title: A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition

abstract: Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called "deep learning", which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google's TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold starts and big data processing of physical activity records.


title: Multiway Attention Networks for Modeling Sentence Pairs

abstract: Modeling sentence pairs plays the vital role for judging the relationship between two sentences, such as paraphrase identification, natural language inference, and answer sentence selection. Previous work achieves very promising results using neural networks with attention mechanism. In this paper, we propose the multiway attention networks which employ multiple attention functions to match sentence pairs under the matching-aggregation framework. Specifically, we design four attention functions to match words in corresponding sentences. Then, we aggregate the matching information from each function, and combine the information from all functions to obtain the final representation. Experimental results demonstrate that the proposed multiway attention networks improve the result on the Quora Question Pairs, SNLI, MultiNLI, and answer sentence selection task on the SQuAD dataset.


Invariant Information Distillation for Unsupervised Image Segmentation and Clustering


title: Layer-wise Relevance Propagation for Explainable Recommendations

abstract: In this paper, we tackle the problem of explanations in a deep-learning based model for recommendations by leveraging the technique of layer-wise relevance propagation. We use a Deep Convolutional Neural Network to extract relevant features from the input images before identifying similarity between the images in feature space. Relationships between the images are identified by the model and layer-wise relevance propagation is used to infer pixel-level details of the images that may have significantly informed the model's choice. We evaluate our method on an Amazon products dataset and demonstrate the efficacy of our approach.


title: DeepFruits: A Fruit Detection System Using Deep Neural Networks

abstract: This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.


title: Techniques for automatically correcting words in text

abstract: Research aimed at correcting words in text has focused on three progressively more difficult problems:(1) nonword error detection; (2) isolated-word error correction; and (3) context-dependent work correction. In response to the first problem, efficient pattern-matching and n -gram analysis techniques have been developed for detecting strings that do not appear in a given word list. In response to the second problem, a variety of general and application-specific spelling correction techniques have been developed. Some of them were based on detailed studies of spelling error patterns. In response to the third problem, a few experiments using natural-language-processing tools or statistical-language models have been carried out. This article surveys documented findings on spelling error patterns, provides descriptions of various nonword detection and isolated-word error correction techniques, reviews the state of the art of context-dependent word correction techniques, and discusses research issues related to all three areas of automatic error correction in text.


title: Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching

abstract: Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the scans, without requiring any preprocessing such as scan denoising or converting into a mesh. First, we construct the descriptor by voxelizing the local shape within a uniquely defined local reference frame and concatenating geometric centroid and point density features extracted from each voxel. Second, we compare two descriptors by employing only corresponding voxels that are both non-empty, thus supporting matching incomplete local shape such as those close to scan boundary. Third, we propose a descriptor saliency measure and compute it from a descriptor-graph to improve shape matching performance. We demonstrate the descriptor’s robustness and effectiveness for shape matching by comparing it with three state-of-the-art descriptors, and applying it to object/scene reconstruction and 3D object recognition.


title: Active opinion-formation in online social networks

abstract: Recommendation systems usually try to “guess” a user's preferences from the system's view. We study another side of recommendation: active opinion-formation from the perspective of the user. In real life, a user's opinion evolves with time and refines when new evidence occurs. Then, how does an online user form his/her own opinion actively in large social networks? The problem has three challenges: the factor, the effect and the open environment. To address those challenges, we investigate: (1) what factors or channels a user will consider, (2) how those channels will take effect, and (3) an incremental approach to incorporate multiple channels. We explore three types of channels: the internal opinion of an individual user, influences from trusted friends, and influences from public channels. A novel simulator, OpinionFormer, is proposed to incorporate those channels incrementally. It differentiates the effects of friends and public channels as well as positive and negative opinions. We validate the performance of OpinionFormer by predicting users' opinions using real-world data sets. Experimental results show that our model can improve accuracy over other models that ignore some channels or that neglect the evolving features.


title: Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

abstract: Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4× resolution-reduced images.


title: Supraharmonics (2 to 150 kHz) and multi-level converters

abstract: This paper summarizes the state of part of the discussions in CIGRE/CIRED/IEEE joined working group C4.24, especially where it concerns supraharmonics. There is an increasing interest from the international standard-setting community in knowledge about distortion in the frequency range 2 to 150 kHz, referred to as supraharmonics. The reduction of emission in the lower-frequency ranges appears to result in an increase in supraharmonic emission by equipment. Power electronics has emerged as a ubiquitous technology, which plays a critical role in almost any areas. Power electronics converter is an important source of waveform distortion, but, as shown in this paper, power electronics can also be the key to mitigate distortion, when the proper technology is employed.


title: High capacity error free wavelet Domain Speech Steganography

abstract: Steganography is the art of hiding information in a cover media without attracting attention. One of the cover media which can be used for steganography is speech. In this paper, we propose a new speech steganography in wavelet domain. In this method, lifting scheme is used to create perfect reconstruction Int2Int wavelets. The data is hidden in some of the Least Significant Bits (LSB) of detail wavelet coefficients. The LSB bits for hiding are selected with a new adaptive algorithm. This algorithm does not hide information in silent parts, so there is no need for silent detection algorithms. This method has zero error in hiding/unhiding process, while normal wavelet domain LSB has about 0.2 % error in equal hiding capacity. This method is a high capacity steganography method which can hide information up to 20% of the input speech. The Signal-to- Noise Ratio (SNR) and listening tests show that the stegano audio is imperceptible from original audio.


title: Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning

abstract: The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search avoids this problem by encouraging a search in all interesting directions. That occurs by replacing a performance objective with a reward for novel behaviors, as defined by a human-crafted, and often simple, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a novelty pressure in image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g. churches, mosques, obelisks, etc.). Here we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: e.g. producing intelligent software, robot controllers, optimized physical components, and art.


title: Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions

abstract: A method for solving boundary value problems (BVPs) is introduced using artificial neural networks (ANNs) for irregular domain boundaries with mixed Dirichlet/Neumann boundary conditions (BCs). The approximate ANN solution automatically satisfies BCs at all stages of training, including before training commences. This method is simpler than other ANN methods for solving BVPs due to its unconstrained nature and because automatic satisfaction of Dirichlet BCs provides a good starting approximate solution for significant portions of the domain. Automatic satisfaction of BCs is accomplished by the introduction of an innovative length factor. Several examples of BVP solution are presented for both linear and nonlinear differential equations in two and three dimensions. Error norms in the approximate solution on the order of 10(-4) to 10(-5) are reported for all example problems.


title: Deep learning, dark knowledge, and dark matter

abstract: Particle colliders are the primary experimental instruments of high-energy physics. By creating conditions that have not occurred naturally since the Big Bang, collider experiments aim to probe the most fundamental properties of matter and the universe. These costly experiments generate very large amounts of noisy data, creating important challenges and opportunities for machine learning. In this work we use deep learning to greatly improve the statistical power on three benchmark problems involving: (1) Higgs bosons; (2) supersymmetric particles; and (3) Higgs boson decay modes. This approach increases the expected discovery significance over traditional shallow methods, by 50%, 2%, and 11% respectively. In addition, we explore the use of model compression to transfer information (dark knowledge) from deep networks to shallow networks.


title: Complex event processing for reactive security monitoring in virtualized computer systems

abstract: The number of security incidents in computer systems is steadily increasing, despite intrusion detection and prevention mechanisms deployed as countermeasures. Many existing intrusion detection and prevention systems struggle to keep up with new threats posed by zero-day attacks and/or have serious performance impacts through extensive monitoring, questioning their effectiveness in most real-life scenarios. In this paper, we present a new approach for reactive security monitoring in a virtualized computer environment based on minimally-intrusive dynamic sensors deployed vertically across virtualization layers and horizontally within a virtual machine instance. The sensor streams are analyzed using a novel federation of complex event processing engines and an optimized query index to maximize the performance of continuous queries, and the results of the analysis are used to trigger appropriate actions on different virtualization layers in response to detected security anomalies. Furthermore, a novel event store that supports fast event logging is utilized for offline analysis of collected historical data. Experiments show that the proposed system can execute tens of thousands of complex, stateful detection rules simultaneously and trigger actions efficiently and with low latency.


