title: Fuzzing: State of the Art

abstract: As one of the most popular software testing techniques, fuzzing can find a variety of weaknesses in a program, such as software bugs and vulnerabilities, by generating numerous test inputs. Due to its effectiveness, fuzzing is regarded as a valuable bug hunting method. In this paper, we present an overview of fuzzing that concentrates on its general process, as well as classifications, followed by detailed discussion of the key obstacles and some state-of-the-art technologies which aim to overcome or mitigate these obstacles. We further investigate and classify several widely used fuzzing tools. Our primary goal is to equip the stakeholder with a better understanding of fuzzing and the potential solutions for improving fuzzing methods in the spectrum of software testing and security. To inspire future research, we also predict some future directions with regard to fuzzing.


title: Domain Adaptation Tracker With Global and Local Searching

abstract: For the convolutional neural network (CNN)-based trackers, most of them locate the target only within a local area, which makes the trackers hard to recapture the target after drifting into the background. Besides, most state-of-the-art trackers spend a large amount of time on training the CNN-based classification networks online to adapt to the current domain. In this paper, to address the two problems, we propose a robust domain adaptation tracker based on the CNNs. The proposed tracker contains three CNNs: a local location network (LL-Net), a global location network (GL-Net), and a domain adaptation classification network (DA-Net). For the former problem, if we come to the conclusion that the tracker drifts into the background based on the output of the LL-Net, we will search for the target in a global area of the current frame based on the GL-Net. For the latter problem, we propose a CNN-based DA-Net with a domain adaptation (DA) layer. By pre-training the DA-Net offline, the DA-Net can adapt to the current domain by only updating the parameters of the DA layer in one training iteration when the online training is triggered, which makes the tracker run five times faster than MDNet with comparable tracking performance. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on three popular benchmarks.


title: Making learning and Web 2.0 technologies work for higher learning institutions in Africa

abstract: Purpose – This paper seeks to assess the extent to which learning and Web 2.0 technologies are utilised to support learning and teaching in Africa's higher learning institutions, with a specific focus on Tanzania's public universities.Design/methodology/approach – A combination of content analysis and semi‐structured interviews was used to collect data. Semi‐structured interviews were conducted with ICT personnel from six of the eight public universities in Tanzania in 2011.Findings – The study found that the adoption of e‐learning and Web 2.0 technologies is still in its infancy in Tanzania's public universities. However, there was much enthusiasm amongst respondents for developing the potential of e‐learning and Web 2.0 tools in their universities.Practical implications – The study seeks to promote academic inquiry about the need for innovative Web 2.0 technologies in learning and teaching and the adoption of these emerging technologies in Africa's higher learning institutions.Originality/value – The st...


title: An Expressive (Zero-Knowledge) Set Accumulator

abstract: We present a new construction of an expressive set accumulator. Unlike existing cryptographic accumulators, ours provides succinct proofs for a large collection of operations over accumulated sets, including intersection, union, set difference, SUM, COUNT, MIN, MAX, and RANGE, as well as arbitrary nestings of the above. We also show how to extend our accumulator to be zero-knowledge. The security of our accumulator is based on extractability assumptions and other assumptions that hold in the generic group model. Our construction has asymptotically optimal verification complexity and proof size, constant update complexity, and public verifiability/updatability—namely, any client who knows the public key and the last accumulator value can verify the supported operations and update the accumulator. The expressiveness of our accumulator comes at the cost of quadratic prover time. However, we show that the cryptographic operations involved are cheap compared to those incurred by generic approaches (e.g., SNARKs) that are equally expressive: our prover runs faster for sets of up to 5 million items. Our accumulator serves as a powerful cryptographic tool with many applications. For example, it can be applied to efficiently support verification of a rich collection of SQL queries when used as a drop-in replacement in existing verifiable database systems (e.g., IntegriDB, CCS 2015).


title: Mel-scaled discrete wavelet coefficients for speech recognition

abstract: In this paper we propose a new feature vector consisting of mel-frequency discrete wavelet coefficients (MFDWC). The MFDWC are obtained by applying the discrete wavelet transform (DWT) to the mel-scaled log filterbank energies of a speech frame. The purpose of using the DWT is to benefit from its localization property in the time and frequency domains. MFDWC are similar to subband-based (SUB) features and multi-resolution (MULT) features in that both attempt to achieve good time and frequency localization. However, MFDWC have better time/frequency localization than SUB features and MULT features. We evaluated the performance of new features for clean speech and noisy speech and compared the performance of MFDWC with mel-frequency cepstral coefficients (MFCC), SUB features and MULT features. Experimental results on a phoneme recognition task showed that a MFDWC-based recognizer gave better results than recognizers based on MFCC, SUB features, and MULT features for the white gaussian noise, band-limited white gaussian noise and clean speech cases.


title: Screening to the converted: an educational intervention in African American churches.

abstract: Abstract Background. African American women have higher incidences of breast and cervical cancers and African American men present with more advanced stages of colon and prostate cancers than do their non‐African American counterparts. Since the church is central to the organization of the African American community, the authors set out to determine whether a church‐directed educational project could influence parishioners to obtain cancer screening. Methods. Three African American churches having memberships of 250, 500, and 1,500, respectively, were selected for their different socioeconomic strata: one congregation was composed mostly of working poor, the second was more affluent, and the third consisted primarily of retirees. During a five‐week summer period, appropriate literature, health fairs, testimonials by cancer survivors, and visits by representatives of the medical community were used to increase awareness of cancer screening. Surveys regarding cancer‐screening behaviors were distributed at t...


A CONJOINT ANALYSIS OF ONLINE CONSUMER SATISFACTION


title: Preservice teachers’ emerging TPACK in a technology-rich methods class

abstract: There is a dearth of research on the mechanisms for preservice teachers’ development of the pedagogical knowledge necessary for effective use of such technologies. We explored the emergent Technological Pedagogical and Content Knowledge (TPACK) (Niess 2005, 2006, 2007) of a group of secondary mathematics preservice teachers in a methods course as they designed and implemented technology-rich teaching materials in field settings. Participant surveys and collected assignments were analyzed through the lens of the TPACK framework. The data were also analyzed to examine the trajectory of the participants’ beliefs about the appropriate role of advanced digital technologies in mathematics. The results indicate that the participants’ understanding of technology shifted from viewing technology as a tool for reinforcement into viewing technology as a tool for developing student understanding. Collected data supports the notion that preservice teacher TPACK development is closely related to a shift in identity from learners of mathematics to teachers of mathematics. In a class where advanced digital technologies were used extensively as a catalyst for promoting inquiry-based learning, preservice teachers retained a great deal of skepticism about the appropriateness of using technology in concept development roles, despite their confidence that they can incorporate technology into their future teaching.


title: EMPFASIS: A Publication of the National Electronics Manufacturing Center of Excellence

abstract: Abstract : The EMPF recently characterized the reliability of surface mount RF components. The RF frequency band of interest was the X band (10.7 to 11.7GHz). A two pronged test for reliability of circuit card assemblies (CCA) was designed for both extreme thermal cycling and vibration. The rapid thermal cycling and extreme vibration testing simulates the total stress encountered by the assembly over the life of the product but accomplishes it in a relatively short period of time. In order to perform the reliability testing, a test vehicle consisting of a printed circuit board with test structures and components, was designed, fabricated, and assembled at the EMPF.


title: Improving point cloud to surface reconstruction with generalized Tikhonov regularization

abstract: Point cloud rendering has a vital role in the user Quality of Experience for applications adopting point cloud based representations. While this is not a new area, it has recently become more relevant with the recent interest on point cloud coding by major standardization groups, notably JPEG and MPEG. The screened Poisson surface reconstruction is a state-of-the-art technique for generating a watertight surface mesh from the point cloud samples. While its screening component allows the surface to better fit the cloud points, this fitting may lead to undesired artifacts in the surface, notably when the point cloud is noisy. This paper proposes to improve this reconstruction method by making it more robust to noise by adopting a generalized Tikhonov regularization term. The proposed regularization approach smooths regions that should be flat while keeping the important details in the edges, thus creating more pleasant surface reconstructions.


