title: Heap bounds protection with low fat pointers

abstract: Heap buffer overflow (underflow) errors are a common source of security vulnerabilities. One prevention mechanism is to add object bounds meta information and to instrument the program with explicit bounds checks for all memory access. The so-called "fat pointers" approach is one method for maintaining and propagating the meta information where native machine pointers are replaced with "fat" objects that explicitly store object bounds. Another approach is "low fat pointers", which encodes meta information within a native pointer itself, eliminating space overheads and also code compatibility issues. This paper presents a new low-fat pointer encoding that is fully compatible with existing libraries (e.g. pre-compiled libraries unaware of the encoding) and standard hardware (e.g. x86_64). We show that our approach has very low memory overhead, and competitive with existing state-of-the-art bounds instrumentation solutions.


title: A dynamic developmental theory of attention-deficit/hyperactivity disorder (ADHD) predominantly hyperactive/impulsive and combined subtypes.

abstract: Attention-deficit/hyperactivity disorder (ADHD) is currently defined as a cognitive/behavioral developmental disorder where all clinical criteria are behavioral. Inattentiveness, overactivity, and impulsiveness are presently regarded as the main clinical symptoms. The dynamic developmental behavioral theory is based on the hypothesis that altered dopaminergic function plays a pivotal role by failing to modulate nondopaminergic (primarily glutamate and GABA) signal transmission appropriately. A hypofunctioning mesolimbic dopamine branch produces altered reinforcement of behavior and deficient extinction of previously reinforced behavior. This gives rise to delay aversion, development of hyperactivity in novel situations, impulsiveness, deficient sustained attention, increased behavioral variability, and failure to inhibit responses (disinhibition). A hypofunctioning mesocortical dopamine branch will cause attention response deficiencies (deficient orienting responses, impaired saecadic eye movements, and poorer attention responses toward a target) and poor behavioral planning (poor executive functions). A hypofunctioning nigrostriatal dopamine branch will cause impaired modulation of motor functions and deficient nondeclarative habit learning and memory. These impairments will give rise to apparent developmental delay, clumsiness, neurological soft signs, and a failure to inhibit responses when quick reactions are required. Hypofunctioning dopamine branches represent the main individual predispositions in the present theory. The theory predicts that behavior and symptoms in ADHD result from the interplay between individual predispositions and the surroundings. The exact ADHD symptoms at a particular time in life will vary and be influenced by factors having positive or negative effects on symptom development. Altered or deficient learning and motor functions will produce special needs for optimal parenting and societal styles. Medication will to some degree normalize the underlying dopamine dysfunction and reduce the special needs of these children. The theory describes how individual predispositions interact with these conditions to produce behavioral, emotional, and cognitive effects that can turn into relatively stable behavioral patterns.


title: Development of mechatronic system integration course for senior students in mechatronics engineering at the University of South Australia

abstract: Background: The courses of Mechatronics I (EEET 2044) and Mechatronics II (EEET 4060) have been offered with two separate curricula in School of Advanced Manufacturing and Mechanical Engineering, and the School of Electrical and Information Engineering at the University of South Australia since 1996. In Mechatronics I, students study the fundamentals of mechatronic systems aiming to identify and determine the key components of mechatronic systems. They also are involved in a group project to design and manufacture pseudo intelligent object avoidance and path following mobile robot. In the subsequent course of Mechatronics II, the concepts and skills of mechatronics are further extended with aims at raising the competency level in designing and implementing mechatronics systems involving the mobile robot system. However, within these two courses students would not become adequately familiar with the relevant practical industrial mechatronic systems. Hence, it was decided to introduce a new course; Mechatronic System Integration (EEET 3044) with the aim of developing a sense of systematic approach to solve industrial mechatronic problems for the students. Purpose: The major reason of investigation in this paper is to develop a systematic approach to introduce what an industrial mechatronic system is comprised of and how various electro-mechanical components can be integrated and work together. This is beyond what they have learnt before, since this course offers an opportunity to make a link between class room theory and industrial practice. It is also aimed to facilitate the graduates' transition from university as a student to industry as an engineer with focuses on their communication and technical skills. Design/Method: A new curriculum has been developed in association with replacing the existing mobile robot development projects by the Modular Production Systems (MPS) which was purchased from FESTO including distribution, testing, and storing stations. The MPS system was selected as part of this course after intensive search, visit and discussion on mechatronic laboratories at a number of universities including Queensland University of Technology (QUT) and Curtin University. Results: By implementing this course, students are expected to be more familiar with real industrial mechatronic systems and equipped with the ability to combine several modular systems into a whole integrated system, in conjunction with being able to incorporate a range of industry standard equipment and components. Also, through a continuous lab logbook practice during the course, students will be able to communicate effectively with others, which is at the core of what will be required as a professional engineer in their future career development. Conclusions: The new developed course for senior students in Mechatronics at University of South Australia will allow the students to be versatile with identifying, integrating, and debugging a large scale mechatronic system encompassing three significant tasks frequently used in industry with substantial development of their multi-disciplinary knowledge in mechanics and effective communication skills.


title: ProM: The Process Mining Toolkit

abstract: The present invention relates to a door for a refrigerator, and more particularly, to a door for use in a refrigerator in which a direction of opening and/or closing the door can be selectively changed. The door of the present invention comprises an external plate 80 which defines at least a front appearance of the door; a door liner 81 which defines a rear appearance of the door and constitutes a space with an insulating layer 83 formed therein in cooperation with the external plate 80; a dispenser 74 which is installed at a front surface of the external plate 80 for dispensing water to the outside of the refrigerator; tube passages 97 and 97' which pass through the insulating layer 83 and allow the dispenser 74 and through-holes 86 for installation of a hinge 57 located at both sides of the door to communicate with each other; a door side tube structure 72 which is installed in at least one of the tube passages 97 and 97' and transfers water from a main body 50 of the refrigerator to the dispenser 74; and a power cable 99 which extends from the dispenser 74 to the respective through-holes 86 and through which electrical signals are transmitted between the door 56 and the main body 50 of the refrigerator. According to the present invention, there are advantages in that the door opening/closing direction can be easily changed and the external appearance of the door can be cleanly and neatly finished.


title: A field study of the software design process for large systems

abstract: The problems of designing large software systems were studied through interviewing personnel from 17 large projects. A layered behavioral model is used to analyze how three of these problems—the thin spread of application domain knowledge, fluctuating and conflicting requirements, and communication bottlenecks and breakdowns—affected software productivity and quality through their impact on cognitive, social, and organizational processes.


How do users know what to say?


title: Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models

abstract: Abstract This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow and Clayton [Breslow, N. E. & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Society 88 (421), 9–25]), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.


title: Effects of media formats on emotions and impulse buying intent

abstract: One way of generating revenue from broadband media content rests upon the assumption that multi-media content may trigger a greater intent to buy products and services impulsively. An experiment was performed in order to explore the effects of media formats on the emotions and impulse buying intentions for music compact discs (CDs). Three distinct media formats of World Wide Web pages were set up: (1) the text of the lyrics, (2) still images from the song's music video and (3) the music video itself. Each had a varying degree of visual/verbal intensity while simultaneously playing the soundtrack in all three conditions. The results of this study indicate that displaying the text of the lyrics had a greater effect on the impulse buying intent than showing still images of the music video. In addition, different media formats caused emotional responses that can explain the participant's impulse buying intent to buy the CD. Unexpectedly, the still images and video did not necessarily generate more buying intention than combinations of the text and music. Therefore, it is recommended that electronic commerce and marketing managers explore innovative ways of integrating visual and verbal media formats for eliciting an effective consumer response.


title: Automatic Shadow Detection and Removal from a Single Image

abstract: We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.


title: A Secure Sharding Protocol For Open Blockchains

abstract: Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol --- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3--7 transactions per second at present, irrespective of the available computation capacity at hand. In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to $1, 600$ nodes confirm ELASTICO's theoretical scaling properties.