title: Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes

abstract: Over the past decade, the rate of care unit (CU) use in the United States has been increasing. With an aging population and ever-growing demand for medical care, effective management of patients’ transitions among different care facilities will prove indispensible for shortening the length of hospital stays, improving patient outcomes, allocating critical care resources, and reducing preventable re-admissions. In this paper, we focus on an important problem of predicting the so-called “patient flow” from longitudinal electronic health records (EHRs), which has not been explored via existing machine learning techniques. By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients’ destination CUs and duration days. Instead of learning a generative point process model via maximum likelihood estimation, we propose a novel discriminative learning algorithm aiming at improving the prediction of transition events in the case of sparse data. By parameterizing the proposed model as a mutually-correcting process, we formulate the estimation problem via generalized linear models, which lends itself to efficient learning based on alternating direction method of multipliers (ADMM). Furthermore, we achieve simultaneous feature selection and learning by adding a group-lasso regularizer to the ADMM algorithm. Additionally, for suppressing the negative influence of data imbalance on the learning of model, we synthesize auxiliary training data for the classes with extremely few samples, and improve the robustness of our learning method accordingly. Testing on real-world data, we show that our method obtains superior performance in terms of accuracy of predicting the destination CU transition and duration of each CU occupancy.


title: Dirichlet Process Mixture Model for Document Clustering with Feature Partition

abstract: Finding the appropriate number of clusters to which documents should be partitioned is crucial in document clustering. In this paper, we propose a novel approach, namely DPMFP, to discover the latent cluster structure based on the DPM model without requiring the number of clusters as input. Document features are automatically partitioned into two groups, in particular, discriminative words and nondiscriminative words, and contribute differently to document clustering. A variational inference algorithm is investigated to infer the document collection structure as well as the partition of document words at the same time. Our experiments indicate that our proposed approach performs well on the synthetic data set as well as real data sets. The comparison between our approach and state-of-the-art document clustering approaches shows that our approach is robust and effective for document clustering.


title: Compact Planar Four-Sector Antenna Comprising Microstrip Yagi-Uda Arrays in a Square Configuration

abstract: In this letter, a simple four-sector antenna configuration is proposed that has a square configuration of microstrip Yagi-Uda arrays and has common parasitic elements with terminations at the corners of the square. The square arrangement of four microstrip Yagi-Uda arrays and commonly shared elements enable downsizing of the antenna, and the termination of commonly shared element enables high front-to-back ratio. From the results of numerical analysis of the radiation pattern, we found that the proposed antenna with termination achieves the high front-to-back ratio of 17 dB, and 10 dB increases compared to that without termination. We also found that the proposed antenna achieves a 50% antenna area reduction compared with the conventional configuration, which shares only the reflector element.


title: Tweets vs. Mendeley readers: How do these two social media metrics differ?

abstract: A set of 1.4 million biomedical papers was analyzed with regards to how often articles are mentioned on Twitter or saved by users on Mendeley. While Twitter is a microblogging platform used by a general audience to distribute information, Mendeley is a reference manager targeted at an academic user group to organize scholarly literature. Both platforms are used as sources for so-called altmetrics to measure a new kind of research impact. This analysis shows in how far they differ and compare to traditional citation impact metrics based on a large set of PubMed papers.


title: Gated recurrent convolution neural network for OCR

abstract: Optical Character Recognition (OCR) aims to recognize text in natural images. Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network (RCNN), we propose a new architecture named Gated RCNN (GRCNN) for solving this problem. Its critical component, Gated Recurrent Convolution Layer (GRCL), is constructed by adding a gate to the Recurrent Convolution Layer (RCL), the critical component of RCNN. The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information. In addition, an efficient Bidirectional Long Short-Term Memory (BLSTM) is built for sequence modeling. The GRCNN is combined with BLSTM to recognize text in natural images. The entire GRCNN-BLSTM model can be trained end-to-end. Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text (SVT) and ICDAR.


title: The Level of Faculty Members’ Spiritual Leadership (SL) Qualities Display According To Students in Faculty of Education

abstract: Abstract Leadership is a process directing to a target of which followers, the participators are shared. For this reason leadership has an important effect on succeeding organizational targets. More importance is given to the leadership studies in order to increase organizational success each day. One of the leadership researches that attracts attention recently is spiritual leadership. Spiritual leadership (SL) is important for imposing ideal to the followers and giving meaning to the works they do. Focusing on SL that has recently taken its place in leadership literature, this study looks into what extend faculty members teaching at Faculty of Education display SL qualities. The study is in descriptive scanning model. 1819 students studying at Kocaeli University Faculty of Education in 2009-2010 academic year constitute the universe of the study. Observing leadership qualities takes long time. Therefore, the sample of the study is determined by deliberate sampling method and includes 432 students studying at the last year of the faculty. Data regarding faculty members’ SL qualities were collected using a questionnaire adapted from Fry's (2003) ‘Spiritual Leadership Scale’. Consequently, university students think that academic stuff shows the features of SL and its sub dimensions in a medium level. According to students, academicians show attitudes related to altruistic love rather than faith and vision. It is found that faculty members couldn’t display leadership qualities enough according to the students at the end of the study.


title: Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations

abstract: The design of convolutional neural network architectures for a new image data set is a laborious and computational expensive task which requires expert knowledge. We propose a novel neuro-evolutionary technique to solve this problem without human interference. Our method assumes that a convolutional neural network architecture is a sequence of neuro-cells and keeps mutating them using function-preserving operations. This novel combination of approaches has several advantages. We define the network architecture by a sequence of repeating neuro-cells which reduces the search space complexity. Furthermore, these cells are possibly transferable and can be used in order to arbitrarily extend the complexity of the network. Mutations based on function-preserving operations guarantee better parameter initialization than random initialization such that less training time is required per network architecture. Our proposed method finds within 12 GPU hours neural network architectures that can achieve a classification error of about 4% and 24% with only 5.5 and 6.5 million parameters on CIFAR-10 and CIFAR-100, respectively. In comparison to competitor approaches, our method provides similar competitive results but requires orders of magnitudes less search time and in many cases less network parameters.


title: Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming

abstract: Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.


title: Analyzing PCA-based face recognition algorithm: Eigenvector’ selection and distance measures. Empirical Evaluation Methods in Computer Vision

abstract: This study examines the role of Eigenvector selection and Eigenspace distance measures on PCA-based face recognition systems. In particular, it builds on earlier results from the FERET face recognition evaluation studies, which created a large face database (1,196 subjects) and a baseline face recognition system for comparative evaluations. This study looks at using a combinations of traditional distance measures (City-block, Euclidean, Angle, Mahalanobis) in Eigenspace to improve performance in the matching stage of face recognition. A statistically significant improvement is observed for the Mahalanobis distance alone when compared to the other three alone. However, no combinations of these measures appear to perform better than Mahalanobis alone. This study also examines questions of how many Eigenvectors to select and according to what ordering criterion. It compares variations in performance due to different distance measures and numbers of Eigenvectors. Ordering Eigenvectors according to a like-image difference value rather than their Eigenvalues is also considered.


title: Oracle database filesystem

abstract: Modern enterprise, web, and multimedia applications are generating unstructured content at unforeseen volumes in the form of documents, texts, and media files. Such content is generally associated with relational data such as user names, location tags, and timestamps. Storage of unstructured content in a relational database would guarantee the same robustness, transactional consistency, data integrity, data recoverability and other data management features consolidated across files and relational contents. Although database systems are preferred for relational data management, poor performance of unstructured data storage, limited data transformation functionalities, and lack of interfaces based on filesystem standards may keep more than eighty five percent of non-relational unstructured content out of databases in the coming decades. We introduce Oracle Database Filesystem (DBFS) as a consolidated solution that unifies state-of-the-art network filesystem features with relational database management ones. DBFS is a novel shared-storage network filesystem developed in the RDBMS kernel that allows content management applications to transparently store and organize files using standard filesystem interfaces, in the same database that stores associated relational content. The server component of DBFS is based on Oracle SecureFiles, a novel unstructured data storage engine within the RDBMS that provides filesystem like or better storage performance for files within the database while fully leveraging relational data management features such as transaction atomicity, isolation, read consistency, temporality, and information lifecycle management. We present a preliminary performance evaluation of DBFS that demonstrates more than 10TB/hr throughput of filesystem read and write operations consistently over a period of 12 hours on an Oracle Exadata Database cluster of four server nodes. In terms of file storage, such extreme performance is equivalent to ingestion of more than 2500 million 100KB document files a single day. The set of initial results look very promising for DBFS towards becoming the universal storage solution for both relational and unstructured content.


