title: Class D Power Amplifier for Audio Beam System

abstract: Audio Beam is a revolutionary way of projecting audio signal in a highly directional manner to the listener. A high efficiency class D power amplifier is presented to drive the piezoelectric load, polyvinylidene fluoride(PVDF) ultrasonic transducer of Audio Beam, between 20 kHz and 60 kHz. The class D amplifier is composed of power MOSFETS, type IRFU2407, in a full H-Bridge configuration driven by HIPxxxxA circuits in bootstrap mode. The specific nature of the problem comes from the fact that the transducer loaded is mostly capacitive around the resonant frequency. The insertion of two appropriate inductors in front of the load introduced an efficient solution to the problem due to the reactance matching and resonance. In the end, the experiment of the realized system testified that the class-D amplifier designed is able to drive the ultrasonic transducer with extreme high efficiency and high power.


title: Efficient Neural Network Robustness Certification with General Activation Functions

abstract: Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a non-trivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for general activation functions still remains largely unexplored. To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions for given input data points. The novelty in our algorithm consists of bounding a given activation function with linear and quadratic functions, hence allowing it to tackle general activation functions including but not limited to four popular choices: ReLU, tanh, sigmoid and arctan. In addition, we facilitate the search for a tighter certified lower bound by adaptively selecting appropriate surrogates for each neuron activation. Experimental results show that CROWN on ReLU networks can notably improve the certified lower bounds compared to the current state-of-the-art algorithm Fast-Lin, while having comparable computational efficiency. Furthermore, CROWN also demonstrates its effectiveness and flexibility on networks with general activation functions, including tanh, sigmoid and arctan.


title: Smarter City: Smart Energy Grid based on Blockchain Technology

abstract: The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger.


title: Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks

abstract: In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False Positive Rate) below $1\%$. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns.


Tutorial on Machine Learning for Spectrum Sharing in Wireless Networks


title: Food matters: how the microbiome and gut–brain interaction might impact the development and course of anorexia nervosa

abstract: Anorexia nervosa (AN) is one of the most common chronic illnesses in female adolescents and exhibits the highest mortality risk of all psychiatric disorders. Evidence for the effectiveness of psychotherapeutic or psychopharmacological interventions is weak. Mounting data indicate that the gut microbiome interacts with the central nervous system and the immune system by neuroendocrine, neurotransmitter, neurotrophic and neuroinflammatory afferent and efferent pathways. There is growing evidence that the gut microbiota influences weight regulation and psychopathology, such as anxiety and depression. This article reviews how the gut–brain interaction may impact the development and course of AN. A “leaky gut”, characterized by antigens traversing the intestinal wall, was demonstrated in an animal model of AN, and could underlie the low-grade inflammation and increased risk of autoimmune diseases found in AN. Moreover, starvation has a substantial impact on the gut microbiome, and diets used for re-nutrition based on animal products may support the growth of bacteria capable of triggering inflammation. As there is currently no empirically derived agreement on therapeutic re-nourishment in AN, this review discusses how consideration of gut–brain interactions may be important for treatment regarding the determination of target weight, rapidity of weight gain, refeeding methods and composition of the diet which might all be of importance to improve long-term outcome of one of the most chronic psychiatric disorders of adolescence.


Unboxing the White-Box Practical attacks against Obfuscated Ciphers


title: Spontaneous-Speech Acoustic-Prosodic Features of Children with Autism and the Interacting Psychologist

abstract: Atypical prosody, often reported in children with Autism Spectrum Disorders, is described by a range of qualitative terms that reflect the eccentricities and variability among persons in the spectrum. We investigate various wordand phonetic-level features from spontaneous speech that may quantify the cues reflecting prosody. Furthermore, we introduce the importance of jointly modeling the psychologist’s vocal behavior in this dyadic interaction. We demonstrate that acoustic-prosodic features of both participants correlate with the children’s rated autism severity. For increasing perceived atypicality, we find children’s prosodic features that suggest ‘monotonic’ speech, variable volume, atypical voice quality, and slower rate of speech. Additionally, we find the psychologist’s features inform their perception of a child’s atypical behavior– e.g., the psychologist’s pitch slope and jitter are increasingly variable and their speech rate generally decreases.


title: Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device

abstract: BACKGROUND ::: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device. ::: ::: ::: METHODS ::: The proposed method consists of two phases: multi-level feature learning and recurrent neural networks-based (RNNs) classification. The feature learning phase is designed to extract low- and mid-level features. Low-level features are extracted from raw signals, capturing temporal and frequency domain properties. Mid-level features are explored based on low-level ones to learn compositions and structural information of signals. Sleep staging is a sequential problem with long-term dependencies. RNNs with bidirectional long short-term memory architectures are employed to learn temporally sequential patterns. ::: ::: ::: RESULTS ::: To better simulate the use of wearable devices in the daily scene, experiments were conducted with a resting group in which sleep was recorded in the resting state, and a comprehensive group in which both resting sleep and non-resting sleep were included. The proposed algorithm classified five sleep stages (wake, non-rapid eye movement 1-3, and rapid eye movement) and achieved weighted precision, recall, and F1 score of 66.6%, 67.7%, and 64.0% in the resting group and 64.5%, 65.0%, and 60.5% in the comprehensive group using leave-one-out cross-validation. Various comparison experiments demonstrated the effectiveness of the algorithm. ::: ::: ::: CONCLUSIONS ::: Our method is efficient and effective in scoring sleep stages. It is suitable to be applied to wearable devices for monitoring sleep at home.


title: Artificial Intelligence: From programs to solvers

abstract: Artificial Intelligence is a brain child of Alan Turing and his universal programmable computer. During the 60’s and 70’s, AI researchers used computers for exploring intuitions about intelligence and for writing programs displaying intelligent behavior. A significant change occurred however in the 80’s, as many AI researchers moved from the early AI paradigm of writing programs for ill-defined problems to writing solvers for well-defined mathematical models like Constraint Satisfaction Problems, Strips Planning, SAT, Bayesian Networks, Partially Observable Markov Decision Processes, and General Game Playing. Solvers are programs that take a compact description of a particular model instance and automatically compute its solution. Unlike the early AI programs, solvers are general as they must deal with any instance that fits the model. Many ideas have been advanced to address this crisp computational challenge from which a number of lessons can be drawn. In this paper, I revisit the problem of generality in AI, look at the way in which this ’Models and Solvers’ agenda addresses the problem, and discuss the relevance of this agenda to the grand AI goal of a computational account of intelligence and human cognition.


title: Query-Conditioned Three-Player Adversarial Network for Video Summarization

abstract: Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discriminator takes three pairs of query-conditioned summaries as the input to discriminate the real summary from a generated and a random one. A three-player loss is introduced for joint training of the generator and the discriminator, which forces the generator to learn better summary results, and avoids the generation of random trivial summaries. Experiments on a recently proposed query-conditioned video summarization benchmark dataset show the efficiency and efficacy of our proposed method.


title: Arabic Diacritization with Gated Recurrent Unit

abstract: Arabic and similar languages require the use of diacritics in order to determine the necessary parameters to pronounce and identify every part of the speech correctly. Therefore, when it comes to perform Natural Language Processing (NLP) over Arabic, diacritization is a crucial step. In this paper we use a gated recurrent unit network as a language-independent framework for Arabic diacritization. The end-to-end approach allows to use exclusively vocalized text to train the system without using external resources. Evaluation is performed versus the state-of-the-art literature results. We demonstrate that we achieve state-of-the-art results and enhance the learning process by scoring better performance in the training and testing timing.


title: Cloud migration patterns: a multi-cloud service architecture perspective

abstract: Many organizations migrate their on-premise software systems to the cloud. However, current coarse-grained cloud migration solutions have made a transparent migration of on-premise applications to the cloud a difficult, sometimes trial-and-error based endeavor. This paper suggests a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud settings and are specified with architectural notations. The proposed migration patterns are based on empirical evi-dence from a number of migration projects, best practices for cloud architectures and a systematic literature review of existing research. The pattern catalogue allows an or-ganization to (1) select appropriate architecture migration patterns based on their ob-jectives, (2) compose them to define a migration plan, and (3) extend them based on the identification of new patterns in new contexts.


