsets. (By a theorem of P. Frankl, this was known whent≧15.) The bound (t+1)(k-t-1) represents the best possible strengthening of the original 1961 theorem of Erdos, Ko, and Rado which reaches the same conclusion under the hypothesisn≧t+(k−t)\(\left( {\begin{array}{*{20}c} k \\ t \\ \end{array} } \right)^3 \). Our proof is linear algebraic in nature; it may be considered as an application of Delsarte’s linear programming bound, but somewhat lengthy calculations are required to reach the stated result. (A. Schrijver has previously noticed the relevance of these methods.) Our exposition is self-contained.


title: Biexciton Emission from Edges and Grain Boundaries of Triangular WS2Monolayers

abstract: Monolayer tungsten disulfides (WS2) constitute a high quantum yield two-dimensional (2D) system, and can be synthesized on a large area using chemical vapor deposition (CVD), suggesting promising nanophotonics applications. However, spatially nonuniform photoluminescence (PL) intensities and peak wavelengths observed in single WS2 grains have puzzled researchers, with the origins of variation in relative contributions of excitons, trions, and biexcitons to the PL emission not well understood. Here, we present nanoscale PL and Raman spectroscopy images of triangular CVD-grown WS2 monolayers of different sizes, with these images obtained under different temperatures and values of excitation power. Intense PL emissions were observed around the edges of individual WS2 grains and the grain boundaries between partly merged WS2 grains. The predominant origin of the main PL emission from these regions changed from neutral excitons to trions and biexcitons with increasing laser excitation power, with biexcitons co...


title: Imbalances in Dietary Consumption of Fatty Acids, Vegetables, and Fruits Are Associated With Risk for Crohn's Disease in Children

abstract: BACKGROUND AND OBJECTIVES ::: The role of dietary factors in the etiology of Crohn's disease (CD) is inconsistent largely due to difficulties in acquiring valid information on consumption habits. We examined the impact of diet on new onset CD in children using a validated food-frequency questionnaire (FFQ). ::: ::: ::: METHODOLOGY ::: A case-control study was carried out. Children < or =20 yr, newly diagnosed with CD, were recruited from 3 pediatric gastroenterology clinics across Canada. Population or hospital controls were selected matched to cases for time of diagnosis (+/-6 months) and area of residence. Dietary consumption 1 yr prior to disease diagnosis was evaluated using a validated FFQ, administered within 1 month of diagnosis. Conditional logistic regression analysis adjusting for potential confounding variables (energy intake, age, gender, body mass index) was carried out. ::: ::: ::: RESULTS ::: A total of 130 CD patients and 202 controls were studied. Mean age at diagnosis (+/-SD) was 14.2 (2.7). There were more male patients (59%). Comparing the highest to the lowest levels of consumption, higher amounts of vegetables (OR 0.69, 95% CI 0.33-1.44, P= 0.03), fruits (OR 0.49, 95% CI 0.25-0.96, P= 0.02), fish (OR 0.46, 95% CI 0.20-1.06, P= 0.02), and dietary fiber (OR 0.12, 95% CI 0.04-0.37, P < 0.001) protected from CD. Consumption of long-chain omega-3 fatty acids (LCN-omega-3) was negatively associated with CD (OR 0.44, 95% CI 0.19-1.00, P < 0.001). A higher ratio of LCN-omega-3/omega-6 fatty acids was significantly associated with lower risks for CD (OR 0.32, 95% CI 0.14-0.71, P= 0.02). ::: ::: ::: CONCLUSIONS ::: Our findings indicate that an imbalance in consumption of fatty acids, vegetables, and fruits is associated with increased risks for CD among Canadian children.


