title: Enhancing Windows Firewall Security Using Fuzzy Reasoning

text: Firewall is a standard security utility within the Microsoft Windows operating system. Most Windows users adopt it as the default security option due to its free availability. Moreover, Windows Firewall is a widely used security tool because of the large market share of the Microsoft Windows operating system. It can be customised for filtering of network traffic based on user-defined inbound and outbound rules. It is supplied with only basic functionality. As a result it cannot be considered as an effective tool for monitoring and analysing of inbound and outbound traffic. Nonetheless, as a freely available and conventional end user security tool, with some enhancement it could perform as a useful security tool for millions of Windows users. Therefore, this paper presents an enhanced Windows Firewall for a more effective monitoring and analysis of network traffic, based upon an intuitive fuzzy reasoning approach. Consequently, it can be used to prevent a greater range of attacks beyond the simple filtering of inbound and outbound network traffic. In this paper, a simulation of ICMP flooding is demonstrated, where the created firewall inbound and outbound rules are insufficient to monitor ICMP flooding. However, the addition of fuzzy reasoning system monitored it successfully and enhanced the standard Windows Firewall functionality to prevent ICMP flooding. The use of this Windows Fuzzy-Firewall can also be extended to prevent TCP flooding, UDP flooding and some other types of denial of service attacks.


title: Deep Model-Based 6D Pose Refinement in RGB

text: We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum. de/Main/FabianManhardt to ensure reproducibility.


title: The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models

text: Defect prediction models help software organizations to anticipate where defects will appear in the future. When training a defect prediction model, historical defect data is often mined from a Version Control System (VCS, e.g., Subversion), which records software changes at the file-level. Software metrics, on the other hand, are often calculated at the class- or method-level (e.g., McCabe's Cyclomatic Complexity). To address the disagreement in granularity, the class- and method-level software metrics are aggregated to file-level, often using summation (i.e., McCabe of a file is the sum of the McCabe of all methods within the file). A recent study shows that summation significantly inflates the correlation between lines of code (Sloc) and cyclomatic complexity (Cc) in Java projects. While there are many other aggregation schemes (e.g., central tendency, dispersion), they have remained unexplored in the scope of defect prediction. In this study, we set out to investigate how different aggregation schemes impact defect prediction models. Through an analysis of 11 aggregation schemes using data collected from 255 open source projects, we find that: (1) aggregation schemes can significantly alter correlations among metrics, as well as the correlations between metrics and the defect count; (2) when constructing models to predict defect proneness, applying only the summation scheme (i.e., the most commonly used aggregation scheme in the literature) only achieves the best performance (the best among the 12 studied configurations) in 11 percent of the studied projects, while applying all of the studied aggregation schemes achieves the best performance in 40 percent of the studied projects; (3) when constructing models to predict defect rank or count, either applying only the summation or applying all of the studied aggregation schemes achieves similar performance, with both achieving the closest to the best performance more often than the other studied aggregation schemes; and (4) when constructing models for effort-aware defect prediction, the mean or median aggregation schemes yield performance values that are significantly closer to the best performance than any of the other studied aggregation schemes. Broadly speaking, the performance of defect prediction models are often underestimated due to our community's tendency to only use the summation aggregation scheme. Given the potential benefit of applying additional aggregation schemes, we advise that future defect prediction models should explore a variety of aggregation schemes.


title: System-level fault-tolerance in large-scale parallel machines with buffered coscheduling

text: Summary form only given. As the number of processors for multiteraflop systems grows to tens of thousands, with proposed petaflops systems likely to contain hundreds of thousands of processors, the assumption of fully reliable hardware has been abandoned. Although the mean time between failures for the individual components can be very high, the large total component count will inevitably lead to frequent failures. It is therefore of paramount importance to develop new software solutions to deal with the unavoidable reality of hardware faults. We will first describe the nature of the failures of current large-scale machines, and extrapolate these results to future machines. Based on this preliminary analysis we will present a new technology that we are currently developing, buffered coscheduling, which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency - requiring no changes to user applications. Preliminary results show that this is attainable with current hardware.


title: A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

text: Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.


title: Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm

text: Millimeter wave (mmWave) communication is a promising technology for future wireless systems, while one practical challenge is to achieve its large-antenna gains with only limited radio frequency (RF) chains for cost-effective implementation. To this end, we study in this paper a new lens antenna array enabled mmWave multiple-input multiple-output (MIMO) communication system. We first show that the array response of lens antenna arrays follows a “sinc” function, where the antenna element with the peak response is determined by the angle of arrival (AoA)/departure (AoD) of the received/transmitted signal. By exploiting this unique property along with the multi-path sparsity of mmWave channels, we propose a novel low-cost and capacity-achieving spatial multiplexing scheme for both narrow-band and wide-band mmWave communications, termed path division multiplexing (PDM), where parallel data streams are transmitted over different propagation paths with simple per-path processing. We further propose a simple path grouping technique with group-based small-scale MIMO processing to effectively mitigate the inter-stream interference due to similar AoAs/AoDs. Numerical results are provided to compare the performance of the proposed mmWave lens MIMO against the conventional MIMO with uniform planar arrays (UPAs) and hybrid analog/digital processing. It is shown that the proposed design achieves significant throughput gains as well as complexity and cost reductions, thus leading to a promising new paradigm for mmWave MIMO communications.


title: The Mutational Landscape of Lethal Castrate Resistant Prostate Cancer

text: Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC). However, less is known about the role of mutations. Here we sequenced the exomes of 50 lethal, heavily pre-treated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment-naive, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPCs (2.00 per megabase) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1 that define a subtype of ETS gene family fusion-negative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in approximately one-third of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Furthermore, we identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with the AR, which is required for AR-mediated signalling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signalling and increases tumour growth. Proteins that physically interact with the AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX (also known as KDM6A) and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signalling deregulated in prostate cancer, and prioritize candidates for future study.


title: Circulating mutant DNA to assess tumor dynamics

text: The measurement of circulating nucleic acids has transformed the management of chronic viral infections such as HIV. The development of analogous markers for individuals with cancer could similarly enhance the management of their disease. DNA containing somatic mutations is highly tumor specific and thus, in theory, can provide optimum markers. However, the number of circulating mutant gene fragments is small compared to the number of normal circulating DNA fragments, making it difficult to detect and quantify them with the sensitivity required for meaningful clinical use. In this study, we applied a highly sensitive approach to quantify circulating tumor DNA (ctDNA) in 162 plasma samples from 18 subjects undergoing multimodality therapy for colorectal cancer. We found that ctDNA measurements could be used to reliably monitor tumor dynamics in subjects with cancer who were undergoing surgery or chemotherapy. We suggest that this personalized genetic approach could be generally applied to individuals with other types of cancer (pages 914–915).


title: ETS gene fusions in prostate cancer: from discovery to daily clinical practice.

text:
CONTEXT
In 2005, fusions between the androgen-regulated transmembrane protease serine 2 gene, TMPRSS2, and E twenty-six (ETS) transcription factors were discovered in prostate cancer.