title: Sliding-mode control of the DC-DC flyback converter in discontinuous conduction mode

abstract: In this paper, a novel approach for two-loop control of the DC-DC flyback converter in discontinuous conduction mode is presented by using sliding mode controller. The proposed controller can regulate output of the converter in wide range of input voltage and load resistance. In order to verify accuracy and efficiency of the developed sliding mode controller, proposed method is simulated in MATLAB/Simulink. It is shown that the developed controller has faster dynamic response compared with standard integrated circuit (MIC38C42-5) based regulators.


title: Modified Starch-Chitosan Edible Films: Physicochemical and Mechanical Characterization

abstract: Starch and chitosan are widely used for preparation of edible films that are of great interest in food preservation. This work was aimed to analyze the relationship between structural and physical properties of edible films based on a mixture of chitosan and modified starches. In addition, films were tested for antimicrobial activity against Listeria innocua. Films were prepared by the casting method using chitosan (CT), waxy (WS), oxidized (OS) and acetylated (AS) corn starches and their mixtures. The CT-starches films showed improved barrier and mechanical properties as compared with those made from individual components, CT-OS film presented the lowest thickness (74 ± 7 µm), water content (11.53% ± 0.85%, w/w), solubility (26.77% ± 1.40%, w/v) and water vapor permeability ((1.18 ± 0.48) × 10−9 g·s−1·m−1·Pa−1). This film showed low hardness (2.30 ± 0.19 MPa), low surface roughness (Rq = 3.20 ± 0.41 nm) and was the most elastic (Young’s modulus = 0.11 ± 0.06 GPa). In addition, films made from CT-starches mixtures reduced CT antimicrobial activity against L. innocua, depending on the type of modified starch. This was attributed to interactions between acetyl groups of AS with the carbonyl and amino groups of CT, leaving CT with less positive charge. Interaction of the pyranose ring of OS with CT led to increased OH groups that upon interaction with amino groups, decreased the positive charge of CT, and this effect is responsible for the reduced antimicrobial activity. It was found that the type of starch modification influenced interactions with chitosan, leading to different films properties.


title: Calibration of strapdown magnetometers in magnetic field domain

abstract: This paper presents an algorithm for calibrating strapdown magnetometers in the magnetic field domain. In contrast to the traditional method of compass swinging, which computes a series of heading correction parameters and thus is limited to use with two-axis systems, this algorithm estimates magnetometer output errors directly. Therefore, this new algorithm can be used to calibrate a full three-axis magnetometer triad. The calibration algorithm uses an iterated, batch least-squares estimator that is initialized using a two-step nonlinear estimator. The algorithm is simulated to validate convergence characteristics and further validated on experimental data collected using a magnetometer triad. It is shown that the postcalibration residuals are small and result in a system with heading errors on the order of 1 to 2 degrees.


title: Multiagent traffic management: an improved intersection control mechanism

abstract: Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by autonomous agents will be possible in the near future. In a previous paper, we proposed a reservation-based system for alleviating traffic congestion, specifically at intersections. This paper extends our prototype implementation in several ways with the aim of making it more implementable in the real world. In particular, we 1) add the ability of vehicles to turn, 2) enable them to accelerate while in the intersection, and 3) augment their interaction capabilities with a detailed protocol such that the vehicles do not need to know anything about the intersection control policy. The use of this protocol limits the interaction of the driver agent and the intersection manager to the extent that it is a reasonable approximation of reliable wireless communication. Finally, we describe how different intersection control policies can be expressed with this protocol and limited exchange of information. All three improvements are fully implemented and tested, and we present detailed empirical results validating their effectiveness.


title: A predictive model of menu performance

abstract: Menus are a primary control in current interfaces, but there has been relatively little theoretical work to model their performance. We propose a model of menu performance that goes beyond previous work by incorporating components for Fitts' Law pointing time, visual search time when novice, Hick-Hyman Law decision time when expert, and for the transition from novice to expert behaviour. The model is able to predict performance for many different menu designs, including adaptive split menus, items with different frequencies and sizes, and multi-level menus. We tested the model by comparing predictions for four menu designs (traditional menus, recency and frequency based split menus, and an adaptive 'morphing' design) with empirical measures. The empirical data matched the predictions extremely well, suggesting that the model can be used to explore a wide range of menu possibilities before implementation.


title: Predicting a Business Star in Yelp from Its Reviews Text Alone

abstract: Yelp online reviews are invaluable source of information for users to choose where to visit or what to eat among numerous available options. But due to overwhelming number of reviews, it is almost impossible for users to go through all reviews and find the information they are looking for. To provide a business overview, one solution is to give the business a 1-5 star(s). This rating can be subjective and biased toward users personality. In this paper, we predict a business rating based on user-generated reviews texts alone. This not only provides an overview of plentiful long review texts but also cancels out subjectivity. Selecting the restaurant category from Yelp Dataset Challenge, we use a combination of three feature generation methods as well as four machine learning models to find the best prediction result. Our approach is to create bag of words from the top frequent words in all raw text reviews, or top frequent words/adjectives from results of Part-of-Speech analysis. Our results show Root Mean Square Error (RMSE) of 0.6 for the combination of Linear Regression with either of the top frequent words from raw data or top frequent adjectives after Part-of-Speech (POS).


title: Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization

abstract: Low-bit deep neural networks (DNNs) become critical for embedded applications due to their low storage requirement and computing efficiency. However, they suffer much from the non-negligible accuracy drop. This paper proposes the stochastic quantization (SQ) algorithm for learning accurate low-bit DNNs. The motivation is due to the following observation. Existing training algorithms approximate the real-valued elements/filters with low-bit representation all together in each iteration. The quantization errors may be small for some elements/filters, while are remarkable for others, which lead to inappropriate gradient direction during training, and thus bring notable accuracy drop. Instead, SQ quantizes a portion of elements/filters to low-bit with a stochastic probability inversely proportional to the quantization error, while keeping the other portion unchanged with full-precision. The quantized and full-precision portions are updated with corresponding gradients separately in each iteration. The SQ ratio is gradually increased until the whole network is quantized. This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs. Experiments show that SQ can consistently and significantly improve the accuracy for different low-bit DNNs on various datasets and various network structures.


title: Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar

abstract: Based on high-resolution radars a new approach for determining the full 2D-motion state (yaw rate, longitudinal and lateral speed) of an extended rigid object in a single measurement is proposed. The system does not rely on any model assumptions and is independent of the exact position, expansion and orientation of the object. In comparison to related methods it is not based on temporal filtering, e.g. a Kalman Filter. These methods are subject to an initialization phase and depend heavily on compliance of the underlying dynamic model. In contrast to temporal filtering, the proposed approach reduces the time to react to critical situations that occur in many safety and advanced driving assistance applications. This paper analyzes the velocity profile (radial velocity over azimuth angles) of the object received by two Doppler radar sensors. The approach can handle white noise and systematic variations (e.g. micro-Doppler of wheels) in the signal. The proposed system is applied to predict the driving path of traffic participants. Measurement results are presented for a set-up with two 77 GHz automotive radar sensors.


title: The neural correlates of sex differences in emotional reactivity and emotion regulation

abstract: Sex differences in emotional responding have been repeatedly postulated but less consistently shown in empirical studies. Because emotional reactions are modulated by cognitive appraisal, sex differences in emotional responding might depend on differences in emotion regulation. In this study, we investigated sex differences in emotional reactivity and emotion regulation using a delayed cognitive reappraisal paradigm and measured whole-brain BOLD signal in 17 men and 16 women. During fMRI, participants were instructed to increase, decrease, or maintain their emotional reactions evoked by negative pictures in terms of cognitive reappraisal. We analyzed BOLD responses to aversive compared to neutral pictures in the initial viewing phase and the effect of cognitive reappraisal in the subsequent regulation phase. Women showed enhanced amygdala responding to aversive stimuli in the initial viewing phase, together with increased activity in small clusters within the prefrontal cortex and the temporal cortex. During cognitively decreasing emotional reactions, women recruited parts of the orbitofrontal cortex, the anterior cingulate, and the dorsolateral prefrontal cortex to a lesser extent than men, while there was no sex effect on amygdala activity. In contrast, compared to women, men showed an increased recruitment of regulatory cortical areas during cognitively increasing initial emotional reactions, which was associated with an increase in amygdala activity. Clinical implications of these findings are discussed.


