title: A dirty dozen: twelve p-value misconceptions.

abstract: The P value is a measure of statistical evidence that appears in virtually all medical research papers. Its interpretation is made extraordinarily difficult because it is not part of any formal system of statistical inference. As a result, the P value's inferential meaning is widely and often wildly misconstrued, a fact that has been pointed out in innumerable papers and books appearing since at least the 1940s. This commentary reviews a dozen of these common misinterpretations and explains why each is wrong. It also reviews the possible consequences of these improper understandings or representations of its meaning. Finally, it contrasts the P value with its Bayesian counterpart, the Bayes' factor, which has virtually all of the desirable properties of an evidential measure that the P value lacks, most notably interpretability. The most serious consequence of this array of P-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism.


title: Exhaustive experimental study of tunnel field effect transistors (TFETs): From materials to architecture

abstract: The goal of this paper is to present and to analyze the Tunnel FET and its specific properties (small subthreshold swing, very low OFF currents). We investigate the opportunities offered by this sub-“kT/q” swing device and review the issues that TFET has to overcome (ambipolar behaviour, low ON current) for future circuit applications. For that purpose we report on experimental results on SOI, Si 1−x Ge x OI & GeOI TFETs, fabricated using a Fully Depleted SOI CMOS process flow with high-k & metal gate stack.


title: Microcontroller-based quadratic buck converter used as led lamp driver

abstract: This paper presents a new proposal for driving LED lamps through the use of a micro controlled quadratic buck DC/DC converter. The main focus is to improve the LED driver's characteristics using the microcontroller PIC16F873A, thus achieving greater durability and efficiency. Mathematical analyses and experimental results are presented in this paper.


Forecasting the future of predictive crime mapping


title: SQLMR : A Scalable Database Management System for Cloud Computing

abstract: As the size of data set in cloud increases rapidly, how to process large amount of data efficiently has become a critical issue. MapReduce provides a framework for large data processing and is shown to be scalable and fault-tolerant on commondity machines. However, it has higher learning curve than SQL-like language and the codes are hard to maintain and reuse. On the other hand, traditional SQL-based data processing is familiar to user but is limited in scalability. In this paper, we propose a hybrid approach to fill the gap between SQL-based and MapReduce data processing. We develop a data management system for cloud, named SQLMR. SQLMR complies SQL-like queries to a sequence of MapReduce jobs. Existing SQL-based applications are compatible seamlessly with SQLMR and users can manage Tera to PataByte scale of data with SQL-like queries instead of writing MapReduce codes. We also devise a number of optimization techniques to improve the performance of SQLMR. The experiment results demonstrate both performance and scalability advantage of SQLMR compared to MySQL and two NoSQL data processing systems, Hive and HadoopDB.


title: Recurrent Neural Networks with External Memory for Language Understanding

abstract: Recurrent Neural Networks (RNNs) have become increasingly popular for the task of language understanding. In this task, a semantic tagger is deployed to associate a semantic label to each word in an input sequence. The success of RNN may be attributed to its ability to memorize long-term dependence that relates the current-time semantic label prediction to the observations many time instances away. However, the memory capacity of simple RNNs is limited because of the gradient vanishing and exploding problem. We propose to use an external memory to improve memorization capability of RNNs. We conducted experiments on the ATIS dataset, and observed that the proposed model was able to achieve the state-of-the-art results. We compare our proposed model with alternative models and report analysis results that may provide insights for future research.


title: Usefulness of Sonazoid contrast-enhanced ultrasonography for hepatocellular carcinoma: comparison with pathological diagnosis and superparamagnetic iron oxide magnetic resonance images

abstract: Objective ::: We investigated the usefulness of Sonazoid contrast-enhanced ultrasonography (Sonazoid-CEUS) in the diagnosis of hepatocellular carcinoma (HCC). The examination was performed by comparing the images during the Kupffer phase of Sonazoid-CEUS with superparamagnetic iron oxide magnetic resonance (SPIO-MRI).


title: Applying PSO to natural language processing tasks: Optimizing the identification of syntactic phrases

abstract: The present article discusses the use of Particle Swarm Optimisation (PSO) in a natural language processing task, namely the creation of a phrasing model which splits any sentence into linguistically-motivated phrases. This involves taking a limited-size training dataset, where sentences are split into syntactically motivated sentences, and learning how to best segment arbitrary sentences into their corresponding phrases. The extrapolation of phrases is needed in numerous applications involving the generation of texts in natural language. One such application is machine translation, namely the automatic translation of unconstrained text from a source language to a target language. The phrasing model to be learnt comprises a number of parameters which need to be optimized based on the training data. To that end, a machine learning approach has been developed, which is based on the concept of attractive and repulsive forces. Instead of previous efforts using manual tuning of the parameters, here PSO is used to achieve the optimization of the model parameters. Experimental results indicate that the proposed PSO approach is promising, giving the most accurate phrasing in this specific application, with statistically significant improvements over earlier results.


title: Online Deep Metric Learning

abstract: Metric learning learns a metric function from training data to calculate the similarity or distance between samples. From the perspective of feature learning, metric learning essentially learns a new feature space by feature transformation (e.g., Mahalanobis distance metric). However, traditional metric learning algorithms are shallow, which just learn one metric space (feature transformation). Can we further learn a better metric space from the learnt metric space? In other words, can we learn metric progressively and nonlinearly like deep learning by just using the existing metric learning algorithms? To this end, we present a hierarchical metric learning scheme and implement an online deep metric learning framework, namely ODML. Specifically, we take one online metric learning algorithm as a metric layer, followed by a nonlinear layer (i.e., ReLU), and then stack these layers modelled after the deep learning. The proposed ODML enjoys some nice properties, indeed can learn metric progressively and performs superiorly on some datasets. Various experiments with different settings have been conducted to verify these properties of the proposed ODML.


DESIGN , DEVELOPMENT AND OPTIMIZATION OF VALSARTAN LIQUISOLID TABLETS USING BOX-BEHNKEN DESIGN


title: Raspberry Pi as a Wireless Sensor node: Performances and constraints

abstract: The main building block of Wireless Sensor Network (WSN) is sensor node consisted of four basic elements: the sensor unit, processing unit, communication and power units. In this paper we propose the Raspberry Pi, cheap, flexible, fully customizable and programmable small computer board and abilities of its usage as WSN node and SensorWeb node. The Raspberry Pi brings the advantages of a PC to the domain of sensor network, what makes it the perfect platform for interfacing with wide variety of external peripherals. Comparative analysis of its key elements and performances with some of current existing wireless sensor nodes have shown that despite few disadvantages, the Raspberry Pi remains an inexpensive computer with its very successfully usage in sensor network domain and diverse range of research applications.


title: An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray

abstract: One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.


abstract: This paper reports on the use of an exercise-based programme in the management of a person with Myasthenia Gravis. This programme was based on current literature pertaining to the therapeutic management of Myasthenia Gravis and other chronic progressive neuromuscular disorders. Although the patient showed moderate gains in clinical measurements of muscle strength and endurance, the effectiveness of the exercise programme was mainly demonstrated by the patient’s decreased levels of fatigue and in the achievement of his stated goals. Davidson L*, Hale L, Mulligan H (2005). Exercise prescription in the physiotherapeutic management of Myasthenia Gravis: a case report. New Zealand Journal of Physiotherapy 33(1) 13-18.


