title: Wikipedia-based Learning Path Generation

abstract: We describe a method for automatic generation of a learning path for education or self- education. As a knowledge base, our method uses the semantic structure view from Wikipedia, leveraging on its broad variety of covered concepts. We evaluate our results by comparing them with the learning paths suggested by a group of teachers. Our algorithm is a useful tool for instructional design process.


Statistical Perspectives on Spatial Social Science


title: Collision Free Object Movement Using Vector Fields

abstract: Presents a technique for automatically providing animation and collision avoidance in a general-purpose computer graphics system. The technique, which relies on an expanded notion of vector fields, allows users to set up and animate objects easily, then prevents objects from colliding as the animation proceeds. This technique automatically generates volume octree vector fields around objects in a scene. These vector fields affect object motion and animation, and also provide for automatic collision avoidance for arbitrary objects. Applications of collision avoidance in an animation system encompass any scene containing object movement above or around other objects.


title: Region of Interest Extraction using Colour Based Methods on the CUAVE Database

abstract: Region of interest (ROI) extraction is an important step in deriving visual features for an audio-visual speech recognition system. Colour based segmentation offers the potential of computationally inexpensive algorithms for ROI selection. This paper presents a comparative study of two colour based techniques, one using hue and accumulated difference, the other chrominance. Results are presented for the CUAVE database. The two methods achieved 69% and 72% correct ROI extraction. The experiment prompted investigation of a new method using a chrominance based accumulated difference image. The new method achieved 79% correct ROI identification. The overall results suggest that a dual approach using chrominance to locate the mouth region and only employing an accumulated difference image when significant motion is not present would offer good robustness with lower computational cost.


title: Binary XML Storage and Query Processing in Oracle 11g

abstract: Oracle RDBMS has supported XML data management for more than six years since version 9i. Prior to 11g, text-centric XML documents can be stored as-is in a CLOB column and schema-based data-centric documents can be shredded and stored in object-relational (OR) tables mapped from their XML Schema. However, both storage formats have intrinsic limitations---XML/CLOB has unacceptable query and update performance, and XML/OR requires XML schema. To tackle this problem, Oracle 11g introduces a native Binary XML storage format and a complete stack of data management operations. Binary XML was designed to address a wide range of real application problems encountered in XML data management---schema flexibility, amenability to XML indexes, update performance, schema evolution, just to name a few. ::: ::: In this paper, we introduce the Binary XML storage format based on Oracle SecureFiles System[21]. We propose a lightweight navigational index on top of the storage and an NFA-based navigational algorithm to provide efficient streaming processing. We further optimize query processing by exploiting XML structural and schema information that are collected in database dictionary. We conducted extensive experiments to demonstrate high performance of the native Binary XML in query processing, update, and space consumption.


title: A DC – 4-GHz True Logarithmic Amplifier : Theory and Implementation

abstract: A 40 dB dynamic range, DC-4 GHz parallel-summation logarithmic amplifier is presented in this paper. The amplifier realizes a piecewise approximation to an exact logarithmic response. A design procedure that yields breakpoints on the exact response is described, along with delay-matching networks for parallel-summation logarithmic amplifiers. The amplifier was constructed in a 35 GHz silicon bipolar process, has /spl plusmn/5 dB logarithmic conformity over a DC-4 GHz bandwidth and has rise and fall times of 100 ps. The integrated circuit has dimensions of 2/spl times/2 mm/sup 2/ and consumes 750 mW from a -5 V supply.


title: Building Earth Mover's Distance on bilingual word embeddings for machine translation

abstract: Following their monolingual counterparts, bilingual word embeddings are also on the rise. As a major application task, word translation has been relying on the nearest neighbor to connect embeddings cross-lingually. However, the nearest neighbor strategy suffers from its inherently local nature and fails to cope with variations in realistic bilingual word embeddings. Furthermore, it lacks a mechanism to deal with many-to-many mappings that often show up across languages. We introduce Earth Mover's Distance to this task by providing a natural formulation that translates words in a holistic fashion, addressing the limitations of the nearest neighbor. We further extend the formulation to a new task of identifying parallel sentences, which is useful for statistical machine translation systems, thereby expanding the application realm of bilingual word embeddings. We show encouraging performance on both tasks.


title: Malignant lymphomas, their classification and relation to leukemia.

abstract: The value of complete hematologic study, including examination of the blood and bone marrow and imprints as well as sections of lymph nodes, for the diagnosis of lymphoblastomas has been indicated. The various forms of these tumors have been described and defined, and their relationships to each other and to leukemia have been discussed and illustrated. A simple working classification has been presented.


title: Aspect-Oriented Refactoring of Legacy Applications: An Evaluation

abstract: The primary claimed benefits of aspect-oriented programming (AOP) are that it improves the understandability and maintainability of software applications by modularizing crosscutting concerns. Before there is widespread adoption of AOP, developers need further evidence of the actual benefits as well as costs. Applying AOP techniques to refactor legacy applications is one way to evaluate costs and benefits. We replace crosscutting concerns with aspects in three industrial applications to examine the effects on qualities that affect the maintainability of the applications. We study several revisions of each application, identifying crosscutting concerns in the initial revision and also crosscutting concerns that are added in later revisions. Aspect-oriented refactoring reduced code size and improved both change locality and concern diffusion. Costs include the effort required for application refactoring and aspect creation, as well as a decrease in performance.


title: Community detection algorithms: a comparative analysis

abstract: Uncovering the community structure exhibited by real networks is a crucial step towards an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.


title: Security of Electronic Payment Systems: A Comprehensive Survey

abstract: This comprehensive survey deliberated over the security of electronic payment systems. In our research, we focused on either dominant systems or new attempts and innovations to improve the level of security of the electronic payment systems. This survey consists of the Card-present (CP) transactions and a review of its dominant system i.e. EMV including several researches at Cambridge university to designate variant types of attacks against this standard which demonstrates lack of a secure “offline” authentication method that is one of the main purpose of using the smart cards instead of magnetic stripe cards which are not able to participate in authentication process, the evaluation of the EMV migration from RSA cryptosystem to ECC based cryptosystem 3. The evaluation of the Card-not-present transactions approaches including 3D Secure, 3D SET, SET/EMV and EMV/CAP, the impact of concept of Tokenization and the role of Blind Signatures schemes in electronic cash and E-payment systems, use of quantum key distribution (QKD) in electronic payment systems to achieve unconditional security rather than only computational assurance of the security level by using traditional cryptography, the evaluation of Near Field Communication (NFC) and the contactless payment systems such as Google wallet, Android Pay and Apple Pay, the assessment of the electronic currency and peer to peer payment systems such as Bitcoin. The criterion of our survey for the measurement and the judgment about the quality of the security in electronic payment systems was this quote: “The security of a system is only as strong as its weakest link”


title: The quest draft: an automated course allocation algorithm

abstract: Course allocation is one of the most complex issues facing any university, due to the sensitive nature of deciding which subset of students should be granted seats in highly-popular (market-scarce) courses. In recent years, researchers have proposed numerous solutions, using techniques in integer programming, combinatorial auction design, and matching theory. In this paper, we present a four-part AI-based course allocation algorithm that was conceived by an undergraduate student, and recently implemented at a small Canadian liberal arts university. This new allocation process, which builds upon the Harvard Business School Draft, has received overwhelming support from students and faculty for its transparency, impartiality, and effectiveness.


title: A GaN pulse width modulation integrated circuit

abstract: We report the first GaN-based pulse width modulation (PWM) circuit for integrated GaN gate driver. This circuit is composed of a sawtooth generator and a comparator, both of which exhibit stable operation at temperatures up to 250 °C and operate properly at 1 MHz. The PWM circuit is able to generate PWM signals whose duty cycle is effectively modulated over a wide range by a reference voltage. This successful demonstration suggests the possibility of an all-GaN solution for power converters by monolithically integrating GaN power switches with the peripheral gate drive circuits, leading to a compact solution with reduced parasitics and improved reliability.


