title: Completed Local Ternary Pattern for Rotation Invariant Texture Classification

abstract: Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter's weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.


title: Thou Shalt Not Depend on Me: Analysing the Use of Outdated JavaScript Libraries on the Web

abstract: Web developers routinely rely on third-party Java-Script libraries such as jQuery to enhance the functionality of their sites. However, if not properly maintained, such dependencies can create attack vectors allowing a site to be compromised. ::: In this paper, we conduct the first comprehensive study of client-side JavaScript library usage and the resulting security implications across the Web. Using data from over 133 k websites, we show that 37% of them include at least one library with a known vulnerability; the time lag behind the newest release of a library is measured in the order of years. In order to better understand why websites use so many vulnerable or outdated libraries, we track causal inclusion relationships and quantify different scenarios. We observe sites including libraries in ad hoc and often transitive ways, which can lead to different versions of the same library being loaded into the same document at the same time. Furthermore, we find that libraries included transitively, or via ad and tracking code, are more likely to be vulnerable. This demonstrates that not only website administrators, but also the dynamic architecture and developers of third-party services are to blame for the Web's poor state of library management. ::: The results of our work underline the need for more thorough approaches to dependency management, code maintenance and third-party code inclusion on the Web.


Digital Storytelling for Reflection and Engagement : a study of the uses and potential of digital storytelling


title: The Preimage of Rectifier Network Activities

abstract: The preimage of the activity at a certain level of a deep network is the set of inputs that result in the same node activity. For fully connected multi layer rectifier networks we demonstrate how to compute the preimages of activities at arbitrary levels from knowledge of the parameters in a deep rectifying network. If the preimage set of a certain activity in the network contains elements from more than one class it means that these classes are irreversibly mixed. This implies that preimage sets which are piecewise linear manifolds are building blocks for describing the input manifolds specific classes, i.e. all preimages should ideally be from the same class. We believe that the knowledge of how to compute preimages will be valuable in understanding the efficiency displayed by deep learning networks and could potentially be used in designing more efficient training algorithms.


title: Ingested plastic transfers hazardous chemicals to fish and induces hepatic stress

abstract: Plastic debris litters aquatic habitats globally, the majority of which is microscopic (< 1 mm), and is ingested by a large range of species. Risks associated with such small fragments come from the material itself and from chemical pollutants that sorb to it from surrounding water. Hazards associated with the complex mixture of plastic and accumulated pollutants are largely unknown. Here, we show that fish, exposed to a mixture of polyethylene with chemical pollutants sorbed from the marine environment, bioaccumulate these chemical pollutants and suffer liver toxicity and pathology. Fish fed virgin polyethylene fragments also show signs of stress, although less severe than fish fed marine polyethylene fragments. We provide baseline information regarding the bioaccumulation of chemicals and associated health effects from plastic ingestion in fish and demonstrate that future assessments should consider the complex mixture of the plastic material and their associated chemical pollutants.


title: Intrusion Tolerance in Distributed Computing Systems

abstract: An intrusion-tolerant distributed system is a system which is designed so that any intrusion into a part of the system will not endanger confidentiality, integrity and availability. This approach is suitable for distributed systems, because distribution enables isolation of elements so that an intrusion gives physical access to only a part of the system. In particular, the intrusion-tolerant authentication and authorization servers enable a consistent security policy to be implemented on a set of heterogeneous, untrusted sites, administered by untrusted (but nonconspiring) people. The authors describe how some functions of distributed systems can be designed to tolerate intrusions. A prototype of the persistent file server presented has been successfully developed and implemented as part of the Delta-4 project of the European ESPRIT program. >


title: How might the brain represent complex symbolic knowledge?

abstract: A novel category of theories is proposed, providing a potential explanation for the representation of complex knowledge in the human (and, more generally, mammalian) brain. Firstly, a "glocal" representation for concepts is suggested, involving localized representations in a sparse network of "concept neurons" in the Medial Temporal Lobe, coupled with a complex dynamical attractor representation in other parts of cortex. Secondly, it is hypothesized that a combi-natory logic like representation is used to encode abstract relationships without explicit use of variable bindings, perhaps using systematic asynchronization among concept neurons to indicate an analogue of the combinatory-logic operation of function application. While unraveling the specifics of the brain's knowledge representation mechanisms will require data beyond what is currently available, the approach presented here provides a class of possibilities that is neurally plausible and bridges the gap between neurophysiological realities and mathematical and computer science concepts.


title: Gapped Transformer Design Methodology and Implementation for LLC Resonant Converters

abstract: In the LLC resonant converter, the air gap is generally positioned in the core of the transformer for proper magnetizing inductance. Traditional transformer design methods assume infinite permeability of the core and no energy stored in the core. The improved design methodology for the gapped transformer is proposed with the optimum relative permeability and gap selection to meet the temperature rise and the magnetizing inductance requirements. The magnetizing current influences the magnetic flux in the core leading to the core saturation and core loss, while the resonant current contributes to the winding loss. The transformer design for a 200 W, 90 kHz LLC resonant converter is presented and experimental results validate the proposed methodology.


title: Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity

abstract: The use of stylometry, authorship recognition through purely linguistic means, has contributed to literary, historical, and criminal investigation breakthroughs. Existing stylometry research assumes that authors have not attempted to disguise their linguistic writing style. We challenge this basic assumption of existing stylometry methodologies and present a new area of research: adversarial stylometry. Adversaries have a devastating effect on the robustness of existing classification methods. Our work presents a framework for creating adversarial passages including obfuscation, where a subject attempts to hide her identity, and imitation, where a subject attempts to frame another subject by imitating his writing style, and translation where original passages are obfuscated with machine translation services. This research demonstrates that manual circumvention methods work very well while automated translation methods are not effective. The obfuscation method reduces the techniques' effectiveness to the level of random guessing and the imitation attempts succeed up to 67% of the time depending on the stylometry technique used. These results are more significant given the fact that experimental subjects were unfamiliar with stylometry, were not professional writers, and spent little time on the attacks. This article also contributes to the field by using human subjects to empirically validate the claim of high accuracy for four current techniques (without adversaries). We have also compiled and released two corpora of adversarial stylometry texts to promote research in this field with a total of 57 unique authors. We argue that this field is important to a multidisciplinary approach to privacy, security, and anonymity.


title: Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey

abstract: Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments and enable to perform complex and delicate operations through small incisions in minimally invasive surgery. These advantages enable their broad applications with minimal trauma and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities. However, their inherent deformable designs make it difficult to realize 3-D intraoperative real-time shape sensing to accurately model their shape. Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, human–robot interaction, and surgical manipulation safety concerns in minimally invasive surgery. Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads. This survey investigates the recent advances in alternative emerging techniques for 3-D shape sensing in this field and focuses on the following categories: fiber-optic-sensor-based, electromagnetic-tracking-based, and intraoperative imaging modality-based shape-reconstruction methods. The limitations of existing technologies and prospects of new technologies are also discussed.


title: Automatic keystone correction for smart projectors with embedded camera

abstract: We propose embedded algorithms for enabling a smart self-adjusting projector that is capable of fully automatic keystone distortion detection and correction, plus zooming and centering the image to fit a screen. The projector has an embedded camera and it automatically detects the screen and a specific pattern during self-adjustment. Proposed algorithms meet severe computational requirements of a portable projector and they exploit the physical constraints in a projector-(embedded) camera pair in a novel way in order to not compromise image quality under different usage conditions. We will demonstrate our projector system during the conference.


title: Man-Made Deserts: Desertization Processes and Threats

abstract: Desertization is defined as the irreversible extension of desert land forms and landscapes to areas where they did not occur in a recent past. Such extension does not result from a would-be increasing aridity as there is no evidence of any global long-term trends in rainfall in arid lands since the period of instrumental record (ca. 150 years). The 0.5° C of global temperature increase over the past 100 years remains well within the range of historical fluctuations. The creation of man-made deserts is actually the result of long-lasting mismanagement of natural resources under the pressure of excessive human and livestock populations. Such land abuse may drive ecosystems beyond their resilience potential for recovery. The causes and mechanisms of arid land degradation pertain to direct and indirect activities, processes, and phenomena, which are not necessarily linked to aridity or drought. Causes, severity, and extent of desertization are quantified from various sources. The potential for biological reco...