title: Recovery Guarantees for One-hidden-layer Neural Networks

abstract: In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to $\mathit{local~strong~convexity}$ in the neighborhood of the ground-truth parameters for the 1NN squared-loss objective. Most popular nonlinear activation functions satisfy the distilled properties, including rectified linear units (ReLUs), leaky ReLUs, squared ReLUs and sigmoids. For activation functions that are also smooth, we show $\mathit{local~linear~convergence}$ guarantees of gradient descent under a resampling rule. For homogeneous activations, we show tensor methods are able to initialize the parameters to fall into the local strong convexity region. As a result, tensor initialization followed by gradient descent is guaranteed to recover the ground truth with sample complexity $ d \cdot \log(1/\epsilon) \cdot \mathrm{poly}(k,\lambda )$ and computational complexity $n\cdot d \cdot \mathrm{poly}(k,\lambda) $ for smooth homogeneous activations with high probability, where $d$ is the dimension of the input, $k$ ($k\leq d$) is the number of hidden nodes, $\lambda$ is a conditioning property of the ground-truth parameter matrix between the input layer and the hidden layer, $\epsilon$ is the targeted precision and $n$ is the number of samples. To the best of our knowledge, this is the first work that provides recovery guarantees for 1NNs with both sample complexity and computational complexity $\mathit{linear}$ in the input dimension and $\mathit{logarithmic}$ in the precision.


title: An EM algorithm for wavelet-based image restoration

abstract: This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.


title: SWARM: An Actuated Wearable for Mediating Affect

abstract: We present SWARM, a wearable affective technology designed to help a user to reflect on their own emotional state, modify their affect, and interpret the emotional states of others. SWARM aims for a universal design (inclusive of people with various disabilities), with a focus on modular actuation components to accommodate users' sensory capabilities and preferences, and a scarf form-factor meant to reduce the stigma of accessible technologies through a fashionable embodiment. Using an iterative, user-centered approach, we present SWARM's design. Additionally, we contribute findings for communicating emotions through technology actuations, wearable design techniques (including a modular soft circuit design technique that fuses conductive fabric with actuation components), and universal design considerations for wearable technology.


Critical Infrastructure Protection


title: Deep Learning: A Primer for Radiologists

abstract: This review illustrates key concepts of deep learning necessary to understand its application to images, describes emerging clinical applications in radiology, and discusses technical requirements and future directions in this field.


title: A homogeneous relational model and query languages for temporal databases

abstract: In a temporal database, time values are associated with data item to indicate their periods of validity. We propose a model for temporal databases within the framework of the classical database theory. Our model is realized as a temporal parameterization of static relations. We do not impose any restrictions upon the schemes of temporal relations. The classical concepts of normal forms and dependencies are easily extended to our model, allowing a suitable design for a database scheme. We present a relational algebra and a tuple calculus for our model and prove their equivalence. Our data model is homogeneous in the sense that the periods of validity of all the attributes in a given tuple of a temporal relation are identical. We discuss how to relax the homogeneity requirement to extend the application domain of our approach.


title: A two-stage training deep neural network for small pedestrian detection

abstract: In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.


title: Efficient Inference in Large Conditional Random Fields

abstract: Conditional Random Fields (CRFs) are widely known to scale poorly, particularly for tasks with large numbers of states or with richly connected graphical structures. This is a consequence of inference having a time complexity which is at best quadratic in the number of states. This paper describes a novel parameterisation of the CRF which ties the majority of clique potentials, while allowing individual potentials for a subset of the labellings. This has two beneficial effects: the parameter space of the model (and thus the propensity to over-fit) is reduced, and the time complexity of training and decoding becomes sub-quadratic. On a standard natural language task, we reduce CRF training time four-fold, with no loss in accuracy. We also show how inference can be performed efficiently in richly connected graphs, in which current methods are intractable.


