abstract: A Structure-Mapping Model of Raven’s Progressive Matrices Andrew Lovett (andrew-lovett@northwestern.edu) Kenneth Forbus (forbus@northwestern.edu) Jeffrey Usher (usher@cs.northwestern.edu) Qualitative Reasoning Group, Northwestern University, 2133 Sheridan Road Evanston, IL 60208 USA Abstract We present a computational model for solving Raven’s Progressive Matrices. This model combines qualitative spatial representations with analogical comparison via structure- mapping. All representations are automatically computed by the model. We show that it achieves a level of performance on the Standard Progressive Matrices that is above that of most adults, and that the problems it fails on are also the hardest for people. Keywords: Analogy, Spatial Cognition, Problem-Solving Introduction There is increasing evidence that visual comparison may rely on the same structural alignment processes used to perform conceptual analogies (Markman & Gentner, 1996; Lovett et al., 2009a; Lovett et al., 2009b). An excellent task for exploring this is the Raven’s Progressive Matrices (RPM) (Raven, Raven, & Court, 2000) In RPM problems (Figure 1), a test-taker is presented with a matrix of images in which the bottom right image is missing, and asked to pick the answer that best completes the matrix. Though RPM is a visual task, performance on it correlates highly with other assessment tasks, many of them non-visual (e.g., Snow & Lohman, 1989; see Raven, Raven, & Court, 2000, for a review). Thus, RPM appears to tap into important, basic cognitive abilities beyond spatial reasoning, such as the ability to perform analogies. This paper presents a computational model that uses analogy to perform the RPM task, building on existing cognitive models of visual representation and analogical comparison. Our claims are: 1) Tasks such as RPM rely heavily on qualitative, structural representations of space (e.g., Biederman, 1987; Forbus, Nielsen, & Faltings, 1991). These representations describe relations between objects in a visual scene, such as their relative location. Importantly, these representations are hierarchical (Palmer, 1977); they can also describe larger-scale relations between groups of objects or smaller- scale relations between parts of an object. 2) Spatial representations are compared via structure- mapping (Gentner, 1983), a process of structural alignment first proposed to explain how people perform analogies. Structure-mapping is used here to compute the similarity of two images, to identify corresponding objects in the images, and to generate abstractions based on commonalities and differences. We previously (Lovett, Forbus, & Usher, 2007) described a model based on these principles that achieved human adult-level performance on two sections of the Standard Progressive Matrices test. That model was unable to handle the more difficult sections of the test because it only considered differences between pairs of images. This paper describes a more advanced model which performs at an above-average level on the hardest four sections of the test. It remains grounded in the same principles but is able to identify patterns of differences across rows of images. Like before, all inputs are automatically computed from vectorized input. We first discuss Carpenter, Just, and Shell’s (1991) computational model of the RPM. We then describe our model and its results on the Standard Progressive Matrices test. We end with conclusions and future work. Background The best-established model of Raven’s Progressive Matrices was developed by Carpenter, Just, and Shell (1991). It was based on both analysis of the test and psychological studies of human performance. The analysis led to the observation that all but two of the problems in the Advanced Progressive Matrices, the hardest version of the test, could be solved via the application of a set of six rules (see Figure 1 for examples). Each rule describes how a set of corresponding objects vary across the three images in a row. The simplest, Constant in a Row, says that the objects stay the same. Quantitative Pairwise Progression (Figure 1A) says that one of the object’s attributes or relations gradually changes. The other rules are more complex, requiring the individual to align objects with different shapes (Distribution of Three), or to find objects that only exist in two of the three images (Figure Addition or Subtraction, Distribution of Two). The psychological studies suggested that most people solved the problems by studying the top row, incrementally generating hypotheses about how the objects varied across that row, and then looking at the middle row to test those hypotheses. This process began by comparing consecutive pairs of images in a row. Armed with their observations, Carpenter et al. built two computational models to solve the Advanced Progressive Matrices: FAIRAVEN and BETTERAVEN. Both models used hand-coded input representations. They solved a problem by: 1) identifying which of the six rules applied to the first two rows, and 2) computing a mapping between those two rows and the bottom row to determine how to apply the same rules in that row.


title: Digital facial engraving

abstract: This contribution introduces the basic techniques for digital facial engraving, which imitates traditional copperplate engraving. Inspired by traditional techniques, we first establish a set of basic rules thanks to which separate engraving layers are built on the top of the original photo. Separate layers are merged according to simple merging rules and according to range shift/scale masks specially introduced for this purpose. We illustrate the introduced technique by a set of black/white and color engravings, showing different features such as engraving-specific image enhancements, mixing different regular engraving lines with mezzotint, irregular perturbations of engraving lines etc. We introduce the notion of engraving style which comprises a set of separate engraving layers together with a set of associated range shift/scale masks. The engraving style helps to port the look and feel of one engraving to another. Once different libraries of pre-defined mappable engraving styles and an appropriate user interface are added to the basic system, producing a decent gravure starting from a simple digital photo will be a matter of seconds. The engraving technique described in this contribution opens new perspectives for digital art, adding unprecedented power and precision to the engraver's work.


title: Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval

abstract: Fine-grained Sketch-based Image Retrieval (Fine-grained SBIR), which uses hand-drawn sketches to search the target object images, has been an emerging topic over the last few years. The difficulties of this task not only come from the ambiguous and abstract characteristics of sketches with less useful information, but also the cross-modal gap at both visual and semantic level. However, images on the web are always exhibited with multimodal contents. In this paper, we consider Fine-grained SBIR as a cross-modal retrieval problem and propose a deep multimodal embedding model that exploits all the beneficial multimodal information sources in sketches and images. In our experiment with large quantity of public data, we show that the proposed method outperforms the state-of-the-art methods for Fine-grained SBIR.


title: Is expanding retrieval a superior method for learning text materials?

abstract: Expanding retrieval practice refers to the idea that gradually increasing the spacing interval between repeated tests ought to promote optimal long-term retention. Belief in the superiority of this technique is widespread, but empirical support is scarce. In addition, virtually all research on expanding retrieval has examined the learning of word pairs in paired-associate tasks. We report two experiments in which we examined the learning of text materials with expanding and equally spaced retrieval practice schedules. Subjects studied brief texts and recalled them in an initial learning phase. We manipulated the spacing of the repeated recall tests and examined final recall 1 week later. Overall we found that (1) repeated testing enhanced retention more than did taking a single test, (2) testing with feedback (restudying the passages) produced better retention than testing without feedback, but most importantly (3) there were no differences between expanding and equally spaced schedules of retrieval practice. Repeated retrieval enhanced long-term retention, but how the repeated tests were spaced did not matter.


title: Design Pattern Detection Using Similarity Scoring

abstract: The identification of design patterns as part of the reengineering process can convey important information to the designer. However, existing pattern detection methodologies generally have problems in dealing with one or more of the following issues: identification of modified pattern versions, search space explosion for large systems and extensibility to novel patterns. In this paper, a design pattern detection methodology is proposed that is based on similarity scoring between graph vertices. Due to the nature of the underlying graph algorithm, this approach has the ability to also recognize patterns that are modified from their standard representation. Moreover, the approach exploits the fact that patterns reside in one or more inheritance hierarchies, reducing the size of the graphs to which the algorithm is applied. Finally, the algorithm does not rely on any pattern-specific heuristic, facilitating the extension to novel design structures. Evaluation on three open-source projects demonstrated the accuracy and the efficiency of the proposed method


title: 5G MmWave Module for the ns-3 Network Simulator

abstract: The increasing demand of data, along with the spectrum scarcity, are motivating a urgent shift towards exploiting new bands. This is the main reason behind identifying mmWaves as the key disruptive enabling technology for 5G cellular networks. Indeed, utilizing new bands means facing new challenges; in this context, they are mainly related to the radio propagation, which is shorter in range and more sensitive to obstacles. The resulting key aspects that need to be taken into account when designing mmWave cellular systems are directionality and link intermittency. The lack of network level results motivated this work, which aims at providing the first of a kind open source mmWave framework, based on the network simulator ns-3. The main focus of this work is the modeling of customizable channel, physical (PHY) and medium access control (MAC) layers for mmWave systems. The overall design and architecture of the model are discussed in details. Finally, the validity of our proposed framework is corroborated through the simulation of a simple scenario.


