title: Coding efficiency comparison of new video coding standards: HEVC vs VP9 vs AVS2 video

abstract: In this paper, coding efficiency comparisons of emerging video codecs, HEVC, VP9 and AVS2 Video, are conducted. The purpose of this paper is to provide useful information about the current state-of-the-art video codecs and to help both academia and industry to get insight for developing advanced techniques on top of them. At first, design differences among the three video codecs are given briefly and then coding efficiency comparisons are conducted in random access and low delay condition. According to experimental results, HEVC outperforms VP9 and AV2 video by 24.9% and 6.5% in random access condition, respectively. In low delay condition, HEVC also outperforms VP9 and AVS2 video by 8.7% and 14.5%, respectively.


title: Design of a 1.26 GHz high gain microstrip patch antenna using double layer with airgap for satellite reconnaissance

abstract: In this paper, we introduce a method using double layer with airgap to design a high gain single element microstrip rectangular patch antenna operating at 1.26 GHz. Double layer technique provide us easy fabrication, besides, under layer consisted of double-sided copper ensure using one side as ground plane and other side as feed network for higher gain array antenna. Low cost fabrication also has been taken into account. To do that, we used a widely available FR4 material and instead of using low-loss and expensive substrates, we inserted an air gap between radiating and ground planes. This air gap reduces both the electric field concentration on the lossy epoxy and the effective dielectric constant of the radiating plane. Therefore, a low-loss and high-gain single element rectangular patch antenna is obtained. The simulation results conducted by CST Microwave Studio and measurement results carried out in an anechoic chamber of the proposed antenna are included in this study. The close similarity between simulation results and measurement results has been observed. This proposed antenna is appropriate to use in especially global position satellite systems, life detection radar systems and wireless communication systems.


title: Art therapy improved depression and influenced fatigue levels in cancer patients on chemotherapy

abstract: Introduction: Cancer patients are particularly vulnerable to depression and anxiety, with fatigue as the most prevalent symptom of those undergoing treatment. The purpose of this study was to determine whether improvement in depression, anxiety or fatigue during chemotherapy following anthroposophy art therapy intervention is substantial enough to warrant a controlled trial. ::: ::: Material and methods: Sixty cancer patients on chemotherapy and willing to participate in once-weekly art therapy sessions (painting with water-based paints) were accrued for the study. Nineteen patients who participated in ⩾4 sessions were evaluated as the intervention group, and 41 patients who participated in ⩽2 sessions comprised the participant group. Hospital Anxiety and Depression Scale (HADS) and the Brief Fatigue Inventory (BFI) were completed before every session, relating to the previous week. ::: ::: Results: BFI scores were higher in the participant group (p=0.06). In the intervention group, the median HADS score for depression was 9 at the beginning and 7 after the fourth appointment (p=0.021). The median BFI score changed from 5.7 to 4.1 (p=0.24). The anxiety score was in the normal range from the beginning. ::: ::: Conclusion: Anthroposophical art therapy is worthy of further study in the treatment of cancer patients with depression or fatigue during chemotherapy treatment. Copyright © 2007 John Wiley & Sons, Ltd.


title: Finding and assessing social media information sources in the context of journalism

abstract: Social media is already a fixture for reporting for many journalists, especially around breaking news events where non-professionals may already be on the scene to share an eyewitness report, photo, or video of the event. At the same time, the huge amount of content posted in conjunction with such events serves as a challenge to finding interesting and trustworthy sources in the din of the stream. In this paper we develop and investigate new methods for filtering and assessing the verity of sources found through social media by journalists. We take a human centered design approach to developing a system, SRSR ("Seriously Rapid Source Review"), informed by journalistic practices and knowledge of information production in events. We then used the system, together with a realistic reporting scenario, to evaluate the filtering and visual cue features that we developed. Our evaluation offers insights into social media information sourcing practices and challenges, and highlights the role technology can play in the solution.


An Image-based Feature Extraction Approach for Phishing Website Detection


title: New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays

abstract: In this brief, we consider the exponential synchronization of chaotic memristive neural networks with time-varying delays using the Lyapunov functional method and inequality technique. The dynamic analysis here employs the theory of differential equations with discontinuous right-hand side as introduced by Filippov. The designing laws in the synchronization of neural networks are proposed via state or output coupling. In addition, the new proposed algebraic criteria are very easy to verify, and they also enrich and improve the earlier publications. Finally, an example is given to show the effectiveness of the obtained results.


title: Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds

abstract: Site-specific weed management is defined as the application of customised control treatments only where weeds are located within the crop-field by using adequate herbicide according to weed emergence. The aim of the study was to generate georeferenced weed seedling infestation maps in two sunflower fields by analysing overlapping aerial images of the visible and near-infrared spectrum (using visible or multi-spectral cameras) collected by an unmanned aerial vehicle (UAV) flying at 30 and 60 m altitudes. The main tasks focused on the configuration and evaluation of the UAV and its sensors for image acquisition and ortho-mosaicking, as well as the development of an automatic and robust image analysis procedure for weed seedling mapping used to design a site-specific weed management program. The control strategy was based on seven weed thresholds with 2.5 steps of increasing ratio from 0 % (herbicide must be applied just when there is presence or absence of weed) to 15 % (herbicide applied when weed coverage >15 %). As a first step of the imagery analysis, sunflower rows were correctly matched to the ortho-mosaicked imagery, which allowed accurate image analysis using object-based image analysis [object-based-image-analysis (OBIA) methods]. The OBIA algorithm developed for weed seedling mapping with ortho-mosaicked imagery successfully classified the sunflower-rows with 100 % accuracy in both fields for all flight altitudes and camera types, indicating the computational and analytical robustness of OBIA. Regarding weed discrimination, high accuracies were observed using the multi-spectral camera at any flight altitude, with the highest (approximately 100 %) being those recorded for the 15 % weed threshold, although satisfactory results from 2.5 to 5 % thresholds were also observed, with accuracies higher than 85 % for both field 1 and field 2. The lowest accuracies (ranging from 50 to 60 %) were achieved with the visible camera at all flight altitudes and 0 % weed threshold. Herbicide savings were relevant in both fields, although they were higher in field 2 due to less weed infestation. These herbicide savings varied according to the different scenarios studied. For example, in field 2 and at 30 m flight altitude and using the multi-spectral camera, a range of 23–3 % of the field (i.e., 77 and 97 % of area) could be treated for 0–15 % weed thresholds. The OBIA procedure computed multiple data which permitted calculation of herbicide requirements for timely and site-specific post-emergence weed seedling management.


title: Multi-function digital relay for power Autotransformer protection

abstract: This paper introduces a multi-function digital relay to provide a complete protection for the three phase power autotransformers against different types of abnormalities. The conventional methods of differential protection have many drawbacks such as Current Transformer (CT) mismatch, vector group and CT saturation problems which cause a false operation for the relay. A typical ATP-EMTP model for 500/220 KV Autotransformer is presented in this paper to evaluate the suggested algorithm. The algorithm is based on calculating the negative sequence differential current, the phase difference between the primary and secondary currents and the fundamental frequency for the primary and secondary currents. The suggested technique satisfies high degree of reliability, security and dependability.


title: Two hearts in three-quarter time: How to waltz the social media/viral marketing dance

abstract: The concept of viral marketing has been discussed in the literature for over 15 years, since Jeffrey Rayport first introduced the term in 1996. However, the more widespread use of social media has recently pushed this idea to a whole new level. We provide insight into the relationship between social media and viral marketing, and illustrate the six steps executives should take in order to dance the social media/viral marketing waltz. We define viral marketing as electronic word-of-mouth whereby some form of marketing message related to a company, brand, or product is transmitted in an exponentially growing way--often through the use of social media applications. We consider the three conditions that need to be fulfilled to create a viral marketing epidemic (i.e., giving the right message to the right messengers in the right environment) and present four different groups of social media viral marketing campaigns (nightmares, strokes-of-luck, homemade issues, and triumphs). We conclude with five points of caution that managers should heed when trying to launch their own viral marketing campaign.


