title: Characteristics of periodontal biotype, its dimensions, associations and prevalence: a systematic review.

abstract: AIM ::: The objectives of this review were as follows: What are characteristics used to define various forms of periodontal biotypes? What are their anatomic dimensions in relation to the definition? In addition, what is the association between these various characteristics in relation to the periodontal biotypes? Furthermore, what is the prevalence of various forms of periodontal biotypes in the population? ::: ::: ::: MATERIAL AND METHODS ::: The PubMed-MEDLINE, the Cochrane-CENTRAL and EMBASE databases were searched through up and till June 2013 to identify any appropriate studies regarding the aim. Appropriate studies were those reporting characteristics of various forms of periodontal biotype and its dimensions. These characteristics were gingival thickness (GT), gingival morphotype (GM), tooth dimensions (TD), keratinized tissue (KT) and bone morphotype (BM). ::: ::: ::: RESULTS AND CONCLUSIONS ::: The search yielded 2581 unique papers, after selection resulted in 12 publications that met the eligibility criteria. In general, the available definitions are found to be unclear and sometimes inconsistent. However, based on the available literature, the three biotypes thin scalloped, thick flat and thick scalloped seem a comprehensive categorization in defining periodontal biotypes in the population. The dental, gingival and osseous dimensions have a weak to moderate association. Only between gingival thickness, keratinized tissue and bone morphotype uniform positive associations are found.


title: A needs theory of governance

abstract: New-institutional economics hypothesizes imperfect rationality, self-seeking preferences, monetary-related needs, and opportunism as fundamental features of human behavior. Consistently, new-institutionalist models of governance highlight the efficiency and transaction costs minimizing features of control rights and governance. Differently, needs theory of governance, as here presented, hypothesizes imperfect rationality, multiple needs, and reciprocity, in which case opportunism is reduced to an exception to individual behavior. Consistently, it presents a theory that links production governance with the wellbeing of those partaking in production. Building on Maslowâ€™s human psychology, the governance model suggested in this paper is aimed at evidencing the self-actualization potential of control rights, organizational structures and practices. The application of Maslowâ€™s theory to the institutional structure of organizations suggests that the deepest organizational layers (control rights and governance) broadly correspond to the most basic needs in Maslowâ€™s theory (survival, security and belonging), while the outer layers (managerial models and employment relations) correspond to the fulfillment of the highest needs (self-esteem and self-actualization). Cooperative firms are used as an illustration of governance solutions consistent with needs theory in human psychology


title: Round table discussion on resonators

abstract: A video signal level clamping arrangement for a television camera in which input video signals are clamped to a fixed potential once during each line flyback. The clamped video signals are fed to a signal path into which signals are gated, each of the signals extending for at least the duration of one line flyback period. The resultant spurious free waveform is clamped to a further fixed potential source which is controlled such that it does not clamp during line flyback or outside field suppression. The waveform resulting from the last mentioned clamping is amplitude clipped to produce a stabilized D.C. level corresponding to a predetermined shade, e.g., black.


title: Understanding the functional neuroanatomy of acquired prosopagnosia

abstract: One of the most remarkable disorders following brain damage is prosopagnosia, the inability to recognize faces. While a number of cases of prosopagnosia have been described at the behavioral level, the functional neuroanatomy of this face recognition impairment, and thus the brain regions critically involved in normal face recognition, has never been specified in great detail. Here, we used anatomical and functional magnetic resonance imaging (fMRI) to present the detailed functional neuroanatomy of a single case of acquired prosopagnosia (PS; Rossion, B., Caldara, R., Seghier, M., Schuller, A.-M., Lazeyras, F., Mayer, E., 2003a. A network of occipito-temporal face-sensitive areas besides the right middle fusiform gyrus is necessary for normal face processing. Brain 126, 2381-95; Rossion, B., Joyce, C.A., Cottrell, G.W., Tarr, M.J., 2003b. Early lateralization and orientation tuning for face, word, and object processing in the visual cortex. Neuroimage 20, 1609-24) with normal object recognition. First, we clarify the exact anatomical location and extent of PS' lesions in relation to (a) retinotopic cortex, (b) face-preferring regions, and (c) other classical visual regions. PS' main lesion - most likely causing her prosopagnosia - is localized in the posterior part of the right ventral occipitotemporal cortex. This lesion causes a left superior paracentral scotoma, as frequently observed in cases of prosopagnosia. While the borders of the early visual areas in the left hemisphere could be delineated well, the extensive posterior right-sided lesion hampered a full specification of the cortical representation of the left visual field. Using multiple scanning runs, face-preferring activation was detected within the right middle fusiform gyrus (MFG) in the so-called 'fusiform face area' ('FFA'), but also in the left inferior occipital gyrus (left 'OFA'), and in the right posterior superior temporal sulcus (STS). The dorsal part of the lateral occipital complex (LOC) and the human middle temporal cortex (hMT+/V5) were localized bilaterally. The color-preferring region V4/V8 was localized only in the left hemisphere. In the right hemisphere, the posterior lesion spared the ventral part of LOC, a region that may be critical for the preserved object recognition abilities of the patient, and the restriction of her deficit to the category of faces. The presumptive functions of both structurally damaged and preserved regions are discussed and new hypotheses regarding the impaired and preserved abilities of the patient during face and non-face object processing are derived. Fine-grained neurofunctional analyses of brain-damaged single cases with isolated recognition deficits may considerably improve our knowledge of the brain regions critically involved in specific visual functions, such as face recognition.


title: A Practical Guide to Developing and Validating Computer Science Knowledge Assessments with Application to Middle School

abstract: Knowledge assessment instruments, or tests, are commonly created by faculty in classroom settings to measure student knowledge and skill. Another crucial role for assessment instruments is in gauging student learning in response to a computer science education research project, or intervention. In an increasingly interdisciplinary landscape, it is crucial to validate knowledge assessment instruments, yet developing and validating these tests for computer science poses substantial challenges. This paper presents a seven-step approach to designing, iteratively refining, and validating knowledge assessment instruments designed not to assign grades but to measure the efficacy or promise of novel interventions. We also detail how this seven-step process is being instantiated within a three-year project to implement a game-based learning environment for middle school computer science. This paper serves as a practical guide for adapting widely accepted psychometric practices to the development and validation of computer science knowledge assessments to support research.


title: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

abstract: Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In this paper we provide an overview of some existing techniques for discovering such embeddings. We then introduce a novel probabilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PCA (DPPCA). The DPPCA model has the additional advantage that the linear mappings from the embedded space can easily be non-linearised through Gaussian processes. We refer to this model as a Gaussian process latent variable model (GP-LVM). Through analysis of the GP-LVM objective function, we relate the model to popular spectral techniques such as kernel PCA and multidimensional scaling. We then review a practical algorithm for GP-LVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes. We demonstrate the model on a range of real-world and artificially generated data sets.


title: Reliability characterization of Chip-on-Wafer-on-Substrate (CoWoS) 3D IC integration technology

abstract: With the size of transistors scaling down, 3D IC packaging emerged as one of the most promising solutions to achieve system integration on the track of Moore's Law. In this article, we demonstrated a sub-system with one 28nm logic device and two 40nm chips on a 600mm2 silicon interposer with Through-Silicon-Via (TSV) integrating 4 layers of high density interconnects. The packages were assembled using our proprietary CoWoS (Chip on Wafer on Substrate) technology that incorporated 270,000 micro-bump (μBump) and 8,700 C4 bumps. Comprehensive reliability characterization and test methods will be presented. It includes copper interconnect reliability of silicon interposer on EM, SM and IMD TDDB with the presence of TSV. μBump EM and TSV EM are characterized to provide a design guideline for maximum current carrying capability; the EM test methodology was also used to optimize the integrated process, e.g. TSV copper plating, μBump joint, and interface treatment of TSV revealing. Package process optimization, bill of material selectio n and qualificatio n we r e conducted on a 28nm Cu/ELK (Extreme low-K) test vehicle taking Chip-Package-Interaction into consideration. Not only component level package reliability tests were performed, board level Thermal Cycling, Power Cycling and Mechanical Shock tests were also executed to understand the reliability margin and potential failure modes in field use condition. The typical failure modes and mechanisms will be discussed. CoWoS technology is eventually successfully developed with enhanced reliability. The results highlight the importance of a highly integrated 3D IC technology from silicon wafer process to assembly packaging. This work shows that in the new paradigm of 3D IC integration, Si Foundry is positioned at a unique leadership to manage the innovation in Si processes and the improvement of Si assembly operating life.


