title: Hardware design of ECG data logger

abstract: The cardiovascular disease is a serious problem in the world. The early diagnosis of heart disease leads to minimization of deaths. The analysis of electrocardiogram (ECG) patterns and cardiac frequency is used for detecting arrhythmias and discovering the nature of disease.. In many cases patients encounter heart problems (pain, sudden change of heart beat) but no longer have an ECG device and/or equipment for measurement. These drawbacks can be overcome using an ECG logger. In this paper it is described hardware design of one-lead ECG data logger. The designed ECG logger is small and compact device without any cables - it is a miniature, hand-held device with fixed electrodes for fingers. Measured data are stored on built-in microSD memory card and then transferred to PC via USB connection.


title: A Computational Approach to Edge Detection

abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.


title: Game based cyber security training: are serious games suitable for cyber security training?

abstract: Security research and training is attracting a lot of investment and interest from governments and the private sector. Most efforts have focused on physical security, while cyber security or digital security has been given less importance. With recent high-profile attacks it has become clear that training in cyber security is needed. Serious Games have the capability to be effective tools for public engagement and behavioural change and role play games, are already used by security professionals. Thus cyber security seems especially well-suited to Serious Games. This paper investigates whether games can be effective cyber security training tools. The study is conducted by means of a structured literature review supplemented with a general web search. While there are early positive indications there is not yet enough evidence to draw any definite conclusions. There is a clear gap in target audience with almost all products and studies targeting the general public and very little attention given to IT professionals and managers. The products and studies also mostly work over a short period, while it is known that short-term interventions are not particularly effective at affecting behavioural change.


title: CMOS Digital Image Stabilization

abstract: In this paper, we present a CMOS digital image stabilization algorithm based on the characteristics of a rolling shutter camera. Due to the rolling shuttering mechanism of a CMOS sensor, a CMOS video frame shows CMOS distortions which are not observed in a CCD video frame, and previous video stabilization techniques cannot handle these distortions properly even though they can make a visually stable CMOS video sequence. In our proposed algorithm, we first suggest a CMOS distortion model. This model is based on a rolling shutter mechanism which provides a solution to solve the CMOS distortion problem. Next, we estimate the global image motion and the CMOS distortion transformation directly from the homography between CMOS frames. Using the two transformations, we remove CMOS distortions as well as jittering motions in a CMOS video. In the experimental results, we demonstrate that our proposed algorithm can handle the CMOS distortion problem more effectively as well as the jittering problem in a CMOS video compared to previous CCD-based digital image stabilization techniques.


title: Visual Aesthetic Quality Assessment with Multi-task Deep Learning.

abstract: This paper considers the problem of assessing visual aesthetic quality with semantic information. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition offers the key to addressing this problem. Based on convolutional neural networks, we propose a general multi-task framework with four different structures. In each structure, aesthetic quality assessment task and semantic recognition task are leveraged, and different features are explored to improve the quality assessment. Moreover, an effective strategy of keeping a balanced effect between the semantic task and aesthetic task is developed to optimize the parameters of our framework. The correlation analysis among the tasks validates the importance of the semantic recognition in aesthetic quality assessment. Extensive experiments verify the effectiveness of the proposed multi-task framework, and further corroborate the above proposition.


title: Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey

abstract: Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values.


title: Formalization and Verification of Event-driven Process Chains

abstract: Abstract For many companies, business processes have become the focal point of attention. As a result, many tools have been developed for business process engineering and the actual deployment of business processes. Typical examples of these tools are Business Process Reengineering (BPR) tools, Enterprise Resource Planning (ERP) systems, and Workflow Management (WFM) systems. Some of the leading products, e.g. SAP R/3 (ERP/WFM) and ARIS (BPR), use Event-driven Process Chains (EPCs) to model business processes. Although the EPCs have become a widespread process modeling technique, they suffer from a serious drawback: neither the syntax nor the semantics of an EPC are well defined. In this paper, this problem is tackled by mapping EPCs (without connectors of type ∨) onto Petri nets. The Petri nets have formal semantics and provide an abundance of analysis techniques. As a result, the approach presented in this paper gives formal semantics to EPCs. Moreover, many analysis techniques are available for EPCs. To illustrate the approach, it is shown that the correctness of an EPC can be checked in polynomial time by using Petri-net-based analysis techniques.


title: Monolithic Integration of GaN Transistors for Higher Efficiency and Power Density in DC-DC Converters

abstract: Power converters are constantly trending towards higher output power, higher efficiency, and higher power density. To provide improved performance better power devices are required. For silicon (Si) power devices, the gains in performance have slowed as the technology has matured and approaches its theoretical limits. Gallium nitride (GaN) devices have emerged as a possible replacement for silicon devices in various power conversion applications and as an enabler of new applications not previously possible. In this paper we will discuss the latest eGaN(r) FETs developments, including a major improvement with the latest generation of discrete devices and introduce a new family of monolithic half bridge ICs offering unmatched high frequency performance. These new families of eGaN FETs are widening the performance gap with the aging power MOSFET in high frequency power conversion by providing significant gains in key switching figures of merit, continued reductions of performance limiting in-circuit parasitics, and improved thermal performance.


title: A 10-bit active RF phase shifter for 5G wireless systems

abstract: This paper presents an active RF phase shifter with 10 bit control word targeted toward the upcoming 5G wireless systems. The circuit is designed and fabricated using 45 nm CMOS SOI technology. An IQ vector modulator (IQVM) topology is used which provides both amplitude and phase control. The design is programmable with exhaustive digital controls available for parameters like bias voltage, resonance frequency, and gain. The frequency of operation is tunable from 12.5 GHz to 15.7 GHz. The mean angular separation between phase points is 1.5 degree at optimum amplitude levels. The rms phase error over the operating band is as low as 0.8 degree. Active area occupied is 0.18 square millimeter. The total DC power consumed from 1 V supply is 75 mW.


title: A 0.5-V Wideband Amplifier for a 1-MHz CT Complex Delta-Sigma Modulator

abstract: This brief presents a fully differential wideband amplifier for 0.5-V supply. The amplifier employs a gate-input two-stage topology and a DC common-mode feedback circuit with a Miller-amplified capacitor for frequency compensation. Designed in a 130-nm triple-well complementary metal-oxide-semiconductor process with regular VT transistors, the amplifier achieves a simulated performance of 51-dB DC open-loop gain, 112-MHz unity gain bandwidth, and 67deg phase margin with a load of 6.5 pF/19.6 kOmega , and consumes 600 muW at 0.5-V supply. The proposed amplifier is incorporated in a continuous-time complex delta-sigma modulator with a 1-MHz signal bandwidth and 64times oversampling ratio. In the simulations, the modulator achieves a 72.5-dB signal-to-noise-plus-distortion ratio and consumes 2.3 mW at 0.5 V.


