title: D2D-Based Grouped Random Access to Mitigate Radio Access Network Congestion in Massive Machine-Type Communications

abstract: Massive Machine-Type Communications (mMTC) presents significant challenges in terms of the number of devices accessing the shared resource, and coordination among those devices. This paper overviews work on RAN congestion control in order to better manage resources in the context of device-to-device (D2D) interaction among the MTCDs. It then proceeds to introduce a novel grouping-assisted random access protocol for mMTC, showing beneficial performance of the concept against parameters such as group size, number of MTCDs in the overall scenario, and reliability of D2D links. Finally, the association is made with a Geolocation Database (GDB) capability to assist the grouping decisions, drawing parallels with recent regulatory-driven initiatives around GDBs, and arguing benefits of the concept.


Models and Methods for Efficiency Estimation of a Marine Electric Power Grid


title: Accurate Ego-Vehicle Global Localization at Intersections Through Alignment of Visual Data With Digital Map

abstract: This paper proposes a method for achieving improved ego-vehicle global localization with respect to an approaching intersection, which is based on the alignment of visual landmarks perceived by the on-board visual system, with the information from a proposed extended digital map (EDM). The visual system relies on a stereovision system that provides a detailed 3-D description of the environment, including road landmark information (lateral lane delimiters, painted traffic signs, curbs, and stop lines) and dynamic environment information (other vehicles). An EDM is proposed, which enriches the standard map information with a detailed description of the intersection required for current lane identification, landmark alignment, and ego-vehicle accurate global localization. A novel approach for lane-delimiter classification, which is necessary for the lane identification, is also presented. An original solution for identifying the current lane, combining visual and map information with the help of a Bayesian network (BN), is proposed. Extensive experiments have been performed, and the results are evaluated with a Global Navigation Satellite System of high accuracy (2 cm). The achieved global localization accuracy is of submeter level, depending on the performance of the stereovision system.


title: Emotion recognition in spontaneous and acted dialogues

abstract: In this work, we compare emotion recognition on two types of speech: spontaneous and acted dialogues. Experiments were conducted on the AVEC2012 database of spontaneous dialogues and the IEMOCAP database of acted dialogues. We studied the performance of two types of acoustic features for emotion recognition: knowledge-inspired disfluency and nonverbal vocalisation (DIS-NV) features, and statistical Low-Level Descriptor (LLD) based features. Both Support Vector Machines (SVM) and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) were built using each feature set on each emotional database. Our work aims to identify aspects of the data that constrain the effectiveness of models and features. Our results show that the performance of different types of features and models is influenced by the type of dialogue and the amount of training data. Because DIS-NVs are less frequent in acted dialogues than in spontaneous dialogues, the DIS-NV features perform better than the LLD features when recognizing emotions in spontaneous dialogues, but not in acted dialogues. The LSTM-RNN model gives better performance than the SVM model when there is enough training data, but the complex structure of a LSTM-RNN model may limit its performance when there is less training data available, and may also risk over-fitting. Additionally, we find that long distance contexts may be more useful when performing emotion recognition at the word level than at the utterance level.


title: Identifying individuals amenable to drug recovery interventions through computational analysis of addiction content in social media

abstract: Drug abuse and addiction is a growing epidemic at the forefront of public health. Within this remit, the illicit use of opioid analgesics alone has emerged as one of the fastest growing forms of drug abuse in the U.S. and the death rate from this epidemic are drawing comparison to the US AIDS epidemic. Traditional methods of epidemiology based on explicit reporting of indicator-based data from patient records or data collected through surveys is often found wanting in modeling and designing effective interventions for addiction. In addition to the non-real time nature of the aforementioned methods, this is also due to a number of reasons including the continual penetration of novel biological/chemical entities into the abuse-cycle, the complex etiology of addiction which includes among others social factors, and the multistage nature of the addiction process. The recent advent of social media presents an intriguing information resource that is free from some of the above deficiencies and may be leveraged to model the addiction process and offer perspectives that are unavailable through traditional methods of epidemiology. In this paper, we use addiction related social media content to design a computational epidemiological approach for predicting a user's propensity for seeking drug recovery interventions. Solving this problem is crucial for designing effective interventions, identifying cohorts who would be most amenable to recovery, and planning resource allocations. Our method characterizes the evolving language of drug use, identifies the interactions that influence a drug user's actions, and using machine learning techniques predicts the extent to which a user is likely to participate in addiction recovery communities. Experimental assessments on real-world data from the social media platforms Reddit and Twitter indicate the proposed method can identify users who are amenable to addiction recovery intervention with high precision, recall, and F1 values.


title: Improving system properties using operator transformations

abstract: It has been demonstrated how non-standard parameterizations can enhance, for example, the identifiability properties of dynamic systems. There is a temptation to extend these ideas of parameter transformations also to other fields of control engineering — however, the theories derived for the standard operators do not necessarily apply to the systems with new operators. This paper presents a solid basis for new parameterizations; the ideas are illustrated using simulations.


title: Green Communication in Energy Renewable Wireless Mesh Networks: Routing, Rate Control, and Power Allocation

abstract: The increasing demand for wireless services has led to a severe energy consumption problem with the rising of greenhouse gas emission. While the renewable energy can somehow alleviate this problem, the routing, flow rate, and power still have to be well investigated with the objective of minimizing energy consumption in multi-hop energy renewable wireless mesh networks (ER-WMNs). This paper formulates the problem of network-wide energy consumption minimization under the network throughput constraint as a mixed-integer nonlinear programming problem by jointly optimizing routing, rate control, and power allocation. Moreover, the min-max fairness model is applied to address the fairness issue because the uneven routing problem may incur the sharp reduction of network performance in multi-hop ER-WMNs. Due to the high computational complexity of the formulated mathematical programming problem, an energy-aware multi-path routing algorithm (EARA) is also proposed to deal with the joint control of routing, flow rate, and power allocation in practical multi-hop WMNs. To search the optimal routing, it applies a weighted Dijkstra's shortest path algorithm, where the weight is defined as a function of the power consumption and residual energy of a node. Extensive simulation results are presented to show the performance of the proposed schemes and the effects of energy replenishment rate and network throughput on the network lifetime.


abstract: A dimmable and power-compensated AC direct LED driver with high efficiency for lighting applications is presented. A Source-Coupled Pair (SCP) is adopted to realize the soft self-commutating. There is no additional sensing or control circuitry, which lower the system power dissipation. A pulse-width modulation (PWM) dimming technique is applied in regulating the LED's brightness. Furthermore, the driver includes a Power Compensation module which enables the output power of LEDs approximately constant under various line voltage. The LED driver IC is designed and implemented in CSMC's 700 V 1 um BCD process. The post-layout simulation results show that under 220 V|rms AC voltage, the driver has a high efficiency of 90.28% and realizes dimming with a 90% duty ratio, 10 KHz dimming signal. The output power compensated only changes 4% under a 10% variation in input voltage.


title: How to assess epidemiological studies

abstract: Assessing the quality of an epidemiological study equates to assessing whether the inferences drawn from it are warranted when account is taken of the methods, the representativeness of the study sample, and the nature of the population from which it is drawn. Bias, confounding, and chance can threaten the quality of an epidemiological study at all its phases. Nevertheless, their presence does not necessarily imply that a study should be disregarded. The reader must first balance any of these threats or missing information with their potential impact on the conclusions of the report.


title: Beam steering in HMSIW LWA at fixed millimeter wave frequency

abstract: Steering of main beam of radiation at fixed millimeter wave frequency in a Substrate Integrated Waveguide (SIW) Leaky Wave Antenna (LWA) has not been investigated so far in literature. In this paper a Half-Mode Substrate Integrated Waveguide (HMSIW) LWA is proposed which has the capability to steer its main beam at fixed millimeter wave frequency of 24GHz. Beam steering is made feasible by changing the capacitance of the capacitors, connected at the dielectric side of HMSIW. The full wave EM simulations show that the main beam scans from 36° to 57° in the first quadrant.