Denoising Deep Monte Carlo Renderings


title: SHOW: Smart Handwriting on Watches

abstract: Smart watch is becoming a new gateway through which people stay connected and track everyday activities, and text-entry on it is becoming a frequent need. With the two de facto solutions: tap-on-screen and voice input, text-entry on the watch remains a tedious task because 1. Tap-on-screen is error prone due to the small screen; 2. Voice input is strongly constrained by the surroundings and suffers from privacy leak. In this paper, we propose SHOW, which enables the user to input as they handwrite on horizontal surfaces, and the only requirement is to use the elbow as the support point. SHOW captures the gyroscope and accelerometer traces and deduces the user's handwriting thereafter. SHOW differs from previous work of gesture recognition in that: 1. it employs a novel rotation injection technique to substantially reduce the effort of data collection; 2. it does not require whole-arm posture, hence is better suited to space-limited places (e.g. vehicles). Our experiments show that SHOW can effectively generate 60 traces from one real handwriting trace and achieve high accuracy at 99.9% when recognizing the 62 different characters written by 10 volunteers. Furthermore, having more screen space after removing the virtual keyboard, SHOW can display 4x candidate words for autocompletion. Aided by the tolerance of character ambiguity and accurate character recognition, SHOW achieves over 70% lower mis-recognition-rate, 43% lower no-response-rate in both daily and general purposed text-entry scenarios, and 33.3% higher word suggestion coverage than the tap-on-screen method using a virtual QWERTY keyboard.


title: A 2.5-ps Bin Size and 6.7-ps Resolution FPGA Time-to-Digital Converter Based on Delay Wrapping and Averaging

abstract: A high-resolution time-to-digital converter (TDC) implemented with field programmable gate array (FPGA) based on delay wrapping and averaging is presented. The fundamental idea is to pass a single clock through a series of delay elements to generate multiple reference clocks with different phases for input time quantization. Due to periodicity, those phases will be equivalently wrapped within one reference clock period to achieve the required fine resolution. In practice, a hybrid delay matrix is created to significantly reduce the required number of delay cells. Multiple TDC cores are constructed for parallel measurements and then exquisite routing control and averaging are applied to smooth out the large quantization errors caused by the inhomogeneity of the TDC delay lines for both linearity and single-shot precision enhancement. To reduce the impact of temperature sensitivity, a cancellation circuit is created to substantially reduce the offset and confine the output difference within 2 LSB for the same input interval over the full operation temperature range of FPGA. With such a fine resolution of 2.5 ps, the integral nonlinearity is measured to be from merely −2.98 to 3.23 LSB and the corresponding rms resolution is 4.99–6.72 ps. The proposed TDC is tested to be fully functional over 0 °C–50 °C ambient temperature range with extremely low resolution variation. Its performance is even superior to many full-custom-designed TDCs.


title: Correlations and One-quantum Pulse Shapes in Photon Pair Generation

abstract: Abstract We examine the correlation between a pair of photons in the information entropy context. Either in two-photon cascade decay of a three-level system or in parametric down-conversion a pure state of two distinguishable photons is produced, with the states of the individual photons entangled. We obtain state amplitudes in the Weisskopf-Wigner approximation and use them to derive the two-photon density matrix in the ‘arrival time’ basis. It is found that only a finite number of eigenvectors of the appropriate density matrices is required to describe the system. That is, despite the continua of wave-vectors involved in the double spontaneous decay the two-photon entanglement is effectively discrete; furthermore, the eigenvectors in the arrival time basis can be interpreted as the fundamental orthogonal spontaneous emission pulses of the two systems.


title: Reactivity of Small Cationic Platinum Clusters

abstract: The gas-phase reactions between Ptn+ clusters (n = 1−5) and the inorganic substrates H2, O2, NH3, H2O, CO2, and N2O have been investigated using FT-ICR mass spectrometry. It is found that Pt2+ is unique in efficiently activating NH3 whereas the other clusters simply add this reactant in the primary reactions. The consecutive products Pt4(NH3)3+ and Pt5NH3+, however, also induce dehydrogenation of further NH3. Reactions of Ptn+ with N2O effect O transfer, those with O2 lead to degradation of the platinum clusters by loss of neutral PtO2. Only Pt5+ is observed to add H2 and H2O with low efficiencies whereas no reactions at all occur with CO2. In addition, the reactions between Ptn+ and CH4 have been reinvestigated. Besides confirming the results previously reported, evidence is found that thermodynamics accounts for the anomalously low reactivity of Pt4+. Comparing the reactivity of Ptn+ clusters with that of Pt surfaces shows distinct similarities and, thus, encourages use of the former in the development ...


title: Executive Functioning, Awareness, and Participation in Daily Life After Mild Traumatic Brain Injury: A Preliminary Study

abstract: OBJECTIVE. We investigated the relationship of executive functioning and self-awareness to participation in daily life of people after mild traumatic brain injury (mTBI) referred to occupational therapy in the postacute phase. METHOD. Thirteen participants who sustained mTBI (average time since injury = 4.7 months, mean age = 43.4 years) were evaluated with the Behavioral Assessment of the Dysexecutive Syndrome, the Dysexecutive Questionnaire, the Self-Awareness of Deficits Interview, and the Participation Index (PI) of the Mayo-Portland Adaptability Inventory. RESULTS. Analysis revealed high frequencies of deficits in executive functions such as planning and shifting. However, self-awareness of the executive deficits was intact. A significant percentage (62%-85%) of participants experienced restrictions in everyday life activities, and PI scores were significantly correlated with measures of executive functioning. CONCLUSIONS. After mTBI, people may be at significant risk for persistent executive deficits and restrictions in participation that warrant occupational therapy intervention.


title: Crowns and extra-coronal restorations: Endodontic considerations: the pulp, the root-treated tooth and the crown

abstract: Endodontic considerations is the fourth in the series on crowns and other extra-coronal restorations. This article focuses strongly on contemporary biological principles, and is not intended to provide a comprehensive review of commercially available materials and techniques. Principles are illustrated in a variety of clinical case scenarios.


title: Creating an Online Learning Community in a Flipped Classroom to Enhance EFL Learners. Oral Proficiency

abstract: Introduction The development of communicative competence is an overarching learning objective, and exposure to communicative practice is generally recognized as an essential element of successful foreign language learning and teaching (Council of Europe, 2001). Oral proficiency in a foreign language is the prerequisite for communication of ideas and intelligent conversation. The ability to speak a language is synonymous with knowing the language, since speech is the most basic means of human communication (Folse, 2006). However, inadequate communication and interaction between teachers and students, excessive teacher-led lectures, and relatively fatiguing test-based teaching methods still suppress the development of student communicative competence. Even with years of English learning, English as a foreign language (EFL) speakers still have difficulty mastering English oral skills and are hesitant when speaking English out loud. Technology, with distinctive features such as mobility, reachability, personalization, spontaneity, and ubiquity, is widely used to facilitate language teaching and learning. In particular, given the benefits and possible learning affordances that mobile-assisted language learning (MALL) offers, incorporating mobile devices appropriately can "have the potential to revolutionize the way we work and learn" (Peters, 2007, p. 1). In recent years, as young users in Asia have been communicating with each other via mobile messaging applications (such LINE, WhatsApp, and WeChat), research into the role of such instant and text messaging technologies in education has revealed their positive effects on providing platforms for socializing, sharing information, and communicating (Sweeny, 2010). Flipped learning is an alternative approach that integrates technology into language learning, and that contributes to ample opportunities for students to learn (Chen Hsieh, Wu, & Marek, 2016; Hung, 2015; McLaughlin et al., 2014; Overmyer, 2012). In a conventional class, new knowledge is introduced in the classroom, usually via lecture, and students practice using the knowledge at home, via homework. Flipped learning reverses this paradigm, with information introduced to students before class using technology (such as mobile devices). This allows more advanced learning activities during in-class time, meaning students are given more opportunities to participate in meaningful engaging activities, thus enhancing the learning outcomes (Boucher, Robertson, Wainner & Sanders, 2013). Studies have shown that flipped learning significantly enhances student learning performance (Chen, Hsieh, Wu, & Marek, 2016; Deslauriers & Wieman, 2011; Hung, 2015; McLaughlin et al., 2014; Sahin, Cavlazoglu, & Zeytuncu, 2015), student engagement (Chen, Hsieh, Wu, & Marek, 2016; Jamaludin & Osman, 2014), and produces enhanced learning outcomes (Chen, Hsieh, Wu, & Marek, 2016; Baepler, Walker & Driessen, 2014; Moravec, Williams, Aguilar-Roca, & O'Dowd, 2010). Bishop and Verleger (2013) contended that a flipped classroom is an educational technique that consists of two important components: (1) the use of computer technologies such as video lectures, and (2) the involvement of interactive learning activities. In fact, flipped learning effectively cultivates student autonomy and arouses student awareness (Yang, 2013), by allowing students to "proceed at their own pace, guide themselves to additional content, and assess their own learning gains" (McLaughlin et al., 2013, p. 196). Furthermore, flipped instruction provides autonomous supportive learning contexts that not only adopt students' different perspective and thoughts but support students' autonomous self-regulation (Reeve, 2009). Simply put, the core of flipped learning is to provide a learning community where students develop knowledge through constructive learning experiences, peer interaction and, collaboration. …


