title: An Approach for Smart Management of Big Data in the Fog Computing Context

abstract: In this paper, a new approach for tackling Big Data in Internet of Things (IoT) systems is presented. We approach the problem from the data perspective rather than only focusing on the computing platform. We design and develop a concept that we call "Smart Data". Taking advantage of a hierarchical fog computing system, we reshape the raw and passive form of the data generated by IoT sensors to intelligent and self-managed data cells that are able to evolve and become more meaningful information with reduced size. We believe that smart data will revolutionize the current perspective of data and will open many potential research directions to tackle emerging big data issues.


title: Physical examination score predicts need for surgery in neonates with necrotizing enterocolitis

abstract: OBJECTIVE ::: To evaluate the utility of a standardized physical exam score (PE-NEC) in predicting need for surgery or death in neonates with necrotizing enterocolitis (NEC). ::: ::: ::: METHODS ::: This prospective, multicenter, observational study was conducted from 3/1/14 to 2/29/16 with three regional perinatal centers in upstate New York. Infants with NEC Bell's Stage ≥ 2 had physical exams and laboratory data recorded at 12-24 h intervals for 48 h following diagnosis. PE-NEC score was comprised of seven components: bowel sounds, capillary refill time, abdominal wall erythema, girth, discoloration, induration, and tenderness. Surgical timing was determined by surgeons blinded to the PE-NEC score. Optimal sensitivity and specificity of PE-NEC score for surgery/death (primary outcome) was determined by receiver operating characteristic curve analysis. ::: ::: ::: RESULTS ::: Of 100 infants with NEC, 5 had pneumoperitoneum at diagnosis and were excluded yielding 95 for analyses. Of those, 35 infants experienced the primary outcome: 3 died from NEC prior to surgery and 32 had surgery (30 laparotomies, 2 drains). The PE-NEC score was found to be sensitive and specific for need for surgery/death (AUC = 0.89, 95% CI 0.82-0.97); a score of ≥3 had a sensitivity of 0.88 (95% CI 0.72-0.97), specificity of 0.81 (95% CI 0.69-0.90). All components of the PE-NEC score were more likely to be present among infants with surgical NEC or who died. ::: ::: ::: CONCLUSION ::: PE-NEC score is sensitive and specific in predicting need for surgery in infants with NEC and should be validated as a clinical decision-making tool.


title: Effects of gender and different augmented reality learning systems on English vocabulary learning of elementary school students

abstract: This study developed and compared two augmented reality learning systems for third-grade students to learn English vocabulary in situated surroundings. One system was developed based on a collective game-based (CGB) design, in which the students had to collect all seven reality targets without being restricted as to which they could start from. The other system was developed based on a sequential-mission gaming (SMG) design, which provided the students with seven stages to complete in sequence. The effects of gender and system on the students’ flow experience, cognitive load, and learning effectiveness were assessed. The results indicated that the students using the two systems had similarly high learning effectiveness. However, those using the CGB system revealed higher flow experience and lower intrinsic cognitive loads in comparison with those using the SMG system. The male students had high flow experience in both systems; however, the flow experience of the females in the CGB system outperformed that of the females in the SMG system.


title: Automatic dynamic range adaptation for image data

abstract: Modern imaging systems are highly sensitive in a large dynamic range, which can give images consisting of many signal levels. Presentation of such images with varying dynamic ranges on a display with a fixed number of greylevels is difficult without losing important visual information. This paper presents several algorithms for automatic dynamic range adaptation for images. All of the algorithms are suitable for and also implemented in realtime applications. They fall mainly into two categories: histogram modification techniques and frequency based techniques. Some of the algorithms are evaluated in a perception experiment, where the goal is to get the visually most attractive images, and the experiment shows that the frequency based techniques are superior to the histogram modification techniques. Some of the proposed algorithms have proven to give visually attractive images, where none or almost none of the important information is lost, for a large selection of images with varying dynamic ranges.


title: SAW and BAW technologies for RF filter applications: A review of the relative strengths and weaknesses

abstract: Acoustic filter technologies based on piezoelectric materials play a key role in wireless communication as they guarantee spectral integrity of RF signals. SAW filters have been used for this purpose since the early days of mobile phones. BAW filters are a relatively recent addition to the technology portfolio. Each technology claims to have unique advantages over the other. This paper gives a detailed review of the strength and weaknesses for both technologies. The aspects covered include process complexity, unique features, performance parameters and limitations. The application space for RF filters will be mapped out to identify areas where either SAW or BAW dominates or where they compete. Both technologies follow a steep path of improvements which involves introducing new materials, processes and design methods. Rivalry put aside there are many areas where advanced SAW and BAW can benefit from each other.


USING THEORY OF REASONED ACTION (TRA) IN RESEARCHING INFORMATION BEHAVIOROF LECTURERS


title: Random Survival Forests Models for SME Credit Risk Measurement

abstract: This paper extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes (SMEs). We propose a non-parametric approach based on Random Survival Forests (RSF) and we compare its performance with a standard logit model. To the authors’ knowledge, no studies in the area of credit risk default for SMEs have used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another. As for the in-sample results, we find that our non-parametric model performs much better that the classical logit model. As for the out-of-sample performances, the evidence is just the opposite, and the logit performs better than the RSF model. We explain this evidence by showing how error in the estimates of default probabilities can affect classification error when the estimates are used in a classification rule.


title: Predicting the Future with Social Media

abstract: In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be utilized to improve the forecasting power of social media.


title: An Experimental Analysis of the Demand for Payday Loans

abstract: The payday loan industry is one of the fastest growing segments of the consumer financial services market in the United States. We design an environment similar to the one that payday loan customers face and then conduct a laboratory experiment to examine what effect, if any, the existence of payday loans has on individuals abilities to manage and to survive financial setbacks. Our primary objective is to examine whether access to payday loans improves or worsens the likelihood of financial survival in our experiment. We also test the degree to which peoples use of payday loans affects their ability to survive financially. We find that payday loans help the subjects to absorb expenditure shocks and therefore survive financially. However, subjects whose demand for payday loans exceeds a certain threshold level are at a greater risk than a corresponding subject in the treatment in which payday loans do not exist.


title: Fast Acquisition of GPS Signal Using Radix-2 and Radix-4 FFT Algorithms

abstract: Acquisition is the important frontend operation of a GPS receiver signal processing. It estimates Doppler change in carrier frequency and delay in PRN code of the GPS signal and gives for tracking of signal. Tracking refines the Doppler shift and delay in code phase and removes the carrier, PRN code from the received GPS signal and acquires the navigational data. The performance of tracking directly depends on performance of acquisition algorithm. Performance of acquisition is compromise between computational time and Doppler frequency resolution. So this paper presents fast acquisition of Doppler shift and code phase from GPS Intermediate Frequency (IF) signal using radix4 and radix2 FFT algorithms. Performance of these acquisition algorithms is compared with time domain correlation and also with signal acquisition using radix2 FFT on decimated with zero padded data. Computational time and correlation magnitude are taken as metrics to evaluate the performance of the algorithms.


title: Solving the PnP Problem with Anisotropic Orthogonal Procrustes Analysis

abstract: In this paper we formulate the Perspective-n-Point (a.k.a. exterior orientation) problem in terms of an instance of the an isotropic orthogonal Procrustes problem, and derive its solution. Experiments with synthetic and real data demonstrate that our method reaches the best trade-off between speed and accuracy. The MATLAB code reported in the paper testifies that it is also exceedingly simple to implement.


title: Robot-Audition-based Human-Machine Interface for a Car

abstract: This paper describes a Robot-Audition based Car Human Machine Interface (RA-CHMI). A RA-CHMI, like a car navigation system, has difficulty dealing with voice commands, since there are many noise sources in a car, including road noise, air-conditioner, music, and passengers. Microphone array processing developed in robot audition, may overcome this problem. Robot audition techniques, including sound source localization, Voice Activity Detection (VAD), sound source separation, and barge-in-able processing, were introduced by considering the characteristics of RA-CHMI. Automatic Speech Recognition (ASR), based on a Deep Neural Network (DNN), improved recognition performance and robustness in a noisy environment. In addition, as an integrated framework, HARK-Dialog was developed to build a multi-party and multi-modal dialog system, enabling the seamless use of cloud and local services with pluggable modular architecture. The constructed multi-party and multimodal RA-CHMI system did not require a push-to-talk button, nor did it require reducing the audio volume or air-conditioner when issuing speech commands. It could also control a four-DOF robot agent to make the system's responses more understandable. The proposed RA-CHMI was validated by evaluating essential techniques in the system, such as VAD and DNN-ASR, using real speech data recorded during driving. The entire design of the RA-CHMI system, including the system response time and the proper use of cloud/local services, are also discussed.


title: Explainable Prediction of Medical Codes from Clinical Text

abstract: Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision @ 8 of 0.7 and a Micro-F1 of 0.52, which are both significantly better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.


