title: Download malware? no, thanks: how formal methods can block update attacks

abstract: In mobile malware landscape there are many techniques to inject malicious payload in a trusted application: one of the most common is represented by the so-called update attack. After an apparently innocuous application is installed on the victim's device, the user is asked to update the application, and a malicious behavior is added to the application. In this paper we propose a static method based on model checking able to identify this kind of attack. In addiction, our method is able to localize the malicious payload at method-level. We obtain an accuracy very close to 1 in identifying families implementing update attack using a real Android dataset composed by 2,581 samples.


title: Adaptive tuning of mutation rates allows fast response to lethal stress in Escherichia coli

abstract: A cell’s DNA can acquire errors over the course of its lifetime. These errors, known as mutations, are often harmful and can cripple the cell. However, some mutations are needed to enable a cell or organism to adapt to changes in its environment. Since there is a trade-off between acquiring beneficial mutations versus harmful ones, cells carefully balance how often they acquire new mutations. Cells have several mechanisms that limit the number of mutations by correcting errors in DNA. Occasionally these repair mechanisms may fail so that a small number of cells in a population accumulate mutations more quickly than other cells. This process is known as “hypermutation” and it enables some cells to rapidly adapt to changing conditions in order to avoid the entire population from becoming extinct. So far, studies on hypermutation have largely been carried out under conditions that are mildly stressful to the cells, which only cause low frequency of hypermutation. However, little is known about the role of this process in cells under near-lethal levels of stress, for example, when drugs target bacteria or cancer cells in the human body. Swings et al. studied hypermutation in populations of a bacterium called Escherichia coli exposed to levels of alcohol that cause the bacteria to experience extreme stress. The experiments show that hypermutation occurs rapidly in these conditions and is essential for bacteria to adapt to the level of alcohol and avoid extinction. Populations of bacteria in which hypermutation did not occur were unable to develop tolerance to the alcohol and perished. Further experiments show that an individual population of bacteria can alter the rate of mutation (that is, how often new mutations arise) several times as a result of changing stress levels. The findings of Swings et al. suggest that hypermutation can rapidly arise in populations of cells that are experiencing extreme stress. Therefore, this process may pose a serious risk in the development of drug resistant bacteria and cancer cells. In the future, developing new drugs that target hypermutation may help to fight bacterial infections and cancer.


title: Dining Cryptographers Revisited

abstract: Dining cryptographers networks (or DC-nets) are a privacy-preserving primitive devised by Chaum for anonymous message publication. A very attractive feature of the basic DC-net is its non-interactivity. Subsequent to key establishment, players may publish their messages in a single broadcast round, with no player-to-player communication. This feature is not possible in other privacy-preserving tools like mixnets. A drawback to DC-nets, however, is that malicious players can easily jam them, i.e., corrupt or block the transmission of messages from honest parties, and may do so without being traced. Several researchers have proposed valuable methods of detecting cheating players in DC-nets. This is usually at the cost, however, of multiple broadcast rounds, even in the optimistic case, and often of high computational and/or communications overhead, particularly for fault recovery. We present new DC-net constructions that simultaneously achieve non-interactivity and high-probability detection and identification of cheating players. Our proposals are quite efficient, imposing a basic cost that is linear in the number of participating players. Moreover, even in the case of cheating in our proposed system, just one additional broadcast round suffices for full fault recovery. Among other tools, our constructions employ bilinear maps, a recently popular cryptographic technique for reducing communication complexity.


title: Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

abstract: Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.


title: Online Anomaly Detection for Hard Disk Drives Based on Mahalanobis Distance

abstract: A hard disk drive (HDD) failure may cause serious data loss and catastrophic consequences. Online health monitoring provides information about the degradation trend of the HDD, and hence the early warning of failures, which gives us a chance to save the data. This paper developed an approach for HDD anomaly detection using Mahalanobis distance (MD). Critical parameters were selected using failure modes, mechanisms, and effects analysis (FMMEA), and the minimum redundancy maximum relevance (mRMR) method. A self-monitoring, analysis, and reporting technology (SMART) data set is used to evaluate the performance of the developed approach. The result shows that about 67% of the anomalies of failed drives can be detected with zero false alarm rate, and most of them can provide users with at least 20 hours during which to backup the data.


title: Decentralized control for parallel operation of distributed generation inverters using resistive output impedance

abstract: In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an ac-distributed system is proposed. This paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed, taking into account the special nature of a low-voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop-control method, the proposed controller uses resistive output impedance, and as a result, a different control law is obtained. The controller is implemented by using a digital signal processor board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6-kVA inverters connected in parallel, showing the features of the proposed wireless control


Adding Semantic Annotation to the Penn TreeBank


title: Adaptive controller for omni-directional walker: Improvement of dynamic model

abstract: An omni-directional walker (ODW) is being developed for both walking rehabilitation and walking support for people with walking disabilities. The ODW cannot accurately follow a training path planned by a physical therapist for walking rehabilitation due to the center-of-gravity shifts and load changes caused by users. To address this issue, a new center-dynamic model of the ODW is derived considering center-of-gravity shifts and load changes. An adaptive control method is shown. Comparison with a dual-loop proportional-integral (PI) controller in simulations shows that the proposed method improves the path tracking accuracy.


title: A mouse model linking viral hepatitis and salivary gland dysfunction.

abstract: Objective: Viral hepatitis is known to cause xerostomia in humans, but this has not been reported in an animal model. We report a severe, acute, highly reproducible saliva deficiency occurring in BALB/c mice as a result of experimental viral hepatitis. ::: ::: ::: ::: Materials and Methods: BALB/c mice, splenectomized or carrying genetic mutations to detect immunological contributions to the saliva deficiency syndrome, were infected intraperitoneally with a non-lethal dose of murine cytomegalovirus. Pilocarpine-stimulated saliva volumes were determined between 0 and 15 days after infection. Salivary gland, liver, spleen, and sera were analyzed for the presence of virus, cytokines, inflammatory infiltrates, and tissue damage. ::: ::: ::: ::: Results: Saliva deficiency was detectable 2 days after cytomegalovirus infection, peaked at 88% below normal by day 7, and resolved partially in all mice by 15 days postinfection as sialoadenitis increased. Neither salivary gland viral titers, sialoadenitis, splenectomy, nor systemic inflammatory markers correlated with hyposalivation severity. Elevated liver enzymes did correlate with hyposalivation, and mice genetically resistant to murine cytomegalovirus-induced hepatitis were significantly protected. ::: ::: ::: ::: Conclusions: Murine cytomegalovirus-induced salivary gland dysfunction is biphasic, with an acute hepatitis-associated phase and a later sialoadenitis-associated phase. Acute murine cytomegalovirus infection of BALB/c mice may provide a model for investigation of hepatitis-associated xerostomia.


title: Design of a 79 dB 80 MHz 8X-OSR Hybrid Delta-Sigma/Pipelined ADC

abstract: A hybrid delta-sigma/pipelined modulator is presented in this paper. The proposed modulator takes advantage of the high resolution and distributed pipelined quantization, and combines it with the noise shaping property of a delta-sigma modulator. As a result, gain, swing, and slew requirements of the integrators are significantly reduced. The modulator also makes use of the latency in the pipelined quantization to enhance noise shaping. These advantages lead to less power dissipation, increased stability, and higher resolution. The prototype chip is implemented in a 0.18 μm CMOS process. With an 80 MHz clock, and an oversampling ratio of 8 (5 MHz bandwidth), the measured dynamic range and SNDR of this prototype IC are 79 dB and 75.4 dB.


title: How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing

abstract: We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude testing and crowdsourcing settings. We devise an active learning/adaptive testing scheme based on a greedy minimization of expected model entropy, which allows a more efficient resource allocation by dynamically choosing the next question to be asked based on the previous responses. We present experimental results that confirm the ability of our model to infer the required parameters and demonstrate that the adaptive testing scheme requires fewer questions to obtain the same accuracy as a static test scenario.


