title: Cyber Security via Signaling Games: Toward a Science of Cyber Security

abstract: In March of 2013, what started as a minor dispute between Spamhaus and Cyberbunker quickly escalated to a distributed denial of service DDoS attack that was so massive, it was claimed to have slowed internet speeds around the globe. The attack clogged servers with dummy internet traffic at a rate of about 300 gigabits per second. By comparison, the largest observed DDoS attacks typically against banks had thus far registered only 50 gigabits per second. The record breaking Spamhaus/Cyberbunker conflict arose 13 years after the publication of best practices on preventing DDoS attacks, and it was not an isolated event. ::: ::: Recently, NYU's Courant Institute and Carnegie Mellon Software Engineering Institute have collaboratively devised a game-theoretic approaches to address various cyber security problems involving exchange of information asymmetrically. This research aims to discover and understand complex structures of malicious use cases within the context of secure systems with the goal of developing an incentives-based measurement system that ensures a high level of resilience to attack.


title: Multi-class SVM based gradient feature for banknote recognition

abstract: Banknote recognition system is the focus of different image processing and pattern recognition research. With the improvement in modern-day banking operations, automated systems for banknote recognition have become pertinent. Recognition of banknotes is a challenging task as banknotes can suffer from defects and images get distorted during acquisition, which raises the need for a robust recognition system to mitigate these flaws. This research proposes a new banknote recognition approach that classifies the principal components of the extracted Histogram of Gradient feature vectors using an efficient error correcting output code technique based on a Multi-Class Support Vector Machine. The method works on both sides of the bank note and efficiently recognize the denomination based on any side of the bill. The system was implemented using the Nigerian Naira, and for experimental evaluation, additional analysis was conducted using the US Dollar, Canadian Dollar, and Euro banknotes. Finally, the system performance was evaluated based on the recognition rate and processing time.


title: MTPV for Continuous Flux-Weakening Strategy Control Law for IPMSM

abstract: Synchronous motors with Interior Permanent Magnets (IPMSM) are particularly effective for operation at high speed due to their high power density. However the speeds generate large electromagnetic forces and it is necessary to set up a flux-weakening. Classical control algorithms do flux-weakening by injecting a negative $\mathbf{I}_{\mathrm{d}}$ current in respect of current norm. The value of this current is usually defined on the LUTs (Look Up Tables) depending on the speed and the torque required reflecting open-loop operation. This article presents continuous control law in closed loop ensuring the operation from the speed zero up to the maximum speed. The generation of the current reference $\mathbf{I}_{\mathrm{d}}$ is based on strategy to Minimum Current per Torque (MinCPT) followed a Maximum Torque per Volt (MTPV) strategy. The goal is to maximize the speed for a given power. The effectiveness of this unified algorithm is tested on an experimental platform.


title: All You Need is: Evading Hate Speech Detection

abstract: With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven state-of-the-art hate speech detection models from prior work, and show that they perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech. A combination of these methods is also effective against Google Perspective - a cutting-edge solution from industry. Our experiments demonstrate that adversarial training does not completely mitigate the attacks, and using character-level features makes the models systematically more attack-resistant than using word-level features.


title: On the Consensus Mechanisms of Blockchain/DLT for Internet of Things

abstract: Internet of Things (IoT) has been experiencing exponential growth in recent years, but still faces many serious challenges. The distributed ledger technology (DLT), e.g., Blockchain, not only appears to be promising to address these technical challenges, but also brings tremendous opportunities for new application and business models. However, the convergence of IoT and DLT is yet a goal far beyond our reach today. Among many problems that have not been sufficiently understood, a fundamental one is how to design appropriate consensus mechanisms for DLT applied to IoT, which is the theme of this paper. We first discuss the potential benefits of applying DLT to IoT, and identify major challenges posed to DLT by IoT. Then we make a survey of existing DLT consensus mechanisms, to summarize major principles and discuss their pros and cons when applied in IoT.


title: Breaking Sticks and Ambiguities with Adaptive Skip-gram

abstract: Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to overcome this limitation and learn multi-prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip-gram model which is a nonparametric Bayesian extension of Skip-gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word-sense induction task.


title: Ontology based information retrieval system for Academic Library

abstract: Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as semantic web which overcome the limitation of keyword based search technique called conceptual or semantic search technique. Natural language processing technique is mostly implemented in QA system for asking user's question and several steps are also followed for conversion of questions to query form for getting an exact answer. In conceptual search, search engine interprets the meaning of user's query and the relation among the concepts that documents contains with respect to a particular domain that produces specific answers instead of giving list of answers. In this paper, we proposed ontology based semantic information retrieval system and Jena semantic web framework in which, user enters an input query which is parsed by Standford Parser then triplet extraction algorithm is used. To all input query, SPARQL query is formed and then it is fired on the knowledge base (Ontology) that finds appropriate RDF triples in knowledge base and retrieve the relevant information using Jena framework.


title: Design a smart waste bin for smart waste management

abstract: In this paper, we presented the smart waste-bin that can managed the waste in a smart city project. The system consist of sensors to measure the weight of waste and the level of waste inside the bin. The system also adapt with network environment, to manage all information from waste management. As the result we proposed a prototype of smart waste-bin that suitable for many kind of conventional waste-bin.


title: Wearable Pulse Wave Monitoring System Based on MEMS Sensors

abstract: Pulse wave monitoring is critical for the evaluation of human health. In this paper, a wearable multi-sensor pulse wave monitoring system is proposed and demonstrated. The monitoring system consists of a measuring unit and an analog circuit processing unit. The main part of the measuring unit is a flexible printed circuit board (PCB) with a thickness of 0.15 mm, which includes three micro-electromechanical system (MEMS) pressure sensors softly packaged by polydimethylsiloxane (PDMS), a blood oxygen detector and a MEMS three-axis accelerometer. The MEMS pressure sensors，the blood oxygen detector and the accelerometer are fixed on the expected locations of the flexible PCB. The analog circuit processing unit includes a power supply module, a filter and an amplifier. The pulse waves of two volunteers are detected by the monitoring system in this study. The output signals of the analog circuit processing module are processed and analyzed. In the preliminary test, the time delay of the three pressure pulse waves has been detected and the calculated pulse wave velocities (PWVs) are 12.50 and 11.36 m/s, respectively. The K value, related to the area of the pulse wave, can be obtained. Both the PWV and K value meet the health parameter standards.


title: Finding Symmetry Plane of 3D Face Shape

abstract: The symmetry plane detection from 3D face shape is much helpful for pose estimation and feature extraction for a wide range of applications, e.g. 3D face recognition. This paper proposes a robust symmetry plane detection method for 3D face models, which is an extension of EGI-based approach. The experiments using two data sets, USF HumanID 3D face database and 3DFED database covering significant variation in facial expression, demonstrate the effectiveness.


title: A New Channel Boosted Convolutional Neural Network using Transfer Learning

abstract: We present a novel architectural enhancement of Channel Boosting in deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of CNN (learning from multiple input channels) and Transfer learning (TL). TL is utilized at two different stages; channel generation and channel exploitation. In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its own original channel. The deep architecture of CNN then exploits the original and boosted channels down the stream for learning discriminative patterns. Churn prediction in telecom is a challenging task due to high dimensionality and imbalanced nature of the data and it is therefore used to evaluate the performance of the proposed Channel Boosted CNN (CB CNN). In the first phase, discriminative informative features are being extracted using a staked autoencoder, and then in the second phase, these features are combined with the original features to form Channel Boosted images. Finally, the knowledge gained by a pre trained CNN is exploited by employing TL. The results are promising and show the ability of the Channel Boosting concept in learning complex classification problem by discerning even minute differences in churners and non churners. The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring of a CNN architecture may increase the representative capacity of the network.


title: Greedy grid scheduling algorithm in dynamic job submission environment

abstract: Grid is a homogeneous or heterogeneous environment of resources. Grid scheduling is a technique by which resources are efficiently utilized to meet user demands. On the user perspective minimization of job turnaround time is the main challenge. Most of the minimization algorithms are implemented in homogeneous resource environment. In this paper a scheduling algorithm is presented to minimize average turnaround time in heterogeneous resource environment. This algorithm is based on greedy method which is used for totally dynamic grid environment where the jobs arrive at different time interval. The turnaround time of each job is minimized individually to minimize the average turnaround time of all submitted jobs in a time slot.


