title: When my robot smiles at me: Enabling human-robot rapport via real-time head gesture mimicry

abstract: People use imitation to encourage each other during conversation. We have conducted an experiment to investigate how imitation by a robot affect people’s perceptions of their conversation with it. The robot operated in one of three ways: full head gesture mimicking, partial head gesture mimicking (nodding), and non-mimicking (blinking). Participants rated how satisfied they were with the interaction. We hypothesized that participants in the full head gesture condition will rate their interaction the most positively, followed by the partial and non-mimicking conditions. We also performed gesture analysis to see if any differences existed between groups, and did find that men made significantly more gestures than women while interacting with the robot. Finally, we interviewed participants to try to ascertain additional insight into their feelings of rapport with the robot, which revealed a number of valuable insights.


title: A security policy oracle: detecting security holes using multiple API implementations

abstract: Even experienced developers struggle to implement security policies correctly. For example, despite 15 years of development, standard Java libraries still suffer from missing and incorrectly applied permission checks, which enable untrusted applications to execute native calls or modify private class variables without authorization. Previous techniques for static verification of authorization enforcement rely on manually specified policies or attempt to infer the policy by code-mining. Neither approach guarantees that the policy used for verification is correct. In this paper, we exploit the fact that many modern APIs have multiple, independent implementations. Our flow- and context-sensitive analysis takes as input an API, multiple implementations thereof, and the definitions of security checks and security-sensitive events. For each API entry point, the analysis computes the security policies enforced by the checks before security-sensitive events such as native method calls and API returns, compares these policies across implementations, and reports the differences. Unlike code-mining, this technique finds missing checks even if they are part of a rare pattern. Security-policy differencing has no intrinsic false positives: implementations of the same API must enforce the same policy, or at least one of them is wrong! Our analysis finds 20 new, confirmed security vulnerabilities and 11 interoperability bugs in the Sun, Harmony, and Classpath implementations of the Java Class Library, many of which were missed by prior analyses. These problems manifest in 499 entry points in these mature, well-studied libraries. Multiple API implementations are proliferating due to cloud-based software services and standardization of library interfaces. Comparing software implementations for consistency is a new approach to discovering "deep" bugs in them.


title: Automatic Fruit Grading and Classification System Using Computer Vision: A Review

abstract: Automation in agriculture comes into play to increase productivity, quality and economic growth of the country. Fruit grading is an important process for producers which affects the fruits quality evaluation and export market. Although the grading and sorting can be done by the human, but it is slow, labor intensive, error prone and tedious. Hence, there is a need of an intelligent fruit grading system. In recent years, researchers had developed numerous algorithms for fruit sorting using computer vision. Color, textural and morphological features are the most commonly used to identify the diseases, maturity and class of the fruits. Subsequently, these features are used to train soft computing technique network. In this paper, use of image processing in agriculture has been reviewed so as to provide an insight to the use of vision based systems highlighting their advantages and disadvantages.


title: Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes

abstract: In recent years, marked point processes have found a natural application in the modeling of ultra-high-frequency financial data since they do not require the integration of the data which is usually needed by other modeling approaches. Among these processes, two main classes have so far been proposed to model financial data: the class of autoregressive conditional duration models of Engle and Russel and the broad class of doubly stochastic Poisson processes with marks. In this paper we consider a class of marked doubly stochastic Poisson processes in which the intensity is driven by another marked point process. In particular, we focus on an intensity with a shot noise form that can be interpreted in terms of the effect caused by news arriving on the market. For models in this class we study likelihood inferential procedures such as Monte Carlo likelihood and importance sampling Monte Carlo expectation maximization by making use of reversible jump Markov chain Monte Carlo algorithms.


title: Online detection of freezing of gait with smartphones and machine learning techniques

abstract: Freezing of gait (FoG) is a common gait deficit in advanced Parkinson's disease (PD). FoG events are associated with falls, interfere with daily life activities and impair quality of life. FoG is often resistant to pharmacologic treatment; therefore effective non-pharmacologic assistance is needed.


abstract: The quintessence of the diagnosis in traditional Chinese medicine is syndrome differentiation and treatment. Syndrome differentiation consists of four methods: observing, hearing as well as smelling, asking, and touching. The examination of the observing is the most important procedure in the method of “tongue.” In recent years, numerous medical studies have identified the close relations between sublingual veins and human organs. Therefore, sublingual pathological symptoms, as well as demographical information of patients, imply pathological changes in the organs, and early diagnosis is beneficial for early treatment. However, the diagnosis of sublingual pathological symptoms is usually influenced by the doctor’s subjective interpretation, experience, and environmental factors. The results can easily be limited by subjective factors such as knowledge, experience, mentality, diagnostic techniques, color perception and interpretation. Different doctors may make different judgments on the same tongue, presenting less than desirable repeatability. Therefore, assisting doctors' diagnoses with scientific methods and standardizing the differentiating process to obtain reliable diagnoses and enhance the clinical applicability of Chinese medicine is an important issue. In its wake, this study aims to construct an Automatic Sublingual Vein Feature Extraction System based on image processing technologies to allow objective and quantified computer readings. The extraction of sublingual vein features mainly captures the back of the tongue and extract the sublingual vein area for feature expression analysis. Firstly, the patient's back of the tongue is photographed and color-graded to compensate for color distortion, and then the tongue-back area is extracted. This study extracts tongue-back imagery by analyzing the RGB color expression of the back of the tongue, lips, teeth and skin, translating it into the HSI color space easily perceived by the human eye, along with skin area removal, rectangle detection, teeth area removal, black area removal and control point detection. The captured tongue-back image goes through histogram equalization and hue shift to enhance color contrast. Sublingual veins are extracted through analyzing RGB color component shift, hues, saturation and brightness. Then the sublingual vein color information and positioning are used to differentiate hues, lengths and branches. Thinning analysis is used to determine the presence of varicose veins. At the same time, the surrounding features of sublingual veins, such as columnar vein, bubbly vein, petechiae and bloodshot, are extracted. The information regarding features and lingual vein conditions are integrated and analyzed for doctors' clinical reference. This study utilizes 199 lingual images for statistic testing and three lingual diagnostic experts in Chinese medicine for lingual reading. The accuracy for the extractions are: tongueback 86%; sublingual vein 80%, varicose veins 90%, branches 87%; and the accuracy rates for columnar veins and bubbly veins are 87%, 88% and 73% respectively. Keywords—tongue diagnosis; sublingual veins; tongue-back imagery; histogram equalization


title: Experiences with CoralCDN: A Five-Year Operational View

abstract: CoralCDN is a self-organizing web content distribution network (CDN). Publishing through CoralCDN is as simple as making a small change to a URL’s hostname; a decentralized DNS layer transparently directs browsers to nearby participating cache nodes, which in turn cooperate to minimize load on the origin webserver. CoralCDN has been publicly available on PlanetLab since March 2004, accounting for the majority of its bandwidth and serving requests for several million users (client IPs) per day. This paper describes CoralCDN’s usage scenarios and a number of experiences drawn from its multi-year deployment. These lessons range from the specific to the general, touching on the Web (APIs, naming, and security), CDNs (robustness and resource management), and virtualized hosting (visibility and control). We identify design aspects and changes that helped CoralCDN succeed, yet also those that proved wrong for its current environment.


Knowledge and Awareness on the Role of Diet in the Incidence of Dental Caries among General Population


title: Hybrid mining approach in the design of credit scoring models

abstract: Unrepresentative data samples are likely to reduce the utility of data classifiers in practical application. This study presents a hybrid mining approach in the design of an effective credit scoring model, based on clustering and neural network techniques. We used clustering techniques to preprocess the input samples with the objective of indicating unrepresentative samples into isolated and inconsistent clusters, and used neural networks to construct the credit scoring model. The clustering stage involved a class-wise classification process. A self-organizing map clustering algorithm was used to automatically determine the number of clusters and the starting points of each cluster. Then, the K-means clustering algorithm was used to generate clusters of samples belonging to new classes and eliminate the unrepresentative samples from each class. In the neural network stage, samples with new class labels were used in the design of the credit scoring model. The proposed method demonstrates by two real world credit data sets that the hybrid mining approach can be used to build effective credit scoring models.