title: A Compact Dual-Band Beam-Sweeping Antenna Based on Active Frequency Selective Surfaces

abstract: A dual-band beam-sweeping antenna based on two independent cylindrical active frequency selective surfaces (AFSSs) is proposed in this paper. This antenna is composed of a dual-band omnidirectional monopole antenna operating at 2.45 and 5.2 GHz and two cylindrical AFSS screens. The dual-band omnidirectional monopole antenna is designed as a radiating source and surrounded by the proposed two cylindrical AFSS screens. The unit cells of the two proposed AFSS screens consist of two metallic crosses connected by a pin diode vertically. By switching the pin diodes, the transmission and reflection characteristics of unit cell of the two AFSS are investigated, respectively, at their own operating frequency. This leads to the variation of the radiation pattern when the cylindrical AFSS screens are loaded around the monopole antenna. Therefore, by switching the pin diodes with specified combinations, the dual-band beamforming antenna with multiple discrete states can be achieved at 2.45 and 5.2 GHz.


title: Interactive intellegent shopping cart using RFID and ZIGBEE modules

abstract: Shopping is a necessity of people, that covers different fields, from vegetable and grocery to clothing. Generally, people used to go separate shops to buy different types of items. Now, shops are integrated so we can find all the necessary items under a single roof, which is known as a shopping complex. The statistics of the minor and major shopping malls grows over the years throughout the world due to the request of the community. Thus, this leads to the increase in the level and structure of a shopping mall. In our country, there is still an abundant need for upgrading the facilities for providing the worthiest shopping experience to customers. Customers frequently deal with difficulties and trouble while shopping. These problems also contain worrying trends about the total amount of money, inadequate information about the items that are for sale and offers of the day, and also waiting in the queue at the billing counter for the bill which leads to the wastage of unnecessary time. For resolving the problems stated above various methods are developed, but the efficacy is still considered for the betterment and improves the shopping experience. Examples of these various existing methods such as barcode technology, where the price is stored in the barcode and RFID technology are implemented.


Security and Privacy in m-Learning and Beyond : Challenges and State-ofthe-art Georgios Kambourakis Laboratory of Information and Communications Systems Security


Gamification of Clinical Routine: The Dr. Fill Approach.


title: Localization and tracking of moving targets with WiFi-based passive radar

abstract: This paper investigates the possibility of obtaining localization and tracking of moving targets by exploiting a passive radar system based on WiFi transmissions. Specifically, the exploitation of multiple passive sensors is considered which allows to collect a set of range/Doppler/DoA measures for a given target when observed at different bistatic geometries. Different target localization strategies are investigated based on proper subsets of the available measures. Experimental results are shown which have been obtained by means of a passive radar prototype developed and fielded at the DIET Dept. — Sapienza University of Rome. This will contribute to demonstrate the fruitful application of the passive radar concept for short range surveillance.


title: Arches: a Framework for Modeling Complex Terrains

abstract: In this paper, we present a framework for representing complex terrains with such features as overhangs, arches and caves and including different materials such as sand and rocks. Our hybrid model combines a volumetric discrete data structure that stores the different materials and an implicit representation for sculpting and reconstructing the surface of the terrain. Complex scenes can be edited and sculpted interactively with high level tools. We also propose an original rock generation technique that enables us to automatically generate complex rocky sceneries with piles of rocks without any computationally demanding physically-based simulation.


title: Ring Resonator Bandpass Filter With Switchable Bandwidth Using Stepped-Impedance Stubs

abstract: This paper presents a novel ring resonator bandpass filter (BPF) with switchable passband bandwidth. Simple techniques for analyzing resonant frequencies and transmission zeros are shown, and the methods of designing the lengths of open, stepped-impedance stubs attached to the ring are introduced to obtain the tunability of the bandwidth. Interdigital-coupled feed lines are used for harmonic suppression by adding transmission zeros outside of the passband, resulting in wide upper and lower stopbands. After the lengths of the open stubs and the design of the feed lines are decided, the means of producing the bandwidth tunability are analyzed. By changing the characteristic impedances of the open stubs, mid-upper or mid-lower passband bandwidths can be separately varied. Simultaneous increases or decreases of each width of the stepped open stubs change the number of resonances, resulting in wide or narrow passband bandwidths. The measured results of the BPF using PIN diodes show that the tunable passband ratio is 1.22:1.13:1 with a maximum insertion loss of 1.35 dB.


title: An Improved Version of the Cervical Vertebral Maturation (CVM) Method for the Assessment of Mandibular Growth

abstract: Abstract The present study aimed to provide a version of the Cervical Vertebral Maturation (CVM) method for the detection of the peak in mandibular growth based on the analysis of the second through fourth cervical vertebrae in a single cephalogram. The morphology of the bodies of the second (odontoid process, C2), third (C3), and fourth (C4) cervical vertebrae were analyzed in six consecutive cephalometric observations (T1 through T6) of 30 orthodontically untreated subjects. Observations for each subject consisted of two consecutive cephalograms comprising the interval of maximum mandibular growth (as assessed by means of the maximum increment in total mandibular length, Co-Gn), together with two earlier consecutive cephalograms and two later consecutive cephalograms. The analysis consisted of both visual and cephalometric appraisals of morphological characteristics of the three cervical vertebrae. The construction of the new version of the CVM method was based on the results of both ANOVA for repeated ...


title: Convolutional neural networks for SAR image segmentation

abstract: Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides this contribution we also suggest a new way to do pixel wise annotation of SAR images that replaces a human expert manual segmentation process, which is both slow and troublesome. Our method for annotation relies on 3D CAD models of objects and scene, and converts these to labels for all pixels in a SAR image. Our algorithms are evaluated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset which was released by the Defence Advanced Research Projects Agency during the 1990s. The method is not restricted to the type of targets imaged in MSTAR but can easily be extended to any SAR data where prior information about scene geometries can be estimated.


title: Lilliput: Ontology-Based Platform for IoT Social Networks

abstract: The progress of the Internet of Things (IoT) has enhanced real-time accessibility on smart things, and the advent of online social network (e.g. Facebook, Twitter, and Google+) have attracted people in sharing their knowledge and experiences with their social organization. Recently, several researches have shown that online social network could act as a user interface of smart things and smart things could be shared by using online social network, and especially we expect that the integration of both of them provide us with novel services which we collectively call IoT social network. However, it is possible that developers or researchers will address complexity of jointly utilizing and understanding both. In this paper, we propose a novel social networking platform for the Internet of Things, called Lilliput. Lilliput is an ontology-based platform providing information of online social networks (e.g. people's profiles, people's social relationships) and smart things (e.g. context, location) as a social graph. To architect consistent unified ontology-based platform, we conduct a research on what the social relationship among people, objects, and places will be, and propose it as IoT social relationship. Also, we show how to exploit this ontologically-connected people, objects, and places by mainly focusing on Human-oriented Access Control for IoT social network (HAC). With this platform, application developers can build IoT social network application without having to understand details in both online social networks and the Internet of Things. We show the feasibility of our architecture by implementing a prototype platform, building a testbed, and creating one of IoT social network applications, called Sorcerer's Book.


