title: Logical Grammars Based on Constraint Handling Rules

abstract: CHR Grammars (CHRGs) are a grammar formalism that provides a constraint-solving approach to language analysis, built on top of Constraint Handling Rules in the same way as Definite Clause Grammars (DCGs) on Prolog. CHRGs work bottom-up and add the following features when compared with DCGs: ::: ::: ::: An inherent treatment of ambiguity without backtracking. ::: ::: ::: Robust parsing; do not give up in case of errors but return the recognized phrases. ::: ::: ::: Flexibility to produce and consume arbitrary hypotheses making it straightforward to deal with abduction, integrity constraints, operators a la assumption grammars, and to incorporate other constraint solvers. ::: ::: ::: References to left and right syntactic context; apply for disambiguation of simple and otherwise ambiguous grammars, coordination in natural language, and tagger-like grammar rules.


title: Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal

abstract: Nowadays sleepiness at the wheel is a problem that affects to the society at large rather more than it at first seemed. The purpose of this thesis is to detect sleepiness and micro-sleeps, which ar ...


title: Stable pinching with fingertips in humanoid robot hand

abstract: It is difficult to generate the action of stably pinching paper or needle, etc. with the finger tips, which is one of the important functions to be realized by humanoid robot hands. In this paper, therefore, the authors firstly propose a small-sized and light-weight robotic hand designed according to the concept of extracting required minimum motor functions and implementing them to the robot, and secondly propose a new robot hand capable of properly realizing a pinching motion with finger tips, by adding the minimum required degree of supplementary freedom which can be realized only with a machine. In the new robot hand, the authors mainly focus on additions of the degrees of freedom of independent motion to the terminal fingers and the degree of freedom of twisting motion to the thumb. Experiments were carried out to investigate force control characteristics of the finger tip joint. The results showed that providing the finger tip with rich force control performance even with weak force is effective for securing delicate control characteristics in a humanoid robot hand.


title: BendFields: Regularized Curvature Fields from Rough Concept Sketches

abstract: Designers frequently draw curvature lines to convey bending of smooth surfaces in concept sketches. We present a method to extrapolate curvature lines in a rough concept sketch, recovering the intended 3D curvature field and surface normal at each pixel of the sketch. This 3D information allows to enrich the sketch with 3D-looking shading and texturing. We first introduce the concept of regularized curvature lines that model the lines designers draw over curved surfaces, encompassing curvature lines and their extension as geodesics over flat or umbilical regions. We build on this concept to define the orthogonal cross field that assigns two regularized curvature lines to each point of a 3D surface. Our algorithm first estimates the projection of this cross field in the drawing, which is nonorthogonal due to foreshortening. We formulate this estimation as a scattered interpolation of the strokes drawn in the sketch, which makes our method robust to sketchy lines that are typical for design sketches. Our interpolation relies on a novel smoothness energy that we derive from our definition of regularized curvature lines. Optimizing this energy subject to the stroke constraints produces a dense nonorthogonal 2D cross field which we then lift to 3D by imposing orthogonality. Thus, one central concept of our approach is the generalization of existing cross field algorithms to the nonorthogonal case. We demonstrate our algorithm on a variety of concept sketches with various levels of sketchiness. We also compare our approach with existing work that takes clean vector drawings as input.


title: Electro-Optical Study of Subphthalocyanine in a Bilayer Organic Solar Cell

abstract: Ultra-thin films of subphthalocyanine (SubPc) were grown onto Si/SiO2 substrates by organic molecular beam deposition and the complex refractive index has been characterized by spectroscopic ellipsometry. The peak maximum in the extinction coefficient is determined to be 1.6 at 590 nm and the dielectric constant equals 3.9 in the limit of long wavelength. These values are extraordinary high when compared to the well-known metal-phthalocyanines and will be beneficial for the performance in a photovoltaic cell. The amorphous SubPc structure on top of indium-tin-oxide (ITO) as well as quartz glass is imaged by atomic force microscopy and scanning electron microscopy and we have characterized the nearly flat surface topology. Next, subphthalocyanine films in combination with buckminsterfullerene (C60) have been studied in a planar bilayer donor/acceptor heterojunction by current density-voltage characterization under AM 1.5 simulated illumination at various light intensities. A power conversion efficiency of 3.0 % under 1 sun was measured. Finally, the external and internal quantum efficiencies demonstrated peak maxima at 590 nm of 46 % and 55 %, respectively. Considering the abrupt junction at the donor/acceptor interface, the electron transfer from SubPc to the acceptor material is thus determined to be highly efficient.


title: A First Step Towards Security Extension for NFV Orchestrator

abstract: Network Functions Virtualization (NFV) has recently emerged as one of the new networking paradigms to significantly change the way that the networks and services are deployed, managed, and operated. One of the major advantages of NFV is to reduce hardware cost, meanwhile increasing service agility and scalability. Recently, there are many platforms for NFV management and orchestration (MANO) are available, however few of them contains dedicated modules or components for security management. This paper is intended to study the feasibility of extending the current NFV orchestrator to have the capability of managing security mechanisms. To do that, we propose a security extension module based on TOSCA data model which is commonly used by NFV MANO architecture. We then develop an access control use case to illustrate the usage of our proposed security extension. Specifically, we integrate the security extension into the Moon framework, which can automatically verify security attributes, generate access control policies, and further enforce the policies through the underlying infrastructure according to the high-level security policies. The preliminary results show that our security extension can work together with the NFV orchestrator to enable fine-grained access control to protect resources and services.


title: A Pursuit of Temporal Accuracy in General Activity Detection

abstract: Detecting activities in untrimmed videos is an important but challenging task. The performance of existing methods remains unsatisfactory, e.g., they often meet difficulties in locating the beginning and end of a long complex action. In this paper, we propose a generic framework that can accurately detect a wide variety of activities from untrimmed videos. Our first contribution is a novel proposal scheme that can efficiently generate candidates with accurate temporal boundaries. The other contribution is a cascaded classification pipeline that explicitly distinguishes between relevance and completeness of a candidate instance. On two challenging temporal activity detection datasets, THUMOS14 and ActivityNet, the proposed framework significantly outperforms the existing state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling activities with various temporal structures.


title: Psychopathic Sexuality: The Thin Line Between Fantasy and Reality

abstract: In two studies, we explored the relations between psychopathic traits and sexual fantasy content. In Study 1, we rated content themes in the fantasy narratives of 195 men and women recruited at a Canadian university. In Study 2, we administered a sexual fantasy questionnaire to a sample of 355 Canadian undergraduate students. In Study 1, we found that psychopathic traits predicted themes of anonymous, uncommitted, and nonromantic sexual activity after controlling for participant sex. In Study 2, we found that psychopathy added to the prediction of self-reported engagement in unrestricted, dominant, submissive, deviant, and adventurous sexual activity, even after controlling for participant sex and level of fantasizing about that activity. Furthermore, an interaction between psychopathy and level of fantasizing was observed for unrestricted and deviant sexual behavior, such that participants who reported high levels of fantasizing about these sexual themes were more likely to engage in that behavior if they also reported high levels of psychopathic traits. These findings suggest that psychopathy is related not only to interest in particular sexual behaviors, but also to whether individuals will translate these fantasized behaviors into reality.


title: Anti-lock regenerative braking torque control strategy for electric vehicle

abstract: Regenerative braking is one of the most effective deceleration control methods in case of electric and hybrid vehicles. The properly controlled regenerative braking force of the electric motor(s) brings better braking safety, shorter braking distance, and less depreciation of the brakes if accompanied by the hydraulic braking modules. When the brake pedal is pressed, the electric motor is treated as a generator to produce negative torque. In this scenario the produced energy can be either dissipated or used to charge the batteries. In this paper, based on the registered brake strength, the braking force is calculated. Accordingly, employing a braking torque distribution strategy between the regenerative braking torque and the hydraulic braking torque, the proper braking torques are determined and the optimal slip ratio is reached by adjusting the braking torques. An anti-lock regenerative braking strategy is proposed to attain anti-slip control for negative accelerations. The performance of the proposed system is evaluated using simulations. The attained simulation results show that optimal braking characteristics are achievable for a variety of conditions using the proposed method.


title: Abstractive News Summarization based on Event Semantic Link Network

abstract: This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.


title: Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios

abstract: We present a method for motion planning in the presence of moving obstacles that is aimed at dynamic on-road driving scenarios. Planning is performed within a geometric graph that is established by sampling deterministically from a manifold that is obtained by combining configuration space and time. We show that these graphs are acyclic and shortest path algorithms with linear runtime can be employed. By reparametrising the configuration space to match the course of the road, it can be sampled very economically with few vertices, and this reduces absolute runtime further. The trajectories generated are quintic splines. They are second order continuous, obey nonholonomic constraints and are optimised for minimum square of jerk. Planning time remains below 20 ms on general purpose hardware.


