title: A Benchmark for Automatic Acral Melanoma Preliminary Screening

abstract: Despite recent progress in artificial intelligence and computer vision, the lack of data for acral melanoma, a particular type of skin disease under nails, makes it difficult to develop its automatic visual diagnosis system. This paper introduces a large data set of dermoscopic images for acral melanoma, which is annotated by senior dermatologists. It contains 6,066 images of two categories: subungual hematomas and other acral melanoma symptoms of malignant tendency (i.e., just bleeding under nails or more critical disorder that requires treatment). We hope this benchmark data set will encourage further research on acral melanoma recognition and will continue to maintain this data set to better serve it. We address the classification task using various computer vision algorithms from conventional techniques to cutting edge deep learning. Currently, we achieve 0.928 accuracy.


title: Deep Homography Based Localization on Videos of Endoscopic Capsules

abstract: Endoscopic capsules are vitamin-sized devices that create 8 to 10 hour videos of the digestive tract. They are the leading diagnosing method for the small bowel, a region not easily accessible with traditional endoscopy techniques. However, these capsules do not provide localization information, even though it is crucial for the diagnosis, follow-ups and surgical interventions. Currently, the capsule localization is either estimated based on scarce gastrointestinal tract landmarks or given by additional hardware that causes discomfort to the patient and represents a cost increase. Current software methods show great potential, but still need to improve in order to overcome their limitations. In this work, a visual odometry method for capsule localization inside the small bowel is proposed.


title: The Effects of Reward and Punishment in Violent Video Games on Aggressive Affect, Cognition, and Behavior

abstract: Three experiments examined the effects of rewarding and punishing violent actions in video games on later aggression-related variables. Participants played one of three versions of the same race-car video game: (a) a version in which all violence was rewarded, (b) a version in which all violence was punished, and (c) a nonviolent version. Participants were then measured for aggressive affect (Experiment 1), aggressive cognition (Experiment 2), and aggressive behavior (Experiment 3). Rewarding violent game actions increased hostile emotion, aggressive thinking, and aggressive behavior. Punishing violent actions increased hostile emotion, but did not increase aggressive thinking or aggressive behavior. Results suggest that games that reward violent actions can increase aggressive behavior by increasing aggressive thinking.


3 D Tracking via Body Radio Reflections by


title: Health companion device using IoT and wearable computing

abstract: Remote Health monitoring means to monitor people's health outside the clinical settings. Our approach is to develop a wearable device that will monitor the temperature and pulse rate of the person on a regular basis and enable early detection of deterioration. If the person's temperature or pulse rate crosses the critical level, the device will alert the person's relatives his/her deteriorating condition with the person's current location. It also includes generation of a daily analysis report helping the doctors to get a better understanding of the person's condition and thus helps in better diagnosis. It continuously uploads data on cloud which helps doctor to create monthly report of particular person. Insights derived from the collected data, help the users to better monitor their health as well as fitness and also helps the doctor to study the details of variations in the user's health conditions.


4 RFID-enabled Supply Chain Traceability : Existing Methods , Applications and Challenges


Key Risk Indicators – Their Role in Operational Risk Management and Measurement


title: I Am The Passenger: How Visual Motion Cues Can Influence Sickness For In-Car VR

abstract: This paper explores the use of VR Head Mounted Displays (HMDs) in-car and in-motion for the first time. Immersive HMDs are becoming everyday consumer items and, as they offer new possibilities for entertainment and productivity, people will want to use them during travel in, for example, autonomous cars. However, their use is confounded by motion sickness caused in-part by the restricted visual perception of motion conflicting with physically perceived vehicle motion (accelerations/rotations detected by the vestibular system). Whilst VR HMDs restrict visual perception of motion, they could also render it virtually, potentially alleviating sensory conflict. To study this problem, we conducted the first on-road and in motion study to systematically investigate the effects of various visual presentations of the real-world motion of a car on the sickness and immersion of VR HMD wearing passengers. We established new baselines for VR in-car motion sickness, and found that there is no one best presentation with respect to balancing sickness and immersion. Instead, user preferences suggest different solutions are required for differently susceptible users to provide usable VR in-car. This work provides formative insights for VR designers and an entry point for further research into enabling use of VR HMDs, and the rich experiences they offer, when travelling.


The Internet of Things ( IoT ) : An Overview Antar


title: Stacked deep convolutional auto-encoders for emotion recognition from facial expressions

abstract: Emotion recognition is critical for everyday living and is essential for meaningful interaction. If we are to progress towards human and machine interaction that is engaging the human user, the machine should be able to recognize the emotional state of the user. Deep Convolutional Neural Networks (CNN) have proven to be efficient in emotion recognition problems. The good degree of performance achieved by these classifiers can be attributed to their ability to self-learn a down-sampled feature vector that retains spatial information through filter kernels in convolutional layers. Given the view that random initialization of weights can lead to convergence to non-optimal local minima, in this paper we explore the impact of training the initial weights in an unsupervised manner. We study the effect of pre-training a Deep CNN as a Stacked Convolutional Auto-Encoder (SCAE) in a greedy layer-wise unsupervised fashion for emotion recognition using facial expression images. When trained with randomly initialized weights, our CNN emotion recognition model achieves a performance rate of 91.16% on the Karolinska Directed Emotional Faces (KDEF) dataset. In contrast, when each layer of the model, including the hidden layer, is pre-trained as an Auto-Encoder, the performance increases to 92.52%. Pre-training our CNN as a SCAE also reduces training time marginally. The emotion recognition model developed in this work will form the basis of a real-time empathic robot system.


title: Example Based 3D Reconstruction from Single 2D Images

abstract: We present a novel solution to the problem of depth reconstruction from a single image. Single view 3D reconstruction is an ill-posed problem. We address this problem by using an example-based synthesis approach. Our method uses a database of objects from a single class (e.g. hands, human figures) containing example patches of feasible mappings from the appearance to the depth of each object. Given an image of a novel object, we combine the known depths of patches from similar objects to produce a plausible depth estimate. This is achieved by optimizing a global target function representing the likelihood of the candidate depth. We demonstrate how the variability of 3D shapes and their poses can be handled by updating the example database on-the-fly. In addition, we show how we can employ our method for the novel task of recovering an estimate for the occluded backside of the imaged objects. Finally, we present results on a variety of object classes and a range of imaging conditions.


title: ADVERTISING EFFECTIVENESS ON SOCIAL NETWORK SITES: AN INVESTIGATION OF TIE STRENGTH , ENDORSER EXPERTISE AND PRODUCT TYPE ON CONSUMER PURCHASE INTENTION

abstract: Social network sites (SNSs) can create new marketing opportunities by leveraging on members’ online social networks. In particular, such sites can produce a new type of product endorser – “friend endorser”, whose effectiveness remains unknown as compared to other types of product endorsers. This paper investigated the impact of three critical factors on advertising effectiveness of different types of product endorser on SNSs - tie strength, endorser expertise, and product type. A 2  2  2 factorial design was used to test the interaction effects among the three variables. The results indicate that, for hedonic products endorsed on SNSs, strong-tie endorsers are more effective than weak-tie endorsers, regardless of their expertise on the endorsed products; but for utilitarian products endorsed on SNSs, high-expertise endorsers, as compared to low-expertise endorsers, result in higher consumers’ purchase intention, regardless of their tie strength with consumers. Based on these results, theoretical and practical implications were discussed, and suggestions for future research in social network advertising were also provided.


title: Evaluative Conditioning and the Development of Attitudes in Early Childhood

abstract: Many attitudes are acquired in early childhood. However, due to a lack of experimental research, little is known about the processes of how they are acquired. Two experiments were therefore conducted with 153 German kindergarten children aged 3–6 years that provide first evidence for childhood attitude formation in terms of evaluative conditioning. Specifically, it was found that children preferred novel stimuli previously paired with liked stimuli over novel stimuli previously paired with disliked stimuli. This effect occurred independently of age, generalized toward similar novel stimuli, and did not depend on children's recollection of how stimuli were paired. The findings are discussed in terms of the processes underlying childhood attitude formation, and implications for related research areas are highlighted.