title: Improvement of GPR tracking by using inertial and GPS combined data

abstract: In most applications of Ground Penetrating Radar (GPR), it is very important to combine the radar with an accurate positioning system. This allows solving errors in the localisation of buried objects, which may be generated by measurement conditions such as the soil slope, in the case of a ground-coupled GPR, and the aerial vehicle altitude, in the case of a GPR mounted on a drone or helicopter. This paper presents the implementation of a low-cost system for positioning, tracking and trimming of GPR data. The proposed system integrates Global Positioning System (GPS) data with those of an Inertial Measurement Unit (IMU). So far, the electronic board including GPS and IMU was designed, developed and tested in the laboratory. As a next step, GPR results will be collected in outdoor scenarios of practical interest and the accuracy of data measured by using our positioning system will be compared to the accuracy of data measured without using it.


title: A 0.5V 2.2pW 2-transistor voltage reference

abstract: A voltage reference using a depletion-mode device is designed in a 0.13µm CMOS process and achieves ultra-low power consumption and sub-1V operation without sacrificing temperature and supply voltage insensitivity. Measurements show a temperature coefficient of 19.4ppm/° (3.4 µV/°), line sensitivity of 0.033%/V, power supply rejection ratio of−67dB, and power consumption of 2.2pW. It requires only two devices and functions down to V dd =0.5V with an area of 1350µm2. A variant for higher Vout is also demonstrated.


title: NuiVend - Next Generation Vending Machine

abstract: Conventional vending machines require users to press buttons and respond to visual cues. This makes them less accessible to some users such as the blind. NuiVend resolves this issue by integrating natural voice commands and gesture interactions into a vending machine, thereby creating many alternative, natural, and more user-friendly ways of interaction. In this paper, we will discuss NuiVend's use of a variety of technologies. Such as: Microsoft Kinect, various Microsoft Cognitive API services, relay and sensor boards, as well as the overall logic of the control software. Finally, we discuss potential improvements to NuiVend as well as Microsoft Language Understanding Intelligent Service (LUIS) techniques that can be applied to many other future NUI based projects.


title: Fuzzy logic, trading uncertainty and technical trading

abstract: From the market microstructure perspective, technical analysis can be profitable when informed traders make systematic mistakes or when uninformed traders have predictable impacts on price. However, chartists face a considerable degree of trading uncertainty because technical indicators such as moving averages are essentially imperfect filters with a nonzero phase shift. Consequently, technical trading may result in erroneous trading recommendations and substantial losses. This paper presents an uncertainty reduction approach based on fuzzy logic that addresses two problems related to the uncertainty embedded in technical trading strategies: market timing and order size. The results of our high-frequency exercises show that ‘fuzzy technical indicators’ dominate standard moving average technical indicators and filter rules for the Euro-US dollar (EUR-USD) exchange rates, especially on high-volatility days.


title: Reflecting on the global burden of musculoskeletal conditions: lessons learnt from the Global Burden of Disease 2010 Study and the next steps forward

abstract: The objective of this paper is to provide an overview of the strengths, limitations and lessons learned from estimating the burden from musculoskeletal (MSK) conditions in the Global Burden of Disease 2010 Study (GBD 2010 Study). It should be read in conjunction with the other GBD 2010 Study papers published in this journal. The strengths of the GBD 2010 Study include: the involvement of a MSK expert group; development of new and more valid case definitions, functional health states, and disability weights to better reflect the MSK conditions; the extensive series of systematic reviews undertaken to obtain data to derive the burden estimates; and the use of a new, more advanced version of the disease-modelling software (DisMod-MR). Limitations include: many regions of the world did not have data; the extent of heterogeneity between included studies; and burden does not include broader aspects of life, such as participation and well-being. A number of lessons were learned. Ongoing involvement of experts is critical to ensure the success of future efforts to quantify and monitor this burden. A paradigm shift is urgently needed among global agencies in order to alleviate the rapidly increasing global burden from MSK conditions. Prevention and control of MSK disability are required, along with health system changes. Further research is needed to improve understanding of the predictors and clinical course across different settings, and the ways in which MSK conditions can be better managed and prevented.


The geopolitics of internet control: censorship, sovereignty, and cyberspace


title: A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

abstract: United States. Air Force Office of Scientific Research (Computational Mathematics Grant FA9550-12-1-0420)


title: Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems

abstract: This article is a review of experiments comparing the effectiveness of human tutoring, computer tutoring, and no tutoring. “No tutoring” refers to instruction that teaches the same content without tutoring. The computer tutoring systems were divided by their granularity of the user interface interaction into answer-based, step-based, and substep-based tutoring systems. Most intelligent tutoring systems have step-based or substep-based granularities of interaction, whereas most other tutoring systems (often called CAI, CBT, or CAL systems) have answer-based user interfaces. It is widely believed as the granularity of tutoring decreases, the effectiveness increases. In particular, when compared to No tutoring, the effect sizes of answer-based tutoring systems, intelligent tutoring systems, and adult human tutors are believed to be d = 0.3, 1.0, and 2.0 respectively. This review did not confirm these beliefs. Instead, it found that the effect size of human tutoring was much lower: d = 0.79. Moreover, the eff...


title: What is Coordination Theory ?

abstract: The primary purpose of this paper is to stimulate discussion about a research agenda for a newinterdisciplinary field. This field--the study of coordination--draws upon a variety of differentdisciplines including computer science, organization theory, management science, economics, andpsychology. Work in this new area will include developing a body of scientific theory, which we willcall "coordination theory," about how the activities of separate actors can be coordinated. Oneimportant use for coordination theory will be in developing and using computer and communicationsystems to help people coordinate their activities in new ways. We will call these systems"coordination technology."


title: The development of macrophages from human CD34+ haematopoietic stem cells in serum-free cultures is optimized by IL-3 and SCF.

abstract: The derivation of human macrophages from peripheral blood monocytes remains a convenient method for the study of macrophage biology. However, for macrophage differentiation, a significant proportion of development has occurred prior to the monocyte stage; monocyte subsets also have varying potential for differentiation. Differentiation of macrophages from a less mature precursor, such as CD34+ haematopoietic stem cells, can further inform with regard to the development of macrophage-lineage cells. CD34+ cells were cultured in serum-free medium containing Flt3L, SCF, IL-3, IL-6 and M-CSF. Using differing combinations of growth factors, the effect on cell proliferation and differentiation to adherent macrophage-like cells was determined. The proliferative response of CD34+ cells to M-CSF was determined during the initial phase of cell culture. Thirteen combinations of SCF, IL-3, IL-6 and M-CSF were then compared to determine the optimum combination for proliferation. Adherence was used to isolate mature macrophages, and the macrophage-like phenotype was confirmed by analyses of surface markers, histo-morphology and phagocytosis. This study refines the means by which large numbers of macrophages are obtained under serum-free conditions from haematopoietic precursors.


title: ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection

abstract: Our system addresses Part 1, Lesion Segmentation and Part 3, Lesion Classification of the ISIC 2017 challenge. Both algorithms make use of deep convolutional networks to achieve the challenge objective.


title: The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms

abstract: The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the different forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.


title: A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images

abstract: Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive $\mathbf L\acute{e}vy$ flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images.


title: Diffusion kernels on statistical manifolds

abstract: A family of kernels for statistical learning is introduced that exploits the geometric structure of statistical models. The kernels are based on the heat equation on the Riemannian manifold defined by the Fisher information metric associated with a statistical family, and generalize the Gaussian kernel of Euclidean space. As an important special case, kernels based on the geometry of multinomial families are derived, leading to kernel-based learning algorithms that apply naturally to discrete data. Bounds on covering numbers and Rademacher averages for the kernels are proved using bounds on the eigenvalues of the Laplacian on Riemannian manifolds. Experimental results are presented for document classification, for which the use of multinomial geometry is natural and well motivated, and improvements are obtained over the standard use of Gaussian or linear kernels, which have been the standard for text classification.


