title: Breaking down Usability

abstract: Good usability of a system is the main goal of interface designers. Determining the usability is usually done afterwards by performing usability tests with users or by going through checklists. On the other hand, design guidelines and design heuristics give the designer assistance in improving the usability while designing. In practice the available checklists, tests, guidelines etc. differ in terms of structure, content and terminology and the suggestion is given that one list is more useful than another. This paper breaks down the concept of usability into a layered model that allows comparisons of the available lists and provides better understanding of them. The model also gives a framework for evaluation by showing which usability aspects can be tested empirically and what can be formally checked by analyzing designs during the design process itself.


title: SYSTEM DEVELOPMENT METHODOLOGIES: UNANSWERED QUESTIONS AND THE RESEARCH-PRACTICE GAP

abstract: There is much we don't know about information system development and the use of systems development methodologies. Research to date has focused on the development of new methodologies. There has been little evaluation of these methodologies or examination of the selection, development, adaptation, or use of methodologies in practice. This paper discusses this lack of knowledge, its ramifications, and key research questions.


title: Unilateral mydriasis due to hemorrhoidal ointment.

abstract: BACKGROUND ::: The finding of a unilaterally dilatated pupil in a patient who is otherwise alert and unimpaired can pose an interesting diagnostic problem. Algorithms have outlined the approach to evaluating anisocoria, but do not stress the importance of toxic exposures. ::: ::: ::: CASE REPORT ::: A patient with a history of depression and chronic headaches was referred to the emergency department from an ophthalmologist's clinic with the findings of asymmetrically dilatated pupils and blurred vision. Detailed history revealed that for several weeks before the onset of symptoms, the patient applied to her eyelids hemorrhoidal ointment that contained an active ingredient known to produce mydriasis. ::: ::: ::: CONCLUSION ::: In the absence of neurologic deficits, clinical history should search for a toxic or pharmacologic etiology of abnormal mydriasis. A detailed physical examination and bedside diagnostic maneuvers can help distinguish benign causes of an abnormally dilatated pupil from more serious causes relating to central nervous system lesions, potentially sparing the patient from unnecessary neuroimaging.


title: Performance Analysis of IEEE 802.11ac DCF with Hidden Nodes

abstract: Recently, the IEEE 802.11 standard based Wireless Local Area Networks (WLAN) have become more popular and are widely deployed. It is anticipated that WLAN will play an important rule in the future wireless communication systems in order to provide several gigabits data rate. IEEE 802.11ac is one of the ongoing WLAN standard aiming to support very high throughput (VHT) with data rate of up to 6 Gbps below the 6 GHz band. In the development of IEEE 802.11ac standard, several new physical layer (PHY) and medium access control layer (MAC) features are taken into consideration, such as employing wider bandwidth in PHY and incrementing the limits of frame aggregation in MAC. However, due to the newly introduced features, some traditional techniques used in previous standards could face some problems. This paper presents a performance analysis of 802.11ac Distributed Coordination Function (DCF) in presence of hidden nodes in overlapping BSS (OBSS) environment. The effectiveness of DCF in IEEE 802.11ac WLAN when using different primary channels and different frequency bandwidth has also been discussed. Our results indicate that the traditional RTS/CTS handshake mechanism faces shortcomings and needs to be modified in order to support the newly defined 802.11ac amendment.


title: DACC: Distributed Access Control in Clouds

abstract: We propose a new model for data storage and access in clouds. Our scheme avoids storing multiple encrypted copies of same data. In our framework for secure data storage, cloud stores encrypted data (without being able to decrypt them). The main novelty of our model is addition of key distribution centers (KDCs). We propose DACC (Distributed Access Control in Clouds) algorithm, where one or more KDCs distribute keys to data owners and users. KDC may provide access to particular fields in all records. Thus, a single key replaces separate keys from owners. Owners and users are assigned certain set of attributes. Owner encrypts the data with the attributes it has and stores them in the cloud. The users with matching set of attributes can retrieve the data from the cloud. We apply attribute-based encryption based on bilinear pairings on elliptic curves. The scheme is collusion secure, two users cannot together decode any data that none of them has individual right to access. DACC also supports revocation of users, without redistributing keys to all the users of cloud services. We show that our approach results in lower communication, computation and storage overheads, compared to existing models and schemes.


title: Three cyber-security strategies to mitigate the impact of a data breach

abstract: We know that our mindsets need to shift these days and we must start by expecting to be hacked. But what then? How do we really manage an effective, secure environment? What steps do we need to consider?The most common approach to keeping organisations safe is through layers of security. Yet frequent high-profile attacks show that this approach is no longer enough to ward off sophisticated threats.Organisations must now alter their mindsets to expect a breach. It's better to assume your systems will be compromised. And with this in mind, they must have a 'response in depth' plan in order to mitigate the breach when it comes. Ben Densham of Nettitude provides a step-by-step guide to implementing an effective cyber-security strategy to ensure a potential disaster can be turned into a manageable incident.


title: Self-Report Dietary Assessment Tools Used in Canadian Research: A Scoping Review

abstract: Choosing the most appropriate dietary assessment tool for a study can be a challenge. Through a scoping review, we characterized self-report tools used to assess diet in Canada to identify patterns in tool use and to inform strategies to strengthen nutrition research. The research databases Medline, PubMed, PsycINFO, and CINAHL were used to identify Canadian studies published from 2009 to 2014 that included a self-report assessment of dietary intake. The search elicited 2358 records that were screened to identify those that reported on self-report dietary intake among nonclinical, non-Aboriginal adult populations. A pool of 189 articles (reflecting 92 studies) was examined in-depth to assess the dietary assessment tools used. Food-frequency questionnaires (FFQs) and screeners were used in 64% of studies, whereas food records and 24-h recalls were used in 18% and 14% of studies, respectively. Three studies (3%) used a single question to assess diet, and for 3 studies the tool used was not clear. A variety of distinct FFQs and screeners, including those developed and/or adapted for use in Canada and those developed elsewhere, were used. Some tools were reported to have been evaluated previously in terms of validity or reliability, but details of psychometric testing were often lacking. Energy and fat were the most commonly studied, reported by 42% and 39% of studies, respectively. For ∼20% of studies, dietary data were used to assess dietary quality or patterns, whereas close to half assessed ≤5 dietary components. A variety of dietary assessment tools are used in Canadian research. Strategies to improve the application of current evidence on best practices in dietary assessment have the potential to support a stronger and more cohesive literature on diet and health. Such strategies could benefit from national and global collaboration.


title: Particle Swarm Optimization Trained Class Association Rule Mining: Application to Phishing Detection

abstract: Association and classification are two important tasks in data mining. Literature abounds with works that unify these two techniques. This paper presents a new algorithm called Particle Swarm Optimization trained Classification Association Rule Mining (PSOCARM) for associative classification that generates class association rules (CARs) from transactional database by formulating a combinatorial global optimization problem, without having to specify minimal support and confidence unlike other conventional associative classifiers. We devised a new rule pruning scheme in order to reduce the number of rules and increasing the generalization aspect of the classifier. We demonstrated its effectiveness for phishing email and phishing website detection. Our experimental results indicate the superiority of our proposed algorithm with respect to accuracy and the number of rules generated as compared to the state-of-the-art algorithms.


title: Type II-C CRISPR-Cas9 Biology, Mechanism, and Application.

abstract: Genome editing technologies have been revolutionized by the discovery of prokaryotic RNA-guided defense system called CRISPR-Cas. Cas9, a single effector protein found in type II CRISPR systems, has been at the heart of this genome editing revolution. Nearly half of the Cas9s discovered so far belong to the type II-C subtype but have not been explored extensively. Type II-C CRISPR-Cas systems are the simplest of the type II systems, employing only three Cas proteins. Cas9s are central players in type II-C systems since they function in multiple steps of the CRISPR pathway, including adaptation and interference. Type II-C CRISPR systems are found in bacteria and archaea from very diverse environments, resulting in Cas9s with unique and potentially useful properties. Certain type II-C Cas9s possess unusually long PAMs, function in unique conditions (e.g., elevated temperature), and tend to be smaller in size. Here, we review the biology, mechanism, and applications of the type II-C CRISPR systems with particular emphasis on their Cas9s.


title: GPS: a graph processing system

abstract: GPS (for Graph Processing System) is a complete open-source system we developed for scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large graphs. This paper serves the dual role of describing the GPS system, and presenting techniques and experimental results for graph partitioning in distributed graph-processing systems like GPS. GPS is similar to Google's proprietary Pregel system, with three new features: (1) an extended API to make global computations more easily expressed and more efficient; (2) a dynamic repartitioning scheme that reassigns vertices to different workers during the computation, based on messaging patterns; and (3) an optimization that distributes adjacency lists of high-degree vertices across all compute nodes to improve performance. In addition to presenting the implementation of GPS and its novel features, we also present experimental results on the performance effects of both static and dynamic graph partitioning schemes, and we describe the compilation of a high-level domain-specific programming language to GPS, enabling easy expression of complex algorithms.


