title: Stabilizing Traffic with Autonomous Vehicles

abstract: Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematical modeling, and control analysis. In this article, we study the ability of small numbers of autonomous vehicles to stabilize a single-lane system of human-driven vehicles. We formalize the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis, and pose the resulting nonlinear optimization problem. In particular, we introduce two conditions which simultaneously stabilize traffic while imposing a safety constraint on the autonomous vehicle and limiting degradation of performance. With this optimal linear controller in a system with typical human driver behavior, we can numerically determine that only a 6% uniform penetration of autonomously controlled vehicles (i.e. one per string of up to 16 human-driven vehicles) is necessary to stabilize traffic across all traffic conditions.


title: Monocular Visual Odometry Scale Recovery Using Geometrical Constraint

abstract: Scale recovery is one of the essential problems for monocular visual odometry. The camera height is usually used as an absolute reference to recover the scale. In this case, the precision of scale recovery depends on the accuracy of the road region detection and road geometrical model calculation. In previous works, road detection and road geometrical model calculation are solved sequentially: the road geometrical model calculation is based on the road detection and the road region detection is based on the color information. However, the color information of a road is not stable enough. In the proposed method, the estimated road geometrical model is taken into consideration to detect the road region as a feedback. Therefore, the road region detection and road geometrical model estimation can benefit each other. Delaunay Triangulation method is used to segment an input image to many triangles with the matched feature points as vertices. Every triangle region is classified as a road region or not by comparing their geometrical model with that of the road and the road geometrical model is updated online. We evaluate our visual odometry scale recovery method on the KITTI dataset and the results show that our method is achieving the best performance among all existing monocular visual odometry scale recovery methods without additional sensors.


title: Bayesian Combination of Sparse and non Sparse Priors in Image Super Resolution ✩

abstract: In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. Two sparse image priors, a Total Variation (TV) prior and a prior based on the @?1 norm of horizontal and vertical first-order differences (f.o.d.), are combined with a non-sparse Simultaneous Auto Regressive (SAR) prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational approximation will produce as many posterior approximations as priors we want to combine. A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimizes a linear convex combination of Kullback-Leibler (KL) divergences. We find this distribution in closed form. The estimated HR images are compared with the ones obtained by other SR reconstruction methods.


title: An overview of research topics and challenges for 5G massive MIMO antennas

abstract: An overview of the main research topics for massive multiple input multiple output (MIMO) antenna arrays is presented. Massive MIMO is expected to be one of the pillars of fifth generation (5G) and beyond cellular systems. In fact, with millimeter wave (mmWave) communications, a large number of antenna elements can be used to form large arrays of reasonable sizes. This entails significant challenges that need to be overcome in practical implementations. In this paper, we present an overview of important research topics related to massive MIMIO, and discuss their associated challenges. Furthermore, we present several application areas where massive MIMO antenna arrays could lead to significant performance enhancements.


title: Extracting View-Dependent Depth Maps from a Collection of Images

abstract: Stereo correspondence algorithms typically produce a single depth map. In addition to the usual problems of occlusions and textureless regions, such algorithms cannot model the variation in scene or object appearance with respect to the viewing position. In this paper, we propose a new representation that overcomes the appearance variation problem associated with an image sequence. Rather than estimating a single depth map, we associate a depth map with each input image (or a subset of them). Our representation is motivated by applications such as view interpolation and depth-based segmentation for model-building or layer extraction. We describe two approaches to extract such a representation from a sequence of images. ::: ::: The first approach, which is more classical, computes the local depth map associated with each chosen reference frame independently. The novelty of this approach lies in its combination of shiftable windows, temporal selection, and graph cut optimization. The second approach simultaneously optimizes a set of self-consistent depth maps at multiple key-frames. Since multiple depth maps are estimated simultaneously, visibility can be modeled explicitly and disparity consistency imposed across the different depth maps. Results, which include a difficult specular scene example, show the effectiveness of our approach.


title: Fides: remote anomaly-based cheat detection using client emulation

abstract: As a result of physically owning the client machine, cheaters in online games currently have the upper-hand when it comes to avoiding detection. To address this problem and turn the table on cheaters, this paper presents Fides, an anomaly-based cheat detection approach that remotely validates game execution. With Fides, a server-side Controller specifies how and when a client-side Auditor measures the game. To accurately validate measurements, the Controller partially emulates the client and collaborates with the server. This paper examines a range of cheat methods and initial measurements that counter them, showing that a Fides prototype is able to efficiently detect several existing cheats, including one state-of-the-art cheat that is advertised as "undetectable".


title: Linear identification of nonlinear systems: A lifting technique based on the Koopman operator

abstract: We exploit the key idea that nonlinear system identification is equivalent to linear identification of the socalled Koopman operator. Instead of considering nonlinear system identification in the state space, we obtain a novel linear identification technique by recasting the problem in the infinite-dimensional space of observables. This technique can be described in two main steps. In the first step, similar to the socalled Extended Dynamic Mode Decomposition algorithm, the data are lifted to the infinite-dimensional space and used for linear identification of the Koopman operator. In the second step, the obtained Koopman operator is "projected back" to the finite-dimensional state space, and identified to the nonlinear vector field through a linear least squares problem. The proposed technique is efficient to recover (polynomial) vector fields of different classes of systems, including unstable, chaotic, and open systems. In addition, it is robust to noise, well-suited to model low sampling rate datasets, and able to infer network topology and dynamics.


title: Towards Online, Accurate, and Scalable QoS Prediction for Runtime Service Adaptation

abstract: Service-based cloud applications are typically built on component services to fulfill certain application logic. To meet quality-of-service (QoS) guarantees, these applications have to become resilient against the QoS variations of their component services. Runtime service adaptation has been recognized as a key solution to achieve this goal. To make timely and accurate adaptation decisions, effective QoS prediction is desired to obtain the QoS values of component services. However, current research has focused mostly on QoS prediction of the working services that are being used by a cloud application, but little on QoS prediction of candidate services that are also important for making adaptation decisions. To bridge this gap, in this paper, we propose a novel QoS prediction approach, namely adaptive matrix factorization (AMF), which is inspired from the collaborative filtering model used in recommender systems. Specifically, our AMF approach extends conventional matrix factorization into an online, accurate, and scalable model by employing techniques of data transformation, online learning, and adaptive weights. Comprehensive experiments have been conducted based on a real-world large-scale QoS dataset of Web services to evaluate our approach. The evaluation results provide good demonstration for our approach in achieving accuracy, efficiency, and scalability.


title: High Performance DES Encryption in Virtex(tm) FPGAs Using Jbits(tm)

abstract: A JBits implementation of the Data Encryption Standard (DES) algorithm in a Virtex FPGA is described. The Virtex architecture efficiently implements the DES primitive operations, and permits a high degree of pipelining. JBits provides a Java-base d Application Programming Interface (API) for the runtime creation and modification of the configuration bitstream. This allows dynamic circuit specialization based on a specific key and mode (encrypt or decrypt). The key schedule is computed entirely in software, and is part of the bitstream. As a result, all cryptographic key input and subkey generation logic are removed from the fully unrolled datapath. When combined with a speed efficient layout, the result is a throughput of over 10 Gigabits per second. This exceeds the performance of a recently announced DES ASIC.


Getting F-Bounded Polymorphism into Shape Technical Report


title: Online Convolutional Dictionary Learning

abstract: Convolutional sparse representations are a form of sparse representation with a structured, translation-invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage and severely limits the training data size that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size than batch methods. This paper extends our prior work, improving a number of aspects of our previous algorithm; proposing an entirely new one, with better performance, that supports the inclusion of a spatial mask for learning from incomplete data; and providing a rigorous theoretical analysis of these methods.


title: Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm

abstract: Real-time functional MRI (rtfMRI) has been used as a basis for brain–computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain–machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects’ thought processes was possible using the rtfMRI-based BMI trials.


