A Patch-Based Saliency Detection Method for Assessing the Visual Privacy Levels of Objects in Photos

Photo privacy protection has recently received increasing attention from the public. However, the overprotection of photo privacy by hiding too much visual information can make photos meaningless. To avoid this, visual information with different degrees of privacy sensitivity can be filtered out using various image-processing techniques. Objects in a photo usually contain visual information that can potentially reveal private information; this potential depends on both the visual saliency of the objects and on the specific categories to which the objects belong. In this paper, we aim to quantitatively evaluate the influence of visual saliency information on privacy and objectively evaluate the levels of visual privacy that objects contain. Meeting this objective faces two challenges: 1) determining a method of effectively detecting generic objects in a photo for the extraction of saliency information and 2) determining a scientific method for assessing the visual private information contained in objects. To cope with these challenges, we first propose a hierarchical saliency detection method that combines a patch-based saliency detection strategy with an objectness estimation strategy to effectively locate salient objects and obtain the saliency information of each object. The proposed method results in a small set of class-independent locations with high quality and a mean average best overlap score of 0.627 at 1150 locations, which is superior to the score of other saliency detection methods. Second, we build a computational privacy assessment system to scientifically calculate and rank the privacy risks of objects in a photo by creating an improved risk matrix and using the Borda count method. The proposed computational privacy assessment method matches human evaluations to a relatively high degree.


Accelerating Apache Hive with MPI for Data Warehouse Systems

Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.


Adaptive figure-ground classification

We propose an adaptive figure-ground classification algorithm to automatically extract a foreground region using a user-provided bounding-box. The image is first over-segmented with an adaptive mean-shift algorithm, from which background and foreground priors are estimated. The remaining patches are iteratively assigned based on their distances to the priors, with the foreground prior being updated online. A large set of candidate segmentations are obtained by changing the initial foreground prior. The best candidate is determined by a score function that evaluates the segmentation quality. Rather than using a single distance function or score function, we generate multiple hypothesis segmentations from different combinations of distance measures and score functions. The final segmentation is then automatically obtained with a voting or weighted combination scheme from the multiple hypotheses. Experiments indicate that our method performs at or above the current state-of-the-art on several datasets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds. In addition, this improvement in accuracy is achieved with low computational cost.


Salamandra Robotica II: An Amphibious Robot to Study Salamander-Like Swimming and Walking Gaits

In this paper, we present Salamandra robotica II: an amphibious salamander robot that is able to walk and swim. The robot has four legs and an actuated spine that allow it to perform anguilliform swimming in water and walking on the ground. The paper first presents the new robot hardware design, which is an improved version of Salamandra robotica I. We then address several questions related to body–limb coordination in robots and animals that have a sprawling posture like salamanders and lizards, as opposed to the erect posture of mammals (e.g., in cats and dogs). In particular, we investigate how the speed of locomotion and curvature of turning motions depend on various gait parameters such as the body–limb coordination, the type of body undulation (offset, amplitude, and phase lag of body oscillations), and the frequency. Comparisons with animal data are presented, and our results show striking similarities with the gaits observed with real salamanders, in particular concerning the timing of the body’s and limbs’ movements and the relative speed of locomotion.


Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning

Distantly supervised approaches have become popular in recent years as they allow training relation extractors without textbound annotation, using instead known relations from a knowledge base and a large textual corpus from an appropriate domain. While state of the art distant supervision approaches use off-theshelf named entity recognition and classification (NERC) systems to identify relation arguments, discrepancies in domain or genre between the data used for NERC training and the intended domain for the relation extractor can lead to low performance. This is particularly problematic for “non-standard” named entities such as album which would fall into the MISC category. We propose to ameliorate this issue by jointly training the named entity classifier and the relation extractor using imitation learning which reduces structured prediction learning to classification learning. We further experiment with Web features different features and compare against using two off-the-shelf supervised NERC systems, Stanford NER and FIGER, for named entity classification. Our experiments show that imitation learning improves average precision by 4 points over an one-stage classification model, while removing Web features results in a 6 points reduction. Compared to using FIGER and Stanford NER, average precision is 10 points and 19 points higher with our imitation learning approach.


Growth Promotion of indigenous Scenedesmus dimorphus strain under different conditions using stirred tank

The effects of culture conditions at different temperature (15-25-35°C) and nitrate concentration (0.1-0.25-0.4g/L) on biomass production and growth rate of scenedesmus dimorphus were determined. The results shown that the dry biomass weight (0.153g/L) at 15°C was the lowest. No significant difference was observed between the dry biomasses at 25°C (0.289g/L) and 35°C (0.240g/L). While the lowest growth rate was at 15°C(0.157d), the highestone was at 25°C (0.229d) and 35°C (0.195d) with insignificant deference, and The nitrate concentration had no effect on both of biomass production and growth rate.


linalg: Matrix Computations in Apache Spark

We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark comes with the mllib.linalg library, which provides abstractions and implementations for distributed matrices. Using these abstractions, we highlight the computations that were more challenging to distribute. When translating single-node algorithms to run on a distributed cluster, we observe that often a simple idea is enough: separating matrix operations from vector operations and shipping the matrix operations to be ran on the cluster, while keeping vector operations local to the driver. In the case of the Singular Value Decomposition, by taking this idea to an extreme, we are able to exploit the computational power of a cluster, while running code written decades ago for a single core. We conclude with a comprehensive set of benchmarks for hardware accelerated matrix computations from the JVM, which is interesting in its own right, as many cluster programming frameworks use the JVM.


Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.


FOR LEAP MOTION CONTROLLER

This thesis studies the new possibilities to gesture interfaces that emerged with a Leap Motion sensor. The Leap Motion is an innovative, 3D motion capturing device designed especially for hands and fingers tracking with precision up to 0.01mm. The outcome of the thesis is the LeapGesture library dedicated to the developers for Leap Motion Controller that contains algorithms allowing to learn and recognize gestures. The authors examined the data provided by the sensor in context of recognition of hand poses (static gestures), hand movements (dynamic gestures) and in task of a finger recognition. The static gestures are recognized using the Support Vector Machine (SVM) with median filtering an input data and using the correspondences between consecutive recognitions. The thesis contains evaluation of different feature sets, which have a significant impact on the recognition rate. The chosen feature set allowed to recognize a set of five gestures with 99% accuracy and a set of ten gestures with 85%. The dynamic gestures (movements of a hand and fingers) are recognized with the Hidden Markov Models (HMM). Recognition with HMMs allowed to achieve accuracy of 80% for a set containing six classes of dynamic gestures. Finger recognition algorithms proposed in this thesis works with 93% accuracy on a recorded dataset. The LeapGesture library contains presented approaches using a C++ interface, that can be easily used in any application for many purposes.