title: Automatic Meter Reading and Theft Control System by Using GSM

abstract: This paper deals with automatic meter reading and theft control system in energy meter. Current transformer is used to measure the total power consumption for house or industrial purpose. This recorded reading is transmitted to the electricity board for every 60 days once. For transmitting the reading of energy meter GSM module is used. To avoid theft, infrared sensor is placed in the screw portion of energy meter seal. If the screw is removed from the meter a message is sent to the electricity board. The measuring of energy meter and monitoring of IR sensor is done with a PIC microcontroller. The informative system will be helpful for the electricity board to monitor the entire supply and the correct billing accordingly without any mishap. This model reduces the manual manipulation work and theft control. Key wordIR Sensor, Magnetic reed switch, PIC16F877A Microcontroller.


title: RETRACTED: Tracheobronchial transplantation with a stem-cell-seeded bioartificial nanocomposite: a proof-of-concept study

abstract: Summary Background Tracheal tumours can be surgically resected but most are an inoperable size at the time of diagnosis; therefore, new therapeutic options are needed. We report the clinical transplantation of the tracheobronchial airway with a stem-cell-seeded bioartificial nanocomposite. Methods A 36-year-old male patient, previously treated with debulking surgery and radiation therapy, presented with recurrent primary cancer of the distal trachea and main bronchi. After complete tumour resection, the airway was replaced with a tailored bioartificial nanocomposite previously seeded with autologous bone-marrow mononuclear cells via a bioreactor for 36 h. Postoperative granulocyte colony-stimulating factor filgrastim (10 μg/kg) and epoetin beta (40 000 UI) were given over 14 days. We undertook flow cytometry, scanning electron microscopy, confocal microscopy epigenetics, multiplex, miRNA, and gene expression analyses. Findings We noted an extracellular matrix-like coating and proliferating cells including a CD105+ subpopulation in the scaffold after the reseeding and bioreactor process. There were no major complications, and the patient was asymptomatic and tumour free 5 months after transplantation. The bioartificial nanocomposite has patent anastomoses, lined with a vascularised neomucosa, and was partly covered by nearly healthy epithelium. Postoperatively, we detected a mobilisation of peripheral cells displaying increased mesenchymal stromal cell phenotype, and upregulation of epoetin receptors, antiapoptotic genes, and miR-34 and miR-449 biomarkers. These findings, together with increased levels of regenerative-associated plasma factors, strongly suggest stem-cell homing and cell-mediated wound repair, extracellular matrix remodelling, and neovascularisation of the graft. Interpretation Tailor-made bioartificial scaffolds can be used to replace complex airway defects. The bioreactor reseeding process and pharmacological-induced site-specific and graft-specific regeneration and tissue protection are key factors for successful clinical outcome. Funding European Commission, Knut and Alice Wallenberg Foundation, Swedish Research Council, StratRegen, Vinnova Foundation, Radiumhemmet, Clinigene EU Network of Excellence, Swedish Cancer Society, Centre for Biosciences (The Live Cell imaging Unit), and UCL Business.


title: AndroTaint: An efficient android malware detection framework using dynamic taint analysis

abstract: Android operating system is widespread in today's smart-phone market due to its open source model, its easy functionality and huge number of Apps and App-Stores (the official and others). The coverage of some 90% of the market, it is also the place-holder for user's personal as well as sensitive information (IMEI, IMSI, etc.). There is a tendency of Apps user to trust on Android OS for securing his/her data but it has been proved that Android OS is more vulnerable to exploitation either for fun or for monetary purpose. Malware detection for Android OS has become an upcoming research problem of interest. Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis. The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform. In order to do this, this paper surveys exhaustively the available literature and works related to dynamic taint analysis. This paper proposes some novel ideas to improve the existing solution with more accuracy. There are two phases of our novel algorithm, the first phase is training phase for feature extraction and the second phase is the analysis phase for automatic tagging and tainting. We have developed a framework named AndroTaint, which is working on Dynamic Taint Analysis. AndroTaint follows a novel supervised and unsupervised Anomaly Detection technique with high precision, recall and harmonic mean. Our Dynamic Taint Analysis algorithm categorizes the App is risky, benign, malicious or aggressive as per their feature and behaviour. We have measured AndroTaint effectiveness on the basis of timeline for building dataset and 10-fold cross validation. AndroTaint covers 90% of malware and benign in analysis phase with less false positive and false negative.


title: Attribute-restricted latent topic model for person re-identification

abstract: Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, and from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Attribute-Restricted Latent Topic Model (ARLTM) to encode targets into semantic topics. Compared to conventional topic models such as LDA and pLSI, ARLTM performs best by imposing semantic restrictions onto the generation of human specific attributes. We use MCMC EM for model learning. Experimental results show that our method achieves state-of-the-art performance.


title: The Self-Serv Environment for Web Services Composition

abstract: Self-Serv aims to enable the declarative composition of new services from existing ones, the multiattribute dynamic selection of services within a composition, and peer-to-peer orchestration of composite service executions. Self-Serv adopts the principle that every service, whether elementary or composite, should provide a programmatic interface based on SOAP and the Web Service Definition Language. This does not exclude the possibility of integrating legacy applications, such as those written in CORBA, into the service's business logic. To integrate such applications, however, first requires the development of appropriate adapters. The paper considers how the mechanism for composing services in Self-Serv is based on two major concepts: the composite service and the service container.


title: Improved method of handwritten digit recognition tested on MNIST database

abstract: Abstract We have developed a novel neural classifier LImited Receptive Area (LIRA) for the image recognition. The classifier LIRA contains three neuron layers: sensor, associative and output layers. The sensor layer is connected with the associative layer with no modifiable random connections and the associative layer is connected with the output layer with trainable connections. The training process converges sufficiently fast. This classifier does not use floating point and multiplication operations. The classifier was tested on two image databases. The first database is the MNIST database. It contains 60,000 handwritten digit images for the classifier training and 10,000 handwritten digit images for the classifier testing. The second database contains 441 images of the assembly microdevice. The problem under investigation is to recognize the position of the pin relatively to the hole. A random procedure was used for partition of the database to training and testing subsets. There are many results for the MNIST database in the literature. In the best cases, the error rates are 0.7, 0.63 and 0.42%. The classifier LIRA gives error rate of 0.61% as a mean value of three trials. In task of the pin–hole position estimation the classifier LIRA also shows sufficiently good results.


title: Twitter-Based User Modeling for News Recommendations

abstract: In this paper, we study user modeling on Twitter. We investigate different strategies for mining user interest profiles from microblogging activities ranging from strategies that analyze the semantic meaning of Twitter messages to strategies that adapt to temporal patterns that can be observed in the microblogging behavior. We evaluate the quality of the user modeling methods in the context of a personalized news recommendation system. Our results reveals that an understanding of the semantic meaning of microposts is key for generating high-quality user profiles.


title: DC/DC fixed frequency resonant LLC full-bridge converter with series-parallel transformers for 10kW high efficiency aircraft application

abstract: In modern aircraft designs, there is a need for new high power isolated DC/DC converters. Full bridge derived topologies are appropriate for these specifications. Aircraft specifications are demanding and a high power density is needed. In order to decrease volume, reactive component, such as the transformer have to be optimized in volume, weight and loses. High frequency is needed to decrease inductive component core size and magnetic integration is a key factor. With the use of high frequency, topologies that take advantage of both ZVS and q-ZCS can offer the best results. Resonant topologies have a lot of benefits for this kind of requirement in this application. In this paper, a comparison of different state of the art full-bridge based topologies is made. A detailed explanation of the benefits of the Series-Parallel solution is provided. Experimental result of the prototype are also shown to prove the concept.


title: Lower Limb Rehabilitation Robot in Sitting Position for Various Therapeutic Exercises

abstract: In this paper, a lower limb rehabilitation robot in sitting position is developed for patients with muscle weakness. The robot is a stationary based type which is able to perform various types of therapeutic exercises. For safe operation, the robot's joint is driven by two-stage cable transmission while the balance mechanism is used to reduce actuator size and transmission ratio. Control algorithms for passive, assistive and resistive exercises are designed to match characteristics of each therapeutic exercises and patients with different muscle strength. Preliminary experiments conducted with a healthy subject have demonstrated that the robot and the control algorithms are promising for lower limb rehabilitation task.


title: Improving Post-Click User Engagement on Native Ads via Survival Analysis

abstract: In this paper we focus on estimating the post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages. To infer relationships between features of the ads and dwell time we resort to the application of survival analysis techniques, which allow us to estimate the distribution of the length of time that the user will spend on the ad. This information is then integrated into the ad ranking function with the goal of promoting the rank of ads that are likely to be clicked and consumed by users (dwell time greater than a given threshold). The online evaluation over live traffic shows that considering post-click engagement has a consistent positive effect on both CTR, decreases the number of bounces and increases the average dwell time, hence leading to a better user post-click experience.