title: Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation

abstract: Unsupervised domain adaptation aims to leverage the labeled source data to learn with the unlabeled target data. Previous trandusctive methods tackle it by iteratively seeking a low-dimensional projection to extract the invariant features and obtaining the pseudo target labels via building a classifier on source data. However, they merely concentrate on minimizing the cross-domain distribution divergence, while ignoring the intra-domain structure especially for the target domain. Even after projection, possible risk factors like imbalanced data distribution may still hinder the performance of target label inference. In this paper, we propose a simple yet effective domain-invariant projection ensemble approach to tackle these two issues together. Specifically, we seek the optimal projection via a novel relaxed domain-irrelevant clustering-promoting term that jointly bridges the cross-domain semantic gap and increases the intra-class compactness in both domains. To further enhance the target label inference, we first develop a ‘sampling-and-fusion’ framework, under which multiple projections are independently learned based on various randomized coupled domain subsets. Subsequently, aggregating models such as majority voting are utilized to leverage multiple projections and classify unlabeled target data. Extensive experimental results on six visual benchmarks including object, face, and digit images, demonstrate that the proposed methods gain remarkable margins over state-of-the-art unsupervised domain adaptation methods.


title: Particle Systems—a Technique for Modeling a Class of Fuzzy Objects

abstract: This paper introduces particle systems--a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems model an object as a cloud of primitive particles that define its volume. Over a period of time, particles are generated into the system, move and change form within the system, and die from the system. The resulting model is able to represent motion, changes of form, and dynamics that are not possible with classical surface-based representations. The particles can easily be motion blurred, and therefore do not exhibit temporal aliasing or strobing. Stochastic processes are used to generate and control the many particles within a particle system. The application of particle systems to the wall of fire element from the Genesis Demo sequence of the film Star Trek H: The Wrath of Khan [10] is presented.


title: A Data Mining Approach for Location Prediction

abstract: Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next intercell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.


title: Interactive Game Based Learning: Advantages and Disadvantages

abstract: Interactive Game-Based Learning might be used to raise the awareness of students concerning questions of sustainability. Sustainability is a very complex topic. By interacting with a simulation game, students can get a more detailed and holistic conception of how sustainability can be achieved in everyday purchasing situations. The SuLi (Sustainable Living) game was developed to achieve this goal. In an evaluation study we found evidence that SuLi is an interesting alternative to more traditional approaches to learning. Nevertheless, there are still many open questions, as, e.g., whether one should combine simulation games with other forms of teaching and learning or how to design simulation games so that students really acquire detailed concepts of the domain.


title: Privacy and Integrity Considerations in Hyperconnected Autonomous Vehicles

abstract: The rapid advances in technology can be witnessed in the emergence of cyber–physical systems that pertain to several domains of our society. In transportation, we see the emergence of self-driving vehicles that utilize a multitude of sensors and intelligent learning techniques to navigate autonomously. Such vehicles are complex cyber–physical systems that are mobile and due to their sensor and intrinsic intelligence are able to collect, analyze, and capitalize upon an unprecedented amount of fine-grained data, as well as collaborate in real time with multiple stakeholders. Although such rich data can play a key role in data-driven economies of scale, this raises questions with respect to privacy- and integrity-dependent scenarios. In this work, the feasibility of ensuring integrity, and hence safety, while preserving privacy in the emerging hyperconnected vehicle scenarios is discussed. An exemplary case study on real-time vehicle interactions pertaining to map updates exemplifies the combination of privacy-enhancing technologies with integrity-protecting mechanisms.


title: A Developmental Perspective on Executive Function

abstract: This review article examines theoretical and methodological issues in the construction of a developmental perspective on executive function (EF) in childhood and adolescence. Unlike most reviews of EF, which focus on preschoolers, this review focuses on studies that include large age ranges. It outlines the development of the foundational components of EF—inhibition, working memory, and shifting. Cognitive and neurophysiological assessments show that although EF emerges during the first few years of life, it continues to strengthen significantly throughout childhood and adolescence. The components vary somewhat in their developmental trajectories. The article relates the findings to long-standing issues of development (e.g., developmental sequences, trajectories, and processes) and suggests research needed for constructing a developmental framework encompassing early childhood through adolescence.