title: Algorithms for Detecting Significantly Mutated Pathways in Cancer

abstract: Recent genome sequencing studies have shown that the somatic mutations that drive cancer development are distributed across a large number of genes. This mutational heterogeneity complicates efforts to distinguish functional mutations from sporadic, passenger mutations. Since cancer mutations are hypothesized to target a relatively small number of cellular signaling and regulatory pathways, a common practice is to assess whether known pathways are enriched for mutated genes. We introduce an alternative approach that examines mutated genes in the context of a genome-scale gene interaction network. We present a computationally efficient strategy for de novo identification of subnetworks in an interaction network that are mutated in a statistically significant number of patients. This framework includes two major components. First, we use a diffusion process on the interaction network to define a local neighborhood of "influence" for each mutated gene in the network. Second, we derive a two-stage multiple hypothesis test to bound the false discovery rate (FDR) associated with the identified subnetworks. We test these algorithms on a large human protein-protein interaction network using somatic mutation data from glioblastoma and lung adenocarcinoma samples. We successfully recover pathways that are known to be important in these cancers and also identify additional pathways that have been implicated in other cancers but not previously reported as mutated in these samples. We anticipate that our approach will find increasing use as cancer genome studies increase in size and scope.


title: Valid population inference for information-based imaging: From the second-level $t$-test to prevalence inference

abstract: In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a $t$-test vs chance level across subjects. We argue that while the random-effects analysis implemented by the $t$-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes, rendering it effectively equivalent to fixed-effects analysis. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.


title: Concept drift detection for streaming data

abstract: Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper presents Linear Four Rates (LFR), a framework for detecting these concept drifts and subsequently identifying the data points that belong to the new concept (for relearning the model). Unlike conventional concept drift detection approaches, LFR can be applied to both batch and stream data; is not limited by the distribution properties of the response variable (e.g., datasets with imbalanced labels); is independent of the underlying statistical-model; and uses user-specified parameters that are intuitively comprehensible. The performance of LFR is compared to benchmark approaches using both simulated and commonly used public datasets that span the gamut of concept drift types. The results show LFR significantly outperforms benchmark approaches in terms of recall, accuracy and delay in detection of concept drifts across datasets.


Depth Estimation of Light Field Images by Optical Flow


title: Visualizations with Venn and Euler Diagrams

abstract: Venn and Euler diagrams intuitively visualize relationships and relative cardinalities of data sets. They are used extensively in areas as biosciences, business and criminology to facilitate data reasoning and analysis. However, current automatic drawing techniques do not always produce desirable diagrams. My research aims to develop novel algorithms to automatically draw readable diagrams that facilitate data analysis. It includes software, theory, and studies demonstrating how such diagrams aid probabilistic judgment.


title: University–industry relationships and open innovation: Towards a research agenda

abstract: Organizations increasingly rely on external sources of innovation via inter-organizational network relationships. This paper explores the diffusion and characteristics of collaborative relationships between universities and industry, and develops a research agenda informed by an open innovation perspective. A framework is proposed, distinguishing university industry relationships from other mechanisms such as technology transfer or human mobility. On the basis of the existing body of research, the role of practices such as collaborative research, university industry research centres, contract research and academic consulting is analysed. The evidence suggests that such university industry relationships are widely practised, whereby differences exist across industries and scientific disciplines. While most existing research focuses on the effects of university industry links on innovation-specific variables such as patents or firm innovativeness, the organizational dynamics of these relationships remain under-researched. A detailed research agenda addresses research needs in two main areas: search and match processes between universities and firms, and the organization and management of collaborative relationships.


title: WarpGAN: Automatic Caricature Generation

abstract: We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating caricatures that not only preserve the identities but also outputs a diverse set of caricatures for each input photo. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated.


title: A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks

abstract: Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack. In both cases, optimization-based attack algorithms can achieve relatively low distortions and high attack success rates. However, they usually suffer from poor time and query complexities, thereby limiting their practical usefulness. In this work, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. In particular, we propose a novel adversarial attack framework for both white-box and black-box settings based on the non-convex Frank-Wolfe algorithm. We show in theory that the proposed attack algorithms are efficient with an $O(1/\sqrt{T})$ convergence rate. The empirical results of attacking Inception V3 model and ResNet V2 model on the ImageNet dataset also verify the efficiency and effectiveness of the proposed algorithms. More specific, our proposed algorithms attain the highest attack success rate in both white-box and black-box attacks among all baselines, and are more time and query efficient than the state-of-the-art.


A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text


title: Robust material classification with a tactile skin using deep learning

abstract: Attaching a flexible tactile skin to an existing robotic system is relatively easy compared to integrating most other tactile sensor designs. In this paper we show that material classification purely based on the spatio-temporal signal of a flexible tactile skin can be robustly performed in a real world setting. We compare different classification algorithms and feature sets, including features adopted and extended from previous works in tactile material classification and that are based on the signal's Fourier spectrum. Our convolutional deep learning network architecture, which we also present here, is directly fed with the raw 24000 dimensional sensor signal and performs best by a large margin, reaching a classification accuracy of up to 97.3%.


title: Kovalevskaya exponents and the space of initial conditions of a quasi-homogeneous vector field

abstract: Formal series solutions and the Kovalevskaya exponents of a quasi-homogeneous polynomial system of differential equations are studied by means of a weighted projective space and dynamical systems theory. A necessary and sufficient condition for the series solution to be a convergent Laurent series is given, which improves the well-known Painleve test. In particular, if a given system has the Painleve property, an algorithm to construct Okamoto's space of initial conditions is given. The space of initial conditions is obtained by weighted blow-ups of the weighted projective space, where the weights for the blow-ups are determined by the Kovalevskaya exponents. The results are applied to the first Painleve hierarchy (2m-th order first Painleve equation).


title: A Multi-component View of Digital Forensics

abstract: We are living in a world where there is an increasing need for evidence in organizations. Good digital evidence is becoming a business enabler. Very few organizations have the structures (management and infrastructure) in place to enable them to conduct cost effective, low-impact and fficient digital investigations [1]. Digital Forensics (DF) is a vehicle that organizations use to provide good and trustworthy evidence and processes. The current DF models concentrate on reactive investigations, with limited reference to DF readiness and live investigations. However, organizations use DF for other purposes for example compliance testing. The paper proposes that DF consists of three components: Pro-active (ProDF), Active (ActDF) and Re-active (ReDF). ProDF concentrates on DF readiness and the proactive responsible use of DF to demonstrate good governance and enhance governance structures. ActDF considers the gathering of live evidence during an ongoing attack with a limited live investigation element whilst ReDF deals with the traditional DF investigation. The paper discusses each component and the relationship between the components.


title: Production and structural elucidation of exopolysaccharide from endophytic Pestalotiopsis sp. BC55.

abstract: There is a little information on exopolysaccharide production by endophytic fungi. In this investigation endophytic Pestalotiopsis sp. BC55 was used for optimization of exopolysaccharide production. One variable at a time method and response surface methodology were adopted to find out the best culture conditions and medium compositions for maximum exopolysaccharide production. The organism produced maximum exopolysaccharide (4.320 ± 0.022 g/l EPS) in 250 ml Erlenmeyer flask containing 75 ml potato dextrose broth supplemented with (g%/l) glucose, 7.66; urea, 0.29; CaCl2, 0.05 with medium pH 6.93; after 3.76 days of incubation at 24°C. Exopolysaccharide [EPS (EP-I)] produced by this organism have Mw ∼2×10(5)Da with a melting point range of 122-124°C. Structural elucidation of the EPS (PS-I) was carried out after a series of experiments. Result indicated the presence of only (1→3)-linked β-d-glucopyranosyl moiety. The structure of the repeating unit was established as - →3)-β-d-Glcp-(1→.