title: Real time face detection/monitor using raspberry pi and MATLAB

abstract: In this paper, we are implementing facial monitoring system by embedding face detection and face tracking algorithm found in MATLAB with the GPIO pins of Raspberry pi B by using RasPi command such that the array of LEDS follows the facial movement by detecting the face using Haar classifier, tracking its position in the range assigned using the eigenfeatures of the face, which are detected by eigenvectors of MATLAB and by face tracking, which is been carried by geometrical transformation so that motion and gesture of the face can be followed. By doing so we are opening up new way of facial tracking on a live streaming by the help of Viola Jones algorithm and an IR camera.


title: Automatic Detection of Naturalistic Hand-over-Face Gesture Descriptors

abstract: One of the main factors that limit the accuracy of facial analysis systems is hand occlusion. As the face becomes occluded, facial features are either lost, corrupted or erroneously detected. Hand-over-face occlusions are considered not only very common but also very challenging to handle. Moreover, there is empirical evidence that some of these hand-over-face gestures serve as cues for recognition of cognitive mental states. In this paper, we detect hand-over-face occlusions and classify hand-over-face gesture descriptors in videos of natural expressions using multi-modal fusion of different state-of-the-art spatial and spatio-temporal features. We show experimentally that we can successfully detect face occlusions with an accuracy of 83%. We also demonstrate that we can classify gesture descriptors (hand shape, hand action and facial region occluded) significantly higher than a naive baseline. To our knowledge, this work is the first attempt to automatically detect and classify hand-over-face gestures in natural expressions.


DelugeNets: Deep Networks with Massive and Flexible Cross-layer Information Inflows


title: Health risks valuations based on public consent

abstract: Health risks for the public due to electric power generation are examined. The risk include those already known to exist due to air pollution and nuclear radiation and the newer potential risks of electromagnetic fields at power frequencies. It is shown that members of populations at risk are capable of making valuations of the worth to them of reduction of such risks. In soliciting their responses, the people are given scientific information on the nature of the hazards involved and their chances of being affected. Thus, the process is one of informed consent, preferable to having the valuation judgment imposed technocratically. The process allows a cost benefit comparison without the moral dilemma of valuing human life and suffering. >


title: Treatment of habitual snoring with botulinum toxin: a pilot study

abstract: The objective of this study was to investigate whether injections of botulinum toxin into the soft palate reduce snoring in a subgroup of patients that present an active process causing habitual snoring. The study was conducted in eight patients with habitual snoring but without evidence of obstructive sleep apnea. Polysomnography was performed for diagnostic purposes and to monitor sleep quality before and after treatment. The patients and their partners completed a questionnaire before and after treatment. Recordings of snoring noise before and after treatment were evaluated on a visual analog scale by a blinded assessor. Doses of 20 U of botulinum toxin type A (Dysport) were injected unilaterally into the muscles of the soft palate. Snoring was reduced in eight cases. The patients reported no major adverse effects. These results justify further studies of botulinum toxin therapy in patients with habitual snoring. The scheme presented for injections of botulinum toxin into the levator veli palatini muscle provides a rational basis for the design of such studies. Therapy with botulinum toxin for habitual snoring is safe, non-invasive, easy to perform, fully reversible, and thus warrants investigation under placebo-controlled, double-blind conditions. This treatment is appropriate for a disorder that is of paramount social importance but does not pose a medical threat to the individuals affected.


title: Approximate likelihood ratio test for branches: a fast, accurate and powerful alternative

abstract: We revisit statistical tests for branches of evolutionary trees reconstructed upon molecular data. A new, fast, approximate likelihood-ratio test (aLRT) for branches is presented here as a competitive alternative to nonparametric bootstrap and Bayesian estimation of branch support. The aLRT is based on the idea of the conventional LRT, with the null hypothesis corresponding to the assumption that the inferred branch has length 0. We show that the LRT statistic is asymptotically distributed as a maximum of three random variables drawn from the chi(0)2 + chi(1)2 distribution. The new aLRT of interior branch uses this distribution for significance testing, but the test statistic is approximated in a slightly conservative but practical way as 2(l1- l2), i.e., double the difference between the maximum log-likelihood values corresponding to the best tree and the second best topological arrangement around the branch of interest. Such a test is fast because the log-likelihood value l2 is computed by optimizing only over the branch of interest and the four adjacent branches, whereas other parameters are fixed at their optimal values corresponding to the best ML tree. The performance of the new test was studied on simulated 4-, 12-, and 100-taxon data sets with sequences of different lengths. The aLRT is shown to be accurate, powerful, and robust to certain violations of model assumptions. The aLRT is implemented within the algorithm used by the recent fast maximum likelihood tree estimation program PHYML (Guindon and Gascuel, 2003).


title: Entrepreneurial impact sourcing: a conceptual framework of social and commercial institutional logics

abstract: This article answers calls for better characterizations of impact sourcing given its potential to create social impacts in conjunction with global sourcing. It introduces a conceptual framework consisting of four dimensions drawn from entrepreneurship literature: primary mission, success criteria, resource mobilization and innovation approach – that characterize entrepreneurial impact sourcing service providers. These four dimensions are anchored across ideal types of social and commercial institutional logics to explicitly account for and capture the dual social and commercial value orientations of impact sourcing service providers as acknowledged in the literature. We evaluate the utility of this framework by using it to assess a US-based business process outsourcing social enterprise that focuses on the underserved workforce of military veterans and spouses of military personnel. We found that this firm used social logic for both the firm's primary mission and the firm's resource mobilization; it used commercial logic for its success criteria, and it used both logics for its innovation. Our analysis of the case substantiates the applicability of the framework for capturing variation in how impact sourcing providers may selectively draw upon different logics across the four dimensions. © 2017 John Wiley & Sons Ltd


title: Fuzzy approach for sentiment analysis

abstract: Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naive Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzy-based approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.


title: Educational games for improving the teaching-learning process of a CLIL subject: Physics and chemistry in secondary education

abstract: The use of educational games in an academic context seems to be a superb alternative to traditional learning activities, such as drill-type exercises, in order to engage 21st-century students. Consequently, this work tries to raise the following objectives: analyze the effectiveness of game-based learning, characterize game elements that may contribute to create playing experiences, comprehend how different player types interact with games and, finally, design interactive games which may create challenges, set goals and provide feedback on progress while motivating learners to study physics and chemistry in a foreign language in the second cycle of Secondary Education (4th E.S.O. that corresponds to the age of 15–16 years old). Specifically, we have used several Web 2.0 tools (Hot Potatoes, Scratch, What2Learn and SMART Notebook 11) and applications (Microsoft PowerPoint and Microsoft Excel) in order to create the games; and these games are based on the successive contents: laboratory safety, laboratory equipment, stoichiometry, atomic structure, electronic configuration, the periodic table, forces, motion and energy.


title: Sign language recognition with recurrent neural network using human keypoint detection

abstract: We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition. Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.


title: Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

abstract: In Cognitive Radio Networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utility. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. A reward of a channel is stochastic with unknown distribution, and statistically different for each user. Each user either explores a channel to learn the channel statistics, or exploits the channel with the highest expected reward based on information collected so far. Further, a channel should be accessed exclusively by one user at a time due to a collision. Using multi-armed bandit framework, we develop a provably efficient solution whose computational complexity is linear to the number of users and channels.


title: Adaptive stair-ascending and stair-descending strategies for powered lower limb exoskeleton

abstract: Enhancing the life quality for patients with spinal cord injuries (SCIs) by means of improving their mobility in daily life assisted by powered lower exoskeletons is practicable and significant. Being a part of typical daily activities, stairs with different sizes are supposed to be a usage scenario of exoskeletons aiming for extending patients' sphere of activities. This paper proposes strategies for automatically ascending/descending stairs with different sizes within the national standard based on the powered lower limb exoskeleton AIDER. The developed strategies employ infrared range sensors, foot pressure sensors and angle encoders to identify stairs whose sizes are computed based on kinematics. In addition, a methodology named “Stick From the Origin” aiming at recognizing stairs more accurately is proposed during the stair-ascending process. The Zero Moment Point (ZMP) stability criterion based on the foot pressures is utilized to ensure safety in the entire process. Experiments have been undertaken to testify the practicability and universality of the proposed strategies, by applying which AIDER can assist patients with various physiological characters to accomplish stair-climbing tasks.


