title: Memory limitations alone do not lead to over-regularization: An experimental and computational investigation

abstract: The “less is more” hypothesis suggests that one reason adults and children differ in their language acquisition abilities is that they also differ in other cognitive capacities. According to one version, children’s relatively poor memory may make them more likely to over-regularize inconsistent input (Hudson Kam & Newport, 2005, 2009). This paper investigates this hypothesis experimentally and computationally. Experiments in which adults were placed under a high cognitive load during a language-learning task reveal that in adults, increased load does not result in increased over-regularization. A computational model offers a possible explanation for these results, demonstrating that over-regularization should occur only in the presence of memory limitations as well as a strong prior bias for over-regularization. Taken together, these findings suggest that the difference in over-regularization between adults and children may not be attributable solely to differences in memory capacity between the two groups.


title: Coherent Multi-Sentence Video Description with Variable Level of Detail

abstract: Humans can easily describe what they see in a coherent way and at varying level of detail. However, existing approaches for automatic video description focus on generating only single sentences and are not able to vary the descriptions’ level of detail. In this paper, we address both of these limitations: for a variable level of detail we produce coherent multi-sentence descriptions of complex videos. To understand the difference between detailed and short descriptions, we collect and analyze a video description corpus of three levels of detail. We follow a two-step approach where we first learn to predict a semantic representation (SR) from video and then generate natural language descriptions from it. For our multi-sentence descriptions we model across-sentence consistency at the level of the SR by enforcing a consistent topic. Human judges rate our descriptions as more readable, correct, and relevant than related work.


title: Cleansing effect of acidic L-arginine on human oral biofilm

abstract: BackgroundDental plaque formed on tooth surfaces is a complex ecosystem composed of diverse oral bacteria and salivary components. Accumulation of dental plaque is a risk factor for dental caries and periodontal diseases. L-arginine has been reported to decrease the risk for dental caries by elevating plaque pH through the activity of arginine deiminase in oral bacteria. Here we evaluated the potential of L-arginine to remove established oral biofilms.MethodsBiofilms were formed using human saliva mixed with Brain Heart Infusion broth supplemented with 1 % sucrose in multi-well plates or on plastic discs. After washing the biofilms with saline, citrate (10 mM, pH3.5), or L-arginine (0.5 M, pH3.5), the retained biofilms were analyzed by crystal violet staining, scanning electron microscopy, and Illumina-based 16S rDNA sequencing.ResultsWashing with acidic L-arginine detached oral biofilms more efficiently than saline and significantly reduced biofilm mass retained in multi-well plates or on plastic discs. Illumina-based microbiota analysis showed that citrate (pH3.5) preferentially washed out Streptococcus from mature oral biofilm, whereas acidic L-arginine prepared with 10 mM citrate buffer (pH3.5) non-specifically removed microbial components of the oral biofilm.ConclusionsAcidic L-arginine prepared with citrate buffer (pH3.5) effectively destabilized and removed mature oral biofilms. The acidic L-arginine solution described here could be used as an additive that enhances the efficacy of mouth rinses used in oral hygiene.


title: Sustainable Wearables: Wearable Technology for Enhancing the Quality of Human Life

abstract: This paper aims to elicit insights about sustainable wearables by investigating recent advancements in wearable technology and their applications. Wearable technology has advanced considerably from a technical perspective, but it has stagnated due to barriers without penetrating wider society despite early positive expectations. This situation is the motivation behind the focus on studies by many research groups in recent years into wearable applications that can provide the best value from a human-oriented perspective. The expectation is that a new means to resolve the issue can be found from a viewpoint of sustainability; this is the main point of this paper. This paper first focuses on the trend of wearable technology like bodily status monitoring, multi-wearable device control, and smart networking between wearable sensors. Second, the development intention of such technology is investigated. Finally, this paper discusses about the applications of current wearable technology from the sustainable perspective, rather than detailed description of the component technologies employed in wearables. In this paper, the definition of sustainable wearables is discussed in the context of improving the quality of individual life, social impact, and social public interest; those wearable applications include the areas of wellness, healthcare, assistance for the visually impaired, disaster relief, and public safety. In the future, wearables will not be simple data trackers or fun accessories but will gain extended objectives and meanings that play a valuable role for individuals and societies. Successful and sustainable wearables will lead to positive changes for both individuals and societies overall.


title: A 32.75-Gb/s Voltage-Mode Transmitter With Three-Tap FFE in 16-nm CMOS

abstract: This paper describes a 32.75-Gb/s voltage-mode transmitter (TX) with three-tap feed forward equalization that is fabricated in a 16-nm FinFET CMOS technology. The TX uses a dual regulator architecture to allow independent control of output swing, output common-mode, and equalization. A hybrid impedance control scheme is presented where the total number of driver slices is used for coarse impedance control, and analog loop-based voltage control is used for fine impedance control of the TX. A finite-impulse response compensation circuit to compensate for the data dependent current due to equalization is also presented. The TX consumes 120.8 mW with 0.9- and 1.2-V supplies, provides 0.25–0.9 Vpp output swing, and achieves total jitter of 6.49 ps–pp and random jitter of 220 fs–rms at 32.75 Gb/s with bit error rate of 1e-12.


title: An Unmanned Aerial Vehicle-Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses

abstract: Abstract:i¾?Road condition data are important in transportation management systems. Over the last decades, significant progress has been made and new approaches have been proposed for efficient collection of pavement condition data. However, the assessment of unpaved road conditions has been rarely addressed in transportation research. Unpaved roads constitute approximately 40% of the U.S. road network, and are the lifeline in rural areas. Thus, it is important for timely identification and rectification of deformation on such roads. This article introduces an innovative Unmanned Aerial Vehicle UAV-based digital imaging system focusing on efficient collection of surface condition data over rural roads. In contrast to other approaches, aerial assessment is proposed by exploring aerial imagery acquired from an unpiloted platform to derive a three-dimensional 3D surface model over a road distress area for distress measurement. The system consists of a low-cost model helicopter equipped with a digital camera, a Global Positioning System GPS receiver and an Inertial Navigation System INS, and a geomagnetic sensor. A set of image processing algorithms has been developed for precise orientation of the acquired images, and generation of 3D road surface models and orthoimages, which allows for accurate measurement of the size and the dimension of the road surface distresses. The developed system has been tested over several test sites with roads of various surface distresses. The experiments show that the system is capable for providing 3D information of surface distresses for road condition assessment. Experiment results demonstrate that the system is very promising and provides high accuracy and reliable results. Evaluation of the system using 2D and 3D models with known dimensions shows that subcentimeter measurement accuracy is readily achieved. The comparison of the derived 3D information with the onsite manual measurements of the road distresses reveals differences of 0.50 cm, demonstrating the potential of the presented system for future practice.


title: Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

abstract: We propose a new algorithm for solving parabolic partial differential equations (PDEs) and backward stochastic differential equations (BSDEs) in high dimension, by making an analogy between the BSDE and reinforcement learning with the gradient of the solution playing the role of the policy function, and the loss function given by the error between the prescribed terminal condition and the solution of the BSDE. The policy function is then approximated by a neural network, as is done in deep reinforcement learning. Numerical results using TensorFlow illustrate the efficiency and accuracy of the proposed algorithms for several 100-dimensional nonlinear PDEs from physics and finance such as the Allen-Cahn equation, the Hamilton-Jacobi-Bellman equation, and a nonlinear pricing model for financial derivatives.


title: Explainable, Normative, and Justified Agency

abstract: In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area.