OBJECTIVE
To review advances in our understanding of ETS gene fusions, focusing on challenges affecting translation to clinical application.

EVIDENCE ACQUISITION
The PubMed database was searched for reports on ETS fusions in prostate cancer.

EVIDENCE SYNTHESIS
Since the discovery of ETS fusions, novel 5' and 3' fusion partners and multiple splice isoforms have been reported. The most common fusion, TMPRSS2:ERG, is present in approximately 50% of prostate-specific antigen (PSA)-screened localized prostate cancers and in 15-35% of population-based cohorts. ETS fusions can be detected noninvasively in the urine of men with prostate cancer, with a specificity rate in PSA-screened cohorts of >90%. Reports from untreated population-based cohorts suggest an association between ETS fusions and cancer-specific death and metastatic spread. In retrospective prostatectomy cohorts, conflicting results have been published regarding associations between ETS fusions and cancer aggressiveness. In addition to serving as a potential biomarker, tissue and functional studies suggest a specific role for ETS fusions in the transition to carcinoma. Finally, recent results suggest that the 5' and 3' ends of ETS fusions as well as downstream targets may be targeted therapeutically.

CONCLUSIONS
Recent studies suggest that the first clinical applications of ETS fusions are likely to be in noninvasive detection of prostate cancer and in aiding with difficult diagnostic cases. Additional studies are needed to clarify the association between gene fusions and cancer aggressiveness, particularly those studies that take into account the multifocal and heterogeneous nature of localized prostate cancer. Multiple promising strategies have been identified to potentially target ETS fusions. Together, these results suggest that ETS fusions will affect multiple aspects of prostate cancer diagnosis and management.


title: Advanced sequencing technologies: methods and goals

text: Nearly three decades have passed since the invention of electrophoretic methods for DNA sequencing. The exponential growth in the cost-effectiveness of sequencing has been driven by automation and by numerous creative refinements of Sanger sequencing, rather than through the invention of entirely new methods. Various novel sequencing technologies are being developed, each aspiring to reduce costs to the point at which the genomes of individual humans could be sequenced as part of routine health care. Here, we review these technologies, and discuss the potential impact of such a 'personal genome project' on both the research community and on society.


title: Word sense disambiguation as a traveling salesman problem

text: Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.


title: An improved k-medoids clustering algorithm

text: In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree. Eventually, we can get k clusters from the root of the CF-Tree. This algorithm improves obviously the drawbacks of the k-medoids algorithm, such as the time complexity, scalability on large dataset, and can't find the clusters of sizes different very much and the convex shapes. Experiments show that this algorithm enhances the quality and scalability of clustering.


title: A Prospective, Open-Label Study of Hyaluronic Acid-Based Filler With Lidocaine (VYC-15L) Treatment for the Correction of Infraorbital Skin Depressions.

text:
BACKGROUND
Infraorbital skin depressions are one of the most troublesome facial areas for aesthetically aware patients.

OBJECTIVE
Evaluate effectiveness and safety of Juvéderm Volbella with Lidocaine (VYC-15L; Allergan plc, Dublin, Ireland) for correction of bilateral infraorbital depressions.

METHODS
In this 12-month, prospective, uncontrolled, open-label study, subjects aged ≥18 years with infraorbital depressions rated ≥1 on the Allergan Infra-oRbital Scale (AIRS) received injections of VYC-15L with optional touch-up treatment on Day 14. The primary efficacy measure was ≥1 AIRS grade improvement from baseline at month 1.

RESULTS
Of 80 subjects initially treated with VYC-15L, 75 (94%) completed the study. All injections were intentionally deep, most using multiple microbolus technique. At 1 month, 99.3% of eyes achieved ≥1 AIRS grade improvement. The responder rate (subjects with ≥1 AIRS grade improvement in both eyes) was 99% at month 1, 92% at month 6, and 54% at month 12. Most injection site reactions (e.g., bruising, redness, irregularities/bumps) were mild and resolved by day 14. Late-onset mild to moderate edema was observed in 11% of eyes at month 6% and 4% of eyes at month 12.

CONCLUSION
VYC-15L is effective and safe for the treatment of infraorbital depressions, with effectiveness lasting up to 12 months.


title: Spreadsheet Validation and Analysis through Content Visualization

text: Visualizing spreadsheet content provides analytic insight and visual validation of large amounts of spreadsheet data. Oculus Excel Visualizer is a point and click data visualization experiment which directly visualizes Excel data and re-users the layout and formatting already present in the spreadsheet.


title: A power-efficient 4-2 Adder Compressor topology

text: The addition is the most used arithmetic operation in Digital Signal Processing (DSP) algorithms, such as filters, transforms and predictions. These algorithms are increasingly present in audio and video processing of battery-powered mobile devices having, therefore, energy constraints. In the context of addition operation, the efficient 4-2 adder compressor is capable to performs four additions simultaneously. This higher order of parallelism reduces the critical path and the internal glitching, thus reducing mainly the dynamic power dissipation. This work proposes two CMOS+ gate-based topologies to further reduce the power, area and delay of the 4-2 adder compressor. The proposed CMOS+ 4-2 adder compressor circuits topologies were implemented with Cadence Virtuoso tool at 45 nm technology and simulated in both electric and layout levels. The results show that a proper choice of gates in 4-2 adder compressor realization can reduce the power, delay and area about 22.41%, 32.45% and 7.4%, respectively, when compared with the literature.


title: IoTScanner: Detecting and Classifying Privacy Threats in IoT Neighborhoods

text: In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments. In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we attempt to classify actively streaming IP cameras from other non-camera devices using simple heuristics. We show that our classification approach achieves a high accuracy in an IoT setting consisting of a large number of IoT devices. While related work usually focuses on detecting either the infrastructure, or eavesdropping on traffic from a specific node, we focus on providing a general overview of operations in all observed networks. We do not assume prior knowledge of used SSIDs, preshared passwords, or similar.