A Robust Heart Sound Segmentation and Classification Algorithm using Wavelet Decomposition and Spectrogram


title: A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks

abstract: Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training cases


title: 3D-printed spherical dipole antenna integrated on small RF node

abstract: New three-dimensional (3D) printing techniques enable the integration of an antenna directly onto the package of a small wireless sensor node. This volume-filling approach ensures near-optimal bandwidth performance of the small antenna, increasing a system's battery life, data rate or range. Simulated results show that the fabricated spherical antenna's bandwidth-efficiency product is more than half of the fundamental limit and radiation pattern measurements exhibit a dipole pattern with −0.7 dBi gain.


title: Secure message transmission algorithm for Vehicle to Vehicle (V2V) communication

abstract: In the highly growing digital world, information sharing is important common. But it must be secured else adversary can either intercept or delete partially/fully information. Hence, it will be useless to specific human but it may be helpful to adversary. In today's road market, vehicles are most useful to assist persons through broadcasting vehicles regarding information such that current position, traffic, environment, etc. to other vehicles in a secure vehicular communication. We will use public-private key mechanism to achieve this scenario in a public domain. The main goal of this paper is to improve security of exchanging message in terms of confidentiality, authentication, access & availability. Hence, we have suggested an approach to fulfill these security needs for information transmission between two or more vehicles in doubtful network infrastructure. Thus, vehicles can send/receive their important credentials through our secure message exchanging algorithm. Because, in our algorithm, we have used idea of public-private key encryption-decryption mechanism in the computation of messages which is sent over public channel.


title: Utility preserving query log anonymization via semantic microaggregation

abstract: Abstract Query logs are of great interest for scientists and companies for research, statistical and commercial purposes. However, the availability of query logs for secondary uses raises privacy issues since they allow the identification and/or revelation of sensitive information about individual users. Hence, query anonymization is crucial to avoid identity disclosure. To enable the publication of privacy-preserved – but still useful – query logs, in this paper, we present an anonymization method based on semantic microaggregation. Our proposal aims at minimizing the disclosure risk of anonymized query logs while retaining their semantics as much as possible. First, a method to map queries to their formal semantics extracted from the structured categories of the Open Directory Project is presented. Then, a microaggregation method is adapted to perform a semantically-grounded anonymization of query logs. To do so, appropriate semantic similarity and semantic aggregation functions are proposed. Experiments performed using real AOL query logs show that our proposal better retains the utility of anonymized query logs than other related works, while also minimizing the disclosure risk.


title: Real-Time Power System Control Using Synchrophasors

abstract: To date, synchronized phasor measurements have been used mainly for power system model validation, postevent analysis, real-time display, and other similar activities. However, synchrophasors have a greater potential than monitoring and visualization. Synchrophasors will increasingly contribute to the reliable and economical operation of power systems as real-time control and protection schemes become broadly used. Synchronous phasor measurements are now available in relays and meters; however, a practical means of processing the data in real time had been lacking. This paper describes a synchronous vector processor and several practical applications, including automated diagnostics, remedial action schemes, direct state measurement, and stability assessment.


title: MARRYING WORK AND THE TECHNICAL ARTIFACT WITHIN THE HEALTHCARE ORGANIZATION : A NARRATIVE NETWORK PERSPECTIVE ON IT INNOVATION -MEDIATED ORGANIZATIONAL CHANGE

abstract: Despite the implicit belief that IT innovations brings beneficial change, medical practitioners and healthcare professionals constantly struggle to realize the innovation potential of electronic medical records (EMR) system in revolutionizing clinical practices. To understand this conundrum, this paper uses an in-depth case study of an EMR implementation to develop a grounded theory of why, when, and how IT-innovation mediated change occur. We propose the Narrative Network Perspective that combines the analysis of the processes of configuration, implementation and use of the system. This combined view allows researchers to understand how “production narrative network”, infrastructure and the macrostructure in healthcare environment co-evolve with the idealized production narrative network inscribed in the EMR system within and across the three phases. By tracing and taking into account all these elements time, this perspective provides plausible answers to when and why organizational innovation occur with the


title: Interconnections in Multi-Core Architectures: Understanding Mechanisms, Overheads and Scaling

abstract: This paper examines the area, power, performance, and design issues for the on-chip interconnects on a chip multiprocessor, attempting to present a comprehensive view of a class of interconnect architectures. It shows that the design choices for the interconnect have significant effect on the rest of the chip, potentially consuming a significant fraction of the real estate and power budget. This research shows that designs that treat interconnect as an entity that can be independently architected and optimized would not arrive at the best multi-core design. Several examples are presented showing the need for careful co-design. For instance, increasing interconnect bandwidth requires area that then constrains the number of cores or cache sizes, and does not necessarily increase performance. Also, shared level-2 caches become significantly less attractive when the overhead of the resulting crossbar is accounted for. A hierarchical bus structure is examined which negates some of the performance costs of the assumed base-line architecture.


title: Academia.edu: Social Network or Academic Network? 1

abstract: Academic social network sites Academia.edu and ResearchGate, and reference sharing sites Mendeley, Bibsonomy, Zotero, and CiteULike, give scholars the ability to publicize their research outputs and connect with each other. With millions of users, these are a significant addition to the scholarly communication and academic information-seeking eco-structure. There is thus a need to understand the role that they play and the changes, if any, that they can make to the dynamics of academic careers. This article investigates attributes of philosophy scholars on Academia.edu, introducing a median-based, time-normalizing method to adjust for time delays in joining the site. In comparison to students, faculty tend to attract more profile views but female philosophers did not attract more profile views than did males, suggesting that academic capital drives philosophy uses of the site more than does friendship and networking. Secondary analyses of law, history, and computer science confirmed the faculty advantage in terms of higher profile views except for females in law and females in computer science. There was also a female advantage for both faculty and students in law and computer science as well as for history students. Hence, Academia.edu overall seems to reflect a hybrid of scholarly norms the faculty advantage and a female advantage that is suggestive of general social networking norms. Finally, traditional bibliometric measures did not correlate with any Academia.edu metrics for philosophers, perhaps because more senior academics use the site less extensively or because of the range informal scholarly activities that cannot be measured by bibliometric methods.


title: A Unified Framework for Multi-Modal Isolated Gesture Recognition

abstract: In this article, we focus on isolated gesture recognition and explore different modalities by involving RGB stream, depth stream, and saliency stream for inspection. Our goal is to push the boundary of this realm even further by proposing a unified framework that exploits the advantages of multi-modality fusion. Specifically, a spatial-temporal network architecture based on consensus-voting has been proposed to explicitly model the long-term structure of the video sequence and to reduce estimation variance when confronted with comprehensive inter-class variations. In addition, a three-dimensional depth-saliency convolutional network is aggregated in parallel to capture subtle motion characteristics. Extensive experiments are done to analyze the performance of each component and our proposed approach achieves the best results on two public benchmarks, ChaLearn IsoGD and RGBD-HuDaAct, outperforming the closest competitor by a margin of over 10% and 15%, respectively. Our project and codes will be released at https://davidsonic.github.io/index/acm_tomm_2017.html.


title: An efficient framework for learning sentence representations

abstract: In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and its context, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time.


title: Cyberbullying classification using text mining

abstract: Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes method and Support Vector Machine (SVM) then applying n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. Naive Bayes yields an average accuracy of 92.81%, SVM with a poly kernel yields an average accuracy of 97.11% It can be concluded that SVM with poly kernel yields higher accuracy than SVM with other kernels, Naive Bayes, and Kelly Reynolds research method of decision tree (J48) and k-NN.


ISSUES IN AUDIOVISUAL TRANSLATION


title: Impaired Autophagy and Defective Mitochondrial Function: Converging Paths on the Road to Motor Neuron Degeneration

abstract: Selective motor neuron degeneration is a hallmark of amyotrophic lateral sclerosis (ALS). Around 10 percent of all cases present as familial ALS (FALS), while sporadic ALS (SALS) accounts for the remaining 90 percent. Diverse genetic mutations leading to FALS have been identified, but the underlying causes of SALS remain largely unknown. Despite the heterogeneous and poorly understood etiology, different types of ALS exhibit overlapping pathology and common phenotypes, including protein aggregation and mitochondrial deficiencies. Here we review the current understanding of mechanisms leading to motor neuron degeneration in ALS, as they pertain to disrupted cellular clearance pathways, ATP biogenesis calcium buffering and mitochondrial dynamics. Through focusing on impaired autophagic and mitochondrial functions, we highlight how the convergence of diverse cellular processes and pathways contributes to common pathology in motor neuron degeneration.