title: Dual Control Memory Augmented Neural Networks for Treatment Recommendations

abstract: Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the premise that an effective treatment plan may have long-term dependencies from previous medical history. We approach the problem by using a memory-augmented neural network, in particular, by leveraging the recent differentiable neural computer that consists of a neural controller and an external memory module. But differing from the original model, we use dual controllers, one for encoding the history followed by another for decoding the treatment sequences. In the encoding phase, the memory is updated as new input is read; at the end of this phase, the memory holds not only the medical history but also the information about the current illness. During the decoding phase, the memory is write-protected. The decoding controller generates a treatment sequence, one treatment option at a time. The resulting dual controller write-protected memory-augmented neural network is demonstrated on the MIMIC-III dataset on two tasks: procedure prediction and medication prescription. The results show improved performance over both traditional bag-of-words and sequence-to-sequence methods.


title: Name usage pattern in the synonym ambiguity problem in bibliographic data

abstract: Individuals often appear with multiple names when considering large bibliographic datasets, giving rise to the synonym ambiguity problem. Although most related works focus on resolving name ambiguities, this work focus on classifying and characterizing multiple name usage patterns--the root cause for such ambiguity. By considering real examples bibliographic datasets, we identify and classify patterns of multiple name usage by individuals, which can be interpreted as name change, rare name usage, and name co-appearance. In particular, we propose a methodology to classify name usage patterns through a supervised classification task and show that different classes are robust (across datasets) and exhibit significantly different properties. We show that the collaboration network structure emerging around nodes corresponding to ambiguous names from different name usage patterns have strikingly different characteristics, such as their common neighborhood and degree evolution. We believe such differences in network structure and in name usage patterns can be leveraged to design more efficient name disambiguation algorithms that target the synonym problem.


SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering


title: On the fragility of skilled performance: What governs choking under pressure

abstract: Experiments 1-2 examined generic knowledge and episodic memories of putting in novice and expert golfers. Impoverished episodic recollection of specific putts among experts indicated that skilled putting is encoded in a procedural form that supports performance without the need for step-by-step attentional control. According to explicit monitoring theories of choking, such proceduralization makes putting vulnerable to decrements under pressure. Experiments 3-4 examined choking and the ability of training conditions to ameliorate it in putting and a nonproceduralized alphabet arithmetic skill analogous to mental arithmetic. Choking occurred in putting but not alphabet arithmetic. In putting, choking was unchanged by dual-task training but eliminated by self-consciousness training. These findings support explicit monitoring theories of choking and the popular but infrequently tested belief that attending to proceduralized skills hurts performance.


title: Age classification with deep learning face representation

abstract: Automatic age estimation from facial images is challenging not only for computers, but also for humans in some cases. Therefore, coarse age groups such as children, teen age, adult and senior adult are considered in age classification, instead of evaluating specific age. In this paper, we propose an approach that provides a significant improvement in performance on benchmark databases and standard protocols for age classification. Our approach is based on deep learning techniques. We optimize the network architecture using the Deep IDentification-verification features, which are proved very efficient for face representation. After reducing the redundancy among the large number of output features, we apply different classifiers to classify the facial images to different age group with the final features. The experimental analysis shows that the proposed approach outperforms the reported state-of-the-arts on both constrained and unconstrained databases.


title: Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals

abstract: Intelligent vehicles use advanced driver assistance systems (ADASs) to mitigate driving risks. There is increasing demand for an ADAS framework that can increase driving safety by detecting dangerous driving behavior from driver, vehicle, and lane attributes. However, because dangerous driving behavior in real-world driving scenarios can be caused by any or a combination of driver, vehicle, and lane attributes, the detection of dangerous driving behavior using conventional approaches that focus on only one type of attribute may not be sufficient to improve driving safety in realistic situations. To facilitate driving safety improvements, the concept of dangerous driving intensity (DDI) is introduced in this paper, and the objective of dangerous driving behavior detection is converted into DDI estimation based on the three attribute types. To this end, we propose a framework, wherein fuzzy sets are optimized using particle swarm optimization for modeling driver, vehicle, and lane attributes and then used to accurately estimate the DDI. The mean opinion scores of experienced drivers are employed to label DDI for a fair comparison with the results of our framework. The experimental results demonstrate that the driver, vehicle, and lane attributes defined in this paper provide useful cues for DDI analysis; furthermore, the results obtained using the framework are in favorable agreement with those obtained in the perception study. The proposed framework can greatly increase driving safety in intelligent vehicles, where most of the driving risk is within the control of the driver.


title: Norms of valence, arousal, and dominance for 13,915 English lemmas

abstract: Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory.


title: RF energy harvesting circuit with on-chip antenna for biomedical applications

abstract: A novel CMOS RF energy harvesting circuit with on-chip antenna is reported. The rectenna system consists of (a) a high efficiency differential full-wave rectifier circuit that converts RF energy to DC power source, and (b) a loop antenna to improve efficiency. To improve area efficiency, the antenna is placed at the perimeter of the chip, occupying only 10-um of each chip edge. Meanwhile, the area enclosed by the loop antenna is fully populated with other circuitries. The system is implemented in a 0.13-um digital CMOS technology, and it is able to harvest 0.5-uW from a 4-GHz +20-dBm RF source 7-cm away. Measured result also shows that the antenna improves RF receive gain by 30-dB compared with the same system without the antenna. The harvested energy is sufficient to power up simple digital circuits such as implantable RFID, and the total area is only 0.16-mm2.


title: In Defense of the Eight-Point Algorithm

abstract: The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images.


title: Development of a bacterial cellulose-based hydrogel cell carrier containing keratinocytes and fibroblasts for full-thickness wound healing

abstract: Bacterial cellulose (BC)/acrylic acid (AA) hydrogel has successfully been investigated as a wound dressing for partial-thickness burn wound. It is also a promising biomaterial cell carrier because it bears some resemblance to the natural soft tissue. This study assessed its ability to deliver human epidermal keratinocytes (EK) and dermal fibroblasts (DF) for the treatment of full-thickness skin lesions. In vitro studies demonstrated that BC/AA hydrogel had excellent cell attachment, maintained cell viability with limited migration, and allowed cell transfer. In vivo wound closure, histological, immunohistochemistry, and transmission electron microscopy evaluation revealed that hydrogel alone (HA) and hydrogel with cells (HC) accelerated wound healing compared to the untreated controls. Gross appearance and Masson’s trichrome staining indicated that HC was better than HA. This study suggests the potential application of BC/AA hydrogel with dual functions, as a cell carrier and wound dressing, to promote full-thickness wound healing.


title: Understanding the recognition of facial identity and facial expression

abstract: Faces convey a wealth of social signals. A dominant view in face-perception research has been that the recognition of facial identity and facial expression involves separable visual pathways at the functional and neural levels, and data from experimental, neuropsychological, functional imaging and cell-recording studies are commonly interpreted within this framework. However, the existing evidence supports this model less strongly than is often assumed. Alongside this two-pathway framework, other possible models of facial identity and expression recognition, including one that has emerged from principal component analysis techniques, should be considered.


