title: Replication strategies in unstructured peer-to-peer networks

abstract: The Peer-to-Peer (P2P) architectures that are most prevalent in today's Internet are decentralized and unstructured. Search is blind in that it is independent of the query and is thus not more effective than probing randomly chosen peers. One technique to improve the effectiveness of blind search is to proactively replicate data. We evaluate and compare different replication strategies and reveal interesting structure: Two very common but very different replication strategies - uniform and proportional - yield the same average performance on successful queries, and are in fact worse than any replication strategy which lies between them. The optimal strategy lies between the two and can be achieved by simple distributed algorithms. These fundamental results o.er a new understanding of replication and show that currently deployed replication strategies are far from optimal and that optimal replication is attainable by protocols that resemble existing ones in simplicity and operation.


title: Research and development of a mobile based women safety application with real-time database and data-stream network

abstract: The application incorporates all the unique features such as real-time location tracking and integrate all the features offered by the existing system such as GPS tracking, SOS. The application requires an initial registration along with emergency contacts and the user is asked to update the emergency contacts from time to time. When the user is travelling from one place to another, the dynamic GPS tracking offered by PubNub's channel is turned on to view the user's location on a map. Users with the same app can monitor other users with this app through the dynamic GPS Tracking system through the PubNub channel. When the SOS button is pressed then an alert message which contains the name of the user, GPS Location and a help message is sent via SMS. The user has access to first-aid information and toll free helpline phone numbers. All the information and data is integrated with Firebase.


title: Adaptive planning for markov decision processes with uncertain transition models via incremental feature dependency discovery

abstract: Solving large scale sequential decision making problems without prior knowledge of the state transition model is a key problem in the planning literature. One approach to tackle this problem is to learn the state transition model online using limited observed measurements. We present an adaptive function approximator (incremental Feature Dependency Discovery (iFDD)) that grows the set of features online to approximately represent the transition model. The approach leverages existing feature-dependencies to build a sparse representation of the state transition model. Theoretical analysis and numerical simulations in domains with state space sizes varying from thousands to millions are used to illustrate the benefit of using iFDD for incrementally building transition models in a planning framework.


Intelligent machinery (Written in 1947.) Universal Turing Machine


title: Data mining for network intrusion detection

abstract: This paper gives an overview of our research in building rare class prediction models for identifying known intrusions and their variations and anomaly/outlier detection schemes for detecting novel attacks whose nature is unknown. Experimental results on the KDDCup’99 data set have demonstrated that our rare class predictive models are much more efficient in the detection of intrusive behavior than standard classification techniques. Experimental results on the DARPA 1998 data set, as well as on live network traffic at the University of Minnesota, show that the new techniques show great promise in detecting novel intrusions. In particular, during the past few months our techniques have been successful in automatically identifying several novel intrusions that could not be detected using state-of-the-art tools such as SNORT. In fact, many of these have been on the CERT/CC list of recent advisories and incident notes.


Aneesah : A Conversational Natural Language Interface to Databases


title: Predicting Object Dynamics in Scenes

abstract: Given a static scene, a human can trivially enumerate the myriad of things that can happen next and characterize the relative likelihood of each. In the process, we make use of enormous amounts of commonsense knowledge about how the world works. In this paper, we investigate learning this commonsense knowledge from data. To overcome a lack of densely annotated spatiotemporal data, we learn from sequences of abstract images gathered using crowdsourcing. The abstract scenes provide both object location and attribute information. We demonstrate qualitatively and quantitatively that our models produce plausible scene predictions on both the abstract images, as well as natural images taken from the Internet.


title: Antibodies against viruses: passive and active immunization

abstract: Antibodies, through passive or active immunization, play a central role in prophylaxis against many infectious agents. While neutralization is a primary function of antibodies in protection against most viruses, the relative contribution of Fc-dependent and complement-dependent anti-viral activities of antibodies was found to vary between different viruses in recent studies. The multiple hit model explains how antibodies neutralize viruses, and recent data on the stoichiometry of antibody neutralization suggest that the organization of viral surface proteins on viruses, in addition to virus size, influences the level of antibody occupancy required for neutralization. These new findings will improve our strategies in therapeutic antibody engineering and rational vaccine design.


title: Recognition of persisting emotional valence from EEG using convolutional neural networks

abstract: Recently there has been considerable interest in EEG-based emotion recognition (EEG-ER), which is one of the utilization of BCI. However, it is not easy to realize the EEG-ER system which can recognize emotions with high accuracy because of the tendency for important information in EEG signals to be concealed by noises. Deep learning is the golden tool to grasp the features concealed in EEG data and enable highly accurate EEG-ER because deep neural networks (DNNs) may have higher recognition capability than humans'. The publicly available dataset named DEAP, which is for emotion analysis using EEG, was used in the experiment. The CNN and a conventional model used for comparison are evaluated by the tests according to 11-fold cross validation scheme. EEG raw data obtained from 16 electrodes without general preprocesses were used as input data. The models classify and recognize EEG signals according to the emotional states "positive" or "negative" which were caused by watching music videos. The results show that the more training data are, the much higher the accuracies of CNNs are (by over 20%). It also suggests that the increased training data need not to belong to the same person's EEG data as the test data so as to get the CNN recognizing emotions accurately. The results indicate that there are not only the considerable amount of the interpersonal difference but also commonality of EEG properties.


title: Multivariate time series classification by combining trend-based and value-based approximations

abstract: Multivariate time series data often have a very high dimensionality. Classifying such high dimensional data poses a challenge because a vast number of features can be extracted. Furthermore, the meaning of the normally intuitive term "similar to" needs to be precisely defined. Representing the time series data effectively is an essential task for decision-making activities such as prediction, clustering and classification. In this paper we propose a feature-based classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry. Our approach encompasses two main phases: representation and classification. ::: ::: For the representation phase, we propose a novel representation of time series which combines trend-based and value-based approximations (we abbreviate it as TVA). It produces a compact representation of the time series which consists of symbolic strings that represent the trends and the values of each variable in the series. The TVA representation improves both the accuracy and the running time of the classification process by extracting a set of informative features suitable for common classifiers. ::: ::: For the classification phase, we propose a memory-based classifier which takes into account the antecedent results of the classification process. The inputs of the proposed classifier are the TVA features computed from the current segment, as well as the predicted class of the previous segment. ::: ::: Our experimental results on real-world multivariate time series show that our approach enables highly accurate and fast classification of multivariate time series.


title: Wideband Low Profile Multiport Antenna With Omnidirectional Pattern and High Isolation

abstract: A wideband high-isolation two-port antenna of low profile and omnidirectional pattern is presented. This two-port system consists of a collocated antenna pair with a cone antenna in the middle and a ring radiator around the cone, both placed over a rectangular ground plane. The overall dimensions are 0.64λ × 0.64λ × 0.11λ at the lowest frequency of operation. The isolation between the two ports is more than 37 dB and achieved by employing polarization orthogonality. The operational band is from 800 to 2700 MHz and the achieved isolation is verified with measurements of a fabricated prototype. Overall, the proposed antenna pair can be employed for simultaneous transmit and receive applications or MIMO and diversity systems in commercial wireless communication bands.


title: Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans

abstract: A patient with selective bilateral damage to the amygdala did not acquire conditioned autonomic responses to visual or auditory stimuli but did acquire the declarative facts about which visual or auditory stimuli were paired with the unconditioned stimulus. By contrast, a patient with selective bilateral damage to the hippocampus failed to acquire the facts but did acquire the conditioning. Finally, a patient with bilateral damage to both amygdala and hippocampal formation acquired neither the conditioning nor the facts. These findings demonstrate a double dissociation of conditioning and declarative knowledge relative to the human amygdala and hippocampus.


title: Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers

abstract: According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy.


title: A survey of learning-based techniques of email spam filtering

abstract: Email spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. In this paper we give an overview of the state of the art of machine learning applications for spam filtering, and of the ways of evaluation and comparison of different filtering methods. We also provide a brief description of other branches of anti-spam protection and discuss the use of various approaches in commercial and non-commercial anti-spam software solutions.


title: Psychological, physical, and academic correlates of cyberbullying and traditional bullying

abstract: Abstract Purpose To examine the relationship between children's and adolescents' experiences with cyberbullying and traditional bullying and psychological health, physical health, and academic performance. Methods Nine hundred thirty-one students in grades 6 through 12 completed an anonymous survey examining their experiences with cyberbullying and traditional bullying. Also included were measures of anxiety, depression, self-esteem, physical well-being, school attendance, and academic performance. Results Participants were categorized as belonging to one of four groups: cyber victims, cyberbullies, cyber bully/victims, and those not involved in cyberbullying. A similar categorization was done with traditional bullying. Those in the bully/victim groups (and particularly the cyber bully/victim group) had the most negative scores on most measures of psychological health, physical, health, and academic performance. Conclusions There appears to be a substantial, although not perfect, overlap between involvement in traditional bullying and cyberbullying. Additionally, the physical, psychological, and academic correlates of the two types of bullying resembled one another.