title: Review of Latest Advances in 3GPP Standardization: D2D Communication in 5G Systems and Its Energy Consumption Models

abstract: Device-to-device (D2D) communication is an essential part of the future fifth generation (5G) system that can be seen as a “network of networks,” consisting of multiple seamlessly-integrated radio access technologies (RATs). Public safety communications, autonomous driving, socially-aware networking, and infotainment services are example use cases of D2D technology. High data rate communications and use of several active air interfaces in the described network create energy consumption challenges for both base stations and the end user devices. In this paper, we review the status of 3rd Generation Partnership Project (3GPP) standardization, which is the most important standardization body for 5G systems. We define a set of application scenarios for D2D communications in 5G networks. We use the recent models of 3GPP long term evolution (LTE) and WiFi interfaces in analyzing the power consumption from both the infrastructure and user device perspectives. The results indicate that with the latest radio interfaces, the best option for energy saving is the minimization of active interfaces and sending the data with the best possible data rate. Multiple recommendations on how to exploit the results in future networks are given.


title: Abracadabra: wireless, high-precision, and unpowered finger input for very small mobile devices

abstract: We present Abracadabra, a magnetically driven input technique that offers users wireless, unpowered, high fidelity finger input for mobile devices with very small screens. By extending the input area to many times the size of the device's screen, our approach is able to offer a high C-D gain, enabling fine motor control. Additionally, screen occlusion can be reduced by moving interaction off of the display and into unused space around the device. We discuss several example applications as a proof of concept. Finally, results from our user study indicate radial targets as small as 16 degrees can achieve greater than 92% selection accuracy, outperforming comparable radial, touch-based finger input.


title: Financing Constraints and Corporate Investment

abstract: Most empirical models of investment rely on the assumption that firms are able to respond to prices set in centralized securities markets (through the "cost of capital" or "q"). An alternative approach emphasizes the importance of cash flow as a determinant of investment spending, because of a "financing hierarchy," in which internal finance has important cost advantages over external finance. We build on recent research concerning imperfections in markets for equity and debt. This work suggests that some firms do not have sufficient access to external capital markets to enable them to respond to changes in the cost of capital, asset prices, or tax-based investment incentives. To the extent that firms are constrained in their ability to raise funds externally, investment spending may be sensitive to the availability of internal finance. That is, investment may display "excess sensitivity" to movements in cash flow. In this paper, we work within the q theory of investment, and examine the importance of a financing hierarchy created by capital-market imperfections. Using panel data on individual manufacturing firms, we compare the investment behavior of rapidly growing firms that exhaust all of their internal finance with that of mature firms paying dividends. We find that q values remain very high for significant periods of time for firms paying no dividends, relative to those for mature firms. We also find that investment is more sensitive to cash flow for the group of firms that our model implies is most likely to face external finance constraints. These results are consistent with the augmented model we propose, which takes into account different financing regimes for different groups of firms. Some extensions and implications for public policy are discussed at the end.


title: Terahertz band communications: Applications, research challenges, and standardization activities

abstract: Terahertz frequency band, 0.1–10THz, is envisioned as one of the possible resources to be utilized for wireless communications in networks beyond 5G. Communications over this band will be feature a number of attractive properties, including potentially terabit-per-second link capacities, miniature transceivers and, potentially, high energy efficiency. Meanwhile, a number of specific research challenges have to be addressed to convert the theoretical estimations into commercially attractive solutions. Due to the diversity of the challenges, the research on THz communications at its early stages was mostly performed by independent communities from different areas. Therefore, the existing knowledge in the field is substantially fragmented. In this paper, an attempt to address this issue and provide a clear and easy to follow introduction to the THz communications is performed. A review on the state-of-the-art in THz communications research is given by identifying the target applications and major open research challenges as well as the recent achievements by industry, academia, and the standardization bodies. The potential of the THz communications is presented by illustrating the basic tradeoffs in typical use cases. Based on the given summary, certain prospective research directions in the field are identified.


title: Channel polarization: A method for constructing capacity-achieving codes

abstract: A method is proposed, called channel polarization, to construct code sequences that achieve the symmetric capacity I(W) of any given binary-input discrete memoryless channel (B-DMC) W. The symmetric capacity I(W) is the highest rate achievable subject to using the input letters of the channel equiprobably and equals the capacity C(W) if the channel has certain symmetry properties. Channel polarization refers to the fact that it is possible to synthesize, out of N independent copies of a given B-DMC W, a different set of N binary-input channels such that the capacities of the latter set, except for a negligible fraction of them, are either near 1 or near 0. This second set of N channels are well-conditioned for channel coding: one need only send data at full rate through channels with capacity near 1 and at 0 rate through the others. The main coding theorem about polar coding states that, given any B-DMC W with I(W) > 0 and any fixed 0 I(W)-delta, and probability of block decoding error Pe les cN-1/4. The codes with this performance can be encoded and decoded within complexity O(N log N).


title: Emergence of Grounded Compositional Language in Multi-Agent Populations

abstract: By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.


title: Biodiversity friend or foe: land use by a top predator, the dingo in contested landscapes of the Australian Wet Tropics

abstract: Dingoes (Canis dingo) in the coastal lowlands of Australia's Wet Tropics are perceived as a major threat to biodiversity and subjected to broad-spectrum lethal control. However, evidence of their impacts is equivocal, and control programmes generally ignore the ecological benefits that dingoes might provide. Previous diet analysis has shown that dingoes in the Wet Tropics primarily prey on common, terrestrial mammals. However, little is known of dingo habitat use or prey acquisition in the region despite these activities having major implications for biodiversity conservation. We investigated land use by dingoes in the lowland Wet Tropics to enable predictions of potential prey types, relative prey use and modes of prey acquisition. Nine dingoes were tracked for 3–6 months. Home ranges and resting areas were estimated using multiple estimators, and habitat use was analysed using compositional analysis of habitat use and generalized additive models. Dingo ranging behaviour suggested that anthropogenic food subsidies were infrequently used. Each territory comprised several sclerophyll forest rest areas with adjacent sugarcane-grassland high activity areas. Individuals used each rest-activity area for extended durations before moving on to another. Sclerophyll and rainforests, which contain the fauna species of primary conservation concern, were generally used for rest/sleep, or movement between rest-activity areas. Activity patterns were consistent with dingoes hunting in open sugarcane-grassland habitats during daylight hours. Dingo activity was low in areas where fauna species of conservation concern occur, which suggests that dingoes do not pose a threat to their survival. Consequently, current broad-spectrum lethal control may have minimal benefits or even incur costs for biodiversity. Maximizing the ecosystem services provided by dingoes while simultaneously minimizing their negative impacts requires a more targeted location-specific management approach, one that assesses and mitigates impacts specifically where background circumstances suggest particular packs may be either a conservation or economic threat.


title: The Complexity of a Pop-Up Book

abstract: Origami is the centuries-old art of folding paper, and recently, it is investigated as science. In this paper, another hundreds-old art of folding paper, a pop-up book, is studied. A model for the pop-up book design problem is given, and its complexity is investigated. We show that both of the opening book problem and the closing book problem are NP-hard.


title: An Overview of End-to-End Verifiable Voting Systems

abstract: Advances in E2E verifiable voting have the potential to fundamentally restore trust in elections and democratic processes in society. In this chapter, we provide a comprehensive introduction to the field. We trace the evolution of privacy and verifiability properties in the research literature and describe the operations of current state-of-the-art E2E voting systems. We also discuss outstanding challenges to the deployment of E2E voting systems, including technical, legal, and usability constraints. ::: Our intention, in writing this chapter, has been to make the innovations in this domain accessible to a wider audience. We have therefore eschewed description of complex cryptographic mechanisms and instead attempt to communicate the fundamental intuition behind the design of E2E voting systems. We hope our work serves as a useful resource and assists in the future development of E2E voting.


title: Synthesis and Characterisation of Poly (Vinylpyrrolidone) – Nickel (II) Complexes

abstract: Main use of polymer - metal complexes lies in their ability to act as catalysts. In the proposed work we have selected poly (vinyl pyrrolidone) as ligand for complexing with nickel. Poly (vinyl pyrrolidone) is a water soluble polymer having a large number of consumer and environment friendly uses which are the consequences of its unique properties like biological compatibility, low toxicity, film forming ability, complexing ability, resistance to thermal degradation in solution and relatively inert behavior towards salts and acids. Nickel, being a transition metal, shows very good catalytic properties. Nickel based catalysts meet the criteria required for the catalysis of a wide range of chemical reactions. Polymer-metal complexes of poly (vinylpyrrolidone) and nickel were prepared by using aqueous solution of PVP (30K) and alcoholic solution of nickel chloride hexahydrate (NiCl2 ∙ 6H2O) at room temperature with different molar compositions. The polymer-metal complexes were obtained in crystalline form by evaporation of the solvent. They were characterized by solubility studies, FTIR, 1 H - NMR and 13 C - NMR. Procedure was repeated with aqueous solution of the salt also. The spectral analysis of the reactants and the polymer-metal complexes confirmed the formation of expected complexes. The conditions for better complexation were optimized.