title: Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing

abstract: Effective unmixing of hyperspectral data cube under a noisy scenario has been a challenging research problem in remote sensing arena. A branch of existing hyperspectral unmixing algorithms is based on Craig's criterion, which states that the vertices of the minimum-volume simplex enclosing the hyperspectral data should yield high fidelity estimates of the endmember signatures associated with the data cloud. Recently, we have developed a minimum-volume enclosing simplex (MVES) algorithm based on Craig's criterion and validated that the MVES algorithm is very useful to unmix highly mixed hyperspectral data. However, the presence of noise in the observations expands the actual data cloud, and as a consequence, the endmember estimates obtained by applying Craig-criterion-based algorithms to the noisy data may no longer be in close proximity to the true endmember signatures. In this paper, we propose a robust MVES (RMVES) algorithm that accounts for the noise effects in the observations by employing chance constraints. These chance constraints in turn control the volume of the resulting simplex. Under the Gaussian noise assumption, the chance-constrained MVES problem can be formulated into a deterministic nonlinear program. The problem can then be conveniently handled by alternating optimization, in which each subproblem involved is handled by using sequential quadratic programming solvers. The proposed RMVES is compared with several existing benchmark algorithms, including its predecessor, the MVES algorithm. Monte Carlo simulations and real hyperspectral data experiments are presented to demonstrate the efficacy of the proposed RMVES algorithm.


title: Simulation of an optimal design of permanent magnet direct-drive wind turbine generator

abstract: Permanent Magnet Direct Drive (PMDD) configuration is seen to provide more advantages compared to its gearbox counterpart, namely absence of gears, less maintenance, higher efficiency, and growing research field. However, PMDD configuration generally has a big ring size and expensive construction cost. To potentially reduce the cost of PMDD, various research designs were investigated, including reduction of weight, lessen of material usage, and simulations on electrical losses. However, as of this time of writing, there has been no research design on the placement of permanent magnets (PM) for optimal energy generation. Thus, this study aims to make an optimal design of a PMDD ring by varying three main parameters, namely in-between magnet clearance, rotor-stator distance, and rotational speed. SolidWorks was used to design the model, ANSYS Fluent was used to simulate the various design combinations, and JMP software was used to analyse the data statistically. Results showed that amongst the three parameters, only the rotor-stator distance significantly influenced the power output of the simulated equipment. Further investigation can be done on the variation of magnet numbers, increase combinations, and investigation of power losses.


title: Foot orthoses for plantar heel pain: a systematic review and meta-analysis

abstract: OBJECTIVE ::: To investigate the effectiveness of foot orthoses for pain and function in adults with plantar heel pain. ::: ::: ::: DESIGN ::: Systematic review and meta-analysis. The primary outcome was pain or function categorised by duration of follow-up as short (0 to 6 weeks), medium (7 to 12 weeks) or longer term (13 to 52 weeks). ::: ::: ::: DATA SOURCES ::: Medline, CINAHL, SPORTDiscus, Embase and the Cochrane Library from inception to June 2017. ::: ::: ::: ELIGIBILITY CRITERIA FOR SELECTING STUDIES ::: Studies must have used a randomised parallel-group design and evaluated foot orthoses for plantar heel pain. At least one outcome measure for pain or function must have been reported. ::: ::: ::: RESULTS ::: A total of 19 trials (1660 participants) were included. In the short term, there was very low-quality evidence that foot orthoses do not reduce pain or improve function. In the medium term, there was moderate-quality evidence that foot orthoses were more effective than sham foot orthoses at reducing pain (standardised mean difference -0.27 (-0.48 to -0.06)). There was no improvement in function in the medium term. In the longer term, there was very low-quality evidence that foot orthoses do not reduce pain or improve function. A comparison of customised and prefabricated foot orthoses showed no difference at any time point. ::: ::: ::: CONCLUSION ::: There is moderate-quality evidence that foot orthoses are effective at reducing pain in the medium term, however it is uncertain whether this is a clinically important change.


title: Modeling of Chemical Dynamic Systems Based on Modified Recursive Neural Network Structures

abstract: A modified recursive neural network structure—dynamic-hide-output recurrent neural network (DHORNN), based on the partial recursive neural network Elman, Jordan, SIRNN,is put forward. Structures of state feedback from hide layer, output feedback, and time-delayed nodes are well combined in the structure. The Levenberg-Marquardt algorithm is successfully used to train the network and to improve its nonlinear-dynamic-modeling capability. The modified recursive neural network structure is used to build models for a continuously stirred tank reactor (CSTR), and then the models are compared with other recursive neural network models. The results showed that the models based on the DHORNN are more effective in chemical dynamic system modeling.


title: Agile Software Development Methods: Review and Analysis

abstract: Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.


title: Empirical analysis of CK metrics for object-oriented design complexity: implications for software defects

abstract: To produce high quality object-oriented (OO) applications, a strong emphasis on design aspects, especially during the early phases of software development, is necessary. Design metrics play an important role in helping developers understand design aspects of software and, hence, improve software quality and developer productivity. In this paper, we provide empirical evidence supporting the role of OO design complexity metrics, specifically a subset of the Chidamber and Kemerer (1991, 1994) suite (CK metrics), in determining software defects. Our results, based on industry data from software developed in two popular programming languages used in OO development, indicate that, even after controlling for the size of the software, these metrics are significantly associated with defects. In addition, we find that the effects of these metrics on defects vary across the samples from two programming languages-C++ and Java. We believe that these results have significant implications for designing high-quality software products using the OO approach.


title: A deep learning approach to detection of splicing and copy-move forgeries in images

abstract: In this paper, we present a new image forgery detection method based on deep learning technique, which utilizes a convolutional neural network (CNN) to automatically learn hierarchical representations from the input RGB color images. The proposed CNN is specifically designed for image splicing and copy-move detection applications. Rather than a random strategy, the weights at the first layer of our network are initialized with the basic high-pass filter set used in calculation of residual maps in spatial rich model (SRM), which serves as a regularizer to efficiently suppress the effect of image contents and capture the subtle artifacts introduced by the tampering operations. The pre-trained CNN is used as patch descriptor to extract dense features from the test images, and a feature fusion technique is then explored to obtain the final discriminative features for SVM classification. The experimental results on several public datasets show that the proposed CNN based model outperforms some state-of-the-art methods.


title: Ontology-Based Automatic Text Summarization Using FarsNet

abstract: To summarize a text means to compress the text source into a shorter text in a way that the informational content is kept the same. With regard to the irregular volume of information available on the internet, manual summarization of huge volume of information by humans will be very arduous and difficult. There have been many activities in the field of automatic summarization so far. However, a lack of having methods capable of achieving a semantic hierarchy available in the documents is still felt. In this article we will propose a method for summarizing Persian documents which uses ontology for recognizing the semantic relationship between different parts of a text and extracting important sentences. For this purpose, mapping an input document with ontology brings about a graph whose vertices are the concepts available in ontology and its edges are the relationships among these concepts. This graph which is a graph-based representation of the input text comprises the necessary computational base for recognizing the important sentences in the input document and the production of a summary. The achieved results indicate the acceptable capability of the proposed method in obtaining semantic relationships available in documents and automatic text summarization. The FarsNet ontology is the base for this article.


title: Early speech perception and later language development: Implications for the “critical period.” Language Learning and Development

abstract: In this article, we present a summary of recent research linking speech perception in infancy to later language development, as well as a new empirical study examining that linkage. Infant phonetic discrimination is initially language universal, but a decline in phonetic discrimination occurs for nonnative phonemes by the end of the 1st year. Exploiting this transition in phonetic perception between 6 and 12 months of age, we tested the hypothesis that the decline in nonnative phonetic discrimination is associated with native-language phonetic learning. Using a standard behavioral measure of speech discrimination in infants at 7 months and measures of their language abilities at 14, 18, 24, and 30 months, we show (a) a negative correlation between infants' early native and nonnative phonetic discrimination skills and (b) that native- and nonnative-phonetic discrimination skills at 7 months differentially predict future language ability. Better native-language discrimination at 7 months predicts accelerate...


title: All aboard the Databus!: Linkedin's scalable consistent change data capture platform

abstract: In Internet architectures, data systems are typically categorized into source-of-truth systems that serve as primary stores for the user-generated writes, and derived data stores or indexes which serve reads and other complex queries. The data in these secondary stores is often derived from the primary data through custom transformations, sometimes involving complex processing driven by business logic. Similarly data in caching tiers is derived from reads against the primary data store, but needs to get invalidated or refreshed when the primary data gets mutated. A fundamental requirement emerging from these kinds of data architectures is the need to reliably capture, flow and process primary data changes. We have built Databus, a source-agnostic distributed change data capture system, which is an integral part of LinkedIn's data processing pipeline. The Databus transport layer provides latencies in the low milliseconds and handles throughput of thousands of events per second per server while supporting infinite look back capabilities and rich subscription functionality. This paper covers the design, implementation and trade-offs underpinning the latest generation of Databus technology. We also present experimental results from stress-testing the system and describe our experience supporting a wide range of LinkedIn production applications built on top of Databus.


