title: Graph-based privacy-preserving data publication

abstract: We propose a graph-based framework for privacy preserving data publication, which is a systematic abstraction of existing anonymity approaches and privacy criteria. Graph is explored for dataset representation, background knowledge specification, anonymity operation design, as well as attack inferring analysis. The framework is designed to accommodate various datasets including social networks, relational tables, temporal and spatial sequences, and even possible unknown data models. The privacy and utility measurements of the anonymity datasets are also quantified in terms of graph features. Our experiments show that the framework is capable of facilitating privacy protection by different anonymity approaches for various datasets with desirable performance.


title: SOLAR ABSORBER WITH A STRUCTURED SURFACE – A WAY TO INCREASE EFFICIENCY

abstract: The basic idea of a solar absorber’s thermal gain increase is the keyhole effect utilization during which the radiation is absorbed by multiple reflections on cavity walls. The lattice of pyramidal or conical cavities on the solar absorber surface can be formed to create a structured surface leading to its overall absorptivity increase and to a reduction of the surface absorptivity dependence on the solar radiation incident beam angle changes caused by the daily and annual solar cycles. This contribution concludes the results of simulations of the effect of cavity geometry, geographical position and absorber orientation on its thermal gain with respect to the technological manufacturability of cavities. Furthermore, the real construction of the absorber with a structured surface using laser welding and parallel hydroforming is briefly described.


title: An On-Chip High-Speed Current Sensor Applied in the Current-Mode DC–DC Converter

abstract: A current-mode dc-dc converter with an on-chip current sensor is presented in this letter. The current sensor has significant improvement on the current-sensing speed. The sensing ratio of the current sensor has low sensitivity to the variation of the process, voltage, temperature and loading. The current sensor combines the sensed inductor current signal with the compensation ramp signal and the output of the error amplifier smoothly. The settling time of the current sensor is less than 10 ns. In the current-mode dc-dc converter application, the differential output of the current sensor can be directly sent to the pulse-width modulation comparator. With the proposed current sensor, the dc-dc converter could realize a low duty cycle with a high switching frequency. The dc-dc converter has been fabricated by CSMC 0.5-μm 5-V CMOS process with a die size of 2.25 mm 2. Experimental results show that the current-mode converter can achieve a duty cycle down to 0.11 with a switching frequency up to 4 MHz. The measured transient response time is less than 6 μs as the load current changes between 50 and 600 mA, rapidly.


title: Android permissions: User attention, comprehension, and behavior

abstract: Android's permission system is intended to inform users about the risks of installing applications. When a user installs an application, he or she has the opportunity to review the application's permission requests and cancel the installation if the permissions are excessive or objectionable. We examine whether the Android permission system is effective at warning users. In particular, we evaluate whether Android users pay attention to, understand, and act on permission information during installation. We performed two usability studies: an Internet survey of 308 Android users, and a laboratory study wherein we interviewed and observed 25 Android users. Study participants displayed low attention and comprehension rates: both the Internet survey and laboratory study found that 17% of participants paid attention to permissions during installation, and only 3% of Internet survey respondents could correctly answer all three permission comprehension questions. This indicates that current Android permission warnings do not help most users make correct security decisions. However, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension. We present recommendations for improving user attention and comprehension, as well as identify open challenges.


title: Discovering Causal Signals in Images

abstract: This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution. Second, we use our causal direction classifier to effectively distinguish between features of objects and features of their contexts in collections of static images. Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.


title: Characterizing MPI and Hybrid MPI+Threads Applications at Scale: Case Study with BFS

abstract: With the increasing prominence of many-core architectures and decreasing per-core resources on large supercomputers, a number of applications developers are investigating the use of hybrid MPI+threads programming to utilize computational units while sharing memory. An MPI-only model that uses one MPI process per system core is capable of effectively utilizing the processing units, but it fails to fully utilize the memory hierarchy and relies on fine-grained internodes communication. Hybrid MPI+threads models, on the other hand, can handle internodes parallelism more effectively and alleviate some of the overheads associated with internodes communication by allowing more coarse-grained data movement between address spaces. The hybrid model, however, can suffer from locking and memory consistency overheads associated with data sharing. In this paper, we use a distributed implementation of the breadth-first search algorithm in order to understand the performance characteristics of MPI-only and MPI+threads models at scale. We start with a baseline MPI-only implementation and propose MPI+threads extensions where threads independently communicate with remote processes while cooperating for local computation. We demonstrate how the coarse-grained communication of MPI+threads considerably reduces time and space overheads that grow with the number of processes. At large scale, however, these overheads constitute performance barriers for both models and require fixing the root causes, such as the excessive polling for communication progress and inefficient global synchronizations. To this end, we demonstrate various techniques to reduce such overheads and show performance improvements on up to 512K cores of a Blue Gene/Q system.


title: Convergent validity of the International Physical Activity Questionnaire (IPAQ): meta-analysis

abstract: OBJECTIVE ::: The purpose of the present study was to use a meta-analytic approach to examine the convergent validity of the international physical activity questionnaire (IPAQ). ::: ::: ::: DESIGN ::: Systematic review by meta-analysis. ::: ::: ::: SETTING ::: The relevant studies were surveyed from five electronic databases. Primary outcomes of interest were the product-moment correlation coefficients between IPAQ and other instruments. Five separate meta-analyses were performed for each physical activity (PA) category of IPAQ: walking, moderate PA (MPA), total moderate PA (TMPA), vigorous PA (VPA) and total PA (TPA). The corrected mean effect size (ESρ) unaffected by statistical artefacts (i.e., sampling error and reliability) was calculated for each PA category. Selected moderator variables were length of IPAQ (i.e., short and long form), reference period (i.e., last 7 d and usual week), mode of administration (i.e., interviewer and self-reported), language (i.e., English and translated) and instruments (i.e., accelerometer, pedometer and subjective measure). ::: ::: ::: SUBJECTS ::: A total of 152 ESρ across five PA categories were retrieved from twenty-one studies. ::: ::: ::: RESULTS ::: The results showed small- to medium-sized ESρ (0.27-0.49). The highest value was observed in VPA while the lowest value was found in MPA. The ESρ were differentiated by some of the moderator variables across PA categories. ::: ::: ::: CONCLUSIONS ::: The study shows the overall convergent validity of IPAQ within each PA category. Some differences in degree of convergent validity across PA categories and moderator variables imply that different research conditions should be taken into account prior to deciding on use of the appropriate type of IPAQ.


title: Theoretical Analysis and Experimental Study on an Avalanche Transistor-Based Marx Generator

abstract: High-voltage, nanosecond, high-repetitive-frequency, and portable pulse generators are always required in many fields. Avalanche transistor-based Marx circuits have been widely studied to generate pulses satisfying the above requirements. However, the basic topology of an avalanche transistor-based circuit, configuration of circuit parameters, and optimal working performance (including output impedance matching) were still scarcely discussed. In this paper, the detailed process of analyzing and designing a compact Marx generator using avalanche transistors was described. Based on the conventional principles of avalanche transistors and Marx circuit, a list of useful and interesting conclusions was discovered by experiment. An example with specific parameter requirements was utilized for the clearness of illustration. The $12~{\rm cm}\times 4$ cm Marx circuit could finally generate pulses with a 2.5-kV amplitude, 6.0-ns width, 10-kHz repetitive frequency, lower than a 100-ps jitter and $\sim 125$ -kW maximum peak output power on a matched 50- $\Omega $ resistive load. The pulse amplitude could be adjusted from 1.5 to 2.5 kV and the pulse width could also be broadened. This paper may help to provide a reference to similar methods of producing high-voltage, nanosecond, and high-repetitive-frequency pulses.


title: Burning, paradoxical hypertrichosis, leukotrichia and folliculitis are four major complications of intense pulsed light hair removal therapy.

