title: A cloud-based web crawler architecture

abstract: Web crawlers work on the behalf of applications or services to find interesting and related information on the web. For example, search engines use web crawlers to index the Internet. Web crawlers have several challenges, such as complexity between links and highly intensive computation requirements when a web crawler wants to retrieve complex connected links. Another issue is the storage of a massive amount of indexed links or downloaded unstructured data, such as binary files, videos or images. As the volume of information on the Internet increases rapidly and requests may search data in a variety of formats including unstructured data, no cloud-based architecture exists in the literatures for web crawlers that could effectively address both highly intensive computing and storage issues. The cloud computing paradigm provides support for elastic resources and unstructured data, and provides pay-peruse features that allow individual businesses to run their own web crawlers for crawling the Internet or a limited web hosts. In this paper, we propose a cloud-based web crawler architecture that uses cloud computing features and the MapReduce programming technique. The proposed web crawler allows us to crawl the web by using distributed agents and each agent stores its own finding on a Cloud Azure Table (NoSQL database). The proposed web crawler also could store unstructured and massive amount of data on Azure Blob storage. We analyze the performance and scalability of the proposed web crawler and we describe the advantages of the proposed web crawler over traditional distributed web crawlers.


title: Oral myiasis: A case report

abstract: Oral Myiasis is a rare pathology in humans and is associated with poor oral hygiene, alcoholism, senility, supuranting lesions, severe halitosis and others conditions. The treatment is a mechanical removal of the maggots one by one but a systemic treatment with Ivermectin, a semi-synthetic macrolide antibiotic, have been used for treatment for oral myiasis. We present a case report of a 32-year-old man indigent, alcohol-dependent with an extensive necrotic area and acute swelling in upper lip and fetid odor. The patient's manegement included topic use of gencian violet, oral therapy with ivermectin (6 mg orally), surgical exploration to remove the larvae and necrotic tissue. After the complete larvae removal the swelling and the wounds were healing normally, the patients was referred to plastic surgery to repair tissue damage. The prevention of human myiasis is by education, but unfortunately in the developing countries some people live in low social condition, predisposing the occurrence of the infestation.


title: A comparison of binarization methods for historical archive documents

abstract: This paper compares several alternative binarization algorithms for historical archive documents, by evaluating their effect on end-to-end word recognition performance in a complete archive document recognition system utilising a commercial OCR engine. The algorithms evaluated are: global thresholding; Niblack's and Sauvola's algorithms; adaptive versions of Niblack's and Sauvola's algorithms; and Niblack's and Sauvola's algorithms applied to background removed images. We found that, for our archive documents, Niblack's algorithm can achieve better performance than Sauvola's (which has been claimed as an evolution of Niblack's algorithm), and that it also achieved better performance than the internal binarization provided as part of the commercial OCR engine.


A Trainable Spaced Repetition Model for Language Learning


title: Type refinement for static analysis of JavaScript

abstract: Static analysis of JavaScript has proven useful for a variety of purposes, including optimization, error checking, security auditing, program refactoring, and more. We propose a technique called type refinement that can improve the precision of such static analyses for JavaScript without any discernible performance impact. Refinement is a known technique that uses the conditions in branch guards to refine the analysis information propagated along each branch path. The key insight of this paper is to recognize that JavaScript semantics include many implicit conditional checks on types, and that performing type refinement on these implicit checks provides significant benefit for analysis precision. To demonstrate the effectiveness of type refinement, we implement a static analysis tool for reporting potential type-errors in JavaScript programs. We provide an extensive empirical evaluation of type refinement using a benchmark suite containing a variety of JavaScript application domains, ranging from the standard performance benchmark suites (Sunspider and Octane), to open-source JavaScript applications, to machine-generated JavaScript via Emscripten. We show that type refinement can significantly improve analysis precision by up to 86% without affecting the performance of the analysis.


title: Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

abstract: Machine-learning ML enables computers to learn how to recognise patterns, make unintended decisions, or react to a dynamic environment. The effectiveness of trained machines varies because of more suitable ML algorithms or because superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. In this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. Such information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights.


title: K-Band GaAs MMIC Doherty Power Amplifier for Microwave Radio With Optimized Driver

abstract: In this paper, a Doherty power amplifier for K-band point-to-point microwave radio, developed in TriQuint GaAs 0.15-μm PWR pHEMT monolithic technology, is presented. Highly efficient driver stages on both the main and auxiliary branches have been designed and optimized to boost gain with minimal impact on power-added efficiency. The selected architecture enables a modular combination to reach higher power levels. Matching network structures have been designed, according to simple equivalent circuit approaches, to obtain the desired 10% fractional bandwidth. The fabricated power amplifier (PA) exhibits, at 24 GHz in continuous-wave conditions, an output power of 30.9 dBm, with a power-added efficiency of 38% at saturation and 20% at 6 dB of output power back-off, together with a gain of 12.5 dB. System-level characterization at 24 GHz, in very demanding conditions, with a 28-MHz channel 7.5-dB peak-to-average ratio modulated signal, showed full compliance with the standard emission mask, adopting a simple predistorter, with average output power of 23.5 dBm, and average efficiency above 14%. The measured performance favorably compare with other academic and commercial K-band PAs for similar applications.


title: Learning to Generate Chairs, Tables and Cars with Convolutional Networks

abstract: We train generative ‘up-convolutional’ neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two different object classes. Moreover, we show that such generative networks can be used to find correspondences between different objects from the dataset, outperforming existing approaches on this task.


title: Boosting for transfer learning

abstract: Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.


title: The rise of deep learning in drug discovery

abstract: Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.


title: A 5G Wideband Patch Antenna With Antisymmetric L-shaped Probe Feeds

abstract: A dual-polarized patch antenna element fed by a pair of antisymmetric L-shaped probes is proposed. The designed twin L-shaped probe feeding structure is able to introduce feed capacitance to the antenna for broadband operation. The lengths of the two L-shaped probe feeds are identical, but the feeds are antisymmetric. This feeding design can minimize the unwanted radiation from the probe effectively. The dual-polarized antenna can be operated in the frequency band 1580–2750 MHz, which covers the current mobile communication systems, 3G and 4G and higher band frequencies. A prototype with dual slanted ±45° polarization has been fabricated for validation. Both the simulation and measured results show that the proposed antenna has wide bandwidth of 54% (SWR < 2) with desirable directional radiation patterns in the vertical and horizontal planes, as well as high isolation better than −30 dB between the two input ports.


title: Puberty suppression and executive functioning: An fMRI-study in adolescents with gender dysphoria

abstract: Summary Adolescents with gender dysphoria (GD) may be treated with gonadotropin releasing hormone analogs (GnRHa) to suppress puberty and, thus, the development of (unwanted) secondary sex characteristics. Since adolescence marks an important period for the development of executive functioning (EF), we determined whether the performance on the Tower of London task (ToL), a commonly used EF task, was altered in adolescents with GD when treated with GnRHa. Furthermore, since GD has been proposed to result from an atypical sexual differentiation of the brain, we determined whether untreated adolescents with GD showed sex-atypical brain activations during ToL performance. We found no significant effect of GnRHa on ToL performance scores (reaction times and accuracy) when comparing GnRHa treated male-to-females (suppressed MFs, n = 8) with untreated MFs (n = 10) or when comparing GnRHa treated female-to-males (suppressed FMs, n = 12) with untreated FMs (n = 10). However, the suppressed MFs had significantly lower accuracy scores than the control groups and the untreated FMs. Region-of-interest (ROI) analyses showed significantly greater activation in control boys (n = 21) than control girls (n = 24) during high task load ToL items in the bilateral precuneus and a trend (p


title: Measuring and Analyzing Write Amplification Characteristics of Solid State Disks

abstract: Write amplification brings endurance challenges to NAND Flash-based solid state disks (SSDs) such as impacts upon their write endurance and lifetime. A large write amplification degrades program/erase cycles (P/Es) of NAND Flashes and reduces the endurance and performance of SSDs. The write amplification problem is mainly triggered by garbage collections, wear-leveling, metadata updates, and mapping table updates. Write amplification is defined as the ratio of data volume written by an SSD controller to data volume written by a host. In this paper, we propose a four-level model of write amplification for SSDs. The four levels considered in our model include the channel level, chip level, die level, and plane level. In light of this model, we design a method of analyzing write amplification of SSDs to trace SSD endurance and performance by incorporating the Ready/Busy (R/B) signal of NAND Flash. Our practical approach aims to measure the value of write amplification for an entire SSD rather than NAND Flashes. To validate our measurement technique and model, we implement a verified SSD (vSSD) system and perform a cross-comparison on a set of SSDs, which are stressed by micro-benchmarks and I/O traces. A new method for SSDs is adopted in our measurements to study the R/B signals of NAND Flashes in an SSD. Experimental results show that our model is accurate and the measurement technique is generally applicable to any SSDs.