title: A new multiclass SVM algorithm and its application to crowd density analysis using LBP features

abstract: Crowd density analysis is a crucial component in visual surveillance for security monitoring. In this paper, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions. The main contribution of this paper is two-fold: First, we propose to learn a discriminant subspace of the high-dimensional Local Binary Pattern (LBP) instead of using raw LBP feature vector. Second, an alternative algorithm for multiclass SVM based on relevance scores is proposed. The effectiveness of the proposed approach is evaluated on PETS dataset, and the results demonstrate the effect of low-dimensional compact representation of LBP on the classification accuracy. Also, the performance of the proposed multiclass SVM algorithm is compared to other frequently used algorithms for multi-classification problem and the proposed algorithm gives good results while reducing the complexity of the classification.


title: The Hadoop Distributed File System

abstract: The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks. By distributing storage and computation across many servers, the resource can grow with demand while remaining economical at every size. We describe the architecture of HDFS and report on experience using HDFS to manage 25 petabytes of enterprise data at Yahoo!.


title: Morphological features help POS tagging of unknown words across language varieties

abstract: Part-of-speech tagging, like any supervised statistical NLP task, is more difficult when test sets are very different from training sets, for example when tagging across genres or language varieties. We examined the problem of POS tagging of different varieties of Mandarin Chinese (PRC-Mainland, PRCHong Kong, and Taiwan). An analytic study first showed that unknown words were a major source of difficulty in cross-variety tagging. Unknown words in English tend to be proper nouns. By contrast, we found that Mandarin unknown words were mostly common nouns and verbs. We showed these results are caused by the high frequency of morphological compounding in Mandarin; in this sense Mandarin is more like German than English. Based on this analysis, we propose a variety of new morphological unknown-word features for POS tagging, extending earlier work by others on unknown-word tagging in English and German. Our features were implemented in a maximum entropy Markov model. Our system achieves state-of-the-art performance in Mandarin tagging, including improving unknown-word tagging performance on unseen varieties in Chinese Treebank 5.0 from 61% to 80% correct.


title: Methane potential and biodegradability of rice straw, rice husk and rice residues from the drying process

abstract: Agricultural solid residues are a potential renewable energy source. Rice harvesting and production in Sancti Spíritus province, Cuba, currently generates residues without an environmentally sustainable disposal route. Rice residues (rice straw, rice husk and rice residues from the drying process) are potentially an important carbon source for anaerobic digestion. For this paper, rice residues were placed for 36 days retention time in anaerobic batch reactor environments at both mesophilic (37 °C) and thermophilic (55 °C) conditions. Biogas and methane yield were determined as well as biogas composition. The results showed that rice straw as well as rice residues from the drying process had the highest biogas and methane yield. Temperature played an important role in determining both biogas yield and kinetics. In all cases, rice straw produced the highest yields; under mesophilic conditions the biogas yield was 0.43 m(3) kg(VS)(-1), under thermophilic conditions biogas yield reached 0.52 m(3) kg(VS)(-1). In the case of the rice husk, the biodegradability was very low. Methane content in all batches was kept above 55% vol. All digested material had a high carbon:nitrogen (C:N) ratio, even though significant biodegradation was recorded with the exception of rice husk. A first-order model can be used to describe the rice crop residues fermentation effectively.


title: Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video

abstract: Understanding video events, i.e., the translation of low-level content in video sequences into high-level semantic concepts, is a research topic that has received much interest in recent years. Important applications of this paper include smart surveillance systems, semantic video database indexing, and interactive systems. This technology can be applied to several video domains including airport terminal, parking lot, traffic, subway stations, aerial surveillance, and sign language data. In this paper, we identify the two main components of the event understanding process: abstraction and event modeling. Abstraction is the process of molding the data into informative units to be used as input to the event model. Due to space restrictions, we will limit the discussion on the topic of abstraction. See the study by Lavee et al. (Understanding video events: A survey of methods for automatic interpretation of semantic occurrences in video, Technion-Israel Inst. Technol., Haifa, Israel, Tech. Rep. CIS-2009-06, 2009) for a more complete discussion. Event modeling is devoted to describing events of interest formally and enabling recognition of these events as they occur in the video sequence. Event modeling can be further decomposed in the categories of pattern-recognition methods, state event models, and semantic event models. In this survey, we discuss this proposed taxonomy of the literature, offer a unifying terminology, and discuss popular event modeling formalisms (e.g., hidden Markov model) and their use in video event understanding using extensive examples from the literature. Finally, we consider the application domain of video event understanding in light of the proposed taxonomy, and propose future directions for research in this field.


title: Food and Mood: Just-in-Time Support for Emotional Eating

abstract: Behavior modification in health is difficult, as habitual behaviors are extremely well-learned, by definition. This research is focused on building a persuasive system for behavior modification around emotional eating. In this paper, we make strides towards building a just-in-time support system for emotional eating in three user studies. The first two studies involved participants using a custom mobile phone application for tracking emotions, food, and receiving interventions. We found lots of individual differences in emotional eating behaviors and that most participants wanted personalized interventions, rather than a pre-determined intervention. Finally, we also designed a novel, wearable sensor system for detecting emotions using a machine learning approach. This system consisted of physiological sensors which were placed into women's brassieres. We tested the sensing system and found positive results for emotion detection in this mobile, wearable system.


Semantic Maps for Robotics


title: Waveform Design for 5G Networks: Analysis and Comparison

abstract: The next-generation cellular network is to provide a large variety of services for different kinds of terminals, from traditional voice and data services over mobile phones to small packet transmission over massive machine-type terminals. Although orthogonal-subcarrier-based waveforms are widely used nowadays in many practical systems, they can hardly meet the requirements in the coming 5G networks. Therefore, more flexible waveforms have been proposed to address the unprecedented challenges. In this paper, we will provide comprehensive analysis and comparison for typical waveforms. To offer an insightful analysis, we will not only introduce the basic principles of the waveforms, but also reveal the underlying characteristics. Moreover, a comprehensive comparison in terms of different performance metrics will also be presented in thispaper, which provides an overall understanding of the new waveforms.


title: An Improved $LLC$ Resonant Inverter for Induction-Heating Applications With Asymmetrical Control

abstract: This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction-heating applications. The LLC load configuration is a combination of a series inductor, a matching transformer, and an inductor and a capacitor connected in parallel. The output power is controlled using the asymmetrical voltage-cancellation technique. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load-parameter variation during the heating process. The proposed configuration has the benefit of smaller inductance and inherent short-circuit protection capability in case a short circuit occurs at the induction coil or from transformer saturation. The validity of the proposed method is verified through computer simulation and hardware experiment at an operating frequency range of 108.7-110.6 kHz.


title: Facility layouts based on differential evolution algorithm

abstract: Facility layout problem (FLP) which deals with the placement of facilities in the plant area is one of the most critical problems in manufacturing systems design. This paper presents a novel facility layout approach based on differential evolution (DE) algorithm. Firstly, mathematical model with constraints for the FLP is established. Then, a new facility layout algorithm based on DE is proposed, which can improve the convergence rate by using the interference checking method. Finally, a simulation platform with three-dimensional visualization is developed. The comparison experiments of the proposed method and genetic algorithm (GA) based on robot work cell layout problem show that the proposed method converges faster and can obtain better optimal layout.