title: A new intra-disk redundancy scheme for high-reliability RAID storage systems in the presence of unrecoverable errors

text: Today's data storage systems are increasingly adopting low-cost disk drives that have higher capacity but lower reliability, leading to more frequent rebuilds and to a higher risk of unrecoverable media errors. We propose an efficient intradisk redundancy scheme to enhance the reliability of RAID systems. This scheme introduces an additional level of redundancy inside each disk, on top of the RAID redundancy across multiple disks. The RAID parity provides protection against disk failures, whereas the proposed scheme aims to protect against media-related unrecoverable errors. In particular, we consider an intradisk redundancy architecture that is based on an interleaved parity-check coding scheme, which incurs only negligible I/O performance degradation. A comparison between this coding scheme and schemes based on traditional Reed--Solomon codes and single-parity-check codes is conducted by analytical means. A new model is developed to capture the effect of correlated unrecoverable sector errors. The probability of an unrecoverable failure associated with these schemes is derived for the new correlated model, as well as for the simpler independent error model. We also derive closed-form expressions for the mean time to data loss of RAID-5 and RAID-6 systems in the presence of unrecoverable errors and disk failures. We then combine these results to characterize the reliability of RAID systems that incorporate the intradisk redundancy scheme. Our results show that in the practical case of correlated errors, the interleaved parity-check scheme provides the same reliability as the optimum, albeit more complex, Reed--Solomon coding scheme. Finally, the I/O and throughput performances are evaluated by means of analysis and event-driven simulation.


title: Understanding and improving recurrent networks for human activity recognition by continuous attention

text: Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean Fl score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.


title: WASSA-2017 Shared Task on Emotion Intensity

text: We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.


title: The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

text: A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster’s members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multipleperspective method increases the interpretability and accountability of both ACA and DCA networks.


title: Global consequences of land use.

text: Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.


title: Plasmacytoid dendritic cells sense self-DNA coupled with antimicrobial peptide

text: Plasmacytoid dendritic cells (pDCs) sense viral and microbial DNA through endosomal Toll-like receptors to produce type 1 interferons. pDCs do not normally respond to self-DNA, but this restriction seems to break down in human autoimmune disease by an as yet poorly understood mechanism. Here we identify the antimicrobial peptide LL37 (also known as CAMP) as the key factor that mediates pDC activation in psoriasis, a common autoimmune disease of the skin. LL37 converts inert self-DNA into a potent trigger of interferon production by binding the DNA to form aggregated and condensed structures that are delivered to and retained within early endocytic compartments in pDCs to trigger Toll-like receptor 9. Thus, our data uncover a fundamental role of an endogenous antimicrobial peptide in breaking innate tolerance to self-DNA and suggest that this pathway may drive autoimmunity in psoriasis.


title: Urban Computing: Concepts, Methodologies, and Applications

text: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.


title: RaceTrack: efficient detection of data race conditions via adaptive tracking

text: Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has been observed. RaceTrack uses a novel hybrid detection algorithm and employs an adaptive approach that automatically directs more effort to areas that are more suspicious, thus providing more accurate warnings for much less over-head. A post-processing step correlates warnings and ranks code segments based on how strongly they are implicated in potential data races. We implemented RaceTrack inside the virtual machine of Microsoft's Common Language Runtime (product version v1.1.4322) and monitored several major, real-world applications directly out-of-the-box,without any modification. Adaptive tracking resulted in a slowdown ratio of about 3x on memory-intensive programs and typically much less than 2x on other programs,and a memory ratio of typically less than 1.2x. Several serious data race bugs were revealed, some previously unknown.


title: ESTIMATION METHODS FOR STOCHASTIC VOLATILITY MODELS : A SURVEY

text: Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been intro duced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.


title: Generative Attention Model with Adversarial Self-learning for Visual Question Answering

text: Visual question answering (VQA) is arguably one of the most challenging multimodal understanding problems as it requires reasoning and deep understanding of the image, the question, and their semantic relationship. Existing VQA methods heavily rely on attention mechanisms to semantically relate the question words with the image contents for answering the related questions. However, most of the attention models are simplified as a linear transformation, over the multimodal representation, which we argue is insufficient for capturing the complex nature of the multimodal data. In this paper we propose a novel generative attention model obtained by adversarial self-learning. The proposed adversarial attention produces more diverse visual attention maps and it is able to generalize the attention better to new questions. The experiments show the proposed adversarial attention leads to a state-of-the-art VQA model on the two VQA benchmark datasets, VQA v1.0 and v2.0.


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

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


title: Automated detection of smuggled high-risk security threats using Deep Learning

text: The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called “small metallic threats” (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).


title: Trends in Automotive Communication Systems

text: The use of networks for communications between the electronic control units (ECU) of a vehicle in production cars dates from the beginning of the 1990s. The specific requirements of the different car domains have led to the development of a large number of automotive networks such as Local Interconnect Network, J1850, CAN, TTP/C, FlexRay, media-oriented system transport, IDB1394, etc. This paper first introduces the context of in-vehicle embedded systems and, in particular, the requirements imposed on the communication systems. Then, a comprehensive review of the most widely used automotive networks, as well as the emerging ones, is given. Next, the current efforts of the automotive industry on middleware technologies, which may be of great help in mastering the heterogeneity, are reviewed. Finally, we highlight future trends in the development of automotive communication systems.


title: Bank Customer Credit Scoring by Using Fuzzy Expert System

text: Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.


title: Semantics matters : cognitively plausible delineation of city centres from point of interest data

text: We sketch a workflow for cognitively plausible recognition of vague geographical concepts, such as a city centre. Our approach imitates a pedestrian strolling through the streets, and comparing his/her internal cognitive model of a city centre with the stimulus from the external world to decide whether he/she is in the city centre or outside. The cognitive model of a British city centre is elicited through an online questionnaire survey and used to delineate referents of city centre from point of interest data. We first compute a measure of ‘city centre-ness’ at each location within a city, and then merge the area of high city centre-ness to a contiguous region. The process is illustrated on the example of the City of Bristol, and the computed city centre area for Bristol is evaluated by comparison to reference areas derived from alternative sources. The evaluation suggests that our approach performs well and produces a representation of a city centre that is near to people’s conceptualisation. The benefits of our work are better (and user-driven) descriptions of complex geographical concepts. We see such models as a prerequisite for generalisation over large changes in detail, and for very specific purposes.