title: Current location-based next POI recommendation

abstract: Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours. First we find the users with similar check-in sequences and depict their check-in sequences as a directed graph, then find the users current location. To recommend the new POI recommendation for next hour we refer to the directed graph we have created. Our algorithm considers both the temporal factor i.e., recommendation time, and the spatial(distance) at the same time. We conduct an experiment on random data collected from Foursquare and Gowalla. Experiment results show that our proposed model outperforms the collaborative-filtering based state-of-the-art recommender techniques.


title: A Step Toward 5G in 2020: Low-cost OTA performance evaluation of massive MIMO base stations.

abstract: Massive multiple-input, multiple-output (MIMO) is seen as an enabling technology to fulfill dramatic improvements in spectral efficiency for fifth-generation (5G) deployment in 2020. For massive MIMO systems, the learning loop from early-stage prototype design to final-stage performance validation is expected to be slow and ineffective. There is a strong need to evaluate massive MIMO base station (BS) performance with over-the-air (OTA) methods. Until now, such OTA solutions have not been discussed for massive MIMO BS systems.


title: Quantitative Evaluation of Intel PEBS Overhead for Online System-Noise Analysis

abstract: Analyzing system-noise incurred to high-throughput systems (e.g., Spark, RDBMS) from the underlying machines must be in the granularity of the message- or request-level to find the root causes of performance anomalies, because messages are passed through many components in very short periods. To this end, we consider using Precise Event Based Sampling (PEBS) equipped in Intel CPUs at higher sampling rates than used normally is promising. It saves context information (e.g., the general purpose registers) at occurrences of various hardware events such as cache misses. The information can be used to associate performance anomalies caused by system noise with specific messages. One challenge is that quantitative analysis of PEBS overhead with high sampling rates has not yet been studied. This is critical because high sampling rates can cause severe overhead but performance problems are often reproducible only in real environments. In this paper, we evaluate the overhead of PEBS and show: (1) every time PEBS saves context information, the target workload slows down by 200-300 ns due to the CPU overhead of PEBS, (2) the CPU overhead can be used to predict actual overhead incurred with complex workloads including multi-threaded ones with high accuracy, and (3) PEBS incurs cache pollution and extra memory IO since PEBS writes data into the CPU cache, and the severity of cache pollution is affected both by the sampling rate and the buffer size allocated for PEBS. To the best of our knowledge, we are the first to quantitatively analyze the overhead of PEBS.


Ergonomic risk and preventive measures of musculoskeletal disorders in the dentistry environment: an umbrella review


title: LeiA: A Lightweight Authentication Protocol for CAN

abstract: Recent research into automotive security has shown that once a single vehicle component is compromised, it is often possible to take full control of the vehicle. This paper proposes LeiA, a lightweight authentication protocol for the Controller Area Network (CAN). This protocol allows critical vehicle Electronic Control Units (ECUs) to authenticate each other providing compartmentalisation and preventing a number of attacks e.g., where a compromised CD player is able to accelerate the vehicle. LeiA is designed to run under the stringent time and bandwidth constraints of automotive applications and is backwards compatible with existing vehicle infrastructure. The protocol is suitable to be implemented using lightweight cryptographic primitives yet providing appropriate security levels by limiting the usage of every key in the system. The security of LeiA is proven under the unforgeability assumption of the MAC scheme under chosen message attacks (uf-cma).


title: Mining API patterns as partial orders from source code: from usage scenarios to specifications

abstract: A software system interacts with third-party libraries through various APIs. Using these library APIs often needs tofollow certain usage patterns. Furthermore, ordering rules (specifications) exist between APIs, and these rules govern the secure and robust operation of the system using these APIs. But these patterns and rules may not be well documented by the API developers. Previous approaches mine frequent association rules, itemsets, or subsequences that capture API call patterns shared by API client code. However, these frequent API patterns cannot completely capture some useful orderings shared by APIs, especially when multiple APIs are involved across different procedures. In this paper, we present a framework to automatically extract usage scenarios among user-specified APIs as partial orders, directly from the source code (API client code). We adapt a model checker to generate interprocedural control-flow-sensitive static traces related to the APIs of interest. Different API usage scenarios are extracted from the static traces by our scenario extraction algorithm and fed to a miner. The miner summarizes different usage scenarios as compact partial orders. Specifications are extracted from the frequent partial orders using our specification extraction algorithm. Our experience of applying the framework on 72 X11 clients with 200K LOC in total has shown that theextracted API partial orders are useful in assisting effective API reuse and checking.


title: Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

abstract: Introduction ::: Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications.


title: Cell genealogies in a plant meristem deduced with the aid of a 'bootstrap' L-system.

abstract: The primary root meristem of maize (Zea mays L.) contains longitudinal files of cells arranged in groups of familial descent (sisters, cousins, etc.). These groups, or packets, show ordered sequences of cell division which are transverse with respect to the apico-basal axis of the root. The sequences have been analysed in three zones of the meristem during the course of the first four cell generations following germination. In this period, the number of cells in the packets increases from one to 16. Theoretically, there are 48 possible division pathways that lead to the eight-cell stage, and nearly 2 x 10(6) that lead to the 16-cell stage. However, analysis shows that only a few of all the possible pathways are used in any particular zone of the root. This restriction of pathways results from inherited sequences of asymmetric cell divisions which lead to sister cells of unequal length. All possible division pathways can be generated by deterministic 'bootstrap' L-systems which assign different lifespans to sister cells of successive generations and hence specify their subsequent sequence of divisions. These systems simulate propagating patterns of cell divisions which agree with those actually found within the growing packets that comprise the root meristem. The patterns of division are specific to cells originating in various regions of the meristem of the germinating root. The importance of such systems is that they simulate patterns of cellular proliferation where there is ancestral dependency. They can therefore be applied in other growing and proliferating systems where this is suspected.


Grammar Inference Using Recurrent Neural Networks


title: A Theory Of Sequential Reciprocity

abstract: Many experimental studies indicate that people are motivated by reciprocity. Rabin [Amer. Econ. Rev. 83 (1993) 1281] develops techniques for incorporating such concerns into game theory and economics. His theory is developed for normal form games, and he abstracts from information about the sequential structure of a strategic situation. We develop a theory of reciprocity for extensive games in which the sequential structure of a strategic situation is made explicit, and propose a new solution concept—sequential reciprocity equilibrium—for which we prove an equilibrium existence result. The model is applied in several examples, and it is shown that it captures very well the intuitive meaning of reciprocity as well as certain qualitative features of experimental evidence.


Dominant Paradigms, Adult Education, and Social Justice.


title: Online popularity and topical interests through the lens of instagram

abstract: Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.


title: Power consumption of IoT access network technologies

abstract: Over the past couple of years, many commentators have hailed the “Internet of Things” (IoT) as the next step in the evolution of the internet. This paper examines, from an energy consumption viewpoint, some options for deploying a network of “Things” and connecting them through their gateway into the Internet or a corporate network. It focuses specifically on the access network from the customer premises to the central office and the implications for this network of carrying uplinkdominant IoT traffic. The power consumption of a number of potential access network technologies and architectures is modelled for a range of IoT traffic and background network traffic levels. It is shown that shared corporate Wi-Fi network with PON backhaul can be the most energy efficient option if the Wi-Fi background traffic level is modest. Otherwise, a 4G Wireless (LTE) access is also very efficient if the site IoT traffic level is low - up to around 100 kb/s. At higher rates a GPON access provides the most energy efficient solution.


