title: Switched state-space model for a switched-capacitor power amplifier

abstract: This paper presents a switched state-space modeling approach for a switched-capacitor power amplifier. In contrast to state of the art behavioral models for nonlinear devices like power amplifiers, the state-space representation allows a straightforward inclusion of the nonidealities of the applied input sources. Hence, adding noise on a power supply or phase distortions on the carrier signal do not require a redesign of the mathematical model. The derived state-space model (SSM), which can be efficiently implemented in any numerical simulation tool, allows a significant reduction of the required simulation run-time (14x speedup factor) with respect to standard Cadence Spectre simulations. The derived state-space model (SSM) has been implemented in MATLAB/Simulink and its results have been verified by comparison with Cadence Spectre simulations.


Bi-directional Conversion Between Graphemes and Phonemes Using a Joint N-gram Model


title: Webcam-Based Accurate Eye-Central Localization

abstract: This paper contains experimental procedure of webcam-based eye-tracker specially for low power devices. This paper consists of five processes. First one is background suppression for reducing average processing requirement. Second one is Haar-cascade feature-based face detection algorithm. Third one is geometrically eye-position determination. The fourth part is to detect and track eye-ball center using mean of gradient vector. The fifth and last step is to detect where the user looking. We simply calculate percentage of movement of eye to detect either it looking left, right, up or down. This procedure is highly effective with satisfactory accuracy. It also requires less processing power.


title: Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis

abstract: Areas where Artificial Intelligence (AI) & related fields are finding their applications are increasing day by day, moving from core areas of computer science they are finding their applications in various other domains.In recent times Machine Learning i.e. a sub-domain of AI has been widely used in order to assist medical experts and doctors in the prediction, diagnosis and prognosis of various diseases and other medical disorders. In this manuscript the authors applied various machine learning algorithms to a problem in the domain of medical diagnosis and analyzed their efficiency in predicting the results. The problem selected for the study is the diagnosis of the Chronic Kidney Disease.The dataset used for the study consists of 400 instances and 24 attributes. The authors evaluated 12 classification techniques by applying them to the Chronic Kidney Disease data. In order to calculate efficiency, results of the prediction by candidate methods were compared with the actual medical results of the subject.The various metrics used for performance evaluation are predictive accuracy, precision, sensitivity and specificity. The results indicate that decision-tree performed best with nearly the accuracy of 98.6%, sensitivity of 0.9720, precision of 1 and specificity of 1.


title: Internet Attack Methods and Internet Security Technology

abstract: The Internet is a complex and dynamic environment in terms of both topology and emerging technology. In such an environment, security measures applied for small well-defined networks cannot work effectively. The lack of adequate knowledge and understanding of software and security engineering leads to security vulnerabilities, e.g. by inappropriate programming, getting even worse under deadline pressure and rush-to-market issues. Some solution may be effective today, but as technology changes, new risks and challenges appear. Moreover, different solutions must be combined to be effective against different types of attacks and the security of the system must be constantly monitored. This paper investigates common Internet attack methods and different Internet security technologies.


title: Edible Bits: Seamless Interfaces between People, Data and Food

abstract: In this paper we introduce the concept of the Edible User Interface (EUI). We present BeanCounter and TasteScreen, two systems employing this novel interaction paradigm. We describe sample applications of these systems and discuss the results of our user study.


Control of Drum Boiler dynamics via an optimized fuzzy controller


title: A cross-program investigation of students' perceptions of agile methods

abstract: Research was conducted on using agile methods in software engineering education. This paper explores the perceptions of students from five different academic levels of agile practices. Information has been gathered through the collection of quantitative and qualitative data over three academic years, and analysis reveals student experiences, mainly positive but also some negative. Student opinions indicate the preference to continue to use agile practices at the workplace if allowed. A way these findings may potentially be extrapolated to the industrial settings is discussed. Finally, this report should encourage other academics considering adoption of agile methods in their computer science or software engineering curricula.


title: A honeycomb-shaped planar monopole antenna for broadband millimeter-wave applications

abstract: This paper investigates a planar monopole antenna for fifth generation (5G) wireless communication networks. The proposed antenna has an ultra-wide band impedance response in millimeter wave (mmW) spectrum, 25–39 GHz covering Ka band. The antenna has unique structural layout resembling hexagonal honeycomb and has low profile (8×7 mm2) on 0.254 mm thick Rogers substrate, enabling the design for incorporation into future mobile phones. This antenna provides peak gain of 4.15 dBi along with 90% efficiency in the working band. The design is also extended to an 8×1 element array presenting maximum gain of 12.7 dBi at central frequency of the antenna.


title: PSSE: An Architecture For A Personalized Semantic Search Engine

abstract: Semantic technologies promise a next generation of semantic search engines. General search engines don’t take into consideration the semantic relationships between query terms and other concepts that might be significant to user. Thus, semantic web vision and its core ontologies are used to overcome this defect. The order in which these results are ranked is also substantial. Moreover, user preferences and interests must be taken into consideration so as to provide user a set of personalized results. In this paper we propose, an architecture for a Personalized Semantic Search Engine (PSSE). PSSE is a crawler-based search engine that makes use of multi-crawlers to collect resources from both semantic as well as traditional web resources. In order for the system to reduce processing time, web pages' graph is clustered, then clusters are annotated using document annotation agents that work in parallel. Annotation agents use methods of ontology matching to find resources of the semantic web as well as means of information extraction techniques in order to provide a well description of HTML documents. System ranks resources based on a final score that's calculated based on traditional link analysis, content analysis and a weighted user profile for more personalized results. We have a belief that the merge of these techniques together enhances search results.


title: A forensic analysis solution of the email network based on email contents

abstract: Forensics analyses of an email network were more based on the email communication structure, while the email content was seldom considered. In this paper, we propose a forensics analysis method for an email network based on the Latent Dirichlet Allocation topic model and the Centrality algorithm. The method takes into full consideration the email content and the email communication structure. In order to calculate the email communication correlation with the topic of the criminal case, we get the similarity of the email content based on the Latent Dirichlet Allocation model. Combined with the Centrality algorithm, we can obtain the mail accounts that have the greater influence on the email network. Experimental results show that this method can more effectively analyze an email network and find the specific email accounts.


title: Computations and relational bundles

abstract: We explore the view of a computation as a relational section of a (trivial) fibre bundle: initial states lie in the base of the bundle and final states lie in the fibres located at their initial states. This leads us to represent a computation in ‘fibre-form' as the angelic choice of its behaviours from each initial state. That view is shown to have the advantage also of permitting final states to be of different types, as might be used for example in a semantics of probabilistic computations, and of providing a natural setting for refinement of computations. ::: ::: However we apply that view in a different direction. On computations more general than code the two standard models, the relational and the predicate-transformer models, obey different laws. One way to understand that difference is to study the laws of more refined models, like the semantics of probabilistic computations. Another is to characterise each model by its laws. In spite of their differences, the relational model is embedded in the transformer model by a Galois connection which can be used to transfer much of the structure on transformers to the relational model. We investigate the extent to which the conjugate on predicate transformers translates to relations and use the result to motivate a characterisation of relational computations, achieved by using fibre-forms.


title: Statistical analysis of SHVC encoded video

abstract: Scalable extension (SHVC) of the High Efficiency Video Coding HEVC was developed to ameliorate the coding efficiency for different types of scalability. In this paper, a survey for SHVC extensions is investigated. We describe its scalability types and explain the different additional coding features that further improve the Enhancement Layer (EL) coding efficiency. Furthermore, we evaluate and prove the effectiveness of the SHVC through experimental results for different coding configurations reduction of about. Based on statistical analysis, depth 0 (64×64) can reach more than 75% of use in B1 frame. This statistics helped us explore deeply on the prediction mode percentages.


title: Male-to-female transsexuals show sex-atypical hypothalamus activation when smelling odorous steroids.