title: E-government adoption in public administration organizations: integrating institutional theory perspective and resource-based view

abstract: We develop and test a theoretical model to investigate the adoption of government-to-government (G2G) information systems in public administration organizations. Specifically, this model explains how top management commitment (TMC) mediates the impact of external institutional pressures on internal organizational resource allocation, which finally leads to the adoption decision. The hypotheses were tested using survey data from public administration organizations in China. Results from partial least squares analyses suggest that coercive and normative pressures positively affect TMC, which then positively affects financial and information technology (IT) human resources in the G2G adoption process. In turn, financial and IT human resources are confirmed to positively affect the intention to adopt G2G. Surprisingly, we do not find support for our hypothesis that mimetic pressures directly influence TMC. Rather, a post hoc analysis implies that mimetic pressures indirectly influence TMC via the influence of coercive pressures. Our findings provide important managerial implications for public administration organizations.


title: Increased mesocorticolimbic dopamine during acute and repeated social defeat stress: modulation by corticotropin releasing factor receptors in the ventral tegmental area

abstract: Rationale ::: Stress activates a subset of dopamine neurons in the ventral tegmental area (VTA), increasing extracellular dopamine in the medial prefrontal cortex (mPFC) and nucleus accumbens shell (NAcSh). The stress neuropeptide corticotropin releasing factor (CRF) and its receptors (CRF-R1 and CRF-R2) are located within the VTA and directly and indirectly influence dopaminergic activity. However, it has yet to be shown in vivo whether VTA CRF receptor activation is necessary for acute and repeated stress-induced dopamine efflux.


title: Attacking the Madry Defense Model with $L_1$-based Adversarial Examples

abstract: The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model. Attacks were constrained to perturb each pixel of the input image by a scaled maximal $L_\infty$ distortion $\epsilon$ = 0.3. This discourages the use of attacks which are not optimized on the $L_\infty$ distortion metric. Our experimental results demonstrate that by relaxing the $L_\infty$ constraint of the competition, the elastic-net attack to deep neural networks (EAD) can generate transferable adversarial examples which, despite their high average $L_\infty$ distortion, have minimal visual distortion. These results call into question the use of $L_\infty$ as a sole measure for visual distortion, and further demonstrate the power of EAD at generating robust adversarial examples.


title: MetaFlow: a Scalable Metadata Lookup Service for Distributed File Systems in Data Centers

abstract: In large-scale distributed file systems, efficient metadata operations are critical since most file operations have to interact with metadata servers first. In existing distributed hash table (DHT) based metadata management systems, the lookup service could be a performance bottleneck due to its significant CPU overhead. Our investigations showed that the lookup service could reduce system throughput by up to 70 percent, and increase system latency by a factor of up to 8 compared to ideal scenarios. In this paper, we present MetaFlow, a scalable metadata lookup service utilizing software-defined networking (SDN) techniques to distribute lookup workload over network components. MetaFlow tackles the lookup bottleneck problem by leveraging B-tree, which is constructed over the physical topology, to manage flow tables for SDN-enabled switches. Therefore, metadata requests can be forwarded to appropriate servers using only switches. Extensive performance evaluations in both simulations and testbed showed that MetaFlow increases system throughput by a factor of up to 3.2, and reduce system latency by a factor of up to 5 compared to DHT-based systems. We also deployed MetaFlow in a distributed file system, and demonstrated significant performance improvement.


title: SSHLDA: A Semi-Supervised Hierarchical Topic Model

abstract: Supervised hierarchical topic modeling and unsupervised hierarchical topic modeling are usually used to obtain hierarchical topics, such as hLLDA and hLDA. Supervised hierarchical topic modeling makes heavy use of the information from observed hierarchical labels, but cannot explore new topics; while unsupervised hierarchical topic modeling is able to detect automatically new topics in the data space, but does not make use of any information from hierarchical labels. In this paper, we propose a semi-supervised hierarchical topic model which aims to explore new topics automatically in the data space while incorporating the information from observed hierarchical labels into the modeling process, called Semi-Supervised Hierarchical Latent Dirichlet Allocation (SSHLDA). We also prove that hLDA and hLLDA are special cases of SSHLDA. We conduct experiments on Yahoo! Answers and ODP datasets, and assess the performance in terms of perplexity and clustering. The experimental results show that predictive ability of SSHLDA is better than that of baselines, and SSHLDA can also achieve significant improvement over baselines for clustering on the FScore measure.


title: A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression

abstract: BackgroundCancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering) to identify subtypes based on gene expression data. The network-level interaction among genes, which is key to understanding the molecular perturbations in cancer, has been rarely considered during the clustering process. The motivation of our work is to develop a method that effectively incorporates molecular interaction networks into the clustering process to improve cancer subtype identification.ResultsWe have developed a new clustering algorithm for cancer subtype identification, called “network-assisted co-clustering for the identification of cancer subtypes” (NCIS). NCIS combines gene network information to simultaneously group samples and genes into biologically meaningful clusters. Prior to clustering, we assign weights to genes based on their impact in the network. Then a new weighted co-clustering algorithm based on a semi-nonnegative matrix tri-factorization is applied. We evaluated the effectiveness of NCIS on simulated datasets as well as large-scale Breast Cancer and Glioblastoma Multiforme patient samples from The Cancer Genome Atlas (TCGA) project. NCIS was shown to better separate the patient samples into clinically distinct subtypes and achieve higher accuracy on the simulated datasets to tolerate noise, as compared to consensus hierarchical clustering.ConclusionsThe weighted co-clustering approach in NCIS provides a unique solution to incorporate gene network information into the clustering process. Our tool will be useful to comprehensively identify cancer subtypes that would otherwise be obscured by cancer heterogeneity, using high-throughput and high-dimensional gene expression data.


title: MR imaging of the lymphatic system in patients with lipedema and lipo-lymphedema.

abstract: OBJECTIVE ::: To assess for the first time the morphology of the lymphatic system in patients with lipedema and lipo-lymphedema of the lower extremities by MR lymphangiography. ::: ::: ::: MATERIALS AND METHODS ::: 26 lower extremities in 13 consecutive patients (5 lipedema, 8 lipo-lymphedema) were examined by MR lymphangiography. 18 mL of gadoteridol and 1 mL of mepivacainhydrochloride 1% were subdivided into 10 portions and injected intracutaneously in the forefoot. MR imaging was performed with a 1.5-T system equipped with high-performance gradients. For MR lymphangiography, a 3D-spoiled gradient-echo sequence was used. For evaluation of the lymphedema a heavily T2-weighted 3D-TSE sequence was performed. ::: ::: ::: RESULTS ::: In all 16 lower extremities (100%) with lipo-lymphedema, high signal intensity areas in the epifascial region could be detected on the 3D-TSE sequence. In the 16 examined lower extremities with lipo-lymphedema, 8 lower legs and 3 upper legs demonstrated enlarged lymphatic vessels up to a diameter of 3 mm. In two lower legs with lipo-lymphedema, an area of dermal back-flow was seen, indicating lymphatic outflow obstruction. In the 10 examined lower extremities with clinically pure lipedema, 4 lower legs and 2 upper legs demonstrated enlarged lymphatic vessels up to a diameter of 2 mm, indicating a subclinical status of lymphedema. In all examined extremities, the inguinal lymph nodes demonstrated a contrast material enhancement in the first image acquisition 15 min after injection. ::: ::: ::: CONCLUSION ::: MR lymphangiography is a safe and accurate minimal-invasive imaging modality for the evaluation of the lymphatic circulation in patients with lipedema and lipo-lymphedema of the lower extremities. If the extent of lymphatic involvement is unclear at the initial clinical examination or requires a better definition for optimal therapeutic planning, MR lymphangiography is able to identify the anatomic and physiological derangements and to establish an objective baseline.


title: Understanding disorders of sexual development.

abstract: Studies estimate that the incidence of genital anomalies could be as high as 1 in 300 births. While it is rare for an infant to present with truly ambiguous genitalia, it is plausible that the pediatric nurse will encounter a patient with disorders of sexual development in his or her career. Cases of disorders of sexual development are challenging due to complexities of diagnosis, gender assignment, uncertain outcomes, treatment options, and psychosocial stressors. This article discusses the evaluation and management of children with disorders of sexual development and the nurse's role as child advocate and family educator.