title: Modeling of massive MIMO transceiver antenna for full-duplex single-channel system (in case of self interference effect)

abstract: Massive MIMO technology can increase capacity, data rate, and link reliability significantly without additional bandwidth or transmission power. Most of the previous study on Massive MIMO systems are using low frequency (2.6 GHz). This frequency is not suitable for future broadband technology, because IoT devices will use high frequency and possible cell coverage would be smaller. This work proposes Massive MIMO transceiver antenna that can be implemented in high frequency (60 GHz). This system provides broadband wireless communication which gives high capacity, high data rate and wide bandwidth. In this works the number of antennas is 64×64. Antennas can be set adaptively to capacity. In this case all of the antennas as a receiver are functioned to get redundancies and 32 antennas as transmitter adjusted to the required capacity. Configuration of antenna has been set to eliminate the mutual coupling effect. The system utilizes full duplex single channel (FDSC) which use the same frequency and time to transmit and receive data. In FDSC, bandwidth can be minimized, but self interference effect appeared and disturbing the system performance of Massive MIMO. The effect of self interference for Massive MIMO transceiver antenna simulated from 10% to 100%. The proposed system provides the channel capacity ≈ 390 bits/s/Hz or ≈ 39× spectral efficiency of SISO system. The simulation result shows that mutual coupling effect ≈ 0. The Massive MIMO have a tolerance of 20% self interference effect, however it could decrease 20% channel capacity as well.


title: A neural autoregressive topic model

abstract: We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical model of word counts that was shown to learn a better generative model and more meaningful document representations. Specifically, we take inspiration from the conditional mean-field recursive equations of the Replicated Softmax in order to define a neural network architecture that estimates the probability of observing a new word in a given document given the previously observed words. This paradigm also allows us to replace the expensive softmax distribution over words with a hierarchical distribution over paths in a binary tree of words. The end result is a model whose training complexity scales logarithmically with the vocabulary size instead of linearly as in the Replicated Softmax. Our experiments show that our model is competitive both as a generative model of documents and as a document representation learning algorithm.


title: Authoring Data-Driven Videos with DataClips

abstract: Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven “clips” together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.


title: Towards a Job Title Classification System

abstract: Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings. Amidst a plethora of approaches to solve such problems, machine-learning techniques have found success in a variety of scenarios. In this paper we discuss the design of a machine learning-based semi-supervised job title classification system for the online job recruitment domain currently in production at CareerBuilder.com and propose enhancements to it. The system leverages a varied collection of classification as well clustering algorithms. These algorithms are encompassed in an architecture that facilitates leveraging existing off-the-shelf machine learning tools and techniques while keeping into consideration the challenges of constructing a scalable classification system for a large taxonomy of categories. As a continuously evolving system that is still under development we first discuss the existing semi-supervised classification system which is composed of both clustering and classification components in a proximity-based classifier setup and results of which are already used across numerous products at CareerBuilder. We then elucidate our long-term goals for job title classification and propose enhancements to the existing system in the form of a two-stage coarse and fine level classifier augmentation to construct a cascade of hierarchical vertical classifiers. Preliminary results are presented using experimental evaluation on real world industrial data.


title: Cyber-Physical-Social Based Security Architecture for Future Internet of Things

abstract: As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM; 2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms; 3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.


title: Open Subtitles Paraphrase Corpus for Six Languages

abstract: This paper accompanies the release of Opusparcus, a new paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The corpus consists of paraphrases, that is, pairs of sentences in the same language that mean approximately the same thing. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows. The informal and colloquial genre that occurs in subtitles makes such data a very interesting language resource, for instance, from the perspective of computer assisted language learning. For each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been checked manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators.


title: Towards an affordable brain computer interface for the assessment of programmers' mental workload

abstract: Abstract This paper provides a proof of concept for the use of wearable technology, and specifically wearable Electroencephalography (EEG), in the field of Empirical Software Engineering. Particularly, we investigated the brain activity of Software Engineers (SEngs) while performing two distinct but related mental tasks: understanding and inspecting code for syntax errors. By comparing the emerging EEG patterns of activity and neural synchrony, we identified brain signatures that are specific to code comprehension. Moreover, using the programmer's rating about the difficulty of each code snippet shown, we identified neural correlates of subjective difficulty during code comprehension. Finally, we attempted to build a model of subjective difficulty based on the recorded brainwave patterns. The reported results show promise towards novel alternatives to programmers’ training and education. Findings of this kind may eventually lead to various technical and methodological improvements in various aspects of software development like programming languages, building platforms for teams, and team working schemes.


title: A Machine Learning Based System for Semi-Automatically Redacting Documents

abstract: Redacting text documents has traditionally been a mostly manual activity, making it expensive and prone to disclosure risks. This paper describes a semi-automated system to ensure a specified level of privacy in text data sets. Recent work has attempted to quantify the likelihood of privacy breaches for text data. We build on these notions to provide a means of obstructing such breaches by framing it as a multi-class classification problem. Our system gives users fine-grained control over the level of privacy needed to obstruct sensitive concepts present in that data. Additionally, our system is designed to respect a user-defined utility metric on the data (such as disclosure of a particular concept), which our methods try to maximize while anonymizing. We describe our redaction framework, algorithms, as well as a prototype tool built in to Microsoft Word that allows enterprise users to redact documents before sharing them internally and obscure client specific information. In addition we show experimental evaluation using publicly available data sets that show the effectiveness of our approach against both automated attackers and human subjects.The results show that we are able to preserve the utility of a text corpus while reducing disclosure risk of the sensitive concept.


title: A 5GS/s 12.2pJ/conv. analog charge-domain FFT for a software defined radio receiver front-end in 65nm CMOS

abstract: This work describes the design of CRAFT (Charge Re-use Analog Fourier Transform): an RF front-end channelizer for software defined radios (SDR) based on a 16 point analog-domain FFT. The design relies on charge re-use to achieve 47dB average SNDR on a 5GS/s I/Q input, and consumes only 12.2pJ/conv. Due to its wide-band and low power channelization capabilities, it is expected to reduce the sample rate and dynamic range requirements for wide-band digitization in SDRs.