abstract: One working hypothesis behind transsexuality is that the normal sex differentiation of certain hypothalamic networks is altered. We tested this hypothesis by investigating the pattern of cerebral activation in 12 nonhomosexual male-to-female transsexuals (MFTRs) when smelling 4,16-androstadien-3-one (AND) and estra-1,3,5(10),16-tetraen-3-ol (EST). These steroids are reported to activate the hypothalamic networks in a sex-differentiated way. Like in female controls the hypothalamus in MFTRs activated with AND, whereas smelling of EST engaged the amygdala and piriform cortex. Male controls, on the other hand, activated the hypothalamus with EST. However, when restricting the volume of interest to the hypothalamus activation was detected in MFTR also with EST, and explorative conjunctional analysis revealed that MFTR shared a hypothalamic cluster with women when smelling AND, and with men when smelling EST. Because the EST effect was limited, MFTR differed significantly only from male controls, and only for EST-AIR and EST-AND. These data suggest a pattern of activation away from the biological sex, occupying an intermediate position with predominantly female-like features. Because our MFTRs were nonhomosexual, the results are unlikely to be an effect of sexual practice. Instead, the data implicate that transsexuality may be associated with sex-atypical physiological responses in specific hypothalamic circuits, possibly as a consequence of a variant neuronal differentiation.


title: SimICS/sun4m: A VIRTUAL WORKSTATION

abstract: System level simulators allow computer architects and system software designers to recreate an accurate and complete replica of the program behavior of a target system, regardless of the availability, existence, or instrumentation support of such a system. Applications include evaluation of architectural design alternatives as well as software engineering tasks such as traditional debugging and performance tuning. ::: ::: We present an implementation of a simulator acting as a virtual workstation fully compatible with the sun4m architecture from Sun Microsystems. Built using the system-level SPARC V8 simulator SimICS, SimICS/sun4m models one or more SPARC V8 processors, supports user-developed modules for data cache and instruction cache simulation and execution profiling of all code, and provides a symbolic and performance debugging environment for operating systems. ::: ::: SimICS/sun4m can boot unmodified operating systems, including Linux 2.0.30 and Solaris 2.6, directly from snapshots of disk partitions. To support essentially arbitrary code, we implemented binary-compatible simulators for several devices, including SCSI, console, interrupt, timers, EEPROM, and Ethernet. The Ethernet simulation hooks into the host and allows the virtual workstation to appear on the local network with full services available (NFS, NIS, rsh, etc). Ethernet and console traffic can be recorded for future playback. ::: ::: The performance of SimICS/sun4m is sufficient to run realistic workloads, such as the database benchmark TPC-D, scaling factor 1/100, or an interactive network application such as Mozilla. The slowdown in relation to native hardware is in the range of 25 to 75 (measured using SPECint95). We also demonstrate some applications, including modeling an 8-processor sun4m version (which does not exist), modeling future memory hierarchies, and debugging an operating system.


Dynamic Process Modeling: Combining Models and Experimental Data to Solve Industrial Problems


The detection of fraudulent financial statements: An integrated language model approach


title: The Influence of Perceived Value and Trust on Online Buying Intention

abstract: This research focuses on the factors influencing online buying intention. The aim of this research is to find out perceived value and trust’s influence on online buying intention, which combined several pervious researches’ result. Perceived value is measured by perceived value and perceived product sacrifice, trust is measured by ability, benevolence and integrity. Based on survey to 238 college students, the author analyzes the questionnaire data with SPSS 15.0 and LISREL 8.8. The results show that perceived value and perceived product sacrifice have significantly influence on online shopper’s buying intention. On the other hand, ability and benevolence also have positive effect on online shopper’s buying intention. But integrity, as a part of trust, has no significantly influence on online shopper’s buying intention.


title: A class of binary burst error-correcting quasi-cyclic codes

abstract: A class of binary quasi-cyclic burst error-correcting codes based upon product codes is studied. An expression for the maximum burst error-correcting capability for each code in the class is given. In certain cases, the codes exist in the class which have the same block length and number of check bits as the Gilbert codes, but correct longer bursts of errors than Gilbert codes. By shortening the codes, it is possible to design codes which achieve the Reiger bound. >


SplineNet : B-spline neural network for efficient classification of 3 D data


Zcash Protocol Specication


title: The Prevalence of Diabetes and Prediabetes in the Adult Population of Jeddah, Saudi Arabia- A Community-Based Survey

abstract: BACKGROUND: ::: ::: Type 2 (T2DM) is believed to be common in Saudi Arabia, but data are limited. In this population survey, we determined the prevalence of T2DM and prediabetes. ::: ::: MATERIALS AND METHODS: ::: ::: A representative sample among residents aged ≥ 18 years of the city of Jeddah was obtained comprising both Saudi and non-Saudi families (N = 1420). Data on dietary, clinical and socio-demographic characteristics were collected and anthropometric measurements taken. Fasting plasma glucose and glycated hemoglobin (HbA1c) were used to diagnose diabetes and prediabetes employing American Diabetes Association criteria. Multiple logistic regression analysis was used to identify factors associated with T2DM. ::: ::: RESULTS: ::: ::: Age and sex standardized prevalence of prediabetes was 9.0% (95% CI 7.5-10.5); 9.4% (7.1-11.8) in men and 8.6% (6.6-10.6) in women. For DM it was 12.1% (10.7-13.5); 12.9% (10.7-13.5) in men and 11.4% (9.5-13.3) in women. The prevalence based on World Population as standard was 18.3% for DM and 11.9% for prediabetes. The prevalence of DM and prediabetes increased with age. Of people aged ≥50 years 46% of men and 44% of women had DM. Prediabetes and DM were associated with various measures of adiposity. DM was also associated with and family history of dyslipidemia in women, cardiovascular disease in men, and with hypertension, dyslipidemia and family history of diabetes in both sexes. ::: ::: DISCUSSION: ::: ::: Age was the strongest predictor of DM and prediabetes followed by obesity. Of people aged 50 years or over almost half had DM and another 10-15% had prediabetes leaving only a small proportion of people in this age group with normoglycemia. Since we did not use an oral glucose tolerance test the true prevalence of DM and prediabetes is thus likely to be even higher than reported here. These results demonstrate the urgent need to develop primary prevention strategies for type 2 diabetes in Saudi Arabia.


title: Requirements of Quality of Service in Wireless Sensor Network

abstract: Sensor networks are distributed networks made up of small sensing devices equipped with processors, memory, and short-range wireless communication. They differ from traditional computer networks in that they have resource constraints, unbalanced mixture traffic, data redundancy, network dynamics, and energy balance. Work within wireless sensor networks (WSNs) Quality of service (QoS) has been isolated and specific either on certain functional layers or application scenarios. However the area of sensor network quality of service (QoS) remains largely open. In this paper we define WSNs QoS requirements within a WSNs application, and then analyzing Issues for QoS Monitoring.


title: Addictive use of social networking sites can be explained by the interaction of Internet use expectancies, Internet literacy, and psychopathological symptoms

abstract: Background and Aims Most people use the Internet in a functional way to achieve certain goals and needs. However, there is an increasing number of people who experience negative consequences like loss of control and distress based on an excessive use of the Internet and its specific online applications. Some approaches postulate similarities with behavioral addictions as well as substance dependencies. They differentiate between a generalized and a specific Internet addiction, such as the pathological use of social networking sites (SIA–SNS). Prior studies particularly identified the use of applications, personal characteristics, and psychopathological symptoms as significant predictors for the development and maintenance of this phenomenon. So far, it remains unclear how psychopathological symptoms like depression and social anxiety interact with individual expectancies of Internet use and capabilities of handling the Internet, summarized as Internet literacy. Methods The current study (N = 334) investig...


title: Cutting-Edge Cryptography Through the Lens of Secret Sharing

abstract: Secret sharing is a mechanism by which a trusted dealer holding a secret “splits” the secret into many “shares” and distributes the shares to a collection of parties. Associated with the sharing is a monotone access structure, that specifies which parties are “qualified” and which are not: any qualified subset of parties can (efficiently) reconstruct the secret, but no unqualified subset can learn anything about the secret. In the most general form of secret sharing, the access structure can be any monotone \({\mathsf {NP}}\) language.