title: Non-ablative 1540 fractional laser: How far could it help injection lipolysis and dermal fillers in lower-face rejuvenation? a randomized controlled trial

abstract: BACKGROUND ::: Rejuvenation of the lower face can be challenging and no single modality can accomplish all its complex events. ::: ::: ::: PATIENTS AND METHODS ::: This 18-month study included 24 female patients with a primary complaint of lower-face aging signs. They were randomly allocated to either Group A, who received injection lipolysis and hyaluronic acid dermal filler, or Group B who in addition received non-ablative 1540 fractional laser. The improvement evaluation score used was the global aesthetic improvement scale (GAIS). Patient's satisfaction level was also recorded. Both were repeated at Months 6, 13 and 18. ::: ::: ::: RESULTS ::: At all evaluations, laser group showed higher degree of improvement. Interestingly, at short-term evaluation (6 month), there was no significant difference between both groups (P > 0.05). However, the laser group improvement in comparison to the other group became significant in the long-term evaluations (13 and 18 months) (P < 0.05). ::: ::: ::: CONCLUSION ::: This study further documents the importance of combination therapy in facial rejuvenation, offering a treatment protocol combining injection lipolysis and hyaluronic acid as an effective, safe, short-term therapeutic option in lower-face rejuvenation. The addition of 1540 non-ablative fractional laser to the protocol offers a higher efficacy with longer-term effects and no adverse events.


title: Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebooks Explanations

abstract: Targeted advertising has been subject to many privacy complaints from both users and policy makers. Despite this attention, users still have little understanding of what data the advertising platforms have about them and why they are shown particular ads. To address such concerns, Facebook recently introduced two transparency mechanisms: a "Why am I seeing this?" button that provides users with an explanation of why they were shown a particular ad (ad explanations), and an Ad Preferences Page that provides users with a list of attributes Facebook has inferred about them and how (data explanations). In this paper, we investigate the level of transparency provided by these two mechanisms. We first define a number of key properties of explanations and then evaluate empirically whether Facebook's explanations satisfy them. For our experiments, we develop a browser extension that collects the ads users receive every time they browse Facebook, their respective explanations, and the attributes listed on the Ad Preferences Page; we then use controlled experiments where we create our own ad campaigns and target the users that installed our extension. Our results show that ad explanations are often incomplete and sometimes misleading while data explanations are often incomplete and vague. Taken together, our findings have significant implications for users, policy makers, and regulators as social media advertising services mature.


title: Altered structural connectivity in neonates at genetic risk for schizophrenia : A combined study using morphological and white matter networks

abstract: Abstract Recently, an increasing body of evidence suggests that developmental abnormalities related to schizophrenia may occur as early as the neonatal stage. Impairments of brain gray matter and wiring problems of axonal fibers are commonly suspected to be responsible for the disconnection hypothesis in schizophrenia adults, but significantly less is known in neonates. In this study, we investigated 26 neonates who were at genetic risk for schizophrenia and 26 demographically matched healthy neonates using both morphological and white matter networks to examine possible brain connectivity abnormalities. The results showed that both populations exhibited small-world network topology. Morphological network analysis indicated that the brain structural associations of the high-risk neonates tended to have globally lower efficiency, longer connection distance, and less number of hub nodes and edges with relatively higher betweenness. Subgroup analysis showed that male neonates were significantly disease-affected, while the female neonates were not. White matter network analysis, however, showed that the fiber networks were globally unaffected, although several subcortical–cortical connections had significantly less number of fibers in high-risk neonates. This study provides new lines of evidence in support of the disconnection hypothesis, reinforcing the notion that the genetic risk of schizophrenia induces alterations in both gray matter structural associations and white matter connectivity.


title: CPI2: CPU performance isolation for shared compute clusters

abstract: Performance isolation is a key challenge in cloud computing. Unfortunately, Linux has few defenses against performance interference in shared resources such as processor caches and memory buses, so applications in a cloud can experience unpredictable performance caused by other programs' behavior. Our solution, CPI2, uses cycles-per-instruction (CPI) data obtained by hardware performance counters to identify problems, select the likely perpetrators, and then optionally throttle them so that the victims can return to their expected behavior. It automatically learns normal and anomalous behaviors by aggregating data from multiple tasks in the same job. We have rolled out CPI2 to all of Google's shared compute clusters. The paper presents the analysis that lead us to that outcome, including both case studies and a large-scale evaluation of its ability to solve real production issues.


title: RpStacks: Fast and Accurate Processor Design Space Exploration Using Representative Stall-Event Stacks

abstract: CPU architects perform a series of slow timing simulations to explore large processor design space. To minimize the exploration overhead, architects make their best efforts to accelerate each simulation step as well as reduce the number of simulations by predicting the exact performance of designs. However, the existing methods are either too slow to overcome the large number of design points, or inaccurate to safely substitute extra simulation steps with performance predictions. In this paper, we propose RpStacks, a fast and accurate processor design space exploration method to 1) identify the current design point's key performance bottlenecks and 2) estimate the exact impacts of latency adjustments without launching an extra step of simulations. The key idea is to selectively collect the information about performance-critical events from a single simulation, construct a small number of event stacks describing the latency of distinctive execution paths, and estimate the overall performance as well as stall-event composition using the stacks. Our proposed method significantly outperforms the existing design space exploration methods in terms of both the latency and the accuracy. For investigating 1,000 design points, RpStacks achieves 26 times speedup on average over a variety of applications while showing high accuracy, when compared to a popular x86 timing simulator.


abstract: The Engineered Barrier System Degradation, Flow, and Transport Process Model Report (EBS PMR) is one of nine PMRs supporting the Total System Performance Assessment (TSPA) being developed by the Yucca Mountain Project for the Site Recommendation Report (SRR). The EBS PMR summarizes the development and abstraction of models for processes that govern the evolution of conditions within the emplacement drifts of a potential high-level nuclear waste repository at Yucca Mountain, Nye County, Nevada. Details of these individual models are documented in 23 supporting Analysis/Model Reports (AMRs). Nineteen of these AMRs are for process models, and the remaining 4 describe the abstraction of results for application in TSPA. The process models themselves cluster around four major topics: ''Water Distribution and Removal Model, Physical and Chemical Environment Model, Radionuclide Transport Model, and Multiscale Thermohydrologic Model''. One AMR (Engineered Barrier System-Features, Events, and Processes/Degradation Modes Analysis) summarizes the formal screening analysis used to select the Features, Events, and Processes (FEPs) included in TSPA and those excluded from further consideration. Performance of a potential Yucca Mountain high-level radioactive waste repository depends on both the natural barrier system (NBS) and the engineered barrier system (EBS) and on their interactions. Although the waste packages are generally considered as components of the EBS, the EBS as defined in the EBS PMR includes all engineered components outside the waste packages. The principal function of the EBS is to complement the geologic system in limiting the amount of water contacting nuclear waste. A number of alternatives were considered by the Project for different EBS designs that could provide better performance than the design analyzed for the Viability Assessment. The design concept selected was Enhanced Design Alternative II (EDA II).


title: Poisson Noise Reduction with Higher-order Natural Image Prior Model

abstract: Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by patch-based non-local methods in recent years. In this paper, we aim to propose a local Poisson denoising model with both structure simplicity and good performance. To this end, we consider a variational modeling to integrate the so-called Fields of Experts (FoE) image prior, that has proven an effective higher-order Markov Random Fields (MRF) model for many classic image restoration problems. We exploit several feasible variational variants for this task. We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR. However, this strategy encounters problem in cases of low SNR. Then we turn to an alternative modeling strategy by using the Anscombe transform and Gaussian statistics derived data term. We retrain the FoE prior model directly in the transform domain. With the newly trained FoE model, we end up with a local variational model providing strongly competitive results against state-of-the-art non-local approaches, meanwhile bearing the property of simple structure. Furthermore, our proposed model comes along with an additional advantage, that the inference is very efficient as it is well-suited for parallel computation on GPUs. For images of size $512 \times 512$, our GPU implementation takes less than 1 second to produce state-of-the-art Poisson denoising performance.


title: Developing a Reliable Core Stability Assessment Battery for Patients with Nonspecific Low Back Pain.

abstract: Study Design. Test-retest design. Objective. The objective was to examine the intrarater (test-retest) reliability of the core stability related tests and to develop a reliable core stability assessment battery. Summary of Background Data. Studies suggest that core stability exercises may improve function and decrease pain in patients with nonspecific low back pain (LBP). Reliable clinical tests are required to implement adequate rehabilitation and to evaluate results of these interventions. Methods. The study had a test-retest design. Thirty-three different tests that might relate to core stability were identified with their mostly used protocols. Five different components of core stability including endurance, flexibility, strength, functional performance, and motor control were assessed in 38 patients with nonspecific LBP. The same testing procedure was performed again after 48 to 72 hours. Intraclass correlation coefficients (ICCs), standard error of measurement, and minimal detectable change were calculated to assess the intrarater reliability. Results. The intrarater reliability of the tests ranged from little to very high (ICC = 0.08–0.98). Partial curl-up (ICC = 0.90), lateral bridge (ICC = 0.95–0.96), trunk flexor endurance (ICC = 0.97), sit and reach (ICC = 0.98), single-legged hop (ICC = 0.98–0.97), lateral step-down (ICC = 0.93–0.92), eyes open right and left leg unilateral stance (ICC = 0.97 and 0.91) tests had the highest intrarater reliability for each core stability component. Conclusion. The results indicated that the partial curl-up test (strength), side bridge and trunk flexor tests (endurance), sit-and-reach test (flexibility), single-legged hop, and lateral step-down (functional), unilateral stance test with eyes open (motor control) had very high intrarater reliability. A core stability assessment battery involving these tests can be used in patients with nonspecific LBP to assess all components of core stability. Conclusion. Level of Evidence: 3


title: Annotation Artifacts in Natural Language Inference Data

abstract: Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem.