User Modeling, Adaptation, and Personalization


title: Towards News Verification: Deception Detection Methods for News Discourse

abstract: News verification is a process of determining whether a particular news report is truthful or deceptive. Deliberately deceptive (fabricated) news creates false conclusions in the readers’ minds. Truthful (authentic) news matches the writer’s knowledge. How do you tell the difference between the two in an automated way? To investigate this question, we analyzed rhetorical structures, discourse constituent parts and their coherence relations in deceptive and truthful news sample from NPR’s “Bluff the Listener”. Subsequently, we applied a vector space model to cluster the news by discourse feature similarity, achieving 63% accuracy. Our predictive model is not significantly better than chance (56% accuracy), though comparable to average human lie detection abilities (54%). Methodological limitations and future improvements are discussed. The long-term goal is to uncover systematic language differences and inform the core methodology of the news verification system.


title: Chemical methods for the production of graphenes

abstract: Interest in graphene centres on its excellent mechanical, electrical, thermal and optical properties, its very high specific surface area, and our ability to influence these properties through chemical functionalization. There are a number of methods for generating graphene and chemically modified graphene from graphite and derivatives of graphite, each with different advantages and disadvantages. Here we review the use of colloidal suspensions to produce new materials composed of graphene and chemically modified graphene. This approach is both versatile and scalable, and is adaptable to a wide variety of applications.


title: Local implicit feedback mining for music recommendation

abstract: Digital music has experienced a quite fascinating transformation during the past decades. Thousands of people share or distribute their music collections on the Internet, resulting in an explosive increase of information and more user dependence on automatic recommender systems. Though there are many techniques such as collaborative filtering, most approaches focus mainly on users' global behaviors, neglecting local actions and the specific properties of music. In this paper, we propose a simple and effective local implicit feedback model mining users' local preferences to get better recommendation performance in both rating and ranking prediction. Moreover, we design an efficient training algorithm to speed up the updating procedure, and give a method to find the most appropriate time granularity to assist the performance. We conduct various experiments to evaluate the performance of this model, which show that it outperforms baseline model significantly. Integration with existing temporal models achieves a great improvement compared to the reported best single model for Yahoo! Music.


title: Execution time prediction of imperative paradigm tasks for grid scheduling optimization

abstract: Summary An efficient functioning of a complicated and dynamic grid environment requires a resource manager to monitor and identify the idling resources and to schedule users’ submitted jobs (or programs) accordingly. A common problem arising in grid computing is to select the most efficient resource to run a particular program. At present the execution time of any program submission depends mostly on guesswork by the user. The inaccuracy of guesswork leads to inefficient resource usage, incurring extra operational costs such as idling queues or machines. Thus, in this paper we propose a job execution time prediction module to aid the user. The proposed system will function as a standalone unit where its services can be offered to users as part of a grid portal. This system focuses on imperative paradigm tasks as they are commonly used in a grid environment. We propose a novel methodology and architecture to predict the execution time of jobs using aspects of static analysis, analytical benchmarking and compiler based approach. Essentially a program is analyzed in segments for execution time and these times are combined together to give the total execution time of the program. The experimental results show that the technique is successful in achieving a prediction accuracy of greater than 80%. Future work may involve handling other paradigms such as object-oriented programming and investigating the possibility of integrating the prediction module into a real grid environment.


title: Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network

abstract: In this paper, we present a novel method for multimodal gesture recognition based on neural networks. Our multi-stream recurrent neural network MRNN is a completely data-driven model that can be trained from end to end without domain-specific hand engineering. The MRNN extends recurrent neural networks with Long Short-Term Memory cells LSTM-RNNs that facilitate the handling of variable-length gestures. We propose a recurrent approach for fusing multiple temporal modalities using multiple streams of LSTM-RNNs. In addition, we propose alternative fusion architectures and empirically evaluate the performance and robustness of these fusion strategies. Experimental results demonstrate that the proposed MRNN outperforms other state-of-the-art methods in the Sheffield Kinect Gesture SKIG dataset, and has significantly high robustness to noisy inputs.


title: Three new versions for the Newton type adaptive filtering algorithm

abstract: In this work, we present three new versions for the Newton type adaptive filtering algorithm FNTF. The first version is based on a temporal division of the basic transverse structure by using local filtering errors for actualization of the different sections of the transverse filter, whereas the second version differs from the first in the use of the final filtering error to update the coefficients of the transverse filter. The third version is based on mixing the second version with the introduction of a scalar parameter into the filtering part. These three versions were proposed to improve the convergence speed of the original Newton algorithm version for the identification of the acoustic impulse response and to improve, also, the tracking capability when these systems vary in time.


title: Interference-Aware Channel Assignment in Multi-Radio Wireless Mesh Networks

abstract: The capacity problem in wireless mesh networks can be alleviated by equipping the mesh routers with multiple radios tuned to non-overlapping channels. However, channel assignment presents a challenge because co-located wireless networks are likely to be tuned to the same channels. The resulting increase in interference can adversely affect performance. This paper presents an interference-aware channel assignment algorithm and protocol for multi-radio wireless mesh networks that address this interference problem. The proposed solution intelligently assigns channels to radios to minimize interference within the mesh network and between the mesh network and co-located wireless networks. It utilizes a novel interference estimation technique implemented at each mesh router. An extension to the conflict graph model, the multi-radio conflict graph, is used to model the interference between the routers. We demonstrate our solution’s practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed. We also report on an extensive evaluation via simulations. In a sample multi-radio scenario, our solution yields performance gains in excess of 40% compared to a static assignment of channels.


title: A Study on the Effects of Loneliness, Depression and Perceived Social Support on Problematic Internet Use among University Students

abstract: The present study investigated the effects of loneliness, depression and perceived social support on problematic Internet use among university students. The participants were 459 students at two universities in Turkey. The study data were collected with a Questionnaire Form, Problematic Internet Use Scale (PIUS), University of California at Los Angeles (UCLA) Loneliness Scale (Version 3), Multidimensional Scale of Perceived Social Support (MSPSS) and Beck Depression Inventory (BDI). The Mann-Whitney U Test and Kruskal-Wallis one-way analysis of variance were conducted to examine the differences; and correlation and regression analyses were used to examine the relationships between variables. There was a positive significant correlation between the PIUS and MSPSS and the UCLA Loneliness Scale and a negative significant correlation between the PIUS and Beck Depression Scale (BDS). The female students had higher total PIUS scores. The results also illustrated that there was a statistically significant difference in total PIUS scores according to having a social network account.


Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery


title: Quantum neural networks (QNN’s): inherently fuzzy feedforward neural networks

abstract: This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack.


title: A Research Study of Hand Gesture Recognition Technologies and Applications for Human Vehicle Interaction

abstract: This paper describes the primary and secondary driving task together with Human Machine Interface (HMI) trends and issues which are driving automotive user interface designers to consider hand gesture recognition as a realistic alternative for user controls. A number of hand gesture recognition technologies and applications for Human Vehicle Interaction (HVI) are also discussed including a summary of current automotive hand gesture recognition research.


title: Analysis of automated administrative and survey databases to study patterns and outcomes of care.

abstract: Large computerized databases often arise from national surveys, insurance claims, and statewide health care registries. These databases are increasingly used to examine patterns of medical care and certain outcomes of care and may be helpful in planning clinical trials. They are highly representative of defined populations, but have limited clinical information. Methods have been developed to identify episodes of low back pain and to quantify the severity of unrelated, comorbid medical conditions. Pitfalls in analysis are discussed, including limitations of diagnosis and procedure coding, cross-sectional nature of most data, limited clinical detail, and the necessarily observational (not experimental) nature of any group comparisons. There is growing interest in expanding the clinical information in such databases, for both quality improvement and research purposes.


title: Improving Internet Security Through Social Information and Social Comparison:

abstract: Cybersecurity is a national priority in this big data era. Because of negative externalities and the resulting lack of economic incentives, companies often underinvest in security controls, despite government and industry recommendations. Although many existing studies on security have explored technical solutions, only a few have looked at the economic motivations. To fill the gap, we propose an approach to increase the incentives of organizations to address security problems. Specifically, we utilize and process existing security vulnerability data, derive explicit security performance information, and disclose the information as feedback to organizations and the public. We regularly release information on the organizations with the worst security behaviors, imposing reputation loss on them. The information is also used by organizations for self-evaluation in comparison to others. Therefore, additional incentives are solicited out of reputation concern and social comparison. To test the effectiveness of our approach, we conducted a field quasi-experiment for outgoing spam for 1,718 autonomous systems in eight countries and published SpamRankings.net, the website we created to release information. We found that the treatment group subject to information disclosure reduced outgoing spam approximately by 16%. We also found that the more observed outgoing spam from the top spammer, the less likely an organization would be to reduce its own outgoing spam, consistent with the prediction by social comparison theory. Our results suggest that social information and social comparison can be effectively leveraged to encourage desirable behavior. Our study contributes to both information architecture design and public policy by suggesting how information can be used as intervention to impose economic incentives. The usual disclaimers apply for NSF grants 1228990 and 0831338.


title: Building Robust Systems an essay

abstract: It is hard to build robust systems: systems that have accept able behavior over a larger class of situations than was anticipated by their designers. The most robust systems are evolvable: they can be easily adapted to new situations with only minor mod ification. How can we design systems that are flexible in this way? Observations of biological systems tell us a great deal about how to make robust and evolvable systems. Techniques origi nally developed in support of symbolic Artificial Intelligence can be viewed as ways of enhancing robustness and evolvability in programs and other engineered systems. By contrast, common practice of computer science actively discourages the construc tion of robust systems.


title: Towards an Ontology Design Pattern Quality Model

abstract: The use of semantic technologies and Semantic Web ontologies in particular have enabled many recent developments in information integration, search engines, and reasoning over formalised knowledge. ...