Analyze your Scratch projects with Dr . Scratch and assess your Computational Thinking skills


title: Efficient Tree-Based Topic Modeling

abstract: Topic modeling with a tree-based prior has been used for a variety of applications because it can encode correlations between words that traditional topic modeling cannot. However, its expressive power comes at the cost of more complicated inference. We extend the SPARSELDA (Yao et al., 2009) inference scheme for latent Dirichlet allocation (LDA) to tree-based topic models. This sampling scheme computes the exact conditional distribution for Gibbs sampling much more quickly than enumerating all possible latent variable assignments. We further improve performance by iteratively refining the sampling distribution only when needed. Experiments show that the proposed techniques dramatically improve the computation time.


title: Extrusion Pretreatment of Lignocellulosic Biomass: A Review

abstract: Bioconversion of lignocellulosic biomass to bioethanol has shown environmental, economic and energetic advantages in comparison to bioethanol produced from sugar or starch. However, the pretreatment process for increasing the enzymatic accessibility and improving the digestibility of cellulose is hindered by many physical-chemical, structural and compositional factors, which make these materials difficult to be used as feedstocks for ethanol production. A wide range of pretreatment methods has been developed to alter or remove structural and compositional impediments to (enzymatic) hydrolysis over the last few decades; however, only a few of them can be used at commercial scale due to economic feasibility. This paper will give an overview of extrusion pretreatment for bioethanol production with a special focus on twin-screw extruders. An economic assessment of this pretreatment is also discussed to determine its feasibility for future industrial cellulosic ethanol plant designs.


title: Local Region Descriptors for Active Contours Evolution

abstract: Edge-based and region-based active contours are frequently used in image segmentation. While edges characterize small neighborhoods of pixels, region descriptors characterize entire image regions that may have overlapping probability densities. In this paper, we propose to characterize image regions locally by defining local region descriptors (LRDs). These are essentially feature statistics from pixels located within windows centered on the evolving contour, and they may reduce the overlap between distributions. LRDs are used to define general-form energies based on level sets. In general, a particular energy is associated with an active contour by means of the logarithm of the probability density of features conditioned on the region. In order to reduce the number of local minima of such energies, we introduce two novel functions for constructing the energy functional which are both based on the assumption that local densities are approximately Gaussian. The first uses a similarity measure between features of pixels that involves confidence intervals. The second employs a local Markov Random Field (MRF) model. By minimizing the associated energies, we obtain active contours that can segment objects that have largely overlapping global probability densities. Our experiments show that the proposed method can accurately segment natural large images in very short time when using a fast level-set implementation.


title: Collusion-Secure Fingerprinting for Digital Data

abstract: This paper discusses methods for assigning code-words for the purpose of fingerprinting digital data, e.g., software, documents, music, and video. Fingerprinting consists of uniquely marking and registering each copy of the data. This marking allows a distributor to detect any unauthorized copy and trace it back to the user. This threat of detection will deter users from releasing unauthorized copies. A problem arises when users collude: for digital data, two different fingerprinted objects can be compared and the differences between them detected. Hence, a set of users can collude to detect the location of the fingerprint. They can then alter the fingerprint to mask their identities. We present a general fingerprinting solution which is secure in the context of collusion. In addition, we discuss methods for distributing fingerprinted data.


title: The interplay of bayesian and frequentist analysis

abstract: The invention contemplates a fundus or the like camera wherein the same incandescent-lamp source may serve to suitably illuminate a retinal field for visual viewing and for video viewing, the arrangement being such that video viewing at substantially elevated field-illuminated level is automatically achieved from the same lamp source, upon selective displacement of a simple element which determines whether the viewing is to be visual or through video techniques.


title: Creative Learning and Innovative Teaching: Final Report on the Study on Creativity and Innovation in Education in EU Member States

abstract: EU policies call for the strengthening of Europe's innovative capacity and the development of a creative and knowledge-intensive economy and society through reinforcing the role of education and training in the knowledge triangle and focusing school curricula on creativity, innovation and entrepreneurship. This report brings evidence to the debate on the status, barriers and enablers for creativity and innovation in compulsory schooling in Europe. It is the final report of the project: ‘Creativity and Innovation in Education and Training in the EU27 (ICEAC)’ carried out by IPTS in collaboration with DG Education and Culture, highlighting the main messages gathered from each phase of the study: a literature review, a survey with teachers, an analysis of curricula and of good practices, stakeholder and expert interviews, and experts workshops. Based on this empirical material, five major areas for improvement are proposed to enable more creative learning and innovative teaching in Europe: curricula, pedagogies and assessment, teacher training, ICT and digital media, and educational culture and leadership. The study highlights the need for action at both national and European level to bring about the changes required for an open and innovative European educational culture based on the creative and innovative potential of its future generation.


title: Gain Enhancement for Broadband Vertical Planar Printed Antenna With H-Shaped Resonator Structures

abstract: A novel broadband high gain vertical planar printed antenna is presented. The proposed antenna is composed of a bowtie-shaped electric dipole, a loop antenna that works as a magnetic dipole, a microstrip-to-coplanar stripline transition balun, and the H-shaped resonator (HSR) structures that are used for gain enhancement, all of which are printed in the same plane perpendicular to the ground. Prototypes of the HSR-loaded antenna and the unloaded one were fabricated and measured. Analyses and comparisons of the proposed antenna are made. The HSR-loaded antenna has a wide impedance bandwidth of 51.9%, ranging from 2.31 GHz to 3.93 GHz and a stable gain of about 7.4-9.7 dBi over the whole operating band. Compared with the unloaded structure, a gain increment of about 0.5-6.0 dB over the whole band is obtained. The good electrical performances make the antenna suitable for the wireless communication systems and phased arrays.


title: Pervasive intra-party conflicts in a democratising Nigeria: Terrains, implications, drivers and options for resolution

abstract: Intra-party conflicts of all shapes and complexions have been part and parcel of Nigeria’s democratic journey. However, in recent times, they have become much more pervasive and even assumed crisis dimensions, with negative implications for democratic stability and consolidation. Drawing from the literature and interpreting the evidence, this article examines the terrain, implications and drivers of intra-party conflicts in a democratising Nigeria with a view to recommending options for resolution. It proceeds from the premise that pervasive intra-party conflicts, which have now assumed crisis dimensions, are not given, but have been nurtured by certain structural factors which have shaped the contours of politics in Nigeria. Specifically, it argues that the crises are closely connected with the neo-patrimonial character of the Nigerian petro state, the nature of politics being played by the political actors, praetorian hangover, and the paucity of democrats who genuinely have democratic temperaments to play the game of democratic politics according to established rules. It calls for, among others, the reform and strengthening of the internal conflict management capacities of political parties in Nigeria. Keywords : democracy, political parties, elections, intra-party conflicts, conflict management, Nigeria


title: ON INTEROPERABILITY IN INFORMATION SYSTEMS : FROM SYSTEM , SYNTAX , STRUCTURE TO SEMANTICS

abstract: Interoperability has been a basic requirement for the modern information systems environment for over two decades. How have key requirements for interoperability changed over that time? How can we understand the full scope of interoperability issues? What has shaped research on information system interoperability? What key progress has been made? This chapter provides some of the answers to these questions. In particular, it looks at different levels of information system interoperability, while reviewing the changing focus of interoperability research themes, past achievements and new challenges in the emerging global information infrastructure (GII). It divides the research into three generations, and discusses some of achievements of the past. Finally, as we move from managing data to information, and in future knowledge, the need for achieving semantic interoperability is discussed and key components of solutions are introduced.


Dynamic Conditional Random Fields for Joint Sentence Boundary and Punctuation Prediction


title: Recycling and recovery routes of plastic solid waste (PSW): a review.

abstract: Plastic solid waste (PSW) presents challenges and opportunities to societies regardless of their sustainability awareness and technological advances. In this paper, recent progress in the recycling and recovery of PSW is reviewed. A special emphasis is paid on waste generated from polyolefinic sources, which makes up a great percentage of our daily single-life cycle plastic products. The four routes of PSW treatment are detailed and discussed covering primary (re-extrusion), secondary (mechanical), tertiary (chemical) and quaternary (energy recovery) schemes and technologies. Primary recycling, which involves the re-introduction of clean scrap of single polymer to the extrusion cycle in order to produce products of the similar material, is commonly applied in the processing line itself but rarely applied among recyclers, as recycling materials rarely possess the required quality. The various waste products, consisting of either end-of-life or production (scrap) waste, are the feedstock of secondary techniques, thereby generally reduced in size to a more desirable shape and form, such as pellets, flakes or powders, depending on the source, shape and usability. Tertiary treatment schemes have contributed greatly to the recycling status of PSW in recent years. Advanced thermo-chemical treatment methods cover a wide range of technologies and produce either fuels or petrochemical feedstock. Nowadays, non-catalytic thermal cracking (thermolysis) is receiving renewed attention, due to the fact of added value on a crude oil barrel and its very valuable yielded products. But a fact remains that advanced thermo-chemical recycling of PSW (namely polyolefins) still lacks the proper design and kinetic background to target certain desired products and/or chemicals. Energy recovery was found to be an attainable solution to PSW in general and municipal solid waste (MSW) in particular. The amount of energy produced in kilns and reactors applied in this route is sufficiently investigated up to the point of operation, but not in terms of integration with either petrochemical or converting plants. Although primary and secondary recycling schemes are well established and widely applied, it is concluded that many of the PSW tertiary and quaternary treatment schemes appear to be robust and worthy of additional investigation.


