title: Applications of Image Processing in Agriculture : A Survey

abstract: Image processing has been proved to be effective tool for analysis in various fields and applications. Agriculture sector where the parameters like canopy, yield, quality of product were the important measures from the farmers’ point of view. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. The analysis of the parameters has proved to be accurate and less time consuming as compared to traditional methods. Application of image processing can improve decision making for vegetation measurement, irrigation, fruit sorting, etc.


title: Deep mutational scanning: a new style of protein science

abstract: Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.


title: Smartphone spectrophotometer for point-of-care diagnostics in low-resource settings

abstract: Colorimetry based point-of-care diagnostics provide an inexpensive technique for healthcare and environmental monitoring during disaster relief operations. Conventional equipments for measuring colorimetric assays are bulky and expensive, making them unsuitable for usage in low-resource settings. This paper proposes a novel approach for taking colorimetric measurements using a miniaturized spectrophotometer prototype attached to a smartphone. The proposed frugal design is portable, easy-to-use and costs less than US$15. High dynamic range imaging is used to process the spectrums captured by smartphone camera. The proposed algorithm supports JPEG image format, allowing the prototype to be used with a wide variety of smartphone models. In the experimental study, spectrums of color dye and milk samples are imaged. Spectrums obtained are found to be well correlated with the sample color and concentration. By providing accurate and fast measurements, smartphone spectrophotometry enables effective application of colorimetric diagnostic techniques in low-resource settings.


title: Online optimal control for VTOL aircraft system based on DHP algorithm

abstract: It is well known that vertical take-off and landing (VTOL) aircraft system is a complex multi-variable nonlinear system with large disturbances. In order to control the VTOL aircraft system efficiently, it is necessary to explore new optimal control methods to stabilize the system states. In this paper, we apply dual heuristic programming (DHP) to design the optimal controller for VTOL aircraft system. As a framework of approximate dynamic programming (ADP) algorithm, DHP consists of three modules: model, critic and actor. The critic module output partial derivatives of cost function with respect to state. It has many advantages such as good dynamics, fast convergence rate, and high control resolution. Simulation and experimental results demonstrate the effectiveness of optimal controller for VTOL aircraft system based on the DHP algorithm.


title: The Forensics Edge Management System: A Concept and Design

abstract: This paper describes the design of the Forensics Edge Management System (FEMS), a system that autonomously provides security and forensic services within the home Internet of Things (IoT) or smart home context. Within smart homes, users are increasingly being allowed the flexibility to manage and maintain all the solutions that entail. This is evident from the growing number of commercial smart home IoT solutions which are being designed to be manageable by end users. This IoT requirement for user-manageable solutions (without direct or indirect input from vendors beyond the provision of robust systems and solutions) presents a challenge to the traditional concept of Digital Forensics (DF) which is currently an expert-led domain. The FEMS design aims to meet these requirements for autonomy and independence, it is a system that can be integrated into a home-IoT network to conduct preliminary forensic investigations and to provide basic security services.


Diagnosis of Heart Disease Using Data Mining Algorithm


title: Adaptive Indexing over Encrypted Numeric Data

abstract: Today, outsourcing query processing tasks to remote cloud servers becomes a viable option; such outsourcing calls for encrypting data stored at the server so as to render it secure against eavesdropping adversaries and/or an honest-but-curious server itself. At the same time, to be efficiently managed, outsourced data should be indexed, and even adaptively so, as a side-effect of query processing. Computationally heavy encryption schemes render such outsourcing unattractive; an alternative, Order-Preserving Encryption Scheme (OPES), intentionally preserves and reveals the order in the data, hence is unattractive from the security viewpoint. In this paper, we propose and analyze a scheme for lightweight and indexable encryption, based on linear-algebra operations. Our scheme provides higher security than OPES and allows for range and point queries to be efficiently evaluated over encrypted numeric data, with decryption performed at the client side. We implement a prototype that performs incremental, query-triggered adaptive indexing over encrypted numeric data based on this scheme, without leaking order information in advance, and without prohibitive overhead, as our extensive experimental study demonstrates.


title: Heart Sound Measurement and Analysis System with Digital Stethoscope

abstract: This paper is concerned with the cardiac sound measurement and analysis system for in-home use of heart abnormality monitoring. The heart sound acquiring system is consisted of a traditional chest-piece, earphone, microph- one, IC recorder. The recorded data is transmitted to a computer by USB interface for analysis based on cardiac sound characteristic waveform method (CSCW), which is extracted from an analytical model of single degree-of-freedom(SDOF). Furthermore, the characteristic parameters (T1, T2, T11, T12) are defined by the time intervals between the crossed points of the CSCW and a threshold value (THV), which are related to the first sound and the second sound and are used for discriminating normal and abnormal heart sounds. Also, an easy-understanding graphical representation for these parameters is considered, so that, even for an inexperienced user he or she is able to monitor his or her pathology progress easily. Finally, a case study on the abnormal/normal cardiac sounds is demonstrated to validate the usefulness and efficiency of this proposed system and the cardiac sound characteristic waveform method.


title: Joinpoint Inference from Behavioral Specification to Implementation

abstract: Aspect-Oriented Programming languages allow pointcut descriptors to quantify over the implementation points of a system. Such pointcuts are problematic with respect to independent development because they introduce strong mutual coupling between base modules and aspects. This paper introduces a new joinpoint selection mechanism based on state machine specifications. Module interfaces include behavioral specifications defined as protocol state machines. These specifications are not defined with respect to potential aspects, but are used to model and simulate the architecture of a system and act as behavioral contracts between the modules of the system. We show how a smart joinpoint selection mechanism is able to infer points that might be located deep inside the implementation of a module, given pointcuts that are expressed entirely in terms of behavioral specification elements. We present a tool, the Motorola WEAVR, which implements this technique in a Model-Driven Engineering environment.


title: User Interface Development for Interactive Television: Extending a Commercial DTV Platform to the Virtual Channel API

abstract: Abstract We explore the generation of interactive computer graphics at digital set-top boxes in place of the fixed graphics that were embedded to the television video before the broadcast. This direction raises new requirements for user interface development, since the graphics are merged with video at each set-top box dynamically, without the traditional quality control from the television producers. Besides the technical issues, interactive computer graphics for television should be evaluated by television viewers. We employ an animated character in an interactive music television application that was evaluated by consumers, and was developed using the Virtual Channel Control Library, a custom high-level API, that was built using Microsoft Windows and TV technologies.


title: A Photometrically Calibrated Benchmark For Monocular Visual Odometry

abstract: We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods. It contains 50 real-world sequences comprising more than 100 minutes of video, recorded across dozens of different environments -- ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position. This allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated. We provide exposure times for each frame as reported by the sensor, the camera response function, and dense lens attenuation factors. We also propose a novel, simple approach to non-parametric vignette calibration, which requires minimal set-up and is easy to reproduce. Finally, we thoroughly evaluate two existing methods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect of image resolution, camera field of view, and the camera motion direction.


title: SALMA: Standard Arabic Language Morphological Analysis

abstract: Morphological analyzers are preprocessors for text analysis. Many Text Analytics applications need them to perform their tasks. This paper reviews the SALMA-Tools (Standard Arabic Language Morphological Analysis) [1]. The SALMA-Tools is a collection of open-source standards, tools and resources that widen the scope of Arabic word structure analysis - particularly morphological analysis, to process Arabic text corpora of different domains, formats and genres, of both vowelized and non-vowelized text. Tag-assignment is significantly more complex for Arabic than for many languages. The morphological analyzer should add the appropriate linguistic information to each part or morpheme of the word (proclitic, prefix, stem, suffix and enclitic); in effect, instead of a tag for a word, we need a subtag for each part. Very fine-grained distinctions may cause problems for automatic morphosyntactic analysis - particularly probabilistic taggers which require training data, if some words can change grammatical tag depending on function and context; on the other hand, fine-grained distinctions may actually help to disambiguate other words in the local context. The SALMA - Tagger is a fine grained morphological analyzer which is mainly depends on linguistic information extracted from traditional Arabic grammar books and prior-knowledge broad-coverage lexical resources; the SALMA - ABCLexicon. More fine-grained tag sets may be more appropriate for some tasks. The SALMA - Tag Set is a standard tag set for encoding, which captures long-established traditional fine-grained morphological features of Arabic, in a notation format intended to be compact yet transparent.