A Survey of Human-Sensing : Methods for Detecting Presence , Count , Location , Track , and Identity


title: Give Me a Customizable Dashboard: Personalized Learning Analytics Dashboards in Higher Education

abstract: With the increased capability of learning analytics in higher education, more institutions are developing or implementing student dashboards. Despite the emergence of dashboards as an easy way to present data to students, students have had limited involvement in the dashboard development process. As part of a larger program of research examining student and academic perceptions of learning analytics, we report here on work in progress exploring student perceptions of dashboards and student preferences for dashboard features. First, we present findings on higher education students’ attitudes towards learning analytic dashboards resulting from four focus groups (N = 41). Thematic analysis of the focus group transcripts identified five key themes relating to dashboards: ‘provide everyone with the same learning opportunities’, ‘to compare or not to compare’, ‘dashboard privacy’, ‘automate alerts’ and ‘make it meaningful—give me a customizable dashboard’. Next we present findings from a content analysis of students’ drawings of dashboards demonstrating that students are interested in features that support learning opportunities, provide comparisons to peers and are meaningful to the student. Finally, we present preliminary findings from a survey of higher education students, reinforcing students’ desire to choose whether to have a dashboard and to be able to customize their dashboards. These findings highlight the potential for providing students with some level of control over learning analytics as a means to increasing self-regulated learning and academic achievement. Future research directions aimed at better understanding students emotional and behavioral responses to learning analytics feedback on dashboards and alerts are outlined.


title: A Novice Researcher's First Walk Through the Maze of Grounded Theory: Rationalization for Classical Grounded Theory

abstract: Being new to grounded theory the onus to understand the methodology and the various versionscan be daunting.Learning and understanding the differences between grounded theories methodologies can be as much a learning of one's own research philosophy and this philosophy is often the deciding factor in methodology selection. Learning the different methodologies is a difficult journey as terminology often sounds similar to the novice researcher, but only by exploring the differences can the researcher rationalize their own choice. This paper offers the new researcher a view into the confusing world of grounded theory, where common terms are usedbut the secret lies in understanding the philosophy of the researcher and the topic of discovery. Glaser was correct, the answer is in the data, but you need to understand the philosophy of the method and if it matches your philosophy of research.


title: Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results

abstract: This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.


title: Turning the Tides: Coping with Trauma and Addiction through Residential Adolescent Group Therapy

abstract: TOPIC ::: Evidence-based group therapy in an inpatient setting that provides an integrated treatment approach for both trauma and addiction in female adolescents. ::: ::: PURPOSE ::: The purpose of this evidence-based practice (EBP) project was to implement and assess the impact of an integrated group therapy approach for both posttraumatic stress disorder (PTSD) and substance use disorder (SUD) in adolescent females as part of a residential treatment program. ::: ::: SOURCES ::: The Iowa Model of EBP guided this EBP project. Judith Herman's three-stage model of trauma recovery and the Skills Training in Affective and Interpersonal Regulation (STAIR) model served as the theoretical framework for the group therapy curriculum. Two programs, Seeking Safety, by Lisa Najavits and VOICES, by Stephanie Covington, provided a guide for group topics and activities. ::: ::: CONCLUSIONS ::: Patients that participated in Turning the Tides© group therapy curriculum reported a decrease in overall PTSD symptoms and decreased functional impairment scores, based on the Child PTSD Symptoms Scale. However, there was a statistically significant increase in the use of as needed medications following the completion of group therapy. Postgroup evaluations from patients indicated a genuine desire to engage in the group therapy as well as an increased sense of trust with facilitators. Implications for psychiatric nursing include the delivery of safe, quality patient care as evidenced by positive improvement in patient outcomes.


title: A racing algorithm for configuring metaheuristics

abstract: This paper describes a racing procedure for finding, in a limited amount of time, a configuration of a metaheuristic that performs as good as possible on a given instance class of a combinatorial optimization problem. Taking inspiration from methods proposed in the machine learning literature for model selection through cross-validation, we propose a procedure that empirically evaluates a set of candidate configurations by discarding bad ones as soon as statistically sufficient evidence is gathered against them. We empirically evaluate our procedure using as an example the configuration of an ant colony optimization algorithm applied to the traveling salesman problem. The experimental results show that our procedure is able to quickly reduce the number of candidates, and allows to focus on the most promising ones.


title: Machine Learning of Coq Proof Guidance: First Experiments

abstract: We report the results of the first experiments with learning proof dependencies from the formalizations done with the Coq system. We explain the process of obtaining the dependencies from the Coq proofs, the characterization of formulas that is used for the learning, and the evaluation method. Various machine learning methods are compared on a dataset of 5021 toplevel Coq proofs coming from the CoRN repository. The best resulting method covers on average 75% of the needed proof dependencies among the first 100 predictions, which is a comparable performance of such initial experiments on other large-theory corpora.


title: FMEA on the Superconducting Torus for the Jefferson Lab 12 GeV Accelerator Upgrade

abstract: As part of the Jefferson Lab 12GeV accelerator upgrade project, Hall B requires two conduction cooled superconducting magnets. One is a magnet system consisting of six superconducting trapezoidal racetrack-type coils assembled in a toroidal configuration and the second is an actively shielded solenoidal magnet system consisting of 5 coils. Both magnets are to be wound with Superconducting Super Collider-36 NbTi strand Rutherford cable soldered into a copper channel. This paper describes the various failure modes in torus magnet along with the failure modes that could be experienced by the torus and its interaction with the solenoid which is located in close proximity.


title: Automatic Arabic Image Captioning using RNN-LSTM-Based Language Model and CNN

abstract: The automatic generation of correct syntaxial and semantical image captions is an essential problem in Artificial Intelligence. The existence of large image caption copra such as Flickr and MS COCO have contributed to the advance of image captioning in English. However, it is still behind for Arabic given the scarcity of image caption corpus for the Arabic language. In this work, an Arabic version that is a part of the Flickr and MS COCO caption dataset is built. Moreover, a generative merge model for Arabic image captioning based on a deep RNN-LSTM and CNN model is developed. The results of the experiments are promising and suggest that the merge model can achieve excellent results for Arabic image captioning if a larger corpus is used.


title: Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks

abstract: Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.


title: Situation Awareness, Automation & Free Flight

abstract: Free flight represents a major change in the way that aircraft may be handled in the U.S. National Airspace System. It has the potential of significantly increasing airspace utilization and thus improving aircraft throughput. The degree to which these objectives can be met, however, without compromising aircraft safety, depends on establishing appropriate changes in the air traffic control system. These changes may include significant incorporation of new air traffic management and cockpit technologies. Studies that examine the affect of automation on situation awareness will be reviewed. These studies indicate that while certain forms of automation may be advantageous, other types of automation may induce losses in situation awareness that can lead out out−of−the−loop performance errors. A recent study was also conducted that examined changes in the locus of control between ATC and the cockpit using existing technology. This study provides an objective evaluation of the effects of free flight on controllers’ ability to maintain an accurate and complete picture of the traffic situation in order to provide needed monitoring and separation functions. The study revealed that aspects of free flight can significantly hinder the situation awareness and performance of controllers. The results of this study provide information for better defining how free flight should be implemented and for determining needed design and procedural modifications to support the concept.


title: Quantic molecular resonance scalpel vs traditional scalpel in the treatment of labial mucocele: a two-center randomized controlled trial.

abstract: OBJECTIVE ::: The aim of this randomized two-center study was to compare quantic molecular resonance scalpel (QMRS) with traditional scalpel (TS) for the surgical treatment of labial mucoceles, in terms of impact on quality of life and postoperative pain (primary outcomes) and postsurgical lip paresthesia and recurrence of the lesion (secondary outcomes). ::: ::: ::: METHOD AND MATERIALS ::: Eighty-five consecutive patients with labial mucoceles were randomized to receive traditional (46) or QMRS surgery (39) in two Italian University Hospital Centers. Patients were asked to complete three different forms during the first week after surgery to evaluate quality of life and postoperative pain. Periodic follow-up examinations were conducted at 1 week, 1 month, and 3 months after surgery, in order to detect recurrence of lesion and/or lip paresthesia. ::: ::: ::: RESULTS ::: Regarding the symptoms reported during the first week after surgery, questionnaires given to the patients and the mean painkiller consumption did not demonstrate any statistically significant differences within the two groups. Women treated with QMRS reported a worse experience than women treated with TS (P < .05). Among all the mucoceles treated, during the follow-up period of 3 months, one that had been eliminated with the cold scalpel was seen to relapse, versus none of those treated with the QMRS (P = 1.0). We reported a localized and terminal paresthesia of the labial mucosa close to the surgical site in 10.58% of the total patients. Comparing the QMRS with TS, we observed a greater incidence of neurologic complications with the latter technique (P = .035). ::: ::: ::: CONCLUSION ::: This is the first prospective randomized study to report outcome data and 3-month follow-up of a cohort of patients suffering from labial mucoceles. QMRS for surgical removal of labial mucoceles seems to be comparable to traditional scalpel in terms of postoperative quality of life, pain, and postsurgical lip paresthesia.