title: PROFINET: an integration platform for heterogeneous industrial communication systems

abstract: Ethernet has become so popular that it has also been further developed/or use at field level in automation technology under realtime conditions. However, for reasons of function and investment protection, the introduction of realtime Ethernet in automation solutions is likely to be a gradual process. Suitable solutions for the integration of existing fieldbus technology into the realtime Ethernet network are therefore required. This paper describes two possible integration concepts of the PROFINET IO realtime Ethernet standard using INTERBUS and PROFIBUS as an example. It shows that it is possible, despite very different device models of the considered fieldbus systems, to implement a high level of integration


title: Performance Evaluation of NoSQL Databases

abstract: NoSQL databases have emerged as a backend to support Big Data applications. NoSQL databases are characterized by horizontal scalability, schema-free data models, and easy cloud deployment. To avoid overprovisioning, it is essential to be able to identify the correct number of nodes required for a specific system before deployment. This paper benchmarks and compares three of the most common NoSQL databases: Cassandra, MongoDB and HBase. We deploy them on the Amazon EC2 cloud platform using different types of virtual machines and cluster sizes to study the effect of different configurations. We then compare the behavior of these systems to high-level queueing network models. Our results show that the models are able to capture the main performance characteristics of the studied databases and form the basis for a capacity planning tool for service providers and service users.


title: Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields

abstract: Robots in a human environment need to be compliant. This compliance requires that a preplanned movement can be adapted to an obstacle that may be moving or appearing unexpectedly. Here, we present a general framework for movement generation and mid-flight adaptation to obstacles. For robust motion generation, Ijspeert et al developed the framework of dynamic movement primitives which represent a demonstrated movement with a set of differential equations. These equations allow adding a perturbing force without sacrificing stability of the desired movement. We extend this framework such that arbitrary movements in end-effector space can be represented - which was not possible before. Furthermore, we include obstacle avoidance by adding to the equations of motion a repellent force - a gradient of a potential field centered around the obstacle. In addition, this article compares different potential fields and shows how to avoid obstacle-link collisions within this framework. We demonstrate the abilities of our approach in simulations and with an anthropomorphic robot arm.


title: Irregular surface area measuring instrument

abstract: The objective here is to develop a flat surface area measuring system which is used to calculate the surface area of any irregular sheet. The irregular leather sheet is used in this work. The system is self protected by user name and password set through software for security purpose. Only authorize user can enter into the system by entering the valid pin code. After entering into the system, the user can measure the area of any irregular sheet, monitor and control the system. The heart of the system is Programmable Logic Controller (Master K80S) which controls the complete working of the system. The controlling instructions for the system are given through the designed Human to Machine Interface (HMI). For communication purpose the GSM modem is also interfaced with the Programmable Logic Controller (PLC). The remote user can also monitor the current status of the devices by sending SMS message to the GSM modem.


A New D.A.R.E. Curriculum Gets Mixed Reviews


title: Retained Austenite Transformation during Heat Treatment of a 5 Wt Pct Cr Cold Work Tool Steel

abstract: Retained austenite transformation was studied for a 5 wt pct Cr cold work tool steel tempered at 798 K and 873 K (525 °C and 600 °C) followed by cooling to room temperature. Tempering cycles with variations in holding times were conducted to observe the mechanisms involved. Phase transformations were studied with dilatometry, and the resulting microstructures were characterized with X-ray diffraction and scanning electron microscopy. Tempering treatments at 798 K (525 °C) resulted in retained austenite transformation to martensite on cooling. The martensite start (M s ) and martensite finish (M f ) temperatures increased with longer holding times at tempering temperature. At the same time, the lattice parameter of retained austenite decreased. Calculations from the M s temperatures and lattice parameters suggested that there was a decrease in carbon content of retained austenite as a result of precipitation of carbides prior to transformation. This was in agreement with the resulting microstructure and the contraction of the specimen during tempering, as observed by dilatometry. Tempering at 873 K (600 °C) resulted in precipitation of carbides in retained austenite followed by transformation to ferrite and carbides. This was further supported by the initial contraction and later expansion of the dilatometry specimen, the resulting microstructure, and the absence of any phase transformation on cooling from the tempering treatment. It was concluded that there are two mechanisms of retained austenite transformation occurring depending on tempering temperature and time. This was found useful in understanding the standard tempering treatment, and suggestions regarding alternative tempering treatments are discussed.


title: Isocube: Exploiting the Cubemap Hardware

abstract: This paper proposes a novel six-face spherical map, isocube, that fully utilizes the cubemap hardware built in most GPUs. Unlike the cubemap, the proposed isocube uniformly samples the unit sphere (uniformly distributed), and all samples span the same solid angle (equally important). Its mapping computation contains only a small overhead. By feeding the cubemap hardware with the six-face isocube map, the isocube can exploit all built-in texturing operators tailored for the cubemap and achieve a very high frame rate. In addition, we develop an anisotropic filtering that compensates aliasing artifacts due to texture magnification. This filtering technique extends the existing hardware anisotropic filtering and can be applied not only to the proposed isocube, but also to other texture mapping applications.


title: The Search for Quasi-Periodicity in Islamic 5-fold Ornament

abstract: T he Penrose tilings are remarkable in that they are non-periodic (have no translational symmetry) but are clearly organised. Their structure, called quasiperiodicity, can be described in several ways, including via self-similar subdivision, tiles with matching rules, and projection of a slice of a cubic lattice in R. The tilings are also unusual for their many centres of local 5-fold and 10fold rotational symmetry, features shared by some Islamic geometric patterns. This resemblance has prompted comparison, and has led some to see precursors of the Penrose tilings and even evidence of quasi-periodicity in traditional Islamic designs. Bonner [2] identified three styles of self-similarity; Makovicky [20] was inspired to develop new variants of the Penrose tiles and later, with colleagues [24], overlaid Penrose-type tilings on traditional Moorish designs; more recently, Lu and Steinhardt [17] observed the use of subdivision in traditional Islamic design systems and overlaid Penrose kites and darts on Iranian designs. The latter article received widespread exposure in the world’s press, although some of the coverage overstated and misrepresented the actual findings. The desire to search for examples of quasi-periodicity in traditional Islamic patterns is understandable, but we must take care not to project modern motivations and abstractions into the past. An intuitive knowledge of group theory is sometimes attributed to any culture that has produced repeating patterns displaying a wide range of symmetry types, even though they had no abstract notion of a group. There are two fallacies to avoid: • abstraction: P knew about X and X is an example of Y therefore P knew Y. • deduction: P knew X and X implies Y therefore P knew Y.


title: Optical properties of biological tissues: a review

abstract: A review of reported tissue optical properties summarizes the wavelength-dependent behavior of scattering and absorption. Formulae are presented for generating the optical properties of a generic tissue with variable amounts of absorbing chromophores (blood, water, melanin, fat, yellow pigments) and a variable balance between small-scale scatterers and large-scale scatterers in the ultrastructures of cells and tissues.


title: Applying long short-term memory recurrent neural networks to intrusion detection

abstract: We claim that modelling network trac as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM) recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each oer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated dierent feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classier provides superior performance in comparison to results previously published results of strong static classiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the rst time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.


title: Decimal floating-point: algorism for computers

abstract: Decimal arithmetic is the norm in human calculations, and human centric applications must use a decimal floating point arithmetic to achieve the same results. Initial benchmarks indicate that some applications spend 50% to 90% of their time in decimal processing, because software decimal arithmetic suffers a 100/spl times/ to 1000/spl times/ performance penalty over hardware. The need for decimal floating point in hardware is urgent. Existing designs, however, either fail to conform to modern standards or are incompatible with the established rules of decimal arithmetic. We introduce a new approach to decimal floating point which not only provides the strict results which are necessary for commercial applications but also meets the constraints and requirements of the IEEE 854 standard. A hardware implementation of this arithmetic is in development, and it is expected that this will significantly accelerate a wide variety of applications.