abstract: BACKGROUND ::: Lasers and intense pulsed light (IPL) are now used worldwide for prolonged photo-epilation. ::: ::: ::: OBJECTIVE ::: To study the side effects of IPL hair removal therapy among 2541 female hirsute patients as the largest series reported so far in the literature. ::: ::: ::: METHODS ::: The first series of 1000 patients were treated by the Lumina IPL system. (Lynton Lasers, UK). The second series of 1541 patients were treated by Vasculight-SR, a multi-functional laser and IPL system (Lumenis, Inc., USA). The fluences used for Lumina varied between 16 and 30 J/cm2. The parameters used for Vasculight-SR were as follows: fluences ranged from 33 to 42 J/cm2; pulse duration was 3-5 ms, and pulse delay was 20-80 ms. The pulse mode was either double or triple. The parameters were chosen according to the patients' Fitzpatrick skin types and tolerance. The cut-off filters frequently used were 695, 755, 645 and 615 nm in descending order of their frequencies. Patients were treated every 4-6 weeks and for eight sessions or more. All patients were followed for up to 20 months. ::: ::: ::: RESULTS ::: Series 1 - burning and its sequelae presented as post-inflammatory hyperpigmentation (PIH), post-inflammatory hypopigmentation (PIHPO), bulla and erosion, and finally scar formation in 75, 10, 64 and one case, respectively. Leukotrichia, folliculitis, and paradoxical hypertrichosis were recorded in 40, 35, and 12 more cases, respectively. Series 2 - PIH and PIHPO were recorded in 28 and four patients. Erosion and crust formation were recorded in 28 patients. Paradoxical effect, leukotrichia and acne formation were seen in 79, 27 and 19 cases, respectively. ::: ::: ::: CONCLUSION ::: Burning and its sequellae, leukotrichia, paradoxical hypertrichosis and folliculitis are four major side effects of IPL hair removal therapy in our study.


title: A Fast-Transient Quasi-V$^{\bf 2}$ Switching Buck Regulator Using AOT Control With a Load Current Correction (LCC) Technique

abstract: A switching regulator with quasi-V2 adaptive on-time (AOT) control that provides a fast load transient response is proposed in this paper. The feed-forward path network allows the proposed switching regulator to achieve a fast transient response and stable operation without requiring an output capacitor with large equivalent series resistance. The proposed adaptive on-time controller makes the switching frequency pseudofixed in the continuous conduction mode and works in the pulse-frequency modulation mode under ultralight-load conditions to maintain the conversion efficiency. The AOT controller adjusts the on-time according to the supply voltage and load current conditions. The measurement results verify that the switching regulator can operate under load current between 5 and 800 mA for the supply voltage of 3.3-4.2 V and an output voltage of 1.2 V. The recovery time is 3.6 μs and the voltage drop is 75 mV when the load current is increased from 5 to 750 mA.


title: Multiple simultaneous acoustic source localization in urban terrain

abstract: Experiences developing a sensor network-based acoustic shooter localization system are presented. The system is able to localize the position of a shooter and the trajectory of the projectile using observed acoustic events, such as the muzzle blast and the ballistic shockwave. The network consists of a large number of cheap sensors communicating through an ad-hoc wireless network, which enables the system to resolve multiple simultaneous acoustic sources, eliminate multipath effects, tolerate multiple sensor failures while providing good coverage and high accuracy, even in such challenging environment as urban terrain. The paper describes the hardware and software platform developed for this application and summarizes the lessons learned during the development of the system.


title: Game-O-Matic: Generating Videogames that Represent Ideas

abstract: In this paper, we describe Game-O-Matic, a videogame authoring tool and generator that creates games that represent ideas. Through using a simple concept map input system, networks of nouns connected by verbs, Game-O-Matic is able to assemble simple arcade style game mechanics into videogames that represent the ideas represented in the concept map. Inspired by a view that videogames convey messages through their mechanics, Game-O-Matic makes use of the rhetorical affordances of explicitly defined abstract gameplay patterns, which we call micro-rhetorics. This paper explains how Game-O-Matic uses the concept map input to select appropriate abstract patterns of gameplay and then how these mash ups of patterns are shaped into coherent playable games that can be said to represent the user's intent.


title: Sleep duration, positive attitude toward life, and academic achievement: the role of daytime tiredness, behavioral persistence, and school start times.

abstract: Sleep timing undergoes profound changes during adolescence, often resulting in inade- quate sleep duration. The present study examines the relationship of sleep duration with positive attitude toward life and academic achievement in a sample of 2716 adolescents in Switzerland (mean age: 15.4 years, SD 1⁄4 0.8), and whether this relationship is mediated by increased daytime tiredness and lower self-discipline/behavioral persistence. Further, we address the question whether adolescents who start school modestly later (20 min; n 1⁄4 343) receive more sleep and report better functioning. Sleeping less than an average of 8 h per night was related to more tiredness, inferior behavioral persistence, less positive attitude toward life, and lower school grades, as compared to longer sleep duration. Daytime tiredness and behavioral persistence medi- ated the relationship between short sleep duration and positive attitude toward life and school grades. Students who started school 20 min later received reliably more sleep and reported less tiredness.


title: Correlates of seeking emergency medical help in the event of an overdose in British Columbia, Canada: Findings from the Take Home Naloxone program

abstract: Abstract Background British Columbia (BC), Canada, is experiencing an unprecedented number of opioid overdoses mainly due to the contamination of illicit drugs with fentanyl and its analogues. Reluctance to seek emergency medical help (i.e., by calling 9-1-1) has been identified as a barrier to optimal care for overdose victims. This study aimed to identify the correlates of seeking help during an overdose event when naloxone was administered via BC’s Take Home Naloxone (THN) program. Methods In this cross-sectional study, we reviewed administrative records (from July 2015 to December 2017) about overdose events submitted by THN participants when they received their replacement naloxone kits (n = 2350). The primary outcome of the study was reported calling 9-1-1 and modified Poisson regression models were built to investigate the factors associated with help-seeking during an overdose event. Results Most overdose victims were men (69.0%) and >30 years old (61.5%). Overall, participants reported calling 9-1-1 in 1310 (55.7%) overdose events. In the multivariable model, the likelihood of calling 9-1-1 was significantly and positively associated with the overdose victim being male and receiving rescue breathing. The likelihood of calling 9-1-1 was significantly and negatively associated with the overdoses occurring in private residences and health regions other than Vancouver Coastal which delivers services to mostly urban residents. Conclusion Overall, medical help was sought for 55.7% of overdoses where naloxone was administered. Overdoses occurring among male victims as well as those receiving higher doses of naloxone and mouth-to-mouth rescue breathing were associated with a higher likelihood of help-seeking by responders. Future interventions need to encourage people who witness an overdose to seek emergency medical help.


title: Internal vs. external information in visual perception

abstract: One of the more compelling beliefs about vision is that it is based on representations that are coherent and complete, with everything in the visual field described in great detail. However, changes made during a visual disturbance are found to be difficult to see, arguing against the idea that our brains contain a detailed, picture-like representation of the scene. Instead, it is argued here that a more dynamic, just-in-time representation is involved, one with deep similarities to the way that users interact with external displays. It is further argued that these similarities can provide a basis for the design of intelligent display systems that can interact with humans in highly effective and novel ways.


title: On-line planning of time-optimal, jerk-limited trajectories

abstract: Service robots which directly interact with humans in highly unstructured, unpredictable and dynamic environments must be able to flexibly adapt their motion in reaction to unforeseen events or obstacles and they must provide a new feasible trajectory in real-time. Hence, algorithms come into focus which replan the motion path and its time evolution from arbitrary initial conditions within milliseconds. We present a real-time algorithm to generate synchronised and time-optimal third-order manipulator trajectories complying maximal motion limits on velocity, acceleration and jerk. Experimental results carried out on a Mitsubishi PA10-7C arm are presented.


title: A Novel Video Dataset for Change Detection Benchmarking

abstract: Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website ::: 1 ::: and will be updated with feedback from academia and industry in the future.


title: Short links under attack: geographical analysis of spam in a URL shortener network

abstract: URL shortener services today have come to play an important role in our social media landscape. They direct user attention and disseminate information in online social media such as Twitter or Facebook. Shortener services typically provide short URLs in exchange for long URLs. These short URLs can then be shared and diffused by users via online social media, e-mail or other forms of electronic communication. When another user clicks on the shortened URL, she will be redirected to the underlying long URL. Shortened URLs can serve many legitimate purposes, such as click tracking, but can also serve illicit behavior such as fraud, deceit and spam. Although usage of URL shortener services today is ubiquituous, our research community knows little about how exactly these services are used and what purposes they serve. In this paper, we study usage logs of a URL shortener service that has been operated by our group for more than a year. We expose the extent of spamming taking place in our logs, and provide first insights into the planetary-scale of this problem. Our results are relevant for researchers and engineers interested in understanding the emerging phenomenon and dangers of spamming via URL shortener services.