title: Factor analysis on information security management in higher education institutions

abstract: Information security has become an important thing for any organization because of information technology adoption, including for higher education institutions. Increasing the number of information security incidents in academic environment make those institutions have to implement a good information security management. However, most of them cannot do it easily because of various factors. This study has a purpose to determine factors that influence information security management in higher education institutions. Variables that related to those factors were divided into five, i.e., awareness, budget, security policy, management support and organization mission. This research used quantitative method with factor analysis technique. The population in this study were universities in Bandung, Indonesia. The sampling technique used was purposive sampling. Based on the results of the factor analysis, two factors were constructed. The first factor consists of four variables; i.e., awareness, budget, information security policy, and top management support. The second factor consists of a variable, mission organization. All the variables in the first factor (awareness, budget, information security policy, and top management support) are more significant in affecting implementation of information security management in higher education.


title: Rough Set Approach for Generation of Classification Rules of Breast Cancer Data

abstract: Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases. ::: ::: In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.


title: Power Supply for a High-Voltage Application

abstract: In this paper, the guidelines to design a high-voltage power converter based on the hybrid series parallel resonant topology, PRC-LCC, with a capacitor as output filter are established. As a consequence of the selection of this topology, transformer ratio, and therefore secondary volume, is reduced. The mathematical analysis provides an original equivalent circuit for the steady-state and dynamical behavior of the topology. A new way to construct high-voltage transformers is also proposed, pointing out the advantages and establishing an original method to evaluate the stray components of the transformer before construction. The way to make compatible the characteristics of both, topology and transformer is illustrated in the frame of a practical application. To demonstrate the feasibility of this solution, a high-voltage, high-power prototype is assembled and tested with good performance and similar behavior to the one predicted by the models. Experimental results are shown on this particular.


title: A new anthropomorphic robotic hand

abstract: This paper presents the new robotic FRH-4 hand. The FRH-4 hand constitutes a new hybrid concept of an anthropomorphic five fingered hand and a three jaw robotic gripper. The hand has a humanoid appearance while maintaining the precision of a robotic gripper. Since it is actuated with flexible fluidic actuators, it exhibits an excellent power to weight ratio. These elastic actuators also ensure that the hand is safe for interacting with humans. In order to fully control the joints, it is equipped with position sensors on all of the 11 joints. The hand is also fitted with tactile sensors based on cursor navigation sensor elements, which allows it to have grasping feedback and the ability for exploration.


title: Discrete Neural Signatures of Basic Emotions

abstract: Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience.


title: Adaptive Task Assignment in Online Learning Environments

abstract: With the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized assignment of learning tasks to student so that to improve his performance in online learning environments. As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments. The SBTS is inspired by the class of multi-armed bandit algorithms. However, in contrast to standard multi-armed bandit approaches, the SBTS aims at acquiring two criteria related to student learning, namely: which topics should the student work on, and what level of difficulty should the task be. The SBTS centers on innovative reward and punishment schemes in a task and skill matrix based on the student behaviour. To verify the algorithm, the complex student behaviour is modelled using a neighbour node selection approach based on empirical estimations of a students learning curve. The algorithm is evaluated with a practical scenario from a basic java programming course. The SBTS is able to quickly and accurately adapt to the composite student competency --- even with a multitude of student models.


title: Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

abstract: The long short-term memory (LSTM) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by LSTM combined with Kalman filters.


title: IVVI 2.0: An intelligent vehicle based on computational perception

abstract: This paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Computational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting-edge applications to cope with perception difficulties, data processing algorithms, expert knowledge, and decision-making. The long-term in-vehicle applications, that are presented in this paper, outperform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and others vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning systems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.


title: Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

abstract: Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.


title: Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)

abstract: Abstract In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN.


title: Automatic Calibration of a Range Sensor and Camera System

abstract: We propose an automated method to recover the full calibration parameters between a 3D range sensor and a monocular camera system. Our method is not only accurate and fully automated, but also relies on a simple calibration target consisting of a single circle. This allows the algorithm to be suitable for applications requiring in-situ calibration. We demonstrate the effectiveness of the algorithm on a camera-lidar system and show results on 3D mapping tasks.


title: Efficient Detection of Split Personalities in Malware

abstract: Malware is the root cause of many security threats on the Internet. To cope with the thousands of new malware samples that are discovered every day, security companies and analysts rely on automated tools to extract the runtime behavior of malicious programs. Of course, malware authors are aware of these tools and increasingly try to thwart their analysis techniques. To this end, malware code is often equipped with checks that look for evidence of emulated or virtualized analysis environments. When such evidence is found, the malware program behaves differently or crashes, thus showing a different “personality” than on a real system. Recent work has introduced transparent analysis platforms (such as Ether or Cobra) that make it significantly more difficult for malware programs to detect their presence. Others have proposed techniques to identify and bypass checks introduced by malware authors. Both approaches are often successful in exposing the runtime behavior of malware even when the malicious code attempts to thwart analysis efforts. However, these techniques induce significant performance overhead, especially for finegrained analysis. Unfortunately, this makes them unsuitable for the analysis of current high-volume malware feeds. In this paper, we present a technique that efficiently detects when a malware program behaves differently in an emulated analysis environment and on an uninstrumented reference host. The basic idea is simple: we just compare the runtime behavior of a sample in our analysis system and on a reference machine. However, obtaining a robust and efficient comparison is very difficult. In particular, our approach consists of recording the interactions of the malware with the operating system in one run and using this information to deterministically replay the program in our analysis environment. Our experiments demonstrate that, by using our approach, one can efficiently detect malware samples that use a variety of techniques to identify emulated analysis environments.


title: Internet Gratifications and Internet Addiction: On the Uses and Abuses of New Media

abstract: Internet addiction has been identified as a pathological behavior, but its symptoms may be found in normal populations, placing it within the scope of conventional theories of media attendance. The present study drew upon fresh conceptualizations of gratifications specific to the Internet to uncover seven gratification factors: Virtual Community, Information Seeking, Aesthetic Experience, Monetary Compensation, Diversion, Personal Status, and Relationship Maintenance. With no parallel in prior research, Virtual Community might be termed a "new" gratification. Virtual Community, Monetary Compensation, Diversion, and Personal Status gratifications accounted for 28% of the variance in Internet Addiction Tendency. The relationship between Internet addiction and gratifications was discussed in terms of the formation of media habits and the distinction between content and process gratifications.


title: A 60 GHz same-channel full-duplex CMOS transceiver and link based on reconfigurable polarization-based antenna cancellation

abstract: This paper describes a direct-conversion 45nm SOI CMOS 60 GHz transceiver for same-channel full duplex applications. A novel polarization-based wideband self-interference cancellation (SIC) technique in the antenna domain is described that can be reconfigured from the IC. In order to achieve the high levels of required SIC, a second RF cancellation path from the transmitter output to the LNA output with >30 dB gain control and >360° phase control is also integrated. The TX and RX share the same LO to reduce the impact of phase noise on SIC. Antenna and RF cancellation together enable >70 dB of total self-interference suppression even in the presence of nearby reflectors. In conjuction with digital SIC impemented in MATLAB, a same-channel full-duplex link is demonstrated over 0.7 m. To the best of our knowledge, this work demonstrates the first fully-integrated full-duplex transceiver front-end and mm-wave link. While not our focus, the transceiver also achieves state-of-the-art saturated output power of +15 dBm, peak TX efficiency of 15.3% and RX NF of 4 dB.


title: An online learned CRF model for multi-target tracking

abstract: We introduce an online learning approach for multitarget tracking. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous approaches which only focus on producing discriminative motion and appearance models for all targets, we further consider discriminative features for distinguishing difficult pairs of targets. The tracking problem is formulated using an online learned CRF model, and is transformed into an energy minimization problem. The energy functions include a set of unary functions that are based on motion and appearance models for discriminating all targets, as well as a set of pairwise functions that are based on models for differentiating corresponding pairs of tracklets. The online CRF approach is more powerful at distinguishing spatially close targets with similar appearances, as well as in dealing with camera motions. An efficient algorithm is introduced for finding an association with low energy cost. We evaluate our approach on three public data sets, and show significant improvements compared with several state-of-art methods.