title: Text/non-text image classification in the wild with convolutional neural networks

abstract: Text in natural images is an important source of information, which can be utilized for many real-world applications. This work focuses on a new problem: distinguishing images that contain text from a large volume of natural images. To address this problem, we propose a novel convolutional neural network variant, called multi-scale spatial partition network (MSP-Net). The network classifies images that contain text or not, by predicting text existence in all image blocks, which are spatial partitions at multiple scales on an input image. The whole image is classified as a text image (an image containing text) as long as one of the blocks is predicted to contain text. The network classifies images very efficiently by predicting all blocks simultaneously in a single forward propagation. Through experimental evaluations and comparisons on public datasets, we demonstrate the effectiveness and robustness of the proposed method. HighlightsWe study a new and important problem: text/non-text image classification in the wild.A new scheme based on block-level classification is proposed to tackle this problem.We propose MSP-Net, a novel CNN variant, to efficiently classify text/non-text images.As a by-product, MSP-Net outputs coarse locations and scales of texts.


title: A planar hopping robot with one actuator: design, simulation, and experimental results

abstract: Animal legs are much more complex than necessary for running on horizontal surfaces. Past research shows that, despite their complexity, they act essentially like simple pogo sticks, or the "spring loaded inverted pendulum" (SLIP). In this paper, the possibility of the realization of a SLIP model using a spring in the leg and only one actuator at the hip is investigated first in numerical simulation and then in experiments. A simple controller results in running at 0.80 m/s (6.7 leg lengths per second). The stability and robustness of the resulting motion is demonstrated.


title: Multicenter Randomized Clinical Trial Evaluating the Effectiveness of the Lokomat in Subacute Stroke

abstract: Objective. To compare the efficacy of robotic-assisted gait training with the Lokomat to conventional gait training in individuals with subacute stroke. Methods. A total of 63 participants <6 months poststroke with an initial walking speed between 0.1 to 0.6 m/s completed the multicenter, randomized clinical trial. All participants received twenty-four 1-hour sessions of either Lokomat or conventional gait training. Outcome measures were evaluated prior to training, after 12 and 24 sessions, and at a 3-month follow-up exam. Self-selected overground walking speed and distance walked in 6 minutes were the primary outcome measures, whereas secondary outcome measures included balance, mobility and function, cadence and symmetry, level of disability, and quality of life measures. Results. Participants who received conventional gait training experienced significantly greater gains in walking speed (P = .002) and distance (P = .03) than those trained on the Lokomat. These differences were maintained at the 3-mon...


title: Interpreting Visual Question Answering Models

abstract: Deep neural networks have shown striking progress and obtained state-of-the-art results in many AI research fields in the recent years. However, it is often unsatisfying to not know why they predict what they do. In this paper, we address the problem of interpreting Visual Question Answering (VQA) models. Specifically, we are interested in finding what part of the input (pixels in images or words in questions) the VQA model focuses on while answering the question. To tackle this problem, we use two visualization techniques -- guided backpropagation and occlusion -- to find important words in the question and important regions in the image. We then present qualitative and quantitative analyses of these importance maps.


title: The Critical Success Factors of ERP implementation in Highe r Education in Saudi Arabia: A Case Study

abstract: An ERP system plays an important role in managing business processes. Recently, many universities have implemented ERP systems. However, little research has been conducted regarding these systems in the higher education sector. This paper explores and analyzes the existing literature on ERP implementation and attempts to identify the critical success factors for a successful implementation of an ERP in higher education's institutions in Saudi Arabia. We use Madar, which is an ERP system developed in King Saud University in Riyadh, Saudi Arabia, as a case study to measure the success in ERP implementation from technical and user perspectives. This paper proves that the most important critical success factors of ERP implementation in Higher Education sector in Saudi Arabia was project management. This study has contributed to academic research by producing the empirical evidence to support the theories of CSFs and ERP implementation success.


title: Don't stand so close to me: investigating the effect of control on the appeal of virtual humans using immersion and a proximity-based behavioral task

abstract: In recent years, there has been much research and media attention devoted to investigating virtual reality environments. In this paper, we are investigating if there are differences in how characters are perceived in immersive virtual reality as opposed to more common, screen-based environments. We were particularly interested if the spatial and immersive components play an important part in perception of interactive, game-like settings, where characters can either be controlled (avatars) or observed (agents). We focus on the subjective reports on perceived realism, affinity, co-presence and agency. Since appearance of the character is an important component of affinity, we introduced the changes in render style, ranging in three realism levels, to test if appearance would even further influence the perception in relation to control condition and platform. Furthermore, we adapted a behavioural method (proximity task) as a novel approach to establishing if behavioural changes could be recorded based on the introduced conditions and compared those values with the subjective reports of the participants. The conclusions have an important value to character design specific to platform and character control.


title: BANDGAP VOLTAGE REFERENCE IC FOR HV AUTOMOTIVE APPLICATIONS WITH PSEUDO-REGULATED BIAS AND SERVICE REGULATOR

abstract: The paper presents a bandgap voltage reference (BGR) implemented in TSMC 0.25 μm BCD technology for an automotive application. To withstand a car's battery large voltage variations, from 5 V to 40 V, the circuit features an embedded pseudo-regulator providing a stable bias current for the bandgap core. High-voltage (HV) MOS count has been kept low thus allowing the design of a compact BGR with an area of 0.118 mm2. The BGR has been designed to operate in automotive extended temperature range (-40°C to 150°C) and it provides a stable voltage of 1.21 V, which is also used as reference for a cascade 3.7 V linear regulator. Measurements carried on fabricated IC samples prove the effectiveness of the BGR design in terms of supported input voltage variations and operating temperature range, temperature drift, line regulation and PSRR performance.


title: Mismatch-Aware Common-Centroid Placement for Arbitrary-Ratio Capacitor Arrays Considering Dummy Capacitors

abstract: Switched capacitors are commonly used in analog circuits to increase the accuracy of analog signal processing and lower power consumption. To take full advantage of switched capacitors, it is very important to achieve accurate capacitance ratios in the layout of the capacitor arrays, which are affected by systematic and random mismatches. A good capacitor placement should have a common-centroid structure with the highest possible degree of dispersion to mitigate mismatches. Several dummy units should be inserted to make the placement shape more square and compact. This paper proposes a simulated-annealing-based approach for mismatch-aware common-centroid placement under the above constraints. A pair-sequence representation is used to record a placement, and a couple of associated operations are developed to find better solutions. The experimental results show that the proposed placements achieve smaller oxide-gradient-induced mismatch and larger overall correlation coefficients (i.e., higher degree of dispersion) than those of previous works.


title: On automated source selection for transfer learning in convolutional neural networks

abstract: Abstract Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task to a target task. In the context of convolutional neural networks (CNNs), transfer learning can be implemented by transplanting the learned feature layers from one CNN (derived from the source task) to initialize another (for the target task). Previous research has shown that the choice of the source CNN impacts the performance of the target task. In the current literature, there is no principled way for selecting a source CNN for a given target task despite the increasing availability of pre-trained source CNNs. In this paper we investigate the possibility of automatically ranking source CNNs prior to utilizing them for a target task. In particular, we present an information theoretic framework to understand the source-target relationship and use this as a basis to derive an approach to automatically rank source CNNs in an efficient, zero-shot manner. The practical utility of the approach is thoroughly evaluated using the Places-MIT dataset, MNIST dataset and a real-world MRI database. Experimental results demonstrate the efficacy of the proposed ranking method for transfer learning.