title: Sensing a live audience

abstract: Psychophysiological measurement has the potential to play an important role in audience research. Currently, such research is still in its infancy and it usually involves collecting data in the laboratory, where during each experimental session one individual watches a video recording of a performance. We extend the experimental paradigm by simultaneously measuring Galvanic Skin Response (GSR) of a group of participants during a live performance. GSR data were synchronized with video footage of performers and audience. In conjunction with questionnaire data, this enabled us to identify a strongly correlated main group of participants, describe the nature of their theatre experience and map out a minute-by-minute unfolding of the performance in terms of psycho-physiological engagement. The benefits of our approach are twofold. It provides a robust and accurate mechanism for assessing a performance. Moreover, our infrastructure can enable, in the future, real-time feedback from remote audiences for online performances.


title: Hierarchical text classification and evaluation

abstract: Hierarchical classification refers to the assignment of one or more suitable categories from a hierarchical category space to a document. While previous work in hierarchical classification focused on virtual category trees where documents are assigned only to the leaf categories, we propose a top-down level-based classification method that can classify documents to both leaf and internal categories. As the standard performance measures assume independence between categories, they have not considered the documents incorrectly classified into categories that are similar to or not far from correct ones in the category tree. We therefore propose category-similarity measures and distance-based measures to consider the degree of misclassification in measuring the classification performance. An experiment has been carried out to measure the performance of our proposed hierarchical classification method. The results showed that our method performs well for a Reuters text collection when enough training documents are given and the new measures have indeed considered the contributions of misclassified documents.


title: A New Torque Control Method for Torque Ripple Minimization of BLDC Motors With Un-Ideal Back EMF

abstract: In classical control of brushless dc (BLDC) motors, flux distribution is assumed trapezoidal and fed current is controlled rectangular to obtain a desired constant torque. However, in reality, this assumption may not always be correct, due to nonuniformity of magnetic material and design trade-offs. These factors, together with current controller limitation, can lead to an undesirable torque ripple. This paper proposes a new torque control method to attenuate torque ripple of BLDC motors with un-ideal back electromotive force (EMF) waveforms. In this method, the action time of pulses, which are used to control the corresponding switches, are calculated in the torque controller regarding actual back EMF waveforms in both normal conduction period and commutation period. Moreover, the influence of finite dc bus supply voltage is considered in the commutation period. Simulation and experimental results are shown that, compared with conventional rectangular current control, the proposed torque control method results in apparent reduction of the torque ripple.


title: On Solvability of Boundary Value Problem for Asymmetric Differential Equation Depending on x

abstract: Abstract We state the conditions of geometrical nature which guarantee the existence of a solution to the boundary value problem x ′′ + 2δx ′ + λf (x + ) − µg(x −) = h(t, x, x ′ ), x(0) = 0 = x(1) with a damping term 2δx ′ and nonnegative parameters λ, µ, provided that f (x +) − g(x −) is a sector-bounded nonlinearity.


title: Closure of non-equilibrium volume-averaged energy equations in high-conductivity porous media

abstract: A formulation for closure of the volume-averaged energy equations under local thermal non-equilibrium conditions has been proposed, providing a general method for obtaining the relevant effective properties for high-conductivity porous materials. The method requires numerical solution of the flow field, as well as closure problems, over a representative volume of porous media. This study considers an array of highly conductive circular cylinders for which all relevant effective properties are obtained. Comparisons are made to results in literature for low Reynolds numbers, while results at higher Reynolds numbers are presented to provide insight into the effects of inertia on the effective properties.


title: A 76.5 GHz microstrip comb-line antenna array for automotive radar system

abstract: This paper presents a 45 degree linearly polarized microstrip comb-line antenna array for the 76.5 GHz automotive radar system. The 13-element array is implemented on Rogers RO3003 substrate with a size of 20 × 2 mm2. The measured gain of the antenna is 11.4 dBi and the sidelobe level is below −16.5 dB at 76.5 GHz.


title: Quality of life after spinal cord injury: a meta-synthesis of qualitative findings

abstract: Meta-synthesis of qualitative research. To identify, compare and synthesize the factors found to contribute to, or detract from the experience of a life worth living following spinal cord injury (SCI). Published articles were identified from the Medline, CINAHL and Sociological Abstracts databases, a hand search through selected journals published since 1990, and from reference lists. These were assessed for their relevance to the focus of interest and appraised for rigour and quality. The key themes that emerged from the data were summarized, compared and synthesized. The search located 64 papers and four books, of which seven papers met the review criteria for relevance and rigour, and in which 10 main concepts were identified: (1) body problems, (2) loss, (3) relationships, (4) responsibility for, and control of one's life, (5) occupation, and ability to contribute, (6) environmental context, (7) new values/perspective transformation, (8) good and bad days, (9) self-worth, (10) self-continuity. This study demonstrates the utility of synthesizing qualitative research to provide a greater depth of insight into the factors that contribute to, and detract from, quality of life (QOL) after SCI. It also provides a more nuanced understanding of the experience of QOL following SCI than is achievable by quantitative methods. Future qualitative research is required to probe further the concepts and connections identified in this study, and to identify how rehabilitation services might best address these issues. N/A.


Exact Inference Techniques for the Dynamic Analysis of Bayesian Attack Graphs


title: History-Dependent Bridge Deck Maintenance and Replacement Optimization with Markov Decision Processes

abstract: Bridge maintenance and replacement optimization methods use deterioration models to predict the future condition of bridge components. The purpose of this paper is to develop a framework for bridge maintenance optimization using a deterioration model that takes into account aspects of the history of the bridge condition and maintenance, while allowing the use of efficient optimization techniques. Markovian models are widely used to represent bridge component deterioration. In existing Markovian models, the state is the bridge component condition, and the history of the condition is not taken into account, which is seen as a limitation. This paper describes a method to formulate a realistic history-dependent model of bridge deck deterioration as a Markov chain, while retaining aspects of the history of deterioration and maintenance as part of the model. This model is then used to formulate and solve a reliability-based bridge maintenance optimization problem as a Markov decision process. A parametric study is conducted to compare the policies obtained in this research with policies derived using a simpler Markovian model.


title: Hierarchical Identity Based Encryption with Constant Size Ciphertext

abstract: We present a Hierarchical Identity Based Encryption (HIBE) system where the ciphertext consists of just three group elements and decryption requires only two bilinear map computations, regardless of the hierarchy depth. Encryption is as efficient as in other HIBE systems. We prove that the scheme is selective-ID secure in the standard model and fully secure in the random oracle model. Our system has a number of applications: it gives very efficient forward secure public key and identity based cryptosystems (with short ciphertexts), it converts the NNL broadcast encryption system into an efficient public key broadcast system, and it provides an efficient mechanism for encrypting to the future. The system also supports limited delegation where users can be given restricted private keys that only allow delegation to bounded depth. The HIBE system can be modified to support sublinear size private keys at the cost of some ciphertext expansion.


title: Empowering or Oppressing? Development and Exploration of the Enjoyment of Sexualization Scale

abstract: Sexualization of girls and women in America is rampant and has many negative consequences. Women, however, often report enjoying being sexually admired by men. Given this paradox, it is unclear whether such enjoyment represents an authentic empowerment of women’s sexuality or is related to traditional feminine norms and sexist beliefs. In Studies 1 and 2, the authors developed and tested the eight-item Enjoyment of Sexualization Scale (ESS). It had good reliability and was differentiated from related constructs including body surveillance, body shame, self-sexualizing behaviors, and appearance-contingent self-esteem. In Study 3, endorsement of traditional gender norms, endorsement of benevolent sexism, and endorsement of hostile sexism were all positively related to the ESS. Moreover, women who both enjoyed sexualization and engaged in self-objectification reported more negative eating attitudes. Overall, there was little support for positive effects of enjoying sexualization. The extent to which enjoying...