title: A tree-based algorithm for distributed mutual exclusion

abstract: We present an algorithm for distributed mutual exclusion in a computer network of N nodes that communicate by messages rather than shared memory. The algorithm uses a spanning tree of the computer network, and the number of messages exchanged per critical section depends on the topology of this tree. However, typically the number of messages exchanged is O (log N ) under light demand, and reduces to approximately four messages under saturated demand. Each node holds information only about its immediate neighbors in the spanning tree rather than information about all nodes, and failed nodes can recover necessary information from their neighbors. The algorithm does not require sequence numbers as it operates correctly despite message overtaking.


title: Pseudo random number generator using Elman neural network

abstract: This paper proposes a pseudo random number generator using Elman neural network. The proposed neural network is a recurrent neural network able to generate pseudo-random numbers from the weight matrices obtained from the layer weights of the Elman network. The proposed method is not computationally demanding and is easy to implement for varying bit sequences. The random numbers generated using our method have been subjected to frequency test and ENT test program. The results show that recurrent neural networks can be used as a pseudo random number generator(prng).


title: Spectrum Exploration and Exploitation for Cognitive Radio: Recent Advances

abstract: The lack of availability of radio spectrum for wireless communication purposes is becoming a serious problem as more wireless systems and services are being developed and operate in crowded spectral bands. The scarcity of useful radio spectrum is mainly due to the static allocation and rigid regulation of the spectrum use rather than the spectrum being actually fully in use. Flexible spectrum use and cognitive radio technologies provide an approach to alleviating this problem by allowing for secondary spectrum use while the spectrum is underutilized by its primary licensed users. Idle spectrum is a time?frequency?location varying resource. It is a resource that also depends on the relative locations of the primary and secondary receivers and transmitters as well as the instantaneous propagation conditions. By acquiring awareness about the current radio environment and the other spectrum users, cognitive radios can more efficiently exploit idle spectrum and manage interference. Doing so requires a means to explore the spectrum to identify high-quality and persistent local spectral resources and access and share them among a number of users while strictly controlling the interference caused to others, in particular, licensed primary users (PUs). Situational awareness about the state of the spectrum allows for optimal exploitation of underutilized spectrum. For example, idle subbands may be allocated, and waveform parameters may be chosen to maximize the sum-rate for the cognitive users while making sure no harmful interference is caused to the other users of the spectrum.


title: MonetDB: Two Decades of Research in Column-oriented Database Architectures

abstract: MonetDB is a state-of-the-art open-source column-store database management system targeting applications in need for analytics over large collections of data. MonetDB is actively used nowadays in health care, in telecommunications as well as in scientiﬁc databases and in data management research, accumulating on average more than 10,000 downloads on a monthly basis. This paper gives a brief overview of the MonetDB technology as it developed over the past two decades and the main research highlights which drive the current MonetDB design and form the basis for its future evolution.


title: Influence of strength training on sprint running performance. Current findings and implications for training.

abstract: Today, it is generally accepted that sprint performance, like endurance performance, can improve considerably with training. Strength training, especially, plays a key role in this process. Sprint performance will be viewed multidimensionally as an initial acceleration phase (0 to 10 m), a phase of maximum running speed (36 to 100 m) and a transition phase in between. Immediately following the start action, the powerful extensions of the hip, knee and ankle joints are the main accelerators of body mass. However, the hamstrings, the m. adductor magnus and the m. gluteus maximus are considered to make the most important contribution in producing the highest levels of speed. Different training methods are proposed to improve the power output of these muscles. Some of them aim for hypertrophy and others for specific adaptations of the nervous system. This includes general (hypertrophy and neuronal activation), velocity specific (speed-strength) and movement specific (sprint associated exercises) strength training. In developing training strategies, the coach has to keep in mind that strength, power and speed are inherently related to one another, because they are all the output of the same functional systems. As heavy resistance training results in a fibre type IIb into fibre type IIa conversion, the coach has to aim for an optimal balance between sprint specific and nonspecific training components. To achieve this they must take into consideration the specific strength training demands of each individual, based on performance capacity in each specific phase of the sprint.


title: Use of Phone Sensors to Enhance Distracted Pedestrians’ Safety

abstract: Studies have shown that using smartphones while walking—called distracted walking —significantly increases the risk of pedestrians colliding with dangerous objects. In this paper, we explore how to mitigate this problem by exploiting the phone's built-in sensors only and developing an application called BumpAlert . BumpAlert provides a generic solution without requiring any prior knowledge of the user's surroundings by estimating distances to nearby objects using the phone's speakers and microphones. This process is enhanced further by using the images acquired from the phone's rear camera, when necessary. We have evaluated BumpAlert under a variety of settings ranging from aisle to outdoor environments with walls, pillars, signboards, dustbins, and people, etc., that are common in our daily surroundings. Our evaluation has shown an average accuracy of BumpAlert to be higher than 95 percent with a less than 2 percent false-positive rate to detect frontal objects 2–4m away, which suffices for the user to react and avoid collision. Even though BumpAlert is unable to detect all dangerous situations, most participants of our user study feel safer when they walk with BumpAlert enabled. Integrating our current design of BumpAlert with other safety systems can provide a practical solution for protecting distracted pedestrians.


Mathematics Takes Center Stage


title: Automated Action Units Vs. Expert Raters: Face off

abstract: User engagement is an essential component of any application design. Finding reliable methods to forecast continuos engagement can aid in creating adaptive applications like web-based interventions, intelligent student tutoring, the creation of socially intelligent human-robots, etc. In this paper, we compare observational estimates from expert raters to vision-based learning, for estimating user engagement. The vision-based approach uses automated computation of Action Units combined with an RNN. Several data collection techniques have been explored in the past that capture different modalities for engagement from obtaining self-reports and gathering external observations via crowd-sourcing or even trained expert raters. Traditional machine learning approaches discard annotations from inconsistent raters, use rater averages or apply raterspecific weighting schemes. Such approaches often end up throwing away expensive annotations. We introduce a novel approach that exploits the inherent confusion and disagreement in raters annotations to build a scalable engagement estimation model that learns to appropriately weigh subjective behavioral cues. We show that actively modeling the uncertainty, either explicitly from expert raters or from automated estimation with AU, significantly improves prediction over prediction from just the average engagement ratings. Our approach performs significantly better or on par with experts in predicting engagement for a trauma-recovery application.


title: Comparative study of histogram equalization algorithms for image enhancement

abstract: Histogram equalization is one of the common tools for improving contrast in digital photography, remote sensing, ::: medical imaging, and scientific visualization. It is a process for recovering lost contrast in an image by remapping the ::: brightness values in such a way that equalizes or more evenly distributes its brightness values. However, Histogram ::: Equalization may significantly change the brightness of the entire image and generate undesirable artifacts. Therefore, ::: many Histogram Equalization based algorithms have been developed to overcome this problem. This paper presents a ::: comprehensive review study of Histogram Equalization based algorithms. Computer simulations and analysis are ::: provided to compare the enhancement performance of several Histogram Equalization based algorithms. A secondderivative- ::: like enhancement measure is introduced to quantitatively evaluate their performance for image enhancement.