title: A microphone array-based 3-D N-best search algorithm for the simultaneous recognition of multiple sound sources in real environments

abstract: Deals with the recognition of distant talking speech and, particularly, with the simultaneous recognition of multiple sound sources. A problem that must be solved in the recognition of distant talking speech is talker localization. In some approaches, the talker is localized by using short- and long-term power. The 3-D Viterbi search based method proposed by Yamada et al.(1998), integrates talker localization and speech recognition. This method provides high recognition rates but its application is restricted to the presence of one talker. In order to deal with multiple talkers, we extended the 3-D Viterbi search method to a 3-D N-best search method enabling the recognition of multiple sound sources. The paper describes our baseline 3-D N-best search-based system and two additional techniques, namely, a likelihood normalization technique and a path distance-based clustering technique. The paper also describes experiments carried out in order to evaluate the performance of the system.


title: Robotic vertical jumping agility via series-elastic power modulation

abstract: Several arboreal mammals have the ability to rapidly and repeatedly jump vertical distances of 2 m, starting from rest. We characterize this performance by a metric we call vertical jumping agility. Through basic kinetic relations, we show that this agility metric is fundamentally constrained by available actuator power. Although rapid high jumping is an important performance characteristic, the ability to control forces during stance also appears critical for sophisticated behaviors. The animal with the highest vertical jumping agility, the galago ( Galago senegalensis ), is known to use a power-modulating strategy to obtain higher peak power than that of muscle alone. Few previous robots have used series-elastic power modulation (achieved by combining series-elastic actuation with variable mechanical advantage), and because of motor power limits, the best current robot has a vertical jumping agility of only 55% of a galago. Through use of a specialized leg mechanism designed to enhance power modulation, we constructed a jumping robot that achieved 78% of the vertical jumping agility of a galago. Agile robots can explore venues of locomotion that were not previously attainable. We demonstrate this with a wall jump, where the robot leaps from the floor to a wall and then springs off the wall to reach a net height that is greater than that accessible by a single jump. Our results show that series-elastic power modulation is an actuation strategy that enables a clade of vertically agile robots.


title: Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory

abstract: With the rise in popularity of artificial intelligence, the technology of verbal communication between man and machine has received an increasing amount of attention, but generating a good conversation remains a difficult task. The key factor in human-machine conversation is whether the machine can give good responses that are appropriate not only at the content level (relevant and grammatical) but also at the emotion level (consistent emotional expression). In our paper, we propose a new model based on long short-term memory, which is used to achieve an encoder-decoder framework, and we address the emotional factor of conversation generation by changing the model’s input using a series of input transformations: a sequence without an emotional category, a sequence with an emotional category for the input sentence, and a sequence with an emotional category for the output responses. We perform a comparison between our work and related work and find that we can obtain slightly better results with respect to emotion consistency. Although in terms of content coherence our result is lower than those of related work, in the present stage of research, our method can generally generate emotional responses in order to control and improve the user’s emotion. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion.


title: AMAP: automatically mining abbreviation expansions in programs to enhance software maintenance tools

abstract: When writing software, developers often employ abbreviations in identifier names. In fact, some abbreviations may never occur with the expanded word, or occur more often in the code. However, most existing program comprehension and search tools do little to address the problem of abbreviations, and therefore may miss meaningful pieces of code or relationships between software artifacts. In this paper, we present an automated approach to mining abbreviation expansions from source code to enhance software maintenance tools that utilize natural language information. Our scoped approach uses contextual information at the method, program, and general software level to automatically select the most appropriate expansion for a given abbreviation. We evaluated our approach on a set of 250 potential abbreviations and found that our scoped approach provides a 57% improvement in accuracy over the current state of the art.


title: How analysts cognitively “connect the dots”

abstract: As analysts attempt to make sense of a collection of documents, such as intelligence analysis reports, they need to “connect the dots” between pieces of information that may initially seem unrelated. We conducted a user study to analyze the cognitive process by which users connect pairs of documents and how they spatialize connections. Users created conceptual stories that connected the dots using a range of organizational strategies and spatial representations. Insights from our study can drive the design of data mining algorithms and visual analytic tools to support analysts' complex cognitive processes.


title: Uncovering Four Strategies to Approach Master Data Management

abstract: Just recently much Information Systems (IS) research focuses on master data management (MDM) which promises to increase an organization's overall core data quality. Above any doubt, however, MDM initiatives confront organizations with multi-faceted and complex challenges that call for a more strategic approach to MDM. In this paper we introduce a framework for approaching MDM projects that has been developed in the course of a design science research study. The framework distinguishes four major strategies of MDM project initiations all featuring their specific assets and drawbacks. The usefulness of our artifact is illustrated in a short case narrative.


DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms


title: Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis ⋆

abstract: We propose a machine learning framework based on sliding windows for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary structural image cue for clinically identifying glaucoma. This localization uses a bundle of sliding windows of different sizes to obtain cup candidates in each disc image, then extracts from each sliding window a new histogram based feature that is learned using a group sparsity constraint. An e-SVR (support vector regression) model based on non-linear radial basis function (RBF) kernels is used to rank each candidate, and final decisions are made with a non-maximal suppression (NMS) method. Tested on the large ORIGA-light clinical dataset, the proposed method achieves a 73.2% overlap ratio with manually-labeled ground-truth and a 0.091 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. The high accuracy of this framework on images from low-cost and widespread digital fundus cameras indicates much promise for developing practical automated/assisted glaucoma diagnosis systems.


title: Investigations of charging noise in future gravitational wave detectors

abstract: Gravitational waves are a result of Einstein's theory of general relativity which postulates that these waves, created by asymmetrically accelerating masses, propagate through the Universe as ripples in the curvature of space-time at the speed of light. Gravitational waves have yet to be directly detected, however, there is sufficient indirect evidence of their existence through observations of inspiraling pulsars and white dwarfs. There is a worldwide network of interferometric detectors that are currently being, or have already been, upgraded to an advanced detector status. In order to get these detectors to a sensitivity high enough to stand a chance of detecting gravitational waves, researchers around the world have been developing innovative and novel technologies, and investigating high quality, low mechanical loss materials, to reduce the limiting noise sources of these instruments. One noise source that may become more conspicuous as gravitational wave detectors become more sensitive is noise due to the movement of electrical charges on the detector optics. Charge can be transferred to the detector optics through various processes such as; dust abrasion on the surface of the optics as the vacuum chamber is being pumped out, contact with nearby structures such as earthquake stops, cleaning of the optics and cosmic rays. In some instances the charge density transferred can be as high as 10^(-7) C/m^2 which would cause the sensitivity of advanced detectors to be significantly reduced at frequencies less than 100Hz. The work contained within this thesis investigates different methods of mitigating the effects of charging noise through the use of glow and corona discharges. It is shown that both of these methods successfully reduce surface charge on silica with an advanced LIGO style optical coating and that these methods do not cause damage to the optical coating within the sensitivity of the equipment. Investigations were carried out to see if the charge deposited on the detector optics during cleaning could be reduced by mixing carbon nano-tubes into the polymer based cleaning solution used to clean the advanced LIGO optics. It is shown that mixing carbon nano-tubes into the polymer cleaning solution does not reduce the amount of charge deposited on the optics. This thesis also presents experimental verification of the frequency dependence of charging noise. Using a torsion balance it was possible to measure charging noise directly and confirm that it does follow the Weiss theory of charging noise.