I-arm-droid: A rewriting framework for in-app reference monitors for android applications


title: Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras

abstract: We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel approach to integrate constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense/semi-dense direct approaches while providing a higher reconstruction density than feature-based methods.


title: Performance evaluation of unified memory and dynamic parallelism for selected parallel CUDA applications

abstract: The aim of this paper is to evaluate performance of new CUDA mechanisms—unified memory and dynamic parallelism for real parallel applications compared to standard CUDA API versions. In order to gain insight into performance of these mechanisms, we decided to implement three applications with control and data flow typical of SPMD, geometric SPMD and divide-and-conquer schemes, which were then used for tests and experiments. Specifically, tested applications include verification of Goldbach’s conjecture, 2D heat transfer simulation and adaptive numerical integration. We experimented with various ways of how dynamic parallelism can be deployed into an existing implementation and be optimized further. Subsequently, we compared the best dynamic parallelism and unified memory versions to respective standard API counterparts. It was shown that usage of dynamic parallelism resulted in improvement in performance for heat simulation, better than static but worse than an iterative version for numerical integration and finally worse results for Golbach’s conjecture verification. In most cases, unified memory results in decrease in performance. On the other hand, both mechanisms can contribute to simpler and more readable codes. For dynamic parallelism, it applies to algorithms in which it can be naturally applied. Unified memory generally makes it easier for a programmer to enter the CUDA programming paradigm as it resembles the traditional memory allocation/usage pattern.


title: Survey on high performance analytics of bigdata with apache spark

abstract: This paper lays attention upon the advantages of Apache Spark over Hadoop MapReduce and analysis of real time data using time-series analysis. As Hadoop MapReduce is a widely used and famous execution engine for working with the storage and analysis of large datasets. In MapReduce, the data is read from the disk and the result is written to the Hadoop Distributed File System (HDFS) after a particular iteration and then the data is read from the HDFS for the next iteration. This whole process consumes a lot of disk space and time as well. The users had been objecting the problem of high latency and fault tolerance of the entire system. To overcome the issues and disadvantages of MapReduce, Apache Spark was developed. Apache Spark is an open-source project that ensures lower latency queries, iterative computations and real time processing on similar data. This paper also focuses on time-series analysis in Hadoop and Spark environment which processes and does analysis of real-time data and generates a pattern out of it to get a clearer glimpse of the statistics and characteristics of data thus making Spark even more efficient over MapReduce.


title: Automatic mining of source code repositories to improve bug finding techniques

abstract: We describe a method to use the source code change history of a software project to drive and help to refine the search for bugs. Based on the data retrieved from the source code repository, we implement a static source code checker that searches for a commonly fixed bug and uses information automatically mined from the source code repository to refine its results. By applying our tool, we have identified a total of 178 warnings that are likely bugs in the Apache Web server source code and a total of 546 warnings that are likely bugs in Wine, an open-source implementation of the Windows API. We show that our technique is more effective than the same static analysis that does not use historical data from the source code repository.


title: Deep learning of directional truncated signed distance function for robust 3D object recognition

abstract: In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression tasks. To achieve this, we first adopt the Truncated Signed Distance Function (TSDF) to encode the point cloud and extract low compact discriminative feature via unsupervised deep learning network. This approach can not only eliminate the dense scale sampling for offline model training but also reduce the distortion by mapping the 3D shape to the 2D plane and overcome the dependence on color cues. Then, we train a Hough forests to achieve multi-object detection and 6-DoF pose estimation simultaneously. In addition, we propose a robust multilevel verification strategy that effectively reduces the unpredictability of output values which occurs in the hough regression module. Experiments on public datasets demonstrate that our approach provides effective results comparable to the state-of-the-arts.


title: Key importance of compression properties in the biophysical characteristics of hyaluronic acid soft-tissue fillers

abstract: Abstract Hyaluronic acid (HA) soft-tissue fillers are the most popular degradable injectable products used for correcting skin depressions and restoring facial volume loss. From a rheological perspective, HA fillers are commonly characterised through their viscoelastic properties under shear-stress. However, despite the continuous mechanical pressure that the skin applies on the fillers, compression properties in static and dynamic modes are rarely considered. In this article, three different rheological tests (shear-stress test and compression tests in static and dynamic mode) were carried out on nine CE-marked cross-linked HA fillers. Corresponding shear-stress ( G ′, tan δ ) and compression ( E ′, tan δ c , normal force F N ) parameters were measured. We show here that the tested products behave differently under shear-stress and under compression even though they are used for the same indications. G ′ showed the expected influence on the tissue volumising capacity, and the same influence was also observed for the compression parameters E ′. In conclusion, HA soft-tissue fillers exhibit widely different biophysical characteristics and many variables contribute to their overall performance. The elastic modulus G ′ is not the only critical parameter to consider amongst the rheological properties: the compression parameters E ′ and F N also provide key information, which should be taken into account for a better prediction of clinical outcomes, especially for predicting the volumising capacity and probably the ability to stimulate collagen production by fibroblasts.


An Integrated Image Processing System for Leaf Disease Detection and Diagnosis.


title: Male-to-female vaginoplasty: Preecha’s surgical technique

abstract: The inverted peno-scrotal flap is considered the standard technique for vaginoplasty in male-to-female transsexuals. Nowadays, great importance is also given by patients to the reconstruction of the clitoro-labial complex; this is also reconstructed with tissue coming from glans penis, penile skin envelop and scrotal skin. Since the first sex reassignment surgery for biological males performed in Thailand in 1975, Dr Preecha and his team developed the surgical technique for vaginoplasty; many refinements have been introduced during the past 40 years, with nearly 3000 patients operated on. The scope of this paper is to present the surgical technique currently in use for vaginoplasty and clitoro-labioplasty and the refinements introduced at the Chulalongkorn University and at the Preecha Aesthetic Institute, Bangkok, Thailand. These refinements consist of cavity dissection with blunt technique, the use of skin graft in addition to the penile flap, shaping of the clitoris complex from penis glans and clitoral hood, and the use of the urethral mucosa to line the anterior fourchette of the neo-vagina. With the refinements introduced, it has been possible to achieve a result that is very close to the biological female genitalia.


title: Eukaryotic expression and purification of anti-epilepsy peptide of Buthus martensii Karsch and its protein interactions

abstract: The Asian scorpion Buthus martensil Karsch is important in the Chinese traditional medicine where it is used for the treatment of some nervous system diseases. The anti-epilepsy peptide (AEP) is a 61-amino-acid polypeptide extracted from the venom of B. martensil Karsch. Research has confirmed that it has anti-epileptic effects on the rat model of epilepsy. In this experiment, a cDNA library of AEP from the venom of B. martensil Karsch was constructed using RT-PCR; the primer was designed and used for the amplification. An expression vector of AEP was constructed using Pichia pastoris. Vector expression was induced, and protein purification was then performed. Bolting of the interaction molecule of AEP was by His pull down. Experimental results indicate high AEP expression, and the obtained protein was purified and compared with the control group. Four conspicuous protein bands were observed, and mass chromatographic analysis indicated that the four proteins were synaptosomal-associated protein of 25 kDa (SNAP-25), glial fibrillary acidic protein (GFAP), Glutamic acid decarboxylase (GAD) and N-methyl-d-aspartate (NMDA). Further, the four protein bands were verified by mammalian two-hybrid experiments and co-immunoprecipitation. AEP was found to interact with SNAP2 and NMDA. This provides experimental evidence for the mechanism of AEP’s anti-epileptic action and for the manufacture of a novel type anti-epileptic drug.


title: Design of via-less planer microstrip-to-waveguide transition with choke structure

abstract: Millimeter-wave technologies have been developed for applications to high-speed wireless communication systems and high angular resolution microwave sensors. Figure 1 shows one of the possible configurations of the millimeter-wave RF module when a microstrip antenna is used. The microstrip-to-waveguide transitions are necessary at the connection of the waveguide with the antenna feed and with the planar RF circuit. We have developed planer microstrip-to-waveguide transitions on a single-layer dielectric substrate in the millimeter-wave band [1], [2]. Via holes surround a rectangular waveguide to prevent leakage of parallel plate mode transmitting in the substrate. However, an additional process for via holes increases production cost and manufacturing errors of via-hole positions increases insertion loss of the transitions. Then, we have proposed a choke structure for replacement from via-hole arrangement [3]. The size of the waveguide short pattern composed of the metal plate on the substrate is designed to form choke structures in the substrate around the waveguide. The impedance of the open circuit with a quarter wavelengths transmission line is equivalent to the short circuit. Therefore, the impedance at the point along the waveguide profile is equivalent to the short circuit, when the open circuit at the edge of the metal plate is spaced by a quarter wavelengths from the waveguide profile in the substrate. First, the planar microstrip-to-waveguide transition is designed. Then, we optimized the choke structure for the transition in this work. The perturbed characteristics of the transition are discussed and the simulated performance is presented in this paper.