title: Stress and decision making: effects on valuation, learning, and risk-taking

abstract: A wide range of stressful experiences can influence human decision making in complex ways beyond the simple predictions of a fight-or-flight model. Recent advances may provide insight into this complicated interaction, potentially in directions that could result in translational applications. Early research suggests that stress exposure influences basic neural circuits involved in reward processing and learning, while also biasing decisions toward habit and modulating our propensity to engage in risk-taking. That said, a substantial array of theoretical and methodological considerations in research on the topic challenge strong cross study comparisons necessary for the field to move forward. In this review we examine the multifaceted stress construct in the context of human decision making, emphasizing stress’ effect on valuation, learning, and risk-taking.


title: Midfield Wireless Powering for Implantable Systems

abstract: Efficient wireless power transfer across tissue is highly desirable for removing bulky energy storage components. Most existing power transfer systems are conceptually based on coils linked by slowly varying magnetic fields (less than 10 MHz). These systems have many important capabilities, but are poorly suited for tiny, millimeter-scale implants where extreme asymmetry between the source and the receiver results in weak coupling. This paper first surveys the analysis of near-field power transfer and associated strategies to optimize efficiency. It then reviews analytical models that show that significantly higher efficiencies can be obtained in the electromagnetic midfield. The performance limits of such systems are explored through optimization of the source, and a numerical example of a cardiac implant demonstrates that millimeter-sized devices are feasible.


title: Combining Hough transform and contour algorithm for detecting vehicles' license-plates

abstract: Vehicle license plate (VLP) recognition is an interesting problem that has attracted many computer vision research groups. One of the most important and difficult tasks of this problem is VLP detecting. It is not only used in VLP recognition systems but also useful for many traffic management systems. Our method is used for a VLP recognition system that deals with Vietnamese VLPs and it can also be applied to other types of VLPs with minor changes. There are various approaches to this problem, such as texture-based, morphology-based and boundary line-based. In this paper, we present the boundary line-based method that optimizes speed and accuracy by combining the Hough transform and contour algorithm. The enhancement of applying the Hough transform to contour images is the much improved speed of the algorithm. In addition, the algorithm can be used on VLP images that have been taken from various distances and have inclined angles between /spl plusmn/30/spl deg/ from the camera. Especially, it can detect plates in images which have more than one VLP. The algorithm was evaluated in two image sets with an accuracy of about 99%.


title: Anagram: A Content Anomaly Detector Resistant to Mimicry Attack

abstract: In this paper, we present Anagram, a content anomaly detector that models a mixture of high-order n-grams (n > 1) designed to detect anomalous and suspicious network packet payloads. By using higher-order n-grams, Anagram can detect significant anomalous byte sequences and generate robust signatures of validated malicious packet content. The Anagram content models are implemented using highly efficient Bloom filters, reducing space requirements and enabling privacy-preserving cross-site correlation. The sensor models the distinct content flow of a network or host using a semi-supervised training regimen. Previously known exploits, extracted from the signatures of an IDS, are likewise modeled in a Bloom filter and are used during training as well as detection time. We demonstrate that Anagram can identify anomalous traffic with high accuracy and low false positive rates. Anagram's high-order n-gram analysis technique is also resilient against simple mimicry attacks that blend exploits with normal appearing byte padding, such as the blended polymorphic attack recently demonstrated in [1]. We discuss randomized n-gram models, which further raises the bar and makes it more difficult for attackers to build precise packet structures to evade Anagram even if they know the distribution of the local site content flow. Finally, Anagram's speed and high detection rate makes it valuable not only as a standalone sensor, but also as a network anomaly flow classifier in an instrumented fault-tolerant host-based environment; this enables significant cost amortization and the possibility of a symbiotic feedback loop that can improve accuracy and reduce false positive rates over time.


title: Large Scale Novel Object Discovery in 3D

abstract: We present a method for discovering never-seen-before objects in 3D point clouds obtained from sensors like Microsoft Kinect. We generate supervoxels directly from the point cloud data and use them with a Siamese network, built on a recently proposed 3D convolutional neural network architecture. We use known objects to train a non-linear embedding of supervoxels, by optimizing the criteria that supervoxels which fall on the same object should be closer than those which fall on different objects, in the embedding space. We test on unknown objects, which were not seen during training, and perform clustering in the learned embedding space of supervoxels to effectively perform novel object discovery. We validate the method with extensive experiments, quantitatively showing that it can discover numerous unseen objects while being trained on only a few dense 3D models. We also show very good qualitative results of object discovery in point cloud data when the test objects, either specific instances or even categories, were never seen during training.


title: Do I use the wrong definition?: DeFuse: definition-use invariants for detecting concurrency and sequential bugs

abstract: Software bugs, such as concurrency, memory and semantic bugs, can significantly affect system reliability. Although much effort has been made to address this problem, there are still many bugs that cannot be detected, especially concurrency bugs due to the complexity of concurrent programs. Effective approaches for detecting these common bugs are therefore highly desired. This paper presents an invariant-based bug detection tool, DefUse, which can detect not only concurrency bugs (including the previously under-studied order violation bugs), but also memory and semantic bugs. Based on the observation that many bugs appear as violations to programmers' data flow intentions, we introduce three different types of definition-use invariants that commonly exist in both sequential and concurrent programs. We also design an algorithm to automatically extract such invariants from programs, which are then used to detect bugs. Moreover, DefUse uses various techniques to prune false positives and rank error reports. We evaluated DefUse using sixteen real-world applications with twenty real-world concurrency and sequential bugs. Our results show that DefUse can effectively detect 19 of these bugs, including 2 new bugs that were never reported before, with only a few false positives. Our training sensitivity results show that, with the benefit of the pruning and ranking algorithms, DefUse is accurate even with insufficient training.


title: Discrete Particle Swarm Optimization for the Orienteering Problem

abstract: In this paper a novel discrete Particle Swarm Optimization (PSO) algorithm is proposed to solve the Orienteering Problem (OP). Discrete evolution is achieved by re-defining all operators and operands used in PSO. To obtain better results, Strengthened-PSO which improves both exploration and exploitation during the search process is employed for experimental evaluation. Our proposed algorithm either achieves or improves the best known solutions compared to previous heuristics for the OP.


title: When agile meets the enterprise

abstract: Context: While renowned agile methods like XP and Scrum were initially intended for projects with small teams, traditional enterprise environments, i.e. environments where plan-driven development is prevalent, have also become attracted by the promises of a faster time to market through agility. Agile software development methods emphasize lightweight software development. Projects within enterprise environments, however, are typically confronted with a large and complex IT landscape, where mission-critical information is at play whose criticality requires prudence regarding design and development. In many an organization, both approaches are used simultaneously. Objective: Find out which challenges the co-existence of agile methods and plan-driven development brings, and how organizations deal with those challenges. Method: We present a grounded theory of the challenges of using agile methods in traditional enterprise environments, based on a Grounded Theory research involving 21 agile practitioners from two large enterprise organizations in the Netherlands. Results: We organized the challenges under two factors: Increased landscape complexity and Lack of business involvement. For both factors, we identify successful mitigation strategies. These mitigation strategies concern the communication between the agile and traditional part of the organization, and the timing of that communication. Conclusion: Agile practices can coexist with plan-driven development. One should, however, keep in mind the context and take actions to mitigate the challenges incurred.


title: 802.11 Disassociation DoS Attack and Its Solutions: A Survey

abstract: Wireless local area networks provide the connectivity at low cost with greater flexibility and mobility. IEEE 802.11 is the defacto wireless standard. It was designed to provide security however it failed to do so. As a result amendments/enhancements were made to overcome the flaws by first introducing Wi-Fi protected access (WPA) and then IEEE 802.11i. But both these enhancements failed to achieve the desired objectives especially availability. Unauthenticated management and control frames provide no protection against identity theft and are main cause of denial of service (DoS) attacks. Spoofed disassociation messages can successfully be used to launch DoS attacks. In this paper we have explained the DoS attack and carried out a survey / analysis of various solutions for this attack.