Unravelling the molecular basis for regulatory T‐cell plasticity and loss of function in disease


title: COSBench: A Benchmark Tool for Cloud Object Storage Services

abstract: With object storage services becoming increasingly accepted as replacements for traditional file or block systems, it is important to effectively measure the performance of these services. Thus people can compare different solutions or tune their systems for better performance. However, little has been reported on this specific topic as yet. To address this problem, we present COSBench (Cloud Object Storage Benchmark), a benchmark tool that we are currently working on in Intel for cloud object storage services. In addition, in this paper, we also share the results of the experiments we have performed so far.


Plate Detection Using AdaBoost


title: A pick-me-up for infants’ exploratory skills: Early simulated experiences reaching for objects using ‘sticky mittens’ enhances young infants’ object exploration skills

abstract: Infants’ early object contact was enriched by giving a group of pre-reaching infants experience with prehension earlier than they would normally acquire it. These infants received 10–14 10-min play sessions wearing “sticky mittens”: mittens with palms that stuck to the edges of toys and allowed the infants to pick up the toys. After these enrichment sessions, the experienced infants’ object engagement and object exploration skills were compared to those of infants who were the same age as the experienced infants but had not received the play sessions. The results showed that the experienced infants showed more object engagement via a number of measures, and showed more sophisticated object exploration strategies compared to their inexperienced peers. The results suggest that the early simulated experience reaching for objects serves to jump-start the process of object engagement in young infants. © 2002 Elsevier Science Inc. All rights reserved.


title: Child Development and the Physical Environment

abstract: Characteristics of the physical environment that influence child development are discussed. Topics include behavioral toxicology, noise, crowding, housing and neighborhood quality, natural settings, schools, and day care settings. Socioemotional, cognitive, motivation, and psychophysiological outcomes in children and youths are reviewed. Necessary methodological and conceptual advances are introduced as well.


title: Catch You if You Misbehave: Ranked Keyword Search Results Verification in Cloud Computing

abstract: With the advent of cloud computing, more and more people tend to outsource their data to the cloud. As a fundamental data utilization, secure keyword search over encrypted cloud data has attracted the interest of many researchers recently. However, most of existing researches are based on an ideal assumption that the cloud server is “curious but honest”, where the search results are not verified. In this paper, we consider a more challenging model, where the cloud server would probably behave dishonestly. Based on this model, we explore the problem of result verification for the secure ranked keyword search. Different from previous data verification schemes, we propose a novel deterrent-based scheme. With our carefully devised verification data, the cloud server cannot know which data owners, or how many data owners exchange anchor data which will be used for verifying the cloud server's misbehavior. With our systematically designed verification construction, the cloud server cannot know which data owners’ data are embedded in the verification data buffer, or how many data owners’ verification data are actually used for verification. All the cloud server knows is that, once he behaves dishonestly, he would be discovered with a high probability, and punished seriously once discovered. Furthermore, we propose to optimize the value of parameters used in the construction of the secret verification data buffer. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes.


title: Recovery and Biodistribution of Ex-Vivo Expanded Human Erythroblasts Injected Into NOD/SCID/IL2Rγnull Mice

abstract: Abstract 338 Ex vivo expanded erythroblasts (EBs) are red blood cell precursors with proliferative capacity that have the potential to serve as alternative transfusion product. In the present study, we investigated the biodistribution and persistence of human EBs expanded ex vivo from cord blood following intravenous administration to NOD/SCID/IL2Rγnull mice. In the first experiment, 107 EBs generated ex vivo from cord blood under Human Erythroid Massive Amplification (HEMA) culture conditions (Migliaccio G et al. Blood Cells Mol Dis. 2002;28:169) were labeled with CFSE and transfused via the tail vein into NOD/SCID/IL2Rγnull mice which had been bled (200 μL) 24 hrs earlier to increase erythropoietin (EPO) levels. The presence of human EBs in bone marrow (BM), spleen and blood of the transfused recipient mice was analyzed by flow cytometry for CSFE and human CD235a. At day 4, 1.5 – 5% of cells in BM and spleen of the animals were CD235apos but no human cells were detectable in blood. To clarify failure of human EBs to generate red blood cells in mice, a second cohort of mice was given 25×106 expanded EBs and sacrificed 4 days thereafter. Their tissues, including BM, liver and spleen, were examined by immunohistochemistry for expression of human markers. Human CD235apos cells were found in the spleen, representing up to 18% of total cells spleen cells of transfused recipients, but the cells were trapped inside larger CD235aneg cells, probably of murine origin. These results indicate that lodging of human EBs in the spleen, where they are probably destroyed by the macrophages, may represent a barrier to using mouse models as a surrogate assay for investigating transfused human EBs. To test this hypothesis, we analyzed the fate and biodistribution of human EBs into normal vs. splenectomized NOD/SCID/IL2Rγnull mice. Cell biodistribution was analyzed using bioluminescence imaging (BLI), following retroviral-mediated transfer of eGFP and the external Gaussia luciferase genes (Santos et al Nat Med 15: 338, 2009) into expanding cord blood-derived EBs. Cord blood-derived CD34pos cells were either expanded in HEMA culture (as control) or cultured for 3 days with TPO, SCF and FLT3L before retroviral transduction. After 3 days, the cells were cultured under HEMA conditions to induce EBs expansion. Mature EBs were detectable after 11 days of culture in the untransduced, control group and the cells expanded 67-fold. By contrast, transduced cord blood cells matured by day 5–7 and amplified only 24-fold (see Figure). Transduction efficiency, as reflected by GFP expression, was on the order of 32–50% in expanded EBs. All the recipient mice were bled 10 hrs before injection (200 μL). Half of them were splenectomized 24 hrs earlier. Mice were given 15×106 each expanded EBs together with 20 units of EPO and the cell biodistribution analyzed by imaging 24 hrs later (see Figure). In intact mice, BLI signal was virtually undetectable (other than in the tail). In contrast, in the splenectomized mice, significant signal levels were also observed in the body of the animals (see Figure). Four days after injection, mice were sacrificed and the presence of human CD235apos cells in the marrow, liver, spleen (intact animals only) and blood analyzed. CD235apos cells were detectable in the marrow (20%) and liver (6%) of the splenectomized mice while in intact animals they were mainly detected in spleen (15%). Detection of human red cells in blood, however, remained low in all the cases. Overall, we have established a model for the tracking and quantification of human EBs transfused into NOD/SCID/IL2Rγnull mice. This model will be very valuable to investigate the in vivo function and persistence of human EBs expanded under different conditions and thereby define the therapeutic potential of ex vivo generated human EBs derived from different stem cell sources. Disclosures: No relevant conflicts of interest to declare.


title: Sleep Staging by Modeling Sleep Stage Transitions using Deep CRF.

abstract: Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy, which maintains the appropriate pressure to ensure continuous airflow. It is widely accepted that in addition to preventing air passage collapse, increase in deep and REM sleep stages would be good metrics for how well the CPAP therapy is working in improving sleep health. Presently, however, there is no mechanism to easily detect a patient's sleep stages from CPAP flow data alone. We propose, for the first time, an automated sleep staging model based only on the flow signal. ::: Recently deep neural networks have shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a deep convolution-recurrent neural networks to extract high-level features from raw flow signal and then uses a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science, especially towards the understanding of sleep architecture trajectory among patients under CPAP therapy.