Introduction to Modern Information Retrieval


title: Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers

abstract: The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video domains. However, recent work has shown that these largely uninterpretable models exhibit glaring security vulnerabilities in the presence of an adversary. In this work, we develop a powerful untargeted adversarial attack for action recognition systems in both white-box and black-box settings. Action recognition models differ from image-classification models in that their inputs contain a temporal dimension, which we explicitly target in the attack. Drawing inspiration from image classifier attacks, we create new attacks which achieve state-of-the-art success rates on a two-stream classifier trained on the UCF-101 dataset. We find that our attacks can significantly degrade a model's performance with sparsely and imperceptibly perturbed examples. We also demonstrate the transferability of our attacks to black-box action recognition systems.


title: A new and rapid colorimetric determination of acetylcholinesterase activity.

abstract: Abstract A photometric method for determining acetylcholinesterase activity of tissue extracts, homogenates, cell suspensions, etc., has been described. The enzyme activity is measured by following the increase of yellow color produced from thiocholine when it reacts with dithiobisnitrobenzoate ion. It is based on coupling of these reactions: The latter reaction is rapid and the assay is sensitive (i.e. a 10 μ1 sample of blood is adequate). The use of a recorder has been most helpful, but is not essential. The method has been used to study the enzyme in human erythrocytes and homogenates of rat brain, kidney, lungs, liver and muscle tissue. Kinetic constants determined by this system for erythrocyte eholinesterase are presented. The data obtained with acetylthiocholine as substrate are similar to those with acetylcholine.


title: The effects of perceived relevance of travel blogs' content on the behavioral intention to visit a tourist destination

abstract: The tourism industry is characterized by ever-increasing competition, causing destinations to seek new methods to attract tourists. Traditionally, a decision to visit a destination is interpreted, in part, as a rational calculation of the costs/benefits of a set of alternative destinations, which were derived from external information sources, including e-WOM (word-of-mouth) or travelers' blogs. There are numerous travel blogs available for people to share and learn about travel experiences. Evidence shows, however, that not every blog exerts the same degree of influence on tourists. Therefore, which characteristics of these travel blogs attract tourists' attention and influence their decisions, becomes an interesting research question. Based on the concept of information relevance, a model is proposed for interrelating various attributes specific to blog's content and perceived enjoyment, an intrinsic motivation of information systems usage, to mitigate the above-mentioned gap. Results show that novelty, understandability, and interest of blogs' content affect behavioral intention through blog usage enjoyment. Finally, theoretical and practical implications are proposed.


title: NLP4BPM - Natural Language Processing Tools for Business Process Management

abstract: Business Process Management is facing a drift in the way process information is used within an organization. To reach a wide audience, organizations keep parallel representations of process information, thus making the processes to be understandable by everyone. However, this poses a challenge on the synchronization and transformation between different process representations. NLP4BPM is an environment to support such crucial tasks. It combines Natural Language Processing tools with process-oriented techniques, and through its web-based interface can be easily accessed from any device.


title: Layer Normalization

abstract: Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.


title: Term Weighting Schemes for Latent Dirichlet Allocation

abstract: Many implementations of Latent Dirichlet Allocation (LDA), including those described in Blei et al. (2003), rely at some point on the removal of stopwords, words which are assumed to contribute little to the meaning of the text. This step is considered necessary because otherwise high-frequency words tend to end up scattered across many of the latent topics without much rhyme or reason. We show, however, that the 'problem' of high-frequency words can be dealt with more elegantly, and in a way that to our knowledge has not been considered in LDA, through the use of appropriate weighting schemes comparable to those sometimes used in Latent Semantic Indexing (LSI). Our proposed weighting methods not only make theoretical sense, but can also be shown to improve precision significantly on a non-trivial cross-language retrieval task.


title: Work Group Diversity and Group Performance: An Integrative Model and Research Agenda.

abstract: Research on the relationship between work group diversity and performance has yielded inconsistent results. To address this problem, the authors propose the categorization-elaboration model (CEM), which reconceptualizes and integrates information/decision making and social categorization perspectives on work-group diversity and performance. The CEM incorporates mediator and moderator variables that typically have been ignored in diversity research and incorporates the view that information/decision making and social categorization processes interact such that intergroup biases flowing from social categorization disrupt the elaboration (in-depth processing) of task-relevant information and perspectives. In addition, the authors propose that attempts to link the positive and negative effects of diversity to specific types of diversity should be abandoned in favor of the assumption that all dimensions of diversity may have positive as well as negative effects. The ways in which these propositions may set the agenda for future research in diversity are discussed.


title: Incarceration as Violence: Inflicting Pain in Portuguese Prisons

abstract: This article sets the groundwork for understanding prison violence as a broad and interrelated phenomenon which is bound by shifting notions of legitimacy. We argue that the concept of violence can help understand how the “pains of imprisonment” are inflicted and punishment is produced in different prison settings. Drawing upon fieldwork carried out in several Portuguese prisons, we describe how unequal sets of circumstances are imposed on groups of inmates, who become differently exposed to the deprivations of confinement. Our argument is that the various types of prison violence should be understood against this backdrop, as well as relationship to the diverse circumstances in which they are produced and the subjectivities of the individuals involved.


How Online Basic Psychological Need Satisfaction Influences Self-Disclosure Online among Chinese Adolescents: Moderated Mediation Effect of Exhibitionism and Narcissism


title: The Juvenile Victimization Questionnaire: Reliability, validity, and national norms

abstract: Objective: To assess the utility and performance of the 34-item Juvenile Victimization Questionnaire (JVQ) in eliciting the recent victimization experiences of a national sample of children ages 2–17. Method: The JVQ was administered in a national random digit dial telephone survey about the experiences of 2,030 children. The experiences of children 10–17 years old were assessed through youth self-report on the JVQ, and the experiences of children 2–9 assessed through JVQ caregiver proxy report. Results: Large numbers of recent victimizations were disclosed using the JVQ (71% of the sample reporting at least one victimization in the last year, with an average of 2.63 victimizations per child). There were few indicators of respondent confusion and little resistance to even the most sensitive questions. In a test of construct validity, endorsements of JVQ items correlated well with measures of traumatic symptoms. The instrument showed adequate test-retest reliability i na3t o 4week re-administration. Large numbers of victimizations were reported across the spectrum of ages, and there were no major discontinuities between the self-reports and proxy reports, suggesting that caregivers provided generally adequate and comparable information to child self-reports about the experiences of children under the age of 10. Conclusion: The JVQ has potential for use in future epidemiological research as well as clinical evaluation concerning the victimization of children. © 2005 Elsevier Ltd. All rights reserved.


title: Compact Tunable Lowpass to CFBW Bandpass Switchable Filter using Concentric Resonators

abstract: A tunable low-pass to bandpass switchable filter is presented in this study. The proposed structure consists of two concentric resonators. Along with varactors, outer resonator is also loaded with a PIN diode switch. The switching conditions of the PIN diode - OFF lead to a low-pass filter (LPF) and ON lead to a bandpass filter. At the ON condition of the switch, the outer resonator behaves as two resonators which are coupled to each other through two ways - inductively coupled through via hole and capacitively coupled through the varactor loaded between the resonators. The coupling coefficients between the two grounded resonators are controlled using varactors to achieve constant fractional bandwidth (CFBW) tunability. The third CFBW tunable band with bandpass characteristic having three transmission zeros across the passband is achieved using inductively coupled inner resonators. A filter is designed, simulated and fabricated. The response shows a tunable LPF having a tuning range of 0.55-1.4 GHz. One tunable CFBW bandpass response with a tuning range of 0.850-1.25 GHz is achieved with a CFBW of 64%. The third tunable band with bandpass filtering behaviour is achieved with a tunable range of 1.04-1.6 GHz with a CFBW of 13.4%.


title: Do School Facilities Affect Academic Outcomes

abstract: On any given school day, about twenty percent of Americans spend time in a school building. The average age of our schools is close to fifty years, and studies by the U.S. General Accounting Office have documented widespread physical deficiencies in many of them. Faced with an aging building stock and growing, shifting student enrollments, states and communities are working hard to build and modernize K–12 facilities.