A Hybrid Ant Colony Optimization Algorithm for Path Planning of Robot in Dynamic Environment

Ant colony optimization and artificial potential field were used respectively as global path planning and local path planning methods in this paper. Some modifications were made to accommodate ant colony optimization to path planning. Pheromone generated by ant colony optimization was also utilized to prevent artificial potential field from getting local minimum. Simulation results showed that the hybrid algorithm could satisfy the real-time demand. The comparison between ant colony optimization and genetic algorithm was also made in this paper.


Co-designing accelerators and SoC interfaces using gem5-Aladdin

Increasing demand for power-efficient, high-performance computing has spurred a growing number and diversity of hardware accelerators in mobile and server Systems on Chip (SoCs). This paper makes the case that the co-design of the accelerator microarchitecture with the system in which it belongs is critical to balanced, efficient accelerator microarchitectures. We find that data movement and coherence management for accelerators are significant yet often unaccounted components of total accelerator runtime, resulting in misleading performance predictions and inefficient accelerator designs. To explore the design space of accelerator-system co-design, we develop gem5-Aladdin, an SoC simulator that captures dynamic interactions between accelerators and the SoC platform, and validate it to within 6% against real hardware. Our co-design studies show that the optimal energy-delay-product (EDP) of an accelerator microarchitecture can improve by up to 7.4× when system-level effects are considered compared to optimizing accelerators in isolation.


Compatibility Family Learning for Item Recommendation and Generation

Compatibility between items, such as clothes and shoes, is a major factor among customer’s purchasing decisions. However, learning “compatibility” is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as “Compatibility Family”. In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.


ON SUPERVISED AND SEMI-SUPERVISED k-NEAREST NEIGHBOR ALGORITHMS

The k-nearest neighbor (kNN) is one of the simplest classification methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this paper kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kNN algorithm to handle multi-label classification settings. Semi-supervision is performed by using data-dependent kernels.


GaN HEMT based Doherty amplifier for 3.5-GHz WiMAX Applications

We have implemented a Doherty amplifier for 3.5-GHz World Interoperability for Microwave Access (WiMAX) applications using Eudyna 90-W (P3 dB) Gallium-Nitride (GaN) High Electron Mobility Transistor (HEMT) because of the poor efficiency of a standard class AB amplifier when the linearity performance is good. The load modulation performance of the GaN HEMT device for the Doherty operation is rather moderate but workable. The linearity is improved using the in-band error cancellation technique of the Doherty amplifier. The implemented Doherty amplifier has been designed at an average output power of 43 dBm, backed-off about 8 dB from the 51 dBm (P3 dB). For WiMAX signal with 28 MHz signal bandwidth, the measured drain efficiency of the amplifier is 27.8%, and the measured Relative Constellation Error (RCE) is -33.17 dB, while those of the comparable class AB amplifier are 19.42% and -24.26 dB, respectively, at the same average output power level.


MICS: an efficient content space representation model for publish/subscribe systems

One of the main challenges faced by content-based publish/subscribe systems is handling large amount of dynamic subscriptions and publications in a multidimensional content space. To reduce subscription forwarding load and speed up content matching, subscription covering, subsumption and merging techniques have been proposed. In this paper we propose MICS, Multidimensional Indexing for Content Space that provides an efficient representation and processing model for large number of subscriptions and publications. MICS creates a one dimensional representation for publications and subscriptions using Hilbert space filling curve. Based on this representation, we propose novel content matching and subscription management (covering, subsumption and merging) algorithms. Our experimental evaluation indicates that the proposed approach significantly speeds up subscription management operations compared to the naive linear approach.


Named Entity Recognition and Compositional Morphology for Word Representations in Chinese

We approach the task of named entity recognition (NER) for Chinese by representing each entity with a composition of its traits: the token-entity, its characters, and its characters’ main radicals. Character and radical-level information for each entity are included to provide additional relationships that might not be strictly captured within a token-entity’s word embedding during training. We learn using neural networks that are some combination of the following traits: unidirectional or bidirectional; single or multi-layer; simple, gated recurrent unit (GRU), or long short term memory (LSTM) celled. We achieve a maximum not-O token-level F1 score of 76 and entity-level F1 of 70.


The extraction of neural strategies from the surface EMG.

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.


Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions

In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users' devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.


Device-to-device communication as an underlay to LTE-advanced networks

In this article device-to-device (D2D) communication underlaying a 3GPP LTE-Advanced cellular network is studied as an enabler of local services with limited interference impact on the primary cellular network. The approach of the study is a tight integration of D2D communication into an LTE-Advanced network. In particular, we propose mechanisms for D2D communication session setup and management involving procedures in the LTE System Architecture Evolution. Moreover, we present numerical results based on system simulations in an interference limited local area scenario. Our results show that D2D communication can increase the total throughput observed in the cell area.


Design aspects of network assisted device-to-device communications

Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design.


Augmenting geospatial data provenance through metadata tracking in geospatial service chaining

In a service-oriented environment, heterogeneous data from distributed data archiving centers and various geo-processing services are chained together dynamically to generate on-demand data products. Creating an executable service chain requires detailed specification of metadata for data sets and service instances. Using metadata tracking, semantics-enabled metadata are generated and propagated through a service chain. This metadata can be employed to validate a service chain, e.g. whether metadata preconditions on the input data of services can be satisfied. This paper explores how this metadata can be further exploited to augment geospatial data provenance, i.e., how a geospatial data product is derived. Provenance information is automatically captured during the metadata tracking process. Semantic Web technologies, including OWL and SPARQL, are used for representation and query of this provenance information. The approach can not only contribute to the automatic recording of geospatial data provenance, but also provide a more informed understanding of provenance information using Semantic Web technologies. & 2009 Elsevier Ltd. All rights reserved.


Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices

—The pedestrian inertial navigation systems are generally based on Pedestrian Dead Reckoning (PDR) algorithm. Considering the physiological characteristics of pedestrian movement, we use the cyclical characteristics and statistics of acceleration waveform and features which are associated with the walking speed to estimate the stride length. Due to the randomness of the pedestrian hand-held habit, the step events cannot always be detected by using the periods of zero velocity updates (ZUPTs). Furthermore, the signal patterns of the sensor could differ significantly depending on the carrying modes and the user’s hand motion. Hence, the step detection and the associated adaptive step length model using a handheld device equipped with accelerometer is required to obtain high-accurate measurements. To achieve this goal, a compositional algorithm of empirical formula and Back-propagation neural network by using handheld devices is proposed to estimate the step length, as well as achieve the accuracy of step detection higher than 98%. Furthermore, the proposed joint step detection and adaptive step length estimation algorithm can help much in the development of Pedestrian Navigation Devices (PNDs) based on the handheld inertial sensors.