title: NIS04-2: Detection of DNS Anomalies using Flow Data Analysis

abstract: The Domain Name System (DNS) is an essential network infrastructure component since it supports the operation of the Web, Email, Voice over IP (VoIP) and other business- critical applications running over the network. Events that compromise the security of DNS can have a significant impact on the Internet since they can affect its availability and its intended operation. This paper describes algorithms used to monitor and detect certain types of attacks to the DNS infrastructure using flow data. Our methodology is based on algorithms that do not rely on known signature attack vectors. The effectiveness of our solution is illustrated with real and simulated traffic examples. In one example, we were able to detect a tunneling attack well before the appearence of public reports of it.


title: An integrated architecture for future car generations

abstract: The DECOS architecture is an integrated architecture that builds upon the validated services of a time-triggered network, which serves as a shared resource for the communication activities of more than one application subsystem. In addition, encapsulated partitions are used to share the computational resources of Electronic Control Units (ECUs) among software modules of multiple application subsystems. This paper investigates the benefits of the DECOS architecture as an electronic infrastructure for future car generations. The shift to an integrated architecture will result in quantifiable cost reductions in the areas of system hardware cost and system development. In the paper we present a current federated Fiat car E/E architecture and discuss a possible mapping to an integrated solution based on the DECOS architecture. The proposed architecture provides a foundation for mixed criticality integration with both safety-critical and non safety-critical subsystems. In particular, this architecture supports applications up to the highest criticality classes (10?9 failures per hour), thereby taking into account the emerging dependability requirements of by-wire functionality in the automotive industry.


Spam Detection Using Artificial Neural Networks ( Perceptron Learning Rule )


title: Learning first-order logic embeddings via matrix factorization

abstract: Many complex reasoning tasks in Artificial Intelligence (including relation extraction, knowledge base completion, and information integration) can be formulated as inference problems using a probabilistic first-order logic. However, due to the discrete nature of logical facts and predicates, it is challenging to generalize symbolic representations and represent first-order logic formulas in probabilistic relational models. In this work, we take a rather radical approach: we aim at learning continuous low-dimensional embeddings for first-order logic from scratch. In particular, we first consider a structural gradient based structure learning approach to generate plausible inference formulas from facts; then, we build grounded proof graphs using background facts, training examples, and these inference formulas. To learn embeddings for formulas, we map the training examples into the rows of a binary matrix, and inference formulas into the columns. Using a scalable matrix factorization approach, we then learn the latent continuous representations of examples and logical formulas via a low-rank approximation method. In experiments, we demonstrate the effectiveness of reasoning with first-order logic embeddings by comparing with several state-of-the-art baselines on two datasets in the task of knowledge base completion.


title: A study of parameter tuning for term frequency normalization

abstract: Most current term frequency normalization approaches for information retrieval involve the use of parameters. The tuning of these parameters has an important impact on the overall performance of the information retrieval system. Indeed, a small variation in the involved parameter(s) could lead to an important variation in the precision/recall values. Most current tuning approaches are dependent on the document collections. As a consequence, the effective parameter value cannot be obtained for a given new collection without extensive training data. In this paper, we propose a novel and robust method for the tuning of term frequency normalization parameter(s), by measuring the normalization effect on the within document frequency of the query terms. As an illustration, we apply our method on Amati \& Van Rijsbergen's so-called normalization 2. The experiments for the ad-hoc TREC-6,7,8 tasks and TREC-8,9,10 Web tracks show that the new method is independent of the collections and able to provide reliable and good performance.


title: Pre-analytical errors management in the clinical laboratory: a five-year study

abstract: Introduction: This study describes quality indicators for the pre-analytical process, grouping errors according to patient risk as critical or major, and assesses their evaluation over a five-year period. Materials and methods: A descriptive study was made of the temporal evolution of quality indicators, with a study population of 751,441 analytical requests made during the period 2007-2011. The Runs Test for randomness was calculated to assess changes in the trend of the series, and the degree of control over the process was estimated by the Six Sigma scale. Results: The overall rate of critical pre-analytical errors was 0.047%, with a Six Sigma value of 4.9. The total rate of sampling errors in the study period was 13.54% (P = 0.003). The highest rates were found for the indicators “haemolysed sample” (8.76%), “urine sample not submitted” (1.66%) and “clotted sample” (1.41%), with Six Sigma values of 3.7, 3.7 and 2.9, respectively. Conclusions: The magnitude of pre-analytical errors was accurately valued. While processes that triggered critical errors are well controlled, the results obtained for those regarding specimen collection are borderline unacceptable; this is particularly so for the indicator “haemolysed sample”.


title: Human-like social skills in dogs ?

abstract: Domestic dogs are unusually skilled at reading human social and communicative behavior – even more so than our nearest primate relatives. For example, they use human social and communicative behavior (e.g. a pointing gesture) to find hidden food, and they know what the human can and cannot see in various situations. Recent comparisons between canid species suggest that these unusual social skills have a heritable component and initially evolved during domestication as a result of selection on systems mediating fear and aggression towards humans. Differences in chimpanzee and human temperament suggest that a similar process may have been an important catalyst leading to the evolution of unusual social skills in our own species. The study of convergent evolution provides an exciting opportunity to gain further insights into the evolutionary processes leading to human-like forms of cooperation and communication.


title: A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS

abstract: The biomimetic CMOS dynamic vision and image sensor described in this paper is based on a QVGA (304×240) array of fully autonomous pixels containing event-based change detection and pulse-width-modulation (PWM) imaging circuitry. Exposure measurements are initiated and carried out locally by the individual pixel that has detected a change of brightness in its field-of-view. Pixels do not rely on external timing signals and independently and asynchronously request access to an (asynchronous arbitrated) output channel when they have new grayscale values to communicate. Pixels that are not stimulated visually do not produce output. The visual information acquired from the scene, temporal contrast and grayscale data, are communicated in the form of asynchronous address-events (AER), with the grayscale values being encoded in inter-event intervals. The pixel-autonomous and massively parallel operation ideally results in lossless video compression through complete temporal redundancy suppression at the pixel level. Compression factors depend on scene activity and peak at ~1000 for static scenes. Due to the time-based encoding of the illumination information, very high dynamic range - intra-scene DR of 143 dB static and 125 dB at 30 fps equivalent temporal resolution - is achieved. A novel time-domain correlated double sampling (TCDS) method yields array FPN of 56 dB (9.3 bit) for >10 Lx illuminance.


title: Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification

abstract: The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification. Recently, the relational learning to rank model, which designs a learnable ranking function following the criterion of maximal marginal relevance, has shown effectiveness in search result diversification [Zhu et al. 2014]. The goodness of a diverse ranking model is usually evaluated with diversity evaluation measures such as α-NDCG [Clarke et al. 2008], ERR-IA [Chapelle et al. 2009], and D#-NDCG [Sakai and Song 2011]. Ideally the learning algorithm would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing relational learning to rank algorithms, however, only train the ranking models by optimizing loss functions that loosely relate to the evaluation measures. To deal with the problem, we propose a general framework for learning relational ranking models via directly optimizing any diversity evaluation measure. In learning, the loss function upper-bounding the basic loss function defined on a diverse ranking measure is minimized. We can derive new diverse ranking algorithms under the framework, and several diverse ranking algorithms are created based on different upper bounds over the basic loss function. We conducted comparisons between the proposed algorithms with conventional diverse ranking methods using the TREC benchmark datasets. Experimental results show that the algorithms derived under the diverse learning to rank framework always significantly outperform the state-of-the-art baselines.


title: The Teaching Dimension of Linear Learners

abstract: Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.


title: Data fusion in the transferable belief model

abstract: When Shafer introduced his theory of evidence based on the use of belief functions, he proposed a rule to combine belief functions induced by distinct pieces of evidence. Since then, theoretical justifications of this so-called Dempster's rule of combination have been produced and the meaning of distinctness has been assessed. The author presents practical applications where the fusion of uncertain data is well achieved by Dempster's rule of combination. It is essential that the meaning of the belief functions used to represent uncertainty be well fixed, as the adequacy of the rule depends strongly on a correct understanding of the context in which they are applied. Missing to distinguish between the upper and lower probabilities theory and the transferable belief model can lead to serious confusion, as Dempster's rule of combination is central in the transferable belief model whereas it hardly fits with the upper and lower probabilities theory.