title: Scalable SVM-Based Classification in Dynamic Graphs

abstract: With the emergence of networked data, graph classification has received considerable interest during the past years. Most approaches to graph classification focus on designing effective kernels to compute similarities for static graphs. However, they become computationally intractable in terms of time and space when a graph is presented in a incremental fashion with continuous updates, i.e., Insertions of nodes and edges. In this paper, we examine the problem of classification in large-scale and incrementally changing graphs. To this end, a framework combining an incremental Support Vector Machine (SVM) with the Weisfeiler-Lehman (W-L) graph kernel has been proposed to study this problem. By retaining the support vectors from each learning step, the classification model is incrementally updated whenever new changes are made to the subject graph. Furthermore, we design an entropy-based sub graph extraction strategy to select informative neighbor nodes and discard those with less discriminative power, to facilitate an effective classification process. We demonstrate the advantages of our learning techniques by conducting an empirical evaluation on two large-scale real-world graph datasets. The experimental results also validate the benefits of our sub graph extraction method when combined with the incremental learning techniques.


title: Article Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes

abstract: We have conducted a comprehensive search for conserved elements in vertebrate genomes, using genome-wide multiple alignments of five vertebrate species (human, mouse, rat, chicken, and Fugu rubripes). Parallel searches have been performed with multiple alignments of four insect species (three species of Drosophila and Anopheles gambiae), two species of Caenorhabditis, and seven species of Saccharomyces. Conserved elements were identified with a computer program called phastCons, which is based on a two-state phylogenetic hidden Markov model (phylo-HMM). PhastCons works by fitting a phylo-HMM to the data by maximum likelihood, subject to constraints designed to calibrate the model across species groups, and then predicting conserved elements based on this model. The predicted elements cover roughly 3%-8% of the human genome (depending on the details of the calibration procedure) and substantially higher fractions of the more compact Drosophila melanogaster (37%-53%), Caenorhabditis elegans (18%-37%), and Saccharaomyces cerevisiae (47%-68%) genomes. From yeasts to vertebrates, in order of increasing genome size and general biological complexity, increasing fractions of conserved bases are found to lie outside of the exons of known protein-coding genes. In all groups, the most highly conserved elements (HCEs), by log-odds score, are hundreds or thousands of bases long. These elements share certain properties with ultraconserved elements, but they tend to be longer and less perfectly conserved, and they overlap genes of somewhat different functional categories. In vertebrates, HCEs are associated with the 3' UTRs of regulatory genes, stable gene deserts, and megabase-sized regions rich in moderately conserved noncoding sequences. Noncoding HCEs also show strong statistical evidence of an enrichment for RNA secondary structure.


A Language Modeling Approach To Predicting Reading Difficulty


title: Graphical Passwords Authentication: A Survey

abstract: In typically text-based passwords, well known users often create memorable passwords that are easy for attackers to guess, but strong system-assigned passwords are difficult for users to remember. Reusing same or easy passwords across different accounts help to memorability, but decrease in security. Text passwords are the most popular user authentication method, but have security and usability problems. Alternatives such as biometric systems and tokens have their own drawbacks Graphical passwords offer another alternative. This paper conducts a comprehensive survey of the existing graphical password techniques also classifies these techniques into three categories: recall, recognition, and cued-recall approaches. This paper discusses security and usability aspects of graphical password techniques and point out the future research directions in this area.


title: Evidences of cognitive effects over auditory steady-state responses by means of artificial neural networks and its use in brain-computer interfaces

abstract: The auditory steady-state response is an EEG potential elicited by the repetitive presentation of auditory stimuli. Researchers have found contradictory results about the influence of cognitive tasks, such as the selective attention, over this potential. It has been proved that selective attention is able to modulate cortex originated steady-state responses, such as the visual. This fact has been widely used to develop brain-computer interfaces. However for complete locked-in patients, such as those in an advanced state of Amyotrophic lateral sclerosis, visual stimuli are not longer suitable, hence the need of another type of stimulus, generally auditory, for both stimulation and feedback. In this paper we present a study based on artificial neural networks that evidences the effects of selective attention over auditory steady-state responses and the use in brain-computer interfaces is discussed.


title: Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity

abstract: Semantic similarity is an essential component of many Natural Language Processing applications. However, prior methods for computing semantic similarity often operate at different levels, e.g., single words or entire documents, which requires adapting the method for each data type. We present a unified approach to semantic similarity that operates at multiple levels, all the way from comparing word senses to comparing text documents. Our method leverages a common probabilistic representation over word senses in order to compare different types of linguistic data. This unified representation shows state-ofthe-art performance on three tasks: semantic textual similarity, word similarity, and word sense coarsening.


title: Corporate Brand Trust as a Mediator in the Relationship between Consumer Perception of CSR, Corporate Hypocrisy, and Corporate Reputation

abstract: The aim of this research is to investigate the relationship between consumer perception of Corporate Social Responsibility (CSR), corporate brand trust, corporate hypocrisy, and corporate reputation. Based on the one-to-one interview method using a structured questionnaire of 560 consumers in South Korea, the proposed model was estimated by structural equation modeling analysis. The model suggests that consumer perception of CSR influences consumer attitudes toward a corporation (i.e., perceived corporate hypocrisy and corporate reputation) by developing corporate brand trust. This in turn further enhances corporate reputation while decreasing corporate hypocrisy. The findings of our study demonstrate that consumer perception of CSR is an antecedent to corporate brand trust, which fully mediates the relationship between consumer perception of CSR and corporate reputation. In addition, corporate brand trust has the role of partial mediator in the relationship between consumer perception of CSR and corporate hypocrisy. These results imply that to better understand the relationship between consumer perception of CSR and consumer attitudes toward a corporation, it is necessary to consider corporate brand trust as an important mediating variable. The theoretical and practical implications of this study are discussed, together with its limitations and potential for future research.


title: Deep Learning to Hash with Multiple Representations

abstract: Hashing seeks an embedding of high-dimensional objects into a similarity-preserving low-dimensional Hamming space such that similar objects are indexed by binary codes with small Hamming distances. A variety of hashing methods have been developed, but most of them resort to a single view (representation) of data. However, objects are often described by multiple representations. For instance, images are described by a few different visual descriptors (such as SIFT, GIST, and HOG), so it is desirable to incorporate multiple representations into hashing, leading to multi-view hashing. In this paper we present a deep network for multi-view hashing, referred to as deep multi-view hashing, where each layer of hidden nodes is composed of view-specific and shared hidden nodes, in order to learn individual and shared hidden spaces from multiple views of data. Numerical experiments on image datasets demonstrate the useful behavior of our deep multi-view hashing (DMVH), compared to recently-proposed multi-modal deep network as well as existing shallow models of hashing.