Native Language Detection Using the I-Vector Framework


title: Development of a capacitive-type 6-axis force-torque sensor

abstract: Force sensing is a crucial task for robots, especially when end effectors such as fingers and hands need to interact with unknown environments; to sense such forces, a force-torque (F/T) sensor is an essential component. In this paper, we propose a small-sized 6-axis F/T sensor with a novel arrangement of 12 transducers using the force transducer we have previously developed. The copper beryllium used in our sensor reduces hysteresis in each transducer. Additionally, the sensor provides digital output via I2C bus to reduce the susceptibility to noise, and reduce the number of required wires. Sensor characteristics such as its sensitivity, signal-to-noise ratio, linearity, and hysteresis are determined. More importantly, we showed that our sensor can detect and measure the 6-axis F/T.


title: Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations

abstract: We introduce the concept of numerical Gaussian processes, which we define as Gaussian processes with covariance functions resulting from temporal discretization of time-dependent partial differential equations. Numerical Gaussian processes, by construction, are designed to deal with cases where: (1) all we observe are noisy data on black-box initial conditions, and (2) we are interested in quantifying the uncertainty associated with such noisy data in our solutions to time-dependent partial differential equations. Our method circumvents the need for spatial discretization of the differential operators by proper placement of Gaussian process priors. This is an attempt to construct structured and data-efficient learning machines, which are explicitly informed by the underlying physics that possibly generated the observed data. The effectiveness of the proposed approach is demonstrated through several benchmark problems involving linear and nonlinear time-dependent operators. In all examples, we are able to recover accurate approximations of the latent solutions, and consistently propagate uncertainty, even in cases involving very long time integration.


title: A Psychotherapist Retires

abstract: Abstract This article reflects on a year of my personal experience as I prepared to retire from my psychotherapy practice of 40 years. While aware that this might be a poignant experience for bothmyself and my clients, my own surprising emotions and dreams demanded that I pay attention to myself. By acknowledging my feelings I was able to direct sensitive attention to the clients' feelings of loss and sadness. Finding the balance between the sadness that filled the therapy room and my own enthusiasm for what awaited me outside that room, was not a simple task


title: Scapular dyskinesis and its relation to shoulder injury.

abstract: The scapula plays a key role in nearly every aspect of normal shoulder function. Scapular dyskinesis-altered scapular positioning and motion-is found in association with most shoulder injuries. Basic science and clinical research findings have led to the identification of normal three-dimensional scapular kinematics in scapulohumeral rhythm and to abnormal kinematics in shoulder injury, the development of clinical methods of evaluating the scapula (eg, scapular assistance test, scapular retraction test), and the formulation of rehabilitation guidelines. Primary scapular presentations such as scapular winging and snapping should be managed with a protocol that is focused on the scapula. Persons with associated conditions such as shoulder impingement, rotator cuff disease, labral injury, clavicle fracture, acromioclavicular joint injury, and multidirectional instability should be evaluated for scapular dyskinesis and treated accordingly.


title: Practical Application-Level Dynamic Taint Analysis of Android Apps

abstract: Dynamic taint analysis traces data flows in applications at runtime and allows detection and consequently prevention of flow-based vulnerabilities, such as data leaks or injection attacks. While dynamic taint analysis spanning all components of the stack is potentially more precise, it requires adaptations of components across the OS stack and thus does not allow to analyze applications in their real runtime environment. In this paper, we introduce a dynamic taint analysis framework for Android applications which injects a taint analysis directly into an application's bytecode and can thus operate on any stock Android platform. Our approach is more precise than previous ones, copes with flow-aware source and sink definitions, and propagates data flows across process boundaries, including propagation over file I/O and inter process communication. We explain how our framework performs with popular apps from the Google Play Store and show that it achieves a precision which is comparable to the most precise platform-level tainting framework.


title: Future Trends of Data Mining in Predicting the Various Diseases in Medical Healthcare System

abstract: The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Disease diagnosis is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. It has also been focused on current research being carried out using the data mining techniques to enhance the disease(s) forecasting process. This research paper provides future trends of current techniques of KDD, using data mining tools for healthcare. It also confers significant issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths. The root causes of all diseases get closer towards drugs i.e. the foremost risk factor of all hilarious diseases. Drug addiction using WEKA has been used that brings into light concerning majority of drug abusers started abusing drugs at age below 20yrs.It is to make aware the druggist about the various diseases that are caused with heavy or long term intake of drugs in their life. So, to make an expert system that will awake the youth about precarious use of drugs and also alert the affected person.


title: THE WISDOM DEVELOPMENT SCALE : FURTHER VALIDITY INVESTIGATIONS

abstract: Researchers are gaining an interest in the concept of wisdom, a more holistic yet often ineffable educational outcome. Models of wisdom abound, but few have rigorously tested measures. This study looks at Brown's (2004a, 2004b) Model of Wisdom Development and its associated measure, the Wisdom Development Scale (WDS; Brown & Greene, 2006). The construct validity, measurement invariance, criterion validity, and reliability of scores from the WDS were assessed with over 3000 participants from two separate groups: one a sample of professionals and the other a sample of college students. Support for construct validity and reliability with these samples was found, along with measurement invariance. Latent means analyses showed predicted discrimination between the groups, and criterion validity evidence, with another measure of collegiate educational outcomes, was found.


The relationship between mobile phone location sensor data and depressive symptom severity


title: Resource Constrained Offloading in Fog Computing

abstract: When focusing on the Internet of Things (IoT), communicating and coordinating sensor--actuator data via the cloud involves inefficient overheads and reduces autonomous behavior. The Fog Computing paradigm essentially moves the compute nodes closer to sensing entities by exploiting peers and intermediary network devices. This reduces centralized communication with the cloud and entails increased coordination between sensing entities and (possibly available) smart network gateway devices. In this paper, we analyze the utility of offloading computation among peers when working in fog based deployments. It is important to study the trade-offs involved with such computation offloading, as we deal with resource (energy, computation capacity) limited devices. Devices computing in a distributed environment may choose to locally compute part of their data and communicate the remainder to their peers. An optimization formulation is presented that is applied to various deployment scenarios, taking the computation and communication overheads into account. Our technique is demonstrated on a network of robotic sensor--actuators developed on the ROS (Robot Operating System) platform, that coordinate over the fog to complete a task. We demonstrate 77.8% latency and 54% battery usage improvements over large computation tasks, by applying this optimal offloading.


title: College student perceptions of teaching and learning quality

abstract: Numerous instructional design models have been proposed over the past sev- eral decades. Instead of focusing on the design process (means), this study investigated how learners perceived the quality of instruction they experienced (ends). An electronic survey instrument containing nine a priori scales was developed. Students responded from 89 different undergraduate and graduate courses at multiple institutions (n = 140). Data analysis indicated strong correlations between student self-reports on academic learning time, how much they learned, First Principles of Instruction, their satisfaction with the course, perceptions of their mastery of course objectives, and global course ratings. Most importantly, these scales measure principles with which instructional developers and teachers can evaluate their products and courses, regardless of design processes used: provide authentic tasks for students to do; activate prior learning; demonstrate what is to be learned; provide repeated opportunities for students to successfully complete authentic tasks with coaching and feedback; and help students integrate what they have learned into their personal lives.