Running title : THEORY OF RECONSOLIDATION The Computational Nature of Memory Reconsolidation


title: Understanding spirituality and spiritual care in nursing

abstract: Spirituality is a complex concept that has different meanings for different people. Spiritual care is a fundamental aspect of nursing and attending to the spiritual needs of patients may improve their health outcomes. This article, the first in a series of three, explores various definitions of spirituality, and the importance of spirituality and spiritual care in healthcare settings. The second article of this series provides an in-depth exploration of the assessment of patients' spiritual care needs, and the third and final article in this short series discusses spiritual care nursing interventions.


title: Network Intrusion Detection Using a HNB Binary Classifier

abstract: Utilization of the data mining techniques in intrusion detection systems is common for the classification of the network events as either normal events or attack events. Naive Bayes (NB) method is a simple, efficient and popular data mining method that is built on conditional independence of attributes assumption. Hidden Naive Bayes (HNB) is an extended form of NB that keeps the NB's simplicity and efficiency while relaxing its independence assumption. Our experimental research claims that the HNB binary classifier model can be applied to intrusion detection problem. Experiment results using classic KDD 1999 Cup intrusion detection dataset indicate that HNB binary classifier has better performance in terms of detection accuracy compared to the traditional NB classifier.


title: The impact of skin colour on human photobiological responses

abstract: Summary ::: Terrestrial solar ultraviolet radiation (UVR) exerts both beneficial and adverse effects on human skin. Epidemiological studies show a lower incidence of skin cancer in people with pigmented skins compared to fair skins. This is attributed to photoprotection by epidermal melanin, as is the poorer vitamin D status of those with darker skins. We summarize a wide range of photobiological responses across different skin colours including DNA damage and immunosuppression. Some studies show the generally modest photoprotective properties of melanin, but others show little or no effect. DNA photodamage initiates non-melanoma skin cancer and is reduced by a factor of about 3 in pigmented skin compared with white skin. This suggests that if such a modest reduction in DNA damage can result in the significantly lower skin cancer incidence in black skin, the use of sunscreen protection might be extremely beneficial for susceptible population. Many contradictory results may be explained by protocol differences, including differences in UVR spectra and exposure protocols. We recommend that skin type comparisons be done with solar-simulated radiation and standard erythema doses or physical doses (J/m2) rather than those based solely on clinical endpoints such as minimal erythema dose (MED).


title: C URRENT OPINION The brain as a flexible task machine: implications for visual rehabilitation using noninvasive vs. invasive approaches

abstract: Purpose of review The exciting view of our brain as highly flexible task-based and not sensory-based raises the chances for visual rehabilitation, long considered unachievable, given adequate training in teaching the brain how to see. Recent advances in rehabilitation approaches, both noninvasive, like sensory substitution devices (SSDs) which present visual information using sound or touch, and invasive, like visual prosthesis, may potentially be used to achieve this goal, each alone, and most preferably together. Recent findings Visual impairments and said solutions are being used as a model for answering fundamental questions ranging from basic cognitive neuroscience, showing that several key visual brain areas are actually highly flexible, modality-independent and, as was recently shown, even visual experience-independent task machines, to technological and behavioral developments, allowing blind persons to ‘see’ using SSDs and other approaches. Summary SSDs can be potentially used as a research tool for assessing the brain’s functional organization; as an aid for the blind in daily visual tasks; to visually train the brain prior to invasive procedures, by taking advantage of the ‘visual’ cortex’s flexibility and task specialization even in the absence of vision; and to augment postsurgery functional vision using a unique SSD–prostheses hybrid. Taken together the reviewed results suggest a brighter future for visual neuro-rehabilitation.


title: Accelerated Mini-Batch Stochastic Dual Coordinate Ascent

abstract: Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batch setting that is often used in practice. Our main contribution is to introduce an accelerated mini-batch version of SDCA and prove a fast convergence rate for this method. We discuss an implementation of our method over a parallel computing system, and compare the results to both the vanilla stochastic dual coordinate ascent and to the accelerated deterministic gradient descent method of Nesterov [2007].


title: Understanding Anti-Vaccination Attitudes in Social Media

abstract: The anti-vaccination movement threatens public health by reducing the likelihood of disease eradication. With social media’s purported role in disseminating anti-vaccine information, it is imperative to understand the drivers of attitudes among participants involved in the vaccination debate on a communication channel critical to the movement: Twitter. Using four years of longitudinal data capturing vaccine discussions on Twitter, we identify users who persistently hold pro and anti attitudes, and those who newly adopt anti attitudes towards vaccination. After gathering each user’s entire Twitter timeline, totaling to over 3 million tweets, we explore differences in the individual narratives across the user cohorts. We find that those with long-term anti-vaccination attitudes manifest conspiratorial thinking, mistrust in government, and are resolute and in-group focused in language. New adoptees appear to be predisposed to form anti-vaccination attitudes via similar government distrust and general paranoia, but are more social and less certain than their long-term counterparts. We discuss how this apparent predisposition can interact with social media-fueled events to bring newcomers into the anti-vaccination movement. Given the strong base of conspiratorial thinking underlying anti-vaccination attitudes, we conclude by highlighting the need for alternatives to traditional methods of using authoritative sources such as the government when correcting misleading vaccination claims.


Procedural generation of populations for storytelling Bas in


title: A fine-grained, controllable, user-to-user delegation method in RBAC

abstract: This paper addresses the issues surrounding user-to-user delegation in RBAC. We show how delegations can be incorporated into the RBAC model in a simple and straightforward manner. A special feature of the model is that it allows fine-grained control over what rights a user wishes to delegate as opposed to delegation at the role level where all the rights of a role must be delegated. In addition, the model provides a rich set of controls regarding further delegations of a right, generic constraints that further control delegations, and an innovative model for revocations. Properties of both delegation and revocation are discussed, and our work is compared with other related research.


title: A Logical Approach to Discrete Math

abstract: Here, the authors strive to change the way logic and discrete math are taught in computer science and mathematics: while many books treat logic simply as another topic of study, this one is unique in its willingness to go one step further. The book traets logic as a basic tool which may be applied in essentially every other area.


Poor Man’s Word-Segmentation: Unsupervised Morphological Analysis for Indonesian


Topic Classification using Latent Dirichlet Allocation at Multiple Levels


title: Cells in Multidimensional Recurrent Neural Networks

abstract: The transcription of handwritten text on images is one task in machine learning and one solution to solve it is using multi-dimensional recurrent neural networks (MDRNN) with connectionist temporal classification (CTC). The RNNs can contain special units, the long short-term memory (LSTM) cells. They are able to learn long term dependencies but they get unstable when the dimension is chosen greater than one. We defined some useful and necessary properties for the one-dimensional LSTM cell and extend them in the multi-dimensional case. Thereby we introduce several new cells with better stability. We present a method to design cells using the theory of linear shift invariant systems. The new cells are compared to the LSTM cell on the IFN/ENIT and Rimes database, where we can improve the recognition rate compared to the LSTM cell. So each application where the LSTM cells in MDRNNs are used could be improved by substituting them by the new developed cells.


title: Energy Management Based on Frequency Approach for Hybrid Electric Vehicle Applications: Fuel-Cell/Lithium-Battery and Ultracapacitors

abstract: This paper presents the ultracapacitors (U) and fuel-cell/lithium-battery connection with an original energy management method for hybrid electric vehicle (HEV) applications. The proposed method is focused on the frequency approach to meet the load energy requirement. The ultracapacitors are connected to the dc link through a buck-boost converter, and the fuel cell is connected to the dc link via a boost converter for the first topology. In the second topology, the lithium battery is connected to the dc link without a converter to avoid the dc-link voltage control. An asynchronous machine is used like the traction motor; it is related to the dc link through a dc/ac converter (inverter). The main contribution of this paper is focused on HEV energy management according to the dynamics (frequency) of the hybrid sources using polynomial correctors. The performances of the proposed method are evaluated through some simulations and the experimental tests, using the New European Driving Cycle (NEDC). This study is extended to an aggressive test cycle, such as the U.S. driving cycle (USDC), to understand the system response and the control performances.