title: Flow Graph Corpus from Recipe Texts

abstract: Academic Center for Computing and Media Studies, Kyoto University Graduate School of Informatics, Kyoto University Yoshidahonmachi, Sakyo-ku, Kyoto, Japan forest@i.kyoto-u.ac.jp, maeta@ar.media.kyoto-u.ac.jp, yamakata@dl.kuis.kyoto-u.ac.jp, sasada@ar.media.kyoto-u.ac.jp Abstract In this paper, we present our attempt at annotating procedural texts with a flow graph as a representation of understanding. The domain we focus on is cooking recipe. The flow graphs are directed acyclic graphs with a special root node corresponding to the final dish. The vertex labels are recipe named entities, such as foods, tools, cooking actions, etc. The arc labels denote relationships among them. We converted 266 Japanese recipe texts into flow graphs manually. 200 recipes are randomly selected from a web site and 66 are of the same dish. We detail the annotation framework and report some statistics on our corpus. The most typical usage of our corpus may be automatic conversion from texts to flow graphs which can be seen as an entire understanding of procedural texts. With our corpus, one can also try word segmentation, named entity recognition, predicate-argument structure analysis, and coreference resolution.


Fuzzy logic Controller Design for gun-turret system


title: Generating stereoscopic HDR images using HDR-LDR image pairs

abstract: A number of novel imaging technologies have been gaining popularity over the past few years. Foremost among these are stereoscopy and high dynamic range (HDR) Imaging. While a large body of research has looked into each of these imaging technologies independently, very little work has attempted to combine them. This is mostly due to the current limitations in capture and display. In this article, we mitigate problems of capturing Stereoscopic HDR (SHDR) that would potentially require two HDR cameras, by capturing an HDR and LDR pair and using it to generate 3D stereoscopic HDR content. We ran a detailed user study to compare four different methods of generating SHDR content. The methods investigated were the following: two based on expanding the luminance of the LDR image, and two utilizing stereo correspondence methods, which were adapted for our purposes. Results demonstrate that one of the stereo correspondence methods may be considered perceptually indistinguishable from the ground truth (image pair captured using two HDR cameras), while the other methods are all significantly distinct from the ground truth.


title: Mindless computing: designing technologies to subtly influence behavior

abstract: Persuasive technologies aim to influence user's behaviors. In order to be effective, many of the persuasive technologies developed so far relies on user's motivation and ability, which is highly variable and often the reason behind the failure of such technology. In this paper, we present the concept of Mindless Computing, which is a new approach to persuasive technology design. Mindless Computing leverages theories and concepts from psychology and behavioral economics into the design of technologies for behavior change. We show through a systematic review that most of the current persuasive technologies do not utilize the fast and automatic mental processes for behavioral change and there is an opportunity for persuasive technology designers to develop systems that are less reliant on user's motivation and ability. We describe two examples of mindless technologies and present pilot studies with encouraging results. Finally, we discuss design guidelines and considerations for developing this type of persuasive technology.


title: Internet/e‐business technologies acceptance in Canada's SMEs: an exploratory investigation

abstract: Purpose – This study aims at contributing to the discussion related to what causes Canadian small and medium‐size enterprises (SMEs) to be reticent about accepting internet and e‐business technologies (IEBT) in their operations. The research also seeks to gain an understanding of the relative importance of each of the selected factors in the research setting.Design/methodology/approach – A survey was conducted in the Atlantic region of Canada. Questionnaires were mailed to key SMEs' informants. Data analysis was performed using the partial least squares (PLS) approach. A research framework based on the technology‐organization‐environment (TOE) frameworks was used to guide the research effort. Such contingent factors as perceived benefits, management commitment/support, organizational IT competence, external pressure, information systems (IS) vendor support, and availability of financial support, were used to develop relevant hypotheses.Findings – The study's findings indicated that perceived benefits, man...


title: Inferring the QoE of HTTP video streaming from user-viewing activities

abstract: HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.


title: Electric Vehicle Driver Clustering using Statistical Model and Machine Learning

abstract: Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches. Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction. Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting.


title: Benford's law behavior of Internet traffic

abstract: In this paper, we analyze the Internet traffic from a different point of view based on Benford's law, an empirical law that describes the distribution of leading digits in a collection of numbers met in naturally occurring phenomena. We claim that Benford's law holds for the inter-arrival times of TCP flows in case of normal traffic. Consequently, any type of anomalies affecting TCP flows, including intentional intrusions or unintended faults and network failures in general, can be detected by investigating the first-digit distributions of the inter-arrival times of TCP SYN packets. In this paper we apply our findings to the detection of intentional attacks, and leave other types of anomalies for future works. We support our claim with related researches that indicate the TCP flow inter-arrival times can be modeled by Weibull distribution with shape parameter less than 1, and show the relation between Weibull distributed data and Benford's law. Finally, we validate our findings on real traffic and achieve encouraging results.


title: A characterization of oblivious message adversaries for which Consensus is solvable

abstract: We consider the Consensus problem on arbitrary oblivious message adversaries. A message adversary models a communication network whose topology evolves from round to round. We make no assumptions about the possible topologies. A message adversary is oblivious if the set of possible topologies is the same at every round.We give the first complete necessary and sufficient condition for message adversaries that admits a Consensus algorithm. For the necessary part we present a specialized bivalency proof. For the sufficiency part, we present a new algorithm that is based upon reconstructing a partial, but significant, view of the actual communications that occurred during the evolution of the network. This reconstruction might be of independent interest.


Video Question Answering via Hierarchical Spatio-Temporal Attention Networks


title: Inkjet-Printed Humidity Sensor for Passive UHF RFID Systems

abstract: This paper presents a novel inkjet-printed humidity sensor tag for passive radio-frequency identification (RFID) systems operating at ultrahigh frequencies (UHFs). During recent years, various humidity sensors have been developed by researchers around the world for HF and UHF RFID systems. However, to our best knowledge, the humidity sensor presented in this paper is one of the first passive UHF RFID humidity sensor tags fabricated using inkjet technology. This paper describes the structure and operation principle of the sensor tag as well as discusses the method of performing humidity measurements in practice. Furthermore, measurement results are presented, which include air humidity-sensitivity characterization and tag identification performance measurements.


title: Large steps in cloth simulation

abstract: The bottle-neck in most cloth simulation systems is that time steps must be small to avoid numerical instability. This paper describes a cloth simulation system that can stably take large time steps. The simulation system couples a new technique for enforcing constraints on individual cloth particles with an implicit integration method. The simulator models cloth as a triangular mesh, with internal cloth forces derived using a simple continuum formulation that supports modeling operations such as local anisotropic stretch or compression; a unified treatment of damping forces is included as well. The implicit integration method generates a large, unbanded sparse linear system at each time step which is solved using a modified conjugate gradient method that simultaneously enforces particles’ constraints. The constraints are always maintained exactly, independent of the number of conjugate gradient iterations, which is typically small. The resulting simulation system is significantly faster than previous accounts of cloth simulation systems in the literature.


title: ALETHEIA: Improving the Usability of Static Security Analysis

abstract: The scale and complexity of modern software systems complicate manual security auditing. Automated analysis tools are gradually becoming a necessity. Specifically, static security analyses carry the promise of efficiently verifying large code bases. Yet, a critical usability barrier, hindering the adoption of static security analysis by developers, is the excess of false reports. Current tools do not offer the user any direct means of customizing or cleansing the report. The user is thus left to review hundreds, if not thousands, of potential warnings, and classify them as either actionable or spurious. This is both burdensome and error prone, leaving developers disenchanted by static security checkers. We address this challenge by introducing a general technique to refine the output of static security checkers. The key idea is to apply statistical learning to the warnings output by the analysis based on user feedback on a small set of warnings. This leads to an interactive solution, whereby the user classifies a small fragment of the issues reported by the analysis, and the learning algorithm then classifies the remaining warnings automatically. An important aspect of our solution is that it is user centric. The user can express different classification policies, ranging from strong bias toward elimination of false warnings to strong bias toward preservation of true warnings, which our filtering system then executes. We have implemented our approach as the Aletheia tool. Our evaluation of Aletheia on a diversified set of nearly 4,000 client-side JavaScript benchmarks, extracted from 675 popular Web sites, is highly encouraging. As an example, based only on 200 classified warnings, and with a policy biased toward preservation of true warnings, Aletheia is able to boost precision by a threefold factor (x 2.868), while reducing recall by a negligible factor (x 1.006). Other policies are enforced with a similarly high level of efficacy.