title: Precise Hausdorff distance computation between polygonal meshes

abstract: We present an exact algorithm for computing the precise Hausdorff distance between two general polyhedra represented as triangular meshes. The locus of candidate points, events where the Hausdorff distance may occur, is fully classified. These events include simple cases where foot points of vertices are examined as well as more complicated cases where extreme distance evaluation is needed on the intersection curve of one mesh with the medial axis of the other mesh. No explicit reconstruction of the medial axis is conducted and only bisectors of pairs of primitives (i.e. vertex, edge, or face) are analytically constructed and intersected with the other mesh, yielding a set of conic segments. For each conic segment, the distance functions to all primitives are constructed and the maximum value of their low envelope function may correspond to a candidate point for the Hausdorff distance. The algorithm is fully implemented and several experimental results are also presented.


title: The Theory is Predictive, but is it Complete?: An Application to Human Perception of Randomness

abstract: When we test a theory using data, it is common to focus on correctness: do the predictions of the theory match what we see in the data? But we also care about completeness: how much of the predictable variation in the data is captured by the theory? This question is difficult to answer, because in general we do not know how much "predictable variation" there is in the problem. In this paper, we consider approaches motivated by machine learning algorithms as a means of constructing a benchmark for the best attainable level of prediction. We illustrate our methods on the task of prediction of human-generated random sequences. Relative to an atheoretical machine learning algorithm benchmark, we find that existing behavioral models explain roughly 10 to 12 percent of the predictable variation in this problem. This fraction is robust across several variations on the problem. We also consider a version of this approach for analyzing field data from domains in which human perception and generation of randomness has been used as a conceptual framework; these include sequential decision-making and repeated zero-sum games. In these domains, our framework for testing the completeness of theories suggest that existing theoretical models may be more complete in their predictions for some domains than for others, suggesting that our methods can offer a comparative perspective across settings. Overall, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness.


title: DiMPP: a complete distributed algorithm for multi-agent path planning

abstract: Multi-agent path planning (MAPP) is a challenging task that aims to find conflict free paths for all the agents in a given domain. Priority-based decoupled approach is one of the several approaches to solve a MAPP problem. It works as follows: first, find paths of individual agents and then restructure these paths based on some priority of the agents. Most of the existing decoupled approaches use centralised algorithms. However, multi-agent systems are inherently distributed, where agents have limited information and each agent may not know the total number of agents in the system. Some of these aspects are incorporated in DMAPP. DMAPP is an existing fully distributed algorithm that works in three phases: (i) individual path planning, (ii) priority decision-making and (iii) plan restructuring. However, DMAPP is incomplete, i.e. DMAPP may fail to find a solution, even if it exists. In this paper, we present a new distributed algorithm (DiMPP) which is complete. The computer simulations performed on some we...


title: Academic performance, career potential, creativity, and job performance: Can one construct predict them all

abstract: This meta-analysis addresses the question of whether I general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices [J. C. Raven, 1965]; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).


title: Attentional multilabel learning over graphs: a message passing approach

abstract: We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, a graph has rich variable-size substructures which are related to the labels in some ways. We believe that uncovering these relations might hold the key to classification performance and explainability. We introduce Graph Attention model for Multi-Label learning (\(\text {GAML}\)), a novel graph neural network that can handle this problem effectively. \(\text {GAML}\) regards labels as auxiliary nodes and models them in conjunction with the input graph. By applying the neural message passing algorithm and attention mechanism to both the label nodes and the input nodes iteratively, \(\text {GAML}\) can capture the relations between the labels and the input subgraphs at various resolution scales. Moreover, our model can take advantage of explicit label dependencies. It also scales linearly with the number of labels and graph size thanks to our proposed hierarchical attention. We evaluate \(\text {GAML}\) on an extensive set of experiments with both graph-structured inputs and classical unstructured inputs. The results show that \(\text {GAML}\) significantly outperforms other competing methods. Importantly, \(\text {GAML}\) enables intuitive visualizations for better understanding of the label-substructure relations and explanation of the model behaviors.


title: Effects of Robot Motion on Human-Robot Collaboration

abstract: Most motion in robotics is purelyfifnctional, planned to achieve the goal and avoid collisions. Such motion is great in isolation, but collaboration affords a human who is watching the motion and making inferences about it, trying to coordinate with the robot to achieve the task. This paper analyzes the benefit of planning motion that explicitly enables the collaborator's inferences on the success of physical collaboration, as measured by both objective and subjective metrics. Results suggest that legible motion, planned to clearly express the robot's intent, leads to more fluent collaborations than predictable motion, planned to match the collaborator's expectations. Furthermore, purely functional motion can harm coordination, which negatively affects both task efficiency, as well as the participants' perception of the collaboration. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics


title: Crowdsourcing in ITS: The State of the Work and the Networking

abstract: In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.


title: Case Studies of E-commerce Adoption in Indonesian SMEs: The Evaluation of Strategic Use

abstract: The use of e-commerce in small medium-sized enterprises (SMEs) has become an important topic in information systems research. At present, there is a large number of well-documented benefits derived from e-commerce for small-medium enterprises (SMEs) both in theoretical and practical literature. Despite the enormous attention given to encourage SMEs to adopt e-commerce both by academics as well as governments, little research has been carried out in identifying strategies of e-commerce adoption for SMEs in developing countries, especially Indonesia. It is, therefore, the aim of this paper to understand the factors and combinations of factors that SMEs need to be considered before embracing e-commerce into their business by providing a closer look at actual experience of Indonesian SMEs. Case study research was carried out to analyse and explain the underlying factors that are likely to determine the varying extent of e-commerce adoption in SMEs, especially the service sector. The findings of the case studies have further extended into the development of proposed practical framework to illustrate how e-commerce adoption should be carried out from a strategic perspective.


title: The Use of Social Media Tools in the Product Life Cycle Phases: A Systematic Literature Review

abstract: In a business world where competitive pressure is constantly increasing, firms are continuously trying to differentiate themselves. The advent of Web 2.0 technologies such as social media allowed firms to communicate and interact with consumers and online users in order to collect information and to perform R&D, marketing and sales tasks. This study uses a systematic literature review in order to identify which social media tools can be used in the product life cycle phases. The results show that most studies focus on the earlier phases of the product life cycle, for innovation purposes. This study offers a systematic overview of literature and suggests many insights to help future researchers and managers in their use of social media in a product life cycle context, which also includes innovation process.


title: How can genetic tests be evaluated for clinical use? Experience of the UK Genetic Testing Network

abstract: The UK Department of Health supported the establishment of the UK Genetic Testing Network (UKGTN) in 2002. The UKGTN is a collaborative network of NHS molecular genetic laboratories that offer tests for human single gene germ-line disorders. Its objective is to provide high quality and equitable services for patients and their families who require genetic advice, diagnosis and management. The UKGTN has developed a 'Gene Dossier' process to evaluate genetic tests and recommend which tests will be provided by the National Health Service. This paper describes the UKGTN organisation and the 'Gene Dossier' process. A brief review of the UKGTN genetic test evaluation experience is presented.


title: Small-reflected substrate integrated waveguide termination with multi-step shape and absorbing material

abstract: A planar wideband substrate integrated waveguide (SIW) termination with multi-step shape and absorbing material is reported. Early reported absorbing material based SIW terminations exhibit fractional bandwidth (FBW) less than 45%. To broaden bandwidth, the multi-section stepped-impedance transformation is introduced into the design of SIW termination based on the theory of small reflection. The multi-step shape such as trident is used to mold the absorbing material, so that the impedance matching between the substrate and absorbing material can be improved and achieve a wider bandwidth. Measurement of the fabricated prototype exhibits a FBW over 70% with return loss better than 17dB, as well as system integration capability and low structure complexity.


title: Audio Based Disambiguation Of Music Genre Tags

abstract: In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres. We show that such embeddings can be used for disambiguating genre tags (identification of different labels for the same genre, tag translation from a tag system to another, inference of hierarchical taxonomies on these genre tags). These embeddings are built by training a deep convolutional neural network genre classifier with large audio datasets annotated with a flat tag system. We show empirically that they makes it possible to retrieve the original taxonomy of a tag system, spot duplicates tags and translate tags from a tag system to another.