Designing Procedural Game Spaces: A Case Study


title: Fully Isotropic Four-Degrees-of-Freedom Parallel Mechanisms for Schoenflies Motion

abstract: This paper presents a novel family of fully isotropic parallel mechanisms whose output link is provided with Schoenflies motion, i.e., it can freely translate in space and rotate about a fixed direction. A methodology is proposed that makes use of the theory of screws to synthesize desired forms for both the direct and the inverse Jacobian matrices. In particular, these are made diagonal and constant throughout the workspace. Motors are mounted one per leg and each one of them actuates one of the degrees of freedom of the output body through a constant one-to-one velocity relation. As a consequence, motors may apply, with equal ease, a twist or a wrench of any amplitude (within the motor operation range) to the end-effector around any screw congruous with the admitted motion, so that full isotropy is achieved. Kinematic analysis is trivial and no computation is required for real-time control. Furthermore, actuator motion ranges can be easily related to the theoretical workspace dimensions and the problem of link interference is potentially simplified.


title: On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes

abstract: In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modelling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.


title: Image and video abstraction by multi-scale anisotropic Kuwahara filtering

abstract: The anisotropic Kuwahara filter is an edge-preserving filter that is especially useful for creating stylized abstractions from images or videos. It is based on a generalization of the Kuwahara filter that is adapted to the local structure of image features. In this work, two limitations of the anisotropic Kuwahara filter are addressed. First, it is shown that by adding thresholding to the weighting term computation of the sectors, artifacts are avoided and smooth results in noise-corrupted regions are achieved. Second, a multi-scale computation scheme is proposed that simultaneously propagates local orientation estimates and filtering results up a low-pass filtered pyramid. This allows for a strong abstraction effect and avoids artifacts in large low-contrast regions. The propagation is controlled by the local variances and anisotropies that are derived during the computation without extra overhead, resulting in a highly efficient scheme that is particularly suitable for real-time processing on a GPU.


title: Modeling Dimensions of Self-Adaptive Software Systems

abstract: It is commonly agreed that a self-adaptive software system is one that can modify itself at run-time due to changes in the system, its requirements, or the environment in which it is deployed. A cursory review of the software engineering literature attests to the wide spectrum of software systems that are described as self-adaptive. The way self-adaptation is conceived depends on various aspects, such as the users' requirements, the particular properties of a system, and the characteristics of the environment. In this paper, we propose a classification of modeling dimensions for self-adaptive software systems. Each modeling dimension describes a particular facet of the system that is relevant to self-adaptation. The modeling dimensions provide the engineers with a common set of vocabulary for specifying the self-adaptive properties under consideration and select suitable solutions. We illustrate how the modeling dimensions apply to several application scenarios.


title: My Phone and Me: Understanding People's Receptivity to Mobile Notifications

abstract: Notifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this paper we present an in-situ study of mobile interruptibility focusing on the effect of cognitive and physical factors on the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone logging and experience sampling we collected 10372 in-the-wild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response time and the perceived disruption from a notification can be influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that even a notification that contains important or useful content can cause disruption. Finally, we observe the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification.


title: Blockchain-based payment collection supervision system using pervasive Bitcoin digital wallet

abstract: After years of tremendous development and research in digital currency, the most famous Bitcoin industry chain has been gradually completed including mining, exchange, currency exchange, ATM, pervasive digital wallet design and so on. Especially, its blockchain technology has become FinTech organizations' emerging business and research directions, also been applied in the interdisciplinary medical science, supply chain and Internet of things. Digitizing currency can solve many problems in physical currency, such as the rampant counterfeit banknotes. Thus, not only the transparency can be cost-effectively preserved in store's ledger, but also customer's rights and interests can be protected while using the digital wallet. For the government, the regulation and auditing of financial transaction can be made simpler and more convenient for tax collection issue. Digitized transaction details can be much easier to audit by any computing device than manually audit the conventional ledger books. Furthermore, to make merchandise store to use digitized currency much easier, in this paper, we propose and deploy a Bitcoin collection supervision system called BPCSS based on blockchain technology with cloud databases for customers and merchandise stores. The preliminary experimental results via the well-known pervasive digital wallet of Testnet Bitcoin demonstrate the proposed BPCSS can cost-effectively collect payment and supervise the transactions between customer and merchandise store running the implemented NFC-enabled Android Apps.


title: Incentives and prosocial behavior

abstract: We develop a theory of prosocial behavior that combines heterogeneity in individual altruism and greed with concerns for social reputation or self-respect. Rewards or punishments (whether material or image-related) create doubt about the true motive for which good deeds are performed and this "overjustification effect" can induce a partial or even net crowding out of prosocial behavior by extrinsic incentives. We also identify settings that are conducive to multiple social norms and those where disclosing one's generosity may backfire. Finally, we analyze the choice by public and private sponsors of incentive levels, their degree of confidentiality and the publicity given to agents' behavior. Sponsor competition is shown to potentially reduce social welfare.


title: Environment-Aware Indoor Localization Using Magnetic Induction

abstract: The Magnetic Induction (MI) communication techniques have enabled or enhanced many wireless applications in the indoor environments where line-of-sight (LOS) links usually do not exist. The position information of each wireless device in such complex environment can also be derived by the same MI systems without additional hardware or infrastructure. However, while MI signals can penetrate most transmission media without significant attenuation and phase shifting, the conductive objects in the indoor environment (e.g., metallic pipelines, beams, and human bodies) can still dramatically influence the MI signals, which can cause significant estimation errors in the MI-based indoor localization. To date, no analysis/solution has been provided to address such problem. In this paper, an environment-aware indoor localization mechanism is proposed for MI-based wireless networks in complex non-LOS environments without preinstalled infrastructures. First, the influence of conductive objects on the MI-based wireless network in indoor environment is investigated. Then based on the influence analysis, a joint device localization and conductive-object tomography algorithm is developed to estimate the position of each wireless devices as well as distribution of objects. The simulation evaluation shows the proposed mechanism can accurately localize each device in a MI-based networks in a complex floor plan with multiple conductive walls and obstructions.


The Impact of Training and Social Norms on Information Security Compliance: A Pilot Study


Dual Attention Network for Visual Question Answering


title: Fault-tolerant clock synchronization in distributed systems

abstract: Existing fault-tolerant clock synchronization algorithms are compared and contrasted. These include the following: software synchronization algorithms, such as convergence-averaging, convergence-nonaveraging, and consistency algorithms, as well as probabilistic synchronization; hardware synchronization algorithms; and hybrid synchronization. The worst-case clock skews guaranteed by representative algorithms are compared, along with other important aspects such as time, message, and cost overhead imposed by the algorithms. More recent developments such as hardware-assisted software synchronization and algorithms for synchronizing large, partially connected distributed systems are especially emphasized. >


title: Data Imbalance Problem solving for SMOTE Based Oversampling: Study on Fault Detection Prediction Model in Semiconductor Manufacturing Process

abstract: Fault detection prediction of FAB (wafer fabrication) process in semiconductor manufacturing process is possible that improve product quality and reliability in accordance with the classification performance. However, FAB process is sometimes due to a fault occurs. And mostly it occurs “pass”. Hence, data imbalance occurs in the pass/fail class. If the data imbalance occurs, prediction models are difficult to predict “fail” class because increases the bias of majority class (pass class). In this paper, we propose the SMOTE (Synthetic Minority Oversampling Technique) based over sampling method for solving problem of data imbalance. The proposed method solve the imbalance of the between pass and fail by oversampling the minority class of fail. In addition, by applying the fault detection prediction model to measure the performance.


title: Modeling how students learn to program

abstract: Despite the potential wealth of educational indicators expressed in a student's approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class.


title: Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey

abstract: With the proliferation of wireless devices, wireless networks in various forms have become global information infrastructure and an important part of our daily life, which, at the same time, incur fast escalations of both data volumes and energy demand. In other words, energy-efficient wireless networking is a critical and challenging issue in the big data era. In this paper, we provide a comprehensive survey of recent developments on energy-efficient wireless networking technologies that are effective or promisingly effective in addressing the challenges raised by big data. We categorize existing research into two main parts depending on the roles of big data. The first part focuses on energy-efficient wireless networking techniques in dealing with big data and covers studies in big data acquisition, communication, storage, and computation; while the second part investigates recent approaches based on big data analytics that are promising to enhance energy efficiency of wireless networks. In addition, we identify a number of open issues and discuss future research directions for enhancing energy efficiency of wireless networks in the big data era.