title: Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

abstract: This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented. The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.


title: Breaking the Barriers to True Augmented Reality

abstract: In recent years, Augmented Reality (AR) and Virtual Reality (VR) have gained considerable commercial traction, with Facebook acquiring Oculus VR for \$2 billion, Magic Leap attracting more than \$500 million of funding, and Microsoft announcing their HoloLens head-worn computer. Where is humanity headed: a brave new dystopia-or a paradise come true? ::: In this article, we present discussions, which started at the symposium "Making Augmented Reality Real", held at Nara Institute of Science and Technology in August 2014. Ten scientists were invited to this three-day event, which started with a full day of public presentations and panel discussions (video recordings are available at the event web page), followed by two days of roundtable discussions addressing the future of AR and VR.


title: A Review of Turbidity Detection Based on Computer Vision

abstract: Computer vision technology has made great progress in practice in recent years, and it also has broad application prospects in turbidity detection. Turbidity detection plays an important role in water environment science, but popular turbidity detection methods have some limitations in aspects of cost, convenience, and space-time coverage. Based on above reasons, researchers are devoted to developing image-based turbidity detection methods as a complementary or even alternative to the popular turbidity detection method. However, the use of computer vision technology to detect turbidity is affected by many factors such as imaging system, feature extraction, model selection, and so on. Currently, there is no comparison and analysis of these methods in a framework. Therefore, this paper introduces typical turbidity detection methods based on computer vision in detail, with their principle, measurement range, accuracy, technical framework, and comparison. In this paper, existing studies are divided into four types according to different image sources, and seven image features mainly used in these studies are pointed out. The objective of this paper is to review the development status, existing problems, future research directions of image-based turbidity detection methods, and establishment of a unified framework which includes principles, technical framework, and main equipment of imaging systems.


title: Mr. DLib's Living Lab for Scholarly Recommendations

abstract: We introduce the first ‘living lab’ for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., recommendations for research papers, citations, conferences, research grants, etc. Recommendations are delivered through the living lab’s API to platforms such as reference management software and digital libraries. The living lab is built on top of the recommender-system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first sixteen months of operating it. During this time, 1,826,643 recommendations were delivered with an average click-through rate of 0.21%.


title: Single n-gram stemming

abstract: Stemming can improve retrieval accuracy, but stemmers are language-specific. Character n-gram tokenization achieves many of the benefits of stemming in a language independent way, but its use incurs a performance penalty. We demonstrate that selection of a single n-gram as a pseudo-stem for a word can be an effective and efficient language-neutral approach for some languages.


Overview of the TREC 2017 Real-Time Summarization Track


title: Armadillo: A Compilation Chain for Privacy Preserving Applications

abstract: In this work we present Armadillo a compilation chain used for compiling applications written in a high-level language (C++) to work on encrypted data. The back-end of the compilation chain is based on homomorphic encryption. The tool-chain further automatically handle a huge amount of parallelism so as to mitigate the performance overhead of using homomorphic encryption.


title: Hesitant fuzzy analytic hierarchy process

abstract: Ordinary fuzzy sets have been recently extended to intuitionistic and hesitant fuzzy sets, which are frequently used for the solution of decision-making problems. All these extensions aim at better defining the membership values or functions of the considered parameters. In this paper we develop a hesitant fuzzy analytic hierarchy process method involving multi-experts' linguistic evaluations aggregated by ordered weighted averaging (OWA) operator. The developed hesitant fuzzy AHP method is applied to a multicriteria supplier selection problem.


title: Real-time Synthesis of Chinese Visual Speech and Facial Expressions using

abstract: This paper describes our initial work in developing a real-time audio-visual Chinese speech synthesizer with a 3D expressive avatar. The avatar model is parameterized according to the MPEG-4 facial animation standard [1]. This standard offers a compact set of facial animation parameters (FAPs) and feature points (FPs) to enable realization of 20 Chinese visemes and 7 facial expressions (i.e. 27 target facial configurations). The Xface [2] open source toolkit enables us to define the influence zone for each FP and the deformation function that relates them. Hence we can easily animate a large number of coordinates in the 3D model by specifying values for a small set of FAPs and their FPs. FAP values for 27 target facial configurations were estimated from available corpora. We extended the dominance blending approach to effect animations for coarticulated visemes superposed with expression changes. We selected six sentiment-carrying text messages and synthesized expressive visual speech (for all expressions, in randomized order) with neutral audio speech. A perceptual experiment involving 11 subjects shows that they can identify the facial expression that matches the text message’s sentiment 85% of the time.


title: Safety and Efficacy of Minimally Invasive Video-Assisted Ablation of Pilonidal Sinus: A Randomized Clinical Trial.

abstract: IMPORTANCE ::: Video-assisted ablation of pilonidal sinus (VAAPS) is a new minimally invasive treatment based on the complete removal of the sinus cavity with a minimal surgical wound. ::: ::: ::: OBJECTIVE ::: To validate the safety and efficacy of VAAPS. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: From January 1, 2012, through December 31, 2013, we enrolled 145 consecutive patients from an intention-to-treat population at a single academic center in Italy. The last date of follow-up for this analysis was December 31, 2014. Patients with chronic nonrecurrent pilonidal sinus were randomized to minimally invasive (76 patients) or conventional (69 patients) treatment of pilonidal sinus. ::: ::: ::: INTERVENTIONS ::: VAAPS and Bascom cleft lift procedure. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: The primary end point of the study was time off work. Secondary end points were the rates of operative success and perioperative complications (infection and recurrence), operative time, patient satisfaction, and intraoperative and postoperative pain ratings. ::: ::: ::: RESULTS ::: Of the 145 patients, 60 (78.9%) were male and 16 (21.1%) were female in the minimally invasive treatment group, and 54 (78.3%) were male and 15 (21.7%) were female in the conventional treatment group. The mean (SD) age of the patients was 25.5 (5.9) years in the minimally invasive treatment group and 25.7 (5.3) in the conventional treatment group. In the minimally invasive treatment group, mean (SD) time off work was significantly less compared with the conventional treatment group (1.6 [1.7] vs 8.2 [3.9] days; P < .001). Mean (SD) operative time was significantly longer in the minimally invasive treatment group (42.9 [9.8] minutes) vs the conventional treatment group (26.5 [8.7] minutes), P < .001. Although the overall complication rate was similar in both groups, there were fewer infections (1 [1.3%] vs 5 [7.2%]; P = .10) recorded in the minimally invasive treatment group. Similarly, patients enrolled in the minimally invasive treatment group expressed significantly less pain and higher satisfaction. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: In the new era of minimally invasive treatment surgery, pilonidal sinus could become a disease treated with an endoscopic approach. Although additional studies are needed to provide a definitive conclusion, our results encourage considering the adoption of this treatment as the most effective way to treat pilonidal sinus. ::: ::: ::: TRIAL REGISTRATION ::: clinicaltrials.gov Identifier: NCT01963273.


title: A Survey on Evaluation Factors for Business Process Management Technology

abstract: Estimating the value of business process management (BPM) technology is a difficult task to accomplish. Computerized business processes have a strong impact on an organization, and BPM projects have a long-term cost amortization. ::: To systematically analyze BPM technology from an economic-driven perspective, we are currently developing an evaluation framework in the EcoPOST project. In order to empirically validate the relevance of assumed evaluation factors (e.g., process knowledge, business process redesign, end user fears, and communication) we have conducted an online survey among 70 BPM experts from more than 50 industrial and academic organizations. This paper summarizes the results of this survey. Our results help both researchers and practitioners to better understand the evaluation factors that determine the value of BPM technology.


title: Susceptibility to acute motion sickness in blind persons.