title: Data-Driven Approximations to NP-Hard Problems

abstract: There exist a number of problem classes for which obtaining the exact solution becomes exponentially expensive with increasing problem size. The quadratic assignment problem (QAP) or the travelling salesman problem (TSP) are just two examples of such NP-hard problems. In practice, approximate algorithms are employed to obtain a suboptimal solution, where one must face a trade-off between computational complexity and solution quality. In this paper, we propose to learn to solve these problem from approximate examples, using recurrent neural networks (RNNs). Surprisingly, such architectures are capable of producing highly accurate solutions at minimal computational cost. Moreover, we introduce a simple, yet effective technique for improving the initial (weak) training set by incorporating the objective cost into the training procedure. We demonstrate the functionality of our approach on three exemplar applications: marginal distributions of a joint matching space, feature point matching and the travelling salesman problem. We show encouraging results on synthetic and real data in all three cases.


title: Passion in Sport: On the Quality of the Coach–Athlete Relationship

abstract: Vallerand et al. (2003) developed a dualistic model of passion, wherein two types of passion are proposed: harmonious (HP) and obsessive (OP) passion that predict adaptive and less adaptive interpersonal outcomes, respectively. In the present research, we were interested in understanding the role of passion in the quality of coach–athlete relationships. Results of Study 1, conducted with athletes (N = 157), revealed that HP positively predicts a high-quality coach–athlete relationship, whereas OP was largely unrelated to such relationships. Study 2 was conducted with coaches (N = 106) and showed that only HP positively predicted the quality of the coach–athlete relationship. Furthermore, these effects were fully mediated by positive emotions. Finally, the quality of the coach–athlete relationship positively predicted coaches’ subjective well-being. Future research directions are discussed in light of the dualistic model of passion.


title: Efficient parallel kNN joins for large data in MapReduce

abstract: In data mining applications and spatial and multimedia databases, a useful tool is the kNN join, which is to produce the k nearest neighbors (NN), from a dataset S, of every point in a dataset R. Since it involves both the join and the NN search, performing kNN joins efficiently is a challenging task. Meanwhile, applications continue to witness a quick (exponential in some cases) increase in the amount of data to be processed. A popular model nowadays for large-scale data processing is the shared-nothing cluster on a number of commodity machines using MapReduce [6]. Hence, how to execute kNN joins efficiently on large data that are stored in a MapReduce cluster is an intriguing problem that meets many practical needs. This work proposes novel (exact and approximate) algorithms in MapReduce to perform efficient parallel kNN joins on large data. We demonstrate our ideas using Hadoop. Extensive experiments in large real and synthetic datasets, with tens or hundreds of millions of records in both R and S and up to 30 dimensions, have demonstrated the efficiency, effectiveness, and scalability of our methods.


title: Subset-codebook precoding for 1-bit massive multiuser MIMO

abstract: We consider a massive multiuser MIMO downlink scenario with 1-bit digital-to-analog converters. QPSK information symbols are transmitted to the users by a channel-dependent mapping of the symbols to 1-bit DAC signals that are transmitted from each base station (BS) antenna. The mapping is selected to emulate the desired information bearing QPSK symbols at the users. Non-linear precoding is considered, where the mapping from the information symbols to the transmit antenna signals is selected based on the combination of information symbols. First, a linear quantized precoder is used to create tentative transmit symbols. For each combination of information symbols, a subset of antennas is selected, and an exhaustive search over a limited size codebook of precoding alternatives is performed. The resulting method significantly lowers the error floor caused by quantization, with a complexity that is linear in the number of transmit antennas.


title: Fast charging technique for Li-Ion battery charger

abstract: In this paper, the compensation of internal resistance of the Li-Ion battery is proposed. The requirement of fast and steady charger becomes the most important issue for power management ICs. Refer to the characteristics of the battery, how to charge the battery with adequate current and fasten the time of charging is critical to the devisers. Due to the impedance of battery pack, the energy of charger charged to cell is consumed in it partly. As a result, the efficiency of charger is reduced inevitably. The previous design has proposed a dynamic circuit for reducing time [1]. However the technique demands the external device to compensate the defeat. Thus, this paper extends the period of the CC mode to charge the battery with a faster speed. Owing to the shifting voltage on the reference voltage, the charger can delay the time that the operation mode from CC mode to CV mode. That is a fast-charging charger can be achieved by a large constant current stored in the battery during a long constant current period. Simulation results verify the success of the fast-charging technique due to the internal resistance compensation.


title: THE TRANSITION FROM MOTHER-OF-ONE TO MOTHER-OF-TWO: MOTHERS’ PERCEPTIONS OF THEMSELVES AND THEIR RELATIONSHIPS WITH THEIR FIRSTBORN CHILDREN

abstract: This qualitative study aimed to explore mothers’ perceptions of the transition to second-time motherhood, and advance methodology for supporting mothers during this transition. Fifty-seven pregnant mothers and their firstborn children (M age = 30.3 months) participated in a laboratory procedure in which the mother exposed her child to a brief episode of differential treatment. The episode was designed to simulate an expected experience of caregiving that would occur after the birth of a second child. During a postnatal visit (M=5.72 weeks following childbirth), mothers provided written feedback on their perceptions of the transition and the contribution of the laboratory experience to that transition. Qualitative content analysis revealed that mothers approached the birth of a second child with apprehension. Intrapersonal themes revealed that participation in the procedure shaped mothers’ understanding of their expanded maternal roles by enhancing their sense of preparedness and confidence in their abilities to support their children. Interpersonal themes revealed that participation helped mothers appreciate the magnitude of the challenge that their children would face and gave mothers reason to believe that their children would adjust successfully. Findings are discussed in terms of their potential contribution toward developing methodology for helping mothers prepare for the addition of a second child.


Transforming Experience: The Potential of Augmented Reality and Virtual Reality for Enhancing Personal and Clinical Change


A New Method Presentation for Fault Location in Power Transformers


title: A Substrate Integrated Waveguide Circular Polarized Slot Radiator and Its Linear Array

abstract: A top-wall substrate integrated waveguide (SIW) slot radiator for generating circular polarized (CP) field is proposed and characterized in this letter. The reflection of the slot radiator is extremely weak, which simplifies the linear traveling wave array design. Based on such a structure, a 16-element CP SIW traveling wave antenna array is designed, fabricated, and measured at 16 GHz. A -23 dB side lobe level (SLL) with an axial ratio (AR) of 1.95 dB is experimentally achieved. The size of the proposed SIW CP linear array antenna is 285 mm times 22 mm. The measured gain is 18.9 dB, and the usable bandwidth is 2.5%.


title: Discourse-Aware Neural Rewards for Coherent Text Generation

abstract: In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn neural rewards to model cross-sentence ordering as a means to approximate desired discourse structure. Empirical results demonstrate that a generator trained with the learned reward produces more coherent and less repetitive text than models trained with cross-entropy or with reinforcement learning with commonly used scores as rewards.


title: Global Multi-armed Bandits with Holder Continuity

abstract: Standard Multi-Armed Bandit (MAB) problems assume that the arms are independent. However, in many application scenarios, the information obtained by playing an arm provides information about the remainder of the arms. Hence, in such applications, this informativeness can and should be exploited to enable faster convergence to the optimal solution. In this paper, formalize a new class of multi-armed bandit methods, Global Multi-armed Bandit (GMAB), in which arms are globally informative through a global parameter, i.e., choosing an arm reveals information about all the arms. We propose a greedy policy for the GMAB which always selects the arm with the highest estimated expected reward, and prove that it achieves bounded parameter-dependent regret. Hence, this policy selects suboptimal arms only nitely many times, and after a nite number of initial time steps, the optimal arm is selected in all of the remaining time steps with probability one. In addition, we also study how the informativeness of the arms about each other’s rewards aects the speed of learning. Specically, we prove that the parameter-free (worst-case) regret is sublinear in time, and decreases with the informativeness of the arms. We also prove a sublinear in time Bayesian risk bound for the GMAB which reduces to the well-known Bayesian risk bound for linearly parameterized bandits when the arms are fully informative. GMABs have applications ranging from drug dosage control to dynamic pricing.


title: Dual marching cubes: primal contouring of dual grids

abstract: We present a method for contouring an implicit function using a grid topologically dual to structured grids such as octrees. By aligning the vertices of the dual grid with the features of the implicit function, we are able to reproduce thin features of the extracted surface without excessive subdivision required by methods such as marching cubes or dual contouring. Dual marching cubes produces a crack-free, adaptive polygonalization of the surface that reproduces sharp features. Our approach maintains the advantage of using structured grids for operations such as CSG while being able to conform to the relevant features of the implicit function yielding much sparser polygonalizations than has been possible using structured grids.