title: Towards Compact and Fast Neural Machine Translation Using a Combined Method

abstract: Neural Machine Translation (NMT) lays intensive burden on computation and ::: memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This paper presents a four stage pipeline to ::: compress model and speed up the decoding for NMT. Our method first introduces ::: a compact architecture based on convolutional encoder and weight shared embeddings. Then weight pruning is applied to obtain a sparse model. Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search. Hence, the time-consuming beam search can be replaced by simple ::: greedy decoding. Finally, vocabulary selection is used to reduce the computation ::: of softmax layer. Our final model achieves 10 × speedup, 17 × parameters reduction, 35MB storage size and comparable performance compared to the baseline model.


title: E-government implementation: A bird's eye view of issues relating to costs, opportunities, benefits and risks

abstract: After more than a decade of comprehensive research work in the area of electronic government (e-government), no attempt has yet been made to undertake a systematic literature review on the costs, opportunities, benefits and risks that influence the implementation of e-government. This is particularly significant given the various related challenges that governments have faced over the years when implementing e-government initiatives. Hence, the aim of this paper is to undertake a comprehensive analysis of relevant literature addressing these issues using a systematic review of 132 studies identified from the Scopus online database and Google Scholar together with a manual review of relevant papers from journals dedicated to electronic government research such as Electronic Government, an International Journal (EGIJ), International Journal of Electronic Government Research (IJEGR) and Transforming Government: People, Process, and Policy (TGPPP). The overall review indicated that although a large number of papers discuss costs, opportunities, benefits and risks, treatment of these issues have tended to be superficial. Moreover, there is a lack of empirical studies which can statistically evaluate the performance of these constructs in relation to the various e-government systems. Therefore, this research would help governments to better analyse the impact of costs, opportunities, benefits and risks on the success of e-government systems and its pre-adoption from an implementation perspective.


title: From the bird’s eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets

abstract: This paper presents stylized facts concerning the spot intra-daily foreign exchange markets. It first describes intra-daily data and proposes a set of definitions for the variables of interest. Empirical regularities of the foreign exchange intra-daily data are then grouped under three major topics: the distribution of price changes, the process of price formation and the heterogeneous structure of the market. The stylized facts surveyed in this paper shed new light on the market structure that appears composed of heterogeneous agents. It also poses several challenges such as the definition of price and of the time-scale, the concepts of risk and efficiency, the modeling of the markets and the learning process.


title: Eigenvalues of covariance matrices: Application to neural-network learning.

abstract: The learing time of a simple neural-network model is obtained through an analytic computation of the eigenvalue spectrum for the Hessian matrix, which describes the second-order properties of the objective function in the space of coupling coefficients. The results are generic for symmetric matrices obtained by summing outer products of random vectors. The form of the eigenvalue distribution suggests new techniques for accelerating the learning process, and provides a theoretical justification for the choice of centered versus biased state variables.


title: Rethinking BPM in a Cognitive World: Transforming How We Learn and Perform Business Processes

abstract: If we are to believe the technology hype cycle, we are entering a new era of Cognitive Computing, enabled by advances in natural language processing, machine learning, and more broadly artificial intelligence. These advances, combined with evolutionary progress in areas such as knowledge representation, automated planning, user experience technologies, software-as-a-service and crowdsourcing, have the potential to transform many industries. In this paper, we discuss transformations of BPM that advances in the Cognitive Computing will bring. We focus on three of the most signficant aspects of this transformation, namely: (a) Cognitive Computing will enable “knowledge acquisition at scale”, which will lead to a transformation in Knowledge-intensive Processes (KiP’s); (b) We envision a new process meta-model will emerge that is centered around a “Plan-Act-Learn” cycle; and (c) Cognitive Computing can enable learning about processes from implicit descriptions (at both design- and run-time), opening opportunities for new levels of automation and business process support, for both traditional business processes and KiP’s. We use the term cognitive BPM to refer to a new BPM paradigm encompassing all aspects of BPM that are impacted and enabled by Cognitive Computing. We argue that a fundamental understanding of cognitive BPM requires a new research framing of the business process ecosystem. The paper presents a conceptual framework for cognitive BPM, a brief survey of state of the art in emerging areas of Cognitive BPM, and discussion of key directions for further research.


title: Antenna array design for multi-UAVs communication in next generation Flying Ad-Hoc Networks (FANETs)

abstract: In this paper, a microstrip patch antenna and its 1×2, 1×4 and 2×4 arrays are proposed for Flying Ad-hoc Network (FANET) communication in multi-UAV systems. The antennas and arrays resonate in the 5GHz band and have been optimized for high bandwidth and gain. The antennas and arrays are proposed and designed using Rogers RT/duroid 5880LZ substrate which is light weight and has specific applications in UAV antennas. The bandwidth achieved by 5880LZ antenna and arrays is 500–530MHz. The efficiency of the single element 5880LZ antenna is 91.7%. The gain and directivity of the 5880LZ antenna has been increased from 9.2dBi to 15.8dBi and 9.6dBi to 17.6dBi respectively by designing 1×2, 1×4 and 2×4 arrays. The antennas are designed using Agilent advance design system - momentum simulator.


title: A methodology for evaluating the impact of data compression on climate simulation data

abstract: High-resolution climate simulations require tremendous computing resources and can generate massive datasets. At present, preserving the data from these simulations consumes vast storage resources at institutions such as the National Center for Atmospheric Research (NCAR). The historical data generation trends are economically unsustainable, and storage resources are already beginning to limit science objectives. To mitigate this problem, we investigate the use of data compression techniques on climate simulation data from the Community Earth System Model. Ultimately, to convince climate scientists to compress their simulation data, we must be able to demonstrate that the reconstructed data reveals the same mean climate as the original data, and this paper is a first step toward that goal. To that end, we develop an approach for verifying the climate data and use it to evaluate several compression algorithms. We find that the diversity of the climate data requires the individual treatment of variables, and, in doing so, the reconstructed data can fall within the natural variability of the system, while achieving compression rates of up to 5:1.


title: Learning to rank code examples for code search engines

abstract: Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user's queries. Essentially, a code search engine provides a ranking schema, which combines a set of ranking features to calculate the relevance between a query and candidate code examples. Consequently, the ranking schema places relevant code examples at the top of the result list. However, it is difficult to determine the configurations of the ranking schemas subjectively. In this paper, we propose a code example search approach that applies a machine learning technique to automatically train a ranking schema. We use the trained ranking schema to rank candidate code examples for new queries at run-time. We evaluate the ranking performance of our approach using a corpus of over 360,000 code snippets crawled from 586 open-source Android projects. The performance evaluation study shows that the learning-to-rank approach can effectively rank code examples, and outperform the existing ranking schemas by about 35.65 % and 48.42 % in terms of normalized discounted cumulative gain (NDCG) and expected reciprocal rank (ERR) measures respectively.


title: A Machine Learning Approach to Android Malware Detection

abstract: With the recent emergence of mobile platforms capable of executing increasingly complex software and the rising ubiquity of using mobile platforms in sensitive applications such as banking, there is a rising danger associated with malware targeted at mobile devices. The problem of detecting such malware presents unique challenges due to the limited resources avalible and limited privileges granted to the user, but also presents unique opportunity in the required metadata attached to each application. In this article, we present a machine learning-based system for the detection of malware on Android devices. Our system extracts a number of features and trains a One-Class Support Vector Machine in an offline (off-device) manner, in order to leverage the higher computing power of a server or cluster of servers.


title: EEG for Automatic Person Recognition

abstract: Electroencephalography is potentially more secure and privacy-compliant than traditional biometric identifiers. A related video can be seen here: http://youtu.be/5f0zflJRnvc. It depicts four maps representing the spatial distribution on the scalp of an EEG signal in time for the four bands Delta, Theta, Alpha, and Beta. Another related video can be seen here: http://youtu.be/X34pUw-4z_8. It depicts the spatial distribution on the scalp of an EEG amplitude spectral density (top) for frequencies ranging in the [1, 30] Hz interval (bottom).


title: Consciousness and Complexity

abstract: Conventional approaches to understanding consciousness are generally concerned with the contribution of specific brain areas or groups of neurons. By contrast, it is considered here what kinds of neural processes can account for key properties of conscious experience. Applying measures of neural integration and complexity, together with an analysis of extensive neurological data, leads to a testable proposal-the dynamic core hypothesis-about the properties of the neural substrate of consciousness.