title: Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation

abstract: In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption that different distributions should be applied for different datasets, we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. An interesting property of the Expectation-Maximization algorithm for the latter is that in the maximization step, each dimension in each component is chosen to be either a Gaussian or a Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks.


title: What makes great teaching? Review of the underpinning research.

abstract: This report reviews over 200 pieces of research to identify the elements of teaching with the strongest evidence of improving attainment. It finds some common practices can be harmful to learning and have no grounding in research. Specific practices which are supported by good evidence of their effectiveness are also examined and six key factors that contribute to great teaching are identified. The report also analyses different methods of evaluating teaching including: using ‘value-added’ results from student test scores; observing classroom teaching; and getting students to rate the quality of their teaching.


title: Decaf: Moving Device Drivers to a Modern Language

abstract: Writing code to interact with external devices is inherently difficult, and the added demands of writing device drivers in C for kernel mode compounds the problem. This environment is complex and brittle, leading to increased development costs and, in many cases, unreliable code. Previous solutions to this problem ignore the cost of migrating drivers to a better programming environment and require writing new drivers from scratch or even adopting a new operating system. ::: ::: We present Decaf Drivers, a system for incrementally converting existing Linux kernel drivers to Java programs in user mode. With support from program-analysis tools, Decaf separates out performance-sensitive code and generates a customized kernel interface that allows the remaining code to be moved to Java. With this interface, a programmer can incrementally convert driver code in C to a Java decaf driver. The Decaf Drivers system achieves performance close to native kernel drivers and requires almost no changes to the Linux kernel. Thus, Decaf Drivers enables driver programming to advance into the era of modern programming languages without requiring a complete rewrite of operating systems or drivers. ::: ::: With five drivers converted to Java, we show that Decaf Drivers can (1) move the majority of a driver's code out of the kernel, (2) reduce the amount of driver code, (3) detect broken error handling at compile time with exceptions, (4) gracefully evolve as driver and kernel code and data structures change, and (5) perform within one percent of native kernel-only drivers.


title: A Review on Augmented Reality for Virtual Heritage System

abstract: Augmented reality is one of the emerging technologies to reconstruct the historical building and monument in the previous era, where the user experiences with the real environment or virtual scene. In education, Virtual Heritage becomes as a platform of learning, motivating and understanding of certain events and historical elements for the students and researchers. The significance of reconstruction of digital culture heritage are to preserve, protect and interpret of our cultural and history. In recent year, there are a number of significant researches and techniques that have been developed, which is focusing on virtual restitution of historical sites. This paper will present an overview on augmented reality in Virtual Heritage system and also consists with the explanation of techniques to reconstruct the historical sites.


title: Wearable medical devices for tele-home healthcare

abstract: The world's ageing population and prevalence of chronic diseases have lead to high demand for tele-home healthcare, in which vital-signs monitoring is essential. An overview of state-of-art wearable technologies for remote patient-monitoring is presented, followed by case studies on a cuffless blood pressure meter, ring-type heart rate monitor, and Bluetoothtrade mark-based ECG monitor. Aim of our project is to develop a tele-home healthcare system which utilizes wearable devices, wireless communication technologies, and multisensor data fusion methods. As an important part of this system, a cuffless BP meter has been developed and tested on 30 subjects in a total of 71 trials over a period of five months. Preliminary results show a mean error (ME) of 1.82 mmHg and standard deviation of error (SDE) of 7.62 mmHg in systolic pressure; while ME and SDE in diastolic pressure are 0.45 mmHg and 5.27 mmHg, respectively.


title: Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment

abstract: PURPOSE ::: Oncological treatment is being increasingly complex, and therefore, decision making in multidisciplinary teams is becoming the key activity in the clinical pathways. The increased complexity is related to the number and variability of possible treatment decisions that may be relevant to a patient. In this paper, we describe validation of a multidisciplinary cancer treatment decision in the clinical domain of head and neck oncology. ::: ::: ::: METHOD ::: Probabilistic graphical models and corresponding inference algorithms, in the form of Bayesian networks, can support complex decision-making processes by providing a mathematically reproducible and transparent advice. The quality of BN-based advice depends on the quality of the model. Therefore, it is vital to validate the model before it is applied in practice. ::: ::: ::: RESULTS ::: For an example BN subnetwork of laryngeal cancer with 303 variables, we evaluated 66 patient records. To validate the model on this dataset, a validation workflow was applied in combination with quantitative and qualitative analyses. In the subsequent analyses, we observed four sources of imprecise predictions: incorrect data, incomplete patient data, outvoting relevant observations, and incorrect model. Finally, the four problems were solved by modifying the data and the model. ::: ::: ::: CONCLUSION ::: The presented validation effort is related to the model complexity. For simpler models, the validation workflow is the same, although it may require fewer validation methods. The validation success is related to the model's well-founded knowledge base. The remaining laryngeal cancer model may disclose additional sources of imprecise predictions.


Emotion AI , Real-Time Emotion Detection using CNN


title: Elasticity of polymer-anchored membranes

abstract: The renormalization of the elastic constants of a polymer-anchored membrane is investigated by means of systematic Monte Carlo simulations. Our results indicate that when polymers are grafted on both sides, and are in the brush regime, the excess bending rigidity scales as N3σα (N and σ being the number of statistical segments and grafting density, respectively) with 7/3 < α < 5. When the membrane is grafted on one side only, the induced excess bending modulus depends on the bare membrane bending modulus, κ0, and approaches a value of (3/2)κ0. These results are largely in agreement with the self-consistent field theory and scaling arguments using the blob picture.


title: Network monitoring using traffic dispersion graphs (tdgs)

abstract: Monitoring network traffic and detecting unwanted applications has become a challenging problem, since many applications obfuscate their traffic using unregistered port numbers or payload encryption. Apart from some notable exceptions, most traffic monitoring tools use two types of approaches: (a) keeping traffic statistics such as packet sizes and interarrivals, flow counts, byte volumes, etc., or (b) analyzing packet content. In this paper, we propose the use of Traffic Dispersion Graphs (TDGs) as a way to monitor, analyze, and visualize network traffic. TDGs model the social behavior of hosts ("who talks to whom"), where the edges can be defined to represent different interactions (e.g. the exchange of a certain number or type of packets). With the introduction of TDGs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.


title: A Recursive Approach to Low Complexity Codes

abstract: A method is described for constructing long error-correcting codes from one or more shorter error-correcting codes, referred to as subcodes, and a bipartite graph. A graph is shown which specifies carefully chosen subsets of the digits of the new codes that must be codewords in one of the shorter subcodes. Lower bounds to the rate and the minimum distance of the new code are derived in terms of the parameters of the graph and the subeodes. Both the encoders and decoders proposed are shown to take advantage of the code's explicit decomposition into subcodes to decompose and simplify the associated computational processes. Bounds on the performance of two specific decoding algorithms are established, and the asymptotic growth of the complexity of decoding for two types of codes and decoders is analyzed. The proposed decoders are able to make effective use of probabilistic information supplied by the channel receiver, e.g., reliability information, without greatly increasing the number of computations required. It is shown that choosing a transmission order for the digits that is appropriate for the graph and the subcodes can give the code excellent burst-error correction abilities. The construction principles


STARPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures


title: From classification to quantification in tweet sentiment analysis

abstract: Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper, we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. “prevalence”) of the different classes in the dataset. The latter task is called quantification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluation measures different from those used for classification. In this paper, we show (by carrying out experiments using two learners, seven quantification-specific algorithms, and 11 TSC datasets) that using quantification-specific algorithms produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers interested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluation measures.


title: Real-Time Multi-scale Action Detection from 3D Skeleton Data

abstract: In this paper we introduce a real-time system for action detection. The system uses a small set of robust features extracted from 3D skeleton data. Features are effectively described based on the probability distribution of skeleton data. The descriptor computes a pyramid of sample covariance matrices and mean vectors to encode the relationship between the features. For handling the intra-class variations of actions, such as action temporal scale variations, the descriptor is computed using different window scales for each action. Discriminative elements of the descriptor are mined using feature selection. The system achieves accurate detection results on difficult unsegmented sequences. Experiments on MSRC-12 and G3D datasets show that the proposed system outperforms the state-of-the-art in detection accuracy with very low latency. To the best of our knowledge, we are the first to propose using multi-scale description in action detection from 3D skeleton data.