title: Flexible inkjet printed sensor for liquid level monitoring

abstract: In this work a method for liquid level measurement is presented, with a support of experimental results. The presented liquid-level measurement method uses two sensors: level and reference interdigital capacitors. Proposed sensors are fabricated in low-cost inkjet printing technology with nanoparticle silver ink on a Novele™ coated polyethylene terephthalate (PET) substrate. The sensors comprise a two interpenetrating comb electrodes with 1.5 mm width of comb electrode, 1.5 mm spacing between each comb and 15 fingers with length of 135 mm and 8 fingers with length of 19 mm for level and reference sensor, respectively. Capacitance of the level sensor fixed on a test tube increases as the liquid level increases (up to 43.5 mm). The sensor characteristics for three liquids with different permittivity are presented. The reference sensor at the bottom of the tube is completely covered by liquid and its capacitance changes as the permittivity of the liquid changes. Experimental results for three different liquids confirmed simple liquid level measurement method which enables the measurement regardless of the liquid type.


title: Stylization and abstraction of photographs

abstract: Good information design depends on clarifying the meaningful structure in an image. We describe a computational approach to stylizing and abstracting photographs that explicitly responds to this design goal. Our system transforms images into a line-drawing style using bold edges and large regions of constant color. To do this, it represents images as a hierarchical structure of parts and boundaries computed using state-of-the-art computer vision. Our system identifies the meaningful elements of this structure using a model of human perception and a record of a user's eye movements in looking at the photo; the system renders a new image using transformations that preserve and highlight these visual elements. Our method thus represents a new alternative for non-photorealistic rendering both in its visual style, in its approach to visual form, and in its techniques for interaction.


title: The dynamic analysis of WannaCry ransomware

abstract: Hacking weapons come in handy for cyber criminals anytime. Ransomware has increased in popularity. Its creators are playing our fears. The rapid proliferation of ransomware attack indicates the growing tendency of ransomware-as-a-service (RaaS) and the integration of hacking weapons. This paper presents the static analysis of the infamous WannaCry ransomware, which is one of the most impacted and propagated malware in 2017. This international wave of cyber threats is reported to have struck over 150 countries worldwide. Through the static analysis, the details of WannaCry processes and functions are revealed. The anatomy of ransomware attack is discussed to dissect the multi-staged execution of Wannacry, including deployment, installation, destruction, and command-and-control. The WannaCry ransomware not only implements the strong encrypting algorithm and key structure, but also integrates the hacking weapons leaked by the Shadow Brokers. In this paper, the reverse engineering analysis is conducted to explore each chain of malware execution.


title: Data Leak Analysis in a Corporate Environment

abstract: Digital Forensics focuses on the collection of evidence from the volatile and non-volatile memory of a computer based system, such as the hard disk and the RAM content. Needless to say, hard disk data are crucial in the investigation, but whether hard disk data collection is possible or not depends on the features of the case, and whether the forensics practitioners have a suitable legal permission. As nowadays the capacity of hard disks is increasing more and more, it takes a considerable amount of time to perform the imaging phase, and thus to deal with different steps in the chain of custody of the case. As a consequence, delaying the collection step in the investigation may have a detrimental effect on the progress of the investigation or may be a barrier to succeed in the investigation. Hence, we suggest an efficient methodology to collect evidence for dealing with data leak cases, by substantially reducing the collection time.


title: Convolutional Neural Networks for Toxic Comment Classification

abstract: Flood of information is produced in a daily basis through the global internet usage arising from the online interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since online texts with high toxicity can cause personal attacks, online harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several attempts to identify an efficient model for online toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.


title: Configuration Knowledge Representation and Reasoning

abstract: Configuration models specify the set of possible configurations (solutions). A configuration model together with a defined set of (customer) requirements are the major elements of a configuration task (problem). In this chapter, we discuss different knowledge representations that can be used for the definition of a configuration model. We provide examples that help to further develop the understanding of the underlying concepts and include a UML-based personal computer (PC) configuration model that is used as a reference example throughout this book.


Ethical Principles for Socially Assistive Robotics


title: Investigating performance of students: a longitudinal study

abstract: This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.


title: A 6.39GHz–14GHz series resonator mode-switching oscillator with 186–188dB FoM and 197dB FoMA in 65nm CMOS

abstract: CMOS LC voltage-controlled oscillators (VCO) with octave frequency tuning-range (FTR) are attractive for wideband radios. An area and power-efficient resonant mode-switching approach is presented that enables wide-FTR oscillators without compromising inductor Q, resulting in low phase noise and high VCO Figure-of-Merit (FoM). The proposed series resonator mode-switching approach results in a 6.4GHz to 14GHz VCO (74.6% FTR) in 65nm CMOS that achieves 186dB–188dB FoM. The scalability of this approach towards achieving even larger FTR is also demonstrated by a triple-mode 2.2GHz to 8.7GHz (119% FTR) VCO.


title: The Operation of the Cross-Border e-commerce Logistics in China

abstract: According to the cross-border e-commerce background, the article is analyzed its operation on the cross-border e-commerce logistics in china. Firstly, this paper illustrates the operation characteristics of cross-border e-commerce logistics, then analyzes some aspects of the cross-border e-commerce logistics, like operations, logistics cost management and so on. Secondly, this paper analyzes existing problems in cross-border e-commerce logistics from the development of electronic commerce logistics cross-border in China. Finally, some suggestions were put forward on cross-border e-commerce logistics operation from the two aspects of macro level of cross-border e-commerce and micro level of cross-border e-commerce enterprise.


title: The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic

abstract: Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Unknown to many, there exists an arguably even simpler and more versatile learning mechanism, namely, the Tsetlin Automaton. Merely by means of a single integer as memory, it learns the optimal action in stochastic environments. In this paper, we introduce the Tsetlin Machine, which solves complex pattern recognition problems with easy-to-interpret propositional formulas, composed by a collective of Tsetlin Automata. To eliminate the longstanding problem of vanishing signal-to-noise ratio, the Tsetlin Machine orchestrates the automata using a novel game. Our theoretical analysis establishes that the Nash equilibria of the game are aligned with the propositional formulas that provide optimal pattern recognition accuracy. This translates to learning without local optima, only global ones. We argue that the Tsetlin Machine finds the propositional formula that provides optimal accuracy, with probability arbitrarily close to unity. In four distinct benchmarks, the Tsetlin Machine outperforms both Neural Networks, SVMs, Random Forests, the Naive Bayes Classifier and Logistic Regression. It further turns out that the accuracy advantage of the Tsetlin Machine increases with lack of data. The Tsetlin Machine has a significant computational performance advantage since both inputs, patterns, and outputs are expressed as bits, while recognition of patterns relies on bit manipulation. The combination of accuracy, interpretability, and computational simplicity makes the Tsetlin Machine a promising tool for a wide range of domains, including safety-critical medicine. Being the first of its kind, we believe the Tsetlin Machine will kick-start completely new paths of research, with a potentially significant impact on the AI field and the applications of AI.