Conditional generative adversarial nets for convolutional face generation


title: Dominant Factors for Online Trust

abstract: The uses of the World Wide Web on the Internet for commerce and information access continue to expand. The e-commerce business has proven to be a promising channel of choice for consumers as it is gradually transforming into a mainstream business activity. However, lack of trust has been identified as a major obstacle to the adoption of online shopping. Empirical study of online trust is constrained by the shortage of high-quality measures of general trust in the e-commence contexts. Based on theoretical or empirical studies in the literature of marketing or information system, nine factors have sound theoretical sense and support from the literature. A survey method was used for data collection in this study. A total of 172 usable questionnaires were collected from respondents. This study presents a new set of instruments for use in studying online trust of an individual. The items in the instrument were analyzed using a factors analysis. The results demonstrated reliable reliability and validity in the instrument.This study identified seven factors has a significant impact on online trust. The seven dominant factors are reputation, third-party assurance, customer service, propensity to trust, website quality, system assurance and brand. As consumers consider that doing business with online vendors involves risk and uncertainty, online business organizations need to overcome these barriers. Further, implication of the finding also provides e-commerce practitioners with guideline for effectively engender online customer trust.


title: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences

abstract: Several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary included many phonetically similar monosyllabic words, therefore the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations. For each parameter set (based on a mel-frequency cepstrum, a linear frequency cepstrum, a linear prediction cepstrum, a linear prediction spectrum, or a set of reflection coefficients), word templates were generated using an efficient dynamic warping method, and test data were time registered with the templates. A set of ten mel-frequency cepstrum coefficients computed every 6.4 ms resulted in the best performance, namely 96.5 percent and 95.0 percent recognition with each of two speakers. The superior performance of the mel-frequency cepstrum coefficients may be attributed to the fact that they better represent the perceptually relevant aspects of the short-term speech spectrum.


Normalized (pointwise) mutual information in collocation extraction


title: The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM

abstract: We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence --- among synchronous or asynchronous methods --- on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.


title: Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization

abstract: We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular, we review a signed variant of the graph Laplacian. We derive our results by considering random walks, graph clustering, graph drawing and electrical networks, showing that they all result in the same formalism for handling negatively weighted edges. We illustrate our methods using examples from social networks with negative edges and bipartite rating graphs.


title: Design topology and electromagnetic field analysis of Permanent Magnet Brushless DC motor for electric scooter application

abstract: This paper presents a Finite Element Method (FEM) based design and analysis of radial flux Permanent Magnet Brushless DC (PMBLDC) motor. The objective of this paper is to develop high power density and high efficiency PMBLDC motor for electric scooter application. The motor considered for FEM design is a 3-phase, 8 pole rectangular wave, which replaces the conventional Internal Combustion Engines (ICEs). The distinct features of this motor design are: (1) The magnetic yoke reduction, which will decrease the weight and size, (2) Coil span equal to one slot pitch which will result in saving of copper and (3) elimination of cogging torque because of fractional number of slots per pole. The outer rotor configuration of PMBLDC motor has been considered in this design. The spokes of the wheel is directly fitted into the outer rotor of the motor. The simulation has been carried out by Pre-flux software. The electromagnetic field analysis is used for the design and optimization of the motor which is based on FEM.


title: Konrad Zuse's Legacy: the Architecture of the Z1 and Z3

abstract: Provides a detailed description of the architecture of the Z1 and Z3 computing machines that Konrad Zuse designed in Berlin between 1936 and 1941. The necessary basic information was obtained from a careful evaluation of the patent application Zuse filed in 1941. Additional insight was gained from a software simulation of the machine's logic. The Z1 was built using purely mechanical components; the Z3 used electromechanical relays. However, both machines shared a common logical structure, and their programming model was the same. I argue that both the Z1 and the Z3 possessed features akin to those of modern computers: the memory and processor were separate units, and the processor could handle floating-point numbers and compute the four basic arithmetical operations as well as the square root of a number. The program was stored on punched tape and was read sequentially. In the last section of this paper, I put the architecture of the Z1 and Z3 into historical perspective by offering a comparison with computing machines built in other countries.


title: Mining ideas from textual information

abstract: This approach introduces idea mining as process of extracting new and useful ideas from unstructured text. We use an idea definition from technique philosophy and we focus on ideas that can be used to solve technological problems. The rationale for the idea mining approach is taken over from psychology and cognitive science and follows how persons create ideas. To realize the processing, we use methods from text mining and text classification (tokenization, term filtering methods, Euclidean distance measure etc.) and combine them with a new heuristic measure for mining ideas. As a result, the idea mining approach extracts automatically new and useful ideas from an user given text. We present these problem solution ideas in a comprehensible way to support users in problem solving. This approach is evaluated with patent data and it is realized as a web-based application, named 'Technological Idea Miner' that can be used for further testing and evaluation.


title: Vehicle Sideslip and Roll Parameter Estimation using GPS

abstract: This paper demonstrates a method for obtaining estimates of key vehicle states using the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor measurements. A Kalman filter integrates the INS sensors with GPS to provide high update estimates of the sensor biases, heading, and vehicle velocities, which can be used to calculate the vehicle slip angle. Since the INS sensors and GPS antennas are attached to the vehicle body, roll and pitch effects from the vehicle motion and road influence the measurements obtained from the sensors and GPS. This paper develops a method that incorporates these roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. With accurate measurements of roll angle and roll rate, it is also possible to estimate key roll parameters, such as roll stiffness and damping ratio, with a second order dynamic model. Comparison of the vehicle state estimates with those predicted by the theoretical vehicle model yields similar results. This similarity verifies that the estimation scheme is giving appropriate estimates of the states.


title: A Quantitative Analysis of Developer Information Needs in Software Ecosystems

abstract: We present the results of an investigation into the nature of information needs of software developers who work in projects that are part of larger ecosystems. This work is based on a quantitative survey of 75 professional software developers. We corroborate the results identified in the survey with needs and motivations proposed in a previous survey and discover that tool support for developers working in an ecosystem context is even more meager than we thought: mailing lists and internet search are the most popular tools developers use to satisfy their ecosystem-related information needs.


title: Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

abstract: A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized PCFG. We also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. Empirical results demonstrate the utility of our methods in predicting human reading times.


title: Atoms of recognition in human and computer vision

abstract: Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.


A Study on Author Identification through Stylometry


title: Perceived barriers to effective knowledge sharing in agile software teams

abstract: While the literature offers several frameworks that explain barriers to knowledge sharing within software development teams, little is known about differences in how team members perceive these barriers. Based on an in-depth multi-case study of four software projects, we investigate how project managers, developers, testers and user representatives think about barriers to effective knowledge sharing in agile development. Adapting comparative causal mapping, we constructed causal maps for each of the four roles and identified overlap and divergence in map constructs and causal linkages. The results indicate that despite certain similarities, the four roles differ in how they perceive and emphasize knowledge-sharing barriers. The project managers put primary emphasis on project setting barriers, while the primary concern of developers, testers and user representatives were project communication, project organization and team capabilities barriers, respectively. Integrating the four causal maps and the agile literature, we propose a conceptual framework with seven types of knowledge-sharing barriers and 37 specific barriers. We argue that to bridge communication gaps and create shared understanding in software teams, it is critical to take the revealed concerns of different roles into account. We conclude by discussing our findings in relation to knowledge sharing in agile teams and software teams more generally.


title: H2O: a hands-free adaptive store

abstract: Modern state-of-the-art database systems are designed around a single data storage layout. This is a fixed decision that drives the whole architectural design of a database system, i.e., row-stores, column-stores. However, none of those choices is a universally good solution; different workloads require different storage layouts and data access methods in order to achieve good performance. In this paper, we present the H2O system which introduces two novel concepts. First, it is flexible to support multiple storage layouts and data access patterns in a single engine. Second, and most importantly, it decides on-the-fly, i.e., during query processing, which design is best for classes of queries and the respective data parts. At any given point in time, parts of the data might be materialized in various patterns purely depending on the query workload; as the workload changes and with every single query, the storage and access patterns continuously adapt. In this way, H2O makes no a priori and fixed decisions on how data should be stored, allowing each single query to enjoy a storage and access pattern which is tailored to its specific properties. We present a detailed analysis of H2O using both synthetic benchmarks and realistic scientific workloads. We demonstrate that while existing systems cannot achieve maximum performance across all workloads, H2O can always match the best case performance without requiring any tuning or workload knowledge.