title: Task-specific signal transmission from prefrontal cortex in visual selective attention

abstract: This study combines transcranial magnetic stimulation (TMS) and electroencephalography (EEG) and finds that TMS over frontal eye fields affects EEG over more posterior areas. This effect was modulated by the task and attentional preparation and provides causal evidence for the existence of a prefrontal top-down control signal.


title: A Reconfigurable K-/Ka-Band Power Amplifier With High PAE in 0.18- $\mu$m SiGe BiCMOS for Multi-Band Applications

abstract: This paper presents a high power efficient broad-band programmable gain amplifier with multi-band switching. The proposed two stage common-emitter amplifier, by using the current reuse topology with a magnetically coupled transformer and a MOS varactor bank as a frequency tunable load, achieves a 55.9% peak power added efficiency (PAE), a peak saturated power of $+$ 11.1 dBm, a variable gain from 1.8 to 16 dB, and a tunable large signal 3-dB bandwidth from 24.3 to 35 GHz. The design is fabricated in a commercial 0.18- $\mu$ m SiGe BiCMOS technology and measured with an output 1-dB gain compression point which is better than $+$ 9.6 dBm and a maximum dc power consumption of 22.5 mW from a single 1.8 V supply. The core amplifier, excluding the measurement pads, occupies a die area of 500 $\mu{\hbox {m}}\times {\hbox {450}}~\mu{\hbox {m}}$ .


title: Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment

abstract: The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map-based methods also define dense shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed.


title: The inconspicuous penis in children

abstract: The term 'inconspicuous penis' refers to a group of anatomical abnormalities in which the penis looks smaller than is expected. Micropenis can be defined as 'true micropenis'--which results from a defect in the hypothalamic-pituitary-gonadal axis--and 'micropenis secondary to congenital anatomical anomalies of the surrounding and overlying structures'--also known as 'concealed penis'. The different forms of concealed penis include webbed penis, congenital megaprepuce and partially hidden penis caused by prepubic adiposity. This disorder can also have iatrogenic causes resulting from adhesions that are secondary to circumcision--this type of concealed penis is known as 'trapped penis'. However, in both groups, micropenis is defined as a stretched penile length that is at least 2.5 SD below the mean for the patient's age, but without any other penile defects. Patients with true micropenis can be managed with testosterone, which has demonstrated good penile elongation results in the long term. Surgery also has a pivotal role in reconstruction for elongating the penis and for correction of anatomical abnormalities in concealed penis.


title: Robust solving of optical motion capture data by denoising

abstract: Raw optical motion capture data often includes errors such as occluded markers, mislabeled markers, and high frequency noise or jitter. Typically these errors must be fixed by hand - an extremely time-consuming and tedious task. Due to this, there is a large demand for tools or techniques which can alleviate this burden. In this research we present a tool that sidesteps this problem, and produces joint transforms directly from raw marker data (a task commonly called "solving") in a way that is extremely robust to errors in the input data using the machine learning technique of denoising. Starting with a set of marker configurations, and a large database of skeletal motion data such as the CMU motion capture database [CMU 2013b], we synthetically reconstruct marker locations using linear blend skinning and apply a unique noise function for corrupting this marker data - randomly removing and shifting markers to dynamically produce billions of examples of poses with errors similar to those found in real motion capture data. We then train a deep denoising feed-forward neural network to learn a mapping from this corrupted marker data to the corresponding transforms of the joints. Once trained, our neural network can be used as a replacement for the solving part of the motion capture pipeline, and, as it is very robust to errors, it completely removes the need for any manual clean-up of data. Our system is accurate enough to be used in production, generally achieving precision to within a few millimeters, while additionally being extremely fast to compute with low memory requirements.


title: Multilingual Recurrent Neural Networks with Residual Learning for Low-Resource Speech Recognition

abstract: The shared-hidden-layer multilingual deep neural network (SHL-MDNN), in which the hidden layers of feed-forward deep neural network (DNN) are shared across multiple languages while the softmax layers are language dependent, has been shown to be effective on acoustic modeling of multilingual low-resource speech recognition. In this paper, we propose that the shared-hidden-layer with Long Short-Term Memory (LSTM) recurrent neural networks can achieve further performance improvement considering LSTM has outperformed DNN as the acoustic model of automatic speech recognition (ASR). Moreover, we reveal that shared-hidden-layer multilingual LSTM (SHL-MLSTM) with residual learning can yield additional moderate but consistent gain from multilingual tasks given the fact that residual learning can allievate the degradation problem of deep LSTMs. Experimental results demonstrate that SHL-MLSTM can relatively reduce word error rate (WER) by 2.1-6.8\% over SHL-MDNN trained using six languages and 2.6-7.3\% over monolingual LSTM trained using the language specific data on CALLHOME datasets. Additional WER reduction, about relatively 2\% over SHL-MLSTM, can be obtained through residual learning on CALLHOME datasets, which demonstrates residual learning is useful for SHL-MLSTM on multilingual low-resource ASR.


title: Reinforcement learning-based multi-agent system for network traffic signal control

abstract: A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cross-blocking. A five-intersection traffic network has been studied in which each intersection is governed by an autonomous intelligent agent. Two types of agents, a central agent and an outbound agent, were employed. The outbound agents schedule traffic signals by following the longest-queue-first (LQF) algorithm, which has been proved to guarantee stability and fairness, and collaborate with the central agent by providing it local traffic statistics. The central agent learns a value function driven by its local and neighbours' traffic conditions. The novel methodology proposed here utilises the Q-Learning algorithm with a feedforward neural network for value function approximation. Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings.


title: CONTROL OF UNICYCLE TYPE ROBOTS Tracking, Path Following and Point Stabilization

abstract: This paper considers the motion control problem of unicycle type mobile robots. We present the mathematical model of the mobile robots taken explicitly into account their dynamics and formulate the respectively motion control strategies of tracking and path-following. Two types of controller s presented in the literature are revised and their performance are illustrated through computer simulations. The problem of regulation to a point is also addressed.


title: How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans

abstract: Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.


title: Supply Chain Management and Retailing

abstract: Retailers are now the dominant partners in most supply systems and have used their positions to re-engineer operations and partnerships with suppliers and other logistic service providers. No longer are retailers the passive recipients of manufacturer allocations, but instead are the active channel controllers organizing supply in anticipation of, and reaction to consumer demand. This paper reflects on the ongoing transformation of retail supply chains and logistics. If considers this transformation through an examination of the fashion, grocery and selected other retail supply chains, drawing on practical illustrations. Current and future challenges are then discussed.


title: Matching As An Econometric Evaluation Estimator

abstract: This paper develops the method of matching as an econometric evaluation estimator. A rigorous distribution theory for kernel-based matching is presented. The method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic. We focus on the method of propensity score matching and show that it is not necessarily better, in the sense of reducing the variance of the resulting estimator, to use the propensity score method even if propensity score is known. We extend the statistical literature on the propensity score by considering the case when it is estimated both parametrically and nonparametrically. We examine the benefits of separability and exclusion restrictions in improving the efficiency of the estimator. Our methods also apply to the econometric selection bias estimator.


title: Coalitional Game Theory for Communication Networks: A Tutorial

abstract: In this tutorial, we provided a comprehensive overview of coalitional game theory, and its usage in wireless and communication networks. For this purpose, we introduced a novel classification of coalitional games by grouping the sparse literature into three distinct classes of games: canonical coalitional games, coalition formation games, and coalitional graph games. For each class, we explained in details the fundamental properties, discussed the main solution concepts, and provided an in-depth analysis of the methodologies and approaches for using these games in both game theory and communication applications. The presented applications have been carefully selected from a broad range of areas spanning a diverse number of research problems. The tutorial also sheds light on future opportunities for using the strong analytical tool of coalitional games in a number of applications. In a nutshell, this article fills a void in existing communications literature, by providing a novel tutorial on applying coalitional game theory in communication networks through comprehensive theory and technical details as well as through practical examples drawn from both game theory and communication application.