title: Communicating quantum processes

abstract: We define a language CQP (Communicating Quantum Processes) for modelling systems which combine quantum and classical communication and computation. CQP combines the communication primitives of the pi-calculus with primitives for measurement and transformation of quantum state; in particular, quantum bits (qubits) can be transmitted from process to process along communication channels. CQP has a static type system which classifies channels, distinguishes between quantum and classical data, and controls the use of quantum state. We formally define the syntax, operational semantics and type system of CQP, prove that the semantics preserves typing, and prove that typing guarantees that each qubit is owned by a unique process within a system. We illustrate CQP by defining models of several quantum communication systems, and outline our plans for using CQP as the foundation for formal analysis and verification of combined quantum and classical systems.


title: Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

abstract: Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (...


title: Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques?

abstract: There has been a recent proliferation in the development of smartphone applications (apps) aimed at modifying various health behaviours. While interventions that incorporate behaviour change techniques (BCTs) have been associated with greater effectiveness, it is not clear to what extent smartphone apps incorporate such techniques. The purpose of this study was to investigate the presence of BCTs in physical activity and dietary apps and determine how reliably the taxonomy checklist can be used to identify BCTs in smartphone apps. The top-20 paid and top-20 free physical activity and/or dietary behaviour apps from the New Zealand Apple App Store Health & Fitness category were downloaded to an iPhone. Four independent raters user-tested and coded each app for the presence/absence of BCTs using the taxonomy of behaviour change techniques (26 BCTs in total). The number of BCTs included in the 40 apps was calculated. Krippendorff’s alpha was used to evaluate interrater reliability for each of the 26 BCTs. Apps included an average of 8.1 (range 2-18) techniques, the number being slightly higher for paid (M = 9.7, range 2-18) than free apps (M = 6.6, range 3-14). The most frequently included BCTs were “provide instruction” (83% of the apps), “set graded tasks” (70%), and “prompt self-monitoring” (60%). Techniques such as “teach to use prompts/cues”, “agree on behavioural contract”, “relapse prevention” and “time management” were not present in the apps reviewed. Interrater reliability coefficients ranged from 0.1 to 0.9 (Mean 0.6, SD = 0.2). Presence of BCTs varied by app type and price; however, BCTs associated with increased intervention effectiveness were in general more common in paid apps. The taxonomy checklist can be used by independent raters to reliably identify BCTs in physical activity and dietary behaviour smartphone apps.


title: Mitochondrial DNA polymorphism, phylogeography, and conservation genetics of the brown bear Ursus arctos in Europe

abstract: Some small European populations of the brown bear (Ursus arctos) are threatened by the risk of extinction in the near future. The reinforcement of these populations with bears from other regions might provide a solution to their future survival. However, before any population transfer, the different conservation units must be identified. The phylogeographic approach has been advocated for this purpose. The different European populations were assayed for mitochondrial (mt) DNA polymorphism. A remarkable degree of concordance was found between the geographic distribution and the mtDNA haplotypes. Two clearly distinct lineages differing by more than 7% in mtDNA control region sequences were found and, furthermore, the western lineage appears to be organized into two clades which correspond to two different ancestral refugia. The potential conservation units can be deduced from these results, and a management policy can consequently be inferred. This study clearly demonstrates the relevance of the molecular phylogeographic approach to the identification of conservation units.


title: MISTRAL: Processing Relational Queries using a Multidimensional Access Technique

abstract: Our thesis investigates the UB-Tree, a multidimensional access method, and its applicability for relational database management systems (RDBMS). In the thesis, we introduce a formal model for multidimensional partitioned relations and discuss several typical query patterns. The model identifies the significance of multidimensional range queries and sort operations. After describing the UB-Tree and its standard algorithms for insertion, deletion, point queries, and range queries, we introduce the spiral algorithm for nearest neighbour queries with UB-Trees and the Tetris algorithm for efficient access to a table in arbitrary sort order. We then describe the complexity of the involved algorithms and give solutions to selected algorithmic problems for a prototype implementation of UB-Trees on top of several RDBMSs. A cost model for sort operations with and without range restrictions is used both for analyzing our algorithms and for comparing UB-Trees with state-of-the-art query processing. Performance comparisons with traditional access methods practically confirm the theoretically expected superiority of UB-Trees and our algorithms over traditional access methods: Query processing in RDBMS is accelerated by several orders of magnitude, while the resource requirements in main memory space and disk space are substantially reduced. Benchmarks on some queries of the TPC-D benchmark as well as the data warehousing scenario of a fruit juice company illustrate the potential impact of our work on relational algebra, SQL, and commercial applications.


title: Virtual Assistants and Self-Driving Cars : To what extent is Artificial Intelligence needed in Next-Generation Autonomous Vehicles ?

abstract: Self-driving cars are technologically a reality and in the next decade they are expected to reach the highest level of automation. While there is general agreement that an advanced human-autonomous vehicle (HAV) interaction is key to achieve the benefits of self-driving cars, it is less clear what role artificial intelligence (AI) should play in this context. While the scientific community is debating on the role and intersections of AI, autonomous vehicles and related issues, above all ethics, the automotive industry is already presenting AI-based products and services that may influence, in a direction or in another, our technological and societal futures. This paper focuses on virtual assistants, the personification of the car intelligence incorporating, among others, an algorithmic “brain”, a synthetic human “voice” and powerful sensor-based “senses”. Should virtual assistants just assist humans or replace them whenever necessary? Should their scope of action be limited to safety-related driving tasks or to any activity performed in the car or controlled from the car? Although at a very early stage of commercial development, the paper will review the state-of-the-art of in-car virtual assistants underlining their role and functions in the connected and automated driving ecosystem. By drawing from earlier reflections on automation, robots and intelligent agents, it will then identify a series of issues to be addressed by the scientific community, policy-makers and the automotive industry stakeholders.


title: A 56 Gb/s PAM-4 linear transimpedance amplifier in 0.13-μm SiGe BiCMOS technology for optical receivers

abstract: This paper describes the implementation of a state-of-the art 56Gb/s single-channel linear transimpedance amplifier (TIA) integrated circuit for PAM-4/NRZ/DMT modulation formats used in data center interconnects (DCI) and base station front haul applications. Fabricated in 130nm SiGe BiCMOS process, the TIA has a single-ended input, differential output configuration, with nominal maximum DC transimpedance gain of ∼5.8 kQ (75 dB), gain dynamic range of 28 dB, average input referred noise (IRN) of 14.9 pA/VHz, typical bandwidth of 38 GHz across the entire gain dynamic range, Total Harmonic Distortion (THD) of 2% for output of 500mVppd, adjustable output voltage swing up to 1V ppd , all operated with typical supply of 3.3 V. At 2.8 V supply, bandwidth of 34 GHz has been achieved. Performance has been measured from −5C to 95C. The TIA has Receive Signal Strength Indicator (RSSI) for measuring the input signal strength, peak detect (PKD) function for measuring the output amplitude and in-built photo-diode (PD) cathode bias network. The chip can be operated in manual gain control (MGC) or automatic gain control (AGC) mode. The die area is 1.6 mmA2. A state-of-the art 4-level Pulse Amplitude Modulation (PAM-4) eye diagram at 56Gb/s has been demonstrated with this linear TIA. The Die has been integrated into an optical product from Discovery Semiconductors and several optical interconnect products.


title: Analysis of weighted networks

abstract: The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a simple proof of the maximum-flow--minimum-cut theorem.