A Light-weight Compaction Tree to Reduce I / O Amplification toward Efficient Key-Value Stores


title: A study of methods for normalizing user ratings in collaborative filtering

abstract: The goal of collaborative filtering is to make recommendations for a test user by utilizing the rating information of users who share interests similar to the test user. Because ratings are determined not only by user interests but also the rating habits of users, it is important to normalize ratings of different users to the same scale. In this paper, we compare two different normalization strategies for user ratings, namely the Gaussian normalization method and the decoupling normalization method. Particularly, we incorporated these two rating normalization methods into two collaborative filtering algorithms, and evaluated their effectiveness on the EachMovie dataset. The experiment results have shown that the decoupling method for rating normalization is more effective than the Gaussian normalization method in improving the performance of collaborative filtering algorithms.


title: Randomized comparison between low-pressure laparoscopic cholecystectomy and gasless laparoscopic cholecystectomy

abstract: BACKGROUND ::: Laparoscopic cholecystectomy using low-pressure pneumoperitoneum (8 mmHg) minimizes adverse hemodynamic effects, reduces postoperative pain, and accelerates recovery. Similar claims are made for gasless laparoscopy using abdominal wall lifting. The aim of this study was to compare gasless laparoscopic cholecystectomy to low-pressure cholecystectomy with respect to postoperative pain and recovery. ::: ::: ::: METHODS ::: Thirty-six patients were randomized to low-pressure or gasless laparoscopic cholecystectomy using a subcutaneous lifting system (Laparotenser). ::: ::: ::: RESULTS ::: The characteristics of the patients were similar in the two groups. The procedure was completed in all patients in the low-pressure group, but two patients in the gasless group were converted to pneumoperitoneum. There were no significant differences in postoperative pain and analgesic consumption, but patients in the gasless group developed shoulder pain more frequently (50% vs 11%, p < 0.05). Gasless operation took longer to perform (95 vs 72.5 min, p = 0.01). ::: ::: ::: CONCLUSIONS ::: Gasless and low-pressure laparoscopic cholecystectomy were similar with respect to postoperative pain and recovery. The gasless technique provided inferior exposure and the operation took longer, but the technique may still have value in high-risk patients with cardiorespiratory disease.


title: TextureGAN: Controlling Deep Image Synthesis with Texture Patches

abstract: In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control. We allow a user to place a texture patch on a sketch at arbitrary locations and scales to control the desired output texture. Our generative network learns to synthesize objects consistent with these texture suggestions. To achieve this, we develop a local texture loss in addition to adversarial and content loss to train the generative network. We conduct experiments using sketches generated from real images and textures sampled from a separate texture database and results show that our proposed algorithm is able to generate plausible images that are faithful to user controls. Ablation studies show that our proposed pipeline can generate more realistic images than adapting existing methods directly.


Text analytics approach to extract course improvement suggestions from students’ feedback


title: A Study of Campus Butterfly Ecology Learning System Based on Augmented Reality and Mobile Learning

abstract: This study used augmented reality and mobile learning technologies to design a virtual butterfly ecological environment for learning butterfly ecology. Students can breed virtual caterpillars on host plants using smart phones, and become familiar with butterflyi¦s life cycle by observing their growth. The virtual campus butterfly ecological environment can reduce the difficulty of building and managing a real butterfly garden, and solve the problems of insufficient species and amount of butterflies. Providing a context-aware learning environment, the system can enhance studentsi¦ learning motivation and interest via virtual breeding and observation activities. Thus, it can be used as an assistant tool for learning butterfly ecology in elementary and junior high schools.


title: Analysis of Noncoherent ASK Modulation-Based RF-Interconnect for Memory Interface

abstract: A noncoherent amplitude shift keying (ASK)-based RF-interconnect (RF-I) system design for off-chip communication is analyzed. The proposed RF-I system exploits the simple architecture and characteristics of noncoherent ASK modulation. This provides an efficient way of increasing interconnect bandwidth by transmitting an RF-modulated data stream simultaneously with a conventional baseband counterpart over a shared off-chip transmission line. Both analysis and tested results prove that the performance of the proposed dual-band (RF+baseband) interconnect system is not limited by thermal noise interference. Therefore, a more sophisticated modulation scheme and/or coherent receiving scheme becomes unnecessary within the scope of system requirements. In addition, it confirms that the proposed inductive coupling network is able to support simultaneous bidirectional communications without using complicated replica circuits or additional filters to isolate simultaneous baseband and RF-band data streams.


title: Identifying content for planned events across social media sites

abstract: User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed content for events that are planned and, therefore, known in advance, across different social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an event on different social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and analyze their effectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr.


title: Grammatical Evolution: Evolving Programs for an Arbitrary Language

abstract: We describe a Genetic Algorithm that can evolve complete programs. Using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved. Other automatic programming methods are described, before our system, Grammatical Evolution, is applied to a symbolic regression problem.


title: Low-profile dual-band circularly polarized microstrip antenna for GNSS applications

abstract: This paper presents a design of a micro-strip circularly polarized antenna intended for the Global Navigation Satellite Systems (GNSS). The presented device is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick and a wideband commercial 3-dB SMT coupler. The combined fullwave antenna results with the measured S-Parameters of the coupler shows very good performances in terms of antenna matching and axial ratio on larger bandwidths.


title: Gastric devascularization—An emergency treatment for hemorrhagic gastritis in the neonate

abstract: A newborn baby suffering from massive hemorrhage due to erosive gastritis, was treated successfully by devascularization of the stomach which is a rather simple procedure. Experience with adults as well as with this patient justifies the use of this operation when other measures fail to stop massive bleeding in hemorrhagic gastritis.


title: Design and development of solar power-assisted manual/electric wheelchair

abstract: INTRODUCTION The elderly face many physical challenges, including nerve and muscular degeneration, reduced motor function and balance, impaired mobility, and so on. Thus, the wheelchair is considered an essential assistive device for improving the mobility, living quality, and dignity of the elderly and those with mobility difficulties arising from physical disabilities, accident-related injuries, and so on [1]. Generally speaking, existing wheelchairs can be categorized as either manual, electric-powered, or power-assisted depending on their mode of propulsion. Traditional manual wheelchairs (MWs) induce a greater oxygen consumption and a higher respiratory exchange ratio and are therefore beneficial to the user's health when properly used. However, the gross mechanical efficiency of such wheelchairs (i.e., the ratio of the external power to the metabolic power) is just 2 to 13.8 percent, depending on the level of injury, the propulsion technique, the adjustments made to the wheelchair interface (e.g., the seat height), and the intensity of the exercise undertaken [2-9]. This low mechanical efficiency, coupled with the high physical strain imposed on the movements of the user, may result in fatigue or even strain-induced injuries in the worst-case scenario [10-12]. As a result, the design and development of electric-powered wheelchairs (EPWs) or power-assisted wheelchairs has attracted increasing attention in recent decades [13-14]. Compared with MWs, EPWs have several important advantages, including a reduced user effort and a lower risk of strain-induced injuries [1,15-22]. Consequently, EPWs have gained in popularity and importance in recent years and have prompted an increasing number of users to switch from manual propulsion to motor-powered propulsion [13]. However, present EPWs still have many disadvantages, including a long charging time, a large physical size, a heavy weight, and a high expense. Furthermore, most EPWs are inconvenient to maintain because of their many mechanical and electrical components. In addition, they cannot be easily folded or disassembled and are therefore cumbersome to store and transport [13,15-22]. Consequently, their market share is not yet as much as that of traditional MWs. Wheelchairs that use a combination of human power and electric power have been developed recently, such as pushrim-activated power-assist wheelchairs (PAPAWs) [13-14]. The human power is delivered by the arms' action on the pushrims, while the electric power from a battery is delivered by an electric motors' torque. A typical PAPAW can sense the torque on the pushrim to generate proper assist torques by motors. The PAPAW can help its user maintain physical condition because the risk of pain and injuries to the upper limbs is greatly reduced. However, the tuning of a PAPAW may be a challenge for individual users because of the strong dependence upon the user's interaction. The motors used to drive electric wheelchairs run on batteries. As a result, they have a relatively limited travel range and require frequent recharging. Various researchers have investigated the feasibility of overcoming these limitations by fitting the wheelchair with a solar power supply [23-26]. However, to the best of the authors' knowledge, only one published patent currently exists for a solar power-assisted electric wheelchair [23]. In their design, the solar panel is fixed rigidly to the back of the wheelchair by a metal frame and cannot be disassembled at will. Moreover, the wheelchair can only be operated in an electric mode, and thus the potential benefits of the wheelchair as an exercise device are lost. Melanson [24] and Messenger [25] have also proposed the integration of a solar panel with an EPW. However, both designs suffer many of the same disadvantages as a traditional EPW, including a large physical size, a heavy weight, and a lack of foldability. Curram et al. recently developed a solar-powered wheelchair in which the weight was reduced by modifying the main frame structure [26]. …