title: Similarity Learning with Higher-Order Proximity for Brain Network Analysis

abstract: In recent years, the similarity learning problem has been widely studied. Most of the existing works focus on images and few of these works could be applied to learn similarity between neuroimages, such as fMRI images and DTI images, which are important data sources for human brain analysis. In this paper, we focus on the similarity learning for fMRI brain network analysis. We propose a general framework called"Multi-hop Siamese GCN"for similarity learning on graphs. This framework provides options for refining the graph representations with high-order structure information, thus can be used for graph similarity learning on various brain network data sets. We apply the proposed Multi-hop Siamese GCN approach on four real fMRI brain network datasets for similarity learning with respect to brain health status and cognitive abilities. Our proposed method achieves an average AUC gain of 82.6% compared to PCA, and an average AUC gain of 42% compared to S-GCN across a variety of datasets, indicating its promising learning ability for clinical investigation and brain disease diagnosis.


title: QUOTA: The Quantile Option Architecture for Reinforcement Learning

abstract: In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making is based on quantiles of a value distribution, not only the mean. QUOTA provides a new dimension for exploration via making use of both optimism and pessimism of a value distribution. We demonstrate the performance advantage of QUOTA in both challenging video games and physical robot simulators.


title: Reputation and Impact in Academic Careers

abstract: Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here we develop an original framework for measuring how a publication's citation rate $\Delta c$ depends on the reputation of its central author $i$, in addition to its net citation count $c$. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly-cited scientists, using the total citations $C_{i}$ of each scientist as his/her reputation measure. We find a citation crossover $c_{\times}$ which distinguishes the strength of the reputation effect. For publications with $c<c_{\times}$, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in $C_{i}$. However, the reputation effect becomes negligible for highly cited publications meaning that for $c\geq c_{\times}$ the citation rate measures scientific impact more transparently. In addition we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.


title: Frustration-Aggression Hypothesis: Examination and Reformulation Leonard Berkowitz

abstract: Examines the Dollard et al. (1939) frustration-aggression hypothesis. The original formulation's main proposition is limited to interference with an expected attainment of a desired goal on hostile (emotional) aggression. Although some studies have yielded negative results, others support the core proposition. Frustrations can create aggressive inclinations even when they are not arbitrary or aimed at the subject personally. Interpretations and attributions can be understood partly in terms of the original analysis but they can also influence the unpleasantness of the thwarting. A proposed revision of the 1939 model holds that frustrations generate aggressive inclinations to the degree that they arouse negative affect. Evidence regarding the aggressive consequences of aversive events is reviewed, and Berkowitz's cognitive-neoassociationistic model is summarized. In 1939, researchers at the Yale University Institute of Human Relations published a small monograph that has had a tremendous impact, directly or indirectly, on almost all of the behavioral sciences. Led by,John Dollard, Leonard Doob, Neal Miller, O. H. Mowrer, and Robert Sears (1939), the group attempted to account for virtually all of human aggression with a few basic ideas. Their book, Frustration and Aggression, quickly attracted considerable attention. Seven articles in one 1941 issue of Psychological Review were devoted to the controversy generated by the monograph, and excerpts from these papers as well as from other related articles were reprinted in a major section of the classic Readings in Social Psychology (Newcomb & Hartley, 1947). Most of the studies investigating the causes and consequences of aggression in the immediately following decades were oriented, to some extent at least, toward issues raised by the Yale group's analysis (see Berkowitz, 1958, 1962; Buss, 1961).


title: Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines

abstract: In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection


title: Tumor detection using threshold operation in MRI brain images

abstract: Medical Image Processing is a complex and challenging field nowadays. Processing of MRI images is one of the parts of this field. This paper proposes a strategy for efficient detection of a brain tumor in MRI brain images. The methodology consists of the following steps: preprocessing by using sharpening and median filters, enhancement of image is performed by histogram equalization, segmentation of the image is performed by thresholding. This approach is then followed by the further application of morphological operations. Finally the tumor region can be obtained by using the technique of image subtraction.


title: Online fuzzy c means

abstract: Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present an online fuzzy clustering algorithm which can be used to cluster streaming data, as well as very large data sets which might be treated as streaming data. Results on several large volumes of magnetic resonance images show that the new algorithm produces partitions which are very close to what you could get if you clustered all the data at one time. So, the algorithm is an accurate approach for online clustering.


title: Monarch butterfly optimization

abstract: In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub ( ::: https://github.com/ggw0122/Monarch-Butterfly-Optimization ::: , C++/MATLAB) and MATLAB Central ( ::: http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization ::: , MATLAB).


title: Joint TDOA and FDOA Estimation: A Conditional Bound and Its Use for Optimally Weighted Localization

abstract: Modern passive emitter-location systems are often based on joint estimation of the time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) of an unknown signal at two (or more) sensors. Classical derivation of the associated Cramer-Rao bound (CRB) relies on a stochastic, stationary Gaussian signal-model, leading to a diagonal Fisher information matrix with respect to the TDOA and FDOA. This diagonality implies that (under asymptotic conditions) the respective estimation errors are uncorrelated. However, for some specific (nonstationary, non-Gaussian) signals, especially chirp-like signals, these errors can be strongly correlated. In this work we derive a “conditional” (or a “signal-specific”) CRB, modeling the signal as a deterministic unknown. Given any particular signal, our CRB reflects the possible signal-induced correlation between the TDOA and FDOA estimates. In addition to its theoretical value, we show that the resulting CRB can be used for optimal weighting of TDOA-FDOA pairs estimated over different signal-intervals, when combined for estimating the target location. Substantial improvement in the resulting localization accuracy is shown to be attainable by such weighting in a simulated operational scenario with some chirp-like target signals.


title: The Associative–Propositional Evaluation Model

abstract: Abstract A central theme in contemporary psychology is the distinction between implicit and explicit evaluations. Research has shown various dissociations between the two kinds of evaluations, including different antecedents, different consequences, and discrepant evaluations of the same object. The associative–propositional evaluation (APE) model accounts for these dissociations by conceptualizing implicit and explicit evaluations as the outcomes of two qualitatively distinct processes. Whereas implicit evaluations are described as the outcome of associative processes, explicit evaluations represent the outcome of propositional processes. Associative processes are further specified as the activation of mental associations on the basis of feature similarity and spatiotemporal contiguity; propositional processes are defined as the validation of activated information on the basis of logical consistency. The APE model includes specific assumptions about the mutual interplay between associative and propositional processes, implying a wide range of predictions about symmetric and asymmetric changes in implicit and explicit evaluations. The current chapter reviews the conceptual and empirical assumptions of the APE model and evidence in support of its predictions. In addition, we discuss conceptual and empirical challenges for the APE model and various directions for future research on implicit and explicit evaluation.


title: Frankenstack: Toward real-time Red Team feedback

abstract: Cyber Defense Exercises have received much attention in recent years, and are increasingly becoming the cornerstone for ensuring readiness in this new domain. Crossed Swords is an exercise directed at training Red Team members for responsive cyber defense. However, prior iterations have revealed the need for automated and transparent real-time feedback systems to help participants improve their techniques and understand technical challenges. Feedback was too slow and players did not understand the visibility of their actions. We developed a novel and modular open-source framework to address this problem, dubbed Frankenstack. We used this framework during Crossed Swords 2017 execution and evaluated its effectiveness by interviewing participants and conducting an online survey. Due to the novelty of Red Team-centric exercises, very little academic research exists on providing real-time feedback during such exercises. Thus, this paper serves as a first foray into a novel research field.