title: Performance evaluation of time-critical communication networks for Smart Grids based on IEC 61850

abstract: Driven by the increasing application of Smart Grid technologies in today's power systems, communication networks are becoming more and more important for exchanging monitoring, control and protection information on local and wide area level. For communication the IEC 61850 standard is a candidate for the Smart Grid and has been introduced for Substation Automation Systems (SAS) some years ago. IEC 61850 provides interoperability among various manufactures and enables systemwide communication between intelligent components of future power systems. However, as IEC 61850 addresses Ethernet (ISO/IEC 8802-3 family) as network technology, especially high performance aspects of Ethernet have become increasingly important for time-critical communication within substation automation systems. In this paper we introduce the generic architecture of IEC 61850 and present our modelling approach for evaluating high performance and real-time capability of communication technologies for future smart grid application. First, we give a short overview of the IEC 61850 protocol and present communication flows in substation automation systems according to the standard. Here we focus on substation automation at bay level, located inside an exemplary substation node taken from the IEEE 39-bus power system network. Afterwards we demonstrate our modeling approach for communication networks based on IEC 61850. For performance evaluation we developed a simulation model along with an analytical approach on basis of Network Calculus, enabling to identify worst case boundaries for intra-substation communication. Finally results for simulative and analytical modelling are provided and cross validated for two bay level scenarios, showing the applicability of Network Calculus for real-time constrained smart grid communication.


title: LiTell: robust indoor localization using unmodified light fixtures

abstract: Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.


title: Extraction of Word Set for Increasing Human-Computer Interaction in Information Retrieval

abstract: We present a mechanism that provides word sets which can make human-computer interaction more active in the course of information retrieval, with natural language processing technology and a mathematic measure for calculating degree of inclusion. We show what type of words should be added to the current query, i.e. keywords which previously had been input, in order to make human-computer interaction more creative. We try to extract related word sets with taxonomical and non-taxonomical relations from documents by employing case-marking particles derived from syntactic analysis. Then, we verify which kind of related words is more useful as an additional word for retrieval support and makes human-computer interaction more fruitful.


Video to Text Summary: Joint Video Summarization and Captioning with Recurrent Neural Networks


title: Manifold Alignment for Person Independent Appearance-Based Gaze Estimation

abstract: We show that dually supervised manifold embedding can improve the performance of machine learning based person-independent and thus calibration-free gaze estimation. For this purpose, we perform a manifold embedding for each person in the training dataset and then learn a linear transformation that aligns the individual, person-dependent manifolds. We evaluate the effect of manifold alignment on the recently presented Columbia dataset, where we analyze the influence on 6 regression methods and 8 feature variants. Using manifold alignment, we are able to improve the person-independent gaze estimation performance by up to 31.2 % compared to the best approach without manifold alignment.


title: ATHENA: Automatic Text Height Extraction for the Analysis of Text Lines in Old Handwritten Manuscripts

abstract: Massive digital acquisition and preservation of deteriorating historical and artistic documents is of particular importance due to their value and fragile condition. The study and browsing of such digital libraries is invaluable for scholars in the Cultural Heritage field but requires automatic tools for analyzing and indexing these datasets. We present two completely automatic methods requiring no human intervention: text height estimation and text line extraction. Our proposed methods have been evaluated on a huge heterogeneous corpus of illuminated medieval manuscripts of different writing styles and with various problematic attributes, such as holes, spots, ink bleed-through, ornamentation, background noise, and overlapping text lines. Our experimental results demonstrate that these two new methods are efficient and reliable, even when applied to very noisy and damaged old handwritten manuscripts.


title: Modeling video evolution for action recognition

abstract: In this paper we present a method to capture video-wide temporal information for action recognition. We postulate that a function capable of ordering the frames of a video temporally (based on the appearance) captures well the evolution of the appearance within the video. We learn such ranking functions per video via a ranking machine and use the parameters of these as a new video representation. The proposed method is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions. We perform a large number of evaluations on datasets for generic action recognition (Hollywood2 and HMDB51), fine-grained actions (MPII- cooking activities) and gestures (Chalearn). Results show that the proposed method brings an absolute improvement of 7–10%, while being compatible with and complementary to further improvements in appearance and local motion based methods.


title: A Novel Recommendation Model Regularized with User Trust and Item Ratings

abstract: We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.


title: A QUASI-PASSIVE LEG EXOSKELETON FOR LOAD-CARRYING AUGMENTATION

abstract: A quasi-passive leg exoskeleton is presented for load-carrying augmentation during walking. The exoskeleton has no actuators, only ankle and hip springs and a knee variabledamper. Without a payload, the exoskeleton weighs 11.7 kg and requires only 2 Watts of electrical power during loaded walking. For a 36 kg payload, we demonstrate that the quasi-passive exoskeleton transfers on average 80% of the load to the ground during the single support phase of walking. By measuring the rate of oxygen consumption on a study participant walking at a self-selected speed, we find that the exoskeleton slightly increases the walking metabolic cost of transport (COT) as compared to a standard loaded backpack (10% increase). However, a similar exoskeleton without joint springs or damping control (zero-impedance exoskeleton) is found to increase COT by 23% compared to the loaded backpack, highlighting the benefits of passive and quasi-passive joint mechanisms in the design of efficient, low-mass leg exoskeletons.


title: Electric Load Forecasting Based on Locally Weighted Support Vector Regression

abstract: The forecasting of electricity demand has become one of the major research fields in electrical engineering. Accurately estimated forecasts are essential part of an efficient power system planning and operation. In this paper, a modified version of the support vector regression (SVR) is presented to solve the load forecasting problem. The proposed model is derived by modifying the risk function of the SVR algorithm with the use of locally weighted regression (LWR) while keeping the regularization term in its original form. In addition, the weighted distance algorithm based on the Mahalanobis distance for optimizing the weighting function's bandwidth is proposed to improve the accuracy of the algorithm. The performance of the new model is evaluated with two real-world datasets, and compared with the local SVR and some published models using the same datasets. The results show that the proposed model exhibits superior performance compare to that of LWR, local SVR, and other published models.