title: Identifying at-risk students in massive open online courses

abstract: Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their successes, a major problem faced by MOOCs is low completion rates. In this paper, we explore the accurate early identification of students who are at risk of not completing courses. We build predictive models weekly, over multiple offerings of a course. Furthermore, we envision student interventions that present meaningful probabilities of failure, enacted only for marginal students. To be effective, predicted probabilities must be both well-calibrated and smoothed across weeks. Based on logistic regression, we propose two transfer learning algorithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks. Experimental results on two offerings of a Coursera MOOC establish the effectiveness of our algorithms.


title: Gender differences in students' mathematics game playing

abstract: The investigation monitored the digital game-playing behaviours of 428 primary-aged students (aged 10-12 years). Chi-square analysis revealed that boys tend to spend more time playing digital games than girls while boys and girls play quite different game genres. Subsequent analysis revealed statistically significant gender differences in terms of the types of mathematics-rich games students prefer to play. Girls preferred to play games that required problem solving, quantitative computations and the interpretation of graphs. Boys preferred games that required visual/spatial engagement. Given the fact that boys outperform girls on spatial tasks and mathematics assessment items that contain graphics, this study has implications for the development of students' mathematics sense making.


title: Improper analysis of trials randomised using stratified blocks or minimisation

abstract: Many clinical trials restrict randomisation using stratified blocks or minimisation to balance prognostic factors across treatment groups. It is widely acknowledged in the statistical literature that the subsequent analysis should reflect the design of the study, and any stratification or minimisation variables should be adjusted for in the analysis. However, a review of recent general medical literature showed only 14 of 41 eligible studies reported adjusting their primary analysis for stratification or minimisation variables. We show that balancing treatment groups using stratification leads to correlation between the treatment groups. If this correlation is ignored and an unadjusted analysis is performed, standard errors for the treatment effect will be biased upwards, resulting in 95% confidence intervals that are too wide, type I error rates that are too low and a reduction in power. Conversely, an adjusted analysis will give valid inference. We explore the extent of this issue using simulation for continuous, binary and time-to-event outcomes where treatment is allocated using stratified block randomisation or minimisation. Copyright © 2011 John Wiley & Sons, Ltd.


title: A Hybrid Particle Level Set Method for Improved Interface Capturing

abstract: In this paper, we propose a new numerical method for improving the mass conservation properties of the level set method when the interface is passively advected in a flow field. Our method uses Lagrangian marker particles to rebuild the level set in regions which are underresolved. This is often the case for flows undergoing stretching and tearing. The overall method maintains a smooth geometrical description of the interface and the implementation simplicity characteristic of the level set method. Our method compares favorably with volume of fluid methods in the conservation of mass and purely Lagrangian schemes for interface resolution. The method is presented in three spatial dimensions.


Conventional Sintering Effects on the Microstructure and Electrical Characteristics of Low-Voltage Ceramic Varistor


Information Technology, Materiality, and Organizational Change: A Professional Odyssey


title: Models, relaxations and exact approaches for the capacitated vehicle routing problem

abstract: In this paper we review the exact algorithms based on the branch and bound approach proposed in the last years for the solution of the basic version of the vehicle routing problem (VRP), where only the vehicle capacity constraints are considered. These algorithms have considerably increased the size of VRPs that can be solved with respect to earlier approaches. Moreover, at least for the case in which the cost matrix is asymmetric, branch and bound algorithms still represent the state-of-the-art with respect to the exact solution. Computational results comparing the performance of different relaxations and algorithms on a set of benchmark instances are presented. We conclude by examining possible future directions of research in this field.


title: An efficient and secure data storage in Mobile Cloud Computing through RSA and Hash function

abstract: Main call for Cloud computing is that users only utilize what they required and only pay for what they really use. Mobile Cloud Computing refers to an infrastructure where data processing and storage can happen away from mobile device. Portio research estimates that mobile subscribers worldwide will reach 6.9 billion by the end of 2013 and 8 billion by the end of 2016. Ericsson also forecasts that mobile subscriptions will reach 9 billion by 2017. Due to increasing use of mobile devices the requirement of cloud computing in mobile devices arise, which gave birth to Mobile Cloud Computing. Mobile devices do not need to have large storage capacity and powerful CPU speed. Due to storing data on cloud there is an issue of data security. Because of the risk associated with data storage many IT professionals are not showing their interest towards Mobile Cloud Computing. To ensure the correctness of users' data in the cloud, we propose an effective mechanism with salient feature of data integrity and confidentiality. This paper proposed a mechanism which uses the concept of RSA algorithm, Hash function along with several cryptography tools to provide better security to the data stored on the mobile cloud.


title: Lane detection and tracking using a new lane model and distance transform

abstract: Lane detection is a significant component of driver assistance systems. Highway-based lane departure warning solutions are in the market since the mid-1990s. However, improving and generalizing vision-based lane detection remains to be a challenging task until recently. Among various lane detection methods developed, strong lane models, based on the global assumption of lane shape, have shown robustness in detection results, but are lack of flexibility to various shapes of lane. On the contrary, weak lane models will be adaptable to different shapes, as well as to maintain robustness. Using a typical weak lane model, particle filtering of lane boundary points has been proved to be a robust way to localize lanes. Positions of boundary points are directly used as the tracked states in the current research. This paper introduces a new weak lane model with this particle filter-based approach. This new model parameterizes the relationship between points of left and right lane boundaries, and can be used to detect all types of lanes. Furthermore, a modified version of an Euclidean distance transform is applied on an edge map to provide information for boundary point detection. In comparison to an edge map, properties of this distance transform support improved lane detection, including a novel initialization and tracking method. This paper fully explains how the application of this distance transform greatly facilitates lane detection and tracking. Two lane tracking methods are also discussed while focusing on efficiency and robustness, respectively. Finally, the paper reports about experiments on lane detection and tracking, and comparisons with other methods.


title: AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service

abstract: Dynamically adjusting the number of virtual machines (VMs) assigned to a cloud application to keep up with load changes and interference from other uses typically requires detailed application knowledge and an ability to know the future, neither of which are readily available to infrastructure service providers or application owners. The result is that systems need to be over-provisioned (costly), or risk missing their performance Service Level Objectives (SLOs) and have to pay penalties (also costly). AGILE deals with both issues: it uses wavelets to provide a medium-term resource demand prediction with enough lead time to start up new application server instances before performance falls short, and it uses dynamic VM cloning to reduce application startup times. Tests using RUBiS and Google cluster traces show that AGILE can predict varying resource demands over the medium-term with up to 3.42× better true positive rate and 0.34× the false positive rate than existing schemes. Given a target SLO violation rate, AGILE can efficiently handle dynamic application workloads, reducing both penalties and user dissatisfaction.


title: PowerNap: eliminating server idle power

abstract: Data center power consumption is growing to unprecedented levels: the EPA estimates U.S. data centers will consume 100 billion kilowatt hours annually by 2011. Much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. Typical idle periods though frequent--last seconds or less, confounding simple energy-conservation approaches. In this paper, we propose PowerNap, an energy-conservation approach where the entire system transitions rapidly between a high-performance active state and a near-zero-power idle state in response to instantaneous load. Rather than requiring fine-grained power-performance states and complex load-proportional operation from each system component, PowerNap instead calls for minimizing idle power and transition time, which are simpler optimization goals. Based on the PowerNap concept, we develop requirements and outline mechanisms to eliminate idle power waste in enterprise blade servers. Because PowerNap operates in low-efficiency regions of current blade center power supplies, we introduce the Redundant Array for Inexpensive Load Sharing (RAILS), a power provisioning approach that provides high conversion efficiency across the entire range of PowerNap's power demands. Using utilization traces collected from enterprise-scale commercial deployments, we demonstrate that, together, PowerNap and RAILS reduce average server power consumption by 74%.