title: An iterative algorithm for linear inverse problems with compound regularizers

abstract: In several imaging inverse problems, it may be of interest to encourage the solution to have characteristics which are most naturally expressed by the combination of more than one regularizer. The resulting optimization problems can not be dealt with by the current state-of-the-art algorithms, which are designed for single regularizers (such as total variation or sparseness-inducing penalties, but not both simultaneously). In this paper, we introduce an iterative algorithm to solve the optimization problem resulting from image (or signal) inverse problems with two (or more) regularizers. We illustrate the new algorithm in a problem of restoration of "group sparse" images, i.e., images displaying a special type of sparseness in which the active pixels tend to cluster together. Experimental results show the effectiveness of the proposed algorithm in solving the corresponding optimization problem.


title: Synthesizing Normalized Faces from Facial Identity Features

abstract: We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar.


title: An Attribute-Based Encryption Scheme to Secure Fog Communications

abstract: Fog computing is deemed as a highly virtualized paradigm that can enable computing at the Internet of Things devices, residing in the edge of the network, for the purpose of delivering services and applications more efficiently and effectively. Since fog computing originates from and is a non-trivial extension of cloud computing, it inherits many security and privacy challenges of cloud computing, causing the extensive concerns in the research community. To enable authentic and confidential communications among a group of fog nodes, in this paper, we propose an efficient key exchange protocol based on ciphertext-policy attribute-based encryption (CP-ABE) to establish secure communications among the participants. To achieve confidentiality, authentication, verifiability, and access control, we combine CP-ABE and digital signature techniques. We analyze the efficiency of our protocol in terms of security and performance. We also implement our protocol and compare it with the certificate-based scheme to illustrate its feasibility.


title: Phrase-Based Statistical Translation of Programming Languages

abstract: Phrase-based statistical machine translation approaches have been highly successful in translating between natural languages and are heavily used by commercial systems (e.g. Google Translate). The main objective of this work is to investigate the applicability of these approaches for translating between programming languages. Towards that, we investigated several variants of the phrase-based translation approach: i) a direct application of the approach to programming languages, ii) a novel modification of the approach to incorporate the grammatical structure of the target programming language (so to avoid generating target programs which do not parse), and iii) a combination of ii) with custom rules added to improve the quality of the translation. To experiment with the above systems, we investigated machine translation from C# to Java. For the training, which takes about 60 hours, we used a parallel corpus of 20,499 C#-to-Java method translations. We then evaluated each of the three systems above by translating 1,000 C# methods. Our experimental results indicate that with the most advanced system, about 60% of the translated methods compile (the top ranked) and out of a random sample of 50 correctly compiled methods, 68% (34 methods) were semantically equivalent to the reference solution.


Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths


title: Computer Attitudes, Gender and Exploratory Behavior: A Developmental Study

abstract: Three studies were conducted which examined computer attitudes, and the effect of particular gender views on exploratory behavior. In the first study, preschool children were interviewed about their computer attitudes before and after an interactive storybook session. The results showed a high level of computer awareness, with boys holding more gender-stereotypical views, and experience did not seem to alter these beliefs. Those holding cross-gender beliefs about computers (mainly the girls) were more restrictive in their exploration of the software. In the other two studies, a questionnaire was used to assess computer attitudes among high school students. All held positive views with gender differences of degree but not kind. Few students held gender-stereotypical beliefs, although, there was a consensus that females were less proficient with computers. Unlike the preschoolers, the exploratory behavior of the older students was not related to attitudes. The results suggest that affirmative action program...


abstract: In this paper, we propose a Remote control of a domestic equipment from an Android application based on Raspberry pi card. Home automation has been recalled in the first stage. In the second stage an application has been developed based on the android system. The different diagrams have been presented. Different codes have been developed to allow the communication between the remote user, the web server, the raspberry pi card and the home components. Also an interface card has been developed to assure the communication between the home components and raspberry pi card. The application has been installed on a Smartphone, a web server and a raspberry pi card to order the shutter of windows. The experimental results are successful.


title: Item Response Theory and Classical Test Theory: An Empirical Comparison of their Item/Person Statistics

abstract: Despite theoretical differences between item response theory (IRT) and classical test theory (CTT), there is a lack of empirical knowledge about how, and to what extent, the IRT- and CTT-based item and person statistics behave differently. This study empirically examined the behaviors of the item and person statistics derived from these two measurement frameworks. The study focused on two issues: (a) What are the empirical relationships between IRT- and CTT-based item and person statistics? and (b) To what extent are the item statistics from IRT and those from CIT invariant across different participant samples? A large-scale statewide assessment database was used in the study. The findings indicate that the person and item statistics derived from the two measurement frameworks are quite comparable. The degree of invariance of item statistics across samples, usually considered as the theoretical superiority IRT models, also appeared to be similar for the two measurement fireworks.


title: Leapfrog Triejoin: a worst-case optimal join algorithm

abstract: Recent years have seen exciting developments in join algorithms. In 2008, Atserias, Grohe and Marx (henceforth AGM) proved a tight bound on the maximum result size of a full conjunctive query, given constraints on the input relation sizes. In 2012, Ngo, Porat, R{\'e} and Rudra (henceforth NPRR) devised a join algorithm with worst-case running time proportional to the AGM bound. Our commercial Datalog system LogicBlox employs a novel join algorithm, \emph{leapfrog triejoin}, which compared conspicuously well to the NPRR algorithm in preliminary benchmarks. This spurred us to analyze the complexity of leapfrog triejoin. In this paper we establish that leapfrog triejoin is also worst-case optimal, up to a log factor, in the sense of NPRR. We improve on the results of NPRR by proving that leapfrog triejoin achieves worst-case optimality for finer-grained classes of database instances, such as those defined by constraints on projection cardinalities. We show that NPRR is \emph{not} worst-case optimal for such classes, giving a counterexample where leapfrog triejoin runs in $O(n \log n)$ time, compared to $\Theta(n^{1.375})$ time for NPRR. On a practical note, leapfrog triejoin can be implemented using conventional data structures such as B-trees, and extends naturally to $\exists_1$ queries. We believe our algorithm offers a useful addition to the existing toolbox of join algorithms, being easy to absorb, simple to implement, and having a concise optimality proof.


title: SC-FEGAN: Face Editing Generative Adversarial Network With User’s Sketch and Color

abstract: We present a novel image editing system that generates images as the user provides free-form masks, sketches and color as inputs. Our system consists of an end-to-end trainable convolutional network. In contrast to the existing methods, our system utilizes entirely free-form user input in terms of color and shape. This allows the system to respond to the user’s sketch and color inputs, using them as guidelines to generate an image. In this work, we trained the network with an additional style loss, which made it possible to generate realistic results despite large portions of the image being removed. Our proposed network architecture SC-FEGAN is well suited for generating high-quality synthetic images using intuitive user inputs.


title: Cryptobotics: Why Robots Need Cyber Safety

abstract: With the expected introduction of robots into our daily lives, providing mechanisms to avoid undesired attacks and exploits in robot communication software is becoming increasingly required. Just as during the beginnings of the computer age (Pfleeger and Pfleeger, 2002), robotics is established in a “happy naivety,” where security rules against external attacks are not adopted, assuming that robotics knowledgeable people are well intended. While this may have been true in the past, the mass adoption of robots will increase the possibilities of attacks. This fact is especially relevant in defense, medical and other critical fields involving humans, where tampering can result in serious bodily harm and/or privacy invasions. For these reasons, we consider that researchers and industry should deploy efforts in cyber safety and acquire good practices when developing and distributing robot software. We propose the term Cryptobotics as a unifying term for research and applications of computer and microcontrollers’ security measures in robotics.