Toward the Minimal Universal Petri Net

A universal Petri net with 14 places, 42 transitions, and 218 arcs was built in the class of deterministic inhibitor Petri nets (DIPNs); it is based on the minimal Turing machine (TM) of Woods and Neary with 6 states, 4 symbols, and 23 instructions, directly simulated by a Petri net. Several techniques were developed, including bi-tag system (BTS) construction on a DIPN, special encoding of TM tape by two stacks, and concise subnets that implement arithmetic encoding operations. The simulation using the BTS has cubic time and linear space complexity, while the resulting universal net runs in exponential time and quadratic space with respect to the target net transitions' firing sequence length. The technique is applicable for simulating any TM by the Petri net.


Transference of kettlebell training to strength, power, and endurance.

Kettlebells are a popular implement in many strength and conditioning programs, and their benefits are touted in popular literature, books, and videos. However, clinical data on their efficacy are limited. The purpose of this study was to examine whether kettlebell training transfers strength and power to weightlifting and powerlifting exercises and improves muscular endurance. Thirty-seven subjects were assigned to an experimental (EXP, n = 23; mean age = 40.9 ± 12.9 years) or a control group (CON; n = 14; mean age = 39.6 ± 15.8 years), range 18-72 years. The participants were required to perform assessments including a barbell clean and jerk, barbell bench press, maximal vertical jump, and 45° back extensions to volitional fatigue before and after a 10-week kettlebell training program. Training was structured in a group setting for 2 d·wk(-1) for 10 weeks. A repeated measures analysis of variance was conducted to determine group × time interactions and main effects. Post hoc pairwise comparisons were conducted when appropriate. Bench press revealed a time × group interaction and a main effect (p < 0.05). Clean and jerk and back extension demonstrated a trend toward a time × group interaction, but it did not reach significance (p = 0.053). However, clean and jerk did reveal a main effect for time (p < 0.05). No significant findings were reported for maximal vertical jump. The results demonstrate a transfer of power and strength in response to 10 weeks of training with kettlebells. Traditional training methods may not be convenient or accessible for strength and conditioning specialists, athletes, coaches, and recreational exercisers. The current data suggest that kettlebells may be an effective alternative tool to improve performance in weightlifting and powerlifting.


Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding

Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.


Skew detection of scanned document images

—Skewing of the scanned image is an inevitable process and its detection is an important issue for document recognition systems. The skew of the scanned document image specifies the deviation of the text lines from the horizontal or vertical axis. This paper surveys methods to detect this skew in


Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.


Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.


Continuous and fine-grained breathing volume monitoring from afar using wireless signals

In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a person's breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the person's body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the person's body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor user's breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.


Polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population.

BACKGROUND
Genome-wide association studies in adults have identified numerous genetic variants related to psychiatric disorders and related traits, such as schizophrenia and educational attainment. However, the effects of these genetic variants on behaviour in the general population remain to be fully understood, particularly in younger populations. We investigated whether polygenic scores of five psychiatric disorders and educational attainment are related to emotional and behaviour problems during early childhood.

METHODS
From the Generation R Study, we included participants with available genotype data and behavioural problems measured with the Child Behavior Checklist (CBCL) at the age of 3 (n = 1,902), 6 (n = 2,202) and 10 years old (n = 1,843). Polygenic scores were calculated for five psychiatric disorders and educational attainment. These polygenic scores were tested for an association with the broadband internalizing and externalizing problem scales and the specific CBCL syndrome scale scores.

RESULTS
Analysis of the CBCL broadband scales showed that the schizophrenia polygenic score was associated with significantly higher internalizing scores at 3, 6 and 10 years and higher externalizing scores at age 3 and 6. The educational attainment polygenic score was associated with lower externalizing scores at all time points and lower internalizing scores at age 3. No associations were observed for the polygenic scores of bipolar disorder, major depressive disorder and autism spectrum disorder. Secondary analyses of specific syndrome scores showed that the schizophrenia polygenic score was strongly related to the Thought Problems scores. A negative association was observed between the educational attainment polygenic score and Attention Problems scores across all age groups.

CONCLUSIONS
Polygenic scores for adult psychiatric disorders and educational attainment are associated with variation in emotional and behavioural problems already at a very early age.


A decision cognizant Kullback-Leibler divergence

In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback-Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback-Leibler divergence (DC-KL), to reduce the contribution of the minority classes, which obscure the true degree of classifier incongruence. We investigate the properties of the novel divergence measure analytically and by simulation studies. The proposed measure is demonstrated to be more robust to minority class clutter. Its sensitivity to estimation noise is also shown to be considerably lower than that of the classical KL divergence. These properties render the DC-KL divergence a much better statistic for discriminating between classifier congruence and incongruence in pattern recognition systems.


Approaches , methods , metrics , measures , and subjectivity in ontology evaluation : A survey

Ontology evaluation is concerned with ascertain two important aspects of ontologies: quality and correctness. The distinction between the two is attempted in this survey as a way to better approach ontology evaluation. The role that ontologies play on the semantic web at large has been has been argued to have catalyzed the proliferation of ontologies in existence. This has also presented the challenge of deciding one the suitability of an given ontology to one’s purposes as compared with another ontology in a similar domain. This survey intends to analyze the state of the art in ontology evaluate spanning such topics as the approaches to ontology evaluation, the metrics and measures used. Particular interest is given to Data-driven ontology evaluation with special emphasis on the notion of bias and it’s relevance to evaluation results. Chief among the outputs of this survey is the gap analysis on the topic of ontology evaluation.


Radar interferometry offers new insights into threats to the Angkor site

The conservation of World Heritage is critical to the cultural and social sustainability of regions and nations. Risk monitoring and preventive diagnosis of threats to heritage sites in any given ecosystem are a complex and challenging task. Taking advantage of the performance of Earth Observation technologies, we measured the impacts of hitherto imperceptible and poorly understood factors of groundwater and temperature variations on the monuments in the Angkor World Heritage site (400 km2). We developed a two-scale synthetic aperture radar interferometry (InSAR) approach. We describe spatial-temporal displacements (at millimeter-level accuracy), as measured by high-resolution TerraSAR/TanDEM-X satellite images, to provide a new solution to resolve the current controversy surrounding the potential structural collapse of monuments in Angkor. Multidisciplinary analysis in conjunction with a deterioration kinetics model offers new insights into the causes that trigger the potential decline of Angkor monuments. Our results show that pumping groundwater for residential and touristic establishments did not threaten the sustainability of monuments during 2011 to 2013; however, seasonal variations of the groundwater table and the thermodynamics of stone materials are factors that could trigger and/or aggravate the deterioration of monuments. These factors amplify known impacts of chemical weathering and biological alteration of temple materials. The InSAR solution reported in this study could have implications for monitoring and sustainable conservation of monuments in World Heritage sites elsewhere.