Deep Learning for Time Series Modeling


title: K-O (Potassium-Oxygen) System

abstract: Potassium oxides are more or less unstable with respect to temperature and applied oxygen pressure. The instability of the compounds increases with oxygen content. This has important implications for the construction of a phase diagram. The published literature on K-O compounds is voluminous and of very variable quality. It is useful here to mention a number of review documents for orientation (Table 1).


title: Color attributes for object detection

abstract: State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-of-the-art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.


title: Applications of hybrid system identification in computer vision

abstract: In this expository paper we illustrate the use of filtering and identification-theoretic techniques in a number of problems in computer vision. We first demonstrate how linear system identification techniques combined with distances for linear systems can be used for modeling, synthesis, classification and recognition of dynamic textures and human gaits. We then show how hybrid system identification techniques can be used for segmentation of dynamic textures. We also highlight some open problems in system identification that are motivated by extensions of the research described in this paper.


title: A translation system for enabling data mining applications on GPUs

abstract: Modern GPUs offer much computing power at a very modest cost. Even though CUDA and other related recent developments are accelerating the use of GPUs for general purpose applications, several challenges still remain in programming the GPUs. Thus, it is clearly desirable to be able to program GPUs using a higher-level interface. In this paper, we offer a solution that targets a specific class of applications, which are the data mining and scientific data analysis applications. Our work is driven by the observation that a common processing structure, that of generalized reductions, fits a large number of popular data mining algorithms. In our solution, the programmers simply need to specify the sequential reduction loop(s) with some additional information about the parameters. We use program analysis and code generation to map the applications to a GPU. Several additional optimizations are also performed by the system. We have evaluated our system using three popular data mining applications, k-means clustering, EM clustering, and Principal Component Analysis (PCA). The main observations from our experiments are as follows. The speedup that each of these applications achieve over a sequential CPU version ranges between 20 and 50. The automatically generated version did not have any noticeable overheads compared to hand written codes. Finally, the optimizations performed in the system resulted in significant performance improvements.


title: Link prediction in drug-target interactions network using similarity indices

abstract: Abstract Background In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning – methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. Results We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. Conclusion This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.


title: An Interval Type-2 Fuzzy Logic Toolbox for Control Applications

abstract: This paper presents the development and design of a graphical user interface and a command line programming toolbox for construction, edition and observation of interval type-2 fuzzy inference systems. The interval type-2 fuzzy logic system toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the toolbox. The toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods and the evaluation of interval type-2 fuzzy inference systems.


title: Wearables with Heart Rate Monitors and Dynamic Workout Plans

abstract: Physical activity has well known physical and psychological benefits. All over the world, people from all ages follow some kind of fitness workout plan to achieve some results in terms of health and wellness. Due to the uniqueness of the human being it is difficult to create generic workout plans that fit everyone’s needs. In many cases people seek the help of professionals for a personal training experience and to get tailored workout plans to maximize results. In this paper, we propose a new approach for assisted generation of dynamic tailored workout plans based on wearables that monitor heart rate data. These workouts have their exercises dynamically generated based on the information provided by the wearables providing an individual training experience that maximizes results by maintaining the individual heart rate inside the zone more adequate for the users’ health condition and the expected results.


title: Real-Time Eye , Gaze , and Face Pose Tracking for Monitoring

abstract: This paper describes a real-time prototype computer vision system for monitoring driver vigilance. The main components of the system consists of a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver's level of vigilance. The visual behaviors include eyelid movement, face orientation, and gaze movement (pupil movement). The system was tested in a simulating environment with subjects of different ethnic backgrounds, different genders, ages, with/without glasses, and under different illumination conditions, and it was found very robust, reliable and accurate.


title: The Role of Knowledge in Disaster Risk Reduction

abstract: Disaster risk reduction policy and practice require knowledge for informed decision making and coordinated action. Although the knowledge production and implementation processes are critical for disaster risk reduction, these issues are seldom systematically addressed in-depth in disaster studies and policy programs. While efforts and improvements have been made with regard to data and information, only limited resources are committed to improving knowledge management structures and integrating knowledge systems at different spatial levels. The recently adopted Sendai Framework for Disaster Risk Reduction 2015–2030 addresses knowledge-related issues and provides the opportunity to highlight the critical role of knowledge in disaster risk reduction. This article presents insights into potential conceptualizations of knowledge that would advance disaster research and policy. We use cases from France to illustrate challenges of and pathways to disaster risk reduction. We suggest to further strengthen efforts that improve our understanding of the connections between disaster risk, knowledge, and learning. A better integration of multiple scales, different societal actors, various knowledge sources, and diverse disciplines into disaster risk research will increase its relevance for decision-makers in policy and practice. Well-targeted incentives and political backing will improve the coherence, coordination, and sharing of knowledge among various actors and arenas.


title: A compact wideband planar inverted-F antenna (PIFA) loaded with metamaterial

abstract: This paper proposes a novel planar inverted-F antenna (PIFA) with metamaterial loaded on the ground plane. The metamaterial is composed of an array of Jerusalem cross-shaped PEC structure. Simulation results show that this novel antenna has a bandwidth of 49.4%, which is from 2.56GHz to 4.24GHz. Compared to conventional PIFA, the proposed antenna reduces 91% in size. And the gain reaches more than 3dB over the whole bandwidth. The proposed antenna has a characteristic of backward radiation in the bandwidth.


title: Urinary tract infections: epidemiology, mechanisms of infection and treatment options

abstract: Urinary tract infections (UTIs) pose a severe public health problem and are caused by a range of pathogens. In this Review, Hultgren and colleagues discuss how basic science studies are elucidating the molecular mechanisms of UTI pathogenesis and how this knowledge is being used for the development of novel clinical treatments for UTIs.


title: Improving Blind Steganalysis in Spatial Domain using a Criterion to Choose the Appropriate Steganalyzer between CNN and SRM+EC

abstract: Conventional state-of-the-art image steganalysis approaches usually consist of a classifier trained with features provided by rich image models. As both features extraction and classification steps are perfectly embodied in the deep learning architecture called Convolutional Neural Network (CNN), different studies have tried to design a CNN-based steganalyzer. This work proposes a criterion to choose either the CNN designed by Xu et al. or the combination Spatial Rich Models (SRM) and Ensemble Classifier (EC) for an input image. Our approach is studied with three steganographic spatial domain algorithms: S-UNIWARD, MiPOD, and HILL, and exhibits detection capabilities better than each method alone. As SRM+EC and the CNN are only trained with MiPOD the proposed method can be seen as an approach for blind steganalysis.


title: A novel trust management framework for multi-cloud environments based on trust service providers

abstract: In this paper, we address the problem of trust management in multi-cloud environments based on a set of distributed Trust Service Providers (TSPs). These are independent third-party providers/trust agents, trusted by Cloud Providers (CPs), Cloud Service Providers (CSPs) and Cloud Service Users (CSUs), that provide trust related services to cloud participants. TSPs are distributed over the clouds, and they elicit raw trust evidence from different sources and in different formats. This evidence is information regarding the adherence of a CSP to a Service Level Agreement (SLA) for a cloud-based service and the feedback sent by CSUs. Using this information, they evaluate an objective trust and a subjective trust of CSPs. TSPs communicate among themselves through a trust propagation network that permits a TSP to obtain trust information about a CSP from other TSPs. Experiments show that our proposed framework is effective and relatively stable in differentiating trustworthy and untrustworthy CSPs in a multi-cloud environment.


title: ConvNets and AGMM based real-time human detection under fisheye camera for embedded surveillance

abstract: Human detection is an essential task in so many applications, especially surveillance systems. Recently, ConvNets (Convolutional Neural Networks)-based YOLO model is a successful method applied for object (including human) detection. It is one of the fastest way to detect directly objects from the input image. However, compared to the ConvNets-based state-of-the-art object detection methods, YOLO model-based object detection method achieved less accuracy. In this paper, we propose a new real-time human detection under fisheye cameras for surveillance purpose based on YOLO model. However, we improve the preciseness by using 2-D input channels consisting of grey-level image channel and foreground-background context information extracted by AGMM (Adaptive Gaussian Mixture Model) instead of original 3-D color input channels for ConvNets-based YOLO model. It is shown through experiments that the proposed method performs better with respect to accuracy and more robust to background scene changes without processing speed degradation compared to YOLO model-based human detection so that it can be successfully employed for embedded surveillance application.


title: Making Bas-reliefs from Photographs of Human Faces

abstract: Bas-reliefs are a form of flattened artwork, part-way between 3D sculpture and 2D painting. Recent research has considered automatic bas-relief generation from 3D scenes. However, little work has addressed the generation of bas-reliefs from 2D images. In this paper, we propose a method to automatically generate bas-relief surfaces from frontal photographs of human faces, with potential applications to e.g. coinage and commemorative medals. Our method has two steps. Starting from a photograph of a human face, we first generate a plausible image of a bas-relief of the same face. Secondly, we apply shape-from-shading to this generated bas-relief image to determine the 3D shape of the final bas-relief. To model the mapping from an input photograph to the image of a corresponding bas-relief, we use a feedforward network. The training data comprises images generated from an input 3D model of a face, and images generated from a corresponding bas-relief; the latter is produced by an existing 3D model-to-bas-relief algorithm. A saliency map of the face controls both model building, and bas-relief generation. Our experimental results demonstrate that the generated bas-relief surfaces are smooth and plausible, with correct global geometric nature, the latter giving them a stable appearance under changes of viewing direction and illumination.