title: Neural regulation of endocrine and autonomic stress responses

abstract: The survival and well-being of all species requires appropriate physiological responses to environmental and homeostatic challenges. The re- establishment and maintenance of homeostasis entails the coordinated activation and control of neuroendocrine and autonomic stress systems. These collective stress responses are mediated by largely overlapping circuits in the limbic forebrain, the hypothalamus and the brainstem, so that the respective contributions of the neuroendocrine and autonomic systems are tuned in accordance with stressor modality and intensity. Limbic regions that are responsible for regulating stress responses intersect with circuits that are responsible for memory and reward, providing a means to tailor the stress response with respect to prior experience and anticipated outcomes.


title: Impact of dietary fiber intake on glycemic control, cardiovascular risk factors and chronic kidney disease in Japanese patients with type 2 diabetes mellitus: the Fukuoka Diabetes Registry

abstract: Dietary fiber is beneficial for the treatment of type 2 diabetes mellitus, although it is consumed differently in ethnic foods around the world. We investigated the association between dietary fiber intake and obesity, glycemic control, cardiovascular risk factors and chronic kidney disease in Japanese type 2 diabetic patients. A total of 4,399 patients were assessed for dietary fiber intake using a brief self-administered diet history questionnaire. The associations between dietary fiber intake and various cardiovascular risk factors were investigated cross-sectionally. Body mass index, fasting plasma glucose, HbA1c, triglyceride and high-sensitivity C-reactive protein negatively associated with dietary fiber intake after adjusting for age, sex, duration of diabetes, current smoking, current drinking, total energy intake, fat intake, saturated fatty acid intake, leisure-time physical activity and use of oral hypoglycemic agents or insulin. The homeostasis model assessment insulin sensitivity and HDL cholesterol positively associated with dietary fiber intake. Dietary fiber intake was associated with reduced prevalence of abdominal obesity, hypertension and metabolic syndrome after multivariate adjustments including obesity. Furthermore, dietary fiber intake was associated with lower prevalence of albuminuria, low estimated glomerular filtration rate and chronic kidney disease after multivariate adjustments including protein intake. Additional adjustments for obesity, hypertension or metabolic syndrome did not change these associations. We demonstrated that increased dietary fiber intake was associated with better glycemic control and more favorable cardiovascular disease risk factors including chronic kidney disease in Japanese type 2 diabetic patients. Diabetic patients should be encouraged to consume more dietary fiber in daily life.


title: Six degree-of-freedom haptic rendering using voxel sampling

abstract: A simple, fast, and approximate voxel-based approach to 6-DOF haptic rendering is presented. It can reliably sustain a 1000 Hz haptic refresh rate without resorting to asynchronous physics and haptic rendering loops. It enables the manipulation of a modestly complex rigid object within an arbitrarily complex environment of static rigid objects. It renders a short-range force field surrounding the static objects, which repels the manipulated object and strives to maintain a voxel-scale minimum separation distance that is known to preclude exact surface interpenetration. Force discontinuities arising from the use of a simple penalty force model are mitigated by a dynamic simulation based on virtual coupling. A generalization of octree improves voxel memory efficiency. In a preliminary implementation, a commercially available 6-DOF haptic prototype device is driven at a constant 1000 Hz haptic refresh rate from one dedicated haptic processor, with a separate processor for graphics. This system yields stable and convincing force feedback for a wide range of user controlled motion inside a large, complex virtual environment, with very few surface interpenetration events. This level of performance appears suited to applications such as certain maintenance and assembly task simulations that can tolerate voxel-scale minimum separation distances.


title: RIABLO: a game system for supporting orthopedic rehabilitation

abstract: In this paper we present a complete game system specifically designed for orthopedic rehabilitation. The system is focused in supporting patients both in the hospital and following them up during their home-based rehabilitation. RIABLO provides the patients with the tools for independently executing the therapy exercises in a correct and pleasure way thus increasing the quality of the whole rehabilitation process. The potential of games in the domain of orthopedic physical rehabilitation is analyzed, challenges discussed and the lessons learnt provided.


title: Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations

abstract: We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space. In this work, we evaluate the utility of these topology-based document representations in traditional NLP tasks, specifically document clustering and sentiment classification. We find that the embeddings do not benefit text analysis. In fact, performance is worse than simple techniques like $\textit{tf-idf}$, indicating that the geometry of the document does not provide enough variability for classification on the basis of topic or sentiment in the chosen datasets.


title: Low-Profile, Quasi-Omnidirectional Substrate Integrated Waveguide (SIW) Multihorn Antenna

abstract: A low-profile, quasi-omnidirectional substrate integrated waveguide (SIW) multihorn antenna is proposed for small nonmetallic unmanned aerial vehicle (UAV) applications. The proposed antenna consists of four H-plane horn antennas and four matching vias. The four H-plane horn antennas are integrated in a single rectangular substrate using the SIW technology and are located concentrically toward the four edges of the substrate. Four shorting vias are designed to match the horn antennas without increasing the antenna size. The proposed antenna is fed by a probe at the center of the substrate. The antenna is vertically polarized and has a very low profile ( $0.028{\lambda _0}$ ). The radiation pattern of the antenna is quasi-omnidirectional on the horizontal plane. A prototype with a dimension of $95.5 \times 95.5 \times 3.5~\hbox{mm}^2$ ( $0.76{\lambda _0} \times 0.76{\lambda _0} \times 0.028{\lambda _0}$ ) is designed, fabricated, and measured. The antenna achieves an impedance bandwidth of 2.1% from 2.39 to 2.44 GHz. The antenna does not need a ground plane. Thus, it is a good candidate for small nonmetallic UAV applications.


title: Big data analytics for healthcare

abstract: Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. In this tutorial, we introduce the characteristics and related mining challenges on dealing with big medical data. Many of those insights come from medical informatics community, which is highly related to data mining but focuses on biomedical specifics. We survey various related papers from data mining venues as well as medical informatics venues to share with the audiences key problems and trends in healthcare analytics research, with different applications ranging from clinical text mining, predictive modeling, survival analysis, patient similarity, genetic data analysis, and public health. The tutorial will include several case studies dealing with some of the important healthcare applications.


title: Two-stage DEA: An application to major Brazilian banks

abstract: Although banking has been widely studied using standard DEA (Data Envelopment Analysis) models and its variations, these models do not in fact account for the internal structure relative to measures characterizing banking operations performance. In this paper, efficiency in Brazilian banking is measured using a two-stage process. In the first stage, called cost efficiency, number of branches and employees are used to attain a certain level of administrative and personnel expenses per year. In the second stage, called productive efficiency, these expenses allow the consecution of two important net outputs: equity and permanent assets. The network-DEA centralized efficiency model is adopted here to optimize both stages simultaneously. Results indicate that Brazilian banks are heterogeneous, with some focusing on cost efficiency and others on productive efficiency. Furthermore, cost efficiency is explained by M&A and size, while productive efficiency is explained by M&A and public status. Policy implications for the Brazilian banking sector are also derived.


title: Microstrip series fed antenna array for millimeter wave automotive radar applications

abstract: A 2D tapered microstrip antenna array is analyzed and designed in the paper by adopting the method of Modified Transmission Line Model. The pattern of the E-plane is determined by 20 linear series fed rectangular microstrip patches with tapered width, and 16 linear arrays compose the 2D array fed by four-stage T-junction power divider network. The excitation coefficients in both E and H plane follow the Taylor distribution. The antenna array achieves a pencil beam of 29dB gain at W band, and its half power beamwidths(HPBW) for E plane and H plane are both about 5°. The Side Lobe Level (SLL) of the pattern at the center frequency is lower than −17dB and 19dB at E and H plane respectively.


title: An Integrated Bayesian Approach for Effective Multi-Truth Discovery

abstract: Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.