title: Survey on PV Modules’ Common Faults After an O&M Flight Extensive Campaign Over Different Plants in Italy

abstract: Nowadays one of the most consolidated inspection methods for photovoltaic (PV) systems is the use of drones. PV modules installed in the last decade show now a quite wide range of defects able to sensibly compromise the performance of the plant. This paper aims to provide a quite comprehensive overview of typical defects observed after an extensive flight campaign in North of Italy made by light multicopter unmanned aerial vehicles over different types of PV plants. The objective of this investigation is to summarize the most observed detectable faults and to give some data of their frequency. Different kinds of sensors have been installed onboard and some peculiarities will be emphasized with respect to the visible, in-field measurements and IR-collected data. The results of this campaign will be discussed in order to provide a clear idea of potential impact of unmanned technology in this sector for future decades.


title: Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks

abstract: The prediction of bank performance is an important issue. The bad performance of banks may first result in bankruptcy, which is expected to influence the economics of the country eventually. Since the early 1970s, many researchers had already made predictions on such issues. However, until recent years, most of them have used traditional statistics to build the prediction model. Because of the vigorous development of data mining techniques, many researchers have begun to apply those techniques to various fields, including performance prediction systems. However, data mining techniques have the problem of parameter settings. Therefore, this study applies particle swarm optimization (PSO) to obtain suitable parameter settings for support vector machine (SVM) and decision tree (DT), and to select a subset of beneficial features, without reducing the classification accuracy rate. In order to evaluate the proposed approaches, dataset collected from Taiwanese commercial banks are used as source data. The experimental results showed that the proposed approaches could obtain a better parameter setting, reduce unnecessary features, and improve the accuracy of classification significantly.


title: Study of authentication with IoT testbed

abstract: The Internet of Things (IoT), connecting things like home appliances and rescue robots to the Internet, is becoming the next revolutionary technology. However, with the IoT penetrating the physical world, potential risks and vulnerabilities cast a shadow upon the dawn of the IoT. Authentication, or verifying the genuineness of smart devices and establishing trust for critical infrastructure, plays a vital role in the realization of the IoT. Testbeds for experimental IoT research have flourished, most focusing on usability issues such as scalability and heterogeneity. With an affordable IoT testbed at a university lab, experiments using tools categorized by SANS Critical Security Controls are conducted from both offense and defense sides. This work focuses on smartcards and physical unclonable functions (PUF) for device authentication, following NIST Guide for Assessing Security Controls. Innovative workflows of authentication protocols are proposed and analyzed. The study prepares the future workforce for a secure IoT.


title: Reference Gene Selection for RT-qPCR Analysis of Flower Development in Chrysanthemum morifolium and Chrysanthemum lavandulifolium

abstract: Quantitative real-time PCR (qPCR) is a popular and powerful tool used to understand the molecular mechanisms of flower development. However, the accuracy of this approach depends on the stability of reference genes. The capitulum of chrysanthemums is very special, which is consist of ray florets and disc florets. There are obvious differences between the two types of florets in symmetry, gender, histological structure and function. Furthermore, the ray florets have various shapes. The objective of present study was to identify the stable reference genes in Chrysanthemum morifolium and Chrysanthemum lavandulifolium during the process of flower development. In this study, nine candidate reference genes were selected and evaluated for their expression stability acrosssamples during the process of flower development, and their stability was validated by four different algorithms (Bestkeeper, NormFinder, GeNorm and Ref-finder). SAND(SAND family protein) was found to be the most stably expressed gene in all samples or different tissues during the process of C. lavandulifolium development. Both SAND and PGK (phosphoglycerate kinase) performed most stable in Chinese large-flowered chrysanthemum cultivars, and PGK was the best in potted chrysanthemums. There were differences in best reference genes among varieties as the genetic background of them were complex. These studies provide guidance for selecting reference genes for analyzing the expression pattern of floral development genes in chrysanthemums.


title: Simulation of static and dynamic wrinkles of skin

abstract: Wrinkles are an extremely important contribution for enhancing the realism of human figure models. We present an approach to generate static and dynamic wrinkles on human skin. For the static model, we consider micro and macro structures of the skin surface geometry. For the wrinkle dynamics, an approach using a biomechanical skin model is employed. The tile texture patterns in the micro structure of skin surface are created using planar Delaunay triangulation. Functions of barycentric coordinates are applied to simulate the curved ridges. The visible (macro) flexure lines which may form wrinkles are predefined edges on the micro structure. These lines act as constraints for the hierarchical triangulation process. Furthermore, the dynamics of expressive wrinkles-controlling their depth and fold-is modeled according to the principal strain of the deformed skin surface. Bump texture mapping is used for skin rendering.