title: Video Summarization for Large Sports Video Archives

abstract: Video summarization is defined as creating a shorter video clip or a video poster which includes only the important scenes in the original video streams. In this paper, we propose two methods of generating a summary of arbitrary length for large sports video archives. One is to create a concise video clip by temporally compressing the amount of the video data. The other is to provide a video poster by spatially presenting the image keyframes which together represent the whole video content. Our methods deal with the metadata which has semantic descriptions of video content. Summaries are created according to the significance of each video segment which is normalized in order to handle large sports video archives. We experimentally verified the effectiveness of our methods by comparing the results with man-made video summaries


title: Pose tracking from natural features on mobile phones

abstract: In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20 Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. We present evaluations on robustness and performance on various devices and finally discuss their appropriateness for augmented reality applications.


title: Scalable Hybrid Wireless Network-on-Chip Architectures for Multicore Systems

abstract: Multicore platforms are emerging trends in the design of System-on-Chips (SoCs). Interconnect fabrics for these multicore SoCs play a crucial role in achieving the target performance. The Network-on-Chip (NoC) paradigm has been proposed as a promising solution for designing the interconnect fabric of multicore SoCs. But the performance requirements of NoC infrastructures in future technology nodes cannot be met by relying only on material innovation with traditional scaling. The continuing demand for low-power and high-speed interconnects with technology scaling necessitates looking beyond the conventional planar metal/dielectric-based interconnect infrastructures. Among different possible alternatives, the on-chip wireless communication network is envisioned as a revolutionary methodology, capable of bringing significant performance gains for multicore SoCs. Wireless NoCs (WiNoCs) can be designed by using miniaturized on-chip antennas as an enabling technology. In this paper, we present design methodologies and technology requirements for scalable WiNoC architectures and evaluate their performance. It is demonstrated that WiNoCs outperform their wired counterparts in terms of network throughput and latency, and that energy dissipation improves by orders of magnitude. The performance of the proposed WiNoC is evaluated in presence of various traffic patterns and also compared with other emerging alternative NoCs.


Parallel K-Means Clustering with Triangle Inequality


title: 3D scan-conversion algorithms for voxel-based graphics

abstract: An assortment of algorithms, termed three-dimensional (3D) scan-conversion algorithms , is presented. These algorithms scan-convert 3D geometric objects into their discrete voxel-map representation within a Cubic Frame Buffer (CFB) . The geometric objects that are studied here include three-dimensional lines, polygons (optionally filled), polyhedra (optionally filled), cubic parametric curves, bicubic parametric surface patches, circles (optionally filled), and quadratic objects (optionally filled) like those used in constructive solid geometry: cylinders, cones, and spheres. All algorithms presented here do scan-conversion with computational complexity which is linear in the number of voxels written to the CFB. All algorithms are incremental and use only additions, subtractions, tests and simpler operations inside the inner algorithm loops. Since the algorithms are basically sequential, the temporal complexity is also linear. However, the polyhedron-fill and sphere-fill algorithms have less than linear temporal complexity, as they use a mechanism for writing a voxel run into the CFB. The temporal complexity would then be linear with the number of pixels in the object's 2D projection. All algorithms have been implemented as part of the CUBE Architecture , which is a voxel-based system for 3D graphics. The CUBE architecture is also presented.


title: General Functional Matrix Factorization Using Gradient Boosting

abstract: Matrix factorization is among the most successful techniques for collaborative filtering. One challenge of collaborative filtering is how to utilize available auxiliary information to improve prediction accuracy. In this paper, we study the problem of utilizing auxiliary information as features of factorization and propose formalizing the problem as general functional matrix factorization, whose model includes conventional matrix factorization models as its special cases. Moreover, we propose a gradient boosting based algorithm to efficiently solve the optimization problem. Finally, we give two specific algorithms for efficient feature function construction for two specific tasks. Our method can construct more suitable feature functions by searching in an infinite functional space based on training data and thus can yield better prediction accuracy. The experimental results demonstrate that the proposed method out-performs the baseline methods on three realworld datasets.


title: Modulation of HERG potassium channel function by drug action.

abstract: Repolarization of cardiomyocytes is mainly performed by the rapid component of the delayed rectifier potassium current, I(Kr), which is encoded by the human ether-a-go-go-related gene (HERG). Inhibition of HERG potassium currents by class III antiarrhythmic drugs causes lengthening of the cardiac action potential, which produces a beneficial antiarrhythmic effect. Conversely, excessive prolongation of the action potential by a wide variety of antiarrhythmic and non-antiarrhythmic drugs may lead to acquired long-QT syndrome, which is associated with a risk for 'torsade de pointes'-arrhythmias and sudden cardiac death. As a result, this undesirable side effect has prompted the withdrawal of several drugs from the market. Recent studies on HERG channel inhibition provide significant insights into the molecular factors that determine state-, voltage-, and use-dependency of HERG current block. In addition, crucial properties of the putative drug binding site in HERG have been identified. The broad diversity in response to pharmacologic treatment among individuals is likely to depend on a combination of multiple factors from the fields of arrhythmia genetics, physiology and pharmacology. In conclusion, the increasing understanding of the molecular mechanisms that underlie HERG channel block by antiarrhythmic and non-antiarrhythmic drugs may improve prevention and treatment of drug-induced long-QT syndrome.


title: Customer churn prediction by hybrid neural networks

abstract: As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN+ANN) and SOM combined with ANN (SOM+ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN+ANN hybrid model significantly performs better than the SOM+ANN hybrid model and the ANN baseline model.


title: A Review of Power Decoupling Techniques for Microinverters With Three Different Decoupling Capacitor Locations in PV Systems

abstract: The reliability of the microinverter is a very important feature that will determine the reliability of the ac-module photovoltaic (PV) system. Recently, many topologies and techniques have been proposed to improve its reliability. This paper presents a thorough study for different power decoupling techniques in single-phase microinverters for grid-tie PV applications. These power decoupling techniques are categorized into three groups in terms of the decoupling capacitor locations: 1) PV-side decoupling; 2) dc-link decoupling; and 3) ac-side decoupling. Various techniques and topologies are presented, compared, and scrutinized in scope of the size of decoupling capacitor, efficiency, and control complexity. Also, a systematic performance comparison is presented for potential power decoupling topologies and techniques.


title: A robust method for road sign detection and recognition

abstract: This paper describes a method for detecting and recognizing road signs in grey-level and colour images acquired by a single camera mounted on a moving vehicle. The method works in three stages. First, the search for the road sign is reduced to a suitable region of the image by using some a priori knowledge on the scene or colour clues (when available). Secondly, a geometrical analysis of the edges extracted from the image is carried out, which generates candidates to be circular and triangular signs. Thirdly, a recognition stage tests by cross-correlation techniques each candidate which, if validated, is classified according to the database of signs. An extensive experimentation has shown that the method is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways. A further improvement on the detection and recognition scheme has been obtained by means of temporal integration based on Kalman filtering methods of the extracted information. The proposed approach can be very helpful for the development of a system for driving assistance.


title: Nonextraction treatment of an open bite with microscrew implant anchorage

abstract: A 16-year-old girl with an anterior open bite was treated with nonextraction therapy that included intrusion of the maxillary and mandibular posterior teeth with microscrew implants. Implants (diameter, 1.2 mm; length, 8 or 6 mm) were placed into alveolar bone near the posterior teeth and used as anchorage for intrusive force. To prevent adverse side effects of buccoversion or linguoversion of the posterior teeth during intrusion, a transpalatal bar and a lingual arch were placed. The 3-mm anterior open bite was corrected in 11 months of treatment, after intrusion of the maxillary and mandibular posterior teeth and autorotation of the mandible. The posterior intrusion relapsed in the early stage of retention, at 8 months; thereafter, no obvious relapse was evident in the vertical position of the molars and the FMA. The treatment mechanics of anterior open bite with posterior intrusion by using microscrew implants were effective but still require a proper retention protocol.


title: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2. Modeling and identification

abstract: Abstract Battery management systems in hybrid electric vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. In order to use EKF to estimate the desired quantities, we first require a mathematical model that can accurately capture the dynamics of a cell. In this paper we “evolve” a suitable model from one that is very primitive to one that is more advanced and works well in practice. The final model includes terms that describe the dynamic contributions due to open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, and the effects of temperature. We also give a means, based on EKF, whereby the constant model parameters may be determined from cell test data. Results are presented that demonstrate it is possible to achieve root-mean-squared modeling error smaller than the level of quantization error expected in an implementation.


title: Security and Privacy Aspects of Low-Cost Radio Frequency Identification Systems

abstract: Like many technologies, low-cost Radio Frequency Identification (RFID) systems will become pervasive in our daily lives when affixed to every- day consumer items as "smart labels". While yielding great productivity gains, RFID systems may create new threats to the security and privacy of individuals or organizations. This paper presents a brief description of RFID systems and their operation. We describe privacy and security risks and how they apply to the unique setting of low-cost RFID devices. We propose several security mech- anisms and suggest areas for future research.