title: Employing git in the classroom

abstract: Git is a well established, well received source version control system for the software development community and beyond. In an effort to better expose my students to the purpose and process of version control, I introduced Git as a mechanism to both disseminate in-class exercises and worked solutions as well as facilitate the submission of continuous assessment. I document in this paper my experience of the experiment and describe the steps required to marshall free third party services to support the tasks undertaken by students and lecturer.


title: Geospatial Databases and Augmented Reality visualization for improving safety in urban excavation operations

abstract: The U.S. has more than 14 million miles of buried pipelines and utilities, many of which are in congested urban environments where several lines share the underground space. Errors in locating excavations for new installation or for repair/rehabilitation of existing utilities can result in significant costs, delays, loss of life, and damage to property (Sterling 2000). There is thus a clear need for new solutions to accurately locate buried infrastructure and improve excavation safety. This paper presents ongoing research being collaboratively conducted by the University of Michigan and DTE Energy (Michigan's largest electric and gas utility company) that is investigating the use of Real-Time Kinematic global positioning system (GPS), combined with Geospatial Databases of subsurface utilities to design a new visual excavator-utility collision avoidance technology. 3D models of buried utilities are created from available geospatial data, and then superimposed over an excavator's work space using geo-referenced Augmented Reality (AR) to provide the operator and the spotter(s) with visual information on the location and type of utilities that exist in the excavator's vicinity. This paper describes the overall methodology and the first results of the research.


title: Cathode-supported hybrid direct carbon fuel cells

abstract: Abstract The direct conversion of coal to heat and electricity by a hybrid direct carbon fuel cell (HDCFC) is a highly efficient and cleaner technology than the conventional combustion power plants. HDCFC is defined as a combination of solid oxide fuel cell and molten carbonate fuel cell. This work investigates cathode-supported cells as an alternative configuration for HDCFC, with better catalytic activity and performance. This study aims to define the best processing route to manufacture highly efficient cathode-supported cells based on La 0.75 Sr 0.25 MnO 3 /yttria-stabilized zirconia infiltrated backbones. The challenges on the development of high-performance backbones are discussed. In this study, cathode-supported configuration was confirmed to be more efficient for the oxidation of carbon than anode supported configuration. The maximum power density of the cathode-supported cell increased almost by a factor of two when compared with the anode-supported cell.


title: Repeat-pass SAR interferometry over forested terrain

abstract: Repeat-pass synthetic aperture radar (SAR) interferometry provides the possibility of producing topographic maps and geocoded as well as radiometrically calibrated radar images. However, the usefulness of such maps and images depends on our understanding of how different types of terrain affect the radar measurements. It is essential that the scene coherence between passes is sdcient. In this paper, we derive a general system model including both radar system and scene scattering properties. The model is used to interpret measurements over a forested area where the scene coherence varies between 0.2 and 0.5. The coherence is found to be sensitive to temperature changes around 0°C but surprisingly insensitive to wind speed. The interferometric height discontinuity at the forest to openfield boundary shows good agreement with in situ tree height measurements for a dense boreal forest, but is observed to decrease for a less dense forest. This suggests the possibility of estimating bole volume from the interferometric tree height and a ground DEM. The decrease of scene coherence over a dense forest with increasing baseline is also used to estimate the effective scattering layer thickness.


title: Clear(ing) space: Mindfulness-based reflective practice

abstract: This paper reports on an enquiry into how practitioners working in healthcare environments bring mindfulness, a particular type of practice of reflection, into their professional and personal lives. Nine participants engaged in group discussion, Haiku poetry writing and the recording of personal audio diaries. Thematic analysis of the diaries indicates a transcending theme that being in the here and now can highlight discomfort, pain and uncertainty. Within this, sub‐themes illustrate that mindfulness can act as a support to personal and professional development, to the content and process of clinical work and as a means to support dealing with work‐related stress. A final theme illustrates participants’ experiences of using the research process to facilitate their ongoing reflective practice. The paper concludes with a note on what has been identified as ‘mindfulness‐based reflective practice’: a practice that can bring vitality and fluidity to critical reflection.


title: Automatic Classification of Points-of-Interest for Land-use Analysis

abstract: This paper describes a methodology for automatic classification of places according to the North American Indus- try Classification System. This taxonomy is applied in many areas, particularly in Urban Planning. The typical approach is to manually classify places/Points-of-Interest that are collected with field surveys. Given the financial costs of the task some semi-automatic approaches have been taken before, but they are still based on field surveys and official census. In this paper, we apply machine learning to fully automatize the classification of Points-of-Interest collected from online sources. We compare the adequacy of several algorithms to the task, using both flat and hierarchical approaches, and validate the results in the Urban Planning context. Keywords-machine learning; space analysis; points-of-interest; urban planning; GIS.


title: Virtual Reality Induces Dissociation and Lowers Sense of Presence in Objective Reality

abstract: Abstract This study utilizes an innovative experimental paradigm to investigate the effects of virtual reality (VR) on dissociative experience and the sense of presence. A nonclinical sample of 30 people were administered measures of dissociation, sense of presence, and immersion before and after an immersion in a virtual environment. Results indicate an increase in dissociative experience (depersonalization and derealization), including a lessened sense of presence in objective reality as the result of exposure to VR. Higher preexisting levels of dissociation and a tendency to become more easily absorbed or immersed were associated with higher increases in dissociative symptoms resulting from VR immersion. Results are discussed in terms of imaginative processes underlying the dissociative experience and potential implications to the treatment of anxiety disorders with VR.


title: Breastfeeding and the Risk of Postneonatal Death in the United States

abstract: OBJECTIVE ::: Breastfed infants in the United States have lower rates of morbidity, especially from infectious disease, but there are few contemporary studies in the developed world of the effect of breastfeeding on postneonatal mortality. We evaluated the effect of breastfeeding on postneonatal mortality in United States using 1988 National Maternal and Infant Health Survey (NMIHS) data. ::: ::: ::: METHODS ::: Nationally representative samples of 1204 infants who died between 28 days and 1 year from causes other than congenital anomaly or malignant tumor (cases of postneonatal death) and 7740 children who were still alive at 1 year (controls) were included. We calculated overall and cause-specific odds ratios for ever/never breastfeeding among all children, conducted race and birth weight-specific analyses, and looked for duration-response effects. ::: ::: ::: RESULTS ::: Overall, children who were ever breastfed had 0.79 (95% confidence interval [CI]: 0.67-0.93) times the risk of never breastfed children for dying in the postneonatal period. Longer breastfeeding was associated with lower risk. Odds ratios by cause of death varied from 0.59 (95% CI: 0.38-0.94) for injuries to 0.84 (95% CI: 0.67-1.05) for sudden infant death syndrome. ::: ::: ::: CONCLUSIONS ::: Breastfeeding is associated with a reduction in risk for postneonatal death. This large data set allowed robust estimates and control of confounding, but the effects of breast milk and breastfeeding cannot be separated completely from other characteristics of the mother and child. Assuming causality, however, promoting breastfeeding has the potential to save or delay approximately 720 postneonatal deaths in the United States each year.


title: The impact of primary school teachers' educational beliefs on the classroom use of computers

abstract: For many years, researchers have searched for the factors affecting the use of computers in the classroom. In studying the antecedents of educational computer use, many studies adopt a rather limited view because only technology-related variables, such as attitudes to computers and computer experience were taken into account. The present study centres on teachers' educational beliefs (constructivist beliefs, traditional beliefs) as antecedent of computer use, while controlling for the impact of technology-related variables (computer experience, general computer attitudes) and demographical variables (sex, age). In order to identify differences in determinants of computer use in the classroom, multilevel modelling was used (N=525). For measuring primary teachers' use of computers to support the leaching or learning process a modified version of the 'Class Use of Computers' scale of van Braak et al. [van Braak, J., Tondeur, J., & Valcke, M. (2004). Explaining different types of computer use among primary school teachers. European Journal of Psychology of Education, 19(4), 407-422] was used. The present article supports the hypothesis that teacher beliefs are significant determinants in explaining why teachers adopt computers in the classroom. Next to the impact of computer experience, general computer attitudes and gender, the results show a positive effect of constructivist beliefs on the classroom use of computers. Traditional beliefs have a negative impact on the classroom use of computers.