Normalization in Social Media by using Spell Correction and Dictionary Based Approach


title: A New Isolation Technique for Reverse Blocking IGBT with Ion Implantation and Laser Annealing to Tapered Chip Edge Sidewalls

abstract: This paper presents a new isolation technique for high breakdown voltage RB-IGBT, whose termination area is extremely small in comparison with conventional isolation technique with thermal diffusion. The p+ isolation layer for the reverse blocking capability was successively fabricated with negligible thermal budget by means of ion implantation and laser annealing to the chip edges. Sufficient blocking capability over 1200V was demonstrated with the new method.


title: A 3.3-V CMOS adaptive analog video line driver with low distortion performance

abstract: An analog line driver for video applications is presented. Utilizing a class-AB error amplifier structure, the design achieved 1.2-V peak-to-peak output swing with better than 42-dB linearity for frequencies up to 5 MHz. An adaptive tuning scheme for output impedance matching using peak detection is used to provide uniform performance across line impedance variations. The circuit is designed in AMI 0.5-/spl mu/m CMOS technology and has a tuning range of 70-180 /spl Omega/ with a power consumption of about 26.4 mW at 75-/spl Omega/ load.


title: CASIA Image Tampering Detection Evaluation Database

abstract: Image forensics has now raised the anxiety of justice as increasing cases of abusing tampered images in newspapers and court for evidence are reported recently. With the goal of verifying image content authenticity, passive-blind image tampering detection is called for. More realistic open benchmark databases are also needed to assist the techniques. Recently, we collect a natural color image database with realistic tampering operations. The database is made publicly available for researchers to compare and evaluate their proposed tampering detection techniques. We call this database CASI-A Image Tampering Detection Evaluation Database. We describe the purpose, the design criterion, the organization and self-evaluation of this database in this paper.


title: Using an Instructional Design Model to Teach Medical Procedures

abstract: Educators are often tasked with developing courses and curricula that teach learners how to perform medical procedures. This instruction must provide an optimal, uniform learning experience for all learners. If not well designed, this instruction risks being unstructured, informal, variable amongst learners, or incomplete. This article shows how an instructional design model can help craft courses and curricula to optimize instruction in performing medical procedures. Educators can use this as a guide to developing their own course instruction.


title: Beyond predictions: biodiversity conservation in a changing climate.

abstract: Climate change is predicted to become a major threat to biodiversity in the 21st century, but accurate predictions and effective solutions have proved difficult to formulate. Alarming predictions have come from a rather narrow methodological base, but a new, integrated science of climate-change biodiversity assessment is emerging, based on multiple sources and approaches. Drawing on evidence from paleoecological observations, recent phenological and microevolutionary responses, experiments, and computational models, we review the insights that different approaches bring to anticipating and managing the biodiversity consequences of climate change, including the extent of species' natural resilience. We introduce a framework that uses information from different sources to identify vulnerability and to support the design of conservation responses. Although much of the information reviewed is on species, our framework and conclusions are also applicable to ecosystems, habitats, ecological communities, and genetic diversity, whether terrestrial, marine, or fresh water.


title: Kinectrack: 3D Pose Estimation Using a Projected Dense Dot Pattern

abstract: Kinectrack is a novel approach to six-DoF tracking that provides agile real-time pose estimation using only commodity hardware. The dot pattern emitter and IR camera components of the standard Kinect device are separated to allow the emitter to roam freely relative to a fixed camera. The six-DoF pose of the emitter component is recovered by matching the dense dot pattern observed by the camera to a pre-captured reference image. A novel matching technique is introduced to obtain the dense dot pattern correspondences efficiently in wide- and adaptive-baseline scenarios that requires only a small subset of the full dense dot pattern to fall within the field of view of the fixed camera. An auto-calibration process is proposed in order to obtain the intrinsic parameters of the fixed camera and the internal dot pattern reference image of the emitter. The system simultaneously recovers the six-DoF pose of the emitter device and the piecewise planar 3D scene structure. Kinectrack provides a low-cost method for tracking an object without any on-board computation, with small size and only simple electronics. This paper extends the original ISMAR 2012 submission, including a demonstration of robust pose tracking for AR and examples of matching in planar and non-planar scenes.


Postparalysis Facial Synkinesis: Clinical Classification and Surgical Strategies


A Connected E-Shape and U-Shape Dual-Band Patch Antenna for Different Wireless Applications


title: LOD Generation for Urban Scenes

abstract: We introduce a novel approach that reconstructs 3D urban scenes in the form of levels of detail (LODs). Starting from raw datasets such as surface meshes generated by multiview stereo systems, our algorithm proceeds in three main steps: classification, abstraction, and reconstruction. From geometric attributes and a set of semantic rules combined with a Markov random field, we classify the scene into four meaningful classes. The abstraction step detects and regularizes planar structures on buildings, fits icons on trees, roofs, and facades, and performs filtering and simplification for LOD generation. The abstracted data are then provided as input to the reconstruction step which generates watertight buildings through a min-cut formulation on a set of 3D arrangements. Our experiments on complex buildings and large-scale urban scenes show that our approach generates meaningful LODs while being robust and scalable. By combining semantic segmentation and abstraction, it also outperforms general mesh approximation approaches at preserving urban structures.


title: A difference in hypothalamic structure between heterosexual and homosexual men.

abstract: The anterior hypothalamus of the brain participates in the regulation of male-typical sexual behavior. The volumes of four cell groups in this region [interstitial nuclei of the anterior hypothalamus (INAH) 1, 2, 3, and 4] were measured in postmortem tissue from three subject groups: women, men who were presumed to be heterosexual, and homosexual men. No differences were found between the groups in the volumes of INAH 1, 2, or 4. As has been reported previously, INAH 3 was more than twice as large in the heterosexual men as in the women. It was also, however, more than twice as large in the heterosexual men as in the homosexual men. This finding indicates that INAH is dimorphic with sexual orientation, at least in men, and suggests that sexual orientation has a biological substrate.


title: A Survey of Explanations in Recommender Systems

abstract: This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications.


title: Improving acoustic estimates of krill: experience from repeat sampling of northern krill (Meganyctiphanes norvegica) in Gullmarsfjord, Sweden

abstract: A series of eight replicated acoustic surveys, four by day and four by night, was undertaken in Gullmarsfjord on the Swedish west coast during two 24-h periods on 8 and 10 September 2003, using a calibrated echosounder operating at 120 and 38 kHz. The difference in signal strength (Sv) was used to distinguish northern krill (Meganyctiphanes norvegica) from other acoustic scatterers. The approach is concluded to be very effective, but it can be improved greatly by applying the following series of simple extensions to current protocols: first, set a very low threshold on both frequencies to minimize sampling bias; second, undertake tests to confirm that the data extracted from each acoustic frequency apply to the same scatterers; third, ensure that the range of Sv is not greater than the TSrange at either frequency; and finally, when abundance estimation is the primary aim, arrange for sampling at the time of day and using the acoustic frequency that together provide the least variance.


title: Combining dry type Resin Impregnated Fiberglass paperless transformer bushings with built-in novel insulation monitoring function

abstract: By comparing the existing types of transformer bushings, this paper reviews distinctive features of RIF™ (Resin Impregnated Fiberglass) paperless condenser bushings; and, in more detail, it introduces principles, construction, characteristics and applications of this type of bushing when used with a new, safer and reliable built-in insulation monitoring function. As the construction of RIF™ insulation would delay the propagation of a core insulation breakdown after the onset of an initial insulation defect, this type of real time monitoring of core insulation condition provides a novel tool to manage bushing defects without any sense of urgency. It offers, for the first time, a very early field detection tool for transformer bushing insulation faults and by way of consequence, a much improved protection of power transformers over their operating life.


title: Beyond Patient Monitoring: Conversational Agents Role in Telemedicine&Healthcare Support For Home-Living Elderly Individuals

abstract: There is a need for systems to dynamically interact with ageing populations to gather information, monitor health condition and provide support, especially after hospital discharge or at-home settings. Several smart devices have been delivered by digital health, bundled with telemedicine systems, smartphone and other digital services. While such solutions offer personalised data and suggestions, the real disruptive step comes from the interaction of new digital ecosystem, represented by chatbots. Chatbots will play a leading role by embodying the function of a virtual assistant and bridging the gap between patients and clinicians. Powered by AI and machine learning algorithms, chatbots are forecasted to save healthcare costs when used in place of a human or assist them as a preliminary step of helping to assess a condition and providing self-care recommendations. This paper describes integrating chatbots into telemedicine systems intended for elderly patient after their hospital discharge. The paper discusses possible ways to utilise chatbots to assist healthcare providers and support patients with their condition.