title: AttSum: Joint Learning of Focusing and Summarization with Neural Attention

abstract: Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well. This paper proposes a novel summarization system called AttSum, which tackles the two tasks jointly. It automatically learns distributed representations for sentences as well as the document cluster. Meanwhile, it applies the attention mechanism to simulate the attentive reading of human behavior when a query is given. Extensive experiments are conducted on DUC query-focused summarization benchmark datasets. Without using any hand-crafted features, AttSum achieves competitive performance. It is also observed that the sentences recognized to focus on the query indeed meet the query need.


title: DETSApp: An App for Disaster Event Tweets Summarization using Images Posted on Twitter

abstract: In this paper, a method for summarization of the images posted on online social media (OSM) is proposed. A method based on near-duplicate image detection is used to cluster the images. The images within each cluster convey some information which is summarized using the textual information posted along with an image. The application is intended for smartphone users and may help the rescue personnel in any disaster situation. The proposed method is tested on a dataset collected from OSMs during the floods in Chennai in December 2015. Some of the results are presented in this paper to show the efficacy of the proposed method.


title: Efficient online recommendation via low-rank ensemble sampling

abstract: The low-rank structure is one of the most prominent features in modern recommendation problems. In this paper, we consider an online learning problem with a low-rank expected reward matrix where both row features and column features are unknown a priori, and the agent aims to learn to choose the best row-column pair (i.e. the maximum entry) in the matrix. We develop a novel online recommendation algorithm based on ensemble sampling, a recently developed computationally efficient approximation of Thompson sampling. Our computational results show that our algorithm consistently achieves order-of-magnitude improvements over the baselines in both synthetic and real-world experiments.


title: Comparative Study between Traditional Machine Learning and Deep Learning Approaches for Text Classification

abstract: In this contemporaneous world, it is an obligation for any organization working with documents to end up with the insipid task of classifying truckload of documents, which is the nascent stage of venturing into the realm of information retrieval and data mining. But classification of such humongous documents into multiple classes, calls for a lot of time and labor. Hence a system which could classify these documents with acceptable accuracy would be of an unfathomable help in document engineering. We have created multiple classifiers for document classification and compared their accuracy on raw and processed data. We have garnered data used in a corporate organization as well as publicly available data for comparison. Data is processed by removing the stop-words and stemming is implemented to produce root words. Multiple traditional machine learning techniques like Naive Bayes, Logistic Regression, Support Vector Machine, Random forest Classifier and Multi-Layer Perceptron are used for classification of documents. Classifiers are applied on raw and processed data separately and their accuracy is noted. Along with this, Deep learning technique such as Convolution Neural Network is also used to classify the data and its accuracy is compared with that of traditional machine learning techniques. We are also exploring hierarchical classifiers for classification of classes and subclasses. The system classifies the data faster and with better accuracy than if done manually. The results are discussed in the results and evaluation section.


title: A Study of Authentication in Daily Life

abstract: We report on a wearable digital diary study of 26 participants that explores people’s daily authentication behavior across a wide range of targets (phones, PCs, websites, doors, cars, etc.) using a wide range of authenticators (passwords, PINs, physical keys, ID badges, fingerprints, etc.). Our goal is to gain an understanding of how much of a burden different kinds of authentication place on people, so that we can evaluate what kinds of improvements would most benefit them. We found that on average 25% of our participants’ authentications employed physical tokens such as car keys, which suggests that token-based authentication, in addition to password authentication, is a worthy area for improvement. We also found that our participants’ authentication behavior and opinions about authentication varied greatly, so any particular solution might not please everyone. We observed a surprisingly high (3–12%) false reject rate across many types of authentication. We present the design and implementation of the study itself, since wearable digital diary studies may prove useful for others exploring similar topics of human behavior. Finally, we provide an example use of participants’ logs of authentication events as simulation workloads for investigating the possible energy consumption of a “universal authentication” device.


title: Are we experiencing a big data bubble?

abstract: Fatma Ozcan IBM Almaden Research Center San Jose, CA fozcan@us.ibm.com Nesime Tatbul Intel Labs and MIT Cambridge, MA tatbul@csail.mit.edu Daniel J. Abadi Yale University New Haven, CT dna@cs.yale.edu Marcel Kornacker Cloudera San Francisco, CA marcel@cloudera.com C Mohan IBM Almaden Research Center San Jose, CA cmohan@us.ibm.com Karthik Ramasamy Twitter, Inc. San Francisco, CA karthik@twitter.com Janet Wiener Facebook, Inc. Menlo Park, CA jlw@fb.com


title: Multi-Information Source Optimization

abstract: We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective. In general, such approximations arise in applications such as reinforcement learning, engineering, and the natural sciences, and are subject to an inherent, unknown bias. This model discrepancy is caused by an inadequate internal model that deviates from reality and can vary over the domain, making the utilization of these approximations a non-trivial task. ::: We present a novel algorithm that provides a rigorous mathematical treatment of the uncertainties arising from model discrepancies and noisy observations. Its optimization decisions rely on a value of information analysis that extends the Knowledge Gradient factor to the setting of multiple information sources that vary in cost: each sampling decision maximizes the predicted benefit per unit cost. ::: We conduct an experimental evaluation that demonstrates that the method consistently outperforms other state-of-the-art techniques: it finds designs of considerably higher objective value and additionally inflicts less cost in the exploration process.


title: An intentional AI for hanabi

abstract: Cooperative games with partial observability are a challenging domain for AI research, especially when the AI should cooperate with a human player. In this paper we investigate one such game, the award-winning card game Hanabi, which has been studied by other researchers before. We present an agent designed to play better with a human cooperator than these previous results by basing it on communication theory and psychology research. To demonstrate that our agent performs better with a human cooperator we ran an experiment in which 224 participants played one or more games of Hanabi with different AIs, and will show that our AI scores higher than previously published work in such a setting.


title: Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks

abstract: Recently, in order to improve reactive fault tolerance techniques in large scale storage systems, researchers have proposed various statistical and machine learning methods based on SMART attributes. Most of these studies have focused on predicting failures of hard drives, i.e., labeling the status of a hard drive as “good” or not. However, in real-world storage systems, hard drives often deteriorate gradually rather than suddenly. Correspondingly, their SMART attributes change continuously towards failure. Inspired by this observation, we introduce a novel method based on Recurrent Neural Networks (RNN) to assess the health statuses of hard drives based on the gradually changing sequential SMART attributes. Compared to a simple failure prediction method, a health status assessment is more valuable in practice because it enables technicians to schedule the recovery of different hard drives according to the level of urgency. Experiments on real-world datasets for disks of different brands and scales demonstrate that our proposed method can not only achieve a reasonable accurate health status assessment, but also achieve better failure prediction performance than previous work.


title: $Z$-Axis Magnetometers for MEMS Inertial Measurement Units Using an Industrial Process

abstract: The combination of multiaxis and multiparameter microelectromechanical systems (MEMS) in the same technology would result in very cheap and smart inertial measurement units (IMUs). In this paper, a $Z$ -axis Lorentz-force-based magnetometer whose design and optimization are reviewed taking into account the constraints of an industrial MEMS technology (process and packaging) already used for accelerometers and gyroscopes is presented. How this impacts the design guidelines is shown; in particular, a very compact device that can fit in the same package of the gyroscope to realize an all-MEMS seven-degree-of-freedom IMU is proposed and experimentally tested. The device shows a mechanical sensitivity of around 0.8 $\hbox{aF}/(\mu\hbox{T} \cdot \hbox{mA})$ with a resolution of 520 $(\hbox{nT} \cdot \hbox{mA})/\sqrt{\hbox{Hz}}$ over a signal bandwidth of 50 Hz at room temperature. Coupled to a transimpedance amplifier, the system shows an overall sensitivity of 150 $\mu\hbox{V}/\mu\hbox{T}$ at 250 $\mu\hbox{A}$ of peak driving current.


title: That's What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships

abstract: Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals' locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms.


title: A 4 $\times$ 4 Ridge Substrate Integrated Waveguide (RSIW) Slot Array Antenna

abstract: The waveguide slot array antenna is known to suffer from a very limited frequency bandwidth, which becomes even more serious in substrate-integrated-waveguide (SIW)-based design because of its reduced waveguide height. In this work, a 4 x 4 slot array antenna based on ridge SIW (RSIW) and T-type folded SIW (TFSIW) techniques is proposed, and an 8.8% bandwidth is easily achieved. The proposed antenna structure is then fabricated. Measured results are well consistent with simulated ones.


title: Hybrid Compression of Bitvectors for the FM-Index

abstract: Compressed bit vectors supporting rank and select operations are the workhorse of compressed data structures. We propose a hybrid scheme for implementing compressed bit vectors, which divides the bit vector into blocks and then chooses the encoding of each block separately from a number of different encoding methods. Hybrid encoding is particularly suitable for bit vectors that have lots of local and regional variation, such as those present in the FM-index, a popular compressed data structure for pattern matching. We propose a specific hybrid combination of three simple encoding methods for FM-index bit vectors achieving superior space-time tradeoffs in experiments.