Potential Application of a Chemically Modified Non-Antimicrobial Tetracycline (CMT-3) Against Metastatic Prostate Cancer


title: Underwater wireless optical communication; recent advances and remaining challenges

abstract: Because of its ability of providing very high data transmission rates over distances up to several tens of meters, underwater wireless optical communication (UWOC) has attracted considerable interest during the past few years. The underwater channel is a challenging environment, especially because of its high attenuation. The difficulty of precise localization underwater also leads to unavoidable link misalignments that can have an important impact on the link availability and otherwise on the quality of signal transmission. In this paper, after a review of the recent research works on UWOC and the available commercialized systems, we present the performance study of a typical UWOC system under some simplifying assumptions for system modeling. We also address the open issues and the challenges that we are faced with in practice.


Toward the Implementation of a Quantum RBM


title: The Complete Chloroplast Genome of Coix lacryma-jobi and a Comparative Molecular Evolutionary Analysis of Plastomes in Cereals

abstract: Graminoid molecular evolution was investigated by chloroplast genome (plastome) scale analyses. A complete plastome from Coix lacryma-jobi (Poaceae) and a draft plastome from Joinvillea plicata (Joinvilleaceae) were sequenced and analyzed. The draft plastome included conserved protein-coding loci routinely analyzed in previous studies plus one additional locus of demonstrated phylogenetic utility. The methodological approach was to directly sequence overlapping amplicons from known plastome regions. Over 100 pairs of amplification and sequencing primers were designed and positioned to flank overlapping 1,200-base pair fragments around the entire plastome. Newly determined sequences were analyzed with published plastomes from representatives of Panicoideae, Ehrhartoideae, and Pooideae. Considerable variation was found for studies within the family and even within Andropogoneae. Readily interpreted mutation patterns were observed, such as small inversions in hairpin-loop regions and indels, which were common in intergenic spacers. Maximum or near-maximum bootstrap support was observed in all analyses resolving relationships between subfamilies. However, the addition of characters from noncoding regions increased the number of parsimony-informative characters and lengthened short internal branches (Andropogoneae), better defining intergeneric relationships. Thus, characters in complete plastomes can be used over a wide scope of phylogenetic studies.


title: Exploiting Geographical Neighborhood Characteristics for Location Recommendation

abstract: Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user's perspective, via modeling the geographical distribution of each individual user's check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region-level characteristics, i.e., locations in the same geographical region may share similar user preferences. In IRenMF, the two levels of geographical characteristics are naturally incorporated into the learning of latent features of users and locations, so that IRenMF predicts users' preferences on locations more accurately. Extensive experiments on the real data collected from Gowalla, a popular LBSN, demonstrate the effectiveness and advantages of our approach.


title: Audio Surveillance: A Systematic Review

abstract: Despite surveillance systems becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been incorporated, both alone or in combination with video, giving birth in the past decade, to a considerable amount of research. In this article, audio-based automated surveillance methods are organized into a comprehensive survey: A general taxonomy, inspired by the more widespread video surveillance field, is proposed to systematically describe the methods covering background subtraction, event classification, object tracking, and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above-described tasks. Differing from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described method and providing the reader with a systematic and schematic view useful for retrieving the most suited algorithms for each specific requirement.


title: Optimum Design of Wideband Compensated and Uncompensated Marchand Baluns With Step Transformers

abstract: This paper presents a design approach for wideband compensated and uncompensated Marchand baluns with stepped-impedance transformers. In order to obtain an equal-ripple bandpass response, conventional Chebyshev polynomials are modified to compensate the effect of the transfer function's dc poles. Unlike the available microwave filter design approaches, which usually require redundant elements, this approach leads to an optimum design by using the minimum number of equal length transmission line elements. Based on this design approach, both compensated and uncompensated Marchand baluns are studied. It is found that increasing difficulty arises when implementing a large bandwidth balun using the widely adopted compensated balun structure. Hence, the uncompensated balun structure becomes a better choice. To validate the proposed design approach, an uncompensated balun is designed on a standard two-sided printed circuit board. The measured results indicate that a return loss greater than 20 dB can be observed from 1 to 7.5 GHz. The phase imbalance is less than 4deg and the amplitude is less than 0.5 dB from dc to 7.2 GHz.


title: A critical analysis of studies that address the use of text mining for citation screening in systematic reviews

abstract: Background: Since the introduction of the systematic review process to Software Engineering in 2004, researchers have investigated a number of ways to mitigate the amount of effort and time taken to filter through large volumes of literature. Aim: This study aims to provide a critical analysis of text mining techniques used to support the citation screening stage of the systematic review process. Method: We critically re-reviewed papers included in a previous systematic review which addressed the use of text mining methods to support the screening of papers for inclusion in a review. The previous review did not provide a detailed analysis of the text mining methods used. We focus on the availability in the papers of information about the text mining methods employed, including the description and explanation of the methods, parameter settings, assessment of the appropriateness of their application given the size and dimensionality of the data used, performance on training, testing and validation data sets, and further information that may support the reproducibility of the included studies. Results: Support Vector Machines (SVM), Naive Bayes (NB) and Committee of classifiers (Ensemble) are the most used classification algorithms. In all of the studies, features were represented with Bag-of-Words (BOW) using both binary features (28%) and term frequency (66%). Five studies experimented with n-grams with n between 2 and 4, but mostly the unigram was used. χ2, information gain and tf-idf were the most commonly used feature selection techniques. Feature extraction was rarely used although LDA and topic modelling were used. Recall, precision, F and AUC were the most used metrics and cross validation was also well used. More than half of the studies used a corpus size of below 1,000 documents for their experiments while corpus size for around 80% of the studies was 3,000 or fewer documents. The major common ground we found for comparing performance assessment based on independent replication of studies was the use of the same dataset but a sound performance comparison could not be established because the studies had little else in common. In most of the studies, insufficient information was reported to enable independent replication. The studies analysed generally did not include any discussion of the statistical appropriateness of the text mining method that they applied. In the case of applications of SVM, none of the studies report the number of support vectors that they found to indicate the complexity of the prediction engine that they use, making it impossible to judge the extent to which over-fitting might account for the good performance results. Conclusions: There is yet to be concrete evidence about the effectiveness of text mining algorithms regarding their use in the automation of citation screening in systematic reviews. The studies indicate that options are still being explored, but there is a need for better reporting as well as more explicit process details and access to datasets to facilitate study replication for evidence strengthening. In general, the reader often gets the impression that text mining algorithms were applied as magic tools in the reviewed papers, relying on default settings or default optimization of available machine learning toolboxes without an in-depth understanding of the statistical validity and appropriateness of such tools for text mining purposes.


title: Flow of information for emotions through temporal and orbitofrontal pathways

abstract: The posterior orbitofrontal cortex, anterior temporal sensory association areas and the amygdala have a key role in emotional processing and are robustly interconnected. By analogy with the pattern of connections in early processing sensory areas, anterior temporal sensory and polymodal association cortices send primarily feedforward projections to posterior orbitofrontal cortex and to the amygdala originating in the supragranular layers, in pathways that may provide signals about the external environment. The amygdala innervates all layers of the posterior orbitofrontal cortex, including the middle, or feedforward, target layers, in a pathway that may convey information about emotional context. The posterior orbitofrontal cortex targets dual systems in the amygdala which have opposite effects on central autonomic structures. Both pathways originate in posterior orbitofrontal cortex, but one targets heavily the inhibitory intercalated masses, whose activation can ultimately disinhibit central autonomic structures during emotional arousal. The other pathway innervates the central nucleus of the amygdala, and can lead to downstream inhibition of central autonomic structures, resulting in autonomic homeostasis. The choice of pathway may depend on emotional context, and probably involves other prefrontal areas, including lateral prefrontal areas, which have executive functions. Lateral prefrontal cortices issue feedforward projections that target layer 5 of orbitofrontal cortex, which is the chief output layer to the amygdala. These laminar-specific pathways suggest sequential and collaborative interactions in evaluating the sensory and emotional aspects of the environment for decision and action in complex behaviour.