title: Performance analysis of objective function MRHOF and OF0 in routing protocol RPL IPV6 over low power wireless personal area networks (6LoWPAN)

abstract: 6LoWPAN is one of the implementations of WSN that using IPv6 as sensor addressing. 6LoWPAN is categorized in the new technology and still being developed. This causes issues about the networks performance to create the communication path and collecting data. Routing Protocol for Low-power-loosy (RPL) is a routing protocol used in 6LoWPAN. In RPL, there are two kind of objective functions development. Objective function is path selection mechanism when formation of the networks. This research is focus on performance analysis in two objective functions RPL that are Minimum Rank with Hysteresis Objective Function (MRHOF) and Objective Function Zero (OFO). This research observed using COOJA as a WSN simulator. The parameter that observed in this research are: networks convergence time, power consumption, ETX, hop, Packet Delivery Ratio (PDR), latency, PDR in mobility node. Based on the simulation we concluded that MRHOF give better performance than OF0 in terms of network quality. The implementation of MRHOF is suitable for use in sensor network that require data delivery in the reliable network. While OF0 is suitable for use in sensor network that require fast formation network link and low power consumption.


title: Using the MEAN stack to implement a RESTful service for an Internet of Things application

abstract: This paper examines the components of the MEAN development stack (MongoDb, Express.js, Angular.js, & Node.js), and demonstrate their benefits and appropriateness to be used in implementing RESTful web-service APIs for Internet of Things (IoT) appliances. In particular, we show an end-to-end example of this stack and discuss in detail the various components required. The paper also describes an approach to establishing a secure mechanism for communicating with IoT devices, using pull-communications.


title: Transcription at different salinities of Haloferax mediterranei sequences adjacent to partially modified PstI sites

abstract: Summary ::: Two genomic sequences from the halophilic archaeon Haloferax mediterranei, where we had found PstI restriction-pattern modifications depending on the salinity of the growth medium, have been studied. A markedly salt-dependent differential expression has been detected in the nearby regions. Two of the open reading frames characterized correspond to two of the differentially expressed transcripts. In both cases the PstI sites were included in purine–pyrimidine alternancies suggestive of Z-DNA structures and located in non–coding regions with frequent repetitive motifs. A long alternating adenine-thymine tract also appears in the upstream regions of one of these open reading frames. A possible role of local DNA configuration in osmoregulation in this organism is discussed.


title: Ally Friendly Jamming: How to Jam Your Enemy and Maintain Your Own Wireless Connectivity at the Same Time

abstract: This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.


title: You are what you include: large-scale evaluation of remote javascript inclusions

abstract: JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them. In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.


title: Development of the conformity to feminine norms inventory

abstract: This article describes the construction of the Conformity to Feminine Norms Inventory (CFNI), which was designed to assess women’s conformity to an array of feminine norms found in the dominant culture in the United States. In addition, we present four studies in which the psychometric properties of the CFNI were examined. In Study 1, factor analysis indicated that the CFNI is comprised of eight distinct factors labeled as Nice in Relationships, Thinness, Modesty, Domestic, Care for Children, Romantic Relationship, Sexual Fidelity, and Invest in Appearance. Results from Study 2 indicated that the CFNI has strong internal consistency estimates and differentiates college women from college men. In addition, Study 2 demonstrated that the CFNI Total score and subscale scores relate to Bem Sex Role Inventory and Feminist Identity Development Scale scores in theoretically consistent patterns. Study 3 indicated that the CFNI Total score and several of the subscales significantly and positively relate to scores on the Eating Disorder Inventory. Finally, Study 4 demonstrated that the CFNI Total score and subscale scores have high test-retest estimates for a 2–3 week period. The discussion focuses on potential uses of the CFNI, limitations to the study, and suggestions for future research.