title: Knowledge and Practices Related to Screening for Breast Cancer among Women in Delhi, India

abstract: Background: Breast cancer is a major public health problem globally. The ongoing epidemiological, socio-cultural ::: and demographic transition by accentuating the associated risk factors has disproportionately increased the incidence ::: of breast cancer cases and resulting mortality in developing countries like India. Early diagnosis with rapid initiation ::: of treatment reduces breast cancer mortality. Therefore awareness of breast cancer risk and a willingness to undergo ::: screening are essential. The objective of the present study was to assess the knowledge and practices relating to screening ::: for breast cancer among women in Delhi. Methods: Data were obtained from 222 adult women using a pretested selfadministered ::: questionnaire. Results: Rates for knowledge of known risk factors of breast cancer were: family history ::: of breast cancer, 59.5%; smoking, 57.7%; old age, 56.3%; lack of physical exercise, 51.9%; lack of breastfeeding, ::: 48.2%; late menopause, 37.4%; and early menarche, 34.7%. Women who were aged < 30 and those who were unmarried ::: registered significantly higher knowledge scores (p ≤ 0.01). Breast self-examination (BSE) was regularly practiced ::: at-least once a month by 41.4% of the participants. Some 48% knew mammography has a role in the early detection ::: of breast cancer. Since almost three-fourths of the participants believed BSE could help in early diagnosis of breast ::: cancer, which is not supported by evidence, future studies should explore the consequences of promoting BSE at the ::: potential expense of screening mammography. Conclusion: Our findings highlight the need for awareness generation ::: among adult women regarding risk factors and methods for early detection of breast cancer.


title: Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time

abstract: Bottom-up and top-down, as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analysing their influence over time. For this purpose we develop a saliency model which is based on the internal representation of a recent early spatial vision model to measure the low-level bottom-up factor. To measure the influence of high-level bottom-up features, we use a recent DNN-based saliency model. To account for top-down influences, we evaluate the models on two large datasets with different tasks: first, a memorisation task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: The first saccade, an initial guided exploration characterised by a gradual broadening of the fixation density, and an steady state which is reached after roughly 10 fixations. Saccade target selection during the initial exploration and in the steady state are related to similar areas of interest, which are better predicted when including high-level features. In the search dataset, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties and as early as 200 ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later this high-level bottom-up control can be overruled by top-down influences.


title: Exploring human mental models of robots through explicitation interviews

abstract: This paper presents the findings of a qualitative study exploring how mental models of a mechanoid robot using dog-inspired affective cues behaviour emerges and impacts the evaluation of the robot after the viewing of a video of an assistive robotics scenario interaction with the robot. It discusses this using contrasting case studies based on the analysis of explicitation interviews with three participants. The analysis suggests that while for some users zoomorphic cues may aid in initial interactions, they need to be framed in an authentic interaction, highlighting the actual capabilities of the robot as a technological artifact, and how these impact the everyday life and interests of the potential user.


title: A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns

abstract: Framing is a sophisticated form of discourse in which the speaker tries to induce a cognitive bias through consistent linkage between a topic and a specific context (frame). We build on political science and communication theory and use probabilistic topic models combined with time series regression analysis (autoregressive distributed-lag models) to gain insights about the language dynamics in the political processes. Processing four years of public statements issued by members of the U.S. Congress, our results provide a glimpse into the complex dynamic processes of framing, attention shifts and agenda setting, commonly known as ‘spin’. We further provide new evidence for the divergence in party discipline in U.S. politics.


title: User Identity Linkage across Online Social Networks: A Review

abstract: The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.


title: A 94-GHz passive imaging receiver using a balanced LNA with embedded Dicke switch

abstract: A fully-integrated silicon-based 94-GHz direct-detection imaging receiver with on-chip Dicke switch and baseband circuitry is demonstrated. Fabricated in a 0.18-µm SiGe BiCMOS technology (f T /f MAX = 200 GHz), the receiver chip achieves a peak imager responsivity of 43 MV/W with a 3-dB bandwidth of 26 GHz. A balanced LNA topology with an embedded Dicke switch provides 30-dB gain and enables a temperature resolution of 0.3–0.4 K. The imager chip consumes 200 mW from a 1.8-V supply.


title: Natural history and predictors of disease severity in chronic hepatitis C.

abstract: Cirrhosis is the end-stage consequence of fibrosis progression in patients with chronic hepatitis C. The median time from infection to cirrhosis is 30 years, with a high inter-individual variability, which is now better understood. Several factors have been clearly shown to be associated with fibrosis progression rate: duration of infection, age, male gender, alcohol consumption, HIV co-infection and low CD4 count. Metabolic conditions such as steatosis, being overweight and diabetes are emerging as independent co-factors of fibrogenesis. The recent validation of non-invasive biomarkers should facilitate the study of fibrosis progression in large populations.


Interactive Depth of Field Using Simulated Diffusion on a GPU


title: Assessing Dimensions of Pathological Narcissism: Psychometric Properties of the Short Form of the Five-Factor Narcissism Inventory in a Sample of Italian University Students

abstract: ABSTRACTTo evaluate the psychometric properties of the Italian translation of the Five-Factor Narcissism Inventory–Short Form (FFNI–SF), 1,063 Italian university students were administered the scale, along with the Pathological Narcissism Inventory (PNI) and the Measure of Disordered Personality Functioning (MDPF). In general, the FFNI–SF scales showed good internal consistency (median α = .76; median interitem r = .44) and structural analyses suggested a 3-component model of FFNI–SF scales that was similar to that reported by Sherman et al. (2015). FFNI–SF second-order scales yielded meaningful convergent and discriminant correlations with PNI second-order grandiose and vulnerable dimensions. Finally, FFNI–SF second-order scales correlated in expected ways with 2 dimensions of personality dysfunction.


title: Knowledge acquisition for adaptive game AI

abstract: Game artificial intelligence (AI) controls the decision-making process of computer-controlled opponents in computer games. Adaptive game AI (i.e., game AI that can automatically adapt the behaviour of the computer players to changes in the environment) can increase the entertainment value of computer games. Successful adaptive game AI is invariably based on the game's domain knowledge. We show that an offline evolutionary algorithm can learn important domain knowledge in the form of game tactics (i.e., a sequence of game actions) for dynamic scripting, an offline algorithm inspired by reinforcement learning approaches that we use to create adaptive game AI. We compare the performance of dynamic scripting under three conditions for defeating non-adaptive opponents in a real-time strategy game. In the first condition, we manually encode its tactics. In the second condition, we manually translate the tactics learned by the evolutionary algorithm, and use them for dynamic scripting. In the third condition, this translation is automated. We found that dynamic scripting performs best under the third condition, and both of the latter conditions outperform manual tactic encoding. We discuss the implications of these results, and the performance of dynamic scripting for adaptive game AI from the perspective of machine learning research and commercial game development.


title: A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret

abstract: We consider algorithms for “smoothed online convex optimization” (SOCO) problems, which are a hybrid between ::: online convex optimization (OCO) and metrical task system ::: (MTS) problems. Historically, the performance metric ::: for OCO was regret and that for MTS was competitive ratio ::: (CR). There are algorithms with either sublinear regret or ::: constant CR, but no known algorithm achieves both simultaneously. We show that this is a fundamental limitation – no algorithm (deterministic or randomized) can achieve sublinear regret and a constant CR, even when the objective functions are linear and the decision space is one dimensional. However, we present an algorithm that, for the important one dimensional case, provides sublinear regret and a CR that grows arbitrarily slowly.


title: Put Your Money Where Your Mouth Is! Explaining Collective Action Tendencies Through Group-Based Anger and Group Efficacy.

abstract: Insights from appraisal theories of emotion are used to integrate elements of theories on collective action. Three experiments with disadvantaged groups systematically manipulated procedural fairness (Study 1), emotional social support (Study 2), and instrumental social support (Study 3) to examine their effects on collective action tendencies through group-based anger and group efficacy. Results of structural equation modeling showed that procedural fairness and emotional social support affected the group-based anger pathway (reflecting emotion-focused coping), whereas instrumental social support affected the group efficacy pathway (reflecting problem-focused coping), constituting 2 distinct pathways to collective action tendencies. Analyses of the means suggest that collective action tendencies become stronger the more fellow group members “put their money where their mouth is.” The authors discuss how their dual pathway model integrates and extends elements of current approaches to collective action.