abstract: Abstract : A group of twelve persons selected only on the basis of their visual defects were exposed to stressful Coriolis accelerations under standardized conditions. All demonstrated differences in susceptibility to acute motion sickness that bore no relation to their rank order of visual deprivation. Insofar as comparison with a group of normal subjects was made possible, no significant differences in susceptibility were demonstrable. It was concluded that vision is not an essential but rather a secondary etiologic factor in the genesis of motion sickness. This is not incompatible with the fact that symptoms characteristic of motion sickness may be visually induced in the absence of 'motion.' (Author)


title: Deep learning cognitive radar for micro UAS detection and classification

abstract: In this paper, we propose an intelligent cognitive radar system for detecting and classifying the micro unmanned aerial systems (micro UASs). In this system, we design a low-complexity binarized deep belief network (DBN) classifier that recognizes the signature patterns generated by using a Doppler radar based solution. To generate the distinguishable patterns, our work employs the spectral correlation function (SCF) that is noise resilient. In the experiment conducted, micro UASs are clamped to be immobile while propellers are on motion. Doppler effects caused by propeller motions of UASs are considered. By employing our binarized DBN, the computationally costly 91600 floating point multiplication operations required in the original DBN are represented by using zero computational cost no connections, simple connections, negation operations, bit-shifting operations, and bit-shifting with negation operations. In the simulation section, we show that the proposed system gives more than 90% accuracy in detecting the micro UASs in the environments with SNR ≥ −3 dB AWGN noise. Furthermore, the classification accuracy of different micro UASs remains more than 90% for environments with SNR ≥ 0 dB AWGN noise.


title: Improved digital design of BPSK modulator using look-up table technique

abstract: This paper present a new and improved digital design of BPSK (Binary Phase Shift Key) modulator. The generic digital design of BPSK modulator has two carrier signals in the form of sine wave having 0 degree and 180 degree phase shift. Generic design uses two sine wave generators which act like carrier signal and MUX gives out any one of the carrier signal depending upon the input bit (0 or 1). Our main aim in this paper is to reduce the hardware from the previous existing digital architecture of the BPSK modulator. The new architecture of the BPSK modulator with optimized resource utilization is proposed. In this improved design instead of using two sine wave generators for producing two different sinusoidal carrier signals having 0 degree and 180 degree phase shift, we have used a single sine wave generator using LUTs technique to generate both sinusoidal carrier signals (sine wave with 0 degree and 180 degree phase shift). Digital Clock Manager (DCM) is used a control block (DCM) this make our architecture more flexible and can work on large frequency range. This new improved architecture is implemented using VHDL language on the Xilinx ISE 9.2i simulator. VHDL Design and simulation of every block of the new architecture is done. Finally the simulation of whole digital design of BPSK is performed and it's gives the satisfactory result which confirms that the new architecture can produce BPSK modulated signal.


title: A Compact DC-30 GHz 0.13-um CMOS SP4T Switch

abstract: This paper presents a DC−30 GHz single-pole- four-throw (SP4T) CMOS switch using 0.13 μ mC MOS process. The CMOS transistor layout is done to minimize the substrate network resistance. The on-chip matching inductors and routing are designed for a very small die area (250 × 180 μm 2 ), and modeled using full-wave EM simulations. The SP4T CMOS switch result in an insertion loss of 1.8 dB and 2.7 dB at 5 GHz and 24 GHz, respectively. The isolation is > 25 dB up to 30 GHz and achieved using a series-shunt switch configuration. The measured input P1dB and IIP3 of the SP4T switch are 9 dBm and 21 dBm, respectively. To our knowledge, this is the first ultra wideband CMOS SP4T switch and with a very small chip area. Index Terms— microwave switch, CMOS switch, SP4T, T/R switch, substrate resistance, RFIC


title: A simple and efficient control scheme to reverse a tractor-trailer system on a trajectory

abstract: Trailer reversing is a problem frequently considered in the literature, usually with fairly complex non-linear control theory based approaches. In this paper, we present a simple method for stabilizing a tractor-trailer system to a trajectory based on the notion of controlling the hitch-angle of the trailer rather than the steering angle of the tractor. The method is intuitive, provably stable, and shown to be viable through various experimental results conducted on our test platform, the CSIRO autonomous tractor.


title: How many child deaths can we prevent this year?

abstract: Summary This is the second of five papers in the child survival series. The first focused on continuing high rates of child mortality (over 10 million each year) from preventable causes: diarrhoea, pneumonia, measles, malaria, HIV/AIDS, the underlying cause of undernutrition, and a small group of causes leading to neonatal deaths. We review child survival interventions feasible for delivery at high coverage in low-income settings, and classify these as level 1 (sufficient evidence of effect), level 2 (limited evidence), or level 3 (inadequate evidence). Our results show that at least one level-1 intervention is available for preventing or treating each main cause of death among children younger than 5 years, apart from birth asphyxia, for which a level-2 intervention is available. There is also limited evidence for several other interventions. However, global coverage for most interventions is below 50%. If level 1 or 2 interventions were universally available, 63% of child deaths could be prevented. These findings show that the interventions needed to achieve the millennium development goal of reducing child mortality by two-thirds by 2015 are available, but that they are not being delivered to the mothers and children who need them.


title: Pair-Matching Method for Estimating 2-D Angle of Arrival With a Cross-Correlation Matrix

abstract: This letter presents a novel method for pair matching of elevation and azimuth angles in two-dimensional (2-D) an- gles-of-arrival estimation employing two uniform linear arrays (ULAs). The proposed pair-matching method exploits the cross-correlation matrix of two received signals impinging on each ULA that shows the characteristics of the corresponding angle pairs, and is conducted by combining it with a propagator method. Numerical simulations using L-shaped array geometry revealed that the proposed method provided better robustness over the SNR, angular separation, and number of sources than that of conventional methods.


title: Ethanol enhances gamma-aminobutyric acid responses in a subpopulation of nucleus accumbens neurons: role of metabotropic glutamate receptors.

abstract: The nucleus accumbens (NAcc) may be a key area in the rewarding effects of abused drugs. We previously showed that low ethanol concentrations decreased both N-methyl-D-aspartate (NMDA)-induced and kainate-induced currents in NAcc core neurons. To explore the effects of ethanol on gamma-aminobutyric acid (GABA) responses in NAcc, we used intracellular voltage-clamp recordings and locally applied GABA in a slice preparation containing the NAcc. Ethanol (11-200 mM) had no effect on resting membrane properties, but 11, 22, 44, 100, and 200 mM ethanol increased GABA currents in 17, 33, 45, 50, and 22% of cells, respectively. Superfusion of low glutamate concentrations that had no direct effect on membrane properties enhanced ethanol potentiation of GABA currents in more than half the NAcc cells. Neither alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid/kainate receptor nor NMDA receptor antagonists affected the percentage of cells showing ethanol enhancement of GABA responses or the degree of ethanol enhancement of GABA currents in NAcc neurons. However, in ethanol-sensitive cells, the metabotropic receptor antagonist alpha-methyl-4-carboxyphenylglycine (MCPG) blocked the ethanol enhancement of GABA currents. In addition, the metabotropic receptor agonist trans-1-aminocyclopentane-1,3-dicarboxylic acid enhanced GABA responses in 50% of cells tested, an effect blocked by MCPG. These data suggest that NAcc core neurons possess both ethanol-sensitive and -insensitive GABA receptors and that glutamate can mimic and enhance the ethanol potentiation of GABA currents in many of these neurons. Furthermore, the ethanol potentiation of GABA currents may involve metabotropic glutamate receptors, perhaps via a phosphorylation mechanism that regulates ethanol sensitivity of GABA receptors in some NAcc neurons.


title: Anti-Inflammatory Effects of a Pomegranate Leaf Extract in LPS-Induced Peritonitis

abstract: Folk medicine suggests that pomegranate (peels, seeds and leaves) has anti-inflammatory properties; however, the precise mechanisms by which this plant affects the inflammatory process remain unclear. Herein, we analyzed the anti-inflammatory properties of a hydroalcoholic extract prepared from pomegranate leaves using a rat model of lipopolysaccharide-induced acute peritonitis. Male Wistar rats were treated with either the hydroalcoholic extract, sodium diclofenac, or saline, and 1 h later received an intraperitoneal injection of lipopolysaccharides. Saline-injected animals (i. p.) were used as controls. Animals were culled 4 h after peritonitis induction, and peritoneal lavage and peripheral blood samples were collected. Serum and peritoneal lavage levels of TNF-α as well as TNF-α mRNA expression in peritoneal lavage leukocytes were quantified. Total and differential leukocyte populations were analyzed in peritoneal lavage samples. Lipopolysaccharide-induced increases of both TNF-α mRNA and protein levels were diminished by treatment with either pomegranate leaf hydroalcoholic extract (57 % and 48 % mean reduction, respectively) or sodium diclofenac (41 % and 33 % reduction, respectively). Additionally, the numbers of peritoneal leukocytes, especially neutrophils, were markedly reduced in hydroalcoholic extract-treated rats with acute peritonitis. These results demonstrate that pomegranate leaf extract may be used as an anti-inflammatory drug which suppresses the levels of TNF-α in acute inflammation.