title: Label Information Guided Graph Construction for Semi-Supervised Learning

text: In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.


title: Flexible sensor for Mckibben pneumatic actuator

text: A flexible electro-conductive rubber sensor for measuring the length of a McKibben pneumatic actuator has been developed. Mckibben actuators are flexible, lightweight, and widely used to actuate power assisting devices for which compliance is required for safety. The actuator's length needs to be measured to control the devices accurately. However, the flexibility and lightweight properties might be lost if rigid sensors such as linear potentionmeters or encoders are directly attached to the actuators. To keep the desirable properties of McKibben actuators, compact and flexible sensors are necessary. In this paper, a flexible sensor using electro-conductive flexible rubber is proposed to measure the length of McKibben actuators. Using this sensor, a higher accuracy can be obtained by measuring the circumference displacement than directly measuring the axial displacement. The estimation accuracy is evaluated and the usefulness of the proposed method is verified by adopting to a multiple-link robot arm driven by the McKibben actuators.


title: Automated Intelligent Pilots for Combat Flight Simulation

text: TacAir-Soar is an intelligent, rule-based system that generates believable “human-like” behavior for large scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the United States military flies in fixed-wing aircraft. It accomplishes this by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currently deployed at the Oceana Naval Air Station WISSARD Lab and the Air Force Research Laboratory in Mesa, AZ. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TacAir-Soar flew all U.S. fixed-wing aircraft.


title: THE INFORMATION CAPACITY OF THE HUMAN MOTOR SYSTEM IN CONTROLLING THE AMPLITUDE OF MOVEMENT

text: Information theory has recently been employed to specify more precisely than has hitherto been possible man's capacity in certain sensory, perceptual, and perceptual-motor functions (5, 10, 13, 15, 17, 18). The experiments reported in the present paper extend the theory to the human motor system. The applicability of only the basic concepts, amount of information, noise, channel capacity, and rate of information transmission, will be examined at this time. General familiarity with these concepts as formulated by recent writers (4,11, 20, 22) is assumed. Strictly speaking, we cannot study man's motor system at the behavioral level in isolation from its associated sensory mechanisms. We can only analyze the behavior of the entire receptor-neural-effector system. How-


title: The mathematical theory of communication

text: T HE recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A basis for such a theory is contained in the important papers of Nyquist 1 and Hartley2 on this subject. In the present paper we will extend the theory to include a number of new factors, in particular the effect of noise in the channel, and the savings possible due to the statistical structure of the original message and due to the nature of the final destination of the information. The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages. The system must be designed to operate for each possible selection, not just the one which will actually be chosen since this is unknown at the time of design. If the number of messages in the set is finite then this number or any monotonic function of this number can be regarded as a measure of the information produced when one message is chosen from the set, all choices being equally likely. As was pointed out by Hartley the most natural choice is the logarithmic function. Although this definition must be generalized considerably when we consider the influence of the statistics of the message and when we have a continuous range of messages, we will in all cases use an essentially logarithmic measure. The logarithmic measure is more convenient for various reasons:


title: RoboCup: The Robot World Cup Initiative

text: The Robot World Cup Initiative (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. The rst RoboCup competition will be held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor-fusion. Unlike AAAI robot competition, which is tuned for a single heavyduty slow-moving robot, RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's nal target is a world cup with real robots, RoboCup o ers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment.


title: Controlling Cooperative Problem Solving in Industrial Multi-Agent Systems Using Joint Intentions

text: One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparative experiments in the real-world domain of electricity transportation management. Finally, the success of the approach of building a system with an explicit and grounded representation of cooperative problem solving is used to outline a proposal for the next generation of multi-agent systems.


title: Laboratory Experiments for Tsunamis Generated by Underwater Landslides: Comparison with Numerical Modeling

text: Three-dimensional experiments and fully nonlinear computations are performed at the University of Rhode Island, to investigate tsunami generation by underwater landslides. Each experiment consists of a solid landslide of idealized smooth shape sliding over a plane slope. Surface elevations are measured using very accurate gages placed at strategic locations. Gage calibration is performed using a newly developed automated system. Landslide acceleration is measured with a micro-accelerometer. The repeatability of experiments is first investigated, and then by varying the initial depth of the landslide, different conditions of wave non-linearity and dispersion are generated and compared. The principle of numerical modeling, using an earlier developed model, is briefly explained. One application is presented and results compared to experiments. The agreement of computations with the latter is quite good. In the model, horizontal velocities are found quite non-uniform over depth above the moving landslide. This would preclude using a long wave model for such landslide tsunami wave generation.


title: CopyCatch: stopping group attacks by spotting lockstep behavior in social networks

text: How can web services that depend on user generated content discern fraudulent input by spammers from legitimate input? In this paper we focus on the social network Facebook and the problem of discerning ill-gotten Page Likes, made by spammers hoping to turn a profit, from legitimate Page Likes. Our method, which we refer to as CopyCatch, detects lockstep Page Like patterns on Facebook by analyzing only the social graph between users and Pages and the times at which the edges in the graph (the Likes) were created. We offer the following contributions: (1) We give a novel problem formulation, with a simple concrete definition of suspicious behavior in terms of graph structure and edge constraints. (2) We offer two algorithms to find such suspicious lockstep behavior - one provably-convergent iterative algorithm and one approximate, scalable MapReduce implementation. (3) We show that our method severely limits "greedy attacks" and analyze the bounds from the application of the Zarankiewicz problem to our setting. Finally, we demonstrate and discuss the effectiveness of CopyCatch at Facebook and on synthetic data, as well as potential extensions to anomaly detection problems in other domains. CopyCatch is actively in use at Facebook, searching for attacks on Facebook's social graph of over a billion users, many millions of Pages, and billions of Page Likes.


title: Automated concolic testing of smartphone apps

text: We present an algorithm and a system for generating input events to exercise smartphone apps. Our approach is based on concolic testing and generates sequences of events automatically and systematically. It alleviates the path-explosion problem by checking a condition on program executions that identifies subsumption between different event sequences. We also describe our implementation of the approach for Android, the most popular smartphone app platform, and the results of an evaluation that demonstrates its effectiveness on five Android apps.


title: SHEEP, GOATS, LAMBS and WOLVES: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation

text: Performance variabilit y in speech and speaker recognition systems can be attributed to many factors. One major factor, which is often acknowledged but seldom analyzed, is inherent differences in the recognizabilit y of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers, including sheep, goats, lambs and wolves, depending on their behavior with respect to automatic recognition systems. In this paper we propose statistical tests for the existence of these animals and apply these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation.