Hot gas distribution in the wind of ζ Pup and ζ Ori


title: A simple method of determining the time course of timolol's effects on aqueous flow in humans.

abstract: We have devised a simple method of determining the time course of the effects of timolol maleate on aqueous flow using changes in aqueous protein concentrations. Aqueous protein concentrations were measured with a laser flare-cell meter. First, the value of the coefficient of protein entry into the anterior chamber (kin) was determined in both eyes of 10 normal subjects. One week later, aqueous protein concentration changes were measured in both eyes of the same subjects before and after instillation of timolol in one eye. From these changes and the value of kin, changes in the aqueous flow rate were calculated using an equation formulating protein kinetics in the anterior chamber. Results of a separate experiment showed that instillation of topical timolol did not change the kin, justifying use of the kin as determined without considering timolol in the calculation. The calculated flow rate had a maximum decrease of 30% +/- 10% (mean +/- SEM, n = 10) 4 hours after instillation in treated eyes and of 10% +/- 6% 3 hours after instillation in fellow, untreated eyes.


title: A progressive error estimation framework for photon density estimation

abstract: We present an error estimation framework for progressive photon mapping. Although estimating rendering error has been established for unbiased rendering algorithms, error estimation for biased rendering algorithms has not been investigated well in comparison. We characterize the error by the sum of a bias estimate and a stochastic noise bound, which is motivated by stochastic error bounds formulation in biased methods. As a part of our error computation, we extend progressive photon mapping to operate with smooth kernels. This enables the calculation of illumination gradients with arbitrary accuracy, which we use to progressively compute the local bias in the radiance estimate. We also show how variance can be computed in progressive photon mapping, which is used to estimate the error due to noise. As an example application, we show how our error estimation can be used to compute images with a given error threshold. For this example application, our framework only requires the error threshold and a confidence level to automatically terminate rendering. Our results demonstrate how our error estimation framework works well in realistic synthetic scenes.


title: User Mobility Evaluation for 5G Small Cell Networks Based on Individual Mobility Model

abstract: With small cell networks becoming core parts of the fifth generation (5G) cellular networks, it is an important problem to evaluate the impact of user mobility on 5G small cell networks. However, the tendency and clustering habits in human activities have not been considered in traditional user mobility models. In this paper, human tendency and clustering behaviors are first considered to evaluate the user mobility performance for 5G small cell networks based on individual mobility model (IMM). As key contributions, user pause probability, user arrival, and departure probabilities are derived in this paper for evaluating the user mobility performance in a hotspot-type 5G small cell network. Furthermore, coverage probabilities of small cell and macro cell BSs are derived for all users in 5G small cell networks, respectively. Compared with the traditional random waypoint (RWP) model, IMM provides a different viewpoint to investigate the impact of human tendency and clustering behaviors on the performance of 5G small cell networks.


title: Structure from Motion with Objects

abstract: This paper shows for the first time that is possible to reconstruct the position of rigid objects and to jointly recover affine camera calibration solely from a set of object detections in a video sequence. In practice, this work can be considered as the extension of Tomasi and Kanade factorization method using objects. Instead of using points to form a rank constrained measurement matrix, we can form a matrix with similar rank properties using 2D object detection proposals. In detail, we first fit an ellipse onto the image plane at each bounding box as given by the object detector. The collection of all the ellipses in the dual space is used to create a measurement matrix that gives a specific rank constraint. This matrix can be factorised and metrically upgraded in order to provide the affine camera matrices and the 3D position of the objects as an ellipsoid. Moreover, we recover the full 3D quadric thus giving additional information about object occupancy and 3D pose. Finally, we also show that 2D points measurements can be seamlessly included in the framework to reduce the number of objects required. This last aspect unifies the classical point-based Tomasi and Kanade approach with objects in a unique framework. Experiments with synthetic and real data show the feasibility of our approach for the affine camera case.


title: Octopus: Hybrid Big Data Integration Engine

abstract: Nowadays large enterprises maintain a huge amount of data in multiple backend systems including traditional database systems and recently popular big data systems. In an example of telecom providers, the key business data (e.g., billing information) is maintained in database systems whereas the huge signaling log data is on HDFS with Hive. How to integrate such data and provide a consolidate query and analytic becomes a challenging task. Neither traditional database warehouse nor recent Big Data system (e.g. Apache Spark and Hadoop) can fully leverage the power of each backend system. In this paper, we build a hybrid data processing engine, called Octopus, to fully integrate backend systems. Given the backend systems, data is distributed at multiple locations. Octopus focuses on the optimization of the amount of data movement. To this end, Octopus proposes a technique of query pushdown for such optimization. A proof-of-concept prototype of Octopus successfully verifies that Octopus can achieve much faster running time than Spark. For example, Octopus outperforms the recent Spark version 1.4.0 by 5.25 X faster running time to process an aggregation query.


title: Towards a TrustZone-Assisted Hypervisor for Real-Time Embedded Systems

abstract: Virtualization technology starts becoming more and more widespread in the embedded space. The penalties incurred by standard software-based virtualization is pushing research towards hardware-assisted solutions. Among the existing commercial off-the-shelf technologies for secure virtualization, ARM TrustZone is attracting particular attention. However, it is often seen with some scepticism due to the dual-OS limitation of existing state-of-the-art solutions. This letter presents the implementation of a TrustZone-based hypervisor for real-time embedded systems, which allows multiple RTOS partitions on the same hardware platform. The results demonstrate that virtualization overhead is less than 2 percent for a 10 milliseconds guest-switching rate, and the system remains deterministic. This work goes beyond related work by implementing a TrustZone-assisted solution that allows the execution of an arbitrary number of guest OSes while providing the foundation to drive next generation of secure virtualization solutions for resource-constrained embedded devices.


title: Advances in Highly Constrained Multi-Phase Trajectory Generation using the General Pseudospectral Optimization Software (GPOPS)

abstract: Abstract : Recent events in hypersonic glide vehicle programs have necessitated a new approach to examine possible flight test trajectories to include new constraints on optimal trajectory generation. Aircraft stability, combined thermo-structural loading, and vehicle ablation are all important considerations for hypersonic vehicle flight; optimal trajectory generation should account for these complex constraints. A collaborative effort was undertaken by the Air Force Research Laboratory Aerospace Systems Directorate and the 412th Test Wing at Edwards Air Force Base to analyze possible optimal control solutions that satisfy these constraints. A three-stage booster aerodynamic and propulsion model and a hypersonic glide vehicle aerodynamic and ablation model were implemented in the General Pseudospectral Optimization Software (GPOPS). The resulting optimal control problem models booster launch through re-entry vehicle impact and incorporates multiple complex constraints including stagnation heating, ablation, no-y zones, aircraft stability, dynamic pressure, time-rate of change of flight path angle, loads and a terminal phase target. Furthermore, the optimal control problem uses derived events as guidance mode or booster stage linkages; including range to target, range from launch site, vehicle loads, altitudes, dynamic pressure and time rate of change of altitude. The optimal control variables are specified to be the guidance variable derivatives; this allows for constraining attitude rates in a translational (point mass) problem. GPOPS is shown to be capable of analyzing highly constrained, multiple-phase optimal control problems using complex vehicle models and flexible enough to incorporate new constraints quickly allowing for exploration of new guidance methodologies.


title: Obfuscation procedure based in dead code insertion into crypter

abstract: The threat that attacks cyberspace is known as malware. In order to infect the technologic devices that are attacked, malware needs to evade the different antivirus systems. To avoid detection, an obfuscation technique must be applied so malware is updated and ready to be performed. No obstant, the technique implementation presents difficulties in terms of its required ability, evasion tests and infection functionality that turn outs to be a problem to keep malware updated. Therefore, a procedure is proposed that allows applying AVFUCKER or DSPLIT techniques. The purpose is to optimize the required technical means, reduce the antivirus analysis and malware functionality check times.


Learning local feature descriptors with triplets and shallow convolutional neural networks.


title: UNLocBoX A matlab convex optimization toolbox using proximal splitting methods

abstract: Nowadays the trend to solve optimization problems is to use specific algorithms rather than very general ones. The UNLocBoX provides a general framework allowing the user to design his own algorithms. To do so, the framework try to stay as close from the mathematical problem as possible. More precisely, the UNLocBoX is a Matlab toolbox designed to solve convex optimization problem of the form $$ \min_{x \in \mathcal{C}} \sum_{n=1}^K f_n(x), $$ using proximal splitting techniques. It is mainly composed of solvers, proximal operators and demonstration files allowing the user to quickly implement a problem.


title: Credit scoring with a data mining approach based on support vector machines

abstract: The credit card industry has been growing rapidly recently, and thus huge numbers of consumers' credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.


title: Creating an actor-specific facial rig from performance capture

abstract: Creating a high quality blendshape rig usually involves a large amount of effort from skilled artists. Although current 3D reconstruction technologies are able to capture accurate facial geometry of the actor, it is still very difficult to build a production-ready blendshape rig from unorganized scans. Removing rigid head motion and separating mixed expressions from the captures are two of the major challenges in this process. We present a technique that creates a facial blendshape rig based on performance capture and a generic face rig. The customized rig accurately captures actor-specific face details while producing a semantically meaningful FACS basis. The resulting rig faithfully serves both artist friendly keyframe animation and high quality facial motion retargeting in production.