title: Optimization of skewed omega for left-handed material characteristics

abstract: Artificial neural network and simulated annealing are applied to obtain optimized design parameters of skewed omega-shaped metamaterial unit cells for providing left-handed material (LHM) with a negative refractive index. The implemented numerical and experimental methods provide evidence for the validity and practicality of the technique. Simulation, with optimized parameters, using CST Microwave Studio, shows that skewed LHMs are low-loss materials.


title: True Knowledge: Open-Domain Question Answering Using Structured Knowledge and Inference

abstract: This article gives a detailed description of True Knowledge: a commercial, open-domain question answering platform. The system combines a large and growing structured knowledge base of common sense, factual and lexical knowledge; a natural language translation system that turns user questions into internal language-independent queries and an inference system that can answer those queries using both directly represented and inferred knowledge. The system is live and answers millions of questions per month asked by internet users.


title: Hypervisor-based fault tolerance

abstract: Protocols to implement a fault-tolerant computing system are described. These protocols augment the hypervisor of a virtual-machine manager and coordinate a primary virtual machine with its backup. The result is a fault-tolerant computing system. No modification to hardware, operating system, or application programs is required. A prototype system was constructed for HP's PA-RISC instruction-set architecture. The prototype was able to run programs about a factor of 2 slower than a bare machine would.


abstract: A simple method for detecting salient regions in images is proposed. It requires only edge detection, threshold decomposition, the distance transform, and thresholding. Moreover, it avoids the need for setting any parameter values. Experiments show that the resulting regions are relatively coarse, but overall the method is surprisingly effective, and has the benefit of easy implementation. Quantitative tests were carried out on Liu et al.'s dataset of 5000 images. Although the ratings of our simple method were not as good as their approach which involved an extensive training stage, they were comparable to several other popular methods from the literature. Further tests on Kootstra and Schomaker's dataset of 99 images also showed promising results.


title: Projective simulation for classical learning agents: a comprehensive investigation

abstract: We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced. 2) Here we provide a detailed analysis of the model and examine its performance, including its achievable efficiency, its learning times and the way both properties scale with the problems’ dimension. In addition, we situate the PS agent in different learning scenarios, and study its learning abilities. A variety of new scenarios are being considered, thereby demonstrating the model’s flexibility. Furthermore, to put the PS scheme in context, we compare its performance with those of Q-learning and learning classifier systems, two popular models in the field of reinforcement learning. It is shown that PS is a competitive artificial intelligence model of unique properties and strengths.


Anti-forensics with a small army of exploits


title: M.: No-redundant metrics for uml class diagram structural complexity

abstract: In software engineering it is widely acknowledged that the usage of metrics at the initial phases of the object oriented software life cycle can help designers to make better decisions and to predict external quality attributes, such as maintainability. Following this idea we have carried out three controlled experiments to ascertain if any correlation exists between the structural complexity and the size of UML class diagrams and their maintainability. We used 8 metrics for measuring the structural complexity of class diagrams due to the usage of UML relationships, and 3 metrics to measure their size. With the aim of determining which of these metrics are really relevant to be used as class diagrams maintainability indicators, we present in this work a study based on Principal Component Analysis. The obtained results show that the metrics related to associations, aggregations, generalizations and dependencies, are the most relevant whilst those related to size seem to be redundant.


title: Are tradable green certificates a cost-efficient policy driving technical change or a rent-generating machine? Lessons from Sweden 2003–2008.” Energy Policy

abstract: In the European policy debate, tradable green certificates (TGC) have been suggested to be a superior regulatory framework for promoting the diffusion of renewable electricity technologies. The purpose of this paper is to assess the performance of the Swedish TGC system, contributing to the European debate on the suitability of different types of frameworks. The expectations of the TGC system were that it would: (a) be effective in terms of increasing the supply of "green" electricity; (b) do this in a cost effective manner (from both a social and a consumer perspective); (c) generate an equitable distribution of costs and benefits and (d) drive technical change. So far, it has performed adequately in terms of effectiveness and social cost effectiveness. However, consumer costs have been substantially higher than expected, very large rents are generated and, at best, it contributes marginally to technical change. Thus, a TGC framework should be selected if the overriding concern is to minimize short term social costs of reaching a certain goal with a high degree of predictability. However, it cannot be expected to also drive technical change, keep consumer costs down and be equitable. Such trade-offs need to be revealed and not obscured by analysts.


title: A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems

abstract: The ad hoc coordination problem is to design an ad hoc agent which is able to achieve optimal flexibility and efficiency in a multiagent system that admits no prior coordination between the ad hoc agent and the other agents. We conceptualise this problem formally as a stochastic Bayesian game in which the behaviour of a player is determined by its type. Based on this model, we derive a solution, called Harsanyi-Bellman Ad Hoc Coordination (HBA), which utilises a set of user-defined types to characterise players based on their observed behaviours. We evaluate HBA in the level-based foraging domain, showing that it outperforms several alternative algorithms using just a few user-defined types. We also report on a human-machine experiment in which the humans played Prisoner's Dilemma and Rock-Paper-Scissors against HBA and alternative algorithms. The results show that HBA achieved equal efficiency but a significantly higher welfare and winning rate.


title: Using County Demographics to Infer Attributes of Twitter Users

abstract: Social media are increasingly being used to complement traditional survey methods in health, politics, and marketing. However, little has been done to adjust for the sampling bias inherent in this approach. Inferring demographic attributes of social media users is thus a critical step to improving the validity of such studies. While there have been a number of supervised machine learning approaches to this problem, these rely on a training set of users annotated with attributes, which can be difficult to obtain. We instead propose training a demographic attribute classifiers that uses county-level supervision. By pairing geolocated social media with county demographics, we build a regression model mapping text to demographics. We then adopt this model to make predictions at the user level. Our experiments using Twitter data show that this approach is surprisingly competitive with a fully supervised approach, estimating the race of a user with 80% accuracy.


title: Abstract Co-management: concepts and methodological implications

abstract: Co-management, or the joint management of the commons, is often formulated in terms of some arrangement of power sharing between the State and a community of resource users. In reality, there often are multiple local interests and multiple government agencies at play, and co-management can hardly be understood as the interaction of a unitary State and a homogeneous community. An approach focusing on the legal aspects of co-management, and emphasizing the formal structure of arrangements (how governance is configured) runs the risk of neglecting the functional side of co-management. An alternative approach is to start from the assumption that co-management is a continuous problem-solving process, rather than a fixed state, involving extensive deliberation, negotiation and joint learning within problem-solving networks. This presumption implies that co-management research should preferably focus on how different management tasks are organized and distributed concentrating on the function, rather than the structure, of the system. Such an approach has the effect of highlighting that power sharing is the result, and not the starting point, of the process. This kind of research approach might employ the steps of (1) defining the social-ecological system under focus; (2) mapping the essential management tasks and problems to be solved; (3) clarifying the participants in the problem-solving processes; (4) analyzing linkages in the system, in particular across levels of organization and across geographical space; (5) evaluating capacity-building needs for enhancing the skills and capabilities of people and institutions at various levels; and (6) prescribing ways to improve policy making and problem-solving.


title: Sequential Neural Models with Stochastic Layers

abstract: How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling.


title: Local Decorrelation For Improved Pedestrian Detection

abstract: Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired by recent work on discriminative decorrelation of HOG features, we instead propose an efficient feature transform that removes correlations in local neighborhoods. The result is an overcomplete but locally decorrelated representation ideally suited for use with orthogonal decision trees. In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost. The overall improvement in accuracy is dramatic: on the Caltech Pedestrian Dataset, we reduce false positives nearly tenfold over the previous state-of-the-art.