title: Rectification of figures and photos in document images using bounding box interface

abstract: This paper proposes an algorithm for the segmentation and rectification of figures and photos in document images. The algorithm requires just a rough user-provided bounding box for the objects in a single-view image. On receiving the user's bounding box, it takes about 1–2 seconds to segment and rectify mega-pixel sized figures. The main feature of the algorithm is a novel segmentation method that exploits the properties of printed figures. Specifically, a set of boundary candidates is generated using the properties, and the optimal boundary in the set is found by using an alternating optimization scheme. This segmentation result is further refined so that it is well localized to the true boundary. In addition to our segmentation method, we also propose a new boundary interpolation method for the rectification of segmented figures. The method improves the quality of output by largely removing perspective distortions compared to conventional boundary interpolation methods. Experimental results on a variety of images show that the method is efficient, robust, and easy to use.


title: Optimized Data Pre-Processing for Discrimination Prevention

abstract: Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling discrimination, limiting distortion in individual data samples, and preserving utility. We characterize the impact of limited sample size in accomplishing this objective, and apply two instances of the proposed optimization to datasets, including one on real-world criminal recidivism. The results demonstrate that all three criteria can be simultaneously achieved and also reveal interesting patterns of bias in American society.


title: FloDB: Unlocking Memory in Persistent Key-Value Stores

abstract: Log-structured merge (LSM) data stores enable to store and process large volumes of data while maintaining good performance. They mitigate the I/O bottleneck by absorbing updates in a memory layer and transferring them to the disk layer in sequential batches. Yet, the LSM architecture fundamentally requires elements to be in sorted order. As the amount of data in memory grows, maintaining this sorted order becomes increasingly costly. Contrary to intuition, existing LSM systems could actually lose throughput with larger memory components. In this paper, we introduce FloDB, an LSM memory component architecture which allows throughput to scale on modern multicore machines with ample memory sizes. The main idea underlying FloDB is essentially to bootstrap the traditional LSM architecture by adding a small in-memory buffer layer on top of the memory component. This buffer offers low-latency operations, masking the write latency of the sorted memory component. Integrating this buffer in the classic LSM memory component to obtain FloDB is not trivial and requires revisiting the algorithms of the user-facing LSM operations (search, update, scan). FloDB's two layers can be implemented with state-of-the-art, highly-concurrent data structures. This way, as we show in the paper, FloDB eliminates significant synchronization bottlenecks in classic LSM designs, while offering a rich LSM API. We implement FloDB as an extension of LevelDB, Google's popular LSM key-value store. We compare FloDB's performance to that of state-of-the-art LSMs. In short, FloDB's performance is up to one order of magnitude higher than that of the next best-performing competitor in a wide range of multi-threaded workloads.


title: Accurate Full Body Scanning from a Single Fixed 3D Camera

abstract: 3D body modeling has been a long studied topic in computer vision and computer graphics. While several solutions have been proposed using either multiple sensors or a moving sensor, we propose here an approach when the user turns, in a natural motion, in front of a fixed 3D low cost camera. This opens the door to a wide range of applications where scanning is performed at home. Our scanning system can be easily set up and the instructions are straightforward to follow. We propose an articulated, part-based cylindrical representation for the body model, and show that accurate 3D shape can be automatically estimated from 4 key views detected from a depth video sequence. The registration between 4 key views is performed in a top-bottom-top manner which fully considers the kinematic constraints. We validate our approach on a large number of users, and compare accuracy to that of a reference laser scan. We show that even using a simplified model (5 cylinders) an average error of 5mm can be consistently achieved.


title: A systematic review and meta analysis of the longevity of anterior and posterior all-ceramic crowns

abstract: Abstract Background Clinical experience suggests that there is a difference in survival between anterior and posterior all ceramic restorations. Objectives This systematic review compared the difference in survival for full coverage all-ceramic materials used in adults to restore anterior or posterior vital teeth, not involved with fixed dental prostheses, but opposed by teeth. Data and sources Searches using Medline, Embase, and the Cochrane Library, including hand searches, with the inclusion criteria containing all-ceramic full coverage crowns in human adults over 17 years of age, prospective and retrospective studies, opposed by teeth, periodontal pocketing ≤5 mm, but not involving implant supported crowns or non-vital teeth. All papers were published between 1980 and March 2014 and available in English. From the selected studies a meta analysis was undertaken. The chi square test, I 2 , Begg’s and Egger’s test were analysed and the publication bias was assessed using a Funnel plot. The, Kappa scores were 0.63, 0.88, and 0.81 at each selection stage. Study selections Pooled data produced 1112 anterior crowns with 73 failures (6.5%) and 1821 posterior crowns with 166 failures (9.1%) with a follow up time from 36 to 223 months. Relative risk meta-analysis of the 14 selected papers demonstrated that anterior all-ceramic crowns were 50% less likely to fail than posterior all-ceramic crowns (p = 0.001). Conclusion These results indicate that there were differences in failure between anterior and posterior all ceramic crowns but the difference was only 3%. Although this has clinical relevance and some caution is needed when prescribing all ceramic posterior crowns the difference was relatively small. Clinical significance The clinically relevant results of this review, based on currently available data, demonstrate a need for some caution when considering posterior all-ceramic crowns. Lithium disilicate restorations were observed to have higher failures on anterior restorations and more research is needed to investigate why.


title: Tutorial: Implementing a Pedestrian Tracker Using Inertial Sensors

abstract: Shoe-mounted inertial sensors offer a convenient way to track pedestrians in situations where other localization systems fail. This tutorial outlines a simple yet effective approach for implementing a reasonably accurate tracker. This Web extra presents the Matlab implementation and a few sample recordings for implementing the pedestrian inertial tracking system using an error-state Kalman filter for zero-velocity updates (ZUPTs) and orientation estimation.


title: Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera

abstract: To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.


title: Deception detection for news: three types of fakes

abstract: A fake news detection system aims to assist users in detecting and filtering out varieties of potentially deceptive news. The prediction of the chances that a particular news item is intentionally deceptive is based on the analysis of previously seen truthful and deceptive news. A scarcity of deceptive news, available as corpora for predictive modeling, is a major stumbling block in this field of natural language processing (NLP) and deception detection. This paper discusses three types of fake news, each in contrast to genuine serious reporting, and weighs their pros and cons as a corpus for text analytics and predictive modeling. Filtering, vetting, and verifying online information continues to be essential in library and information science (LIS), as the lines between traditional news and online information are blurring.


title: BD-NET: A Multiplication-Less DNN with Binarized Depthwise Separable Convolution

abstract: In this work, we propose a multiplication-less deep convolution neural network, called BD-NET. As far as we know, BD-NET is the first to use binarized depthwise separable convolution block as the drop-in replacement of conventional spatial-convolution in deep convolution neural network (CNN). In BD-NET, the computation-expensive convolution operations (i.e. Multiplication and Accumulation) are converted into hardware-friendly Addition/Subtraction operations. In this work, we first investigate and analyze the performance of BD-NET in terms of accuracy, parameter size and computation cost, w.r.t various network configurations. Then, the experiment results show that our proposed BD-NET with binarized depthwise separable convolution can achieve even higher inference accuracy to its baseline CNN counterpart with full-precision conventional convolution layer on the CIFAR-10 dataset. From the perspective of hardware implementation, the convolution layer of BD-NET achieves up to 97.2%, 88.9%, and 99.4% reduction in terms of computation energy, memory usage, and chip area respectively.