title: Spreadsheet table transformations from examples

abstract: Every day, millions of computer end-users need to perform tasks over large, tabular data, yet lack the programming knowledge to do such tasks automatically. In this work, we present an automatic technique that takes from a user an example of how the user needs to transform a table of data, and provides to the user a program that implements the transformation described by the example. In particular, we present a language of programs TableProg that can describe transformations that real users require.We then present an algorithm ProgFromEx that takes an example input and output table, and infers a program in TableProg that implements the transformation described by the example. When the program is applied to the example input, it reproduces the example output. When the program is applied to another, potentially larger, table with a 'similar' layout as the example input table, then the program produces a corresponding table with a layout that is similar to the example output table. A user can apply ProgFromEx interactively, providing multiple small examples to obtain a program that implements the transformation that the user desires. Moreover, ProgFromEx can help identify 'noisy' examples that contain errors. To evaluate the practicality of TableProg and ProgFromEx, we implemented ProgFromEx as a module for the Microsoft Excel spreadsheet program. We applied the module to automatically implement over 50 table transformations specified by endusers through examples on online Excel help forums. In seconds, ProgFromEx found programs that satisfied the examples and could be applied to larger input tables. This experience demonstrates that TableProg and ProgFromEx can significantly automate the tasks over tabular data that users need to perform.


title: The classic serial on television and radio

abstract: Acknowledgements List of Illustrations Introduction Definitions, Early History: The Classic Drama Serial The 1970s: Signs of Change The Blockbusters The Nineties: Renaissance of the Classic Serial The Pride and Prejudice Factor The Pride and Prejudice Effect: A Promising Future for the Past Boz Rides Again, and This is Where We Came In The Albatross which was Really a Phoenix Select Bibliography Index


title: Genomics of Ovarian Cancer Progression Reveals Diverse Metastatic Trajectories Including Intraepithelial Metastasis to the Fallopian Tube

abstract: Accumulating evidence has supported the fallopian tube rather than the ovary as the origin for high-grade serous ovarian cancer (HGSOC). To understand the relationship between putative precursor lesions and metastatic tumors, we performed whole-exome sequencing on specimens from eight HGSOC patient progression series consisting of serous tubal intraepithelial carcinomas (STIC), invasive fallopian tube lesions, invasive ovarian lesions, and omental metastases. Integration of copy number and somatic mutations revealed patient-specific patterns with similar mutational signatures and copy-number variation profiles across all anatomic sites, suggesting that genomic instability is an early event in HGSOC. Phylogenetic analyses supported STIC as precursor lesions in half of our patient cohort, but also identified STIC as metastases in 2 patients. Ex vivo assays revealed that HGSOC spheroids can implant in the fallopian tube epithelium and mimic STIC lesions. That STIC may represent metastases calls into question the assumption that STIC are always indicative of primary fallopian tube cancers. ::: ::: ::: SIGNIFICANCE ::: We find that the putative precursor lesions for HGSOC, STIC, possess most of the genomic aberrations present in advanced cancers. In addition, a proportion of STIC represent intraepithelial metastases to the fallopian tube rather than the origin of HGSOC. Cancer Discov; 6(12); 1342-51. ©2016 AACR.See related commentary by Swisher et al., p. 1309This article is highlighted in the In This Issue feature, p. 1293.


title: Inverter-based low-noise amplifier topologies for ultra-wideband applications

abstract: In this paper two ultra-wideband (UWB) low noise amplifier (LNA) configurations in UMC 0.18μm CMOS technology are presented. Both amplifiers are based on a shunt-feedback inverter-configuration with inductive peaking. In first LNA topology inductor is placed at the input node of the inverter cell. In the frequency range from 3.1 GHz - 10.6 GHz, this amplifier achieves power gain (S21) of 12.23 dB, noise figure of 5.62 dB and input/output return loss of -8 dB/-11 dB, while consuming 9.61 mA from 1.8 V supply. In second LNA peaking inductor is applied at the gate of NMOS transistor in inverter stage. With this technique the -3 dB roll-off frequency is increased from 9 GHz, obtained with previous technique, to 11.11 GHz, without additional power consumption. Achieved power gain from 3.1 GHz - 10.6 GHz is 12.64 dB, noise figure is 4.04 dB and input/output return loss is -7 dB/-10 dB. This amplifier consumes 9.48 mA from the 1.8 V supply.


title: Cognitive control and attentional functions

abstract: Abstract Cognitive control is essential to flexible, goal-directed behavior under uncertainty, yet its underlying mechanisms are not clearly understood. Because attentional functions are known to allocate mental resources and prioritize the information to be processed, we propose that the attentional functions of alerting, orienting, and executive control and the interactions among them contribute to cognitive control in the service of uncertainty reduction. To test this hypothesis, we examined the relationship between cognitive control and attentional functions. We used the Majority Function Task (MFT) to manipulate uncertainty in order to evoke cognitive control along with the Revised Attention Network Test (ANT-R) to measure the efficiency and the interactions of attentional functions. A backwards, stepwise regression model revealed that performance on the MFT could be significantly predicted by attentional functions and their interactions as measured by the ANT-R. These results provide preliminary support for our theory that the attentional functions are involved in the implementation of cognitive control as required to reduce uncertainty, though further investigation is needed.


title: 900 MHz CMOS RF-to-DC converter using a cross-coupled charge pump for energy harvesting

abstract: This paper presents a CMOS RF-to-DC converter with a cross-coupled charge pump for energy harvesting. The proposed RF-to-DC converter consists of a cross-coupled rectifier and a cross-coupled charge pump. The charge pump can operate with a non-overlapping clock with a switching frequency of 25 MHz using a pump capacitance of 4 pF. The RF-to-DC converter IC was designed and fabricated using a 0.13 μm CMOS process. An output voltage of 2.05 V was achieved at an RF input power of −6 dBm with an input frequency of 900 MHz and the load of 100 KΩ. For this condition, the charge pump exhibited a high efficiency of about 74 %


title: Beneficial effects of Lactobacillus paracasei subsp. paracasei NTU 101 and its fermented products

abstract: It is well-known that probiotics have a number of beneficial health effects in humans and animals, including the reduction of symptoms in lactose intolerance and enhancement of the bioavailability of nutrients. Probiotics have showed to possess antimutagenic, anticarcinogenic and hypocholesterolemic properties. Further, they were also observed to have antagonistic actions against intestinal and food-borne pathogens, to decrease the prevalence of allergies in susceptible individuals and to have immunomodulatory effects. Typically, the bacteria colonise the intestinal tract first and then reinforce the host defence systems by inducing a generalised mucosal immune response, balanced T-helper cell response, self-limited inflammatory response and secretion of polymeric IgA. Scientific reports showed that the Taiwan native lactic acid bacterium from newborn infant faeces identified as Lactobacillus paracasei subsp. paracasei NTU 101 and its fermented products proved to be effective for the management of blood cholesterol and pressure, prevention of gastric mucosal lesion development, immunomodulation and alleviation of allergies, anti-osteoporosis and inhibition the fat tissue accumulation. This review article describes that the beneficial effects of this Lactobacillus strains and derivative products may be suitable for human and animals.


title: On the Computability of AIXI

abstract: How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are proposed answers to this question. Both are known to be incomputable. In this paper, we quantify this using the arithmetical hierarchy, and prove upper and corresponding lower bounds for incomputability. We show that AIXI is not limit computable, thus it cannot be approximated using finite computation. Our main result is a limit-computable {\epsilon}-optimal version of AIXI with infinite horizon that maximizes expected rewards.


title: Large-Vocabulary Speech Recognition Algorithms

abstract: By making the advances necessary to implement next-generation speech recognition applications, researchers could develop systems within a decade that match human performance levels.


title: WattApp: an application aware power meter for shared data centers

abstract: The increasing heterogeneity between applications in emerging virtualized data centers like clouds introduce significant challenges in estimating the power drawn by the data center. In this work, we presentWattApp: an application-aware power meter for shared data centers that addresses this challenge. In order to deal with heterogeneous applications, WattApp introduces application parameters (e.g, throughput) in the power modeling framework. WattApp is based on a carefully designed set of experiments on a mix of diverse applications: power benchmarks, web-transaction workloads, HPC workloads and I/O-intensive workloads. Given a set of N applications and M server types, WattApp runs in O(N) time, uses O(NxM) calibration runs, and predicts the power drawn by any arbitrary placement within 5%of the real power for the applications studied.