title: Psychedelics and the essential importance of context

abstract: Psychedelic drugs are making waves as modern trials support their therapeutic potential and various media continue to pique public interest. In this opinion piece, we draw attention to a long-recognised component of the psychedelic treatment model, namely ‘set’ and ‘setting’ – subsumed here under the umbrella term ‘context’. We highlight: (a) the pharmacological mechanisms of classic psychedelics (5-HT2A receptor agonism and associated plasticity) that we believe render their effects exceptionally sensitive to context, (b) a study design for testing assumptions regarding positive interactions between psychedelics and context, and (c) new findings from our group regarding contextual determinants of the quality of a psychedelic experience and how acute experience predicts subsequent long-term mental health outcomes. We hope that this article can: (a) inform on good practice in psychedelic research, (b) provide a roadmap for optimising treatment models, and (c) help tackle unhelpful stigma still surrounding ...


title: Optimising the semantic web service composition process using bio-inspired methods

abstract: This paper presents two bio-inspired methods one inspired by the cuckoo's breeding behaviour, and another one inspired by natural evolution and genetics for selecting the optimal or near-optimal solution in web service composition. The proposed methods are applied on an enhanced planning graph structure which models the composition search space for a given user request. The cuckoo-inspired selection method applies a 1-OPT heuristic to expand the search space in a controlled way such that the stagnation in a local optimum solution is avoided. The genetic-based selection method uses two memory structures to avoid the stagnation in a local optimum solution on one hand, and to ensure that exploitation and exploration are properly performed. The quality of a composition solution is evaluated in terms of QoS attributes and semantic quality. To validate the proposed methods we have implemented an experimental prototype and carried out experiments on a set of scenarios with different complexities. Finally, we comparatively analyse the experimental results obtained by applying the two selection methods.


title: Uplink grant-free pattern division multiple access (GF-PDMA) for 5G radio access

abstract: Massive machine type communication (mMTC) is one of the key application scenarios for the fifth generation mobile communication (5G). Grant-free (GF) transmission can reduce the high signaling overhead in mMTC. Non-orthogonal multiple access (NMA) can support more users for mMTC than orthogonal frequency division multiple access (OFDMA). Applying GF transmission in NMA system becomes an active topic recently. The in-depth study on applying GF transmission in pattern division multiple access (PDMA), a competitive candidate scheme of NMA, is investigated in this paper. The definition, latency and allocation of resource and transmission mechanism for GF-PDMA are discussed in detail. The link-level and system-level evaluations are provided to verify the analysis. The analysis and simulation results demonstrate that the proposed GF-PDMA has lower latency than grant based PDMA (GB-PDMA), possesses strong scalability to confront collision and provides almost 2.15 times gain over GF-OFDMA in terms of supporting the number of active users in the system.


Can PET be performed without an attenuation scan?


title: Structure and messaging techniques for online peer learning systems that increase stickiness

abstract: When students work with peers, they learn more actively, build richer knowledge structures, and connect material to their lives. However, not every peer learning experience online sees successful adoption. This paper articulates and addresses three adoption challenges for global-scale peer learning. First, peer interactions struggle to bootstrap critical mass. However, class incentives can signal importance and spur initial usage. Second, online classes have limited peer visibility and awareness, so students often feel alone even when surrounded by peers. We find that highlighting interdependence and strengthening norms can mitigate this issue. Third, teachers can readily access "big" aggregate data but not "thick" contextual data that helps build intuitions, so software should guide teachers' scaffolding of peer interactions. We illustrate these challenges through studying 8,500 students' usage of two peer learning platforms, Talkabout and PeerStudio. This paper measures efficacy through sign-up and participation rates and the structure and duration of student interactions.


title: Approximations to magic: finding unusual medical time series

abstract: In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is 3 to 4 orders of magnitude faster than brute force, while guaranteed to produce identical results.


title: Fast bayesian compressive sensing using Laplace priors

abstract: In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity of the unknown signal. This signal prior includes some of the existing models as special cases and achieves a high degree of sparsity. We develop a constructive (greedy) algorithm resulting from this formulation where necessary parameters are estimated solely from the observation and therefore no user-intervention is needed. We provide experimental results with synthetic 1D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.


title: A systematic analysis of performance measures for classification tasks

abstract: This paper presents a systematic analysis of twenty four performance measures used in the complete spectrum of Machine Learning classification tasks, i.e., binary, multi-class, multi-labelled, and hierarchical. For each classification task, the study relates a set of changes in a confusion matrix to specific characteristics of data. Then the analysis concentrates on the type of changes to a confusion matrix that do not change a measure, therefore, preserve a classifier's evaluation (measure invariance). The result is the measure invariance taxonomy with respect to all relevant label distribution changes in a classification problem. This formal analysis is supported by examples of applications where invariance properties of measures lead to a more reliable evaluation of classifiers. Text classification supplements the discussion with several case studies.


title: Dynamic adaptive streaming over HTTP dataset

abstract: The delivery of audio-visual content over the Hypertext Transfer Protocol (HTTP) got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is now available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which -- in most cases -- is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset including our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.


title: Metasurface Superstrate Antenna With Wideband Circular Polarization for Satellite Communication Application

abstract: A new wideband circularly polarized antenna using metasurface superstrate for C-band satellite communication application is proposed in this letter. The proposed antenna consists of a planar slot coupling antenna with an array of metallic rectangular patches that can be viewed as a polarization-dependent metasurface superstrate. The metasurface is utilized to adjust axial ratio (AR) for wideband circular polarization. Furthermore, the proposed antenna has a compact structure with a low profile of 0.07λ ::: 0 ::: ( λ ::: 0 ::: stands for the free-space wavelength at 5.25 GHz) and ground size of 34.5×28 mm ::: 2 ::: . Measured results show that the -10-dB impedance bandwidth for the proposed antenna is 33.7% from 4.2 to 5.9 GHz, and 3-dB AR bandwidth is 16.5% from 4.9 to 5.9 GHz with an average gain of 5.8 dBi. The simulated and measured results are in good agreement to verify the good performance of the proposed antenna.


title: Model checking graphical user interfaces using abstractions

abstract: Symbolic model checking techniques have been widely and successfully applied to statically analyze dynamic properties of hardware systems. Efforts to apply this same technology to the analysis of software systems has met with a number of obstacles, such as the existence of non-finite state-spaces. This paper investigates abstractions that make it possible to cost-effectively model check specifications of software for graphical user interface (GUI) systems. We identify useful abstractions for this domain and demonstrate that they can be incorporated into the analysis of a variety of systems with similar structural characteristics. The resulting domain-specific model checking yields fast verification of naturally occur ring specifications of intended GUI behavior.


title: DLID: Deep Learning for Domain Adaptation by Interpolating between Domains

abstract: In many real world applications of machine learning, the distribution of the training data (on which the machine learning model is trained) is dierent from the distribution of the test data (where the learnt model is actually deployed). This is known as the problem of Domain Adaptation. We propose a novel deep learning model for domain adaptation which attempts to learn a predictively useful representation of the data by taking into account information from the distribution shift between the training and test data. Our key proposal is to successively learn multiple intermediate representations along an \interpolating path" between the train and test domains. Our experiments on a standard object recognition dataset show a signicant performance improvement over the state-of-the-art.