title: Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

text: Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.


title: TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

text: Current state-of-the-art methods solve spatio-temporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called action micro-tubes. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera do. Assuming we evaluate n 2D anchors in each frame, then the number of possible transitions from each 2D anchor to he next, for a sequence of f consecutive frames, is in the order of O(n ), expensive even for small values of f . To avoid this problem we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from O(n ) to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translationinvariant. Finally, we show that our network is able to handle sparse annotations such as those available in the DALY dataset, while allowing for both dense (accurate) or sparse (efficient) evaluation within a single model. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.


title: Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving

text: Scale drift is a crucial challenge for monocular autonomous driving to emulate the performance of stereo. This paper presents a real-time monocular SFM system that corrects for scale drift using a novel cue combination framework for ground plane estimation, yielding accuracy comparable to stereo over long driving sequences. Our ground plane estimation uses multiple cues like sparse features, dense inter-frame stereo and (when applicable) object detection. A data-driven mechanism is proposed to learn models from training data that relate observation covariances for each cue to error behavior of its underlying variables. During testing, this allows per-frame adaptation of observation covariances based on relative confidences inferred from visual data. Our framework significantly boosts not only the accuracy of monocular self-localization, but also that of applications like object localization that rely on the ground plane. Experiments on the KITTI dataset demonstrate the accuracy of our ground plane estimation, monocular SFM and object localization relative to ground truth, with detailed comparisons to prior art.


title: The B2C group-buying model on the Internet

text: Internet-based group-buying has become a new growth point for China's e-commerce. By studying B2C group-buying model, this paper has collected data of 20 major Chinese group-buying websites, studied factors that might influence Internet users' group-buying behavior, proposed a new model for evaluating the value of Internet enterprises, and provided a feasible reference model for the evaluation and performance of Internet enterprises.


title: Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

text: Unsupervised anomaly detection on multior high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score.


title: Introducing qualitative research in psychology Adventures in theory and method

text: All rights reserved. Except for the quotation of short passages for the purpose of criticism and review, no part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the publisher or a licence from the Copyright Licensing Agency Limited. Details of such licences (for reprographic reproduction) may be obtained from the Copyright Introducing qualitative research in psychology: adventures in theory and method / Carla Willig. p. cm. Includes bibliographical references and index.


title: Visual reaction time and high-speed ball games.

text: Laboratory measures of visual reaction time suggest that some aspects of high-speed ball games such as cricket are 'impossible' because there is insufficient time for the player to respond to unpredictable movements of the ball. Given the success with which some people perform these supposedly impossible acts, it has been assumed by some commentators that laboratory measures of reaction time are not applicable to skilled performers. An analysis of high-speed film of international cricketers batting on a specially prepared pitch which produced unpredictable movement of the ball is reported, and it is shown that, when batting, highly skilled professional cricketers show reaction times of around 200 ms, times similar to those found in traditional laboratory studies. Furthermore, professional cricketers take roughly as long as casual players to pick up ball flight information from film of bowlers. These two sets of results suggest that the dramatic contrast between the ability of skilled and unskilled sportsmen to act on the basis of visual information does not lie in differences in the speed of operation of the perceptual system. It lies in the organisation of the motor system that uses the output of the perceptual system.


title: Combining Confocal Imaging and Descattering

text: In translucent objects, light paths are affected by multiple scattering, which is polluting any observation. Confocal imaging reduces the influence of such global illumination effects by carefully focusing illumination and viewing rays from a large aperture to a specific location within the object volume. The selected light paths still contain some global scattering contributions, though. Descattering based on high frequency illumination serves the same purpose. It removes the global component from observed light paths. We demonstrate that confocal imaging and descattering are orthogonal and propose a novel descattering protocol that analyzes the light transport in a neighborhood of light transport paths. In combination with confocal imaging, our descattering method achieves optical sectioning in translucent media with higher contrast and better resolution.


title: A Survey on Survey of Migration of Legacy Systems

text: Legacy systems are mission critical complex systems which are hard to maintain owing to shortage of skill sets and monolithic code architecture with tightly coupled tiers, all of which are indication of obsoleteness of the system technology. Thus, they have to be migrated to the latest technology(ies). Migration is an offspring research in Software Engineering which is almost three decades old research and numerous publications have emerged in many topics in the migration domain with focus areas of code migration, architecture migration, case study on migration and effort estimation on migration. In addition, various survey works surveying different aspects of migration have also emerged. This paper provides a survey of these survey works in order to provide a consolidation of these survey works. As an outcome of the survey on survey of migration, a road map of migration evolution from the early stages to the recent works on migration comprising of significant milestones in migration research has been presented in this work.


title: Distance Preserving Dimension Reduction for Manifold Learning

text: Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data analysis and bioinformatics. The focus of this paper is on developing algorithms for reducing the computational complexity of manifold learning algorithms, in particular, we consider the case when the number of features is much larger than the number of data points. To handle the large number of features, we propose a preprocessing method, distance preserving dimension reduction (DPDR). It produces t-dimensional representations of the high-dimensional data, wheret is the rank of the original dataset. It exactly preserves the Euclidean L2-norm distances as well as cosine similarity measures between data points in the original space. With the original data projected to the t-dimensional space, manifold learning algorithms can be executed to obtain lower dimensional parameterizations with substantial reduction in computational cost. Our experimental results illustrate that DPDR significantly reduces computing time of manifold learning algorithms and produces low-dimensional parameterizations as accurate as those obtained from the original datasets.