title: A low-loss compact stripline low-pass filter for millimeter-wave applications

abstract: In this work, a low-loss compact stripline low-pass-filter for millimeter-wave applications is to be presented. The proposed filter, with a pass-band from 26.5 GHz to 40 GHz and 55 dB of rejection up to 65 GHz, shows an improved performance over the classical alternative (a stepped-impedance filter). The filter has been fabricated in LTCC technology and the effects of the fabrication errors have been studied with a sensitivity analysis. The study shows that the proposed filter is more robust to these errors which, together with the reduced size, makes it more suitable for later integration in a high-frequency system.


title: Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias

abstract: While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts. Our analysis identifies an important but long overlooked issue of existing visual emotion benchmarks in the form of dataset biases. We design a series of tests to show and measure how such dataset biases obstruct learning a generalizable emotion recognition model. Based on our analysis, we propose a webly supervised approach by leveraging a large quantity of stock image data. Our approach uses a simple yet effective curriculum guided training strategy for learning discriminative emotion features. We discover that the models learned using our large scale stock image dataset exhibit significantly better generalization ability than the existing datasets without the manual collection of even a single label. Moreover, visual representation learned using our approach holds a lot of promise across a variety of tasks on different image and video datasets.


title: E-services: problems, opportunities, and digital platforms

abstract: Services that are delivered over the Internet-e-services-pose unique problems yet offer unprecedented opportunities. In this paper, we classify e-services along the dimensions of their level of digitization and the nature of their target markets (business-to-business, business-to-consumer, consumer-to-consumer). Using the case of application services, we analyze how they differ from traditional software procurement and development. Next, we extend the concept of modular platforms to this domain and identify how knowledge management can be used to rapidly assemble new application services. We also discuss how such traceability-based knowledge management can facilitate e-service evolution and version-based market segmentation.


title: The Role of Aggregation in the Measurement of IT-Related Organizational Innovation

abstract: The extent of organizational innovation with information technology, an important construct in the IT innovation literature, has been measured in many different ways. Some measures have a narrow focus while others aggregate innovative behaviors across a set of innovations or stages in the assimilation lifecycle. There appear to be some significant tradeoffs involving aggregation: more aggregated measures can be more robust and generalizable and can promote stronger predictive validity, while less aggregated measures allow more context-specific investigations and can preserve clearer theoretical interpretations. This article begins with a conceptual analysis that identifies the circumstances when these tradeoffs are most likely to favor aggregated measures. It is found that aggregation should be favorable when: (1) the researcher's interest is in general innovation or a model that generalizes to a class of innovations, (2) antecedents have effects in the same direction in all assimilation stages, (3) characteristics of organizations can be treated as constant across the innovations in the study, (4) characteristics of innovations can not be treated as constant across organizations in the study, (5) the set of innovations being aggregated includes substitutes or moderate complements, and (6) sources of noise in the measurement of innovation may be present. The article then presents an empirical study using data on the adoption of software process technologies by 608 U.S. based corporations. This study-which had circumstances quite favorable to aggregation- found that aggregating across three innovations within a technology class more than doubled the variance explained compared to single innovation models. Aggregating across assimilation stages also had a slight positive effect on predictive validity. Taken together, these results provide initial confirmation of the conclusions from the conceptual analysis regarding the circumstances favoring aggregation.


title: A prospective randomised study of alginate-drenched low stretch bandages as an alternative to conventional lymphologic compression bandaging

abstract: Background ::: Breast-cancer-related lymphoedema, either caused by the tumour itself or its therapy, can be found in approximately 24% of all patients. It results in disabilities, psychological distress and reduced quality of life. Therefore, proper therapy for this entity is very important. Guidelines recommend a therapy in two phases, an intensive phase I for 3 weeks for volume reduction and, between the cycles of phase I, a reduced phase II to maintain the result. During phase I therapy, manual lymphatic drainage often cannot be administered on weekends or holidays; only a reduced therapy, mainly by application of a more or less passive compression by bandaging, is administered. For this, conventional low-stretch bandages are hitherto being used. Several attempts have been made to overcome this disadvantage by either impregnating or covering the bandage with sticky or adhesive substances such as india rubber, elastomeres, polyacrylates, etc. Recently, new bandages are available, which are drenched with alginate that becomes semi-rigid after drying for approximately 6 h. It was the aim of this study to compare alginate bandaging to a conventional lymphologic-multilayered low-stretch bandaging with individual supportive lining as to their effect concerning their congestive capacity in exactly delimited time periods of reduced decongestive therapy as well as the patients’ tolerance.


title: Racism, Segregation, and Risk of Obesity in the Black Women's Health Study

abstract: We assessed the relation of experiences of racism to the incidence of obesity and the modifying impact of residential racial segregation in the Black Women's Health Study, a follow-up study of US black women. Racism scores were created from 8 questions asked in 1997 and 2009 about the frequency of “everyday” racism (e.g., “people act as if you are dishonest”) and of “lifetime” racism (e.g., unfair treatment on the job). Residential segregation was measured by linking participant addresses to 2000 and 2010 US Census block group data on the percent of black residents. We used Cox proportional hazard models to estimate incidence rate ratios and 95% confidence intervals. Based on 4,315 incident cases of obesity identified from 1997 through 2009, both everyday racism and lifetime racism were positively associated with increased incidence. The incidence rate ratios for women who were in the highest category of everyday racism or lifetime racism in both 1997 and 2009, relative to those in the lowest category, were 1.69 (95% confidence interval: 1.45, 1.96; Ptrend < 0.01) and 1.38 (95% confidence interval: 1.15, 1.66; Ptrend < 0.01), respectively. These associations were not modified by residential segregation. These results suggest that racism contributes to the higher incidence of obesity among African American women.


A mixture model of clustering ensembles


REVISITING SOCIAL BREAKDOWN SYNDROME: THE AUSTRALIAN NATIONAL SURVEY OF HIGH IMPACT PSYCHOSIS (SHIP)


title: Automated User Modeling for Personalized Digital Libraries

abstract: Digital libraries (DLs) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from DLs. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in DLs has been user driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct DLs that satisfy a user's necessity for information: Adaptive DLs, libraries that automatically learn user preferences and goals and personalize their interaction using this information.


title: Adaptive scenario-based object-oriented test frameworks for testing embedded systems

abstract: This paper presents a process to develop adaptive object-oriented scenario-based test frameworks for testing embedded systems. Embedded systems often require rigorous testing due to the mission-critical nature of their applications, and they are often developed as a family of products. The process uses techniques such as design-for-change, design patterns, scenarios, ripple effect analysis, and regression testing. This paper then uses an example to illustrate this process by applying it to test a mobile phone system, and the framework constructed can facilitate generation of numerous test cases quickly with minimum effort, and it can also accommodate many changes suggested by another party without changing the overall structure of the framework.


title: The director's lens: an intelligent assistant for virtual cinematography

abstract: We present the Director's Lens, an intelligent interactive assistant for crafting virtual cinematography using a motion-tracked hand-held device that can be aimed like a real camera. The system employs an intelligent cinematography engine that can compute, at the request of the filmmaker, a set of suitable camera placements for starting a shot. These suggestions represent semantically and cinematically distinct choices for visualizing the current narrative. In computing suggestions, the system considers established cinema conventions of continuity and composition along with the filmmaker's previous selected suggestions, and also his or her manually crafted camera compositions, by a machine learning component that adapts shot editing preferences from user-created camera edits. The result is a novel workflow based on interactive collaboration of human creativity with automated intelligence that enables efficient exploration of a wide range of cinematographic possibilities, and rapid production of computer-generated animated movies.


title: UHF broadband slot antenna with bent aperture

abstract: The described is the bow-tie slot antenna with a slightly bent aperture. The use of the bent aperture allows to increase gain in the upper frequency band compared to an antenna with a flat aperture.


title: Mining Fine Grained Opinions by Using Probabilistic Models and Domain Knowledge

abstract: The explosive growth of the user-generated content on the Web has offered a rich data source for mining opinions. However, the large number of diverse review sources challenges the individual users and organizations on how to use the opinion information effectively. Therefore, automated opinion mining and summarization techniques have become increasingly important. Different from previous approaches that have mostly treated product feature and opinion extraction as two independent tasks, we merge them together in a unified process by using probabilistic models. Specifically, we treat the problem of product feature and opinion extraction as a sequence labeling task and adopt Conditional Random Fields models to accomplish it. As part of our work, we develop a computational approach to construct domain specific sentiment lexicon by combining semi-structured reviews with general sentiment lexicon, which helps to identify the sentiment orientations of opinions. Experimental results on two real world datasets show that the proposed method is effective.