title: Bema: A multimodal interface for expert experiential analysis of political assemblies at the Pnyx in ancient Greece

abstract: We present Bema, a multimodal user interface that enables scholars of Greek rhetoric and oratory to perform virtual reality studies of ancient political assemblies at the hill of the Pnyx. Named after the flat stone speakers' platform utilized at the Pnyx, the Bema interface supports the high-level task of gaining a nuanced understanding of what it would feel like to give or receive a speech together with as many as 14,000 Athenian citizens and, further, how this experience must have changed as a result of at least two massive renovations captured in the archaeological record. Bema integrates solutions for several low-level interaction tasks, including navigating in virtual space and time, adjusting data visualization parameters, interacting with virtual characters, and analyzing spatial audio and architecture. Navigation is accomplished through a World-in-Miniature technique, re-conceived to support multi-touch input within a 4-wall Cave environment. Six degree-of-freedom head tracking and a sound level meter are used to analyze speeches delivered by users. Comparative analysis of different historical phases and assembly sizes is facilitated by the use of crowd simulation to generate realistic spatial arrangements for the assemblymen and staged animated transitions that preserve context while comparing two or more scenarios. An evaluation with our team's scholar of ancient Greek rhetoric and oratory provides support for the most important design decisions and affirms the value of this user interface for experiential analysis.


title: Review of Social Features in Social Network Games

abstract: Although social network games on Facebook have become popular, their actual sociability has been questioned. In this paper, we review the social features of 16 social games and, as a result, present a list of 30 social features in three categories: presence, communication, and interaction. A common set of features, which was found in all of the examined games, is mainly focused on the presence and communication aspects, while neglecting player interaction. In addition, social features are primarily used for acquisition and retention purposes, rather than monetization. These findings are useful for the study and design of social features in social games and in other games with social network integration.


title: Adversarial Ladder Networks

abstract: The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializing the weights of the network and after these weights are refined with the use of supervised data. On the other hand adversarial noise has improved the results of clas- sical supervised learning. Recently, a new neural network topology called Ladder Network, where the key idea is based in some properties of hierar- chichal latent variable models, has been proposed as a technique to train a neural network using supervised and unsupervised data at the same time with what is called semi-supervised learning. This technique has reached state of the art classification. In this work we add adversarial noise to the ladder network and get state of the art classification, with several important conclusions on how adversarial noise can help in addition with new possible lines of investi- gation. We also propose an alternative to add adversarial noise to unsu- pervised data.


title: Optimization of powder core inductors of buck-boost converters for Hybrid Electric Vehicles

abstract: In the present paper, the characteristics of Mega-Flux ® , JNEX-Core ® , amorphous and ferrite cores are compared to the inductor of buck-boost converters for Hybrid Electric Vehicles. Core losses are analyzed at the condition of 10 ㎑ sine wave excitations, and permeability fluctuations vs. temperature and magnetizing force will be analyzed and discussed. Under the specifications of the buck-boost converter for 20 ㎾ THS-Ⅱ, the power inductor will be designed with Mega-Flux ® and JNEX-Core ® , and informative simulation results will be provided with respect to dc bias characteristics, core and copper losses.


title: Smart cities at the forefront of the future internet

abstract: Smart cities have been recently pointed out by M2M experts as an emerging market with enormous potential, which is expected to drive the digital economy forward in the coming years. However, most of the current city and urban developments are based on vertical ICT solutions leading to an unsustainable sea of systems and market islands. In this work we discuss how the recent vision of the Future Internet (FI), and its particular components, Internet of Things (IoT) and Internet of Services (IoS), can become building blocks to progress towards a unified urban-scale ICT platform transforming a Smart City into an open innovation platform. Moreover, we present some results of generic implementations based on the ITU-T's Ubiquitous Sensor Network (USN) model. The referenced platform model fulfills basic principles of open, federated and trusted platforms (FOTs) at two different levels: the infrastructure level (IoT to support the complexity of heterogeneous sensors deployed in urban spaces), and at the service level (IoS as a suit of open and standardized enablers to facilitate the composition of interoperable smart city services). We also discuss the need of infrastructures at the European level for a realistic large-scale experimentally-driven research, and present main principles of the unique-in-the-world experimental test facility under development within the SmartSantander EU project.


title: Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype

abstract: Background: Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient’s recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms. Therefore, more studies where digital data is collected in larger scales are needed to collect scientific evidence on the capacities of these platforms. Most of the existing platforms for digital psychiatry studies are designed as monolithic systems for a certain type of study; publications from these studies focus on their results, rather than the design features of the data collection platform. Inevitably, more tools and platforms will emerge in the near future to fulfill the need for digital data collection for psychiatry. Currently little knowledge is available from existing digital platforms for future data collection platforms to build upon. Objective: The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features. Methods: We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data. Results: The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies. Conclusions: The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry. [JMIR Res Protoc 2017;6(6):e110]


title: Separation and purification of phosphatidylcholine and phosphatidylethanolamine from soybean degummed oil residues by using solvent extraction and column chromatography

abstract: Abstract Natural phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were separated and purified from soybean degummed oil residues in this work. Crude PC and PE were first separated from degummed oil residues by extraction with 95% ethanol, and then the crude PC and PE were used as raw materials to prepare high purity PC and PE by using column chromatography of silica gel (100–200 mesh) with different eluents and elution modes. The high purity PC (content >90%) was obtained from the crude PC by using isocratic elution with methanol as eluent. Compared with the methods reported by using isocratic elution with mixed solvents as eluent or gradient elution, the procedure proposed exhibits low cost and industry potentialities because of some advantages, such as operation simplicity, cheap equipment and solvent to be recovered easily. The purity of the PE product prepared from the crude PE was more than 75%. The gradient elution was preferable to isocratic elution for reducing the elution time and eluent consumption when to prepare PE from the crude PE. The effects of loading amount and the flow-rate on separation efficiency were also investigated. For obtaining high separation efficiency, the loading amount should be less than 2.0 g crude PC or PE/100 g silica gel, and the flow-rate should be controlled under 4 ml/min for crude PC and 3 ml/min for crude PE, respectively.


A Semantic Loss Function for Deep Learning Under Weak Supervision


title: Stochastic Load Balancing for Virtual Resource Management in Datacenters

abstract: Cloud computing offers a cost-effective and elastic computing paradigm that facilitates large scale data storage and analytics. By deploying virtualization technologies in the datacenter, cloud enables efficient resource management and isolation for various big data applications. Since the hotspots (i.e., overloaded machines) can degrade the performance of these applications, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). However, the previous load balancing schemes make migration decisions based on deterministic resource demand estimation and workload characterization, without considering their stochastic properties. By studying real world traces, we show that the resource demand and workload of virtual machines are highly dynamic and bursty, which can cause these schemes to make inefficient migrations for load balancing. To address this problem, in this paper we propose a stochastic load balancing scheme which aims to provide probabilistic guarantee against the resource overloading with virtual machine migration, while minimizing the total migration overhead. Our scheme effectively addresses the prediction of the distribution of resource demand and the multidimensional resource requirements with stochastic characterization. Moreover, as opposed to the previous works that measure the migration cost without considering the network topology, our scheme explicitly takes into account the distance between the source physical machine and the destination physical machine for a virtual machine migration. The trace-driven experiments show that our scheme outperforms the previous schemes in terms of SLA violation and the migration cost.


title: Polarimetric Convolutional Network for PolSAR Image Classification

abstract: The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix are used as the main research object to extract features. In this paper, we focus on the original polarimetric scattering matrix and propose a polarimetric scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on the convolution network to combine this feature. Based on the polarimetric scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for polarimetric scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding .


title: T2FA: Transparent Two-Factor Authentication

abstract: Traditional username and password-based single-factor authentication is easy to deploy but vulnerable to dictionary attacks, snooping, and brute force attacks. Two-factor authentication (2FA) has been proposed to improve the security, where smart devices are used as the second authentication factor. However, the interaction between human and the smart device is required, which is inconvenient to users. In addition, an attacker is able to get the second authentication factor through fraud, thus invalidating current 2FA mechanisms. In order to solve these problems, we propose a transparent two-factor authentication (T2FA) based on physical unclonable function (PUF) and voiceprint. The second authentication authenticates the user’s mobile phone through the PUF. The third one is to determine whether the login terminal and the user’s mobile phone are in the same environment with the environment voiceprint. The second and third authentication in the second factor is completely transparent to users. Therefore, T2FA avoids the tedious interaction and provides the same high user experience satisfaction as the single-factor authentication and exhibits high security simultaneously. Moreover, the fraud is eliminated technically due to the transparency of the authentication.


title: A Practical Approach to the Automatic Classification of Security-Relevant Commits

abstract: The lack of reliable sources of detailed information on the vulnerabilities of open-source software (OSS) components is a major obstacle to maintaining a secure software supply chain and an effective vulnerability management process. Standard sources of advisories and vulnerability data, such as the National Vulnerability Database (NVD), are known to suffer from poor coverage and inconsistent quality. To reduce our dependency on these sources, we propose an approach that uses machine-learning to analyze source code repositories and to automatically identify commits that are security-relevant (i.e., that are likely to fix a vulnerability). We treat the source code changes introduced by commits as documents written in natural language, classifying them using standard document classification methods. Combining independent classifiers that use information from different facets of commits, our method can yield high precision (80%) while ensuring acceptable recall (43%). In particular, the use of information extracted from the source code changes yields a substantial improvement over the best known approach in state of the art, while requiring a significantly smaller amount of training data and employing a simpler architecture.


title: Deep metric learning for person Re-identification and De-identification

abstract: Large amounts of visual data are gathered from various surveillance systems across different places and times, and have to be processed in order to infer the current state of the world. One of the common problems in surveillance scenarios is person re-identification, the task of associating a person across different cameras. On the other hand, these scenarios raise privacy concerns, which lead to the need for person de-identification, i.e. concealing person identity. This task is related to the re-identification in two aspects: (i) multiple appearances of the same person could be de-identified in similar manner; and (ii) if we discover the features useful for re-identification, we could try to hide the identity by modifying those features. Re-identification can be addressed as a classification problem. The state-of-the-art classification methods are based on deep learning. In this paper we explore the applicability of the recently proposed Triplet network architecture to the person re-identification problem, by applying it on VIPeR dataset. We show that the network is able to learn useful feature-space embeddings, and analyze its benefits and limitations.


title: Optimal FemtoCell Density for Maximizing Throughput in 5G Heterogeneous Networks under Outage Constraints

abstract: Heterogeneous networks (HetNets), which involve densely deployed femtocells underlaid traditional macrocell network, is a promising solution to the extremely high data rate requirements of the future 5G communications. In this paper, we analyze the closed-form optimal deployment of femtocells in HetNets to maximize the network throughput under the outage constraints from both macrocells and femtocells. Specifically, we model the random distribution of macro cell users (MUEs) and femtocell base stations (FBSs) as Poisson Point Processes (PPPs). Then, the closed form expressions for outage probabilities in both uplink and downlink transmissions are derived. Further, we study the network throughput maximization problem under the outage probability constraints. Finally, With the help of convex optimization, the interval of FBS density, which contains the maximum network throughput is obtained in closed form. Simulation results validate the impact of the system parameters on the different optimal FBS density as well as the influence of interference to the maximum network throughput.


title: Wearable Wireless Health Monitoring: Current Developments, Challenges, and Future Trends

abstract: Wearable health-monitoring systems are becoming very popular, especially in enabling the noninvasive diagnosis of vital functions of the human body. Besides typical singular heartbeat or perspiration sensors, which have been commercially available in recent years, the deployment of a series of body-worn sensors can enable an effective health-monitoring mechanism. The combined information obtained from such systems can either be relayed directly to any health-monitoring personnel in the case of emergencies or can be logged and analyzed as a part of preventive health measures. However, the deployment of on-body nodes for humans must be performed with care, as they may interfere with the patients' regular movements. This is especially challenging because the relationship between the electromagnetic waves is influenced by the patient's movements, distance from the nearest base station, operating environment, etc. Additional challenges to the deployment of such mechanisms are also faced in situations where the nodes require additional on-body space, impose additional weight, or are not conformal enough to the patient's body. On the hardware design aspect, the sensory and communication functions on the electronic node have to be designed using special materials to avoid reliability issues or damage due to repeated or intense movements. Finally, and perhaps the most important aspect that needs to be addressed concerning such systems, is their electromagnetic safety level, which is defined by their specific absorption rates (SARs). This article aims to review the latest developments in body-worn wireless health-monitoring systems and their current challenges and limitations and to discuss future trends for such worn devices for these applications.


A Value Driven Agent: Instantiation of a Case-Supported Principle-Based Behavior Paradigm.


title: Integrated Compensation of Magnetometer Array Magnetic Distortion Field and Improvement of Magnetic Object Localization

abstract: A fluxgate magnetometer array for magnetic object localization is designed, where hard-iron and soft-iron magnetic distortion fields are the major factors influencing measurement accuracy. A vector compensation method is proposed to suppress error, in which magnetometer error, misalignment error, and magnetic distortion fields are considered. The experimental system mainly consists of a plane cross magnetometer array, a magnet (to be hard-iron), a steel block (to be soft-iron), and a deployment platform (to change the attitude of the magnetometer array). Experimental results show that integrated compensation parameters can be obtained accurately, and array difference errors are reduced about two orders, thus proving the effectiveness of the vector compensation method. The compensated array is used for static and dynamic localization in 3-D. In static situation, localization errors are reduced from 0.17 m, 0.28 m, and 0.27 m to 0.03 m, 0.05 m, and 0.14 m, respectively. On the object deployment trace, error intensity is reduced from 0.17 to 0.04 m. In particular, the dynamic localization results are unreliable without compensation, and the error intensity is reduced from 2.47 to 0.05 m using the proposed method, thus improving the localization accuracy.


title: Federated Machine Learning: Concept and Applications

abstract: Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.


title: A survey of EEG based user authentication schemes

abstract: Electroencephalography (EEG) is the recording of electrical activity occurring in the brain, which is recorded from the scalp through placement of voltage sensitive electrodes. It has been repeatedly demonstrated that the brain emits voltage fluctuations on a continuous basis. These fluctuations are a reflection of the on-going brain dynamics, which present as a series of fluctuations that have characteristic waveforms and amplitude patterns, depending on the cognitive state of the subject. A number of published reports have indicated that there is enough depth in the EEG recording, rendering it suitable as a tool for person authentication. This idea has a solid underpinning in that recent evidence suggests much of the on-going EEG recordable activity within brains has a genetic component. This study presents the common steps for developing a human identification systems based on EEG signals. It will also present some of the important techniques used.


title: A domain-specific architecture for deep neural networks

abstract: Tensor processing units improve performance per watt of neural networks in Google datacenters by roughly 50x.


title: An Interpretive Perspective on Generating Theory From Qualitative Field Studies

abstract: This chapter presents a set of principles for the use of Grounded Theory techniques in qualitative field studies. Some issues and controversies relating to rigor in Grounded Theory generation are discussed. These include: inductive theory generation and emergence, how theoretical saturation may be judged, the extent to which coding schemes should be formalized, the objectivistsubjectivist debate, and the assessment of quality and rigor in interpretive research. It is argued that Grounded Theory is often criticized for a lack of rigor because we apply positivist evaluations of rigor to research that derives from an interpretive worldview. Alternative assessments of rigor are suggested, that emphasize reflexivity in the inductive-deductive cycle of substantive theory


title: Deep Anomaly Detection with Outlier Exposure

abstract: It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.