title: Towards a Real-Time Campus-Scale Water Balance Monitoring System

abstract: In this paper, we describe the design of a real time water balance monitoring system, suitable for large campuses. The battery operated sensor nodes consist of an ultra-sound level sensor, a 16-bit microcontroller and a sub-gigahertz radio to setup a hub and spoke system. Real time data from the sensors is pushed to a server on the cloud to log as well as perform analytics. Industrial design of the device allows flexible mounting on a variety of tanks. Experimental results from a trial deployment in a medium sized campus are shown to illustrate the usefulness of such a system towards better management of campus water resources.


title: Step-by-step design and simulation of a simple CPU architecture

abstract: This paper describes a sequence of assignments, each building upon the next, leading students to a working simulation of a simple 8-bit CPU (Central Processing Unit). The design features a classic Von Neumann architecture comprising a simple data path with a few registers, a simple ALU (Arithmetic Logic Unit), and a microprogram to direct all the control signals. The first step involves the design of the ALU which is capable of eight basic operations. The second step guides students to construct a datapath complete with several 8-bit registers. The third step involves the design and implementation of a control unit which uses a microprogram to implement machine code instructions. The microprogram implements nine basic machine language instructions which are sufficient for writing many simple programs. The final step involves adding program memory and an input and output device to form a simple working simulation of a computer. At this point, students may hand-assemble code for their CPU and simulate its execution. All simulations are performed using a free and open source simulator called Logisim which performs digital logic simulations with the ability to build larger circuits from smaller subcircuits. Students can set an adjustable clock rate and observe the internal CPU state and registers as it retrieves instructions and steps through the microcode. The basic CPU architecture provides many opportunities for more advanced exercises, such as adding an instruction fetch unit, adding pipelining, or adding more machine language instructions. The assignments were introduced in a second year course on computer organization, providing an effective hands-on approach to understanding how a CPU actually operates.


title: Reducing intergroup prejudice and conflict using the media: A field experiment in Rwanda

abstract: Can the media reduce intergroup prejudice and conflict? Despite the high stakes of this question, understanding of the mass media’s role in shaping prejudiced beliefs, norms, and behaviors is very limited. A yearlong field experiment in Rwanda tested the impact of a radio soap opera about two Rwandan communities in conflict, which featured messages about reducing intergroup prejudice, violence, and trauma. Compared to communities who listened to a control radio soap opera, listeners’ perceptions of social norms and their behaviors changed concerning some of the most critical issues for Rwanda’s post conflict society, namely intermarriage, open dissent, trust, empathy, cooperation and discussion of personal trauma. However, the radio program did little to influence listeners’ personal beliefs. Group discussion was a notable feature of the listening experience. Taken together, the results suggest that radio can communicate social norms and influence behaviors that contribute to intergroup tolerance and reconciliation.


title: Robust Strategy against Unknown Risk-averse Attackers in Security Games

abstract: Stackelberg security games (SSGs) are now established as a powerful tool in security domains. In this paper, we consider a new dimension of security games: the risk preferences of the attacker. Previous work assumes a risk-neutral attacker that maximizes his expected reward. However, extensive studies show that the attackers in some domains are in fact risk-averse, e.g., terrorist groups in counter-terrorism domains. The failure to incorporate the risk aversion in SSG models may lead the defender to suffer significant losses. Additionally, defenders are uncertain about the degree of attacker's risk aversion. Motivated by this challenge this paper provides the following five contributions: (i) we propose a novel model for security games against risk-averse attackers with uncertainty in the degree of their risk aversion; (ii) we develop an intuitive MIBLP formulation based on previous security games research, but find that it finds locally optimal solutions and is unable to scale up; (iii) based on insights from our MIBLP formulation, we develop our scalable BeRRA algorithm that finds globally e-optimal solutions; (iv) our BeRRA algorithm can also be extended to handle other risk-aware attackers, e.g., risk-seeking attackers; (v) we show that we do not need to consider attacker's risk attitude in zero-sum games.


title: TOWARD AN ONTOLOGY BASED APPROACH FOR DATA WAREHOUSING STATE OF THE ART AND PROPOSAL

abstract: The design process of a data warehouse (DW) raises many problems and then is considered as a complex and tedious task. DW designers follow different levels concerning the design process; among these levels the conceptual level gains a significant care in the literature so far. Recently, using a hybrid approach conjugated to a semantic resource is considered as a successful way for designing a DW that meets the decision makers' requirements, the organization’s operational data source, and the design quality. Many works have been proposed, as well, different approaches or/and methods have been applied for designing a DW. However, still there is not yet a common approach that designers can follow to design a DW. This paper is twofold objectives: First, we conduct a comprehensive survey among proposed works in order to compare their methods based on a set of significant criteria. Secondly, considering the result of this study, we suggest an ontologybased hybrid approach for the DW design. The result is a framework consisting in three main steps: i) Building star schemas based on business-requirements; ii) Building star schemas from an operational data-source; and iii) Matching the two sets of schemas to produce a DW model compliant to user and data source simultaneously. In order to enhance these steps, each one uses an ontology as a knowledge representation to alleviate semantic issues.


title: Building Standard Dataset for Quran Tafseer

abstract: The growing number of scholars and students of the Quran Tafseer and its science has led to the increase of computer-based researches. Additionally, the evaluation of these researches requires computer-based experiments to be performed with a real Quran Tafseer documents. The available Tafseer documents are in text file or image file and are in Arabic language whereas the majority of the computer programming is in English language. Furthermore, the researchers will have difficulty to compare their research with other researchers. Therefore, the requirement for standard Tafseer dataset is very necessary for the current researches. This paper proposed a Tafseer dataset that will be used by researchers in Quran Tafseer and information processing fields. The dataset is organized in XML format to provide simple access and usage from the computer applications. A computer program is developed to create the dataset in XML structure. By applying the XML language computer applications users will be able to access the dataset and manipulate the content in easily.


Self-Reflective Sentiment Analysis


title: A comparison of typical ℓ p minimization algorithms

abstract: Recently, compressed sensing has been widely applied to various areas such as signal processing, machine learning, and pattern recognition. To find the sparse representation of a vector w.r.t. a dictionary, an @?"1 minimization problem, which is convex, is usually solved in order to overcome the computational difficulty. However, to guarantee that the @?"1 minimizer is close to the sparsest solution, strong incoherence conditions should be imposed. In comparison, nonconvex minimization problems such as those with the @?"p(0


title: Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features

abstract: All people with diabetes have the risk of developing diabetic retinopathy (DR), a vision-threatening complication. Early detection and timely treatment can reduce the occurrence of blindness due to DR. Computer-aided diagnosis has the potential benefit of improving the accuracy and speed in DR detection. This study is concerned with automatic classification of images with microaneurysm (MA) and neovascularization (NV), two important DR clinical findings. Together with normal images, this presents a 3-class classification problem. We propose a modified color auto-correlogram feature (AutoCC) with low dimensionality that is spectrally tuned towards DR images. Recognizing the fact that the images with or without MA or NV are generally different only in small, localized regions, we propose to employ a multi-class, multiple-instance learning framework for performing the classification task using the proposed feature. Extensive experiments including comparison with a few state-of-art image classification approaches have been performed and the results suggest that the proposed approach is promising as it outperforms other methods by a large margin.


title: A Comparison and Critique of Eucalyptus, OpenNebula and Nimbus

abstract: Eucalyptus, Open Nebula and Nimbus are three major open-source cloud-computing software platforms. The overall function of these systems is to manage the provisioning of virtual machines for a cloud providing infrastructure-as-a-service. These various open-source projects provide an important alternative for those who do not wish to use a commercially provided cloud. We provide a comparison and analysis of each of these systems. We begin with a short summary comparing the current raw feature set of these projects. After that, we deepen our analysis by describing how these cloud management frameworks relate to the many other software components required to create a functioning cloud computing system. We also analyse the overall structure of each of these projects and address how the differing features and implementations reflect the different goals of each of these projects. Lastly, we discuss some of the common challenges that emerge in setting up any of these frameworks and suggest avenues of further research and development. These include the problem of fair scheduling in absence of money, eviction or preemption, the difficulties of network configuration, and the frequent lack of clean abstractions.