title: A Novel Approach to Structural Facial Volume Replacement

abstract: UNLABELLED ::: Improved understanding of the anatomy and physiology of the aging face has laid the foundation for adopting an earlier and more comprehensive approach to facial rejuvenation, shifting the focus from individual wrinkle treatment and lift procedures to a holistic paradigm that considers the entire face and its structural framework. This article presents an overview of a comprehensive method to address facial aging. The key components to the reported strategy for improving facial cosmesis include, in addition to augmentation of volume loss, protection with sunscreens and antioxidants; promotion of epidermal cell turnover with techniques such as superficial chemical peels; microlaser peels and microdermabrasion; collagen stimulation and remodeling via light, ultrasound, or radiofrequency (RF)-based methods; and muscle control with botulinum toxin. For the treatment of wrinkles and for the augmentation of pan-facial dermal lipoatrophy, several types of fillers and volumizers including hyaluronic acid (HA), autologous fat, and calcium hydroxylapatite (CaHA) or injectable poly-L-lactic acid (PLLA) are available. A novel bimodal, trivector technique to restore structural facial volume loss that combines supraperiosteal depot injections of volume-depleted fat pads and dermal/subcutaneous injections for panfacial lipoatrophy with PLLA is presented. The combination of treatments with fillers; toxins; light-, sound-, and RF-based technologies; and surgical procedures may help to forestall the facial aging process and provide more natural results than are possible with any of these techniques alone. ::: ::: ::: LEVEL OF EVIDENCE V ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing

abstract: Existing distributed graph processing frameworks, e.g., Pregel, Giraph, GPS and GraphLab, mainly exploit main memory to support flexible graph operations for efficiency. Due to the complexity of graph analytics, huge memory space is required especially for those graph analytics that spawn large intermediate results. Existing frameworks may terminate abnormally or degrade performance seriously when the memory is exhausted or the external storage has to be used. ::: ::: In this paper, we propose MOCgraph, a scalable distributed graph processing framework to reduce the memory footprint and improve the scalability, based on message online computing. MOCgraph consumes incoming messages in a streaming manner, so as to handle larger graphs or more complex analytics with the same memory capacity. MOCgraph also exploits message online computing with external storage to provide an efficient out-of-core support. We implement MOCgraph on top of Apache Giraph, and test it against several representative graph algorithms on large graph datasets. Experiments illustrate that MOCgraph is efficient and memory-saving, especially for graph analytics with large intermediate results.


title: Rigid scene flow for 3D LiDAR scans

abstract: The perception of the dynamic aspects of the environment is a highly relevant precondition for the realization of autonomous robot system acting in the real world. In this paper, we propose a novel method for estimating dense rigid scene flow in 3D LiDAR scans. We formulate the problem as an energy minimization problem, where we assume local geometric constancy and incorporate regularization for smooth motion fields. Analyzing the dynamics at point level helps in inferring the fine-grained details of motion. We show results on multiple sequences of the KITTI odometry dataset, where we seamlessly estimate multiple motions pertaining to different dynamic objects. Furthermore, we test our approach on a dataset with pedestrians to show how our method adapts to a case with non-rigid motion. For comparison we use the ground truth from KITTI and show how our method outperforms different ICP-based methods.


title: Electrophysiological Studies of Human Face Perception. I: Potentials Generated in Occipitotemporal Cortex by Face and Non-face Stimuli

abstract: This and the following two papers describe event-related potentials (ERPs) evoked by visual stimuli in 98 patients in whom electrodes were placed directly upon the cortical surface to monitor medically intractable seizures. Patients viewed pictures of faces, scrambled faces, letter-strings, number-strings, and animate and inanimate objects. This paper describes ERPs generated in striate and peristriate cortex, evoked by faces, and evoked by sinusoidal gratings, objects and letter-strings. Short-latency ERPs generated in striate and peristriate cortex were sensitive to elementary stimulus features such as luminance. Three types of face-specific ERPs were found: (i) a surface-negative potential with a peak latency of ~200 ms (N200) recorded from ventral occipitotemporal cortex, (ii) a lateral surface N200 recorded primarily from the middle temporal gyrus, and (iii) a late positive potential (P350) recorded from posterior ventral occipitotemporal, posterior lateral temporal and anterior ventral temporal cortex. Face-specific N200s were preceded by P150 and followed by P290 and N700 ERPs. N200 reflects initial face-specific processing, while P290, N700 and P350 reflect later face processing at or near N200 sites and in anterior ventral temporal cortex. Face-specific N200 amplitude was not significantly different in males and females, in the normal and abnormal hemisphere, or in the right and left hemisphere. However, cortical patches generating ventral face-specific N200s were larger in the right hemisphere. Other cortical patches in the same region of extrastriate cortex generated grating-sensitive N180s and objectspecific or letter-string-specific N200s, suggesting that the human ventral object recognition system is segregated into functionally discrete regions.


title: A study of prefix hijacking and interception in the internet

abstract: There have been many incidents of prefix hijacking in the Internet. The hijacking AS can blackhole the hijacked traffic. Alternatively, it can transparently intercept the hijacked traffic by forwarding it onto the owner. This paper presents a study of such prefix hijacking and interception with the following contributions: (1). We present a methodology for prefix interception, (2). We estimate the fraction of traffic to any prefix that can be hijacked and intercepted in the Internet today, (3). The interception methodology is implemented and used to intercept real traffic to our prefix, (4). We conduct a detailed study to detect ongoing prefix interception. ::: We find that: Our hijacking estimates are in line with the impact of past hijacking incidents and show that ASes higher up in the routing hierarchy can hijack a significant amount of traffic to any prefix, including popular prefixes. A less apparent result is that the same holds for prefix interception too. Further, our implementation shows that intercepting traffic to a prefix in the Internet is almost as simple as hijacking it. Finally, while we fail to detect ongoing prefix interception, the detection exercise highlights some of the challenges posed by the prefix interception problem.


Recovering baseline and orientation from essential matrix


title: The importance of residual renal function in chronic dialysed patients

abstract: In the last decade, many researches have reached to the conclusion that preservation of residual renal function (RRF) is important after initiating dialysis, as well as in the predialysis period. ::: ::: RRF has been proven to contribue to the quality of life of dialysis patients. Longer preservation of RRF provides a better small and middle molecule removal, improved volemic status and arterial pressure control, diminished risk of vascular and valvular calcification due to better phosphate removal. Deterioration of RRF results in worsening of anemia, inflammation and malnutrition. It is now proven a direct relationship between RRF value and survival in dialysis patient. ::: ::: Several therapeutical intervention have been proven to ameliorate the decline of RRF in dialysis patients. Some of them are identical with those before initiating dialysis: ACE–inhibitors and/or angiotensin–receptor blockers, limiting the use of nephrotoxic drugs, avoiding contrast media procedures, adequate control of blood pressure. Others are specific for dialysis period: adequate dialysis dose, avoiding excessive ultrafiltration, preventing arterial hypotension during dialysis sessions, using biocompatible dialysis membranes, ultrapure water for dialysis, dietary interventions.


title: Mobile 3D wood pile surveying

abstract: We present a novel method for photogrammetric wood pile surveying, which runs on mobile devices as well as on desktop computers. The demand for measurement techniques for wood piles has strongly increased in the last years. Unlike existing methods, our method is not limited to a single image and uses 3D reconstruction techniques on a set of images taken with a smartphone or digital camera. The reconstructed 3D model is then used to identify individual wood logs and perform photogrammetric surveying of the entire wood pile. An extensive evaluation is conducted on 246 data sets (7655 images) from the publicly available HAWK-wood database. For the wood log detection benchmark a true positive rate of 98.8% with a false positive rate of 0.7% is achieved. The volume computation showed an average absolute difference of 2.2% (contour volume) and 5.6% (solid wood volume).


title: The Objectives of Sustainable Development - Ways to Achieve Welfare

abstract: Abstract A sustainable development must be backed up by economical growth, in line with the requirments for ecological balance and the entire human development. Regarding sustainable development, there are several relevant conclusions. These are: sustainable development is a global aim; analysis of sustainable development implies a systematical approach of the economico-social-ecological three-way system; sustainable development implies a profund change in the mentality of the social system. Therefore, the acceptance of sustainable development ideas is heavily required by the recognition of the fact that the past models for economical growth, all have limitations and flaws.As an answer for the above conclusions, it can attach three categories of economical objectives to sustainable development. These objectives are: economical objectives, social objectives (welfare and equity) and ecological objectives (biodiversity and resources).