A fast and efficient sift detector using the mobile GPU

Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.


Home, habits, and energy: examining domestic interactions and energy consumption

This paper presents findings from a qualitative study of people's everyday interactions with energy-consuming products and systems in the home. Initial results from a large online survey are also considered. This research focuses not only on "conservation behavior" but importantly investigates interactions with technology that may be characterized as "normal consumption" or "over-consumption." A novel vocabulary for analyzing and designing energy-conserving interactions is proposed based on our findings, including: cutting, trimming, switching, upgrading, and shifting. Using the proposed vocabulary, and informed by theoretical developments from various literatures, this paper demonstrates ways in which everyday interactions with technology in the home are performed without conscious consideration of energy consumption but rather are unconscious, habitual, and irrational. Implications for the design of energy-conserving interactions with technology and broader challenges for HCI research are proposed.


What affects web credibility perception - an analysis of textual justifications

In this paper, we present the findings of a qualitative analysis of 15,750 comments left by 2,041 participants in a Reconcile web credibility evaluation study. While assessing the credibility of the presented pages, respondents of the Reconcile studies were also asked to justify their ratings in writing. This work attempts to give an insight into the factors that affected the credibility assessment. To the best of our knowledge, the presented study is the most-recent large-scale study of its kind carried out since 2003, when the Fogg et al. śHow do users evaluate the credibility of Web sites? A study with over 2,500 participants’ paper was published. The performed analysis shows that the findings made a decade ago are still mostly valid today despite the passage of time and the advancement of Internet technologies. However we report a weaker impact of webpage appearance. A much bigger dataset (as compared to Fogg’s studies) allowed respondents to reveal additional features, which influenced the credibility evaluations.


Semantics and implementation of continuous sliding window queries over data streams

In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.


Atrial Fibrillation Detection Based on Poincaré plot of RR Intervals

Atrial fibrillation (AF) is one of the most common types of arrhythmia which significantly increases the risk factor of stroke – especially in elderly population. In this paper an algorithm is presented which is suitable for the effective detection of AF, by using merely heart rate data as input. The method is based on the processing of Poincaré plots constructed from the set of 30 RR intervals. During the analysis of Poincaré plots the dispersion of points around the diagonal line is calculated and the number of clusters is determined by a self-developed cluster analyzer. The decision criterion of AF relies on these two parameters. On the one hand, the algorithm was tested on 10 AF and 10 normal rhythm ECG signals of the PhysioNet Database, achieving the average sensitivity (Se) of 98.69% and the average specificity (Sp) of 99.59%. On the other hand, 10 AF and 10 normal clinically confirmed records of a heart rate meter were also processed, resulting the average Se and Sp of 96.89% and 99.00%, respectively.


FEBEI-Face Expression Based Emoticon Identification CSB 657 Computer Vision

The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.


Analysis and Application of Discrete Halbach Magnet Array With Unequal Arc Lengths and Unequally Changed Magnetization Directions

This paper presents a general process to analyze and optimize the performance for an arbitrary discrete Halbach magnet array which is “unnormal” on magnetization direction and arc length of each segment. Besides the original type, a new arrangement of Halbach array is proposed, which does not have main PM segments. Two-dimensional field analytical models for predicting the air-gap field distribution of both arrangements are offered and validated by an FEA method. Then, a multi-objective optimizer was applied for different requirements, and comparison between normal and optimized 3-segment Halbach array is given to verify the effect of optimization. At last, some potential applications and follow-up study are discussed.


Three-dimensional menus: A survey and taxonomy

Various interaction techniques have been developed in the field of virtual and augmented reality. Whereas techniques for object selection, manipulation, travel, and wayfinding have already been covered in existing taxonomies in some detail, application control techniques have not yet been sufficiently considered. However, they are needed by almost every mixed reality application, e.g. for choosing from alternative objects or options. For this purpose a great variety of distinct three-dimensional (3D) menu selection techniques is available. This paper surveys existing 3D menus from the corpus of literature and classifies them according to various criteria. The taxonomy introduced here assists developers of interactive 3D applications to better evaluate their options when choosing, optimizing, and implementing a 3D menu technique. Since the taxonomy spans the design space for 3D menu solutions, it also aids researchers in identifying opportunities to improve or create novel virtual menu techniques. r 2006 Elsevier Ltd. All rights reserved.


ShoeSoleSense: proof of concept for a wearable foot interface for virtual and real environments

ShoeSoleSense is a proof of concept, novel body worn interface - an insole that enables location independent hands-free interaction through the feet. Forgoing hand or finger interaction is especially beneficial when the user is engaged in real world tasks. In virtual environments as moving through safety training applications is often conducted via finger input, which is not very suitable. To enable a more intuitive interaction, alternative control concepts utilize gesture control, which is usually tracked by statically installed cameras in CAVE-like-installations. Since tracking coverage is limited, problems may also occur. The introduced prototype provides a novel control concept for virtual reality as well as real life applications. Demonstrated functions include movement control in a virtual reality installation such as moving straight, turning and jumping. Furthermore the prototype provides additional feedback by heating up the feet and vibrating in dedicated areas on the surface of the insole.


A discussion of cybersickness in virtual environments

An important and troublesome problem with current virtual environment (VE) technology is the tendency for some users to exhibit symptoms that parallel symptoms of classical motion sickness both during and after the VE experience. This type of sickness, cybersickness, is distinct from motion sickness in that the user is often stationary but has a compelling sense of self motion through moving visual imagery. Unfortunately, there are many factors that can cause cybersickness and there is no foolproof method for eliminating the problem. In this paper, I discuss a number of the primary factors that contribute to the cause of cybersickness, describe three conflicting cybersickness theories that have been postulated, and discuss some possible methods for reducing cybersickness in VEs.


ChairIO--the Chair-Based Interface


Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning

The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.


Design guidelines for wireless sensor networks: communication, clustering and aggregation

When sensor nodes are organized in clusters, they could use either single hop or multi-hop mode of communication to send their data to their respective cluster heads. We present a systematic cost-based analysis of both the modes, and provide results that could serve as guidelines to decide which mode should be used for given settings. We determine closed form expressions for the required number of cluster heads and the required battery energy of nodes for both the modes. We also propose a hybrid communication mode which is a combination of single hop and multi-hop modes, and which is more cost-effective than either of the two modes. Our problem formulation also allows for the application to be taken into account in the overall design problem through a data aggregation model. 2003 Elsevier B.V. All rights reserved.