title: An Eight-Way Radial Switch Based on SIW Power Divider

abstract: This paper presents a single-pole eight-throw switch, based on an eight-way power divider, using substrate integrate waveguide(SIW) technology. Eight sectorial-lines are formed by inserting radial slot-lines on the top plate of SIW power divider. Each sectorial-line can be controlled independently with high level of isolation. The switching is accomplished by altering the capacitance of the varactor on the line, which causes different input impedances to be seen at a central probe to each sectorial line. The proposed structure works as a switching circuit and an eight-way power divider depending on the bias condition. The change in resonant frequency and input impedance are estimated by adapting a tapered transmission line model. The detailed design, fabrication, and measurement are discussed.


title: A new ultrawideband printed monopole antenna: the planar inverted cone antenna (PICA)

abstract: A new antenna, the planar inverted cone antenna (PICA), provides ultrawideband (UWB) performance with a radiation pattern similar to monopole disk antennas , but is smaller in size. Extensive simulations and experiments demonstrate that the PICA antenna provides more than a 10:1 impedance bandwidth (for VSWR<2) and supports a monopole type omnidirectional pattern over 4:1 bandwidth. A second version of the PICA with two circular holes changes the current flow on the metal disk and extends the high end of the operating frequency range, improving the pattern bandwidth to 7:1.


title: Efficient activity detection with max-subgraph search

abstract: We propose an efficient approach that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem over a learned space-time graph constructed on the test sequence. We show this permits an efficient branch-and-cut solution for the best-scoring — and possibly non-cubically shaped — portion of the video for a given activity classifier. The upshot is a fast method that can evaluate a broader space of candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on three datasets, and show its speed and accuracy advantages over multiple existing search strategies.


title: Language identification of short text segments with n-gram models

abstract: There are many accurate methods for language identification of long text samples, but identification of very short strings still presents a challenge. This paper studies a language identification task, in which the test samples have only 5–21 characters. We compare two distinct methods that are well suited for this task: a naive Bayes classifier based on character n-gram models, and the ranking method by Cavnar and Trenkle (1994). For the n-gram models, we test several standard smoothing techniques, including the current state-of-theart, the modified Kneser-Ney interpolation. Experiments are conducted with 281 languages using the Universal Declaration of Human Rights. Advanced language model smoothing techniques improve the identification accuracy and the respective classifiers outperform the ranking method. The higher accuracy is obtained at the cost of larger models and slower classification speed. However, there are several methods to reduce the size of an n-gram model, and our experiments with model pruning show that it provides an easy way to balance the size and the identification accuracy. We also compare the results to the language identifier in Google AJAX Language API, using a subset of 50 languages.


title: Step-by-step design and control of LCL filter based three phase grid-connected inverter

abstract: This paper proposes a detailed step-by-step design procedure and control of an LCL filter for grid connected three phase sine PWM voltage source inverter. The goal of the design is to ensure high quality of grid current as well as to minimize the size of filter magnetics. In order to ensure unity power factor injection into grid a current controller is designed with a constraint that only the inverter side inductor current is sensed. Good agreement between simulation and experimental results verify the effectiveness of the designed controller and the LCL filter.


title: A prediction method of network traffic using time series models

abstract: This paper describes a method to derive an appropriate prediction model for network traffic and verify its trustfulness. The proposal is not only an analysis of network packets but also finding a prediction method for the number of packets. We use time series prediction models and evaluate whether the model can predict network traffic exactly or not. In order to predict network packets in a certain time, the AR, MA, ARMA, and ARIMA model are applied. Our purpose is to find the most suitable model which can express the nature of future traffic among these models. We evaluate whether the models satisfy the stationary assumption for network traffic. The stationary assumption is obtained by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function) using a suitable significance. As the result, when network traffic is classified on a daily basis, the AR model is a good method to predict network packets exactly. The proposed prediction method can be used on a routing protocol as a decision factor for managing traffic data dynamically in a network.


title: Transitions in Romantic Relationships and Development of Self-Esteem

abstract: Research suggests that self-esteem increases during late adolescence and young adulthood, but that there is large interindividual variability in this development. However, little is known about the factors accounting for these findings. Using propensity score matching, we tested whether important transitions in the domain of romantic relationships (i.e., beginning a relationship, marrying, and breaking up) explain why individuals differ in the particular self-esteem trajectory they follow. Data came from a longitudinal German study with a large sample of 3 nationally representative cohorts of late adolescents and young adults (total N = 9,069). The analyses were based on 4 assessments across a 3-year period. Using matched samples, the results showed that beginning a relationship increased self-esteem and that the increase persisted when the relationship held at least for 1 year. Experiencing a relationship break-up decreased self-esteem, but the effect disappeared after 1 year, even if the participant stayed single. Marrying did not influence self-esteem. Additionally, we tested for selection effects of self-esteem on the later occurrence of relationship transitions. High self-esteem predicted the beginning of a relationship and low self-esteem predicted relationship break-up. All findings held across gender, age, and migration background. Furthermore, relationship quality mediated the effect of self-esteem on relationship break-up and the effect of beginning a longer versus a short relationship on self-esteem. The findings have significant implications because they show that self-esteem influences whether important transitions occur in the relationship domain and that, in turn, experiencing these transitions influences the further development of self-esteem.


title: Differentiable Learning-to-Normalize via Switchable Normalization

abstract: We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig.1). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, and Kinetics. Analyses of SN are also presented. We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been made available in https://github.com/switchablenorms/.


title: On-Street Parking Guidance with Real-Time Sensing Data for Smart Cities

abstract: On-street parking is an essential component of parking infrastructure for smart cities, which allows users to park near their destinations for short term. However, due to limited capacity, saturated on-street parking becomes a serious and widespread problem for urban transportation systems. Greedily searching for an on-street parking spot in a saturated area is often a frustrating task for drivers, and cruising for vacant parking spots results in additional delays and impaired local circulation. With the recent development of networked smart parking meter, real-time city-wide on- street parking information becomes available for more efficient parking management. In this paper, we design an online parking guidance system that recommends parking spots in real-time based on the parking availability prediction. With a receding horizon optimization framework, our solution minimizes the user's driving and walking cost by adapting the spatiotemporally dynamic supply and demand in the local area, significantly reducing parking competitions in a timely manner. We implement and evaluate our solution with a dataset of 13,503,655 parking records collected from 5228 in-ground sensors distributed in the Australian city Melbourne. The evaluation results show that our approach achieves up to 63.8% delay reduction compared with existing solutions.


title: Sentiment analysis to predict election results using Python

abstract: Sentiment analysis is an evaluation of the opinion of the speaker, writer or other subject with regard to some topic. In US presidential election 2016, Donald Trump, Hillary Clinton and Bernie Sanders were among the top election candidates. The opinion of the public for a candidate will impact the potential leader of the country. Twitter is used to acquire a large diverse data set representing the current public opinions of the candidates. The collected tweets are analyzed using lexicon based approach to determine the sentiments of public. In this paper, we determine the polarity and subjectivity measures for the collected tweets that help in understanding the user opinion for a particular candidate. Further, a comparison is made among the candidates over the type of sentiment. Also, a word cloud is plotted representing most frequently appearing words in the tweets.


title: Stochastic synapse reinforcement learning (SSRL)

abstract: Over the past several decades, reinforcement learning has emerged as one of the major paradigms in machine learning because it allows an agent to learn through interaction with its environment, so long as there is some mechanism by which the agent can gain evaluative feedback on the effects of its actions. However, there are still many open questions as to the most appropriate reinforcement learning approach, particularly for difficult problems such as those dealing with delayed reward, unknown reward structures, continuous state and/or action spaces, perceptual aliasing, and/or environmental change. Here we present a new learning algorithm for these types of difficult problems. It combines the eligibility traces approach to reinforcement learning with artificial neural networks for generalization and pushes the idea of stochastic computations down to the level of the synapse. A proof-of-concept experiment in the domain of robotics demonstrates that the approach has promise.