title: Improvement of power supply rejection ratio of LDO deteriorated by reducing power consumption

abstract: In this work, the bulk-gate controlled circuit to improve the power supply ripple ratio (PSRR) of a Low Dropout Regulator (LDO) which deteriorates due to lowering power consumption is proposed. Designing with 0.25 mum CMOS process, the simulation results by HSPICE shown that the proposed circuit provides a high performance of PSRR even though 1/10 of the power consumption is reduced compare to the conventional circuit. It is confirmed that about 40[dB] at 10[Hz] frequency and 20[dB] at 1[kHz] frequency of PSRR are together improved.


title: Adapting the Sample Size in Particle Filters Through KLD-Sampling

abstract: Over the past few years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process. The key idea of the KLD-sampling method is to bound the approximation error introduced by the sample-based representation of the particle filter. The name KLD-sampling is due to the fact that we measure the approximation error using the Kullback-Leibler distance. Our adaptation approach chooses a small number of samples if the density is focused on a small part of the state space, and it chooses a large number of samples if the state uncertainty is high. Both the implementation and computation overhead of this approach are small. Extensive experiments using mobile robot localization as a test application show that our approach yields drastic improvements over particle filters with fixed sample set sizes and over a previously...


title: ORBIT: Hybrid movie recommendation engine

abstract: Today, users will get so many movie recommendations websites, which suggest users best movies according to their interests. All these websites have implemented one of the conventional content, context and collaborative recommendations algorithms. Alone, these algorithms are failed to recommend best and efficient recommendations to user. So, there is a need to evolve a unique algorithm which combines the features of conventional algorithm along with its new features. This paper describes the ORBIT, which is a movie recommendation engine, based on a unique Hybrid recommendation algorithm, satisfies a user by providing best and efficient books recommendations. Comparative case study of conventional recommendation algorithms to ORBIT's Hybrid movie recommendation algorithm has also been studied and presented in this paper. This case study is based on evaluating criteria of recommendation algorithm i.e. accuracy, precision, recall, F-measure etc. Results of this case study are represented in the form of tables and graphs to clearly specify the need of ORBIT.


title: A triple-band monopole antenna for WLAN and WiMAX applications

abstract: In this paper, a triple-band monopole antenna for WLAN and WiMAX wireless communication applications is presented. The antenna has a simple structure designed for 2.4/5.2/5.8 GHz WLAN and 3.5/5.5 GHz WiMAX bands. The radiator is composed of just two branches and a short stub. The antenna is designed on a 40 × 40 × 0.8 mm3 substrate using computer simulation. For verification of simulation results, a prototype is fabricated and measured. Results show that the antenna can provide three impedance bandwidths, 2.35–2.58 GHz, 3.25–4 GHz and 4.95–5.9 GHz, for the WLAN and WiMAX applications. The simulated and measured radiation patterns, efficiencies and gains of the antenna are all presented.


title: Fully-integrated core chip for X-band phased array T/R modules

abstract: This paper discusses the final result of development effort leading to a fully-integrated phased-array core chip. The MMIC is especially useful for active electronically-steered arrays, and integrates a full functionality including low-noise amplifier (LNA), a medium-power amplifier (MPA) matched to common high-power amplifiers (HPA) and all control functions. The design and measurement results will be discussed. The integration of an LNA and MPA makes this MMIC an ideal candidate for a two chip T/R module solution. The MMIC is realised in the 0.25/spl mu/m pHEMT and process of UMS.


title: Security Analysis of the Estonian Internet Voting System

abstract: Estonia was the first country in the world to use Internet voting nationally, and today more than 30% of its ballots are cast online. In this paper, we analyze the security of the Estonian I-voting system based on a combination of in-person election observation, code review, and adversarial testing. Adopting a threat model that considers the advanced threats faced by a national election system---including dishonest insiders and state-sponsored attacks---we find that the I-voting system has serious architectural limitations and procedural gaps that potentially jeopardize the integrity of elections. In experimental attacks on a reproduction of the system, we demonstrate how such attackers could target the election servers or voters' clients to alter election results or undermine the legitimacy of the system. Our findings illustrate the practical obstacles to Internet voting in the modern world, and they carry lessons for Estonia, for other countries considering adopting such systems, and for the security research community.


title: Automatic Genre Classification Using Large High-Level Musical Feature Sets

abstract: This paper presents a system that extracts 109 musical features from symbolic recordings (MIDI, in this case) and uses them to classify the recordings by genre. The features used here are based on instrumentation, texture, rhythm, dynamics, pitch statistics, melody and chords. The classification is performed hierarchically using different sets of features at different levels of the hierarchy. Which features are used at each level, and their relative weightings, are determined using genetic algorithms. Classification is performed using a novel ensemble of feedforward neural networks and k-nearest neighbour classifiers. Arguments are presented emphasizing the importance of using high-level musical features, something that has been largely neglected in automatic classification systems to date in favour of low-level features. The effect on classification performance of varying the number of candidate features is examined in order to empirically demonstrate the importance of using a large variety of musically meaningful features. Two differently sized hierarchies are used in order to test the performance of the system under different conditions. Very encouraging classification success rates of 98% for root genres and 90% for leaf genres are obtained for a hierarchical taxonomy consisting of 9 leaf genres.


title: Implementation of 8B/10B encoder-decoder for Gigabit Ethernet Frame

abstract: This paper describe a byte oriented transmission code and its hardware implementation with elaborating a method for implementation of the DC-balanced 8B/10B coding using a very fast FPGA from Spartan family. This code is particularly well suited for high-speed local area networks. This technique can be used by other high speed buses such as PCI Express, IEEE 1394b, Serial ATA, SAS, Fiber channel, SSA. Gigabit Ethernet INFIBAND, XAUI, Serial Rapid IO, uses the same coding module. Using the Look-up Table and memory with fast technique made this design efficient to be implemented. A very simple implementation of the code has been accomplished by the partitioning of the coder into 5B/6B and 3B/4B subordinates coders. For increasing its performance more RTL logic is required.


title: Hyperelastic pressure sensing with a liquid-embedded elastomer

abstract: A hyperelastic pressure transducer is fabricated by embedding silicone rubber with microchannels of conductive liquid eutectic gallium–indium. Pressing the surface of the elastomer with pressures in the range of 0–100 kPa will deform the cross-section of underlying channels and change their electric resistance by as much as 50%. Microchannels with dimensions as small as 25 µm are obtained with a maskless, soft lithography process that utilizes direct laser exposure. Change in electrical resistance is measured as a function of the magnitude and area of the surface pressure as well as the cross-sectional geometry, depth and relative lateral position of the embedded channel. These experimentally measured values closely match closed-form theoretical predictions derived from plane strain elasticity and contact mechanics.


title: Routing Attacks and Countermeasures in the RPL-Based Internet of Things

abstract: The Routing Protocol for Low-Power and Lossy Networks (RPL) is a novel routing protocol standardized for constrained environments such as 6LoWPAN networks. Providing security in IPv6/RPL connected 6LoWPANs is challenging because the devices are connected to the untrusted Internet and are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL, 6LoWPAN, and CoAP/CoAPs. In this paper we provide a comprehensive analysis of IoT technologies and their new security capabilities that can be exploited by attackers or IDSs. One of the major contributions in this paper is our implementation and demonstration of well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol. We implement these attacks in the RPL implementation in the Contiki operating system and demonstrate these attacks in the Cooja simulator. Furthermore, we highlight novel security features in the IPv6 protocol and exemplify the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.


title: Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production

abstract: Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales.


title: Edge Mining the Internet of Things

abstract: This paper examines the benefits of edge mining -data mining that takes place on the wireless, battery-powered, and smart sensing devices that sit at the edge points of the Internet of Things. Through local data reduction and transformation, edge mining can quantifiably reduce the number of packets that must be sent, reducing energy usage, and remote storage requirements. In addition, edge mining has the potential to reduce the risk in personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to three specific algorithms: linear Spanish inquisition protocol (L-SIP), ClassAct, and bare necessities (BN), which are all instantiations of general SIP. In general, the benefits provided by edge mining are related to the predictability of data streams and availability of precise information requirements; results show that L-SIP typically reduces packet transmission by around 95% (20-fold), BN reduces packet transmission by 99.98% (5000-fold), and ClassAct reduces packet transmission by 99.6% (250-fold). Although energy reduction is not as radical because of other overheads, minimization of these overheads can lead up to a 10-fold battery life extension for L-SIP, for example. These results demonstrate the importance of edge mining to the feasibility of many IoT applications.