title: Pregel Algorithms for Graph Connectivity Problems with Performance Guarantees

abstract: Graphs in real life applications are often huge, such as the Web graph and various social networks. These massive graphs are often stored and processed in distributed sites. In this paper, we study graph algorithms that adopt Google's Pregel, an iterative vertex-centric framework for graph processing in the Cloud. We first identify a set of desirable properties of an efficient Pregel algorithm, such as linear space, communication and computation cost per iteration, and logarithmic number of iterations. We define such an algorithm as a practical Pregel algorithm (PPA). We then propose PPAs for computing connected components (CCs), biconnected components (BCCs) and strongly connected components (SCCs). The PPAs for computing BCCs and SCCs use the PPAs of many fundamental graph problems as building blocks, which are of interest by themselves. Extensive experiments over large real graphs verified the efficiency of our algorithms.


title: Additive manufacturing based on optimized mask video projection for improved accuracy and resolution

abstract: Abstract Additive manufacturing (AM) processes based on mask image projection such as digital micro-mirror devices (DMD) have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on such digital devices. However, the accuracy and resolution of the related AM processes are constrained by the limited number of mirrors in a DMD. In this paper, a novel AM process based on the mask video projection has been presented. For each layer, a set of mask images instead of a single image are planned based on the principle of the optimized pixel blending. The planned images are then projected in synchronization with the small movement of the building platform. A mask image planning method has been presented for the formulated optimization problem. Experimental results have verified that the mask video projection process can significantly improve the accuracy and resolution of built components.


An Overview of the Development of Safety-critical Software


title: Bilateral juvenile nasopharyngeal angiofibroma: Report of a case

abstract: Background ::: ::: Juvenile nasopharyngeal angiofibroma (JNA) is the most common benign neoplasm of the nasopharynx. Almost always unilateral on diagnosis, JNAs are locally invasive and may extend across the midline, giving a false bilateral appearance; as such, true bilateral JNA is exceedingly rare. We present a recent case of true bilateral JNA. ::: ::: ::: ::: Methods ::: ::: Single case report of a patient with bilateral JNA, including clinical presentation, diagnosis, and management. ::: ::: ::: ::: Results ::: ::: The patient presented with unilateral nasal obstruction and recurrent epistaxis. Computed tomography and magnetic resonance imaging demonstrated bilateral, noncontiguous masses. Angiography revealed independent vascular supplies from each respective side with no bilateral supply noted. The patient underwent preoperative embolization followed by endoscopic surgical removal of the larger mass; no complications were noted. Follow-up at 2 years demonstrated no recurrence or growth. ::: ::: ::: ::: Conclusion ::: ::: The vast majority of JNAs are unilateral, though invasive growth to the contralateral side may appear “bilateral” in presentation. Proper identification of true bilateral JNA is helpful in guiding management, wherein excision of both tumors may not be necessary.


title: Grade prediction with models specific to students and courses

abstract: The accurate estimation of students’ grades in future courses is important as it can inform the selection of next term’s courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future course grade predictions methods based on sparse linear and low-rank matrix factorization models that are specific to each course or student–course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student–course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota, for two different departments with different characteristics. This evaluation showed that focusing on course-specific data improves the accuracy of grade prediction.


title: Argument discovery via crowdsourcing

abstract: The amount of controversial issues being discussed on the Web has been growing dramatically. In articles, blogs, and wikis, people express their points of view in the form of arguments, i.e., claims that are supported by evidence. Discovery of arguments has a large potential for informing decision-making. However, argument discovery is hindered by the sheer amount of available Web data and its unstructured, free-text representation. The former calls for automatic text-mining approaches, whereas the latter implies a need for manual processing to extract the structure of arguments. In this paper, we propose a crowdsourcing-based approach to build a corpus of arguments, an argumentation base, thereby mediating the trade-off of automatic text-mining and manual processing in argument discovery. We develop an end-to-end process that minimizes the crowd cost while maximizing the quality of crowd answers by: (1) ranking argumentative texts, (2) pro-actively eliciting user input to extract arguments from these texts, and (3) aggregating heterogeneous crowd answers. Our experiments with real-world datasets highlight that our method discovers virtually all arguments in documents when processing only 25% of the text with more than 80% precision, using only 50% of the budget consumed by a baseline algorithm.


title: Novel three-DOF ankle mechanism for lower-limb exoskeleton: Kinematic analysis and design of passive-type ankle module

abstract: In this paper, a novel three-DOF ankle mechanism is introduced. A lower-limb exoskeleton is developed by our research group to strengthen wearer's muscle power in military missions. Owing to the tough operational condition, each module of the exoskeleton should be carefully designed for safety and to reduce the sense of fatigue of the wearer. To this end, it is desirable for the ankle module to be lightweight and compact and to have a remote center of rotation which is closely located to the rotation center of the wearer's ankle. In order to satisfy these design considerations, the proposed ankle mechanism consists of the parallel connection of a spherical five-bar linkage and an RSU (revolute-spherical-universal)- serial chain. The spherical five-bar linkage realizes two-DOF ankle rotations of inversion/eversion and internal/external rotation, and the RSU-serial chain controls the ankle dorsiflexion/plantar flexion. The forward and inverse kinematics are derived, and instantaneous kinematics as well as force relations are analyzed by deriving the screw-based Jacobian and the reciprocal Jacobian. Finally, based on the gait data obtained from a normal subject with 4km/h of walking speed and 45kg of backpack load, the initial version of it is designed as a fully passive-type mechanism with a torsional spring. It is shown from a simulation that 42% reduction of the wearer's ankle peak torque is expected by using the proposed ankle exoskeleton.


title: How does high dimensionality affect collaborative filtering?

abstract: A crucial operation in memory-based collaborative filtering (CF) is determining nearest neighbors (NNs) of users/items. This paper addresses two phenomena that emerge when CF algorithms perform NN search in high-dimensional spaces that are typical in CF applications. The first is similarity concentration and the second is the appearance of hubs (i.e. points which appear in $k$-NN lists of many other points). Through theoretical analysis and experimental evaluation we show that these phenomena are inherent properties of high-dimensional space, unrelated to other data properties like sparsity, and that they can impact CF algorithms by questioning the meaning and representativeness of discovered NNs. Moreover, we show that it is not easy to mitigate the phenomena using dimensionality reduction. Studying these phenomena aims to provide a better understanding of the limitations of memory-based CF and motivate the development of new algorithms that would overcome them.


Research in management accounting innovations : an overview of its development


title: Segmentation-based Fractal Texture Analysis and Color Layout Descriptor for Content Based Image Retrieval

abstract: Due to the information technology which is rapidly developing, digital content is becoming increasingly difficult to handle. This include images that are kept on digital cameras, CCTV and medical scanners. Areas such as medical and forensic science are using these databases to do critical tasks which include diagnosing of diseases or identification of criminal suspects. However, to manage and search the similar images from these databases are not an easy task. Content Based Image Retrieval (CBIR) is one of the techniques used to manage and search similar images from a database. The performance of CBIR depends on the low level (Texture, Color and Shape) features. In this paper, a new feature vector to represent the image in terms of low level features and to improve the performance of CBIR is proposed. The proposed approach used texture and color feature namely SFTA-CLD. SFTA-CLD is based on Segmentation-based Fractal Texture Analysis (SFTA) and Color Layout Descriptor (CLD). SFTA-CLD is assessed using Coral image gallery and validated by comparing the performance in terms of average precision with previous CBIR techniques.


title: Anisotropic Ambient Volume Shading

abstract: We present a novel method to compute anisotropic shading for direct volume rendering to improve the perception of the orientation and shape of surface-like structures. We determine the scale-aware anisotropy of a shading point by analyzing its ambient region. We sample adjacent points with similar scalar values to perform a principal component analysis by computing the eigenvectors and eigenvalues of the covariance matrix. In particular, we estimate the tangent directions, which serve as the tangent frame for anisotropic bidirectional reflectance distribution functions. Moreover, we exploit the ratio of the eigenvalues to measure the magnitude of the anisotropy at each shading point. Altogether, this allows us to model a data-driven, smooth transition from isotropic to strongly anisotropic volume shading. In this way, the shape of volumetric features can be enhanced significantly by aligning specular highlights along the principal direction of anisotropy. Our algorithm is independent of the transfer function, which allows us to compute all shading parameters once and store them with the data set. We integrated our method in a GPU-based volume renderer, which offers interactive control of the transfer function, light source positions, and viewpoint. Our results demonstrate the benefit of anisotropic shading for visualization to achieve data-driven local illumination for improved perception compared to isotropic shading.


title: Particle Pollution Estimation Based on Image Analysis

abstract: Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.