Identification of Quantitative Trait Loci for Immune Traits in Landrace Purebred Swine


title: Glaucoma detection based on deep convolutional neural network

abstract: Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.


title: A new method for gray-level picture thresholding using the entropy of the histogram

abstract: Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput. Graphics Image Process.16, 1981, 210–239) have been carefully and critically examined. A new method with a sound theoretical foundation is proposed. Examples are given on a number of real and artifically generated histograms.


title: False-Positive Psychology : Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

abstract: In this article, we accomplish two things. First, we show that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.


title: Design of high speed CCD data acquisition system based on FPGA and USB3.0

abstract: The high-speed area array CCD (Charge Coupled Devices) signal acquisition system based on FPGA and USB3.0 interface is introduced in this paper. The hardware components of the system, the firmware of the USB interface control chip and an interface module in FPGA to fit the GPIF II are discussed. A PC application is also developed based on MFC with Microsoft VC++. The testing results indicate that the designed system works reliably, which can acquire and save the CCD data at a high speed with a good performance.


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

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


Chapter 1 Computational Modelling of Music Cognition and Musical Creativity


title: Greedy Transition-Based Dependency Parsing with Stack LSTMs

abstract: We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks-the stack long short-term memory unit LSTM. Like the conventional stack data structures used in transition-based parsers, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. Our model captures three facets of the parser's state: i unbounded look-ahead into the buffer of incoming words, ii the complete history of transition actions taken by the parser, and iii the complete contents of the stack of partially built tree fragments, including their internal structures. In addition, we compare two different word representations: i standard word vectors based on look-up tables and ii character-based models of words. Although standard word embedding models work well in all languages, the character-based models improve the handling of out-of-vocabulary words, particularly in morphologically rich languages. Finally, we discuss the use of dynamic oracles in training the parser. During training, dynamic oracles alternate between sampling parser states from the training data and from the model as it is being learned, making the model more robust to the kinds of errors that will be made at test time. Training our model with dynamic oracles yields a linear-time greedy parser with very competitive performance.


title: Family Attitude, Academic Procrastination and Test Anxiety as Predictors of Academic Achievement

abstract: In this study, the relationship between high school students' test anxiety, academic procrastination behaviours, family attitudes and academic achievements were examined; the predicting of test anxiety, academic procrastination and family attitudes on the academic achievement was also investigated. The participants of the study were composed of 496 high school students. The end-of-term grade average of the students was defined as the criterion of their academic achievements. The students' test anxiety was measured by the Revised Test Anxiety Scale and the academic procrastination behaviours by the Academic Procrastination Scale. The family attitudes were measured by determining the frequency of the activities that support the academic achievement of the students. Data were analysed by correlation and regression methods. The findings indicate that there was a negative correlation between “achievement score” and “anxiety”, “test irrelevant thinking” and “academic procrastination” while there was a positive correlation between “achievement score” and “family attitude”. The findings also revealed that “tension”, “bodily symptoms” and “family attitude” significantly predicted the “end-of-term grade average” positively, while “worry”, “test irrelevant thinking” and “academic procrastination” significantly predicted the “achievement score” negatively. The findings were discussed in the light of literature along with the suggestions for further studies.


title: Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems

abstract: Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature.


title: Automated process adjustments of chip cutting operations using neural network and statistical approaches

abstract: This research explores the automated process adjustments of chip cutting operations by using neural network and statistical approaches in a computer-aided vision system. Multilayer ceramic capacitors (MLCC), owing to their excellent electronic characteristics, are applied in the design of high-density PC boards. The excellences of MLCCs are accomplished through rigorous controlling of every production step, especially the accuracy-demanding chip cutting operation. This research applies computer vision techniques to find mass centers of chips, locate cutting lines and calculate cutting distances for the automated, precise, and high-speed cutting of MLCCs. The statistical bounded adjustment method with response surface methodology and the radial basis function network model are proposed to solve the cutting deviation problems and to timely and quantitatively adjust the process towards the target values. Two common kinds of process deviations, shift and trend deviations, are explored in this research. Experimental results show that the statistical bounded adjustment and the radial basis function network, respectively, increase the effective adjustment rates of cutting deviations by 35% and 60% more than the current cutting method.


title: Lessons from the business sector for successful knowledge management in health care: A systematic review

abstract: BackgroundThe concept of knowledge management has been prevalent in the business sector for decades. Only recently has knowledge management been receiving attention by the health care sector, in part due to the ever growing amount of information that health care practitioners must handle. It has become essential to develop a way to manage the information coming in to and going out of a health care organization. The purpose of this paper was to summarize previous studies from the business literature that explored specific knowledge management tools, with the aim of extracting lessons that could be applied in the health domain.MethodsWe searched seven databases using keywords such as "knowledge management", "organizational knowledge", and "business performance". We included articles published between 2000-2009; we excluded non-English articles.Results83 articles were reviewed and data were extracted to: (1) uncover reasons for initiating knowledge management strategies, (2) identify potential knowledge management strategies/solutions, and (3) describe facilitators and barriers to knowledge management.ConclusionsKM strategies include such things as training sessions, communication technologies, process mapping and communities of practice. Common facilitators and barriers to implementing these strategies are discussed in the business literature, but rigorous studies about the effectiveness of such initiatives are lacking. The health care sector is at a pinnacle place, with incredible opportunities to design, implement (and evaluate) knowledge management systems. While more research needs to be done on how best to do this in healthcare, the lessons learned from the business sector can provide a foundation on which to build.


title: What stands out in a scene? A study of human explicit saliency judgment

abstract: Eye tracking has become the de facto standard measure of visual attention in tasks that range from free viewing to complex daily activities. In particular, saliency models are often evaluated by their ability to predict human gaze patterns. However, fixations are not only influenced by bottom-up saliency (computed by the models), but also by many top-down factors. Thus, comparing bottom-up saliency maps to eye fixations is challenging and has required that one tries to minimize top-down influences, for example by focusing on early fixations on a stimulus. Here we propose two complementary procedures to evaluate visual saliency. We seek whether humans have explicit and conscious access to the saliency computations believed to contribute to guiding attention and eye movements. In the first experiment, 70 observers were asked to choose which object stands out the most based on its low-level features in 100 images each containing only two objects. Using several state-of-the-art bottom-up visual saliency models that measure local and global spatial image outliers, we show that maximum saliency inside the selected object is significantly higher than inside the non-selected object and the background. Thus spatial outliers are a predictor of human judgments. Performance of this predictor is boosted by including object size as an additional feature. In the second experiment, observers were asked to draw a polygon circumscribing the most salient object in cluttered scenes. For each of 120 images, we show that a map built from annotations of 70 observers explains eye fixations of another 20 observers freely viewing the images, significantly above chance (dataset by Bruce and Tsotsos (2009); shuffled AUC score 0.62 ± 0.07, chance 0.50, t-test p < 0.05). We conclude that fixations agree with saliency judgments, and classic bottom-up saliency models explain both. We further find that computational models specifically designed for fixation prediction slightly outperform models designed for salient object detection over both types of data (i.e., fixations and objects). Published by Elsevier Ltd.


title: On Coreference Resolution Performance Metrics

abstract: The paper proposes a Constrained Entity-Alignment F-Measure (CEAF) for evaluating coreference resolution. The metric is computed by aligning reference and system entities (or coreference chains) with the constraint that a system (reference) entity is aligned with at most one reference (system) entity. We show that the best alignment is a maximum bipartite matching problem which can be solved by the Kuhn-Munkres algorithm. Comparative experiments are conducted to show that the widely-known MUC F-measure has serious flaws in evaluating a coreference system. The proposed metric is also compared with the ACE-Value, the official evaluation metric in the Automatic Content Extraction (ACE) task, and we conclude that the proposed metric possesses some properties such as symmetry and better interpretability missing in the ACE-Value.


title: ONCOMINE: a cancer microarray database and integrated data-mining platform

abstract: DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.