title: Managing for Stakeholders: The Role of Stakeholder-Based Management in Project Success

abstract: Today, project management practices play a key role in different industries and sectors. Project management is promoted as an organizational strategic component that leads innovation, creates value and turns vision into reality. Despite the importance of projects and project management their high rate of failures and challenges is a major concern of both industry and academia. Among the reasons that affect project outcomes, stakeholder influential attributes and more importantly, their understanding and effective utilization and management are identified as the key to project success. This study utilizes the body of knowledge developed in the field of project management and uses stakeholder theory combined with a number of complementary theories to achieve its goals and objectives. The study moves beyond the traditional power-based frameworks employing six key influential attributes to examine their direct and mediating effects on project success. The quantitative survey data are analyzed using SEM statistical techniques and procedures to produce research results. The research results have led to the development of a new typology of stakeholder influential attributes (TSIA) and a stakeholder-based project management model (SBPMM) that aid managing for stakeholders’ strategy and principle.


title: Why Do Online Product Reviews Have a J-Shaped Distribution? Overcoming Biases in Online Word-of-Mouth Communication

abstract: Online word-of-mouth communication in the form of product reviews is a major information source for consumers and marketers about product quality. The literature has used the mean of online reviews to predict product sales, assuming that the mean reflects product quality. However, using a combination of econometric, experimental, and analytical results, we show that the mean is a biased estimator of product quality due to two self-selection biases (purchasing and under-reporting bias). First, econometric results with secondary data from Amazon.com show that almost all products have an asymmetric bimodal (J-shaped) distribution with more positive than negative reviews. Second, experimental results where all respondents wrote reviews show that their reviews have an approximately normal distribution with roughly equal number of positive and negative reviews. This implies two biases: (1) purchasing bias - only consumers with favorable disposition towards a product purchase the product and have the opportunity to write a product review, and (2) under-reporting bias - consumers with polarized (either positive or negative) reviews are more likely to report their reviews than consumers with moderate reviews. This results in a J-shaped distribution of online product reviews that renders the mean a biased estimator of product quality. Third, we develop an analytical model to derive the conditions for the mean to become an unbiased estimator of product quality. Based on these conditions, we build a new model that integrates three distributional parameters - mean, standard deviation, and the two modes of the online product reviews (to overcome under-reporting bias) and product price (to overcome purchasing bias). This model is shown to be a superior predictive model of future product sales compared to competing models.


title: Timely Dataflow: A Model

abstract: This paper studies timely dataflow, a model for data-parallel computing in which each communication event is associated with a virtual time. It defines and investigates the could-result-in relation which is central to this model, then the semantics of timely dataflow graphs.


title: Multi-Query Optimization for Subgraph Isomorphism Search

abstract: Existing work on subgraph isomorphism search mainly focuses on a-query-at-a-time approaches: optimizing and answering each query separately. When multiple queries arrive at the same time, sequential processing is not always the most efficient. In this paper, we study multi-query optimization for subgraph isomorphism search. We first propose a novel method for efficiently detecting useful common sub-graphs and a data structure to organize them. Then we propose a heuristic algorithm based on the data structure to compute a query execution order so that cached intermediate results can be effectively utilized. To balance memory usage and the time for cached results retrieval, we present a novel structure for caching the intermediate results. We provide strategies to revise existing single-query subgraph isomorphism algorithms to seamlessly utilize the cached results, which leads to significant performance improvement. Extensive experiments verified the effectiveness of our solution.


title: Deep Salient Object Detection by Integrating Multi-level Cues

abstract: A key problem in salient object detection is how to effectively exploit the multi-level saliency cues in a unified and data-driven manner. In this paper, building upon the recent success of deep neural networks, we propose a fully convolutional neural network based approach empowered with multi-level fusion to salient object detection. By integrating saliency cues at different levels through fully convolutional neural networks and multi-level fusion, our approach could effectively exploit both learned semantic cues and higher-order region statistics for edge-accurate salient object detection. First, we fine-tune a fully convolutional neural network for semantic segmentation to adapt it to salient object detection to learn a suitable yet coarse perpixel saliency prediction map. This map is often smeared across salient object boundaries since the local receptive fields in the convolutional network apply naturally on both sides of such boundaries. Second, to enhance the resolution of the learned saliency prediction and to incorporate higher-order cues that are omitted by the neural network, we propose a multi-level fusion approach where super-pixel level coherency in saliency is exploited. Our extensive experimental results on various benchmark datasets demonstrate that the proposed method outperforms the state-of the-art approaches.


title: An active volumetric model for 3D reconstruction

abstract: In this paper, we present an active volumetric model (AVM) for 3D reconstruction from multiple calibrated images of a scene. The AVM is a physically motivated 3D deformable model which shrinks actively under the influence of multiple simulated forces towards the real scene by throwing away some of its voxels. It provides a computational framework to integrate several constraints in an intelligible way. In the current work, we use three forces derived respectively from the smooth constraint, the compulsory silhouette constraint, and the color consistency constraint. Based on the composition of the 3 forces, our algorithm can significantly restrain holes and floating voxels, which plague voxel coloring algorithms, and produce precise and smooth models. We test our algorithm by experiments based on both synthetic and real data.


title: Production of pyrolytic liquids from industrial sewage sludges in an induction-heating reactor

abstract: Abstract With the application of induction-heating, the pyrolytic experiments have been carried out for three sewage sludges from the food processing factories in an externally heated fixed-bed reactor. The thermochemical characteristics of sludge samples were first analyzed. The results indicated that the calorific value had about 15 MJ/kg on an average, suggesting that it had a potential for biomass energy source. However, its nitrogen concentration was relatively high. From the thermogravimetric analysis (TGA) curves, it showed that the pyrolysis reaction can be almost finished in the temperature range of 450–750 °C. The yields of resulting liquid and char products from the pyrolysis of sewage sludge were discussed for examining the effects of pyrolysis temperature (500–800 °C), heating rate (200–500 °C/min), and holding time (1–8 min). Overall, the variation of yield was not so significant in the experimental conditions for three sewage sludges. All results of the resulting liquid products analyzed by elemental analyzer, pH meter, Karl-Fischer moisture titrator and bomb calorimeter were in consistence with those analyses by FTIR spectroscopy. Furthermore, the pyrolysis liquid products contained large amounts of water (>73% by weight) mostly derived from the bound water in the biosludge feedstocks and the condensation reactions during the pyrolysis reaction, and fewer contents of oxygenated hydrocarbons composing of carbonyl and nitrogen-containing groups, resulting in low pH and low calorific values.


title: Stepwise Optimal Subspace Pursuit for Improving Sparse Recovery

abstract: We propose a new iterative algorithm to reconstruct an unknown sparse signal x from a set of projected measurements y = Φx . Unlike existing methods, which rely crucially on the near orthogonality of the sampling matrix Φ , our approach makes stepwise optimal updates even when the columns of Φ are not orthogonal. We invoke a block-wise matrix inversion formula to obtain a closed-form expression for the increase (reduction) in the L2-norm of the residue obtained by removing (adding) a single element from (to) the presumed support of x . We then use this expression to design a computationally tractable algorithm to search for the nonzero components of x . We show that compared to currently popular sparsity seeking matching pursuit algorithms, each step of the proposed algorithm is locally optimal with respect to the actual objective function. We demonstrate experimentally that the algorithm significantly outperforms conventional techniques in recovering sparse signals whose nonzero values have exponentially decaying magnitudes or are distributed N(0,1) .


title: ACStream: Enforcing Access Control over Data Streams

abstract: In this demo proposal, we illustrate ACStream, a system built on top of Stream Base [1], to specify and enforce access control policies over data streams. ACStream supports a very flexible role-based access control model specifically designed to protect against unauthorized access to streaming data. The core component of ACStream is a query rewriting mechanism that, by exploiting a set of secure operators proposed by us in [2], rewrites a user query in such a way that it does not violate the specified access control policies during its execution. The demo will show how policies modelling a variety of access control requirements can be easily specified and enforced using ACStream.


title: Semantic parsing of street scenes from video

abstract: Semantic models of the environment can significantly improve navigation and decision making capabilities of autonomous robots or enhance level of human and robot interaction. We present a novel approach for semantic segmentation of street scene images into coherent regions, while simultaneously categorizing each region as one of the predefined categories representing commonly encountered object and background classes. We formulate the segmentation on small blob-based superpixels and exploit a visual vocabulary tree as an intermediate image representation. The main novelty of our approach is the introduction of an explicit model of spatial co-occurrence of visual words associated with superpixels and utilization of appearance, geometry and contextual cues in a probabilistic framework. We demonstrate how individual cues contribute towards global segmentation accuracy and how their combination yields superior performance compared with the best known method on the challenging benchmark dataset which exhibits diversity of street scenes with varying viewpoints, a large number of categories, captured in daylight and dusk.