title: Impact of Smoking on Outcomes of HPV-related Oropharyngeal Cancer Treated with Primary Radiation or Surgery

abstract: Purpose Tobacco exposure is known to affect the biological behavior of human papilloma virus (HPV)-positive oropharyngeal carcinoma (OPC) with intermediate outcomes relative to tumors that are HPV associated with no smoking exposure and smoking-related HPV-negative tumors. We aim to evaluate the impact of smoking on the outcomes of patients with HPV-associated locally advanced OPC when stratified by treatment modality. Methods and Materials A retrospective chart review was undertaken for 352 patients with known p16-overexpressing locally advanced OPC who were managed with curative-intent therapy from 2006 to 2015. The impact of smoking status on overall survival (OS) and recurrence-free survival were compared using the Kaplan-Meier method. Results Of the 352 patients, 67.6% (n = 238) were managed with primary chemoradiation therapy (CRT) and 32.4% (n = 114) with primary surgery ± adjuvant therapy. The median smoking pack-year was 15. Twenty-seven percent of patients were active smokers at the time of presentation, with 40.3% identifying as former smokers and 32.7% having never smoked. Median follow-up for surviving patients was 4.2 years. Current smokers had a significantly worse relapse-free survival and OS compared with never and former smokers (P = .03 and P = .0001, respectively), with outcomes significantly worsening with increasing smoking exposure. The 5-year OS for more than 10, 20, and 30 pack-year smoking history was 73.2%, 64.7%, and 59.1%, respectively. Current smokers managed with CRT had a 5-year OS of 64.2% compared with former and never smokers (93.1% and 78.2%, respectively). For current smokers managed primarily by surgery the 5-year OS was 57.6% compared with former and never smokers (69.6% and 73.5%, respectively). Conclusions Current smokers and those with higher smoking exposure had poorer outcomes irrespective of their primary modality of treatment. Although not the specific focus of the study, definitive CRT appeared to at least be equivalent to surgery with respect to disease outcomes for patients with HPV-associated oropharyngeal cancer, regardless of smoking status.


title: 3-D Depth Reconstruction from a Single Still Image

abstract: We consider the task of 3-d depth estimation from a single still image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured indoor and outdoor environments which include forests, sidewalks, trees, buildings, etc.) and their corresponding ground-truth depthmaps. Then, we apply supervised learning to predict the value of the depthmap as a function of the image. Depth estimation is a challenging problem, since local features alone are insufficient to estimate depth at a point, and one needs to consider the global context of the image. Our model uses a hierarchical, multiscale Markov Random Field (MRF) that incorporates multiscale local- and global-image features, and models the depths and the relation between depths at different points in the image. We show that, even on unstructured scenes, our algorithm is frequently able to recover fairly accurate depthmaps. We further propose a model that incorporates both monocular cues and stereo (triangulation) cues, to obtain significantly more accurate depth estimates than is possible using either monocular or stereo cues alone.


title: Motion artifact reduction in photoplethysmography using independent component analysis

abstract: Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.


title: Understanding Convolutional Neural Networks for Text Classification

abstract: We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the networks process and classify text. We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors. We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest. Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions).


title: The Spartanizer: Massive automatic refactoring

abstract: The Spartanizer is an eclipse plugin featuring over one hundred and fifty refactoring techniques, all aimed at reducing various size complexity of the code, without changing its design, i.e., inheritance relations, modular structure, etc. Typical use case of the Spartanizer is in an automatic mode: refactoring operations are successively selected and applied by the tool, until the code is reshaped in spartan style (a frugal coding style minimizing the use of characters, variables, tokens, etc.). The Spartanizer demonstrates the potential of automatic refactoring: tens of thousands of transformations are applied in matter of seconds, chains of dependent applications of transformations with tens of operations in them, significant impact on code size, and extent reaching almost every line of code, even of professional libraries.


title: Investigating the use of "Grounded Theory" in information systems research

abstract: "Grounded Theory" has been employed quite widely in studies of information systems (IS) phenomena. A survey of IS literature reveals conflict in the understanding and use of "grounded theory". The term "grounded theory" is often used as a catch phrase to denote usage of a grounded theory approach to conducting research. A variety of grounded theory approaches have been employed in IS research. The purpose of this investigation was to establish the alternative approaches employed, and the extent to which each was used. To achieve this purpose, a comprehensive review of IS studies that employed "grounded theory" was carried out. Articles from the commonly ranked top 50 IS-centric journals were used as the frame of reference. These journals most closely represent the status quo in IS research. Articles for the period 1985 to 2007 were examined. The analysis revealed four main grounded theory approaches in IS research. These can be classified as the "Glaserian" grounded theory approach, the "Straussian" grounded theory approach, the use of "grounded theory" as part of a mixed methodology, and the simple application of grounded theory techniques, typically for data analysis purposes. The latter has been the most common application of "grounded theory" in IS research. The "Glaserian" approach was the least often employed, with many studies opting for the "Straussian" approach. These and other findings are discussed and implications drawn.


title: Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

abstract: This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.


title: Citadel: Efficiently Protecting Stacked Memory from Large Granularity Failures

abstract: Stacked memory modules are likely to be tightly integrated with the processor. It is vital that these memory modules operate reliably, as memory failure can require the replacement of the entire socket. To make matters worse, stacked memory designs are susceptible to newer failure modes (for example, due to faulty through-silicon vias, or TSVs) that can cause large portions of memory, such as a bank, to become faulty. To avoid data loss from large-granularity failures, the memory system may use symbol-based codes that stripe the data for a cache line across several banks (or channels). Unfortunately, such data-striping reduces memory level parallelism causing significant slowdown and higher power consumption. This paper proposes Citadel, a robust memory architecture that allows the memory system to retain each cache line within one bank, thus allowing high performance, lower power and efficiently protects the stacked memory from large-granularity failures. Citadel consists of three components, TSV-Swap, which can tolerate both faulty data-TSVs and faulty address-TSVs, Tri Dimensional Parity (3DP), which can tolerate column failures, row failures, and bank failures, and Dynamic Dual Granularity Sparing (DDS), which can mitigate permanent faults by dynamically sparing faulty memory regions either at a row granularity or at a bank granularity. Our evaluations with real-world data for DRAM failures show that Citadel provides performance and power similar to maintaining the entire cache line in the same bank, and yet provides 700x higher reliability than Chip Kill-like ECC codes.


title: Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection

abstract: It is estimated that approximately $700 billion is lost due to fraud, waste, and abuse in the US healthcare system. Medicaid has been particularly susceptible target for fraud in recent years, with a distributed management model, limited cross- program communications, and a difficult-to-track patient population of low-income adults, their children, and people with certain disabilities. For effective fraud detection, one has to look at the data beyond the transaction-level. This paper builds upon Sparrow's fraud type classifications and the Medicaid environment and to develop a Medicaid multidimensional schema and provide a set of multidimensional data models and analysis techniques that help to predict the likelihood of fraudulent activities. These data views address the most prevalent known fraud types and should prove useful in discovering the unknown unknowns. The model is evaluated by functionally testing against known fraud cases


title: Media Meets Semantic Web - How the BBC Uses DBpedia and Linked Data to Make Conections

abstract: In this paper, we describe how the BBC is working to integrate data and linking documents across BBC domains by using Semantic Web technology, in particular Linked Data, MusicBrainz and DBpedia. We cover the work of BBC Programmes and BBC Music building Linked Data sites for all music and programmes related brands, and we describe existing projects, ongoing development, and further research we are doing in a joint collaboration between the BBC, Freie Universitat Berlin and Rattle Research in order to use DBpedia as the controlled vocabulary and semantic backbone for the whole BBC.


title: An Intelligent Location Management approaches in GSM Mobile Network

abstract: Location management refers to the problem of updating and searching the current location of mobile nodes in a wireless network. To make it efficient, the sum of update costs of location database must be minimized. Previous work relying on fixed location databases is unable to fully exploit the knowledge of user mobility patterns in the system so as to achieve this minimization. The study presents an intelligent location management approach which has interacts between intelligent information system and knowledge-base technologies, so we can dynamically change the user patterns and reduce the transition between the VLR and HLR. The study provides algorithms are ability to handle location registration and call delivery.


title: SeCReT: Secure Channel between Rich Execution Environment and Trusted Execution Environment

abstract: We propose SeCReT to ameliorate this problem. SeCReT is a framework that builds a secure channel between the REE and TEE by enabling REE processes to use session keys in the REE that is regarded as unsafe region. SeCReT provides the session key to a requestor process only when the requestor’s code and control flow integrity are verified. To prevent the key from being exposed to an attacker who already compromised the REE kernel, SeCReT flushes the key from the memory every time the processor switches into kernel mode. In this paper, we present the design and implementation of SeCReT to show how it protects the key in the REE. Our prototype is implemented on Arndale board, which offers a Cortex-A15 dual-core processor with TrustZone as its security extension. We performed a security analysis by using a kernel rootkit and also ran LMBench microbenchmark to evaluate the performance overhead imposed by SeCReT.