THE USE OF SOCIAL MEDIA IN THE SUPPLY CHAIN: SURVEY AND EXTENSIONS


title: Single and sparse view 3D reconstruction by learning shape priors

abstract: In this paper, we aim to reconstruct free-form 3D models from only one or few silhouettes by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: (1) a framework for learning the shape prior of the 3D objects, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects and (2) novel probabilistic inference schemes for automatically reconstructing 3D shapes from the silhouette(s) in the single view or sparse views. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach.


title: A construction of cryptography system based on quantum neural network

abstract: Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs).The purpose of this paper is to introduce an application of QNNs in construction of cryptography system in which two networks exchange their outputs (in qubits) and the key to be synchronized between two communicating parties. This system is based on multilayer qubit QNNs trained with back-propagation algorithm.


title: Affective commitment to the organization : The contribution of perceived organizational support

abstract: Three studies examined the interrelationships among work experiences, perceived organizational support (POS), affective commitment (AC), and employee turnover. Using a diverse sample of 367 employees drawn from a variety of organizations, Study 1 found that POS mediated positive associations of organizational rewards, procedural justice, and supervisor support with AC. Study 2 examined changes of POS and AC in retail employees over a 2-year span (N = 333) and a 3-year span (N = 226). POS was positively related to temporal changes in AC, suggesting that POS leads to AC. Study 3 found a negative relationship between POS and subsequent voluntary employee turnover that was mediated by AC in retail employees (N = 1,124) and in poultry- and feed-processing workers (N = 262). These results suggest that favorable work conditions operate via POS to increase AC, which, in turn, decreases employee withdrawal behavior.


title: Autopsy findings for a case of acute gastric volvulus in a child.

abstract: Acute gastric volvulus resulting in abdominal compartment syndrome was determined to be the cause of death in a 4-year-old girl who presented with abdominal distension. At about 1AM on the day of her death, she was brought to our emergency medical center. Physical examination and plain abdominal X-ray revealed pronounced gastric dilatation. A decompression procedure was performed, followed by observation. She went into cardiopulmonary arrest around 1PM on the same day and died. Postmortem investigation, including an autopsy and computed tomography (CT), was performed to determine the cause of death. The findings included that the stomach was severely distended. Evidence was seen of mucosal hemorrhage in the gastric mucosa on the greater curvature side, which was thinned in places but without perforation. No necrosis of the gastric mucosa was observed; reversible changes were evident on histopathological examination. The postmortem CT images suggested that the pyloric region was positioned cranioventrally to the cardiac region. None of the findings indicated sudden blockage, and the cause of death was determined to be acute gastric volvulus resulting in abdominal compartment syndrome. The abnormal placement of the organs was difficult to determine based on physical examination alone; postmortem CT and careful examination were helpful in conducting the autopsy in this case.


title: Dual CNN Models for Unsupervised Monocular Depth Estimation

abstract: The unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth. In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss. In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation.


title: Trip analyzer through smartphone apps

abstract: Broad usage of Smartphones and mobile apps enables both individual trip summary and regional travel demand analysis. In this paper, we describe a trip analysis system, as part of smarter transit service. This trip analysis system consists of mobile apps and a centralized analyzer. It identifies the travel mode and purpose of the trips sensed by mobile devices, provides trip summaries and insights to mobile subscribers, and generates meaningful patterns to support traffic operation planning and transit system design. It is developed and deployed to the Smartphones of the volunteers in Dubuque, IA, to serve both the volunteers and the transit agencies. Preliminary evaluation has demonstrated the applicability of the design.


title: Improving software developers' fluency by recommending development environment commands

abstract: Software developers interact with the development environments they use by issuing commands that execute various programming tools, from source code formatters to build tools. However, developers often only use a small subset of the commands offered by modern development environments, reducing their overall development fluency. In this paper, we use several existing command recommender algorithms to suggest new commands to developers based on their existing command usage history, and also introduce several new algorithms. By running these algorithms on data submitted by several thousand Eclipse users, we describe two studies that explore the feasibility of automatically recommending commands to software developers. The results suggest that, while recommendation is more difficult in development environments than in other domains, it is still feasible to automatically recommend commands to developers based on their usage history, and that using patterns of past discovery is a useful way to do so.


title: AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back

abstract: We present AutoTutor and Affective AutoTutor as examples of innovative 21st century interactive intelligent systems that promote learning and engagement. AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by interacting with them in natural language with adaptive dialog moves similar to those of human tutors. AutoTutor constructs a cognitive model of students' knowledge levels by analyzing the text of their typed or spoken responses to its questions. The model is used to dynamically tailor the interaction toward individual students' zones of proximal development. Affective AutoTutor takes the individualized instruction and human-like interactivity to a new level by automatically detecting and responding to students' emotional states in addition to their cognitive states. Over 20 controlled experiments comparing AutoTutor with ecological and experimental controls such reading a textbook have consistently yielded learning improvements of approximately one letter grade after brief 30--60-minute interactions. Furthermore, Affective AutoTutor shows even more dramatic improvements in learning than the original AutoTutor system, particularly for struggling students with low domain knowledge. In addition to providing a detailed description of the implementation and evaluation of AutoTutor and Affective AutoTutor, we also discuss new and exciting technologies motivated by AutoTutor such as AutoTutor-Lite, Operation ARIES, GuruTutor, DeepTutor, MetaTutor, and AutoMentor. We conclude this article with our vision for future work on interactive and engaging intelligent tutoring systems.


title: Optical Versus Video See-Through Head-Mounted Displays in Medical Visualization

abstract: We compare two technological approaches to augmented reality for 3-D medical visualization: optical and video see-through devices. We provide a context to discuss the technology by reviewing several medical applications of augmented-reality re search efforts driven by real needs in the medical field, both in the United States and in Europe. We then discuss the issues for each approach, optical versus video, from both a technology and human-factor point of view. Finally, we point to potentially promising future developments of such devices including eye tracking and multifocus planes capabilities, as well as hybrid optical/video technology.


title: Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening

abstract: The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.


title: Application development for recognizing type of infant's cry sound

abstract: Crying infant is a sign of baby who has a problem. But, some people are not able to recognize the meaning of infant's cry. Several researches to recognize infant's cry sound had been done by some researchers, but there is still no research that develop an application which able to recognize type of infant's cry sound based on web. In this research, an application is developed to help users identify the sound of crying infant based on Dunstan Baby Language. The method applied in this application are Mel-Frequency Cepstral Coefficient (MFCC) feature extraction for infant's cry sound, normalization of feature extraction result, and K-nearest neighbor classification. From the various tests performed, it can be concluded that highest average accuracy of 75.95 percent can be obtained by using parameters consist of 0.08 seconds wintime in MFCC feature extraction, 85 percent of training data and 15 percent of test data from any type of infant's cry sound, feature extraction normalization by Standard Deviation Normalization, and K-nearest Neighbor with k equal to 1 classification. While testing application by using all data, average accuracy of 96.57 percent can be obtained by using parameters consist of 0.08 seconds wintime in MFCC feature extraction, 85 percent of training data from any type of infant's cry sound, feature extraction normalization by Standard Deviation Normalization, and K-nearest Neighbor k equal to 1 classification. From that test, it can be concluded that the application has been running well when classifying all types of infant's cry sound data.


title: Rational quantitative attribution of beliefs, desires and percepts in human mentalizing

abstract: A Bayesian theory of mind model is shown to infer and quantify the mental state and judgements of humans in decision-making scenarios. The model is a key step towards enabling machines to ‘intuit’ human thoughts and desires.