title: Assessing Demand for Transparency in Intelligent Systems Using Machine Learning

abstract: Intelligent systems offering decision support can lessen cognitive load and improve the efficiency of decision making in a variety of contexts. These systems assist users by evaluating multiple courses of action and recommending the right action at the right time. Modern intelligent systems using machine learning introduce new capabilities in decision support, but they can come at a cost. Machine learning models provide little explanation of their outputs or reasoning process, making it difficult to determine when it is appropriate to trust, or if not, what went wrong. In order to improve trust and ensure appropriate reliance on these systems, users must be afforded increased transparency, enabling an understanding of the systems reasoning, and an explanation of its predictions or classifications. Here we discuss the salient factors in designing transparent intelligent systems using machine learning, and present the results of a user-centered design study. We propose design guidelines derived from our study, and discuss next steps for designing for intelligent system transparency.


title: Designing a Blockchain-Based IoT With Ethereum , Swarm , and LoRa

abstract: Today, the number of Internet of Things (IoT) devices in all aspects of life is increasing exponentially. Our cities are getting smarter and informing us about our surroundings in a contextual manner. However, we face significant challenges in deploying, managing, and collecting data from these devices. In addition, we must address the problem of storing and mining that data for higher-quality IoT services. Blockchain technology, even in today's nascent form, has the potential to be the foundation for a common, distributed, trustless, and autonomous infrastructure system. This article describes a standardized IoT infrastructure where data are stored on a distributed storage service that is fault-tolerant and resistant to distributed denial of service (DDOS) attacks and data access is managed by a decentralized, trustless blockchain. The illustrated system used LoRa as the emerging network technology, Swarm as the distributed data storage platform, and Ethereum as the blockchain platform. Such a data back end will ensure high availability with minimal security risks while replacing traditional back-end systems with a single "smart contract."


title: Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

abstract: Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.


title: Association rule mining to detect factors which contribute to heart disease in males and females

abstract: This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms - Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.


title: Blended learning at maths with aerospace engineering freshmen

abstract: Instructing mathematics to engineering freshmen is a challenge not only because of their diverse backgrounds but also because of their high skills in digital technology. For this reason, a new approach has been considered in Aerospace Engineering at Technical University of Valencia (UPV) which, in addition to master classes, includes assignments specially designed to achieve mathematical competencies in the first year. These assignments reinforce the learning process of students and increase their competencies before each assessment test. On the other hand PoliformaT, an educational platform developed by UPV, and based on the Sakai project, has facilitated to develop a flipped teaching methodology in the one hour lab sessions in the first year of Aerospace Engineering. Thus a blended learning methodology, a combination of computer technologies and face-to-face classes, has provided very good learning outcomes results for Spanish standards. We present some results concerning our first year Mathematics course in Aerospace Engineering and the opinion of the students about this blended learning methodology.


title: Performance and power profiling for emulated Android systems

abstract: Simulation is a common approach for assisting system design and optimization. For system-wide optimization, energy and computational resources are often the two most critical issues. Monitoring the energy state of each hardware component and measuring the time spent in each state is needed for accurate energy and performance prediction. For software optimization, it is important to profile the energy and the time consumed by each software construct in a realistic operating environment with a proper workload. However, the conventional approaches of simulation often fail to produce satisfying data. First, building a cycle-accurate simulation environment for a complex system, such as an Android smartphone, is difficult and can take a long time. Second, a slow simulation can significantly alter the behavior of multithreaded, I/O-intensive applications and can affect the accuracy of profiles. Third, existing software-based profilers generally do not work on simulators, which makes it difficult for performance analysis of complicated software, for example, Java applications executed by the Dalvik VM in an Android system. To address these aforementioned problems, we proposed and prototyped a framework, called virtual performance analyzer (VPA). VPA takes advantage of an existing emulator or virtual machine monitor to reduce the complexity of building a simulator. VPA allows the user to selectively and incrementally integrate timing models and power models into the emulator with our carefully designed performance/power monitors, tracing facility, and profiling tools to evaluate and analyze the emulated system. The emulated system can perform at different levels of speed to help verify if the profile data are impacted by the emulation speed. Finally, VPA supports existing software-based profiles and enables non-intrusive tracing/profiling by minimizing the probe effect. Our experimental results show that the VPA framework allows users to quickly establish a performance/power evaluation environment and gather useful information to support system design and software optimization for Android smartphones.


title: Pose from Flow and Flow from Pose

abstract: Human pose detectors, although successful in localising faces and torsos of people, often fail with lower arms. With fast movements body motion estimation is often inaccurate. We build a segmentation-detection algorithm that mediates the information between body parts recognition, and multi-frame motion grouping to improve both pose detection and tracking. Motion of body parts, though not accurate, is often sufficient to segment them from their backgrounds. Such segmentations are crucial for extracting hard to detect body parts out of their interior body clutter. By matching these segments to exemplars we obtain pose labeled body segments. The pose labeled segments and corresponding articulated joints are used to improve the motion flow fields by proposing kinematically constrained affine displacements on body parts. The pose-based articulated motion model is shown to handle large limb rotations and displacements. Our algorithm can detect people under rare poses, frequently missed by pose detectors, showing the benefits of jointly reasoning about pose, segmentation and motion in videos.


title: Fitting Tweedie’s compound Poisson model to insurance claims data

abstract: Abstract We discuss the estimation and inference problems for the Tweedie compound Poisson process and its application to tarification. For data in the form of the total claim and number of claims for a given time interval and a given exposure, the Tweedie process corresponds to a Poisson process of claims and gamma distributed claim sizes. The model has three parameters, namely the mean claim rate, a dispersion parameter and a shape parameter, and the exposure enters as a weight via the dispersion parameter. The Tweedie process is an exponential dispersion model for fixed value of the shape parameter, and hence regression models for the claim rate may be fitted as in generalized linear models. The shape parameter is estimated by maximum likelihood, and inference is based on the likelihood ratio test, rather than the usual analysis of deviance. A GLIM 4 program for estimation in the model is presented.


title: Analyzing and Mining Comments and Comment Ratings on the Social Web

abstract: An analysis of the social video sharing platform YouTube and the news aggregator Yahooe News reveals the presence of vast amounts of community feedback through comments for published videos and news stories, as well as through metaratings for these comments. This article presents an in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahooe News. In this study, comment ratings are considered first-class citizens. Their dependencies with textual content, thread structure of comments, and associated content (e.g., videos and their metadata) are analyzed to obtain a comprehensive understanding of the community commenting behavior. Furthermore, this article explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior. Results from this study have potential application in guiding the design of community-oriented online discussion platforms.


title: Customer Relationship Management: Strategies And Practices In Selected Banks Of Pakistan

abstract: As economic globalization intensifies competition and creates a climate of constant change, winning and keeping customers has never been more important. Nowadays, Banks have realized that customer relationships are a very important factor for their success. Customer relationship management (CRM) is a strategy that can help them to build long-lasting relationships with their customers and increase their revenues and profits. CRM in the banking sector is of greater importance. The aim of this study is to explore and analyze the strategic implementation of CRM in selected banks of Pakistan, identify the benefits, the problems, as well as the success and failure factors of the implementation and develop a better understanding of CRM impact on banking competitiveness as well as provide a greater understanding of what constitutes good CRM practices. In this study, CMAT (Customer Management Assessment Tool) model is used which encompasses all the essential elements of practical customer relationship management. Data is collected through questionnaires from the three major banks (HBL, MCB, and Citibank) of Pakistan. The evidence supports that CRM is gradually being practiced in studied banks; however the true spirit of CRM is still needed to be on the active agenda of the banking sector in Pakistan. This study contributes to the financial services literature as it is one of the very few that have examined CRM applications, a comparatively new technology, in the Pakistani banking sector, where very limited research has taken place on the implementation of CRM.