Bad Company - Neighborhoods in Neural Embedding Spaces Considered Harmful


title: MR imaging of the pericardium.

abstract: Imaging of the pericardium requires understanding of anatomy and the normal and abnormal physiology of the pericardium. MR imaging is well-suited for answering clinical questions regarding suspected pericardial disease. Pericardial diseases that may be effectively imaged with MR imaging include pericarditis, pericardial effusion, cardiac-pericardial tamponade, constrictive pericarditis, pericardial cysts, absence of the pericardium, and pericardial masses. Although benign and malignant primary tumors of the pericardium may be occasionally encountered, the most common etiology of a pericardial mass is metastatic disease.


title: Chukwa: A system for reliable large-scale log collection

abstract: Large Internet services companies like Google, Yahoo, and Facebook use the MapReduce programming model to process log data. MapReduce is designed to work on data stored in a distributed filesystem like Hadoop's HDFS. As a result, a number of log collection systems have been built to copy data into HDFS. These systems often lack a unified approach to failure handling, with errors being handled separately by each piece of the collection, transport and processing pipeline. ::: ::: We argue for a unified approach, instead. We present a system, called Chukwa, that embodies this approach. Chukwa uses an end-to-end delivery model that can leverage local on-disk log files for reliability. This approach also eases integration with legacy systems. This architecture offers a choice of delivery models, making subsets of the collected data available promptly for clients that require it, while reliably storing a copy in HDFS. We demonstrate that our system works correctly on a 200-node testbed and can collect in excess of 200 MB/sec of log data. We supplement these measurements with a set of case studies describing real-world operational experience at several sites.


title: Improving biotech education through gamified laboratory simulations

abstract: Gamified laboratory simulations motivate students and improve learning outcomes compared with traditional teaching methods.


title: Business intelligence systems: design and implementation strategies

abstract: Managing an organization requires access to information in order to monitor activities and assess performance. Trying to understand what information an organization has can be challenging because the information systems collect and process vast amount of data in various forms. To flow in the running stream of rapidly changing, increasingly competitive global market scenario and increasingly volatile consumer and market behavior and rapidly shortening product life cycles, business enterprises today are necessary to analyze accurate and timely information about financial operations, customers, and products using familiar business terms, in order to gain analytical insight into business problems and opportunities. Enterprises are building business intelligence systems that support business analysis and decision making to help them better understand their operations and compete in the marketplace. This paper describes the life cycle comprising various phases in the development of a BI system. The paper elaborates the implementation issues of BI in an organization focusing a case study


title: Exploring the design space of robots: Children's perspectives

abstract: Children's perceptions and evaluations of different robot designs are an important unexplored area within robotics research considering that many robots are specifically designed for children. To examine children's feelings and attitudes towards robots, a large sample of children (N=159) evaluated 40 robot images by completing a questionnaire for each image, which enquired about robot appearance, robot personality dimensions and robot emotions. Results showed that depending on a robot's appearance children clearly distinguished robots in terms of their intentions (i.e. friendly vs. unfriendly), their capability to understand, and their emotional expression. Results of a principal components analysis of the children's ratings of the robots' personality attributes revealed two dimensions labelled 'Behavioural Intention' and 'Emotional Expression'. Robots were classified according to their scores on these two dimensions and a content analysis of their appearance was conducted in an attempt to identify salient features of different robot personalities. Children judged human-like robots as aggressive, but human-machine robots as friendly. Results on children's perceptions of the robots' behavioural intentions provided tentative empirical support for the Uncanny Valley, hypothesized by (Mori, M., 1970), reflecting a situation where robots are very human-like, but still distinguishable from humans, evoking a feeling of discomfort or repulsion. The paper concludes with a discussion of design implications for robots, and the use of robots in educational contexts.


CS599: Structure and Dynamics of Networked Information (Spring 2005) 02/02/2005: α-communities


title: Improved Neural Text Attribute Transfer with Non-parallel Data

abstract: Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes. In this work, we propose multiple improvements over the existing approaches that enable the encoder-decoder framework to cope with the text attribute transfer from non-parallel data. We perform experiments on the sentiment transfer task using two datasets. For both datasets, our proposed method outperforms a strong baseline in two of the three employed evaluation metrics.


title: Dark Silicon: From Computation to Communication

abstract: In the emerging Dark Silicon era, not all parts of an on-chip system (i.e., cores, Network-on-Chip, and memory resources) can be simultaneously powered-on at the full speed. This paper aims at exposing dark silicon challenges to the NOCS community with an overview of some of the early research efforts that are attempting to shape the design and run-time management of future generation heterogeneous dark silicon processors. The goal is to cover both the computation and communication perspectives. In particular, we exploit computation and communication heterogeneity at multiple levels of system abstractions to design and manage dark silicon processors. The available dark silicon is leveraged to improve power/energy, performance, and reliability efficiency.


Predicting Soccer Match Results in the English Premier League


title: Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images

abstract: In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation. Watershed regions that are unlikely to represent nuclei are removed in the postprocessing stage. The proposed algorithm is evaluated on a number of images that are representative of the diversity in pathology in this type of tissue. The method shows good performance in terms of the number of segmented nuclei and segmentation accuracy.


title: Not knowing what I feel: Emotional empathy in autism spectrum disorders

abstract: While there is a general consensus in the literature that individuals with autism spectrum disorder have difficulty with cognitive empathy, much less is known about emotional empathy processing in these individuals. Most research has employed subjective self-report measures, which can often be misinterpreted or under-reported/over-reported. More objective measures such as psychophysiological recordings of arousal offer a more objective response. Furthermore, combining physiological responses with self-report ratings allows us to explore the relationship between these two responses to emotionally charged stimuli. A total of 25 individuals with autism spectrum disorder were compared with 25 matched controls on their physiological (arousal) and psychological (self-report) responses to emotionally distressing video scenes. These responses were also then compared with self-report cognitive and emotional trait empathy. Results indicate that while individuals with autism spectrum disorder appear to respond simil...


title: Rapid adaptation of video game AI

abstract: Current approaches to adaptive game AI require either a high quality of utilised domain knowledge, or a large number of adaptation trials. These requirements hamper the goal of rapidly adapting game AI to changing circumstances. In an alternative, novel approach, domain knowledge is gathered automatically by the game AI, and is immediately (i.e., without trials and without resource-intensive learning) utilised to evoke effective behaviour. In this paper we discuss this approach, called dasiarapidly adaptive game AIpsila. We perform experiments that apply the approach in an actual video game. From our results we may conclude that rapidly adaptive game AI provides a strong basis for effectively adapting game AI in actual video games.


title: Graphical One-Time Password (GOTPass): A usability evaluation

abstract: Complying with a security policy often requires users to create long and complex passwords to protect their accounts. However, remembering such passwords is difficult for many and may lead to insecure practices, such as choosing weak passwords or writing them down. In addition, they are vulnerable to various types of attacks, such as shoulder surfing, replay, and keylogger attacks Gupta, Sahni, Sabbu, Varma, & Gangashetty, 2012 One-Time Passwords OTPs aim to overcome such problems Gupta et al., 2012; however, most implemented OTP techniques require special hardware, which not only adds cost, but there are also issues regarding its availability Brostoff, Inglesant, & Sasse, 2010. In contrast, the use of graphical passwords is an alternative authentication mechanism designed to aid memorability and ease of use, often forming part of a multifactor authentication process. This article is complementary to the earlier work that introduced and evaluated the security of the new hybrid user-authentication approach: Graphical One-Time Password GOTPass Alsaiari et al., 2015. The scheme aims to combine the usability of recognition-based and draw-based graphical passwords with the security of OTP. The article presents the results of an empirical user study that investigates the usability features of the proposed approach, as well as pretest and posttest questionnaires. The experiment was conducted during three separate sessions, which took place over five weeks, to measure the efficiency, effectiveness, memorability, and user satisfaction of the new scheme. The results showed that users were able to easily create and enter their credentials as well as remember them over time. Participants carried out a total of 1,302 login attempts with a 93% success rate and an average login time of 24.5 s.


title: Exergames for Older Adults with Subthreshold Depression: Does Higher Playfulness Lead to Better Improvement in Depression?

abstract: Abstract Objective: The use of exergames is increasingly prevalent in the healthcare promotion among older adults. The current study aimed to examine whether the playfulness may influence the antidepressant effect of exergames on older adults. Method: Two experimental conditions, high playfulness (Wii™ Sport games) and low playfulness (Wii Fit™ training), were implemented in a 6-week randomized controlled study. A manipulation check was conducted first to confirm the significant difference in playfulness between the two conditions. A total of 49 community-dwelling older adults diagnosed with subthreshold depression have finished the study. Their depression, positive emotions, and self-efficacy were measured at both pre- and post-test. A multivariate analysis of covariance (MANCOVA) was then conducted between two conditions, with age and gender as the covariates. Results: Findings suggest that older adults in both two exergame conditions have improvements in subthreshold depression [t(48) = 9.48, P < 0.001...