title: Back-Propagation Control Algorithm for Power Quality Improvement Using DSTATCOM

abstract: This paper presents an implementation of a three phase distribution static compensator (DSTATCOM) using a back propagation (BP) control algorithm for its functions such as harmonic elimination, load balancing and reactive power compensation for power factor correction, and zero voltage regulation under nonlinear loads. A BP-based control algorithm is used for the extraction of the fundamental weighted value of active and reactive power components of load currents which are required for the estimation of reference source currents. A prototype of DSTATCOM is developed using a digital signal processor, and its performance is studied under various operating conditions. The performance of DSTATCOM is found to be satisfactory with the proposed control algorithm for various types of loads.


title: Unite neuroscience , supercomputing , and nanotechnology to discover , demonstrate , and deliver the brain ’ s core algorithms

abstract: Unite neuroscience, supercomputing, and nanotechnology to discover, demonstrate, and deliver the brain's core algorithms.


title: Multicategory classification for support vector machines

abstract: We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik's Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k > 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.


title: Effectiveness of mindfulness-based cognitive therapy as an adjuvant to pharmacotherapy in patients with panic disorder or generalized anxiety disorder

abstract: Background: Mindfulness-based cognitive therapy (MBCT) has been widely used to treat patients with depressive disorder to prevent relapse. The objective of this study was to examine the effectiveness of newly developed MBCT program as an adjuvant to pharmacotherapy in the treatment of patients with panic disorder or generalized anxiety disorder. Methods: Forty-six patients with panic disorder or generalized anxiety disorder were assigned to either MBCTor an anxiety disorder education (ADE) program for a period of 8 weeks. The Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), and Symptom Checklist-90Revised (SCL-90-R) were used to assess the patients at 0 week and after the two programs had been running for 2, 4, and 8 weeks. Results: The MBCT group demonstrated significantly more improvement than the ADE group according to all anxiety (HAM-A, po0.01; BAI, po0.01; anxiety subscale of SCL-90-R, p 50.01) and depression (HAM-D, po0.01; BDI, po0.01; depression subscale of SCL-90-R, po0.01) scale scores. The obsessive-compulsive and phobic subscales of the SCL-90-R also showed significantly more improvement in the MBCT group. However, no significant improvement was observed in the MBCT group versus the ADE group in terms of the somatization, interpersonal sensitivity, paranoid ideation, or psychoticism subscale scores of the SCL-90-R. Conclusions: MBCT may be effective at relieving anxiety and depressive symptoms in patients with panic disorder or generalized anxiety disorder. However, well-designed, randomized controlled trials are needed. Depression and Anxiety 26:601–606, 2009. r 2009 Wiley-Liss, Inc.


title: Surgical Scar Location Preference for Pediatric Kidney and Pelvic Surgery: A Crowdsourced Survey

abstract: Purpose: The benefits of minimally invasive surgery in pediatric urology, such as reduced length of hospital stay and postoperative pain, are less predictable compared to findings in the adult literature. We evaluated the choices that adult patients make for themselves and their children regarding scar location.Materials and Methods: We surveyed the preference for scar location/size based on surgery for bladder and kidney procedures with additional questions assessing the impact of a hidden incision, length of hospital stay and pain. The survey was posted to Amazon® Mechanical Turk®.Results: We analyzed a total of 954 completed surveys. Surgical history was reported in 660 surveys (69%) with scar bother reported in 357 (54.2%). For pelvic surgery the initial choice was a Pfannenstiel incision for 434 respondents (45.5%), laparoscopy port incisions for 392 (41.1%) and no preference for incision location for 126 (13.2%). When incisions were illustrated relative to undergarments, 718 respondents (75.3%) chos...


title: Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks

abstract: Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic constraints among aspect term extraction and opinion term extraction to uncover their intra-task and inter-task relationship, which seeks an optimal solution over the neural predictions for both tasks. Extensive evaluations on three benchmark datasets demonstrate that our global inference approach is able to bring consistent improvements over several base models in different scenarios.


Cartoon-to-Photo Facial Translation with Generative Adversarial Networks


title: Network Service Chaining in Fog and Cloud Computing for the 5G Environment: Data Management and Security Challenges

abstract: In the last few years, we have seen an exponential increase in the number of Internet-enabled devices, which has resulted in popularity of fog and cloud computing among end users. End users expect high data rates coupled with secure data access for various applications executed either at the edge (fog computing) or in the core network (cloud computing). However, the bidirectional data flow between the end users and the devices located at either the edge or core may cause congestion at the cloud data centers, which are used mainly for data storage and data analytics. The high mobility of devices (e.g., vehicles) may also pose additional challenges with respect to data availability and processing at the core data centers. Hence, there is a need to have most of the resources available at the edge of the network to ensure the smooth execution of end-user applications. Considering the challenges of future user demands, we present an architecture that integrates cloud and fog computing in the 5G environment that works in collaboration with the advanced technologies such as SDN and NFV with the NSC model. The NSC service model helps to automate the virtual resources by chaining in a series for fast computing in both computing technologies. The proposed architecture also supports data analytics and management with respect to device mobility. Moreover, we also compare the core and edge computing with respect to the type of hypervisors, virtualization, security, and node heterogeneity. By focusing on nodes' heterogeneity at the edge or core in the 5G environment, we also present security challenges and possible types of attacks on the data shared between different devices in the 5G environment.


title: Constraint-Aware Role Mining via Extended Boolean Matrix Decomposition

abstract: The role mining problem has received considerable attention recently. Among the many solutions proposed, the Boolean matrix decomposition (BMD) formulation has stood out, which essentially discovers roles by decomposing the binary matrix representing user-to-permission assignment (UPA) into two matrices-user-to-role assignment (UA) and permission-to-role assignment (PA). However, supporting certain embedded constraints, such as separation of duty (SoD) and exceptions, is critical to the role mining process. Otherwise, the mined roles may not capture the inherent constraints of the access control policies of the organization. None of the previously proposed role mining solutions, including BMD, take into account these underlying constraints while mining. In this paper, we extend the BMD so that it reflects such embedded constraints by proposing to allow negative permissions in roles or negative role assignments for users. Specifically, by allowing negative permissions in roles, we are often able to use less roles to reconstruct the same given user-permission assignments. Moreover, from the resultant roles we can discover underlying constraints such as separation of duty constraints. This feature is not supported by any existing role mining approaches. Hence, we call the role mining problem with negative authorizations the constraint-aware role mining problem (CRM). We also explore other interesting variants of the CRM, which may occur in real situations. To enable CRM and its variants, we propose a novel approach, extended Boolean matrix decomposition (EBMD), which addresses the ineffectiveness of BMD in its ability of capturing underlying constraints. We analyze the computational complexity for each of CRM variants and present heuristics for problems that are proven to be NP-hard.


title: Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources

abstract: In order to extract event information from text, a machine reading model must learn to accurately read and interpret the ways in which that information is expressed. But it must also, as the human reader must, aggregate numerous individual value hypotheses into a single coherent global analysis, applying global constraints which reflect prior knowledge of the domain. ::: In this work we focus on the task of extracting plane crash event information from clusters of related news articles whose labels are derived via distant supervision. Unlike previous machine reading work, we assume that while most target values will occur frequently in most clusters, they may also be missing or incorrect. ::: We introduce a novel neural architecture to explicitly model the noisy nature of the data and to deal with these aforementioned learning issues. Our models are trained end-to-end and achieve an improvement of more than 12.1 F$_1$ over previous work, despite using far less linguistic annotation. We apply factor graph constraints to promote more coherent event analyses, with belief propagation inference formulated within the transitions of a recurrent neural network. We show this technique additionally improves maximum F$_1$ by up to 2.8 points, resulting in a relative improvement of $50\%$ over the previous state-of-the-art.


title: Predicting popularity of online videos using Support Vector Regression

abstract: In this work, we propose a regression method to predict the popularity of an online video measured by its number of views. Our method uses Support Vector Regression with Gaussian radial basis functions. We show that predicting popularity patterns with this approach provides more precise and more stable prediction results, mainly thanks to the nonlinear character of the proposed method as well as its robustness. We prove the superiority of our method against the state of the art using datasets containing almost 24 000 videos from YouTube and Facebook. We also show that using visual features, such as the outputs of deep neural networks or scene dynamics’ metrics, can be useful for popularity prediction before content publication. Furthermore, we show that popularity prediction accuracy can be improved by combining early distribution patterns with social and visual features and that social features represent a much stronger signal in terms of video popularity prediction than the visual ones.