title: Efficient online coflow routing and scheduling

abstract: A coflow is a collection of related parallel flows that occur typically between two stages of a multi-stage compute task in a network, such as shuffle flows in MapReduce. The coflow abstraction allows applications to convey their semantics to the network so that application-level requirements (e.g., minimizing the completion time of the slowest flow) can be better satisfied. In this paper, we study the routing and scheduling of multiple coflows to minimize the average coflow completion time (CCT). We first propose a rounding-based randomized approximation algorithm, called OneCoflow, for single coflow routing and scheduling. The multiple coflow problem is more challenging as coexisting coflows will compete for the same network resources such as link bandwidths. To minimize the average CCT, we derive an online multiple coflow routing and scheduling algorithm, called OMCoflow, and prove that it has a reasonably good competitive ratio. To the best of our knowledge, this is the first online algorithm with theoretical performance guarantees which considers routing and scheduling simultaneously for multi-coflows. Compared with existing methods, OMCoflow runs more efficiently, and it avoids the problem of frequently rerouting the flows. Extensive simulations on a Facebook data trace show that OMCoflow outperforms the state-of-the-art heuristic schemes significantly (e.g., reducing the average CCT by up to 41.8% and the execution time by up to 99.2% against RAPIER [28]).


title: Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network

abstract: Automobile driver information as displayed on marked road signs indicates the state of the road, traffic conditions, proximity to schools, etc. These signs are important to insure the safety of the driver and pedestrians. They are also important input to the automated advanced driver assistance system (ADAS), installed in many automobiles. Over time, the arrow-road markings may be eroded or otherwise damaged by automobile contact, making it difficult for the driver to correctly identify the marking. Failure to properly identify an arrow-road marker creates a dangerous situation that may result in traffic accidents or pedestrian injury. Very little research exists that studies the problem of automated identification of damaged arrow-road marking painted on the road. In this study, we propose a method that uses a convolutional neural network (CNN) to recognize six types of arrow-road markings, possibly damaged, by visible light camera sensor. Experimental results with six databases of Road marking dataset, KITTI dataset, Malaga dataset 2009, Malaga urban dataset, Naver street view dataset, and Road/Lane detection evaluation 2013 dataset, show that our method outperforms conventional methods.


Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet


title: Supervised topic models for multi-label classification

abstract: Recently, some publications indicated that the generative modeling approaches, i.e., topic models, achieved appreciated performance on multi-label classification, especially for skewed data sets. In this paper, we develop two supervised topic models for multi-label classification problems. The two models, i.e., Frequency-LDA (FLDA) and Dependency-Frequency-LDA (DFLDA), extend Latent Dirichlet Allocation (LDA) via two observations, i.e., the frequencies of the labels and the dependencies among different labels. We train the models by the Gibbs sampler algorithm. The experiment results on well known collections demonstrate that our two models outperform the state-of-the-art approaches.


title: First demonstration of broadband W-band and D-band GaN MMICs for next generation communication systems

abstract: High-performing GaN MMICs that can cover broadband applications at W and D-Band have been fabricated and tested. A five stage 60–105 GHz LNA has >23 dB of gain across the band and a six stage D-Band LNA has 25 dB of gain from 110–170 GHz. A double balanced resistive FET mixer has −11 to −15 dB of conversion loss from 74–94 GHz. To our knowledge, these are the first reported GaN MMICs with high broadband gain at these frequencies.


title: The Hopping Discrete Fourier Transform [sp Tips&Tricks]

abstract: The discrete Fourier transform (DFT) produces a Fourier representation for finite-duration data sequences. In addition to its theoretical importance, the DFT plays a key role in the implementation of a variety of digital signal-?processing algorithms. Several algorithms including the fast Fourier transform (FFT) and the Goertzel algorithm have been introduced for the fast implementation of the DFT [1], [2].


title: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions

abstract: Word embedding is a useful approach to capture co-occurrence structures in large text corpora. However, in addition to the text data itself, we often have additional covariates associated with individual corpus documents---e.g. the demographic of the author, time and venue of publication---and we would like the embedding to naturally capture this information. We propose CoVeR, a new tensor decomposition model for vector embeddings with covariates. CoVeR jointly learns a \emph{base} embedding for all the words as well as a weighted diagonal matrix to model how each covariate affects the base embedding. To obtain author or venue-specific embedding, for example, we can then simply multiply the base embedding by the associated transformation matrix. The main advantages of our approach are data efficiency and interpretability of the covariate transformation. Our experiments demonstrate that our joint model learns substantially better covariate-specific embeddings compared to the standard approach of learning a separate embedding for each covariate using only the relevant subset of data, as well as other related methods. Furthermore, CoVeR encourages the embeddings to be"topic-aligned"in that the dimensions have specific independent meanings. This allows our covariate-specific embeddings to be compared by topic, enabling downstream differential analysis. We empirically evaluate the benefits of our algorithm on datasets, and demonstrate how it can be used to address many natural questions about covariate effects. Accompanying code to this paper can be found at http://github.com/kjtian/CoVeR.


Motivation and Burnout in Professional Rugby Players.


title: Dynamic Match Kernel With Deep Convolutional Features for Image Retrieval

abstract: For image retrieval methods based on bag of visual words, much attention has been paid to enhancing the discriminative powers of the local features. Although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (CNN). Such images should not be considered as relevant pairs. To tackle this problem, we propose to construct a dynamic match kernel by adaptively calculating the matching thresholds between query and candidate images based on the pairwise distance among deep CNN features. In contrast to the typical static match kernel which is independent to the global appearance of retrieved images, the dynamic one leverages the semantical similarity as a constraint for determining the matches. Accordingly, we propose a semantic-constrained retrieval framework by incorporating the dynamic match kernel, which focuses on matched patches between relevant images and filters out the ones for irrelevant pairs. Furthermore, we demonstrate that the proposed kernel complements recent methods, such as hamming embedding, multiple assignment, local descriptors aggregation, and graph-based re-ranking, while it outperforms the static one under various settings on off-the-shelf evaluation metrics. We also propose to evaluate the matched patches both quantitatively and qualitatively. Extensive experiments on five benchmark data sets and large-scale distractors validate the merits of the proposed method against the state-of-the-art methods for image retrieval.


title: Technical Privacy Metrics: a Systematic Survey

abstract: The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.


title: A low-kickback-noise latched comparator for high-speed flash analog-to-digital converters

abstract: In traditional comparators especially for flash ADCs, one serious problem is the kick back noise, which disturbs the input signal voltages and consequently might cause errors at the outputs of the ADCs. In this paper, we propose a novel CMOS latched comparator with very low kickback noise for high-speed flash ADCs. The proposed comparator separates analog preamplifier from the positive feedback digital dynamic latch so as to reduce the influence of the kickback noise. Simulation results based on a mixed signal CMOS 0.35 /spl mu/m technology show that, this comparator can work at a maximum clock frequency of 500 MHz with very reduced kickback noise compared with conventional architectures.


title: Low-Latency Audio Pitch Tracking: A Multi-Modal Sensor-Assisted Approach

abstract: This paper presents a multi-modal approach to musical instrument pitch tracking combining audio and position sensor data. Finger location on a violin fingerboard is measured using resistive sensors, allowing rapid detection of approximate pitch. The initial pitch estimate is then used to restrict the search space of an audio pitch tracking algorithm. Most audio pitch tracking algorithms face a fundamental tradeoff between accuracy and latency, with longer analysis windows producing better pitch estimates at the cost of noticeable lag in a live performance environment. Conversely, sensor-only strategies struggle to achieve the fine pitch accuracy a human listener would expect. While this paper is violin centric, it demonstrates a more general concept for augmented instruments that by combining the two differing approaches, high accuracy and low latency pitch can be simultaneiously achieved.