title: Influence of preparation design and existing condition of tooth structure on load to failure of ceramic laminate veneers

abstract: Statement of problem Although investigators have evaluated the effect of ceramic veneer preparation design, limited information is available regarding preparation design in association with the condition of existing tooth structure. Purpose The purpose of this in vitro study was to evaluate the effect of preparation design and the amount of existing tooth structure on the fracture resistance of pressable ceramic laminate veneers. Material and methods Thirty-two extracted human maxillary central incisors were allocated into 4 groups (n=8) to test for 2 variables: (1) the preparation design (a 2 mm incisal reduction shoulder finish line with or without palatal chamfer) and (2) the existing amount of tooth structure (non-worn tooth or worn tooth). Measurement of the remaining enamel thickness on the inciso-occlusal surface was made after the tooth was prepared. All prepared teeth were restored with pressable ceramic (IPS Empress) veneers, and the veneers were luted with resin cement (Rely-X Veneer). These luted specimens were loaded to failure in a universal testing machine, in the compression mode, with a crosshead speed of 0.05 mm/min. The data were analyzed using a 2-way ANOVA and Tukey's HSD multiple comparison test (α=.05) Results Preparation design and the amount of existing tooth structure had a significant effect on the load to failure value ( P P =.702). Mean (SD) load to failure values were as follows: a preparation design with a palatal chamfer margin with a non-worn tooth (166.67 N (28.89)) revealed a significantly higher failure load than the group with a shoulder finish line alone (131.84 N (18.88)) ( P P P Conclusions Preparation design and the amount of existing tooth structure had a significant effect on load to failure for ceramic veneers. This study revealed that using a palatal chamfer margin design significantly increased the load to failure compared to a shoulder finish line.


Introduction: Behavioral phenotypes in neurogenetic syndromes


title: Pattern-Based Emotion Classification on Social Media

abstract: Sentiment analysis can go beyond the typical granularity of polarity that assumes each text to be positive, negative or neural. Indeed, human emotions are much more diverse, and it is interesting to study how to define a more complete set of emotions and how to deduce these emotions from human-written messages. In this book chapter we argue that using Plutchik’s wheel of emotions model and a rule-based approach for emotion detection in text makes it a good framework for emotion classification on social media. We provide a detailed description of how to define rule-based patterns for Plutchik’s wheel emotion detection, how to learn them from the annotated social media and how to apply them for classifying emotions in the previously unseen texts. The results of the experimental study suggest that the described framework is promising and that it advances the current state-of-the-art in emotion detection.


title: MomentsNet: a simple learning-free method for binary image recognition

abstract: In this paper, we propose a new simple and learning-free deep learning network named MomentsNet, whose convolution layer, nonlinear processing layer and pooling layer are constructed by Moments kernels, binary hashing and block-wise histogram, respectively. Twelve typical moments (including geometrical moment, Zernike moment, Tchebichef moment, etc.) are used to construct the MomentsNet whose recognition performance for binary image is studied. The results reveal that MomentsNet has better recognition performance than its corresponding moments in almost all cases and ZernikeNet achieves the best recognition performance among MomentsNet constructed by twelve moments. ZernikeNet also shows better recognition performance on binary image database than that of PCANet, which is a learning-based deep learning network.


title: Self-organizing maps for outlier detection

abstract: In this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection ofthe trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data.


title: Towards Accurate Object Localization with Smartphones

abstract: In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions.


title: Predicting First Impressions with Deep Learning

abstract: Describable visual facial attributes are now commonplace in human biometrics and affective computing, with existing algorithms even reaching a sufficient point of maturity for placement into commercial products. These algorithms model objective facets of facial appearance, such as hair and eye color, expression, and aspects of the geometry of the face. A natural extension, which has not been studied to any great extent thus far, is the ability to model subjective attributes that are assigned to a face based purely on visual judgements. For instance, with just a glance, our first impression of a face may lead us to believe that a person is smart, worthy of our trust, and perhaps even our admiration - regardless of the underlying truth behind such attributes. Psychologists believe that these judgements are based on a variety of factors such as emotional states, personality traits, and other physiognomic cues. But work in this direction leads to an interesting question: how do we create models for problems where there is no ground truth, only measurable behavior? In this paper, we introduce a new convolutional neural network-based regression framework that allows us to train predictive models of crowd behavior for social attribute assignment. Over images from the AFLW face database, these models demonstrate strong correlations with human crowd ratings.


title: A Live Face Swapper

abstract: In this technical demonstration, we propose a face swapping framework, which is able to interactively change the appearance of a face in the wild to a different person/creature's face in real time on a mobile device. To realize this objective, we develop a deep learning-based face detector which is able to accurately detect faces in the wild. Our face feature points tracking system based on progressive initialization ensures accurate and robust localization of facial landmarks under extreme poses and expressions in real time. Relying on the advances of our face detector and face feature points tracker, we construct the Face Swapper which can smoothly replace the face appearance of a user in real time.


title: Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering

abstract: Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover, these algorithms cannot effectively use the learner's historical sequential patterns of material accessing in recommendation. For addressing these problems and improving the accuracy and quality of recommendation, a new material recommender system framework based on sequential pattern mining and multidimensional attribute-based collaborative filtering (CF) is proposed. In the sequential pattern based approach, modified Apriori and PrefixSpan algorithms are implemented to discover latent patterns in accessing of materials and use them for recommendation. Leaner Preference Tree (LPT) is introduced to take into account multidimensional-attribute of materials, and learners' rating and model dynamic and multi-preference of learners in the multidimensional attribute-based CF approach. Finally, the recommendation results of two approaches are combined using cascade, weighted and mixed methods. The proposed method outperforms the previous algorithms on the classification accuracy measures and the learner's real learning preference can be satisfied accurately according to the real-time up dated contextual information.


title: M.: Real-time 3D soccer ball tracking from multiple cameras

abstract: A novel model for 3D soccer ball tracking is proposed, using multiple fixed cameras as input. The main problems are: successfully filtering false alarms, tracking through missing observations and estimating 3D positions from single or multiple camera inputs. The key innovation is to categorise all motion of the ball into four possible phases ‐ rolling, flying, in possession, and out of play, and in different phases the ball trajectory is further modelled as linear or curve segments. Then, triangulations and phase-specific models are employed to estimate 3D ball positions. The system accuracy is evaluated by comparing the estimated ball phases and positions with manual groundtruth, over several minutes of real data sequences. Unlike existing systems using shadows and manual assistance, our approach demonstrates fully automatic ball tracking with the potential for accurate and robust results.


title: Leadership theory and research in the new millennium: Current theoretical trends and changing perspectives

abstract: Scholarly research on the topic of leadership has witnessed a dramatic increase over the last decade, resulting in the development of diverse leadership theories. To take stock of established and developing theories since the beginning of the new millennium, we conducted an extensive qualitative review of leadership theory across 10 top-tier academic publishing outlets that included The Leadership Quarterly, Administrative Science Quarterly, American Psychologist, Journal of Management, Academy of Management Journal, Academy of Management Review, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Organizational Science, and Personnel Psychology. We then combined two existing frameworks (Gardner, Lowe, Moss, Mahoney, & Cogliser, 2010; Lord & Dinh, 2012) to provide a process-oriented framework that emphasizes both forms of emergence and levels of analysis as a means to integrate diverse leadership theories. We then describe the implications of the findings for future leadership research and theory.


title: Impact of ICT-based innovations on organizational performance: The role of corporate entrepreneurship

abstract: Purpose ::: ::: ::: ::: ::: Research has shown that information and communication technologies (ICT) are crucial for economic growth. The purpose of this paper is to develop a framework that would depict and examine the nature of the relationship between ICT use and organizational performance in the Lebanese market, taking into consideration the impact that innovation and corporate entrepreneurship may have on this relationship. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: To investigate the proposed model a survey targeting employees, and managers who adopted ICT applications in SMEs located in Lebanon was conducted. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results indicate that ICT and innovation are strategic resources. However, their contribution to sustainable competitive advantage vitally depends on the implicitness and entrepreneurial behaviors of those involved. It is through this capability that ICT and ICT-based innovations could make a difference in organization’s performance – both present and future. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: First, the respondents were selected using the convenience sampling technique. Second, the data were collected through self-report questionnaires. Finally, the use of perceptual data related to performance may have a bias effect on the study results. ::: ::: ::: ::: ::: Practical/implications ::: ::: ::: ::: ::: At the practical level, the study results have repercussions for managers, technology suppliers, and innovation adopters and managers, as this may contribute to better understanding of the factors that could influence the adoption, management, and use of ICT resources for enhancing the competitiveness level of the firm. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The results of this study have implications for ICT adoption in Lebanese SMEs. More importantly, they suggest a framework which depicts the relationship between ICT and the organization’s innovation level on one hand, and a company’s performance on the other, taking entrepreneurship as a mediator in this relationship.