title: Al-Azizi , "Automatic Vehicle Location Tracking System Based on GIS Environment

abstract: Recently, automatic vehicle location (AVL) has become more widely used, affordable and popular than ever before. AVL is used for different tracking purposes, especially for those related to tracking one vehicle or a fleet of vehicles. Tracking system technology was made possible by the integration of three new technologies: navigational technologies such as global positioning system (GPS), database technologies such as geographic information system (GIS) and communication technology such as general packet radio service (GPRS). The proposed software design ‘tracking system’ is used to pinpoint the position, ground speed and fuel level of a given vehicle. This improves fleet management by making it secure and more efficient. The system has the ability to detect the optimal path between source and destination, depending on many factors such as travel time, jam, topography and number of traffic lights. The authors applied greedy techniques (GT) such as Dijkstra's and Kruskal's algorithms to a graph weight depending on the proposed cost function (CF). The geofencing technique is applied to the system based on real coordinates and grants security and safety to the fleet of vehicles. The designed software offers more flexibility in loading digital maps. This proposed software has the ability to visualise the real position of vehicles on maps and to take decisions according to real-time information.


title: Human Endogenous Retrovirus-K(II) Envelope Induction Protects Neurons during HIV/AIDS

abstract: Human endogenous retroviruses (HERVs) are differentially expressed depending on the cell type and physiological circumstances. HERV-K has been implicated in the pathogenesis of several diseases although the functional consequences of its expression remain unknown. Human immunodeficiency virus (HIV) infection causes neuroinflammation with neuronal damage and death. Herein, we investigated HERV-K(II)/(HML-2) envelope (Env) expression and its actions in the brain during HIV/AIDS. HERV-K(II) Env expression was assessed in healthy brain tissues, autopsied HIV HIV− infected (HIV+) and uninfected (HIV−) brains and in neural cell cultures by real time RT-PCR, massively parallel (deep) sequencing, immunoblotting and immunohistochemistry. Neuronal and neural stem cells expressing HERV-K(II) Env were analyzed in assays of host responses including cellular viability, immune responses and neurobehavioral outcomes. Deep sequencing of human brain transcriptomes disclosed that RNA sequences encoded by HERV-K were among the most abundant HERV sequences detected in human brain. Comparison of different cell types revealed that HERV-K(II) env RNA abundance was highest in cultured human neurons but was suppressed by epidermal growth factor exposure. HERV-K(II) Env immunoreactivity was increased in the cerebral cortex from persons with HIV/AIDS, principally localized in neurons. Human neuronal cells transfected with HERV-K(II) Env exhibited increased NGF and BDNF expression. Expression of HERV-K(II) Env in neuronal cells increased cellular viability and prevented neurotoxicity mediated by HIV-1 Vpr. Intracerebral delivery of HERV-K(II) Env expressed by neural stem cells suppressed TNF-α expression and microglial activation while also improving neurobehavioral deficits in vpr/RAG1−/− mice. HERV-K(II) Env was highly expressed in human neurons, especially during HIV/AIDS, but in addition exerted neuroprotective effects. These findings imply that HERV gene products might exert adaptive effects in circumstances of pathophysiological stress, perhaps underlying the conservation of HERVs within the human genome.


title: Barriers to the Successful Integration of ICT in Teaching and Learning Environments : A Review of the Literature

abstract: The use of ICT in the classroom is very important for providing opportunities for students to learn to operate in an information age. Studying the obstacles to the use of ICT in education may assist educators to overcome these barriers and become successful technology adopters in the future. This paper provides a meta-analysis of the relevant literature that aims to present the perceived barriers to technology integration in science education. The findings indicate that teachers had a strong desire for to integrate ICT into education; but that, they encountered many barriers. The major barriers were lack of confidence, lack of competence, and lack of access to resources. Since confidence, competence and accessibility have been found to be the critical components of technology integration in schools, ICT resources including software and hardware, effective professional development, sufficient time, and technical support need to be provided to teachers. No one component in itself is sufficient to provide good teaching. However, the presence of all components increases the possibility of excellent integration of ICT in learning and teaching opportunities. Generally, this paper provides information and recommendation to those responsible for the integration of new technologies into science education.


Augmenting decisions of taxi drivers through reinforcement learning for improving revenues


1 Spectral Methods for Dimensionality Reduction


title: Model-Free Dual Heuristic Dynamic Programming

abstract: Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one hidden layer for both the action and the critic networks design, and use delayed objective functions to train both the action and the critic networks online over time. We test both the MF-DHP and MB-DHP approaches with a discrete time example and a continuous time example under the same parameter settings. Our simulation results demonstrate that the MF-DHP approach can obtain a control performance competitive with that of the traditional MB-DHP approach while requiring less computational resources.


Approximate Bayesian Computation for Distance-Dependent Learning


title: Synthesis and structural analysis of Ni0.45Cu0.55Mn2O4 by Williamson–Hall and size–strain plot methods

abstract: Abstract This paper describes synthesis and structural properties of Ni0.45Cu0.55Mn2O4 nanopowder, obtained by co-precipitation route. XRD pattern reveals cubic structure with lattice parameter 8.305 Å. We report crystallite size (D), micro strain (ε), dislocation density (ρD), and hopping lengths (LA and LB). We also report preferential orientation by texture coefficients [Tc (h k l)]. The Williamson-Hall plot and stress-strain plot also employed to understand the mechanical properties of materials.


title: Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system

abstract: Although the benefits of e-learning have been discussed in various previous studies; it is a critical issue of better understanding the reasons why some learners are dissatisfied with the e-learning experience. Therefore, this research investigates learners' satisfaction, behavioral intentions, and the effectiveness of the Blackboard e-learning system. A total of 424 university students were surveyed using a standard questionnaire. The results showed that perceived self-efficacy is a critical factor that influences learners' satisfaction with the Blackboard e-learning system. Perceived usefulness and perceived satisfaction both contribute to the learners' behavioral intention to use the e-learning system. Furthermore, e-learning effectiveness can be influenced by multimedia instruction, interactive learning activities, and e-learning system quality. This research proposes a conceptual model for understanding learners' satisfaction, behavioral intention, and effectiveness of using the e-learning system.


Security analysis of end-to-end encryption in Telegram


title: Enhanced LSB technique for audio steganography

abstract: The idea of this paper is to invent a new strategy in Steganography to get the minimum effect in audio which is used to hide data into it. “Progress always involves risk” Fredrick Wilcox observed that technological progress of computer science and the Internet altered the way we lived, and will continue to cast our life[1] . In this paper we have presented a Steganography method of embedding text data in an audio file. The basic approach behind this paper is to provide a good, well-organized method for hiding the data and sent to the destination in safer manner. In the proposed technique first the audio file is sampled and then appropriate bit is modified. In selected sample one bit is modified at least significant bit .The remaining bits may be used but it may be cause noise. We have attempted to provide an overview, theoretical framework about audio Steganography techniques and a novel approach to hide data in an audio using least significant bit (LSB).


title: Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources

abstract: One of the most important, but often ignored, parts of any clustering and classification algorithm is the computation of the similarity matrix. This is especially important when integrating high throughput biological data sources because of the high noise rates and the many missing values. In this paper we present a new method to compute such similarities for the task of classifying pairs of proteins as interacting or not. Our method uses direct and indirect information about interaction pairs to constructs a random forest (a collection of decision tress) from a training set. The resulting forest is used to determine the similarity between protein pairs and this similarity is used by a classification algorithm (a modified kNN) to classify protein pairs. Testing the algorithm on yeast data indicates that it is able to improve coverage to 20% of interacting pairs with a false positive rate of 50%. These results compare favorably with all previously suggested methods for this task indicating the importance of robust similarity estimates.


title: Exploring individual personality factors as drivers of M‐shopping acceptance

abstract: Purpose – The purpose of this paper is to evaluate how personality variables related to technology (innovativeness, compatibility and affinity) can modify the influence of classical technology acceptance model (TAM) variables on behavioural adoption intention of mobile shopping.Design/methodology/approach – The impact of innovativeness, compatibility, affinity, TAM beliefs (ease of use and usefulness) and attitude on mobile shopping adoption is tested through structural equation modelling techniques. The sample consisted of 470 Spanish mobile telephone users selected on the basis of convenience.Findings – Data analysis shows that the effect of perceived usefulness and, in a minor degree, perceived ease of use are over dimensioned if personality variables are omitted making intention formation to be perceived as more rational than it really is. Personality variables (affinity to mobile telephones, compatibility and innovativeness) have a direct and positive influence on the intention to engage in M‐shoppin...