Job Seekers' Acceptance of Job Recommender Systems: Results of an Empirical Study


A structured framework to assess the business application landscape of blockchain technologies.


title: Filtering technique in ultrasound for kidney, liver and pancreas image using Matlab

abstract: Ultrasound is a well-known medical imaging modality on its ability to visualize kidney, liver and pancreas accurately. However, speckle noise, caused by constructive and destructive interferences of the wavelets will disturb the image of the organs. The main goal of this paper is to identify the most suitable and reasonable filtering technique for each organ. 50 ultrasound images for each organ- kidney, liver and pancreas are taken using Toshiba Nemio 10 ultrasound machine with 3.75 MHz PVM-375AT transducer. The images are cropped into specific sizes before converted into grayscale image. Each of the images then filtered using median filter, Wiener filter, 2-D order-statistic filter, N-D filter, and Entropy filter. Performance of each filter are analysed with MSE and PSNR. The results show that Median filter is the most suitable filter for kidney and pancreas while Wiener filter is the most effective filter in removing noise for liver.


The heel effect.


title: Chinese–Spanish neural machine translation enhanced with character and word bitmap fonts

abstract: Recently, machine translation systems based on neural networks have reached state-of-the-art results for some pairs of languages (e.g., German–English). In this paper, we are investigating the performance of neural machine translation in Chinese–Spanish, which is a challenging language pair. Given that the meaning of a Chinese word can be related to its graphical representation, this work aims to enhance neural machine translation by using as input a combination of: words or characters and their corresponding bitmap fonts. The fact of performing the interpretation of every word or character as a bitmap font generates more informed vectorial representations. Best results are obtained when using words plus their bitmap fonts obtaining an improvement (over a competitive neural MT baseline system) of almost six BLEU, five METEOR points and ranked coherently better in the human evaluation.


title: A data mining approach to predict forest fires using meteorological data

abstract: Forest fires are a major environmental issue, creating economical and ecological damage while endangering human lives. Fast detection is a key element for controlling such phenomenon. To achieve this, one alternative is to use automatic tools based on local sensors, such as provided by meteorological stations. In effect, meteorological conditions (e.g. temperature, wind) are known to influence forest fires and several fire indexes, such as the forest Fire Weather Index (FWI), use such data. In this work, we explore a Data Mining (DM) approach to predict the burned area of forest fires. Five different DM techniques, e.g. Support Vector Machines (SVM) and Random Forests, and four distinct feature selection setups (using spatial, temporal, FWI components and weather attributes), were tested on recent real-world data collected from the northeast region of Portugal. The best configuration uses a SVM and four meteorological inputs (i.e. temperature, relative humidity, rain and wind) and it is capable of predicting the burned area of small fires, which are more frequent. Such knowledge is particularly useful for improving firefighting resource management (e.g. prioritizing targets for air tankers and ground crews).


title: Fully automatic pose-invariant face recognition via 3D pose normalization

abstract: An ideal approach to the problem of pose-invariant face recognition would handle continuous pose variations, would not be database specific, and would achieve high accuracy without any manual intervention. Most of the existing approaches fail to match one or more of these goals. In this paper, we present a fully automatic system for pose-invariant face recognition that not only meets these requirements but also outperforms other comparable methods. We propose a 3D pose normalization method that is completely automatic and leverages the accurate 2D facial feature points found by the system. The current system can handle 3D pose variation up to ±45° in yaw and ±30° in pitch angles. Recognition experiments were conducted on the USF 3D, Multi-PIE, CMU-PIE, FERET, and FacePix databases. Our system not only shows excellent generalization by achieving high accuracy on all 5 databases but also outperforms other methods convincingly.


title: Camouflage Treatment of a Severe Open Bite Case

abstract: Skeletal open bite is a complex and multifactorial anomaly. There are many reasons for the occurrence of open bite, including skeletal abnormal growth pattern; dental, respiratory, neurologic, and habitual factors; and tongue posture and function. Anterior open bite is one of the most difficult problems to treat in orthodontics. Various treatment modalities for the correction of an anterior open bite have been proposed such as extrusion of anterior teeth using intermaxillary elastics, multiloop edgewise archwire technique developed by Kim, inhibition of molar eruption using bite blocks and high-pull headgear during growth, and extraction therapy. The most effective treatment option in adult patients is the repositioning of both the maxilla and mandible through a surgical correction. Although satisfactory results can be achieved with orthognathic surgery, the complexity, risks and costs of surgery are disadvantages of the surgical treatment. With the advent of skeletal anchorage, a number of studies were released to introduce the treatment of anterior open bite by intrusion of maxillary posterior teeth. This report presents the treatment and long-term stability of an adult case of a severe anterior open bite treated by means of extraction of the 4 first molars.


Exploiting Linguistic Features for Sentence Completion


title: Business Process Analytics

abstract: Business Process Management systems (BPMS) are a rich source of events that document the execution of processes and activities within these systems. Business Process Management analytics is the family of methods and tools that can be applied to these event streams in order to support decision making in organizations. The analysis of process events can focus on the behavior of completed processes, evaluate currently running process instances, or focus on predicting the behavior of process instances in the future. This chapter provides an overview of the different methods and technologies that can be employed in each of these three areas of process analytics. We discuss the underlying format and types of process events as the common source of analytics information, present techniques for the aggregation and composition of these events, and outline methods that support backward- and forward-looking process analytics.


title: Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems

abstract: High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.


title: Efficient human activity recognition using hyperdimensional computing

abstract: Human activity recognition is a key task of many Internet of Things (IoT) applications to understand underlying contexts and react with the environments. Machine learning is widely exploited to identify the activities from sensor measurements, however, they are often overcomplex to run on less-powerful IoT devices. In this paper, we present an alternative approach to efficiently support the activity recognition tasks using brain-inspired hyperdimensional (HD) computing. We show how the HD computing method can be applied to the recognition problem in IoT systems while improving the accuracy and efficiency. In our evaluation conducted for three practical datasets, the proposed design achieves the speedup of the model training by up to 486x as compared to the state-of-the-art neural network training. In addition, our design improves the performance of the HD-based inference procedure by 7x on a low-power ARM processor.


title: Soft Assurance: Coping with Uncertainty Through Haptic Sensations

abstract: Uncertainty is an inescapable element of human life. But how do people deal with it? To date, most research has focused on the cognitive strategies people adopt to do so. In four experiments we examine, whether people may also use an alternative experiential route to cope with uncertainty. We demonstrate that (1) when faced with uncertainty, people seek soft haptic sensations (Experiments 1 and 2) and (2) that doing so is functional (Experiments 3 and 4). More specifically, we show that people shift their preference to objects with soft (i.e., soft-grip pen, soft candy) rather than hard properties (i.e., hard-grip pen, hard candy) when feeling uncertain. Furthermore, we show that holding something soft (i.e., a soft-grip pen, a soft cloth) as compared to something hard (i.e., a hard-grip pen, a hard cloth) reduces uncertainty on a subsequent ambiguous task and helps to shield against uncertainty in daily life by increasing tolerance towards uncertainty. Overall, this research reveals that humans may use their oldest and most fundamental sense – touch – as a basic experiential device to cope with uncertainty.


title: Learning a Convolutional Neural Network for Image Compact-Resolution

abstract: We study the dual problem of image super-resolution (SR), which we term image compact-resolution (CR). Opposite to image SR that hallucinates a visually plausible high-resolution image given a low-resolution input, image CR provides a low-resolution version of a high-resolution image, such that the low-resolution version is both visually pleasing and as informative as possible compared to the high-resolution image. We propose a convolutional neural network (CNN) for image CR, namely, CNN-CR, inspired by the great success of CNN for image SR. Specifically, we translate the requirements of image CR into operable optimization targets for training CNN-CR: the visual quality of the compact resolved image is ensured by constraining its difference from a naively downsampled version and the information loss of image CR is measured by upsampling/super-resolving the compact-resolved image and comparing that to the original image. Accordingly, CNN-CR can be trained either separately or jointly with a CNN for image SR. We explore different training strategies as well as different network structures for CNN-CR. Our experimental results show that the proposed CNN-CR clearly outperforms simple bicubic downsampling and achieves on average 2.25 dB improvement in terms of the reconstruction quality on a large collection of natural images. We further investigate two applications of image CR, i.e., low-bit-rate image compression and image retargeting. Experimental results show that the proposed CNN-CR helps achieve significant bits saving than High Efficiency Video Coding when applied to image compression and produce visually pleasing results when applied to image retargeting.