title: A Uniplanar Compact Photonic-Bandgap (UC-PBG) Structure and Its Applications for Microwave Circuits

abstract: This paper presents a novel photonic bandgap (PBG) structure for microwave integrated circuits. This new PBG structure is a two-dimensional square lattice with each element consisting of a metal pad and four connecting branches. Experimental results of a microstrip on a substrate with the PEG ground plane displays a broad stopband, as predicted by finite-difference time-domain simulations. Due to the slow-wave effect generated by this unique structure, the period of the PBG lattice is only 0.1/spl lambda//sub 0/ at the cutoff frequency, resulting in the most compact PEG lattice ever achieved. In the passband, the measured slow-wave factor (/spl beta//k/sub 0/) is 1.2-2.4 times higher and insertion loss is at the same level compared to a conventional 50-/spl Omega/ line. This uniplanar compact PBG (UC-PBG) structure can be built using standard planar fabrication techniques without any modification. Several application examples have also been demonstrated, including a nonleaky conductor-backed coplanar waveguide and a compact spurious-free bandpass filter. This UC-PBG structure should find wide applications for high-performance and compact circuit components in microwave and millimeter-wave integrated circuits.


title: Reading the legends of Roman Republican coins

abstract: Coin classification is one of the main aspects of numismatics. The introduction of an automated image-based coin classification system could assist numismatists in their everyday work and allow hobby numismatists to gain additional information on their coin collection by uploading images to a respective Web site. For Roman Republican coins, the inscription is one of the most significant features, and its recognition is an essential part in the successful research of an image-based coin recognition system. This article presents a novel way for the recognition of ancient Roman Republican coin legends. Traditional optical character recognition (OCR) strategies were designed for printed or handwritten texts and rely on binarization in the course of their recognition process. Since coin legends are simply embossed onto a piece of metal, they are of the same color as the background and binarization becomes error prone and prohibits the use of standard OCR. Therefore, the proposed method is based on state-of-the-art scene text recognition methods that are rooted in object recognition. Sift descriptors are computed for a dense grid of keypoints and are tested using support vector machines trained for each letter of the respective alphabet. Each descriptor receives a score for every letter, and the use of pictorial structures allows one to detect the optimal configuration for the lexicon words within an image; the word causing the lowest costs is recognized. Character and word recognition capabilities of the proposed method are evaluated individually; character recognition is benchmarked on three and word recognition on different datasets. Depending on the Sift configuration, lexicon, and dataset used, the word recognition rates range from 29% to 67%.


title: A Matlab-based modeling and simulation package for electric and hybrid electric vehicle design

abstract: This paper discusses a simulation and modeling package developed at Texas A&M University, V-Elph 2.01. V-Elph facilitates in-depth studies of electric vehicle (EV) and hybrid EV (HEV) configurations or energy management strategies through visual programming by creating components as hierarchical subsystems that can be used interchangeably as embedded systems. V-Elph is composed of detailed models of four major types of components: electric motors, internal combustion engines, batteries, and support components that can be integrated to model and simulate drive trains having all electric, series hybrid, and parallel hybrid configurations. V-Elph was written in the Matlab/Simulink graphical simulation language and is portable to most computer platforms. This paper also discusses the methodology for designing vehicle drive trains using the V-Elph package. An EV, a series HEV, a parallel HEV, and a conventional internal combustion engine (ICE) driven drive train have been designed using the simulation package. Simulation results such as fuel consumption, vehicle emissions, and complexity are compared and discussed for each vehicle.


title: A Survey on Ciphertext-Policy Attribute-based Encryption (CP-ABE) Approaches to Data Security on Mobile Devices and its Application to IoT

abstract: The growth in Cloud Computing and the ubiquity of Mobile devices to access Cloud services has generated a new paradigm, Mobile Cloud Computing (MCC). While the benefits of storing and accessing data in the Cloud are well documented there are concerns relating to the security of such data through data corruption, theft, exploitation or deletion. Innovative encryption schemes have been developed to address the challenges of data protection in the Cloud and having greater control over who should be accessing what data, one of which is Attribute-Based Encryption (ABE). ABE is a type of role-based access control encryption solution which allows data owners and data consumers or users to encrypt and decrypt based on their personal attributes (e.g. department, location, gender, role). A number of ABE schemes have been developed over the years but ABE in MCC has established its own paradigm driven by a) the use of mobile devices to access private data hosted in the Cloud and b) the physical limitations of the mobile device to perform complex computation in support of encryption and decryption in ABE. ABE in MCC is an evolving research field but given the breadth and strength of interest at time of writing it is timely to perform a survey. Due to the sheer volume of research, the survey has focused on one aspect of ABE - Ciphertext-Policy Attribute-Based Encryption - in line with its prominence in ABE in MCC research to date. Further, given the significant developments and interest in IoT, the survey has since been extended to assess whether the research into mobile devices has been translated to the application of attribute-based encryption in IoT where the challenges to support complex computation and data transmission are potentially more complex given the much greater heterogeneity and resource restrictions of IoT devices.


title: Detecting Data-Flow Errors in BPMN 2.0

abstract: Data-flow errors in BPMN 2.0 process models, such as missing or unused data, lead to undesired process executions. In particular, since BPMN 2.0 with a standardized execution semantics allows specifying alternatives for data as well as optional data, identifying missing or unused data systematically is difficult. In this paper, we propose an approach for detecting data-flow errors in BPMN 2.0 process models. We formalize BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We define a set of anti-patterns representing data-flow errors of BPMN 2.0 process models. By employing the anti-patterns, our tool performs model checking for the unfolded Petri Nets. The evaluation shows that it detects all data-flow errors identified by hand, and so improves process quality.


title: Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation

abstract: We propose an online, end-to-end, neural generative conversational model for open-domain dialog. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on a diversity-promoting heuristic for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of meaningful, relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.


title: Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

abstract: Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.


title: Market States and Momentum

abstract: We test overreaction theories of short-run momentum and long-run reversal in the cross section of stock returns. Momentum profits depend on the state of the market, as predicted. From 1929 to 1995, the mean monthly momentum profit following positive market returns is 0.93%, whereas the mean profit following negative market returns is −0.37%. The up-market momentum reverses in the long-run. Our results are robust to the conditioning information in macroeconomic factors. Moreover, we find that macroeconomic factors are unable to explain momentum profits after simple methodological adjustments to take account of microstructure concerns. SEVERAL BEHAVIORAL THEORIES have been developed to jointly explain the shortrun cross-sectional momentum in stock returns documented by Jegadeesh and Titman (1993) and the long-run cross-sectional reversal in stock returns documented by DeBondt and Thaler (1985). 1 Daniel, Hirshleifer, and Subrahmanyam (1998; hereafter DHS) and Hong and Stein (1999; hereafter HS) each employ different behavioral or cognitive biases to explain these anomalies. 2, 3


title: What kind of graphical model is the brain?

abstract: If neurons are treated as latent variables, our visual systems are non-linear, densely-connected graphical models containing billions of variables and thousands of billions of parameters. Current algorithms would have difficulty learning a graphical model of this scale. Starting with an algorithm that has difficulty learning more than a few thousand parameters, I describe a series of progressively better learning algorithms all of which are designed to run on neuron-like hardware. The latest member of this series can learn deep, multi-layer belief nets quite rapidly. It turns a generic network with three hidden layers and 1:7 million connections into a very good generative model of handwritten digits. After learning, the model gives classification performance that is comparable to the best discriminative methods.