Estimating the Voltage Stability of a Power System


The Dynamic Relationship between Volatility , Volume and Open Interest in CSI 300 Futures Market


title: Validating an innovative real-time Delphi approach - A methodological comparison between real-time and conventional Delphi studies

abstract: A novel and innovative real-time Delphi technique is introduced in order to address previously identified weaknesses of the conventional Delphi method, such as complicated facilitator tasks, lack of real-time presentation of results, and difficulties in tracking progress over time. We demonstrate how the real-time (computer-based) method increases the efficiency of the process, accommodates expert availability, and reduces drop-out-rates. Modifications in the Delphi procedure (e.g. change of iteration principle) not only increase efficiency but also change the nature and process of the survey technique itself. By identifying and analysing three individual effects (initial condition effect, feedback effect, and iteration effect) we examine whether the modifications in the survey process cause deviations to the survey results. Empirical data obtained from both conventional as well as real-time Delphi studies is analysed based on multiple statistical analyses. The research findings indicate that significant differences between the two Delphi survey formats do not exist and final survey results are not affected by changes in the survey procedure.


title: MODELING AND SIMULATION OF SOLAR PHOTOVOLTAIC MODULE USING MATLAB/SIMULINK

abstract: This paper presents the circuit model of photovoltaic (PV) module. Simulation and modeling is done using MATLAB/ Simulink software package. The proposed model is user friendly and can be used as a common platform for both applied physics scientist and power electronics engineers. A detailed modeling procedure is presented. The designed model is verified by using STP255-20/Wd PV module. The IV and PV characteristics are simulated at different temperature and irradiance conditions and the results are verified. The proposed model is very simple fast and accurate. The designed model can be used for analysis of PV characteristics and for simulation of maximum power point tracking algorithms. I. INTRODUCTION Energy is required for large number of purposes. Traditional energy is used from coal, natural gas, oil and nuclear energy. But they are exhaustible and polluting. So we need to find an alternate source which is renewable energy source. This can be more extensively used in many applications. Solar energy is a good option and the electricity produced is clean, long lasting, no moving parts, less maintenance and silent. It has experienced a remarkable growth for the past two decades in its widespread use from standalone and utility interactive PV systems (1, 2). The increase in a number of photovoltaic system installed all over the world brought the need for proper supervision and control algorithms as well as modelling and simulation tool for researcher and practitioners involved in its application is very mandatory. The modelling and simulation of PV have made a great transition and play a vital role in power generation. Simple circuit models have been proposed in literature (3, 4). Even though they are interesting, they are very tedious complicated and impractical. Thus because of numerical challenges, an accurate and comprehensive design of PV system in MATLAB Simulink model is proposed. In this paper modelling is limited to simulation of PV module characteristics. In section II, a simple circuit with detailed circuit modelling of PV module is presented for the design of PV system. In section III, physical equations governing the PV module are presented. A Simulink model is developed for each equation of PV module. In section IV, the numerical result for different temperature and irradiance conditions is presented. Section V presents the discussion and conclusion of the modelling process. II. BASIC CIRCUIT AND MATHEMATICAL MODEL A PV module consists of number of solar cells connected in series and parallel as per the requirement. When solar light falls on PV cell, light energy is converted to electrical energy without any moving parts. The transmitted light is absorbed within the semiconductor, by using this light energy to excite the free electrons from the low energy status to an unoccupied higher energy level. These excess electron hole pairs contribute to the flow of current. A general mathematical description of I-V output characteristics for a PV cell has been researched and analysed for the past four decades. Such an equivalent based circuit based model is mainly used for the Maximum power point tracking techniques (MPPT) (5, 6). Authors (7, 8, 9) discussed about solar power extraction. The equivalent circuit of the general model which consists of a photon current, a diode, a parallel resistor expressing a leakage current, and a series resistor describing an internal resistance to the flow of current is shown in Fig. 1. The current (Ipv) - voltage (Vpv) equation of a solar cell is given by:


Deep Convolutional Neural Models for Picture Quality Prediction


title: Segway robotic mobility platform

abstract: The Segway Robotic Mobility Platform (RMP) is a new mobile robotic platform based on the self-balancing Segway Human Transporter (HT). The Segway RMP is faster, cheaper, and more agile than existing comparable platforms. It is also rugged, has a small footprint, a zero turning radius, and yet can carry a greater payload. The new geometry of the platform presents researchers with an opportunity to examine novel topics, including people-height sensing and actuation modalities. This paper describes the history and development of the platform, its characteristics, and a summary of current research projects involving the platform at various institutions across the United States.


title: Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

abstract: This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.


title: End-to-End Incremental Learning

abstract: Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model -a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees. We evaluate our method extensively on the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and show state-of-the-art performance.


title: Where are the Blobs: Counting by Localization with Point Supervision

abstract: Object counting is an important task in computer vision due to its growing demand in applications such as surveillance, traffic monitoring, and counting everyday objects. State-of-the-art methods use regression-based optimization where they explicitly learn to count the objects of interest. These often perform better than detection-based methods that need to learn the more difficult task of predicting the location, size, and shape of each object. However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods. Our contributions are three-fold: (1) we propose a novel loss function that encourages the network to output a single blob per object instance using point-level annotations only; (2) we design two methods for splitting large predicted blobs between object instances; and (3) we show that our method achieves new state-of-the-art results on several challenging datasets including the Pascal VOC and the Penguins dataset. Our method even outperforms those that use stronger supervision such as depth features, multi-point annotations, and bounding-box labels.


title: Validity and reliability of measurement instruments used in research

abstract: Purpose. Issues related to the validity and reliability of measurement instruments used in research are reviewed. ::: ::: Summary. Key indicators of the quality of a measuring instrument are the reliability and validity of the measures. The process of developing and validating an instrument is in large part focused on reducing error in the measurement process. Reliability estimates evaluate the stability of measures, internal consistency of measurement instruments, and interrater reliability of instrument scores. Validity is the extent to which the interpretations of the results of a test are warranted, which depends on the particular use the test is intended to serve. The responsiveness of the measure to change is of interest in many of the applications in health care where improvement in outcomes as a result of treatment is a primary goal of research. Several issues may affect the accuracy of data collected, such as those related to self-report and secondary data sources. Self-report of patients or subjects is required for many of the measurements conducted in health care, but self-reports of behavior are particularly subject to problems with social desirability biases. Data that were originally gathered for a different purpose are often used to answer a research question, which can affect the applicability to the study at hand. ::: ::: Conclusion. In health care and social science research, many of the variables of interest and outcomes that are important are abstract concepts known as theoretical constructs. Using tests or instruments that are valid and reliable to measure such constructs is a crucial component of research quality.


title: Sexual Attraction to Corpses: A Psychiatric Review of Necrophilia

abstract: The authors review 122 cases (88 from the world literature and 34 unpublished cases) manifesting necrophilic acts or fantasies. They distinguish genuine necrophilia from pseudonecrophilia and classify true necrophilia into three types: necrophilic homicide, “regular” necrophilia, and necrophilic fantasy. Neither psychosis, mental retardation, nor sadism appears to be inherent in necrophilia. The most common motive for necrophilia is possession of an unresisting and unrejecting partner. Necrophiles often choose occupations that put them in contact with corpses. Some necrophiles who had occupational access to corpses committed homicide nevertheless. Psychodynamic themes, defense mechanisms, and treatment for this rare disorder are discussed.


title: Language engineering in the context of a popular, inexpensive robot platform

abstract: Language engineering - the theory and practice of building language processors and compilers, has long been recognized as important subject in Computer Science curricula. However, due to lack of suitable target systems, educators face significant challenges to teach language engineering classes effectively. Leveraging the emerging inexpensive robot devices, this paper presents a new approach of using robots as system context to teach language engineering topics. We designed the Chirp-Scribbler Language, which targets the popular Scribbler robot; combined together, they provide an engaging and feature-rich platform to teach a wide range of topics in language engineering. This paper describes the Chirp-Scribbler Language, its integration with the target robot, and the teaching practice of using them to teach language translation basics in an undergraduate programming course.