title: Bacteriophage therapy: exploiting smaller fleas.

abstract: Although bacteriophages have been used for the treatment of patients with bacterial infections in some regions of the world for >9 decades, adequate clinical trials of the safety and efficacy of the treatment have not been reported. The increasing problem of antibiotic resistance has, however, rekindled interest in this approach to therapy. Although potentially significant obstacles to systemic administration of phages exist, topical and oral administration of phages and/or phage products, such as lysins, are feasible in the short term. In addition to exploitation of the effects of native phages and phage products, bioengineering of phages will allow directed specificity and their use as delivery systems for antimicrobial and antivirulence molecules. This brief overview of the history and status of phage therapy, along with speculation about its future, provides a background for understanding of this imminent therapeutic modality.


title: Methods of teaching a computer science course for prospective teachers

abstract: This article focuses on a "Methods of Teaching Computer Science in the High School" course (abbreviated MTCS). It presents the rationale and need for the course, and suggests optional course frameworks and implementations that are not limited to a particular programming language, programming paradigm, level of students, or curriculum.


title: Grouper: A Dynamic Clustering Interface to Web Search Results

abstract: Abstract Users of Web search engines are often forced to sift through the long ordered list of document `snippets' returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on most major search engines. The NorthernLight search engine organizes its output into `custom folders' based on pre-computed document labels, but does not reveal how the folders are generated or how well they correspond to users' interests. In this paper, we introduce Grouper, an interface to the results of the HuskySearch meta-search engine, which dynamically groups the search results into clusters labeled by phrases extracted from the snippets. In addition, we report on the first empirical comparison of user Web search behavior on a standard ranked-list presentation versus a clustered presentation. By analyzing HuskySearch logs, we are able to demonstrate substantial differences in the number of documents followed, and in the amount of time and effort expended by users accessing search results through these two interfaces.


title: A High-Power $Ka$-Band (31–36 GHz) Solid-State Amplifier Based on Low-Loss Corporate Waveguide Combining

abstract: A method of using low-loss waveguide septum combiners is developed into a high-power -band (31-36 GHz) amplifier producing 50 W at 33 GHz (Ka-band) using 32 low-power (>2 W) solid-state amplifier modules. By using low-loss waveguide combining and a packaged monolithic microwave integrated circuit with a low-loss microstrip-to-waveguide launcher, the output loss is minimized, allowing for the overall power-combining efficiency to remain high, 80% (average insertion loss of combiner < 0.7 dB and average insertion loss of launcher <0.3 dB) over 31-36 GHz. In the past, lower power-combining efficiencies have limited the number of modules that can be combined at -band, and hence, have limited the power output. The approach demonstrated in this paper, with high power-combining efficiency, allows a very large number (32) of solid-state amplifier modules to be combined to produce high powers. Greater than 50 W was demonstrated with low power modules, but even higher powers 120 W are possible. The current approach is based on corporate combining, using low-loss waveguide septum combiners that provide isolation, maintaining the true graceful degradation of a modular solid-state amplifier system.


title: A survey on self-healing systems: approaches and systems

abstract: Present large-scale information technology environments are complex, heterogeneous compositions often affected by unpredictable behavior and poor manageability. This fostered substantial research on designs and techniques that enhance these systems with an autonomous behavior. In this survey, we focus on the self-healing branch of the research and give an overview of the current existing approaches. The survey is introduced by an outline of the origins of self-healing. Based on the principles of autonomic computing and self-adapting system research, we identify self-healing systems’ fundamental principles. The extracted principles support our analysis of the collected approaches. In a final discussion, we summarize the approaches’ common and individual characteristics. A comprehensive tabular overview of the researched material concludes the survey.


title: Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG

abstract: Modelling and exploiting teammates' policies in cooperative multi-agent systems have long been an interest and also a big challenge for the reinforcement learning (RL) community. The interest lies in the fact that if the agent knows the teammates' policies, it can adjust its own policy accordingly to arrive at proper cooperations; while the challenge is that the agents' policies are changing continuously due to they are learning concurrently, which imposes difficulty to model the dynamic policies of teammates accurately. In this paper, we present \emph{ATTention Multi-Agent Deep Deterministic Policy Gradient} (ATT-MADDPG) to address this challenge. ATT-MADDPG extends DDPG, a single-agent actor-critic RL method, with two special designs. First, in order to model the teammates' policies, the agent should get access to the observations and actions of teammates. ATT-MADDPG adopts a centralized critic to collect such information. Second, to model the teammates' policies using the collected information in an effective way, ATT-MADDPG enhances the centralized critic with an attention mechanism. This attention mechanism introduces a special structure to explicitly model the dynamic joint policy of teammates, making sure that the collected information can be processed efficiently. We evaluate ATT-MADDPG on both benchmark tasks and the real-world packet routing tasks. Experimental results show that it not only outperforms the state-of-the-art RL-based methods and rule-based methods by a large margin, but also achieves better performance in terms of scalability and robustness.


title: New color GPHOG descriptors for object and scene image classification

abstract: This paper presents a novel set of image descriptors that encodes information from color, shape, spatial and local features of an image to improve upon the popular Pyramid of Histograms of Oriented Gradients (PHOG) descriptor for object and scene image classification. In particular, a new Gabor-PHOG (GPHOG) image descriptor created by enhancing the local features of an image using multiple Gabor filters is first introduced for feature extraction. Second, a comparative assessment of the classification performance of the GPHOG descriptor is made in grayscale and six different color spaces to further propose two novel color GPHOG descriptors that perform well on different object and scene image categories. Finally, an innovative Fused Color GPHOG (FC---GPHOG) descriptor is presented by integrating the Principal Component Analysis (PCA) features of the GPHOG descriptors in the six color spaces to combine color, shape and local feature information. Feature extraction for the proposed descriptors employs PCA and Enhanced Fisher Model (EFM), and the nearest neighbor rule is used for final classification. Experimental results using the MIT Scene dataset and the Caltech 256 object categories dataset show that the proposed new FC---GPHOG descriptor achieves a classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT) based Pyramid Histograms of visual Words descriptor, Color SIFT four Concentric Circles, Spatial Envelope, and Local Binary Patterns.


title: A robust sclera segmentation algorithm

abstract: Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm's robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities' space generated by the classifiers at the stage 1. The proposed method was ranked 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.


title: Enhancing the search ability of differential evolution through orthogonal crossover

abstract: Differential evolution (DE) is a class of simple yet powerful evolutionary algorithms for global numerical optimization. Binomial crossover and exponential crossover are two commonly used crossover operators in current popular DE. It is noteworthy that these two operators can only generate a vertex of a hyper-rectangle defined by the mutant and target vectors. Therefore, the search ability of DE may be limited. Orthogonal crossover (OX) operators, which are based on orthogonal design, can make a systematic and rational search in a region defined by the parent solutions. In this paper, we have suggested a framework for using an OX in DE variants and proposed OXDE, a combination of DE/rand/1/bin and OX. Extensive experiments have been carried out to study OXDE and to demonstrate that our framework can also be used for improving the performance of other DE variants.