title: Quantum computing in molecular magnets

abstract: Shor and Grover demonstrated that a quantum computer can outperform any classical computer in factoring numbers1 and in searching a database2 by exploiting the parallelism of quantum mechanics. Whereas Shor's algorithm requires both superposition and entanglement of a many-particle system3, the superposition of single-particle quantum states is sufficient for Grover's algorithm4. Recently, the latter has been successfully implemented5 using Rydberg atoms. Here we propose an implementation of Grover's algorithm that uses molecular magnets6,7,8,9,10, which are solid-state systems with a large spin; their spin eigenstates make them natural candidates for single-particle systems. We show theoretically that molecular magnets can be used to build dense and efficient memory devices based on the Grover algorithm. In particular, one single crystal can serve as a storage unit of a dynamic random access memory device. Fast electron spin resonance pulses can be used to decode and read out stored numbers of up to 105, with access times as short as 10-10 seconds. We show that our proposal should be feasible using the molecular magnets Fe8 and Mn12.


title: Are Word Embedding-based Features Useful for Sarcasm Detection?

abstract: This paper makes a simple increment to state-of-the-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embeddings. We augment word embedding-based features to four feature sets reported in the past. We also experiment with four types of word embeddings. We observe an improvement in sarcasm detection, irrespective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improvement in F-score of around 4\% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used. Finally, a comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.


title: Agent Communication Languages: The Current Landscape

abstract: Despite the substantial number of multiagent systems that use an agent communication language, the dust has not yet settled over the ACL landscape. Although semantic specification issues have monopolized the debate, other important pragmatic issues must be resolved quickly if ACLs are to support the development of robust agent systems. We introduce some concepts useful in discussing agent communication languages and then compare and evaluate the two major ACLs.


title: Channel Feasibility for Outdoor Non-Line-of-Sight mmWave Mobile Communication

abstract: Due to the scarcity of spectrum below 3 GHz for wireless communications, there have been proposals to explore millimeter wave (mmWave) spectrum (3- 300GHz) for commercial mobile applications. MmWave spectrum provides unique advantages such as availability of GHz bandwidth and use of antenna arrays with beamforming to compensate for path loss. While there exist well-established models for mmWave indoor non-line-of-sight (60 GHz) and mmWave outdoor line-of-sight (backhaul) communication, mmWave channels for outdoor, non-line-of-sight, mobile communication have not been explored sufficiently. We present penetration and reflection measurements for different materials and line-of-sight and non- line-of-sight measurements for outdoor mmWave mobile communication. We find that while well-known lossy objects such as human body and concrete can have poor penetration, they are good reflectors at these frequencies, enabling the receiver to capture secondary reflections for non-line-of-sight communication. We also show that a wide beam width, low gain antenna at the mobile receiver can capture more energy in scattered non-line-of-sight environments and thus, can provide more gain than a narrow beam, high gain antenna for mobile communication. Our initial measurements motivate utilizing mmWave frequencies for outdoor non-line- of-sight mobile communication.


title: Isolate the Clutter: Pure and Hybrid Linear-Frequency-Modulated Continuous-Wave (LFMCW) Radars for Indoor Applications

abstract: Radars are among the best exponents of radio-frequency (RF)/microwave devices for remote sensing [1], [2]. Long-range radars have demonstrated outstanding potential not only for defense/security environments, such as the detection, location, and tracking of targets, but also on a plurality of civilian scenarios, such as meteorological/weather measurement tasks, imaging of the Earth's surface through synthetic aperture radar (SAR) techniques, monitoring of animal migrations, and many others [3]-[7].


title: Mitigating Use-After-Free Attack with Application Program Loader

abstract: In the area of software security, use-after-free vulnerabilities have been reported since 2006. When the target vulnerable application is run, the attack exploits a dangling pointer after the heap memory is released. Until today, use-after-free attacks have been frequently reported in popular software such as browsers. This is a serious software security problem because a use-after-free attack allows an attacker to execute an arbitrary code to hijack an application control flow or to force a system crash. Some countermeasures have been proposed to thwart such attacks. However, most of these countermeasures have some problems such as the necessity of a source code or the problem of dependency. In this paper, we propose and evaluate the implementation of an application-level program loader to mitigate the use-after-free attack.


From Patches to Images: A Nonparametric Generative Model


title: Conspicuous Consumption versus Charitable Behavior in Response to Social Exclusion: A Differential Needs Explanation

abstract: Social exclusion has been shown to produce a number of different responses. This research examines the proposition that social exclusion may produce either self-focused or prosocial responses, depending on which needs are threatened. Different types of social exclusion threaten different needs, which in turn produce distinct outcomes (differential needs hypothesis). Social exclusion in the form of being implicitly ignored increased conspicuous consumption, whereas being explicitly rejected increased helping and donation behavior. However, when efficacy needs (power, meaningful existence) were bolstered, the effects of being ignored were eliminated, whereas when relational needs (self-esteem) were bolstered, the effects of being rejected were eliminated. The results indicate that certain types of social exclusion produce prosocial responses, whereas others produce self-focused and attention-getting responses.


title: Semi-dense visual odometry for AR on a smartphone

abstract: We present a direct monocular visual odometry system which runs in real-time on a smartphone. Being a direct method, it tracks and maps on the images themselves instead of extracted features such as keypoints. New images are tracked using direct image alignment, while geometry is represented in the form of a semi-dense depth map. Depth is estimated by filtering over many small-baseline, pixel-wise stereo comparisons. This leads to significantly less outliers and allows to map and use all image regions with sufficient gradient, including edges. We show how a simple world model for AR applications can be derived from semi-dense depth maps, and demonstrate the practical applicability in the context of an AR application in which simulated objects can collide with real geometry.


title: Diagonal scaling in Douglas-Rachford splitting and ADMM

abstract: The performance of Douglas-Rachford splitting and the alternating direction method of multipliers (ADMM) (i.e. Douglas-Rachford splitting on the dual problem) are sensitive to conditioning of the problem data. For a restricted class of problems that enjoy a linear rate of convergence, we show in this paper how to precondition the optimization data to optimize a bound on that rate. We also generalize the preconditioning methods to problems that do not satisfy all assumptions needed to guarantee a linear convergence. The efficiency of the proposed preconditioning is confirmed in a numerical example, where improvements of more than one order of magnitude are observed compared to when no preconditioning is used.


Fine-Grained Image Retrieval: the Text/Sketch Input Dilemma


title: iNEAT: Incomplete Network Alignment

abstract: Network alignment and network completion are two fundamental cornerstones behind many high-impact graph mining applications. The state-of-the-arts have been addressing these tasks in parallel. In this paper, we argue that network alignment and completion are inherently complementary with each other, and hence propose to jointly address them so that the two tasks can benefit from each other. We formulate it from the optimization perspective, and propose an effective algorithm iNEAT to solve it. The proposed method offers two distinctive advantages. First (Alignment accuracy), our method benefits from higher-quality input networks while mitigates the effect of incorrectly inferred links introduced by the completion task itself. Second (Alignment efficiency), thanks to the low-rank structure of the complete networks and alignment matrix, the alignment can be significantly accelerated. The extensive experiments demonstrate the performance of our algorithm.


title: Joint Radio and Computational Resource Allocation in IoT Fog Computing

abstract: The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users’ delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational resource allocation in the fog computing. In this work, we investigate a joint radio and computational resource allocation problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project allocation (SPA) game, to provide a distributed solution for the formulated joint resource allocation problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.