title: Periodic subgraph mining in dynamic networks

abstract: In systems of interacting entities such as social networks, interactions that occur regularly typically correspond to significant, yet often infrequent and hard to detect, interaction patterns. To identify such regular behavior in streams of dynamic interaction data, we propose a new mining problem of finding a minimal set of periodically recurring subgraphs to capture all periodic behavior in a dynamic network. We analyze the computational complexity of the problem and show that it is polynomial, unlike many related subgraph or itemset mining problems. We propose an efficient and scalable algorithm to mine all periodic subgraphs in a dynamic network. The algorithm makes a single pass over the data and is also capable of accommodating imperfect periodicity. We demonstrate the applicability of our approach on several real-world networks and extract interesting and insightful periodic interaction patterns. We also show that periodic subgraphs can be an effective way to uncover and characterize the natural periodicities in a system.


title: Top Down Approach to Multiple Plane Detection

abstract: Detecting multiple planes in images is a challenging problem, but one with many applications. Recent work such as J-Linkage and Ordered Residual Kernels have focussed on developing a domain independent approach to detect multiple structures. These multiple structure detection methods are then used for estimating multiple homographies given feature matches between two images. Features participating in the multiple homographies detected, provide us the multiple scene planes. We show that these methods provide locally optimal results and fail to merge detected planar patches to the true scene planes. These methods use only residues obtained on applying homography of one plane to another as cue for merging. In this paper, we develop additional cues such as local consistency of planes, local normals, texture etc. to perform better classification and merging . We formulate the classification as an MRF problem and use TRWS message passing algorithm to solve non metric energy terms and complex sparse graph structure. We show results on challenging dataset common in robotics navigation scenarios where our method shows accuracy of more than 85 percent on average while being close or same as the actual number of scene planes.


title: Next generation of thales ADS-B ground stations - supporting the US Surveillance Broadcasting Services System Program

abstract: Thales is delivering the ground station equipment for the US Federal Aviation Administration (FAA) Surveillance Broadcast Services (SBS) Program which is part of the FAA Next Generation Air Traffic System (NGATS or NextGen). Demanded by the constraints and requirements laid out in the FAA essential and critical services specification, this new generation of automatic dependent surveillance broadcast (ADS-B) ground station equipment provides significantly higher performance and flexibility than its already successful predecessor AS680. This paper presents background, function, and performance of the new equipment generation.


title: Using Multi-Feature and Classifier Ensembles to Improve Malware Detection

abstract: With the rapid growth of internet application, malware has become one of the major threats to information security. Traditionally, anti-virus products use signature matching to detect malware, but the drawback is that they can not detect new and unknown malware. Recent studies showed that the use of machine learning can successfully detect new and unknown malware, but the limitation of this technique is its high false rate. The performance of machine learning is influenced by two main factors: (1) the features used to represent the instances; and (2) the algorithm used to generate classifier. In this paper, we improved the accuracy of machine learning from these two factors. On the one hand we combined features extracted from both content-based and behavior-based analyses to represent the instances; on the other hand, we used classifier ensembles to replace individual classifier. Based on our methodology, a hybrid-classifier was implemented to classify unknown executables as either malicious or benign. Experimental results show that the methods proposed in this paper can improve the accuracy of malware detection effectively.


title: Compact Dual-Band GPS Microstrip Antenna Using Multilayer LTCC Substrate

abstract: This letter presents the design of a single-feed dual-band microstrip antenna for global positioning satellite (GPS) operations at 1227 MHz (L2) and 1575 (L1) MHz using low-temperature cofired ceramic (LTCC) technology. Details of the GPS antenna design and simulated and experimental results are presented and discussed. The measured results confirm the validity of this design, which can meet the requirement of GPS application at L1 and L2 bands. Furthermore, the proposed structure can be deployed in a space-limited environment and multi-dielectric-layer antenna applications.


Sequence Discriminative Distributed Training of Long Short-Term Memory Recurrent Neural Networks


title: De-anonymizing Web Browsing Data with Social Networks

abstract: Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show---theoretically, via simulation, and through experiments on real user data---that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one's feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user's social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time.To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is---to our knowledge---the largest-scale demonstrated de-anonymization to date.


title: An Integrated AC Choke Design for Common-Mode Current Suppression in Neutral-Connected Power Converter Systems

abstract: This paper presents an integrated ac choke that incorporates the common-mode (CM) suppression function into a three-phase differential-mode (DM) inductor. The proposed choke, together with other filter components, provides a transformerless solution to issues caused by the switching CM voltage in neutral-connected power converter systems. The required high CM-to-DM inductance ratio is generated by different magnetic paths in the novel structure. An approach based on magnetic circuit calculation can be applied to the weight-minimized design in which a reasonably small core loss confirms the choke's heat dissipation capability. The existence and uniqueness of the optimization are proven by a design example and analysis. Comparison to separate DM and CM inductors demonstrates the benefits of integration: the iron and copper material savings, the volume and weight reduction, and overall system efficiency improvement. The finite element method is used to tune the design parameters with which a prototype is implemented. The simulation and experimental results of the prototype in a voltage source converter verify the feasibility and effectiveness of the integrated choke.


title: Executive functions and the frontal lobes: a conceptual view

abstract: Several problems in understanding executive functions and their relationships to the frontal lobes are discussed. Data are then presented from several of our studies to support the following statements: (1) the examination of patients with focal frontal lobe lesions is a necessary first step in defining the relation of executive functions to the frontal lobes; (2) there is no unitary executive function. Rather, distinct processes related to the frontal lobes can be differentiated which converge on a general concept of control functions; (3) a simple control-automatic distinction is inadequate to explain the complexity of control-automatic processes; (4) the distinction between complex and simple tasks cannot explain the differences in functions between the frontal lobes and other brain regions; and (5) the most important role of the frontal lobes may be for affective responsiveness, social and personality development, and self-awareness and unconsciousness.


title: Quantification of 99mTc-DPD concentration in the lumbar spine with SPECT/CT

abstract: Routine single-photon emission computed tomography (SPECT) currently lacks quantitative information on regional activity concentration (ACC) of the injected tracer (e.g. kBq/ml). Furthermore, little is known on the skeletal absolute concentration of 99mTc-DPD after intravenous injection in bone scintigraphy. The aim of this study is to determine ACC in the healthy lumbar vertebrae of patients using a recently published quantitative SPECT/computed tomography (CT) protocol. Lumbar vertebrae ACC estimates were performed in 50 female patients (mean age 69.88 ± 13.73 years) who had been administered 562.84 ± 102.33 MBq of 99mTc-DPD and had undergone SPECT acquisition 4 h after the injection. The SPECT/CT system was calibrated against a well counter. Images were reconstructed with Flash3D. ACC and CT tissue density were measured in volumes of interest drawn over the spongious bone tissue of the three lower lumbar vertebral bodies when these exhibited no focal CT or SPECT pathology. Average ACC measured in the normal spongious bone tissue was 48.15 ± 13.66 kBq/ml (95% confidence interval (CI) 45.81 to 50.50 kBq/ml). This corresponds to a mean standardised uptake value (SUV) of (5.91 ± 1.54) (95% CI (5.64 to 6.17) SUV). SUV correlated significantly with Hounsfield units (HU) (r = 0.678, p < 0.0001). Significant negative correlations were observed between age and HU (r = −0.650, p < 0.0001) and between age and SUV (r = −0.385, p < 0.0001). The SUVs determined for 99mTc-DPD uptake 4 h post injection are in the same range as those reported for [18F]fluoride in positron emission tomography. The strong correlation of SUV with bone CT density underlines the physiological significance of this variable. Our data suggest further investigation of the potential value of ACC measurement in the diagnosis of pathological conditions such as osteoporosis or in following up osseous metastases under therapy.