title: Android taint flow analysis for app sets

abstract: One approach to defending against malicious Android applications has been to analyze them to detect potential information leaks. This paper describes a new static taint analysis for Android that combines and augments the FlowDroid and Epicc analyses to precisely track both inter-component and intra-component data flow in a set of Android applications. The analysis takes place in two phases: given a set of applications, we first determine the data flows enabled individually by each application, and the conditions under which these are possible; we then build on these results to enumerate the potentially dangerous data flows enabled by the set of applications as a whole. This paper describes our analysis method, implementation, and experimental results.


title: Toward improved traceability of non-functional requirements

abstract: This position paper examines current practices and challenges for tracing non-functional requirements (NFRs). Anecdotal evidence suggests that many organizations do not effectively trace NFRs and that functional changes are often implemented with very little understanding as to how system qualities such as safety, security, and performance will be impacted. The tendency for NFRs to have broad ranging impact upon a software system, and the strong interdependencies and tradeoffs that exist between NFRs and the software architecture leave typical existing traceability methods insufficient for tracing NFRs. This paper focuses on post-requirements traceability of NFRs. It first identifies three critical areas in which NFRs require traceability support, it then evaluates existing traceability methods, and finally proposes a more holistic traceability environment named Goal Centric Traceability that supports impact analysis of NFRs within the context of the software architecture in which they are deployed.


PATCH-BASED IMAGE INTERPOLATION: ALGORITHMS AND APPLICATIONS


title: Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications

abstract: What is the growth pattern of social networks, like Facebook and WeChat? Does it truly exhibit exponential early growth, as predicted by textbook models like the Bass model, SI, or the Branching Process? How about the count of links, over time, for which there are few published models? We examine the growth of several real networks, including one of the world's largest online social network, ``WeChat'', with 300 million nodes and 4.75 billion links by 2013; and we observe power law growth for both nodes and links, a fact that completely breaks the sigmoid models (like SI, and Bass). In its place, we propose NETTIDE, along with differential equations for the growth of the count of nodes, as well as links. Our model accurately fits the growth patterns of real graphs; it is general, encompassing as special cases all the known, traditional models (including Bass, SI, log-logistic growth); while still remaining parsimonious, requiring only a handful of parameters. Moreover, our NETTIDE for link growth is the first one of its kind, accurately fitting real data, and naturally leading to the densification phenomenon. We validate our model with four real, time-evolving social networks, where NETTIDE gives good fitting accuracy, and, more importantly, applied on the WeChat data, our NETTIDE forecasted more than 730 days into the future, with 3% error.


title: The Role of Intermediaries in Electronic Marketplaces: Developing a Contingency Model

abstract: Abstract. Early research in electronic markets seemed to suggest that e- commerce transactions would result in decreased costs for buyers and sellers alike, and would therefore ultimately lead to the elimination of intermediaries from electronic value chains. However, a careful analysis of the structure and functions of electronic marketplaces reveals a different picture. Intermediaries provide many value-adding functions that cannot be easily substituted or ‘internalized’ through direct supplier–buyer dealings, and hence mediating parties may continue to play a significant role in the e-commerce world. In this paper we provide an analysis of the potential roles of intermediaries in electronic markets and we articulate a number of hypotheses for the future of intermediation in such markets. Three main scenarios are discussed: the disintermediation scenario, in which market dynamics will favour direct buyer–seller transactions; the reintermediation scenario, in which traditional intermediaries will be forced to differentiate themselves and re-emerge in the electronic marketplace; and the cybermediation scenario, in which wholly new markets for intermediaries will be created. The analysis suggests that the likelihood of each scenario dominating a given market is primarily dependent on the exact functions that intermediaries play in each case. A detailed discussion of such functions is presented in the paper, together with an analysis of likely outcomes in the form of a contingency model for intermediation in electronic markets.


title: ECDH based security model for IoT using ESP8266

abstract: Now a days, Internet of Things (IoT) is the emerging trend in technology that aims at making our life simpler using sensors and smart devices. All of them are connected to the Internet. Because of this it is possible to access and control them from any place and at any time. The most important aspect in this networking is communication between the smart devices. The correctness in the behavior of the devices mostly depends on its efficiency to send the data properly. Thus security is very important in IoT implementations. In this paper Elliptic Curve Diffie-Hellman(ECDH) key exchange on NIST P-192 curve for secured communication between ESP8266 modules has been discussed and implemented using NodeMCU.


title: Low-Cost, High Resolution X-Band Laboratory Radar System for Synthetic Aperture Radar Applications

abstract: Entry into the field of radar cross section measurements or synthetic aperture radar (SAR) algorithm development is often difficult due to the cost of high-end precision pulsed IF or other precision radar test instruments. A low-cost entry-level alternative was developed in order to provide an intermediate step between high-end high precision radar systems and ad-hock spare parts systems. The system developed is a frequency modulated continuous wave radar utilizing a homodyne radar architecture. Transmit chirp covers 8 GHz to 10.5 GHz with 18 dBm of transmit power. Due to the fairly wide transmit bandwidth, this radar is capable of better than 12 inches of range resolution. The dynamic range of this system was measured to be 60 dB. Such a low-cost, high resolution X-band laboratory radar system could be utilized as a linear rail SAR, inverse SAR, or for motion compensation experiments.


title: Special Section on Visual Analytics: Anomaly detection for visual analytics of power consumption data

abstract: Commercial buildings are significant consumers of electrical power. Also, energy expenses are an increasing cost factor. Many companies therefore want to save money and reduce their power usage. Building administrators have to first understand the power consumption behavior, before they can devise strategies to save energy. Second, sudden unexpected changes in power consumption may hint at device failures of critical technical infrastructure. The goal of our research is to enable the analyst to understand the power consumption behavior and to be aware of unexpected power consumption values. In this paper, we introduce a novel unsupervised anomaly detection algorithm and visualize the resulting anomaly scores to guide the analyst to important time points. Different possibilities for visualizing the power usage time series are presented, combined with a discussion of the design choices to encode the anomaly values. Our methods are applied to real-world time series of power consumption, logged in a hierarchical sensor network.


title: An Analysis of Anonymity in the Bitcoin System

abstract: Anonymity in Bitcoin, a peer-to-peer electronic currency system, is a complicated issue. Within the system, users are identified by public-keys only. An attacker wishing to de-anonymize its users will attempt to construct the one-to-many mapping between users and public-keys and associate information external to the system with the users. Bitcoin tries to prevent this attack by storing the mapping of a user to his or her public-keys on that user's node only and by allowing each user to generate as many public-keys as required. In this chapter we consider the topological structure of two networks derived from Bitcoin's public transaction history. We show that the two networks have a non-trivial topological structure, provide complementary views of the Bitcoin system and have implications for anonymity. We combine these structures with external information and techniques such as context discovery and flow analysis to investigate an alleged theft of Bitcoins, which, at the time of the theft, had a market value of approximately half a million U.S. dollars.