Guidelines for community-based ecotourism development


title: Deconvolutional Latent-Variable Model for Text Sequence Matching

abstract: A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (generator), providing learned latent codes with more semantic information and better generalization. Our model, trained in an unsupervised manner, yields stronger empirical predictive performance than a decoder based on Long Short-Term Memory (LSTM), with less parameters and considerably faster training. Further, we apply it to text sequence-matching problems. The proposed model significantly outperforms several strong sentence-encoding baselines, especially in the semi-supervised setting.


title: Magnet skew in cogging torque minimization of axial gap permanent magnet motors

abstract: Several cogging torque minimization techniques exist for permanent magnet (PM) machines, and these are well documented in earlier studies. However, the majority are used to reduce the cogging torque in radial flux PM (RFPM) machines. In this paper, cogging torque minimization techniques for axial-flux PM (AFPM) disk machines are briefly reviewed, and one of the foremost cogging torque minimization method, magnet skew, is examined in detail. Various magnet skewing methods, types, and effects on the machine performance are also investigated. The results are compared with a reference AFPM machine, and the improvements are demonstrated.


title: Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

abstract: Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called `adversarial subspaces') in which adversarial examples lie. In particular, effective measures are required to discriminate adversarial examples from normal examples in such regions. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors. We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the detection of adversarial examples generated using the state-of-the-art attacks. We show that when applied for adversarial detection, an LID-based method can outperform several state-of-the-art detection measures by large margins for five attack strategies across three benchmark datasets. Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.


title: Messages behind the sound: real-time hidden acoustic signal capture with smartphones

abstract: With the ever-increasing use of smart devices, recent research endeavors have led to unobtrusive screen-camera communication channel designs, which allow simultaneous screen viewing and hidden screen-camera communication. Such practices, albeit innovative and effective, require well-controlled alignment of camera and screen and obstacle-free access. In this paper, we develop Dolphin, a novel form of real-time acoustics-based dual-channel communication, which uses a speaker and the microphones on off-the-shelf smartphones to achieve concurrent audible and hidden communication. By leveraging masking effects of the human auditory system and readily available audio signals in our daily lives, Dolphin ensures real-time unobtrusive speaker-microphone data communication without affecting the primary audio-hearing experience for human users, while, at the same time, it overcomes the main limitations of existing screen-camera links. Our Dolphin prototype, built using off-the-shelf smartphones, realizes real-time hidden communication, supports up to 8-meter signal capture distance and ±90° listening angle and achieves decoding rate above 80% without error correction. Further, it achieves average data rates of up to 500bps while keeping the decoding rate above 95% within a distance of 1m.


title: Noise and gain optimization technique for RF-integrated CMOS low noise amplifier

abstract: In this paper, very simple and insightful sets of noise parameters expressions for power-constrained simultaneous noise and input matching CMOS LNA design technique are newly introduced. Based on the noise parameters expression, the design principle, advantages, and limitations are clearly explained. Additionally, this paper proposes a gain enhancement technique that is implemented by using simple positive feedback. The proposed LNA is optimized for 5 GHz WLAN applications based on 0.18 /spl mu/m CMOS technology. Measurement results show power gain of 18 dB, NF of 1.5 dB, and IIP3 of -5 dBm while dissipates the DC current of 4 mA at supply voltage of 2.5 V.


title: Towards the enhancement of e-democracy: identifying the notion of the ‘middleman paradox’

abstract: The challenge towards e-democracy, through the electronic transforma- tion of political systems, has become increasingly evident within developed econ- omies. It is regarded as an approach for increased and better quality citizen participation in the democratic processes. E-democracy forms a component of overall e-government initiatives where technology adoption and diffusion, to enhance wider access to, and the delivery of, government services, are apparent. However, previous research demonstrates that very few e-democracy proposals survive the stage of formal political decision-making to become substantive e- government projects within national or international agendas. Furthermore, the implementation of e-democracy projects is undertaken at a much slower pace and with dramatically less support than the implementation of other, so-called e-administration, activities in the public sector. The research in this paper considers the notion of the 'middleman paradox', presenting theoretical and empirical evidence that further investigates the phen- omenon associated with potential e-democracy improvements. Specifically, the paper adds a new dimension to existing theories on the hesitant evolution of e-democracy that clearly identifies politicians as an inhibiting factor. Proposals are made for an enhancement of these processes, and suggestions for further appli- cable research are demonstrated.


title: The role of auditory and premotor cortex in sensorimotor transformations.

abstract: This review first summarizes three functional magnetic resonance imaging studies conducted to elucidate the neural basis for interactions between the auditory and motor systems in the context of musical rhythm perception and production. The second part of the paper discusses these findings in the context of a proposed model for auditory‐ motor interactions that engage the posterior aspects of the superior temporal gyrus, and the ventral and dorsal premotor cortex. In the last section, we present outstanding issues that encompass topics, such as the role of auditory versus parietal cortex in sensorimotor integration, sensorimotor integration as an emergent property, the role of mirror neurons, and clinical applications.


title: A planar Ku band antenna for satellite communications

abstract: In this study a planar Ku band antenna is designed and built for satellite communications especially to receive TV signals in Turkey. This paper summarizes the design and manufacturing phases of the study and demonstrates the simulation results of the prototype antenna.


DESIGN , SIMULATION AND HARDWARE IMPLEMENTATION OF EFFICIENT SOLAR POWER CONVERTER WITH HIGH MPP TRACKING ACCURACY FOR DC MICROGRID APPLICATIONS


title: A quadruped robot with parallel mechanism legs

abstract: Summary form only given. The design and control of quadruped robots has become a fascinating research field because they have better mobility on unstructured terrains. Until now, many kinds of quadruped robots were developed, such as JROB-1 [1], BISAM [2], BigDog [3], LittleDog [4], HyQ [5] and Cheetah cub [6]. They have shown significant walking performance. However, most of them use serial mechanism legs and have animal like structure: the thigh and the crus. To swing the crus in swing phase and support the body's weight in stance phase, a linear actuator is attached on the thigh [2, 3, 5, 6], or instead, a rotational actuator is installed on the knee joint [1, 4]. To make the robot more useful in the wild environment, e.g., the detection or manipulation tasks, the payload capability is very important. To carry the sensors or tools, heavy load legged robot is very necessary. Thus the knee actuator should be lightweight, powerful and easy to maintain. However, this can be very costly and hard to satisfy at the same time.


title: Incorporating Chinese Characters of Words for Lexical Sememe Prediction

abstract: Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.


title: IoT based classification of vital signs data for chronic disease monitoring

abstract: Nowadays chronic diseases are the leading cause of deaths in India. These diseases which include various ailments in the form of diabetes, stroke, cardiovascular diseases, mental health illness, cancers, and chronic lung diseases. Chronic diseases are the biggest challenge for India and these diseases are the main cause of hospitalization for elder people. People who have suffered from chronic diseases are needed to repeatedly monitor the vital signs periodically. The number of nurses in hospital is relative low compared to the number of patients in hospital, there may be a chance to miss to monitor any patient vital signs which may affect patient health. In this paper, real time monitoring vital signs of a patient is developed using wearable sensors. Without nurse help, patient know the vital signs from the sensors and the system stored the sensor value in the form of text document. By using data mining approaches, the system is trained for vital sign data. Patients give their text document to the system which in turn they know their health status without any nurse help. This system enables high risk patients to be timely checked and enhance the quality of a life of patients.


title: An Improved Image Segmentation Algorithm Based on the Otsu Method

abstract: By analyzing the basic principle of Otsu method and its application in image segmentation, and according to the distribution characteristics of the target and background, an improved threshold image segmentation algorithm based on the Otsu method is developed. By narrowing the selection range of threshold and searching the minimum variance ratio, the improved algorithm selects the optimal threshold. Through the compared with the Otsu method and other methods, the results show that the new improved algorithm has these advantages such as high segmentation precision and fast computation speed.


title: Why Early-Stage Software Startups Fail: A Behavioral Framework

abstract: Software startups are newly created companies with little operating history and oriented towards producing cutting-edge products. As their time and resources are extremely scarce, and one failed project can put them out of business, startups need effective practices to face with those unique challenges. However, only few scientific studies attempt to address characteristics of failure, especially during the early-stage. With this study we aim to raise our understanding of the failure of early-stage software startup companies. This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach. The results present how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework. Despite strategies reveal the first need to understand the problem/solution fit, actual executions prioritize the development of the product to launch on the market as quickly as possible to verify product/market fit, neglecting the necessary learning process.


title: Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells

abstract: We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T-cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immuno-therapy cancer treatment. Segmenting individual touching cells in cluttered regions is challenging as the feature distribution on shared borders and cell foreground are similar thus difficulting discriminating pixels into proper classes. We present two novel weight maps applied to the weighted cross entropy loss function which take into account both class imbalance and cell geometry. Binary ground truth training data is augmented so the learning model can handle not only foreground and background but also a third touching class. This framework allows training using U-Net. Experiments with our formulations have shown superior results when compared to other similar schemes, outperforming binary class models with significant improvement of boundary adequacy and instance detection. We validate our results on manually annotated microscope images of T-cells.