title: Measuring the quality of experience of HTTP video streaming

abstract: HTTP video streaming, such as Flash video, is widely deployed to deliver stored media. Owing to TCP's reliable service, the picture and sound quality would not be degraded by network impairments, such as high delay and packet loss. However, the network impairments can cause rebuffering events which would result in jerky playback and deform the video's temporal structure. These quality degradations could adversely affect users' quality of experience (QoE). In this paper, we investigate the relationship among three levels of quality of service (QoS) of HTTP video streaming: network QoS, application QoS, and user QoS (i.e., QoE). Our ultimate goal is to understand how the network QoS affects the QoE of HTTP video streaming. Our approach is to first characterize the correlation between the application and network QoS using analytical models and empirical evaluation. The second step is to perform subjective experiments to evaluate the relationship between application QoS and QoE. Our analysis reveals that the frequency of rebuffering is the main factor responsible for the variations in the QoE.


title: Entity Disambiguation for Knowledge Base Population

abstract: The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves performance of up to 95% on entities mentioned from newswire and 80% on a public test set that was designed to include challenging queries.


title: Evaluating surgical skills from kinematic data using convolutional neural networks

abstract: The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.


title: Polymicrobial Multi-functional Approach for Enhancement of Crop Productivity.

abstract: There is an increasing global need for enhancing the food production to meet the needs of the fast-growing human population. Traditional approach to increasing agricultural productivity through high inputs of chemical nitrogen and phosphate fertilizers and pesticides is not sustainable because of high costs and concerns about global warming, environmental pollution, and safety concerns. Therefore, the use of naturally occurring soil microbes for increasing productivity of food crops is an attractive eco-friendly, cost-effective, and sustainable alternative to the use of chemical fertilizers and pesticides. There is a vast body of published literature on microbial symbiotic and nonsymbiotic nitrogen fixation, multiple beneficial mechanisms used by plant growth-promoting rhizobacteria (PGPR), the nature and significance of mycorrhiza-plant symbiosis, and the growing technology on production of efficacious microbial inoculants. These areas are briefly reviewed here. The construction of an inoculant with a consortium of microbes with multiple beneficial functions such as N(2) fixation, biocontrol, phosphate solubilization, and other plant growth-promoting properties is a positive new development in this area in that a single inoculant can be used effectively for increasing the productivity of a broad spectrum of crops including legumes, cereals, vegetables, and grasses. Such a polymicrobial inoculant containing several microorganisms for each major function involved in promoting the plant growth and productivity gives it greater stability and wider applications for a range of major crops. Intensifying research in this area leading to further advances in our understanding of biochemical/molecular mechanisms involved in plant-microbe-soil interactions coupled with rapid advances in the genomics-proteomics of beneficial microbes should lead to the design and development of inoculants with greater efficacy for increasing the productivity of a wide range of crops.


Investigation of oxide-type oxidation catalysts by reactions of oxygen isotopic exchange.


title: The chicken-and-egg problem in wordnet design: synonymy, synsets and constitutive relations

abstract: Wordnets are built of synsets, not of words. A synset consists of words. Synonymy is a relation between words. Words go into a synset because they are synonyms. Later, a wordnet treats words as synonymous because they belong in the same synset $$\ldots$$ ? Such circularity, a well-known problem, poses a practical difficulty in wordnet construction, notably when it comes to maintaining consistency. We propose to make a wordnet a net of words or, to be more precise, lexical units. We discuss our assumptions and present their implementation in a steadily growing Polish wordnet. A small set of constitutive relations allows us to construct synsets automatically out of groups of lexical units with the same connectivity. Our analysis includes a thorough comparative overview of systems of relations in several influential wordnets. The additional synset-forming mechanisms include stylistic registers and verb aspect.


title: GPU-based multi-volume ray casting within VTK for medical applications

abstract: Purpose ::: Multi-volume visualization is important for displaying relevant information in multimodal or multitemporal medical imaging studies. The main objective with the current study was to develop an efficient GPU-based multi-volume ray caster (MVRC) and validate the proposed visualization system in the context of image-guided surgical navigation.


Evolution of data modeling for databases


title: Trichoscopic findings in alopecia areata and their relation to disease activity, severity and clinical subtype in Turkish patients.

abstract: BACKGROUND/OBJECTIVE ::: Trichoscopic studies of alopecia areata are helpful in the non-invasive diagnosis and prediction of the course of the disease. The objective was to determine the relationship of trichoscopic findings in alopecia areata with disease activity, severity and clinical subtype in Turkish patients. ::: ::: ::: METHODS ::: Trichoscopic examinations of 39 patients with alopecia areata were compared with 309 alopecia patients including psoriasis (n = 31), seborrhoeic dermatitis (n = 112), female androgenetic alopecia (n = 138), male androgenetic alopecia (n = 63), female androgenetic alopecia of male pattern (n = 5), telogen effluvium (n = 22) and trichotillomania (n = 4). A χ(2) test and logistic regression analysis were used for the statistical analysis. The odds ratios were calculated by cross tabulation. ::: ::: ::: RESULTS ::: There was no relationship between the alopecia areata subtype and trichoscopic findings. On the other hand, a honeycomb hyperpigmentation pattern, cumulus-like clustered white dots, white dots and black dotted pigmentation related to severe disease, while exclamation mark hairs related to mild disease. Exclamation mark hairs were found to be related to active disease while atypical red vessels and white dots were negatively related to disease activity. ::: ::: ::: CONCLUSION ::: In our study, further characteristic trichoscopic findings were detected in alopecia areata such as clustered white dots, multi-hair follicular unit, hidden hairs and black dotted pigmentation, in addition to previous findings. Hence, it is concluded that the identification and prediction of alopecia areata might be straightforward with the help of these new signs such as activation and severity findings.


title: Soft-Margin Softmax for Deep Classification

abstract: In deep classification, the softmax loss (Softmax) is arguably one of the most commonly used components to train deep convolutional neural networks (CNNs). However, such a widely used loss is limited due to its lack of encouraging the discriminability of features. Recently, the large-margin softmax loss (L-Softmax [1]) is proposed to explicitly enhance the feature discrimination, with hard margin and complex forward and backward computation. In this paper, we propose a novel soft-margin softmax (SM-Softmax) loss to improve the discriminative power of features. Specifically, SM-Softamx only modifies the forward of Softmax by introducing a non-negative real number m, without changing the backward. Thus it can not only adjust the desired continuous soft margin but also be easily optimized by the typical stochastic gradient descent (SGD). Experimental results on three benchmark datasets have demonstrated the superiority of our SM-Softmax over the baseline Softmax, the alternative L-Softmax and several state-of-the-art competitors.


Socialization and gender roles within the family: a study on adolescents and their parents in Great Britain


title: Low-pass and high-pass microwave filters with transmission zero based on metamaterial concepts

abstract: In this work, highly selective filters based on periodic arrays of electrically small resonators are pointed out. The high-pass filters are implemented in microstrip technology by etching complementary split ring resonators (CSRRs), or complementary spiral resonators (CSRs), in the ground plane, and series capacitive gaps, or interdigital capacitors, in the signal strip. The structure exhibits a composite right/left handed (CRLH) behavior and, by properly tuning the geometry of the elements, a high pass response with a sharp transition band is obtained. The low-pass filters, also implemented in microstrip technology, are designed by cascading open complementary split ring resonators (OCSRRs) in the signal strip. These low pass filters do also exhibit a narrow transition band. The high selectivity of these microwave filters is due to the presence of a transmission zero. Since the resonant elements are small, filter dimensions are compact. Several prototype device examples are reported in this paper.


title: Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

abstract: We present an approach for cyberbullying detection based on state-of-the-art text classification and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective userinteraction paradigms with the goal of promoting empathy among social network participants. We propose an air traffic control-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that incontext, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.


title: A direct method for locating the focus of expansion

abstract: We address the problem of recovering the motion of a monocular observer relative to a rigid scene. We do not make any assumptions about the shapes of the surfaces in the scene, nor do we use estimates of the optical flow or point correspondences. Instead, we exploit the spatial gradient and the time rate of change of brightness over the whole image and explicitly impose the constraint that the surface of an object in the scene must be in front of the camera for it to be imaged.