title: Factorization Models for Forecasting Student Performance

abstract: Predicting student performance, one of the tasks in educational data mining, has been taken into account recently [Toscher and Jahrer 2010; Yu et al. 2010; Cetintas et al. 2010; Thai-Nghe et al. 2011]. It was selected as a challenge task for the KDD Cup 2010 [Koedinger et al. 2010]. Concretely, predicting student performance is the task where we would like to know how the students learn (e.g. generally or narrowly), how quickly or slowly they adapt to new problems or if it is possible to infer the knowledge requirements to solve the problems directly from student performance data [Corbett and Anderson 1995; Feng et al. 2009], and eventually, we would like to know whether the students perform the tasks (exercises) correctly (or with some levels of certainty). As discussed in Cen et al. [2006], an improved model for predicting student performance could save millions of hours of students’ time and effort in learning algebra. In that time, students could move to other specific fields of their study or doing other things they enjoy. From educational data mining point of view, an accurate and reliable model in predicting student performance may replace some current standardized tests, and thus, reducing the pressure, time, as well as effort on “teaching and learning for examinations” [Feng et al. 2009; Thai-Nghe et al. 2011]. To address the problem of predicting student performance, many papers have been published but most of them are based on traditional classification/regression techniques [Cen et al. 2006; Feng et al. 2009; Yu et al. 2010; Pardos and Heffernan 2010]. Many other works can be found in Romero et al. [2010]. Recently, [Thai-Nghe et al. 2010; Toscher and Jahrer 2010; Thai-Nghe et al. 2011] have proposed using recommendation techniques, e.g. matrix factorization, for predicting student performance. The authors have shown that predicting student performance can be considered as rating prediction since the student, task, and performance would become user, item, and rating in recommender systems, respectively. We know that learning and problem-solving are complex cognitive and affective processes that are different to shopping and other e-commerce transactions, however, as discussed in Thai-Nghe et al. [2011], the factorization models in recommender systems are implicitly able to encode latent factors of students and tasks (e.g. “slip” and “guess”), and especially in case where we do not have enough meta data about students and tasks (or even we have not enough background knowledge of the domain), this mapping is a reasonable approach.


title: Machine learning of image analysis with convolutional networks and topological constraints

abstract: Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.


title: Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications

abstract: The current electric grid is an inefficient system that wastes significant amounts of the electricity it produces because there is a disconnect between the amount of energy consumers require and the amount of energy produced from generation sources. Power plants typically produce more power than necessary to ensure adequate power quality. By taking advantage of energy storage within the grid, many of these inefficiencies can be removed. When using battery energy storage systems (BESS) for grid storage, advanced modeling is required to accurately monitor and control the storage system. A battery management system (BMS) controls how the storage system will be used and a BMS that utilizes advanced physics-based models will offer for much more robust operation of the storage system. The paper outlines the current state of the art for modeling in BMS and the advanced models required to fully utilize BMS for both lithium-ion batteries and vanadium redox-flow batteries. In addition, system architecture and how it can be useful in monitoring and control is discussed. A pathway for advancing BMS to better utilize BESS for grid-scale applications is outlined.


title: Internet of Things in the 5G Era: Enablers, Architecture, and Business Models

abstract: The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.


title: Parsing Natural Scenes and Natural Language with Recursive Neural Networks

abstract: Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole. We introduce a max-margin structure prediction architecture based on recursive neural networks that can successfully recover such structure both in complex scene images as well as sentences. The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to outperform alternative approaches for semantic scene segmentation, annotation and classification. For segmentation and annotation our algorithm obtains a new level of state-of-the-art performance on the Stanford background dataset (78.1%). The features from the image parse tree outperform Gist descriptors for scene classification by 4%.


title: Continuous-Time User Modeling in Presence of Badges: A Probabilistic Approach

abstract: User modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements, are among those factors that are neglected, while they have a strong impact on user participation in online services. In this paper, we propose interdependent multi-dimensional temporal point processes that capture the impact of badges on user participation besides the peer influence and content factors. We extend the proposed processes to model user actions over the community based question and answering websites, and propose an inference algorithm based on Variational-EM that can efficiently learn the model parameters. Extensive experiments on both synthetic and real data gathered from Stack Overflow show that our inference algorithm learns the parameters efficiently and the proposed method can better predict the user behavior compared to the alternatives.


title: CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis

abstract: This paper presents a novel dataset for traffic accidents analysis. Our goal is to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads. Through the analysis of the proposed dataset, we observed a significant degradation of object detection in pedestrian category in our dataset, due to the object sizes and complexity of the scenes. To this end, we propose to integrate contextual information into conventional Faster R-CNN using Context Mining (CM) and Augmented Context Mining (ACM) to complement the accuracy for small pedestrian detection. Our experiments indicate a considerable improvement in object detection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate the performance of accident forecasting in our dataset using Faster R-CNN and an Accident LSTM architecture. We achieved an average of 1.684 seconds in terms of Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage for the paper is this https URL


High-level semantic optimization of numerical codes


title: Generalized Semi-Orthogonal Multiple-Access for Massive MIMO

abstract: We propose a novel framework to enable concurrent transmission of multiple users to an access node equipped with a massive multiple-input multiple-output (mMIMO), which encompasses and extends the conventional time-division duplex (TDD) protocol and the recently proposed semi-orthogonal multiple-access (SOMA). The new solution is referred to generalized semi-orthogonal multiple-access (GSOMA). To enable GSOMA, the users are grouped such that the users within each group are assigned mutually orthogonal pilot sequences. However the pilot sequences can be reused in different groups and the users in different group are coordinated such that their pilot sequences do not interfere by a semi-orthogonal mechanism enabled by a resource-offset sequence. We describe the general framework and analyze a case of GSOMA and show that a properly designed GSOMA can outperform the conventional TDD and SOMA.


title: Goal directed design of serial robotic manipulators

abstract: Computing the optimal geometric structure of manipulators is one of the most intricate problems in contemporary robot kinematics. Robotic manipulators are designed and built to perform certain predetermined tasks. It is therefore important to incorporate such task requirements during the design and synthesis of the robotic manipulators. Such task requirements and performance constraints can be specified in terms of the required end-effector positions, orientations along the task trajectory. In this work, we define, develop and test a methodology that can generate optimal manipulator geometric structures based on the task requirements. Another objective of this work is to guarantee task performance under user defined joint constraints. Using this methodology, task-based optimal manipulator structures can be generated that guarantee task performance under set operating constraints.


title: Content-Based Citation Recommendation

abstract: We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup. We release an online portal (http://labs.semanticscholar.org/citeomatic/) for citation recommendation based on our method, and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.


title: Multilevel classification of security concerns in cloud computing

abstract: Threats jeopardize some basic security requirements in a cloud. These threats generally constitute privacy breach, data leakage and unauthorized data access at different cloud layers. This paper presents a novel multilevel classification model of different security attacks across different cloud services at each layer. It also identifies attack types and risk levels associated with different cloud services at these layers. The risks are ranked as low, medium and high. The intensity of these risk levels depends upon the position of cloud layers. The attacks get more severe for lower layers where infrastructure and platform are involved. The intensity of these risk levels is also associated with security requirements of data encryption, multi-tenancy, data privacy, authentication and authorization for different cloud services. The multilevel classification model leads to the provision of dynamic security contract for each cloud layer that dynamically decides about security requirements for cloud consumer and provider.


title: Detection of centerline crossing in abnormal driving using CapsNet

abstract: This paper presents the detection of centerline crossing in abnormal driving using a CapsNet. The benefit of the CapsNet is that the capsule contains all the data about the status of objects and recognizes objects as vectors; hence, these can be used to classify driving as normal or abnormal. The datasets use the Creative Commons Licenses from YouTube to obtain traffic accident footages and six time-flow images composed of data with our quantitative basis. A comparison of our proposed architecture with the CNN model showed that our method produces better results.


title: Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification

abstract: In hierarchical classification, the prediction paths may be required to always end at leaf nodes. This is called mandatory leaf node prediction (MLNP) and is particularly useful when the leaf nodes have much stronger semantic meaning than the internal nodes. However, while there have been a lot of MLNP methods in hierarchical multiclass classification, performing MLNP in hierarchical multilabel classification is much more difficult. In this paper, we propose a novel MLNP algorithm that (i) considers the global hierarchy structure; and (ii) can be used on hierarchies of both trees and DAGs. We show that one can efficiently maximize the joint posterior probability of all the node labels by a simple greedy algorithm. Moreover, this can be further extended to the minimization of the expected symmetric loss. Experiments are performed on a number of real-world data sets with tree- and DAG-structured label hierarchies. The proposed method consistently outperforms other hierarchical and flat multilabel classification methods.