title: Ensuring performance measurement integrity in logistics using blockchain

abstract: Information and communication technology has been a positive dynamic force behind improving performance. Simultaneously enabling digital business ecosystems to become an increasingly popular concept for modelling and building distributed systems in heterogeneous, decentralized and open environments. These business solutions have created an opportunity for automated business relations and transactions as well as implementing blockchain based performance measurement systems (PMS), allowing PMS to be more advanced giving validation and verification of received data hence the indicators. This paper evaluates feasibility of blockchain based PMS for logistics industry. Today the performance of supply chain stakeholders including transport logistics is lacking transparency and restraining innovative digital improvements towards a connected, smart and efficient ecosystem. Current public blockchains are not feasible for given use case due to limited throughput, and transaction cost. In this paper we have evaluated different technological alternatives and found several new developments that make blockchain technology suitable for ensuring logistics sector performance measurement integrity. Validated indicators can add trustworthiness and visibility to management and process improvement decisions, as well as be subject to smart contracts and monetary transactions.


title: Myofascial Trigger Point: Symptoms, Diagnosis, Intervention

abstract: It is acknowledged in medicine that being physically active has overall positive health benefits by reducing the risk of chronic health problems such as cardiovascular disease, diabetes, some forms of cancer, osteoporosis, obesity, falls and fractures, and some mental health problems (Brukner & Brown, 2005; Owen et al., 2007). However, some of us take it to the extreme in sports, whilst others need to be persuaded to be active on urgent medical advice. Even so, regardless of our level of activity or occupation, most of us would have experienced musculoskeletal (MSK) pain at one point or another. It is estimated that between 13.5 and 47% of the general population have MSK pain (Cimmino & Ferrone, 2011; Fleckenstein et al., 2010). The threat of MSK pain can prevent us from enjoying daily physical activities. With significant implications for public health and wellbeing, MSK injury prevention, diagnosis, intervention, rehabilitation occupies an important space in the general population and contemporary sports medicine.


title: Vision based distance measurement system using single laser pointer design for underwater vehicle

abstract: As part of a continuous research and development of underwater robotics technology at ITB, a vision- based distance measurement system for an Unmanned Underwater vehicle (UUV) has been designed. The proposed system can be used to predict horizontal distance between underwater vehicle and wall in front of vehicle. At the same time, it can be used to predict vertical distance between vehicle and the surface below it as well. A camera and a single laser pointer are used to obtain data needed by our algorithm. The vision-based navigation consists of two main processes which are the detection of a laser spot using image processing and the calculation of the distance based on laser spot position on the image.


title: A comparison of pilot-aided channel estimation methods for OFDM systems

abstract: This paper deals with the estimation of the channel impulse response (CIR) in orthogonal frequency division multiplexed (OFDM) systems. In particular, we focus on two pilot-aided schemes: the maximum likelihood estimator (MLE) and the Bayesian minimum mean square error estimator (MMSEE). The advantage of the former is that it is simpler to implement as it needs no information on the channel statistics. On the other hand, the MMSEE is expected to have better performance as it exploits prior information about the channel. Theoretical analysis and computer simulations are used in the comparisons. At SNR values of practical interest, the two schemes are found to exhibit nearly equal performance, provided that the number of pilot tones is sufficiently greater than the CIRs length. Otherwise, the MMSEE is superior. In any case, the MMSEE is more complex to implement.


title: An efficient approach for clustering face images

abstract: Investigations that require the exploitation of large volumes of face imagery are increasingly common in current forensic scenarios (e.g., Boston Marathon bombing), but effective solutions for triaging such imagery (i.e., low importance, moderate importance, and of critical interest) are not available in the literature. General issues for investigators in these scenarios are a lack of systems that can scale to volumes of images of the order of a few million, and a lack of established methods for clustering the face images into the unknown number of persons of interest contained in the collection. As such, we explore best practices for clustering large sets of face images (up to 1 million here) into large numbers of clusters (approximately 200 thousand) as a method of reducing the volume of data to be investigated by forensic analysts. Our analysis involves a performance comparison of several clustering algorithms in terms of the accuracy of grouping face images by identity, run-time, and efficiency in representing large datasets of face images in terms of compact and isolated clusters. For two different face datasets, a mugshot database (PCSO) and the well known unconstrained dataset, LFW, we find the rank-order clustering method to be effective in clustering accuracy, and relatively efficient in terms of run-time.


title: Essential Oils and Future Antibiotics: New Weapons against Emerging ‘Superbugs’?

abstract: Antibiotic resistance is emerging at an alarming rate, outpacing current research and development efforts to combat this trend. As a result, many infectious diseases have become difficult to treat; in some cases, no treatment options exist. The search for new antibiotics must accelerate to avoid returning to the ‘pre-antibiotic’ era. Ancient remedies, including essential oils and their components, have been explored on a limited basis as a source of new antimicrobials. Many are known to possess significant antimicrobial activity against a wide range of microorganisms. Elucidation of the mechanism of action of these compounds may lead to identification new antibiotic targets. Such targets, once identified, may represent biosynthetic or regulatory pathways not currently inhibited by available drugs. Novel drugs and targets are vital for continued control of infectious diseases worldwide.


title: Robust hand tracking by integrating appearance, location and depth cues

abstract: In this paper, a robust hand tracking algorithm is proposed for 3D sign language recognition application. The most challenging problems in hand tracking are the complex background, various illumination and articulated hand motion. In our work, based on the color and depth information simultaneously, two hands are well tracked by similarity optimization framework. However, in the fusion procedure, one important problem must be considered is that the data from the color and the depth channels are not always synchronous for the hardware reason. A two-layer difference comparison scheme is presented to determine whether the color and depth data are consistent. According to this consistency determination, the depth data can be used confidently or be deserted. Experiments on 300 sign language videos convincingly show the accuracy and robustness of the proposed hand tracking method. The visualized results also show the good performance even for the complex two-hand overlapping situations.


title: Cascaded seven level inverter with reduced number of switches using level shifting PWM technique

abstract: A multilevel inverter is a power electronic device that is used for high voltage and high power applications and has many advantages like, low switching stress, low total harmonic distortion (THD). Hence, the size and bulkiness of passive filters can be reduced. This paper proposes two new topologies of a 7-level cascaded multilevel inverter with reduced number of switches than that of conventional type which has 12 switches. The topologies consist of circuits with 9 switches and 7 switches for the same 7-level output. Therefore with less number of switches, there will be a reduction in gate drive circuitry and also very few switches will be conducting for specific intervals of time. The SPWM technique is implemented using multicarrier wave signals. Level Shifted triangular waves are used in comparison with sinusoidal reference to generate Sine PWM switching sequence. The number of level shifted triangular waves depends on the number of levels in the output. i.e. for n levels, n-1 number of carrier waves. This paper uses 1 KHz SPWM pulses with a modulation index of 0.8. The circuits are simulated using SPWM technique and the effect of the harmonic spectrum is analyzed. A comparison is made for the topologies with 9 switches and 7 switches and an effective reduction in THD has been observed for the circuits with less number of switches. The THD for 9 switches is 14% and the THD for 7 switches is 12.5%. The circuits are modeled and simulated with the help of MATLAB/SIMULINK.


title: The Relationship between Organization Culture and Knowledge sharing towards Business System Success

abstract: Understanding the business systems success factors has been a challenging process for both public and private organizations. Organizational culture is measured as a critical factor promoting knowledge sharing among employees. Based on the competing value framework (CVF), this research shows how various dimensions of organization culture influence knowledge sharing towards business systems success at the individual and organization level. A quantitative approach was applied to test the relationship between organizational cultures, knowledge sharing and business systems success in Saudi enterprise.


title: Doctors and the Dangers of Driving

abstract: Automobiles have been the subject of more than a century's worth of varied public health warnings in the Journal. Doctors have remained ambivalent, however, about taking aggressive action to combat the dangers of driving.