title: Auto cropping for application of heart abnormalities detection through Iris based on mobile devices

abstract: As the WHO says, heart disease is a main cause of death and examining it by current methods in hospitals is not cheap. Iridology is one of the most popular alternative ways to detect the condition of organs. Iridology is a science field that makes a health non-expert or practitioner to study signs in the iris that are capable of showing abnormalities in the body, including basic genetics, toxin deposition, dam circulation, and other weaknesses. Research on computer iridology has been done before. One is about the computer's iridology system to detect heart conditions. Some previous works used manual cropping to get iris images and auto cropping on PC. This research presents auto cropping on iris images base on mobile devices. There are several stages such as capture eye base on target, pre-processing, cropping, segmentation, feature extraction and classification using Thresholding algorithms. The system we proposed was tested at Mugi Barokah Clinic Surabaya. This research resulted in an experimental using 20 data indicating that the auto cropping results is 45% success for cropping process and all the success cropping brings accuracy for classification.


title: A semantic approach for extracting domain taxonomies from text

abstract: In this paper we present a framework for the automatic building of a domain taxonomy from text corpora, called Automatic Taxonomy Construction from Text (ATCT). This framework comprises four steps. First, terms are extracted from a corpus of documents. From these extracted terms the ones that are most relevant for a specific domain are selected using a filtering approach in the second step. Third, the selected terms are disambiguated by means of a word sense disambiguation technique and concepts are generated. In the final step, the broader-narrower relations between concepts are determined using a subsumption technique that makes use of concept co-occurrences in a text. For evaluation, we assess the performance of the ATCT framework using the semantic precision, semantic recall, and the taxonomic F-measure that take into account the concept semantics. The proposed framework is evaluated in the field of economics and management as well as the medical domain.


title: A Cascade Multilevel Converter Topology With Reduced Number of Switches

abstract: This paper introduces a new multilevel converter topology that has many steps with fewer power electronic switches. The proposed circuit consists of series-connected submultilevel converters blocks. The optimal structures of this topology are investigated for various objectives, such as minimum number of switches and capacitors, and minimum standing voltage on switches for producing maximum output voltage steps. A new algorithm for determination of dc voltage sourcespsila magnitudes has also been presented. The proposed topology results in reduction of the number of switches, losses, installation area, and converter cost. The operation and performance of the proposed multilevel converter has been verified by the simulation and experimental results of a single-phase 53-level multilevel converter.


title: 1024-Channel 3D ultrasound digital beamformer in a single 5W FPGA

abstract: 3D ultrasound, an emerging medical imaging technique that is presently only used in hospitals, has the potential to enable breakthrough telemedicine applications, provided that its cost and power dissipation can be minimized. In this paper, we present an FPGA architecture suitable for a portable medical 3D ultrasound device. We show an optimized design for the digital part of the imager, including the delay calculation block, which is its most critical part. Our computationally efficient approach requires a single FPGA for 3D imaging, which is unprecedented. The design is scalable; a configuration supporting a 32×32-channel probe, which enables high-quality imaging, consumes only about 5W.


Strengths , and Limitations of Systematic Reviews and Meta-Analyses


title: A broadband dual-polarized base station antenna element for European Digital Dividend, CDMA800 and GSM900 applications

abstract: In this paper, a novel broadband dual-polarized (slant ±45°) base station antenna element operating at 790–960 MHz is proposed. The antenna element consists of two pairs of symmetrical dipoles, four couples of baluns, a cricoid pedestal and two kinds of plastic fasteners. Specific shape metal reflector is also designed to achieve stable radiation pattern and high front-to-back ratio (FBR). All the simulated and measured results show that the proposed antenna element has wide impedance bandwidth (about 19.4%), low voltage standing wave ratio (VSWR 21 9.66 dBi) are also achieved. The proposed antenna element fabricated by integrated metal casting technology has great mechanical properties such as compact structure, low profile, good stability, light weight and easy to fabricate. Due to its good electrical and mechanical characteristics, the antenna element is suitable for European Digital Dividend, CDMA800 and GSM900 bands in base station antenna of modern mobile communication.


title: Interactive goal model analysis for early requirements engineering

abstract: In goal-oriented requirements engineering, goal models have been advocated to express stakeholder objectives and to capture and choose among system requirement candidates. A number of highly automated procedures have been proposed to analyze goal achievement and select alternative requirements using goal models. However, during the early stages of requirements exploration, these procedures are difficult to apply, as stakeholder goals are typically high-level, abstract, and hard-to-measure. Automated procedures often require formal representations and/or information not easily acquired in early stages (e.g., costs, temporal constraints). Consequently, early requirements engineering (RE) presents specific challenges for goal model analysis, including the need to encourage and support stakeholder involvement (through interactivity) and model improvement (through iterations). This work provides a consolidated and updated description of a framework for iterative, interactive, agent-goal model analysis for early RE. We use experiences in case studies and literature surveys to guide the design of agent-goal model analysis specific to early RE. We introduce analysis procedures for the i* goal-oriented framework, allowing users to ask "what if?" and "are certain goals achievable? how? or why not?" The i* language and our analysis procedures are formally defined. We describe framework implementation, including model visualization techniques and scalability tests. Industrial, group, and individual case studies are applied to test framework effectiveness. Contributions, including limitations and future work, are described.


title: BioWar: scalable agent-based model of bioattacks

abstract: While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.


title: The Economics of Computer Hacking

abstract: This paper considers various classes of computer hackers, with a special emphasis on fame-driven versus profit-driven hackers. We use simple economic analysis to examine how each of these hacking markets work. The resulting framework is employed to evaluate current U.S. policy aimed at reducing the threat of computer hacking and shows that this policy is largely effective. We consider policy adjustments consistent with the insights of the framework provided as a means of strengthening cyber security.


title: Analysis of Single-Event Effects in a Radiation-Hardened Low-Jitter PLL Under Heavy Ion and Pulsed Laser Irradiation

abstract: A radiation-hardened low-jitter phase-locked loop (PLL) with a low-mismatch charge pump and a robust voltage-controlled oscillator is designed in a 130 nm PD-SOI process. In order to evaluate the overall response to single-event effects, the accumulated phase jitter has been put forward, which can exclude the inherent noise floor and accumulate all the radiation-induced noise. Then the single-event sensitivity of the proposed PLL is comprehensively analyzed by heavy ion and pulsed laser tests.


title: Brewster’s angle silicon wafer terahertz linear polarizer

abstract: We present a new cost-effective terahertz linear polarizer made from a stack of silicon wafers at Brewster's angle, andevaluate its performances. We show that this polarizer is wide-band, has a high extinction ratio (> 6 × 10(3)) and very small insertion losses (< 1%). We provide measurements of the temporal waveforms after linearly polarizing the THz beam and show that there is no distortion of the pulse. We compare its performances with a commercial wire-grid polarizer, and show that the Brewster's angle polarizer can conveniently be used to control the power of a terahertz beam.


title: Estimating arch length discrepancy through Little's Irregularity Index for epidemiological use

abstract: The objective of this study was to evaluate the diagnostic capability of Little's Irregularity Index (LII) in order to estimate the arch length discrepancy (ALD) in a dental arch. Dental casts with a full permanent dentition, excluding third molars, from 200 12- to-16-year-old schoolchildren from a representative high school located in Lima, Peru, were used. Incisal irregularity was measured using the LII, whereas ALD was calculated as the difference between available and required space in each dental arch anterior to the first permanent molars. The receiver-operator characteristic (ROC) curve was used to contrast the LII with three different dichotomized ALDs and locate optimized cut-off points. Correlation between ALD and LII was -0.68 (P < 0.001). According to ROC curves, LIIs of 2.45, 4.00, and 4.55 mm were the optimized cut-off points to estimate negative ALDs higher than 0, 3, and 6 mm, respectively. LII's highest diagnostic capability was found for estimating negative ALD greater than 3 mm with a sensibility of 0.78 and a specificity of 0.76. Based on the present findings, LII could potentially be used in epidemiological surveys as a valid and less time-consuming measurement of crowding compared with ALD; however, further studies are needed to test the reliability of this approach in field settings.


title: Morphological and Molecular Analyses of Closely Related Species In the Stygobiontic Genus Niphargus (Amphipoda)

abstract: The present study investigates morphologically similar species in the amphipod genus Niphargus, with special emphasis on three presumably closely-related species: N. fontanus, N. aquilex, and N. schellenbergi. The distribution ranges of these species overlap in Central Europe, and ambiguity of the current diagnostic characters has likely resulted in misidentifications. To address this issue, we carried out molecular-phylogenetic analyses of Cytochrome Oxidase Subunit 1 gene (COI) and 28S rDNA sequences for 34 individuals of the three species from 27 sites in Central Europe and Great Britain. We also included 14 GenBank sequences for two additional species, N. virei and N. rhenorhodanensis, to increase the resolution potential among our in-group taxa. In addition, about 100 specimens of the three species were examined for a reappraisal of diagnostic characters. Representative specimens taken from various subterranean habitat types were examined for each of the three species. A new morphological key, utilizing improved characters, is presented to clearly distinguish between N. fontanus, N. aquilex, and N. schellenbergi. Molecular-phylogenetic analysis using COI sequence data indicates geographically well-delimited clades for N. aquilex and N. fontanus. Our data suggest that some previously recorded niphargids from the Harz Mountains of Germany, initially identified as N. fontanus or N. aquilex, were all N. schellenbergi.