title: UAV vision aided positioning system for location and landing

abstract: The research and use of UAVs (Unmanned Aerial Vehicles) have grown in attention and new ideas in the last couple of decades, mainly due to technology improvement of its construction parts, sensors and controllers. When it comes to its autonomous behavior, robust techniques make themselves necessary to assure the security of the aircraft and people around it. This work presents the use of computer vision techniques to improve the positioning of a quadrotor UAV in the landing moment, adding security and precision, overcoming intrinsic errors of other positioning sensors, such as GPS. A landmark was built to be detected by the camera with contour detection, image thresholding and other mathematical techniques in order to obtain fast, simple and robust processing and results. A GoPro camera model was used with a developed algorithm to remove the image distortion. With this set up, the vehicle can be programmed to develop any mission, with this works control algorithm taking part with the vision aided positioning in the landing moment. The quadrotor was conceived and built at the GRIn (Grupo de Robtica Inteligente) laboratory at UFJF (Juiz de Fora Federal University), and uses open source code for its control, with Mavlink communication protocol to exchange messages with the vision-based control algorithm for mission and landing. This algorithm was developed in Python language, and is in charge of image characteristics segmentation, extracting distances and altitude from the data. The control commands are sent to the quadrotor using the mentioned protocol. The system is structured with a remote laptop connected with a serial port to the quadrotor, and a router transmits messages back and forth among the GCS (Ground Control Station, on the laptop) and the high-level control algorithm (mission planning and vision based control) through TCP ports to the quadrotor itself. The camera is connected via Wi-Fi built-in network to the laptop, so all the image processing is done inside the last one. The results obtained in outdoor tests show the efficacy and fast processing of the vision methods and ensure safety in the landing moment. Furthermore, the positioning techniques can be extended to other UAV applications, such as transmission lines and utility equipment inspection and monitoring. Future works look forward to embedding the process in a companion board attached to the UAV, so the process would become automatic and the risk of communication interference and lost heavily reduced.


title: Modeling annotated data

abstract: We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in correspondence latent Dirichlet allocation, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type. We conduct experiments on the Corel database of images and captions, assessing performance in terms of held-out likelihood, automatic annotation, and text-based image retrieval.


Effect of tyre inflation preassure on the vehicle dynamics during braking manouvre


title: Big Data and Predictive Analytics Methods for Modeling and Analysis of Semiconductor Manufacturing Processes

abstract: Semiconductor manufacturing fabs generate huge amount of data. The big data approaches of data management have increased speed, quality and accessibility of the data. This paper discusses harnessing value from this data using predictive analytics methods. Various aspects predictive analytics in the context of semiconductor manufacturing are discussed. The limitations of standard methods of analysis and the need to adopt robust methods of modeling and analysis are highlighted. The robust prediction modeling method is implemented on wafer sensor data resulting in improved prediction ability of wafer quality characteristics.


title: VALIDITY CONVERGENT AND RELIABILITY TEST-RETEST OF THE REY AUDITORY VERBAL LEARNING TEST

abstract: Objective: This study provides evidence for the validity and reliability of the Rey Auditory Verbal Learning Test (RAVLT). Method: The reliability was measured by internal consistency and the test-retest method with a mean interval of 35 days. To determine the convergent and divergent validity, it was compared the performance on the RAVLT with the Benton Visual Retention Test (BVRT) and the Trail Making Test (TMT), respectively. The test was been taken by 34 healthy participants of both genders, ages 17 to 40 and 11.2±0.7 years of education. Results: All test-retest correlation coefficients achieved significance, ranging between 0.36 and 0.68. The A2 measure obtained the weakest correlation (r =0.28), and the sum of A1-A5 (r =0.68) was the strongest. The rest of the measures obtained moderate correlations. The value of the Cronbach’s Alpha coefficient was 0.80. The two RAVLT measures, the sum of A1-A5 and A7, did not significantly correlate with the TMT measures. In contrast, these measures did exhibit significant but modest correlations with the measures of BVRT (ranging from 0.37 to 0.44). Conclusions: The results had adequate divergent and convergent validity and good reliability in terms of internal consistency. The evidence collected in this study indicates that RAVLT is a valid and reliable psychometric instrument in neuropsychological assessment.


title: Improving data association by joint modeling of pedestrian trajectories and groupings

abstract: We consider the problem of data association in a multiperson tracking context. In semi-crowded environments, people are still discernible as individually moving entities, that undergo many interactions with other people in their direct surrounding. Finding the correct association is therefore difficult, but higher-order social factors, such as group membership, are expected to ease the problem. However, estimating group membership is a chicken-and-egg problem: knowing pedestrian trajectories, it is rather easy to find out possible groupings in the data, but in crowded scenes, it is often difficult to estimate closely interacting trajectories without further knowledge about groups. To this end, we propose a third-order graphical model that is able to jointly estimate correct trajectories and group memberships over a short time window. A set of experiments on challenging data underline the importance of joint reasoning for data association in crowded scenarios.


title: Matching resumes and jobs based on relevance models

abstract: We investigate the difficult problem of matching semi-structured resumes and jobs in a large scale real-world collection. We compare standard approaches to Structured Relevance Models (SRM), an extensionof relevance-based language model for modeling and retrieving semi-structured documents. Preliminary experiments show that the SRM approach achieved promising performance and performed better than typical unstructured relevance models.


title: Scalable Upconversion Medium for Static Volumetric Display

abstract: Efficient up-conversion emitters are necessary to generate a full-color 3D display. Rare-earth, co-doped fluorides that efficiently convert near infrared diode laser emission to visible red, green, and blue light by sequential two photon absorption are necessary to accomplish this. An up-conversion medium for a 3D display, particularly the CSpace “static volumetric display,” can be fabricated by grinding rare earth-doped fluoride bulk crystals and then dispersing the resultant microcrystals within an index matched host. This in turn leads to a reduction in display cost, weight, and growing time, as well as facilitating display scalability. To demonstrate a scalable medium for the CSpace display, several rare earth-doped fluoride bulk crystals were ground into micro crystal powders and then dispersed in liquids of different refractive index, including 1.45, 1.456, 1.46, 1.464, 1.468, 1.47, 1.474, 1.476, 1.48, 1.484, and 1.49. Fluorescence strength and transmission measurements were taken. Different particle concentrations were tested and demonstrated as well, and the detailed experimental results are described. A real volumetric 3D image was constructed inside a prototype display medium of ${\hbox{40}}\times \hbox{40}\times \hbox{10 mm}^{3}$ using the CSpace display. A potential future solution is presented, and suggestions to improve the scalable medium are given.


title: D.O.: Analysis of KDD ‘99 Intrusion Detection Dataset for Selection of Relevance Features

abstract: The rapid development of business and other transaction systems over the Internet makes computer security a critical issue. In recent times, data mining and machine learning have been subjected to extensive research in intrusion detection with emphasis on improving the accuracy of detection classifier. But selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented the relevance of each feature in KDD '99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class. Empirical results show that seven features were not relevant in the detection of any class. network-based. The former operates on information collected from within an individual computer system and the latter collect raw networks packets as the data source from the network and analyze for signs of intrusions. The two different detection techniques employed in IDS to search for attack patterns are Misuse and Anomaly. Misuse detection systems find known attack signatures in the monitored resources. Anomaly detection systems find attacks by detecting changes in the pattern of utilization or bahaviour of the system. Majority of the IDS currently in use are either rule-based or expert-system based. Their strengths depend largely on the ability of the security personnel that develops them. The former can only detect known attack types and the latter is prone to generation of false positive alarms. This leads to the use of an intelligence technique known as data mining/machine learning technique as an alternative to expensive and strenuous human input. These techniques automatically learn from data or extract useful pattern from data as a reference for normal/attack traffic behaviour profile from existing data for subsequent classification of network


title: Short text classification by detecting information path

abstract: Short text is becoming ubiquitous in many modern information systems. Due to the shortness and sparseness of short texts, there are less informative word co-occurrences among them, which naturally pose great difficulty for classification tasks on such data. To overcome this difficulty, this paper proposes a new way for effectively classifying the short texts. Our method is based on a key observation that there usually exists ordered subsets in short texts, which is termed ``information path'' in this work, and classification on each subset based on the classification results of some pervious subsets can yield higher overall accuracy than classifying the entire data set directly. We propose a method to detect the information path and employ it in short text classification. Different from the state-of-art methods, our method does not require any external knowledge or corpus that usually need careful fine-tuning, which makes our method easier and more robust on different data sets. Experiments on two real world data sets show the effectiveness of the proposed method and its superiority over the existing methods.