title: Skin: a constructive approach to modeling free-form shapes

abstract: We present a new particle-based surface representation with which a user can interactively sculpt free-form surfaces. The particles maintain mesh connectivity and operate under rules that lead them to form triangulations with properties that make them suitable for use in subdivision. A user interactively guides the particles, which we call skin, to grow over a given collection of polyhedral elements (or skeletons), yielding a smooth surface (through subdivision) that approximates the underlying skeletal shapes. Skin resembles blobby modeling in the constructive approach to modeling it supports, but allows a richer vocabulary of skeleton shapes, supports sharp creases where desired, and provides a convenient mechanism for adding multiresolution surface detail. CR


title: Decoupled Powered Caster Wheel for omnidirectional mobile platforms

abstract: This paper presents a novel design of a Powered Caster Wheel (PCW) that decouples the steering and rolling motions. The working principles and mechanical design of the PCW are introduced in details. Kinematic modeling, trajectory planning and control algorithms are derived for PCW-based omnidirectional mobile platforms. To demonstrate the new PCW design, a PCW prototype has been developed and an omnidirectional mobile platform based on the decoupled PCW has also been constructed and tested.


Microsoft Word - 01 Sensors and Signals Introduction.doc


title: Modeling visual attention via selective tuning

abstract: A model for aspects of visual attention based on the concept of selective tuning is presented. It provides for a solution to the problems of selection in an image, information routing through the visual processing hierarchy and task-specific attentional bias. The central thesis is that attention acts to optimize the search procedure inherent in a solution to vision. It does so by selectively tuning the visual processing network which is accomplished by a top-down hierarchy of winner-take-all processes embedded within the visual processing pyramid. Comparisons to other major computational models of attention and to the relevant neurobiology are included in detail throughout the paper. The model has been implemented; several examples of its performance are shown. This model is a hypothesis for primate visual attention, but it also outperforms existing computational solutions for attention in machine vision and is highly appropriate to solving the problem in a robot vision system.


title: Impact analysis of emotion in figurative language

abstract: Due to the implicit traits embedded in tweets, handling figurative languages appear as the most trending topics in computational linguistics. While recognition of a single language is hard to capture, differentiating several languages at once is the most challenging task. To achieve this purpose, we employ a set of emotion-based features in order to individuate between humor, irony, sarcasm, satire and true. We use eight basic emotions excerpted from EmoLex supplement with tweets polarity. We apply these features in two datasets: balanced dataset (collected using hashtag-based approach) and class-imbalanced dataset (collected from streaming tweets). As a result, the model not only outperform a word-based baseline but also handle both balanced and class-imbalanced datasets in multi-figurative language detection.


title: Time-Contrastive Networks: Self-Supervised Learning from Video

abstract: We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose. We train our representations using a triplet loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems. Video results, open-source code and dataset are available at sermanet.github.io/imitate


title: Antecedents of big data quality: An empirical examination in financial service organizations

abstract: Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of big data quality in financial institutions. This will help to understand how data quality from big data analysis can be improved. For this, a literature review was performed and data was collected using three case studies, followed by content analysis. The overall findings indicate that there are no fundamentally new data quality issues in big data projects. Nevertheless, the complexity of the issues is higher, which makes it harder to assess and attain data quality in big data projects compared to the traditional projects. Ten antecedents of big data quality were identified encompassing data, technology, people, process and procedure, organization, and external aspects.


title: Color image edge detection using cluster analysis

abstract: A color image edge detection algorithm is proposed based on the idea that use global color information to guide local gradient computation. The major chromatic components of an image are first extracted through cluster analysis. According to these color clusters, a set of linear chromatic transforms are generated. An appropriate chromatic transform is chosen for each pixel to maximize the gradient magnitude. In this way, edges are treated as transitions from one cluster to another. The algorithm is implemented and experimental results for real color images are included.