Intelligent Critiquing of Design Sketches


title: Optimizing referral reward programs under impression management considerations

abstract: We examine referral reward programs (RRP) that are intended for a service firm to encourage its current customers (inductors) to entice their friends (inductees) to purchase the firm's service. By considering the interplay among the firm, the inductor, and the inductee, we solve a "nested" Stackelberg game so as to determine the optimal RRP in equilibrium. We determine the conditions under which it is optimal for the firm to reward the inductor only, reward the inductee only, or reward both. Also, our results suggest that RRP dominates direct marketing when the firm's current market penetration or the inductor's referral effectiveness is sufficiently high. We then extend our model to incorporate certain key impression management factors: the inductor's intrinsic reward of making a positive impression by being seen as helping a friend, the inductor's concerns about creating a negative impression when making an incentivized referral, and the inductee's impression of the inductor's credibility when an incentive is involved. In the presence of these impression management factors, we show that the firm should reward the inductee more and the inductor less. Under certain conditions, it is optimal for the firm to reward neither the inductor nor the inductee so that the optimal RRP relies purely on unincentivized word of mouth.


title: Estimating Consumers’ Knowledge and Attitudes Towards Over-The-Counter Analgesic Medication in Greece in the Years of Financial Crisis: The Case of Paracetamol

abstract: Introduction ::: Non-prescription over-the-counter (OTC) drugs are widely used by patients to control aches, pain, and fever. One of the most frequently used OTC medications worldwide is paracetamol (acetaminophen). The aim of the present study was to fill the current knowledge gap regarding the beliefs and attitudes of people in Greece associated with the use of paracetamol during the years of financial crisis.


title: Millimeter-Wave Wideband High-Efficiency Circularly Polarized Planar Array Antenna

abstract: A Ka-band circularly polarized (CP) planar array antenna with wide axial ratio (AR) bandwidth and high efficiency is presented in this paper. A crossed slot with four parasitic patches is proposed as the CP element, which is fed by a unique 90° delay line. There are three minimal AR points within the operational frequency band to widen the bandwidth. The 90° delay line has three branches to realize the phase delay, the amplitude compensation, and the impedance matching, respectively. Thus, the design process is clear and simple. Several short-circuited posts are added in the feeding layer to suppress the TEM wave propagating outside the feeding network. Simulated results of the element show an AR bandwidth ( $\text{AR} ) of 21.2% from 25.3 to 31.3 GHz and a gain of 5.7–6.7 dBic over the same frequency band. Measured results of a fabricated $4 \times 4$ array demonstrate about 14% AR bandwidth and more than 17.4 dBic gain within the frequency band of 26.4–30.3 GHz. The maximum realized gain reaches to 18.2 dBic. The array antenna with a waveguide transition is fabricated through multilayer PCB process and has a size of $72\;\text{mm} \times 48\;\text{mm} \times 2.2\;\text{mm}$ .


title: Closing the AI Knowledge Gap

abstract: AI researchers employ not only the scientific method, but also methodology from mathematics and engineering. However, the use of the scientific method - specifically hypothesis testing - in AI is typically conducted in service of engineering objectives. Growing interest in topics such as fairness and algorithmic bias show that engineering-focused questions only comprise a subset of the important questions about AI systems. This results in the AI Knowledge Gap: the number of unique AI systems grows faster than the number of studies that characterize these systems' behavior. To close this gap, we argue that the study of AI could benefit from the greater inclusion of researchers who are well positioned to formulate and test hypotheses about the behavior of AI systems. We examine the barriers preventing social and behavioral scientists from conducting such studies. Our diagnosis suggests that accelerating the scientific study of AI systems requires new incentives for academia and industry, mediated by new tools and institutions. To address these needs, we propose a two-sided marketplace called TuringBox. On one side, AI contributors upload existing and novel algorithms to be studied scientifically by others. On the other side, AI examiners develop and post machine intelligence tasks designed to evaluate and characterize algorithmic behavior. We discuss this market's potential to democratize the scientific study of AI behavior, and thus narrow the AI Knowledge Gap.


title: Emerging tumor spheroids technologies for 3D in vitro cancer modeling

abstract: Cancer is a leading cause of mortality and morbidity worldwide. Around 90% of deaths are caused by metastasis and just 10% by primary tumor. The advancement of treatment approaches is not at the same rhythm of the disease; making cancer a focal target of biomedical research. To enhance the understanding and prompts the therapeutic delivery; concepts of tissue engineering are applied in the development of in vitro models that can bridge between 2D cell culture and animal models, mimicking tissue microenvironment. Tumor spheroid represents highly suitable 3D organoid-like framework elucidating the intra and inter cellular signaling of cancer, like that formed in physiological niche. However, spheroids are of limited value in studying critical biological phenomenon such as tumor-stroma interactions involving extra cellular matrix or immune system. Therefore, a compelling need of tailoring spheroid technologies with physiologically relevant biomaterials or in silico models, is ever emerging. The diagnostic and prognostic role of spheroids rearrangements within biomaterials or microfluidic channel is indicative of patient management; particularly for the decision of targeted therapy. Fragmented information on available in vitro spheroid models and lack of critical analysis on transformation aspects of these strategies; pushes the urge to comprehensively overview the recent technological advancements (e.g. bioprinting, micro-fluidic technologies or use of biomaterials to attain the third dimension) in the shed of translationable cancer research. In present article, relationships between current models and their possible exploitation in clinical success is explored with the highlight of existing challenges in defining therapeutic targets and screening of drug efficacy.


title: Factors affecting teachers’ use of Information and Communications Technology: A review of the literature

abstract: This article reports on the literature associated with practising teachers' uptake of information and communications technology (ICT). Studies reveal a number of factors which influence teachers' decisions to use ICT in the classroom: access to resources, quality of software and hardware, ease of use, incentives to change, support and collegiality in their school, school and national polices, commitment to professional learning and background in formal computer training. The review highlights the role of pedagogy and suggests that teachers' beliefs about teaching and learning with ICT are central to integration. It is suggested that successful implementation of ICT needs to address three interlocking frameworks for change: the teacher, the school and policy makers.


title: Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data

abstract: Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.


title: Natural feature tracking in JavaScript

abstract: We present an efficient natural feature tracking pipeline solely implemented in JavaScript. It is embedded in a web technology-based Augmented Reality system running plugin-free in web browsers. The evaluation shows that real-time framerates on desktop computers and while on smartphones interactive framerates are achieved.


title: Anticipating the future by watching unlabeled video

abstract: In many computer vision applications, machines will need to reason beyond the present, and predict the future. This task is challenging because it requires leveraging extensive commonsense knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently obtaining this knowledge is through the massive amounts of readily available unlabeled video. In this paper, we present a large scale framework that capitalizes on temporal structure in unlabeled video to learn to anticipate both actions and objects in the future. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. We experimentally validate this idea on two challenging "in the wild" video datasets, and our results suggest that learning with unlabeled videos significantly helps forecast actions and anticipate objects.


title: The chameleon effect : The perception-behavior link and social interaction

abstract: The chameleon effect refers to nonconscious mimicry of the postures, mannerisms, facial expressions, and other behaviors of one's interaction partners, such that one's behavior passively and unintentionally changes to match that of others in one's current social environment. The authors suggest that the mechanism involved is Has. perception-behavior link, the recently documented finding (e.g., J. A. Bargh, M. Chen, & L. Burrows, 1996) that the mere perception of another's behavior automatically increases the likelihood of engaging in that behavior oneself. Experiment 1 showed that the motor behavior of participants unintentionally matched that of strangers with whom they worked on a task. Experiment 2 had confederates mimic the posture and movements of participants and showed that mimicry facilitates the smoothness of interactions and increases liking between interaction partners. Experiment 3 showed that dispositionally empathic individuals exhibit the chameleon effect to a greater extent than do other people.