title: Morphological observation and CBCT of the bony canal structure of the groove and the location of blood vessels and nerves in the palatine of elderly human cadavers

abstract: PURPOSE ::: The greater and lesser palatine nerves and vessels supply the hard and soft palates, and the roots of these vessels and nerves run through a bony structure. However, the arrangement of blood vessels in the maxilla requires attention during clinical treatments, but detailed morphological information about changes in the greater and lesser palatine arteries and nerves during aging is unavailable. We therefore need detailed investigations of the morphology of the donor cadaver palatine using cone-beam computed tomography (CBCT) and macroscopic observations. ::: ::: ::: METHODS ::: We investigated 72 donor cadavers using macroscopic segmentation and CBCT. The results' analysis examined differences in skull measurement parameters and differences between dentate and edentulous cases. ::: ::: ::: RESULTS ::: The greater palatine artery and nerve showed different macroscopic arrangements in dentate and edentulous cadavers. We also classified three types of bony structures of the nerve and vessel roots in the molar regions of the palatine using CBCT images: the shallow groove, deep groove, and flat groove. The deep groove is the deepest of the three and is remarkable in edentulous elderly cadavers. ::: ::: ::: CONCLUSION ::: This study of macroscopic and CBCT data provides information useful for planning dental implant surgeries and autogenous bone harvesting.


title: Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

abstract: We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.


title: Better Mini-Batch Algorithms via Accelerated Gradient Methods

abstract: Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient methods. We provide a novel analysis, which shows how standard gradient methods may sometimes be insufficient to obtain a significant speed-up and propose a novel accelerated gradient algorithm, which deals with this deficiency, enjoys a uniformly superior guarantee and works well in practice.


title: Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology

abstract: The purpose of this paper is to propose foundations for a theory of situation awareness based on the analysis of interactions between agents (i.e. both human and non-human) in subsystems. This approach may help to promote a better understanding of technology-mediated interaction in systems, as well as helping in the formulation of hypotheses and predictions concerning distributed situation awareness. It is proposed that agents within a system each hold their own situation awareness, which may be very different from (although compatible with) that of other agents. It is argued that we should not always hope for, or indeed want, sharing of this awareness, as different system agents have different purposes. This view marks situation awareness as a dynamic and collaborative process binding agents together on tasks on a moment-by-moment basis. Implications of this viewpoint for the development of a new theory of, and accompanying methodology for, distributed situation awareness are offered.


title: Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study

abstract: OBJECTIVE It is vital to detect the full safety profile of a drug throughout its market life. Current pharmacovigilance systems still have substantial limitations, however. The objective of our work is to demonstrate the feasibility of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes. DESIGN Narrative discharge summaries were collected from the Clinical Information System at New York Presbyterian Hospital (NYPH). MedLEE, an NLP system, was applied to the collection to identify medication events and entities which could be potential adverse drug events (ADEs). Co-occurrence statistics with adjusted volume tests were used to detect associations between the two types of entities, to calculate the strengths of the associations, and to determine their cutoff thresholds. Seven drugs/drug classes (ibuprofen, morphine, warfarin, bupropion, paroxetine, rosiglitazone, ACE inhibitors) with known ADEs were selected to evaluate the system. RESULTS One hundred thirty-two potential ADEs were found to be associated with the 7 drugs. Overall recall and precision were 0.75 and 0.31 for known ADEs respectively. Importantly, qualitative evaluation using historic roll back design suggested that novel ADEs could be detected using our system. CONCLUSIONS This study provides a framework for the development of active, high-throughput and prospective systems which could potentially unveil drug safety profiles throughout their entire market life. Our results demonstrate that the framework is feasible although there are some challenging issues. To the best of our knowledge, this is the first study using comprehensive unstructured data from the EHR for pharmacovigilance.


title: Quadratic guaranteed cost analysis for a class of nonlinear uncertain systems

abstract: This paper focuses on the problem of quadratic guaranteed cost analysis for a class of nonlinear uncertain systems. Results on the analysis are presented. The robust guaranteed cost bound for the nonlinear uncertainty is proposed. A Riccati equation approach is developed.


title: Impacts of increasing volume of digital forensic data

abstract: A major challenge to digital forensic analysis is the ongoing growth in the volume of data seized and presented for analysis. This is a result of the continuing development of storage technology, including increased storage capacity in consumer devices and cloud storage services, and an increase in the number of devices seized per case. Consequently, this has led to increasing backlogs of evidence awaiting analysis, often many months to years, affecting even the largest digital forensic laboratories. Over the preceding years, there has been a variety of research undertaken in relation to the volume challenge. Solutions posed range from data mining, data reduction, increased processing power, distributed processing, artificial intelligence, and other innovative methods. This paper surveys the published research and the proposed solutions. It is concluded that there remains a need for further research with a focus on real world applicability of a method or methods to address the digital forensic data volume challenge.


title: Handheld Computing and Programming for Mobile Commerce

abstract: Using Internet‐enabled mobile handheld devices to access the World Wide Web is a promising addition to the Web and traditional e‐commerce. Mobile handheld devices provide convenience and portable access to the huge information on the Internet for mobile users from anywhere and at anytime. However, mobile commerce has not enjoyed the same level of success as the e‐commerce has so far because mobile Web contents are scarce and mostly awkward for browsing. The major reason of the problems is most software engineers are not familiar with handheld devices, let alone programming for them. To help software engineers better understand this subject, this article gives a comprehensive study of handheld computing and programming for mobile commerce. It includes five major topics: (i) mobile commerce systems, (ii) mobile handheld devices, (iii) handheld computing, (iv) server‐side handheld computing and programming, and (v) client‐side handheld computing and programming. The most popular server‐side handheld applicat...


title: Contents lists available at ScienceDirect Clinical Psychology Review Psychological flexibility as a fundamental aspect of health

abstract: Traditionally, positive emotions and thoughts, strengths, and the satisfaction of basic psychological needs for belonging, competence, and autonomy have been seen as the cornerstones of psychological health. Without disputing their importance, these foci fail to capture many of the fluctuating, conflicting forces that are readily apparent when people navigate the environment and social world. In this paper, we review literature to offer evidence for the prominence of psychological flexibility in understanding psychological health. Thus far, the importance of psychological flexibility has been obscured by the isolation and disconnection of research conducted on this topic. Psychological flexibility spans a wide range of human abilities to: recognize and adapt to various situational demands; shift mindsets or behavioral repertoires when these strategies compromise personal or social functioning; maintain balance among important life domains; and be aware, open, and committed to behaviors that are congruent with deeply held values. In many forms of psychopathology, these flexibility processes are absent. In hopes of creating a more coherent understanding, we synthesize work in emotion regulation, mindfulness and acceptance, social and personality psychology, and neuropsychology. Basic research findings provide insight into the nature, correlates, and consequences of psychological flexibility and applied research provides details on promising interventions. Throughout, we emphasize dynamic approaches that might capture this fluid construct in the real-world.