title: Adapting Security Policy at Runtime for Connected Autonomous Vehicles

abstract: Models@run.time has appeared as a new paradigm for continuously monitoring software systems execution to enable self-adaptation. This adaptation happens whenever a change, a failure or a bug is introduced within the execution environment. It is done by a causal bidirectional connection between design and execution artifacts supported by feedback-loop information. We developed an approach to integrate a secure lightweight dynamic adaptation of access control policies when new security requirements are introduced at runtime. The proposed runtime adaptation is supported by a fast checking of design models at runtime against security constraints. A vehicle-2-x security case study is used to demonstrate the benefits of our approach.


title: Group signatures with verifier-local revocation

abstract: Group signatures have recently become important for enabling privacy-preserving attestation in projects such as Microsoft's ngscb effort (formerly Palladium). Revocation is critical to the security of such systems. We construct a short group signature scheme that supports Verifier-Local Revocation (VLR). In this model, revocation messages are only sent to signature verifiers (as opposed to both signers and verifiers). Consequently there is no need to contact individual signers when some user is revoked. This model is appealing for systems providing attestation capabilities. Our signatures are as short as standard RSA signatures with comparable security. Security of our group signature (in the random oracle model) is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear groups. We give a precise model for VLR group signatures and discuss its implications.


If I Cannot Have Her Everybody Can: Sexual Disclosure and Privacy Law


title: Web caching on smartphones: ideal vs. reality

abstract: Web caching in mobile networks is critical due to the unprecedented cellular traffic growth that far exceeds the deployment of cellular infrastructures. Caching on handsets is particularly important as it eliminates all network-related overheads. We perform the first network-wide study of the redundant transfers caused by inefficient web caching on handsets, using a dataset collected from 3 million smartphone users of a large commercial cellular carrier, as well as another five-month-long trace contributed by 20 smartphone users. Our findings suggest that redundant transfers contribute 18% and 20% of the total HTTP traffic volume in the two datasets. Also they are responsible for 17% of the bytes, 7% of the radio energy consumption, 6% of the signaling load, and 9% of the radio resource utilization of all cellular data traffic in the second dataset. Most of such redundant transfers are caused by the smartphone web caching implementation that does not fully support or strictly follow the protocol specification, or by developers not fully utilizing the caching support provided by the libraries. This is further confirmed by our caching tests of 10 popular HTTP libraries and mobile browsers. Improving the cache implementation will bring considerable reduction of network traffic volume, cellular resource consumption, handset energy consumption, and user-perceived latency, benefiting both cellular carriers and customers.


title: Can organizational knowledge capabilities affect knowledge sharing behavior ?

abstract: Recent concerns about the issue of knowledge management (KM) within an organization have accentuated the need for more efficient and effective knowledge sharing - that is, knowledge sharing plays an increasingly significant role in determining the outcomes of KM. Most scholars explore this issue from the economic perspective, while only a few research studies have focussed on organizational capabilities. Drawing from the theories of resource-based view and KM, this study aims to investigate the relationship between organizational knowledge capabilities (OKC) and knowledge sharing (KS). Using regression analysis with data from questionnaires collected in different industries with a valid response rate of 62.4%, the results show that OKC have a positive association with KS. Technical, structural, and human knowledge capabilities are significant for organizational knowledge sharing. Moreover, the effects of implementing KM on OKC and KS are also significant.


title: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences

abstract: We present and evaluate a person re-identification scheme for multi-camera surveillance system. Our approach uses matching of signatures based on interest-points descriptors collected on short video sequences. One of the originalities of our method is to accumulate interest points on several sufficiently time-spaced images during person tracking within each camera, in order to capture appearance variability. A first experimental evaluation conducted on a publicly available set of low-resolution videos in a commercial mall shows very promising inter-camera person re-identification performances (a precision of 82% for a recall of 78%). It should also be noted that our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making re- identification among hundreds of persons computationally feasible in less than ~ 1/5 second.


title: Income and Happiness: Towards a Unified Theory

abstract: Material aspirations are initially fairly similar among income groups; consequently more income brings greater happiness. Over the life cycle, however, aspirations grow along with income, and undercut the favourable effect of income growth on happiness, although the cross-sectional happiness-income difference persists. People think they were less happy in the past and will be happier in the future, because they project current aspirations to be the same throughout the life cycle, while income grows. But since aspirations actually grow along with income, experienced happiness is systematically different from projected happiness. Consequently, choices turn out to be based on false expectations.


title: Layout

abstract: As mobile devices are equipped with more memory and computational capability, a novel peer-to-peer communication model for mobile cloud computing is proposed to interconnect nearby mobile devices through various short range radio communication technologies to form mobile cloudlets, where every mobile device works as either a computational service provider or a client of a service requester. Though this kind of computation offloading benefits compute-intensive applications, the corresponding service models and analytics tools are remaining open issues. In this paper we categorize computation offloading into three modes: remote cloud service mode, connected ad hoc cloudlet service mode, and opportunistic ad hoc cloudlet service mode. We also conduct a detailed analytic study for the proposed three modes of computation offloading at ad hoc cloudlet.


title: Disk space guarantees as a distributed resource management problem: A case study

abstract: In the single system UNIX, successful completion of a write system call implies a guarantee of adequate disk space for any new pages created by the system call. To support such a guarantee in a distributed file system designers need to solve the problems of accurately estimating the space needed, communication overhead, and fault tolerance. In the Calypso file system, which is a cluster-optimized, distributed UNIX file system, we solve these problems using an advance-reservation scheme. Measurements show that the overhead of this scheme for typical UNIX usage patterns is 1% to 3%.


title: Demonstration of zero-offset-field operation for top-pinned MTJ with synthetic antiferromagnetic free layer

abstract: We have designed and demonstrated a top-pinned magnetic tunnel junction (MTJ) with a synthetic antiferromagnetic (SAF) free layer, which eliminated the offset field. The key SAF structure was designed on the basis of a prediction derived from micromagnetic simulations. As a result, fabricated MTJ with an SAF free layer exhibited stable switching without applying an external field.


title: Parallel Image Processing Based on CUDA

abstract: CUDA (compute unified device architecture) is a novel technology of general-purpose computing on the GPU, which makes users develop general GPU (graphics processing unit) programs easily. This paper analyzes the distinct features of CUDA GPU, summarizes the general program mode of CUDA. Furthermore, we implement several classical image processing algorithms by CUDA, such as histogram equalization, removing clouds, edge detection and DCT encode and decode etc., especially introduce the first two algorithms. If we donpsilat take the data transfer time in experiment between host memory and device memory into account, as the image size increase, histogram computation can get a more than 40x speedup, removing clouds can get an about 79x speedup, DCT can gain around 8x and edge detection more than 200x.


title: Rise of Augmented Reality: Current and Future Application Areas

abstract: The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.


title: A gender identity interview for children.

abstract: A 12-item gender identity interview schedule was administered to 85 children referred for concerns regarding their gender identity development and 98 clinical and normal control children. Factor analysis identified two factors, which were labeled Affective Gender Confusion and Cognitive Gender Confusion. The gender-referred group gave significantly more deviant responses than did the controls on both factors. Results were discussed with regard to several diagnostic and assessment issues pertaining to children with gender identity disorder.


title: Impact of research on practice in the field of inspections, reviews and walkthroughs: learning from successful industrial uses

abstract: Software inspections, reviews, and walkthroughs have become a standard process component in many software development domains. Maturity level 3 of the CMM-I requires establishment of peer reviews [12] and substantial sustained improvements in quality and productivity have been reported as a result of using reviews ([16], [21], [22], [27]). The NSF Impact project identifies the degree to which these industrial success cases have been instigated and improved by research in software engineering. This research identifies that there is widespread adoption of inspections, reviews or walkthroughs but that companies do not generally exploit their full potential. However there exist sustained industrial success cases with respect to the wide-spread and measurably successful application of them. It also identifies research in software engineering that can be credibly documented as having influenced the industrial success cases. Credible documentation may exist in the form of publications or documented reports by witnesses. Due to the semi-formal nature of inspections, reviews, and walkthroughs, a specific focus is given to empirical research results as motivators for adoption. Through the examination of one detailed case study, it is shown that software engineering research has had a significant impact on practice and that the impact can be traced in this case from research to that practice. The case study chosen provides evidence of both success and failure regarding sustained application in practice. Thus the analysis of historic impact chains of research reveals a clear impact of software engineering research on sustained industrial success for inspections, reviews and walkthroughs. More importantly, in impact chains where the empirical results have not been established, we conclude that success has not been achieved or has not been sustained. The paper closes with (1) lessons learned for creating the sustained use and impact of semi-formal software engineering processes, (2) a request for researchers and practitioners to further consider how their work can improve the effectiveness of research and practice, and (3) a request to contribute additional success cases and impact factors to the authors database for future enhancements of this paper.