Grounded Learning of Color Semantics with Autoencoders

Humans learn language by grounding word meaning in the physical world. Recent efforts in natural language processing have attempted to model such multimodal learning by incorporating visual information into word and sentence representations. Here, we explore the task of grounding lexical color descriptions in their visual referents. We propose an RNN-based autoencoder model to learn vector representations of sentences that reflect their associated color values. Our model effectively learns a joint visual-lexical space that demonstrates compositionality and generalizes to unseen color names. As a demonstration of such a space learned, we show that our model can predict captions from color representations and color representations from captions. In addition to successfully modeling color language, this work provides a novel framework for grounded language learning.


Behavior recognition based on machine learning algorithms for a wireless canine machine interface

Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.


Encoding User as More Than the Sum of Their Parts: Recurrent Neural Networks and Word Embedding for People-to-people Recommendation

Neural networks and word embeddings are powerful tools to capture latent factors. These tools can provide effective measures of similarities between users or items in the context of sparse data. We propose a novel approach that relies on neural networks and word embeddings to the problem of matching a learner looking for mentoring, and a tutor that is willing to provide this mentoring. Tutors and learners can issue multiple offers/requests on different topics. The approach matches over the whole array of topics specified by learners and tutors. Its performance for tutor-learner matching is compared with the state of the art. It yields similar results in terms of precision, but improves the recall.


MATLAB / SIMULINK Based Modelling Photovoltaic Array Fed T-Source Inverter

In order to utilize the solar energy for industrial, commercial and domestic applications the power conversion schemes plays an important role. The problem exists in conventional power conversion schemes are low efficiency, poor transient response, low voltage gain and more reactive components are being used .This paper proposes a single stage power conversion scheme called T-source inverter(TSI) to overcome these drawbacks. T-Source inverter (TSI) with simple boost control scheme is used as interface circuit between PV array and load. The PV array is analyzed under different irradiation and temperature value. The mathematical equations are verified with simulation and hardware. The verification shows the voltage gain of TSI was comparatively higher than ZSI. The reactive components in the circuit are less, fast transient response and low output ripple.


A near-threshold 7T SRAM cell with high write and read margins and low write time for sub-20 nm FinFET technologies

In this paper, a 7T SRAM cell with differential write and single ended read operations working in the near-threshold region is proposed. The structure is based on modifying a recently proposed 5T cell which uses high and low VTH transistors to improve the read and write stability. To enhance the read static noise margin (RSNM) while keeping the high write margin and low write time, an extra access transistor is used and the threshold voltages of the SRAM transistors are appropriately set. In addition, to maintain the low leakage power of the cell and increase the Ion/Ioff ratio of its access transistors, a high VTH transistor is used in the pull down path of the cell. To assess the efficacy of the proposed cell, its characteristics are compared with those of 5T, 6T, 8T, and 9T SRAM cells. The characteristics are obtained from HSPICE simulations using 20 nm, 16 nm, 14 nm, 10 nm, and 7 nm FinFET technologies assuming a supply voltage of 500 mV. The results reveal high write and read margins, the highest Ion/Ioff ratio, a fast write, and ultra-low leakage power in the hold “0” state for the cell. Therefore, the suggested 7T cell may be considered as one of the better design choices for both high performance and low power applications. Also, the changes of cell parameters when the temperature rises from 40 1C to 100 1C are investigated. Finally, the write margin as well as the read and hold SNMs of the cell in the presence of the process variations are studied at two supply voltages of 400 mV and 500 mV. The study shows that the proposed cell meets the required cell sigma value (6σ) under all conditions. & 2015 Elsevier B.V. All rights reserved.


Microcontroller-based quadratic buck converter used as led lamp driver

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


Integrating Grid-Based and Topological Maps for Mobile Robot Navigation

Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms—grid-based and topological—, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room envi-


Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach

The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation.


Gamification solutions for software acceptance: A comparative study of Requirements Engineering and Organizational Behavior techniques

Gamification is a powerful paradigm and a set of best practices used to motivate people carrying out a variety of ICT-mediated tasks. Designing gamification solutions and applying them to a given ICT system is a complex and expensive process (in time, competences and money) as software engineers have to cope with heterogeneous stakeholder requirements on one hand, and Acceptance Requirements on the other, that together ensure effective user participation and a high level of system utilization. As such, gamification solutions require significant analysis and design as well as suitable supporting tools and techniques. In this work, we compare concepts, tools and techniques for gamification design drawn from Software Engineering and Human and Organizational Behaviors. We conduct a comparison by applying both techniques to the specific Meeting Scheduling exemplar used extensively in the Requirements Engineering literature.


Organic food: buying more safety or just peace of mind? A critical review of the literature.

Consumer concern over the quality and safety of conventional food has intensified in recent years, and primarily drives the increasing demand for organically grown food, which is perceived as healthier and safer. Relevant scientific evidence, however, is scarce, while anecdotal reports abound. Although there is an urgent need for information related to health benefits and/or hazards of food products of both origins, generalized conclusions remain tentative in the absence of adequate comparative data. Organic fruits and vegetables can be expected to contain fewer agrochemical residues than conventionally grown alternatives; yet, the significance of this difference is questionable, inasmuch as actual levels of contamination in both types of food are generally well below acceptable limits. Also, some leafy, root, and tuber organic vegetables appear to have lower nitrate content compared with conventional ones, but whether or not dietary nitrate indeed constitutes a threat to human health is a matter of debate. On the other hand, no differences can be identified for environmental contaminants (e.g. cadmium and other heavy metals), which are likely to be present in food from both origins. With respect to other food hazards, such as endogenous plant toxins, biological pesticides and pathogenic microorganisms, available evidence is extremely limited preventing generalized statements. Also, results for mycotoxin contamination in cereal crops are variable and inconclusive; hence, no clear picture emerges. It is difficult, therefore, to weigh the risks, but what should be made clear is that 'organic' does not automatically equal 'safe.' Additional studies in this area of research are warranted. At our present state of knowledge, other factors rather than safety aspects seem to speak in favor of organic food.


Induction of pluripotent stem cells from fibroblast cultures

Clinical application of embryonic stem (ES) cells faces difficulties regarding use of embryos, as well as tissue rejection after implantation. One way to circumvent these issues is to generate pluripotent stem cells directly from somatic cells. Somatic cells can be reprogrammed to an embryonic-like state by the injection of a nucleus into an enucleated oocyte or by fusion with ES cells. However, little is known about the mechanisms underlying these processes. We have recently shown that the combination of four transcription factors can generate ES-like pluripotent stem cells directly from mouse fibroblast cultures. The cells, named induced pluripotent stem (iPS) cells, can be differentiated into three germ layers and committed to chimeric mice. Here we describe detailed methods and tips for the generation of iPS cells.


Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience

This work evaluates the feasibility of a motor imagery-based optical brain-computer interface (BCI) for humanoid robot control. The functional near-infrared spectroscopy (fNIRS) based BCI-robot system developed in this study operates through a high-level control mechanism where user specifies a target action through the BCI and the robot performs the set of micro operations necessary to fulfill the identified goal. For the evaluation of the system, four motor imagery tasks (left hand, right hand, left foot, and right foot) were mapped to operational commands (turn left, turn right, walk forward, walk backward) that were sent to the robot in real time to direct the robot navigating a small room. An ecologically valid offline analysis with minimal preprocessing shows that seven subjects could achieve an average accuracy of 32.5 %. This was increased to 43.6 % just by including calibration data from the same day of the robot control using the same cap setup, indicating that day-of calibration following the initial training may be important for BCI control.


Understanding Availability

This paper addresses a simple, yet fundamental question in the design of peer-to-peer systems: What does it mean when we say “availability” and how does this understanding impact the engineering of practical systems? We argue that existing measurements and models do not capture the complex timevarying nature of availability in today’s peer-to-peer environments. Further, we show that unforeseen methodological shortcomings have dramatically biased previous analyses of this phenomenon. As the basis of our study, we empirically characterize the availability of a large peer-to-peer system over a period of 7 days, analyze the dependence of the underlying availability distributions, measure host turnover in the system, and discuss how these results may affect the design of high-availability peer-to-peer services.


Photonic Crystal-Structures for THz Vacuum Electron Devices

The technology of photonic crystals (PhCs) is investigated here to improve the performance of THz vacuum electron devices. Compared with conventional metallic waveguides, the PhC arrangement alleviates typical issues in THz vacuum electron tubes, i.e. difficult vacuum pumping process and assembling, and improves the input/output coupling. A slow-wave structure (SWS) based on a corrugated waveguide assisted by PhC lateral walls and the efficient design of a PhC coupler for sheet-beam interaction devices are demonstrated. Based on the proposed technology, a backward-wave oscillator (BWO) is designed in this paper. Cold parameters of the novel PhC SWS as well as 3-D particle-in-cell simulations of the overall BWO are investigated, obtaining more than 70-mW-peak output power at 0.650 THz for beam voltage of 11 kV and beam current of 6 mA.


Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving

To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.


Kernel-Based Learning of Hierarchical Multilabel Classification Models

We present a kernel-based algorithm for hierarchical text c lassification where the documents are allowed to belong to more than one category at a time. The clas sification model is a variant of the Maximum Margin Markov Network framework, where the classifi cation hierarchy is represented as a Markov tree equipped with an exponential family defined o n the edges. We present an efficient optimization algorithm based on incremental conditi onal gradient ascent in single-example subspaces spanned by the marginal dual variables. The optim ization is facilitated with a dynamic programming based algorithm that computes best update dire ctions in the feasible set. Experiments show that the algorithm can feasibly optimize t raining sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light clas sifiers for each node. The algorithm’s predictive accuracy was found to be competitive with other r ecently introduced hierarchical multicategory or multilabel classification learning algorithms .


A novel ultra-wideband 80 GHz FMCW radar system for contactless monitoring of vital signs

In this paper an ultra-wideband 80 GHz FMCW-radar system for contactless monitoring of respiration and heart rate is investigated and compared to a standard monitoring system with ECG and CO2 measurements as reference. The novel FMCW-radar enables the detection of the physiological displacement of the skin surface with submillimeter accuracy. This high accuracy is achieved with a large bandwidth of 10 GHz and the combination of intermediate frequency and phase evaluation. This concept is validated with a radar system simulation and experimental measurements are performed with different radar sensor positions and orientations.


Optimizing F-measure: A Tale of Two Approaches

F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario.


Diffusion maps for high-dimensional single-cell analysis of differentiation data

MOTIVATION
Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.

RESULTS
Here, we propose the use of diffusion maps to deal with the problem of defining differentiation trajectories. We adapt this method to single-cell data by adequate choice of kernel width and inclusion of uncertainties or missing measurement values, which enables the establishment of a pseudotemporal ordering of single cells in a high-dimensional gene expression space. We expect this output to reflect cell differentiation trajectories, where the data originates from intrinsic diffusion-like dynamics. Starting from a pluripotent stage, cells move smoothly within the transcriptional landscape towards more differentiated states with some stochasticity along their path. We demonstrate the robustness of our method with respect to extrinsic noise (e.g. measurement noise) and sampling density heterogeneities on simulated toy data as well as two single-cell quantitative polymerase chain reaction datasets (i.e. mouse haematopoietic stem cells and mouse embryonic stem cells) and an RNA-Seq data of human pre-implantation embryos. We show that diffusion maps perform considerably better than Principal Component Analysis and are advantageous over other techniques for non-linear dimension reduction such as t-distributed Stochastic Neighbour Embedding for preserving the global structures and pseudotemporal ordering of cells.

AVAILABILITY AND IMPLEMENTATION
The Matlab implementation of diffusion maps for single-cell data is available at https://www.helmholtz-muenchen.de/icb/single-cell-diffusion-map.

CONTACT
fbuettner.phys@gmail.com, fabian.theis@helmholtz-muenchen.de

SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.


Algorithms for the Assignment and Transiortation Troblems*

In this paper we presen algorithms for the solution of the general assignment and transportation problems. In Section 1, a statement of the algorithm for the assignment problem appears, along with a proof for the correctness of the algorithm. The remarks which constitute the proof are incorporated parenthetically into the statement of the algorithm. Following this appears a discussion of certain theoretical aspects of the problem. In Section 2, the algorithm is generalized to one for the transportation problem. The algorithm of that section is stated as concisely as possible, with theoretical remarks omitted.


Segmentation of Dermoscopy Images Using Wavelet Networks

This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.


Global cancer statistics.

The global burden of cancer continues to increase largely because of the aging and growth of the world population alongside an increasing adoption of cancer-causing behaviors, particularly smoking, in economically developing countries. Based on the GLOBOCAN 2008 estimates, about 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008; of these, 56% of the cases and 64% of the deaths occurred in the economically developing world. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths. Lung cancer is the leading cancer site in males, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Breast cancer is now also the leading cause of cancer death among females in economically developing countries, a shift from the previous decade during which the most common cause of cancer death was cervical cancer. Further, the mortality burden for lung cancer among females in developing countries is as high as the burden for cervical cancer, with each accounting for 11% of the total female cancer deaths. Although overall cancer incidence rates in the developing world are half those seen in the developed world in both sexes, the overall cancer mortality rates are generally similar. Cancer survival tends to be poorer in developing countries, most likely because of a combination of a late stage at diagnosis and limited access to timely and standard treatment. A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination (for liver and cervical cancers), and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake. Clinicians, public health professionals, and policy makers can play an active role in accelerating the application of such interventions globally.