The acquisition of gender labels in infancy : Implications for sex-typed play


title: Software Implementation of the NIST Elliptic Curves Over Prime Fields

abstract: This paper presents an extensive study of the software implementation on workstations of the NIST-recommended elliptic curves over prime fields. We present the results of our implementation in C and assembler on a Pentium II 400MHz workstation. We also provide a comparison with the NIST-recommended curves over binary fields.


title: Measurement of within and between genetic variability in duck breeds by RAPD markers.

abstract: A total of 200 genomic DNAs were isolated from the four breeds of duck: Muscovy, Pekin, Khaki Campbell and Native, through a modified salting out procedure. The samples were used in a Polymerase Chain Reaction (PCR) with 27 RAPD markers. Amplified PCR-products with the markers were separated on a 2% agarose gel and stained with ethidium bromide. To evaluate the bands, polymorphic and monomorphic bands were described. Genetic similarity and genetic distance were calculated. The RAPD analysis data from 7 primers were utilized in estimating genetic similarity between four breeds, which ranged from 0.46 to 0.77 between the breeds. The maximum genetic distance was observed between Muscovy and Native duck breeds (0.76).


title: Quantifying and Predicting Mental Illness Severity in Online Pro-Eating Disorder Communities

abstract: Social media sites have struggled with the presence of emotional and physical self-injury content. Individuals who share such content are often challenged with severe mental illnesses like eating disorders. We present the first study quantifying levels of mental illness severity (MIS) in social media. We examine a set of users on Instagram who post content on pro-eating disorder tags (26M posts from 100K users). Our novel statistical methodology combines topic modeling and novice/clinician annotations to infer MIS in a userâ s content. Alarmingly, we find that proportion of users whose content expresses high MIS have been on the rise since 2012 (13%/year increase). Previous MIS in a userâ s content over seven months can predict future risk with 81% accuracy. Our model can also forecast MIS levels up to eight months in the future with performance better than baseline. We discuss the health outcomes and design implications as well as ethical considerations of this line of research.


CALTRANSCENSE : A REAL-TIME SPEAKER IDENTIFICATION SYSTEM


title: Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

abstract: Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.


ACCURACY ANALYSIS OF KINECT DEPTH DATA


title: Equilibrium existence for zero-sum games and spatial models of elections

abstract: Abstract A theorem on existence of mixed strategy equilibria in discontinuous zero-sum games is proved and applied to three models of elections. First, the existence theorem yields a mixed strategy equilibrium in the multidimensional spatial model of elections with three voters. A nine-voter example shows that a key condition of the existence theorem is violated for general finite numbers of voters and illustrates an obstacle to a general result. Second, the theorem provides a simple and self-contained proof of Kramer's existence result for the multidimensional model with a continuum of voters [Kramer, G., 1978. Existence of electoral equilibrium. In: Ordeshook, P. (Ed.), Game Theory and Political Science. NYU Press, New York]. Third, existence follows for a class of multidimensional probabilistic voting models with discontinuous probability-of-winning functions.


title: Predicting Returns with Financial Ratios

abstract: This article provides a new test of the predictive ability of aggregate financial ratios. Predictive regressions are subject to small-sample biases, but the correction in previous studies can substantially understate forecasting power. Dividend yield predicts aggregate market returns from 1946 Â€O 2000, as well as in various subperiods. Book-to-market and the earnings-price ratio predict returns during the shorter 1963 Â€O 2000 sample. The evidence remains strong despite the unusual price run-up in recent years


title: Introduction of the S-UHE, a single-wafer ultra-high energy ion implanter

abstract: In order to address the process requirements of leading-edge image sensors, a new single-wafer ultra-high energy ion implanter, the S-UHE, has been developed. This product incorporates two exceptional subassemblies. One is the eighteen-stage RF linear accelerator from the UHE, a multi-wafer ultra-high energy implanter, offering maximum beam energy of 2MeV per charge. The other is the field proven end station used by the MC3-II/GP, a single-wafer medium current implanter, which can provide throughput of over 450 wafers/hour. The S-UHE has a unique beam line concept where beam energy analyzing magnets bend the accelerated beam 180°. This system minimizes tool footprint, providing additional space for maintenance. Other key elements of the beam line include an electrostatic scanner, parallelizing lens and energy filter. The electrostatic scanner provides higher scan speed than mechanical systems - significantly improving dose uniformity compared to a batch high energy implanter. Additionally, the S-UHE ensures accurate implant angles and ultra-low level of metal contamination, both of which are very important parameters for advanced image sensors.


title: Understanding the Relationship between Frustration and the Severity of Usability Problems: What can Psychophysiological Data (Not) Tell Us?

abstract: Frustration is used as a criterion for identifying usability problems (UPs) and for rating their severity in a few of the existing severity scales, but it is not operationalized. No research has systematically examined how frustration varies with the severity of UPs. We aimed to address these issues with a hybrid approach, using Self-Assessment Manikin, comments elicited with Cued-Recall Debrief, galvanic skin responses (GSR) and gaze data. Two empirical studies involving a search task with a website known to have UPs were conducted to substantiate findings and improve on the methodological framework, which could facilitate usability evaluation practice. Results showed no correlation between GSR peaks and severity ratings, but GSR peaks were correlated with frustration scores -- a metric we developed. The Peak-End rule was partially verified. The problematic evaluator effect was the limitation as it confounded the severity ratings of UPs. Future work is aimed to control this effect and to develop a multifaceted severity scale.


title: Mechanical design and analysis of light weight hip joint Parallel Elastic Actuator for industrial exoskeleton

abstract: Industrial wearable exoskeletons can assist the workers during manual handling of loads at manufacturing facilities. Today, one of their design challenges is to reduce weight so the worker can wear them for an extended length of time, without compromising torque and power requirements. Actuators can largely contribute to the overall weight of such devices. An elastic element in parallel can reduce the technical specifications of the actuator. However, such elastic elements are heavy with a large footprint. We present an innovative Parallel Elastic Actuator (PEA) using an elastic cord made of natural rubber elastomer, which can store energy during lowering and release it while lifting. Trunk exoskeleton requirements are analysed based on human subject data for industrial lowering and lifting scenarios. The mechanical design concept of a PEA for the hip joint of an industrial exoskeleton is discussed in detail. We formulate the mathematical model of the human-exoskeleton motion in the sagittal plane. We perform the virtual testing on industrial lowering and lifting scenarios to verify the actuator performance. The results show the improvement in weight, peak torque and peak power by 20%, 50% and 40% respectively as compared with the current prototype. The new integrated actuator consists of the direct current (DC) motor, the harmonic drive (HD) and the parallel natural rubber elements which reduce size and complexity of the trunk exoskeleton.