title: The phone lock: audio and haptic shoulder-surfing resistant PIN entry methods for mobile devices

abstract: Tangible user interfaces are portals to digital information. In the future, securing access to such material will be an important concern. This paper describes the design, implementation and evaluation of a PIN entry system based on audio or haptic cues that is suitable for integration into such physical systems. The current implementation links movements on a mobile phone touch screen with the display of non-visual cues; selection of a sequence of these cues composes a password. Studies reveal the validity of this approach in terms of task times and error rates that improve over prior art. In sum, this paper demonstrates the potential of non-visual PINs as a mechanism for securing access to a range of systems, ultimately incorporating mobile, ubiquitous or tangible interfaces.


title: A comparison of hard-state and soft-state signaling protocols

abstract: One of the key infrastructure components in all telecommunication networks, ranging from the telephone network, to VC-oriented data networks, to the Internet, is its signaling system. Two broad approaches towards signaling can be identified: so-called hard-state and soft-state approaches. Despite the fundamental importance of signaling, our understanding of these approaches - their pros and cons and the circumstances in which they might best be employed - is mostly anecdotal (and occasionally religious). In this paper, we compare and contrast a variety of signaling approaches ranging from a "pure" soft state, to soft-state approaches augmented with explicit state removal and/or reliable signaling, to a "pure" hard state approach. We develop an analytic model that allows us to quantify state inconsistency in single- and multiple-hop signaling scenarios, and the "cost" (both in terms of signaling overhead, and application-specific costs resulting from state inconsistency) associated with a given signaling approach and its parameters (e.g., state refresh and removal timers). Among the class of soft-state approaches, we find that a soft-state approach coupled with explicit removal substantially improves the degree of state consistency while introducing little additional signaling message overhead. The addition of reliable explicit setup/update/removal allows the soft-state approach to achieve comparable (and sometimes better) consistency than that of the hard-state approach.


title: Ontology-Based Semantic Annotation of Arabic Language Text

abstract: Semantic annotation is the process of adding semantic metadata to resources. Semantic metadata is data concerning the meaning of entities and the relationships that exist. Semantic annotation cannot be performed without an ontology suitable for the task. In this research paper, we describe the design, implementation, and evaluation of a lexical ontology for Arabic semantic relations. The main purpose of the ontology is to facilitate the task of semantic annotation of the Arabic textual content. The ontology was evaluated for usability and usefulness using a prototype system for the automated semantic annotation of Arabic text. The results of the evaluation indicated that the ontology was fit for the purpose of semantic annotation of Arabic text with lexical relations. The evaluation has also revealed important findings and recommendations for designing Arabic semantic annotation tools.


title: A framework for integrating IoT and SDN using proposed OF-enabled management device

abstract: The world today is becoming smart in-terms of technology. Internet of Things is an enabler for smart world. However the problem with internet of Things is that they are devices which support heterogeneous methods of communication. Another problem is that the existing network is already loaded and the surge of data increase once more and more devices are connected will be enormous. In this paper we propose a framework to easily imbibe Internet of Things devices into the network with better control than the existing scenario. Also we proposed a device to solve the challenge of heterogeneous communication methods used in IoT. We also gave the sequence of steps which is the flow of data from the IoT to the remote processing device. Our future work is to provide the implementation details of the proposed device.


title: Semiparametric Decolorization With Laplacian-Based Perceptual Quality Metric

abstract: While the RGB2GRAY conversion with fixed parameters is a classical and widely used tool for image decolorization, recent studies showed that adapting weighting parameters in a two-order multivariance polynomial model has great potential to improve the conversion ability. In this paper, by viewing the two-order model as the sum of three subspaces, it is observed that the first subspace in the two-order model has the dominating importance and the second and the third subspace can be seen as refinement. Therefore, we present a semiparametric strategy to take advantage of both the RGB2GRAY and the two-order models. In the proposed method, the RGB2GRAY result on the first subspace is treated as an immediate grayed image, and then the parameters in the second and the third subspace are optimized. Experimental results show that the proposed approach is comparable to other state-of-the-art algorithms in both quantitative evaluation and visual quality, especially for images with abundant colors and patterns. This algorithm also exhibits good resistance to noise. In addition, instead of the color contrast preserving ratio using the first-order gradient for decolorization quality metric, the color contrast correlation preserving ratio utilizing the second-order gradient is calculated as a new perceptual quality metric.


title: The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents

abstract: Objective ::: This study was designed to investigate the revised and short version of the smartphone addiction scale and the proof of its validity in adolescents. In addition, it suggested cutting off the values by gender in order to determine smartphone addiction and elaborate the characteristics of smartphone usage in adolescents. ::: ::: ::: Method ::: A set of questionnaires were provided to a total of 540 selected participants from April to May of 2013. The participants consisted of 343 boys and 197 girls, and their average age was 14.5 years old. The content validity was performed on a selection of shortened items, while an internal-consistency test was conducted for the verification of its reliability. The concurrent validity was confirmed using SAS, SAPS and KS-scale. Receiver operating characteristics analysis was conducted to suggest cut-off. ::: ::: ::: Results ::: The 10 final questions were selected using content validity. The internal consistency and concurrent validity of SAS were verified with a Cronbach's alpha of 0.911. The SAS-SV was significantly correlated with the SAS, SAPS and KS-scale. The SAS-SV scores of gender (p<.001) and self-evaluation of smartphone addiction (p<.001) showed significant difference. The ROC analysis results showed an area under a curve (AUC) value of 0.963(0.888–1.000), a cut-off value of 31, sensitivity value of 0.867 and specificity value of 0.893 in boys while an AUC value of 0.947(0.887–1.000), a cut-off value of 33, sensitivity value of 0.875, and a specificity value of 0.886 in girls. ::: ::: ::: Conclusions ::: The SAS-SV showed good reliability and validity for the assessment of smartphone addiction. The smartphone addiction scale short version, which was developed and validated in this study, could be used efficiently for the evaluation of smartphone addiction in community and research areas.


title: MR imaging of skeletal soft tissue infection: utility of diffusion-weighted imaging in detecting abscess formation

abstract: PURPOSE ::: Our objectives were to assess if diffusion-weighted imaging (DWI) can help identify abscess formation in the setting of soft tissue infection and to assess whether abscess formation can be diagnosed confidently with a combination of DWI and other unenhanced sequences. ::: ::: ::: METHODS ::: Eight cases of soft tissue infection imaged with MRI including DWI were retrospectively reviewed. ::: ::: ::: RESULTS ::: Two male and six female patients were studied (age range 23-50 years). Unenhanced MRI including DWI was performed in all patients. Post-contrast images were obtained in seven patients. All patients had clinically or surgically confirmed abscesses. Abscesses demonstrated restricted diffusion. DWI in conjunction with other unenhanced imaging showed similar confidence levels as post-contrast images in diagnosing abscess formation in four cases. In two cases, although the combined use of DWI and other unenhanced imaging yielded the same confidence levels as post-contrast imaging, DWI was more definitive for demonstrating abscess formation. In one case, post-contrast images had a better confidence for suggesting abscess. In one case, DWI helped detected the abscess, where gadolinium could not be administered because of a contraindication. ::: ::: ::: CONCLUSION ::: This preliminary study suggests that DWI is a useful adjunct in the diagnosis of skeletal soft tissue abscesses.


title: Internet of Things: A Definition & Taxonomy

abstract: The Internet of Things (IoT) has various fields of application including health care, resource management, asset tracking, etc. Depending on the use case, various technologies like RFID, Wireless Sensor Network (WSN) or Smart Objects can be used. With each of these comes a specific vision of what the IoT and connected objects are and -- to our knowledge -- there is no global picture of the IoT. The issue with this approach is that specific problems have been addressed before global ones: what if something has been missed? We propose a definition and taxonomy for connected objects and the IoT.


title: Risk perception and risk management in cloud computing: Results from a case study of Swiss companies

abstract: Abstract In today's economic turmoil, the pay-per-use pricing model of cloud computing, its flexibility and scalability and the potential for better security and availability levels are alluring to both SMEs and large enterprises. However, cloud computing is fraught with security risks which need to be carefully evaluated before any engagement in this area. This article elaborates on the most important risks inherent to the cloud such as information security, regulatory compliance, data location, investigative support, provider lock-in and disaster recovery. We focus on risk and control analysis in relation to a sample of Swiss companies with regard to their prospective adoption of public cloud services. We observe a sufficient degree of risk awareness with a focus on those risks that are relevant to the IT function to be migrated to the cloud. Moreover, the recommendations as to the adoption of cloud services depend on the company's size with larger and more technologically advanced companies being better prepared for the cloud. As an exploratory first step, the results of this study would allow us to design and implement broader research into cloud computing risk management in Switzerland.