title: Mindfulness meditation and the immune system: a systematic review of randomized controlled trials

abstract: Mindfulness meditation represents a mental training framework for cultivating the state of mindful awareness in daily life. Recently, there has been a surge of interest in how mindfulness meditation improves human health and well-being. Although studies have shown that mindfulness meditation can improve self-reported measures of disease symptomatology, the effect that mindfulness meditation has on biological mechanisms underlying human aging and disease is less clear. To address this issue, we conducted the first comprehensive review of randomized controlled trials examining the effects of mindfulness meditation on immune system parameters, with a specific focus on five outcomes: (1) circulating and stimulated inflammatory proteins, (2) cellular transcription factors and gene expression, (3) immune cell count, (4) immune cell aging, and (5) antibody response. This analysis revealed substantial heterogeneity across studies with respect to patient population, study design, and assay procedures. The findings suggest possible effects of mindfulness meditation on specific markers of inflammation, cell-mediated immunity, and biological aging, but these results are tentative and require further replication. On the basis of this analysis, we describe the limitations of existing work and suggest possible avenues for future research. Mindfulness meditation may be salutogenic for immune system dynamics, but additional work is needed to examine these effects.


title: Boosting for transfer learning with multiple sources

abstract: Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target. Rather than improving the learning, brute force leveraging of a source poorly related to the target may decrease the classifier performance. One strategy to reduce this negative transfer is to import knowledge from multiple sources to increase the chance of finding one source closely related to the target. This work extends the boosting framework for transferring knowledge from multiple sources. Two new algorithms, MultiSource-TrAdaBoost, and TaskTrAdaBoost, are introduced, analyzed, and applied for object category recognition and specific object detection. The experiments demonstrate their improved performance by greatly reducing the negative transfer as the number of sources increases. TaskTrAdaBoost is a fast algorithm enabling rapid retraining over new targets.


title: Autism spectrum disorder: Parenting stress, family functioning and health-related quality of life.

abstract: Abstract The prevalence of Autism Spectrum Disorder (ASD) is 1 in 110 persons in the U.S. Both parents of children with ASD are under stress that may impact their health-related quality of life (HRQL) (physical and mental health). The purpose of the current study was to explore the relationship of parenting stress, support from family functioning and the HRQL (physical and mental health) of both parents. Female (n = 64) and male (n = 64) parents of children with ASD completed Web-based surveys examining parenting stress, family functioning, and physical and mental health. Results of a Wilcoxon signed-ranks test showed that female parent discrepant (D) scores between "what is" and "should be" family functioning were significantly larger than male parents, p = .002. Results of stepwise linear regression for the male-female partners showed that (1) higher female caregiving stress was related to lower female physical health (p < .001), (2) a higher discrepancy score in family functioning predicted lower mental health (p < .001), accounting for 31% of the variance for females and (3) male parent personal and family life stress (p < .001) and family functioning discrepant (D) score (p < .001) predicted poor mental health, with the discrepancy score accounting for 35% of the variance. These findings suggest that there may be differences in mothers' and fathers' perceptions and expectations about family functioning and this difference needs to be explored and applied when working with families of children with ASD.


title: Thirty years of heterogeneous vehicle routing

abstract: It has been around 30 years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems.


title: Improving background subtraction using Local Binary Similarity Patterns

abstract: Most of the recently published background subtraction methods can still be classified as pixel-based, as most of their analysis is still only done using pixel-by-pixel comparisons. Few others might be regarded as spatial-based (or even spatiotemporal-based) methods, as they take into account the neighborhood of each analyzed pixel. Although the latter types can be viewed as improvements in many cases, most of the methods that have been proposed so far suffer in complexity, processing speed, and/or versatility when compared to their simpler pixel-based counterparts. In this paper, we present an adaptive background subtraction method, derived from the low-cost and highly efficient ViBe method, which uses a spatiotemporal binary similarity descriptor instead of simply relying on pixel intensities as its core component. We then test this method on multiple video sequences and show that by only replacing the core component of a pixel-based method it is possible to dramatically improve its overall performance while keeping memory usage, complexity and speed at acceptable levels for online applications.