title: Security review on the Internet of Things

abstract: This paper looks at Internet of Things devices from a security perspective to help provide an understanding on the impact Plug and Play Internet of Things devices have on organizations and Home networks. Such devices which are common within IoT are Internet connected devices such as mobile phones, routers or fridges. The papers key areas are to focus on the attack surface such as Malware within the Internet of Things domain, poor web interfaces which are exposed to the Internet, improper network connectivity and default credentials being used after purchase. In addition to discussing the attack surface, the paper looks at security best practices and outlines how to properly administer and manage IoT devices within controlled networks such as private organizations, government and home networks. This focuses on reasons for allowing such devices as well as guidance for best practices through the developing, manufacturing and consumer life cycle.


title: Sensing Pressure Distribution on a Lower-Limb Exoskeleton Physical Human-Machine Interface

abstract: A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer's skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented.


title: The Cyber Aggression in Relationships Scale: a new multidimensional measure of technology-based intimate partner aggression

abstract: The purpose of this study was to develop and provide initial validation for a measure of adult cyber intimate partner aggression (IPA): the Cyber Aggression in Relationships Scale (CARS). Drawing on recent conceptual models of cyber IPA, items from previous research exploring general cyber aggression and cyber IPA were modified and new items were generated for inclusion in the CARS. Two samples of adults 18 years or older were recruited online. We used item factor analysis to test the factor structure, model fit, and invariance of the measure structure across women and men. Results confirmed that three-factor models for both perpetration and victimization demonstrated good model fit, and that, in general, the CARS measures partner cyber aggression similarly for women and men. The CARS also demonstrated validity through significant associations with in-person IPA, trait anger, and jealousy. Findings suggest the CARS is a useful tool for assessing cyber IPA in both research and clinical settings.


title: Image Splicing Detection via Camera Response Function Analysis

abstract: Recent advances on image manipulation techniques have made image forgery detection increasingly more challenging. An important component in such tools is to fake motion and/or defocus blurs through boundary splicing and copy-move operators, to emulate wide aperture and slow shutter effects. In this paper, we present a new technique based on the analysis of the camera response functions (CRF) for efficient and robust splicing and copy-move forgery detection and localization. We first analyze how non-linear CRFs affect edges in terms of the intensity-gradient bivariable histograms. We show distinguishable shape differences on real vs. forged blurs near edges after a splicing operation. Based on our analysis, we introduce a deep-learning framework to detect and localize forged edges. In particular, we show the problem can be transformed to a handwriting recognition problem an resolved by using a convolutional neural network. We generate a large dataset of forged images produced by splicing followed by retouching and comprehensive experiments show our proposed method outperforms the state-of-the-art techniques in accuracy and robustness.


title: A framework for analyzing semantic change of words across time

abstract: Recently, large amounts of historical texts have been digitized and made accessible to the public. Thanks to this, for the first time, it became possible to analyze evolution of language through the use of automatic approaches. In this paper, we show the results of an exploratory analysis aiming to investigate methods for studying and visualizing changes in word meaning over time. In particular, we propose a framework for exploring semantic change at the lexical level, at the contrastive-pair level, and at the sentiment orientation level. We demonstrate several kinds of NLP approaches that altogether give users deeper understanding of word evolution. We use two diachronic corpora that are currently the largest available historical language corpora. Our results indicate that the task is feasible and satisfactory outcomes can be already achieved by using simple approaches.


title: A hybrid machine learning approach to automatic plant phenotyping for smart agriculture

abstract: Recently, a new ICT approach to agriculture called “Smart Agriculture” has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.


title: The Internet of Battle Things

abstract: The battlefield of the future will be densely populated by a variety of entities ("things") -- some intelligent and some only marginally so -- performing a broad range of tasks: sensing, communicating, acting, and collaborating with each other and human warfighters. We call this the Internet of Battle Things, IoBT. In some ways, IoBT is already becoming a reality, but 20-30 years from now it is likely to become a dominant presence in warfare. To become a reality, however, this bold vision will have to overcome a number of major challenges. As one example of such a challenge, the communications among things will have to be flexible and adaptive to rapidly changing situations and military missions. In this paper, we explore this and several other major challenges of IoBT, and outline key research directions and approaches towards solving these challenges.


title: Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

abstract: Recently, researchers have demonstrated state-of-the-art performance on sequential prediction problems using deep neural networks and Reinforcement Learning (RL). For some of these problems, oracles that can demonstrate good performance may be available during training, but are not used by plain RL methods. To take advantage of this extra information, we propose AggreVaTeD, an extension of the Imitation Learning (IL) approach of Ross & Bagnell (2014). AggreVaTeD allows us to use expressive differentiable policy representations such as deep networks, while leveraging training-time oracles to achieve faster and more accurate solutions with less training data. Specifically, we present two gradient procedures that can learn neural network policies for several problems, including a sequential prediction task and several high-dimensional robotics control problems. We also provide a comprehensive theoretical study of IL that demonstrates that we can expect up to exponentially-lower sample complexity for learning with AggreVaTeD than with plain RL algorithms. Our results and theory indicate that IL (and AggreVaTeD in particular) can be a more effective strategy for sequential prediction than plain RL.


title: Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation

abstract: Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in Filed Programmable Gate Array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.


title: Distributed Clustering-Task Scheduling for Wireless Sensor Networks Using Dynamic Hyper Round Policy

abstract: Prolonging the network life cycle is an essential requirement for many types of Wireless Sensor Network (WSN) applications. Dynamic clustering of sensors into groups is a popular strategy to maximize the network lifetime and increase scalability. In this strategy, to achieve the sensor nodes’ load balancing, with the aim of prolonging lifetime, network operations are split into rounds, i.e., fixed time intervals. Clusters are configured for the current round and reconfigured for the next round so that the costly role of the cluster head is rotated among the network nodes, i.e., Round-Based Policy (RBP). This load balancing approach potentially extends the network lifetime. However, the imposed overhead, due to the clustering in every round, wastes network energy resources. This paper proposes a distributed energy-efficient scheme to cluster a WSN, i.e., Dynamic Hyper Round Policy (DHRP), which schedules clustering-task to extend the network lifetime and reduce energy consumption. Although DHRP is applicable to any data gathering protocols that value energy efficiency, a Simple Energy-efficient Data Collecting (SEDC) protocol is also presented to evaluate the usefulness of DHRP and calculate the end-to-end energy consumption. Experimental results demonstrate that SEDC with DHRP is more effective than two well-known clustering protocols, HEED and M-LEACH, for prolonging the network lifetime and achieving energy conservation.


title: Reliability Analysis of Converter Valves for VSC-HVDC Power Transmission System

abstract: In this paper reliability model of the converter valves in VSC-HVDC system is analyzed. The internal structure and functions of converter valve are presented. Taking the StakPak IGBT from ABB Semiconductors for example, the mathematical reliability model for converter valve and its sub-module is established. By means of calculation and analysis, the reliability indices of converter valve under various voltage classes and redundancy designs are obtained, and then optimal redundant scheme is chosen.


title: Edge Computing Framework for Cooperative Video Processing in Multimedia IoT Systems

abstract: Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision, and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves suboptimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.


title: A Comparison of Neural Network Methods for Unsupervised Representation Learning on the Zero Resource Speech Challenge

abstract: The success of supervised deep neural networks (DNNs) in speech recognition cannot be transferred to zero-resource languages where the requisite transcriptions are unavailable. We investigate unsupervised neural network based methods for learning frame-level representations. Good frame representations eliminate differences in accent, gender, channel characteristics, and other factors to model subword units for withinand acrossspeaker phonetic discrimination. We enhance the correspondence autoencoder (cAE) and show that it can transform Mel Frequency Cepstral Coefficients (MFCCs) into more effective frame representations given a set of matched word pairs from an unsupervised term discovery (UTD) system. The cAE combines the feature extraction power of autoencoders with the weak supervision signal from UTD pairs to better approximate the extrinsic task’s objective during training. We use the Zero Resource Speech Challenge’s minimal triphone pair ABX discrimination task to evaluate our methods. Optimizing a cAE architecture on English and applying it to a zero-resource language, Xitsonga, we obtain a relative error rate reduction of 35% compared to the original MFCCs. We also show that Xitsonga frame representations extracted from the bottleneck layer of a supervised DNN trained on English can be further enhanced by the cAE, yielding a relative error rate reduction of 39%.