title: Influence of Data Granularity on Smart Meter Privacy

abstract: Through smart metering in the smart grid end-user domain, load profiles are measured per household. Personal data can be inferred from these load profiles by using nonintrusive appliance load monitoring methods, which has led to privacy concerns. Privacy is expected to increase with longer intervals between measurements of load curves. This paper studies the impact of data granularity on edge detection methods, which are the common first step in nonintrusive load monitoring algorithms. It is shown that when the time interval exceeds half the on-time of an appliance, the appliance use detection rate declines. Through a one-versus-rest classification modeling, the ability to detect an appliance's use is evaluated through F-scores. Representing these F-scores visually through a heatmap yields an easily understandable way of presenting potential privacy implications in smart metering to the end-user or other decision makers.


title: A study of count-based exploration and bonus for reinforcement learning

abstract: In order to better balance exploration and exploitation and solve the problem of sparse reward in reinforcement learning, this paper focus on changing the traditional exploration strategy, such as e-greedy strategy, by introducing the count for state-action visitation into the Boltzmann distribution method as a new behavioral exploration strategy, and adding count-based exploration bonus to guide the agent to exploration, proposes a method of count-based exploration and bonus for reinforcement learning. Selecting Sarsa(λ) learning algorithm as the basis, the effectiveness of the count-based Sarsa(λ) learning algorithm is verified by carrying out comparative experiments of the Q learning algorithm based on the e-greedy strategy, the Sarsa(λ) learning algorithm based on the e-greedy strategy, and the count-based Sarsa(λ) learning algorithm in the tank combat simulation problem. Due to its simplicity, it provides an easy yet powerful baseline for solving MDPs that require informed exploration.


title: Evaluating the benefits of augmented reality for task localization in maintenance of an armored personnel carrier turret

abstract: We present the design, implementation, and user testing of a prototype augmented reality application to support military mechanics conducting routine maintenance tasks inside an armored vehicle turret. Our prototype uses a tracked head-worn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, location, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: an untracked headworn display with text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.


title: ABOD3: A Graphical Visualization and Real-Time Debugging Tool for BOD Agents

abstract: Current software for AI development requires the use of programming languages to develop intelligent agents. This can be disadvantageous for AI designers, as their work needs to be debugged and treated as a generic piece of software code. Moreover, such approaches are designed for experts, requiring a steep initial learning curve, as they are tailored for programmers. This can be disadvantageous for implementing transparent inspection of agents, as additional work is needed to expose and represent information to end users. We are working towards the development of a new editor, ABOD3, shown in fig.~\ref{abod3}. It allows the graphical visualisation of BOD-based plans, including its two major derivatives: POSH and Instinct. The new editor is designed to allow not only the development of reactive plans, but also to debug such plans in real time to reduce the time required to develop an agent. This allows the development and testing of plans from a same application. The editor provides a user-customisable user interface (UI) aimed at supporting both the development and debug of agents. Plan elements, their subtrees, and debugging-related information can be hidden, to allow different levels of abstraction and present only relevant information. The graphical representation of the plan can be generated automatically, and the user can override its default layout by moving elements to suit his needs and preferences. The simple UI and customisation allows the editor to be employed not only as a developer's tool, but also to present transparency related information to the end-user to help them develop more accurate mental models of the agent. Alpha testers have already used ABOD3 in experiments to determine the effects of transparency on the mental models formed by humans. Their experiments consisted of a non-humanoid robot, powered by the BOD-based Instinct reactive planner. They have demonstrated that subjects can show marked improvement in the accuracy of their mental model of a robot observed, if they also see an accompanying display of the robot's real-time decision making as provided by ABOD3. They concluded that providing transparency information by using ABOD3 does help users to understand the behaviour of the robot, calibrating their expectations. We plan to continue developing this new editor, implementing debug functions such as ``fast-forward'' in pre-recorded log files and usage of breakpoints in real-time. A transparent agent, with an inspectable decision-making mechanism, could also be debugged in a similar manner to the way in which traditional, non-intelligent software is commonly debugged. The developer would be able to see which actions the agent is selecting, why this is happening, and how it moves from one action to the other. This is similar to the way in which popular Integrated Development Environments (IDEs) provide options to follow different streams of code with debug points. Moreover, we will enhance its plan design capabilities by introducing new views, to view and edit specific types of plan-elements and through a public beta testing to gather feedback by both experienced and inexperienced AI developers.


title: New challenges and innovation in forensic toxicology: Focus on the “New Psychoactive Substances”

abstract: Abstract In the recent years, new molecules have appeared in the illicit market, claimed to contain “non-illegal” compounds, although exhibiting important psychoactive effects; this heterogeneous and rapidly evolving class of compounds are commonly known as “New Psychoactive Substances” or, less properly, “Smart Drugs” and are easily distributed through the e-commerce or in the so-called “Smart Shops”. They include, among other, synthetic cannabinoids, cathinones and tryptamine analogs of psylocin. Whereas cases of intoxication and death have been reported, the phenomenon appears to be largely underestimated and is a matter of concern for Public Health. One of the major points of concern depends on the substantial ineffectiveness of the current methods of toxicological screening of biological samples to identify the new compounds entering the market. These limitations emphasize an urgent need to increase the screening capabilities of the toxicology laboratories, and to develop rapid, versatile yet specific assays able to identify new molecules. The most recent advances in mass spectrometry technology, introducing instruments capable of detecting hundreds of compounds at nanomolar concentrations, are expected to give a fundamental contribution to broaden the diagnostic spectrum of the toxicological screening to include not only all these continuously changing molecules but also their metabolites. In the present paper a critical overview of the opportunities, strengths and limitations of some of the newest analytical approaches is provided, with a particular attention to liquid phase separation techniques coupled to high accuracy, high resolution mass spectrometry.


title: Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering

abstract: In this paper, we propose a method for vehicle tracking on roadways using measurements of magnetometers and accelerometers. The measurements are used to build a low-cost, low-complexity vehicle tracking sensor platform for highway traffic monitoring. First, the problem is formulated by introducing the process model for the motion of the vehicle on the road and two measurement models: one for each of the sensors. Second, it is shown how the measurements of the sensors can be fused using particle filtering. The standard sampling importance resampling (SIR) particle filter is extended for processing of multirate sensor measurements and models that employ unknown static parameters. The latter are treated by Rao–Blackwellization. The performance of the method is demonstrated by computer simulations. It is found that it is feasible to fuse the two sensors for vehicle tracking and that the proposed multirate particle filter performs better than particle filters that process only measurements of one of the sensors. The main contribution of this paper is the novel approach of fusing the measurements of road-mounted magnetometers and accelerometers for vehicle tracking and traffic monitoring.


Sexual Masochism Disorder with Asphyxiophilia: A Deadly yet Underrecognized Disease


title: Application of ferrate(VI) in the treatment of industrial wastes containing metal-complexed cyanides : a green treatment.

abstract: Ferrate(VI) was employed for the oxidation of cyanide (CN) and simultaneous removal of copper or nickel in the mixed/complexed systems of CN-Cu, CN-Ni, or CN-Cu-Ni. The degradation of CN (1.00 mmol/L) and removal of Cu (0.095 mmol/L) were investigated as a function of Fe(VI) doses from 0.3-2.00 mmol/L at pH 10.0. It was found that Fe(VI) could readily oxidize CN and the reduction of Fe(VI) into Fe(III) might serve efficiently for the removal of free copper ions. The increase in Fe(VI) dose apparently favoured the CN oxidation as well as Cu removal. Moreover, the pH dependence study (pH 10.0-13.0) revealed that the oxidation of CN was almost unaffected in the studied pH range (10.0-13.0), however, the maximum removal efficiency of Cu was obtained at pH 13.0. Similarly, treatment was carried out for CN-Ni system having the initial Ni concentration of 0.170 mmol/L and CN concentration of 1.00 mmol with Fe(VI) dose 2.00 mmol at various pH values (10.0-12.0). Results showed a partial oxidation of CN and partial removal of Ni. It can be observed that Fe(VI) can partially degrade the CN-Ni complex in this pH range. Further, Fe(VI) was applied for the treatment of simulated industrial waste/effluent waters treatment containing CN, Cu, and Ni.