title: 3-Speed Transmission Using Dual Motors and One-Way Clutches

abstract: The use of electric vehicles has begun to increase, and the ratio of electric vehicles will continually increase due to environmental regulations. The speed–torque characteristic of an electric motor is better than that of a combustion engine for vehicle driving due to the high torque in a low-speed range. However, the efficiency of the motor declines in the low-speed range; therefore, when an electric vehicle climbs a steep slope slowly, severe heat may be generated due to the high current in the low-efficiency range. In this regard, for driving at a high efficiency, electric vehicles also require mechanical multispeed transmissions, but a conventional mechanical multispeed transmission is bulky, heavy, and energy consuming. This paper suggests a 3-speed transmission using dual-electric motors and one-way clutches. The transmission has a structure simple enough to be used in an in-wheel drive unit, and it does not have actively moved clutches, resulting in high reliability and eliminating the need for energy to move the clutches during speed changes. The operational principle is explained, and a prototype and its experimental results are presented.


title: Modeling and Simulation of Autonomous Logistic Processes

abstract: Today, manufacturing systems face an increasing dynamic environment. This results in a need for adaptive control strategies. Autonomous Control enables logistic objects to render their own local decisions. The logistic performance of autonomous systems depends on a careful design of the system. Therefore, it is necessary to model and test autonomous processes before implementing them. The Autonomous Logistic Engineering Methodology is designed to develop autonomous processes. To enable testing and validating the models, the methodology is extended by a simulation component. This article presents a procedure, to transform the process models into executable simulation models. This procedure uses concepts and techniques of the Model Driven Architecture.


title: OntoVPA - An Ontology-Based Dialogue Management System for Virtual Personal Assistants.

abstract: Dialogue management (DM) is a difficult problem. We present OntoVPA, an Ontology-Based Dialogue Management System (DMS) for Virtual Personal Assistants (VPAs). The features of OntoVPA are offered as generic solutions to core DM problems, such as dialogue state tracking, anaphora and coreference resolution, etc. To the best of our knowledge, OntoVPA is the first commercially available, fully implemented DMS that employs ontologies and ontology-based rules for (a) domain model representation and reasoning, (b) dialogue representation and state tracking, and (c) response generation. OntoVPA is a declarative, knowledge-based system which can be customized to a new VPA domain by modifying and exchanging ontologies and rule bases, with very little to no conventional programming required.


title: Effects of selected activities on affective meaning in psychiatric patients.

abstract: Occupational therapists have used activity analysis to ensure the therapeutic use of activities. Recently, they have begun to explore the affective components of activities. This study explores the feelings (affective responses) that chronic psychiatric patients have toward selected activities commonly used in occupational therapy. Twenty-two participating chronic psychiatric patients were randomly assigned to one of three different activity groups: cooking, craft, or sensory awareness. Immediately following participation, each subject was asked to rate the activity by using Osgood's semantic differential, which measures the evaluation, power, and action factors of affective meaning. Data analysis revealed significant differences between the cooking activity and the other two activities on the evaluation factor. The fact that the three activities were rated differently is evidence that different activities can elicit different responses in one of the target populations of occupational therapy. The implications of these findings to occupational therapists are discussed and areas of future research are indicated.


title: Communication and personality profiles of global software developers

abstract: ContextPrior research has established that a small proportion of individuals dominate team communication during global software development. It is not known, however, how these members' contributions affect their teams' knowledge diffusion process, or whether their personality profiles are responsible for their dominant presence. ObjectiveWe set out to address this gap through the study of repository artifacts. MethodArtifacts from ten teams were mined from the IBM Rational Jazz repository. We employed social network analysis (SNA) to group practitioners into two clusters, Top Members and Others, based on the numbers of messages they communicated and their engagement in task changes. SNA metrics (density, in-degree and closeness) were then used to study practitioners' importance in knowledge diffusion. Thereafter, we performed psycholinguistic analysis on practitioners' messages using linguistic dimensions that had been previously correlated with the Big Five personality profiles. ResultsFor our sample of 146 practitioners we found that Top Members occupied critical roles in knowledge diffusion, and demonstrated more openness to experience than the Others. Additionally, all personality profiles were represented during teamwork, although openness to experience, agreeableness and extroversion were particularly evident. However, no specific personality predicted members' involvement in knowledge diffusion. ConclusionTask assignment that promotes highly connected team communication networks may mitigate tacit knowledge loss in global software teams. Additionally, while members expressing openness to experience are likely to be particularly driven to perform, this is not entirely responsible for a global team's success.


title: A healthy nutrition expert system for children

abstract: Healthy food is one of the most critical aspects for the family, especially for their children as healthy diet for children results in better life including the increase of their ability to learn, exercise, and even behaving. Therefore, a need for a full support for providing the children with healthy food is a vital target to reach. In this paper, we propose a prototype for a children nutrition expert system which aim is to provide its users with the nutrition expertise. It generates healthy meals for children in different ages according to different criteria including their growth stage, gender, and their health status. A web application is developed and two case studies are applied to demonstrate how the proposed prototype can be applied for determining child's nutrition.


title: Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns

abstract: Background ::: Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA).


title: Comprehensive analysis and comparative evaluation of the isolated true bridgeless Cuk single-phase PFC rectifier system

abstract: In the public low voltage mains the stress caused by harmonic currents is strongly increased due to the higher demand of electronic equipment in everyday use. Therefore, standards were created in order to limit the harmonic currents injected into the network, thus maintaining a high voltage quality of these networks. Hence, converter systems with active PFC are indispensable, where in general the compliance of the created directives for lower harmonics can be easily fulfilled. Typically, in single-phase systems, boost PFC converter topologies are used, which offer a wide input voltage range and a controlled output voltage. Depending on the underlying applications, a subsequent reduction and/or isolation of the output voltage based on a DC/DC-converter is needed. The major disadvantages of this converter cascading are the reduction of the overall efficiency, the large component count and the increasing control complexity. In comparison to the conventional two-stage PFC converter system, the true bridgeless Cuk PFC rectifier system can perform the PFC functionality and the galvanic isolation in a single-stage, thus eliminating the mentioned disadvantages. In this paper a comprehensive analysis of the operation, design and limits of the recently proposed single-stage Cuk concept is investigated. In addition, a comparative evaluation concerning efficiency and power density against a conventional two-stage approach based on bridgeless PFC rectifier and a subsequent LLC-resonant DC/DC-converter is presented.


title: Feature selection for spatially enhanced LBP: application to face recognition

abstract: Block-based local binary patterns a.k.a. enhanced local binary patterns (ELBPs) have proven to be a highly discriminative descriptor for face recognition and image retrieval. Since this descriptor is mainly composed by histograms, little work (if any) has been done for selecting its relevant features (either the bins or the blocks). In this paper, we address feature selection for both the classic ELBP representation and the recently proposed color quaternionic LBP (QLBP). We introduce a filter method for the automatic weighting of attributes or blocks using an improved version of the margin-based iterative search Simba algorithm. This new improved version introduces two main modifications: (i) the hypothesis margin of a given instance is computed by taking into account the K-nearest neighboring examples within the same class as well as the K-nearest neighboring examples with a different label; (ii) the distances between samples and their nearest neighbors are computed using the weighted \(\chi ^2\) distance instead of the Euclidean one. This algorithm has been compared favorably with several competing feature selection algorithms including the Euclidean-based Simba as well as variance and Fisher score algorithms giving higher performances. The proposed method is useful for other descriptors that are formed by histograms. Experimental results show that the QLBP descriptor allows an improvement of the accuracy in discriminating faces compared with the ELBP. They also show that the obtained selection (attributes or blocks) can either improve recognition performance or maintain it with a significant reduction in the descriptor size.