title: Reducing the effect of email interruptions on employees

abstract: It is generally assumed that because it is not necessary to react to email messages when they arrive, employees will read their messages in their own time with minimum interruption to their work. This research has shown that email messages do have some disruptive effect by interrupting the user. Employees at the Danwood Group in the UK were monitored to see how they used email. It was found that most employees had their email software check for incoming messages every 5min and responded to the arrival of a message within 6s. A recovery time between finishing reading the email and returning to normal work also existed though it was shorter than published recovery times for a telephone interrupt. This analysis has suggested that a number of methods can be employed to reduce this interrupt effect. Employee training, changing the settings and modes of using the email software and the introduction of a one line email facility are all shown to have beneficial effects. This has led to a series of recommendations that will enable the Danwood Group to make better use of email communication and increase employee effectiveness.


title: An Integrated Reasoning Approach to Moral Decision-Making

abstract: We present a computational model, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision-making. Current theories of moral decision-making extend beyond pure utilitarian models by relying on contextual factors that vary with culture. MoralDM uses a natural language system to produce formal representations from psychological stimuli, to reduce tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to determine consequences and utilities when making moral judgments. We describe how MoralDM works and show that it can model psychological results and improve its performance via accumulating examples.


title: Parallel merge sort

abstract: We give a parallel implementation of merge sort on a CREW PRAM that uses n processors and $O(\log n)$ time; the constant in the running time is small. We also give a more complex version of the algorithm for the EREW PRAM; it also uses n processors and $O(\log n)$ time. The constant in the running time is still moderate, though not as small.


title: Health-State Estimation and Prognostics in Machining Processes

abstract: Failure mechanisms of electromechanical systems usually involve several degraded health-states. Tracking and forecasting the evolution of health-states and impending failures, in the form of remaining-useful-life (RUL), is a critical challenge and regarded as the Achilles' heel of condition-based-maintenance (CBM). This paper demonstrates how this difficult problem can be addressed through Hidden Markov models (HMMs) that are able to estimate unobservable health-states using observable sensor signals. In particular, implementation of HMM based models as dynamic Bayesian networks (DBNs) facilitates compact representation as well as additional flexibility with regard to model structure. Both regular HMM pools and hierarchical HMMs are employed here to estimate online the health-state of drill-bits as they deteriorate with use on a CNC drilling machine. Hierarchical HMM is composed of sub-HMMs in a pyramid structure, providing functionality beyond an HMM for modeling complex systems. In the case of regular HMMs, each HMM within the pool competes to represent a distinct health-state and adapts through competitive learning. In the case of hierarchical HMMs, health-states are represented as distinct nodes at the top of the hierarchy. Monte Carlo simulation, with state transition probabilities derived from a hierarchical HMM, is employed for RUL estimation. Detailed results on health-state and RUL estimation are very promising and are reported in this paper. Hierarchical HMMs seem to be particularly effective and efficient and outperform other HMM methods from literature.


title: Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units

abstract: Abstract Basic uniform pseudo-random number generators are implemented on ATI Graphics Processing Units (GPU). The performance results of the realized generators (multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR, RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The obtained speed up factor is hundreds of times in comparison with CPU. RANLUX generator is found to be the most appropriate for using on GPU in Monte Carlo simulations. The brief review of the pseudo-random number generators used in modern software packages for Monte Carlo simulations in high-energy physics is presented.


title: Evaluation measures for hierarchical classification: a unified view and novel approaches

abstract: Hierarchical classification addresses the problem of classifying items into a hierarchy of classes. An important issue in hierarchical classification is the evaluation of different classification algorithms, an issue which is complicated by the hierarchical relations among the classes. Several evaluation measures have been proposed for hierarchical classification using the hierarchy in different ways without however providing a unified view of the problem. This paper studies the problem of evaluation in hierarchical classification by analysing and abstracting the key components of the existing performance measures. It also proposes two alternative generic views of hierarchical evaluation and introduces two corresponding novel measures. The proposed measures, along with the state-of-the-art ones, are empirically tested on three large datasets from the domain of text classification. The empirical results illustrate the undesirable behaviour of existing approaches and how the proposed methods overcome most of these problems across a range of cases.


title: Food Image Recognition by Using Convolutional Neural Networks (CNNs)

abstract: Food image recognition is one of the promising applications of visual object recognition in computer vision. In this study, a small-scale dataset consisting of 5822 images of ten categories and a five-layer CNN was constructed to recognize these images. The bag-of-features (BoF) model coupled with support vector machine (SVM) was first evaluated for image classification, resulting in an overall accuracy of 56%; while the CNN model performed much better with an overall accuracy of 74%. Data augmentation techniques based on geometric transformation were applied to increase the size of training images, which achieved a significantly improved accuracy of more than 90% while preventing the overfitting issue that occurred to the CNN based on raw training data. Further improvements can be expected by collecting more images and optimizing the network architecture and hyper-parameters.


title: On the design and implementation of a home energy management system

abstract: To reduce energy consumption and wastage, effective energy management at home is key and an integral part of the future Smart Grid. In this paper, we present the design and implementation of Green Home Service (GHS) for home energy management. Our approach addresses the key issues of home energy management in Smart Grid: a holistic management solution, improved device manageability, and an enabler of Demand-Response. We also present the scheduling algorithms in GHS for smart energy management and show the results in simulation studies.


title: Selective aberrant functional connectivity of resting state networks in social anxiety disorder

abstract: Abstract Several functional MRI (fMRI) activation studies have highlighted specific differences in brain response in social anxiety disorder (SAD) patients. Little is known, so far, about the changes in the functional architecture of resting state networks (RSNs) in SAD during resting state. We investigated statistical differences in RSNs on 20 SAD and 20 controls using independent component analysis. A diffuse impact on widely distributed RSNs and selective changes of RSN intrinsic functional connectivity were observed in SAD. Functional connectivity was decreased in the somato-motor (primary and motor cortices) and visual (primary visual cortex) networks, increased in a network including medial prefrontal cortex which is thought to be involved in self-referential processes, and increased or decreased in the default mode network (posterior cingulate cortex/precuneus, bilateral inferior parietal gyrus, angular gyrus, middle temporal gyrus, and superior and medial frontal gyrus) which has been suggested to be involved in episodic memory, and self-projection, the dorsal attention network (middle and superior occipital gyrus, inferior and superior parietal gyrus, and middle and superior frontal gyrus) which is thought to mediate goal-directed top-down processing, the core network (insula-cingulate cortices) which is associated with task control function, and the central-executive network (fronto-parietal cortices). A relationship between functional connectivity and disease severity was found in specific regions of RSNs, including medial and lateral prefrontal cortex, as well as parietal and occipital regions. Our results might supply a novel way to look into neuro-pathophysiological mechanisms in SAD patients.


title: Persistent Lexical Entrainment in HRI

abstract: In this study, we set out to ask three questions. First, does lexical entrainment with a robot interlocutor persist after an interaction? Second, how does the influence of social robots on humans compare with the influence of humans on each other? Finally, what role is played by personality traits in lexical entrainment to robots, and how does this compare with the role of personality in entrainment to other humans? Our experiment shows that first, robots can indeed prompt lexical entrainment that persists after an interaction is over. This finding is interesting since it demonstrates that speakers can be linguistically influenced by a robot, in a way that is not merely motivated by a desire to be understood. Second, we find similarities between lexical entrainment to the robot peer and lexical entrainment to a human peer, although the effects are stronger when the peer is human. Third, we find that whether the peer is a robot or a human, similar personality traits contribute to lexical entrainment. In both peer conditions, participants who score higher on ``Openness to experience" are more likely to adopt less conventional terminology.


title: Introducing an adverse drug event manager

abstract: Objectives Under reporting of adverse drug events (ADEs) is a global challenge. ADEs contribute to increased morbidity and mortality and many systems have been tried to overcome the lack of reporting. The aim of our study was to introduce an ADE manager (ADEM) at a university hospital in Denmark to increase the number of ADEs reported. Methods An ADEM was introduced in five medical wards at a university hospital in Denmark in the period October 2010–September 2011. The function of the ADEM was to complete the ADE report, whenever a physician required assistance. By using electronic health records, the ADEM was able to collect the necessary information about the ADE and complete a report to the Danish Medicines Agency (DMA), now part of Danish Health and Medicines Authority. Results The clinicians spent 2–3 min reporting the ADE to the ADEM, who spent 30 min completing the report. The study contributed to an increase in reported ADEs from 30 in 2009 to 162 in the 12-month project period at the university hospital. Conclusions An ADEM relieves the clinicians of the administrative burden of reporting ADEs and increases the number of ADEs reported to the DMA.