title: High gain 2.4GHz patch antenna array for rural area application

abstract: The design and implementation of high gain 2.4GHz patch antenna array for wireless communications application in rural area is presented. The patch antenna array with high gain is expected to minimize the need of tower that almost requires high cost of construction. In order to achieve high gain, the proposed antenna is constructed by 4×4 rectangular patches fed by microstrip line corporate feeding network which is developed using a λ/4 transformer impedance matching technique. The antenna structure is then deployed on a Flame Retardant (FR) 4 Epoxy dielectric substrate which the thickness and dielectric constant of 1.6mm, and 4.4, respectively. Prior hardware realization, some antenna parameters including return loss, voltage standing wave ratio (VSWR), radiation pattern, and gain are characterized through simulation to obtain an optimum design of antenna. While from the measurement, it shows that the characteristics of realized patch antenna array have good agreements with the design results in which the realized antenna has the measured gain of 15.59dB at the center frequency with the return loss of 19.52dB which corresponds to VSWR of 1.24 and the bandwidth response of 130MHz ranges from the frequency of 2.31GHz–2.44GHz.


title: Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

abstract: Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.


title: Performance of H.264, H.265, VP8 and VP9 Compression Standards for High Resolutions

abstract: Recently multimedia services increase, especially in video domain, leads to requirements of quality assessment. The main factors effected that quality are compression technology and transmission link. This paper presents a coding efficiency comparison of the well-known video compression standards Advanced Video Coding (H.264/AVC), High-Efficiency Video Coding (H.265/HEVC), VP8 and VP9 using objective metrics. An extensive range of bitrates from low to high bitrates was selected and encoded. The evaluation was done for four types of sequences in high resolutions depending on content. All four video sequences were encoded by using same encoding configurations for all the examined video codecs. The results showed that the quality of all compression standards rises logarithmically with increasing bitrate – in low bitrates the quality grows faster than in high bitrates. The efficiency of the new compression standards outperforms the older ones. The efficiency of VP8 compression standard outperforms the H.264/AVC compression standard. The efficiency of H.265/HEVC and VP9 compression standards is almost the same. The results also showed that VP8 codec cannot allow encoding by low bitrates. According to the results can be also said that the efficiency of the new compression standards decreases with the resolution. The results showed that the effectiveness of compression depends on the type of test sequence.


title: System dynamics archetypes for capacity management of energy systems

abstract: Abstract Energy systems are increasingly becoming more and more complex due to dynamic interactions of various supply-demand related variables in the systems. These variable interactions and the underlying feedback loop structures contribute to the realized overall system behavior. As a result, managing the capacity of energy systems is a complex task. The purpose of this paper is to present typical system dynamics archetypes for capacity management of energy systems. First, two archetypes are identified and modelled based on causal loop analysis: (i) limit to growth, and (ii) growth and underinvestment. The archetypes help the analyst to effectively visualize the entire system and forecast the reaction of the system. Second, stock flow analysis models are then presented. Third and finally, “what-if” simulation experiments are conducted to illustrate the key effects of limited capacity growth as well as growth with and underinvestment in the presence of time delays. The study demonstrates the importance of taking a systems thinking approach when managing the capacity of complex energy systems. Feedback loops and time delays must be considered seriously. By so doing, unwanted and unpredictable system fluctuations can be avoided when making capacity adjustment decisions.


title: Depth inpainting by tensor voting

abstract: Depth maps captured by range scanning devices or by using optical cameras often suffer from missing regions due to occlusions, reflectivity, limited scanning area, sensor imperfections, etc. In this paper, we propose a fast and reliable algorithm for depth map inpainting using the tensor voting (TV) framework. For less complex missing regions, local edge and depth information is utilized for synthesizing missing values. The depth variations are modeled by local planes using 3D TV, and missing values are estimated using plane equations. For large and complex missing regions, we collect and evaluate depth estimates from self-similar (training) datasets. We align the depth maps of the training set with the target (defective) depth map and evaluate the goodness of depth estimates among candidate values using 3D TV. We demonstrate the effectiveness of the proposed approaches on real as well as synthetic data.


title: A low-power 75 dB digitally programmable variable-gain amplifier in 0.18μm CMOS

abstract: Variable-gain amplifiers (VGAs) are essential building blocks of many communication systems. In this paper, a monolithic low-power digitally programmable VGA with 75 dB of gain range is presented. The core of the design is based on a low-distortion source-degenerated differential amplifier structure. The gain is varied by changing the source-degeneration resistor and tuning the resistors in the common-mode feedback circuitry. The complete VGA consists of three gain stages. As a proof of concept, a 24 dB single-gain stage with 2 dB gain steps is fabricated in a 0.18 ?m CMOS technology. The prototype chip is tested, and measurement results are obtained. Based on these results, the gain stage is redesigned to optimize its performance, and a three-stage 75 dB VGA is designed and simulated. Each stage has a digitally tunable gain range of 25 dB. The overall gain can be varied from ?15 dB to 60 dB in 2.5 dB gain steps. The bandwidth of the multi-stage VGA is higher than 140 MHz, and the gain error is less than 0.3 dB. The overall VGA draws 6.5mA from a 1.8V power supply. The noise figure of the system at maximum gain is 12.5 dB, and the third-order intermodulation intercept point (IIP3) at minimum gain is 14.4 dBm.


You are a Game Bot!: Uncovering Game Bots in MMORPGs via Self-similarity in the Wild


title: A Joint Graph Model for Pinyin-to-Chinese Conversion with Typo Correction

abstract: It is very import for Chinese language processing with the aid of an efficient input method engine (IME), of which pinyinto-Chinese (PTC) conversion is the core part. Meanwhile, though typos are inevitable during user pinyin inputting, existing IMEs paid little attention to such big inconvenience. In this paper, motivated by a key equivalence of two decoding algorithms, we propose a joint graph model to globally optimize PTC and typo correction for IME. The evaluation results show that the proposed method outperforms both existing academic and commercial IMEs.


title: Surface form and memory in question answering

abstract: Speakers tend to repeat materials from previous talk. This tendency is experimentally established and manipulated in various question-answering situations. It is shown that a question's surface form can affect the format of the answer given, even if this form has little semantic or conversational consequence, as in the pair Q: (At) what time do you close. A: “(At)five o'clock.” Answerers tend to match the utterance to the prepositional (nonprepositional) form of the question. This “correspondence effect” may diminish or disappear when, following the question, additional verbal material is presented to the answerer. The experiments show that neither the articulatory buffer nor long-term memory is normally involved in this retention of recent speech. Retaining recent speech in working memory may fulfill a variety of functions for speaker and listener, among them the correct production and interpretation of surface anaphora. Reusing recent materials may, moreover, be more economical than regenerating speech anew from a semantic base, and thus contribute to fluency. But the realization of this strategy requires a production system in which linguistic formulation can take place relatively independent of, and parallel to, conceptual planning.


title: AdaBoosting Neural Networks: Application to on-line Character Recognition

abstract: “Boosting” is a general method for improving the performance of any weak learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [4]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [3], in particular decision trees [1,2,5]. In this paper we use AdaBoost to improve the performances of a strong learning algorithm: a neural network based on-line character recognition system. In particular we will show that it can be used to learn automatically a great variety of writing styles even when the amount of training data for each style varies a lot. Our system achieves about 1.4 % error on a handwritten digit data base of more than 200 writers.


title: An unequal cluster-based routing protocol in wireless sensor networks

abstract: Clustering provides an effective method for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spot problem in multihop sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavier relay traffic and tend to die much faster, leaving areas of the network uncovered and causing network partitions. To mitigate the hot spot problem, we propose an Unequal Cluster-based Routing (UCR) protocol. It groups the nodes into clusters of unequal sizes. Cluster heads closer to the base station have smaller cluster sizes than those farther from the base station, thus they can preserve some energy for the inter-cluster data forwarding. A greedy geographic and energy-aware routing protocol is designed for the inter-cluster communication, which considers the tradeoff between the energy cost of relay paths and the residual energy of relay nodes. Simulation results show that UCR mitigates the hot spot problem and achieves an obvious improvement on the network lifetime.


title: Challenges in Open Source Intelligence

abstract: A host of tools and techniques are now available for data mining on the Internet. The explosion in social media usage and people reporting brings a new range of problems related to trust and credibility. Traditional media monitoring systems have now reached such sophistication that real time situation monitoring is possible. The challenge though is deciding what reports to believe, how to index them and how to process the data. Vested interests allow groups to exploit both social media and traditional media reports for propaganda purposes. The importance of collecting reports from all sides in a conflict and of balancing claims and counter-claims becomes more important as ease of publishing increases. Today the challenge is no longer accessing open source information but in the tagging, indexing, archiving and analysis of the information. This requires the development of general-purpose and domain specific knowledge bases. Intelligence tools are needed which allow an analyst to rapidly access relevant data covering an evolving situation, ranking sources covering both facts and opinions.