title: Ultra Low Power Wake-Up Radios: A Hardware and Networking Survey

abstract: In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.


title: Comparison Between Traditional Texture Methods and Deep Learning Descriptors for Detection of Nitrogen Deficiency in Maize Crops

abstract: Every year, efficient maize production is very important to the economy of many countries. Since nutritional deficiencies in maize plants are directly reflected in their grains productivity, early detection is needed to maximize the chances of proper recovery of these plants. Traditional texture methods recently showed interesting results in the identification of nutritional deficiencies. On the other hand, deep learning techniques are increasingly outperforming hand-crafted features on many tasks. In this paper, we propose a simple transfer learning approach from pre-trained cnn models and compare their results with those from traditional texture methods in the task of nitrogen deficiency identification. We perform experiments in a real-world dataset that contains digitalized images of maize leaves at different growth stages and with different levels of nitrogen fertilization. The results show that deep learning based descriptors achieve better success rates than traditional texture methods.


title: Connotation Lexicon: A Dash of Sentiment Beneath the Surface Meaning

abstract: Understanding the connotation of words plays an important role in interpreting subtle shades of sentiment beyond denotative or surface meaning of text, as seemingly objective statements often allude nuanced sentiment of the writer, and even purposefully conjure emotion from the readers’ minds. The focus of this paper is drawing nuanced, connotative sentiments from even those words that are objective on the surface, such as “intelligence”, “human”, and “cheesecake”. We propose induction algorithms encoding a diverse set of linguistic insights (semantic prosody, distributional similarity, semantic parallelism of coordination) and prior knowledge drawn from lexical resources, resulting in the first broad-coverage connotation lexicon.


title: Sensing techniques for mobile interaction

abstract: We describe sensing techniques motivated by unique aspects of human-computer interaction with handheld devices in mobile settings. Special features of mobile interaction include changing orientation and position, changing venues, the use of computing as auxiliary to ongoing, real-world activities like talking to a colleague, and the general intimacy of use for such devices. We introduce and integrate a set of sensors into a handheld device, and demonstrate several new functionalities engendered by the sensors, such as recording memos when the device is held like a cell phone, switching between portrait and landscape display modes by holding the device in the desired orientation, automatically powering up the device when the user picks it up the device to start using it, and scrolling the display using tilt. We present an informal experiment, initial usability testing results, and user reactions to these techniques.


title: Automatic generation of test scripts from formal test specifications

abstract: TSL is a language for writing formal test specifications of the functions of a software system. The test specifications are compiled into executable test scripts that establish test environments, assign values to input variables, perform necessary setup and cleanup operations, run the test cases, and check the correctness of test results. TSL is a working system that has been used to test commercial software in a production environment.


title: Gender and Competition

abstract: Laboratory studies have documented that women often respond less favorably to competition than men. Conditional on performance, men are often more eager to compete, and the performance of men tends to respond more positively to an increase in competition. This means that few women enter and win competitions. We review studies that examine the robustness of these differences as well the factors that may give rise to them. Both laboratory and field studies largely confirm these initial findings, showing that gender differences in competitiveness tend to result from differences in overconfidence and in attitudes toward competition. Gender differences in risk aversion, however, seem to play a smaller and less robust role. We conclude by asking what could and should be done to encourage qualified males and females to compete.


title: CFD Analysis of Axial Flow Fans with Skewed Blades

abstract: The present work deals with axial flow fans that are primarily used for providing the required airflow for heat and mass transfer operations in the various industrial equipment and processes. It can be used for cooling, ventilation or for drying purposes. These include cooling towers for air-conditioning and ventilation, humidifiers in textile mills, air heat exchangers for various chemical processes, ventilation and exhaust as in mining industry. In consideration of all these application various efforts in the past have been done to evaluate the performance of axial flow fans. In the present work computational investigation of axial flow fan with Forward and Backward Skewed blade profile carried out using CFD software FLUENT 6.3 and the results are compared with the experimental results from literature. The CFD analysis is done by modeling the axial fan in GAMBIT 2.2 and using Standard k-є model with the Standard wall function for modeling turbulence. The analysis is carried out with blade stagger angle of 25 o , Skewed angle of 8.3 o and at 1440 rpm and 1800 rpm. The aim is to analyze fan with these two types of profiles and compare the static pressure, flow rate, flow coefficient and pressure coefficient generated by fan and hence finding the efficiency of an axial flow fan. After carrying out computational investigation it is found that computational results holds good agreement with the experimental results taken from the literature.


Tag Recommender for StackOverflow Questions CSE 255 Assignment 1


BUSINESS PROCESS REENGINEERING METHOD VERSUS KAIZEN METHOD


title: A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using an RGB Camera

abstract: Recording and monitoring vital signs is an essential part of home-based healthcare. Using contact sensors to record physiological signals can cause discomfort to patients, especially after prolonged use. Hence, remote physiological measurement approaches have attracted considerable attention, as they do not require physical contact with the patient’s skin. Several studies proposed techniques to measure heart rate (HR) and heart rate variability by detecting the blood volume pulse from human facial video recordings while the subject is in a resting condition. In this paper, we focus on the measurement of HR. We adopt an algorithm that uses the independent component analysis (ICA) to separate the source (physiological) signal from noise in the RGB channels of a facial video. We generalize existing methods to support subject movement during video recording. Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. With respect to the ECG measurement ground truth, the proposed method decreases the RMSE by 27% compared with the state of the art in the stationary condition. When the subject is in motion, our proposed method achieves an RMSE of 1.12 bpm.


Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?


title: Neuroaesthetics in fashion: Modeling the perception of fashionability

abstract: In this paper, we analyze the fashion of clothing of a large social website. Our goal is to learn and predict how fashionable a person looks on a photograph and suggest subtle improvements the user could make to improve her/his appeal. We propose a Conditional Random Field model that jointly reasons about several fashionability factors such as the type of outfit and garments the user is wearing, the type of the user, the photograph's setting (e.g., the scenery behind the user), and the fashionability score. Importantly, our model is able to give rich feedback back to the user, conveying which garments or even scenery she/he should change in order to improve fashionability. We demonstrate that our joint approach significantly outperforms a variety of intelligent baselines. We additionally collected a novel heterogeneous dataset with 144,169 user posts containing diverse image, textual and meta information which can be exploited for our task. We also provide a detailed analysis of the data, showing different outfit trends and fashionability scores across the globe and across a span of 6 years.


title: Ontology Based Expert-System for Suspicious Transactions Detection

abstract: The development of an effective mechanism to detect suspicious transactions is a critical problem for financial institutions in their endeavor to prevent anti-money laundering activities. This research addresses this problem by proposing an ontology based expert-system for suspicious transaction detection. The ontology consists of domain knowledge and a set of (SWRL) rules that together constitute an expert system. The native reasoning support in ontology is used to deduce new knowledge from the predefined rules about suspicious transactions. The presented expert-system has been tested on a real data set of more than 8 million transactions of a commercial bank. The novelty of the approach lies in the use of ontology driven technique that not only minimizes the data modeling cost but also makes the expert-system extendable and reusable for different applications.


title: Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

abstract: We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS$_2$ score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial fibrillation. Our model is as interpretable as CHADS$_2$, but more accurate.


title: Ready for a paradigm shift? Part 1: Introducing the philosophy of qualitative research

abstract: The manual therapy professions have almost exclusively focused on the use of quantitative research to help inform their practices. This paper argues that a greater use of qualitative research will help develop a more robust and comprehensive knowledge base in manual therapy. The types of knowledge used in practice and generated from the two research paradigms are explored. It is hoped that an understanding of the philosophical and theoretical underpinnings of qualitative research may encourage more manual therapists to value and use this approach to help further inform their practice; for some, this may involve a paradigm shift in thinking.