title: Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

abstract: X-ray computed tomography (CT) using sparse projection views is often used to reduce the radiation dose. However, due to the insufficient projection views, a reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-net have demonstrated impressive performance for sparse view CT reconstruction. However, theoretical justification is still lacking. The main goal of this paper is, therefore, to develop a mathematical theory and to discuss how to improve these algorithms. In particular, inspired by the recent theory of deep convolutional framelets, we show that the U-net relies on a sub-optimal non-local bases that overly emphasizes low frequency components. The discovery leads to a dual frame and a tight frame U-net architectures for effective recovery of directional image components.


title: Establishing pairwise keys in distributed sensor networks

abstract: Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensors, it is infeasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). To facilitate the study of novel pairwise key predistribution techniques, this paper presents a general framework for establishing pairwise keys between sensors on the basis of a polynomial-based key predistribution protocol [2]. This paper then presents two efficient instantiations of the general framework: a random subset assignment key predistribution scheme and a grid-based key predistribution scheme. The analysis in this paper indicates that these two schemes have a number of nice properties, including high probability (or guarantee) to establish pairwise keys, tolerance of node captures, and low communication overhead. Finally, this paper presents a technique to reduce the computation at sensors required by these schemes.


title: Uruguay's IT Industry: Fundamentals and Growth Opportunities

abstract: The authors look at the IT industry in Uruguay, examining its evolution, where it stands now, and future challenges.


title: Coverage criteria for GUI testing

abstract: A widespread recognition of the usefulness of graphical user interfaces (GUIs) has established their importance as critical components of today's software. GUIs have characteristics different from traditional software, and conventional testing techniques do not directly apply to GUIs. This paper's focus is on coverage critieria for GUIs, important rules that provide an objective measure of test quality. We present new coverage criteria to help determine whether a GUI has been adequately tested. These coverage criteria use events and event sequences to specify a measure of test adequacy. Since the total number of permutations of event sequences in any non-trivial GUI is extremely large, the GUI's hierarchical structure is exploited to identify the important event sequences to be tested. A GUI is decomposed into GUI components, each of which is used as a basic unit of testing. A representation of a GUI component, called an event-flow graph, identifies the interaction of events within a component and intra-component criteria are used to evaluate the adequacy of tests on these events. The hierarchical relationship among components is represented by an integration tree, and inter-component coverage criteria are used to evaluate the adequacy of test sequences that cross components. Algorithms are given to construct event-flow graphs and an integration tree for a given GUI, and to evaluate the coverage of a given test suite with respect to the new coverage criteria. A case study illustrates the usefulness of the coverage report to guide further testing and an important correlation between event-based coverage of a GUI and statement coverage of its software's underlying code.


title: Block periodization of high-intensity aerobic intervals provides superior training effects in trained cyclists

abstract: The purpose of this study was to compare the effect of two different methods of organizing endurance training in trained cyclists. One group of cyclists performed block periodization, wherein the first week constituted five sessions of high-intensity aerobic training (HIT), followed by 3 weeks of one weekly HIT session and focus on low-intensity training (LIT) (BP; n = 10, VO2max = 62 ± 2 mL/kg/min). Another group of cyclists performed a more traditional organization, with 4 weeks of two weekly HIT sessions interspersed with LIT (TRAD; n = 9, VO2max = 63 ± 2 mL/kg/min). Similar volumes of both HIT and LIT was performed in the two groups. While BP increased VO2max , peak power output (Wmax) and power output at 2 mmol/L [la(-)] by 4.6 ± 3.7%, 2.1 ± 2.8%, and 10 ± 12%, respectively (P < 0.05), no changes occurred in TRAD. BP showed relative improvements in VO2max compared with TRAD (P < 0.05). Mean effect size (ES) of the relative improvement in VO2max , Wmax , and power output at 2 mmol/L [la(-)] revealed large to moderate effects of BP training compared with TRAD training (ES = 1.34, ES = 0.85, and ES = 0.71, respectively). The present study suggests that block periodization of training provides superior adaptations to traditional organization during a 4-week endurance training period, despite similar training volume and intensity.


title: Target Detection in SFCW Ground Penetrating Radar with C3 Algorithm and Hough Transform based on GPRMAX Simulation and Experimental Data

abstract: We perform target detection in B-scan images obtained using stepped frequency continuous wave (SFCW) ground-penetrating radar (GPR) simulation with Matlab and the open-source software for electromagnetic propagation simulation gprMax and using experimental data from measurements available on the Web. We perform hyperbola detection and target depth estimation using the Hough transform. Additionally, we reduce the complexity of Hough transform using the Column-Connection Clustering (C3) algorithm. The results show that gprMax can be used with SFCW modulated signals, besides its use with conventional impulse signals (impulse GPR). The proposed combination of C3 and Hough transform shows very good detection capability. These algorithms are also tested on experimental GPR data available on the Web to show their applicability in real soil conditions. Mean subtraction method and Singular Value Decomposition (SVD) algorithms are used to reduce the effects of the ground reflected wave.


title: Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

abstract: Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation’s recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.


A Study of Human Gaits across Different Speeds


title: New unidirectional high-efficiency three-level single-phase bridgeless PFC rectifier

abstract: This work presents a new multilevel unidirectional single-phase PFC rectifier topology well-suited for applications targeting for high efficiency and/or high power density. The characteristics of this rectifier topology, including the principles of operation, modulation strategy, feedback control scheme, and dimensioning equations are described in this paper. Finally, a 3 kW laboratory prototype is constructed and used in order to verify the characteristics of the new converter.


title: A systematic review of distributed Agile software engineering

abstract: The combination of Agile methods and distributed software development via remote teams represents an emerging approach to address the challenges such as late feedback, slow project timelines, and high cost, typically associated with software development projects. However, when projects are implemented using an Agile model with distributed human resources, there are a number of challenges that need to be considered and mitigated. The objectives of our work are multifold. First, we would like to understand the reasons and conditions that lead to the adoption of distributed Agile software engineering DASE practices. Second, we would like to investigate and find out the most important risks that threaten a DASE approach and what mitigation strategies exist to address them. Finally, we would like to highlight which of the available approaches among the existing Agile methodologies has been successfully adopted by the community. We intend to solidify our findings by exploring the strength of the evidence that has been reported in the literature.


title: Evaluation of News-Based Trading Strategies

abstract: The marvel of markets lies in the fact that dispersed information is instantaneously processed by adjusting the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we attempt to design trading strategies that are built on textual news in order to obtain higher profits than benchmark strategies achieve. Essentially, we succeed by showing evidence that a news-based trading strategy indeed outperforms our benchmarks by a 9.06-fold performance.


title: Binaural Classification for Reverberant Speech Segregation Using Deep Neural Networks

abstract: Speech signal degradation in real environments mainly results from room reverberation and concurrent noise. While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. We treat the binaural segregation problem as binary classification, and employ deep neural networks (DNNs) for the classification task. The binaural features of the interaural time difference and interaural level difference are used as the main auditory features for classification. The monaural feature of gammatone frequency cepstral coefficients is also used to improve classification performance, especially when interference and target speech are collocated or very close to one another. We systematically examine DNN generalization to untrained spatial configurations. Evaluations and comparisons show that DNN-based binaural classification produces superior segregation performance in a variety of multisource and reverberant conditions.