title: A model-driven architecture-based data quality management framework for the internet of Things

abstract: The internet of Things (IoT) is a data stream environment where a large scale deployment of smart things continuously report readings. These data streams are then consumed by pervasive applications, i.e. data consumers, to offer ubiquitous services. The data quality (DQ) is a key criteria for IoT data consumers especially when considering the inherent uncertainty of sensor-enabled data. However, DQ is a highly subjective concept and there is no standard agreement of how to determine “good” data. Moreover, the combinations of considered measured attributes and associated DQ information are as diverse as the needs of data consumers. This introduces expensive overheads for data consumers that desire a specifically built system for managing their DQ information. To effectively handle these various perceptions of DQ, we propose a Model-Driven Architecture-based approach that allows the data consumer to easily and efficiently express, through models, his vision of DQ and its requirements using an easy-to-use graphical model editor. The defined DQ specifications are then automatically transformed to generate an entire infrastructure for DQ management that fits perfectly the data consumer's requirements. We demonstrate the flexibility and the efficiency of our approach through a real life data stream environment scenario.


title: Tensor-Train Recurrent Neural Networks for Video Classification

abstract: The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing. These models, however, turn out to be impractical and difficult to train when exposed to very high-dimensional inputs due to the large input-to-hidden weight matrix. This may have prevented RNNs' large-scale application in tasks that involve very high input dimensions such as video modeling; current approaches reduce the input dimensions using various feature extractors. To address this challenge, we propose a new, more general and efficient approach by factorizing the input-to-hidden weight matrix using Tensor-Train decomposition which is trained simultaneously with the weights themselves. We test our model on classification tasks using multiple real-world video datasets and achieve competitive performances with state-of-the-art models, even though our model architecture is orders of magnitude less complex. We believe that the proposed approach provides a novel and fundamental building block for modeling high-dimensional sequential data with RNN architectures and opens up many possibilities to transfer the expressive and advanced architectures from other domains such as NLP to modeling high-dimensional sequential data.


title: Synthesis and characterization of cuprous oxide dendrites: New simplified green hydrothermal route

abstract: Abstract Cuprous oxide (Cu 2 O) dendrites were prepared by simple hydrothermal route at two different temperatures using starch as reducing and stabilizing agent. Scanning Electron Microscopy (SEM) revealed the alterations in morphology with reaction temperature and time. The spherical nanoparticles obtained at lower reaction temperature self-assembled into distinct dendritic nanostructures at high temperature. The mechanism of formation of dendrite over the polysaccharide template has been discussed. Transmission Electron Microscopy (TEM) revealed that the crystalline size of these dendrites in one dimension is about 50 nm. The nanoparticles were characterized by UV–vis and photoluminescence (PL) spectroscopy, X-ray diffraction (XRD), FT-IR and Thermal Gravimetry Analyzer (TGA). Impedance analysis of the nanostructures showed conductivity to be a function of temperature.


Effect of Craniocervical Alignment on Body Sway


title: Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review

abstract: It is generally accepted that augmented feedback, provided by a human expert or a technical display, effectively enhances motor learning. However, discussion of the way to most effectively provide augmented feedback has been controversial. Related studies have focused primarily on simple or artificial tasks enhanced by visual feedback. Recently, technical advances have made it possible also to investigate more complex, realistic motor tasks and to implement not only visual, but also auditory, haptic, or multimodal augmented feedback. The aim of this review is to address the potential of augmented unimodal and multimodal feedback in the framework of motor learning theories. The review addresses the reasons for the different impacts of feedback strategies within or between the visual, auditory, and haptic modalities and the challenges that need to be overcome to provide appropriate feedback in these modalities, either in isolation or in combination. Accordingly, the design criteria for successful visual, auditory, haptic, and multimodal feedback are elaborated.


title: A unified approach to interpreting model predictions

abstract: Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.


FAF-Drugs3: a web server for compound property calculation and chemical library design


title: Cyber anonymity based on software-defined networking and Onion Routing (SOR)

abstract: Cyber anonymity tools have attracted wide attention in resisting network traffic censorship and surveillance, and have played a crucial role for open communications over the Internet. The Onion Routing (Tor) is considered the prevailing technique for circumventing the traffic surveillance and providing cyber anonymity. Tor operates by tunneling a traffic through a series of relays, making such traffic to appear as if it originated from the last relay in the traffic path, rather than from the original user. However, Tor faced some obstructions in carrying out its goal effectively, such as insufficient performance and limited capacity. This paper presents a cyber anonymity technique based on software-defined networking; named SOR, which builds onion-routed tunnels across multiple anonymity service providers. SOR architecture enables any cloud tenants to participate in the anonymity service via software-defined networking. Our proposed architecture leverages the large capacity and robust connectivity of the commercial cloud networks to elevate the performance of the cyber anonymity service.


title: Perceptions of Internet Information Credibility

abstract: People increasingly rely on Internet and web-based information despite evidence that it is potentially inaccurate and biased. Therefore, this study sought to assess people's perceptions of the cred...


title: Hole-filling Based on Disparity Map for DIBR

abstract: Due to sharp depth transition, big holes may be found in the novel view that is synthesized by depth-image-based rendering (DIBR). A hole-filling method based on disparity map is proposed. One important aspect of the method is that the disparity map of destination image is used for hole-filling, instead of the depth image of reference image. Firstly, the big hole detection based on disparity map is conducted, and the start point and the end point of the hole are recorded. Then foreground pixels and background pixels are distinguished for hole-dilating according to disparity map, so that areas with matching errors can be determined and eliminated. In addition, parallaxes of pixels in the area with holes and matching errors are changed to new values. Finally, holes are filled with background pixels from reference image according to these new parallaxes. Experimental results show that the quality of the new view after hole-filling is quite well; and geometric distortions are avoided in destination image, in contrast to the virtual view generated by depth-smoothing methods and image inpainting methods. Moreover, this method is easy for hardware implementation.


title: Dynamic User Modeling in Social Media Systems

abstract: Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top-k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.


title: KIBO robot demo: engaging young children in programming and engineering

abstract: Robotics offers a playful and tangible way for young children to engage with technology and engineering concepts during their foundational early childhood years. This paper describes the development of KIBO, a newly created robotics kit for children ages 4-7 developed at Tufts University through funding from the National Science Foundation. Designed explicitly for very young children, the KIBO kit has gone through several design iterations based on feedback from teachers and children over the past three years. The newest version of KIBO allows young children to become engineers by constructing robots using motors, sensors, and craft materials. Children also become programmers by exploring sequences, loops, and variables. In line with screen-time recommendations for young children, KIBO is programmed to move using tangible programming blocks- no computer, tablet, or screen required.


title: Hiding Hardware Trojan Communication Channels in Partially Specified SoC Bus Functionality

abstract: On-chip bus implementations must be bug-free and secure to provide the functionality and performance required by modern system-on-a-chip (SoC) designs. Regardless of the specific topology and protocol, bus behavior is never fully specified, meaning there exist cycles/conditions where some bus signals are irrelevant, and ignored by the verification effort. We highlight the susceptibility of current bus implementations to Hardware Trojans hiding in this partially specified behavior, and present a model for creating a covert Trojan communication channel between SoC components for any bus topology and protocol. By only altering existing bus signals during the period where their behaviors are unspecified , the Trojan channel is very difficult to detect. We give Trojan channel circuitry specifics for AMBA AXI4 and advanced peripheral bus (APB), then create a simple system comprised of several master and slave units connected by an AXI4-Lite interconnect to quantify the overhead of the Trojan channel and illustrate the ability of our Trojans to evade a suite of protocol compliance checking assertions from ARM. We also create an SoC design running a multiuser Linux OS to demonstrate how a Trojan communication channel can allow an unprivileged user access to root-user data. We then outline several detection strategies for this class of Hardware Trojan.


title: BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity

abstract: The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. Thus, this method is able to address some open questions regarding accurate and efficient computations that occur in the brain. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs.