The Human Factor in


HUX – Measuring Holistic User Experience


Data Poisoning Attacks on Multi-Task Relationship Learning


Heating the patient : a promising approach ?


title: Vaginal labiaplasty: current practices and a simplified classification system for labial protrusion.

abstract: BACKGROUND ::: Vaginal labiaplasty has been described for the management of functional and aesthetic problems associated with protrusion of the labia minora. Despite increasing numbers of procedures performed, there is a paucity of data to guide treatment paradigms. This systematic review aims to establish a simple, unifying classification scheme for labial protrusion and summarize current labiaplasty techniques and practices. ::: ::: ::: METHODS ::: A systematic literature review was performed using the PubMed database. Additional articles were selected after reviewing references of identified articles. ::: ::: ::: RESULTS ::: The search returned 247 articles. After applying inclusion criteria to identify prospective and retrospective studies evaluating different techniques, outcomes, complications, and patient satisfaction, 19 articles were selected. Labiaplasty of the labia minora was described in 1949 patients. Seven different surgical techniques were used for labiaplasty, including deepithelialization, direct excision, W-shaped resection, wedge resection, composite reduction, Z-plasty, and laser excision. Patient satisfaction rates for each technique ranged from 94 to 100 percent. The most common postoperative complication for all techniques was wound dehiscence (4.7 percent). Key areas for perioperative patient management were defined. ::: ::: ::: CONCLUSIONS ::: Labiaplasty is safe and carries a high satisfaction rate. However, current practices remain exceedingly diverse. The authors propose a simplified classification system based on the distance of the lateral edge of the labia minora from that of the labia majora, rather than from the introitus. Key areas for perioperative patient management include patient anesthesia, resection technique used, wound closure, and postoperative care. Further randomized studies using a standardized classification system are required to better compare different techniques and establish best practices.


title: Virtual Reality Exposure Therapy

abstract: It has been proposed that virtual reality (VR) exposure may be an alternative to standard in vivo exposure. Virtual reality integrates real-time computer graphics, body tracking devices, visual displays, and other sensory input devices to immerse a participant in a computer- generated virtual environment. Virtual reality exposure is potentially an efficient and cost-effective treatment of anxiety disorders. VR exposure therapy reduced the fear of heights in the first controlled study of virtual reality in treatment of a psychiatric disorder. A case study supported the efficacy of VR exposure therapy for the fear of flying. The potential for virtual reality exposure treatment for these and other disorders is explored, and therapeutic issues surrounding the delivery of VR exposure are discussed.


title: A Literature Review on the Impact of Enterprise Systems on Management Accounting

abstract: This paper provides a comprehensive review of previous research at the interface of enterprise systems (i.e. enterprise resource planning (ERP), business intelligence (BI) systems) and management accounting. To date, research has focused mainly on ERP systems with the exception of one study which also investigated BI systems. Studies are reviewed under three categories: those that have employed a positivist approach, an interpretivist approach, and a combination of both approaches. The findings of the literature review suggest that the majority of previous empirical studies has tended to focus on describing changes in management accounting practice and in the role of the management accountant resulting from ERP implementations rather than focusing on analyzing and understanding these changes. The paper presents a research agenda which aims to help academics and practitioners achieve a better understanding of the impact that enterprise systems may have on management accounting.


title: Adopting customer relationship management technology

abstract: Now customer relationship marketing (CRM) solutions might be the hottest topic in business world. CRM impelled the growth of both B2B and B2C markets. But the issue is how to apply the cutting‐edge CRM solutions. Do people really understand what CRM is and why they should install CRM? The mistaken concept of CRM may have disastrous effects on the company. This paper begins with the basic concepts of CRM, elaborates the characteristics, reviews its brief history and addresses the current status of CRM. Then it develops the extended concepts of CRM from micro‐ and macro‐ perspectives. In the “Implementation and tips” section, it concludes the proper steps to approach CRM and how to bear a right attitude towards CRM solutions. Related Government rules are also covered.


title: Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

abstract: Understanding and forecasting mobile traffic of large scale cellular networks is extremely valuable for service providers to control and manage the explosive mobile data, such as network planning, load balancing, and data pricing mechanisms. This paper targets at extracting and modeling traffic patterns of 9,000 cellular towers deployed in a metropolitan city. To achieve this goal, we design, implement, and evaluate a time series analysis approach that is able to decompose large scale mobile traffic into regularity and randomness components. Then, we use time series prediction to forecast the traffic patterns based on the regularity components. Our study verifies the effectiveness of our utilized time series decomposition method, and shows the geographical distribution of the regularity and randomness component. Moreover, we reveal that high predictability of the regularity component can be achieved, and demonstrate that the prediction of randomness component of mobile traffic data is impossible.


title: Analyzing Tracklets for the Detection of Abnormal Crowd Behavior

abstract: This paper presents a novel video descriptor, referred to as Histogram of Oriented Track lets, for recognizing abnormal situation in crowded scenes. Unlike standard approaches that use optical flow, which estimates motion vectors only from two successive frames, we built our descriptor over long-range motion trajectories which is called track lets in the literature. Following the standard procedure, we divided video sequences in spatio-temporal cuboids within which we collected statistics on the track lets passing through them. In particular, we quantized orientation and magnitude in a 2-dimensional histogram which encodes the motion patterns expected in each cuboid. We classify frames as normal and abnormal by using Latent Dirichlet Allocation and Support Vector Machines. We evaluated the effectiveness of the proposed descriptors on three datasets: UCSD, Violence in Crowds and UMN. The experiments demonstrated (i) very promising results in abnormality detection, (ii) setting new state-of-the-art on two of them, and (iii) outperforming former descriptors based on the optical flow, dense trajectories and the social force model.


title: SVM Tutorial - Classification, Regression and Ranking.

abstract: Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. Two special properties of SVMs are that SVMs achieve (1) high generalization by maximizing the margin and (2) support an efficient learning of nonlinear functions by kernel trick. This chapter introduces these general concepts and techniques of SVMs for learning classification, regression, and ranking functions. In particular, we first present the SVMs for binary classification in Section 2, SVR in Section 3, ranking SVM in Section 4, and another recently developed method for learning ranking SVM called Ranking Vector Machine (RVM) in Section 5.


title: Focused ultrasound modulates region-specific brain activity

abstract: We demonstrated the in vivo feasibility of using focused ultrasound (FUS) to transiently modulate (through either stimulation or suppression) the function of regional brain tissue in rabbits. FUS was delivered in a train of pulses at low acoustic energy, far below the cavitation threshold, to the animal's somatomotor and visual areas, as guided by anatomical and functional information from magnetic resonance imaging (MRI). The temporary alterations in the brain function affected by the sonication were characterized by both electrophysiological recordings and functional brain mapping achieved through the use of functional MRI (fMRI). The modulatory effects were bimodal, whereby the brain activity could either be stimulated or selectively suppressed. Histological analysis of the excised brain tissue after the sonication demonstrated that the FUS did not elicit any tissue damages. Unlike transcranial magnetic stimulation, FUS can be applied to deep structures in the brain with greater spatial precision. Transient modulation of brain function using image-guided and anatomically-targeted FUS would enable the investigation of functional connectivity between brain regions and will eventually lead to a better understanding of localized brain functions. It is anticipated that the use of this technology will have an impact on brain research and may offer novel therapeutic interventions in various neurological conditions and psychiatric disorders.


title: Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus

abstract: Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads. We show that the models learned from one cell type can be applied to make predictions in other cell or tissue types. Our work not only provides a computational framework to enhance Hi-C data resolution but also reveals features underlying the formation of 3D chromatin interactions.


title: Assessment of energy distribution losses for increasing penetration of distributed generation

abstract: High levels of penetration of distributed generation (DG) are a new challenge for traditional electric power systems. Power injections from DGs change network power flows modifying energy losses. Although it is considered that DG reduce losses, this paper shows that this is not always true. This paper presents an approach to compute annual energy losses variations when different penetration and concentration levels of DG are connected to a distribution network. In addition, the impact on losses of different DG technologies, such as combined heat and power, wind power, photovoltaic, and fuel-cells, is analyzed. Results show that energy losses variation, as a function of the DG penetration level, presents a characteristic U-shape trajectory. Moreover, when DG units are more dispersed along network feeders, higher losses reduction can be expected. Regarding DG technologies, it should be noted that wind power is the one that shows the worst behavior in losses reduction. Finally, DG units with reactive power control provide a better network voltage profile and lower losses.


title: Too much of a good thing: curvilinear relationships between personality traits and job performance.