title: Resource BLAT—The BLAST-Like Alignment Tool

abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT's speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome.


title: Evaluation methods for topic models

abstract: A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean method and empirical likelihood method. In this paper, we demonstrate experimentally that commonly-used methods are unlikely to accurately estimate the probability of held-out documents, and propose two alternative methods that are both accurate and efficient.


title: Simplified feeding for a modified printed Yagi antenna

abstract: A modified printed Yagi antenna with a simplified feeding mechanism is proposed. In the new design, the driver dipole is fed by a transmission line formed by two parallel strips printed on the opposite sides of the dielectric substrate. The parallel strips are connected to a microstrip line with a truncated ground plane. This simplified feeding structure results in the reduction of the transmission line length, and, consequently, the radiation losses. The analysis of the modified Yagi antenna is performed numerically using commercial FEM and FD-TD software.


title: Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features

abstract: Depression is a serious mental disorder that affects millions of people all over the world. Traditional clinical diagnosis methods are subjective, complicated and need extensive participation of experts. Audio-visual automatic depression analysis systems predominantly base their predictions on very brief sequential segments, sometimes as little as one frame. Such data contains much redundant information, causes a high computational load, and negatively affects the detection accuracy. Final decision making at the sequence level is then based on the fusion of frame or segment level predictions. However, this approach loses longer term behavioural correlations, as the behaviours themselves are abstracted away by the frame-level predictions. We propose to on the one hand use automatically detected human behaviour primitives such as Gaze directions, Facial action units (AU), etc. as low-dimensional multi-channel time series data, which can then be used to create two sequence descriptors. The first calculates the sequence-level statistics of the behaviour primitives and the second casts the problem as a Convolutional Neural Network problem operating on a spectral representation of the multichannel behaviour signals. The results of depression detection (binary classification) and severity estimation (regression) experiments conducted on the AVEC 2016 DAIC-WOZ database show that both methods achieved significant improvement compared to the previous state of the art in terms of the depression severity estimation.


title: A Smart IoT Platform for Personalized Healthcare Monitoring Using Semantic Technologies

abstract: Today, numerous technological advances in electronic and information technologies are rapidly transforming our modern life. Industries are being completely transformed and health systems are not oblivious to these changes. All these new technologies are opening up a wide range of new opportunities and challenges for researchers, physicians and patients. The emergent paradigm of Internet of Things is one of the promising technologies introduced into the world of health care. In fact, IoT has been widely applied to interconnect available medical devices and sensors which allow patients to take control of their health condition in real time, also physicians to accurately remotely monitor the health of their patients. The ultimate goal of achieving high quality of healthcare practices depends on the ability to effectively integrate data incoming from heterogeneous sources, share the collected data while keeping their security and privacy, use powerful data analytics tools to extract useful information from these data, and the ability to have an expressive and personalized visualization. In this paper, we propose the SM-IoT platform, an IoT-based platform for intelligent and personalized healthcare, dedicated to patients, as well as caregivers. The aim of this platform is to improve the remote patient monitoring and promote healthcare services. SM-IoT platform is able to collect data from heterogeneous information sources, integrate them by using a flexible semantic web, store them in the cloud for further analysis, visualized these data with user-friendly interfaces and facilitate their sharing by taking into account their privacy aspect.


CHD 7 Targets Active Gene Enhancer Elements to Modulate ES Cell-Specific Gene Expression


title: Antenna on glass for 60GHz WiGig application

abstract: In this paper, we investigate antenna on glass and compare the property with antenna on ceramic substrate at 60GHz band for Wireless Gigabit alliance application. The design of Antenna on Corning EAGLE XG Glass is dependent on the result of simulation. The structure of a glass antenna with one redistribution layer (RDL) on the front-side and one ground metal on the backside has been fabricated. The antenna property is compared with the simulation result and on ceramic substrate. The results indicate that performance of this antenna on glass is better than on low temperature co-fired ceramic (LTCC) substrate. The data show the feasibility of glass substrate for electronics applications.


title: Combining structural and citation-based evidence for text classification

abstract: This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM Digital Library and the ACM Computing Classification System show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.


title: Should I do that? using relational reinforcement learning and declarative programming to discover domain axioms

abstract: Robots assisting humans in complex domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper focuses on the challenge of incrementally and interactively discovering previously unknown axioms governing domain dynamics, and describes an architecture that integrates declarative programming and relational reinforcement learning to address this challenge. Answer Set Prolog (ASP), a declarative programming paradigm, is used to represent and reason with incomplete domain knowledge for planning and diagnostics. For any given goal, unexplained failure of plans created by ASP-based inference is taken to indicate the existence of unknown domain axioms. The task of discovering these axioms is formulated as a reinforcement learning problem, and a relational representation is used to incrementally generalize from specific axioms identified over time. These generic axioms are then added to the ASP-based representation for subsequent inference. The architecture's capabilities are demonstrated and evaluated in two domains, Blocks World and Robot Butler.


title: Effective regulation of affect: An action control perspective on emotion regulation

abstract: The present review adopts an action control perspective on emotion regulation, contextualising the gap between emotion control goals (e.g., I want to remain calm) and emotional outcomes (e.g., anger, anxiety, and aggression) in terms of the broader literature on goal pursuit. We propose that failure to effectively regulate emotions can result from difficulties with the self-regulatory tasks of (i) identifying the need to regulate, (ii) deciding whether and how to regulate, and (iii) enacting a regulation strategy. Next we review evidence that a technique traditionally associated with regulating behavioural goals (forming implementation intentions or “if-then” planning) can help to overcome these difficulties. Meta-analysis indicated that forming implementation intentions is effective in modifying emotional outcomes, with a large effect relative to no regulation instructions (k = 21, N = 1306 d + = 0.91, 95% CI = 0.61 to 1.20) and a medium-sized effect relative to goal intention instructions (k = 29, N = 1208, d + = 0.53, 95% CI = 0.42 to 0.65). Our conclusion is that research on emotion regulation might benefit from an action control perspective and the interventions that this perspective offers.


title: Agricultural Productivity Enhancement System & Livestock Management using Internet of Things

abstract: The more man has advanced towards technology, more and more problems affecting Mother Nature have aroused. ‘Not now, then when? Not earth, then where? Not we, then who’ are all the rhetoric questions that in themselves stress the need for us to become more aware about Global Warming. Global Warming has vastly affected different human sectors but the worst of the effects are seen in the Agricultural sector where the unpredictable climatic changes are causing to be of great trouble due to which many losses have been incurred. There were days back when our ancestors could predict the climate through ancient methods and to our surprise; most of their predictions did turn out to be true. Now that there are sudden climate changes, the previously used prediction methods have proven futile. There is an acute need to develop more sophisticated prediction methods using technology where more accurate decisions can be made. Using Embedded Systems and IOT, we aim at bringing sophistication to the crop prediction techniques to enhance the agricultural productivity even while the climatic trends keep on changing continuously. Adding on to the utility of this project is the Livestock Management module which further adds value to the project.