title: Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

abstract: Inertial measurement units (IMUs) are widely used in motion measurement. However, the drift of IMUs results in significant accumulated errors for long-term position and orientation measurement. This paper reports a method to compensate the drift of inertial sensors with the assist of ultrasonic sensors and magnetometers. The magnetometer is combined with a three-axis accelerometer as a digital compass for orientation determination in static state by measuring the gravity and the earth's magnetic field vectors. A three-axis gyroscope is used to measure the orientation in dynamic state to complement the digital compass. Displacement determination is implemented using the accelerometer through double integration, and an ultrasonic sensor is employed to periodically calibrate the accumulated errors of the accelerometer. The redundant data from the multi-sensors are fused using extended Kalman filter (EKF), in which the position measured by the ultrasonic sensor and the orientation measured by the digital compass are defined as the observation values, and the position, velocity, and orientation are included in the state vector. Experimental results show that the accumulated errors of inertial sensors are reduced by ultrasonic sensors and magnetometers, and EKF improves the accuracy of orientation and position measurements.


title: Urban water quality prediction based on multi-task multi-view learning

abstract: Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. In this work, we forecast the water quality of a station over the next few hours, using a multitask multi-view learning method to fuse multiple datasets from different domains. In particular, our learning model comprises two alignments. The first alignment is the spaio-temporal view alignment, which combines local spatial and temporal information of each station. The second alignment is the prediction alignment among stations, which captures their spatial correlations and performs copredictions by incorporating these correlations. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach.


title: Distributed Data Vending on Blockchain

abstract: Recent advances in blockchain technologies have provided exciting opportunities for decentralized applications. Specifically, blockchain-based smart contracts enable credible transactions without authorized third parties. The attractive properties of smart contracts facilitate distributed data vending, allowing for proprietary data to be securely exchanged on a blockchain. Distributed data vending can transform domains such as healthcare by encouraging data distribution from owners and enabling large-scale data aggregation. However, one key challenge in distributed data vending is the trade-off dilemma between the effectiveness of data retrieval and the leakage risk from indexing the data. In this paper, we propose a framework for distributed data vending through a combination of data embedding and similarity learning. We illustrate our framework through a practical scenario of distributing and aggregating electronic medical records on a blockchain. Extensive empirical results demonstrate the effectiveness of our framework.


title: A bi-directional, isolated, single-stage, DAB-based AC-DC converter with open-loop power factor correction and other advanced features

abstract: In this paper, a control method for an AC-DC converter is proposed that simultaneously has the following features: a) galvanic isolation b) bi-directional power flow, c) Zero Current Switching (ZCS) for the primary side switches and Zero Voltage Switching (ZVS) turn-on for the secondary side, d) linear power relationship for easy control implementation, e) unity power factor with open-loop control and f) single-stage power conversion. It is thoroughly analyzed by first assuming it to be a DC-DC converter in a push-pull topology and then extending the results to analyze an AC-DC converter. The conclusions of the analysis are confirmed by simulations.


Unsupervised Surgical Task Segmentation with Milestone Learning


title: Understanding Instructions on Large Scale for Human-Robot Interaction

abstract: Correctly interpreting human instructions is the first step to human-robot interaction. Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain. Annotating large enough instructions with semantic forms needs exhaustive engineering efforts. Hence, we propose propagating the semantic lexicon to learn a semantic parser from limited annotations, whereas the parser still has the ability of interpreting instructions on a large scale. We assume that the semantically-close words have the same semantic form based on the fact that human usually uses different words to refer to a same object or task. Our approach softly maps the unobserved words/phrases to the semantic forms learned from the annotated copurs through a metric for knowledge-based lexical similarity. Experiments on the collected instructions showed that the semantic parser learned with lexicon propagation outperformed the baseline. Our approach provides an opportunity for the robots to understand the human instructions on a large scale.


title: Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences

abstract: Private regression has received attention from both database and security communities. Recent work by Fredrikson et al. (USENIX Security 2014) analyzed the functional mechanism (Zhang et al. VLDB 2012) for training linear regression models over medical data. Unfortunately, they found that model accuracy is already unacceptable with differential privacy when $\varepsilon = 5$. We address this issue, presenting an explicit connection between differential privacy and stable learning theory through which a substantially better privacy/utility tradeoff can be obtained. Perhaps more importantly, our theory reveals that the most basic mechanism in differential privacy, output perturbation, can be used to obtain a better tradeoff for all convex-Lipschitz-bounded learning tasks. Since output perturbation is simple to implement, it means that our approach is potentially widely applicable in practice. We go on to apply it on the same medical data as used by Fredrikson et al. Encouragingly, we achieve accurate models even for $\varepsilon = 0.1$. In the last part of this paper, we study the impact of our improved differentially private mechanisms on model inversion attacks, a privacy attack introduced by Fredrikson et al. We observe that the improved tradeoff makes the resulting differentially private model more susceptible to inversion attacks. We analyze this phenomenon formally.


title: Multilayer neural networks and Bayes decision theory

abstract: Abstract There are many applications of multilayer neural networks to pattern classification problems in the engineering field. Recently, it has been shown that Bayes a posteriori probability can be estimated by feedforward neural networks through computer simulation. In this paper, Bayes decision theory is combined with the approximation theory on three-layer neural networks, and the two-category n-dimensional Gaussian classification problem is studied. First, we prove theoretically that three-layer neural networks with at least 2n hidden units have the capability of approximating the a posteriori probability in the two-category classification problem with arbitrary accuracy. Second, we prove that the input–output function of neural networks with at least 2n hidden units tends to the a posteriori probability as Back-Propagation learning proceeds ideally. These results provide a theoretical basis for the study of pattern classification by computer simulation.


title: The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue

abstract: The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroencephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient ( Cp ) for beta, alpha, and delta bands and the character path length ( Lp ) for all EEG bands. The normalized measures $\boldsymbol{\gamma}$ showed significant increases in beta, alpha, and delta bands, and $\boldsymbol{\lambda}$ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.


title: Motivating physical activity at work: using persuasive social media for competitive step counting

abstract: Previous research has suggested that social and competitive interaction over online social networking sites could be harnessed in order to motivate behaviour change in users. This paper presents the design and in-the-wild evaluation of StepMatron, a Facebook application designed to provide social and competitive context for daily pedometer readings in order to motivate physical activity in the working environment. A study was conducted in order to determine whether interactions between users via the application more successfully motivated physical activity than simply recording daily step counts in a similar application. Ten participants (1 male), all nurses working in a UK hospital, used the application across two conditions over the course of the study. In the socially-enabled condition, participants could view each other's step data and make comparisons and comments. In the non-social condition, participants could only view their own personal step data. A significant increase in step activity was observed in the socially enabled condition. Our findings highlight the potential of social media as a means for generating positive behaviour change. They also suggest that simple mobile devices can function as an inexpensive, accessible and powerful trigger towards this behaviour change without necessitating the use of overly complex and expensive mobile applications or devices.