title: Analysis and parameters optimization of a contactless IPT system for EV charger

abstract: This paper discusses the characteristics of a series-series compensated inductive power transfer system (IPT) with theoretical analysis and experimental results. To maximize system efficiency, two-stage structure, which includes a PFC stage and a resonant DC-DC stage operating in ZVS region, is proposed. One of the major design challenges in implementation of a practical contactless EV charger is the variation of the coupling condition of the loosely coupled transformer. This combined with the battery charger's wide range load variation makes parameters design of the resonant DC-DC stage more complex. To optimize the parameter design for all coupling and load conditions, a parameter sweeping method is proposed. The design procedure searches parameters set to minimize the averaged primary current while keeping the voltage stress across the primary capacitor below the preset limit; both the high coupling and low coupling conditions are considered. To validate the analysis, a 4 kW resonant DC-DC prototype was built and tested. At 4 cm gap distance with 0.526 coupling coefficient, an efficiency of 97% for the DC-DC stage was achieved with output power range from 0.8 kW to 4 kW.


title: Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

abstract: Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.


title: Real-time MTPA and field-weakening method for IPMSM in the full speed region

abstract: IPMSM can be operated with its full potential using MTPA and field weakening. Generally, it is not determined which motor is connected to inverter. Therefore, among various methods for MTPA and field weakening, LUT(Look-Up Table) generation test in consideration of inductance change by its saturation is not feasible. In this paper, real-time calculation of current reference is addressed in the full speed region including MTPA and field weakening. Two-dimensional Newton-Raphson method is used to get the solution.


title: Learning Vertex Representations for Bipartite Networks

abstract: Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages.


title: NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler

abstract: Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points. Furthermore, they typically require large networks parameterized by many weights, which makes them hard to train. In this paper, we propose an auto-encoder architecture that can both encode and decode clouds of arbitrary size and demonstrate its effectiveness at upsampling sparse point clouds. Interestingly, we can do so using less than half as many parameters as state-of-the-art architectures while still delivering better performance. We will make our code base fully available.


title: Symbol Emergence in Robotics: A Survey

abstract: Humans can learn a language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form symbol systems and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted regarding the construction of robotic systems and machine learning methods that can learn a language through embodied multimodal interaction with their environment and other systems. Understanding human?-social interactions and developing a robot that can smoothly communicate with human users in the long term require an understanding of the dynamics of symbol systems. The embodied cognition and social interaction of participants gradually alter a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER represents a constructive approach towards a symbol emergence system. The symbol emergence sys...


title: A new stacked NPC converter: 3L-topology and control

abstract: In this paper a new three-level (3L) voltage source converter is presented. This new topology called Stacked Neutral Point Clamped (3L-SNPC) increases the apparent switching frequency compared with other 3L structures (3L-Stacked Cells, 3L-Neutral Point Clamped and 3L-Active NPC). This advantage leads to a better balancing of total losses in power switches under all specified operating conditions (low or high modulation index). The topology has more degrees of freedom and allows a parallel conversion. The load current passes simultaneously or alternatively through two parallel paths, depending on PWM strategy. In the paper three PWM strategies are proposed and their switching states and commutations are analyzed. The total losses in power switches are calculated and compared to the most popular 3L structures. Experimental and simulation results are illustrated in the paper to confirm the operation of the proposed topology.


title: Journal of Documentation (2004), 60(3), 336-345. MULTITASKING INFORMATION BEHAVIOR AND INFORMATION TASK SWITCHING: AN EXPLORATORY STUDY

abstract: Recent studies show that humans engage in multitasking information behaviors, often in libraries, as they seek and search for information on more than one information task. Multitasking information behaviors may consist of library search and use behaviors, or database or Web search sessions on multiple information tasks. However, few human information behavior models of seeking, searching or use, or library use models, include considerations of multitasking information behavior. This paper reports results from a case study exploring multitasking information behavior by an information seeker in a public library using diary, observation and interview data collection techniques. The information seeker sought information on four unrelated personal information tasks during two public library visits. Findings include a taxonomy of information behaviors; a sequential flowchart of the information seeker's complex and iterative processes, including multitasking information behavior, electronic searches, physical library searches, serendipitous browsing, and successive searches; and that the information seeker engaged in a process of 17 information task switches over two library visits. A model of information multitasking and information task switching is presented. Implications for library services and bibliographic instruction are also discussed.


title: A secure location verification method for ADS-B

abstract: At the heart of the NextGen Air Traffic Control System, there is the ADS-B (Automatic Dependent Surveillance — Broadcast) that provides aircraft location data. Despite its importance, ADS-B has been developed without consideration of security risks and is subject to various types of attacks. Although a number of techniques have been proposed to protect ADS-B, there are many challenges remaining for applying them to real world. We have developed a practical method that can reject virtually all spoofed ADS-B messages by monitoring the radio propagation time between senders and receivers. To measure the actual propagation time, it uses a small timestamp value; hence we call it ADS-B with Timestamp (ADS-BT). ADS-BT monitors the discrepancy between the time of flight based on the timestamp values and the time of flight based on the location data. In spoofed ADS-B messages, the discrepancy between them diverges over time, which allows us to identify spoofed ADS-B messages accurately. Our study shows a 99% detection rate within a few seconds and 100% rate within 25 seconds, with 0% false positive ratio. ADS-BT does not require any special hardware or 3rd party stations to collaborate. It does not require synchronized clocks or cryptographic processing. In this paper, we describe how the time discrepancies are measured, how the timestamps are implemented and used, and how the spoofed message frames are rejected passively and actively. We then demonstrate its performance through simulation study under various attack scenarios. If ADS-BT is adopted in the NextGen ATC, the security risk for the ADS-B can be substantially reduced.


title: COBAYN: Compiler Autotuning Framework Using Bayesian Networks

abstract: The variety of today’s architectures forces programmers to spend a great deal of time porting and tuning application codes across different platforms. Compilers themselves need additional tuning, which has considerable complexity as the standard optimization levels, usually designed for the average case and the specific target architecture, often fail to bring the best results. This article proposes COBAYN: Compiler autotuning framework using BAYesian Networks, an approach for a compiler autotuning methodology using machine learning to speed up application performance and to reduce the cost of the compiler optimization phases. The proposed framework is based on the application characterization done dynamically by using independent microarchitecture features and Bayesian networks. The article also presents an evaluation based on using static analysis and hybrid feature collection approaches. In addition, the article compares Bayesian networks with respect to several state-of-the-art machine-learning models. Experiments were carried out on an ARM embedded platform and GCC compiler by considering two benchmark suites with 39 applications. The set of compiler configurations, selected by the model (less than 7p of the search space), demonstrated an application performance speedup of up to 4.6 × on Polybench (1.85 × on average) and 3.1 × on cBench (1.54 × on average) with respect to standard optimization levels. Moreover, the comparison of the proposed technique with (i) random iterative compilation, (ii) machine learning--based iterative compilation, and (iii) noniterative predictive modeling techniques shows, on average, 1.2 × , 1.37 × , and 1.48 × speedup, respectively. Finally, the proposed method demonstrates 4 × and 3 × speedup, respectively, on cBench and Polybench in terms of exploration efficiency given the same quality of the solutions generated by the random iterative compilation model.


CHAPTER TWO Self-Distancing : Theory , Research , and Current Directions


title: New edge directed interpolation

abstract: This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.


title: 3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels

abstract: RGB-D data is getting ever more interest from the research community as both cheap cameras appear in the market and the applications of this type of data become more common. A current trend in processing image data is the use of convolutional neural networks (CNNs) that have consistently beat competition in most benchmark data sets. In this paper, we investigate the possibility of transferring knowledge between CNNs when processing RGB-D data with the goal of both improving accuracy and reducing training time. We present experiments that show that our proposed approach can achieve both these goals.


Polynomials Approximating Complex Functions


title: Analysis of short circuit type II and III of high voltage SiC MOSFETs with fast current source gate drive principle

abstract: The Silicon Carbide (SiC) MOSFET is considered to be the leading candidate for future 1.7 kV and 3.3 kV switches in 2-level voltage source converters (VSC) up to 2 MW. For those converters, short circuit (SC) in the dc-link loop can occur due to a number of reasons, e.g. faulty semiconductor modules, faulty gate drivers (GDs), or electro-magnetic interference (EMI). Termination of such SCs is important in order to protect components and reduce the damage in the converter box. This paper presents a new short circuit protection scheme based on a universal current-source GD principle without dedicated hardware components. The performance of the design is evaluated for SC in the dc-link loop under load conditions, called type II and type III. Moreover, measurement results are presented using the proposed GD connected to a 1700 V 300 A SiC MOSFET tested during SC type II and III at two different dc-link stray inductances, 30 nH and 100 nH, and at two different temperatures, 25 °C and 125 °C. The conclusions are that the proposed scheme is able to terminate both SC type II and III with fast reaction time, with low energy dissipation, with a margin of about 15 times below the destructive level for dc-link voltages and load currents up to 1050 V and 300 A respectively.