title: Cerebral Cortex doi:10.1093/cercor/bhm027 Local Potential Connectivity in Cat Primary Visual Cortex

abstract: Time invariant description of synaptic connectivity in cortical circuits may be precluded by the ongoing growth and retraction of dendritic spines accompanied by the formation and elimination of synapses. On the other hand, the spatial arrangement of axonal and dendritic branches appears stable. This suggests that an invariant description of connectivity can be cast in terms of potential synapses, which are locations in the neuropil where an axon branch of one neuron is proximal to a dendritic branch of another neuron. In this paper, we attempt to reconstruct the potential connectivity in local cortical circuits of the cat primary visual cortex (V1). Based on multiple single-neuron reconstructions of axonal and dendritic arbors in 3 dimensions, we evaluate the expected number of potential synapses and the probability of potential connectivity among excitatory (pyramidal and spiny stellate) neurons and inhibitory basket cells. The results provide a quantitative description of structural organization of local cortical circuits. For excitatory neurons from different cortical layers, we compute local domains, which contain their potentially pre- and postsynaptic excitatory partners. These domains have columnar shapes with laminar specific radii and are roughly of the size of the ocular dominance column. Therefore, connections between most excitatory neurons in the ocular dominance column can be implemented by local synaptogenesis. Structural connectivity involving inhibitory basket cells is generally weaker than excitatory connectivity. Here, only nearby neurons are capable of establishing more than one potential synapse, implying that within the ocular dominance column these connections have more limited potential for circuit remodeling.


title: Design and Fabrication of a Dual-Polarization Waveguide Slot Array Antenna With High Isolation and High Antenna Efficiency for the 60 GHz Band

abstract: We propose a slot array antenna for dual-polarization operation in the 60 GHz band. To realize the dual-polarization, cross-shaped radiating slots and a multilayer feeding structure are employed. A 16 $\,\times\,$ 16-element array is fabricated by diffusion bonding of laminated thin copper plates, which has the advantages of high precision and low loss characteristics. To suppress the grating lobes, we employ several design techniques, such as a thick cavity structure and dense element spacing. The high antenna gain higher than 32.0 dBi and the antenna efficiency near 80.0% are obtained over 60.0–64.0 GHz for two different polarizations. The 1-dB-down gain bandwidths reach 10.4% and 10.9%. High isolation above 50 dB is achieved between the two input ports.


title: Orthogonal Random Features

abstract: We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error. We call this technique Orthogonal Random Features (ORF), and provide theoretical and empirical justification for this behavior. Motivated by this discovery, we further propose Structured Orthogonal Random Features (SORF), which uses a class of structured discrete orthogonal matrices to speed up the computation. The method reduces the time cost from $\mathcal{O}(d^2)$ to $\mathcal{O}(d \log d)$, where $d$ is the data dimensionality, with almost no compromise in kernel approximation quality compared to ORF. Experiments on several datasets verify the effectiveness of ORF and SORF over the existing methods. We also provide discussions on using the same type of discrete orthogonal structure for a broader range of applications.


title: The rules of Facebook friendship: A two-stage examination of interaction rules in close, casual, and acquaintance friendships

abstract: The present study examined friendship rules on the online social networking site Facebook. Study 1 used focus group data to inductively create a list of 36 Facebook friendship rules. Study 2 utilized survey data to examine college students’ endorsement of the various rules in close, casual, and acquaintance friendships. Results indicated five categories of Facebook friendship rules, which included rules regarding communication channels, deception and control, relational maintenance, negative consequences for the self, and negative consequences for a friend. Additionally, close friends, casual friends, and acquaintances significantly differed in their endorsement of four of the five rules categories. Results suggested that interaction rules provide a useful framework for the study of online social networking sites.


title: Step by Step Towards Creating a Safe Smart Contract: Lessons and Insights from a Cryptocurrency Lab

abstract: We document our experiences in teaching smart contract programming to undergraduate students at the University of Maryland, the first pedagogical attempt of its kind. Since smart contracts deal directly with the movement of valuable currency units between contractual parties, security of a contract program is of paramount importance.


title: Online Data Partitioning in Distributed Database Systems

abstract: Most of previous studies on automatic database partitioning focus on deriving a (near-)optimal (re)partition scheme according to a specic pair of database and query workload and oversees the problem about how to eciently deploy the derived partition scheme into the underlying database system. In fact, (re)partition scheme deployment is often non-trivial and challenging, especially in a distributed OLTP system where the repartitioning is expected to take place online without interrupting and disrupting the processing of normal transactions. In this paper, we propose SOAP, a system framework for scheduling online database repartitioning for OLTP workloads. SOAP aims to minimize the time frame of executing the repartition operations while guaranteeing the correctness and performance of the concurrent processing of normal transactions. SOAP packages the repartition operations into repartition transactions, and then mixes them with the normal transactions for holistic scheduling optimization. SOAP utilizes a cost-based approach to rank the repartition transactions’ scheduling priorities, and leverages a feedback model in control theory to determine in which order and at which frequency the repartition transactions should be scheduled for execution. When the system is under heavy workload or resource shortage, SOAP takes a further step by allowing repartition operations to piggyback onto the normal transactions so as to mitigate the resource contention. We have built a prototype on top of PostgreSQL and conducted a comprehensive experimental study on Amazon EC2 to validate SOAP’s signicant performance advantages.


title: NDN.p4: Programming information-centric data-planes

abstract: Although content-awareness at the network level is becoming more and more needed, Information-Centric Networking (ICN)-based solutions struggle to emerge. Research on ICN has already produced insightful outputs, nevertheless architecture-tied designs of ICN devices cannot be easily deployed and tested in operational networks; further those designs are hard to share. In the meantime, the vision of Software-Defined Networking has grown and taken new shapes. Network players desire to change devices' behavior often and drastically, even though performances are still crucial to operate at line-speed. This has been leading to a rethink of network devices designs with the aim to offer full-programmability through high-level programming languages for packet processors, like P4. The paper presents a preliminary open source implementation of the Named-Data Networking (NDN), that is, an ICN instance, written in P4.


title: HARF: Hierarchy-Associated Rich Features for Salient Object Detection

abstract: The state-of-the-art salient object detection models are able to perform well for relatively simple scenes, yet for more complex ones, they still have difficulties in highlighting salient objects completely from background, largely due to the lack of sufficiently robust features for saliency prediction. To address such an issue, this paper proposes a novel hierarchy-associated feature construction framework for salient object detection, which is based on integrating elementary features from multi-level regions in a hierarchy. Furthermore, multi-layered deep learning features are introduced and incorporated as elementary features into this framework through a compact integration scheme. This leads to a rich feature representation, which is able to represent the context of the whole object/background and is much more discriminative as well as robust for salient object detection. Extensive experiments on the most widely used and challenging benchmark datasets demonstrate that the proposed approach substantially outperforms the state-of-the-art on salient object detection.


title: M2: A Language for Mapping Spreadsheets to OWL

abstract: In this paper, we describe a mapping language for converting data contained in arbitrary spreadsheets into the Web Ontology Language (OWL). The developed language overcomes shortcomings with existing spreadsheet mapping techniques, including their restriction to well-formed spreadsheets reminiscent of a single relational database table and verbose syntax for expressing mapping rules when transforming spreadsheet contents into OWL. We additionally present an implementation of the mapping approach, Mapping Master, which is available as a plug-in for the Protege ontology editor.


Commentary on: Efficacy of Retrobulbar Hyaluronidase Injection for Vision Loss Resulting from Hyaluronic Acid Filler Embolization


title: Paper 3: The Continuous Improvement and Innovation Process

abstract: Continuous Improvement and Innovation (CII; and Creation and Synthesis. Focus and Refocus steps are added at the front and back respectively. We also describe how to use CI&I as rapid improvement and innovation.


title: Digital forensics: Latest challenges and response

abstract: Twenty first century has witnessed an explosive growth in use of computers and networks in every facet of life. With this explosive growth, new advancements in the field of secure computing, network technologies and systems have also been witnessed at an unprecedented rate. These developments have made existing digital forensics tools and techniques ineffective. Moreover, easy access to science of digital forensics and associated techniques has lead to use of anti-forensics measures by malicious actors to cover their traces. The paper highlights the impact of latest trends, technological advancement and use of anti-forensics measures on digital forensics along with the response of digital forensics to them. Shortfalls in the said response have also been covered along with recommended measures to address them. Moreover, historical perspective and established procedures of digital forensics are also discussed.