Ties, Nets and an Elastic Bund: Community in the Postmodern City.


title: Beat them or ban them: The characteristics and social functions of anger and contempt.

abstract: This article reports 3 studies in which the authors examined (a) the distinctive characteristics of anger and contempt responses and (b) the interpersonal causes and effects of both emotions. In the 1st study, the authors examined the distinction between the 2 emotions; in the 2nd study, the authors tested whether contempt could be predicted from previous anger incidents with the same person; and in the 3rd study, the authors examined the effects of type of relationship on anger and contempt reactions. The results of the 3 studies show that anger and contempt often occur together but that there are clear distinctions between the 2 emotions: Anger is characterized more by short-term attack responses but long-term reconciliation, whereas contempt is characterized by rejection and social exclusion of the other person, both in the short-term and in the long-term. The authors also found that contempt may develop out of previously experienced anger and that a lack of intimacy with and perceived control over the behavior of the other person, as well as negative dispositional attributions about the other person, predicted the emergence of contempt.


title: Planning Text For Advisory Dialogues: Capturing Intentional And Rhetorical Information

abstract: To participate in a dialogue a system must be capable of reasoning about its own previous utterances. Follow-up questions must be interpreted in the context of the ongoing conversation, and the system's previous contributions form part of this context. Furthermore, if a system is to be able to clarify misunderstood explanations or to elaborate on prior explanations, it must understand what it has conveyed in prior explanations. Previous approaches to generating multisentential texts have relied solely on rhetorical structuring techniques. In this paper, we argue that, to handle explanation dialogues successfully, a discourse model must include information about the intended effect of individual parts of the text on the hearer, as well as how the parts relate to one another rhetorically. We present a text planner that records this information and show how the resulting structure is used to respond appropriately to a follow-up question.


title: A Novel CPW Fed Multiband Circular Microstrip Patch Antenna for Wireless Applications

abstract: In this paper, a novel design of coplanar micro strip patch antenna is presented for ultra wideband (UWB) and smart grid applications. It is designed on a dielectric substrate and fed by a coplanar wave guide (CPW). Micro strip patch antenna consists of a circular patch with an elliptical shaped eye opening in the center to provide multiband operations. The antenna has wide bandwidth of 5 GHz between 10.8-15.8 GHz and 3 GHz between 5.8-8.8GHz, high return loss of-34 dB at 3.9 GHz and -29dB at 6.8 GHz with satisfactory radiation properties. The parameters that affect the performance of the antenna in terms of its frequency domain characteristics are investigated. The antenna design has been simulated on Ansoft's High Frequency Structure Simulator (HFSS). It is a compact design of 47×47mm2 area on Rogers RO3003 (tm) substrate with dielectric constant of 3 and thickness of 1.6 mm. The simulated antenna has three operating frequency bands at 2.82, 5.82-8.8, 10.8-15.8GHz and shows the band-notch characteristic in the UWB band to avoid interferences, which is caused by WLAN at 5.15 -- 5.825 GHz and Wi-MAX at 5.25 -- 5.85 GHz. The designed antenna structure with 5.68 dBi gain is planar, simple and compact, hence it can be easily embedded in wireless communication systems and integrated with microwave circuitry for low manufacturing cost.


title: Model-driven reverse engineering of legacy graphical user interfaces

abstract: Businesses are increasingly beginning to modernise those of their legacy systems that were originally developed with Rapid Application Development (RAD) or Fourth Generation Language (4GL) environments, in order to benefit from new platforms and technologies. In these systems, the Graphical User Interface (GUI) layout is implicitly provided by the position of the GUI elements (i.e. coordinates). However, taking advantage of current features of GUI technologies often requires an explicit, high-level layout model. We propose a Model-Driven Engineering process with which to perform the automatic reverse engineering of RAD-built GUIs, which is focused on discovering the implicit layout, and produces a GUI model in which the layout is explicit. As an example of the approach, we apply an automatic reengineering process to this model in order to generate a Java Swing user interface.


title: Resolving semantic heterogeneity in schema integration

abstract: Interoperability and integration of data sources are becoming ever more impor- tant issues as both, the amount of data and the number of data producers are growing. Interoper- ability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Semantics refer to the meaning of data in contrast to syntax, which only defines the structure of the schema items (e.g., classes and attributes). We focus on the part of semantics related to the meanings of the terms used as identifiers in schema definitions. This paper pre- sents an approach to integrate schemas from different communities, where each such commu- nity is using its own ontology. The approach is based on merging ontologies based on similarity relations among concepts of different ontologies. We present formal definitions of similarity relations based on intensional definitions and conclude the extensional consequences. The pro- cess of merging ontologies based on the detected similarity relations is discussed. The merged ontology is finally used to derive an integrated schema. The resulting schema can be used as the global schema in a federated database system.


title: Offline Writer Identification Using Convolutional Neural Network Activation Features

abstract: Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of \(\mathrm {mAP}\) on the challenging bilingual ICDAR dataset.


Corpus-analysis for NLG


title: The network in the garden: an empirical analysis of social media in rural life

abstract: History repeatedly demonstrates that rural communities have unique technological needs. Yet, we know little about how rural communities use modern technologies, so we lack knowledge on how to design for them. To address this gap, our empirical paper investigates behavioral differences between more than 3,000 rural and urban social media users. Using a dataset collected from a broadly popular social network site, we analyze users' profiles, 340,000 online friendships and 200,000 interpersonal messages. Using social capital theory, we predict differences between rural and urban users and find strong evidence supporting our hypotheses. Namely, rural people articulate far fewer friends online, and those friends live much closer to home. Our results also indicate that the groups have substantially different gender distributions and use privacy features differently. We conclude by discussing design implications drawn from our findings; most importantly, designers should reconsider the binary friend-or-not model to allow for incremental trust-building.


title: Automatic Question Generation from Children’s Stories for Companion Chatbot

abstract: With the rising trends of dialog system, interactions with questions become a vital human-machine interface in realizing intentions and generating adequate feedback, thereby establishing question generation as an important and interesting research topic. In the education domain, question generation is also useful for reducing time and effort for teachers, as well as parents. Extensive research has been conducted on question generation recently but little focused on question generation in Chinese. In addition, previous papers examining question generation in Chinese, have been poorly reviewed due to the clumsy handling of sentence complexities of Chinese. In this article, we focus on developing a question generation technique based on children’s tales. For the training and testing dataset, approximately one hundred articles from the website, 兒童 故事(http://www.yfes.tn.edu.tw/yfesvi/story.htm) were used. To achieve better quality of the questions automatically generated, approaches and methods were carefully chosen in this system. The results show that questions generated from our system are mostly acceptable, which reflects the main objective of our system.


title: Phase-Gradient Meta-Dome for Increasing Grating-Lobe-Free Scan Range in Phased Arrays

abstract: This paper presents a simple and effective way to increase the scan range of phased array antennas by using a metasurface (MTS) curved dome. The technique is based on a phase-gradient passive MTS that deflects the incident rays coming from the array aperture, and on a predistortion of amplitude and phase applied to the original phased array. We assume, therefore, to have at disposal an array capable of electronic beam scanning over a limited angular range with control of amplitude and phase. Geometrical optics ray tracing is used for the design of the metadome and physical optics for the analysis. For the sake of simplicity, the idea is illustrated here for a 2-D scan case; however, the extension to full 3-D scanning appears to be straightforward.


title: An Intelligent Hotel Room

abstract: This paper presents an innovative application of ambient technology in the domain of tourism and leisure that aims to improve the quality of services offered by the hospitality industry. The main objective is to formulate an ambient ecosystem that observes its surroundings using non-invasive technology and adapts its behavior, in real-time, to deliver "intelligent" and personalized services to the occupants of a guest room. Towards that direction, the proposed system aims to leverage the disadvantages of being away from home and offer a unique user experience.


title: GazeSim: simulating foveated rendering using depth in eye gaze for VR

abstract: We present a novel technique of implementing customized hardware that uses eye gaze focus depth as an input modality for virtual reality applications. By utilizing eye tracking technology, our system can detect the point in depth the viewer focusses on, and therefore promises more natural responses of the eye to stimuli, which will help overcoming VR sickness and nausea. The obtained information for the depth focus of the eye allows the utilization of foveated rendering to keep the computing workload low and create a more natural image that is clear in the focused field, but blurred outside that field.