title: Automatic Simplification of Obfuscated JavaScript Code: A Semantics-Based Approach

abstract: JavaScript is a scripting language that is commonly used to create sophisticated interactive client-side web applications. However, JavaScript code can also be used to exploit vulnerabilities in the web browser and its extensions, and in recent years it has become a major mechanism for web-based malware delivery. In order to avoid detection, attackers often take advantage of the dynamic nature of JavaScript to create highly obfuscated code. This paper describes a semantics-based approach for automatic deobfuscation of JavaScript code. Experiments using a prototype implementation indicate that our approach is able to penetrate multiple layers of complex obfuscations and extract the core logic of the computation, which makes it easier to understand the behavior of the code.


title: Sensorless control of a BLDC PM motor based on I-f starting and back-EMF zero-crossing detection

abstract: The present paper introduces a novel sensorless control of brushless dc permanent magnet (BLDC-PM) motor based on I-f principle for starting (from any initial position) with seamless commutation to BEMF zero crossing detection above a certain speed. The starting method assures a fast response and the criteria for switching to speed sensorless control introduces short transients. This way, for general drives, even with notable load torque at start, the motor will start, although after a short hesitation depending on the initial position of the rotor. Several scenarios were simulated in Matlab/Simulink ( with Plecs Library) to confirm the proposed control strategy. Experiments for I-f starting are ongoing.


title: A Review on Artificial Intelligence in Special Education

abstract: Innovative educational technologies have started to open new ways of interacting with students with special educational needs (SEN). Amongst the most effective approaches during the last decade (2001-2010) are those based on Artificial Intelligence (A.I.) techniques. The effective application of A.I. methods is seen as a means of improving the quality of life of SEN learners. Hence, a need for introducing A.I. techniques arises in order to develop both diagnosis and intervention processes. This paper presents a brief overview of the most representative studies of the past ten years, used for the above purposes.


title: Semi-Supervised Classification with Graph Convolutional Networks

abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.


title: Big data analytics: a survey

abstract: The age of big data is now coming. But the traditional data ::: analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and ::: how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. ::: Some important open issues and further research directions will also be presented for the next step of big data analytics.


title: Latent Support Measure Machines for Bag-of-Words Data Classification

abstract: In many classification problems, the input is represented as a set of features, e.g., the bag-of-words (BoW) representation of documents. Support vector machines (SVMs) are widely used tools for such classification problems. The performance of the SVMs is generally determined by whether kernel values between data points can be defined properly. However, SVMs for BoW representations have a major weakness in that the co-occurrence of different but semantically similar words cannot be reflected in the kernel calculation. To overcome the weakness, we propose a kernel-based discriminative classifier for BoW data, which we call the latent support measure machine (latent SMM). With the latent SMM, a latent vector is associated with each vocabulary term, and each document is represented as a distribution of the latent vectors for words appearing in the document. To represent the distributions efficiently, we use the kernel embeddings of distributions that hold high order moment information about distributions. Then the latent SMM finds a separating hyperplane that maximizes the margins between distributions of different classes while estimating latent vectors for words to improve the classification performance. In the experiments, we show that the latent SMM achieves state-of-the-art accuracy for BoW text classification, is robust with respect to its own hyper-parameters, and is useful to visualize words.


Chapter 11 QUEUEING ANALYSIS IN HEALTHCARE


title: Impulse generator targeting the European UWB mask

abstract: This paper presents a monolithic impulse generator IC targeting the spectral mask for ultra-wideband applications allocated in the European Union. The multicycle impulse is based on a spike triggered resonant circuit and has a peak to peak output amplitude of 32mV and a time domain extension of 0.83 ns (full width at half maximum). It can generate single pulses as well as repetition rates exceeding the 200MHz shown in this paper. The IC includes a conversion stage, which can generate the triggering spike from a low slew rate signal. The IC is fabricated in a Si/SiGe HBT production technology, has a power consumption of 58.6mW at 200MHz repetition rate and an on-chip area of 480 × 880µm2, both including the conversion stage. Based on the time domain measurement a model of the impulse transient for the use in system simulations is also presented. The modell equation applies a summation of two Gaussian bell shapes as the envelope function, which is multiplied with a phase-corrected sinusoidal waveform to arrive at the final shape.


title: A Supervised Approach to Predict Company Acquisition With Factual and Topic Features Using Profiles and News Articles on TechCrunch

abstract: Merger and Acquisition (M&A) prediction has been an interesting and challenging research topic in the past a few decades. However, past work has only adopted numerical features in building models, and yet the valuable textual information from the great variety of social media sites has not been touched at all. To fully explore this information, we used the profiles and news articles for companies and people on TechCrunch, the leading and largest public database for the tech world, which anybody can edit. Specifically, we explored topic features via topic modeling techniques, as well as a set of other novel features of our design within a machine learning framework. We conducted experiments of the largest scale in the literature, and achieved a high true positive rate (TP) between 60% to 79.8% with a false positive rate (FP) mostly between 0% and 8.3% over company categories with a small number of missing attributes in the CrunchBase profiles.


title: A Novel Secure Architecture for the Internet of Things

abstract: With the development of the IoT, different wireless communication technologies and edge network infrastructures are continuously integrated. Therefore, the corresponding communication network environment has become more complex and the security issues involved in the IoT are more complex than any existing network infrastructures. The trust relationship between the heterogeneous entities, security communication, security system and other security issues are more complex and difficult to solve. In order to solve this kind of security problems, this paper gives the system structure, analyzes the security challenges and threats, proposes a novel security architecture for the IoT based on the research of these security demands, and eventually gives details of the designed security verification system.


title: Prevalence and persistence of depression among undergraduate medical students: a longitudinal study at one UK medical school

abstract: Objectives To determine the prevalence of depression among male and female medical students, its change over time and whether depression persists for affected students. Design Longitudinal study comprising annual questionnaire surveys which included the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D). Participants Between 2007 and 2010 all 1112 students entering the Core Science component (Year 1) and all 542 students entering the Clinical component (Year 4) of the Cambridge (UK) medical course were followed-up annually. Methods We analysed, separately for men and women, mean HADS-D scores, the proportions whose scores indicated depression at different time-points and for students maintaining participation, the number of occasions on which their HADS-D scores indicated depression. Results 725 Core Science and 364 Clinical students participated. Mean HADS-D scores ranged between 3.34 and 3.49 among all Core Science students and between 2.16 and 2.91 among all Clinical students. There was no difference between men and women in median HADS-D scores. Prevalence of depression ranged between 5.7% and 10.6% among all Core Science students and between 2.7% and 8.2% among all Clinical students. Over time Core Science students displayed no increase in mean HADS-D score. Among Clinical students only men displayed a small increase (time coefficient 0.33 (95% CI 0.11 to 0.55)). Prevalence did not increase over time. 220 Core Science and 150 Clinical students participated throughout the study. Of these, 18.2% and 10.6%, respectively, recorded HADS-D scores indicating depression on at least one occasion. Of 56 students recording depression at some point, 37 did so only once. Conclusions Prevalence of depression among participants was similar to that reported for comparable groups. Among men approaching the end of clinical studies depression scores increased. In all years a minority of students displayed depression; for some this persisted. Mechanisms are needed to identify and support students suffering from depression, particularly when persistent.


title: Fingerprinting Web Users Through Font Metrics

abstract: We describe a web browser fingerprinting technique based on measuring the onscreen dimensions of font glyphs. Font rendering in web browsers is affected by many factors—browser version, what fonts are installed, and hinting and antialiasing settings, to name a few—that are sources of fingerprintable variation in end-user systems. We show that even the relatively crude tool of measuring glyph bounding boxes can yield a strong fingerprint, and is a threat to users’ privacy. Through a user experiment involving over 1,000 web browsers and an exhaustive survey of the allocated space of Unicode, we find that font metrics are more diverse than User-Agent strings, uniquely identifying 34 % of participants, and putting others into smaller anonymity sets. Fingerprinting is easy and takes only milliseconds. We show that of the over 125,000 code points examined, it suffices to test only 43 in order to account for all the variation seen in our experiment. Font metrics, being orthogonal to many other fingerprinting techniques, can augment and sharpen those other techniques.