Mindfulness Training for Healthy Aging: Impact on Attention, Well-Being, and Inflammation


title: Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding

abstract: With the introduction of the Electric Health Records, large amounts of digital data become available for analysis and decision support. When physicians are prescribing treatments to a patient, they need to consider a large range of data variety and volume, making decisions increasingly complex. Machine learning based Clinical Decision Support systems can be a solution to the data challenges. In this work we focus on a class of decision support in which the physicians' decision is directly predicted. Concretely, the model would assign higher probabilities to decisions that it presumes the physician are more likely to make. Thus the CDS system can provide physicians with rational recommendations. We also address the problem of correlation in target features: Often a physician is required to make multiple (sub-)decisions in a block, and that these decisions are mutually dependent. We propose a solution to the target correlation problem using a tensor factorization model. In order to handle the patients' historical information as sequential data, we apply the so-called Encoder-Decoder-Framework which is based on Recurrent Neural Networks (RNN) as encoders and a tensor factorization model as a decoder, a combination which is novel in machine learning. With experiments with real-world datasets we show that the proposed model does achieve better prediction performances.


title: Effects of Video Caption Modes on English Listening Comprehension and Vocabulary Acquisition Using Handheld Devices

abstract: This study investigates the effects of different display modes of video captions on mobile devices, including non-caption, full-caption, and target-word modes, on the English comprehension and vocabulary acquisition of fifth graders. During the one-month experiment, the status of the students' English listening comprehension and vocabulary acquisition was evaluated on a weekly basis. From the experimental results, it was found that the learning achievement of the English target-word group was as good as that of the full-caption group in terms of vocabulary acquisition, while both groups outperformed the non-caption group. When the students’ learning preferences were taken into consideration, the visual style students in both the English target-word group and the full-caption group showed significantly better learning effectiveness in terms of vocabulary acquisition than those in the control group. Furthermore, in terms of listening comprehension, the students in all three groups made remarkable progress.


title: The Autosar XML Schema and Its Relevance for Autosar Tools

abstract: The Autosar standard represents an emerging approach to unifying virtually all aspects of the development workflow of automotive embedded software. The standard features an XML schema used as a basis for interchanging documents containing Autosar model content. Although the schema is primarily for document exchange, it could also be the basis for creating a metamodel. However, a tool metamodel should probably be designed differently.


title: Separation of Fine Granular Mixtures in S-Plate-Type Electrostatic Separators

abstract: Plate-type electrostatic separators are commonly used for the selective sorting of conductive from nonconductive constituents of a granular mixture. This paper addresses the more delicate issue of granular materials that are characterized by finite nonzero resistivities. The study was conducted with three materials having similar granule size range and specific weight but slightly different electrical properties. Two sets of experiments were performed for evaluating the effect of electrode geometry on the behavior of each of the materials. These results were then employed for predicting the outcome of the electrostatic separation of binary mixtures of good and poor conductors. A third set of experiments validated the predictions and pointed out a couple of nonelectric factors that might deteriorate the performance of a plate-type electrostatic separator in an industrial environment.


Traffic Signal Settings Optimization Using Gradient Descent


title: Beyond Weiser: From Ubiquitous to Collective Computing

abstract: Considering the technological changes across computing's first three generations, how might the next serve humanity? Three critical technologies--the cloud, the crowd, and the shroud of devices connecting the physical and digital worlds--define the fourth generation of collective computing.


title: On certain aspects of Kazakh part-of-speech tagging

abstract: We compare and discuss various approaches to the problem of part of speech (POS) tagging of texts written in Kazakh, an agglutinative and highly inflectional Turkic language. In Kazakh a single root may produce hundreds of word forms, and it is difficult, if at all possible, to label enough training data to account for a vast set of all possible word forms in the language. Thus, current state of the art statistical POS taggers may not be as effective for Kazakh as for morphologically less complex languages, e.g. English. Also the choice of a POS tag set may influence the informativeness and the accuracy of tagging.


title: Joint Hypergraph Learning for Tag-Based Image Retrieval

abstract: As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval. It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated. However, most existing methods use these visual features separately or sequentially. In this paper, we propose a global and local visual features fusion approach to learn the relevance of images by hypergraph approach. A hypergraph is constructed first by utilizing global, local visual features, and tag information. Then, we propose a pseudo-relevance feedback mechanism to obtain the pseudo-positive images. Finally, with the hypergraph and pseudo relevance feedback, we adopt the hypergraph learning algorithm to calculate the relevance score of each image to the query. Experimental results demonstrate the effectiveness of the proposed approach.


title: A High-Performance Cable-Drive Module for the Development of Wearable Devices

abstract: Bowden cables are frequently used as remote transmissions on wearable devices because of their light weight, simplicity, and flexibility of design. However, a Bowden cable suffers from inefficiency, nonlinear backlash, and cable tension variation due to the bending of the cable housing and friction losses, leading to deteriorating force control performance, and a wearers comfort level and augmentation effectiveness. In this paper, to enhance wearable device performance and to overcome the above drawbacks, we propose a novel high-performance, cable-drive module with rolling cable transmissions (RoCT) that can efficiently transmit motion and mechanical power from an input to an output via an inner cable/rope. The RoCT is compact, modular, lightweight, extremely stiff, highly backdrivable, and is freely rotatable and translatable during manipulation. It maintains the cable tension regardless of the transmission angle between the input and output. The configuration of the proposed tethered cable-drive module, the working principle of the rolling cable transmission, and its properties, are introduced. Mechanical designs based on the working principle are also provided. Finally, as one of the applications of the proposed module, a tethered ankle exoskeleton system and its experimental results demonstrating the properties of the proposed transmission are presented.


title: Calibration requirements and procedures for augmented reality (No

abstract: Augmented reality entails the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they blend into the real world in terms of alignments, perspectives, illuminations, etc. For practical reasons the information necessary to obtain this realistic blending cannot be known a priori, and cannot be hard-wired into a system. Instead a number of calibration procedures are necessary so that the location and parameters of each of the system components are known. In this paper we identify the calibration steps necessary to build a complete computer model of the real world and then, using the augmented reality system developed at ECRC (Grasp) as an example, we describe each of the calibration processes.


title: Generation of reactive oxygen species in cyanobacteria and green algae induced by allelochemicals of submerged macrophytes.

abstract: Inhibition of phytoplankton by allelochemicals released by submerged macrophytes is reported to be one of the mechanisms that maintain a clear-water state in shallow lakes. In order to elucidate this mechanism, the ability of six polyphenols and two long-chain fatty acids to induce the generation of reactive oxygen species (ROS) in phytoplankton was studied using the ROS sensitive probe 2',7'- dichlorodihydrofluorescein diacetate (DCFH-DA). The results showed that only (+)-catechin (CA) and pyrogallic acid (PA) could induce ROS formation in Microcystis aeruginosa and Pseudokirchneriella subcapitata. 25 mg L⁻¹ CA caused 1.2, 1.4 and 1.8 times increase of ROS levels in M. aeruginosa at 1, 2 and 4h exposure, respectively, and, correspondingly in P. subcapitata cells, these values were 3.7, 6.2 and 7.7, respectively. PA also significantly increased the levels of intracellular ROS in P. subcapitata (P < 0.01); however, significant ROS generation in M. aeruginosa was observed at only 4h exposure (P < 0.01). Light enhanced ROS generation in CA treated cells, but not in the cells treated with PA. CA and PA may act as redox cyclers after uptake by test organisms and produce ROS successively. These results suggest that the oxidative stress induced by the redox cycling property of allelochemicals may be one of the important causes for the inhibitory effect of some submerged macrophytes towards undesired phytoplankton in natural aquatic ecosystems.


title: NOVEL REAL-TIME FPGA-BASED QRS DETECTOR USING ADAPTIVE THRESHOLD WITH THE PREVIOUS SMALLEST PEAK OF ECG SIGNAL

abstract: The QRS complex of the ECG signal is the reference point for the most ECG applications. In this paper, we aim to describe the design and the implementation of an embedded system for detection of the QRS complexes in real-time. The design is based on the notorious algorithm of Pan & Tompkins, with a novel simple idea for the decision stage of this algorithm. The implementation uses a circuit of the current trend, i.e. the FPGA, and it is developed with the Xilinx design tool, System Generator for DSP. In the authors’ view, the specific feature, i.e. authenticity and simplicity of the proposed model, is that the threshold value is updated from the previous smallest peak; in addition, the model is entirely designed simply with MCode blocks. The hardware design is tested with five 30 minutes data records obtained from the MIT-BIH Arrhythmia database. Its accuracy exceeds 96%, knowing that four records among the five represent the worst cases in the database. In terms of the resources utilization, our implementation occupies around 30% of the used FPGA device, namely the Xilinx Spartan 3E XC3S500.