title: Derivative Security Markets, Market Manipulation, and Option Pricing Theory

abstract: AbstractThis paper studies a new theory for pricing options in a large trader economy. This theory necessitates studying the impact that derivative security markets have on market manipulation. In an economy with a stock, money market account, and a derivative security, it is shown, by example, that the introduction of the derivative security generates market manipulation trading strategies that would otherwise not exist. A sufficient condition is provided on the price process such that no additional market manipulation trading strategies are introduced by a derivative security. Options are priced under this condition, where it is shown that the standard binomial option model still applies but with random volatilities.


title: Automobile Anti-theft System Based on GSM and GPS Module

abstract: In view of automobile anti-theft systems mostly used static real-time detection and alarm at present, in the paper we design an automobile anti-theft system based on GSM and GPS module. The system is developed based on the high speed mixed type single-chip C8051F120 and detect automobile stolen to the automobile owner by vibration sensor. Automobile location can be obtained with the GPS module integrated in anti-theft system. The system can keep in touch with automobile owner through the GSM module, to monitor the safety and reliability of automobile.


title: Analysis of Massive MIMO-Enabled Downlink Wireless Backhauling for Full-Duplex Small Cells

abstract: Recent advancements in self-interference (SI) cancellation capability of low-power wireless devices motivate in-band full-duplex (FD) wireless backhauling in small cell networks (SCNs). In-band FD wireless backhauling concurrently allows the use of the same frequency spectrum for the backhaul as well as access links of the small cells. In this paper, using tools from stochastic geometry, we develop a framework to model the downlink rate coverage probability of a user in a given SCN with massive multiple-input–multiple-output (MIMO)-enabled wireless backhauls. The considered SCN is composed of a mixture of small cells that are configured in either in-band or out-of-band backhaul modes with a certain probability. The performance of the user in the considered hierarchical network is limited by several sources of interference, such as the backhaul interference, small cell base station (SBS)-to-SBS interference, and the SI. Moreover, due to the channel hardening effect in massive MIMO, the backhaul links only experience long term channel effects, whereas the access links experience both the long term and the short term channel effects. Consequently, the developed framework is flexible to characterize different sources of interference while capturing the heterogeneity of the access and backhaul channels. In specific scenarios, the framework enables deriving closed-form coverage probability expressions. Under perfect backhaul coverage, the simplified expressions are utilized to optimize the proportion of in-band and out-of-band small cells in the SCN in the closed form. Finally, a few remedial solutions are proposed that can potentially mitigate the backhaul interference and in turn improve the performance of in-band FD wireless backhauling. Numerical results investigate the scenarios in which in-band wireless backhauling is useful and demonstrate that maintaining a correct proportion of in-band and out-of-band FD small cells is crucial in wireless backhauled SCNs.


title: 3D Modeling from Wide Baseline Range Scans Using Contour Coherence

abstract: Registering 2 or more range scans is a fundamental problem, with application to 3D modeling. While this problem is well addressed by existing techniques such as ICP when the views overlap significantly at a good initialization, no satisfactory solution exists for wide baseline registration. We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization. Inspired by ICP, we maximize the contour coherence by building robust corresponding pairs on apparent contours and minimizing their distances in an iterative fashion. We use the contour coherence under a multi-view rigid registration framework, and this enables the reconstruction of accurate and complete 3D models from as few as 4 frames. We further extend it to handle articulations, and this allows us to model articulated objects such as human body. Experimental results on both synthetic and real data demonstrate the effectiveness and robustness of our contour coherence based registration approach to wide baseline range scans, and to 3D modeling.


WhatsApp Usage Patterns and Prediction Models


title: Model-Based Design Structure Matrix: Deriving a DSM from an Object-Process Model

abstract: We investigate potential benefits of employing Design Structure Matrix (DSM) in the context of Model-Based Systems Engineering (MBSE) for the purposes of analyzing and improving the design of a product-project ensemble. Focusing on process DSM, we present an algorithm for bidirectional transformation frame between a product-project system model and its corresponding Model-Based DSM (MDSM). Using Object-Process Methodology (OPM) as the underlying modeling language, we examine and characterize useful and insightful relationships between the system model and its MDSM. An unmanned aerial vehicle case study demonstrates the semantics of and analogy between various types of relationships as they are reflected in both the OPM system model and the MDSM derived from it. Finally, we conclude with further research direction on showing how clustering of DSM processes can be reflected back as an improvement of the OPM model.


title: Approach to Hand Tracking and Gesture Recognition Based on Depth-Sensing Cameras and EMG Monitoring

abstract: In this paper, a new approach for hand tracking and gesture recognition based on the Leap Motion device and surface electromyography (SEMG) is presented. The system is about to process the depth image information and the electrical activity produced by skeletal muscles on forearm. The purpose of such combination is enhancement in the gesture recognition rate. As a first we analyse the conventional approaches toward hand tracking and gesture recognition and present the results of various researches. Successive topic gives brief overview of depth-sensing cameras with focus on Leap motion device where we test its accuracy of fingers recognition. The vision-SEMG-based system is to be potentially applicable to many areas of human computer interaction.


title: Brand Name Logo Recognition of Fast Food and Healthy Food among Children

abstract: The fast food industry has been increasingly criticized for creating brand loyalty in young consumers. Food marketers are well versed in reaching children and youth given the importance of brand loyalty on future food purchasing behavior. In addition, food marketers are increasingly targeting the Hispanic population given their growing spending power. The fast food industry is among the leaders in reaching youth and ethnic minorities through their marketing efforts. The primary objective of this study was to determine if young children recognized fast food restaurant logos at a higher rate than other food brands. Methods Children (n = 155; 53% male; 87% Hispanic) ages 4-8 years were recruited from elementary schools and asked to match 10 logo cards to products depicted on a game board. Parents completed a survey assessing demographic and psychosocial characteristics associated with a healthy lifestyle in the home. Results Older children and children who were overweight were significantly more likely to recognize fast food restaurant logos than other food logos. Moreover, parents' psychosocial and socio-demographic characteristics were associated with the type of food logo recognized by the children. Conclusions Children's high recognition of fast food restaurant logos may reflect greater exposure to fast food advertisements. Families' socio-demographic characteristics play a role in children's recognition of food logos.


title: Discriminating rapeseed varieties using computer vision and machine learning

abstract: We developed an expert system for detection of rapeseed variety using computer vision and machine learning.Scanner images can be used to discriminate rapeseed variety.Developed algorithm determines successfully variety of rapeseed with an accuracy rate of 99.24%. Rapeseed is widely cultivated throughout the world for the production of animal feed, vegetable fat for human consumption, and biodiesel. Since the seeds are evaluated in many areas for sowing and oilseed processing, they must be identified quickly and accurately for selection of a correct variety. An affordable method based on computer vision and machine learning was proposed to classify the seven rapeseed varieties. Different types of feature sets, feature models, and machine learning classifiers were investigated to obtain the best predictive model for rapeseed classification. The training and test sets were used to tune the model parameters during the training epochs by varying the complexity of the predictive models with grid-search and K-fold cross validation. After obtaining optimized models for each level of complexity, a dedicated validation set was used to validate predictive models. The developed computer vision system provided an overall accuracy rate of 99.24% for the best predictive model in discriminating rapeseed variety.


title: PATS: pattern aware scheduling and power gating for GPGPUs

abstract: General purpose computing using graphics processing units (GPGPUs) is an attractive option to achieve power efficient throughput computing. But the power efficiency of GPGPUs can be significantly curtailed in the presence of divergence. This paper evaluates two important facets of this problem. First, we study the branch divergence behavior of various GPGPU workloads. We show that only a few branch divergence patterns are dominant in most workloads. In fact only five branch divergence patterns account for 60% of all the divergent instructions in our workloads. In the second part of this work we exploit this branch divergence pattern bias to propose a new divergence pattern aware warp scheduler, called PATS. PATS prioritizes scheduling warps with the same divergence pattern so as to create long idleness windows for any given execution lane. The long idleness windows are then exploited for efficiently power gating the unused lanes while amortizing the gating overhead. We describe the architectural implementation details of PATS and evaluate the power and performance impact of PATS. Our proposed design significantly improves power gating efficiency of GPGPUs with minimal performance overhead.


title: Forecasting stock market movement direction with support vector machine

abstract: Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods.


title: Region-Based Convolutional Networks for Accurate Object Detection and Segmentation

abstract: Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012—achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network . Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.