title: Comparison between full-bridge-forward converter and DAB converter

abstract: This paper analyzes and compares the dual active bridge (DAB) converter and the full-bridge-forward integrated DC-DC converter when applied to a microgrid energy storage bidirectional system that demands high power flow at the discharging operation mode and low power flow at the charging operation mode. The phase-shift modulation (PSM) is generally applied on the DAB converter, but it presents practical limitations, such as maximum and minimum phase-shift angle restrictions. In order to obtain a wide power variation range on the discharging operation mode, a hybrid modulation scheme can be applied. On the other hand, the full-bridge-forward DC-DC converter is also an alternative, which is specifically designed for different power level bidirectional processes and therefore presents low number of active devices. Theoretical analysis and experimental results of both converters are presented.


title: The competitiveness of on-line assignments

abstract: Consider the on-line problem where a number of servers are ready to provide service to a set of customers. Each customer's job can be handled by any of a subset of the servers. Customers arrive one-by-one and the problem is to assign each customer to an appropriate server in a manner that will balance the load on the servers. This problem can be modeled in a natural way by a bipartite graph where the vertices of one side (customers) appear one at a time and the vertices of the other side (servers) are known in advance. We derive tight bounds on the competitive ratio in both deterministic and randomized cases. Let n denote the number of servers. In the deterministic case we provide an on-line algorithm that achieves a competitive ratio of k = [log 2 n ] (up to an additive 1) and prove that this is the best competitive ratio that can be achieved by any deterministic on-line algorithm. In a similar way we prove that the competitive ratio for the randomized case is k =ln( n ) (up to an additive 1). We conclude that for this problem, randomized algorithms differ from deterministic ones by precisely a constant factor.


title: Big Learning with Bayesian Methods

abstract: Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.


title: Hiding a Needle in a Haystack: Privacy Preserving Apriori algorithm inMapReduce Framework

abstract: In the last few years, Hadoop become a "de facto" standard to process large scale data as an open source distributed system. With combination of data mining techniques, Hadoop improve data analysis utility. That is why, there are amount of research is studied to apply data mining technique to mapreduce framework in Hadoop. However, data mining have a possibility to cause a privacy violation and this threat is a huge obstacle for data mining using Hadoop. To solve this problem, numerous studies have been conducted. However, existing studies were insufficient and had several drawbacks. In this paper, we propose the privacy preserving data mining technique in Hadoop that is solve privacy violation without utility degradation. We focus on association rule mining algorithm that is representative data mining algorithm. We validate the proposed technique to satisfy performance and preserve data privacy through the experimental results.


Agents that Explain Their Own Actions


title: Governance of an Enterprise Social Intranet Implementation: The Statkraft Case.

abstract: Recent studies demonstrate that the implementation of enterprise social systems (ESSs) will transfer organizations into new paradigm of social business which results in enormous economic returns and competitive advantage. Social business creates a completely new way of working and organizing characterised by social collaboration, intrinsic knowledge sharing, voluntarily mass participation, just name a few. Thus, implementation of ESSs should tackle the uniqueness of the new way of working and organizing. However, there is a shortage of knowledge about implementation of these large enterprise systems. The purpose of this paper is to study governance model of ESSs implementation. A case study is conducted to investigate the implementation of the social intranet called the ‘Stream’ at Statkraft, which is a world-leading energy company in Norway. The governance model of ‘Stream’ emphasizes the close cooperation and accountability between corporate communication, human resources and IT, which implies paradigm shift in governance of implementing ESSs. Benefits and challenges in the implementation are also identified. Based on the knowledge and insights gained in the study, recommendations are proposed to assist the company in improving governance of ESSs implementation. The study contributes knowledge/know-how on governance of ESSs implementation.


title: A dissociation between object manipulation spatial ability and spatial orientation ability

abstract: We developed psychometric tests of spatial orientation ability, in which people are shown a two dimensional array of objects, imagine taking a perspective within the array, and indicate the direction to a target object from this perspective. Patterns of errors on these tests were consistent with experimental studies of perspective taking. Characteristic errors and verbal protocols supported the validity of the perspective-taking tests, suggesting that people encoded the objects in the display with respect to a body-centered coordinate system when the imagined perspective was more than 90° different from the orientation of the display. By comparing alternative models in a confirmatory factor analysis, we found that the ability to mentally rotate and manipulate an imagined object (as measured by tests of spatial visualization and spatial relations) and the ability to reorient the imagined self (as measured by the perspective-taking tests) are separable spatial abilities.


title: Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs

abstract: Most analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users query search engines in order to accomplish tasks at a variety of granularities, issuing multiple queries as they attempt to accomplish tasks. In this work we study real sessions manually labeled into hierarchical tasks, and show that timeouts, whatever their length, are of limited utility in identifying task boundaries, achieving a maximum precision of only 70%. We report on properties of this search task hierarchy, as seen in a random sample of user interactions from a major web search engine's log, annotated by human editors, learning that 17% of tasks are interleaved, and 20% are hierarchically organized. No previous work has analyzed or addressed automatic identification of interleaved and hierarchically organized search tasks. We propose and evaluate a method for the automated segmentation of users' query streams into hierarchical units. Our classifiers can improve on timeout segmentation, as well as other previously published approaches, bringing the accuracy up to 92% for identifying fine-grained task boundaries, and 89-97% for identifying pairs of queries from the same task when tasks are interleaved hierarchically. This is the first work to identify, measure and automatically segment sequences of user queries into their hierarchical structure. The ability to perform this kind of segmentation paves the way for evaluating search engines in terms of user task completion.


title: A bidirectional soft pneumatic fabric-based actuator for grasping applications

abstract: THIS paper presents the development of a bidirectional fabric-based soft pneumatic actuator requiring low fluid pressurization for actuation, which is incorporated into a soft robotic gripper to demonstrate its utility. The bidirectional soft fabric-based actuator is able to provide both flexion and extension. Fabrication of the fabric actuators is simple as compared to the steps involved in traditional silicone-based approach. In addition, the fabric actuators are able to generate comparably larger vertical grip resistive force at lower operating pressure than elastomeric actuators and 3D-printed actuators, being able to generate resistive grip force up to 20N at 120 kPa. Five of the bidirectional soft fabric-based actuators are deployed within a five-fingered soft robotic gripper, complete with five casings and a base. It is capable of grasping a variety of objects with maximum width or diameter closer to its bending curvature. A cutting task involved bimanual manipulation was demonstrated successfully with the gripper. To incorporate intelligent control for such a task, a soft force made completely of compliant material was attached to the gripper, which allows determination of whether the cutting task is completed. To the authors' knowledge, this work is the first study which incorporates two soft robotic grippers for bimanual manipulation with one of the grippers sensorized to provide closed loop control.


title: Lightweight Monitoring of Distributed Streams

abstract: As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds. ::: ::: Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated. ::: ::: Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.