abstract: This paper proposes a new dc-dc static power converter, designated Bidirectional Current-Fed Flyback-Push-Pull DC-DC Converter. Circuit operation, analysis, simulation, design example and experimental results are included in the paper. What distinguishes the proposed converter from the previous circuits is the existence of input and output inductors, which provides a significant reduction of both the power source and the load side current ripple. The proposed converter is suitable for the renewable electric power systems, such as those having fuel cells as the DC-source power supply. It is also a good candidate for electric vehicle power systems, where bidirectional power flow related with battery charge and discharge is necessary.


title: Synapse: a microservices architecture for heterogeneous-database web applications

abstract: The growing demand for data-driven features in today's Web applications -- such as targeting, recommendations, or predictions -- has transformed those applications into complex conglomerates of services operating on each others' data without a coherent, manageable architecture. We present Synapse, an easy-to-use, strong-semantic system for large-scale, data-driven Web service integration. Synapse lets independent services cleanly share data with each other in an isolated and scalable way. The services run on top of their own databases, whose layouts and engines can be completely different, and incorporate read-only views of each others' shared data. Synapse synchronizes these views in real-time using a new scalable, consistent replication mechanism that leverages the high-level data models in popular MVC-based Web applications to replicate data across heterogeneous databases. We have developed Synapse on top of the popular Web framework Ruby-on-Rails. It supports data replication among a wide variety of SQL and NoSQL databases, including MySQL, Oracle, PostgreSQL, MongoDB, Cassandra, Neo4j, and Elasticsearch. We and others have built over a dozen microservices using Synapse with great ease, some of which are running in production with over 450,000 users.


title: From novice to expert: the use of intuitive knowledge as a basis for district nurse education.

abstract: Abstract Curriculum development in nursing should be a dynamic process set within a context which unites theory and practice. Nursing is operating within a climate of ubiquitous change and reliance must be placed upon a robust and professionally enhancing curriculum framework. At the University of Liverpool, the recently validated Diploma in District Nursing has been based upon the work of Patricia Benner ‘From Novice to Expert’ (1984). Benner's proposals are gaining considerable international credence and, whilst acknowledging that North American models are not always readily transferable into practice within the United Kingdom, the author explains why the combination of skills acquisition and clinical expertise provide an exciting framework pertinent to post registration courses such as district nursing. Those limitations of interpretation derived from culture and the institutional settings of the research are identified. The article which follows explains the reasons which underpin the adaptation of Benner's model to the education and practice of district nursing, identifies the advantages and limitations and concludes by describing how the educational change process was managed to support practice.


title: A Joint Model for Word Embedding and Word Morphology

abstract: This paper presents a joint model for performing unsupervised morphological analysis on words, and learning a character-level composition function from morphemes to word embeddings. Our model splits individual words into segments, and weights each segment according to its ability to predict context words. Our morphological analysis is comparable to dedicated morphological analyzers at the task of morpheme boundary recovery, and also performs better than word-based embedding models at the task of syntactic analogy answering. Finally, we show that incorporating morphology explicitly into character-level models helps them produce embeddings for unseen words which correlate better with human judgments.


title: Towards visual ego-motion learning in robots

abstract: Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped ego-motion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.


title: Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

abstract: The current review contributes with an extensive overview of decision support systems in diagnosing heart diseases in clinical settings. The investigators independently screened and abstracted studies related to heart diseases-based clinical decision support system (DSS) published until 8-June-2015 in PubMed, CINAHL and Cochrane Library. The data extracted from the twenty full-text articles that met the inclusion criteria was classified under the following fields; heart diseases, methods for data sets formation, machine learning algorithms, machine learning-based DSS, comparator types, outcome evaluation and clinical implications of the reported DSS. Out of total of 331 studies 20 met the inclusion criteria. Most of the studies relate to ischemic heart diseases with neural network being the most common machine learning (ML) technique. Among the ML techniques, ANN classifies myocardial infarction with 97% and myocardial perfusion scintigraphy with 87.5% accuracy, CART classifies heart failure with 87.6%, neural network ensembles classifies heart valve with 97.4%, support vector machine classifies arrhythmia screening with 95.6%, logistic regression classifies acute coronary syndrome with 72%, artificial immune recognition system classifies coronary artery disease with 92.5% and genetic algorithms and multi-criteria decision analysis classifies chest-pain patients with 91% accuracy respectively. There were 55% studies that validated the results in clinical settings while 25% validated the results through experimental setups. Rest of the studies (20%) did not report the applicability and feasibility of their methods in clinical settings. The study categorizes the ML techniques according to their performance in diagnosing various heart diseases. It categorizes, compares and evaluates the comparator based on physician’s performance, gold standards, other ML techniques, different models of same ML technique and studies with no comparison. It also investigates the current, future and no clinical implications. In addition, trends of machine learning techniques and algorithms used in the diagnosis of heart diseases along with the identification of research gaps are reported in this study. The reported results suggest reliable interpretations and detailed graphical self-explanatory representations by DSS. The study reveals the need for establishment of non-ambiguous real-time clinical data for proper training of DSS before it can be used in clinical settings. The future research directions of the ML-based DSS is mostly directed towards development of generalized systems that can decide on clinical measurements which are easily accessible and assessable in real-time.


Author Name Disambiguation in MEDLINE Based on Journal Descriptors and Semantic Types


title: Nonparametric Joint Shape and Feature Priors for Image Segmentation

abstract: In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.


title: A novel solid-state DC-breaker based on cascaded SiC MOSFETs

abstract: A novel control topology based on a single standard gate driver for cascaded SiC MOSFETs is proposed firstly in this paper. Then, the 2.4kV, 20A experimental prototype validates the analysis and shows the availability of the proposed topology. Furthermore, the operating characteristics of solid-state DC breaker is analyzed, some elements are added to the proposed cascade SiC MOSFETs stack based on the analysis to make it suitable to DC-breaker application. The 2.4kV, 20A DC-breaker prototype verifies the proposed DC-breaker topology, the voltage across two cascaded devices shows a consistency during shutdown process of the short circuit current.


title: Wealth effect, credit price effect, and the inter‐relationships between Hong Kong's property market and stock market

abstract: Purpose – This paper aims to test the short‐ and long‐run interrelationships between Hong Kong's residential property market and stock market, as well as market fundamentals and China‐related factors, between 1990 and 2006.Design/methodology/approach – The authors observed changes in the relationship between property prices and stock indexes by using Granger causality test, variance decomposition and CUSUM test. In contrast to some other studies, they have, at the same time, identified the break point(s) and variation of relation.Findings – The findings reveal that the correlation between residential property price and stock index had become weaker over time, even though the trend of residential property price is similar to that of stock index during the sample period. Under such circumstances, it is more likely for investors to reap more benefits through portfolio diversifications.Research limitations/implications – The credit price effect from the property market to the stock market, as observed between...


title: Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

abstract: In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. In this paper, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an, a priori unknown, safety constraint that depends on states and actions. We aim to explore the MDP under this constraint, assuming that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop a novel algorithm for this task and prove that it is able to completely explore the safely reachable part of the MDP without violating the safety constraint. To achieve this, it cautiously explores safe states and actions in order to gain statistical confidence about the safety of unvisited state-action pairs from noisy observations collected while navigating the environment. Moreover, the algorithm explicitly considers reachability when exploring the MDP, ensuring that it does not get stuck in any state with no safe way out. We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover.