title: Investigation of 1.2 kV SiC MOSFETs for hard- and soft-switching converters

abstract: In this paper, current silicon carbide (SiC) MOSFETs from two different manufacturers are evaluated including static and dynamic characteristics for different gate resistances, different load currents and at various temperatures. These power semiconductors are operated continuously at a high switching frequency of 1MHz comparing a hard- and a soft-switching converter. A calorimetric power loss measurement method is realized in order to achieve a good measurement accuracy, and the results are compared to the electrical measurements.


title: Neural mechanisms of birdsong memory

abstract: Birdsong learning in avian species has strong similarities with speech acquisition in human infants. Recent research on the song system has shed fresh light on the neural substrate of song memory and sensorimotor learning in both male and female songbirds.


title: Organizational Ambidexterity in Action: How Managers Explore and Exploit

abstract: Dynamic capabilities have been proposed as a useful way to understand how organizations are able to adapt to changes in technology and markets. Organizational ambidexterity the ability of senior managers to seize opportunities through the orchestration and integration of existing assets to overcome inertia and path dependence, is a core dynamic capability. While promising, research on dynamic capabilities and ambidexterity has not yet been able to specify the specific mechanisms through which senior managers are actually able to reallocate resources and reconfigure assets to simultaneously explore and exploit. Using interviews and qualitative case studies from thirteen organizations, this article explores the actions senior managers took to implement ambidextrous designs and identify which ones helped or hindered them in their attempts. A set of interrelated choices of organization design and senior team process determine which attempts to build ambidextrous organizations are successful.


title: Interactive clustering

abstract: An interactive strategy for applying cluster-analytic techniques in behavioral research is presented. The two-part approach stresses the use of on-line computers for both data collection and analysis. In data collection, an extension of multidimensional unfolding to clustering reduces the number of judgments required of subjects by as much as 50%, During data analysis, the interactive procedures described permit the testing of multiple clustering models from an extensive family. With each selection, the goodness of fit of the model to the data can be tested. In addition to improving efficiency, the interactive strategy promoted here combines the advantages of the original nonmetric clustering procedures (e.g., Johnson, 1967) with those of the latest linear additive models (e.g., Sattath & Tversky, 1977; Shepard & Arabie, 1979).


title: Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent

abstract: We address the rectangular matrix completion problem by lifting the unknown matrix to a positive semidefinite matrix in higher dimension, and optimizing a nonconvex objective over the semidefinite factor using a simple gradient descent scheme. With $O( \mu r^2 \kappa^2 n \max(\mu, \log n))$ random observations of a $n_1 \times n_2$ $\mu$-incoherent matrix of rank $r$ and condition number $\kappa$, where $n = \max(n_1, n_2)$, the algorithm linearly converges to the global optimum with high probability.


title: Aqueous wet coatings for transparent plastic glazing

abstract: Abstract Electrostatically stabilized particulate sols, e.g. aqueous silica or alumina sols, have been surface-modified to reduce the particle/particle interaction and to obtain electrosterically stabilized sols. Epoxy functionalized alkoxysilanes were used as surface modifiers for alumina, silica or zirconia particles to achieve water dispersible nanoparticulate coating materials. After evaporating the organic solvent (produced by hydrolysis and condensation) aqueous sol-gel coatings have been prepared with organic solvent contents below 5%. The storage stability of these materials has been investigated by 29 Si-NMR. An amino functionalized alkoxysilane was used as condensation catalyst and the nanocomposite material was thermally cured at 130°C after flow or spray coating on pretreated polycarbonate substrates. The coatings show excellent adhesion which was shown by cross cut and tape test. After 1000 cycles Taber abrasion test (CS-10F, 500 g, DIN 52347) losses of transmittance due to scattering of 2–6% were achieved with a coating thickness of about 5 μm.


title: Rank-based distance metric learning: An application to image retrieval

abstract: We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements, has been studied widely, for example pairwise constraint-based distance metric learning. However, the capacity of existing algorithms is limited, because they usually assume that the distance between two similar objects is smaller than the distance between two dissimilar objects. This assumption may not hold, especially in the case of information retrieval when the input space is heterogeneous. To address this problem explicitly, we propose rank-based distance metric learning. Our approach overcomes the drawback of existing algorithms by comparing the distances only among the relevant and irrelevant objects for a given query. To avoid over-fitting, a regularizer based on the Burg matrix divergence is also introduced. We apply the proposed framework to tattoo image retrieval in forensics and law enforcement application domain. The goal of the application is to retrieve tattoo images from a gallery database that are visually similar to a tattoo found on a suspect or a victim. The experimental results show encouraging results in comparison to the standard approaches for distance metric learning.


title: Frequency-Dependent Resistance of Planar Coils in Printed Circuit Board With Litz Structure

abstract: Printed circuit board (PCB) windings are convenient for many applications given their ease of manufacture, high repeatability, and low profile. In many cases, the use of multistranded litz wires is appropriate due to the rated power, frequency range, and efficiency constraints. This paper proposes a manufacturing technique and a semianalytical loss model for PCB windings using planar litz structure to obtain a similar ac loss reduction to that of conventional windings of round wires with litz structure. Different coil prototypes have been tested in several configurations to validate the proposal.


title: Radioligand binding evidence implicates the brain 5-HT2 receptor as a site of action for LSD and phenylisopropylamine hallucinogens

abstract: Alterations in brain serotonergic function have been implicated in the mechanism of action of LSD, mescaline, and other similarly acting hallucinogenic drugs of abuse such as STP (2,5-dimethoxyphenylisopropylamine; DOM). In order to test the hypothesis that the mechanism of action of LSD and phenylisopropylamine hallucinogens is through stimulation of a specific brain serotonin receptor sub-type, the affinities of these compounds for radiolabelled 5-HT2, 5-HT1A, 5-HT1B, and 5-HT1C receptors have been determined using recently developed in vitro radioligand binding methodologies. The 5-HT2 receptor was labelled with the agonist/hallucinogen radioligand 3H-DOB (4-bromo-2,5-dimethoxyphenylisopropylamine). The 5-HT1A, 5-HT1B, and 5-HT1C receptors were labelled with 3H-OH-DPAT, 3H-5-HT, and 3H-mesulergine, respectively. In general, the phenylisopropylamines displayed 10–100 fold higher affinities for the 5-HT2 receptor than for the 5-HT1C receptor and 100–1000 fold higher affinities for the 5-HT2 receptor than for the 5-HT1A or 5-HT1B receptor. There was a strong correlation between hallucinogenic potencies and 5-HT2 receptor affinities of the phenylisopropylamines (r=0.90); the correlation coefficients for the 5-HT1A, 5-HT1B, and 5-HT1C were 0.73, 0.85, and 0.78, respectively. Because there is no evidence that 5-HT1A-selective or 5-HT1B-selective agonists are hallucinogenic and because the phenylisopropylamines are potent hallucinogens, a 5-HT2 receptor interaction is implicated and supports our previous suggestions to this effect. A secondary role for 5-HT1C receptors cannot be discounted at this time. These results, when integrated with other receptor pharmacological information, indicate that an important component of the mechanism of action of LSD and the phenylisopropylamine hallucinogens is through stimulation of brain 5-HT2 receptors.


title: Mobile Learning and Student Retention.

abstract: Student retention in open and distance learning (ODL) is comparatively poor to traditional education and, in some contexts, embarrassingly low. Literature on the subject of student retention in ODL indicates that even when interventions are designed and undertaken to improve student retention, they tend to fall short. Moreover, this area has not been well researched. The main aim of our research, therefore, is to better understand and measure students’ attitudes and perceptions towards the effectiveness of mobile learning. Our hope is to determine how this technology can be optimally used to improve student retention at Bachelor of Science programmes at Indira Gandhi National Open University (IGNOU) in India. For our research, we used a survey. Results of this survey clearly indicate that offering mobile learning could be one method improving retention of BSc students, by enhancing their teaching/ learning and improving the efficacy of IGNOU’s existing student support system. The biggest advantage of this technology is that it can be used anywhere, anytime. Moreover, as mobile phone usage in India explodes, it offers IGNOU easy access to a larger number of learners. This study is intended to help inform those who are seeking to adopt mobile learning systems with the aim of improving communication and enriching students’ learning experiences in their ODL institutions.