Detection of fraud in mobile telecommunications


title: Who interacts on the Web?: The intersection of users' personality and social media use

abstract: In the increasingly user-generated Web, users' personality traits may be crucial factors leading them to engage in this participatory media. The literature suggests factors such as extraversion, emotional stability and openness to experience are related to uses of social applications on the Internet. Using a national sample of US adults, this study investigated the relationship between these three dimensions of the Big-Five model and social media use (defined as use of social networking sites and instant messages). It also examined whether gender and age played a role in that dynamic. Results revealed that while extraversion and openness to experiences were positively related to social media use, emotional stability was a negative predictor, controlling for socio-demographics and life satisfaction. These findings differed by gender and age. While extraverted men and women were both likely to be more frequent users of social media tools, only the men with greater degrees of emotional instability were more regular users. The relationship between extraversion and social media use was particularly important among the young adult cohort. Conversely, being open to new experiences emerged as an important personality predictor of social media use for the more mature segment of the sample.


title: Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control

abstract: Reinforcement learning and evolutionary strategy are two major approaches in addressing complicated control problems. Both have strong biological basis and there have been recently many advanced techniques in both domains. In this paper, we present a thorough comparison between the state of the art techniques in both domains in complex continuous control tasks. We also formulate the parallelized version of the Proximal Policy Optimization method and the Deep Deterministic Policy Gradient method.


title: Broadband RF energy harvesting system covering CDMA, GSM900, GSM1800, 3G bands with inherent impedance matching

abstract: A broadband RF energy harvesting system for harvesting energy from 800 MHz to 2.5 GHz is presented. A passive voltage amplification scheme and inherent matching technique using higher antenna impedance is employed to enhance the output voltage of the rectifier. A planar broadband folded dipole antenna having an input impedance of 300Ω is designed and fabricated. The measured VSWR is less than 2 from 825MHz to 1.27GHz and 1.67GHz to 2.17GHz. A RF energy harvesting system consisting of the high impedance antenna, Schottky diode based rectifier and a self oscillating boost converter is used to charge a 12µAh battery to 3.8V. At 844MHz for a received power of −10.7dBm the battery charges to 3.8V. At 1.73GHz the battery charges to 3.8V for a received power of −5.72dBm. Also using the designed RF energy harvesting system, a voltage of 4.4 V is measured for a load of 3 kΩ at 10 cm from a cell phone making a call.


title: Side-Channel Watchdog: Run-Time Evaluation of Side-Channel Vulnerability in FPGA-Based Crypto-systems

abstract: Besides security against classical cryptanalysis, its important for cryptographic implementations to have sufficient robustness against side-channel attacks. Many countermeasures have been proposed to thwart side channel attacks, especially power trace measurement based side channel attacks. Additionally, researchers have proposed several evaluation metrics to evaluate side channel security of crypto-system. However, evaluation of any crypto-system is done during the testing phase and is not part of the actual hardware. In our approach, we propose to implement such evaluation metrics on-chip for run-time side channel vulnerability estimation of a cryptosystem. The objective is to create a watchdog on the hardware which will monitor the side channel leakage of the device, and will alert the user if that leakage crosses a pre-determined threshold, beyond which the system might be considered vulnerable. Once such alert signal is activated, proactive countermeasures can be activated either at the device level or at the protocol level, to prevent the impending side channel attack. A FPGA based prototype designed by us show low hardware overhead, and is an effective option that avoids the use of bulky and inconvenient on-field measurement setup.


title: Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

abstract: This research synthesizes a taxonomy for classifying detection methods of new malicious code by Machine Learning (ML) methods based on static features extracted from executables. The taxonomy is then operationalized to classify research on this topic and pinpoint critical open research issues in light of emerging threats. The article addresses various facets of the detection challenge, including: file representation and feature selection methods, classification algorithms, weighting ensembles, as well as the imbalance problem, active learning, and chronological evaluation. From the survey we conclude that a framework for detecting new malicious code in executable files can be designed to achieve very high accuracy while maintaining low false positives (i.e. misclassifying benign files as malicious). The framework should include training of multiple classifiers on various types of features (mainly OpCode and byte n-grams and Portable Executable Features), applying weighting algorithm on the classification results of the individual classifiers, as well as an active learning mechanism to maintain high detection accuracy. The training of classifiers should also consider the imbalance problem by generating classifiers that will perform accurately in a real-life situation where the percentage of malicious files among all files is estimated to be approximately 10%.


title: Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis.

abstract: Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.


title: MOfinder: A Novel Algorithm for Detecting Overlapping Modules from Protein-Protein Interaction Network

abstract: Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.


title: Emotion Distribution Recognition from Facial Expressions

abstract: Most existing facial expression recognition methods assume the availability of a single emotion for each expression in the training set. However, in practical applications, an expression rarely expresses pure emotion, but often a mixture of different emotions. To address this problem, this paper deals with a more common case where multiple emotions are associated to each expression. The key idea is to learn the specific description degrees of all basic emotions for each expression and the mapping from the expression images to the emotion distributions by the proposed emotion distribution learning (EDL) method.The databases used in the experiments are the s-JAFFE database and the s-BU\_3DFE database as they are the databases with explicit scores for each emotion on each expression image. Experimental results show that EDL can effectively deal with the emotion distribution recognition problem and perform remarkably better than the state-of-the-art multi-label learning methods.


title: QTL identification on two genetic systems for rapeseed glucosinolate and erucic acid contents over two seasons

abstract: Glucosinolate and erucic acid are important plant compounds in rapeseed believed to have numerous functions in rapeseed-environment interactions. However, little is known about the QTL information related to the two different genetic systems including the embryo nuclear chromosomes and maternal plant nuclear chromosomes for glucosinolate content (GSLC) and erucic acid content (EAC) in rapeseed. Differences in QTL distribution between these two genetic systems, which control the performance of GSLC and EAC across different environmental conditions, were analyzed in the present study. A set of 202 DH populations derived from an elite hybrid cross of ‘Tapidor’ × ‘Ningyou7’ and their two backcross populations BC1F1 1 (DHs × Tapidor) and BC1F1 2 (DHs × Ningyou7) generated in two years were used as experimental materials for the study. A total of nine loci for GSLC and three loci for EAC with significant embryo additive main effects, embryo dominant main effects and/or maternal additive main effects, explaining 83.8 and 89.7 %, respectively, of their phenotypic variation, were identified. Although QTL × environment interaction effects were also detected in the present experiment, they played a minimum role in influencing the phenotypic variation. It was noted that qEAC-7-1 for EAC mapped on linkage group A7 was detected as the major QTL and could explain 68.32 % of the phenotypic variation for this trait. These results could be useful for the molecular maker-assisted breeding of GSLC and EAC quality traits based on the influence of two genetic systems.


Stylometry with R: A Package for Computational Text Analysis


Mechanism of renal necrosis induced by bromobenzene or chlorobenzene.


title: Neural correlates underlying insight problem solving: Evidence from EEG alpha oscillations

abstract: Previous studies on insight problem solving using Chinese logogriphs as insight problems only investigated the time- and phase-locked changes of electrocortical responses triggered by Chinese logogriphs, but did not focus on what kind of brain state facilitates individuals to solve insight problems. To investigate this, we focused on participants’ alpha activities (8–12 Hz) that closely correlates with insight problem solving and defocused attention while they were solving Chinese logogriphs. Results indicated that in the time window of 800–1400 ms after the presentation of target logogriphs, alpha power over parieto-central electrodes decreased relative to the reference interval in both the successful and unsuccessful logogriphs solving conditions. However, alpha power increased at parieto-occipital electrode sites in successful conditions compared with that in unsuccessful condition. The decrease in alpha activity in both conditions may reflect the cognitive demands in solving the target logogriphs. Furthermore, difference in alpha power between the successful and unsuccessful conditions at parieto-occipital electrode sites is associated with the process of heuristic information. Alpha synchronization observed in the successful condition compared to the unsuccessful condition might reflect a state of defocused attention that facilitates insight problem solving.


title: Current trends and future prospects of biotechnological interventions through tissue culture in apple

abstract: Apple (Malus domestica Borkh.), which is a ::: widely cultivated, important economic fruit crop with ::: nutritive and medicinal importance, has emerged as a ::: model horticultural crop in this post-genomic era. Apple ::: cultivation is heavily dependent on climatic condition and ::: is susceptible to several diseases caused by fungi, bacteria, ::: viruses, insects, etc. Extensive research work has been ::: carried out to standardize tissue culture protocols and utilize ::: them in apple improvement. We review the in vitro ::: shoot multiplication, rooting, transformation and regeneration ::: methodologies in apple and tabulate various such ::: protocols for easy reference. The utility and limitation of ::: transgenesis in apple improvement have also been summarized. ::: The concepts of marker-free plants, use of nonantibiotic ::: resistance selectable markers, and cisgenic and ::: intragenic approaches are highlighted. Furthermore, the ::: limitations, current trends and future prospects of tissue ::: culture-mediated biotechnological interventions in apple ::: improvement are discussed.