Holographic Feature Representations of Deep Networks


Semi-structured interviews : guidance for novice researchers february


title: Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

abstract: Nowadays software is mainly developed through outsourcing and it has become one of the most important business practice strategies for the software industry. However, outsourcing projects are often affiliated with high failure rate. Therefore to ensure success in outsourcing projects, past research has aimed to develop intelligent risk prediction models to evaluate the success rate and cost-effectiveness of software projects. In this study, we first summarized related work over the past 20years and observed that all existing prediction models assume equal misclassification costs, neglecting actual situations in the management of software projects. In fact, overlooking project failure is far more serious than the misclassification of a success-prone project as a failure. Moreover, ensemble learning, a technique well-recognized to improve prediction performance in other fields, has not yet been comprehensively studied in software project risk prediction. This study aims to close the research gaps by exploring cost-sensitive analysis and classifier ensemble methods. Comparative analysis with T-test on 60 different risk prediction models using 327 outsourced software project samples suggests that the ideal model is a homogeneous ensemble model of decision trees (DT) based on bagging. Interestingly, DT underperformed Support Vector Machine (SVM) in accuracy (i.e., assuming equal misclassification cost), but outperformed in cost-sensitive analysis under the proposed framework. In conclusion, this study proposes the first cost-sensitive and ensemble-based hybrid modeling framework (COSENS) for software project risk prediction. In addition, it establishes a new rigorous evaluation standard for assessing software risk prediction models by considering misclassification costs. Display Omitted The first cost-sensitive and ensemble framework to predict software project riskA comprehensive T-test method was used for rigorous performance comparison.A total of 60 models were built and compared based on 327 real project samples.Decision tree underperformed SVM in accuracy, but outperformed in cost analysis.A new rigorous model standard for software project risk analysis is established.


title: A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging

abstract: In this paper we study a first-order primal-dual algorithm for non-smooth convex optimization problems with known saddle-point structure. We prove convergence to a saddle-point with rate O(1/N) in finite dimensions for the complete class of problems. We further show accelerations of the proposed algorithm to yield improved rates on problems with some degree of smoothness. In particular we show that we can achieve O(1/N 2) convergence on problems, where the primal or the dual objective is uniformly convex, and we can show linear convergence, i.e. O(? N ) for some ??(0,1), on smooth problems. The wide applicability of the proposed algorithm is demonstrated on several imaging problems such as image denoising, image deconvolution, image inpainting, motion estimation and multi-label image segmentation.


title: Robust Transfer Metric Learning for Image Classification

abstract: Metric learning has attracted increasing attention due to its critical role in image analysis and classification. Conventional metric learning always assumes that the training and test data are sampled from the same or similar distribution. However, to build an effective distance metric, we need abundant supervised knowledge (i.e., side/label information), which is generally inaccessible in practice, because of the expensive labeling cost. In this paper, we develop a robust transfer metric learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain. Specifically, RTML exploits knowledge transfer to mitigate the domain shift in two directions, i.e., sample space and feature space. In the sample space, domain-wise and class-wise adaption schemes are adopted to bridge the gap of marginal and conditional distribution disparities across two domains. In the feature space, our metric is built in a marginalized denoising fashion and low-rank constraint, which make it more robust to tackle noisy data in reality. Furthermore, we design an explicit rank constraint regularizer to replace the rank minimization NP-hard problem to guide the low-rank metric learning. Experimental results on several standard benchmarks demonstrate the effectiveness of our proposed RTML by comparing it with the state-of-the-art transfer learning and metric learning algorithms.


Tagging Ingush - Language Technology For Low-Resource Languages Using Resources From Linguistic Field Work


title: Children’s Direct Exposure to Types of Domestic Violence Crime: A Population-based Investigation

abstract: Police officers served as public health sentinels to collect data on children exposed to domestic violence across an entire municipality for 1 year. This study extended research by investigating a typology of domestic violence crimes and children’s direct sensory exposure to these types. Police officers used a standard, validated protocol to collect data on all substantiated domestic violence. Findings revealed that almost half of all events had children present, and 81% of these children were directly exposed to the violence. Children under the age of 6 years old were at greater risk of exposure. Identified domestic violence households with children were more likely to be low-income, non-White, and headed by a single female, compared to households at large. Cluster analysis revealed seven domestic violence event profiles. Typology showed that children were disproportionately exposed to the most unstable and dangerous profiles including weapon use, mutual assault, and substance abuse.


title: A Proposal of New Classification for Dental Implant Complications

abstract: AIM ::: The aims of this study are to review the available literature related to implant complications and propose a new classification method for dental implant complications. ::: ::: ::: MATERIALS AND METHODS ::: Dental literature was reviewed via PubMed focusing on articles published in English, which included data regarding dental implants, complications, and classification from January 2000 to January 2018. The author, who has experience with implant placement and restorations for 15 years, and ten of his colleagues, formed a list of implant complications that they have encountered in their practices. ::: ::: ::: RESULTS ::: After 3,736 articles were found in the initial search, a total of 613 potentially relevant review articles were identified in the database. After the full-text analysis of 25 articles, only 6 review articles with complication classifications were utilized in this study. In addition, a clinically based classification named "Turkyilmaz's Classification of Implant Complications" including three categories was created. Types of complications in these three groups (Mild, Moderate, and Severe) were listed, and some of them were illustrated. Also, recommendations for clinicians were made on how to avoid these problems and/or overcome them. ::: ::: ::: CONCLUSION ::: It has been suggested that categorical data regarding complications of dental implants are limited, and the new complication classification presented in this article may help clinicians identify and overcome commonly encountered implant complications. ::: ::: ::: CLINICAL SIGNIFICANCE ::: For clinicians, it is important to know possible complications regarding dental implants. In this article, a group of dentists created and suggested a new classification for implant complications, which may help clinicians identify commonly encountered complications and how to handle them in the clinic.


title: A person identification system combining recognition of face and lip-read passwords

abstract: This paper presents a person identification system which combines recognition of facial features as well as spoken word using visual features alone. It incorporates a face recognition algorithm to identify the person, followed by spoken word recognition of ‘lip-read’ password. For face recognition, PCA is used for feature extraction, followed by a KNN based classification on the reduced dimensionality features. Spoken word recognition of passwords is performed using a Visual Lip reading (Visual ASR). The visual features corresponding to the spoken word is extracted using DWT, which are then recognized using a HMM based approaches. Since evidences from face recognition and visual lip reading could be complementary in nature, the scores from the two modalities are combined. Based on the combined evidences, decision making is for person identification is carried out. The performance for face identification is 90% while the accuracy for visual speech recognition is 72%. By combining these evidences, an improved accuracy 98% is achieved.


title: Dense Multi-view 3D-reconstruction Without Dense Correspondences

abstract: We introduce a variational method for multi-view shape-from-shading under natural illumination. The key idea is to couple PDE-based solutions for single-image based shape-from-shading problems across multiple images and multiple color channels by means of a variational formulation. Rather than alternatingly solving the individual SFS problems and optimizing the consistency across images and channels which is known to lead to suboptimal results, we propose an efficient solution of the coupled problem by means of an ADMM algorithm. In numerous experiments on both simulated and real imagery, we demonstrate that the proposed fusion of multiple-view reconstruction and shape-from-shading provides highly accurate dense reconstructions without the need to compute dense correspondences. With the proposed variational integration across multiple views shape-from-shading techniques become applicable to challenging real-world reconstruction problems, giving rise to highly detailed geometry even in areas of smooth brightness variation and lacking texture.


title: Detailed diagnosis in enterprise networks

abstract: By studying trouble tickets from small enterprise networks, we conclude that their operators need detailed fault diagnosis. That is, the diagnostic system should be able to diagnose not only generic faults (e.g., performance-related) but also application specific faults (e.g., error codes). It should also identify culprits at a fine granularity such as a process or firewall configuration. We build a system, called NetMedic, that enables detailed diagnosis by harnessing the rich information exposed by modern operating systems and applications. It formulates detailed diagnosis as an inference problem that more faithfully captures the behaviors and interactions of fine-grained network components such as processes. The primary challenge in solving this problem is inferring when a component might be impacting another. Our solution is based on an intuitive technique that uses the joint behavior of two components in the past to estimate the likelihood of them impacting one another in the present. We find that our deployed prototype is effective at diagnosing faults that we inject in a live environment. The faulty component is correctly identified as the most likely culprit in 80% of the cases and is almost always in the list of top five culprits.