title: EXPLORING USERS' ATTITUDES AND INTENTIONS TOWARD THE ADOPTION OF CLOUD COMPUTING IN SAUDI ARABIA: AN EMPIRICAL INVESTIGATION

abstract: Over the past few years, cloud computing has evolve d as one of the major advances in the field of Information Technology (IT) utilizing third-party s ervices. Therefore, trust in cloud vendors as well as the determination of potential risks, such as privacy a nd security issues, are crucial for ensuring the su ccessful adoption of an appropriate cloud. Prior research ha ve addressed the technical aspects of cloud-based environments, such as cloud virtualization, scalabi lity and security. Nevertheless, it is argued that the biggest obstacle of cloud computing is not technolo gical, rather it is perceptual or attitudinal. The adoption of cloud computing has been central to several scho larly research areas, particularly user acceptance. This study presents an extended Technology Acceptance Model (TAM), which integrates Trust (TR), Anxiety (ANX) and Perceived Risk (PR), to investigate users ’ attitudes and intentions toward the adoption of c loud computing. The proposed model was empirically examined using the Structural Equation Model (SEM) to analyze data gathered by a survey of both IT profes sionals and end users. The results herein suggested that trust, anxiety and PR can be successfully integrate d within the TAMs. Trust has demonstrated to have a strong positive influence on Perceived Ease of Use (PEOU), but it had no significant effects on Perceived Use fulness (PU). Both anxiety and PR were found to have significant negative effects on PEOU and PU. In addition, Behavioral Intention (BI) to use the cloud can be p redicted by trust, attitudes and PU, as PR was show n to have no significant effect on BI. The proposed model, including PR, trust and anxiety, has been demonstrated to be a true predictor of user intentions, toward t he use of cloud computing, within the context of Sa udi Arabia.


title: Stages in Digital Business Transformation: Results of an Empirical Maturity Study

abstract: Managers and decision makers need to transform their organizational routines and structures to meet the challenges of the digital age. Even though organizational change is by no means a new topic, many companies struggle to recognize and make sense of the often very disruptive changes affecting all industries. In order to foster an understanding of the phenomenon and the development of a digital transformation strategy, this research derives typical stages in a digital business transformation process from empirical data. The nine dimensions of the digital maturity model (DMM) provide a more profound understanding of the relevant levers for managing digital transformation. The DMM was implemented together with a survey of 547 individuals from 417 organizations in Switzerland and Germany. Based on the survey data, we used the Rasch-algorithm and cluster analysis to derive five maturity stages. The findings show that while digital affinity and experimenting with digital technology are already prevalent in companies, a strategically planned transformation and usage of advanced data analytics in business processes are less common. The results from this study yield insights into how activities in digital business transformation are currently tackled and prioritized and thus con-tribute to the body of knowledge about organizational transformation.


title: Chaff from the Wheat: Characterizing and Determining Valid Bug Reports

abstract: Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports.


title: Adaptive Sampling and Fast Low-Rank Matrix Approximation

abstract: We prove that any real matrix A contains a subset of at most 4k/e+ 2k log(k+1) rows whose span “contains” a matrix of rank at most k with error only (1+e) times the error of the best rank-k approximation of A. We complement it with an almost matching lower bound by constructing matrices where the span of any k/2e rows does not “contain” a relative (1+e)-approximation of rank k. Our existence result leads to an algorithm that finds such rank-k approximation in time ::: ::: $ O \left( M \left( \frac{k}{\epsilon} + k^{2} \log k \right) + (m+n) \left( \frac{k^{2}}{\epsilon^{2}} + \frac{k^{3} \log k}{\epsilon} + k^{4} \log^{2} k \right) \right), $ ::: ::: i.e., essentially O(Mk/e), where M is the number of nonzero entries of A. The algorithm maintains sparsity, and in the streaming model [12,14,15], it can be implemented using only 2(k+1)(log(k+1)+1) passes over the input matrix and $O \left( \min \{ m, n \} (\frac{k}{\epsilon} + k^{2} \log k) \right)$ additional space. Previous algorithms for low-rank approximation use only one or two passes but obtain an additive approximation.


title: A Model for Real-Time Computation in Generic Neural Microcircuits

abstract: A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry.


title: Design of lane keeping assist system for autonomous vehicles

abstract: Lane detection and tracking and departure warning systems are important components of Intelligent Transportation Systems. They have particularly attracted great interest from industry and academia. Many architectures and commercial systems have been proposed in the literature. In this paper, we discuss the design of such systems regarding the following stages: pre-processing, detection, and tracking. For each stage, a short description of its working principle as well as their advantages and shortcomings are introduced. Our paper may possibly help in designing new systems that overcome and improve the shortcomings of current architectures.


title: The Cardiac Insufficiency Bisoprolol Study II (CIBIS-II): a randomised trial

abstract: Summary Background In patients with heart failure, β-blockade has improved morbidity and left-ventricular function, but the impact on survival is uncertain. We investigated the efficacy of bisoprolol, a β, selective adrenoceptor blocker in decreasing all-cause mortality in chronic heart failure. Methods In a multicentre double-blind randomised placebo-controlled trial in Europe, we enrolled 2647 symptomatic patients in New York Heart Association class III or IV, with left-ventricular ejection fraction of 35% or less receiving standard therapy with diuretics and inhibitors of angiotensin-converting enzyme. We randomly assigned patients bisoprolol 1·25 mg (n=1327) or placebo (n=1320) daily, the drug being progressively increased to a maximum of 10 mg per day. Patients were followed up for a mean of 1·3 years. Analysis was by intention to treat. Findings CIBIS-II was stopped early, after the second interim analysis, because bisoprolol showed a significant mortality benefit. All-cause mortality was significantly lower with bisoprolol than on placebo (156 [11·8%] vs 228 [17·3%] deaths with a hazard ratio of 0·66 (95% CI 0·54–0·81, p vs 83 [6·3%] deaths), with a hazard ratio of 0·56 (0·39–0·80, p=0·0011). Treatment effects were independent of the severity or cause of heart failure. Interpretation β-blocker therapy had benefits for survival in stable heart-failure patients. Results should not, however, be extrapolated to patients with severe class IV symptoms and recent instability because safety and efficacy has not been established in these patients.


title: Phosphorylation of serine 1137/1138 of mouse insulin receptor substrate (IRS) 2 regulates cAMP-dependent binding to 14-3-3 proteins and IRS2 protein degradation.

abstract: Insulin receptor substrate (IRS) 2 as intermediate docking platform transduces the insulin/IGF-1 (insulin like growth factor 1) signal to intracellular effector molecules that regulate glucose homeostasis, β-cell growth, and survival. Previously, IRS2 has been identified as a 14-3-3 interaction protein. 14-3-3 proteins can bind their target proteins via phosphorylated serine/threonine residues located within distinct motifs. In this study the binding of 14-3-3 to IRS2 upon stimulation with forskolin or the cAMP analog 8-(4-chlorophenylthio)-cAMP was demonstrated in HEK293 cells. Binding was reduced with PKA inhibitors H89 or Rp-8-Br-cAMPS. Phosphorylation of IRS2 on PKA consensus motifs was induced by forskolin and the PKA activator N(6)-Phe-cAMP and prevented by both PKA inhibitors. The amino acid region after position 952 on IRS2 was identified as the 14-3-3 binding region by GST-14-3-3 pulldown assays. Mass spectrometric analysis revealed serine 1137 and serine 1138 as cAMP-dependent, potential PKA phosphorylation sites. Mutation of serine 1137/1138 to alanine strongly reduced the cAMP-dependent 14-3-3 binding. Application of cycloheximide revealed that forskolin enhanced IRS2 protein stability in HEK293 cells stably expressing IRS2 as well as in primary hepatocytes. Stimulation with forskolin did not increase protein stability either in the presence of a 14-3-3 antagonist or in the double 1137/1138 alanine mutant. Thus the reduced IRS2 protein degradation was dependent on the interaction with 14-3-3 proteins and the presence of serine 1137/1138. We present serine 1137/1138 as novel cAMP-dependent phosphorylation sites on IRS2 and show their importance in 14-3-3 binding and IRS2 protein stability.


title: Information Technology and India’s Economic Development ∗

abstract: This paper discusses the possibilities for broad-based IT-led economic growth in India, including increasing value-added, using better telecom links to capture more benefits domestically through offshore development for developed country firms, greater spillovers to the local economy, broadening the IT industry with production of telecom access devices, improving the functioning of the economy through a more extensive and denser communications network, and improving governance. We also examine the policy environment, arguing that government policy is better focused on removing labor market distortions and infrastructure constraints, rather than providing output or export subsidies to the software industry.