title: Building Privacy-Preserving Cryptographic Credentials from Federated Online Identities

abstract: Federated identity providers, e.g., Facebook and PayPal, offer a convenient means for authenticating users to third-party applications. Unfortunately such cross-site authentications carry privacy and tracking risks. For example, federated identity providers can learn what applications users are accessing; meanwhile, the applications can know the users' identities in reality. This paper presents Crypto-Book, an anonymizing layer enabling federated identity authentications while preventing these risks. Crypto-Book uses a set of independently managed servers that employ a (t,n)-threshold cryptosystem to collectively assign credentials to each federated identity (in the form of either a public/private keypair or blinded signed messages). With the credentials in hand, clients can then leverage anonymous authentication techniques such as linkable ring signatures or partially blind signatures to log into third-party applications in an anonymous yet accountable way. We have implemented a prototype of Crypto-Book and demonstrated its use with three applications: a Wiki system, an anonymous group communication system, and a whistleblower submission system. Crypto-Book is practical and has low overhead: in a deployment within our research group, Crypto-Book group authentication took 1.607s end-to-end, an overhead of 1.2s compared to traditional non-privacy-preserving federated authentication.


title: Olfactory performance in spinocerebellar ataxia type 7 patients.

abstract: Abstract A large body of evidence has shown olfactory deficits in many neurodegenerative diseases. However, the nature of the olfactory impairment remains poorly understood partly because the majority of studies have only explored smell identification capabilities. The purpose of the present study was twofold. First we wanted to test if patients with spinocerebellar ataxia type 7 (SCA7), a progressive neurodegenerative disorder characterized by cerebellar ataxia and visual loss, also have olfactory deficits. Secondly, we wanted to test the nature of the olfactory deficits by testing not only the identification level but also olfactory threshold and discrimination. Based on the olfactory dysfunction found in different neurodegenerative diseases and functional neuroimaging data showing cerebellar activation during olfaction, we hypothesized that SCA7 patients would show an olfactory impairment. To test this hypothesis we studied twenty-eight genetically confirmed SCA7 patients and twenty-seven matched controls using the Sniffing Sticks Test and the University of Pennsylvania Smell Identification Test (UPSIT). The results show that SCA7 patients' ability to discriminate and identify odors is significantly impaired, although their odor detection thresholds were at normal levels. These results suggest that SCA7 neurological damage affects olfactory perception but spares the patients' olfactory sensory capabilities.


title: A loss function for classification based on a robust similarity metric

abstract: We present a margin-based loss function for classification, inspired by the recently proposed similarity measure called correntropy. We show that correntropy induces a nonconvex loss function that is a closer approximation to the misclassification loss (ideal 0–1 loss). We show that the discriminant function obtained by optimizing the proposed loss function using a neural network is insensitive to outliers and has better generalization performance as compared to using the squared loss function which is common in neural network classifiers. The proposed method of training classifiers is a practical way of obtaining better results on real world classification problems, that uses a simple gradient based online training procedure for minimizing the empirical risk.


title: Fuzzy Set Theory Based Decision Model for Determining Market Position and Developing Strategy for Hospital Service Quality

abstract: Many crucial aspects of service management are multifaceted. However, the traditional approach to measuring complex dimensions such as service quality clearly is inadequate. Essentially, this study integrated the five dimensions of SERVQUAL and adopted a fuzzy set theory based research design. The five dimensions of SERVQUAL were tested for five hospitals using Fuzzy set theory to clarify the positioning of service quality in the healthcare market and suggest service strategy implementation priorities of service strategies. This result enables managers to collate the service strategy of the benchmarking hospital and competitors in the local market, and also permits hospital decision-makers to consider the weight of the five dimensions while modifying the service strategy. Such an approach can also clarify the fundamental strengths and weaknesses of service quality.


title: CRONets: Cloud-Routed Overlay Networks

abstract: Overlay networking and ISP-assisted tunneling are effective solutions to overcome problematic BGP routes and bypass troublesome autonomous systems. Despite their demonstrated effectiveness, overlay support is not broadly available. In this paper, we propose Cloud-Routed Overlay Networks (CRONets), whereby users can readily build their own overlays using nodes from global and well-provisioned cloud providers like IBM Softlayer or Amazon EC2. While previous studies have demonstrated the benefits of overlay networks with the high-speed experimental Internet2 backbone, we are the first to evaluate the improvements in a realistic -- cloud -- setting. We conduct a large-scale experiment where we observe 6,600 Internet paths. The results show that CRONets improve the throughput for 78% of the default Internet paths with a median and average improvement factors of 1.67 and 3.27 times respectively, at a tenth of the cost of leasing private lines of comparable performance. We also performed a longitudinal measurement, and demonstrate that the performance gains are consistent over time with only a small number of overlay nodes needed to be deployed. However, given the size and dynamic nature of the Internet routing system (e.g., due to congestion and failures), selecting the proper path is still a challenging problem. To address it, we propose a novel solution based on the newly-introduced MPTCP extensions. Our experiments show that MPTCP can achieve the maximum observed throughput across the different overlay paths.


title: STRIDER: A Black-box, State-based Approach to Change and Configuration Management and Support

abstract: We describe a new approach, called Strider, to Change and Configuration Management and Support (CCMS). Strider is a black-box approach: without relying on specifications, it uses state differencing to identify potential causes of differing program behaviors, uses state tracing to identify actual, run-time state dependencies, and uses statistical behavior modeling for noise filtering. Strider is a state-based approach: instead of linking vague, high-level descriptions and symptoms to relevant actions, it models management and support problems in terms of individual, named pieces of low-level configuration state and provides precise mappings to user-friendly information through a computer genomics database. We use troubleshooting of configuration failures to demonstrate that the Strider approach reduces problem complexity by several orders of magnitude, making root cause analysis possible.


title: CNN-based shot boundary detection and video annotation

abstract: With the explosive growth of video data, content-based video analysis and management technologies such as indexing, browsing and retrieval have drawn much attention. Video shot boundary detection (SBD) is usually the first and important step for those technologies. Great efforts have been made to improve the accuracy of SBD algorithms. However, most works are based on signal rather than interpretable features of frames. In this paper, we propose a novel video shot boundary detection framework based on interpretable TAGs learned by Convolutional Neural Networks (CNNs). Firstly, we adopt a candidate segment selection to predict the positions of shot boundaries and discard most non-boundary frames. This preprocessing method can help to improve both accuracy and speed of the SBD algorithm. Then, cut transition and gradual transition detections which are based on the interpretable TAGs are conducted to identify the shot boundaries in the candidate segments. Afterwards, we synthesize the features of frames in a shot and get semantic labels for the shot. Experiments on TRECVID 2001 test data show that the proposed scheme can achieve a better performance compared with the state-of-the-art schemes. Besides, the semantic labels obtained by the framework can be used to depict the content of a shot.


IOT Based Secured Smart Home Automation Using Raspberry Pi


title: Object Detection Free Instance Segmentation With Labeling Transformations

abstract: Instance segmentation has attracted recent attention in computer vision and existing methods in this domain mostly have an object detection stage. In this paper, we study the intrinsic challenge of the instance segmentation problem, the presence of a quotient space (swapping the labels of different instances leads to the same result), and propose new methods that are object proposal- and object detection- free. We propose three alternative methods, namely pixel-based affinity mapping, superpixel-based affinity learning, and boundary-based component segmentation, all focusing on performing labeling transformations to cope with the quotient space problem. By adopting fully convolutional neural networks (FCN) like models, our framework attains competitive results on both the PASCAL dataset (object-centric) and the Gland dataset (texture-centric), which the existing methods are not able to do. Our work also has the advantages in its transparency, simplicity, and being all segmentation based.


title: Stabilization and Path Following of a Spherical Robot

abstract: In this paper, we present a spherical mobile robot BYQ_III, for planetary surface exploration and security tasks. The driving torque for the rolling robot is generated by a new type of mechanism equipped with a counter-pendulum. This robot is nonholonomic in nature, and underactuated. In this paper, the three-dimensional (3-D) nonlinear dynamic model is developed, then decoupled to the longitudinal and lateral motions by linearization. Two sliding-mode controllers are proposed to asymptotically stabilize the tracking errors in lean angle and spinning angular velocity, respectively, and indirectly to stabilize the desired path curvature, because the robot steers only by leaning itself to a predefined angle. For the task of path following, a path curvature controller, based on a geometrical notion, is employed. The stability and performance analyses are performed, and also the effectiveness of the controllers is shown by numerical simulations. To the best of author's knowledge, similar results could not be obtained in the previous spherical robot control system based on the dynamics. The work is of significance in understanding and developing this type of planning and controlling motions of nonholonomic systems.