The Philippines: Archaeology in the Philippines to 1950


title: Multi-robot target reaching using modified Q-learning and PSO

abstract: In this paper, a group of mobile robots learns to solve a target reaching problem in a simulated grid environment filled with obstacles. Each robot knows its distance to the target and can communicate with each other. The proposed learning algorithm combines a reinforcement learning algorithm and a swarm optimization algorithm. Q-learning, which is a reinforcement learning algorithm, is modified to learn a policy by specifying rewards and punishment for certain robot actions. Particle swarm optimization (PSO), which is a swarm optimization algorithm, is modified for grid environment and used to accelerate the learning process for multiple robots. The proposed algorithm outperforms the original Q-learning in both training and testing. It learns 2.23 times faster and required 6.52 fewer steps to reach the destination. Moreover, it uses less memory than the original Q-learning. We also experiment on various numbers of robots. The result shows that more robots learn faster in most cases.


title: On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

abstract: Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system (McCall et al., 2007) to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane-change maneuver at a particular point in the future. Quantitative results from a naturalistic driving study are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction. The addition of eye gaze does not improve performance as much as simpler head dynamics cues. The advantage of head data over eye data is shown to be statistically significant (p < 0.01) 3 s ahead of lane-change situations, indicating that there may be a biological basis for head motion to begin earlier than eye motion during "lane-change"-related gaze shifts.


title: Unsupervised feature extraction with autoencoder trees

abstract: Abstract The autoencoder is a popular neural network model that learns hidden representations of unlabeled data. Typically, single- or multilayer perceptrons are used in constructing an autoencoder, but we use soft decision trees (i.e., hierarchical mixture of experts) instead. Such trees have internal nodes that implement soft multivariate splits through a gating function and all leaves are weighted by the gating values on their path to get the output. The encoder tree converts the input to a lower dimensional representation in its leaves, which it passes to the decoder tree that reconstructs the original input. Because the splits are soft, the encoder and decoder trees can be trained back to back with stochastic gradient-descent to minimize reconstruction error. In our experiments on handwritten digits, newsgroup posts, and images, we observe that the autoencoder trees yield as small and sometimes smaller reconstruction error when compared with autoencoder perceptrons. One advantage of the tree is that it learns a hierarchical representation at different resolutions at its different levels and the leaves specialize at different local regions in the input space. An extension with locally linear mappings in the leaves allows a more flexible model. We also show that the autoencoder tree can be used with multimodal data where a mapping from one modality (i.e., image) to another (i.e., topics) can be learned.


Maternal and child nutrition


Myriad Functions of Stanniocalcin-1 (STC1) Cover Multiple Therapeutic Targets in the Complicated Pathogenesis of Idiopathic Pulmonary Fibrosis (IPF)


title: Analysis and Optimization of Graph Decompositions by Lifted Multicuts

abstract: We study the set of all decompositions (clusterings) of a graph through its characterization as a set of lifted multicuts. This leads us to practically relevant insights related to the definition of a class of decompositions by must-join and must-cut constraints and related to the comparison of clusterings by metrics. To find optimal decompositions defined by minimum cost lifted multicuts, we establish some properties of some facets of lifted multicut polytopes, define efficient separation procedures and apply these in a branch-and-cut algorithm.


title: Content-Centric Energy Management of Mobile Displays

abstract: Despite numerous studies to reduce the power consumption of the display-related components of mobile devices, previous works have led to a deterioration in user experience due to compromised graphic quality. In this paper, we propose an effective scheme to reduce the energy consumption of the display subsystems of mobile devices without compromising user experience. In preliminary experiments, we noticed that mobile devices typically perform redundant display updates even if the display content does not change. Based on this observation, we first propose a metric called the content rate , which is defined as the number of meaningful frame changes in a second. Our scheme then estimates an optimal refresh rate based on the content rate in order to eliminate redundant display updates. Also proposed is the flicker compensation technique, which prevents the flickering problem caused by the reduced refresh rate. Extensive experiments conducted on the latest smartphones demonstrated that our system effectively reduces the overall power consumption of mobile devices by 35 percent while simultaneously maintaining satisfactory display quality.


title: Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

abstract: While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data from potentially untrustworthy sources, providing adversaries with the opportunity to manipulate them by inserting carefully crafted samples into the training set. Recent work has shown that this type of attack, called a poisoning attack, allows adversaries to insert backdoors or trojans into the model, enabling malicious behavior with simple external backdoor triggers at inference time and only a blackbox perspective of the model itself. Detecting this type of attack is challenging because the unexpected behavior occurs only when a backdoor trigger, which is known only to the adversary, is present. Model users, either direct users of training data or users of pre-trained model from a catalog, may not guarantee the safe operation of their ML-based system. In this paper, we propose a novel approach to backdoor detection and removal for neural networks. Through extensive experimental results, we demonstrate its effectiveness for neural networks classifying text and images. To the best of our knowledge, this is the first methodology capable of detecting poisonous data crafted to insert backdoors and repairing the model that does not require a verified and trusted dataset.


An Integrative Neuroscience Framework for the Treatment of Chronic Pain: From Cellular Alterations to Behavior


title: Change Detection between Multimodal Remote Sensing Data Using Siamese CNN

abstract: Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring. In practice, remote sensing data are often available in different modalities and at different time epochs. Change detection between multimodal data can be very challenging since the data show different characteristics. Given 3D laser scanning point clouds and 2D imagery from different epochs, this paper presents a framework to detect building and tree changes. First, the 2D and 3D data are transformed to image patches, respectively. A Siamese CNN is then employed to detect candidate changes between the two epochs. Finally, the candidate patch-based changes are grouped and verified as individual object changes. Experiments on the urban data show that 86.4\% of patch pairs can be correctly classified by the model.


title: On the Generative Discovery of Structured Medical Knowledge

abstract: Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce medical information access cost for both individuals and societies. However, expanding the scale of high-quality yet structured medical knowledge usually comes with tedious efforts in data preparation and human annotation. To promote the benefits while minimizing the data requirement in expanding medical knowledge, we introduce a generative perspective to study the relational medical entity pair discovery problem. A generative model named Conditional Relationship Variational Autoencoder is proposed to discover meaningful and novel medical entity pairs by purely learning from the expression diversity in the existing relational medical entity pairs. Unlike discriminative approaches where high-quality contexts and candidate medical entity pairs are carefully prepared to be examined by the model, the proposed model generates novel entity pairs directly by sampling from a learned latent space without further data requirement. The proposed model explores the generative modeling capacity for medical entity pairs while incorporating deep learning for hands-free feature engineering. It is not only able to generate meaningful medical entity pairs that are not yet observed, but also can generate entity pairs for a specific medical relationship. The proposed model adjusts the initial representations of medical entities by addressing their relational commonalities. Quantitative and qualitative evaluations on real-world relational medical entity pairs demonstrate the effectiveness of the proposed method in generating relational medical entity pairs that are meaningful and novel.


title: Electrowetting Actuation for a Sessile Liquid Drop: Experiments and Simulations

abstract: In this paper, experiments and numerical simulations are performed to study the effects of electric field on the contact angle of a sessile liquid drop resting on a substrate in electrowetting-on-dielectric (EWOD) application. In the experiments for studying the electrowetting, a mercury droplet of 20μL was dispensed manually on a soldermask-coated PCB (Printed Circuit Board) and different values of AC voltage were applied between the droplet and the insulator. High quality images were captured using a CCD camera in all experiments and a program was developed using the MATLAB software for image-processing purposes to obtain the contact angle and other geometrical parameters of the droplet. A numerical model was also used to simulate the drop deformation under an electric field. The continuity and momentum equations along with an equation for tracking the liquid free surface were solved. The free surface advection and reconstruction were performed based on the volume-of-fluid method using Youngs’ algorithm. To evaluate the effect of the electric field on the free surface, the electrostatic potential was first solved for the entire computational domain. Next, the electric field intensity and the surface density of the electric charge were calculated on the free surface after which the electric force could be determined. Calculated droplet shapes agreed well with those of the experiments.© 2011 ASME


title: Learning Contextual Inquiry and Distributed Cognition: a case study on technology use in anaesthesia

abstract: Abstract ::: There have been few studies on how analysts learn or use frameworks to support gathering and analysis of field data. Distributed Cognition for Teamwork (DiCoT) is a framework that has been developed to facilitate the learning of Distributed Cognition (DCog), focusing on analysing small team interactions. DiCoT, in turn, exploits representations from Contextual Inquiry (CI). The present study is a reflective account of the experience of learning first CI and then DiCoT for studying the use of infusion devices in operating theatres. We report on how each framework supported a novice analyst (the first author) in structuring his data gathering and analysis, and the challenges that he faced. There are three contributions of this work: (1) an example of learning CI and DCog in a semi-structured way; (2) an account of the process and outcomes of learning and using CI and DiCoT in a complex setting; and (3) an outline account of information flow in anaesthesia. While CI was easier to learn and consequently gave better initial support to the novice analyst entering a complex work setting, DiCoT gave added value through its focus on information propagation and transformation as well as the roles of people and artefacts in supporting communication and situation awareness. This study makes visible many of the challenges of learning to apply a framework that are commonly encountered but rarely reported.