title: Sparsity-Inspired Sphere Decoding (SI-SD): A Novel Blind Detection Algorithm for Uplink Grant-Free Sparse Code Multiple Access

abstract: Sparse code multiple access (SCMA), as a promising non-orthogonal multiple access scheme for the 5G system, aims to achieve massive connections and grant-free transmission in the radio access scenario. In this paper, we propose a blind detection scheme for the uplink grant-free SCMA transmission based on a novel sparsity-inspired sphere decoding (SI-SD) algorithm. By introducing one additional all-zero code word, each user’s status and data can be jointly detected, thus avoiding the redundant pilot overhead. Considering the sparsity features in the grant-free SCMA transmission, we establish its mathematical model where the degree of sparsity is characterized by a transmission probability parameter, which will be estimated during the SI-SD detection process. With such a priori probability, the proposed SI-SD algorithm will achieve the maximum a posteriori (MAP) detection. Furthermore, unlike the conventional sphere decoding, in the grant-free SCMA scenario, strong constraints are proven to exist among the nodes in the proposed SI-SD algorithm which can be utilized to early remove some improbable transmit hypotheses. In addition, a reduced sparsity-inspired MAP metric constitutes a tight sphere constraint which in turn implies less valid hypotheses within the search sphere. By using the above two strategies, the complexity of the SI-SD can be efficiently reduced.


title: Dual-Band Base Station Array Using Filtering Antenna Elements for Mutual Coupling Suppression

abstract: This paper presents a novel dual-band base station antenna array using filtering antenna elements for size miniaturization. It consists of two $1 \times 6$ subarrays arranged side by side, which are designed for Digital Cellular System (DCS: 1710–1880 MHz) and Wideband Code Division Multiple Access (WCDMA: 1920–2170 MHz) applications. The two subarrays are composed of filtering antenna elements with high in-band radiation efficiency and out-of-band radiation rejection levels. The radiation of the DCS subarray is suppressed in the WCDMA band and vice versa. Mutual coupling between the two subarrays, therefore, can be suppressed and high isolation can be obtained with reduced subarray spacing. For demonstration, a dual-band filtering antenna array is designed and fabricated. The overall width of the array is only 206 mm, which is much narrower than that of typical industrial products ( $\sim 290$ mm). An isolation of 35 dB is obtained between the two subarrays without any decoupling network. The measured antenna gains are about 14.2 and 14.5 dBi for DCS and WCDMA bands, respectively, and the 3-dB beamwidths of the horizontal radiation patterns are 65° ± 5°. In addition, null filling below the main beam in the vertical radiation patterns is realized by elaborately designing a feed network to manipulate the output magnitude and phase of each array element. The proposed array is suitable for potential base station applications.


title: A Study of Expansions of Posterior Distributions

abstract: Johnson (1970) obtained expansions for marginal posterior distributions through Taylor expansions. Here, the posterior expansion is expressed in terms of the likelihood and the prior together with their derivatives. Recently, Weng (2010) used a version of Stein's identity to derive a Bayesian Edgeworth expansion, expressed by posterior moments. Since the pivots used in these two articles are the same, it is of interest to compare these two expansions. We found that our O(t −1/2) term agrees with Johnson's arithmetically, but the O(t −1) term does not. The simulations confirmed this finding and revealed that our O(t −1) term gives better performance than Johnson's.


title: RFID based attendance system

abstract: Most educational institutions' administrators are concerned about student irregular attendance. Truancies can affect student overall academic performance. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. Radio Frequency Identification (RFID) based attendance system is one of the solutions to address this problem. This system can be used to take attendance for student in school, college, and university. It also can be used to take attendance for workers in working places. Its ability to uniquely identify each person based on their RFID tag type of ID card make the process of taking the attendance easier, faster and secure as compared to conventional method. Students or workers only need to place their ID card on the reader and their attendance will be taken immediately. With real time clock capability of the system, attendance taken will be more accurate since the time for the attendance taken will be recorded. The system can be connected to the computer through RS232 or Universal Serial Bus (USB) port and store the attendance taken inside database. An alternative way of viewing the recorded attendance is by using HyperTerminal software. A prototype of the system has been successfully fabricated.


title: A RFID-based Material Tracking Information System

abstract: Radio Frequency Identification (RFID) is the next generation wireless communication technology applicable to various areas. For the beneficial features of RFID, we integrate RFID readers into the Material Tracking Information System. In this paper, we propose the function and solution architecture for the system. Two methods for the client application to communicate with the RFID readers are introduced, one of which is to build a RFID middleware server. The design of the RFID middleware is discussed. RFID reader and back-end database both help to automatically record and store the circulation history and current status of a tracked object, and they cooperate to synchronize the information flow and material flow. Prototypical intelligent RFID guided vehicle used in the warehouse is presented in the paper. Then the flowchart for one tracking subsystem is given and web server is built to provide convenient verbal and graphic interface for tracking.


title: Bayesian Inference for PCFGs via Markov Chain Monte Carlo

abstract: This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using the Inside-Outside algorithm. We illustrate these methods by estimating a sparse grammar describing the morphology of the Bantu language Sesotho, demonstrating that with suitable priors Bayesian techniques can infer linguistic structure in situations where maximum likelihood methods such as the Inside-Outside algorithm only produce a trivial grammar.


title: Phase measurements using direct conversion AM radio navigation

abstract: This paper presents a number of approaches for precision navigation using AM broadcasts and analog phase measurement techniques. By mixing signals down to a low intermediate frequency (IF) and locking on with a phase lock loop (PLL), phase differences can be measured with /spl plusmn/0.5/spl deg/ accuracy. This provides /spl plusmn/0.32 meters range accuracy with a 1300 kHz AM radio signal.


Scenario-based performance analysis of routing protocols for mobile ad-hoc networks


title: Privacy-preserving crowdsourced spectrum sensing

abstract: Crowdsourced spectrum sensing has great potential in improving current spectrum database services. Without strong incentives and location privacy protection in place, however, mobile users will be reluctant to act as mobile crowdsourcing workers for spectrum sensing tasks. In this paper, we present PriCSS, the first framework for a crowdsourced spectrum sensing service provider to select spectrum-sensing participants in a differentially privacy-preserving manner. Thorough theoretical analysis and simulation studies show that PriCSS can simultaneously achieve differential location privacy, approximate social cost minimization, and truthfulness.


title: Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview

abstract: Background modeling for foreground detection is often used in different applications ::: to model the background and then detect the moving objects in the scene like in video ::: surveillance. The last decade witnessed very significant publications in this field. Furthermore, ::: several surveys can be found in literature but none of them addresses an overall ::: review in this field. So, the purpose of this paper is to provide a complete survey ::: of the traditional and recent approaches. First, we categorize the different approaches ::: found in literature. We have classified them in terms of the mathematical models used ::: and we have discussed them in terms of the critical situations that they claim to handle. ::: Furthermore, we present the available resources, datasets and libraries. Then, we ::: conclude with several promising directions for future research.


title: Resource allocation for multiclass service in IoT uplink communications

abstract: The Internet of Things has drawn lots of research attention as the growing number of devices connected to the Internet. Long Term Evolution-Advanced (LTE-A) is a promising technology for wireless communication and it's also promising for IoT. The main challenge of incorporating IoT devices into LTE-A is a large number of IoT devices attempting to access the network in a short period which will greatly reduce the network performance. In order to improve the network utilization, we adopted a hierarchy architecture using a gateway for connecting the devices to the eNB and proposed a multiclass resource allocation algorithm for LTE based IoT communication. Simulation results show that the proposed algorithm can provide good performance both on data rate and latency for different QoS applications both in saturated and unsaturated environment.


title: Summarizing Search Results with Community-Based Question Answering

abstract: Previous work on snippet generation focused mainly on how to produce one snippet for an individual search result. This paper aims to generate snippets as a comprehensive overview for an entity query (e.g., flu) in a search-result page. Our approach first extracts the attributes (e.g., Symptom and diagnose) of the categories (e.g., Disease) from a community-based question-answering (CQA) website, and then generates the snippets based on how central a sentence is to the meaning of the query, its category, and how well it diversifies the attributes. Integer Linear Programming (ILP) is adopted to find the optimal sentence set. The experiments are conducted on Wikipedia and Yahoo! Answers. Experimental results demonstrate the effectiveness of our approach, compared to an existing commercial search engine and several summarization baselines.