title: A Logical Basis for the D Combinator and Normal Form in CCG

abstract: The standard set of rules defined in Combinatory Categorial Grammar (CCG) fails to provide satisfactory analyses for a number of syntactic structures found in natural languages. These structures can be analyzed elegantly by augmenting CCG with a class of rules based on the combinator D (Curry and Feys, 1958). We show two ways to derive the D rules: one based on unary composition and the other based on a logical characterization of CCG’s rule base (Baldridge, 2002). We also show how Eisner’s (1996) normal form constraints follow from this logic, ensuring that the D rules do not lead to spurious ambiguities.


title: Development and experiment of facial robot SHFR-III

abstract: As a main direction in the research field of humanoid robot, facial robot take an important role in human-robot interaction. This paper introduces the development and experiment of facial robot SHFR-III. The robot's head is consist of eyebrow mechanism, eyeball mechanism, lid mechanism, jaw mechanism and neck mechanism. As eyeball and neck mechanism need a more precise motion control in human-robot interaction, the inverse kinematic problems of eyeball and neck mechanism are analyzed in the paper. Experiment of recognition rate for the robot's 8 basic expression is also presented. Last, summary and further research plan are attached.


title: Quantum information theory

abstract: We survey the field of quantum information theory. In particular, we discuss the fundamentals of the field, source coding, quantum error-correcting codes, capacities of quantum channels, measures of entanglement and quantum cryptography.


title: From Context to Distance: Learning Dissimilarity for Categorical Data Clustering

abstract: Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical attribute, since the values are not ordered. In this article, we propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute Ai can be determined by the way in which the values of the other attributes Aj are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of Ai a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes Aj. We validate our approach by embedding our distance learning framework in a hierarchical clustering algorithm. We applied it on various real world and synthetic datasets, both low and high-dimensional. Experimental results show that our method is competitive with respect to the state of the art of categorical data clustering approaches. We also show that our approach is scalable and has a low impact on the overall computational time of a clustering task.


title: Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge

abstract: Background:Genome sciences have experienced an increasing demand for efficient text-processing tools that can extract biologically relevant information from the growing amount of published literature. In response, a range of text-mining and information-extraction tools have recently been developed specifically for the biological domain. Such tools are only useful if they are designed to meet real-life tasks and if their performance can be estimated and compared. The BioCreative challenge (Critical Assessment of Information Extraction in Biology) consists of a collaborative initiative to provide a common evaluation framework for monitoring and assessing the state-of-the-art of text-mining systems applied to biologically relevant problems.Results:The Second BioCreative assessment (2006 to 2007) attracted 44 teams from 13 countries worldwide, with the aim of evaluating current information-extraction/text-mining technologies developed for one or more of the three tasks defined for this challenge evaluation. These tasks included the recognition of gene mentions in abstracts (gene mention task); the extraction of a list of unique identifiers for human genes mentioned in abstracts (gene normalization task); and finally the extraction of physical protein-protein interaction annotation-relevant information (protein-protein interaction task). The 'gold standard' data used for evaluating submissions for the third task was provided by the interaction databases MINT (Molecular Interaction Database) and IntAct.Conclusion:The Second BioCreative assessment almost doubled the number of participants for each individual task when compared with the first BioCreative assessment. An overall improvement in terms of balanced precision and recall was observed for the best submissions for the gene mention (F score 0.87); for the gene normalization task, the best results were comparable (F score 0.81) compared with results obtained for similar tasks posed at the first BioCreative challenge. In case of the protein-protein interaction task, the importance and difficulties of experimentally confirmed annotation extraction from full-text articles were explored, yielding different results depending on the step of the annotation extraction workflow. A common characteristic observed in all three tasks was that the combination of system outputs could yield better results than any single system. Finally, the development of the first text-mining meta-server was promoted within the context of this community challenge.


title: Personalized tag recommendation through nonlinear tensor factorization using gaussian kernel

abstract: Personalized tag recommendation systems recommend a list of tags to a user when he is about to annotate an item. It exploits the individual preference and the characteristic of the items. Tensor factorization techniques have been applied to many applications, such as tag recommendation. Models based on Tucker Decomposition can achieve good performance but require a lot of computation power. On the other hand, models based on Canonical Decomposition can run in linear time and are more feasible for online recommendation. In this paper, we propose a novel method for personalized tag recommendation, which can be considered as a nonlinear extension of Canonical Decomposition. Different from linear tensor factorization, we exploit Gaussian radial basis function to increase the model's capacity. The experimental results show that our proposed method outperforms the state-of-the-art methods for tag recommendation on real datasets and perform well even with a small number of features, which verifies that our models can make better use of features.


title: Accurate UWB and IMU based indoor localization for autonomous robots

abstract: Real-time monitoring and tracking of mobile robots in an indoor environment is very important for numerous applications. In this paper a method to accurately locate mobile robots with sensor fusion is proposed. The acceleration from an inertial measurement unit (IMU) and the 2-D coordinates received from the Ultra-Wideband(UWB) anchors are fused together in a Kalman filter to achieve an accurate location estimation. The proposed method increases robustness, scalability, and accuracy of location. The measurement results, which was obtained using the proposed fusion, show considerable improvements in accuracy of the location estimation which can be used in different Indoor Positioning System (IPS) applications requiring precision.


title: Innovation diffusion in global contexts : determinants of post-adoption digital transformation of European companies

abstract: Grounded in the diffusion of innovation theory and the technology–organization–environment framework, we develop an integrative model to study the determinants of post-adoption stages of innovation diffusion, using enterprise digital transformation as an example of technology-enabled innovations. We specify four innovation characteristics (relative advantage, compatibility, costs and security concern) and four contextual factors (technology competence, organization size, competitive pressure and partner readiness) as determinants of post-adoption usage, and postulate usage as an intermediate link to impact on firm performance. We test the proposed model using a dataset of 1415 companies from six European countries. We find that the innovation needs to be used extensively in value-chain activities before its impact can be realized. Among the innovation characteristics, we find that compatibility is the strongest driver, and security concern outweighs cost as a usage inhibitor. Among the contextual variables, technology competence, partner readiness and competitive pressure significantly drive e-business usage, and the structural inertia of large firms tends to slow down its penetration. Collectively, these results indicate that innovation diffusion can be better understood by including both innovation characteristics and contextual factors, whereas earlier literature has traditionally treated the two separately. Finally, we evaluate an international dimension among European countries and tease out important boundary conditions that would not have been evident in a single-country dataset. Our results show that careful attention must be paid to the economic and regulatory factors that may result in uneven innovation diffusion even among developed European countries.


title: Bottleneck Conditional Density Estimation

abstract: We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input $x$ and target $y$, where both are high-dimensional. Crucially, we propose a new hybrid training method that blends the conditional generative model with a joint generative model. Hybrid blending is the key to effective training of the BCDE, which avoids overfitting and provides a novel mechanism for leveraging unlabeled data. We show that our hybrid training procedure enables models to achieve competitive results in the MNIST quadrant prediction task in the fully-supervised setting, and sets new benchmarks in the semi-supervised regime for MNIST, SVHN, and CelebA.


title: Photo-Realistic Talking-Heads from Image Samples

abstract: This paper describes a system for creating a photo-realistic model of the human head that can be animated and lip-synched from phonetic transcripts of text. Combined with a state-of-the-art text-to-speech synthesizer (TTS), it generates video animations of talking heads that closely resemble real people. To obtain a naturally looking head, we choose a "data-driven" approach. We record a talking person and apply image recognition to extract automatically bitmaps of facial parts. These bitmaps are normalized and parameterized before being entered into a database. For synthesis, the TTS provides the audio track, as well as the phonetic transcript from which trajectories in the space of parameterized bitmaps are computed for all facial parts. Sampling these trajectories and retrieving the corresponding bitmaps from the database produces animated facial parts. These facial parts are then projected and blended onto an image of the whole head using its pose information. This talking head model can produce new never recorded speech of the person who was originally recorded. Talking-head animations of this type are useful as a front-end for agents and avatars in multimedia applications such as virtual operators, virtual announcers, help desks, educational, and expert systems.