A phonetic approach to the phonology of v: A case study from Hungarian and Slovak


title: Memory encryption: A survey of existing techniques

abstract: Memory encryption has yet to be used at the core of operating system designs to provide confidentiality of code and data. As a result, numerous vulnerabilities exist at every level of the software stack. Three general approaches have evolved to rectify this problem. The most popular approach is based on complex hardware enhancements; this allows all encryption and decryption to be conducted within a well-defined trusted boundary. Unfortunately, these designs have not been integrated within commodity processors and have primarily been explored through simulation with very few prototypes. An alternative approach has been to augment existing hardware with operating system enhancements for manipulating keys, providing improved trust. This approach has provided insights into the use of encryption but has involved unacceptable overheads and has not been adopted in commercial operating systems. Finally, specialized industrial devices have evolved, potentially adding coprocessors, to increase security of particular operations in specific operating environments. However, this approach lacks generality and has introduced unexpected vulnerabilities of its own. Recently, memory encryption primitives have been integrated within commodity processors such as the Intel i7, AMD bulldozer, and multiple ARM variants. This opens the door for new operating system designs that provide confidentiality across the entire software stack outside the CPU. To date, little practical experimentation has been conducted, and the improvements in security and associated performance degradation has yet to be quantified. This article surveys the current memory encryption literature from the viewpoint of these central issues.


title: Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

abstract: With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more “cyber” conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).


title: Towards Explanation of DNN-based Prediction with Guided Feature Inversion

abstract: While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics. Existing attempts based on local interpretations aim to identify relevant features contributing the most to the prediction of DNN by monitoring the neighborhood of a given input. They usually simply ignore the intermediate layers of the DNN that might contain rich information for interpretation. To bridge the gap, in this paper, we propose to investigate a guided feature inversion framework for taking advantage of the deep architectures towards effective interpretation. The proposed framework not only determines the contribution of each feature in the input but also provides insights into the decision-making process of DNN models. By further interacting with the neuron of the target category at the output layer of the DNN, we enforce the interpretation result to be class-discriminative. We apply the proposed interpretation model to different CNN architectures to provide explanations for image data and conduct extensive experiments on ImageNet and PASCAL VOC07 datasets. The interpretation results demonstrate the effectiveness of our proposed framework in providing class-discriminative interpretation for DNN-based prediction.


title: Approximate Personalized PageRank on Dynamic Graphs

abstract: We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted. Our algorithms are natural dynamic versions of two known local variations of power iteration. One, Forward Push, propagates probability mass forwards along edges from a source node, while the other, Reverse Push, propagates local changes backwards along edges from a target. In both variations, we maintain an invariant between two vectors, and when an edge is updated, our algorithm first modifies the vectors to restore the invariant, then performs any needed local push operations to restore accuracy. For Reverse Push, we prove that for an arbitrary directed graph in a random edge model, or for an arbitrary undirected graph, given a uniformly random target node t, the cost to maintain a PPR vector to t of additive error e as k edges are updated is O(k + d/e, where d is the average degree of the graph. This is O(1) work per update, plus the cost of computing a reverse vector once on a static graph. For Forward Push, we show that on an arbitrary undirected graph, given a uniformly random start node s, the cost to maintain a PPR vector from s of degree-normalized error e as k edges are updated is O(k + 1/e, which is again O(1) per update plus the cost of computing a PPR vector once on a static graph.


title: Text Mining at the Term Level

abstract: Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. Previous work in text mining focused at the word or the tag level. This paper presents an approach to performing text mining at the term level. The mining process starts by preprocessing the document collection and extracting terms from the documents. Each document is then represented by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxonomy. In this paper we will describe the Term Extraction module of the Document Explorer system, and provide experimental evaluation performed on a set of 52,000 documents published by Reuters in the years 1995–1996.


title: Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain

abstract: Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool for localizing and analyzing brain activity. This study focuses on one very important aspect of the functional properties of human brain, specifically the estimation of the level of parallelism when performing complex cognitive tasks. Using fMRI as the main modality, the human brain activity is investigated through a purely data-driven signal processing and dimensionality analysis approach. Specifically, the fMRI signal is treated as a multi-dimensional data space and its intrinsic `complexity' is studied via dataset fractal analysis and blind-source separation (BSS) methods. One simulated and two real fMRI datasets are used in combination with Independent Component Analysis (ICA) and fractal analysis for estimating the intrinsic (true) dimensionality, in order to provide data-driven experimental evidence on the number of independent brain processes that run in parallel when visual or visuo-motor tasks are performed. Although this number is can not be defined as a strict threshold but rather as a continuous range, when a specific activation level is defined, a corresponding number of parallel processes or the casual equivalent of `cpu cores' can be detected in normal human brain activity.


title: Audio Signal Reconstruction Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)

abstract: We propose a novel audio signal reconstruction model that makes use of a non-linear estimation algorithm called Cartesian Genetic Programming evolved Artificial Neural Network (CGPANN). CGPANN estimates the non-linear graphs of audio signals with much better accuracy than its counterparts: the interpolation and extrapolation. We have compared them in terms of SNR improvement and ability to deal with disputed data. Unlike other conventional reconstruction algorithms, the proposed algorithm can restore the signal which is damaged up to 50% by noise. A state-of-the-art approach for reconstructing an audio signal utilizing machine learning is presented in this paper. The performance of algorithm is evaluated by measuring its Signal-to-Noise (SNR) improvement and difference between original and reconstructed signal in terms of Mean Absolute Percentage Error (MAPE). SNR improvement of up to 20 dB is recorded for single point estimation with 25% missing samples, 19 dB for multi-point (up to 5) estimation in which half of the data is missing and 16 dB for a signal with random variable noise.


title: Making B+-tree efficient in PCM-based main memory

abstract: Phase change memory (PCM) is a promising technology for building future large-scale and low-power main memory systems. Main memory databases (MMDBs) can benefit from the high density of PCM. However, its long write latency, high write energy, and limited lifetime, bring challenges to database algorithm design for PCM-based memory systems. In this paper, we focus on making B+-tree PCM-friendly by reducing the write accesses to PCM. We propose three different schemes. Experimental results show that they can efficiently improve the performance, reduce the memory energy consumption, and improve the lifetime for PCM memory.


title: Depth from Defocus in the Wild

abstract: We consider the problem of two-frame depth from defocus in conditions unsuitable for existing methods yet typical of everyday photography: a handheld cellphone camera, a small aperture, a non-stationary scene and sparse surface texture. Our approach combines a global analysis of image content—3D surfaces, deformations, figure-ground relations, textures—with local estimation of joint depth-flow likelihoods in tiny patches. To enable local estimation we (1) derive novel defocus-equalization filters that induce brightness constancy across frames and (2) impose a tight upper bound on defocus blur—just three pixels in radius—through an appropriate choice of the second frame. For global analysis we use a novel piecewise-spline scene representation that can propagate depth and flow across large irregularly-shaped regions. Our experiments show that this combination preserves sharp boundaries and yields good depth and flow maps in the face of significant noise, uncertainty, non-rigidity, and data sparsity.


title: Assessment and occurrence of various heavy metals in surface water of Ganga river around Kolkata: a study for toxicity and ecological impact

abstract: A study was conducted during November, 2005-October, 2006 to evaluate the surface water quality of river Ganga around Kolkata. The samples were analyzed for a number of physico-chemical parameters using standard laboratory procedures and giving prime thrust to determine the heavy metal concentrations (Fe, Mn, Cu, Zn, Pb, Cd, Cr, and Ni) of surface water at four different locations of the river Ganga around Kolkata from two points (middle of the river stream and a discharge point) at each location. Out of 96 samples analyzed, Fe, Mn, Cu, Zn, and Ni were detected in 71, 47, 38, 60, and 45 samples in the concentrations ranging from 0.013 to 5.49, 0.022 to 1.78, 0.003 to 0.033, 0.005 to 0.293, and 0.045 to 0.24 mg L(-1), respectively. Cd and Pb were detected in six and 21 samples in the range of 0.005 to 0.006 and 0.05 to 0.53 mg L(-1), respectively. But Cr was not detected in any of the samples analyzed. The metals exhibited no significant variation with respect to sampling locations as well as discharge points. However, the concentration of those metals varied with season, being higher in rainy and lower in winter season.