title: Performance Improvement in Construction Project based on Six Sigma Principles

abstract: The importance of quality improvement and excellent performance in the highly competitive ::: world market, lead many organizations, their top managers, project managers, and engineers to ::: implement the ...


title: Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

abstract: Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance function with a finite Fourier series approximation and treat it as a random variable. The random covariance function has a posterior, on which a variational distribution is placed. The variational distribution transforms the random covariance function to fit the data. We study the properties of our approximate inference, compare it to alternative ones, and extend it to the distributed and stochastic domains. Our approximation captures complex functions better than standard approaches and avoids over-fitting.


title: Version 6 of the system identification toolbox

abstract: Abstract This paper describes the new developments in Mathwork's SYSTEM IDENTIFICATION TOOLBOX to be run with MATLAB. There are three main additions to the new version: (1) Frequency domain input/output data as well as frequency response data can be directly used to fit models. (2) Simple continuous-time process models of the type Static Gain, Time Constant, Dead Time can be directly estimated as a new model object class. (3) A function advice can be applied both to data sets and to estimated models, in order to provide guidance and advice through the sometime complex identification process.


title: Efficient Combining Keypoints and Correlation Filter for Long-Term Tracking

abstract: I72 this paper, we propose a tracker (CKCF) to tackle the challenges of long-term tracking where the target encounter occlusion, deformation. For these problems, we decompose the task of the long-term tracking into translation filter and re-detector, where the re-detector consists of point tracking, a simple method for estimating location and GMS (Grid-based Motion Statistics) filter and it is activated in case of tracking failure, which is detected by computing a score. Finally, the CKCF was evaluated on OTB2015 datasets and the experimental results show that our tracker outperforms favorably many state-of-art trackers in term of speed (∼50 FPS), accuracy and robustness, especially occlusion is encountered.


title: Group-Level Emotion Recognition Using Hybrid Deep Models Based on Faces, Scenes, Skeletons and Visual Attentions

abstract: This paper presents a hybrid deep learning network submitted to the 6th Emotion Recognition in the Wild (EmotiW 2018) Grand Challenge [9], in the category of group-level emotion recognition. Advanced deep learning models trained individually on faces, scenes, skeletons and salient regions using visual attention mechanisms are fused to classify the emotion of a group of people in an image as positive, neutral or negative. Experimental results show that the proposed hybrid network achieves 78.98% and 68.08% classification accuracy on the validation and testing sets, respectively. These results outperform the baseline of 64% and 61%, and achieved the first place in the challenge.


title: Field-aware probabilistic embedding neural network for CTR prediction

abstract: For Click-Through Rate (CTR) prediction, Field-aware Factorization Machines (FFM) have exhibited great effectiveness by considering field information. However, it is also observed that FFM suffers from the overfitting problem in many practical scenarios. In this paper, we propose a Field-aware Probabilistic Embedding Neural Network (FPENN) model with both good generalization ability and high accuracy. FPENN estimates the probability distribution of the field-aware embedding rather than using the single point estimation (the maximum a posteriori estimation) to prevent overfitting. Both low-order and high-order feature interactions are considered to improve the accuracy. FPENN consists of three components, i.e., FPE component, Quadratic component and Deep component. FPE component outputs probabilistic embedding to the other two components, where various confidence levels for feature embeddings are incorporated to enhance the robustness and the accuracy. Quadratic component is designed for extracting low-order feature interactions, while Deep component aims at capturing high-order feature interactions. Experiments are conducted on two benchmark datasets, Avazu and Criteo. The results confirm that our model alleviates the overfitting problem while having a higher accuracy.


title: Evaluation of Real-time Deep Learning Turn-taking Models for Multiple Dialogue Scenarios

abstract: The task of identifying when to take a conversational turn is an important function of spoken dialogue systems. The turn-taking system should also ideally be able to handle many types of dialogue, from structured conversation to spontaneous and unstructured discourse. Our goal is to determine how much a generalized model trained on many types of dialogue scenarios would improve on a model trained only for a specific scenario. To achieve this goal we created a large corpus of Wizard-of-Oz conversation data which consisted of several different types of dialogue sessions, and then compared a generalized model with scenario-specific models. For our evaluation we go further than simply reporting conventional metrics, which we show are not informative enough to evaluate turn-taking in a real-time system. Instead, we process results using a performance curve of latency and false cut-in rate, and further improve our model's real-time performance using a finite-state turn-taking machine. Our results show that the generalized model greatly outperformed the individual model for attentive listening scenarios but was worse in job interview scenarios. This implies that a model based on a large corpus is better suited to conversation which is more user-initiated and unstructured. We also propose that our method of evaluation leads to more informative performance metrics in a real-time system.


title: Modeling the Propagation of Worms in Networks: A Survey

abstract: There are the two common means for propagating worms: scanning vulnerable computers in the network and spreading through topological neighbors. Modeling the propagation of worms can help us understand how worms spread and devise effective defense strategies. However, most previous researches either focus on their proposed work or pay attention to exploring detection and defense system. Few of them gives a comprehensive analysis in modeling the propagation of worms which is helpful for developing defense mechanism against worms' spreading. This paper presents a survey and comparison of worms' propagation models according to two different spreading methods of worms. We first identify worms characteristics through their spreading behavior, and then classify various target discover techniques employed by them. Furthermore, we investigate different topologies for modeling the spreading of worms, analyze various worms' propagation models and emphasize the performance of each model. Based on the analysis of worms' spreading and the existing research, an open filed and future direction with modeling the propagation of worms is provided.


title: What can quantum theory bring to information retrieval

abstract: The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.


title: The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

abstract: The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.


Architectural Styles and the Design of Network-based Software Architectures


title: Online flow size prediction for improved network routing

abstract: We describe an emerging application of data mining in the context of computer networks. This application concerns the problem of predicting the size of a flow and detecting elephant flows (very large flows). Flow size is a very important statistic that can be used to improve routing, load balancing and scheduling in computer networks. Flow size prediction is particularly challenging since flow patterns continuously change and predictions must be done in real time (milliseconds) to avoid delays. We describe how to formulate the problem as an online machine learning task to continuously adjust to changes in flow traffic. We evaluate the predictive nature of a set of features and the accuracy of three online predictors based on neural networks, Gaussian process regression and online Bayesian Moment Matching on three datasets of real traffic. We also demonstrate how to use such online predictors to improve routing (i.e., reduced flow completion time) in a network simulation.


title: Fully soft-switched bidirectional resonant dc-dc converter with a new CLLC tank

abstract: A bidirectional dc-dc converter with a new CLLC type resonant tank, which features in ZVS for the input inverting port switches and ZCS for the output rectifier port switches, regardless of the direction of the power flow, is proposed. If the MOSFETs are implemented as all of the main switches, the proposed converter has a minimized switching loss. The detail operation principles, as well as the design considerations, are presented. A prototype, which interfacing the 400V/48V DC buses for the UPS system with a power rating of 500VA, whose highest conversion efficiencies for the bidirectional mode both exceed 96%, was developed to verify the validity and applicability of this proposed converter.


title: Multimodal Open-domain Conversations with the Nao Robot

abstract: In this paper we discuss the design of human-robot interaction focussing especially on social robot communication and multimodal information presentation. As a starting point we use the WikiTalk application, an open-domain conversational system which has been previously developed using a robotics simulator. We describe how it can be implemented on the Nao robot platform, enabling Nao to make informative spoken contributions on a wide range of topics during conversation. Spoken interaction is further combined with gesturing in order to support Nao’s presentation by natural multimodal capabilities, and to enhance and explore natural communication between human users and robots.


title: The Taboo Trap: Behavioural Detection of Adversarial Samples

abstract: Deep Neural Networks (DNNs) have become a powerful toolfor a wide range of problems. Yet recent work has found an increasing variety of adversarial samplesthat can fool them. Most existing detection mechanisms against adversarial attacksimpose significant costs, either by using additional classifiers to spot adversarial samples, or by requiring the DNN to be restructured. In this paper, we introduce a novel defence. We train our DNN so that, as long as it is workingas intended on the kind of inputs we expect, its behavior is constrained, in that some set of behaviors are taboo. If it is exposed to adversarial samples, they will often cause a taboo behavior, which we can detect. Taboos can be both subtle and diverse, so their choice can encode and hide information. It is a well-established design principle that the security of a system should not depend on the obscurity of its design, but on some variable (the key) which can differ between implementations and bechanged as necessary. We discuss how taboos can be used to equip a classifier with just such a key, and how to tune the keying mechanism to adversaries of various capabilities. We evaluate the performance of a prototype against a wide range of attacks and show how our simple defense can defend against cheap attacks at scale with zero run-time computation overhead, making it a suitable defense method for IoT devices.