title: BCS: A Binary Cuckoo Search algorithm for feature selection

abstract: Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.


title: Sensor-based M2M Agriculture Monitoring Systems for Developing Countries: State and Challenges

abstract: Machine to machine (M2M) communication networks consist of thousands of low cost, low energy, low computational power and memory sensors nodes. Due to the autonomous monitoring, self organization, low power consumptions and remote accessibility of sensors, M2M communication networks gain momentum in environmental monitoring, pollution detection, agriculture, disasters monitoring and many similar applications. It is evident that sensor-based agriculture monitoring systems are being designed and implemented mostly in the context of developed countries. However, these monitoring systems do not focus on the implementation of sensor-based M2M networks for the benefit of farmers in developing countries. This paper presents (i) the current state of the art research in sensor-based M2M communication networks for agriculture monitoring, (ii) several existing agricultural monitoring systems and compare them on different design factors, (iii) the technical framework of some recent deployment of agriculture monitoring systems in developing countries and identify their design challenges and (iv) major design and implementation differences of these monitoring systems in developed and developing countries.


title: Sharing rides with friends: a coalition formation algorithm for ridesharing

abstract: We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system modelling such problem as a graph constrained coalition formation (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (Geo-Life) and social data (Twitter), to validate the applicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to -36.22%). Moreover, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).


title: Context-assisted face clustering framework with human-in-the-loop

abstract: Automatic face clustering, which aims to group faces referring to the same people together, is a key component for face tagging and image management. Standard face clustering approaches that are based on analyzing facial features can already achieve high-precision results. However, they often suffer from low recall due to the large variation of faces in pose, expression, illumination, occlusion, etc. To improve the clustering recall without reducing the high precision, we leverage the heterogeneous context information to iteratively merge the clusters referring to same entities. We first investigate the appropriate methods to utilize the context information at the cluster level, including using of “common scene”, people co-occurrence, human attributes, and clothing. We then propose a unified framework that employs bootstrapping to automatically learn adaptive rules to integrate this heterogeneous contextual information, along with facial features, together. Finally, we discuss a novel methodology for integrating human-in-the-loop feedback mechanisms that leverage human interaction to achieve the high-quality clustering results. Experimental results on two personal photo collections and one real-world surveillance dataset demonstrate the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.


title: Identifying Personality Traits, and Especially Traits Resulting in Violent Behavior through Automatic Handwriting Analysis

abstract: Handwriting analysis is a process that has been carried out for centuries. But its effectiveness when analyzing the behavior and personality of an individual is still a debate. Is it possible to detect potential deviant behavior and personality traits of an individual by carrying out an analysis of his/her handwriting? There are two methods of handwriting analysis: Graphology is the method of psychological analysis, while forensic document examination or handwriting identification which is the examination of documents and writing samples by a known source, or person. In this paper we have carried out research of the various state of the art technologies available in analyzing an individual’s behavior based on their handwriting and the effectiveness of predicting the character and personality of that individual. We also wanted to determine if we can uncover handedness, authorship and gender through analysis. Apart from working on LewinsonZubin method of analyzing handwriting, various online tools are also available for handwriting analysis, such as: NEURO SCRIPT, WANDA, CEDAR-FOX, and Gaussian Mixture Model.


title: Promoting Relevance in IS Research: An Informing System for Design Science Research

abstract: Introduction The accusation of research irrelevance has been leveled at virtually every academic community at one time or another. Management (Morhman, Gibson, & Mohrman, 2001) engineering (Scalzi, 1996) and even health care (Belkhodja, Amara, Landry, & Ouimet 2007) have been repeatedly called to task. The challenge to increase research relevance to practice has also been issued to the three information technology disciplines identified by Glass, Ramesh and Vessey (2004)--Computer Science (CS), Software Engineering (SE) and Information Systems (IS)--and each has responded to a different degree. Computer Science is the most mathematically grounded and self-referential of the three (Glass, et al., 2004). While most individual academics in CS wish their research to be relevant, there is little published concern on the topic in CS journals and conferences. Software engineering, likely due to the higher direct visibility of its products in the business community (Windows and SAP are just two global examples), expresses the need for relevance to practice much more strongly. Without doubt however, the information technology field most concerned with research relevance to practice is that of the authors, Information Systems. With its academic departments frequently housed in colleges of business and its subject matter drawn from CS, SE, and management science, IS has always been extremely conscious of itself as a "spanning" discipline, between business and computer technology. Thirty years of defending its place as a relevant profession has made the field introspective and as a result a segment of IS has refined a research technique practiced in many disciplines--design [science] research--that makes the sternest attempt we are aware of to insure the relevance of research to practice. In addition it leverages the artifact build-test cycle to assess its results in a more rigorous manner than is commonly found in information technology disciplines. From its inception academic research in Information Systems has taken two different approaches: behavioral (management oriented) research and constructivist (build-evaluate) research. Over the last ten years the constructivist approach, termed design science research by its practice community, has gained in number of adherents and importance. We, along with other IS academics, insert the word 'science' into design research to distinguish it from design research as the phrase is understood by non-constructivist researchers in a variety of fields: the study of how artifacts are designed and the characteristics of designers and their practices. In brief, design science research in IS (DSRIS) uses the construction of an information technology artifact and its evaluation learning through building--as the research method. The DSRIS community is therefore much closer to the CS/SE origins of IS than to its management origins, and for this reason we feel DSR as developed in IS has much to offer to all information technology disciplines. At this point we can hear CS and SE readers saying, "OK, you build something and test it. So what? We've always done that." True; the difference is that as a result of reflecting on the constructivist method and formalizing it DSRIS produces more than just a validated artifact. Every DSRIS effort should be targeted to produce an artifact that is a (partial) solution to an acknowledged business information technology problem. In addition, it produces a "design theory" that prescribes the requirements for a class of artifacts to address similar problems (Jones & Gregor, 2007). Recently a number of academic design science researchers have suggested methods for still further leveraging a DSRIS project to produce mid-range theory describing the phenomena by which the artifact operates in addition to the prescriptive design theory (Kuechler & Vaishnavi, 2008a). Finally, because the design science methodology has been formalized to a greater degree than has previously been the case in information technology disciplines, it can be better taught to new generations of researchers. …


title: Broadband dial-up user behavior identification and analysis

abstract: With the evolvement of the Internet and network services, broadband user behavior tends to diversify. It is a rising challenge for the network operators to understand network user behavior. In this paper, we present a general approach for identification and profiling user behavior model by using data mining technique to analyze user activity data. We present analysis of a data set comprised of the dial-up users' usage data in a typical metropolitan area network of a national broadband operator in China. The data recorded activity of over 250,000 unique user accounts. We identify several main behavior groups by using K-means algorithm, and further summarize behavior profile of each user group as main user behavior models. The results show that our approach can properly identify user behavior models. Our results provide a basis for network operators to understand dial-up user behavior, optimize network planning and adjust customer related policies accordingly.


title: Bagging, Boosting and the Random Subspace Method for Linear Classifiers

abstract: Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented.


title: Mobile attachment causes and consequences for emotional bonding with mobile phones

abstract: This paper addresses the phenomenon of emotional attachments to mobile phones. We introduce the term "mobile attachment" and define it as a bond between a person's self and a mobile phone that varies in strength. Based on a critical reflection of interdisciplinary literature, a conceptual mobile attachment model is developed. Within this model causes, consequences and influencing factors of mobile attachment are exposed and elaborated. We argue that mobile attachment emerges when the mobile phone becomes part of the user's self concept. The link between the user and their mobile phone may be fostered when it empowers, enriches, or gratifies the user's self. Attachment causes lead to "design space determinants" that enable user experience designers to design for mobile attachment. Attachment consequences may be operationalized for user experience evaluation.