title: Behavioral Models of the Praying Mantis as a Basis for Robotic Behavior

abstract: Abstract Formal models of animal sensorimotor behavior can provide effective methods for generating robotic intelligence. In this article we describe how schema-theoretic models of the praying mantis derived from behavioral and neuroscientific data can be implemented on a hexapod robot equipped with a real time color vision system. This implementation incorporates a wide range of behaviors, including obstacle avoidance, prey acquisition, predator avoidance, mating, and chantlitaxia behaviors that can provide guidance to neuroscientists, ethologists, and roboticists alike. The goals of this study are threefold: to provide an understanding and means by which fielded robotic systems are not competing with other agents that are more effective at their designated task; to permit them to be successful competitors within the ecological system and capable of displacing less efficient agents; and that they are ecologically sensitive so that agent–environment dynamics are well-modeled and as predictable as possible whenever new robotic technology is introduced.


title: Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems

abstract: This paper presents a new optimization technique called water cycle algorithm (WCA) which is applied to a number of constrained optimization and engineering design problems. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. A comparative study has been carried out to show the effectiveness of the WCA over other well-known optimizers in terms of computational effort (measures as number of function evaluations) and function value (accuracy) in this paper.


title: Indoor scene segmentation using a structured light sensor

abstract: In this paper we explore how a structured light depth sensor, in the form of the Microsoft Kinect, can assist with indoor scene segmentation. We use a CRF-based model to evaluate a range of different representations for depth information and propose a novel prior on 3D location. We introduce a new and challenging indoor scene dataset, complete with accurate depth maps and dense label coverage. Evaluating our model on this dataset reveals that the combination of depth and intensity images gives dramatic performance gains over intensity images alone. Our results clearly demonstrate the utility of structured light sensors for scene understanding.


title: Bengali Named Entity Recognition using Support Vector Machine

abstract: Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes , defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) . In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages. Keywords—Named Entity (NE); Named Entity Recognition (NER); Support Vector Machine (SVM); Bengali; Hindi.


title: Ecology of Microbial Saprophytes and Pathogens in Tissue Culture and Field-Grown Plants: Reasons for Contamination Problems In Vitro

abstract: Abstract This review compares published surveys of microbial populations in plant tissue and cell cultures with the microbial saprophytes and pathogens found on field grown plants and microbial populations in the laboratory environment. From this comparison and the measured reduction in contamination after improvements in working practices in the laboratory, conclusions can be drawn about the importance of the explant and the laboratory as sources of contamination. Mechanisms of pathogenicity in vitro are described to explain why bacteria, fungi, and yeasts that are not pathogenic to plants in the field become pathogens in plant tissue cultures. Conversely, plant metabolism and its effect on the tissue culture environment are described to explain why prokaryotes, viruses, and viroids that cause disease in the field can stay latent in vitro. Detection methods for latent contaminants in plant tissue cultures are summarized, and the strategies and methods for prevention or treatment of contamination are disc...


title: Experiments on deep learning for speech denoising

abstract: In this paper we present some experiments using a deep learning model for speech denoising. We propose a very lightweight procedure that can predict clean speech spectra when presented with noisy speech inputs, and we show how various parameter choices impact the quality of the denoised signal. Through our experiments we conclude that such a structure can perform better than some comparable single-channel approaches and that it is able to generalize well across various speakers, noise types and signal-to-noise ratios.


title: Data quality management, data usage experience and acquisition intention of big data analytics

abstract: Abstract Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.


title: A Distributed Magnetic Circuit Approach to Analysis of Multiphase Induction Machines With Nonsinusoidal Supply

abstract: A 15-phase induction machine supplied by voltages with third-order harmonic injection has nonsinusoidal magnetomotive forces distribution. This paper introduces a distributed magnetic circuit approach to perform magnetic circuit calculations in order to determine the magnetizing inductance for both the fundamental and third-harmonic component. The proposed approach divides the half-pole or one-pole machine model into a large number of fan-shaped sectors. The air-gap flux densities for all sectors are then solved iteratively. The fundamental and third-harmonic inductances are extracted via Fourier analysis of air-gap flux density distribution. Calculated fundamental and third-order harmonic inductances are compared with those obtained from no-load tests of a 15-phase prototype induction machine. The good agreement between calculated and experimental results verifies the accuracy of the presented method.


title: COOL: A Language for Describing Coordination in Multi Agent Systems

abstract: Agent interaction takes place at several levels. Current work in the ARPA Knowledge Sharing Effort has addressed the information content level by the KIF language and the intentional level by the KQML language. In this paper we address the coordination level by means of our Coordination Language (COOL) that relies on speech act based communication, but integrates it in a structured conversation framework that captures the coordination mechanisms agents use when working together. We are currently using this language (i) to represent coordination mechanisms for the supply chain of manufacturing enterprises modeled as intelligent agents and (ii) as an environment for designing and validating coordination protocols for multi-agent systems. This paper describes the basic elements of this language: conversation objects, conversation rules, error recovery rules, continuation rules, conversation nesting. The actual COOL source code and a running trace for the n-queens problem are presented in the Appendix. Topic areas: Coordination, Intelligent agents in enterprise integration


title: Classification of High Content Screening Data by Deep Convolutional Neural Networks

abstract: In drug discovery, high content screening (HCS) is an imaging-based method forcell-based screening of large libraries of drug compounds. HCS generates enormous amounts of images that need to be ana ...


title: A survey on clustering methods in web service discovery

abstract: Several unclassified web services are available in the internet which is difficult for the user to choose the correct web services. This raises service discovery cost, transforming data time between services and service searching time. Adequate methods, tools, technologies for clustering the web services have been developed. The clustering of web services is done manually. This survey is organized based on clustering of web service discovery methods, tools and technologies constructed on following list of parameters. The parameters are clustering model, graphs and environment, different technologies, advantages and disadvantages, theory and proof of concepts. Based on the user requirements results are different and better than one another. If the web service clustering is done automatically that can create an impact in the service discovery and fulfills the user requirements. This article gives the overview of the significant issues of the different methods and discusses the lack of technologies and automatic tools of the web service discovery.


title: Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids

abstract: Monitoring systems are expected to play a major role in active distribution grids, and the design of the measurement infrastructure is a critical element for an effective operation. The use of any available and newly installed, though heterogeneous, metering device providing more accurate and real-time measurement data offers a new paradigm for the distribution grid monitoring system. In this paper the authors study the meter placement problem for the measurement infrastructure of an active distribution network, where heterogeneous measurements provided by Phasor Measurement Units (PMUs) and other advanced measurement systems such as Smart Metering systems are used in addition to measurements that are typical of distribution networks, in particular substation measurements and a-priori knowledge. This work aims at defining a design approach for finding the optimal measurement infrastructure for an active distribution grid. The design problem is posed in terms of a stochastic optimization with the goal of bounding the overall uncertainty of the state estimation using heterogeneous measurements while minimizing the investment cost. The proposed method is also designed for computational efficiency so to cover a wide set of scenarios.


title: Applying a Conceptual Framework to Analyze Online Reputation of Tourism Destinations

abstract: Destination managers are investing considerable efforts (time and money) in order to market their destination online without considering that unofficial information competitors (e.g. blogs, wiki, media sharing website etc) are gaining more and more popularity among internet users. This research uses online reputation as a metric to make sense out of the huge amount of user generated contents available online applying a conceptual framework to the reputation analysis: Destination Online Reputation (DORM). The model, derived from the popular models used in corporate reputation analysis has been tested within the tourism online domain accessible trough search engine of a popular English destination: London. Results demonstrate the validity of the model in understanding and managing destination online reputation.


title: Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

abstract: Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings, which could be a possible way to bring interpretability to the process. In this paper, we investigate if graph embeddings are approximating something analogous to traditional vertex-level graph features. If such a relationship can be found, it could be used to provide a theoretical insight into how graph embedding approaches function. We perform this investigation by predicting known topological features, using supervised and unsupervised methods, directly from the embedding space. If a mapping between the embeddings and topological features can be found, then we argue that the structural information encapsulated by the features is represented in the embedding space. To explore this, we present extensive experimental evaluation with five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features. We demonstrate that several topological features are indeed being approximated in the embedding space, allowing key insight into how graph embeddings create good representations.


title: Dual band Axially Displaced Ellipse reflector antenna for radar and broadband applications

abstract: In the scope of this paper a dual-reflector antenna called Axially Displaced Ellipse (ADE) antenna for being used in X-Band, K-Band radar and any other broadband applications were focused. The antenna design consists of feed antenna and dual-reflector configuration. Broadband Double Ridged Horn (DRH) antenna is designed and used as a feeder. Most used radar frequencies at X-Band and K-Band which are 10 GHz, 24 GHz, 35 GHz are considered for design and performance. The simulation program “CST Microwave Studio” was used to test the antenna designs by simulating them. The goal of this study is designing the antenna with a good value of S11 and remarkable radiation characteristics along the whole frequency range.