Poisson Naive Bayes For Text Classification With Feature Weighting


title: SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction

abstract: Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of the scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians but ignore the scene layouts in pedestrian trajectory prediction. In this paper, a novel hierarchical LSTM-based network is proposed to consider both the influence of social neighbourhood and scene layouts. Our SS-LSTM, which stands for Social-Scene-LSTM, uses three different LSTMs to capture person, social and scene scale information. We also use a circular shape neighbourhood setting instead of the traditional rectangular shape neighbourhood in the social scale. We evaluate our proposed method against two baseline methods and a state-of-art technique on three public datasets. The results show that our method outperforms other methods and that using circular shape neighbourhood improves the prediction accuracy.


title: The antioxidant effect of fermented papaya preparation involves iron chelation.

abstract: Abstract Iron-overload is a major clinical problem in various diseases. Under this condition, serum iron which surpasses the binding capacity of transferrin is present as non-transferrin bound iron and cellular unbound Labile Iron Pool (LIP) is increased. LIP participates in generation of free radicals, including reactive oxygen species (ROS). Increased ROS, with concomitant decrease in anti-oxidants, results in oxidative stress and toxicity to the liver, heart and other tissues, causing serious morbidity and eventually mortality. Therapeutic iron chelation reduces the LIP and thereby ameliorates oxidative stress-mediated toxicity. Many food-derived antioxidants have the capacities to scavenge ROS and chelate iron. We have reported that fermented papaya preparation (FPP) has ROS scavenging effect on blood cells in vitro or in vivo (in thalassemic patients and experimental animals). We now investigated FPP's iron chelating effect - its ability to prevent (and revert) LIP accumulation. Liver- and heart-derived cells, and RBCs were exposed to non-transferrin bound iron in the form of ferrous ammonium sulfate and the effect of FPP on their LIP content and ROS generation was measured by flow-cytometry. The results indicated that FPP reduces LIP and ROS, and suggest that its antioxidant mechanism is related, at least in part, to iron chelation.


title: Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations

abstract: The item cold-start problem is inherent to collaborative filtering (CF) recommenders where items and users are represented by vectors in a latent space. It emerges since CF recommenders rely solely on historical user interactions to characterize their item inventory. As a result, an effective serving of new and trendy items to users may be delayed until enough user feedback is received, thus, reducing both users' and content suppliers' satisfaction. To mitigate this problem, many commercial recommenders apply random exploration and devote a small portion of their traffic to explore new items and gather interactions from random users. Alternatively, content or context information is combined into the CF recommender, resulting in a hybrid system. Another hybrid approach is to learn a mapping between the item attribute space and the CF latent feature space, and use it to characterize the new items providing initial estimates for their latent vectors. In this paper, we adopt the attribute-to-feature mapping approach to expedite random exploration of new items and present LearnAROMA - an advanced algorithm for learning the mapping, previously proposed in the context of classification. In particular, LearnAROMA learns a Gaussian distribution over the mapping matrix. Numerical evaluation demonstrates that this learning technique achieves more accurate initial estimates than logistic regression methods. We then consider a random exploration setting, in which new items are further explored as user interactions arrive. To leverage the initial latent vector estimates with the incoming interactions, we propose DynamicBPR - an algorithm for updating the new item latent vectors without retraining the CF model. Numerical evaluation reveals that DynamicBPR achieves similar accuracy as a CF model trained on all the ratings, using 71% less exploring users than conventional random exploration.


title: A Bayesian hierarchical detection framework for parking space detection

abstract: In this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter- occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.


title: DP 2 PN 2 Solver : A flexible dynamic programming solver software tool

abstract: Dynamic programming (DP) is a very general op- timization technique, which can be applied to numerous decision problems that typically require a sequence of decisions to be made. The solver software DP2PN2Solver presented in this paper is a general, flexible, and expandable software tool that solves DP prob- lems. It consists of modules on two levels. A level one module takes the specification of a discrete DP problem instance as input and produces an intermediate Petri net (PN) representation called Bellman net (Lew, 2002; Lew, Mauch, 2003, 2004) as output — a middle layer, which concisely captures all the essential elements of a DP problem in a standardized and mathematically precise fashion. The optimal solution for the problem instance is computed by an "executable" code (e.g. Java, Spreadsheet, etc.) derived by a level two module from the Bellman net representation. DP2PN2Solver's unique potential lies in its Bellman net repre- sentation. In theory, a PN's intrinsic concurrency allows to distrib- ute the computational load encountered when solving a single DP problem instance to several computational units.


title: A Modified DTC with Capability of Regenerative Braking Energy in BLDC driven Electric Vehicles Using Adaptive Control Theory

abstract: This paper represents a novel regenerative braking approach for the Electric Vehicles. The proposed method solves the short-range problem which is corresponding to the charge of the battery pack. The DTC switching algorithm has been modified to recover the electrical energy from Electrical Vehicle (EV), driven by Brushless DC motor, without using the additional power converter or the other electrical energy storage devices. During regenerative braking process, different switching pattern is applied to the inverter to convert the mechanical energy to the electrical energy through the reverse diodes. This switching pattern is different from the normal operation due to the special arrangement of voltage vectors which is considered to convert the mechanical energy to electrical energy. The state of charge of the battery is used as a performance indicator of the method. Simultaneously, a model reference adaptive system has been designed to tune the system parameters. Several simulations are carried out to validate the performance and effectiveness of the proposed methods. The results show the high capability and performance of the designed method.


title: Functional pearl: a SQL to C compiler in 500 lines of code

abstract: We present the design and implementation of a SQL query processor that outperforms existing database systems and is written in just about 500 lines of Scala code -- a convincing case study that high-level functional programming can handily beat C for systems-level programming where the last drop of performance matters. The key enabler is a shift in perspective towards generative programming. The core of the query engine is an interpreter for relational algebra operations, written in Scala. Using the open-source LMS Framework (Lightweight Modular Staging), we turn this interpreter into a query compiler with very low effort. To do so, we capitalize on an old and widely known result from partial evaluation known as Futamura projections, which state that a program that can specialize an interpreter to any given input program is equivalent to a compiler. In this pearl, we discuss LMS programming patterns such as mixed-stage data structures (e.g. data records with static schema and dynamic field components) and techniques to generate low-level C code, including specialized data structures and data loading primitives.


title: Verifiable agent interaction in abductive logic programming: The SCIFF framework

abstract: SCIFF is a framework thought to specify and verify interaction in open agent societies. The SCIFF language is equipped with a semantics based on abductive logic programming; SCIFF's operational component is a new abductive logic programming proof procedure, also named SCIFF, for reasoning with expectations in dynamic environments. In this article we present the declarative and operational semantics of the SCIFF language, and the termination, soundness, and completeness results of the SCIFF proof procedure, and we demonstrate SCIFF's possible application in the multiagent domain.


title: Feature Selection and Composition Using PyOracle

abstract: A system is described which uses the Audio Oracle algorithm for music analysis and machine improvisation. Some improvements on previous Factor Oracle-based systems are presented, including automatic model calibration based on measures from Music Information Dynamics, facilities for compositional structuring and automation, and an audio-based query mode which uses the input signal to influence the output of the generative system.


title: Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

abstract: Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.


title: Study on novel Curvature Features for 3D fingerprint recognition

abstract: The human finger is a three-dimensional object. More information will be provided if 3D fingerprint images are available compared with 2D fingerprints. This paper explores 3D fingerprint features, as well as their possible applications. Novel fingerprint features, which are defined as Curvature Features (e.g. curve-skeleton, overall maximum curvatures), are for the first time proposed and investigated in this paper. Those features are then employed to assist more accurate fingerprint matching or classify human gender after analyzing their characteristics. A series of experiments are conducted to evaluate the effectiveness of employing these novel fingerprint features to fingerprint recognition based on the established database with 541 fingers. Results show that an Equal error Rate (EER) of ~15% can be achieved when only curve-skeleton is used for recognition. But, promising EER of ~3.4% is realized by combining curve-skeleton with classical 2D fingerprint features for recognition that indicates the prospect of 3D fingerprint recognition. The proposed overall maximum curvatures are found to be helpful for human gender classification.


title: Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context †

abstract: The Internet of Things (IoT) has opened productive ways to cultivate soil with the use of low-cost hardware (sensors/actuators) and communication (Internet) technologies. Remote equipment and crop monitoring, predictive analytic, weather forecasting for crops or smart logistics and warehousing are some examples of these new opportunities. Nevertheless, farmers are agriculture experts but, usually, do not have experience in IoT applications. Users who use IoT applications must participate in its design, improving the integration and use. In this work, different industrial agricultural facilities are analysed with farmers and growers to design new functionalities based on IoT paradigms deployment. User-centred design model is used to obtain knowledge and experience in the process of introducing technology in agricultural applications. Internet of things paradigms are used as resources to facilitate the decision making. IoT architecture, operating rules and smart processes are implemented using a distributed model based on edge and fog computing paradigms. A communication architecture is proposed using these technologies. The aim is to help farmers to develop smart systems both, in current and new facilities. Different decision trees to automate the installation, designed by the farmer, can be easily deployed using the method proposed in this document.