title: Sculpting: an interactive volumetric modeling technique

abstract: We present a new interactive modeling technique based on the notion of sculpting a solid material. A sculpting tool is controlled by a 3D input device and the material is represented by voxel data; the tool acts by modifying the values in the voxel array, much as a "paint" program's "paintbrush" modifies bitmap values. The voxel data is converted to a polygonal surface using a "marching-cubes" algorithm; since the modifications to the voxel data are local, we accelerate this computation by an incremental algorithm and accelerate the display by using a special data structure for determining which polygons must be redrawn in a particular screen region. We provide a variety of tools: one that cuts away material, one that adds material, a "sandpaper" tool, a "heat gun," etc. The technique provides an intuitive direct interaction, as if the user were working with clay or wax. The models created are free-form and may have complex topology; however, they are not precise, so the technique is appropriate for modeling a boulder or a tooth but not for modeling a crankshaft.


title: Structure-preserving speckle reduction of SAR images using nonlocal means filters

abstract: This paper proposes a structure-preserving speckle reduction (SPSR) algorithm for synthetic aperture radar (SAR) images by exploiting self-similarity of structural patterns based on nonlocal means filter. The SPSR algorithm is featured by discerning pixels of similar structural patterns, which is crucial for a despeckling process to avoid blurring image structure. To alleviate the impact of speckle noise to similarity measure, a two-stage filtering scheme is introduced into the SPSR algorithm. Filtering at the first stage aims at an accurate approximation of true structural similarity, followed by the filtering at the second stage to group pixels with similar neighborhood in a large area. Compared to the traditional Lee filter, enhanced Lee filter and the speckle reducing anisotropic diffusion (SRAD), evaluation results have shown that the SPSR algorithm substantially improves the despeckling performance especially on structure preservation and speckle reduction in homogeneous regions.


title: Design, Modeling, and Control of an Aerial Robot DRAGON: A Dual-Rotor-Embedded Multilink Robot With the Ability of Multi-Degree-of-Freedom Aerial Transformation

abstract: In this letter, we introduce a novel transformable aerial robot called DRAGON, which is a dual-rotor-embedded multilink robot with the ability of multi-degree-of-freedom (DoF) aerial transformation. The new aerial robot can control the full pose in $\mathsf {SE}(3)$ regarding the center of gravity (CoG) of multilinks and can render the multi-DoF aerial transformation, which is accomplished by the original two-DoF force vectoring mechanism on each link called the dual-rotor gimbal module. The dynamics is derived on the basis of the special definition of CoG orientation, followed by a control method decoupled into thrust force control and rotor gimbal control. In the thrust force control, the minimum force norm is considered to avoid force saturation, and the rotor gimbal control method is developed to enhance both translational and rotational stabilities during hovering and large-scale aerial transformation. The prototype composed of four links is constructed, and associated preliminary experiments are performed. The feasibility of the novel mechanical design and the proposed control method for the aerial transformation is demonstrated.


Fashion, Fiction, Function : Mediating Wearable Design Through Fashion Film


title: A Stroke Order Verification Method for On-Line Handwritten Chinese Characters Based on Tempo-spatial Consistency Analysis

abstract: This paper proposes a method to recognize stroke orders of on-line handwritten Chinese characters based on analyzing both spatial and temporal information. A novel control-point-based analysis method is presented for spatial information analysis to match strokes with various shapes and styles. Its computation complexity is much lower than image correlation method and is suitable for applications on mobile devices. For temporal information analysis, Hidden Markov Model is adopted and a proposed rectification method is integrated to find the optimal pair-wise matching result of stroke sequences. Experimental results proved the effectiveness of the proposed method. The verification rate is 99.6% on the test set.


title: The requirements engineering body of knowledge (REBoK)

abstract: A body of knowledge is a term used to represent the complete set of concepts, terms and activities that make up a professional domain. It encompasses the core teachings, skills and research in a field or industry. So far, the discipline of RE is lacking an official Requirements Engineering Body of Knowledge (REBoK). This working session brings together researchers and practitioners to elaborate the goals, requirements and constraints for a REBoK that shall serve as commonly agreed basis for developing a draft over the following months.


title: Robust object localization based on error patterns learning for dexterous mobile manipulation

abstract: In this article we describe an approach for object detection and pose estimation from stereo RGB frames for robot manipulation in manufacturing scenarios. This solution was developed in the framework of the second challenge of the EuRoC project, and meets the need of a registration method invariant to the view perspective and robust to the structural symmetries and ambiguities of the target objects. Our contribution consists of automatic correction of sub-optimal results of registration algorithms. As most registration algorithms only converge on local optima, a tool for recognizing and correcting wrong alignments is highly desirable. Our insight is that, for a given target point cloud, it is important to study the alignment space offline and identify sub-optimal solutions before the registration. The convergence of the algorithm leads to the error pattern knowledge that can be used to discard the wrong solutions, and recover the correct alignment. Experiments on synthesized and real data show that exploiting the known information about the spatial properties of the objects, together with appropriate pre-processing and refining of the data, we can have a substantial improvement in discarding wrong hypothesis for geometrically ambiguous items.


title: TrustZone Explained: Architectural Features and Use Cases

abstract: ARM TrustZone is a hardware security extension technology, which aims to provide secure execution environment by splitting computer resources between two execution worlds, namely normal world and secure world. TrustZone is supported on different flavors of ARM architectures, that include architecture deployed on targets running regular applications, such as mobile devices and architecture for micro-controllers. As ARM is widely deployed on the majority of mobile and micro-controller devices, TrustZone's goal is to provide security for those platforms. In this paper, we will discuss details of different ARM architectures that support TrustZone technology. Then, we will review how TrustZone is implemented in the hardware and software of ARM products. We will also compare TrustZone with other implementations of trusted execution environments on the market.


title: Backside failure analysis techniques: What's the gain of silicon getting thinner?

abstract: Failure analysis (FA) of electronic devices today is mostly conducted through the device backside. Advanced silicon (Si) backside preparation for this purpose has developed over the past years, with a final Si thickness from around 300μm to 100μm down to around 20μm to 10μm. This paper discusses what to expect if Si can be processed a little thinner, from 20μm down to a few μm. Improvement of optical imaging, spectral extension of photon emission and expanded optical interaction for stimulation techniques are investigated. Further, Si thickness is coming close to the penetration depth of particle beams. The interaction potential for device analysis is discussed, preliminary results are presented.


Fingerprint classification techniques: a review


title: Suppression of HIV-1 Reverse Transcriptase Activity by Culture Supernatants of Mycoplasmas

abstract: Coinfection with mycoplasmas has been shown to enhance cytopathic changes in T lymphocytes in culture brought about by human immunodeficiency virus type-1 (HIV-1), accelerate disease progression, and suppress reverse transcriptase (RT) activity simultaneously. We attempted to identify the components in culture supernatants of mycoplasmas which suppress RT activity. The marked inhibitory effect on RT by culture supernatants was dependent upon Mg2+. The culture supernatants exhibited the activities of DNase and RNase, which degraded the products and substrates in RT assay, respectively. Gel filtration studies revealed that two major protein peaks, peak 1 (MW 67-100 kDa) and peak 2 (MW 10-25 kDa), exhibited DNase and/or RNase activities, and that both peaks contained a significant degree of inhibitory activity on RT. These results indicate that suppression of RT activity by the culture supernatants of mycoplasmas is due to DNase and RNase activities in the culture supernatants. The results of the present investigation suggest that RT assay of certain biological materials that are contaminated with mycoplasmas must be conducted carefully.


title: Cash demand forecasting in ATMs by clustering and neural networks

abstract: To improve ATMs’ cash demand forecasts, this paper advocates the prediction of cash demand for groups of ATMs with similar day-of-the week cash demand patterns. We first clustered ATM centers into ATM clusters having similar day-of-the week withdrawal patterns. To retrieve “day-of-the-week” withdrawal seasonality parameters (effect of a Monday, etc.) we built a time series model for each ATMs. For clustering, the succession of seven continuous daily withdrawal seasonality parameters of ATMs is discretized. Next, the similarity between the different ATMs’ discretized daily withdrawal seasonality sequence is measured by the Sequence Alignment Method (SAM). For each cluster of ATMs, four neural networks viz., general regression neural network (GRNN), multi layer feed forward neural network (MLFF), group method of data handling (GMDH) and wavelet neural network (WNN) are built to predict an ATM center’s cash demand. The proposed methodology is applied on the NN5 competition dataset. We observed that GRNN yielded the best result of 18.44% symmetric mean absolute percentage error (SMAPE), which is better than the result of Andrawis, Atiya, and El-Shishiny (2011). This is due to clustering followed by a forecasting phase. Further, the proposed approach yielded much smaller SMAPE values than the approach of direct prediction on the entire sample without clustering. From a managerial perspective, the clusterwise cash demand forecast helps the bank’s top management to design similar cash replenishment plans for all the ATMs in the same cluster. This cluster-level replenishment plans could result in saving huge operational costs for ATMs operating in a similar geographical region.