title: A Comprehensive Memory Modeling Tool and Its Application to the Design and Analysis of Future Memory Hierarchies

abstract: In this paper we introduce CACTI-D, a significant enhancement of CACTI 5.0. CACTI-D adds support for modeling of commodity DRAM technology and support for main memory DRAM chip organization. CACTI-D enables modeling of the complete memory hierarchy with consistent models all the way from SRAM based L1 caches through main memory DRAMs on DIMMs. We illustrate the potential applicability of CACTI-D in the design and analysis of future memory hierarchies by carrying out a last level cache study for a multicore multithreaded architecture at the 32nm technology node. In this study we use CACTI-D to model all components of the memory hierarchy including L1, L2, last level SRAM, logic process based DRAM or commodity DRAM L3 caches, and main memory DRAM chips. We carry out architectural simulation using benchmarks with large data sets and present results of their execution time, breakdown of power in the memory hierarchy, and system energy-delay product for the different system configurations. We find that commodity DRAM technology is most attractive for stacked last level caches, with significantly lower energy-delay products.


title: An anthropomorphic hand exoskeleton to prevent astronaut hand fatigue during extravehicular activities

abstract: This correspondence presents a prototype of a powered hand exoskeleton that is designed to fit over the gloved hand of an astronaut and offset the stiffness of the pressurized space suit. This will keep the productive time spent in extravehicular activity from being constrained by hand fatigue. The exoskeleton has a three-finger design, the third and fourth fingers being combined to lighten and simplify the assembly. The motions of the hand are monitored by an array of pressure sensors mounted between the exoskeleton and the hand. Controller commands are determined by a state-of-the-art programmable microcontroller using pressure sensor input. These commands are applied to a PWM driven DC motor array which provides the motive power to move the exoskeleton fingers. The resultant motion of the exoskeleton allows the astronaut to perform both precision grasping tasks with the thumb and forefinger, as well as a power grasp with the entire hand.


title: Seed dormancy and the control of germination

abstract: Seed dormancy is an innate seed property that defines the environmental conditions in which the seed is able to germinate. It is determined by genetics with a substantial environmental influence which is mediated, at least in part, by the plant hormones abscisic acid and gibberellins. Not only is the dormancy status influenced by the seed maturation environment, it is also continuously changing with time following shedding in a manner determined by the ambient environment. As dormancy is present throughout the higher plants in all major climatic regions, adaptation has resulted in divergent responses to the environment. Through this adaptation, germination is timed to avoid unfavourable weather for subsequent plant establishment and reproductive growth. In this review, we present an integrated view of the evolution, molecular genetics, physiology, biochemistry, ecology and modelling of seed dormancy mechanisms and their control of germination. We argue that adaptation has taken place on a theme rather than via fundamentally different paths and identify similarities underlying the extensive diversity in the dormancy response to the environment that controls germination.


title: Design considerations and development of gate driver for 15 kV SiC IGBT

abstract: The 15 kV SiC N-IGBT is the state-of-the-art high voltage power semiconductor device developed by Cree. The SiC IGBT is exposed to a peak stress of 10-11 kV in power converter systems, with punch-through turn-on dv/dt over 100 kV/μs and turn-off dv/dt about 35 kV/μs. Such high dv/dt requires ultralow coupling capacitance in the dc-dc isolation stage of the gate driver for maintaining fidelity of the signals on the control-supply ground side. Accelerated aging of the insulation in the isolation stage is another serious concern. In this paper, a simple transformer based isolation with a toroid core is investigated for the above requirements of the 15 kV IGBT. The gate driver prototype has been developed with over 100 kV dc insulation capability, and its inter-winding coupling capacitance has been found to be 3.4 pF and 13 pF at 50 MHz and 100 MHz respectively. The performance of the gate driver prototype has been evaluated up to the above mentioned specification using double-pulse tests on high-side IGBT in a half-bridge configuration. The continuous testing at 5 kHz has been performed till 8 kV, and turn-on dv/dt of 85 kV/μs on a buck-boost converter. The corresponding experimental results are presented. Also, the test methodology of evaluating the gate driver at such high voltage, without a high voltage power supply is discussed. Finally, experimental results validating fidelity of the signals on the control-ground side are provided to show the influence of increased inter-winding coupling capacitance on the performance of the gate driver.


10 x 10 : A Case Study in Highly-Programmable and Energy-Efficient Federated Heterogeneous Architecture


title: Understanding Individual Neuron Importance Using Information Theory

abstract: In this work, we characterize the outputs of individual neurons in a trained feed-forward neural network by entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence. By cumulatively ablating neurons in the network, we connect these information-theoretic measures to the impact their removal has on classification performance on the test set. We observe that, looking at the neural network as a whole, none of these measures is a good indicator for classification performance, thus confirming recent results by Morcos et al. However, looking at specific layers separately, both mutual information and class selectivity are positively correlated with classification performance. We thus conclude that it is ill-advised to compare these measures across layers, and that different layers may be most appropriately characterized by different measures. ::: We then discuss pruning neurons from neural networks to reduce computational complexity of inference. Drawing from our results, we perform pruning based on information-theoretic measures on a fully connected feed-forward neural network with two hidden layers trained on MNIST dataset and compare the results to a recently proposed pruning method. We furthermore show that the common practice of re-training after pruning can partly be obviated by a surgery step called bias balancing, without incurring significant performance degradation.


title: Towards an Ontology-Driven Blockchain Design for Supply Chain Provenance

abstract: An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods--e.g. tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods--has often not been possible with today's items that are produced and transported in complex, inter-organizational, often internationally-spanning supply chains. Recent adoption of Internet of Things and Blockchain technologies give promise at better supply chain provenance. We are particularly interested in the blockchain as many favoured use cases of blockchain are for provenance tracking. We are also interested in applying ontologies as there has been some work done on knowledge provenance, traceability, and food provenance using ontologies. In this paper, we make a case for why ontologies can contribute to blockchain design. To support this case, we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the Ethereum blockchain platform.


Cannabis sativa: The Plant of the Thousand and One Molecules


Estimating the extent of illegal traditional Chinese medicine trade in Guangzhou , China using occupancy modelling


title: A 77 GHz Frequency Doubling Two-Path Phased-Array FMCW Transceiver for Automotive Radar

abstract: A fully-integrated 77 GHz frequency doubling two-path phased-array frequency-modulated continuous-wave (FMCW) transceiver for automotive radar applications is proposed. By utilizing the frequency doubling scheme, the chirp bandwidth is improved, and the complexity of the frequency synthesizer and the insertion loss of the local-oscillating (LO) distribution network are both reduced. Top-injected coupled resonator based wide locking range technique is proposed in the frequency doublers to minimize the required injection power to cover the chirp bandwidth plus enough PVT variation margin, and therefore reduce the power consumption of the LO distribution network. Current-reused coupled resonator technique is utilized to implement the LO phase shifting in each receiving path. The digitally controlled artificial dielectric-based transmission lines are inserted in the low noise amplifiers to provide the operation frequency calibration capability. The receiving two-path signals are converted into intermediate frequency by low flicker noise current-mode passive mixers and then combined in the trans-impedance amplifier, followed by the reconfigurable analog baseband processing. Fabricated in 65 nm CMOS, the FMCW transceiver has achieved 1.93 GHz maximum chirp bandwidth, $12.9\sim 13.2$ dBm maximum transmitting power, and $47.8\sim 100.7$ dB programmable receiving conversion gain. The transceiver consumes 343 mW power and 4.64 mm2 chip area including all of the pads.


title: Application of TRIZ framework for resolving security issues in IOT

abstract: The traditional security mechanisms are not feasible to work with the IOT systems. The ubiquitous sensors are subject to tampering and data can be manipulated as a result. The processing capabilities of the IOT devices are not suitable for the current encryption techniques, also with all the data from the devices being collected the user privacy is at a risk as a result. Additionally, with the peta, zettabytes of data that will be collected through IOT systems effective mechanisms of data management techniques need to be developed. There is an urgent need to find new techniques to find answers to the security challenges and prevent data theft. Considering the above factors, the authors were motivated to find ways to enhance the security system for IOT devices. The authors apply the TRIZ methodology to resolve the underlying contradictions in order to the find the solutions to the problems with IOT devices.