title: Misdiagnosis of High Function Autism Spectrum Disorders in Adults: An Italian Case Series

abstract: High Function (HF) Autism Spectrum Disorders (ASD) in adulthood is highly prevalent but insufficiently recognized. In Italy, in particular, awareness of this condition is still insufficient and many psychiatrists have no cases of HF ASD to mention. Adult patients with HF ASD come to the attention of Mental Health Services complaining of difficulties within their social context and interpersonal relationships. Objectives: Describe emblematic clinical examples of misdiagnosed HF ASD to understand reasons that conducted to misdiagnosis. Procedure: We contact five specialized Italian Center in diagnosis of ASD. Each center have to describe two or three emblematic cases of adult patient with diagnosis of ASD validated by ADOS-4 but referred to clinicians with another diagnosis, discussing about possible reasons of misdiagnosis. Sample and Results: We have collected 12 case reports (2 from Bologna center, 3 from Torino center, 3 from Pavia center, 2 from Verona center and 2 from Catania center) of adult HF ASD previously misdiagnosed. These cases shows important similarity across centers and highlight that if are taken into account only problems or symptoms that conduct patients to ask help, cases can easily suggest other psychiatric or personality disorders. Diagnosis becomes clear only after considering all the clinical features and a detailed developmental history. Conclusion: Psychiatrists who have insufficient experience of ASD may overlook some symptoms of the overall clinical picture and misdiagnose ASD as personality disorders, schizophrenia, phobia or even as a non-psychiatric condition, so is hopeful for future increased knowledge about HF ASD in adulthood.


title: Toward in vivo diagnosis of skin cancer using multimode imaging dermoscopy: (II) molecular mapping of highly pigmented lesions

abstract: We have developed a multimode imaging dermoscope that combines polarization and hyperspectral imaging with a computationally rapid analytical model. This approach employs specific spectral ranges of visible and near infrared wavelengths for mapping the distribution of specific skin bio-molecules. This corrects for the melanin-hemoglobin misestimation common to other systems, without resorting to complex and computationally intensive tissue optical models that are prone to inaccuracies due to over-modeling. Various human skin measurements including a melanocytic nevus, and venous occlusion conditions were investigated and compared with other ratiometric spectral imaging approaches. Access to the broad range of hyperspectral data in the visible and near-infrared range allows our algorithm to flexibly use different wavelength ranges for chromophore estimation while minimizing melanin-hemoglobin optical signature cross-talk.


title: Trajectory clustering via deep representation learning

abstract: Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher-level applications like location prediction. While a plethora of trajectory clustering techniques have been proposed, they often rely on spatiotemporal similarity measures that are not space- and time-invariant. As a result, they cannot detect trajectory clusters where the within-cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low-dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behavior features that capture space- and time-invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements, and further employ a sequence to sequence autoencoder to learn fixed-length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space- and time-invariant clusters. We evaluate the proposed method on both synthetic and real data, and observe significant performance improvements over existing methods.


title: Inheritance of apomeiosis (diplospory) in fleabanes (Erigeron, Asteraceae)

abstract: Unreduced egg formation (apomeiosis) in flowering plants is rare except when it is coupled with parthenogenesis to yield gametophytic apomixis via apospory or diplospory. Results from genetic mapping studies in diverse apomictic taxa suggest that apomeiosis and parthenogenesis are genetically linked, a finding that is compatible with the conventional rationale that apomeiosis is unlikely to evolve independently because of deleterious fitness consequences. An Erigeron annuus (apomictic) × E. strigosus (sexual) genetic mapping population, however, included a high proportion of plants that were highly apomeiotic (diplosporous) but nonapomictic; that is, they lacked autonomous seed production. To evaluate the function and inheritance of diplospory in Erigeron, a diplosporous triploid (2n=3x=27) seed parent was crossed with a sexual diploid (2n=2x=18) E. strigosus pollen parent to produce an F1 of 31 plants. Chromosome numbers and molecular markers (AFLPs) document the inheritance of the maternal genome through unreduced eggs resulting in recombinant but predominantly (77%) tetraploid F1s (2n=4x=36; 2n+n, BIII). Quantitative evaluation shows continuous variation in the proportion of diplosporous (vs meiotic) ovules (41–89%) in tetraploid F1s despite the presumed equal genetic contribution from the diplosporous mother. These findings demonstrate the functional independence of diplospory and suggest that variation in the trait in F1s is likely due to segregating paternal modifiers. In addition, of six aneuploid (4x−1, 4x−2) F1s, three lack a subset of maternal AFLP markers. These plants likely arose from aberrant megagametogenesis resulting in the loss of maternal chromatin prior to fertilization.


title: Boosting Vocabulary Learning by Verbal Cueing During Sleep

abstract: Reactivating memories during sleep by re-exposure to associated memory cues (e. g., odors or sounds) improves memory consolidation. Here, we tested for the first time whether verbal cueing during sleep can improve vocabulary learning. We cued prior learned Dutch words either during non-rapid eye movement sleep (NonREM) or during active or passive waking. Re-exposure to Dutch words during sleep improved later memory for the German translation of the cued words when compared with uncued words. Recall of uncued words was similar to an additional group receiving no verbal cues during sleep. Furthermore, verbal cueing failed to improve memory during active and passive waking. High-density electroencephalographic recordings revealed that successful verbal cueing during NonREM sleep is associated with a pronounced frontal negativity in eventrelated potentials, a higher frequency of frontal slow waves as well as a cueing-related increase in right frontal and left parietal oscillatory theta power. Our results indicate that verbal cues presented during NonREM sleep reactivate associated memories, and facilitate later recall of foreign vocabulary without impairing ongoing consolidation processes. Likewise, our oscillatory analysis suggests that both sleep-specific slow waves as well as theta oscillations (typically associated with successful memory encoding during wakefulness) might be involved in strengthening memories by cueing during sleep.


title: DLAU: A Scalable Deep Learning Accelerator Unit on FPGA

abstract: As the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses significant challenge to construct a high performance implementations of deep learning neural networks. In order to improve the performance as well as to maintain the low power cost, in this paper we design deep learning accelerator unit (DLAU), which is a scalable accelerator architecture for large-scale deep learning networks using field-programmable gate array (FPGA) as the hardware prototype. The DLAU accelerator employs three pipelined processing units to improve the throughput and utilizes tile techniques to explore locality for deep learning applications. Experimental results on the state-of-the-art Xilinx FPGA board demonstrate that the DLAU accelerator is able to achieve up to $36.1 {\times }$ speedup comparing to the Intel Core2 processors, with the power consumption at 234 mW.


title: Emotion Attention to Friends on Social Networking Services

abstract: Social networking services are an important channel for people to keep in contact nowadays. Through the messages posted by friends, the user is able to know his/ her friends' recent activities, news and emotion status. In this research, we aimed at developing a system that can screen and analyze the messages posted by friends on social networking services to know which friends need instant attention/care. The posts on Facebook Wall of a friend are filtered first to obtain the messages by the friend, excluding the posts by games or other applications. The obtained messages are then classified into one of eight categories, including six emotion categories, special events, and others. The category information of the terms in the messages is then further analyzed for the proposed attention index. After the attention indices of selected friends are decided, they are ranked by the indices. The ones with higher attention indices need the user to show his/her care in a timely manner. We have developed a Facebook application that can display the desired information in a graphical interface.


title: A Tutor Agent for MOBA Games

abstract: Digital games have become a key player in the entertainment industry, attracting millions of new players each year. In spite of that, novice players may have a hard time when playing certain types of games, such as MOBAs and MMORPGs, due to their steep learning curves and not so friendly online communities. In this paper, we present an approach to help novice players in MOBA games overcome these problems. An artificial intelligence agent plays alongside the player analyzing his/her performance and giving tips about the game. Experiments performed with the game {\em League of Legends} show the potential of this approach.


title: A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases

abstract: Tasks such as question answering and semantic search are dependent on the ability of querying & reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model which can be applied to querying & reasoning over heterogeneous knowledge bases (KBs). The approach can be used for approximative reasoning, querying and associational knowledge discovery. In this paper we focus on commonsense reasoning as the main motivational scenario for the approach. The approach focuses on addressing the following problems: (i) providing a semantic selection mechanism for facts which are relevant and meaningful in a specific reasoning & querying context and (ii) allowing coping with information incompleteness in large KBs. The approach is evaluated using ConceptNet as a commonsense KB, and achieved high selectivity, high scalability and high accuracy in the selection of meaningful navigational paths. Distributional semantics is also used as a principled mechanism to cope with information incompleteness.