title: Quantum Field Theory in Curved Spacetime

text: We review the mathematically rigorous formulation of the quantum theory of a linear field propagating in a globally hyperbolic spacetime. This formulation is accomplished via the algebraic approach, which, in essence, simultaneously admits all states in all possible (unitarily inequivalent) Hilbert space constructions. The physically nonsingular states are restricted by the requirement that their two-point function satisfy the Hadamard condition, which insures that the ultra-violet behavior of the state be similar to that of the vacuum state in Minkowski spacetime, and that the expected stress-energy tensor in the state be finite. We briefly review the Unruh and Hawking effects from the perspective of the theoretical framework adopted here. A brief discussion also is given of several open issues and questions in quantum field theory in curved spacetime regarding the treatment of “backreaction”, the validity of some version of the “averaged null energy condition”, and the formulation and properties of quantum field theory in causality violating


title: Tensor2Tensor for Neural Machine Translation

text: Tensor2Tensor is a library for deep learning models that is very well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model. 1 Neural Machine Translation Background Machine translation using deep neural networks achieved great success with sequence-tosequence models Sutskever et al. (2014); Bahdanau et al. (2014); Cho et al. (2014) that used recurrent neural networks (RNNs) with LSTM cells Hochreiter and Schmidhuber (1997). The basic sequence-to-sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed-sized state vector. This is followed by an RNN decoder, which generates the target sentence, one token at a time, from the state vector. While a pure sequence-to-sequence recurrent neural network can already obtain good translation results Sutskever et al. (2014); Cho et al. (2014), it suffers from the fact that the whole input sentence needs to be encoded into a single fixed-size vector. This clearly manifests itself in the degradation of translation quality on longer sentences and was partially overcome in Bahdanau et al. (2014) by using a neural model of attention. Convolutional architectures have been used to obtain good results in word-level neural machine translation starting from Kalchbrenner and Blunsom (2013) and later in Meng et al. (2015). These early models used a standard RNN on top of the convolution to generate the output, which creates a bottleneck and hurts performance. Fully convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et al. (2016). The model in Kaiser and Bengio (2016) (Extended Neural GPU) used a recurrent stack of gated convolutional layers, while the model in Kalchbrenner et al. (2016) (ByteNet) did away with recursion and used left-padded convolutions in the decoder. This idea, introduced in WaveNet van den Oord et al. (2016), significantly improves efficiency of the model. The same technique was improved in a number of neural translation models recently, including Gehring et al. (2017) and Kaiser et al. (2017).


title: Semantics in Visual Information Retrieval

text: V isual information retrieval systems have entered a new era. First-generation systems allowed access to images and videos through textual data. 1,2 Typical searches for these systems include, for example, " all images of paintings of the Florentine school of the 15th century " or " all images by Cezanne with landscapes. " Such systems expressed information through alphanumeric keywords or scripts. They employed representation schemes like relational models, frame models, and object-oriented models. On the other hand, current-generation retrieval systems support full retrieval by visual content. 3,4 Access to visual information is not only performed at a conceptual level, using keywords as in the textu-al domain, but also at a perceptual level, using objective measurements of visual content. In these systems, image processing, pattern recognition , and computer vision constitute an integral part of the system's architecture and operation. They objectively analyze pixel distribution and extract the content descriptors automatically from raw sensory data. Image content descriptors are commonly represented as feature vectors, whose elements correspond to significant parameters that model image attributes. Therefore, visual attributes are regarded as points in a multidimen-sional feature space, where point closeness reflects feature similarity. These advances (for comprehensive reviews of the field, see the " Further Reading " sidebar) have paved the way for third-generation systems, featuring full multimedia data management and networking support. Forthcoming standards such as MPEG-4 and MPEG-7 (see the Nack and Lindsay article in this issue) provide the framework for efficient representation, processing, and retrieval of visual information. Yet many problems must still be addressed and solved before these technologies can emerge. An important issue is the design of indexing structures for efficient retrieval from large, possibly distributed , multimedia data repositories. To achieve this goal, image and video content descriptors can be internally organized and accessed through multidimensional index structures. 5 A second key problem is to bridge the semantic gap between the system and users. That is, devise representations capturing visual content at high semantic levels especially relevant for retrieval tasks. Specifically, automatically obtaining a representation of high-level visual content remains an open issue. Virtually all the systems based on automatic storage and retrieval of visual information proposed so far use low-level perceptual representations of pictorial data, which have limited semantics. Building up a representation proves tantamount to defining a model of the world, possibly through a formal description language, whose semantics capture only a few …


title: A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation

text: The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.


title: A survey of RDF data management systems

text: RDF is increasingly being used to encode data for the semantic web and data exchange. There have been a large number of works that address RDF data management following different approaches. In this paper we provide an overview of these works. This review considers centralized solutions (what are referred to as warehousing approaches), distributed solutions, and the techniques that have been developed for querying linked data. In each category, further classifications are provided that would assist readers in understanding the identifying characteristics of different approaches.


title: Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

text: Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental property of randomly shaped real-world graph instances. By utilizing memory bandwidth more efficiently, our method shows improved performance over the current state-of-the-art implementation and increases its advantage as the size of the graph increases. We then propose a hybrid method which, for each level of the BFS algorithm, dynamically chooses the best implementation from: a sequential execution, two different methods of multicore execution, and a GPU execution. Such a hybrid approach provides the best performance for each graph size while avoiding poor worst-case performance on high-diameter graphs. Finally, we study the effects of the underlying architecture on BFS performance by comparing multiple CPU and GPU systems, a high-end GPU system performed as well as a quad-socket high-end CPU system.


title: Optimising Results from Minimal Access Cranial Suspension Lifting (MACS-Lift)

text: Between November 1999 and February 2005, 450 minimal access cranial suspension (MACS) lifts were performed. Starting with the idea of suspension for sagging soft tissues using permanent purse-string sutures, a new comprehensive approach to facial rejuvenation was developed in which the vertical vector appeared to be essential. The neck is corrected by extended submental liposuction and strong vertical traction on the lateral part of the platysma by means of a first vertical purse-string suture. The volume of the jowls and the cheeks is repositioned in a cranial direction with a second, slightly oblique purse-string suture. The descent of the midface is corrected by suspending the malar fat pad in a nearly vertical direction. In 23 cases (5.1%), the result in the neck was unsatisfactory, and additional work had to be done secondarily, or in later cases, primarily. The problem that appeared was unsatisfactory correction of platysmal bands (resolved with an additional anterior cervicoplasty) or vertical skin folds that appeared in the infralobular region (corrected with an additional posterior cervicoplasty). This article describes two ancillary procedures that, although not frequently necessary, can optimise the result of MACS lifting.


title: A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

text: As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.


title: Human activity recognition using inertial sensors with invariance to sensor orientation

text: This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.


title: A simple circularly polarized loop tag antenna for increased reading distance

text: A simple circularly polarized (CP) loop tag antenna is proposed to overcome the polarization mismatch problem between reader antenna and tag antenna. Good CP radiation can be achieved by introducing an open gap onto the square loop. Also, the technique of loading a matching stub across the RFID chip is applied to the proposed loop tag antenna, and desirable impedance matching between the antenna and RFID chip can be realized by tuning the matching stub. The proposed loop tag antenna shows good reading distance performance, and its measured reading distance reaches to 17.6 m.