Microbial diagnosis in the early-onset of disease is a recommended procedure to determine the status of disease


title: A New Emotion Database: Considerations, Sources and Scope

abstract: Research on the expression of emotion is underpinned by databases. Reviewing available resources persuaded us of the need to develop one that prioritised ecological validity. The basic unit of the database is a clip, which is an audiovisual recording of an episode that appears to be reasonably selfcontained. Clips range from 10 – 60 secs, and are captured as MPEG files. They were drawn from two main sources. People were recorded discussing emotive subjects either with each other, or with one of the research team. We also recorded extracts from television programs where members of the public interact in a way that at least appears essentially spontaneous. Associated with each clip are two additional types of file. An audio file (.wav format) contains speech alone, edited to remove sounds other than the main speaker. An interpretation file describes the emotional state that observers attribute to the main speaker, using the FEELTRACE system to provide a continuous record of the perceived ebb and flow of emotion. Clips have been extracted for 100 speakers, with at least two for each speaker (one relatively neutral and others showing marked emotions of different kinds).


title: A differential voltage reference generator for pipelined ADCs

abstract: A differential voltage reference generator for pipelined analog-to-digital converters (ADCs) is presented in this paper. A modified Brokaw bandgap reference (BGR) is adopted to generate an adjustable sub-1V reference voltage. A new single-ended to differential converter based on current conversion is proposed to obtain the required differential voltage reference from the output of BGR directly. Fabricated in TSMC 0.18μm CMOS technology, the proposed circuit generates a 0.5-V differential reference voltage with a temperature coefficient of less than 46 ppm/ °C. The power consumption is 3.5 mW under a 1.8-V power supply.


High Notes: Audiologist Serves as Consultant to Orchestra


title: Removing Electroencephalographic Artifacts by Blind Source Separation

abstract: Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.


title: Design of Synchronous Reluctance Motors With Multiobjective Optimization Algorithms

abstract: The automatic design of synchronous reluctance (SyR) machines is considered in this paper by means of finite-element analysis and multiobjective optimization algorithms (MOOAs). The research focuses on the design of the rotor geometry, which is the key aspect of the SyR machine design. In particular, the performance of three popular MOOAs is analyzed and compared in terms of quality of the final design and computational time. A procedure to minimize the computational burden of the optimized design process is introduced and applied to a three-layer and a five layer rotor. Two prototypes experimentally demonstrate the feasibility of the design procedure.


title: Ku-band dielectric-loaded SIW horn for vertically-polarized multi-sector antennas

abstract: This paper presents a compact and lightweight Ku-band dielectric-loaded H-plane sectoral horn antenna, using the substrate integrated waveguide (SIW) technology, suitable to be utilized as an element of a steerable multi-sector antenna with the ability of scanning a full 360° on the horizon. A dielectric loading together with metallic strips in front of the SIW horn aperture are utilized to optimize the performances in terms of bandwidth, gain and front‐to‐back ratio (FTBR). To experimentally demonstrate the performance of the proposed design, a three‐element prototype, intended later to be a part of an eight‐sector antenna, has been fabricated. The measurement process of different characteristics of the prototype is discussed in detail and a comparison between simulated and measured results is given.


title: Dermal fillers in aesthetics: an overview of adverse events and treatment approaches

abstract: Background The everexpanding range of dermal filler products for aesthetic soft tissue augmentation is of benefit for patients and physicians, but as indications and the number of procedures performed increase, the number of complications will likely also increase. Objective To describe potential adverse events associated with dermal fillers and to provide structured and clear guidance on their treatment and


Identification of Common Molecular Subsequences


title: Self-esteem as an interpersonal monitor: The sociometer hypothesis.

abstract: Five studies tested hypotheses derived from the sociometer model of self-esteem according to which the self-esteem system monitors others' reactions and alerts the individual to the possibility of social exclusion. Study 1 showed that the effects of events on participants' state self-esteem parallel


title: Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks

abstract: Functional groups (FGs) serve as a foundation for analyzing chemical properties of organic molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. Here, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties. Molecules are modeled as undirected graphs with atoms as nodes and bonds as edges. Using this graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different methods: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. Grad- CAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.


title: Cross-Modulation Networks for Few-Shot Learning

abstract: A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art.


title: A Path Tracking Algorithm Using Future Prediction Control with Spike Detection for an Autonomous Vehicle Robot

abstract: Trajectory tracking is an important aspect of autonomous vehicles. The idea behind trajectory tracking is the ability of the vehicle to follow a predefined path with zero steady state error. The difficulty arises due to the nonlinearity of vehicle dynamics. Therefore, this paper proposes a stable tracking control for an autonomous vehicle. An approach that consists of steering wheel control and lateral control is introduced. This control algorithm is used for a non-holonomic navigation problem, namely tracking a reference trajectory in a closed loop form. A proposed future prediction point control algorithm is used to calculate the vehicle's lateral error in order to improve the performance of the trajectory tracking. A feedback sensor signal from the steering wheel angle and yaw rate sensor is used as feedback information for the controller. The controller consists of a relationship between the future point lateral error, the linear velocity, the heading error and the reference yaw rate. This paper also introduces a spike detection algorithm to track the spike error that occurs during GPS reading. The proposed idea is to take the advantage of the derivative of the steering rate. This paper aims to tackle the lateral error problem by applying the steering control law to the vehicle, and proposes a new path tracking control method by considering the future coordinate of the vehicle and the future estimated lateral error. The effectiveness of the proposed controller is demonstrated by a simulation and a GPS experiment with noisy data. The approach used in this paper is not limited to autonomous vehicles alone since the concept of autonomous vehicle tracking can be used in mobile robot platforms, as the kinematic model of these two platforms is similar.


title: Adiponectin, resistin and leptin response to dietary intervention in diabetic nephropathy.

abstract: OBJECTIVE ::: Adipokines play an important role in metabolic regulations. Obesity, diabetes, and renal disturbances affect adipokine profile by influencing their complex effects on metabolism. Our objective was to assess the effect of low-energy diet intervention on serum adiponectin, leptin, and resistin levels in diabetic nephropathy. ::: ::: ::: METHODS ::: Seventeen subjects with diabetes type 2 and nephropathy participated in the study. After estimation of individual resting metabolic rates by indirect calorimetry, diets introducing 20% energy deficit were applied. At baseline and after 2 months of dieting, the following parameters were measured: body composition by dual x-ray spectrometry and serum adiponectin (Adp), leptin (Lep), resistin (Res), insulin, urea, creatinine, glucose, glycosylated hemoglobin, C-reactive protein, and tumor necrosis factor-alpha concentrations. Homeostatic model assessment (HOMA) was used to quantify insulin resistance. ::: ::: ::: RESULTS ::: Total energy, protein, and fat intakes diminished significantly with intentional dieting. Significant decreases in total body fat mass (FM) and its percentage in body mass (FM%) and trunk and gynoid fat mass, as well as in serum resistin and tumor necrosis factor-alpha levels, were also observed. Responses of adipokines to dietary treatment varied individually. Generally, they were affected by FM. Alterations in Lep concentrations correlated negatively with baseline FM, FM%, and android and gynoid fat mass and positively with changes in intake of protein, carbohydrates, and total energy of the consumed diet. Changes in Adp were inversely related to FM after therapy. Alterations in Res concentrations correlated positively with android fat mass before therapy and initial Lep levels. Adiponectin was inversely related to HOMA index before and after treatment. ::: ::: ::: CONCLUSIONS ::: Low-energy diet applied in diabetic nephropathy may decrease serum resistin levels and inflammation. In addition, responses of all adipokines to dieting appear to be affected by body fat mass, especially android fat mass.


title: Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors

abstract: This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.


title: Runtime power estimation of mobile AMOLED displays

abstract: Modeling and estimating power consumption of OLED displays are necessary to understand the energy behavior of emerging mobile devices. Although previous study exists to model and estimate the power consumption of stationary display images, to the best of our knowledge, no prior work is found to deal with runtime power behavior of OLED display running real applications. This paper proposes a runtime power estimation scheme for OLED displays that involves monitoring kernel activities that capture the screen change events of running applications. The experiment results show that the proposed scheme estimates the display energy consumption of running applications with reasonable accuracy.


title: Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

abstract: Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or the amount of computation, we find that they do not necessarily result in lower energy consumption. Therefore, these targets do not serve as a good metric for energy cost estimation. To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses the energy consumption of a CNN to guide the pruning process. The energy estimation methodology uses parameters extrapolated from actual hardware measurements. The proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in the output feature maps instead of the filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet is reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. We also show that reducing the number of target classes in AlexNet greatly decreases the number of weights, but has a limited impact on energy consumption.