title: 3D Lidar-based static and moving obstacle detection in driving environments

abstract: Artificial perception, in the context of autonomous driving, is the process by which an intelligent system translates sensory data into an effective model of the environment surrounding a vehicle. In this paper, and considering data from a 3D-LIDAR mounted onboard an intelligent vehicle, a 3D perception system based on voxels and planes is proposed for ground modeling and obstacle detection in urban environments. The system, which incorporates time-dependent data, is composed of two main modules: (i) an effective ground surface estimation using a piecewise plane fitting algorithm and RANSAC-method, and (ii) a voxel-grid model for static and moving obstacles detection using discriminative analysis and ego-motion information. This perception system has direct application in safety systems for intelligent vehicles, particularly in collision avoidance and vulnerable road users detection, namely pedestrians and cyclists. Experiments, using point-cloud data from a Velodyne LIDAR and localization data from an Inertial Navigation System were conducted for both a quantitative and a qualitative assessment of the static/moving obstacle detection module and for the surface estimation approach. Reported results, from experiments using the KITTI database, demonstrate the applicability and efficiency of the proposed approach in urban scenarios. A complete framework for ground surface estimation and static/moving obstacle detection in driving environments is proposed.A piecewise surface fitting algorithm, based on a 'multi-region' strategy and Velodyne LIDAR scans behavior is proposed to estimate a finite set of multiple surfaces that fit the road and its vicinity.A 3D voxel-based representation, using discriminative analysis is proposed for obstacle modeling. The proposed approach detects moving obstacles by integrating and processing information from previous measurements.A set of diversified experiments, and corresponding result analysis, aimed at evaluating the performance of the proposed approach were performed.


title: ROI Selection for Remote Photoplethysmography

abstract: Camera-based remote photoplethysmography (rPPG) is a technique that can be used to measure vital signs contactlessly. In order to optimize the extraction of photoplethysmographic signals from video sequences, we investigate the spatial dependence of the photoplethysmographic signal. For an evaluation of the suitability of various regions of interest for rPPG measurements, we conducted a study on 20 healthy subjects. We analysed the videos using a refined pulse amplitude mapping approach. Our results show that the signal-to-noise ratio of rPPG signals can be improved by limiting the region of interest to certain regions of the face.


title: Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots

abstract: An algorithm is presented for wheeled mobile robot trajectory generation that achieves a high degree of generality and efficiency. The generality derives from numerical linearization and inversion of forward models of propulsion, suspension, and motion for any type of vehicle. Efficiency is achieved by using fast numerical optimization techniques and effective initial guesses for the vehicle controls parameters. This approach can accommodate such effects as rough terrain, vehicle dynamics, models of wheel-terrain interaction, and other effects of interest. It can accommodate boundary and internal constraints while optimizing an objective function that might, for example, involve such criteria as obstacle avoidance, cost, risk, time, or energy consumption in any combination. The algorithm is efficient enough to use in real time due to its use of nonlinear programming techniques that involve searching the space of parameterized vehicle controls. Applications of the presented methods are demonstrated for planetary rovers.


title: Non Intrusive Load Monitoring: Systems, Metrics and Use Cases

abstract: Buildings across the world contribute significantly to the overall energy consumption. Targeted feedback can help occupants optimise energy consumption. In our first work we present techniques for actionable feedback across fridges and air conditioning (HVAC) units, which can save upto 25% of fridge energy and identify homes needing feedback on HVAC setpoint schedule with 84% accuracy. In our next work, we do an extensive sensor deployment in a home in Delhi, India; monitoring appliance level power, home aggregate power and other ambient parameters. Our study presents various insights unseen in the developed world, such as: frequent voltage brownouts, poor network reliability, long lasting blackouts, heavy dominance of fridge and HVAC to overall energy consumption. Our study verifies that measuring appliance level power scales poorly in cost and maintenance. Non-intrusive load monitoring (NILM) is viewed as a viable alternative where machine learning techniques are used to break down aggregate household energy consumption into contributing appliances. Despite the existence of a rich volume of literature in NILM, it remained virtually impossible to compare NILM works due to: i) lack of existence of benchmarks; ii) previous work tested on single data set; iii) inconsistent metrics. To address these challenges we developed an open source toolkit: Non-intrusive load monitoring toolkit (NILMTK), designed specifically to enable the comparison of NILM algorithms. While many new NILM techniques have been proposed in recent times, it is not clear if these can enable energy saving and whether higher accuracy translates to higher energy saving. We explore these questions in our recent work and find that existing energy disaggregation techniques do not provide power traces with sufficient fidelity to support the feedback techniques we developed in our earlier work. Our results indicate a need to revisit the metrics by which disaggregation is evaluated.


title: Distance measure with computer vision and neural networks for underwater applications

abstract: This document describes a distance measurement system for underwater applications with computer vision and an Artificial Neural Network (ANN). The developed system is installed in the observation class remotely operated vehicle (ROV) Visor3, to be used in experimental testing. An integrated camera with two laser pointers are used to obtain input data to a Multi-Layer Perceptron (MLP) ANN, trained with a resilient backpropagation algorithm (RPROP). The solution described in this paper is an alternative to other methods found in the literature. Experimental results are presented.


title: A Fast Random Sampling Algorithm for Sparsifying Matrices

abstract: We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-Hoeffding bounds. Despite the simplicity, the approximation is comparable and sometimes better than previous work. ::: ::: Our algorithm computes the sparse matrix approximation in a single pass over the data. Further, most of the entries in the output matrix are quantized, and can be succinctly represented by a bit vector, thus leading to much savings in space.


title: Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling

abstract: Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These methods suffer from two major drawbacks: 1) the set of basic units is fixed, such as the set of words, characters or phonemes in speech recognition, and 2) the decomposition of target sequences is fixed. These drawbacks usually result in sub-optimal performance of modeling sequences. In this pa- per, we extend the popular CTC loss criterion to alleviate these limitations, and propose a new loss function called Gram-CTC. While preserving the advantages of CTC, Gram-CTC automatically learns the best set of basic units (grams), as well as the most suitable decomposition of tar- get sequences. Unlike CTC, Gram-CTC allows the model to output variable number of characters at each time step, which enables the model to capture longer term dependency and improves the computational efficiency. We demonstrate that the proposed Gram-CTC improves CTC in terms of both performance and efficiency on the large vocabulary speech recognition task at multiple scales of data, and that with Gram-CTC we can outperform the state-of-the-art on a standard speech benchmark.


title: Real time face and object tracking as a component of a perceptual user interface

abstract: As a step towards a perceptual user interface, an object tracking algorithm is developed and demonstrated tracking human faces. Computer vision algorithms that are intended to form part of a perceptual user interface must be fast and efficient. They must be able to track in real time and yet not absorb a major share of computational resources. An efficient, new algorithm is described here based on the mean shift algorithm. The mean shift algorithm robustly finds the mode (peak) of probability distributions. We first describe histogram based methods of producing object probability distributions. In our case, we want to track the mode of an object's probability distribution within a video scene. Since the probability distribution of the object can change and move dynamically in time, the mean shift algorithm is modified to deal with dynamically changing probability distributions. The modified algorithm is called the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm. CAMSHIFT is then used as an interface for games and graphics.


title: An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter

abstract: In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning.


title: Benchmarking of Beyond-CMOS Exploratory Devices for Logic Integrated Circuits

abstract: A new benchmarking of beyond-CMOS exploratory devices for logic integrated circuits is presented. It includes new devices with ferroelectric, straintronic, and orbitronic computational state variables. Standby power treatment and memory circuits are included. The set of circuits is extended to sequential logic, including arithmetic logic units. The conclusion that tunneling field-effect transistors are the leading low-power option is reinforced. Ferroelectric transistors may present an attractive option with faster switching delay. Magnetoelectric effects are more energy efficient than spin transfer torque, but the switching speed of magnetization is a limitation. This article enables a better focus on promising beyond-CMOS exploratory devices.


Mass Collaboration Systems on the World-Wide Web


title: Serial Order: A Parallel Distributed Processing Approach

abstract: ABSTRACT A theory of learned sequential behavior is presented, with a focus on coarticulatory phenomena in speech. The theory is implemented as a recurrent parallel distributed processing network that is trained via a generalized error-correcting algorithm. The basic idea underlying the theory is that both serial order and coarticulatory overlap can be represented in terms of relative levels of activation in a network if a clear distinction is made between the state of the network and the output of the network.


title: Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

abstract: We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM_CNN) model that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM_CNN model is evaluated on standard datasets on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN_LSTM model. The paper also shows that the proposed LSTM_CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.


title: Spectral Signatures in Backdoor Attacks

abstract: A recent line of work has uncovered a new form of data poisoning: so-called \emph{backdoor} attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary. In this paper, we identify a new property of all known backdoor attacks, which we call \emph{spectral signatures}. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards designing ML systems secure against such backdoor attacks