pal - The Pålsjö Algorithm Language


title: Religious coping and health status in medically ill hospitalized older adults.

abstract: Associations between specific religious coping (RC) behaviors and health status in medically ill hospitalized older patients were examined and compared with associations between nonreligious coping (NRC) behaviors and health status. The sample consisted of 577 patients age 55 or over consecutively admitted to the general medical inpatient services of Duke University Medical Center (78%) or the Durham VA Medical Center (22%). Information was gathered on 21 types of RC, 11 types of NRC, and 3 global indicators of religious activity (GIRA). Health measures included multiple domains of physical health, depressive symptoms, quality of life, stress-related growth, cooperativeness, and spiritual growth. Demographic factors, education, and admitting hospital were control variables. "Negative" and "positive" types of religious coping were identified. Negative RC behaviors related to poorer physical health, worse quality of life, and greater depression were reappraisals of God as punishing, reappraisals involving demonic forces, pleading for direct intercession, and expression of spiritual discontent. Coping that was self-directed (excluding God's help) or involved expressions reflecting negative attitudes toward God, clergy, or church members were also related to greater depression and poorer quality of life. Positive RC behaviors related to better mental health were reappraisal of God as benevolent, collaboration with God, seeking a connection with God, seeking support from clergy/church members, and giving religious help to others. Of 21 RC behaviors, 16 were positively related to stress-related growth, 15 were related to greater cooperativeness, and 16 were related to greater spiritual growth. These relationships were both more frequent and stronger than those found for NRC behaviors. Certain types of RC are more strongly related to better health status than other RC types. Associations between RC behaviors and mental health status are at least as strong, if not stronger, than those observed with NRC behaviors.


title: Mixed-Level Iteration Schemes for Nonlinear Model Predictive Control

abstract: Abstract In general, numerical schemes for nonlinear model predictive control (NMPC) require the (approximate) solution of a nonlinear program in each sample for feedback generation. Thus, the application of NMPC to processes that need fast feedback poses a major computational challenge. Recently, new multi–level iteration schemes have been proposed, extending the well–known idea of real–time iterations. These algorithms take into account different time scales inherent in the dynamic model by updating the data of the feedback–generating quadratic program (QP), i.e., Hessians and Jacobians, gradients, and constraint residuals, on different levels. In this contribution we consider new mixed–level updates of the QP data, which interval–wise apply different update levels. In particular, we apply higher–level updates more frequently on the first intervals of the control horizon, given their importance in the context of model predictive control in general. Targeting at modern computers with multi–core processing units, we describe an efficient parallel implementation of the mixed–level iteration approach and apply it to a benchmark problem from automotive engineering.


title: Electromagnetic Wave Propagation into Fresh Water

abstract: Wireless communications from air to fresh water are studied here. Our analysis relies on plane wave propagation mod-els. Specifically, the transmission loss and propagation loss of RF waves penetrating into fresh water are calculated for various propagation depths. Even though RF wireless communications are not well suited for seawater due to its high attenuation, our paper illustrates that RF communications from air to fresh water are possible. Finally, this work de-rives the optimum frequencies, which provide minimum attenuation and maximum propagation depth, for RF commu-nications from air to fresh water.


Facebook Filter: Create Awareness among Users


title: Panoptic Segmentation

abstract: We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation. For more analysis and up-to-date results, please check the arXiv version of the paper: {\small\url{https://arxiv.org/abs/1801.00868}}.


title: Mental Health Counselor Qualities for a Diverse Clientele: Linking Empathy, Universal-Diverse Orientation, and Emotional Intelligence

abstract: We examined how culturally relevant variables, including universal-diverse orientation (UDO), an attitude characterized by awareness and acceptance of similarities and differences between self and others, and emotional intelligence, the ability to accurately monitor emotions, might be related to empathy.Two-hundred-and-eleven counseling graduate students completed measures of these variables, as well as a demographic sheet. Hierarchical regression analyses indicated that UDO and emotional intelligence, along with gender, significantly explained variance in empathy. Implications for effective mental health counseling across diverse settings are discussed.


title: A New Concept of Deep Reinforcement Learning based Augmented General Tagging System

abstract: In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence tagging model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence tagging tasks using ATIS and CoNLL-2003 benchmark datasets, to demonstrate the new system's outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9% and that on CoNLL-2003 dataset by 1.4%.


title: Finding Overlapping Communities in Social Networks

abstract: Increasingly, methods to identify community structure in networks have been proposed which allow groups to overlap. These methods have taken a variety of forms, resulting in a lack of consensus as to what characteristics overlapping communities should have. Furthermore, overlapping community detection algorithms have been justified using intuitive arguments, rather than quantitative observations. This lack of consensus and empirical justification has limited the adoption of methods which identify overlapping communities. In this text, we distil from previous literature a minimal set of axioms which overlapping communities should satisfy. Additionally, we modify a previously published algorithm, Iterative Scan, to ensure that these properties are met. By analyzing the community structure of a large blog network, we present both structural and attribute based verification that overlapping communities naturally and frequently occur.


title: AutoExtend: Combining Word Embeddings with Semantic Resources

abstract: We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks.


title: A Multiband CPW-Fed Slot Antenna with Fractal Stub and Parasitic Line

abstract: This paper presents a multiband CPW-fed slot antenna with fractal stub and parasitic line. The conven- tional wideband slot antenna with fractal stub is modified by inserting the parasitic line surrounding the fractal stub that affects the attribution to be a multiband operation suitable for some applications in wireless communication systems. The parasitic line surrounding the fractal stub can generate a dual-notched frequency that can be controlled by varying the parameters of the parasitic structure. The lengths of slit and stub on both sides of the parasitic line can control the lower and higher notched frequencies, respectively. Additionally, the prototype of the proposed antenna can operate and cover the applications of DCS 1800, WiMAX IEEE 802.16, WLAN IEEE 802.11a/b/g, and IMT advance systems.


title: Learning What and Where to Draw

abstract: Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers. While existing models can synthesize images based on global constraints such as a class label or caption, they do not provide control over pose or object location. We propose a new model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes images given instructions describing what content to draw in which location. We show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset, conditioned on both informal text descriptions and also object location. Our system exposes control over both the bounding box around the bird and its constituent parts. By modeling the conditional distributions over part locations, our system also enables conditioning on arbitrary subsets of parts (e.g. only the beak and tail), yielding an efficient interface for picking part locations.


title: Analysis of SiC MOSFETs under hard and soft-switching

abstract: Analytical models for hard-switching and soft-switching SiC MOSFETs and their experimental validation are described in this paper. The models include the high frequency parasitic components in the circuit and enable very fast, accurate simulation of the switching behaviour of SiC MOSFET using only datasheet parameters. The much higher switching speed of SiC devices over Si counterparts necessitates a clear detailed analysis. Each switching transient was divided into four distinct sub-periods and their respective equivalent circuits were solved to approximate the circuit state variables. Nonlinearities in the junction capacitances of SiC devices were considered in the model. Analytical modelling results were close to the LTspice simulation results with a threefold reduction in the simulation time. The effect of snubber capacitors on the soft-switching waveforms is also explained analytically and validated experimentally, which enables the analytical model to be used to evaluate future soft-switching solutions. It was found that the snubber branch can significantly reduce the turn off ringing of the SiC MOSFET in addition to the reduction of switching losses.