title: The Impact of a Flagship vs. a Brand Store on Brand Attitude, Brand Attachment and Brand Equity

abstract: How can flagships and brand stores contribute to building brands? We inquire about the relationships between store image, brand experience, brand attitude, brand attachment and brand equity using store intercepts. We find that flagships, due to the powerful brand experiences they allow, have a stronger impact on brand attitude, brand attachment and brand equity compared to brand stores. We provide retail marketers with avenues to offer increased in-store brand experiences by appealing to consumers’ emotions, senses, behaviors, and cognition.


title: Color image segmentation based on multiobjective artificial bee colony optimization

abstract: A new color image segmentation method based on Improved Bee Colony Algorithm for Multi-Objective Optimization (IBMO) is presented.The proposed method is applied on several natural images obtained from Berkeley segmentation database.The obtained results are compared the ones obtained from Fuzzy C-means which is one of the most popular methods used in image segmentation, Nondominated Sorting Genetic Algorithm, and Nondominated Sorted Particle Swarm Optimization.The comparative results of performance metrics show that the adapted version of IBMO is a promising method for color image segmentation. This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.


title: Chorus: a crowd-powered conversational assistant

abstract: Despite decades of research attempting to establish conversational interaction between humans and computers, the capabilities of automated conversational systems are still limited. In this paper, we introduce Chorus, a crowd-powered conversational assistant. When using Chorus, end users converse continuously with what appears to be a single conversational partner. Behind the scenes, Chorus leverages multiple crowd workers to propose and vote on responses. A shared memory space helps the dynamic crowd workforce maintain consistency, and a game-theoretic incentive mechanism helps to balance their efforts between proposing and voting. Studies with 12 end users and 100 crowd workers demonstrate that Chorus can provide accurate, topical responses, answering nearly 93% of user queries appropriately, and staying on-topic in over 95% of responses. We also observed that Chorus has advantages over pairing an end user with a single crowd worker and end users completing their own tasks in terms of speed, quality, and breadth of assistance. Chorus demonstrates a new future in which conversational assistants are made usable in the real world by combining human and machine intelligence, and may enable a useful new way of interacting with the crowds powering other systems.


title: Peripheral Popout: The Influence of Visual Angle and Stimulus Intensity on Popout Effects

abstract: By exploiting visual popout effects, interface designers can rapidly draw a user's attention to salient information objects in a display. A variety of different visual stimuli can be used to achieve popout effects, including color, shape, size, motion, luminance, and flashing. However, there is a lack of understanding about how accurately different intensities of these effects support popout, particularly as targets move further from the center of the visual field. We therefore conducted a study to examine the accuracy of popout target identification using different visual variables, each at five different levels of intensity, and at a wide range of angles from the display center. Results show that motion is a strong popout stimulus, even at low intensities and wide angles. Identification accuracy decreases rapidly across visual angle with other popout stimuli, particularly with shape and color. The findings have relevance to a wide variety of applications, particularly as multi-display desktop environments increase in size and visual extent.


title: The Benefits of a Model of Annotation

abstract: Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus. In a case study of word sense annotation, conventional methods for evaluating labels from trained annotators are contrasted with a probabilistic annotation model applied to crowdsourced data. The annotation model provides far more information, including a certainty measure for each gold standard label; the crowdsourced data was collected at less than half the cost of the conventional approach.


title: Large Scale URL-based Classification Using Online Incremental Learning

abstract: We address the problem of large-scale topic classification of web pages based on the minimal text available in the URLs. This problem is challenging because of the sparsity of feature vectors that are derived from the URL text, and the typical asymmetry between the cardinality of train and test sets due to non-availability of sufficient sets of annotated URLs for training and very large test sets (e.g., in the case of large-scale focused crawling). We propose an online incremental learning algorithm which addresses these issues. Our experiments based on large publicly available datasets demonstrate an improvement of 0.11 -- 0.12 in terms of F-measure over the baseline algorithms, like Support Vector Machine, in difficult scenarios where the cardinality of train set is just a fraction of that of the test set.


title: Human computation for procedural content generation in platform games

abstract: One of the major challenges in procedural content generation in computer games is to automatically evaluate whether the generated content has good quality. In this paper we describe a system which uses human computation to evaluate small portions of levels generated by an existing system for the game of Infinite Mario Bros. Several such evaluated portions are then combined into a full level of the game. The composition of the small portions into a full level is done by accounting for the human-annotated information and the mathematical model of tension arcs used in interactive drama and storytelling. We tested our system with human subjects and the results show that our approach is able to generate levels with better visual aesthetics and that are more enjoyable to play than other existing approaches.


title: Opinion extraction and summarization on the Web

abstract: The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sources containing such opinions, e.g., product reviews, forums, discussion groups. and blogs. Techniques are now being developed to exploit these sources to help organizations and individuals to gain such important information easily and quickly. In this paper, we first discuss several aspects of the problem in the AI context, and then present some results of our existing work published in KDD-04 and WWW-05.


title: Mobile augmented reality for books on a shelf

abstract: Retrieving information about books on a bookshelf by snapping a photo of book spines with a mobile device is very useful for book-stores, libraries, offices, and homes. In this paper, we develop a new mobile augmented reality system for book spine recognition. Our system achieves very low recognition delays, around 1 second, to support real-time augmentation on a mobile device's viewfinder. We infer user interest by analyzing the motion of objects seen in the viewfinder. Our system initiates a query during each low-motion interval. This selection mechanism eliminates the need to press a button and avoids using degraded motion-blurred query frames during high-motion intervals. The viewfinder is augmented with a book's identity, prices from different vendors, average user rating, location within the enclosing bookshelf, and a digital compass marker. We present a new tiled search strategy for finding the location in the bookshelf with improved accuracy in half the time as in a previous state-of-the-art system. Our AR system has been implemented on an Android smartphone.


title: Ultra low-voltage, rail-to-rail input/output stage Operational Transconductance Amplifier (OTA) with high linearity and its application in a Gm-C filter

abstract: This paper presents an ultra low-voltage, rail-to-rail input/output stage Operational Transconductance Amplifier (OTA) which uses quasi floating gate input transistors. This OTA works with ±0.3v and consumes 57µw. It has near zero variation in small/large-signal behavior (i.e. transconductance and slew rate) in whole range of the common mode voltage of input signals. Using source degeneration technique for linearity improvement, make it possible to obtain −42.7 dB, HD3 for 0.6v P-P sine wave input signal with the frequency of 1MHz. The used feedback amplifier in input stage also enhances common mode rejection ratio (CMRR), such that in DC, CMRR is 146 dB. OTA is used for implementation of a wide-tunable third-order elliptic filter with 237 KHz–2.18 MHz cutoff frequencies. Proposed OTA and filter have been simulated in 0.18µm TSMC CMOS technology with Hspice.


title: Data Generation as Sequential Decision Making

abstract: We connect a broad class of generative models through their shared reliance on sequential decision making. Motivated by this view, we develop extensions to an existing model, and then explore the idea further in the context of data imputation - perhaps the simplest setting in which to investigate the relation between unconditional and conditional generative modelling. We formulate data imputation as an MDP and develop models capable of representing effective policies for it. We construct the models using neural networks and train them using a form of guided policy search [9]. Our models generate predictions through an iterative process of feedback and refinement. We show that this approach can learn effective policies for imputation problems of varying difficulty and across multiple datasets.