title: Species-specific responses of Late Quaternary megafauna to climate and humans

abstract: Despite decades of research, the roles of climate and humans in driving the dramatic extinctions of large-bodied mammals during the Late Quaternary period remain contentious. Here we use ancient DNA, species distribution models and the human fossil record to elucidate how climate and humans shaped the demographic history of woolly rhinoceros, woolly mammoth, wild horse, reindeer, bison and musk ox. We show that climate has been a major driver of population change over the past 50,000 years. However, each species responds differently to the effects of climatic shifts, habitat redistribution and human encroachment. Although climate change alone can explain the extinction of some species, such as Eurasian musk ox and woolly rhinoceros, a combination of climatic and anthropogenic effects appears to be responsible for the extinction of others, including Eurasian steppe bison and wild horse. We find no genetic signature or any distinctive range dynamics distinguishing extinct from surviving species, emphasizing the challenges associated with predicting future responses of extant mammals to climate and human-mediated habitat change.


title: Physics-Based Generative Adversarial Models for Image Restoration and Beyond

abstract: We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we find that these problems can be solved by generative models with adversarial learning. However, the basic formulation of generative adversarial networks (GANs) does not generate realistic images, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose a physics model constrained learning algorithm so that it can guide the estimation of the specific task in the conventional GAN framework. The proposed algorithm is trained in an end-to-end fashion and can be applied to a variety of image restoration and related low-level vision problems. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art algorithms.


title: Self-Interest, Altruism, Incentives, and Agency Theory

abstract: Many people are suspicious of self-interest and incentives and oppose motivating humans through incentives. I analyze the meaning of incentives in the logic of choice and argue that it is inconceivable that purposeful actions are anything other than responses to incentives. Money is not always the best way to motivate people. But where money incentives are required, they are required precisely because people are motivated by things other than money. Self-interest is consistent with altruistic motives. Agency problems, however, cannot be solved by instilling greater altruism in people because altruism, the concern for the well-being of others, does not make a person into a perfect agent who does the bidding of others. I discuss the universal tendency of people to behave in non-rational ways. Though they are Resourceful, Evaluative Maximizers (REMMs) humans are imperfect because their brains are biologically structured so as to blind them from perceiving and correcting errors that cause psychic pain. The result is systematic, non-functional behavior. I discuss a Pain Avoidance Model (PAM) that complements REMM by capturing the non-rational component of human behavior (the crux of human self-control problems). Recognizing these self- control problems leads to an expansion of agency theory since they are a second source of agency costs in addition to those generated by conflicts of interest between people. See also the related paper by Jensen and Meckling The Nature of Man.


title: Web Mining Techniques in E-Commerce Applications

abstract: ABSTRACT Today web is the best medium of communication in modern business. Many companies are redefining their business strategies to improve the business output. Business over internet provides the opportunity to customers and partners where their products and specific business can be found. Nowadays online business breaks the barrier of time and space as compared to the physical office. Big companies around the world are realizing that e-commerce is not just buying and selling over Internet, rather it improves the efficiency to compete with other giants in the market. For this purpose data mining sometimes called as knowledge discovery is used. Web mining is data mining technique that is applied to the WWW. There are vast quantities of information available over the Internet. General Terms Data mining techniques, e-commerce applications and web mining. Keywords Electronic commerce, data mining, web mining.


title: Secure Ranging With Message Temporal Integrity

abstract: In this paper, we address the problem of delay attacks on radio frequency time of arrival (ToA) secure ranging. In secure ranging, two mutually trusted devices try to determine their distance in the presence of an attacker. A delay attack consists of delaying the ranging messages exchanged between the devices, resulting in an increase of the measured message arrival times and thus in an increase of the measured distance. In this work, we propose the first secure ranging protocol that enables the detection of delay attacks on ranging. This protocol therefore enables two trusted devices to obtain a secure estimate of their mutual distance; existing solutions enabled the devices only to obtain an upper bound on their mutual distance. We further discuss possible implementations of our secure ranging protocol using Ultra-Wide-Band radio technology. Finally, we introduce and formally define the notion of message temporal integrity, a message security property which relates to message delay and advancement.


title: Unconventional serine proteases: variations on the catalytic Ser/His/Asp triad configuration.

abstract: Serine proteases comprise nearly one-third of all known proteases identified to date and play crucial roles in a wide variety of cellular as well as extracellular functions, including the process of blood clotting, protein digestion, cell signaling, inflammation, and protein processing. Their hallmark is that they contain the so-called "classical" catalytic Ser/His/Asp triad. Although the classical serine proteases are the most widespread in nature, there exist a variety of "nonclassical" serine proteases where variations to the catalytic triad are observed. Such variations include the triads Ser/His/Glu, Ser/His/His, and Ser/Glu/Asp, and include the dyads Ser/Lys and Ser/His. Other variations are seen with certain serine and threonine peptidases of the Ntn hydrolase superfamily that carry out catalysis with a single active site residue. This work discusses the structure and function of these novel serine proteases and threonine proteases and how their catalytic machinery differs from the prototypic serine protease class.


title: Question type classification using a part-of-speech hierarchy

abstract: Question type (or answer type) classification is the task of determining the correct type of the answer expected to a given query. This is often done by defining or discovering syntactic patterns that represent the structure of typical queries of each type, and classify a given query according to which pattern they satisfy. In this paper, we combine the idea of using informer spans as patterns with our own part-of-speech hierarchy in order to propose both a new approach to pattern-based question type classification and a new way of discovering the informers to be used as patterns. We show experimentally that using our part-of-speech hierarchy greatly improves type classification results, and allows our system to learn valid new informers.


title: Neuromorphic Artificial Touch for Categorization of Naturalistic Textures

abstract: We implemented neuromorphic artificial touch and emulated the firing behavior of mechanoreceptors by injecting the raw outputs of a biomimetic tactile sensor into an Izhikevich neuronal model. Naturalistic textures were evaluated with a passive touch protocol. The resulting neuromorphic spike trains were able to classify ten naturalistic textures ranging from textiles to glass to BioSkin, with accuracy as high as 97%. Remarkably, rather than on firing rate features calculated over the stimulation window, the highest achieved decoding performance was based on the precise spike timing of the neuromorphic output as captured by Victor Purpura distance. We also systematically varied the sliding velocity and the contact force to investigate the role of sensing conditions in categorizing the stimuli via the artificial sensory system. We found that the decoding performance based on the timing of neuromorphic spike events was robust for a broad range of sensing conditions. Being able to categorize naturalistic textures in different sensing conditions, these neurorobotic results pave the way to the use of neuromorphic tactile sensors in future real-life neuroprosthetic applications.


title: Neurons in the orbitofrontal cortex encode economic value

abstract: Economic choice is the behaviour observed when individuals select one among many available options. There is no intrinsically 'correct' answer: economic choice depends on subjective preferences. This behaviour is traditionally the object of economic analysis and is also of primary interest in psychology. However, the underlying mental processes and neuronal mechanisms are not well understood. Theories of human and animal choice have a cornerstone in the concept of 'value'. Consider, for example, a monkey offered one raisin versus one piece of apple: behavioural evidence suggests that the animal chooses by assigning values to the two options. But where and how values are represented in the brain is unclear. Here we show that, during economic choice, neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods. Notably, OFC neurons encode value independently of visuospatial factors and motor responses. If a monkey chooses between A and B, neurons in the OFC encode the value of the two goods independently of whether A is presented on the right and B on the left, or vice versa. This trait distinguishes the OFC from other brain areas in which value modulates activity related to sensory or motor processes. Our results have broad implications for possible psychological models, suggesting that economic choice is essentially choice between goods rather than choice between actions. In this framework, neurons in the OFC seem to be a good candidate network for value assignment underlying economic choice.


title: You Are Your Photographs: Detecting Multiple Identities of Vendors in the Darknet Marketplaces

abstract: Darknet markets are online services behind Tor where cybercriminals trade illegal goods and stolen datasets. In recent years, security analysts and law enforcement start to investigate the darknet markets to study the cybercriminal networks and predict future incidents. However, vendors in these markets often create multiple accounts (\em i.e., Sybils), making it challenging to infer the relationships between cybercriminals and identify coordinated crimes. In this paper, we present a novel approach to link the multiple accounts of the same darknet vendors through photo analytics. The core idea is that darknet vendors often have to take their own product photos to prove the possession of the illegal goods, which can reveal their distinct photography styles. To fingerprint vendors, we construct a series deep neural networks to model the photography styles. We apply transfer learning to the model training, which allows us to accurately fingerprint vendors with a limited number of photos. We evaluate the system using real-world datasets from 3 large darknet markets (7,641 vendors and 197,682 product photos). A ground-truth evaluation shows that the system achieves an accuracy of 97.5%, outperforming existing stylometry-based methods in both accuracy and coverage. In addition, our system identifies previously unknown Sybil accounts within the same markets (23) and across different markets (715 pairs). Further case studies reveal new insights into the coordinated Sybil activities such as price manipulation, buyer scam, and product stocking and reselling.