title: Semantic-Based Surveillance Video Retrieval

abstract: Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene


title: A new multiphase buck converter with optimum-damping-control and current-balanced techniques

abstract: This paper presents a multiphase buck converter with optimum-damping-control and current-balanced techniques. The proposed optimum-damping-control and current-balanced techniques can achieve current balance to avoid output current flow in one phase. The phase controller is used to control multiphase buck converter. This configuration provides wide output current range from 400 mA to 4 A. It also has low output voltage and current ripple. The proposed buck converter has been fabricated in TSMC 0.35µm CMOS 2P4M technology, and the areas of the chips are 1.896 mm × 1.852 mm and 1.339 mm × 1.439 mm. The power efficiency can be up to 89.5%.


title: Input Sentence Splitting And Translating

abstract: We propose a method to split and translate input sentences for speech translation in order to overcome the long sentence problem. This approach is based on three criteria used to judge the goodness of translation results. The criteria utilize the output of an MT system only and assumes neither a particular language nor a particular MT approach. In an experiment with an EBMT system, in which prior methods cannot work or work badly, the proposed split-and-translate method achieves much better results in translation quality.


title: The Semantic Web Revisited

abstract: The article included many scenarios in which intelligent agents and bots undertook tasks on behalf of their human or corporate owners. Of course, shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks: they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years


title: Temporally coherent local tone mapping of HDR video

abstract: Recent subjective studies showed that current tone mapping operators either produce disturbing temporal artifacts, or are limited in their local contrast reproduction capability. We address both of these issues and present an HDR video tone mapping operator that can greatly reduce the input dynamic range, while at the same time preserving scene details without causing significant visual artifacts. To achieve this, we revisit the commonly used spatial base-detail layer decomposition and extend it to the temporal domain. We achieve high quality spatiotemporal edge-aware filtering efficiently by using a mathematically justified iterative approach that approximates a global solution. Comparison with the state-of-the-art, both qualitatively, and quantitatively through a controlled subjective experiment, clearly shows our method's advantages over previous work. We present local tone mapping results on challenging high resolution scenes with complex motion and varying illumination. We also demonstrate our method's capability of preserving scene details at user adjustable scales, and its advantages for low light video sequences with significant camera noise.


title: Proton++: a customizable declarative multitouch framework

abstract: Proton++ is a declarative multitouch framework that allows developers to describe multitouch gestures as regular expressions of touch event symbols. It builds on the Proton framework by allowing developers to incorporate custom touch attributes directly into the gesture description. These custom attributes increase the expressivity of the gestures, while preserving the benefits of Proton: automatic gesture matching, static analysis of conflict detection, and graphical gesture creation. We demonstrate Proton++'s flexibility with several examples: a direction attribute for describing trajectory, a pinch attribute for detecting when touches move towards one another, a touch area attribute for simulating pressure, an orientation attribute for selecting menu items, and a screen location attribute for simulating hand ID. We also use screen location to simulate user ID and enable simultaneous recognition of gestures by multiple users. In addition, we show how to incorporate timing into Proton++ gestures by reporting touch events at a regular time interval. Finally, we present a user study that suggests that users are roughly four times faster at interpreting gestures written using Proton++ than those written in procedural event-handling code commonly used today.


title: Energy Harvesting Wireless Communications: A Review of Recent Advances

abstract: This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.


title: Multitask Learning: A Knowledge-Based Source of Inductive Bias

abstract: This paper suggests that it may be easier to learn several hard tasks at one time than to learn these same tasks separately. In effect, the information provided by the training signal for each task serves as a domain-specific inductive bias for the other tasks. Frequently the world gives us clusters of related tasks to learn. When it does not, it is often straightforward to create additional tasks. For many domains, acquiring inductive bias by collecting additional teaching signal may be more practical than the traditional approach of codifying domain-specific biases acquired from human expertise. We call this approach Multitask Learning (MTL). Since much of the power of an inductive learner follows directly from its inductive bias, multitask learning may yield more powerful learning. An empirical example of multitask connectionist learning is presented where learning improves by training one network on several related tasks at the same time. Multitask decision tree induction is also outlined.


title: Affective-feature-based sentiment analysis using SVM classifier

abstract: Based on the methods of the traditional topic-based text classification, machine learning method was performed to the coarse-grained sentiment classification of reviews. Sentiment classification involved a lot of problems. In this paper, the sentiment Vector Space Model (s-VSM) was used for text representation to solve data sparseness. In addition, the critical issues of the sentiment classification, i.e. the selection of classification algorithms, the determination of feature selection method and the selection of feature dimension, are verified by experiments. Furthermore, in order to consider the entire corpus contribution of features and each category contribution of features, the feature selection method of Chi-square Difference between the Positive and Negative Categories (CDPNC) was proposed. It combined DF with CHI and had the better performance. Experiments showed that the Macro-F and Micro-F achieved 90.18% and 90.08% respectively.


title: Compact Quarter-Wave Resonator and Its Applications to Miniaturized Diplexer and Triplexer

abstract: Novel and compact composite right/left-handed (CRLH) quarter-wave type resonators are proposed in this paper. The resonator can resonate at the frequency where the electrical length is phase-leading or negative, which results in a smaller size as compared to the conventional phase-delayed microstrip-line resonator. Furthermore, it is only half the size of the CRLH half-wave resonator resonating at the same frequency. In addition, the proposed resonator is capable of engineering the multiresonances very close to each other, which makes it suitable to implement the miniaturized multiband microwave components such as diplexers and triplexers. A very compact diplexer and a very compact triplexer are proposed based on the proposed CRLH quarter-wave resonators in this paper and both of them have demonstrated very good performance. Specifically, compared to the referenced works based on the conventional microstrip resonators, the proposed diplexer and triplexer are 50% and 76% smaller than their microstrip counterparts, respectively.


title: Syntactic Parse Fusion

abstract: Model combination techniques have consistently shown state-of-the-art performance across multiple tasks, including syntactic parsing. However, they dramatically increase runtime and can be difficult to employ in practice. We demonstrate that applying constituency model combination techniques to n-best lists instead of n different parsers results in significant parsing accuracy improvements. Parses are weighted by their probabilities and combined using an adapted version of Sagae and Lavie (2006). These accuracy gains come with marginal computational costs and are obtained on top of existing parsing techniques such as discriminative reranking and self-training, resulting in state-of-the-art accuracy: 92.6% on WSJ section 23. On out-of-domain corpora, accuracy is improved by 0.4% on average. We empirically confirm that six well-known n-best parsers benefit from the proposed methods across six domains.


title: Sexting Among Young Adults

abstract: Purpose: Sexting has stirred debate over its legality and safety, but few researchers have documented the relationship between sexting and health. We describe the sexting behavior of young adults in the United States, and examine its association with sexual behavior and psychological well-being. Methods: Using an adapted Web version of respondent-driven sampling, we recruited a sample of U.S. young adults (aged 18 –24 years, N 3,447). We examined participant sexting behavior using four categories of sexting: (1) nonsexters, (2) receivers, (3) senders, and (4) two-way sexters. We then assessed the relationships between sexting categories and sociodemographic characteristics, sexual behavior, and psychological well-being. Results: More than half (57%) of the respondents were nonsexters, 28.2% were two-way sexters, 12.6% were receivers, and 2% were senders. Male respondents were more likely to be receivers than their female counterparts. Sexually active respondents were more likely to be two-way sexters than non–sexually active ones. Among participants who were sexually active in the past 30 days, we found no differences across sexting groups in the number of sexual partners or the number of unprotected sex partners in the past 30 days. We also found no relationship between sexting and psychological well-being. Conclusions: Our results suggest that sexting is not related to sexual risk behavior or psychological well-being. We discuss the findings of this study and propose directions for further research on sexting.


title: RegNet: Multimodal sensor registration using deep neural networks

abstract: In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28 degrees for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20 degrees.