title: Business artifacts: An approach to operational specification

text: Any business, no matter what physical goods or services it produces, relies on business records. It needs to record details of what it produces in terms of concrete information. Business artifacts are a mechanism to record this information in units that are concrete, identifiable, self-describing, and indivisible. We developed the concept of artifacts, or semantic objects, in the context of a technique for constructing formal yet intuitive operational descriptions of a business. This technique, called OpS (Operational Specification), was developed over the course of many business-transformation and business-process-integration engagements for use in IBM’s internal processes as well as for use with customers. Business artifacts (or business records) are the basis for the factorization of knowledge that enables the OpS technique. In this paper we present a comprehensive discussion of business artifacts—what they are, how they are represented, and the role they play in operational business modeling. Unlike the more familiar and popular concept of business objects, business artifacts are pure instances rather than instances of a taxonomy of types. Consequently, the key operation on business artifacts is recognition rather than classification.


title: Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies

text: Besides attracting a billion dollar economy, Bitcoin revolutionized the field of digital currencies and influenced many adjacent areas. This also induced significant scientific interest. In this survey, we unroll and structure the manyfold results and research directions. We start by introducing the Bitcoin protocol and its building blocks. From there we continue to explore the design space by discussing existing contributions and results. In the process, we deduce the fundamental structures and insights at the core of the Bitcoin protocol and its applications. As we show and discuss, many key ideas are likewise applicable in various other fields, so that their impact reaches far beyond Bitcoin itself.


title: QoS-aware middleware for Web services composition

text: The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.


title: Cryptocurrencies, smart contracts, and artificial intelligence

text: Recent developments in "cryptocurrencies" and "smart contracts" are creating new opportunities for applying AI techniques. These economic technologies would benefit from greater real world knowledge and reasoning as they become integrated with everyday commerce. Cryptocurrencies and smart contracts may also provide an infrastructure for ensuring that AI systems follow specified legal and safety regulations as they become more integrated into human society.


title: To See or not to See – Innovative Display Technologies as Enablers for Ergonomic Cockpit Concepts Ergonomic requirements future mobility future functionality

text: Introduction Given the fact that the driving task will remarkably change by the increase of assistance and information systems in the future innovative display technologies play a central role for the ergonomic realization of the driver vehicle interaction. Increasing assistance and even automation of the driving task do not lead to a decrease or disappearance of visual information but instead request new and in some cases revolutionary concepts to close the loop between driver, vehicle and traffic environment. Augmenting information in contact analog head-up-displays for navigation and driver assistance is a very promising approach. Replacement of mirrors via camera monitor systems is a further example. Free programmable cluster instruments in combination with HUD promise to resolve the problem of information density produced by an increasing amount of ADAS and IVIS functionality.


title: An electrostatic gripper for flexible objects

text: We demonstrate a flexible, electrostatic adhesive gripper designed to controllably grasp and manipulate soft goods in space. The 8-fingered gripper has 50 cm2 of active electrodes operating at 3 kV. It generates electrostatic adhesion forces up to 3.5 N (0.70 kPa) on Ge-coated polyimide film and 1.2 N on MLI blanket, a film composite used for satellite thermal insulation. Extremely low-force gripper engagement (0.08 N) and release (0.04 N) of films is ideal for micro-gravity. Individual fingers generate shear adhesion forces up to 4.76 N (5.04 kPa) using electrostatic adhesive and 45.0 N (47.6 kPa) with a hybrid electrostatic / gecko adhesive. To simulate a satellite servicing task, the gripper was mounted on a 7-DoF robot arm and performed a supervised grasp, manipulate, and release sequence on a hanging, Al-coated PET film.


title: The optimum received power levels of uplink non-orthogonal multiple access (NOMA) signals

text: Non-orthogonal multiple access (NOMA) has been recently considered as a promising multiple access technique for fifth generation (5G) mobile networks as an enabling technology to meet the demands of low latency, high reliability, massive connectivity, and high throughput. The two dominants types of NOMA are: power-domain and code-domain. The key feature of power-domain NOMA is to allow different users to share the same time, frequency, and code, but with different power levels. In code-domain NOMA, different spread-spectrum codes are assigned to different users and are then multiplexed over the same time-frequency resources. This paper concentrates on power-domain NOMA. In power-domain NOMA, Successive Interference Cancellation (SIC) is employed at the receiver. In this paper, the optimum received uplink power levels using a SIC detector is determined analytically for any number of transmitters. The optimum uplink received power levels using the SIC decoder in NOMA strongly resembles the μ-law encoding used in pulse code modulation (PCM) speech companders.


title: Analysis, synthesis, and perception of voice quality variations among female and male talkers.

text: Voice quality variations include a set of voicing sound source modifications ranging from laryngealized to normal to breathy phonation. Analysis of reiterant imitations of two sentences by ten female and six male talkers has shown that the potential acoustic cues to this type of voice quality variation include: (1) increases to the relative amplitude of the fundamental frequency component as open quotient increases; (2) increases to the amount of aspiration noise that replaces higher frequency harmonics as the arytenoids become more separated; (3) increases to lower formant bandwidths; and (4) introduction of extra pole zeros in the vocal-tract transfer function associated with tracheal coupling. Perceptual validation of the relative importance of these cues for signaling a breathy voice quality has been accomplished using a new voicing source model for synthesis of more natural male and female voices. The new formant synthesizer, KLSYN88, is fully documented here. Results of the perception study indicate that, contrary to previous research which emphasizes the importance of increased amplitude of the fundamental component, aspiration noise is perceptually most important. Without its presence, increases to the fundamental component may induce the sensation of nasality in a high-pitched voice. Further results of the acoustic analysis include the observations that: (1) over the course of a sentence, the acoustic manifestations of breathiness vary considerably--tending to increase for unstressed syllables, in utterance-final syllables, and at the margins of voiceless consonants; (2) on average, females are more breathy than males, but there are very large differences between subjects within each gender; (3) many utterances appear to end in a "breathy-laryngealized" type of vibration; and (4) diplophonic irregularities in the timing of glottal periods occur frequently, especially at the end of an utterance. Diplophonia and other deviations from perfect periodicity may be important aspects of naturalness in synthesis.


title: FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning

text: Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate the metadata and data service and negatively impact other users. In this paper, we present FanStore, a transient runtime file system that optimizes DL I/O on existing hardware/software stacks. FanStore distributes datasets to the local storage of compute nodes, and maintains a global namespace. With the techniques of system call interception, distributed metadata management, and generic data compression, FanStore provides a POSIX-compliant interface with native hardware throughput in an efficient and scalable manner. Users do not have to make intrusive code changes to use FanStore and take advantage of the optimized I/O. Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90% scaling efficiency.