title: Towards cloud-based image-integrated similarity search in big data

abstract: The volume of data accumulated by institutions and individuals and containing valuable and even critical information grows steadily with time complicating timely access to this information. In particular, this concerns the domain of healthcare. One approach to leverage the increasing amounts of data is to use advanced similarity search and case retrieval-based solutions. In this paper, we share our experience with translation of a similarity search-based clinical decision support system Case Reasoner from a standalone desktop application into a mobile Web-based solution. In particular, translation of patient similarity visualization and of a Web-based medical imaging toolkit are considered.


title: Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories

abstract: The biggest challenge in fostering a virtual community is the supply of knowledge, namely the willingness to snare Knowledge with other members. This paper integrates the Social Cognitive Theory and the Social Capital Theory to construct a model for investigating the motivations behind people's knowledge sharing in virtual communities. The study holds that the facets of social capital -- social interaction ties, trust, norm of reciprocity, identification, shared vision and shared language -- will influence individuals' knowledge sharing in virtual communities. We also argue that outcome expectations -- community-related outcome expectations and personal outcome expectations -- can engender knowledge sharing in virtual communities. Data collected from 310 members of one professional virtual community provide support for the proposed model. The results help in identifying the motivation underlying individuals' knowledge sharing behavior in professional virtual communities. The implications for theory and practice and future research directions are discussed.


title: Chapter 9 A SURVEY OF LINK PREDICTION IN SOCIAL NETWORKS

abstract: Link prediction is an important task for analying social networks which also has applications in other domains like, information retrieval, bioinformatics and e-commerce. There exist a variety of techniques for link prediction, ranging from feature-based classification and kernel-based method to matrix factorization and probabilistic graphical models. These methods differ from each other with respect to model complexity, prediction performance, scalability, and generalization ability. In this article, we survey some representative link prediction methods by categorizing them by the type of the models. We largely consider three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model. Second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models. And, finally the linear algebraic approach which computes the similarity between the nodes in a network by rank-reduced similarity matrices. We discuss various existing link prediction models that fall in these broad categories and analyze their strength and weakness. We conclude the survey with a discussion on recent developments and future research direction.


title: Improved 3D ear reconstruction based on 3D EMM

abstract: Ear has been proven to be a good candidate for non-contact recognition. In order to acquire ear's 3D information as well as reserve its non-contact advantage, 3D reconstruction using 2D images can be a promising way. However ear is a small object with abundant structure information, which makes the 3D shape estimating a challenge problem. An improved sophisticated 3D ear reconstruction method based on 3D ear morphable model is demonstrated. We propose a novel ear contour feature points extraction method based on the automatically detection of ear contour which combines both intensity and depth image. Abundant experiment results show that more realistic 3D ear sample with smooth contour can be generated using the automatically detected feature points, not to mention it can greatly reduce the workload. After statistical model training and model fitting based on sparse points, the reconstruction accuracy is discussed. This is the first 3D ear reconstruction work which demonstrates the reconstruction accuracy quantitatively. Abundant experiment results show the efficiency of our proposed method.


title: Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision

abstract: A new image-based approach for fast and robust vehicle tracking from a moving platform is presented. Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle.


title: Anticipatory affect: neural correlates and consequences for choice

abstract: 'Anticipatory affect' refers to emotional states that people experience while anticipating significant outcomes. Historically, technical limitations have made it difficult to determine whether anticipatory affect influences subsequent choice. Recent advances in the spatio-temporal resolution of functional magnetic resonance imaging, however, now allow researchers to visualize changes in neural activity seconds before choice occurs. We review evidence that activation in specific brain circuits changes during anticipation of monetary incentives, that this activation correlates with affective experience and that activity in these circuits may influence subsequent choice. Specifically, an activation likelihood estimate meta-analysis of cued response studies indicates that nucleus accumbens (NAcc) activation increases during gain anticipation relative to loss anticipation, while anterior insula activation increases during both loss and gain anticipation. Additionally, anticipatory NAcc activation correlates with self-reported positive arousal, whereas anterior insula activation correlates with both self-reported negative and positive arousal. Finally, NAcc activation precedes the purchase of desirable products and choice of high-risk gambles, whereas anterior insula activation precedes the rejection of overpriced products and choice of low-risk gambles. Together, these findings support a neurally plausible framework for understanding how anticipatory affect can influence choice.


title: Reinforced Mnemonic Reader for Machine Reading Comprehension

abstract: In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.


title: Foreground-Adaptive Background Subtraction

abstract: Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The most successful background subtraction methods apply probabilistic models to background intensities evolving in time; nonparametric and mixture-of-Gaussians models are but two examples. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this paper, we adapt this threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model, we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity. We also apply a Markov model to change labels to improve spatial coherence of the detections. The proposed methodology is applicable to other background models as well.


title: Accounting Information System Risk Assessment Algorithm Based on Analytic Hierarchy Process

abstract: So far, there is little research on accounting information system risk assessment in our country. In order to provide the basis to meet the security needs of the accounting information system and reduce the risk of accounting information system security, reduce financial loses and improve the work efficiency, a model of enterprise accounting information system risk assessment method based on Analytic Hierarchy Process is proposed. The analytic hierarchy process model is applied to one corporate accounting information system for risk assessment. It can be concluded that the proposed method get better result of risk assessment, and have strong operability and effectiveness of risk assessment in the accounting information system for enterprise.


title: Convolutional neural network‐based encoding and decoding of visual object recognition in space and time

abstract: Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1,000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feed-forward sweep across the visual hierarchy between 75 and 200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.


title: Portfolio Allocation for Bayesian Optimization

abstract: Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.


title: Verifying Anaconda's expert rating by competing against Chinook: experiments in co-evolving a neural checkers player

abstract: Abstract Since the early days of artificial intelligence, there has been interest in having a computer teach itself how to play a game of skill, like checkers, at a level that is competitive with human experts. To be truly noteworthy, such efforts should minimize the amount of human intervention in the learning process. Recently, co-evolution has been used to evolve a neural network (called Anaconda ) that, when coupled with a minimax search, can evaluate checker-boards and play to the level of a human expert, as indicated by its rating of 2045 on an international web site for playing checkers. The neural network uses only the location, type, and number of pieces on the board as input. No other features that would require human expertise are included. Experiments were conducted to verify the neural network's expert rating by competing it in 10 games against a “novice-level” version of Chinook, a world-champion checkers program. The neural network had 2 wins, 4 losses, and 4 draws in the 10-game match. Based on an estimated rating of Chinook at the novice level, the results corroborate Anaconda's expert rating.


A Computational Analysis of the Language of Drug Addiction


title: General Introduction: Evaluation of Diagnostic Procedures

abstract: box Whereas the development of diagnostic technologies has greatly accelerated, the methodology of diagnostic research still lags behind that of evaluation of treatment. Diagnostic research appears to be more comprehensive and complex than treatment research as it evaluates the connection between diagnostic and prognostic assessment with choosing optimal interventions. Objectives of diagnostic testing are (1) detecting or excluding disorders, (2) contributing to further diagnostic and therapeutic management, (3) assessing prognosis, (4) monitoring clinical course, and (5) measuring general health or fitness. Methodological challenges include dealing with complex relations, the “gold standard” problem, spectrum and selection bias, “soft” outcome measures, observer variability and bias, optimizing clinical relevance, appropriate sample size, and rapid progress of applicable knowledge over time. Choosing the appropriate study design depends on the research question; the most important designs are the cross-sectional study (to determine the accuracy and added discriminatory value of diagnostic procedures) and the randomized controlled trial (to evaluate the clinical impact of [additional] testing). To synthesize the results of various studies on the same topic, diagnostic systematic reviews and meta-analyses are powerful tools. (continued)


title: Circularly-polarized SIW slot antenna for MMW applications

abstract: A novel low profile circularly polarized antenna using Substrate Integrated Waveguide technology (SIW) for millimeter-wave (MMW) application is proposed. The antenna employs an X-shaped slot excited by a rectangular SIW and backed by circular cavity. The optimized design has an operating frequency range from 34.7 GHZ to 36.1 GHz with a bandwidth of 4.23%. The overall antenna realized gain is around 6.7 dB over the operating band. The simulated results using both HFSS and CSTMWS show a very good agreement between them.


Teachers’ Oral Feedback Impact on EFL Students’ Oral Proficiency. Case of Undergraduate Classes of the English Branch at MKU of Biskra.


15 Planar Antenna Technology for mm-Wave Automotive Radar , Sensing