A PROPOSED UNDER-VOLTAGE LOCKOUT OF COMPENSATED TEMPERATURE COEFFICIENT THRESHOLD VOLTAGE WITHOUT COMPARATOR


title: Building Virtual and Augmented Reality museum exhibitions

abstract: A system that allows museums to build and manage Virtual and Augmented Reality exhibitions based on 3D models of artifacts is presented. Dynamic content creation based on pre-designed visualization templates allows content designers to create virtual exhibitions very efficiently. Virtual Reality exhibitions can be presented both inside museums, e.g. on touch-screen displays installed inside galleries and, at the same time, on the Internet. Additionally, the presentation based on Augmented Reality technologies allows museum visitors to interact with the content in an intuitive and exciting manner.


title: On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA

abstract: A pre-trained convolutional deep neural network (CNN) is a feed-forward computation perspective, which is widely used for the embedded systems, requires highly power-and-area efficiency. This paper proposes a binarized CNN on an FPGA which treats only binary 2-values∼(+1/-1) for the inputs and the weights. In this case, the multiplier is replaced into an XNOR circuit instead of a dedicated DSP block. For hardware implementation, using binarized inputs and weights is more suitable. However, the binarized CNN requires the batch normalization techniques to retain the classification accuracy. In that case, the additional multiplication and addition require extra hardware, also, the memory access for its parameters reduces system performance. In this paper, we propose a batch normalization free binarized CNN which is mathematically equivalent to one using batch normalization. The proposed CNN treats the binarized inputs and weights with the integer bias. We implemented the VGG-16 benchmark CNN on the Xilinx Inc. Zynq UltraScale+ MPSoC zcu102 evaluation board. Our binarized CNN stores all the weights, inputs, and output to on-chip BRAMs those are faster and dissipate lower power than an off-chip memory, such as a DDR4SDRAM. Compared with the conventional FPGA realizations, although the classification accuracy is 6.5% decayed, the performance is 2.45 times faster, the power efficiency is slightly better, and the area efficiency is 2.68 times better. Compared with the ARM Cortex-A57, it is 136.8 times faster, it dissipates 3.1 times much power, and its performance per power efficiency is 44.7 times better. Also, compared with the Maxwell embedded GPU, it is 4.9 times faster, it dissipates 1.3 times much power, and its performance per power efficiency is 3.8 times better. Thus, our method is suitable for the embedded computer system.


title: An Empirical Study of Recent Face Alignment Methods

abstract: The problem of face alignment has been intensively studied in the past years. A large number of novel methods have been proposed and reported very good performance on benchmark dataset such as 300W. However, the differences in the experimental setting and evaluation metric, missing details in the description of the methods make it hard to reproduce the results reported and evaluate the relative merits. For instance, most recent face alignment methods are built on top of face detection but from different face detectors. In this paper, we carry out a rigorous evaluation of these methods by making the following contributions: 1) we proposes a new evaluation metric for face alignment on a set of images, i.e., area under error distribution curve within a threshold, AUC$_\alpha$, given the fact that the traditional evaluation measure (mean error) is very sensitive to big alignment error. 2) we extend the 300W database with more practical face detections to make fair comparison possible. 3) we carry out face alignment sensitivity analysis w.r.t. face detection, on both synthetic and real data, using both off-the-shelf and re-retrained models. 4) we study factors that are particularly important to achieve good performance and provide suggestions for practical applications. Most of the conclusions drawn from our comparative analysis cannot be inferred from the original publications.


title: The Orphan Problem in ZigBee Wireless Networks

abstract: ZigBee is a communication standard which is considered to be suitable for wireless sensor networks. In ZigBee, a device (with a permanent 64-bit MAC address) is said to join a network if it can successfully obtain a 16-bit network address from a parent device. Parent devices calculate addresses for their child devices by a distributed address assignment scheme. This assignment is easy to implement, but it restricts the number of children of a device and the depth of the network. We observe that the ZigBee address assignment policy is too conservative, thus usually making the utilization of the address pool poor. Those devices that cannot receive network addresses will be isolated from the network and become orphan nodes. In this paper, we show that the orphan problem can be divided into two subproblems: the bounded-degree-and-depth tree formation (BDDTF) problem and the end-device maximum matching (EDMM) problem. We then propose algorithms to relieve the orphan problem. Our simulation results show that the proposed schemes can effectively reduce the number of orphan devices compared to the ZigBee strategy.


title: A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

abstract: Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of the clustering-based CF models only utilize historical rating information in the clustering procedure but ignore other data resources in recommender systems such as the social connections of users and the correlations between items. In this article, we propose HMCoC, a Hybrid Multigroup CoClustering recommendation framework, which can cluster users and items into multiple groups simultaneously with different information resources. In our framework, we first integrate information of user--item rating records, user social networks, and item features extracted from the DBpedia knowledge base. We then use an optimization method to mine meaningful user--item groups with all the information. Finally, we apply the conventional CF method in each cluster to make predictions. By merging the predictions from each cluster, we generate the top-n recommendations to the target users for return. Extensive experimental results demonstrate the superior performance of our approach in top-n recommendation in terms of MAP, NDCG, and F1 compared with other clustering-based CF models.


title: Fully Deep Neural Networks Incorporating Unsupervised Feature Learning for Audio Tagging

abstract: In this paper we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a fully deep neural network (DNN) framework incorporating unsupervised feature learning to handle the multi-label classification task in a regression way. Considering that only chunk-level rather than frame-level labels are available, the whole or almost whole frames of the chunk are fed into the DNN to perform a multi-label regression for the expected tags. The fully DNN, which is regarded as an encoding function, can map the audio features sequence to a multi-tag vector. For the unsupervised feature learning, we propose to use a deep auto-encoder (AE) to generate new features with non-negative representation from the basic features. The new feature can further improve the performance of audio tagging. A deep pyramid structure was also designed to extract more robust high-level features related to the target tags. Further improved methods were adopted, such as the dropout and background noise aware training, to enhance the generalization capability of DNNs for new audio recordings in mismatched environments. Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method is able to utilize the long-term temporal information with the whole chunk as the input. The results show that our approach obtains a 19.1% relative improvement compared with the official GMM-based baseline method of DCASE 2016 audio tagging task.


title: Big Graph Analytics: The State of the Art and Future Research Agenda

abstract: Analytics over big graphs is becoming a first-class challenge in database research, with fast-growing interest from both the academia and the industrial community. This problem arises in several application scenarios, ranging from social networks to large-scale network systems, from knowledge discovery to cybersecurity, and so forth. Following this major trend, this paper explores actual state-of-the-art results in the area of analytics over big graphs and discusses open research issues and actual trends in such area.


title: Computational Features of the Thinking and the Thinking Attributes of Computing: on Computational Thinking

abstract: The paper aims at revealing the essence and connotation of Computational Thinking. It analyzed some of the international academia's research results of Computational Thinking. The author thinks Computational Thinking is discipline thinking or computing philosophy, and it is very critical to understand Computational Thinking to grasp the thinking' s computational features and the computing's thinking attributes. He presents the basic rules of screening the representative terms of Computational Thinking and lists some representative terms based on the rules. He thinks Computational Thinking is contained in the commonalities of those terms. The typical thoughts of Computational Thinking are structuralization, formalization, association-and-interaction, optimization and reuse-and-sharing. Training Computational Thinking must base on the representative terms and the typical thoughts. There are three innovations in the paper: the five rules of screening the representative terms, the five typical thoughts and the formalized description of Computational Thinking.