title: BLE-Based Accurate Indoor Location Tracking for Home and Office

text: Nowadays the use of smart mobile devices and the accompanying needs for emerging services relying on indoor location-based services (LBS) for mobile devices are rapidly increasing. For more accurate location tracking using Bluetooth Low Energy (BLE), this paper proposes a novel trilateration-based algorithm and presents experimental results that demonstrate its effectiveness.


title: Automated text summarisation and evidence-based medicine: A survey of two domains

text: The practice of evidence-based medicine (EBM) urges medical practitioners to utilise the latest research evidence when making clinical decisions. Because of the massive and growing volume of published research on various medical topics, practitioners often find themselves overloaded with information. As such, natural language processing research has recently commenced exploring techniques for performing medical domain-specific automated text summarisation (ATS) techniques-- targeted towards the task of condensing large medical texts. However, the development of effective summarisation techniques for this task requires cross-domain knowledge. We present a survey of EBM, the domain-specific needs for EBM, automated summarisation techniques, and how they have been applied hitherto. We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.


title: Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis

text: This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.


title: Hemi-cylinder unwrapping algorithm of fish-eye image based on equidistant projection model

text: This paper presents a novel fish-eye image unwrapping algorithm based on equidistant projection mode. We discuss a framework to estimate the image distortion center using vanishing points extraction. Then we propose a fish-eye image unwrapping algorithm using hemi-cylinder projection in 3D space. Experimental results show that our algorithm is efficient and effective. In particular, the hemi-cylinder unwrapping results do not reduce the horizontal field of view which is very useful for panoramic surveillance with applications in important sites safety compared with other fish-eye image correction methods.


title: III-nitride micro-emitter arrays : development and applications

text: III-nitride micro-emitter array technology was developed in the authors’ laboratory around 1999. Since its inception, much progress has been made by several groups and the technology has led to the invention of several novel devices. This paper provides an overview on recent progress in single-chip ac-micro-size light emitting diodes (μLEDs) that can be plugged directly into standard high ac voltage power outlets, self-emissive microdisplays and interconnected μLEDs for boosting light emitting diodes’s wall-plug efficiency, all of which were evolved from III-nitride micro-emitter array technology. Finally, potential applications of III-nitride visible micro-emitter arrays as a light source for DNA microarrays and future prospects of III-nitride deep ultraviolet micro-emitter arrays for label-free protein analysis in microarray format by taking advantage of the direct excitation of intrinsic protein fluorescence are discussed. (Some figures in this article are in colour only in the electronic version)


title: Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration

text: We present a novel CNN-based image editing method that allows the user to change the semantic information of an image over a user-specified region. Our method makes this possible by combining the idea of manifold projection with spatial conditional batch normalization (sCBN), a version of conditional batch normalization with userspecifiable spatial weight maps. With sCBN and manifold projection, our method lets the user perform (1) spatial class translation that changes the class of an object over an arbitrary region of user’s choice, and (2) semantic transplantation that transplants semantic information contained in an arbitrary region of the reference image to an arbitrary region in the target image. These two transformations can be used simultaneously, and can realize a complex composite image-editing task like “change the nose of a beagle to that of a bulldog, and open her mouth”. The user can also use our method with intuitive copy-paste-style manipulations. We demonstrate the power of our method on various images. Code will be available at https://github. com/pfnet-research/neural-collage.


title: A study of deep convolutional auto-encoders for anomaly detection in videos

text: The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. Convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since they learn features automatically during the training process. From the anomaly detection perspective, the Convolutional Autoencoder (CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE architecture, we also propose a method for aggregating high-level spatial and temporal features with the input frames and investigate how they affect the CAE performance. An easy-to-use measure of video spatial complexity was devised and correlated with the classification performance of the CAE. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area. © 2017 Published by Elsevier B.V.


title: Novelty Detection via Topic Modeling in Research Articles

text: In today’s world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to “Research Articles”. A method of identifying novelty at each sections of the article is highly required for determining the novel idea proposed in the research paper. Since research articles are semistructured, detecting novelty of information from them requires more accurate systems. Topic model provides a useful means to process them and provides a simple way to analyze them. This work compares the most predominantly used topic modelLatent Dirichlet Allocation with the hierarchical Pachinko Allocation Model. The results obtained are promising towards hierarchical Pachinko Allocation Model when used for document retrieval.


title: Making B+-Trees Cache Conscious in Main Memory

text:
Previous research has shown that cache behavior is important for main memory index structures. Cache conscious index structures such as Cache Sensitive Search Trees (CSS-Trees) perform lookups much faster than binary search and T-Trees. However, CSS-Trees are designed for decision support workloads with relatively static data. Although B+-Trees are more cache conscious than binary search and T-Trees, their utilization of a cache line is low since half of the space is used to store child pointers. Nevertheless, for applications that require incremental updates, traditional B+-Trees perform well.
Our goal is to make B+-Trees as cache conscious as CSS-Trees without increasing their update cost too much. We propose a new indexing technique called “Cache Sensitive B+-Trees” (CSB+-Trees). It is a variant of B+-Trees that stores all the child nodes of any given node contiguously, and keeps only the address of the first child in each node. The rest of the children can be found by adding an offset to that address. Since only one child pointer is stored explicitly, the utilization of a cache line is high. CSB+-Trees support incremental updates in a way similar to B+-Trees.
We also introduce two variants of CSB+-Trees. Segmented CSB+-Trees divide the child nodes into segments. Nodes within the same segment are stored contiguously and only pointers to the beginning of each segment are stored explicitly in each node. Segmented CSB+-Trees can reduce the copying cost when there is a split since only one segment needs to be moved. Full CSB+-Trees preallocate space for the full node group and thus reduce the split cost. Our performance studies show that CSB+-Trees are useful for a wide range of applications.


title: H-TCP : TCP for high-speed and long-distance networks

text: In this paper we present a congestion control protocol that is suitable for deployment in highspeed and long-distance networks. The new protocol, H-TCP, is shown to fair when deployed in homogeneous networks, to be friendly when competing with conventional TCP sources, to rapidly respond to bandwidth as it becomes available, and to utilise link bandwidth in an efficient manner. Further, when deployed in conventional networks, H-TCP behaves as a conventional TCP-variant.