title: Treatment of thoracolumbar burst fractures: short-segment pedicle instrumentation versus kyphoplasty.

abstract: UNLABELLED ::: The management of amyelic thoracolumbar burst fractures remains controversial. In this study, we compared the clinical efficacy of percutaneous kyphoplasty (PKP) and short-segment pedicle instrumentation (SSPI). Twenty-three patients were treated with PKP, and 25 patients with SSPI. They all presented with Type A3 amyelic thoracolumbar fractures. Clinical outcomes were evaluated by a Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) preoperatively, postoperatively, and at two years follow-up. Radiographic data including the anterior and posterior vertebral body height, kyphotic angle, as well as spinal canal compromise was also evaluated. The patients in both groups were similar regarding age, bone mineral density (BMD), follow-up period, severity of the deformity and fracture. Blood loss, operation time, and bed-rest time were less in the PKP group. VAS, ODI score improved more rapidly after surgery in the PKP group. No significant difference was found in VAS and ODI scores between the two groups at final follow-up (p > 0.05). Meanwhile, the height of anterior vertebrae (Ha), the height of posterior vertebrae (Hp) and the kyphosis angle showed significant improvement in each group (p < 0.05). The postoperative improvement in spinal canal compromise was not statistically significant in the PKP group (p > 0.05); there was a significant improvement in the SSPI group (p < 0.05). Moreover, these postoperative radiographic assessments showed significant differences between the two groups regarding the improvement of canal compromise (p < 0.05). At final follow-up, remodeling of spinal canal compromise was detected in both groups. ::: ::: ::: CONCLUSION ::: Both PKP and SSPI appeared as effective and reliable operative techniques for selected amyelic thoracolumbar fractures in the short-term. PKP had a significantly smaller blood loss and shorter bed-rest time, but SSPI provided a better reduction. Long-time studies should be conducted to support these clinical outcomes.


title: Older Americans Need To Make Every Calorie Count

abstract: ing energy needs mean they must eat better while eating less. USDA food consumption survey data indicate that most older Americans are having trouble fitting the recommended number of daily food group servings into their decreased “calorie budgets.” While the basic nutrition advice in the Dietary Guidelines for Americans and the Food Guide Pyramid applies to healthy adults of all ages, the elderly face some special challenges, particularly declining energy (calorie) needs as metabolism slows down. For some older adults, decreased physical activity may further reduce energy needs, although the Dietary Guidelines emphasize that healthy seniors, just like younger adults, should be physically active each day. Because the amount of food they can eat while maintaining calorie balance is more limited than when they were younger, older individuals must choose wisely, selecting nutrient-dense foods and limiting “extras.” To help older adults and others visualize what this means, researchers at the Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University created an “elderly pyramid” that is narrower than the Food Guide Pyramid for the general population (see box). The more narrow shape of the elderly pyramid indicates that most individuals age 70 and older should choose the smaller numbers of servings within the range recommended by the Food Guide Pyramid. A more quantitative way of providing food choice guidance would be to compute benchmark food densities for younger and older men and women. A benchmark food density is the number of servings per 1,000 calories an individual consuming a given number of calories would need to consume to meet the Food Guide Pyramid recommendations. For example, a person who consumes 2,200 calories daily should consume four servings of vegetables, according to the Food Guide Pyramid. This consumption level would translate into a benchmark density of 1.8 servings of vegetables per 1,000 calories. Data from USDA’s Continuing Survey of Food Intakes by Individuals, 1994-96 (CSFII 1994-96) were used to examine food intakes of younger men and women, age 1959, and seniors, age 60 and older. Survey respondents lived in household settings, not in institutional settings, such as nursing homes. Data were collected from a nationwide sample, yielding results representative of the noninstitutionalized American population. The survey collected information on what, when, where, and how much individuals ate during the 3-year survey period. We used 1-day dietary intake data from this survey to examine how much younger and


title: A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment.

abstract: Veterinary antibiotics (VAs) are widely used in many countries worldwide to treat disease and protect the health of animals. They are also incorporated into animal feed to improve growth rate and feed efficiency. As antibiotics are poorly adsorbed in the gut of the animals, the majority is excreted unchanged in faeces and urine. Given that land application of animal waste as a supplement to fertilizer is often a common practice in many countries, there is a growing international concern about the potential impact of antibiotic residues on the environment. Frequent use of antibiotics has also raised concerns about increased antibiotic resistance of microorganisms. We have attempted in this paper to summarize the latest information available in the literature on the use, sales, exposure pathways, environmental occurrence, fate and effects of veterinary antibiotics in animal agriculture. The review has focused on four important groups of antibiotics (tylosin, tetracycline, sulfonamides and, to a lesser extent, bacitracin) giving a background on their chemical nature, fate processes, occurrence, and effects on plants, soil organisms and bacterial community. Recognising the importance and the growing debate, the issue of antibiotic resistance due to the frequent use of antibiotics in food-producing animals is also briefly covered. The final section highlights some unresolved questions and presents a way forward on issues requiring urgent attention.


title: Coordinated Population Activity Underlying Texture Discrimination in Rat Barrel Cortex

abstract: Rodents can robustly distinguish fine differences in texture using their whiskers, a capacity that depends on neuronal activity in primary somatosensory "barrel" cortex. Here we explore how texture was collectively encoded by populations of three to seven neuronal clusters simultaneously recorded from barrel cortex while a rat performed a discrimination task. Each cluster corresponded to the single-unit or multiunit activity recorded at an individual electrode. To learn how the firing of different clusters combines to represent texture, we computed population activity vectors across moving time windows and extracted the signal available in the optimal linear combination of clusters. We quantified this signal using receiver operating characteristic analysis and compared it to that available in single clusters. Texture encoding was heterogeneous across neuronal clusters, and only a minority of clusters carried signals strong enough to support stimulus discrimination on their own. However, jointly recorded groups of clusters were always able to support texture discrimination at a statistically significant level, even in sessions where no individual cluster represented the stimulus. The discriminative capacity of neuronal activity was degraded when error trials were included in the data, compared to only correct trials, suggesting a link between the neuronal activity and the animal's performance. These analyses indicate that small groups of barrel cortex neurons can robustly represent texture identity through synergistic interactions, and suggest that neurons downstream to barrel cortex could extract texture identity on single trials through simple linear combination of barrel cortex responses.


title: A deep neural network for time-domain signal reconstruction

abstract: Supervised speech separation has achieved considerable success recently. Typically, a deep neural network (DNN) is used to estimate an ideal time-frequency mask, and clean speech is produced by feeding the mask-weighted output to a resynthesizer in a subsequent step. So far, the success of DNN-based separation lies mainly in improving human speech intelligibility. In this work, we propose a new deep network that directly reconstructs the time-domain clean signal through an inverse fast Fourier transform layer. The joint training of speech resynthesis and mask estimation yields improved objective quality while maintaining the objective intelligibility performance. The proposed system significantly outperforms a recent non-negative matrix factorization based separation system in both objective speech intelligibility and quality.


title: Bringing Semantics to Web Services with OWL-S

abstract: Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation for automating the use of Web Services. Representational techniques being developed for the Semantic Web can be used to augment these standards. The resulting Web Service specifications enable the development of software programs that can interpret descriptions of unfamiliar Web Services and then employ those services to satisfy user goals. OWL-S ("OWL for Services") is a set of notations for expressing such specifications, based on the Semantic Web ontology language OWL. It consists of three interrelated parts: a profile ontology, used to describe what the service does; a process ontology and corresponding presentation syntax, used to describe how the service is used; and a grounding ontology, used to describe how to interact with the service. OWL-S can be used to automate a variety of service-related activities involving service discovery, interoperation, and composition. A large body of research on OWL-S has led to the creation of many open-source tools for developing, reasoning about, and dynamically utilizing Web Services.