title: Cavamax W7 Composite Ethosomal Gel of Clotrimazole for Improved Topical Delivery: Development and Comparison with Ethosomal Gel

abstract: The present research work was aimed to formulate clotrimazole encapsulated Cavamax W7 composite ethosomes by injection method for improved delivery across epidermis. 3(2) factorial design was used to design nine formulations (F1-F9) and compared with ethosomal formulations (F10-F12). F9 with vesicle size of 202.8 ± 4.8 nm, highest zeta potential (-83.6 ± 0.96 mV) and %EE of 98.42 ± 0.15 was selected as optimized composite ethosome and F12 as reference ethosomal formulation. As revealed by transmission electron microscopy F9 vesicles were more condensed, uniformly spherical in shape than F12 vesicles. Vesicular stability studies indicated F9 to be more stable as compared to F12. Both F9 and F12 were incorporated in carbopol 934 gel base to get G1-G8 gel formulations and evaluated for in vitro skin permeability. Cavamax W7 composite ethosomal optimized gel (G5) showed higher in vitro percent cumulative drug permeation (88.53 ± 2.10%) in 8 h and steady state flux (J(ss)) of 3.39 ± 1.45 μg/cm(2)/min against the J(ss) of 1.57 ± 0.23 μg/cm(2)/min for ethosomal gel (G1) and 1.13 ± 0.06 μg/cm(2)/min for marketed formulation. The J(ss) flux of G5 was independent of amount of drug applied/unit area of skin. In vivo confocal laser scanning microscopic study of G5 depicted uniform and deeper penetration of rhodamine B (marker) in epidermis from Cavamax W7 composite ethosomal gel in comparison to G1. Finally, G5 demonstrated better (p < 0.05) antifungal activity against Candida albicans and Aspergillus niger than G1 thus, signifying that Cavamax W7 composite ethosomes present a superior stable and efficacious vesicular system than ethosomal formulation for topical delivery of clotrimazole.


title: SoundWrite II: Ambient Acoustic Sensing for Noise Tolerant Device-Free Gesture Recognition

abstract: Acoustic sensing has brought forth the advances of prosperous applications such as gesture recognition. Specifically, ambient acoustic sensing has drawn many contentions due to the ease of use property. Unfortunately, the inherent ambient noise is the major reason for unstable gesture recognition. In this work, we propose “SoundWrite II”, which is an improved version of our previously designed system. Compared with our previous design, we utilize the two threshold values to identify the effective signals from the original noisy input, and leverage the MFCC (Mel frequency cepstral coefficient) to extract the stable features from different gestures. These enhancements could effectively improve the noise tolerant performance for previous design. Implementation on the Android system has realized the real time processing of the feature extraction and gesture recognition. Extensive evaluations have validated our design, where the noise tolerant property is fully tested under different experimental settings and the recognition accuracy could be 91% with 7 typical gestures.


title: Feature tracking and matching in video using programmable graphics hardware

abstract: This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.


title: Detecting depression from facial actions and vocal prosody

abstract: Current methods of assessing psychopathology depend almost entirely on verbal report (clinical interview or questionnaire) of patients, their family, or caregivers. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of psychological disorder, much of which may occur outside the awareness of either individual. We compared clinical diagnosis of major depression with automatically measured facial actions and vocal prosody in patients undergoing treatment for depression. Manual FACS coding, active appearance modeling (AAM) and pitch extraction were used to measure facial and vocal expression. Classifiers using leave-one-out validation were SVM for FACS and for AAM and logistic regression for voice. Both face and voice demonstrated moderate concurrent validity with depression. Accuracy in detecting depression was 88% for manual FACS and 79% for AAM. Accuracy for vocal prosody was 79%. These findings suggest the feasibility of automatic detection of depression, raise new issues in automated facial image analysis and machine learning, and have exciting implications for clinical theory and practice.


Global positioning system: signals, measurements, and performance [Book Review]


title: The future of forensic DNA analysis

abstract: The author's thoughts and opinions on where the field of forensic DNA testing is headed for the next decade are provided in the context of where the field has come over the past 30 years. Similar to the Olympic motto of 'faster, higher, stronger', forensic DNA protocols can be expected to become more rapid and sensitive and provide stronger investigative potential. New short tandem repeat (STR) loci have expanded the core set of genetic markers used for human identification in Europe and the USA. Rapid DNA testing is on the verge of enabling new applications. Next-generation sequencing has the potential to provide greater depth of coverage for information on STR alleles. Familial DNA searching has expanded capabilities of DNA databases in parts of the world where it is allowed. Challenges and opportunities that will impact the future of forensic DNA are explored including the need for education and training to improve interpretation of complex DNA profiles.


title: Accelerating ODE-Based Simulation of General and Heterogeneous Biophysical Models Using a GPU

abstract: Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.


title: On performance study of UWB real time locating system

abstract: Real time locating systems (RTLS) with centimeter accuracy have recently been made available for commercial deployment by employing ultra wideband (UWB) technology. DecaWave's DW1000 is a commercial UWB integrated circuit that utilizes time-of-flight (ToF) measurement technique and two-way-ranging (TWR) mechanism to accurately estimate the distance between an anchor and a tag. With the distances between the tag and at least three anchors, a trilateration algorithm can be applied to estimate the tag's location on the same two-dimensional (2D) plane as the anchors. Moreover, if four or more anchors are available, the tag's location in three-dimensional space can also be estimated. This work presents a performance study on location accuracy of DecaWave's TREK1000, a two-way ranging RTLS IC evaluation kit. Four anchors and one tag were set up as an RTLS deployed in two different indoor areas. The difficulties encountered during the calculation of the tag's location at certain locations were pointed out. It was also found that the change in performance of the 2D localization of the system when set up with three and four anchors was insignificant. Additionally, the accuracy in the third dimension or the height of the tag above the ground was found to be less accurate.


title: A Comparison of Mesh Morphing Methods for 3D Shape Optimization

abstract: The ability to automatically morph an existing mesh to conform to geometry modifications is a necessary capability to enable rapid prototyping of design variations. This paper compares six methods for morphing hexahedral and tetrahedral meshes, including the previously published FEMWARP and LBWARP methods as well as four new methods. Element quality and performance results show that different methods are superior on different models. We recommend that designers of applications that use mesh morphing consider both the FEMWARP and a linear simplex based method.


title: Compacting Neural Network Classifiers via Dropout Training

abstract: We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per unit dropout retention probability so that the optimizer can effectively prune hidden units during training. By changing the prior hyperparameters, we can control the size of the resulting network. We performed a systematic comparison of dropout compaction and competing methods on several real-world speech recognition tasks and found that dropout compaction achieved comparable accuracy with fewer than 50% of the hidden units, translating to a 2.5x speedup in run-time.


title: Generative Adversarial Neural Networks for Pigmented and Non-Pigmented Skin Lesions Detection in Clinical Images

abstract: Skin cancer is one of the most common malignancies in fair skin population. It can be divided in two main classes: melanoma and non-melanoma skin cancer. This means that pigmented and, also, non-pigmented skin lesions might raise an important risk. Due to the fact that melanoma is more aggressive, pigmented skin lesions gained more attention in terms of automatic diagnosis. One of the most important steps in this procedure is to correctly identify the skin lesion in an image (acquired with a dermoscope or a standard camera). Based on the accurate identification of the lesion, specific automatic algorithms for cancer diagnosis can be developed. In this paper we propose and evaluate an artificial intelligence method for pigmented and non-pigmented lesion segmentation. The method uses generative adversarial neural networks. The network was trained and tested on a large set of images acquired with smartphone cameras. The results show that approximately 92% of the lesions are correctly identified on the test set.


FLOCKING BY THE FUSION OF SONAR AND ACTIVE INFRARED SENSORS ON PHYSICAL AUTONOMOUS MOBILE ROBOTS


title: A Systemic Smartphone Usage Pattern Analysis: Focusing on Smartphone Addiction Issue

abstract: Despite the recent massive popularity of smartphones and their impact on our lifestyles, only few objective studies have been conducted on smartphone usage patterns. In this paper, using a comprehensive smartphone usage logging system (a client app and a server), various statistical analysis results from more than 800 man-days usage logs are presented. The analysis shows 1) significant difference in usage frequency and time statistics among the applications, 2) different application category preferences between addicts and non-addicts, and 3) little usage variation between weekdays and weekends, but higher usage in night time for addicts.