Semantically Equivalent Adversarial Rules for Debugging NLP models


title: Photovoltaic supplied grid-connected modular multilevel converter with active power injection and reactive power compensation capability

abstract: This paper discusses the active and reactive power control method for a modular multilevel converter (MMC) based grid-connected PV system. The voltage vector space analysis is performed by using average value models for the feasibility analysis of reactive power compensation (RPC). The proposed double-loop control strategy enables the PV system to handle unidirectional active power flow and bidirectional reactive power flow. Experiments have been performed on a laboratory-scaled modular multilevel PV inverter. The experimental results verify the correctness and feasibility of the proposed strategy.


title: A survey of cloud-based network intrusion detection analysis

abstract: As network traffic grows and attacks become more prevalent and complex, we must find creative new ways to enhance intrusion detection systems (IDSes). Recently, researchers have begun to harness both machine learning and cloud computing technology to better identify threats and speed up computation times. This paper explores current research at the intersection of these two fields by examining cloud-based network intrusion detection approaches that utilize machine learning algorithms (MLAs). Specifically, we consider clustering and classification MLAs, their applicability to modern intrusion detection, and feature selection algorithms, in order to underline prominent implementations from recent research. We offer a current overview of this growing body of research, highlighting successes, challenges, and future directions for MLA-usage in cloud-based network intrusion detection approaches.


WordNet Atlas : a web application for visualizing WordNet as a zoomable map


title: Inferring friendship network structure by using mobile phone data

abstract: Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.


title: A regression framework for learning ranking functions using relative relevance judgments

abstract: Effective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish two types of relevance judgments used as the training data: 1) absolute relevance judgments arising from explicit labeling of search results; and 2) relative relevance judgments extracted from user click throughs of search results or converted from the absolute relevance judgments. We propose a novel optimization framework emphasizing the use of relative relevance judgments. The main contribution is the development of an algorithm based on regression that can be applied to objective functions involving preference data, i.e., data indicating that a document is more relevant than another with respect to a query. Experimental results are carried out using data sets obtained from a commercial search engine. Our results show significant improvements of our proposed methods over some existing methods.


title: Clustering and Feature Selection using Sparse Principal Component Analysis

abstract: In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d’Aspremont et al. (SIAM Rev. 49(3):434–448, 2007). We then apply these results to some classic clustering and feature selection problems arising in biology.


title: Natural Language Processing: A Paninian Perspective

abstract: This book is presented with a paninian perspective [Panini 500 B was a grammarian and well known for his contribution to the grammar and structure of the language.] It introduces three western grammar frameworks using examples from English.


title: Power Distribution in Coupled Multiple-Coil Inductors for Induction Heating Appliances

abstract: In this paper, we propose a theoretical model of multiple-coil inductors supplied by resonant half-bridge inverters in induction hobs. The model includes coupling effects and allows calculating the coil currents and the inverter's output power for any modulation parameters, even with different switching frequencies and duty cycle. Furthermore, we calculate the power distribution in the pan and obtain the relationship with the output powers of the power stage, so that they can be controlled to obtain a homogeneous power distribution and a more uniform heating.


Interpretable Anomaly Detection for Monitoring of High Performance Computing Systems


title: A framework for automated corpus compilation for KeyXtract: Twitter model

abstract: The corpus is a limiting factor for a keyword extraction process with a word matching stage. This paper proposes a framework to automate the corpus generation stage required for the Twitter Model of KeyXtract, an algorithm used for essential keyword extraction from tweets. The initial algorithm was designed with two manually compiled corpora that limited the adaptability of the system. The automated framework proposed in the present research is an extension to the keyword extraction process of KeyXtract and would address this limitation of the system. The design was carried out using open-class words of the source text and by matching them against the bag of words compiled by analyzing the tweets. The automated corpus had a total of 138 words, out of which 74 words were also found in the handpicked corpus (which had a total of 206 words). However, when the corpus was used with the keyword extraction system, the average F1 scores of the system showed a decrease of 0.07, proving that the automated corpus cannot perform parallel to the human-made corpus in complexity. This was because the human-made corpus was compiled using syntactic, semantic and pragmatic features while the automated framework focused only on the syntactic features. However, there were individual tweets in which the F1 score showed an increase. Thus, this was a promising first step in the corpus automation process. The automatic corpus generation framework could be made more accurate by including the semantic analysis of the lexical items. Thus, the present framework is able to substantially address the limitation of the corpus compilation which was present in the Twitter Model of KeyXtract.


title: Greenhouse Monitoring with Wireless Sensor Network

abstract: In modern greenhouses, several measurement points are required to trace down the local climate parameters in different parts of the big greenhouse to make the greenhouse automation system work properly. Cabling would make the measurement system expensive and vulnerable. Moreover, the cabled measurement points are difficult to relocate once they are installed. Thus, a wireless sensor network (WSN) consisting of small-size wireless sensor nodes equipped with radio and one or several sensors, is an attractive and cost-efficient option to build the required measurement system. In this work, we developed a wireless sensor node for greenhouse monitoring by integrating a sensor platform provided by sensinode Ltd. [1] with three commercial sensors capable to measure four climate variables. The feasibility of the developed node was tested by deploying a simple sensor network into Martens Greenhouse Research Foundation's greenhouse in Narpio town in Western Finland. During a one day experiment, we collected data to evaluate the network reliability and its ability to detect the microclimate layers, which typically exist in the greenhouse between lower and upper flora. We were also able to show that the network can detect the local differences in the greenhouse climate caused by various disturbances, such as direct sunshine near the greenhouse walls. This article is our first step in the area of greenhouse monitoring and control, and it is all about the developed sensor network feasibility and reliability.


title: Design and development of infrared LED based non invasive blood glucometer

abstract: Diabetes affects more than 285 million people globally according to estimates by the International Diabetes Federation (IDF). It is a metabolic pathological condition of concern, which affects vital organs of body if not diagnosed and treated on time. Regular monitoring of blood glucose is important to avoid further complication. Commonly used glucose measurement methods are invasive which generally involve finger puncturing. These methods are painful and frequent pricking cause calluses on the skin. It also has a risk of spreading infectious diseases if the needle is contaminated or used more than once. Therefore there is a need to develop a non-invasive monitoring system which can measure blood glucose continuously without posing much problem and easy to use for the diabetic population. In this paper, near infrared optical measurement is applied to overcome the invasive method drawbacks like frequent puncturing, high recurring consumable cost and danger of spreading infectious diseases. The designed device consists of an infrared LED having a wavelength of 900 to 1100 nm as emitter which placed over the fingertip for measurement of blood glucose optically. The intensity of received light depends on the glucose concentration present in blood. The signal is then amplified and fed as input to microcontroller Arduino uno for displaying glucose signal on a computer after carrying out Regression analysis. By analyzing the variation in voltages received after reflection of incident light in the cases the approximate glucose level of the individual is going to be predicted. A compact framework for non-invasive blood glucose measurement has been designed and tested successfully. The results obtained were validated by using various statistical techniques like standard deviation, standard error, percentage linearity etc. It is found that statistical analysis gives correlation coefficient of 0.93. The result obtained shows that voltage intensity level due to pulsatile nature of blood flow and blood glucose level has correlation.