Border detection in dermoscopy images using statistical region merging.

BACKGROUND
As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it.

METHODS
In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm.

RESULTS
The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method).

CONCLUSION
The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.


Lesion border detection in dermoscopy images

BACKGROUND
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.

METHODS
In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects.

CONCLUSION
Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.


Segmentation of digitized dermatoscopic images by two-dimensional color clustering

A color-based segmentation scheme applied to dermatoscopic images is proposed. The RGB image is processed in the L*u*v* color space. A 2D histogram is computed with the two principal components and then smoothed with a Gaussian low-pass filter. The maxima location and a set of features are computed from the histogram contour lines. These features are the number of enclosed pixels, the surface of the base and the height of the maximum. They allow for the selection of valid clusters which determine the number of classes. The image is then segmented using a modified version of the fuzzy c-means (FCM) clustering technique that takes into account the cluster orientation. Finally, the segmented image is cleaned using mathematical morphology, the region borders are smoothed and small components are removed.


NICBPM: Non-invasive cuff-less blood pressure monitor

An ultra-low power, ultra portable, wearable integrated bio-medical device, the Non-Invasive Cuff-less Blood Pressure Monitor (NICBPM) is proposed to measure the Pulse Transit Time (PTT). The relationship between PTT and Blood Pressure (BP) is investigated using linear and non linear regression models. Simultaneous reading obtained from an FDA approved, commercially available Blood Pressure monitor and the (Electrocardiogram) ECG and (photoplethsymogram) PPG signal from the NICBPM are used to calibrate the device for 30 subjects during cardiopulmonary exercise test. Bland-Altman plots for the linear regression model revealed limits of agreement of −10.6 to 10.6 mmHg for systolic BP (sBP) and −10.5 to 10.5 mmHg for distolic BP (dBP). Bland-Altman plots for the non linear regression model revealed marginally better limits of agreement between −8.5 to 8.5 mmHg for sBP and −6.6 to 6.6 mmHg for dBP. sBP can be estimated from the NICBPM with 99% accuracy and 99% sensitivity.


A Dolev-Yao Model for Zero Knowledge

In cryptographic protocols, zero knowledge proofs are employed for a principal A to communicate some non-trivial information t to B while at the same time ensuring that B cannot derive any information “stronger” than t . Oen this implies that B can verify that some property holds without being able to produce a proof of this. While a rich theory of zero knowledge proofs exists, there are few formal models addressing verification questions.We propose an extension of the standard Dolev-Yao model of cryptographic protocols which involves not only constructibility of terms but also a form of verifiability. We present a proof system for term derivability, which is employed to yield a decision procedure for checking whether a given protocol meets its zero knowledge specification.


Tools for the Automatic Generation of Ontology Documentation: A Task-Based Evaluation

Ontologies are knowledge constructs essential for creation of the Web of Data. Good documentation is required to permit people to understand ontologies and thus employ them correctly, but this is costly to create by tradition authorship methods, and is thus inefficient to create in this way until an ontology has matured into a stable structure. The authors describe three tools, LODE, Parrot and the OWLDoc-based Ontology Browser, that can be used automatically to create documentation from a well-formed OWL ontology at any stage of its development. They contrast their properties and then report on the authors’ evaluation of their effectiveness and usability, determined by two task-based user testing sessions.


Digital Mosaic Frameworks - An Overview

Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we survey in a unified framework several methods to transform raster input images into good quality mosaics. For each of the major different approaches in literature the paper reports a short description and a discussion of the most relevant issues. To complete the survey comparisons among the different techniques both in terms of visual quality and computational complexity are provided.


Human movement analysis using stereophotogrammetry. Part 1: theoretical background.

This paper sets the stage for a series of reviews dealing with the problems associated with the reconstruction and analysis of in vivo skeletal system kinematics using optoelectronic stereophotogrammetric data. Instantaneous bone position and orientation and joint kinematic variable estimations are addressed in the framework of rigid body mechanics. The conceptual background to these exercises is discussed. Focus is placed on the experimental and analytical problem of merging the information relative to movement and that relative to the morphology of the anatomical body parts of interest. The various global and local frames that may be used in this context are defined. Common anatomical and mathematical conventions that can be used to describe joint kinematics are illustrated in a comparative fashion. The authors believe that an effort to systematize the different theoretical and experimental approaches to the problems involved and related nomenclatures, as currently reported in the literature, is needed to facilitate data and knowledge sharing, and to provide renewed momentum for the advancement of human movement analysis.


Differentially Private Smart Metering with Battery Recharging

The energy industry has recently begun using smart meters to take fine-grained readings of energy usage. These smart meters enable flexible timeof-use billing, forecasting, and demand response, but they also raise serious user privacy concerns. We propose a novel technique for provably hiding sensitive power consumption information in the overall power consumption stream. Our technique relies on a rechargeable battery that is connected to the household’s power supply. This battery is used to modify the household’s power consumption by adding or subtracting noise (i.e., increasing or decreasing power consumption), in order to establish strong privacy guarantees in the sense of differential privacy. To achieve these privacy guarantees in realistic settings, we first investigate the influence of, and the interplay between, capacity and throughput bounds that batteries face in reality. We then propose an integrated method based on noise cascading that allows for recharging the battery on-the-fly so that differential privacy is retained, while adhering to capacity and throughput constraints, and while keeping the additional consumption of energy induced by our technique to a minimum.


Diagnosing performance overheads in the xen virtual machine environment

Virtual Machine (VM) environments (e.g., VMware and Xen) are experiencing a resurgence of interest for diverse uses including server consolidation and shared hosting. An application's performance in a virtual machine environment can differ markedly from its performance in a non-virtualized environment because of interactions with the underlying virtual machine monitor and other virtual machines. However, few tools are currently available to help debug performance problems in virtual machine environments.In this paper, we present Xenoprof, a system-wide statistical profiling toolkit implemented for the Xen virtual machine environment. The toolkit enables coordinated profiling of multiple VMs in a system to obtain the distribution of hardware events such as clock cycles and cache and TLB misses. The toolkit will facilitate a better understanding of performance characteristics of Xen's mechanisms allowing the community to optimize the Xen implementation.We use our toolkit to analyze performance overheads incurred by networking applications running in Xen VMs. We focus on networking applications since virtualizing network I/O devices is relatively expensive. Our experimental results quantify Xen's performance overheads for network I/O device virtualization in uni- and multi-processor systems. With certain Xen configurations, networking workloads in the Xen environment can suffer significant performance degradation. Our results identify the main sources of this overhead which should be the focus of Xen optimization efforts. We also show how our profiling toolkit was used to uncover and resolve performance bugs that we encountered in our experiments which caused unexpected application behavior.