title: Biometric authentication via keystroke sound

abstract: Unlike conventional “one shot” biometric authentication schemes, continuous authentication has a number of advantages, such as longer time for sensing, ability to rectify authentication decisions, and persistent verification of a user's identity, which are critical in applications demanding enhanced security. However; traditional modalities such as face, fingerprint and keystroke dynamics, have various drawbacks in continuous authentication scenarios. In light of this, this paper proposes a novel nonintrusive and privacy-aware biometric modality that utilizes keystroke sound. Given the keystroke sound recorded by a low-cost microphone, our system extracts discriminative features and performs matching between a gallery and a probe sound stream. Motivated by the concept of digraphs used in modeling keystroke dynamics, we learn a virtual alphabet from keystroke sound segments, from which the digraph latency within pairs of virtual letters as well as other statistical features are used to generate match scores. The resultant multiple scores are indicative of the similarities between two sound streams, and are fused to make a final authentication decision. We collect a first-of-its-kind keystroke sound database of 45 subjects typing on a keyboard. Experiments on static text-based authentication, demonstrate the potential as well as limitations of this biometric modality.


title: Semantic Similarity from Natural Language and Ontology Analysis

abstract: Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -- most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -- intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies. (...) Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures.


title: A Hybrid Framework for Text Modeling with Convolutional RNN

abstract: In this paper, we introduce a generic inference hybrid framework for Convolutional Recurrent Neural Network (conv-RNN) of semantic modeling of text, seamless integrating the merits on extracting different aspects of linguistic information from both convolutional and recurrent neural network structures and thus strengthening the semantic understanding power of the new framework. Besides, based on conv-RNN, we also propose a novel sentence classification model and an attention based answer selection model with strengthening power for the sentence matching and classification respectively. We validate the proposed models on a very wide variety of data sets, including two challenging tasks of answer selection (AS) and five benchmark datasets for sentence classification (SC). To the best of our knowledge, it is by far the most complete comparison results in both AS and SC. We empirically show superior performances of conv-RNN in these different challenging tasks and benchmark datasets and also summarize insights on the performances of other state-of-the-arts methodologies.


title: Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production

abstract: The Food and Drug Administration (FDA) regulates pharmaceutical drug products to ensure a continuous supply of high-quality drugs in the USA. Continuous processing has a great deal of potential to address issues of agility, flexibility, cost, and robustness in the development of pharmaceutical manufacturing processes. Over the past decade, there have been significant advancements in science and engineering to support the implementation of continuous pharmaceutical manufacturing. These investments along with the adoption of the quality-by-design (QbD) paradigm for pharmaceutical development and the advancement of process analytical technology (PAT) for designing, analyzing, and controlling manufacturing have progressed the scientific and regulatory readiness for continuous manufacturing. The FDA supports the implementation of continuous manufacturing using science- and risk-based approaches.


Program Repair Using Code Repositories


title: The BellKor 2008 Solution to the Netflix Prize

abstract: Our RMSE=0.8643 solution is a linear blend of over 100 results. Some of them are new to this year, whereas many others belong to the set that was reported a year ago in our 2007 Progress Prize report [3]. This report is structured accordingly. In Section 2 we detail methods new to this year. In general, our view is that those newer methods deliver a superior performance compared to the methods we used a year ago. Throughout the description of the methods, we highlight the specific predictors that participated in the final blended solution. Nonetheless, the older methods still play a role in the blend, and thus in Section 3 we list those methods repeated from a year ago. Finally, we conclude with general thoughts in Section 4.


Cross-lingual syntactically informed distributed word representations


title: Towards Real-time Simulation of Hyperelastic Materials

abstract: We present a new method for real-time physics-based simulation supporting many different types of hyperelastic materials. Previous methods such as Position Based or Projective Dynamics are fast, but support only limited selection of materials; even classical materials such as the Neo-Hookean elasticity are not supported. Recently, Xu et al. [2015] introduced new "spline-based materials" which can be easily controlled by artists to achieve desired animation effects. Simulation of these types of materials currently relies on Newton's method, which is slow, even with only one iteration per timestep. In this paper, we show that Projective Dynamics can be interpreted as a quasi-Newton method. This insight enables very efficient simulation of a large class of hyperelastic materials, including the Neo-Hookean, spline-based materials, and others. The quasi-Newton interpretation also allows us to leverage ideas from numerical optimization. In particular, we show that our solver can be further accelerated using L-BFGS updates (Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm). Our final method is typically more than 10 times faster than one iteration of Newton's method without compromising quality. In fact, our result is often more accurate than the result obtained with one iteration of Newton's method. Our method is also easier to implement, implying reduced software development costs.


title: A general line tracking algorithm based on computer vision

abstract: This paper presents a general algorithm to track the line on the ground based on computer vision, which utilizes Raspberry Pi 2 to complete the image processing. The algorithm accomplishes image processing firstly, then recognizes the line by edge detection and scanning model. The proposed algorithm can be used in a variety of platforms including intelligent car system, robot platform, UAV platform and other mobile platforms. In this paper, the algorithm is verified by quadrotor platform and achieves great line-tracking effect by tuning parameters. Series of experiments show that the algorithm has great validity, robustness, and generality.


title: Impact of battery energy storage on power system with high wind penetration

abstract: The penetration of renewable resources in power system has been increasing in recent years. Many of these resources are uncontrollable and variable in nature, wind in particular, are relatively unpredictable. At high penetration levels, volatility of wind power production could cause problems for power system to maintain system security and reliability. One of the solutions being proposed to improve reliability and performance of the system is to integrate energy storage devices into the network. In this paper, unit commitment and dispatch schedule in power system with and without energy storage is examined for different level of wind penetration. Battery energy storage (BES) is considered as an alternative solution to store energy. The SCUC formulation and solution technique with wind power and BES is presented. The proposed formulation and model is validated with eight-bus system case study. Further, a discussion on the role of BES on locational pricing, economic, peak load shaving, and transmission congestion management had been made.


title: New Security Perspectives around BYOD

abstract: The dramatic growth of cloud computing services and mobility trends, in terms of 3/4G availability and smart devices, is creating a new phenomenon called "Consumerization" that affects the consumers habits in all facets of the their life. Also during the working time people prefer to stay in their consumer environment because that's their comfort zone. A collateral phenomenon is called BYOD (Bring Your Own Device), that means the employees use their own devices also during their working time. These changing of habits represent an opportunity and a challenge for the enterprises. The opportunity is related to two main aspects: the productivity increase and the costs reduction. In a BYOD scenario the end users would pay totally or partially the devices and would work independently from time and location. On the opposite side, the new scenario bring some risks that could be critical. The use of devices for both personal and working activities opens to new security threats to face for IT organization. Also, the direct comparison between public cloud services for personal use and company's IT services could be a frustrating user experience, that's because of the public cloud services are often almost more effective and usable than typical IT company's services. The aim of this work is presenting a brief survey about the emerging methods and models to approach the BYOD phenomenon from the security point of view.


title: A model of customer relationship management and business intelligence systems for catalogue and online retailers

abstract: As more retailers evolve into customer-centric and segment-based business, business intelligence (BI) and customer relationship management (CRM) systems are playing a key role in achieving and maintaining competitive advantage. For the past ten years, the authors have had the rare opportunity of observing and interviewing employees and managers of three different management teams at three separate Fingerhut companies as they experimented with various ITs for their companies. When the first Fingerhut company peaked in 1998, as many as 200 analysts and 40 statisticians mined the database for insights that helped predict consumer shopping patterns and credit behaviour. Data mining and BI helped Fingerhut spot shopping patterns, bring product offerings to the right customers, and nurture customer relationships. By 1998, Fingerhut was the second largest catalogue retailer in the U.S. with revenues nearing $2 billion. However, after Federated acquired Fingerhut in 1999 and made it a subsidiary, Fingerhut Net, it suffered great losses and was eventually liquidated. Finally, a new company, Fingerhut Direct Marketing, was resurrected in 2002 under a new management team, and it once again became successful. What went right? What went wrong? The paper concludes with CRM and BI systems success factors and a discussion of lessons learned.


title: Review: Student modeling approaches: A literature review for the last decade

abstract: This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10years are described, the aspects of students' characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student's mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students' learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.