title: Sleep Interference Effects of Pathological Electronic Media Use during Adolescence

abstract: Adolescents increasingly use electronic media as a night-time activity, amid concerns about the potential negative impact on sleep and daytime functioning. The present study examined electronic media use and pathological media use in relation to sleep activity in a normative sample of Australian adolescents. A total of 1,287 high school students aged 12–18 years (50 % female) were recruited from seven secondary schools in South Australia. Adolescents completed a questionnaire assessing electronic media use, pathological media use, and sleep factors. Adolescents reported non-optimal sleep duration on weekday (71 %) and weekend nights (53 %). One in five adolescents reported nightly bedtime delay as a consequence of electronic media use. Adolescent pathological media users reported significantly more sleep problems than their non-pathological peers. These data contribute to current knowledge of how electronic media use may negatively affect adolescent sleep patterns, particularly in regard to sleep displacement and sleep-onset latency effects. Further research is needed in light of the increasing accessibility and uptake of portable electronic media devices, as well as the growing use of media as a sleeping aid, among young people.


title: Filling Gaps in the Boundary of a Polyhedron

abstract: Abstract In this paper we present an algorithm for detecting and repairing defects in the boundary of a polyhedron. These defects, usually caused by problems in CAD software, consist of small gaps bounded by edges that are incident to only one polyhedron face. The algorithm uses a partial curve matching technique for matching parts of the defects, and an optimal triangulation of 3-D polygons for resolving the unmatched parts. It is also shown that finding a consistent set of partial curve matches with maximum score, a subproblem which is related to our repairing process, is NP-hard. Experimental results on several polyhedra are presented.


title: DPICO: a high speed deep packet inspection engine using compact finite automata

abstract: Deep Packet Inspection (DPI)has been widely adopted in detecting network threats such as intrusion, viruses and spam. It is challenging, however, to achieve high speed DPI due to the expanding rule sets and ever increasing line rates. A key issue is that the size of the finite automata falls beyond the capacity of on-chip memory thus incurring expensive off-chip accesses. In this paper we present DPICO a hardware based DPI engine that utilizes novel techniques to minimize the storage requirements for finite automata. The techniques proposed are modified content addressable memory (mCAM), interleaved memory banks, and data packing. The experiment results show the scalable performance of DPICO can achieve up to 17.7 Gbps throughput using a contemporary FPGA chip. Experiment data also show that a DPICO based accelerator can improve the pattern matching performance of a DPI server by up to 10 times.


Ear Biometrics Based on Geometrical Feature Extraction


A Comparison between Agile and Traditional Software Development Methodologies


title: TOWARDS FOURTH-PARTY LOGISTICS PROVIDERS A Business Model for Cloud-Based Autonomous Logistics

abstract: Cloud computing denotes a paradigm shift in computing that enables a flexible allocation of hardware and software resources on demand. Therewith, it is particularly appealing for applications with a high degree of computational complexity and dynamics. This paper identifies logistics planning and control as a promising application for clouds. However, two prerequisites must be met for cloud-based logistics control. Firstly, the platform-as-a-service layer must provide a synchronisation of the physically distributed real-world material flows and the data flows in the cloud. Secondly, appropriate and scalable control software must be implemented on the software-as-a-service layer. Apart from outlining the technical foundations, this paper describes how both steps enable a business model that is usually referred to as fourth-party logistics.


title: Combining Instance-Based and Model-Based Learning

abstract: This paper concerns learning tasks that require the prediction of a continuous value rather than a discrete class. A general method is presented that allows predictions to use both instance-based and model-based learning. Results with three approaches to constructing models and with eight datasets demonstrate improvements due to the composite method. ::: ::: Keywords: learning with continuous classes, instance-based learning, model-based learning, empirical evaluation.


title: Fundamental Tradeoff between Storage and Latency in Cache-Aided Wireless Interference Networks

abstract: This paper studies the fundamental tradeoff between storage and latency in a general wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by an information-theoretic metric, normalized delivery time (NDT), which is the worst case delivery time of the actual traffic load at a transmission rate specified by degrees of freedom of a given channel. We obtain both an achievable upper bound and a theoretical lower bound of the minimum NDT for any number of transmitters, any number of receivers, and any feasible cache size tuple. We show that the achievable NDT is exactly optimal in certain cache size regions, and is within a bounded multiplicative gap to the theoretical lower bound in other regions. In the achievability analysis, we first propose a novel cooperative transmitter/receiver coded caching strategy. It offers the freedom to adjust file splitting ratios for NDT minimization. We then propose a delivery strategy that transforms the considered interference network into a new class of cooperative X-multicast channels. It leverages local caching gain, coded multicasting gain, and transmitter cooperation gain (via interference alignment and interference neutralization) opportunistically. Finally, the achievable NDT is obtained by solving a linear programming problem. This paper reveals that with caching at both transmitter and receiver sides, the network can benefit simultaneously from traffic load reduction and transmission rate enhancement, thereby effectively reducing the content delivery latency.


title: Extending SME to Handle Large‐Scale Cognitive Modeling

abstract: Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine (SME) since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: (a) Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O(n2 log(n)); (b) Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; (c) Ubiquitous predicates model the varying degrees to which items may suggest alignment; (d) Structural evaluation of analogical inferences models aspects of plausibility judgments; (e) Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before.


THE USE OF INFLUENCE TACI ICS AMONG MID-LEVEL MANAGERS


title: A game theoretic approach to controller design for hybrid systems

abstract: We present a method to design controllers for safety specifications in hybrid systems. The hybrid system combines discrete event dynamics with nonlinear continuous dynamics: the discrete event dynamics model linguistic and qualitative information and naturally accommodate mode switching logic, and the continuous dynamics model the physical processes themselves, such as the continuous response of an aircraft to the forces of aileron and throttle. Input variables model both continuous and discrete control and disturbance parameters. We translate safety specifications into restrictions on the system's reachable sets of states. Then, using analysis based on optimal control and game theory for automata and continuous dynamical systems, we derive Hamilton-Jacobi equations whose solutions describe the boundaries of reachable sets. These equations are the heart of our general controller synthesis technique for hybrid systems, in which we calculate feedback control laws for the continuous and discrete variables, which guarantee that the hybrid system remains in the "safe subset" of the reachable set. We discuss issues related to computing solutions to Hamilton-Jacobi equations. Throughout, we demonstrate out techniques on examples of hybrid automata modeling aircraft conflict resolution, autopilot flight mode switching, and vehicle collision avoidance.


title: Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction

abstract: Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVMs remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the \emph{first} \emph{static} feature and sample reduction method for sparse SVM. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude.


title: Message in a Sealed Bottle: Privacy Preserving Friending in Social Networks

abstract: Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with a matched profile within a certain distance. A challenging task in these applications is to protect the privacy of the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search persons with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms also establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is privacy-preserving (no participants' profile and the submitted preference-profile are exposed), verifiable (both the initiator and the unmatched user cannot cheat each other to pretend to be matched), and efficient in both communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient than existing solutions.