The Framework for the Selection of Personnel Based on the SWARA and ARAS Methods Under Uncertainties


title: Test-driven development as a defect-reduction practice

abstract: Test-driven development is a software development practice that has been used sporadically for decades. With this practice, test cases (preferably automated) are incrementally written before production code is implemented. Test-driven development has recently re-emerged as a critical enabling practice of the extreme programming software development methodology. We ran a case study of this practice at IBM. In the process, a thorough suite of automated test cases was produced after UML design. In this case study, we found that the code developed using a test-driven development practice showed, during functional verification and regression tests, approximately 40% fewer defects than a baseline prior product developed in a more traditional fashion. The productivity of the team was not impacted by the additional focus on producing automated test cases. This test suite aids in future enhancements and maintenance of this code. The case study and the results are discussed in detail.


title: Interactive 3D Modeling with a Generative Adversarial Network

abstract: This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface. The user edits a voxel grid with a painting interface (like Minecraft). Yet, at any time, he/she can execute a SNAP command, which projects the current voxel grid onto a latent shape manifold with a learned projection operator and then generates a similar, but more realistic, shape using a learned generator network. Then the user can edit the resulting shape and snap again until he/she is satisfied with the result. The main advantage of this approach is that the projection and generation operators assist novice users to create 3D models characteristic of a background distribution of object shapes, but without having to specify all the details. The core new research idea is to use a GAN to support this application. 3D GANs have previously been used for shape generation, interpolation, and completion, but never for interactive modeling. The new challenge for this application is to learn a projection operator that takes an arbitrary 3D voxel model and produces a latent vector on the shape manifold from which a similar and realistic shape can be generated. We develop algorithms for this and other steps of the SNAP processing pipeline and integrate them into a simple modeling tool. Experiments with these algorithms and tool suggest that GANs provide a promising approach to computer-assisted interactive modeling.


title: Partitioned logistic regression for spam filtering

abstract: Naive Bayes and logistic regression perform well in different regimes. While the former is a very simple generative model which is efficient to train and performs well empirically in many applications,the latter is a discriminative model which often achieves better accuracy and can be shown to outperform naive Bayes asymptotically. In this paper, we propose a novel hybrid model, partitioned logistic regression, which has several advantages over both naive Bayes and logistic regression. This model separates the original feature space into several disjoint feature groups. Individual models on these groups of features are learned using logistic regression and their predictions are combined using the naive Bayes principle to produce a robust final estimation. We show that our model is better both theoretically and empirically. In addition, when applying it in a practical application, email spam filtering, it improves the normalized AUC score at 10% false-positive rate by 28.8% and 23.6% compared to naive Bayes and logistic regression, when using the exact same training examples.


MOOC Leaner Motivation and Learning Pattern Discovery


title: Towards intelligent caring agents for Aging-In-Place: Issues and challenges

abstract: The aging of the world's population presents vast societal and individual challenges. The relatively shrinking workforce to support the growing population of the elderly leads to a rapidly increasing amount of technological innovations in the field of elderly care. In this paper, we present an integrated framework consisting of various intelligent agents with their own expertise and responsibilities working in a holistic manner to assist, care, and accompany the elderly around the clock in the home environment. To support the independence of the elderly for Aging-In-Place (AIP), the intelligent agents must well understand the elderly, be fully aware of the home environment, possess high-level reasoning and learning capabilities, and provide appropriate tender care in the physical, cognitive, emotional, and social aspects. The intelligent agents sense in non-intrusive ways from different sources and provide wellness monitoring, recommendations, and services across diverse platforms and locations. They collaborate together and interact with the elderly in a natural and holistic manner to provide all-around tender care reactively and proactively. We present our implementation of the collaboration framework with a number of realized functionalities of the intelligent agents, highlighting its feasibility and importance in addressing various challenges in AIP.


title: Performance trends and limitations of power electronic systems

abstract: In 2003 the Roadmapping Initiative of the European Center of Power Electronics (ECPE) has been started based on a future vision of society in 2020 in order to define the future role of power electronics, and to identify technological barriers and prepare new technologies well in time. In the framework of this initiative a new mathematically supported approach for the roadmapping in power electronics has been developed. As described in this paper the procedure relies on a comprehensive mathematical modeling and subsequent multi-objective optimization of a converter system. The relationship between the technological base and the performance of the system then exists as a mathematical representation, whose optimization assures the best possible exploitation of the available degrees of freedom and technologies. Thus an objective Technology Node of a system is obtained, whereby physical limits are implicitly taken into account. Furthermore, the sensitivity of the system performance with regard to the technological base can be calculated directly and the internal interdependence of Performance Indices directly studied. Accordingly, the improvement in performance achievable by improvements in the technology base can be tested and assessed in advance. Moreover, different system concepts, i.e. circuit topologies, control procedures, etc. can be evaluated and directly compared with regard to achievable efficiency, power density and costs in the form of the associated Pareto Front which defines the boundary of the Feasible Performance Space. If the target performance lies outside the Pareto Envelope of known system concepts and state-of-the-art technologies, a new technology must be employed. The necessity of a technological leap, i.e. the introduction of a Disruptive Technology can thus be recognized at an early stage. This offers an excellent basis for effective roadmapping for various main application areas in power electronics.


title: Streaming Pointwise Mutual Information

abstract: Recent work has led to the ability to perform space efficient, approximate counting over large vocabularies in a streaming context. Motivated by the existence of data structures of this type, we explore the computation of associativity scores, otherwise known as pointwise mutual information (PMI), in a streaming context. We give theoretical bounds showing the impracticality of perfect online PMI computation, and detail an algorithm with high expected accuracy. Experiments on news articles show our approach gives high accuracy on real world data.


title: An Integrated Security Architecture for Next Generation Networks

abstract: NGN is an IP based heterogeneous network environment, converges wired and wireless network technologies, offer a wide range of services to the customers with generalized mobility. It enables the customers to avail unrestricted services from different service providers of different networks, ensures enhanced and consistent services and reduces network and operational complexities. NGN, though, is embedded with unique characteristics of offering voice, data and video services over the same network with guaranteed quality of service, there are numerous critical security issues exist which need to be addressed. There are quite a lot of proposals for the security architecture of the NGN, but the ever growing vulnerabilities, threats and the attacks on NGN environment, demand the necessity for NGN security mechanisms. Hence, in this paper, a novel integrated security architecture for NGN has been proposed.


title: Simulation of bubbles

abstract: We present a novel framework based on a continuous fluid simulator for general simulation of realistic bubbles, with which we can handle as many significant dynamic bubble effects as possible. To capture a very thin liquid film of bubbles, we have developed a regional level set method allowing multi-manifold interface tracking. Based on the definitions of regional distance and its five operators, the implementation of the regional level set method is very easy. An implicit surface of liquid film with arbitrary thickness can be reconstructed from the regional level set function. To overcome the numerical instability problem, we exploit a new semi-implicit surface tension model which is unconditionally stable and makes the simulation of surface tension dominated phenomena much more efficient. An approximated film thickness evolution model is proposed to control the bubble's lifecycle. All these new techniques combine into a general framework that can produce various realistic dynamic effects of bubbles.


title: Fuzzy Clustering for Topic Analysis and Summarization of Document Collections

abstract: Large document collections, such as those delivered by Internet search engines, are difficult and time-consuming for users to read and analyse. The detection of common and distinctive topics within a document set, together with the generation of multi-document summaries, can greatly ease the burden of information management. We show how this can be achieved with a clustering algorithm based on fuzzy set theory, which (i) is easy to implement and integrate into a personal information system, (ii) generates a highly flexible data structure for topic analysis and summarization, and (iii) also delivers excellent performance.


title: Obtaining accurate trajectories from repeated coarse and inaccurate location logs

abstract: Context awareness is a key property for enabling context aware services. For a mobile device, the user's location or trajectory is one of the crucial contexts. One common challenge for detecting location or trajectory by mobile devices is to manage the tradeoff between accuracy and power consumption. Typical approaches are (1) controlling the frequency of usage of sensors and (2) sensor fusion technique. The algorithm proposed in this paper takes a different approach to improve the accuracy by merging repeatedly measured coarse and inaccurate location data from cell tower. The experimental result shows that the mean error distance between the detected trajectory and the ground truth is improved from 44m to 10.9m by merging data from 41 days' measurement.


title: Design parameters of rating scales for web sites

abstract: The effects of design parameters of rating scales on the perceived quality of interaction with web sites were investigated, using four scales (Disorientation, Perceived ease of use, Perceived usefulness and Flow). Overall, the scales exhibited good psychometric properties. In Experiment 1, psychometric results generally converged between two response formats (visual analogue scale and Likert scale). However, in Experiment 2, presentation of one questionnaire item per page was better than all items presented on a single page and direct interaction (using radio buttons) was better than indirect interaction (using a drop-down box). Practical implications and a framework for measurement are presented.