title: Grandiose and vulnerable narcissism from the perspective of the interpersonal circumplex

abstract: Abstract A growing empirical literature documents the existence of two distinct dimensions of narcissism, grandiose and vulnerable. In order to better understand the nature of these dimensions, we examined them in the context of the interpersonal circumplex (IPC). Using a sample collected on-line (N = 277), we examined the relations between these two narcissism dimensions – generated as a result of an exploratory factor analysis of 15 narcissism and narcissism-related scales – and two measures of the IPC. GN was most strongly linked with high agency and low communion. Conversely, vulnerable narcissism was most strongly linked with low communion. The data also suggest that the assessment of IPC can substantially influence the pattern of findings for vulnerable narcissism.


title: The effect of electrical vagal stimulation on canine pancreatic exocrine function.

abstract: The effect of electrical vagal stimulation on canine pancreatic exocrine function was studied in conscious dogs by stimulating intact thoracic vagus nerves, the distal ends of cut vagus nerves in animals with intact gastric denervation, and the distal ends of cut vagus nerves in dogs whose stomachs had been previously selectively denervated. The effectiveness of the stimulus was confirmed by monitoring gastric hydrogen ion output. The results indicate that stimulation of intact nerves produced minimal alteration in pancreatic output and bicarbonate and protein secretion while significantly increasing gastric fistula hydrogen ion output. Stimulation of the distal ends (efferent fibers) of cut vagus nerves in dogs with intact gastric innervation significantly increased the volume and protein output of the pancreas and the acid output of the stomach. Stimulation of the distal ends of cut right and both vagus nerves in dogs whose stomach had been denervated previously, again, significantly increased the volume and protein output of the pancreas without stimulation of stomach hydrogen ion output. The data presented in this study suggest that the canine pancreas is innervated directly by vagal fibers, which when stimulated produce an increase in protein (enzyme) output and volume of secretion. Maintenance of the pancreatic response following denervation of the stomach suggests that the response is primarily the result of direct vagal innervation and is not produced by gastrin released from the antrum.


Atypical Herpetic Corneal Endotheliitis: A Case Report


title: Varieties of User-Centeredness

abstract: This paper critically examines user-centeredness as a multidimensional concept along four aspects: as user focus; as work-centeredness; as user participation; and as system personalization. Each aspect loads user-centeredness with different meanings. It is discussed whether user centered design can be considered an information systems development approach on its own. In its current form, we do not see it as a separate approach, because it is neither horizontally nor vertically complete, and because of the overall confusion regarding its goals, principles and practices. The four dimensions identified can be used for evaluating information systems development methods and approaches to what extent they adhere to the ideals of user-centeredness.


title: The Psychology of Security for the Home Computer User

abstract: The home computer user is often said to be the weakest link in computer security. They do not always follow security advice, and they take actions, as in phishing, that compromise themselves. In general, we do not understand why users do not always behave safely, which would seem to be in their best interest. This paper reviews the literature of surveys and studies of factors that influence security decisions for home computer users. We organize the review in four sections: understanding of threats, perceptions of risky behavior, efforts to avoid security breaches and attitudes to security interventions. We find that these studies reveal a lot of reasons why current security measures may not match the needs or abilities of home computer users and suggest future work needed to inform how security is delivered to this user group.


title: Behavioural and neurotoxic effects of ayahuasca infusion (Banisteriopsis caapi and Psychotria viridis) in female Wistar rat

abstract: Abstract Ayahuasca, a psychoactive beverage used by indigenous and religious groups, is generally prepared by the coction of Psychotria viridis and Banisteriopsis caapi plants containing N , N -dimethyltryptamine (DMT) and β-carboline alkaloids, respectively. To investigate the acute toxicity of ayahuasca, the infusion was administered by gavage to female Wistar rats at doses of 30X and 50X the dose taken during a religious ritual, and the animals observed for 14 days. Behavioural functions were investigated one hour after dosing at 15X and 30X using the open field, elevated plus maze, and forced swimming tests. Neuronal activation (c- fos marked neurons) and toxicity (Fluoro-Jade B and Nissl/Cresyl staining) were investigated in the dorsal raphe nuclei (DRN), amygdaloid nucleus, and hippocampal formation brain areas of rats treated with a 30X ayahuasca dose. The actual lethal oral dose in female Wistar rats could not be determined in this study, but was shown to be higher than the 50X (which corresponds to 15.1 mg/kg bw DMT). The ayahuasca and fluoxetine treated groups showed a significant decrease in locomotion in the open field and elevated plus-maze tests compared to controls. In the forced swimming test, ayahuasca treated animals swam more than controls, a behaviour that was not significant in the fluoxetine group. Treated animals showed higher neuronal activation in all brain areas involved in serotoninergic neurotransmission. Although this led to some brain injury, no permanent damage was detected. These results suggest that ayahuasca has antidepressant properties in Wistar female at high doses, an effect that should be further investigated.


title: Nonnegative Weak Solutions of Thin Film Equations Related to Viscous Flows in Cylindrical Geometries

abstract: Motivated by models for thin films coating cylinders in two physical cases proposed in Kerchman (J Fluid Mech 290:131–166, 1994) and Kerchman and Frenkel (Theor Comput Fluid Dyn 6:235–254, 1994), we analyze the dynamics of corresponding thin-film models. The models are governed by nonlinear, fourth-order, degenerate, parabolic PDEs. We prove, given positive and suitably regular initial data, the existence of weak solutions in all length scales of the cylinder, where all solutions are only local in time. We also prove that given a length constraint on the cylinder, long time and global in time weak solutions exist. This analytical result is motivated by numerical work on related models of Reed Ogrosky (Modeling liquid film flow inside a vertical tube, Ph.D. thesis, The University of North Carolina at Chapel Hill, 2013) in conjunction with the works (Camassa et al. in Phys Rev E 86(6):066305, 2012; Physica D Nonlinear Phenom 333:254–265, 2016; J Fluid Mech 745:682–715, 2014; J Fluid Mech 825:1056–1090, 2017).


title: Photonic crystals: emerging biosensors and their promise for point-of-care applications

abstract: Biosensors are extensively employed for diagnosing a broad array of diseases and disorders in clinical settings worldwide. The implementation of biosensors at the point-of-care (POC), such as at primary clinics or the bedside, faces impediments because they may require highly trained personnel, have long assay times, large sizes, and high instrumental cost. Thus, there exists a need to develop inexpensive, reliable, user-friendly, and compact biosensing systems at the POC. Biosensors incorporated with photonic crystal (PC) structures hold promise to address many of the aforementioned challenges facing the development of new POC diagnostics. Currently, PC-based biosensors have been employed for detecting a variety of biotargets, such as cells, pathogens, proteins, antibodies, and nucleic acids, with high efficiency and selectivity. In this review, we provide a broad overview of PCs by explaining their structures, fabrication techniques, and sensing principles. Furthermore, we discuss recent applications of PC-based biosensors incorporated with emerging technologies, including telemedicine, flexible and wearable sensing, smart materials and metamaterials. Finally, we discuss current challenges associated with existing biosensors, and provide an outlook for PC-based biosensors and their promise at the POC.


title: Supervised tensor learning

abstract: This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n/sup th/-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.


title: Beyond the lock icon: real-time detection of phishing websites using public key certificates

abstract: We propose a machine-learning approach to detect phishing websites using features from their X.509 public key certificates. We show that its efficacy extends beyond HTTPSenabled sites. Our solution enables immediate local identification of phishing sites. As such, this serves as an important complement to the existing server-based anti-phishing mechanisms which predominately use blacklists. Blacklisting suffers from several inherent drawbacks in terms of correctness, timeliness, and completeness. Due to the potentially significant lag prior to site blacklisting, there is a window of opportunity for attackers. Other local client-side phishing detection approaches also exist, but primarily rely on page content or URLs, which are arguably easier to manipulate by attackers. We illustrate that our certificatebased approach greatly increases the difficulty of masquerading undetected for phishers, with single millisecond delays for users. We further show that this approach works not only against HTTPS-enabled phishing attacks, but also detects HTTP phishing attacks with port 443 enabled.


title: The 10 kVA SiC-based grid connected AC/DC converter with extended functionalities — Experimental investigation

abstract: The paper presents a SiC-based 10 kVA AC/DC converter and its experimental verification during various operation modes when is connected to the 3x 400V grid. Besides a standard feature of the AC/DC converter — bidirectional power flow — new functionalities are enabled: a compensation of highorder harmonics and reactive power, a fast response to step load changes. These features are available due applied control method based on Direct Power Control and the elevated switching frequency (40 kHz). Finally, the conducted measurements show that for the discussed AC/DC converter peak efficiency close to 98.75% is available.