title: Universally composable zero-knowledge protocol using trusted platform modules

abstract: Cryptographic protocols that are established as secure in the Universally Composable (UC) model of security provide strong security assurances even when run in complex environments. Unfortunately, in order to achieve such strong security properties, UC protocols are often impractical, and most non-trivial two-party protocols cannot be secure in the UC model without some sort of external capability (or "setup assumption") being introduced. Recent work by Hofheinz et al provided an important breakthrough in designing realistic universally composable two party protocols, in which they use trusted, tamper proof hardware as a special type of helping functionality which they call a catalyst. Hofheinz et al. use government issued signature cards as a catalyst to design universally composable protocols for zero-knowledge proofs and commitments, but did not give a complete security proof for either protocol. In this thesis, we consider another form of security hardware, Trusted Platform Modules (TPMs), which are more widespread than signature cards and are currently shipped as a part of almost every business laptop or desktop. Trusted Module Platforms are tamper evident devices which support cryptographic functionalities including digital signatures, but have a different key management model from signature cards. In this thesis we consider TPMs as catalysts and describe a universally composable zero knowledge protocol using Trusted Platform Modules. We also present a complete security proof for both the Hofheinz's universally composable zero knowledge protocol from signature cards and our universally composable zero knowledge protocol using TPMs as a catalyst.


title: Practical animation of liquids

abstract: We present a general method for modeling and animating liquids. The system is specifically designed for computer animation and handles viscous liquids as they move in a 3D environment and interact with graphics primitives such as parametric curves and moving polygons. We combine an appropriately modified semi-Lagrangian method with a new approach to calculating fluid flow around objects. This allows us to efficiently solve the equations of motion for a liquid while retaining enough detail to obtain realistic looking behavior. The object interaction mechanism is extended to provide control over the liquid s 3D motion. A high quality surface is obtained from the resulting velocity field using a novel adaptive technique for evolving an implicit surface.


title: Fusing Monocular Information in Multicamera SLAM

abstract: This paper explores the possibilities of using monocular simultaneous localization and mapping (SLAM) algorithms in systems with more than one camera. The idea is to combine in a single system the advantages of both monocular vision (bearings-only, infinite range observations but no 3-D instantaneous information) and stereovision (3-D information up to a limited range). Such a system should be able to instantaneously map nearby objects while still considering the bearing information provided by the observation of remote ones. We do this by considering each camera as an independent sensor rather than the entire set as a monolithic supersensor. The visual data are treated by monocular methods and fused by the SLAM filter. Several advantages naturally arise as interesting possibilities, such as the desynchronization of the firing of the sensors, the use of several unequal cameras, self-calibration, and cooperative SLAM with several independently moving cameras. We validate the approach with two different applications: a stereovision SLAM system with automatic self-calibration of the rig's main extrinsic parameters and a cooperative SLAM system with two independent free-moving cameras in an outdoor setting.


Robo-Advising As a Human-Machine Interaction System


Secure Electronic Voting Application Based on Face Recognition and Ciphering


title: Simple and effective visual question answering in a single modality

abstract: Visual question answering (VQA) comes as a result of great development in computer vision and natural language processing, which requires deep understanding of images and questions and effective integration of them. Current works on VQA simply concatenated visual and textual features or compared them via dot product, which were unable to eliminate the semantic difference between them. We argue to transfer VQA problem into a single modality and propose a simple and effective baseline method, utilizing Long Short-Term Memory (LSTM) properties to filter particular information specified by questions in the generic descriptions of the image. We provide thorough analysis and extensive experiments on VQA benchmark dataset to discuss performance of different methods and prove the effectiveness of our proposed method.


Learning to Play Computer Games with Deep Learning and Reinforcement Learning Final Report


title: A random finite set approach for dynamic occupancy grid maps with real-time application

abstract: Grid mapping is a well established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.


Enhanced End-of-Turn Detection for Speech to a Personal Assistant.


title: KeyDrown: Eliminating Keystroke Timing Side-Channel Attacks

abstract: Besides cryptographic secrets, side-channel attacks also leak sensitive user input. The most accurate attacks exploit cache timings or interrupt information to monitor keystroke timings and subsequently infer typed words and sentences. Previously proposed countermeasures fail to prevent keystroke timing attacks as they do not protect keystroke processing among the entire software stack. ::: We close this gap with KeyDrown, a new defense mechanism against keystroke timing attacks. KeyDrown injects a large number of fake keystrokes in the kernel to prevent interrupt-based attacks and Prime+Probe attacks on the kernel. All keystrokes, including fake keystrokes, are carefully propagated through the shared library in order to hide any cache activity and thus to prevent Flush+Reload attacks. Finally, we provide additional protection against Prime+Probe for password input in user space programs. We show that attackers cannot distinguish fake keystrokes from real keystrokes anymore and we evaluate KeyDrown on a commodity notebook as well as on two Android smartphones. We show that KeyDrown eliminates any advantage an attacker can gain from using interrupt or cache side-channel information.


title: Reliability of lower limb motor evoked potentials in stroke and healthy populations: How many responses are needed?

abstract: Abstract Objective To determine the intra- and inter-session reliability of motor evoked potential (MEP) size parameters in the lower limb of patients with stroke, focussing on the number of MEPs collected and the method of measuring MEP size. Methods Transcranial magnetic stimulation was used to elicit MEPs in the soleus muscle of patients with stroke ( n = 13) and age-matched healthy participants ( n = 13) during low level muscle activation. Two sets of 10 responses were collected in the first session and a further 10 responses collected in a second session held 7 days later. Four MEP size measurements were made using 4, 6, 8, or all 10 of the MEPs collected. Intra- and inter-session reliability was examined using intraclass correlation coefficients (ICC) and typical percentage error. Results Intrasession ICC statistics using 6 or more MEPs were >0.85 in the stroke group but intersession values were all Conclusions Reliability of intrasession MEP size is excellent in the lower limb of patients with stroke using as few as 6 MEPs but intersession reliability is poor. Significance Comparing MEP size measures across two or more sessions is questionable in the lower limb of patients with stroke.


title: Query-feature graphs: bridging user vocabulary and system functionality

abstract: This paper introduces query-feature graphs, or QF-graphs. QF-graphs encode associations between high-level descriptions of user goals (articulated as natural language search queries) and the specific features of an interactive system relevant to achieving those goals. For example, a QF-graph for the GIMP graphics manipulation software links the query "GIMP black and white" to the commands "desaturate" and "grayscale." We demonstrate how QF-graphs can be constructed using search query logs, search engine results, web page content, and localization data from interactive systems. An analysis of QF-graphs shows that the associations produced by our approach exhibit levels of accuracy that make them eminently usable in a range of real-world applications. Finally, we present three hypothetical user interface mechanisms that illustrate the potential of QF-graphs: search-driven interaction, dynamic tooltips, and app-to-app analogy search.


title: The Objective Structured Clinical Examination (OSCE): AMEE Guide No. 81. Part I: An historical and theoretical perspective

abstract: The Objective Structured Clinical Examination (OSCE) was first described by Harden in 1975 as an alternative to the existing methods of assessing clinical performance (Harden et al. 1975). The OSCE was designed to improve the validity and reliability of assessment of performance, which was previously assessed using the long case and short case examinations. Since then the use of the OSCE has become widespread within both undergraduate and postgraduate clinical education. We recognise that the introduction of the OSCE into an existing assessment programme is a challenging process requiring a considerable amount of theoretical and practical knowledge. The two parts of this Guide are designed to assist all those who intend implementing the OSCE into their assessment systems. Part I addresses the theoretical aspects of the OSCE, exploring its historical development, its place within the range of assessment tools and its core applications. Part II offers more practical information on the process of implementing an OSCE, including guidance on developing OSCE stations, choosing scoring rubrics, training examiners and standardised patients and managing quality assurance processes. Together we hope these two parts will act as a useful resource both for those choosing to implement the OSCE for the first time and also those wishing to quality assure their existing OSCE programme.


title: Proposing measures of flicker in the low frequencies for lighting applications

abstract: The IEEE Standards Working Group, IEEE PAR1789 "Recommending practices for modulating current in High Brightness LEDs for mitigating health risks to viewers" has been formed to advise the lighting industry and standards groups about the emerging concern of flicker in LED lighting. This paper intends to introduce new measures and definitions of lamp flicker in lighting. The discussion represents on-going work in IEEE PAR1789 that is vital to designing safe LED lamp drivers.