Eye-Blink Detection Using Facial Landmarks


title: Integrated information theory of consciousness: an updated account.

abstract: This article presents an updated account of integrated information theory of consciousness (IIT) and some of its implications. IIT stems from thought experiments that lead to phenomenological axioms and ontological postulates. The information axiom asserts that every experience is one out of many, i.e. specific – it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is one, i.e. unified – it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite – it is limited to particular things and not others and flows at a particular speed and resolution. IIT formalizes these intuitions with three postulates. The information postulate states that only “differences that make a difference” from the intrinsic perspective of a system matter: a mechanism generates cause-effect information if its present state has specific past causes and specific future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects – a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated at the optimal spatio-temporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system’s ability to match the causal structure of the world. After introducing an updated definition of information integration and related quantities, the article presents some theoretical considerations about the relationship between information and causation and about the relational structure of concepts within a quale. It also explores the relationship between the temporal grain size of information integration and the dynamic of metastable states in the corticothalamic complex. Finally, it summarizes how IIT accounts for empirical findings about the neural substrate of consciousness, and how various aspects of phenomenology may in principle be addressed in terms of the geometry of information integration.


title: Defending Cyberspace with Fake Honeypots

abstract: Honeypots are computer systems designed for no purpose other than recording attacks on them. Cyberattackers should avoid them since honeypots jeopardize the secrecy of attack methods and it is hard to launch attacks from them. This suggests that a computer system might pretend to be a honeypot to scare away attackers, reducing the number of attacks and their severity. This could be done on ordinary computer systems as a kind of “vaccination” of those systems, to create what we call “fake honeypots”. After some background, we examine this idea from three perspectives. We develop a mathematical model of what would make an attacker go away. We report experiments with deliberate distortions on text to see at what point people could detect deception, and discover they can respond to subtle clues. We then report experiments with real attackers against a honeypot. Results show that attacks on it decreased over time (which may indicate that attackers are being scared away), irregular outages of the honeypot stimulated attacks, and other changes occurred in response to our manipulations. We conclude with some speculation about the escalation of honeypot-antihoneypot techniques.


title: Single-Neuron Responses in Humans during Execution and Observation of Actions

abstract: Summary Direct recordings in monkeys have demonstrated that neurons in frontal and parietal areas discharge during execution and perception of actions [1–8]. Because these discharges "reflect" the perceptual aspects of actions of others onto the motor repertoire of the perceiver, these cells have been called mirror neurons . Their overlapping sensory-motor representations have been implicated in observational learning and imitation, two important forms of learning [9]. In humans, indirect measures of neural activity support the existence of sensory-motor mirroring mechanisms in homolog frontal and parietal areas [10, 11], other motor regions [12–15], and also the existence of multisensory mirroring mechanisms in nonmotor regions [16–19]. We recorded extracellular activity from 1177 cells in human medial frontal and temporal cortices while patients executed or observed hand grasping actions and facial emotional expressions. A significant proportion of neurons in supplementary motor area, and hippocampus and environs, responded to both observation and execution of these actions. A subset of these neurons demonstrated excitation during action-execution and inhibition during action-observation. These findings suggest that multiple systems in humans may be endowed with neural mechanisms of mirroring for both the integration and differentiation of perceptual and motor aspects of actions performed by self and others.


title: Reactive power control of grid-connected wind farm based on adaptive dynamic programming

abstract: Optimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and efficient electricity. Among many enabling technologies, the latest research results from both the power and energy community and computational intelligence (CI) community have demonstrated that CI research could provide key technical innovations into this challenging problem. In this paper, a neural network based controller is presented for the reactive power control of wind farm with doubly fed induction generators (DFIG). Specifically, we investigate the on-line learning and control approach based on adaptive dynamic programming (ADP) for wind farm control and integration with the grid. This controller can effectively dampen the oscillation of the wind farm system after the ground fault of the grid. Compared to previous control strategies, this controller is on-line and ''model free'', and therefore, can reduce the control complexity. Simulation studies are carried out in Matlab/Simulink and the results demonstrated the effectiveness of the ADP controller.


title: EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications

abstract: The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.


title: Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts

abstract: Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies that combine the small text content with prior knowledge and use more than just bag-of-words. In this work we propose a new deep convolutional neural network that exploits from characterto sentence-level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Treebank (SSTb), which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS), which contains Twitter messages. For the SSTb corpus, our approach achieves state-of-the-art results for single sentence sentiment prediction in both binary positive/negative classification, with 85.7% accuracy, and fine-grained classification, with 48.3% accuracy. For the STS corpus, our approach achieves a sentiment prediction accuracy of 86.4%.


title: Nonsense-mediated mRNA decay: splicing, translation and mRNP dynamics

abstract: Studies of nonsense-mediated mRNA decay in mammalian cells have proffered unforeseen insights into changes in mRNA-protein interactions throughout the lifetime of an mRNA. Remarkably, mRNA acquires a complex of proteins at each exon-exon junction during pre-mRNA splicing that influences the subsequent steps of mRNA translation and nonsense-mediated mRNA decay. Complex-loaded mRNA is thought to undergo a pioneer round of translation when still bound by cap-binding proteins CBP80 and CBP20 and poly(A)-binding protein 2. The acquisition and loss of mRNA-associated proteins accompanies the transition from the pioneer round to subsequent rounds of translation, and from translational competence to substrate for nonsense-mediated mRNA decay.


title: Predicting Effectiveness of IR-Based Bug Localization Techniques

abstract: Recently, many information retrieval (IR) based bug localization approaches have been proposed in the literature. These approaches use information retrieval techniques to process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recent approaches can achieve reasonable accuracy, however, even a state-of-the-art bug localization tool outputs many ranked lists where buggy files appear very low in the lists. This potentially causes developers to distrust bug localization tools. Parnin and Orso recently conduct a user study and highlight that developers do not find an automated debugging tool useful if they do not find the root cause of a bug early in a ranked list. To address this problem, we build an oracle that can automatically predict whether a ranked list produced by an IR-based bug localization tool is likely to be effective or not. We consider a ranked list to be effective if a buggy file appears in the top-N position of the list. If a ranked list is unlikely to be effective, developers do not need to waste time in checking the recommended files one by one. In such cases, it is better for developers to use traditional debugging methods or request for further information to localize bugs. To build this oracle, our approach extracts features that can be divided into four categories: score features, textual features, topic model features, and metadata features. We build a separate prediction model for each category, and combine them to create a composite prediction model which is used as the oracle. We name our proposed approach APRILE, which stands for Automated Prediction of IR-based Bug Localization's Effectiveness. We have evaluated APRILE to predict the effectiveness of three state-of-the-art IR based bug localization tools on more than three thousands bug reports from AspectJ, Eclipse, and SWT. APRILE can achieve an average precision, recall, and F-measure of at least 70.36%, 66.94%, and 68.03%, respectively. Furthermore, APRILE outperforms a baseline approach by 84.48%, 17.74%, and 31.56% for the AspectJ, Eclipse, and SWT bug reports, respectively.


title: CNN based key frame extraction for face in video recognition

abstract: Nowadays we see an increasing demand for face in video recognition. However, in order to overcome the large variations in face quality in video streams, as well as for the purpose of improving the processing speed of face recognition system, frame selection becomes a necessary and essential step prior to performing face recognition. In this paper, we propose a convolutional neural network (CNN) based key-frame extraction (KFE) engine with Graphic Processing Unit (GPU) acceleration, which targets at extracting key-frames with high quality faces correctly and swiftly. We evaluated our method with ChokePoint dataset following NIST standards and compared against several representative key-frame selection approaches. The experimental results show that our CNN-based KFE engine can largely reduce the total processing time for face in video recognition, as well as improves the recognition accuracy of the face recognition back-end. With GPU acceleration, our KFE engine reaches and exceeds real-time processing speed requirement under HD resolution, making it capable of processing multiple video steams on the fly. On top ofthat, our proposed KFE engine is adaptive to different face recognition back-end.