abstract: The relationships between personality traits and performance are often assumed to be linear. This assumption has been challenged conceptually and empirically, but results to date have been inconclusive. In the current study, we took a theory-driven approach in systematically addressing this issue. Results based on two different samples generally supported our expectations of the curvilinear relationships between personality traits, including Conscientiousness and Emotional Stability, and job performance dimensions, including task performance, organizational citizenship behavior, and counterproductive work behaviors. We also hypothesized and found that job complexity moderated the curvilinear personality-performance relationships such that the inflection points after which the relationships disappear were lower for low-complexity jobs than they were for high-complexity jobs. This finding suggests that high levels of the two personality traits examined are more beneficial for performance in high- than low-complexity jobs. We conclude by discussing the implications of these findings for the use of personality in personnel selection.


title: Adversarial nets with perceptual losses for text-to-image synthesis

abstract: Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work.


title: iPark: identifying parking spaces from trajectories

abstract: A wide variety of desktop and mobile Web applications involve geo-tagged content, e.g., photos and (micro-) blog postings. Such content, often called User Generated Geo-Content (UGGC), plays an increasingly important role in many applications. However, a great demand also exists for "core" UGGC where the geo-spatial aspect is not just a tag on other content, but is the primary content, e.g., a city street map with up-to-date road construction data. Along these lines, the iPark system aims to turn volumes of GPS data obtained from vehicles into information about the locations of parking spaces, thus enabling effective parking search applications. In particular, we demonstrate how iPark helps ordinary users annotate an existing digital map with two types of parking, on-street parking and parking zones, based on vehicular tracking data.


title: Humanoid arm with the integrated serial elastic actuator

abstract: This paper proposes the design and development of an integrated serial elastic actuator (SEA) which is used as the elbow joint in the soft humanoid arm. First, requirements of SEA are illustrated. And six different types of elastic elements (e.g. the discoid element, the cylindrical) are analyzed using the design of experiment (DOE) method. And then, a discoid elastic element with larger flexibility is developed. Based on this, an integrated SEA containing the position sensors and the torque sensor is developed. And a 7-degree-of-freedom humanoid arm with joint flexibilities is designed. Finally, experiments of SEA demonstrate the approximation of the stiffness between the desired SEA and the prototype.


title: Research into Cyberbullying: Student Perspectives on Cybersafe Learning Environments

abstract: This paper reports a qualitative study designed to investigate the issues of cybersafety and cyberbullying and report how students are coping with them. Through discussion with 74 students, aged from 10 to 17, in focus groups divided into three age levels, data were gathered in three schools in Victoria, Australia, where few such studies had been set. Social networking sites and synchronous chat sites were found to be the places where cyberbullying most commonly occurred, with email and texting on mobile phones also used for bullying. Grades 8 and 9 most often reported cyberbullying and also reported behaviours and internet contacts that were cybersafety risks. Most groups preferred to handle these issues themselves or with their friends rather then alert parents and teachers who may limit their technology access. They supported education about these issues for both adults and school students and favoured a structured mediation group of their peers to counsel and advise victims.


title: A Deeper Look at Experience Replay

abstract: Experience replay plays an important role in the success of deep reinforcement learning (RL) by helping stabilize the neural networks. It has become a new norm in deep RL algorithms. In this paper, however, we showcase that varying the size of the experience replay buffer can hurt the performance even in very simple tasks. The size of the replay buffer is actually a hyper-parameter which needs careful tuning. Moreover, our study of experience replay leads to the formulation of the Combined DQN algorithm, which can significantly outperform primitive DQN in some tasks.


Removing Secrets from Android's TLS


title: Efficient Program Analyses Using Deductive and Semantic Methodologies

abstract: Program analysis is the process of gathering deeper insights about a source code and analysing them to resolve software problems of arbitrary complexity. The key challenge in program analysis is to keep it fast, precise and straightforward. This research focuses on three key objectives to achieve an efficient program analysis: (i) expressive data representation, (ii) optimised data structure and (iii) fast data processing mechanisms. State of the art technologies such as Resource Description Framework (RDF) as data representation format, triplestores as the storage a processing layer, and datalog to represent program analysis rules are considered in our research. diagram(BDD) to be embedded in the triplestore. Additionally, an ontology is being designed to standardise the definitions of concepts and representation of the knowledge in the program analysis domain.


title: A probabilistic approach to optimal robust path planning with obstacles

abstract: Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. Previous approaches that used a constrained optimization approach to solve for finite sequences of optimal control inputs have been highly effective. For robust execution, it is essential to take into account the inherent uncertainty in the problem, which arises due to uncertain localization, modeling errors, and disturbances. Prior work has handled the case of deterministically bounded uncertainty. We present here an alternative approach that uses a probabilistic representation of uncertainty, and plans the future probabilistic distribution of the vehicle state so that the probability of collision with obstacles is below a specified threshold. This approach has two main advantages; first, uncertainty is often modeled more naturally using a probabilistic representation (for example in the case of uncertain localization); second, by specifying the probability of successful execution, the desired level of conservatism in the plan can be specified in a meaningful manner. The key idea behind the approach is that the probabilistic obstacle avoidance problem can be expressed as a disjunctive linear program using linear chance constraints. The resulting disjunctive linear program has the same complexity as that corresponding to the deterministic path planning problem with no representation of uncertainty. Hence the resulting problem can be solved using existing, efficient techniques, such that planning with uncertainty requires minimal additional computation. Finally, we present an empirical validation of the new method with a number of aircraft obstacle avoidance scenarios.


title: A Method for Hand Kinematics Designers7 Billion Perfect Hands

abstract: In the past decade the design of kinematic models for hands has been addressed using several different approaches such as numerical optimization or direct measurement of human hands. ::: However, once the mechanical and structural design constraints are included, no existing kinematic model of the hand fits the needs of the DLR Hand Arm System. The diversity of human hands reveals that the functional aspects are more important than the kinematics itself. In order to develop a suitable model for the hand arm system a more empirical method was developed. Based on an basic hand skeleton, that incorporates the structural constraints, incremental variations are performed to improve the parameters to fit the functional needs of the hand. The tests are derived from a set of common grasps as well as some tests performed by surgeons. To be able to rapidly appreciate the results simple cardboard prototypes are constructed. The resulting kinematics revealed to be a promising and suitable hand kinematics for the DLR Hand Arm System. The design is realized in hardware.


title: Cyclophosphamide and cancer: golden anniversary

abstract: Cyclophosphamide remains one of the most successful and widely utilized antineoplastic drugs. Moreover, it is also a potent immunosuppressive agent and the most commonly used drug in blood and marrow transplantation (BMT). It was initially synthesized to selectively target cancer cells, although the hypothesized mechanism of tumor specificity (activation by cancer cell phosphamidases) transpired to be irrelevant to its activity. Nevertheless, cyclophosphamide's unique metabolism and inactivation by aldehyde dehydrogenase is responsible for its distinct cytotoxic properties. Differential cellular expression of aldehyde dehydrogenase has an effect on the anticancer therapeutic index and immunosuppressive properties of cyclophosphamide. This Review highlights the chemistry, pharmacology, clinical toxic effects and current clinical applications of cyclophosphamide in cancer and autoimmune disorders. We also discuss the development of high-dose cyclophosphamide for BMT and the treatment of autoimmune diseases.


On Practical Aspects of Using RNNs for Fault Detection in Sparsely-Labeled Multi-sensor Time Series


title: Comparison with Parametric Optimization in Credit Card Fraud Detection

abstract: We apply five classification methods, neural nets (NN), Bayesian nets (BN), naive Bayes (NB), artificial immune systems (AIS) and decision trees (DT), to credit card fraud detection. For a fair comparison, we fine adjust the parameters for each method either through exhaustive search, or through genetic algorithm (GA). Furthermore, we compare these classification methods in two training modes: a cost sensitive training mode where different costs for false positives and false negatives are considered in the training phase; and a plain training mode. The exploration of possible cost-sensitive metaheuristics to be applied is not in the scope of this work and all executions are run using Weka, a publicly available software. Although NN is claimed to be widely used in the market today, the evaluated implementation of NN in plain training leads to quite poor results. Our experiments are consistent with the early result of Maes et al. (2002) which concludes that BN is better than NN. Cost sensitive training substantially improves the performance of all classification methods apart from NB and, independently of the training mode, DT and AIS with, optimized parameters, are the best methods in our experiments.


title: Field classification-based novel fuzzing case generation for ICS protocols

abstract: An industrial control system combined with IT is not a special thing; however, cyber security in this field does not mature. Therefore, vulnerability analysis techniques for protocols used in this field are clearly needed. In this paper, we propose a novel test case generation technique for a fuzzing test that can be used for various industrial control system protocols. The proposed fuzzing test is designed for generating a cross-field fuzzing test case because of field dependencies, a characteristic of industrial control system protocols. Additionally, we focus on multilayer testing because the weaknesses of lower layer protocols are inherited by upper layer protocols.