title: Social bonds and Internet pornographic exposure among adolescents

abstract: Abstract Concern has grown regarding possible harm to the social and psychological development of children and adolescents exposed to Internet pornography. Parents, academics and researchers have documented pornography from the supply side, assuming that its availability explains consumption satisfactorily. The current paper explored the user's dimension, probing whether pornography consumers differed from other Internet users, as well as the social characteristics of adolescent frequent pornography consumers. Data from a 2004 survey of a national representative sample of the adolescent population in Israel were used ( n = 998). Adolescent frequent users of the Internet for pornography were found to differ in many social characteristics from the group that used the Internet for information, social communication and entertainment. Weak ties to mainstream social institutions were characteristic of the former group but not of the latter. X-rated material consumers proved to be a distinct sub-group at risk of deviant behaviour.


title: A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR

abstract: Permanent Scatterer SAR Interferometry (PSInSAR) aims to identify coherent radar targets exhibiting high phase stability over the entire observation time period. These targets often correspond to point-wise, man-made objects widely available over a city, but less present in non-urban areas. To overcome the limits of PSInSAR, analysis of interferometric data-stacks should aim at extracting geophysical parameters not only from point-wise deterministic objects (i.e., PS), but also from distributed scatterers (DS). Rather than developing hybrid processing chains where two or more algorithms are applied to the same data-stack, and results are then combined, in this paper we introduce a new approach, SqueeSAR, to jointly process PS and DS, taking into account their different statistical behavior. As it will be shown, PS and DS can be jointly processed without the need for significant changes to the traditional PSInSAR processing chain and without the need to unwrap hundreds of interferograms, provided that the coherence matrix associated with each DS is properly “squeezed” to provide a vector of optimum (wrapped) phase values. Results on real SAR data, acquired over an Alpine area, challenging for any InSAR analysis, confirm the effectiveness of this new approach.


title: Sensitivity Study of the Dynamics of Three-Phase Photovoltaic Inverters With an LCL Grid Filter

abstract: An accurate small-signal model of three-phase photovoltaic (PV) inverters with a high-order grid filter is derived in this paper. The proposed model takes into account the influence of both the inverter operating point and the PV panel characteristics on the inverter dynamic response. A sensitivity study of the control loops to variations of the DC voltage, PV panel transconductance, supplied power, and grid inductance is performed using the proposed small-signal model. Analytical and experimental results carried out on a 100-kW PV inverter are presented.


title: The Fog Computing Paradigm: Scenarios and Security Issues

abstract: Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.


title: Effect of rosary prayer and yoga mantras on autonomic cardiovascular rhythms: comparative study.

abstract: OBJECTIVE ::: To test whether rhythmic formulas such as the rosary and yoga mantras can synchronise and reinforce inherent cardiovascular rhythms and modify baroreflex sensitivity. ::: ::: ::: DESIGN ::: Comparison of effects of recitation of the Ave Maria (in Latin) or of a mantra, during spontaneous and metronome controlled breathing, on breathing rate and on spontaneous oscillations in RR interval, and on blood pressure and cerebral circulation. ::: ::: ::: SETTING ::: Florence and Pavia, Italy. ::: ::: ::: PARTICIPANTS ::: 23 healthy adults. ::: ::: ::: MAIN OUTCOME MEASURES ::: Breathing rate, regularity of breathing, baroreflex sensitivity, frequency of cardiovascular oscillations. ::: ::: ::: RESULTS ::: Both prayer and mantra caused striking, powerful, and synchronous increases in existing cardiovascular rhythms when recited six times a minute. Baroreflex sensitivity also increased significantly, from 9.5 (SD 4.6) to 11.5 (4.9) ms/mm Hg, P<0.05. ::: ::: ::: CONCLUSION ::: Rhythm formulas that involve breathing at six breaths per minute induce favourable psychological and possibly physiological effects.


title: Validity and Utility of Alternative Predictors of Job Performance

abstract: Meta-analysis of the cumulative research on various predictors of job performance shows that for entry-level jobs there is no predictor with validity equal to that of ability, which has a mean validity of .53. For selection on the basis of current job performance, the work sample test, with mean validity of .54, is slightly better. For federal entry-level jobs, substitution of an alternative predictor would cost from $3.12 billion (job tryout) to $15.89 billion per year (age). Hiring on ability has a utility of $15.61 billion per year, but affects minority groups adversely. Hiring on ability by quotas would decrease this utility by 5%. A third strategy—using a low cutoff score—would decrease utility by 83%. Using other predictors in conjunction with ability tests might improve validity and reduce adverse impact, but there is as yet no data base for studying this possibility.


title: Ordinary magic. Resilience processes in development.

abstract: Abstract The study of resilience in development has overturned many negative assumptions and deficit-focused models about children growing up under the threat of disadvantage and adversity. The most surprising conclusion emerging from studies of these children is the ordinariness of resilience. An examination of converging findings from variable-focused and person-focused investigations of these phenomena suggests that resilience is common and that it usually arises from the normative functions of human adaptational systems, with the greatest threats to human development being those that compromise these protective systems. The conclusion that resilience is made of ordinary rather than extraordinary processes offers a more positive outlook on human development and adaptation, as well as direction for policy and practice aimed at enhancing the development of children at risk for problems and psychopathology.


title: The Infinite Gaussian Mixture Model

abstract: In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the "right" number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.


title: Efficiency and linearity of power amplifiers with external harmonic injection

abstract: This paper discusses a method for improving the efficiency of linear power amplifiers by externally injecting power into the output at the second harmonic frequency. An experimental proof-of-concept PA based on class-A/AB mode with a 10-W GaN pHEMT at 2.45GHz is presented, and its efficiency improved from 58% to 75% with −6.5 dBc injected 2nd harmonic power. Two-tone measurements confirm improved linearity with simultaneous increase in efficiency, accompanied by gain compression at higher input power.


title: An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics

abstract: A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed. Its functions include adaptive cruise control and forward collision warning/avoidance. The research data came from driver car-following tests in real traffic environments. Based on the data analysis, a driver model imitating the driver's operation is established to generate the desired throttle depression and braking pressure. Algorithms for collision warning and automatic braking activation are designed based on the driver's pedal deflection timing during approach (gap closing). A self-learning algorithm for driver characteristics is proposed based on the recursive least-square method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data in the manual operation phase, and the identification result is applied during the automatic control phase in real time. A test bed with an electronic throttle and an electrohydraulic brake actuator is developed for system validation. The experimental results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics.


title: MNC knowledge transfer , subsidiary absorptive capacity , and HRM

abstract: Based on a sample of 169 subsidiaries of multinational corporations (MNCs) operating in the USA, Russia, and Finland, this paper investigates the relationship between MNC subsidiary HRM practices, absorptive capacity and knowledge transfer. First, we examine the relationship between the application of specific HRM practices and the level of the absorptive capacity. Second, we suggest that absorptive capacity should be conceptualized as being comprised of both employees’ ability and motivation. Further, results indicate that both ability and motivation (absorptive capacity) are needed to facilitate the transfer of knowledge from other parts of the MNC.