title: Switching Frequency Determination of DC–DC Converters With Hysteretic Control

abstract: Hysteretic control provides unique advantages in dc–dc converter applications but it is less popular than other types of control because the switching frequency depends on the operating point, the feedback ripple, and the component parasitics. Furthermore, it is difficult to predict the frequency due to the complex interaction of those factors. This paper proposes harmonic balance analysis to systematically determine the switching frequency of the buck and boost-like converters with hysteretic current control, and extends the method to buck converters with hysteretic voltage control. The analysis includes the most important second-order effects (feedback ripple, component parasitics, a second output capacitor, and switching delays). For the less complex cases the switching frequency is expressed in closed form, and for the more complex cases the paper proposes a graphical solution. The predictions are verified by simulations and, for the buck converter with hysteretic current control, by experiments. The derived results are useful for designing a converter with predictable switching frequency, and can also be employed during the design process of a converter with synchronized or stabilized frequency. Given an arbitrary control scheme or circuit variation, the switching frequency can be readily obtained by following the proposed approach.


title: Audio-to-score alignment of piano music using RNN-based automatic music transcription

abstract: We propose a framework for audio-to-score alignment on piano performance that employs automatic music transcription (AMT) using neural networks. Even though the AMT result may contain some errors, the note prediction output can be regarded as a learned feature representation that is directly comparable to MIDI note or chroma representation. To this end, we employ two recurrent neural networks that work as the AMT-based feature extractors to the alignment algorithm. One predicts the presence of 88 notes or 12 chroma in frame-level and the other detects note onsets in 12 chroma. We combine the two types of learned features for the audio-to-score alignment. For comparability, we apply dynamic time warping as an alignment algorithm without any additional post-processing. We evaluate the proposed framework on the MAPS dataset and compare it to previous work. The result shows that the alignment framework with the learned features significantly improves the accuracy, achieving less than 10 ms in mean onset error.


title: Batch Reinforcement Learning

abstract: Batch reinforcement learning is a subfield of dynamic programming-based reinforcement learning. Originally defined as the task of learning the best possible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the efficient use of collected data and the stability of the learning process, this research area has attracted a lot of attention recently. In this chapter, we introduce the basic principles and the theory behind batch reinforcement learning, describe the most important algorithms, exemplarily discuss ongoing research within this field, and briefly survey real-world applications of batch reinforcement learning.


title: HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion

abstract: While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HumanEva datasets contain multiple subjects performing a set of predefined actions with a number of repetitions. On the order of 40,000 frames of synchronized motion capture and multi-view video (resulting in over one quarter million image frames in total) were collected at 60 Hz with an additional 37,000 time instants of pure motion capture data. A standard set of error measures is defined for evaluating both 2D and 3D pose estimation and tracking algorithms. We also describe a baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering. In the context of this baseline algorithm we explore a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters. Our experiments suggest that image observation models and motion priors play important roles in performance, and that in a multi-view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets and the software are made available to the research community. This infrastructure will support the development of new articulated motion and pose estimation algorithms, will provide a baseline for the evaluation and comparison of new methods, and will help establish the current state of the art in human pose estimation and tracking.


title: Thyroid-stimulating hormone (TSH) binding to extrathyroidal human tissues: TSH binding to extrathyroidal human tissues: TSH and thyroid-stimulating immunoglobulin effects on adenosine 3',5'-monophosphate in testicular and adrenal tissues.

abstract: Binding of [125I]bovine TSH to human thyroid, testicular, fat, adrenal, liver, kidney, pancreas, and lung cell membranes has been studied. The first four tissues were found to have comparable high affinity constant values; the rest of the tissues lacked high affinity sites. With the exception of fat tissue, the capacities of the high affinity sites of the first four tissues were similar. Bovine TSH concentrations of 100-20,000 microIU/ml stimulated increased cAMP production in human cryopreserved testicular slices. Forty percent of the specimens of thyroid-stimulating immunoglobulin (TSI) from Graves' disease sera also increased human testicular cAMP production. In addition, bovine TSH caused a significant rise in cAMP in the whole decapsulated rat testis. Twenty-five percent of the TSI specimens tested also induced such responses. The rat adrenal gland responded with increased cAMP production to concentrations of 1,000 microU/ml bovine TSH. The physiological significance of high affinity bovine TSH and TSI binding and subsequent cAMP production in nonthyroidal tissues in not known. However, since these stimulators are present in hypothyroidism and hyperthyroidism, respectively, it is possible that the pathophysiological effects of this binding could be of some importance.


CHAPTER 12 – Applying Social Psychological Theory to the Problems of Group Work


title: Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping

abstract: This paper discusses the development of a natural gesture user interface that tracks and recognizes in real time hand gestures based on depth data collected by a Kinect sensor. The interest space corresponding to the hands is first segmented based on the assumption that the hand of the user is the closest object in the scene to the camera. A novel algorithm is proposed to improve the scanning time in order to identify the first pixel on the hand contour within this space. Starting from this pixel, a directional search algorithm allows for the identification of the entire hand contour. The $k$ -curvature algorithm is then employed to locate the fingertips over the contour, and dynamic time warping is used to select gesture candidates and also to recognize gestures by comparing an observed gesture with a series of prerecorded reference gestures. The comparison of results with state-of-the-art approaches shows that the proposed system outperforms most of the solutions for the static recognition of sign digits and is similar in terms of performance for the static and dynamic recognition of popular signs and for the sign language alphabet. The solution simultaneously deals with static and dynamic gestures as well as with multiple hands within the interest space. An average recognition rate of 92.4% is achieved over 55 static and dynamic gestures. Two possible applications of this work are discussed and evaluated: one for interpretation of sign digits and gestures for a friendlier human-machine interaction and the other one for the natural control of a software interface.


On Being Criticized


title: Asymmetric Information Effects on Loan Spreads

abstract: This paper estimates the cost arising from information asymmetry between the lead bank and members of the lending syndicate. In a lending syndicate, the lead bank retains only a fraction of the loan but acts as the intermediary between the borrower and the syndicate participants. Theory predicts that asymmetric information will cause participants to demand a higher interest rate and that a large loan ownership by the lead bank should reduce this effect. In equilibrium, however, the asymmetric information premium demanded by participants is offset by the diversification premium demanded by the lead. Using shifts in the idiosyncratic credit risk of the lead bank's loan portfolio as an instrument, I measure the asymmetric information effect of the lead's share on the loan spread and find that it accounts for approximately 4% of the total cost of credit.


title: 234 nm and 246 nm AlN-Delta-GaN quantum well deep ultraviolet light-emitting diodes

abstract: Deep ultraviolet (DUV) AlN-delta-GaN quantum well (QW) light-emitting diodes (LEDs) with emission wavelengths of 234 nm and 246 nm are proposed and demonstrated in this work. Our results reveal that the use of AlN-delta-GaN QW with ∼1–3 monolayer GaN delta-layer can achieve a large transverse electric (TE)-polarized spontaneous emission rate instead of transverse magnetic-polarized emission, contrary to what is observed in conventional AlGaN QW in the 230–250 nm wavelength regime. The switching of light polarization in the proposed AlN-delta-GaN QW active region is attributed to the rearrangement of the valence subbands near the Γ-point. The light radiation patterns obtained from angle-dependent electroluminescence measurements for the Molecular Beam Epitaxy (MBE)-grown 234 nm and 246 nm AlN-delta-GaN QW LEDs show that the photons are mainly emitted towards the surface rather than the edge, consistent with the simulated patterns achieved by the finite-difference time-domain modeling. The results demonstra...


title: Generalized linear discriminant sequence kernels for speaker recognition

abstract: Support Vector Machines have recently shown dramatic performance gains in many application areas. We show that the same gains can be realized in the area of speaker recognition via sequence kernels. A sequence kernel provides a numerical comparison of speech utterances as entire sequences rather than a probability at the frame level. We introduce a novel sequence kernel derived from generalized linear discriminants. The kernel has several advantages. First, the kernel uses an explicit expansion into “feature space”-this property allows all of the support vectors to be collapsed into a single vector creating a small speaker model. Second, the kernel retains the computational advantage of generalized linear discriminants trained using mean-squared error training. Finally, the kernel shows dramatic reductions in equal error rates over standard mean-squared error training in matched and mismatched conditions on a NIST speaker recognition task.