title: Parameter Meta-optimization of Metaheuristic Optimization Algorithms

abstract: The quality of a heuristic optimization algorithm is strongly dependent on its parameter values. Finding the optimal parameter values is a laborious task which requires expertise and knowledge about the algorithm, its parameters and the problem. This paper describes, how the optimization of parameters can be automated by using another optimization algorithm on a meta-level. To demonstrate this, a meta-optimization problem which is algorithm independent and allows any kind of algorithm on the meta- and base-level is implemented for the open source optimization environment HeuristicLab. Experimental results of the optimization of a genetic algorithm for different sets of base-level problems with different complexities are shown.


abstract: A novel design of compact 2.4 GHz microstrip antennas, where the radiating patch is designed in fractal configuration, namely, square and pentagon shape respectively, is proposed. The newly designed microstrips provide an optimized patch area resulting into a substantial reduction in size compared to a typical microstrip patch designed at the same frequency of operation. It was found that the characteristics of the novel fractal patch antennas are comparable to the conventional patch antenna, while their gain and radiating efficiency are noticeably improved.


title: An auditory oddball (P300) spelling system for brain‐computer interfaces

abstract: This study was designed to develop and test an auditory event-related potential (ERP) based spelling system for a brain-computer interface (BCI) and to compare user’s performance between the auditory and visual modality. The spelling system, where letters in a matrix were coded with acoustically presented numbers, was tested on a group of healthy volunteers. The results were compared with a visual spelling system. Nine of the 13 participants presented with the auditory ERP spelling system scored above a predefined criterion level control for communication. Compared to the visual spelling system, users’ performance was lower and the peak latencies of the auditorily evoked ERPs were delayed. It was concluded that auditorily evoked ERPs from the majority of the users could be reliably classified. High accuracies were achieved in these users, rendering item selection with a BCI based on auditory stimulation feasible for communication. Descriptors: Electroencephalogram, Auditory stimulation, Event-related potential, P300, Brain-computer interface


title: An algorithm for local geoparsing of microtext

abstract: The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms.


title: Preliminary Guidelines for Empirical Research in Software Engineering

abstract: Empirical software engineering research needs research guidelines to improve the research and reporting processes. We propose a preliminary set of research guidelines aimed at stimulating discussion among software researchers. They are based on a review of research guidelines developed for medical researchers and on our own experience in doing and reviewing software engineering research. The guidelines are intended to assist researchers, reviewers, and meta-analysts in designing, conducting, and evaluating empirical studies. Editorial boards of software engineering journals may wish to use our recommendations as a basis for developing guidelines for reviewers and for framing policies for dealing with the design, data collection, and analysis and reporting of empirical studies.


title: A secure user anonymity-preserving biometric-based multi-server authenticated key agreement scheme using smart cards

abstract: Abstract Advancement in communication technology provides a scalable platform for various services, where a remote user can access the server from anywhere without moving from its place. It provides a unique opportunity for online services such that a user does not need to be physically present at the service center. These services adopt authentication and key agreement protocols in order to ensure authorized and secure access to the resources. Most of the authentication schemes proposed in the literature support a single-server environment, where the user has to register with each server. If a user wishes to access multiple application servers, he/she requires to register with each server. The multi-server authentication introduces a scalable platform such that a user can interact with any server using single registration. Recently, Chuang and Chen proposed an efficient multi-server authenticated key agreement scheme based on a user’s password and biometrics (Chuang and Chen, 2014). Their scheme is a lightweight, which requires the computation of only hash functions. In this paper, we first analyze Chuang and Chen’s scheme and then identify that their scheme does not resist stolen smart card attack which causes the user’s impersonation attack and server spoofing attack. We also show that their scheme fails to protect denial-of-service attack. We aim to propose an efficient improvement on Chuang and Chen’s scheme to overcome the weaknesses of their scheme, while also retaining the original merits of their scheme. Through the rigorous informal and formal security analysis, we show that our scheme is secure against various known attacks including the attacks found in Chuang and Chen’s scheme. Furthermore, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool and show that our scheme is secure against the replay and man-in-the-middle attacks. In addition, our scheme is comparable in terms of the communication and computational overheads with Chuang and Chen’s scheme and other related existing schemes.


title: Engineering Deep Representations for Modeling Aesthetic Perception

abstract: Many aesthetic models in computer vision suffer from two shortcomings: 1) the low descriptiveness and interpretability of those hand-crafted aesthetic criteria (i.e., nonindicative of region-level aesthetics), and 2) the difficulty of engineering aesthetic features adaptively and automatically toward different image sets. To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting. More specifically, using a bag-ofwords (BoW) representation of the frequent Flickr image tags, a sparsity-constrained subspace algorithm discovers a compact set of textual attributes (e.g., landscape and sunset) for each image. Then, a weakly-supervised learning algorithm projects the textual attributes at image-level to the highly-responsive image patches at pixel-level. These patches indicate where humans look at appealing regions with respect to each textual attribute, which are employed to learn the visual attributes. Psychological and anatomical studies have shown that humans perceive visual concepts hierarchically. Hence, we normalize these patches and feed them into a five-layer convolutional neural network (CNN) to mimick the hierarchy of human perceiving the visual attributes. We apply the learned deep features on image retargeting, aesthetics ranking, and retrieval. Both subjective and objective experimental results thoroughly demonstrate the competitiveness of our approach.


title: Development of Intelligent Core Network for Tactile Internet and Future Smart Systems

abstract: One of the main design aspects of the Tactile Internet system is the 1 ms end-to-end latency, which is considered as being the main challenge with the system realization. Forced by recent development and capabilities of the fifth generation (5G) cellular system, the Tactile Internet will become a real. One way to overcome the 1 ms latency is to employ a centralized controller in the core of the network with a global knowledge of the system, together with the concept of network function virtualization (NFV). This is the idea behind the software defined networking (SDN). This paper introduces a Tactile Internet system structure, which employs SDN in the core of the cellular network and mobile edge computing (MEC) in multi-levels. The work is mainly concerned with the structure of the core network. The system is simulated over a reliable environment and introduces a round trip latency of orders of 1 ms. This can be interpreted by the reduction of intermediate nodes that are involved in the communication process.


title: A complete hand-drawn sketch vectorization framework

abstract: Vectorizing hand-drawn sketches is an important but challenging task. Many businesses rely on fashion, mechanical or structural designs which, sooner or later, need to be converted in vectorial form. For most, this is still a task done manually. This paper proposes a complete framework that automatically transforms noisy and complex hand-drawn sketches with different stroke types in a precise, reliable and highly-simplified vectorized model. The proposed framework includes a novel line extraction algorithm based on a multi-resolution application of Pearson's cross correlation and a new unbiased thinning algorithm that can get rid of scribbles and variable-width strokes to obtain clean 1-pixel lines. Other contributions include variants of pruning, merging and edge linking procedures to post-process the obtained paths. Finally, a modification of the original Schneider's vectorization algorithm is designed to obtain fewer control points in the resulting Bezier splines. All the steps presented in this framework have been extensively tested and compared with state-of-the-art algorithms, showing (both qualitatively and quantitatively) their outperformance. Moreover they exhibit fast real-time performance, making them suitable for integration in any computer graphics toolset.


title: Principles of Data Mining

abstract: The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.


title: Interest cash: an application-based countermeasure against interest flooding for dynamic content in named data networking

abstract: As a design of information-centric network architecture, Named Data Networking (NDN) provides content-based security. The signature binding the name with the content is the key point of content-based security in NDN. However, signing a content will introduce a significant computation overhead, especially for dynamically generated content. Adversaries can take advantages of such computation overhead to deplete the resources of the content provider. In this paper, we propose Interest Cash, an application-based countermeasure against Interest Flooding for dynamic content. Interest Cash requires a content consumer to solve a puzzle before it sends an Interest. The content consumer should provide a solution to this puzzle as cash to get the signing service from the content provider. The experiment shows that an adversary has to use more than 300 times computation resources of the content provider to commit a successful attack when Interest Cash is used.


title: Combining Multimodal Features with Hierarchical Classifier Fusion for Emotion Recognition in the Wild

abstract: Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features from video and audio to evaluate their discriminative ability to human emotion analysis. For each clip, we extract SIFT, LBP-TOP, PHOG, LPQ-TOP and audio features. We train different classifiers for every kind of features on the dataset from EmotiW 2014 Challenge, and we propose a novel hierarchical classifier fusion method for all the extracted features. The final achievement we gained on the test set is 47.17% which is much better than the best baseline recognition rate of 33.7%.