Experimental Assessment of Private Information Disclosure in LTE Mobile Networks


title: Enabling tangible interaction on capacitive touch panels

abstract: We propose two approaches to sense tangible objects on capacitive touch screens, which are used in off-the-shelf multi-touch devices such as Apple iPad, iPhone, and 3M's multi-touch displays. We seek for the approaches that do not require modifications to the panels: spatial tag and frequency tag. Spatial tag is similar to fiducial tag used by tangible tabletop surface interaction, and uses multi-point, geometric patterns to encode object IDs. Frequency tag simulates high-frequency touches in the time domain to encode object IDs, using modulation circuits embedded inside tangible objects to simulate high-speed touches in varying frequency. We will show several demo applications. The first combines simultaneous tangible + touch input system. This explores how tangible inputs (e.g., pen, easer, etc.) and some simple gestures work together on capacitive touch panels.


title: Seamless Flow of Voluminous Data in High Speed Network without Congestion Using Feedback Mechanism

abstract: Continuously growing needs for Internet applications that transmit massive amount of data has led to the emergence of high speed network. Data transfer must take place without any congestion and hence feedback parameters must be transferred from the receiver end to the sender end so as to restrict the sending rate in order to avoid congestion. Even though TCP tries to avoid congestion by restricting the sending rate and window size, it never announces the sender about the capacity of the data to be sent and also it reduces the window size by half at the time of congestion therefore resulting in the decrease of throughput, low utilization of the bandwidth and maximum delay. In this paper, XCP protocol is used and feedback parameters are calculated based on arrival rate, service rate, traffic rate and queue size and hence the receiver informs the sender about the throughput, capacity of the data to be sent and window size adjustment, resulting in no drastic decrease in window size, better increase in sending rate because of which there is a continuous flow of data without congestion. Therefore as a result of this, there is a maximum increase in throughput, high utilization of the bandwidth and minimum delay. The result of the proposed work is presented as a graph based on throughput, delay and window size. Thus in this paper, XCP protocol is well illustrated and the various parameters are thoroughly analyzed and adequately presented.


title: Identification of machine parameters of a synchronous motor

abstract: This paper presents a method for estimating the machine parameters of a synchronous motor. The presented method is equally applicable for a wound field synchronous motor, synchronous reluctance motor, or permanent magnet (PM) synchronous motor, both the interior and surface-mount types. The method works particularly well for machines having a significant amount of space harmonics, such as synchronous reluctance and interior PM (IPM) machines, where the harmonics are predominantly the slot harmonics. It is also well suited for surface PM machines operating under saturation, where the harmonics are saturation-induced. The presence of these harmonics makes the parameter identification difficult. Most of the methods presented so far in the literature have failed to properly identify machine parameters in the presence of space harmonics. In this paper, the machine parameters, identified by using a proposed algorithm, are compared with the finite element and the experimental results to demonstrate the effectiveness of the presented method. Both interior PM and surface PM machines are considered.


title: Subject-oriented programming: a critique of pure objects

abstract: Object-Oriented technology is often described in terms of an interwoven troika of themes: encapsulation, polymorphism, and inheritance. But these themes are firmly tied with the concept of identity. If object-oriented technology is to be successfully scaled from the development of independent applications to development of integrated suites of applications, it must relax its emphasis on the objecf. The technology must recognize more directly that a multiplicity of subjective views delocalizes the concept of object, and must emphasize more the binding concept of identity to tie them together. This paper explores this shift to a style of objectoriented technology that emphasizes the subjective views: Subject-Oriented Programming.


title: A Deep Matrix Factorization Method for Learning Attribute Representations

abstract: Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies cannot interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.


title: Path Planning Strategies for UAVS in 3D Environments

abstract: The graph-search algorithms developed between 60s and 80s were widely used in many fields, from robotics to video games. The A* algorithm shall be mentioned between some of the most important solutions explicitly oriented to motion-robotics, improving the logic of graph search with heuristic principles inside the loop. Nevertheless, one of the most important drawbacks of the A* algorithm resides in the heading constraints connected with the grid characteristics. Different solutions were developed in the last years to cope with this problem, based on post-processing algorithms or on improvements of the graph-search algorithm itself. A very important one is Theta* that refines the graph search allowing to obtain paths with “any” heading. In the last two years, the Flight Mechanics Research Group of Politecnico di Torino studied and implemented different path planning algorithms. A Matlab based planning tool was developed, collecting four separate approaches: geometric predefined trajectories, manual waypoint definition, automatic waypoint distribution (i.e. optimizing camera payload capabilities) and a comprehensive A*-based algorithm used to generate paths, minimizing risk of collision with orographic obstacles. The tool named PCube exploits Digital Elevation Maps (DEMs) to assess the risk maps and it can be used to generate waypoint sequences for UAVs autopilots. In order to improve the A*-based algorithm, the solution is extended to tri-dimensional environments implementing a more effective graph search (based on Theta*). In this paper the application of basic Theta* to tri-dimensional path planning will be presented. Particularly, the algorithm is applied to orographic obstacles and in urban environments, to evaluate the solution for different kinds of obstacles. Finally, a comparison with the A* algorithm will be introduced as a metric of the algorithm performances.


title: Quantum Walk Neural Networks for Graph-Structured Data

abstract: In recent years, neural network architectures designed to operate on graph-structured data have pushed the state-of-the-art in the field. A large set of these architectures utilize a form of classical random walks to diffuse information throughout the graph. We propose quantum walk neural networks (QWNN), a novel graph neural network architecture based on quantum random walks, the quantum parallel to classical random walks. A QWNN learns a quantum walk on a graph to construct a diffusion operator which can then be applied to graph-structured data. We demonstrate the use of this model on a variety of prediction tasks on graphs involving temperature, biological, and molecular datasets.


title: MedLDA: maximum margin supervised topic models for regression and classification

abstract: Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.


title: Leishmania in the Old World: 4. The distribution of L. donovani sensu lato zymodemes

abstract: Abstract Isoenzyme profiles of 67 stocks of Leishmania donovani sensu lato from across the Old World were compared with those of reference strains of L. donovani sensu stricto , L. infantum , L. major , L. tropica and L. aethiopica using starch-gel electrophoresis of 13 enzymes (GPI, GD, ES, PGM, PEPD, NH, ASAT, ALAT, PK, MPI, 6PGD, SOD, MDH). 12 zymodemes were seen. Isolates from man, Canis familiaris , Vulpes vulpes , Rattus rattus , Arvicanthis sp. and Phkbotomus martini were examined. Several zymodemes comprised stocks from man and C. familiaris , two of which also included wild animal isolates. Isolates from cases of post-kala-azar dermal leishmaniasis and visceral leishmaniasis were indistinguishable. L. donovani s.l. , including L. donovani s.s . and L. infantum , formed a coherent group with a striking degree of enzymic homogeneity. Only one enzyme pattern was held in common with the L. tropica and L. aethiopica reference strains and two with the L. major reference strain enzyme profile.


Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing


Model-based image segmentation in medical applications


title: Model Predictive Control in Power Electronics: A Hybrid Systems Approach

abstract: The field of power electronics poses challenging control problems that cannot be treated in a complete manner using traditional modelling and controller design approaches. The main difficulty arises from the hybrid nature of these systems due to the presence of semiconductor switches that induce different modes of operation and operate with a high switching frequency. Since the control techniques traditionally employed in industry feature a significant potential for improving the performance and the controller design, the field of power electronics invites the application of advanced hybrid systems methodologies. The computational power available today and the recent theoretical advances in the control of hybrid systems allow one to tackle these problems in a novel way that improves the performance of the system, and is systematic and implementable. In this paper, this is illustrated by two examples, namely the Direct Torque Control of three-phase induction motors and the optimal control of switch-mode dc-dc converters.


title: Harmonic Suppressed Slot Loop Antenna Fed by Coplanar Waveguide

abstract: A harmonic suppressed coplanar waveguide (CPW)-fed slot-coupled circular slot loop antenna is proposed. Two coupling slots placed inside the radiating slot loop and connected directly to the CPW are used for the harmonic suppression. By properly adjusting the coupling slot lengths, the second and third harmonics can be suppressed separately or simultaneously. The principle of harmonic suppression is discussed, and simulation and measurement results are also presented and discussed.


title: Using clustering algorithms in legacy systems remodularization

abstract: Incited by the observation that cluster analysis and the remodularization of software systems solve similar problems, we have done research in both these areas in order to provide theoretical background for the application of cluster analysis in systems remodularization. We present an overview of cluster analysis and of systems remodularization. It appears that system remodularization techniques often either reinvent clustering techniques or could be augmented by them. We also give directions for further research.