title: Workflow scheduling in cloud computing environment using Cat Swarm Optimization

abstract: Cloud computing is new era of network based computing, where resources are distributed over the network and shared among its users. Any user can use these resources through internet on the basis of Pay-As-Per-Use system. A service used by any user can produce a very large amount of data. So in this case, the data transfer cost between two dependent resources will be very high. In addition, a complex application can have a large number of tasks which may cause an increase in total cost of execution of that application, if not scheduled in an optimized way. So to overcome these problems, the authors present a Cat Swarm Optimization (CSO) - based heuristic scheduling algorithm to schedule the tasks of an application onto available resources. The CSO heuristic algorithm considers both data transmission cost between two dependent resources and execution cost of tasks on different resources. The authors experiment with the proposed CSO algorithm using a hypothetical workflow and compare the workflow scheduling results with the existing Particle Swarm Optimization (PSO) algorithm. The experimental results show - (1) CSO gives an optimal task-to-resource (TOR) scheduling scheme that minimizes the total cost, (2) CSO shows an improvement over existing PSO in terms of number of iterations, and (3) CSO ensures fair load distribution on the available resources.


title: Spatial Presence and Emotions during Video Game Playing: Does It Matter with Whom You Play?

abstract: The authors examined whether the nature of the opponent (computer, friend, or stranger) influences spatial presence, emotional responses, and threat and challenge appraisals when playing video games. In a within-subjects design, participants played two different video games against a computer, a friend, and a stranger. In addition to self-report ratings, cardiac interbeat intervals (IBIs) and facial electromyography (EMG) were measured to index physiological arousal and emotional valence. When compared to playing against a computer, playing against another human elicited higher spatial presence, engagement, anticipated threat, post-game challenge appraisals, and physiological arousal, as well as more positively valenced emotional responses. In addition, playing against a friend elicited greater spatial presence, engagement, and self-reported and physiological arousal, as well as more positively valenced facial EMG responses, compared to playing against a stranger. The nature of the opponent influences spatial presence when playing video games, possibly through the mediating influence on arousal and attentional processes.


title: Accidental strangulation resulted from hypoxyphilia associated with multiple paraphilias and substance abuse: a psychological autopsy case report

abstract: We report a case of a 34-year-old male who was found hanged by his neck in his own apartment. The deceased was dressed in women's nylon panties, bra, nylon pantyhose, and a wig. The immediate cause of his death was found to be asphyxia due to hanging but the manner of his death was initially considered to be equivocal. The psychological autopsy based on the structured interview with the deceased's parents, sexual partner, and closed friends using Beck's Suicide Intent Scale (SIS), Paykel's Interview for Recent Life Events (IRLE), Duke Social Support Index (DSSI), the Structured Clinical Interview for DSM-IV-TR (SCID-I), Hamilton Depression Rating Scale (HAM-D), and our structured suicide information questionnaire based on the pamphlet "Suicide Prevention and Psychological Autopsy" (Department of U.S. Army) and some published articles was performed and the data were analyzed. The interviews with the informants showed that the deceased was single; was engaged in homosexual behavior; was well educated with a university degree; was employed; has not been diagnosed with any chronic illnesses; never had suicidal attempts in the past; didn't have any history of alcohol and drug abuse; wasn't recently emotionally upset; didn't have any chronic and recent interpersonal problems with his family, sexual partner, and friends; had plans for the future. The deceased has not being diagnosed with any psychiatric illnesses during his life. But the scrupulous crime scene investigation was indicative that diseased was meeting the DSM-IV-TR diagnostic criteria for substance abuse, multiple paraphilias involving sexual masochism (hypoxyphilia), transvestic fetishism, and agalmatophilia (a paraphilia that involves being sexually attracted to a doll, statue or mannequin).


title: Reverse Engineering of Geometric Models

abstract: In many areas of industry, it is desirable to create geometric models of existing objects for which no such model is available. This paper reviews the process of reverse engineering of shapes. After identifying the purpose of reverse engineering and the main application areas, the most important algorithmic steps are outlined and various reconstruction strategies are presented. Pros and cons of various data acquisition techniques are described with related problems of boundary representation model construction. Specific issues addressed include characterization of geometric models and related surface representations, segmentation and surface fitting for simple and free-form shapes, multiple view combination and creating consistent and accurate B-rep models. The limitations of currently known solutions are also described, and we point out areas in which further work is required before reverse engineering of shape becomes a practical, widely-available engineering tool.


Neonatal Marfan syndrome-A case report


title: Image Difference Threshold Strategies and Shadow Detection

abstract: The paper considers two problems associated with the detection and classification of motion in image sequences obtained from a static camera. Motion is detected by differencing a reference and the "current" image frame, and therefore requires a suitable reference image and the selection of an appropriate detection threshold. Several threshold selection methods are investigated, and an algorithm based on hysteresis thresholding is shown to give acceptably good results over a number of test image sets. The second part of the paper examines the problem of detecting shadow regions within the image which are associated with the object motion. This is based on the notion of a shadow as a semi-transpare nt region in the image which retains a (reduced contrast) representation of the underlying surface pattern, texture or grey value. The method uses a region growing algorithm which uses a growing criterion based on a fixed attenuation of the photometric gain over the shadow region, in comparison to the reference image.


title: Analyzing Latency-Aware Self-Adaptation Using Stochastic Games and Simulations

abstract: Self-adaptive systems must decide which adaptations to apply and when. In reactive approaches, adaptations are chosen and executed after some issue in the system has been detected (e.g., unforeseen attacks or failures). In proactive approaches, predictions are used to prepare the system for some future event (e.g., traffic spikes during holidays). In both cases, the choice of adaptation is based on the estimated impact it will have on the system. Current decision-making approaches assume that the impact will be instantaneous, whereas it is common that adaptations take time to produce their impact. Ignoring this latency is problematic because adaptations may not achieve their effect in time for a predicted event. Furthermore, lower impact but quicker adaptations may be ignored altogether, even if over time the accrued impact is actually higher. In this article, we introduce a novel approach to choosing adaptations that considers these latencies. To show how this improves adaptation decisions, we use a two-pronged approach: (i) model checking of Stochastic Multiplayer Games (SMGs) enables us to understand best- and worst-case scenarios of optimal latency-aware and non-latency-aware adaptation without the need to develop specific adaptation algorithms. However, since SMGs do not provide an algorithm to make choices at runtime, we propose a (ii) latency-aware adaptation algorithm to make decisions at runtime. Simulations are used to explore more detailed adaptation behavior and to check if the performance of the algorithm falls within the bounds predicted by SMGs. Our results show that latency awareness improves adaptation outcomes and also allows a larger set of adaptations to be exploited.


title: Shadow Detection and Removal from a Single Image Using LAB Color Space

abstract: A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.


title: A Coordination Architecture for Spacecraft Formation Control

abstract: This paper addresses the problem of coordinating multiple spacecraft to fly in tightly controlled formations. The main contribution of the paper is to introduce a coordination architecture that subsumes leader-following, behavioral, and virtual-structure approaches to the multiagent coordination problem. The architecture is illustrated through a detailed application of the ideas to the problem of synthesizing a multiple spacecraft interferometer in deep space.


title: Design aspects for a reference M2M communication platform for Smart Cities

abstract: For the last decades, we have witnessed new technological evolutions in the Internet, wireless networks, and sensors fields. Currently, we are able to build smart systems that improve quality of life and enhance environment management. However, most available smart systems are implemented based on proprietary hardware/software solutions, restricting interoperation, which is required for large-scale Smart City solutions. To enable the implementation of a general Smart City solution, a platform is needed to fulfill the communication requirements between heterogeneous access technologies. In this paper, we address the requirements and design aspects of a reference Machine-to-Machine (M2M) communication platform as an enabler for Smart Cities.


title: Machine learning in bioinformatics

abstract: This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.


title: Privacy & Social Media in the Context of the Arab Gulf

abstract: Theories of privacy and how it relates to the use of Information Communication Technology (ICT) have been a topic of research for decades. However, little attention has been paid to the perception of privacy from the perspective of technology users in the Middle East. In this paper, we delve into interpretations of privacy from the approach of Arab Gulf citizens. We consider how privacy is practiced and understood in technology-mediated environments among this population, paying particular attention to the role of Islam and cultural traditions in constructing norms around privacy. We then offer culturally sensitive design principles and suggestions for future research that incorporates previously unexplored characteristics of privacy, which play a role in how users navigate social media.