title: Ranking opinionated blog posts using OpinionFinder

abstract: The aim of an opinion finding system is not just to retrieve relevant documents, but to also retrieve documents that express an opinion towards the query target entity. In this work, we propose a way to use and integrate an opinion-identification toolkit, OpinionFinder, into the retrieval process of an Information Retrieval (IR) system, such that opinionated, relevant documents are retrieved in response to a query. In our experiments, we vary the number of top-ranked documents that must be parsed in response to a query, and investigate the effect on opinion retrieval performance and required parsing time. We find that opinion finding retrieval performance is improved by integrating OpinionFinder into the retrieval system, and that retrieval performance grows as more posts are parsed by OpinionFinder. However, the benefit eventually tails off at a deep rank, suggesting that an optimal setting for the system has been achieved.


title: Factors influencing ERP implementation in retail sector: an empirical study from India

abstract: Purpose – The purpose of this paper is to explore the factors influencing the enterprise resource planning (ERP) implementation success in Indian retail sector. Additionally, the study also addresses the relationship between factors that influence ERP implementation and the success of ERP implementation empirically. Strategic, Technological, People and Project management are the examined factors. Design/methodology/approach – Empirical data were collected through survey questionnaire from practitioner like project sponsors, project managers, implementation consultants and team members who were involved in ERP implementation in retail sector. Findings – The results of the study has empirically verified that Strategic, Technological, People and Project management factors are positively influencing ERP implementation success. All four hypotheses were supported by results of the study. Practical implications – This study will provide valuable insights to researchers, practicing managers and those who are plan...


title: Multi-task joint-learning of deep neural networks for robust speech recognition

abstract: Although deep neural networks (DNNs) have achieved great success in automatic speech recognition (ASR), significant performance degradation still exists in noisy environments. In this paper, a novel multi-task joint-learning framework is proposed to address the noise robustness for speech recognition. The architecture integrates two different DNNs, including the regressive denoising DNN and the discriminative recognition DNN, into a complete multi-task structure and all the parameters can be optimized in a real joint-learning mode just from the beginning in model training. In addition, the basic multi-task structure is further explored and reorganized into a more general framework which can get substantial gains. Furthermore, noise adaptive training can also be easily incorporated within this architecture to achieve further performance improvement. Experiments on the Aurora4 task showed that the proposed approach can achieve a WER below 10% without using adaptation or sequence training, a very large and significant (more than 20% relative) improvement over a strong DNN-HMM baseline.


title: Lay Definitions of Happiness across Nations: The Primacy of Inner Harmony and Relational Connectedness

abstract: In well-being research the term happiness is often used as synonymous with life satisfaction. However, little is known about lay people’s understanding of happiness. Building on the available literature, this study explored lay definitions of happiness across nations and cultural dimensions, analyzing their components and relationship with participants’ demographic features. Participants were 2799 adults (age range=30-60, 50% women) living in urban areas of Argentina, Brazil, Croatia, Hungary, India, Italy, Mexico, New Zealand, Norway, Portugal, South Africa and United States. They completed the Eudaimonic and Hedonic Happiness Investigation (EHHI), reporting, among other information, their own definition of happiness. Answers comprised definitions referring to a broad range of life domains, covering both the contextual-social sphere and the psychological sphere. Across countries and with little variation by age and gender, inner harmony predominated among psychological definitions, and family and social relationships among contextual definitions. Whereas relationships are widely acknowledged as basic happiness components, inner harmony is substantially neglected. Nevertheless, its cross-national primacy, together with relations, is consistent with the view of an ontological interconnectedness characterizing living systems, shared by several conceptual frameworks across disciplines and cultures. At the methodological level, these findings suggest the potential of a bottom-up, mixed method approach to contextualize psychological dimensions within culture and lay understanding.


title: Blockchain distributed ledger technologies for biomedical and health care applications

abstract: Objectives ::: To introduce blockchain technologies, including their benefits, pitfalls, and the latest applications, to the biomedical and health care domains. ::: ::: ::: Target Audience ::: Biomedical and health care informatics researchers who would like to learn about blockchain technologies and their applications in the biomedical/health care domains. ::: ::: ::: Scope ::: The covered topics include: (1) introduction to the famous Bitcoin crypto-currency and the underlying blockchain technology; (2) features of blockchain; (3) review of alternative blockchain technologies; (4) emerging nonfinancial distributed ledger technologies and applications; (5) benefits of blockchain for biomedical/health care applications when compared to traditional distributed databases; (6) overview of the latest biomedical/health care applications of blockchain technologies; and (7) discussion of the potential challenges and proposed solutions of adopting blockchain technologies in biomedical/health care domains.


title: Work time lost to sickness, unemployment and stoppages: measurement and application

abstract: The paper considers a number of methods of estimating the work time lost due to sickness, unemployment and stoppages. The most satisfactory approach suggested is based on a multiple state working life table. Numerical examples of the measurement of work time lost are provided for particular application to the actuarial assessment of damages (arising out of personal injury or fatal accident). Other applications, including the pricing of unemployment insurance, are also discussed.


title: Ischemic mitral regurgitation: long-term outcome and prognostic implications with quantitative Doppler assessment.

abstract: BACKGROUND ::: Myocardial infarction (MI) can directly cause ischemic mitral regurgitation (IMR), which has been touted as an indicator of poor prognosis in acute and early phases after MI. However, in the chronic post-MI phase, prognostic implications of IMR presence and degree are poorly defined. ::: ::: ::: METHODS AND RESULTS ::: We analyzed 303 patients with previous (>16 days) Q-wave MI by ECG who underwent transthoracic echocardiography: 194 with IMR quantitatively assessed in routine practice and 109 without IMR matched for baseline age (71+/-11 versus 70+/-9 years, P=0.20), sex, and ejection fraction (EF, 33+/-14% versus 34+/-11%, P=0.14). In IMR patients, regurgitant volume (RVol) and effective regurgitant orifice (ERO) area were 36+/-24 mL/beat and 21+/-12 mm(2), respectively. After 5 years, total mortality and cardiac mortality for patients with IMR (62+/-5% and 50+/-6%, respectively) were higher than for those without IMR (39+/-6% and 30+/-5%, respectively) (both P<0.001). In multivariate analysis, independently of all baseline characteristics, particularly age and EF, the adjusted relative risks of total and cardiac mortality associated with the presence of IMR (1.88, P=0.003 and 1.83, P=0.014, respectively) and quantified degree of IMR defined by RVol >/=30 mL (2.05, P=0.002 and 2.01, P=0.009) and by ERO >/=20 mm(2) (2.23, P=0.003 and 2.38, P=0.004) were high. ::: ::: ::: CONCLUSIONS ::: In the chronic phase after MI, IMR presence is associated with excess mortality independently of baseline characteristics and degree of ventricular dysfunction. The mortality risk is related directly to the degree of IMR as defined by ERO and RVol. Therefore, IMR detection and quantification provide major information for risk stratification and clinical decision making in the chronic post-MI phase.


Blockchain Thinking : The Brain as a DAC ( Decentralized Autonomous Organization )


title: Stop Consonant Productions of Korean-English Bilingual Children.

abstract: The purpose of this study was to conduct an acoustic examination of the obstruent stops produced by Korean–English bilingual children in connection with the question of whether bilinguals establish distinct categories of speech sounds across languages. Stop productions were obtained from ninety children in two age ranges, five and ten years: thirty Korean–English bilinguals, thirty monolingual Koreans and thirty monolingual English speakers. Voice-Onset-Time (VOT) lag at word-initial stop and fundamental frequency (f0) in the following vowel (hereafter vowel-onset f0) were measured. The bilingual children showed different patterns of VOT in comparison to both English and Korean monolinguals, with longer VOT in their production of Korean stop consonants and shorter VOT for English. Moreover, the ten-year-old bilinguals distinguished all stop categories using both VOT and vowel-onset f0, whereas the five-year-olds tended to make stop distinctions based on VOT but not vowel-onset f0. The results of this study suggest that bilingual children at around five years of age do not yet have fully separate stop systems, and that the systems continue to evolve during the developmental period.


title: Cholecystectomy in patients with cirrhosis. A surgical challenge.