title: Crew Assignment via Constraint Programming: Integrating Column Generation and Heuristic Tree Search

abstract: The Airline Crew Assignment Problem (ACA) consists of assigning lines of work to a set of crew members such that a set of activities is partitioned and the costs for that assignment are minimized. Especially for European airline companies, complex constraints defining the feasibility of a line of work have to be respected. We developed two different algorithms to tackle the large scale optimization problem of Airline Crew Assignment. The first is an application of the Constraint Programming (CP) based Column Generation Framework. The second approach performs a CP based heuristic tree search. We present how both algorithms can be coupled to overcome their inherent weaknesses by integrating methods from Constraint Programming and Operations Research. Numerical results show the superiority of the hybrid algorithm in comparison to CP based tree search and column generation alone. Copyright Kluwer Academic Publishers 2002


title: Efficacy and safety of etodolac-paracetamol fixed dose combination in patients with knee osteoarthritis flare-up: a randomized, double-blind comparative evaluation.

abstract: OBJECTIVE ::: To evaluate the efficacy and safety of etodolac-paracetamol combination in comparison with etodolac alone in patients with knee osteoarthritis (OA) flare-up. ::: ::: ::: METHODS ::: In this double-blind, double-dummy, randomized, comparative, multicentric, parallel group study, 220 patients of either sex in the age range of 40 to 70 years with an OA flare-up were randomized either to etodolac (300 mg)-paracetamol (500 mg) combination or etodolac (300 mg) alone twice a day for 10 days. Efficacy outcomes were an average daily pain intensity score on 11-point visual analog scale, Western Ontario and McMaster score (WOMAC), and Lequesne Severity Index; total pain relief score at 30 minutes, 1, 2, and 4 hours after first-dose administration; OA flare-up symptoms resolution; patient's and investigator's overall assessment of study treatments. ::: ::: ::: RESULTS ::: Etodolac-paracetamol was significantly superior to etodolac alone in reducing pain intensity (P<0.001), achieving pain relief (P<0.05) during the first 4 hours after the study dose administration, and resolution of the clinical signs and symptoms of OA flare-up such as morning stiffness, swelling/inflammation, and erythema. The combination showed significantly greater improvement in WOMAC scores and Lequesne Severity Index (P<0.001) than in etodolac monotherapy. Peak pain intensity difference over a period of 10 days was also significantly (P<0.001) higher in combination-treated patients compared with monotherapy-treated patients. The combination had significantly better patient's and investigator's global efficacy assessment (P=0.001). Both treatments were well tolerated and safe in patients with OA flare-up. ::: ::: ::: DISCUSSION ::: For the treatment of painful OA flare-ups, the etodolac-paracetamol combination can offer improved clinical outcomes by targeting multiple pain pathways. The results of the current study show that etodolac-paracetamol is more effective in the treatment of OA flare-up than etodolac alone.


title: A highly accurate and computationally efficient approach for unconstrained iris segmentation

abstract: Biometric research has experienced significant advances in recent years given the need for more stringent security requirements. More important is the need to overcome the rigid constraints necessitated by the practical implementation of sensible but effective security methods such as iris recognition. An inventive iris acquisition method with less constrained image taking conditions can impose minimal to no constraints on the iris verification and identification process as well as on the subject. Consequently, to provide acceptable measures of accuracy, it is critical for such an iris recognition system to be complemented by a robust iris segmentation approach to overcome various noise effects introduced through image capture under different recording environments and scenarios. This research introduces a robust and fast segmentation approach towards less constrained iris recognition using noisy images contained in the UBIRIS.v2 database (the second version of the UBIRIS noisy iris database). The proposed algorithm consists of five steps, which include: (1) detecting the approximate localization of the eye area of the noisy image captured at the visible wavelength using the extracted sclera area, (2) defining the outer iris boundary which is the boundary between iris and sclera, (3) detecting the upper and lower eyelids, (4) conducting the verification and correction for outer iris boundary detection and (5) detecting the pupil area and eyelashes and providing means for verification of the reliability of the segmentation results. The results demonstrate that the accuracy is estimated as 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at >=97% in a ''Noisy Iris Challenge Evaluation (NICE.I)'' in an international competition that involved 97 participants worldwide, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time.


title: Efficient planning in non-Gaussian belief spaces and its application to robot grasping

abstract: The limited nature of robot sensors make many important robotics problems partially observable. These problems may require the system to perform complex information-gathering operations. One approach to solving these problems is to create plans in belief-space, the space of probability distributions over the under-lying state of the system. The belief-space plan encodes a strategy for performing a task while gaining information as necessary. Most approaches to belief-space planning rely upon representing belief state in a particular way (typically as a Gaussian). Unfortunately, this can lead to large errors between the assumed density representation of belief state and the true belief state. This paper proposes a new sample-based approach to belief-space planning that has fixed computational complexity while allowing arbitrary implementations of Bayes filtering to be used to track belief state. The approach is illustrated in the context of a simple example and compared to a prior approach. Then, we propose an application of the technique to an instance of the grasp synthesis problem where a robot must simultaneously localize and grasp an object given initially uncertain object parameters by planning information-gathering behavior. Experimental results are presented that demonstrate the approach to be capable of actively localizing and grasping boxes that are presented to the robot in uncertain and hard-to-localize configurations.


title: Aberrant Behavior Detection in Time Series for Network Monitoring

abstract: The open-source software RRDtool and Cricket provide a solution to the problem of collecting, storing, and visualizing service network time series data for the real-time monitoring task. However, simultaneously monitoring all service network time series of interest is an impossible task even for the accomplished network technician. The solution is to integrate a mathematical model for automatic aberrant behavior detection in time series into the monitoring software. While there are many such models one might choose, the primary goal should be a model compatible with real-time monitoring. At WebTV, the solution was to integrate a model based on exponential smoothing and Holt-Winters forecasting into the Cricket/RRDtool architecture. While perhaps not optimal, this solution is flexible, efficient, and effective as a tool for automatic aberrant behavior detection.


title: Generating animated paper pop-ups from the motion of articulated characters

abstract: Pop-up books are fascinating books comprised of paper pieces that pop out when opened to form interesting three-dimensional structures. But more than just reproducing 3D shapes, pop-up artists also use the movement of the paper pieces during the opening process to convey motion and produce some form of animation. Similarly, previous automated methods have focused on reproducing the 3D shape of an input mesh. In our work, we focus in recreating motion. We study the movement of the paper pieces of the different mechanisms used in pop-up structures to automatically design animated pop-ups. Our input is an animation file, containing a 3D character with an armature and motion. We map each of the linkage chains to a specific pop-up mechanism based on the type of motion it can produce. We then obtain the initial values of the parameters of the mechanisms, such as lengths and orientations of the patches, based on our formulations and parameter estimation. Subsequently, we utilize simulated annealing to search for a plausible layout from a valid configuration space. Finally, we produce a printable design layout of the animated pop-up.


title: Knowledge, strategy, and the theory of the firm

abstract: This paper argues that firms have particular institutional capabilities that allow them to protect knowledge from expropriation and imitation more effectively than market contracting. I argue that it is these generalized institutional capabilities that allow firms to generate and protect the unique resources and capabilities that are central to the strategic theory of the firm.


title: Universal Shape Formation for Programmable Matter

abstract: We envision programmable matter consisting of systems of computationally limited devices (which we call particles) that are able to self-organize in order to achieve a desired collective goal without the need for central control or external intervention. Central problems for these particle systems are shape formation and coating problems. In this paper, we present a universal shape formation algorithm which takes an arbitrary shape composed of a constant number of equilateral triangles of unit size and lets the particles build that shape at a scale depending on the number of particles in the system. Our algorithm runs in O(√n) asynchronous execution rounds, where $n$ is the number of particles in the system, provided we start from a well-initialized configuration of the particles. This is optimal in a sense that for any shape deviating from the initial configuration, any movement strategy would require Ω(√n) rounds in the worst case (over all asynchronous activations of the particles). Our algorithm relies only on local information (e.g., particles do not have ids, nor do they know n, or have any sort of global coordinate system), and requires only a constant-size memory per particle.


title: The Scrum Software Development Process for Small Teams

abstract: In today's software development environment, requirements often change during the product development life-cycle to meet shifting business demands, creating endless headaches for development teams. We discuss our experience in implementing the Scrum software development process to address these concerns.


title: Smart heterogeneous precision agriculture using wireless sensor network based on extended Kalman filter

abstract: Smart heterogeneous precision agriculture (SHPA) using a wireless sensor network is introduced for limited energy sensor nodes, deployed in an agricultural farm which is divided into a number of heterogeneous agricultural areas. At each time step, the extended Kalman filter is adopted to measure and to predict the agricultural parameters including the soil moisture and temperature so that the noise associated with noisy measurements is filtered. After that, sensor node selection algorithm proactively selects the sensor nodes for each individual area to sense the agricultural parameters. Thus, the network lifetime and sensing accuracy are improved. The sampling interval for each crop is predefined based on the crop types and agricultural requirements so that the crop yields are improved. Also, the design, framework, algorithms, and architecture of SHPA are considered and proposed. Compared with other schemes, simulation results show that the proposed SHPA scheme eliminates the noise associated with the measurements, improves the network lifetime and sensing accuracy, and enhances the crop yields.