title: Global Aesthetics Consensus: Hyaluronic Acid Fillers and Botulinum Toxin Type A—Recommendations for Combined Treatment and Optimizing Outcomes in Diverse Patient Populations

abstract: BACKGROUND ::: Combination of fillers and botulinum toxin for aesthetic applications is increasingly popular. Patient demographics continue to diversify, and include an expanding population receiving maintenance treatments over decades. ::: ::: ::: METHODS ::: A multinational panel of plastic surgeons and dermatologists convened the Global Aesthetics Consensus Group to develop updated guidelines with a worldwide perspective for hyaluronic acid fillers and botulinum toxin. This publication considers strategies for combined treatments, and how patient diversity influences treatment planning and outcomes. ::: ::: ::: RESULTS ::: Global Aesthetics Consensus Group recommendations reflect increased use of combined treatments in the lower and upper face, and some midface regions. A fully patient-tailored approach considers physiologic and chronologic age, ethnically associated facial morphotypes, and aesthetic ideals based on sex and culture. Lower toxin dosing, to modulate rather than paralyze muscles, is indicated where volume deficits influence muscular activity. Combination of toxin with fillers is appropriate for several indications addressed previously with toxin alone. New scientific data regarding hyaluronic acid fillers foster an evidence-based approach to selection of products and injection techniques. Focus on aesthetic units, rather than isolated rhytides, optimizes results from toxin and fillers. It also informs longitudinal treatment planning, and analysis of toxin nonresponders. ::: ::: ::: CONCLUSIONS ::: The emerging objective of injectable treatment is facial harmonization rather than rejuvenation. Combined treatment is now a standard of care. Its use will increase further as we refine the concept that aspects of aging are intimately related, and that successful treatment entails identifying and addressing the primary causes of each. ::: ::: ::: CLINICAL QUESTION/LEVEL OF EVIDENCE ::: Therapeutic, V.


Analysing Political Discourse : Toward a Cognitive Approach 1


title: Sequential pattern recognition for medical records analysis

abstract: Automatic recognition of patterns is an important problem in a variety of engineering and scientific disciplines. The most common application in medicine relates to medical imaging for diagnostics' purpose. This paper presents another field of pattern recognition application - mining medical records. Analysis of treatment pathways (in which diagnostic and therapeutic procedures are sequenced on a timeline) as a mean of providing and standardized patient care of high quality, is one of the issues gaining in importance in the last decade in the field of medical informatics. As there is a need for scalable computational tools for pattern exploration, we propose a conceptual framework, adapted to Poland's context, for expressing data mining tasks to discover knowledge about patients and providers similarities involving multiple time and procedure granularities.


title: CoDine: an interactive multi-sensory system for remote dining

abstract: The pervasiveness of computing has extended into domestic realms, including the dining room. Beyond simply a place to consume food, the dining room is a social hub where family members meet and share experiences. Yet busy lifestyles can make it difficult to spend social time with your family. To provide a new solution for family bonding, this paper presents the CoDine system, a dining table embedded with interactive subsystems that augment and transport the experience of communal family dining to create a sense of coexistence among remote family members. CoDine connects people in different locations through shared dining activities: gesture-based screen interaction, mutual food serving, ambient pictures on an animated tablecloth, and the transportation of edible messages. Rather than focusing on functionality or efficiency, CoDine aims to provide people with an engaging interactive dining experience through enriched multi-sensory communication.


title: Evaluation of mobile games using playability heuristics

abstract: Mobile games currently occupy a very large market share amongst mobile applications. The success of these games depends on how well they appeal to their users. In this paper, four mobile games have been analyzed based on playability heuristics from the literature study. The results give us a glimpse of the success of these games in creating a positive user experience. Results of the analysis were then compared with the market statistics of the individual games to understand whether success of the games in the market can be attributed to the judicious following of the playability heuristics.


title: Which Training Methods for GANs do actually Converge?

abstract: Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn a generative image model of all 1000 Imagenet classes in a single GAN with little hyperparameter tuning.


title: Towards secure and dependable software-defined networks

abstract: Software-defined networking empowers network operators with more flexibility to program their networks. With SDN, network management moves from codifying functionality in terms of low-level device configurations to building software that facilitates network management and debugging. By separating the complexity of state distribution from network specification, SDN provides new ways to solve long-standing problems in networking --- routing, for instance --- while simultaneously allowing the use of security and dependability techniques, such as access control or multi-path. However, the security and dependability of the SDN itself is still an open issue. In this position paper we argue for the need to build secure and dependable SDNs by design. As a first step in this direction we describe several threat vectors that may enable the exploit of SDN vulnerabilities. We then sketch the design of a secure and dependable SDN control platform as a materialization of the concept here advocated. We hope that this paper will trigger discussions in the SDN community around these issues and serve as a catalyser to join efforts from the networking and security & dependability communities in the ultimate goal of building resilient control planes.


title: Recent structural studies of carbohydrate-binding modules

abstract: Carbohydrate-binding modules (CBMs) are found in many carbohydrate-active enzymes. CBMs bind to a range of polysaccharides, their primary function being to increase the catalytic efficiency of the carbohydrate-active enzymes against soluble and/or insoluble substrates. CBMs bind to their target ligands with high specificities and affinities. Thus, CBM systems are excellent models to study the mechanism of protein-carbohydrate interaction. To date, CBMs have been classified into 45 different families and many structural and functional studies have been reported. At present, three-dimensional structures of CBMs from 31 different families have been determined. These structures demonstrate that the fold most commonly found in CBMs is the beta-sandwich. In the past few years, about 10 new structures from different families have been reported. These enable detailed classification of CBM structures. This article reviews recent structural and functional studies of CBMs and discusses the sub-classification of beta-sandwich CBMs.


title: Dialing while Driving? A Bounded Rational Analysis of Concurrent Multi-task Behavior

abstract: When people conduct multiple tasks in tandem, such as dialing a cell phone while driving a car, they often interleave the two tasks, for instance by returning attention to the primary driving task after entering bursts of three of four digits at a time. In order to explain why people tend to interleave these tasks at this particular interval, a control model of steering behavior is described that focuses on understanding how environmental and psychological constraints interact to determine driver performance. We use this model to predict the amount of time that people are prepared to stray from the driving task while engaging in a secondary in-car task and, by consequence, the degree of task interleaving. In particular, a modeling experiment was conducted to determine the consequences of systematically varying the time interval between consecutive steering updates for driving performance. The results of this analysis were then used to demonstrate why returning attention to driving after entering bursts of three of four digits at a time is a particularly efficient strategy: It does not allow driving performance to become too egregious, while at the same time keeping the additional time costs that are incurred as a result of interleaving tasks minimal.


title: The Relationship Between Job Satisfaction and Organizational Commitment: A Case of Alps Malaysia Sdn Bhd.

abstract: This research purpose is to evaluate the relationship between the commitment level of staff and officers of the ALPS Malaysia Sdn Bhd which located at Jengka, Pahang. This study will try to find the relationship between sub dimensions of job satisfaction and the connection with the organizational commitment including salary/pay, chance for promotion, job condition, supervision and co-worker. This study also investigated the relationship between organizational commitment based on demographic factors such as designation/position, gender, race, age, education, marital status, working experience, department attached and salary. The questionnaire is developed based on Organization Commitment Questionnaire (OCQ) which introduced by Mowday, Steers and Porter, 1979 to investigate the organizational commitment level among the staff. While to measure the level of job satisfaction, the Job Description Index (JDI) which developed by Smith, Kendall and Hulin, 1969, will be used for the questionnaires. The research question comprise with 44 items using the Likert scale where about 157 peoples are chosen to participate in this assessment. Data were analyzed using Statistical Software Package for Social Science (SPSS) version 19.0. There are two types of statistics are used including the descriptive statistics to see the percentage and statistical inference using t tests, ANOVA and Pearson bivariate correlation to examine the relationship between selected variables. The result of the study found a significant relationship between the selected of 8 demographic factors and 5 dimensions of job satisfaction on the organizational commitment. Hopefully the company can maintain the highest level of the organizational commitment among their staff for ensure the company more productive and more competitive in the future.