title: Automated super-voxel based features classification of urban environments by integrating 3D point cloud and image content

abstract: In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point cloud captured by a high definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. We show how to significantly reduce the need for manually labeled training data by reduction of scene complexity using non-supervised ground and building segmentation. Our system first automatically segments grounds point cloud. Then, using binary range image processing building facades will be detected. Remained point cloud will grouped into voxels which are then transformed to super voxels. Local 3D features extracted from super voxels are classified by trained boosted decision trees and labeled with semantic classes e.g. tree, pedestrian, car. Given labeled 3D points cloud and 2D image with known viewing camera pose, the proposed association module aligned collections of 3D points to the groups of 2D image pixel to parsing 2D cubic images.


title: Tunable RF MEMS-Based Frequency-Dependent Power Limiter

abstract: A novel approach to frequency-dependent power limiters (FDPLs) is proposed. RF microelectromechanical systems (MEMS) switches are integrated with bandpass filters to form a power limiter, where the output RF power is limited to specific levels, based on the frequency bands. Both the nonplanar and planar versions of FDPLs are presented using circulators and hybrids, and RF MEMS-based power limiters are analyzed theoretically and experimentally for one frequency band. The limiter attenuates the high-power signal only within the bandwidth of the integrated filter. The design of the proposed power limiter is expanded to achieve power limiting for various frequency bands. The flatness of the threshold level can be set to the desired value by controlling the return loss of the filters used in the FDPL circuit. Measured results for an FDPL circuit are presented, demonstrating that the limiting power level can be controlled by adjusting the dc bias of the MEMS switches. The commercially available electrostatically actuated switches OMRON and Radant are employed for the realization of the FDPL. Additionally, a varactor-based tunable filter is designed and implemented in FDPL circuits. The tunable FDPL circuits are fabricated and measured, demonstrating the feasibility of realizing adaptive FDPLs.


title: Face recognition with temporal invariance: A 3D aging model

abstract: The variation caused by aging has not received adequate attention compared with pose, lighting, and expression variations. Aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). While the facial age modeling has been widely studied in computer graphics community, only a few studies have been reported in computer vision literature on age-invariant face recognition. We propose an automatic aging simulation technique that can assist any existing face recognition engine for aging-invariant face recognition. We learn the aging patterns of shape and the corresponding texture in 3D domain by adapting a 3D morphable model to the 2D aging database (public domain FG-NET). At recognition time, each probe and all gallery images are modified to compensate for the age-induced variation using an intermediate 3D model deformation and a texture modification, prior to matching. The proposed approach is evaluated on a set of age-separated probe and gallery data using a state-of-the-art commercial face recognition engine, FaceVACS. Use of 3D aging model improves the rank-1 matching accuracy on FG-NET database from 28.0% to 37.8%, on average.


title: Faster variational inducing input Gaussian process classification

abstract: Gaussian processes (GP) provide a prior over functions and allow finding complex regularities in data. Gaussian processes are successfully used for classification/regression problems and dimensionality reduction. In this work we consider the classification problem only. The complexity of standard methods for GP-classification scales cubically with the size of the training dataset. This complexity makes them inapplicable to big data problems. Therefore, a variety of methods were introduced to overcome this limitation. In the paper we focus on methods based on so called inducing inputs. This approach is based on variational inference and proposes a particular lower bound for marginal likelihood (evidence). This bound is then maximized w.r.t. parameters of kernel function of the Gaussian process, thus fitting the model to data. The computational complexity of this method is $O(nm^2)$, where $m$ is the number of inducing inputs used by the model and is assumed to be substantially smaller than the size of the dataset $n$. Recently, a new evidence lower bound for GP-classification problem was introduced. It allows using stochastic optimization, which makes it suitable for big data problems. However, the new lower bound depends on $O(m^2)$ variational parameter, which makes optimization challenging in case of big m. In this work we develop a new approach for training inducing input GP models for classification problems. Here we use quadratic approximation of several terms in the aforementioned evidence lower bound, obtaining analytical expressions for optimal values of most of the parameters in the optimization, thus sufficiently reducing the dimension of optimization space. In our experiments we achieve as well or better results, compared to the existing method. Moreover, our method doesn't require the user to manually set the learning rate, making it more practical, than the existing method.


title: A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval

abstract: Image Retrieval is the basic requirement of today’s life in present scenario. Because of huge amount of different types of images are added in database from different sources for retrieval of the image, different kinds of processing is required to extract the relevant features from them. In this paper, comparisons of combination texture and shape features are done with texture Gray Level Co-occurrence Matrix and Hu-moments and the combination of tamura texture and shape invariant Hu-moments. For the performance evaluation of the system we use most commonly used methods namely precision and recall.


title: A second order accurate projection method for the incompressible Navier-Stokes equations on non-graded adaptive grids

abstract: We present an unconditionally stable second order accurate projection method for the incompressible Navier-Stokes equations on non-graded adaptive Cartesian grids. We employ quadtree and octree data structures as an efficient means to represent the grid. We use the supra-convergent Poisson solver of C.-H. Min, F. Gibou, H. Ceniceros, A supra-convergent finite difference scheme for the variable coefficient Poisson equation on fully adaptive grids, CAM report 05-29, J. Comput. Phys. (in press)], a second order accurate semi-Lagrangian method to update the momentum equation, an unconditionally stable backward difference scheme to treat the diffusion term and a new method that guarantees the stability of the projection step on highly non-graded grids. We sample all the variables at the grid nodes, producing a scheme that is straightforward to implement. We propose two and three-dimensional examples to demonstrate second order accuracy for the velocity field and the divergence free condition in the L1 and L∞ norms.


General Business Anthropology


utomatic detection of skin defects in citrus fruits using a multivariate image nalysis approach


title: Clustering techniques in data mining: A comparison

abstract: Clustering is a technique in which a given data set is divided into groups called clusters in such a manner that the data points that are similar lie together in one cluster. Clustering plays an important role in the field of data mining due to the large amount of data sets. This paper reviews the various clustering algorithms available for data mining and provides a comparative analysis of the various clustering algorithms like DBSCAN, CLARA, CURE, CLARANS, K-Means etc.


title: An Effective Deferentially Private Data Releasing Algorithm for Decision Tree

abstract: Differential privacy is a strong definition for protecting individual privacy in data releasing and mining. However, it is a rigid definition introducing a large amount of noise to the original dataset, which significantly decreases the quality of data mining results. Recently, how to design a suitable data releasing algorithm for data mining purpose is a hot research area. In this paper, we propose a differential private data releasing algorithm for decision tree construction. The proposed algorithm provides a non-interactive data releasing method through which miner can obtain the complete dataset for data mining purpose. With a given privacy budget, the proposed algorithm generalizes the original dataset, and then specializes it in a differential privacy constrain to construct decision trees. As the designed novel scheme selection operation can fully utilize the allocated privacy budget, the data set released by the proposed algorithm can yield better decision tree models than other method. Experimental results demonstrate that the proposed algorithm outperforms existing methods for private decision tree construction.


title: Self-Transfer Learning for Fully Weakly Supervised Object Localization

abstract: Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data/task-driven features instead of hand-crafted features. Although location information of region-of-interests (ROIs) gives good prior for object localization, it requires heavy annotation efforts from human resources. Thus a weakly supervised framework for object localization is introduced. The term "weakly" means that this framework only uses image-level labeled datasets to train a network. With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features. However, those approaches cannot be used for some applications which do not have pre-trained networks or well-localized large scale images. Medical image analysis is a representative among those applications because it is impossible to obtain such pre-trained networks. In this work, we present a "fully" weakly supervised framework for object localization ("semi"-weakly is the counterpart which uses pre-trained filters for weakly supervised localization) named as self-transfer learning (STL). It jointly optimizes both classification and localization networks simultaneously. By controlling a supervision level of the localization network, STL helps the localization network focus on correct ROIs without any types of priors. We evaluate the proposed STL framework using two medical image datasets, chest X-rays and mammograms, and achieve signiticantly better localization performance compared to previous weakly supervised approaches.