Discrete Mittag-Leffler function and its applications (New Developments in the Research of Integrable Systems : Continuous, Discrete, Ultra-discrete)


title: Strong reciprocity, human cooperation, and the enforcement of social norms

abstract: This paper provides strong evidence challenging the self-interest assumption that dominates the behavioral sciences and much evolutionary thinking. The evidence indicates that many people have a tendency to voluntarily cooperate, if treated fairly, and to punish non-cooperators. We call this behavioral propensity ‘strong reciprocity’ and show empirically that it can lead to almost universal cooperation in circumstances in which purely self-interested behavior would cause a complete breakdown of cooperation. In addition, we show that people are willing to punish those who behaved unfairly towards a third person or who defected in a Prisoner’s Dilemma game with a third person. This suggests that strong reciprocity is a powerful device for the enforcement of social norms like, e.g., food-sharing norms or collective action norms. Strong Reciprocity cannot be rationalized as an adaptive trait by the leading evolutionary theories of human cooperation, i.e., by kin selection theory, reciprocal altruism theory, indirect reciprocity theory and costly signaling theory. However, multi-level selection theories and theories of cultural evolution are consistent with strong reciprocity.


title: An Expert System for Analyzing Firewall Rules

abstract: When deploying firewalls in an organization, it is essential to verify that the firewalls are configured properly. The problem of finding out what a given firewall configuration does occurs, for instance, when a new network administrator takes over, or a third party performs a technical security audit for the organization. While the problem can be approached via testing, non-intrusive techniques are often preferred. Existing tools for analyzing firewall configurations usually rely on hard-coded algorithms for analyzing access lists. In this paper we present a tool based on constraint logic programming (CLP) which allows the user to write higher level operations for, e.g., detecting common configuration mistakes. Our tool understands Cisco router access lists, and it is implemented using Eclipse, a constraint logic programming language. The problem of analyzing firewall configurations lends itself quite naturally to be solved by an expert system. We found it surprisingly easy to use logic statements to express knowledge on networking, firewalls, and common configuration mistakes, for instance. Using an existing generic inference engine allowed us to focus on defining the core concepts and relationships in the knowledge base.


title: PWM Switched Double Stage Buck BoostConverter with LC Filter for LED LightingApplications

abstract: An integrated double buck–boost(IDBB) converter is proposed as a high-power-factor offline power supply for power-LED lamps. The IDBB converter features just one controlled switch and two inductors and is able to supply a solid-state lamp from the mains, providing high power factor and good efficiency. The IDBB converter is analyzed, and a design methodology is proposed. It is demonstrated that, with a careful design of the converter, the filter capacitances can be made small enough so that film capacitors may be used. In this way, the converter mean time between failures can be made as high as that of the solid-state lamp. PWM switching is used for controlling the switching pulses. Feedback circuit is provided with PI controller for accurate result. The converter is simulated using MATLAB 2010 and waveforms are analysed. Simulation results demonstrate that the output voltage of the desired converter can be stably maintained at 200 V and power factor can be improved upto 0.90.


title: Monitoring service clouds in the future internet

abstract: Service Clouds are a key emerging feature of the Future Internet which will provide a platform to execute virtualized services. To effectively operate a service cloud there needs to be a monitoring system which provides data on the actual usage and changes in resources of the cloud and of the services running in the cloud. We present the main aspects of Lattice, a new monitoring framework, which has been specially designed for monitoring resources and services in virtualized environments. Finally, we discuss the issues related to federation of service clouds, and how this affects monitoring in particular. © 2010 The authors and IOS Press. All rights reserved.


title: An Analysis of Single-Layer Networks in Unsupervised Feature Learning

abstract: A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several simple factors, such as the number of hidden nodes in the model, may be more important to achieving high performance than the learning algorithm or the depth of the model. Specifically, we will apply several othe-shelf feature learning algorithms (sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures) to CIFAR, NORB, and STL datasets using only singlelayer networks. We then present a detailed analysis of the eect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (“stride”) between extracted features, and the eect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance—so critical, in fact, that when these parameters are pushed to their limits, we achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyperparameters to tune beyond the model structure itself, and is very easy to implement. Despite the simplicity of our system, we achieve accuracy beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.2% respectively).


title: A System View of Financial Blockchains

abstract: Blockchain (BC) has received significant attention recently. This paper presents system-related issues for BCs for financial applications. This paper first presents the design of a BC without consideration of any application scenarios, and issues such as performance, security, performance and scalability lead to specific BC designs. Sample BC scenarios are analyzed and these lead to additional BC designs. Specifically, two new kinds of BC emerge: for storing information at transactional level, for storing account information. By splitting traditional BCs into these two BCs allow one to optimize the system with respect to scalability and privacy.


title: Design of high frequency compensation submount for 40Gbit/s lumped electroabsorption modulated lasers

abstract: A high frequency compensation technique is proposed by a T-resonator scheme on the submount for 40Gbit/s lumped electroabsorption modulated lasers (EMLs) package. Reflected microwave generated by the T-resonator enhances the electrical signal over the modulator, which can be controlled by the size of the T-resonator.


title: Feature-guided image stippling

abstract: This paper presents an automatic method for producing stipple renderings from photographs, following the style of professional hedcut illustrations. For effective depiction of image features, we introduce a novel dot placement algorithm which adapts stipple dots to the local shapes. The core idea is to guide the dot placement along 'feature flow' extracted from the feature lines, resulting in a dot distribution that conforms to feature shapes. The sizes of dots are adaptively determined from the input image for proper tone representation. Experimental results show that such feature-guided stippling leads to the production of stylistic and feature-emphasizing dot illustrations.


title: JSFlow: tracking information flow in JavaScript and its APIs

abstract: JavaScript drives the evolution of the web into a powerful application platform. Increasingly, web applications combine services from different providers. The script inclusion mechanism routinely turns barebone web pages into full-fledged services built up from third-party code. Such code provides a range of facilities from helper utilities (such as jQuery) to readily available services (such as Google Analytics and Tynt). Script inclusion poses a challenge of ensuring that the integrated third-party code respects security and privacy. This paper presents JSFlow, a security-enhanced JavaScript interpreter for fine-grained tracking of information flow. We show how to resolve practical challenges for enforcing information-flow policies for the full JavaScript language, as well as tracking information in the presence of libraries, as provided by browser APIs. The interpreter is itself written in JavaScript, which enables deployment as a browser extension. Our experiments with the extension provide in-depth understanding of information manipulation by third-party scripts such as Google Analytics. We find that different sites intended to provide similar services effectuate rather different security policies for the user's sensitive information: some ensure it does not leave the browser, others share it with the originating server, while yet others freely propagate it to third parties.


title: Densely Connected Convolutional Networks for Speech Recognition

abstract: This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition. DenseN-ets are very deep, compact convolutional neural networks, which have demonstrated incredible improvements over the state-of-the-art results on several data sets in computer vision. Our experimental results show that DenseNet can be used for AM significantly outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore, results on Wall Street Journal revealed that with only a half of the training data DenseNet was able to outperform other models trained with the full data set by a large margin.