Empirical Analysis of Algorithms is Easy (or is it?)


title: Compressed Sensing with Coherent and Redundant Dictionaries

abstract: This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or incoherent dictionary, but in a truly redundant dictionary. This work thus bridges a gap in the literature and shows not only that compressed sensing is viable in this context, but also that accurate recovery is possible via an L1-analysis optimization problem. We introduce a condition on the measurement/sensing matrix, which is a natural generalization of the now well-known restricted isometry property, and which guarantees accurate recovery of signals that are nearly sparse in (possibly) highly overcomplete and coherent dictionaries. This condition imposes no incoherence restriction on the dictionary and our results may be the first of this kind. We discuss practical examples and the implications of our results on those applications, and complement our study by demonstrating the potential of L1-analysis for such problems.


title: What is this evasive beast we call user satisfaction

abstract: Abstract The notion of ‘user satisfaction’ plays a prominent role in HCI, yet it remains evasive. This exploratory study reports three experiments from an ongoing research program. In this program we aim to uncover (1) what user satisfaction is, (2) whether it is primarily determined by user expectations or by the interactive experience, (3) how user satisfaction may be related to perceived usability, and (4) the extent to which satisfaction rating scales capture the same interface qualities as uncovered in self-reports of interactive experiences. In all three experiments reported here user satisfaction was found to be a complex construct comprising several concepts, the distribution of which varied with the nature of the experience. Expectations were found to play an important role in the way users approached a browsing task. Satisfaction and perceived usability was assessed using two methods: scores derived from unstructured interviews and from the Web site Analysis MeasureMent Inventory (WAMMI) rating scales. Scores on these two instruments were somewhat similar, but conclusions drawn across all three experiments differed in terms of satisfaction ratings, suggesting that rating scales and interview statements may tap different interface qualities. Recent research suggests that ‘beauty’, or ‘appeal’ is linked to perceived usability so that what is ‘beautiful’ is also perceived to be usable [Interacting with Computers 13 (2000) 127]. This was true in one experiment here using a web site high in perceived usability and appeal. However, using a site with high appeal but very low in perceived usability yielded very high satisfaction, but low perceived usability scores, suggesting that what is ‘beautiful’ need not also be perceived to be usable. The results suggest that web designers may need to pay attention to both visual appeal and usability.


title: Efficient matrix completion for seismic data reconstruction

abstract: ABSTRACTDespite recent developments in improved acquisition, seismic data often remain undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We have interpreted the missing-trace interpolation problem in the context of matrix completion (MC), and developed three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We use an optimization program that restores this low-rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we found experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by using the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to red...


Road Sign Detection and Recognition Using Hidden Markov Model †


title: Theory and Practice of Projective Rectification

abstract: This paper gives a new method for image rectification, the process of resampling pairs of stereo images taken from widely differing viewpoints in order to produce a pair of “matched epipolar projections”. These are projections in which the epipolar lines run parallel with the x-axis and consequently, disparities between the images are in the x-direction only. The method is based on an examination of the fundamental matrix of Longuet-Higgins which describes the epipolar geometry of the image pair. The approach taken is consistent with that advocated by Faugeras (1992) of avoiding camera calibration. The paper uses methods of projective geometry to determine a pair of 2D projective transformations to be applied to the two images in order to match the epipolar lines. The advantages include the simplicity of the 2D projective transformation which allows very fast resampling as well as subsequent simplification in the identification of matched points and scene reconstruction.


title: Anchor Free Network for Multi-Scale Face Detection

abstract: Anchor-based deep methods are the most widely used methods for face detection and have reached the state-of-the-art result. Compared with anchor-based methods that estimates the bounding-box rely on some pre-defined anchor boxes, anchor-free methods perform the localization by predicting the offsets of a pixel inside a face to its outside boundaries whose accuracies are much more precise. However, anchor-free methods suffer the drawback of low recall-rate mainly because 1) only using single scale features lead to miss detection of small faces, 2) the highly intra-class imbalance problem among different size faces. In this paper, to address these problems, we propose a unified anchor-free network for detecting multi-scale faces by leveraging the local and global contextual information of multi-layer features. We also utilize a scale aware sampling strategy to mitigate the intra-class imbalance issue which can adaptivity select the positive samples. Furthermore, a revised focal loss function is adopted to deal with the foreground/background imbalance issue. Experimental results on two benchmark datasets demonstrate the effective of our proposed method.


title: Towards generating arcade game rules with VGDL

abstract: We describe an attempt to generate complete arcade games using the Video Game Description Language (VGDL) and the General Video Game Playing environment (GVG-AI). Games are generated by an evolutionary algorithm working on genotypes represented as VGDL descriptions. In order to direct evolution towards good games, we need an evaluation function that accurately estimates game quality. The evaluation function used here is based on the differential performance of several game-playing algorithms, or Relative Algorithm Performance Profiles (RAPP): it is assumed that good games allow good players to play better than bad players. For the purpose of such evaluations, we introduce two new game tree search algorithms, DeepSearch and Explorer; these perform very well on benchmark games and constitute a substantial subsidiary contribution of the paper. In the end, the attempt to generate arcade games is only partially successful, as some of the games have interesting design features but are barely playable as generated. An analysis of these shortcomings yields several suggestions to guide future attempts at arcade game generation.


title: An automatic tongue detection and segmentation framework for computer-aided tongue image analysis

abstract: Traditional Chinese Medicine (TCM) has a long history and has been recognised as a popular alternative medicine in western countries. Tongue diagnosis is a significant procedure in computer–aided TCM, where tongue image analysis plays a dominant role. In this paper, we propose a fully automatic tongue detection and segmentation framework that includes a Principal Component Analysis (PCA)–based tongue detection algorithm as well as a new hybrid tongue segmentation algorithm that fully exploits the robustness of the Mean Shift algorithm and the efficiency of the Canny Edge Detection algorithm, together with the outlier removal capability of the Tensor Voting algorithm. Compared with other existing methods, our method is fully automatic without the need of adjusting parameters for different images and do not need any initialisation.


title: Non-intrusive load monitoring using prior models of general appliance types

abstract: Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback.


title: Space–time wiring specificity supports direction selectivity in the retina

abstract: Motion detection by the retina is thought to rely largely on the biophysics of starburst amacrine cell dendrites; here machine learning is used with gamified crowdsourcing to draw the wiring diagram involving amacrine and bipolar cells to identify a plausible circuit mechanism for direction selectivity; the model suggests similarities between mammalian and insect vision.


title: Performance and Overhead Study of Containers Running on Top of Virtual Machines

abstract: Cloud computing is a widespread and commonly used computing paradigm. Cloud relied on virtualization technology to be a scalable and efficient distributed system. In traditional cloud infrastructures, hypervisors like KVM create and manage Virtual Machines according to users’ demands. Recently a more lightweight virtualization technique is being rapidly adopted and it is based on containers.Although running a container on top of Virtual Machine is a common case, according to our research it has never been studied before how this affects the container’s performance. In this work, we conducted several experiments to study how the container performance is affected by the additional virtualization layer of the Virtual Machine. We used Docker to deploy and run our containers, KVM hypervisor for the Virtual Machines and we executed a series of well-known benchmarks on different operating systems.


title: Racer: A Core Inference Engine for the Semantic Web

abstract: In this paper we describe Racer, which can be considered as a core inference engine for the semantic web. The Racer inference server offers two APIs that are already used by at least three different network clients, i.e., the ontology editor OilEd, the visualization tool RICE, and the ontology development environment Protege 2. The Racer server supports the standard DIG protocol via HTTP and a TCP based protocol with extensive query facilities. Racer currently supports the web ontology languages DAML+OIL, RDF, and OWL.


title: Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks

abstract: While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54% to 86.88%.