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title: Group-based multi-trajectory modeling

abstract: Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood ...


title: A Visual Model For Blast Waves and Francture

abstract: The expense, danger, planning and precision required to create explosions suggests that the computational visual modelling of explosions is worthwhile. However, the short time scale at which explosions occur, and their sheer complexity, poses a difficult modelling challenge. After describing the basic phenomenology of explosion events, we present an efficient computational model of isotropic blast wave transport and an algorithm for fracturing objects in their wake. Our model is based on the notion of a blast curve that gives the force-loading profile of an explosive material on an object as a function of distance from the explosion’s centre. We also describe a technique for fracturing materials loaded by a blast. Our approach is based on the notion of rapid fracture: that microfractures in a material together with loading forces seed a fracturing process that quickly spreads across the material and causes it to fragment.


title: Griffon: a man‐portable hybrid UGV/UAV

abstract: To demonstrate proof‐of‐concept of the Griffon man‐portable hybrid unmanned ground vehicle/unmanned aerial vehicle (UGV/UAV) based on the iRobot PackBot we developed the Griffon air mobility system consisting of a gasoline‐powered propeller engine, a steerable parafoil, and a radio‐controlled servo system. We integrated the AMS with a PackBot prototype, and we conducted ground and flight tests to validate this concept. The Griffon prototype was capable of remote‐controlled flight, take‐off, and landing. The Griffon achieved speeds of over 20 mph and altitudes of up to 200 feet. We demonstrated the feasibility of developing a man‐portable hybrid UGV/UAV. Future work may explore the possibilities for teleoperated, semi‐autonomous, and fully autonomous control using the Griffon concept. The parafoil wing limits the usability of this vehicle in windy conditions, but this could be addressed using a lightweight fixed wing instead. Man‐portable hybrid UGV/UAVs may be used by the military to perform reconnaissanc...


title: Non-negative matrix factorization with constraints

abstract: Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation. However, NMF is essentially an unsupervised method and can not make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Non-negative Matrix Factorization, which takes the label information as additional constraints. Specifically, we require that the data points sharing the same label have the same coordinate in the new representation space. This way, the learned representations can have more discriminating power. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.


title: Mitogen-activated protein kinase pathway is involved in α6 integrin gene expression in androgen-independent prostate cancer cells: role of proximal Sp1 consensus sequence

abstract: Abstract Metastatic diseases of prostate cancer reveal high expression of α6 integrin and the activation of mitogen-activated protein kinases (MAP kinase). Therefore, the present study was conducted to examine whether MAP kinase pathway is involved in the α6 integrin gene expression in androgen-independent prostate cancer cell lines. α6 integrin mRNA expression, the α6 integrin promoter-induced luciferase activities and MAP kinase enzyme activities in androgen-independent LNCaP and PC-3 cell lines were higher than those in androgen-dependent LNCaP. Deletion and mutation analysis showed that Sp1 consensus sequence at −48 to −43 bp from the transcription start site was necessary for basal promoter activity. Binding of Sp1 to its consensus sequence in three cell lines was confirmed by electrophoretic mobility shift assays. Sp1 binding to its consensus sequence, as well as promoter activity and mRNA expression, were found to be inhibited by an inhibitor of MAP kinase kinase 1 and 2, U0126, in the androgen-independent cell lines. Our results indicate that the proximal Sp1 is necessary for basal promoter activity of the α6 integrin, suggesting that signal transduction from MAP kinases to activation of Sp1 might be involved in α6 integrin gene expression in androgen-independent prostate cancer cell lines.


title: String diagrams for free monads (functional pearl)

abstract: We show how one can reason about free monads using their universal properties rather than any concrete implementation. We introduce a graphical, two-dimensional calculus tailor-made to accommodate these properties.


title: A 10-bit Charge-Redistribution ADC Consuming 1.9 $\mu$W at 1 MS/s

abstract: This paper presents a 10 bit successive approximation ADC in 65 nm CMOS that benefits from technology scaling. It meets extremely low power requirements by using a charge-redistribution DAC that uses step-wise charging, a dynamic two-stage comparator and a delay-line-based controller. The ADC requires no external reference current and uses only one external supply voltage of 1.0 V to 1.3 V. Its supply current is proportional to the sample rate (only dynamic power consumption). The ADC uses a chip area of approximately 115×225 μm2. At a sample rate of 1 MS/s and a supply voltage of 1.0 V, the 10 bit ADC consumes 1.9 μW and achieves an energy efficiency of 4.4 fJ/conversion-step.


title: Multi-label learning by exploiting label dependency

abstract: In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the conditional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other well-established methods.


title: A First Step Toward Network Security Virtualization: From Concept To Prototype

abstract: Network security management is becoming more and more complicated in recent years, considering the need of deploying more and more network security devices/middle-boxes at various locations inside the already complicated networks. A grand challenge in this situation is that current management is inflexible and the security resource utilization is not efficient. The flexible deployment and utilization of proper security devices at reasonable places at needed time with low management cost is extremely difficult. In this paper, we present a new concept of network security virtualization, which virtualizes security resources/functions to network administrators/users, and thus maximally utilizing existing security devices/middle-boxes. In addition, it enables security protection to desirable networks with minimal management cost. To verify this concept, we further design and implement a prototype system, NETSECVISOR, which can utilize existing pre-installed (fixed-location) security devices and leverage software-defined networking technology to virtualize network security functions. At its core, NETSECVISOR contains: 1) a simple script language to register security services and policies; 2) a set of routing algorithms to determine optimal routing paths for different security policies based on different needs; and 3) a set of security response functions/strategies to handle security incidents. We deploy NETSECVISOR in both virtual test networks and a commercial switch environment to evaluate its performance and feasibility. The evaluation results show that our prototype only adds a very small overhead while providing desired network security virtualization to network users/administrators.


title: Abstract Knowledge Management Governance: A Multifaceted Approach to Organisational Decision and Innovation Support

abstract: Knowledge management is a strategy to support enhanced decision making through effective control of organizational knowledge. This paper presents a preliminary investigation into the governance of strategies to manage knowledge and the relationship between such strategies and organizational structures. It finds that the federated information transfer model is the 'best fit' with a stakeholder model of governance which ensures accountability and responsibility for tasks meeting the needs of all organizational stakeholders. Further, the governance structure established may predicate the ability of the organization to effectively establish a strategy to manage their knowledge.


title: Discriminatively Trained Dense Surface Normal Estimation

abstract: In this work we propose the method for a rather unexplored problem of computer vision - discriminatively trained dense surface normal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding. We apply our method to two challenging data sets containing images of man-made environments, the indoor NYU2 data set and the outdoor KITTI data set. Our surface normal predictor achieves results better than initially expected, significantly outperforming state-of-the-art.


title: Brushless alternator in automotive applications

abstract: This paper deals with reliability problems of common types of generators in hard conditions. It shows possibilities of construction changes that should increase the machine reliability. This contribution is dedicated to the study of brushless alternator for automotive industry. There are described problems with usage of common types of alternators and main benefits and disadvantages of several types of brushless alternators.