title: A survey of edge-preserving image denoising methods

abstract: Reducing noise has always been one of the standard problems of the image analysis and processing community. Often though, at the same time as reducing the noise in a signal, it is important to preserve the edges. Edges are of critical importance to the visual appearance of images. So, it is desirable to preserve important features, such as edges, corners and other sharp structures, during the denoising process. This paper presents a review of some significant work in the area of image denoising. It provides a brief general classification of image denoising methods. The main aim of this survey is to provide evolution of research in the direction of edge-preserving image denoising. It characterizes some of the well known edge-preserving denoising methods, elaborating each of them, and discusses the advantages and drawbacks of each. Basic ideas and improvement of the denoising methods are also comprehensively summarized and analyzed in depth. Often, researchers face difficulty in selecting an appropriate denoising method that is specific to their purpose. We have classified and systemized these denoising methods. The key goal of this paper is to provide researchers with background on a progress of denoising methods so as to make it easier for researchers to choose the method best suited to their aims.


title: Ultrasonic and Infrared Sensors Performance in a Wireless Obstacle Detection System

abstract: Ultrasonic (US) and infrared (IR) sensors are broadly used in mobile applications for distance measurements. In this project, an obstacle detection system is built based on these two types of sensors. The system is intended for use by the elderly and people with vision impairment. The prototype developed has been tested to detect obstacles and shows accuracies of 95% to 99% for distance measurements if the sensor circuits are calibrated properly and their output linearized. The system also demonstrates good detection for different obstacle materials (e.g., wood, plastic, mirror, plywood and concretes) and colors. The minimum size of an obstacle that the system can detect is 5 cm x 5 cm.


title: Small-signal modeling of a three-phase isolated inverter with both voltage and frequency droop control

abstract: In conventional power system operation, droop control methods are used to facilitate load sharing among different generation sources. This method compensates for both active and reactive power imbalances by adjusting the output voltage magnitude and frequency of the generating unit. Both P-ω and Q-V droops have been used in synchronous machines for decades. Similar droop controllers were used in this study to develop a control algorithm for a three-phase isolated (islanded) inverter. Controllers modeled in a synchronous dq reference frame were simulated in PLECS and validated with the hardware setup. A small-signal model based on an averaged model of the inverter was developed to study the system's dynamics. The accuracy of this mathematical model was then verified using the data obtained from the experimental and simulation results. This validated model is a useful tool for the further dynamic analysis of a microgrid.


title: Misinformation in Online Social Networks: Detect Them All with a Limited Budget

abstract: Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors. In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its #P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.


Bag of What? Simple Noun Phrase Extraction for Text Analysis


title: Missing Data Techniques for Structural Equation Modeling.

abstract: As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods. Virtually all methods of statistical analysis are plagued by problems with missing data, and structural equation modeling is no exception. It is well known that the use of inappropriate methods for handling missing data can lead to bias in parameter estimates (Jones, 1996), bias in standard errors and test statistics (Glasser, 1964), and inefficient use of the data (Afifi & Elashoff, 1966). This article surveys various methods that are available for handling missing data in the estimation of structural equation models (SEMs). After reviewing such conventional methods as listwise deletion, pairwise deletion, and regression imputation, I focus on the implementation of two newer methods, maximum likelihood and multiple imputation. These methods have much better statistical properties than conventional methods have under considerably weaker assumptions, a rare combination for new statistical


title: On the Methodology of Linguistics: A Case Study

abstract: Prolegomena introduction to game-theoretical semantics the principles of GB methodological comparisons direct comparisons between GB theories and GTS comparing explanatory strategies thematic relations co-reference in GTS co-reference and simple donkey sentences co-reference and complex donkey sentences other recent theories reflexives and co-reference.


title: An improved ring amplifier with process- and supply voltage-insensitive dead-zone

abstract: Conventional ring amplifier suffers from stability problem, the problem is exacerbated when process, voltage and temperature (PVT) variations are taken into considerations. Prior works are mainly focused on stability problem, the influence of PVT on transient response were barely discussed. In this paper the influence of process variations on stability and transient response is analyzed, a process- and supply voltage-robust ring amplifier is presented. The mechanism of the proposed process- and voltage-insensitive dead-zone (DZ) is analyzed. Simulations showed that the proposed ring amplifier stays stable and maintains good transient performance over different process corners, the stability with supply voltage variations is also improved.


title: Efficiency improvement of a 12-pole 17.5 kW induction motor using converter-fed wound rotor

abstract: This paper investigates an induction machine using a novel concept of a converter-fed rotor. The stator is directly connected to the grid while the rotor is fed by a converter with a floating capacitor over the dc-link. In earlier work this topology has shown to be capable of improving the power factor and efficiency of a 4-pole induction machine. This paper explores the possibility to improve the power factor and efficiency of a squirrel-cage 12-pole 17.5 kW induction machine by redesigning the cage rotor to a wound rotor to enable connection of the rotor windings to the converter. A standard 12-pole squirrel-cage induction machine (SCIM) with an inherent low power factor is studied. A wound rotor induction machine (WRIM) is then analytically designed. The performance of the designed WRIM using the analytical model is shown to agree with the results from the finite element (FEM) simulations, thus verifying the correctness of the model. The analytical model is then further used to design different WRIMs with different dimensions and rotor slot numbers. Performances of these designed WRIMs show good potential for power factor and efficiency improvement. Particularly, an optimum efficiency improvement of 6.8%, compared with the original SCIM, has been achieved.


title: Cybersecurity Practices for E - Government: An Assessment in Bhutan

abstract: The main goal of e-government implementation is to improve the effectiveness, efficiency and quality of public service delivery using Information and Communication Technologies (ICT). However, its success is dependent on the provision of information security goals such as confidentiality, integrity, availability and trust. Therefore, cybersecurity is vital for the successful adoption of e-government systems. This paper presents an assessment of cybersecurity practices, cyber threats and other factors affecting effective implementation of cybersecurity program in government organizations in Bhutan. Selected cybersecurity practices included in the study were cyber policy, risk management, training and awareness, and access controls for protection of network including mobile computing devices. Out of 280 potential respondents, 157 respondents completed the survey. The results show that, in many organizations, there is very limited use of or a lack of formal cybersecurity policy, risk management, awareness, or incident management practices. The results also indicate that many organizations have either suffered from, or been affected by, cybersecurity threats such as malware, hacking and phishing scams. The study recommends both managerial and technological practices to improve cybersecurity posture of government organizations and to improve people’s level of trust and confidence in e-government services.


title: Usability for Mobile Commerce Across Multiple Form Factors

abstract: Current research on usability for mobile commerce has focused on single platforms and very limited aspects of commerce activities. We conducted an exploratory study to examine usability problems and to identify potential research questions concerning wireless solutions for consumer e-commerce. By using cognitive walkthrough and heuristic evaluation methods, we evaluated the usability of ten wireless sites in three platforms: WAP-enabled mobile phones, Palm OS based wireless PDAs, and Pocket PCs running Windows CE operating systems. This article discusses our usability findings pertaining to user tasks, content presentation, search, navigation systems, and the design constraints imposed by form factors. It also provides design guidelines based on our study and examines research implications for wireless interface design.


title: Scanning Rate Enhancement of Leaky-Wave Antennas Using Slow-Wave Substrate Integrated Waveguide Structure

abstract: In this communication, we propose a high scanning rate leaky-wave antenna (LWA) based on a slow-wave substrate integrated waveguide (SIW) structure. The slow-wave effect is introduced by etching periodical slots on the top surface of SIW. The LWA radiation is subsequently realized by introducing sinusoidal modulation to the slots profile. Such a structure significantly improves the scanning rate of LWA due to the small group velocity at the slow-wave region. Simulation and measured results indicate that the proposed LWA scans a wide angle in a narrow bandwidth near the cutoff frequency of surface plasmon polariton. Within the frequency band 13.5–13.9 GHz (3% relative bandwidth), the measured scanning angle is from 2° to 37° with the measured gain all above 9.2 dBi.


title: When Gaussian Process Meets Big Data: A Review of Scalable GPs

abstract: The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process regression (GPR), a well-known nonparametric, and interpretable Bayesian model, which suffers from cubic complexity to data size. To improve the scalability while retaining desirable prediction quality, a variety of scalable GPs have been presented. However, they have not yet been comprehensively reviewed and analyzed to be well understood by both academia and industry. The review of scalable GPs in the GP community is timely and important due to the explosion of data size. To this end, this article is devoted to reviewing state-of-the-art scalable GPs involving two main categories: global approximations that distillate the entire data and local approximations that divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations that modify the prior but perform exact inference, posterior approximations that retain exact prior but perform approximate inference, and structured sparse approximations that exploit specific structures in kernel matrix; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and capability of scalable GPs are reviewed. Finally, the extensions and open issues of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues.