Adaptive Courseware: A Literature Review

This paper gives a detailed description of how adaptivity is realized in e-learning systems, with a focus on adaptivity of courseware. The review aims to answer three basic questions on adaptive courseware: what has been adapted, how and why. Researchers have tried to adapt according to these different elements: student knowledge, behaviour, interests, preferences, learning style, cognitive style, goals, courseware presentation, navigation, annotation, sequencing and knowledge testing. An historical background of adaptivity is described: from word definition, through adaptive instructions and adaptive systems, all the way to adaptive courseware. The biggest challenge was to find the most prominent representatives of adaptive systems in courseware generation that were selected after an exhaustive search through relevant scientific paper databases. Each prevailing system is briefly described and the student features, to which the system adapts, are highlighted, together with the level of adaptation. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from elearning systems in general to intelligent tutoring systems in particular. Such an analysis has not been done so far, especially in a way that adaptive systems are described uniformly. Finally, a comparative analysis of those systems and conclusions and demands about the ultimate adaptive system is given. This paper can be used as a guide for making decisions about what to adapt, how and why, while designing an adaptive e-learning system.


Influence of Rectifiers on High-Speed Permanent Magnet Generator Electromagnetic and Temperature Fields in Distributed Power Generation Systems

A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on-off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.


A Cross-Type Thermal Wind Sensor With Self-Testing Function

A 2-D smart flow sensor with self-testing function was designed, fabricated and tested in this paper. It is composed of cross-structure heaters and four symmetrically located sensing parts. When the flow angle changes in the clockwise direction, the temperature differences among the four parts, namely Mode 1 given by T13(T24) and Model 2 given by T12(T14) will be Sine(Cosine) functions of wind direction. Further study shows that the magnitude of mode 1 is 1.414 times that of Mode 2, and the phase of Mode 2 leads Mode 1 by 45°. By using the fixed phase gap between the two modes, the self-test function can be realized without additional components. In order to achieve a high sensitivity and robust sensor, thermal insulated substrate was introduced to fabricate calorimetric flow sensor instead of silicon wafers. The proposed sensor was fabricated on the Pyrex7740 substrate using single liftoff process. Finally, a wind tunnel test was carried out in constant power (CP) mode, and the test results show reasonable agreement with the simulation curves.


Modeling the ownership of source code topics

Exploring linguistic topics in source code is a program comprehension activity that shows promise in helping a developer to become familiar with an unfamiliar software system. Examining ownership in source code can reveal complementary information, such as who to contact with questions regarding a source code entity, but the relationship between linguistic topics and ownership is an unexplored area. In this paper we combine software repository mining and topic modeling to measure the ownership of linguistic topics in source code. We conduct an exploratory study of the relationship between linguistic topics and ownership in source code using 10 open source Java systems. We find that classes that belong to the same linguistic topic tend to have similar ownership characteristics, which suggests that conceptually related classes often share the same owner(s). We also find that similar topics tend to share the same ownership characteristics, which suggests that the same developers own related topics.


Behavioural Finance : A Review and Synthesis

I provide a synthesis of the Behavioural finance literature over the past two decades. I review the literature in three parts, namely, (i) empirical and theoretical analyses of patterns in the cross-section of average stock returns, (ii) studies on trading activity, and (iii) research in corporate finance. Behavioural finance is an exciting new field because it presents a number of normative implications for both individual investors and CEOs. The papers reviewed here allow us to learn more about these specific implications.


PATTERNS OF INFORMATION SEEKING ON THE WEB: A QUALITATIVE STUDY OF DOMAIN EXPERTISE AND WEB EXPERTISE

This research examines the pattern of Web information seeking in four groups of nurses with different combinations of domain expertise and Web expertise. Protocols were gathered as the nurses carried out information-seeking tasks in the domain of osteoporosis. Domain and Web novices searched breadth-first and did little or no evaluation of the results. Domain expert/Web novices also searched breadth-first but evaluated information more thoroughly using osteoporosis knowledge. Domain novice/Web experts searched in a mixed, breadth-first/depth-first pattern and attempted to evaluate information using general criteria. Domain expert/Web experts carried out depth-first searches, following deep trails of information and evaluated information based on the most varied and sophisticated criteria. The results suggest that there are distinct differences in searching patterns related to expertise. Implications of these findings and suggestions for future research are provided. _______________________ Christine A. Jenkins is a Solution Architect with Sprint Corporation. christine_jenkin@hotmail.com. Cynthia L. Corritore is a professor at the College of Business Administration, Creighton University, Omaha, NE 68178 USA. cindy@creighton.edu. Susan Wiedenbeck is a professor at the College of Information Science and Technology, Drexel University, Philadelphia, PA 19104 USA. susan.wiedenbeck@cis.drexel.edu.


A Softwarization Architecture for UAVs and WSNs as Part of the Cloud Environment

The development of Unmanned Aerial Vehicles (UAV) and wireless sensor network (WSN) applications depends on the availability of resources in UAVs and sensor nodes. In traditional applications, UAVs obtain the required data from specific sensors. However, this tightly coupled architecture restricts the usage of the infrastructure for specific applications. This paper proposes a softwarization architecture for UAVs and WSNs. In addition, the higher layers are proposed to be part of the cloud to benefit from the various cloud opportunities. The proposed architecture is based on the network softwarization concept and the Software Defined Networks (SDN) concept along with the Network Functional Virtualization (NFV) technologies. These concepts are associated with decoupling the hardware infrastructure from the control layer that virtualizes the infrastructure resources for the higher layers. The architecture is illustrated with an agricultural example for a cooperative system of a WSN and UAVs. We implement a prototype system that consists of a sensing node, UAV, WSN controller, UAV controller, and orchestration layer to provide a proof of concept for the proposed architecture.


A Comprehensive Study for Software Testing and Test Cases Generation Paradigms

Software testing is accounted to be an essential part in software development life cycle in terms of cost and manpower, where its total cost is considerable high. Consequently, many studies [48] have been conducted to minimize the associated cost and human effort to fix bugs and errors, and to improve the quality of testing process by automatically generating test cases. Test cases can be generated from different phases (requirement phase, design phase and after development phase). Though, test case generation at early stages is more effective rather than that after development, where time and effort used for finding and fixing errors and bugs is less than that after development. At later stage, fixing errors results in enormous code correction, consuming a lot of time and effort. In this paper, we study the different paradigms of testing techniques for generating test cases, where we investigate their coverage and associated capabilities. We finally propose a preliminary model for a generic automated test cases generation.