title: Path Optimization in Stream-Based Overlay Networks

abstract: The emergence of sensor networks and distributed applications that generate data streams has created a need for Internet overlays designed for streaming data. Such stream-based overlay network (SBONs) consist of a set of Internet hosts that collect, process, and deliver stream-based data to multiple applications. A key challenge in the design and implementation of SBONs is efficient path optimization when mapping logical query streams to physical network hosts and paths. Suboptimal placements can induce poor utilization of network resources, leading to severe performance penalties, link saturation, and network hotspots. Our goal is to realize efficient stream placement that takes the physical topology of the Internet into account, thereby minimizing overall network utilization. In this paper, we describe a novel, network-aware path optimization algorithm for stream-based overlay networks. Our approach is based on a spring relaxation model that operates in a metric space defined by the pairwise node latency within the SBON. This relaxation placement algorithm utilizes the underlying network resources efficiently, is capable of performing inter-stream optimization, and can be implemented in a scalable and decentralized way. To evaluate its performance, we define a set of evaluation metrics for path optimization in terms of network utilization, application delay, and resource contention. Our simulation experiments with a realistic network topology show that relaxation placement approaches optimal network utilization without introducing an undue delay penalty or resource contention. Compared to an optimal placement strategy, relaxation placement causes only 15% more network traffic on average, while adding a 24% delay penalty for the application. We validate our simulation results with actual measurements involving 70 PlanetLab nodes.


title: Review of statistical network analysis: models, algorithms, and software

abstract: The analysis of network data is an area that is rapidly growing, both within and outside of the discipline of statistics. ::: ::: This review provides a concise summary of methods and models used in the statistical analysis of network data, including the Erdős–Renyi model, the exponential family class of network models, and recently developed latent variable models. Many of the methods and models are illustrated by application to the well-known Zachary karate dataset. Software routines available for implementing methods are emphasized throughout. ::: ::: The aim of this paper is to provide a review with enough detail about many common classes of network models to whet the appetite and to point the way to further reading. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 ::: ::: (This material is based upon work supported by the Science Foundation Ireland under Grant No. 08/SRC/I1407: Clique: Graph & Network Analysis Cluster.)


title: Health-Related Disaster Communication and Social Media: Mixed-Method Systematic Review

abstract: ABSTRACTThis mixed-method evidence synthesis drew on Cochrane methods and principles to systematically review literature published between 2003 and 2016 on the best social media practices to promote health protection and dispel misinformation during disasters. Seventy-nine studies employing quantitative, qualitative, and mixed methods on risk communication during disasters in all UN-languages were reviewed, finding that agencies need to contextualize the use of social media for particular populations and crises. Social media are tools that still have not become routine practices in many governmental agencies regarding public health in the countries studied. Social media, especially Twitter and Facebook (and equivalents in countries such as China), need to be incorporated into daily operations of governmental agencies and implementing partners to build familiarity with them before health-related crises happen. This was especially observed in U.S. agencies, local government, and first responders but also fo...


title: Machine learning, medical diagnosis, and biomedical engineering research - commentary

abstract: A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique.


title: Rapid, Reliable Shape Setting of Superelastic Nitinol for Prototyping Robots

abstract: Shape setting Nitinol tubes and wires in a typical laboratory setting for use in superelastic robots is challenging. Obtaining samples that remain superelastic and exhibit desired precurvatures currently requires many iterations, which is time consuming and consumes a substantial amount of Nitinol. To provide a more accurate and reliable method of shape setting, in this paper we propose an electrical technique that uses Joule heating to attain the necessary shape setting temperatures. The resulting high power heating prevents unintended aging of the material and yields consistent and accurate results for the rapid creation of prototypes. We present a complete algorithm and system together with an experimental analysis of temperature regulation. We experimentally validate the approach on Nitinol tubes that are shape set into planar curves. We also demonstrate the feasibility of creating general space curves by shape setting a helical tube. The system demonstrates a mean absolute temperature error of 10°C.


title: A Link-Based Approach to the Cluster Ensemble Problem

abstract: Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations, while those among clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over 10 data sets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and regularly illustrate higher clustering quality in comparison to several state-of-the-art techniques.


title: Economic reasoning and artificial intelligence

abstract: The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.


title: Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques

abstract: Learner modeling is a basis of personalized, adaptive learning. The research literature provides a wide range of modeling approaches, but it does not provide guidance for choosing a model suitable for a particular situation. We provide a systematic and up-to-date overview of current approaches to tracing learners' knowledge and skill across interaction with multiple items, focusing in particular on the widely used Bayesian knowledge tracing and logistic models. We discuss factors that influence the choice of a model and highlight the importance of the learner modeling context: models are used for different purposes and deal with different types of learning processes. We also consider methodological issues in the evaluation of learner models and their relation to the modeling context. Overall, the overview provides basic guidelines for both researchers and practitioners and identifies areas that require further clarification in future research.


title: A 2.8–3.2-GHz Fractional- $N$ Digital PLL With ADC-Assisted TDC and Inductively Coupled Fine-Tuning DCO

abstract: A 2.8-3.2-GHz fractional-N digital PLL, implemented in 0.18- μm CMOS, is presented. The PLL architecture has the form of a classic delta-sigma fractional-N PLL. A PFD generates up and down pulses from the reference and divided-down digitally controlled oscillator (DCO) clock. The time-to-digital converter (TDC) converts the width of up pulses to digital words. The quantization noise introduced by a third-order delta-sigma modulator through the multi-modulus divider is canceled at the TDC output. A resistively interpolated ADC is employed to boost TDC resolution by a factor of five. A dither-less DCO with an inductively coupled fine-tuned varactor bank improves tuning step-size by a factor of 16.6, to 20 kHz. With a 52-MHz reference clock, a 3.2-GHz output clock, and a loop-bandwidth of 950 kHz, this prototype achieves 230-fs rms jitter, integrated from a 1-kHz to 40-MHz offset, while drawing 17 mW from a 1.8-V supply. The in-band phase noise floor is -111.6 dBc/Hz at a 500-kHz offset. The reference spur is -75 dBc and the worst-case fractional-N spur, by sweeping the multiplication ratio near 61, is -55 dBc. An FOM of -240.4 dB is achieved, and this design occupies a core area of 0.62 mm2 .


title: Microservices: The Journey So Far and Challenges Ahead

abstract: Microservices are an architectural approach emerging out of service-oriented architecture, emphasizing self-management and lightweightness as the means to improve software agility, scalability, and autonomy. This article examines microservice evolution from the technological and architectural perspectives and discusses key challenges facing future microservice developments.


title: Learning Disentangled Multimodal Representations for the Fashion Domain

abstract: In many visual domains (like fashion, furniture, etc.) the search for products on online platforms requires matching textual queries to image content. For example, the user provides a search query in natural language (e.g.,pink floral top) and the results obtained are of a different modality (e.g., the set of images of pink floral tops). Recent work on multimodal representation learning enables such cross-modal matching by learning a common representation space for text and image. While such representations ensure that the n-dimensional representation of pink floral top is very close to representation of corresponding images, they do not ensure that the first k1 (< n) dimensions correspond to color, the next k2 (< n) correspond to style and so on. In other words, they learn entangled representations where each dimension does not correspond to a specific attribute. We propose two simple variants which can learn disentangled common representations for the fashion domain wherein each dimension would correspond to a specific attribute (color, style, silhoutte, etc.). Our proposed variants can be integrated with any existing multimodal representation learning method. We use a large fashion dataset of over 700K fashion items crawled from multiple fashion e-commerce portals to evaluate the learned representations on four different applications from the fashion domain, namely, cross-modal image retrieval, visual search, image tagging, and query expansion. Our experimental results show that the proposed variants lead to better performance for each of these applications while learning disentangled representations.


title: Accurate Estimation of the Degree Distribution of Private Networks

abstract: We describe an efficient algorithm for releasing a provably private estimate of the degree distribution of a network. The algorithm satisfies a rigorous property of differential privacy, and is also extremely efficient, running on networks of 100 million nodes in a few seconds. Theoretical analysis shows that the error scales linearly with the number of unique degrees, whereas the error of conventional techniques scales linearly with the number of nodes. We complement the theoretical analysis with a thorough empirical analysis on real and synthetic graphs, showing that the algorithm's variance and bias is low, that the error diminishes as the size of the input graph increases, and that common analyses like fitting a power-law can be carried out very accurately.