title: Recent developments in heterogeneous photocatalytic water treatment using visible light-responsive photocatalysts: a review

abstract: Visible light-responsive photocatalytic technology holds great potential in water treatment to enhance purification efficiency, as well as to augment water supply through the safe usage of unconventional water sources. This review summarizes the recent progress in the design and fabrication of visible light-responsive photocatalysts via various synthetic strategies, including the modification of traditional photocatalysts by doping, dye sensitization, or by forming a heterostructure, coupled with π-conjugated architecture, as well as the great efforts made within the exploration of novel visible light-responsive photocatalysts. Background information on the fundamentals of heterogeneous photocatalysis, the pathways of visible light-responsive photocatalysis, and the unique features of visible light-responsive photocatalysts are presented. The photocatalytic properties of the resulting visible light-responsive photocatalysts are also covered in relation to the water treatment, i.e., regarding the photocatalytic degradation of organic compounds and inorganic pollutants, as well as photocatalytic disinfection. Finally, this review concludes with a summary and perspectives on the current challenges faced and new directions in this emerging area of research.


title: Model-based Gaussian and non-Gaussian Clustering

abstract: Abstract : The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of Friedman and Rubin (1967). However, as currently implemented, it does not allow the specification of which features (orientation, size and shape) are to be common to all clusters and which may differ between clusters. Also, it is restricted to Gaussian distributions and it does not allow for noise. We propose ways of overcoming these limitations. A reparameterization of the covariance matrix allows us to specify that some features, but not all, be the same for all clusters. A practical framework for non-Gaussian clustering is outlined, and a means of incorporating noise in the form of a Poisson process is described. An approximate Bayesian method for choosing the number of clusters is given. The performance of the proposed methods is studied by simulation, with encouraging results. The methods are applied to the analysis of a data set arising in the study of diabetes, and the results seem better than those of previous analyses. (RH)


title: Learning to Log: Helping Developers Make Informed Logging Decisions

abstract: Logging is a common programming practice of practical importance to collect system runtime information for postmortem analysis. Strategic logging placement is desired to cover necessary runtime information without incurring unintended consequences (e.g., Performance overhead, trivial logs). However, in current practice, there is a lack of rigorous specifications for developers to govern their logging behaviours. Logging has become an important yet tough decision which mostly depends on the domain knowledge of developers. To reduce the effort on making logging decisions, in this paper, we propose a "learning to log" framework, which aims to provide informative guidance on logging during development. As a proof of concept, we provide the design and implementation of a logging suggestion tool, Log Advisor, which automatically learns the common logging practices on where to log from existing logging instances and further leverages them for actionable suggestions to developers. Specifically, we identify the important factors for determining where to log and extract them as structural features, textual features, and syntactic features. Then, by applying machine learning techniques (e.g., Feature selection and classifier learning) and noise handling techniques, we achieve high accuracy of logging suggestions. We evaluate Log Advisor on two industrial software systems from Microsoft and two open-source software systems from Git Hub (totally 19.1M LOC and 100.6K logging statements). The encouraging experimental results, as well as a user study, demonstrate the feasibility and effectiveness of our logging suggestion tool. We believe our work can serve as an important first step towards the goal of "learning to log".


title: The perfect search engine is not enough: a study of orienteering behavior in directed search

abstract: This paper presents a modified diary study that investigated how people performed personally motivated searches in their email, in their files, and on the Web. Although earlier studies of directed search focused on keyword search, most of the search behavior we observed did not involve keyword search. Instead of jumping directly to their information target using keywords, our participants navigated to their target with small, local steps using their contextual knowledge as a guide, even when they knew exactly what they were looking for in advance. This stepping behavior was especially common for participants with unstructured information organization. The observed advantages of searching by taking small steps include that it allowed users to specify less of their information need and provided a context in which to understand their results. We discuss the implications of such advantages for the design of personal information management tools.


title: Agile Team Perceptions of Productivity Factors

abstract: In this paper, we investigate agile team perceptions of factors impacting their productivity. Within this overall goal, we also investigate which productivity concept was adopted by the agile teams studied. We here conducted two case studies in the industry and analyzed data from two projects that we followed for six months. From the perspective of agile team members, the three most perceived factors impacting on their productivity were appropriate team composition and allocation, external dependencies, and staff turnover. Teams also mentioned pair programming and collocation as agile practices that impact productivity. As a secondary finding, most team members did not share the same understanding of the concept of productivity. While some known factors still impact agile team productivity, new factors emerged from the interviews as potential productivity factors impacting agile teams.


title: Data Imbalance Problem in Text Classification

abstract: Aimming at the ever-present problem of imbalanced data in text classification, the authors study on several forms of imbalanced data, such as text number, class size, subclass and class fold. Some useful conclusions are gotten from a series of correlative experiments: first, when the text of two class is almost the same number, the difference of word number become major factor to affect the accuracy of the classification, second, to improve the accuracy of the classification through increasing the small class size is limited, third, in the case of unbalanced data, the same words which are appeared in two class often carry strong class information, that is, class overlap will not affect the classification accuracy.


The Buller-McGinnis Model of Serial Homicidal Behavior : An Integrated Approach


title: Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model

abstract: Financial market prediction and trading presents a challenging task that attracts great interest from researchers and investors because success may result in substantial rewards. This paper describes the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series. A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named irregular shaped membership function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data.


title: High dimensional biological data retrieval optimization with NoSQL technology

abstract: BackgroundHigh-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data.ResultsIn this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB.ConclusionsThe performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data.


title: Subtelomeric analysis detects a familial 10p;12p rearrangement in two relatives with a distinct syndrome.

abstract: In recent years, subtelomeric rearrangements have been identified as a major cause of multiple congenital anomalies (MCA)/mental retardation (MR) syndromes. Currently, more than 2,500 individuals with MR have been tested and subtelomeric rearrangements were detected in about 6%. Therefore, subtelomeric FISH analysis is indicated as a second tier test after high-resolution G-banding analysis, in subjects with otherwise unexplained developmental delay/MR and/or MCA. We describe a female patient and her maternal aunt, both showing a distinct phenotype, associated with the same complex subtelomeric rearrangement. Subtelomeric FISH testing performed between 1 year 9 months and 20 years after the initial karyotype showed, in both patients, distal trisomy 12p and distal monosomy 10p as follows: 46,XX.ish der(10)t(10;12)(p15.3;p13.31). Parental subtelomeric FISH analysis showed the proposita's mother (sister of Patient 2) and grandmother (mother to Patient 2), to have a balanced 10p:12p translocation. Both girls showed a similar phenotype with pre/postnatal growth retardation, moderate-to-severe developmental delay/MR, very poor/absent speech, hypotonia, lax ligaments, and a distinct pattern of malformation. On examination there were blepharophimosis; bilateral ptosis/epicanthus; broad, depressed nasal bridge with a beaked nose; short philtrum; low-set, posteriorly rotated, overfolded ears; micrognathia; mild webbing of the neck; mild broadening of thumbs; puffy hands/feet; long hallux; and sacral/coccygeal dimples. A slow overall improvement was seen in both patients over time. To our knowledge, a complex subtle rearrangement as the one seen in our patients has not been reported thus far. Our patients show features of partial 10p deletion syndrome rather than those of partial duplication 12p, confirming the general rule that deletions are more phenotypically penetrant than duplications. © 2006 Wiley-Liss, Inc.


title: Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors

abstract: Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time. For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated. These images have been used to train and test different convolutional neural network architectures. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors.


title: An untethered miniature origami robot that self-folds, walks, swims, and degrades

abstract: A miniature robotic device that can fold-up on the spot, accomplish tasks, and disappear by degradation into the environment promises a range of medical applications but has so far been a challenge in engineering. This work presents a sheet that can self-fold into a functional 3D robot, actuate immediately for untethered walking and swimming, and subsequently dissolve in liquid. The developed sheet weighs 0.31 g, spans 1.7 cm square in size, features a cubic neodymium magnet, and can be thermally activated to self-fold. Since the robot has asymmetric body balance along the sagittal axis, the robot can walk at a speed of 3.8 body-length/s being remotely controlled by an alternating external magnetic field. We further show that the robot is capable of conducting basic tasks and behaviors, including swimming, delivering/carrying blocks, climbing a slope, and digging. The developed models include an acetone-degradable version, which allows the entire robot's body to vanish in a liquid. We thus experimentally demonstrate the complete life cycle of our robot: self-folding, actuation, and degrading.