Perspective Rectangle Detection


title: An architecture for 1-bit localized compressive sensing with applications to EEG

abstract: Compressed sensing exploits special signal features to extract its information content with a smaller amount of samples with respect to acquisition based on Nyquist theorem. While many theoretical results have proved the capabilities of this new paradigm, hardware implementations are still far from being practical. Here, we present a new architecture of analog to information converter that produces 1-bit compressive measurements. The performance of the architecture can be boosted if the signal to acquire features, beyond the classically required sparsity, also some sort of localization of its energy. The effectiveness of the architecture and of its enhancement is shown in the measurement of EEG, that presents a non-uniform spectral profile.


title: Power distribution protection scheme enhanced with communication

abstract: The paper proposes a communication-based protection scheme for distribution networks with distributed generators. Emphasis is put on making the scheme flexible for various network configurations. Extensive simulation studies demonstrate the proper operation of the protection scheme. The paper looks additionally at the current capabilities in communication-enhanced protection made possible by standardized protocols (IEC 61850) and mechanisms (Ethernet). It finally discusses techniques to encompass intersubstation communications and examines issues that need to be addressed to create a successful, secure and dependable communication network.


title: Robust Maneuverability of a Miniature, Low-Cost Underwater Robot Using Multiple Fin Actuation

abstract: In this letter, we present the design of a miniature (100 mm) autonomous underwater robot that is low-cost ($ 100), easy to manufacture, and highly maneuverable. A key aspect of the robot design that makes this possible is the use of low-cost magnet-in-coil actuators, which have a small profile and minimal sealing requirements. This allows us to create a robot with multiple flapping fin propulsors that independently control robot motions in surge, heave, and yaw. We present several results on the robot, including 1) quantified open-loop swimming characteristics; 2) autonomous behaviors using a pressure sensor and an inertial measurement unit (IMU) to achieve controlled swimming of prescribed trajectories; 3) feedback from an optic sensor to enable homing to a light source. The robot is designed to form the basis for underwater swarm robotics testbeds, where low cost and ease of manufacture are critical, and three-dimensional (3-D) maneuverability allows testing complex coordination inspired by natural fish schools. Individually, miniature and low-cost underwater robots can also provide a platform for the study of 3-D autonomy and marine vehicle dynamics in educational and resource-constrained settings.


title: An investigation on the design and performance assessment of double-PID and LQR controllers for the inverted pendulum

abstract: The widespread application of inverted pendulum principles requires better dynamic performance and steady state performance of the inverted pendulum system. The objective of this paper is to design and investigate the time specification performance of the inverted pendulum controllers. Two control methods are proposed in this paper, an innovative double PID control method and a modern LQR (liner quadratic regulator) control method. Dynamic performance and steady state performance are investigated and compared of the two controllers. This paper proves that the LQR controller can guarantee the inverted pendulum a faster and smoother stabilizing process and with better robustness and less oscillation than the double-PID controller. The novelty of this paper is the design of the two controllers, and the adoption of limits cycles as the performance assessment method for the inverted pendulum, which not only makes the steady state performance assessment available, but also, provides an effective way for the evaluation of any equilibrium control problem with friction involved.


title: A Survey on Object Detection in Optical Remote Sensing Images

abstract: Abstract Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.


Gracewipe: Secure and Verifiable Deletion under Coercion


Stabilization of a Quadrotor With Uncertain Suspended Load Using Sliding Mode Control


title: Exploring phrase-compositionality in skip-gram models

abstract: In this paper, we introduce a variation of the skip-gram model which jointly learns distributed word vector representations and their way of composing to form phrase embeddings. In particular, we propose a learning procedure that incorporates a phrase-compositionality function which can capture how we want to compose phrases vectors from their component word vectors. Our experiments show improvement in word and phrase similarity tasks as well as syntactic tasks like dependency parsing using the proposed joint models.


title: Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

abstract: Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion ::: related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions ::: as features and examined five popular machine learning classifiers in the task of identifying users with selfreported ::: mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features ::: and combined features showed promising improvements to the performance on this task.