title: Incentives for Tax Planning and Avoidance: Evidence from the Field

abstract: ABSTRACT: We analyze survey responses from nearly 600 corporate tax executives to investigate firms' incentives and disincentives for tax planning. While many researchers hypothesize that reputational concerns affect the degree to which managers engage in tax planning, this hypothesis is difficult to test with archival data. Our survey allows us to investigate reputational influences and, indeed, we find that reputational concerns are important—69 percent of executives rate reputation as important and the factor ranks second in order of importance among all factors explaining why firms do not adopt a potential tax planning strategy. We also find that financial accounting incentives play a role. For example, 84 percent of publicly traded firms respond that top management at their company cares at least as much about the GAAP ETR as they do about cash taxes paid and 57 percent of public firms say that increasing earnings per share is an important outcome from a tax planning strategy. JEL Classifications: D8...


title: A Silicon-Based 0.3 THz Frequency Synthesizer With Wide Locking Range

abstract: A 300 GHz frequency synthesizer incorporating a triple-push VCO with Colpitts-based active varactor (CAV) and a divider with three-phase injection is introduced. The CAV provides frequency tunability, enhances harmonic power, and buffers/injects the VCO fundamental signal from/to the divider. The locking range of the divider is vastly improved due to the fact that the three-phase injection introduces larger allowable phase change and injection power into the divider loop. Implemented in 90 nm SiGe BiCMOS, the synthesizer achieves a phase-noise of -77.8 dBc/Hz (-82.5 dBc/Hz) at 100 kHz (1 MHz) offset with a crystal reference, and an overall locking range of 280.32-303.36 GHz (7.9%).


title: Tweet Recommendation with Graph Co-Ranking

abstract: As one of the most popular micro-blogging services, Twitter attracts millions of users, producing millions of tweets daily. Shared information through this service spreads faster than would have been possible with traditional sources, however the proliferation of user-generation content poses challenges to browsing and finding valuable information. In this paper we propose a graph-theoretic model for tweet recommendation that presents users with items they may have an interest in. Our model ranks tweets and their authors simultaneously using several networks: the social network connecting the users, the network connecting the tweets, and a third network that ties the two together. Tweet and author entities are ranked following a co-ranking algorithm based on the intuition that that there is a mutually reinforcing relationship between tweets and their authors that could be reflected in the rankings. We show that this framework can be parametrized to take into account user preferences, the popularity of tweets and their authors, and diversity. Experimental evaluation on a large dataset shows that our model outperforms competitive approaches by a large margin.


title: Effects of iron supplementation in nonanemic pregnant women, infants, and young children on the mental performance and psychomotor development of children: a systematic review of randomized controlled trials

abstract: Background: Uncertainty exists regarding the effects of iron supplementation on neurodevelopmental outcomes in the absence of anemia. Objective: Our objective was to evaluate the effects of iron supplementation in nonanemic pregnant women and in nonanemic healthy children aged ,3 y on the mental performance and psychomotor development of children. Design: In this systematic review, MEDLINE, EMBASE, and The Cochrane Library were searched through December 2009 for randomized controlled trials (RCTs). Results: None of 5 RCTs individually showed a beneficial effect of iron supplementation during early life on the Mental Developmental Index of the Bayley Scales of Infant Development at different ages throughout the first 18 mo. Meta-analysis of 3 RCTs (n = 561) showed that, compared with placebo, supplementation with iron had no significant effect on children’s Mental Developmental Index at ’12 mo of age (weighted mean difference: 1.66; 95% CI: 20.14, 3.47). Three of 5 RCTs showed a beneficial effect of iron supplementation on the Psychomotor Development Index at some time points, whereas 2 did not. Meta-analysis of 3 RCTs (n = 561) showed significant improvement on the Psychomotor Development Index at ’12 mo of age in the iron-supplemented group compared with the control group (weighted mean difference: 4.21; 95% CI: 2.31, 6.12). Two RCTs showed no effect of iron supplementation on behavior. Neither of the 2 RCTs that addressed the influence of prenatal iron supplementation showed an effect of iron on either the intelligence quotient or behavioral status of the children. Conclusion: Limited available evidence suggests that iron supplementation in infants may positively influence children’s psychomotor development, whereas it does not seem to alter their mental development or behavior. Am J Clin Nutr doi: 10.3945/ajcn. 2010.29191.


title: Effective Dynamic Voltage Scaling Through CPU-Boundedness Detection

abstract: Dynamic voltage scaling (DVS) allows a program to execute at a non-peak CPU frequency in order to reduce CPU power, and hence, energy consumption; however, it is oftentimes done at the expense of performance degradation. For a program whose execution time is bounded by peripherals' performance rather than the CPU speed, applying DVS to the program will result in negligible performance penalty. Unfortunately, existing DVS-based power-management algorithms are conservative in the sense that they overly exaggerate the impact that the CPU speed has on the execution time. We propose a new DVS algorithm that detects the CPU-boundedness of a program on the fly (via a regression method on the past MIPS rate) and then adjusts the CPU frequency accordingly. To illustrate its effectiveness, we compare our algorithm with other DVS algorithms on real systems via physical measurements.


title: Interdomal Suture through a Nondelivery Endonasal Approach: A New Technique

abstract: The nasal tip is one of the most important features of facial aesthetics, and successful rhinoplasty depends on achieving adequate nasal tip refinement and definition. Asymmetry, excessive width and poor definition of the tip are common problems after primary and secondary rhinoplasty, and achieving a pleasant shape of the nasal tip has become one of the most challenging problems in rhinoplasty.1,2 Multiple techniques for nasal tip refinement and definition have been proposed with unquestionable effectiveness, including the standard techniques of cephalic trim of the lower lateral cartilages, scoring and transection of the alar crura and the use of multiple types of cartilage grafts, among others.2–6 In recent decades, the use of tip sutures has gained large popularity and become one of the leading techniques for achieving adequate tip refinement and definition. Suture techniques to address the nasal tip are old as rhinoplasty. Joseph3 is widely recognized as the first to use sutures to treat tip deformities. He described 2 suturing techniques. The columellar-septal suture serves to rotate the nasal tip, providing increased projection while stabilizing the tip lobule complex. The second was what is now known as the interdomal suture, which was the first direct interdomal suture reported in the literature. It was often used to stabilize the remaining lower lateral cartilages after significant resection and sculpturing. No additional reports on tip sutures are found in the literature until 1985 when McCollough and English7 described the “double-dome unit” procedure to increase nasal tip projection and definition. Their technique was presented as an alternative to Goldman’s8 procedure and included morselization of the domes and posterior suturing of the crura beneath them, all through a delivery approach. Tardy and Cheng9 modified the technique in 1987 by excising the interdomal soft tissue and positioning the knot deep in the interdomal space. With the advent of the open rhinoplasty era in the eighties, a new age began for tip suturing. Daniel2 and Tebbetts1 reported on intradomal dome-shaping sutures and several variations of tip sutures. Gruber10 presented the lateral crura convexity control suture and Guyuron,11 the medial crura footplate suture refinement. The only reference to a tip suture performed through a nondelivery endonasal approach was in 2012 in an article by Pasinato et al,12 where they used a straight needle introduced through the vestibular skin in a back-and-forth mattress-like suture that grabbed the domes and narrowed the tip, leaving the knot lateral to the external vestibular aspect of the dome. When performing rhinoplasty, aggressive reduction may set the stage for unpredictable long-term healing and undesirable sequelae. For these reasons, rhinoplasty and nasal tip surgery, in particular, have evolved in recent decades to procedures characterized by tissue conservation, reorientation and even augmentation using grafting techniques rather than reduction. The use of grafts introduced the possibility of improving lobular definition and has a special role in tip refinement techniques. However, any grafting technique poses a new set of risks that can affect the healing process and often results in evident visibility or migration, particularly in patients with thin skin. Based on experience accumulated over several decades, rather than excising the tip cartilage or inserting cartilage grafts, the tendency today is to focus on lateral crura preservation and tip cartilage modification through precise suture placement and tension control. Our personal experience, reinforced by other authors’ statements13 has shown that surgical results are more predictable with suturing than with excisional or grafting techniques because intraoperatively, the effects are immediately visible, and can be adjusted or reversed depending on the aesthetic look of the tip, something very difficult to achieve with cartilaginous excisions or grafts. Open rhinoplasty has been the platform on which most tip suturing techniques were developed. The classical nondelivery endonasal rhinoplasty remained mostly a reductive procedure, whereas tip suturing was performed only through the delivery approach.14,15 To address these issues, the purpose of this article is 2-fold: first to describe a technique designed to refine the nasal tip with an interdomal suture placed through a nondelivery endonasal approach and second to provide data and information regarding the results that can be achieved with this technique and long-term outcomes.