abstract: Cholecystectomy or cholecystostomy was performed in 49 patients with cirrhosis with a mortality of 10.2%. Massive intraoperative blood loss was found in 16.3% and major wound problems (dehiscence, abscess) in 12.2%. Intraoperative blood loss, amount of blood transfused, and mortality were correlated with the Child classification of hepatic reserve. Mortality was 23.5% for Child C patients vs 0% for Child A patients. Excessive blood loss from a hypervascular biliary bed and resulting liver failure and sepsis were the usual causes of death. Elective surgical intervention for Child A and B patients with symptomatic cholelithiasis is warranted. In Child C patients, however, every attempt should be made to increase the class to a Child B.


title: Efficient Restoration Method for Images Corrupted with Impulse Noise

abstract: This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in real-time digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.


title: Example-Based Sketch Segmentation and Labeling Using CRFs

abstract: We introduce a new approach for segmentation and label transfer in sketches that substantially improves the state of the art. We build on successful techniques to find how likely each segment is to belong to a label, and use a Conditional Random Field to find the most probable global configuration. Our method is trained fully on the sketch domain, such that it can handle abstract sketches that are very far from 3D meshes. It also requires a small quantity of annotated data, which makes it easily adaptable to new datasets. The testing phase is completely automatic, and our performance is comparable to state-of-the-art methods that require manual tuning and a considerable amount of previous annotation [Huang et al. 2014].


title: Gated Attentive-Autoencoder for Content-Aware Recommendation

abstract: The rapid growth of Internet services and mobile devices provides an excellent opportunity to satisfy the strong demand for the personalized item or product recommendation. However, with the tremendous increase of users and items, personalized recommender systems still face several challenging problems: (1) the hardness of exploiting sparse implicit feedback; (2) the difficulty of combining heterogeneous data. To cope with these challenges, we propose a gated attentive-autoencoder (GATE) model, which is capable of learning fused hidden representations of items' contents and binary ratings, through a neural gating structure. Based on the fused representations, our model exploits neighboring relations between items to help infer users' preferences. In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder. The word-level attention learns the item hidden representations from items' word sequences, while favoring informative words by assigning larger attention weights. The neighbor-level attention learns the hidden representation of an item's neighborhood by considering its neighbors in a weighted manner. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on four real-world datasets. The experimental results not only demonstrate the effectiveness of our model on top-N recommendation but also provide interpretable results attributed to the attention modules.


title: Understanding 2D-BarCode Technology and Applications in M-Commerce - Design and Implementation of A 2D Barcode Processing Solution

abstract: With the swift increase of the number of mobile device users, more wireless information services and mobile commerce applications are needed. Since various barcodes have been used for decades as a very effective means in many traditional commerce systems, today people are looking for innovative solutions to use barcodes in the wireless world. Recently, the mobile industry began to pay more attention to barcode applications in m-commerce because 2D-barcodes not only provide a simple and inexpensive method to present diverse commerce data, but also improve mobile user experience by reducing their inputs. This paper first discusses 2D-barcode concepts, types and classifications, major technology players, and applications in mobile commerce. Then, it reports a research project to develop a 2D-barcode processing solution to support mobile applications. Moreover, the paper also presents the application examples, and case study using the solution.


title: An Optimised Density Based Clustering Algorithm

abstract: The DBSCAN [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. As the original DBSCAN algorithm uses the distance measures to compute the distance between objects, it consumes so much processing time and its computation complexity comes as O (N). In this paper we have proposed a new algorithm to improve the performance of DBSCAN algorithm. The existing algorithms A Fast DBSCAN Algorithm[6] and Memory effect in DBSCAN algorithm[7] has been combined in the new solution to speed up the performance as well as improve the quality of the output. As the RegionQuery operation takes long time to process the objects, only few objects are considered for the expansion and the remaining missed border objects are handled differently during the cluster expansion. Eventually the performance analysis and the cluster output show that the proposed solution is better to the existing algorithms.


title: CDM-ESD induced damage in components using stacked-die packaging

abstract: CDM-ESD robustness of stacked-die packages is investigated and compared with single-die packages. The peak discharge current is not increased significantly by die stacking. The inter-die signal interfaces are shown to be well protected against CDM by placing just a small ESD protection clamp at the receiver, if certain package integration guidelines are followed.


title: Rapid Development of Spoken Language Understanding Grammars

abstract: To facilitate the development of spoken dialog systems and speech enabled applications, we introduce SGStudio (Semantic Grammar Studio), a grammar authoring tool that enables regular software developers with little speech/linguistic background to rapidly create quality semantic grammars for automatic speech recognition (ASR) and spoken language understanding (SLU). We focus on the underlying technology of SGStudio, including knowledge assisted example-based grammar learning, grammar controls and configurable grammar structures. While the focus of SGStudio is to increase productivity, experimental results show that it also improves the quality of the grammars being developed.


title: Solar-wind hybrid energy system using MPPT

abstract: Energy demand is ever increasing in the world, searching for fossil fuel is done on priority basis. These fuels are not sustainable, they pollute the environment. Shortage of fossil fuels resources and adverse environment affects made use of Renewable Energy Sources (RES) as Solar energy and Wind energy essential. Solar energy and Wind energy are natural resources which are not depleted by use and are more popular. Availability and ease to obtain electric power made Solar and Wind power as alternative energy sources. Solar energy and Wind energy combined to form Solar-Wind Hybrid Power System (SWHPS), which will enhance the qualities of each other and another. To reduce the power demand on the conventional power generation sector, the optimized utilization of these natural resources is essential to produce power. Various methodologies are in practice for generation of power using Solar-Wind Hybrid System with Maximum Power Point Tracking (MPPT). Constant voltage method is used for maximum power transfer. This method should have some key features to increase the stability and efficiency.


title: Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning

abstract: Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.


title: Towards a variable topology truss for shoring

abstract: This paper introduces a new type of versatile modular reconfigurable robot. This robot is a self-reconfigurable truss structure. The requirements and challenges involved to build a functioning system are explored. These include the functional requirements, and the implications on hardware design, software issues and topological analysis. One potential application for this robot is the shoring of damaged structures during search and rescue operations.


title: Automatic target scoring system using machine vision

abstract: This paper reports on the development of a machine vision system for assessing targeting accuracy of ballistic, projectile-firing, weapon systems. The machine vision system developed here can monitor the target continuously, report precise quantitative targeting information, and simultaneously provide a color-coded display of impacts. The only other automated target scoring system on the market today is based on the analysis of acoustic signatures of projectiles using a heavily instrumented target. The proposed vision-based technique, in contrast, is inherently passive, requiring little or no modification of the existing set up.


title: Exploration of tree-based hierarchical softmax for recurrent language models

abstract: Recently, variants of neural networks for computational linguistics have been proposed and successfully applied to neural language modeling and neural machine translation. These neural models can leverage knowledge from massive corpora but they are extremely slow as they predict candidate words from a large vocabulary during training and inference. As an alternative to gradient approximation and softmax with class decomposition, we explore the tree-based hierarchical softmax method and reform its architecture, making it compatible with modern GPUs and introducing a compact tree-based loss function. When combined with several word hierarchical clustering algorithms, improved performance is achieved in language modelling task with intrinsic evaluation criterions on PTB, WikiText-2 and WikiText-103 datasets.


title: Organizational citizenship behavior and the quantity and quality of work group performance.

abstract: Despite the widespread interest in the topic of organizational citizenship behaviors (OCBs), little empirical research has tested the fundamental assumption that these forms of behavior improve the effectiveness of work groups or organizations in which they are exhibited. In the present study, the effects of OCBs on the quantity and quality of the performance of 218 people working in 40 machine crews in a paper mill located in the Northeastern United States were examined. The results indicate that helping behavior and sportsmanship had significant effects on performance quantity and that helping behavior had a significant impact on performance quality. However, civic virtue had no effect on either performance measure.


title: SAR ADC Algorithm with Redundancy and Digital Error Correction

abstract: This paper describes an algorithm for Successive Approximation Register (SAR) ADCs with overlapping steps that allow comparison decision errors (due to, such as DAC incomplete settling) to be digitally corrected. We generalize this non-binary search algorithm, and clarify which decision errors it can digitally correct. This algorithm requires more SAR ADC conversion steps than a binary search algorithm, but we show that the sampling speed of an SAR ADC using this algorithm can be faster than that of a conventional binary-search SAR ADC—because the latter must wait for the settling time of the DAC inside the SAR ADC.