Software Defect Reduction Top 10 List


title: Working Paper: Please do not quote or cite without permissionTurning Visitors Into Customers: A Usability-Centric Perspective on Purchase Behavior in Electronic Channels

abstract: We develop a theoretical model for predicting purchase behavior in electronic channels. The model suggests that website use (i.e., technology use), a key indicator of the degree to which a site is sticky, is a significant antecedent of purchase behavior. Furthermore, we relate the usability of a website to use behavior and purchase behavior. Specifically, individual characteristics and product type are argued to differentially influence the weights that customers place on five different categories of usability. The weighted ratings of the five categories together determine use behavior and purchase behavior, after controlling for purchase need, experience with similar sites, and previous purchase on the specific sites. The model was tested in a longitudinal field study among 757 customers who provided usability assessments for multiple websites from four different industriesi.e., airlines, online bookstores, automobile manufacturers, and car rental agencies. Six months later, 370 of these individuals provided responses to help understand the transition from visitor to customer, i.e., whether they actually transacted with a specific website. Results provided strong support for the model and yield important theoretical and practical implications.


title: High-Efficiency Wideband 3-D On-Chip Antennas for Subterahertz Applications Demonstrated at 200 GHz

abstract: This work presents an efficient method for realizing a laterally radiating 3-D monopole antenna for millimeter-wave and terahertz applications. The vertical antenna rod is formed with a standard $\text{17-}{\mu} \text{m}$ aluminum wire on a programmable semiautomatic ultrasonic wedge bonder, which allows for a good reproducibility. The key features are a high gain of up to 4.9 dBi, a ${\text{360}}^{\circ }$ azimuthal coverage, an estimated radiation efficiency of around 90%, a uniform behavior over a wide frequency range of 60 GHz, and a low chip area requirement of ${\text{0.06}}\ \text{mm}^{2}$ . The on-chip structure offers the unique ability to be alternatively utilized for direct characterization with standard measurement probes. In addition, an analytic model is derived and compared to electromagnetic field simulations. To verify the radiation performance, a physical transmission link is demonstrated for distances between 5 and 15 mm and for a frequency ranging from 140 to 220 GHz. The obtained results agree well with the theoretical and simulated estimations.


title: Addressing data veracity in big data applications

abstract: Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. Such scenarios lead to partial data problem and raise the issue of data veracity in big data applications. We describe a novel solution to the problem of making short term predictions (up to a few hours ahead) in absence of real-time data from sensors in Smart Grid. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We thus reduce the communication complexity involved in transmitting sensory data in Smart Grids. We use real-world electricity consumption data from smart meters to empirically demonstrate the usefulness of our method. Our dataset consists of data collected at 15-min intervals from 170 smart meters in the USC Microgrid for 7 years, totaling 41,697,600 data points.


title: Real time data mining-based intrusion detection

abstract: We present an overview of our research in real time data mining-based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment. We describe our approaches to address three types of issues: accuracy, efficiency, and usability. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from intrusions; we use artificial anomalies along with normal and/or intrusion data to produce more effective misuse and anomaly detection models. To improve efficiency, the computational costs of features are analyzed and a multiple-model cost-based approach is used to produce detection models with low cost and high accuracy. We also present a distributed architecture for evaluating cost-sensitive models in real-time. To improve usability, adaptive learning algorithms are used to facilitate model construction and incremental updates; unsupervised anomaly detection algorithms are used to reduce the reliance on labeled data. We also present an architecture consisting of sensors, detectors, a data warehouse, and model generation components. This architecture facilitates the sharing and storage of audit data and the distribution of new or updated models. This architecture also improves the efficiency and scalability of the IDS.


title: On-the-Job Learning with Bayesian Decision Theory

abstract: Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowd-sourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets—named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels. We also achieve a 8% F1 improvement over having a single human label the whole set, and a 28% F1 improvement over online learning.


title: Sparse Estimation From Noisy Observations of an Overdetermined Linear System

abstract: This note studies a method for the estimation of a finite number of unknown parameters from linear equations, which are perturbed by Gaussian noise. In the case the unknown parameters have only few nonzero entries, the proposed estimator performs more efficiently than a traditional approach. The method consists of three steps: (1) a classical Least Squares Estimate (LSE); (2) the support is recovered through a Linear Programming (LP) optimization problem which can be computed using a soft-thresholding step; (3) a de-biasing step using a LSE on the estimated support set. The main contribution of this note is a formal derivation of an associated ORACLE property of the final estimate. That is, with probability 1, the estimate equals the LSE based on the support of the true parameters when the number of observations goes to infinity.


Face Recognition using Template Matching


Using Pre-Trained Models for Fine-Grained Image Classification in Fashion Field


A NOVEL TRIPLE-BAND ELECTROMAGNETIC BANDGAP (EBG) STRUCTURE


title: Camera geolocation from mountain images

abstract: This paper presents a novel approach to camera geolocation from mountain images. Existing mountain view-based geolocation techniques use only mountain skylines and assume that such skylines are available and can be reliably extracted from query images. However, in real-life scenarios the skyline in a query image may be blurred or invisible, due to occlusions, adverse weather conditions and atmospheric effects, and poor image quality. Geolocating mountain view images with poor skylines is a challenge. In addition, when geolocating a query image, existing techniques do not estimate the camera roll angle, assuming that the roll angle is always small and its impact to the geolocation accuracy is negligible. However, in our research we have observed that even a small camera roll angle of only a few degrees can significantly alter the skyline features extracted from the query image and thus cause geolocation failure due to feature mismatching between the query and feature database. It is another challenge to reliably handle query images with non-negligible camera roll angles. In this paper, we propose a novel solution to these challenges by exploiting additional visual features extracted from mountain ridges beyond skylines and performing search-based camera roll angle estimation. Our proposed approach has been extensively tested on real-world challenging query images from five different regions in three continents. The experimental results from our proposed approach is significantly superior to those obtained from state-of-the-art skyline-based image geolocation approaches.


title: Factors Affecting Purchase Intention of Online Game Prepayment Card - Evidence from Thailand

abstract: Over the past decade, the most popular leisure activity in the world is to play online games. However, it is difficult to make the online gamers will to pay for the privilege of playing the game in present online game industry, especially in developing countries. Therefore, the purpose of the research was to study factors affecting purchase intention of online game prepayment card which is the main revenue source for free-to-play game. This study collected 335 respondent data from Thailand to test the research hypotheses. The results show the perceived enjoyment value, monetary value and promotion programs are significant positive factors to influence the purchase intention of online game prepayment card. The research findings can contribute to the understandings of online game consumer behavior in developing country such as Thailand and be adopted as marketing planning guideline for online game business managers to promote prepayment card purchase intention of consumers.


Using Dependency Relations for Text Classification


title: A logic for reasoning about time and reliability

abstract: We present a logic for stating properties such as, "after a request for service there is at least a 98\045 probability that the service will be carried out within 2 seconds". The logic extends the temporal logic CTL by Emerson, Clarke and Sistla with time and probabil- ities. Formulas are interpreted over discrete time Markov chains. We give algorithms for checking that a given Markov chain satis- fies a formula in the logic. The algorithms require a polynomial number of arithmetic operations, in size of both the formula and\003This research report is a revised and extended version of a paper that has appeared under the title "A Framework for Reasoning about Time and Reliability" in the Proceeding of the 10thIEEE Real-time Systems Symposium, Santa Monica CA, December 1989. This work was partially supported by the Swedish Board for Technical Development (STU) as part of Esprit BRA Project SPEC, and by the Swedish Telecommunication Administration.1the Markov chain. A simple example is included to illustrate the algorithms.