Programming Rework in Software Processes


title: Semantic Segmentation Based Traffic Light Detection at Day and at Night

abstract: Traffic light detection from a moving vehicle is an important technology both for new safety driver assistance functions as well as for autonomous driving in the city. In this paper we present a machine learning framework for detection of traffic lights that can handle in real-time both day and night situations in a unified manner. A semantic segmentation method is employed to generate traffic light candidates, which are then confirmed and classified by a geometric and color features based classifier. Temporal consistency is enforced by using a tracking by detection method.


Hallucinations of past experience and experiential responses to stimulation of temporal cortex.


title: Privacy-preserving Prediction

abstract: Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learning without privacy. At the same time, in many applications it is not necessary to expose the model itself. Instead users may be allowed to query the prediction model on their inputs only through an appropriate interface. Here we formulate the problem of ensuring privacy of individual predictions and investigate the overheads required to achieve it in several standard models of classification and regression. We first describe a simple baseline approach based on training several models on disjoint subsets of data and using standard private aggregation techniques to predict. We show that this approach has nearly optimal sample complexity for (realizable) PAC learning of any class of Boolean functions. At the same time, without strong assumptions on the data distribution, the aggregation step introduces a substantial overhead. We demonstrate that this overhead can be avoided for the well-studied class of thresholds on a line and for a number of standard settings of convex regression. The analysis of our algorithm for learning thresholds relies crucially on strong generalization guarantees that we establish for all differentially private prediction algorithms.


title: Introduction to smart learning analytics: foundations and developments in video-based learning

abstract: Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.


title: Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

abstract: The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.


title: The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study

abstract: Presents the findings of a study performed on data from a large bank’s retail‐banking operations. Illustrates the relationship of customer satisfaction to customer loyalty, and customer loyalty to profitability, using multiple measures of satisfaction, loyalty, and profitability. An estimate of the effects of increased customer satisfaction on profitability (assuming hypothesized causality) suggests that attainable increases in satisfaction could dramatically improve profitability.


title: Co-author Relationship Prediction in Heterogeneous Bibliographic Networks

abstract: The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, \i.e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (\e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called\emph{Path Predict}, \i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.


title: Cellular-Connected UAV: Potential, Challenges, and Promising Technologies

abstract: Enabling high-rate, low-latency and ultra-reliable wireless communications between UAVs and their associated ground pilots/users is of paramount importance to realize their large-scale usage in the future. To achieve this goal, cellular- connected UAV, whereby UAVs for various applications are integrated into the cellular network as new aerial users, is a promising technology that has drawn significant attention recently. Compared to conventional cellular communication with terrestrial users, cellular-connected UAV communication possesses substantially different characteristics that present new research challenges as well as opportunities. In this article, we provide an overview of this emerging technology, by first discussing its potential benefits, unique communication and spectrum requirements, as well as new design considerations. We then introduce promising technologies to enable the future generation of 3D heterogeneous wireless networks with coexisting aerial and ground users. Last, we present simulation results to corroborate our discussions and highlight key directions for future research.


title: Potential Forensic Analysis of IoT Data: An Overview of the State-of-the-Art and Future Possibilities

abstract: Academics and practicing digital forensics experts have previously proposed several models for tackling the end to end procedure of investigating IoT devices. However, these approaches are still very high level or focuses on one specific evidence collection technique. There is not, as yet, a comprehensive enumeration of classes of forensic data collected by IoT devices, nor even a comprehensive list of techniques for extracting specific forensic information from specific IoT devices. This paper addresses these issues by describing the potential forensic information that can be gathered, derived, or inferred from IoT-collected data. We also present an overview of existing IoT data collection methods for the Amazon Echo, Z-wave protocol, and home routers to illustrate the types of forensic data already being collected and the techniques used to obtain it. Finally, we present possible avenues for additional mechanisms for obtaining forensic data from these devices.


Nonlinear Optimal Line-Of-Sight Stabilization with Fuzzy Gain-Scheduling


title: Detection of potato diseases using image segmentation and multiclass support vector machine

abstract: Modern phenotyping and plant disease detection provide promising step towards food security and sustainable agriculture. In particular, imaging and computer vision based phenotyping offers the ability to study quantitative plant physiology. On the contrary, manual interpretation requires tremendous amount of work, expertise in plant diseases, and also requires excessive processing time. In this work, we present an approach that integrates image processing and machine learning to allow diagnosing diseases from leaf images. This automated method classifies diseases (or absence thereof) on potato plants from a publicly available plant image database called ‘Plant Village’. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 300 images with an accuracy of 95%. Thus, the proposed approach presents a path toward automated plant diseases diagnosis on a massive scale.


title: Teaching Introductory Artificial Intelligence through Java-based Games

abstract: We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.


Seasonal Fluctuations of Some Sucrose Metabolizing Enzymes and Sugar and Organic Acid Contents in Broccoli


title: DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

abstract: We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D convolutional networks, high-class imbalance arising from the low percentage of vessel voxels, and unavailability of accurately annotated training data - and offer solutions as the building blocks of DeepVesselNet. ::: First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate synthetic dataset using a computational angiogenesis model capable of generating vascular trees under physiological constraints on local network structure and topology and use these data for transfer learning. ::: DeepVesselNet is optimized for segmenting and analyzing vessels, and we test the performance on a range of angiographic volumes including clinical MRA data of the human brain, as well as X-ray tomographic microscopy scans of the rat brain. Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0.07 when compared to full 3-D filters). Our class balancing metric is crucial for training the network and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks.


title: Synesketch: An Open Source Library for Sentence-Based Emotion Recognition

abstract: Online human textual interaction often carries important emotional meanings inaccessible to computers. We propose an approach to textual emotion recognition in the context of computer-mediated communication. The proposed recognition approach works at the sentence level and uses the standard Ekman emotion classification. It is grounded in a refined keyword-spotting method that employs: a WordNet-based word lexicon, a lexicon of emoticons, common abbreviations and colloquialisms, and a set of heuristic rules. The approach is implemented through the Synesketch software system. Synesketch is published as a free, open source software library. Several Synesketch-based applications presented in the paper, such as the the emotional visual chat, stress the practical value of the approach. Finally, the evaluation of the proposed emotion recognition algorithm shows high accuracy and promising results for future research and applications.


title: A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

abstract: This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students’ mathematics performance. We define progress in three areas: improved student cognition, engagement, and affect, and we attribute this improvement to specific components and interventions that are inherently affective, cognitive, and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students’ metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and student self-reports, and supported through affective learning companions and progress reports. Collectively, these studies elucidate a suite of effective strategies to support advanced personalized learning via an intelligent adaptive tutor that can be tailored to the individual needs, emotions, cognitive states, and metacognitive skills of learners.