title: Intelligent accident detection classification using mobile phones

abstract: Road accidents are one of the leading causes of mortality. While most accidents merely affect the exterior of the cars of the drivers involved, some of them have led to serious and fatal injuries. It is imperative that the Emergency Medical Services (EMS) are given as much information about the crash site as possible before their arrival at the scene. In this paper, a mobile phone application is developed that, when placed inside a car, intelligently classifies the type of accident it is involved in and notifies the EMS team of this classification along with the car's GPS location. The classification mechanism is built through a collection of data sets from a simulation of three types of collisions, which creates a knowledge base for an artificial intelligence-based classifier software. The experimental setup for data collection and the functionality of the mobile phone application called ‘Crash Detect’ are explored.


title: Automatic single-image 3d reconstructions of indoor manhattan world scenes

abstract: 3d reconstruction from a single image is inherently an ambiguous problem. Yet when we look at a picture, we can often infer 3d information about the scene. Humans perform single-image 3d reconstructions by using a variety of single-image depth cues, for example, by recognizing objects and surfaces, and reasoning about how these surfaces are connected to each other. In this paper, we focus on the problem of automatic 3d reconstruction of indoor scenes, specifically ones (sometimes called “Manhattan worlds”) that consist mainly of orthogonal planes. We use a Markov random field (MRF) model to identify the different planes and edges in the scene, as well as their orientations. Then, an iterative optimization algorithm is applied to infer the most probable position of all the planes, and thereby obtain a 3d reconstruction. Our approach is fully automatic—given an input image, no human intervention is necessary to obtain an approximate 3d reconstruction.


title: The need for multivocal literature reviews in software engineering: complementing systematic literature reviews with grey literature

abstract: Systematic Literature Reviews (SLR) may not provide insight into the "state of the practice" in SE, as they do not typically include the "grey" (non-published) literature. A Multivocal Literature Review (MLR) is a form of a SLR which includes grey literature in addition to the published (formal) literature. Only a few MLRs have been published in SE so far. We aim at raising the awareness for MLRs in SE by addressing two research questions (RQs): (1) What types of knowledge are missed when a SLR does not include the multivocal literature in a SE field? and (2) What do we, as a community, gain when we include the multivocal literature and conduct MLRs? To answer these RQs, we sample a few example SLRs and MLRs and identify the missing and the gained knowledge due to excluding or including the grey literature. We find that (1) grey literature can give substantial benefits in certain areas of SE, and that (2) the inclusion of grey literature brings forward certain challenges as evidence in them is often experience and opinion based. Given these conflicting viewpoints, the authors are planning to prepare systematic guidelines for performing MLRs in SE.


title: Deep learning for sentence classification

abstract: Most of the machine learning algorithms requires the input to be denoted as a fixed-length feature vector. In text classifications (bag-of-words) is a popular fixed-length features. Despite their simplicity, they are limited in many tasks; they ignore semantics of words and loss ordering of words. In this paper, we propose a simple and efficient neural language model for sentence-level classification task. Our model employs Recurrent Neural Network Language Model (RNN-LM). Particularly, Long Short-Term Memory (LSTM) over pre-trained word vectors obtained from unsupervised neural language model to capture semantics and syntactic information in a short sentence. We achieved outstanding empirical results on multiple benchmark datasets, IMDB Sentiment analysis dataset, and Stanford Sentiment Treebank (SSTb) dataset. The empirical results show that our model is comparable with neural methods and outperforms traditional methods in sentiment analysis task.


title: Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

abstract: Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.


title: To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques

abstract: Class imbalance problem is quite pervasive in our nowadays human practice. This problem basically refers to the skewness in the data underlying distribution which, in turn, imposes many difficulties on typical machine learning algorithms. To deal with the emerging issues arising from multi-class skewed distributions, existing efforts are mainly divided into two categories: model-oriented solutions and data-oriented techniques. Focusing on the latter, this paper presents a new over-sampling technique which is inspired by Mahalanobis distance. The presented over-sampling technique, called MDO (Mahalanobis Distance-based Over-sampling technique), generates synthetic samples which have the same Mahalanobis distance from the considered class mean as other minority class examples. By preserving the covariance structure of the minority class instances and intelligently generating synthetic samples along the probability contours, new minority class instances are modelled better for learning algorithms. Moreover, MDO can reduce the risk of overlapping between different class regions which are considered as a serious challenge in multi-class problems. Our theoretical analyses and empirical observations across wide spectrum multi-class imbalanced benchmarks indicate that MDO is the method of choice by offering statistical superior MAUC and precision compared to the popular over-sampling techniques.


title: Judy -- a mutation testing tool for Java

abstract: Popular code coverage measures, such as branch coverage, are indicators of the thoroughness rather than the fault detection capability of test suites. Mutation testing is a fault-based technique that measures the effectiveness of test suites for fault localisation. Unfortunately, use of mutation testing in the software industry is rare because generating and running vast numbers of mutants against the test cases is time-consuming and difficult to do without an automated, fast and reliable tool. Our objective is to present an innovative approach to mutation testing that takes advantage of a novel aspect-oriented programming mechanism, called ‘pointcut and advice’, to avoid multiple compilation of mutants and, therefore, to speed up mutation testing. An empirical comparison of the performance of the developed tool, called Judy, with the MuJava mutation testing tool on 24 open-source projects demonstrates the value of the presented approach. The results show that there is a statistically significant (t(23)=−12.28, p < 0.0005, effect size d=3.43) difference in the number of mutants generated per second between MuJava (M=4.15, SD=1.42) and Judy (M=52.05, SD=19.69). Apart from being statistically significant, this effect is considered very large and, therefore, represents a substantive finding. This therefore allows us to estimate the fault detection effectiveness of test suites of much larger systems.


title: Deep Quantization Network for efficient image retrieval

abstract: Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing improves the quality of hash coding by exploiting the semantic similarity on data pairs and has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand-crafted or machine-learned features, then quantized by a separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, since the quantization error is not statistically minimized and the feature representation is not optimally compatible with the hash coding. In this paper, we propose a novel Deep Quantization Network (DQN) architecture for supervised hashing, which learns image representation for hash coding and formally control the quantization error. The DQN model constitutes four key components: (1) a sub-network with multiple convolution-pooling layers to capture deep image representations; (2) a fully connected bottleneck layer to generate dimension-reduced representation optimal for hash coding; (3) a pairwise cosine loss layer for similarity-preserving learning; and (4) a product quantization loss for controlling hashing quality and the quantizability of bottleneck representation. Extensive experiments on standard image retrieval datasets show the proposed DQN model yields substantial boosts over latest state-of-the-art hashing methods.


title: Binary RDF representation for publication and exchange (HDT)

abstract: The current Web of Data is producing increasingly large RDF datasets. Massive publication efforts of RDF data driven by initiatives like the Linked Open Data movement, and the need to exchange large datasets has unveiled the drawbacks of traditional RDF representations, inspired and designed by a document-centric and human-readable Web. Among the main problems are high levels of verbosity/redundancy and weak machine-processable capabilities in the description of these datasets. This scenario calls for efficient formats for publication and exchange. This article presents a binary RDF representation addressing these issues. Based on a set of metrics that characterizes the skewed structure of real-world RDF data, we develop a proposal of an RDF representation that modularly partitions and efficiently represents three components of RDF datasets: Header information, a Dictionary, and the actual Triples structure (thus called HDT). Our experimental evaluation shows that datasets in HDT format can be compacted by more than fifteen times as compared to current naive representations, improving both parsing and processing while keeping a consistent publication scheme. Specific compression techniques over HDT further improve these compression rates and prove to outperform existing compression solutions for efficient RDF exchange.