title: Interactive Visualization of Gene Regulatory Networks with Associated Gene Expression Time Series Data

abstract: We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes, and the edges represent interactions between a gene and its targets. GENeVis adds features that are currently lacking in existing tools, such as mapping of expression value and corresponding p-value (or other statistic) to a single visual attribute, multiple time point visualization, and visual comparison of multiple time series in one view. Various interaction mechanisms, such as panning, zooming, regulator and target highlighting, data selection, and tooltips support data analysis and exploration. Subnetworks can be studied in detail in a separate view that shows the network context, expression data plots, and tables containing the raw expression data. We present a case study, in which gene expression time series data acquired in-house are analyzed by a biological expert using GENeVis. The case study shows that the application fills the gap between present biological interpretation of time series experiments, performed on a gene-by-gene basis, and analysis of global classes of genes whose expression is regulated by regulator proteins.


title: DopeLearning: A Computational Approach to Rap Lyrics Generation

abstract: Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.


title: The Big Five Personality Traits and the Life Course: A 45-Year Longitudinal Study

abstract: Abstract One hundred sixty-three men who have been followed prospectively for over 45 years were rated on a set of 25 personality traits at the end of their college careers and took the NEO-PI at approximately ages 67–68. The college traits were transformed, via a rating procedure, to scales assessing each of the Big Five dimensions and related to the NEO-PI. Three traits—Neuroticism, Extraversion, and Openness—exhibited significant correlations across the 45-year interval. Furthermore, the trait profiles remained relatively stable over that interval. Both sets of personality traits were related to a wide variety of life course variables representing the domains of global adult adjustment, career functioning/success, creativity, social relations, mental health, substance abuse, childhood characteristics, familial history of pathology, maturity of defenses, and political attitudes. Conscientiousness in college was the best predictor of what happened to the men in the future, whereas Neuroticism in late midlife was the best correlate of life course functioning across a variety of domains.


title: Real-time attitude and position estimation for small UAVs using low-cost sensors

abstract: Small unmanned air vehicles (UAVs) and micro air vehicles (MAVs) have payload and power constraints that prohibit heavy sensors and powerful processors. This paper presents real-time attitude and position estimation solutions that use small, inexpensive sensors and low-power microprocessors. In connection with an Extended Kalman Filter attitude estimation scheme, a novel method for dealing with latency in real-time is presented using a distributed-in-time architecture. Essential to small UAV or MAV missions is the ability to navigate precisely. To reduce computational overhead and to simplify design, a cascaded filter approach to position estimation is used. The design is insensitive to noise and to loss of GPS lock. Simulation and hardware tests show that the algorithms operate in real-time and are suitable for control, stabilization, and navigation.


title: Genetic Algorithm-Based Solver for Very Large Multiple Jigsaw Puzzles of Unknown Dimensions and Piece Orientation

abstract: In this paper we propose the first genetic algorithm (GA)-based solver for jigsaw puzzles of unknown puzzle dimensions and unknown piece location and orientation. Our solver uses a novel crossover technique, and sets a new state-of-the-art in terms of the puzzle sizes solved and the accuracy obtained. The results are significantly improved, even when compared to previous solvers assuming known puzzle dimensions. Moreover, the solver successfully contends with a mixed bag of multiple puzzle pieces, assembling simultaneously all puzzles.


title: Construction of fair surfaces over irregular meshes

abstract: This paper describes the process of constructing a fair, open or closed C1 surface over a given irregular curve mesh. The input to the surface construction consists of point and/or curve data which are individually marked to be interpolated or approximated and are arranged according to an arbitrary irregular curve mesh topology. The surface constructed from these data will minimize flexibly chosen fairness criteria. The set of available fairness criteria is able to measure surface characteristics related to curvature, variation of curvature and higher order surface derivatives based on integral functionals of quadratic form derived from the second, third and higher order parametric derivatives of the surface. The choice is based on the desired shape character. The construction of the surface begins with a midpoint refinement decomposition of the irregular mesh into aggregates of patch complexes in which the only remaining type of building block is the quadrilateral Bezier patch of degrees 4 by 4. The fairing process may be applied regionally or to the entire surface. The fair surface is built up either in a single global step or iteratively in a three stage local process, successively accounting for vertex, edge curve and patch interior continuity and fairness requirements. This surface fairing process will be illustrated by two main examples, a benchmark test performed on a topological cube, resulting in many varieties of fair shapes for a closed body, and a practical application to a ship hull surface for a modern container ship, which is subdivided into several local fairing regions with suitable transition pieces. The examples will demonstrate the capability of the fairing approach of contending with irregular mesh topologies, dealing with multiple regions, applying global and local fairing processes and will illustrate the influence of the choice of criteria upon the character of the resulting shapes.


title: A Universal Semantic Space

abstract: Multilingual embeddings build on the success of monolingual embeddings and have applications in crosslingual transfer, in machine translation and in the digital humanities. We present the first multilingual embedding space for thousands of languages, a much larger number of languages than in prior work.


title: Realtime Caustics using Distributed Photon Mapping

abstract: With the advancements in realtime ray tracing and new global illumination algorithms we are now able to render the most important illumination effects at interactive rates. One of the major remaining issues is the fast and efficient simulation of caustic illumination, such as e.g. the illumination from a car headlight. The photon mapping algorithm is a simple and robust approach that generates high-quality results and is the preferred algorithm for computing caustic illumination. However, photon mapping has a number of properties that make it rather slow on today's processors. Photon mapping has also been notoriously difficult to parallelize efficiently. ::: ::: In this paper, we present a detailed analysis of the performance issues of photon mapping together with significant performance improvements for all aspects of the photon mapping technique. The solution forms a complete framework for realtime photon mapping that efficiently combines realtime ray tracing, optimized and improved photon mapping algorithms, and efficient parallelization across commodity PCs. The presented system achieves realtime photon mapping performance of up to 22 frames per second on non-trivial scenes, while still allowing for interactively updating all aspects of the scene, including lighting, material properties, and geometry.


title: Differential expression in RNA-seq: A matter of depth

abstract: Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach—NOISeq—that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.


title: Understanding, scoping and defining user experience: a survey approach

abstract: Despite the growing interest in user experience (UX), it has been hard to gain a common agreement on the nature and scope of UX. In this paper, we report a survey that gathered the views on UX of 275 researchers and practitioners from academia and industry. Most respondents agree that UX is dynamic, context-dependent, and subjective. With respect to the more controversial issues, the authors propose to delineate UX as something individual (instead of social) that emerges from interacting with a product, system, service or an object. The draft ISO definition on UX seems to be in line with the survey findings, although the issues of experiencing anticipated use and the object of UX will require further explication. The outcome of this survey lays ground for understanding, scoping, and defining the concept of user experience.


title: Mixing Complexity and its Applications to Neural Networks

abstract: We suggest analyzing neural networks through the prism of space constraints. We observe that most training algorithms applied in practice use bounded memory, which enables us to use a new notion introduced in the study of space-time tradeoffs that we call mixing complexity. This notion was devised in order to measure the (in)ability to learn using a bounded-memory algorithm. In this paper we describe how we use mixing complexity to obtain new results on what can and cannot be learned using neural networks.


title: COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting

abstract: COCO is a platform for Comparing Continuous Optimizers in a black-box setting. It aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. We present the rationals behind the development of the platform as a general proposition for a guideline towards better benchmarking. We detail underlying fundamental concepts of COCO such as its definition of a problem, the idea of instances, the relevance of target values, and runtime as central performance measure. Finally, we give a quick overview of the basic code structure and the available test suites.