title: Magnetic Resonance Imaging–Guided Focused Ultrasound Treatment of Symptomatic Uterine Fibroids

abstract: Objectives: The aim of this study was to assess the impact of the advanced technology of the new ExAblate 2100 system (Insightec Ltd, Haifa, Israel) for magnetic resonance imaging (MRI)-guided focused ultrasound surgery on treatment outcomes in patients with symptomatic uterine fibroids, as measured by the nonperfused volume ratio. Materials and Methods: This is a retrospective analysis of 115 women (mean age, 42 years; range, 27-54 years) with symptomatic fibroids who consecutively underwent MRI-guided focused ultrasound treatment in a single center with the new generation ExAblate 2100 system from November 2010 to June 2011. Mean +/- SD total volume and number of treated fibroids (per patient) were 89 +/- 94 cm(3) and 2.2 +/- 1.7, respectively. Patient baseline characteristics were analyzed regarding their impact on the resulting nonperfused volume ratio. Results: Magnetic resonance imaging-guided focused ultrasound treatment was technically successful in 115 of 123 patients (93.5%). In 8 patients, treatment was not possible because of bowel loops in the beam pathway that could not be mitigated (n = 6), patient movement (n = 1), and system malfunction (n = 1). Mean nonperfused volume ratio was 88% +/- 15% (range, 38%-100%). Mean applied energy level was 5400 +/- 1200 J, and mean number of sonications was 74 +/- 27. No major complications occurred. Two cases of first-degree skin burn resolved within 1 week after the intervention. Of the baseline characteristics analyzed, only the planned treatment volume had a statistically significant impact on nonperfused volume ratio. Conclusions: With technological advancement, the outcome of MRI-guided focused ultrasound treatment in terms of the nonperfused volume ratio can be enhanced with a high safety profile, markedly exceeding results reported in previous clinical trials.


title: Half-Mode Substrate Integrated Waveguide Transverse Slot Array Antennas

abstract: Transverse slot array antennas fed by a half-mode substrate integrated waveguide (HMSIW) are proposed and developed in this paper. The design concept of these new radiating structures is based on the study of the field distribution and phase constant along the HMSIW as well as on the resonant characteristics of a single slot etched on its top conducting wall. Two types of HMSIW-fed slot array antennas, operating, respectively, in X-band and Ka-band, are designed following a procedure similar to the design of slot array antennas fed by a dielectric-filled rectangular waveguide. Compared with slot array antennas fed by a conventional rectangular waveguide, such proposed HMSIW-fed slot array antennas possess the advantages of low profile, compact size, low cost, and easy integration with other microwave and millimeter wave planar circuits. It is worth noting that the width of HMSIW slot array antennas is reduced by nearly half compared to that of slot array antennas fed by a substrate integrated waveguide.


title: Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform

abstract: Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system. With so many components we need a reliable system that detects the possible changes in the system and alerts the engineering team. In this paper we describe the mechanism that we invented for recovering the representative metrics and detecting the change in their behavior. We show that this mechanism is able to detect the possible problems in time by describing some incident cases.


title: Image Matching: An Application-oriented Benchmark

abstract: Image matching approaches have been widely used in computer vision applications in which the image-level matching performance of matchers is critical. However, it has not been well investigated by previous works which place more emphases on evaluating local features. To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods. The proposed metrics are application-oriented as they emphasize application requirements for matchers. The dataset contains two portions for benchmarking video frame matching and unordered image matching separately, where each portion consists of real-world image sequences and each sequence has a specific attribute. Subsequently, we carry out a comprehensive performance evaluation of different state-of-the-art methods and conduct in-depth analyses regarding various aspects such as application requirements, matching types, and data diversity. Moreover, we shed light on how to choose appropriate approaches for different applications based on empirical results and analyses. Conclusions in this benchmark can be used as general guidelines to design practical matching systems and also advocate potential future research directions in this field.


title: Single-Stage, Universal-Input AC/DC LED Driver With Current-Controlled Variable PFC Boost Inductor

abstract: This paper presents a single-stage flyback power-factor-correction (PFC) circuit with a variable boost inductance for high-brightness LED applications for the universal input voltage (90-270 Vrms). The proposed circuit overcomes the limitations of the conventional single-stage PFC flyback with a constant boost inductance that cannot be designed to achieve a practical maximum bulk-capacitor voltage level (i.e., less than 450 V) at high line while meeting required line-current harmonic specifications at low line. According to the proposed method for achieving variable boost inductance, the boost inductance has a constant high value at high line, while at low line it is reduced proportionally to the load current, so that the IEC 61000-3-2 class C and corresponding Japanese JIS C 61000-3-2 class C line-current harmonic limits are satisfied. The proposed single-stage PFC flyback LED driver with the variable boost inductor is experimentally verified on a 24-V/91-W prototype circuit.


title: Computational Higher Type Theory IV: Inductive Types

abstract: This is the fourth in a series of papers extending Martin-L\"of's meaning explanation of dependent type theory to higher-dimensional types. In this installment, we show how to define cubical type systems supporting a general schema of cubical inductive types, inductive types whose constructors may take dimension parameters and may have specified boundaries. Using this schema, we are able to specify and implement many of the higher inductive types which have been postulated in homotopy type theory, including homotopy pushouts, the torus, $W$-quotients, truncations, and arbitrary localizations. We also construct one indexed inductive type, the fiber family of a term. Using the fiber family, it is possible to define an identity type whose eliminator satisfies an exact computation rule on the reflexivity constructor. We believe that the techniques used to construct the fiber family could be straightforwardly combined with our schema for inductive types in order to give a schema for indexed cubical inductive types. The addition of higher inductive types and identity types makes computational higher type theory a model of homotopy type theory, capable of interpreting almost all of the constructions in the HoTT Book (with the exception of general indexed inductive types and inductive-inductive types). This is the first such model with an explicit canonicity theorem stating that all closed terms of boolean type evaluate either to true or to false.


title: A study of the effect of term proximity on query expansion

abstract: Query expansion terms are often used to enhance original query formulations in document retrieval. Such terms are usually selected from the entire documents or from windows or passages surrounding query term occurrences. Arguably, the semantic relatedness between terms weakens with the increase in the distance separating them. In this paper we report a study that was conducted to systematically evaluate different distance functions for selecting query expansion terms. We propose a distance factor that can be effectively combined with the statistical term association measure of mutual information for selecting query expansion terms. Evaluation of the TREC collection shows that distance-weighted mutual information is more effective than mutual information alone in selecting terms for query expansion.


title: A 40 Gb/s CMOS Serial-Link Receiver With Adaptive Equalization and Clock/Data Recovery

abstract: This paper presents a 40 Gb/s serial-link receiver including an adaptive equalizer and a CDR circuit. A parallel-path equalizing filter is used to compensate the high-frequency loss in copper cables. The adaptation is performed by only varying the gain in the high-pass path, which allows a single loop for proper control and completely removes the RC filters used for separately extracting the high- and low-frequency contents of the signal. A full-rate bang-bang phase detector with only five latches is proposed in the following CDR circuit. Minimizing the number of latches saves the power consumption and the area occupied by inductors. The performance is also improved by avoiding complicated routing of high-frequency signals. The receiver is able to recover 40 Gb/s data passing through a 4 m cable with 10 dB loss at 20 GHz. For an input PRBS of 2 7-1, the recovered clock jitter is 0.3 psrms and 4.3 pspp. The retimed data exhibits 500 mV pp output swing and 9.6 pspp jitter with BER <10-12. Fabricated in 90 nm CMOS technology, the receiver consumes 115 mW , of which 58 mW is dissipated in the equalizer and 57 mW in the CDR.


title: Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism

abstract: To extract and learn representations leading to generative mechanisms from data, especially without making arbitrary decisions and biased assumptions, is a central challenge in most areas of scientific research particularly in connection to current major limitations of influential topics and methods of machine and deep learning as they have often lost sight of the model component. Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative mechanisms. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our method and find that it can disentangle observations from discrete dynamic systems, random and complex networks. We think that these causal inference techniques can contribute as key pieces of information for estimations of probability distributions complementing other more statistical-oriented techniques that otherwise lack model inference capabilities.