title: Differences of "Traditional Marketing" in opposition to "Electronic Marketing"

abstract: This paper initially seeks to document and review Differences of traditional marketing in opposition to Electronic marketing.Nowadays; the methods of business conduction have been changed by the revolution of information communication technology (ICT) that most of the transactions are related to internet. Execution of Electronic marketing all over the world can transform the forms of transactions due to the rapid proliferation of the Internet. The World Wide Web, online communication skills has created fast growing modern electronic distribution channels for Electronic marketing that If we surf through the internet, we will find their goals that they can convince customers to purchase their needs. As far as we concerned, advertising and selling methods are much cheaper, useful, and suitable for producers and suppliers all over the world. It is considered that, there are different types of modern marketing, for instance, internet marketing, and email marketing are all drive transactions to be doing well.


title: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

abstract: Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.


title: Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation

abstract: This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimate UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system.


title: An Overview of Grounded Theory Design in Educational Research

abstract: Over past decades, grounded theory is increasingly popular in a broad range of research primarily in educational research. The current paper aims to provide useful information for the new-comers and fit them well in grounded theory research. This paper starts with definitions, origin and applications of grounded theory, followed by types of grounded theory research designs and the key characteristics of grounded theory. Other aspects covered include data collection and data analysis, general steps, and ethical issues in grounded theory. Discussions on the strengths and limitations of grounded theory, as well as evaluation aspects, are found in the last part of this paper.


title: Detection of Leukemia in microscopic images using image processing

abstract: Leukemia occurs when lot of abnormal white blood cells produced by the bone marrow. Hematologist makes use of microscopic study of human blood, which leads to need of methods, including microscopic color imaging, segmentation, classification and clustering that can allow identification of patients suffering from Leukemia. The microscopic images will be inspected visually by hematologists and the process is time consuming and tiring. The automatic image processing system is urgently needed and can overcome related constraints in visual inspection. The proposed system will be on microscopic images to detect Leukemia. The early and fast identification of Leukemia greatly aids in providing the appropriate treatment. Initial segmentation is done using Statistical parameters such as mean, standard deviation which segregates white blood cells from other blood components i.e. erythrocytes and platelets. Geometrical features such as area, perimeter of the white blood cell nucleusis investigated for diagnostic prediction of Leukemia. The proposed method is successfully applied to a large number of images, showing promising results for varying image quality. Different image processing algorithms such as Image Enhancement, Thresholding, Mathematical morphology and Labelling are implemented using LabVIEW and MATLAB.


title: Output Reachable Set Estimation and Verification for Multilayer Neural Networks

abstract: In this paper, the output reachable estimation and safety verification problems for multi-layer perceptron neural networks are addressed. First, a conception called maximum sensitivity in introduced and, for a class of multi-layer perceptrons whose activation functions are monotonic functions, the maximum sensitivity can be computed via solving convex optimization problems. Then, using a simulation-based method, the output reachable set estimation problem for neural networks is formulated into a chain of optimization problems, which can be efficiently solved by using convex optimization tools. Finally, an automated safety verification is developed based on the output reachable set estimation result. An application to the safety verification for a robotic arm model with two joints is presented to show the effectiveness of proposed approaches.


title: An extensive repertoire of type III secretion effectors in Escherichia coli O157 and the role of lambdoid phages in their dissemination

abstract: Several pathogenic strains of Escherichia coli exploit type III secretion to inject "effector proteins" into human cells, which then subvert eukaryotic cell biology to the bacterium's advantage. We have exploited bioinformatics and experimental approaches to establish that the effector repertoire in the Sakai strain of enterohemorrhagic E. coli (EHEC) O157:H7 is much larger than previously thought. Homology searches led to the identification of >60 putative effector genes. Thirteen of these were judged to be likely pseudogenes, whereas 49 were judged to be potentially functional. In total, 39 proteins were confirmed experimentally as effectors: 31 through proteomics and 28 through translocation assays. At the protein level, the EHEC effector sequences fall into >20 families. The largest family, the NleG family, contains 14 members in the Sakai strain alone. EHEC also harbors functional homologs of effectors from plant pathogens (HopPtoH, HopW, AvrA) and from Shigella (OspD, OspE, OspG), and two additional members of the Map/IpgB family. Genes encoding proven or predicted effectors occur in >20 exchangeable effector loci scattered throughout the chromosome. Crucially, the majority of functional effector genes are encoded by nine exchangeable effector loci that lie within lambdoid prophages. Thus, type III secretion in E. coli is linked to a vast phage "metagenome," acting as a crucible for the evolution of pathogenicity.


title: Modeling and interpreting mesoscale network dynamics

abstract: Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.


title: A Type VI Secretion System Is Involved in Pseudomonas fluorescens Bacterial Competition

abstract: Protein secretion systems are crucial mediators of bacterial interactions with other organisms. Among them, the type VI secretion system (T6SS) is widespread in Gram-negative bacteria and appears to inject toxins into competitor bacteria and/or eukaryotic cells. Major human pathogens, such as Vibrio cholerae, Burkholderia and Pseudomonas aeruginosa, express T6SSs. Bacteria prevent self-intoxication by their own T6SS toxins by producing immunity proteins, which interact with the cognate toxins. We describe here an environmental P. fluorescens strain, MFE01, displaying an uncommon oversecretion of Hcp (hemolysin-coregulated protein) and VgrG (valine-glycine repeat protein G) into the culture medium. These proteins are characteristic components of a functional T6SS. The aim of this study was to attribute a role to this energy-consuming overexpression of the T6SS. The genome of MFE01 contains at least two hcp genes (hcp1 and hcp2), suggesting that there may be two putative T6SS clusters. Phenotypic studies have shown that MFE01 is avirulent against various eukaryotic cell models (amebas, plant or animal cell models), but has antibacterial activity against a wide range of competitor bacteria, including rhizobacteria and clinical bacteria. Depending on the prey cell, mutagenesis of the hcp2 gene in MFE01 abolishes or reduces this antibacterial killing activity. Moreover, the introduction of T6SS immunity proteins from S. marcescens, which is not killed by MFE01, protects E. coli against MFE01 killing. These findings suggest that the protein encoded by hcp2 is involved in the killing activity of MFE01 mediated by effectors of the T6SS targeting the peptidoglycan of Gram-negative bacteria. Our results indicate that MFE01 can protect potato tubers against Pectobacterium atrosepticum, which causes tuber soft rot. Pseudomonas fluorescens is often described as a major PGPR (plant growth-promoting rhizobacterium), and our results suggest that there may be a connection between the T6SS and the PGPR properties of this bacterium.


title: Cultural stereotypes as gatekeepers: increasing girls’ interest in computer science and engineering by diversifying stereotypes

abstract: Despite having made significant inroads into many traditionally male-dominated fields (e.g., biology, chemistry), women continue to be underrepresented in computer science and engineering. We propose that students' stereotypes about the culture of these fields-including the kind of people, the work involved, and the values of the field-steer girls away from choosing to enter them. Computer science and engineering are stereotyped in modern American culture as male-oriented fields that involve social isolation, an intense focus on machinery, and inborn brilliance. These stereotypes are compatible with qualities that are typically more valued in men than women in American culture. As a result, when computer science and engineering stereotypes are salient, girls report less interest in these fields than their male peers. However, altering these stereotypes-by broadening the representation of the people who do this work, the work itself, and the environments in which it occurs-significantly increases girls' sense of belonging and interest in the field. Academic stereotypes thus serve as gatekeepers, driving girls away from certain fields and constraining their learning opportunities and career aspirations.


title: Cues to Coup Plotters : Elections as Coup Triggers in Dictatorships

abstract: A large proportion of coup attempts in autocracies occur in the aftermath of elections, yet little systematic research exists on the topic. Drawing on recent literature on elections in autocracies, we present an argument to explain postelection coups. While we recognize that electoral institutions have the potential to stabilize autocracies, we illustrate that the election event can spark instability when incumbents reveal electoral weakness. Electoral outcomesâ€”in the form of vote shares and opposition reactionsâ€”are signals containing information about the strength of the opposition, and indirectly about the likelihood of a successful full-scale revolution that would compromise the privileged positions of regime elites. In these situations, coups are likely to be initiated to avoid a revolution, either by serving as concessions to the opposition or by facilitating increased repression. We perform a large- N study that supports our argument, significantly nuancing the claim that elections stabilize autocracies.