title: An Authoring Tool for Location-Based Mobile Games with Augmented Reality Features

abstract: This paper presents an authoring tool for building location-based mobile games, enhanced with augmented reality capabilities. These games are a subclass of pervasive games in which the gameplay evolves and progresses according to player's location. We have conducted a literature review on authoring tools and pervasive games to (i) collect the common scenarios of current location-based mobile games, and (ii) features of authoring tools for these games. Additionally, we have also used the focus groups methodology to find new scenarios for location-based mobile games, in particular regarding how augmented reality can be used in these games. Both literature review and focus groups provide us a set of requirements to design a software architecture for our authoring tool, a web-based application where games are created and executed. In our approach, games are designed as a set of missions that can be ordered or not. Players use mobile devices to perform these missions in order to complete each game. Our main objective is to provide a software solution to enable non-programmers users to design, build and run location-based mobile games. In order to evaluate our tool, we present a game design called "Battle for Fortaleza", and how this game is implemented in our solution.


title: Improving Fingerprint Orientation Extraction

abstract: Computation of local orientations is a primary step in fingerprint recognition. A large number of approaches have been proposed in the literature, but no systematic quantitative evaluations have been done yet. We implemented and tested several well know methods and a plethora of their variants over a novel, specifically designed, benchmark, made available in the FVC-onGoing framework. We proved that parameter optimizations, pre- and post-processing stages can markedly improve accuracy of the baseline methods on bad quality fingerprints. Finally, in this paper we propose a novel adaptive method which selectively exploits accuracy of local-based analysis and learning-based global methods, thus achieving the overall best performance on a challenging dataset.


title: Real Time AMR & Control of Household Energy Meter with Zigbee Communication

abstract: In today's world almost all industrial systems are based on wireless communication, as it has many advantages. This paper presents the design and implementation of Automatic Meter Reading (AMR) and Energy meter control using Zigbee communication. The design presents a novice method of combined Zigbee and Global System for Mobile communication (GSM) technology to monitor the power consumption and controlling of the meters remotely. This system avoids the human intervention in power management. If the consumer doesn't pay the bill in time, the user is informed through sms system using GSM. If still user does not pay the bill then after the designed late consideration, the power connection will be disconnected from the remote server automatically. It displays the corresponding billing information on LCD and sends data to the server through the Zigbee Module. The entire programming is based on VB Language. The developed mechanism provides efficient monitoring of meter reading, power control mechanism, avoiding the billing error and reduces the maintenance cost.


title: Approximation theory and feedforward networks

abstract: Approximation of real functions by feedforward networks of the usual kind is shown to be based on the fundamental principle of approximation by piecewise-constant functions. This principle underlies a simple construction given for three-layer networks and suggests possible difficulties in determining two-layer networks.


title: CMMI ® or Agile : Why Not Embrace Both !

abstract: iv 1 Problem Definition 1 2 Origins from Two Extremes 3 2.1 The Origins of Agile Methods 3 2.2 The Origins of CMMI 5 3 Factors that Affect Perception 7 3.1 Misuse 7 3.2 Lack of Accurate Information 8 3.3 Terminology Difficulties 9 4 The Truth About CMMI 11 4.1 CMMI Is a Model, Not a Process Standard 11 4.2 Process Areas, Not Processes 13 4.3 SCAMPI Appraisals 14 5 The Truth About Agile 16 6 There Is Value in Both Paradigms 20 6.1 Challenges When Using Agile 20 6.2 Challenges When Using CMMI 22 6.3 Current Published Research 23 6.4 The Hope in Recent Trends 24 7 Problems Not Solved by CMMI nor Agile 27


title: A Survey of Scheduling and Interference Mitigation in LTE

abstract: Among the goals of 3GPP LTE networks are higher user bit rates, lower delays, increased spectrum efficiency, support for diverse QoS requirements, reduced cost, and operational simplicity. Resource scheduling and interference mitigation are two functions which are key to achieving these goals. This paper provides a survey of related techniques which have been proposed and shown to be promising. A brief discussion of the challenges for LTE-Advanced, the next step in the evolution, is also provided.


title: Strategies of Adapting and Replacing Artistic Leisure Occupations to Maintain Participation and Identity: A Qualitative Study of Women With Arthritis

abstract: This qualitative study explored older women's qualitative accounts regarding their strategies for maintaining or adapting their participation in visual art-making in the context of arthritis. Ten participants were interviewed, aged 62–81 years, all having lived with arthritis for at least 7 years. Interpretative phenomenological analysis identified four convergent themes. Participants described adapting their strategies of art-making in ways that maintained the aesthetic quality of their artwork while also accommodating their arthritis. They described devising more accessible creative spaces. Planning, design, and anticipating new creative activities enhanced choice and control during flares in symptoms. Positive attitudes toward self-managing illness informed the practical adaptations described.


title: THE GOOD NUTRITION GAME: Extending the Good Behavior Game to Promote Fruit and Vegetable Intake.

abstract: BACKGROUND ::: Consumption of diverse and nutritious food is challenging for children diagnosed with Autism Spectrum Disorder. ::: ::: ::: OBJECTIVE ::: We created the Good Nutrition Game and implemented the intervention among students diagnosed with Autism Spectrum Disorder at a therapeutic after-school socialization program. ::: ::: ::: METHOD ::: We divided students into two teams in each session. The team earned one point per bite of fruit or vegetables by a teammate. The winning team congratulated each other for winning and was given the privilege to choose a small prize item as reinforcement. ::: ::: ::: LESSONS LEARNED ::: The approach showed promising findings to increase fruit and vegetable consumption among students diagnosed with Autism Spectrum Disorder in a classroom setting. While the approach needs to be implemented with more students in a longer term trial, other challenges include objectively measuring the number of bites instead of self-report by team members and addressing different eating habits among students.


title: Identification of overlapping community structure in complex networks using fuzzy c-means clustering

abstract: Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, we devise a novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG's Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-means clustering. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters.


title: Unpopular, Overweight, and Socially Inept: Reconsidering the Stereotype of Online Gamers

abstract: Abstract Online gaming has become an activity associated with a highly specific, caricatured, and often negative image. This “stereotype” has permeated the collective consciousness, as online gamers have become common caricatures in popular media. A lack of comprehensive demographic inquiries into the online gaming population has made it difficult to dispute these stereotypical characteristics and led to rising concerns about the validity of these stereotypes. The current study aims to clarify the basis of these negative characterizations, and determine whether online video game players display the social, physical, and psychological shortcomings stereotypically attributed them. Sampling and recruiting was conducted using a two-stage approach. First, a representative sample of 50,000 individuals aged 14 and older who were asked about their gaming behavior in an omnibus telephone survey. From this sample, 4,500 video game players were called for a second telephone interview, from which the current data wer...


title: Improvement of the quality factor of RF integrated inductors by layout optimization

abstract: A systematic method to improve the quality factor of RF integrated inductors is proposed. The method is based on the inductor's layout optimization, and has been successfully applied to the design of square spiral inductors performed using a silicon based MCM technology and silicon micromachining post processing.


title: Explaining Explanations: An Overview of Interpretability of Machine Learning

abstract: There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.


title: Deep video portraits

abstract: We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modified target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network -- thus taking full control of the target. With the ability to freely recombine source and target parameters, we are able to demonstrate a large variety of video rewrite applications without explicitly modeling hair, body or background. For instance, we can reenact the full head using interactive user-controlled editing, and realize high-fidelity visual dubbing. To demonstrate the high quality of our output, we conduct an extensive series of experiments and evaluations, where for instance a user study shows that our video edits are hard to detect.


title: BlockCNN: A Deep Network for Artifact Removal and Image Compression

abstract: We present a general technique that performs both artifact removal and image compression. For artifact removal, we input a JPEG image and try to remove its compression artifacts. For compression, we input an image and process its 8 by 8 blocks in a sequence. For each block, we first try to predict its intensities based on previous blocks; then, we store a residual with respect to the input image. Our technique reuses JPEG's legacy compression and decompression routines. Both our artifact removal and our image compression techniques use the same deep network, but with different training weights. Our technique is simple and fast and it significantly improves the performance of artifact removal and image compression.


A Study on the Relevance of Feature Selection Methods in Microarray Data