title: Software Bug Prediction using Machine Learning Approach

abstract: Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes. This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature. This paper presents a software bug prediction model based on machine learning (ML) algorithms. Three supervised ML algorithms have been used to predict future software faults based on historical data. These classifiers are Naive Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs). The evaluation process showed that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance.


title: Augmented Reality for teaching spatial relations

abstract: unchen, Germany Abstract: This paper describes an approach for increasing the understanding and ease the learning of chemistry for students by visualizing and controlling virtual models of molecules in a intuitive way. With the Help of Augmented Reality, we developed a tool with the name Augmented Chemical Reactions. This program visualizes models of molecules rendered to a camera picture at the position of special markers hold in the hands of the users. The intuitive controlling of the position and orientation of the molecules is done by moving and rotating the markers in front of a camera, so the virtual objects behave as they would have been manipulated themselves. For a better understanding of the subject of chemistry, Augmented Chemical Reactions also shows the dynamic deformation of molecules when they come close to each other. Here the users can have a better view on certain behaviours between molecules. This program has the potential to increase the understanding and ease learning chemistry because of its intuitive controlling of the 3D structure of molecules. In the same way that Augmented Chemical Reactions can help students, it also has the potential to speed up the process of designing new molecules. Scientists can inspect the created molecules and see, if they meet the spatial requirements for specific reactions. This approach prevents time consuming reactions in the laboratory to create those molecules and test them for their desired attributes.


title: The Influence of Excessive Product Packaging on Green Brand Attachment: The Mediation Roles of Green Brand Attitude and Green Brand Image

abstract: This study develops an original framework to explore the influence of excessive product packaging on green brand attachment and to discuss the mediation roles of green brand attitude and green brand image. Structural Equation Modeling (SEM) is applied to verify the research framework. The results from a dataset of 238 valid questionnaires show that excessive product packaging has no direct effect on green brand attachment. However, green brand attitude and green brand image fully mediate the negative relationship between excessive product packaging and green brand attachment. Managerially, this study helps firms understand that excessive product packaging may bring damage to green brand attitude and green brand image, which positively relate to green brand attachment. Thus, committing to promoting the functional benefit of green products, firms must not neglect the negative effects of excessive product packaging.


title: On the Depth and Dynamics of Online Search Behavior 1

abstract: This paper examines search across competing e-commerce sites. By analyzing panel data from over 10,000 Internet households and three commodity-like products (books, compact discs (CDs), and air travel services), we show that the amount of online search is actually quite limited. On average, households visit only 1.2 book sites, 1.3 CD sites, and 1.8 travel sites during a typical active month in each category. Using probabilistic models, we characterize search behavior at the individual level in terms of (1) depth of search, (2) dynamics of search, and (3) activity of search.We model an individual's tendency to search as a logarithmic process, finding that shoppers search across very few sites in a given shopping month. We extend the logarithmic model of search to allow for time-varying dynamics that may cause the consumer to evolve and, perhaps, learn to search over time. We find that for two of the three product categories studied, search propensity does not change from month to month. However, in the third product category we find mild evidence of time-varying dynamics, where searchdecreases over time from already low levels. Finally, we model the level of a household's shopping activity and integrate it into our model of search. The results suggest that more-active online shoppers tend also to search across more sites. This consumer characteristic largely drives the dynamics of search that can easily be mistaken as increases from experience at the individual level.


title: Improved spiral test using digitized graphics tablet for monitoring Parkinson's disease

abstract: The monitoring of the progression of Parkinson’s disease (PD) is held on frequently in the clinic and an inconvenient and time-consuming process since PD is generally observed mostly in elderly people whose physical visits to the clinic are troublesome, and the physical examinations must be performed by trained medical staff. Besides, from the machine learning perspective, building a generalizable non-invasive PD diagnosis and monitoring decision support system requires enough number of training samples collected from PD patients at regular intervals. Therefore, self-administered and noninvasive telemonitoring applications that enable the PD patients to collect data at home and transmit it over the internet to a dedicated server have recently become popular. In addition to these, the use of computer systems to collect data from subjects enables the researchers to develop various tests which cannot be easily performed with paper and pencil. In this study, considering that PD affects the handwriting motor abilities of patients, we collected handwriting samples of PD patients who have admitted to the Department of Neurology in Cerrahpasa Faculty of Medicine, Istanbul University via a graphics tablet which was used in many biomedical studies before like cancer imaging, influence of stress on servical myelopathy, and brain imaging. In addition to the static spiral drawing test (SST) which can also be performed traditionally with pencil and paper, we propose a new test called dynamic spiral test (DST) which, unlike SST, can only be performed with the use of electronic equipments such as a tablet and a computer. We present the comparative results of SST and DST, and show that handwriting samples collected with a computerized system can be used to build generalizable PD telemonitoring systems.


title: 3D AVM system for automotive applications

abstract: The car body structure and vision limitations of the rear view mirror may lead to inner wheel difference and blind spots, which results in traffic accidents. To solve this problem, many automakers develop the Around View Monitoring (AVM) actively. The AVM system provides the information around the car for drivers with videos. This paper is based on the 2D AMV and further develops the 3D AVM, which uses four fisheye cameras to set in the front, in the rear, and in the rear view mirrors of both sides to record videos. This paper applies fisheye correction, perspective transform, image stitching, and scale-invariant feature transform to synthesize four input videos into 2D AVM, and the 3D AVM is created by 2D AVM to 3D AVM algorithm. Compared to the 2D AVM in previous methods, the 3D AVM proposed in this paper can help drivers to determine the collision distance while crossing with another car in narrow roads and parking, which can prevent traffic accidents. Moreover, drivers can switch perspectives arbitrarily according to the requirements without the limitation of fixed overlooking view. Therefore, the driving safety is improved.


title: Asynchronous Data Aggregation for Training End to End Visual Control Networks

abstract: Robust training of deep neural networks requires a large amount of data. However gathering and labeling this data can be expensive and determining which distribution of features are needed for training is not a trivial problem. This is compounded when training neural networks for autonomous navigation in continuous non-deterministic environments using only visual input. Increasing the quantity of demonstrated data does not solve this problem as the demonstrated sequences of actions are not guaranteed to produce the same outcomes and slight changes in orientation generate drastically different visual representations. This results in a training set with a different distribution than what the agent will typically encounter in application. Here, we develop a method that can grow a training set from the same distribution as the agent's experiences and capture useful features not found in demonstrated behavior. Additionally, we show that our approach scales to efficiently handle complex tasks that require a large amount of data (experiences) for training. Concretely, we propose the deep asynchronous Dagger framework, which combines the Dagger algorithm with an asynchronous actor-learner architecture for parallel dataset aggregation and network policy learning. We apply our method to the task of navigating 3D mazes in Minecraft with randomly changing block types and analyze our results.


title: Implication of Genetic Algorithm in Cryptography to Enhance Security

abstract: In today’s age of information technology secure transmission of information is a big challenge. Symmetric and asymmetric cryptosystems are not appropriate for high level of security. Modern hash function based systems are better than traditional systems but the complex algorithms of generating invertible functions are very time consuming. In traditional systems data is being encrypted with the key but still there are possibilities of eavesdrop the key and altered text. Therefore, key must be strong and unpredictable, so a method has been proposed which take the advantage of theory of natural selection. Genetic Algorithms are used to solve many problems by modeling simplified genetic processes and are considered as a class of optimization algorithms. By using Genetic Algorithm the strength of the key is improved that ultimately make the whole algorithm good enough. In the proposed method, data is encrypted by a number of steps. First, a key is generated through random number generator and by applying genetic operations. Next, data is diffused by genetic operators and then logical operators are performed between the diffused data and the key to encrypt the data. Finally, a comparative study has been carried out between our proposed method and two other cryptographic algorithms. It has been observed that the proposed algorithm has better results in terms of the key strength but is less computational efficient than other two.


title: What Does a TextCNN Learn?

abstract: TextCNN, the convolutional neural network for text, is a useful deep learning algorithm for sentence classification tasks such as sentiment analysis and question classification. However, neural networks have long been known as black boxes because interpreting them is a challenging task. Researchers have developed several tools to understand a CNN for image classification by deep visualization, but research about deep TextCNNs is still insufficient. In this paper, we are trying to understand what a TextCNN learns on two classical NLP datasets. Our work focuses on functions of different convolutional kernels and correlations between convolutional kernels.