Modesty Doesn’t Become Me Narcissism and the Big Five among Male and Female Candidates for the Big Brother TV Show


title: A Magnetoelectric Dipole Leaky-Wave Antenna for Millimeter-Wave Application

abstract: This paper presents a novel frequency beam scanning slotted leaky-wave magnetoelectric (ME) dipole antenna array for the fifth generation (5G) application. The proposed antenna, which has eighteen elements of slots and electric dipoles, is built on a two-layer printed circuit board. In the lower layer, it is a conventional slotted substrate integrated waveguide (SIW) leaky-wave antenna (LWA). In the upper layer, electric dipoles are attached to the design. As for each element unit, the magnetic dipole is realized by each lower-layer aperture, while the electric dipole is realized by each pair of patches in the upper layer. The design concept is that two modes are excited together in orthogonal directions to realize the ME dipole. By introducing electric dipoles to the conventional slotted LWA, the antenna exhibits less gain variation over a wide bandwidth. The SIW leaky-wave ME dipole antenna array is designed and fabricated to operate at the 28-GHz band. It operates with wide impedance bandwidth and a peak gain of 16.55 dBi with less than 3-dB gain variation throughout the frequency range from 27 to 32 GHz.


title: Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition

abstract: We investigate the effectiveness of generative adversarial networks (GANs) for speech enhancement, in the context of improving noise robustness of automatic speech recognition (ASR) systems. Prior work demonstrates that GANs can effectively suppress additive noise in raw waveform speech signals, improving perceptual quality metrics; however this technique was not justified in the context of ASR. In this work, we conduct a detailed study to measure the effectiveness of GANs in enhancing speech contaminated by both additive and reverberant noise. Motivated by recent advances in image processing, we propose operating GANs on log-Mel filterbank spectra instead of waveforms, which requires less computation and is more robust to reverberant noise. While GAN enhancement improves the performance of a clean-trained ASR system on noisy speech, it falls short of the performance achieved by conventional multi-style training (MTR). By appending the GAN-enhanced features to the noisy inputs and retraining, we achieve a 7% WER improvement relative to the MTR system.


title: Style compatibility for 3D furniture models

abstract: This paper presents a method for learning to predict the stylistic compatibility between 3D furniture models from different object classes: e.g., how well does this chair go with that table? To do this, we collect relative assessments of style compatibility using crowdsourcing. We then compute geometric features for each 3D model and learn a mapping of them into a space where Euclidean distances represent style incompatibility. Motivated by the geometric subtleties of style, we introduce part-aware geometric feature vectors that characterize the shapes of different parts of an object separately. Motivated by the need to compute style compatibility between different object classes, we introduce a method to learn object class-specific mappings from geometric features to a shared feature space. During experiments with these methods, we find that they are effective at predicting style compatibility agreed upon by people. We find in user studies that the learned compatibility metric is useful for novel interactive tools that: 1) retrieve stylistically compatible models for a query, 2) suggest a piece of furniture for an existing scene, and 3) help guide an interactive 3D modeler towards scenes with compatible furniture.


title: Blue-black rule: a simple dermoscopic clue to recognize pigmented nodular melanoma.

abstract: BACKGROUND ::: Dermoscopy improves melanoma recognition, but most criteria were described in the context of superficial spreading melanoma. ::: ::: ::: OBJECTIVES ::: To test whether pigmented nodular melanoma could be recognized dermoscopically by the presence of a combination of blue and black colour within the lesion. ::: ::: ::: METHODS ::: Dermoscopic images of histopathologically diagnosed pigmented nodular tumours with no (or only minimal) flat component were evaluated for the presence of standard melanoma criteria and for the presence of a new feature named blue-black (BB) colour. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for standard criteria and BB feature in relation to the diagnosis of melanoma and to diagnosis of malignancy. ::: ::: ::: RESULTS ::: Of 283 lesions, 160 were malignant, including 78 (27·6%) melanomas, and 123 were benign. The BB feature and the standard criteria had 78·2% and 43·6% sensitivity for melanoma, respectively, whereas a combined method based on the presence of either the BB feature or one (or more) of the standard criteria reached 84·6% sensitivity, with 80·5% specificity and 93·2% negative predictive value. Sensitivity values for malignant lesions were only 24·4%, 56·9% and 60% for standard criteria, BB feature and the combined method, respectively. However, the combined method gave 91·9% specificity and 90·6% positive predictive value for malignancy. ::: ::: ::: CONCLUSIONS ::: Using a method based on the BB feature or one of the standard melanoma criteria, only 9·4% of positive pigmented nodular lesions were found to be benign and only 6·8% of negative lesions were found to be melanoma histopathologically.


title: HMDB: A large video database for human motion recognition

abstract: With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.


title: Effective Social Graph Deanonymization Based on Graph Structure and Descriptive Information

abstract: The study of online social networks has attracted increasing interest. However, concerns are raised for the privacy risks of user data since they have been frequently shared among researchers, advertisers, and application developers. To solve this problem, a number of anonymization algorithms have been recently developed for protecting the privacy of social graphs. In this article, we proposed a graph node similarity measurement in consideration with both graph structure and descriptive information, and a deanonymization algorithm based on the measurement. Using the proposed algorithm, we evaluated the privacy risks of several typical anonymization algorithms on social graphs with thousands of nodes from Microsoft Academic Search, LiveJournal, and the Enron email dataset, and a social graph with millions of nodes from Tencent Weibo. Our results showed that the proposed algorithm was efficient and effective to deanonymize social graphs without any initial seed mappings. Based on the experiments, we also pointed out suggestions on how to better maintain the data utility while preserving privacy.


title: LipPass: Lip Reading-based User Authentication on Smartphones Leveraging Acoustic Signals

abstract: To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.


title: A Fast PM-Based DOA Estimation Method with Automatic Pairing

abstract: massive MIMO technology, considered as one of the fifth generation (5G) system candidates, has been more and more important in research. The direction of arrival (DOA) estimation is closely related with massive MIMO system. Hence, a precise and low computational method to estimate DOA of the signals is necessary. In this paper, we extended the signal model in a 2D problem with a plane antenna array and proposed an improvements of Propagator method (PM). Based on root MUSIC method, the angle parameter can be estimated by another angle parameter. It does not need to reconstruct the system model and achieve automatic pairing. So this method can reduce the computational complexity significantly and has well performance in low SNR condition.


title: Optimization of Various Essential Oils and Their Effect on the Microbial and Sensory Attributes of Chicken Sausages

abstract: The present study was undertaken to standardize the method for incorporation of essential oils (EOs) in chicken sausages. Swabbing was found to be the best method in comparison to direct or along with oil method, for incorporation of EOs into chicken sausages. This was followed by the optimization of various individual EOs viz., cassia oil and thyme oil, in fresh chicken sausages. In the first part of the study, based on the results of the total plate count and sensory scores, level of incorporation for cassia oil and thyme oil was optimized at 0.25 and 0.125%, respectively, in fresh chicken sausages. In the second part of study, on the basis of the results of total plate count and sensory attributes, blends of different EOs viz, Blend-1, Blend-2, Blend-3 were optimized each at 0.25% level and Blend-4 was optimized at 0.125% level for incorporation in emulsion-based chicken sausages. Thus, out of two individual EOs and four blends tried, Cassia oil and Blend-1 at 0.25% level each is recommended for use in fresh and emulsion-based chicken sausages, respectively.


title: SVM classifier incorporating feature selection using GA for spam detection

abstract: The use of SVM (Support Vector Machines) in detecting e-mail as spam or nonspam by incorporating feature selection using GA (Genetic Algorithm) is investigated. An GA approach is adopted to select features that are most favorable to SVM classifier, which is named as GA-SVM. Scaling factor is exploited to measure the relevant coefficients of feature to the classification task and is estimated by GA. Heavy-bias operator is introduced in GA to promote sparse in the scaling factors of features. So, feature selection is performed by eliminating irrelevant features whose scaling factor is zero. The experiment results on UCI Spam database show that comparing with original SVM classifier, the number of support vector decreases while better classification results are achieved based on GA-SVM.


title: A Security Framework in RFID Multi-domain System

abstract: In previous approaches, it's generally assumed that all radio frequency identification (RFID) tags belong to a single RFID domain system (we name this as RFID single domain system). To date, most researches in the RFID single domain system have been on authentication protocols against a variety of attacks. This paper considers the security and privacy problems regarding that RFID tags used by different two or more RFID domains (we name this as RFID multi-domain system). We divided the security and privacy mechanisms in RFID multi-domain system into 3 conceptual parts: RFID forehand system security, RFID backend system security, and RFID inter-domain system security. First, we review RFID forehand and backend system security issues. Second, we present a security framework in RFID multi-domain system. Third, we propose and evaluate authentication and authorization for RFID inter-domain system with a case study


title: DistancePPG: Robust non-contact vital signs monitoring using a camera

abstract: Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.