Natural Products From Marine Algae


title: Transcutaneous measurement of glomerular filtration rate using FITC-sinistrin in rats

abstract: BACKGROUND ::: Inulin/sinistrin (I/S) clearance is a gold standard for an accurate assessment of glomerular filtration rate (GFR). Here we describe and validate an approach for a transcutaneous determination of GFR by using fluorescein-isothiocyanate-labelled sinistrin (FITC-S) in rats. ::: ::: ::: METHODS ::: Using a small animal imager, fluorescence is measured over the depilated ear of a rat after the injection of FITC-S. The decay curve of fluorescence is used for the calculation of half-life and GFR. The thus obtained transcutaneous data were validated by simultaneously performed enzymatic and fluorometric measurements in plasma of both FITC-S and sinistrin. ::: ::: ::: RESULTS ::: The results of enzymatic sinistrin determination versus transcutaneous half-life of FITC-S or plasma fluorescence correlated well with each other (R(2) > 0.90). Furthermore, Bland-Altman analyses proved a good degree of agreement of the three methods used. The measurements performed in healthy animals as well as different models of renal failure demonstrate its appropriateness in a wide range of renal function. ::: ::: ::: CONCLUSIONS ::: The transcutaneous method described offers a precise assessment of GFR in small animals. As neither blood and/or urine sampling nor time-consuming lab work is required, GFR can be determined immediately after the clearance procedure is finished. This method, therefore, simplifies and fastens GFR determinations in small lab animals compared to conventional bolus clearance techniques based on blood sampling. A low-cost device for the measurement of transcutaneous fluorescence intensity over time is under construction.


Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models


title: Emotion recognition using facial expressions with active appearance models

abstract: Recognizing emotion using facial expressions is a key element in human communication. In this paper we discuss a framework for the classification of emotional states, based on still images of the face. The technique we present involves the creation of an active appearance model (AAM) trained on face images from a publicly available database to represent shape and texture variation key to expression recognition. Parameters from the AAM are used as features for a classification scheme that is able to successfully identify faces related to the six universal emotions. The results of our study demonstrate the effectiveness of AAMs in capturing the important facial structure for expression identification and also help suggest a framework for future development.


title: Shadow systems: the good, the bad and the ugly

abstract: A convection diffuser and charge support system for an annealing furnace utilizes a diffuser base assembly that includes a vane-carrying center casting which underlies a charge support plate, and which is perimetrically surrounded by a pair of outer and inner cooling rings. The components of the diffuser base assembly cooperate to define an array of horizontally extending gas circulation passages that are shielded from above by the charge support plate. The horizontal passages extend among a plurality of upstanding heat exchange fins that are carried by the inner cooling ring, and terminate in upwardly extending outer end regions that are defined by grooves formed in the outer cooling ring. A centrifugal fan draws hot gases through a central opening in the charge support plate and conveys the gases radially outwardly through the horizontally extending gas flow passages. The gases flow outwardly through the horizontal passages, among the fins of the inner cooling ring, and are diverted upwardly by the grooves of the outer cooling ring for movement along generally helical flow paths about a charge of material being annealed. The outer cooling ring is provided with a depending, perimetrically extending skirt. In an annealing process, cooling of the hot gases within the furnace enclosure is carried out in a two stage procedure that is initiated sufficiently gradually to avoid collapse of the positive pressure atmosphere within the furnace enclosure.


title: Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores

abstract: Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brain's function and efficiency. The non-von Neumann nature of the TrueNorth architecture necessitates a novel approach to efficient system design. To this end, we have developed a set of abstractions, algorithms, and applications that are natively efficient for TrueNorth. First, we developed repeatedly-used abstractions that span neural codes (such as binary, rate, population, and time-to-spike), long-range connectivity, and short-range connectivity. Second, we implemented ten algorithms that include convolution networks, spectral content estimators, liquid state machines, restricted Boltzmann machines, hidden Markov models, looming detection, temporal pattern matching, and various classifiers. Third, we demonstrate seven applications that include speaker recognition, music composer recognition, digit recognition, sequence prediction, collision avoidance, optical flow, and eye detection. Our results showcase the parallelism, versatility, rich connectivity, spatio-temporality, and multi-modality of the TrueNorth architecture as well as compositionality of the corelet programming paradigm and the flexibility of the underlying neuron model.


title: Automatically generating features for learning program analysis heuristics for C-like languages

abstract: We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been developed, which mine existing codebases and automatically learn heuristics for finding a cost-effective abstraction for a given analysis task. Such approaches reduce the burden of the analysis designers, but they do not remove it completely; they still leave the nontrivial task of designing so called features to the hands of the designers. Our technique aims at automating this feature design process. The idea is to use programs as features after reducing and abstracting them. Our technique goes through selected program-query pairs in codebases, and it reduces and abstracts the program in each pair to a few lines of code, while ensuring that the analysis behaves similarly for the original and the new programs with respect to the query. Each reduced program serves as a boolean feature for program-query pairs. This feature evaluates to true for a given program-query pair when (as a program) it is included in the program part of the pair. We have implemented our approach for three real-world static analyses. The experimental results show that these analyses with automatically-generated features are cost-effective and consistently perform well on a wide range of programs.


title: Significance of Clustering Coefficient over Jaccard Index

abstract: Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information network and citation network to predict the future links. Every network has got certain structural properties. For predicting the accuracy of future links, structural properties might play an important role. Current work is an effort to find out the correlation between structural properties of network and prediction accuracy. Positive Correlation will signify that knowing the structural properties of a Network will assist us to know the prediction accuracy in advance.


title: Protection Strategies for Medium-Voltage Direct-Current Microgrid at a Remote Area Mine Site

abstract: This paper presents protection strategies for a medium-voltage dc (MVDC) microgrid at a remote area mine site. The microgrid is operated to provide high power quality and reliability to sensitive loads and to improve the energy efficiency of the mining equipment. In the MVDC microgrid, various local distributed energy resources have been used, including photovoltaic arrays, wind turbines, a fuel-cell stack, an energy storage system, and mobile diesel generators. For the protection of transmission lines, a communication-based differential protection scheme with solid-state electronic relays is employed to isolate the faulted part of the MVDC microgrid. This is further reinforced by dc overcurrent protection as a backup. Earlier research work had neglected the backup protection for dc systems. In addition, communication-based dc directional overcurrent protective relays are used for both source protection and load protection to support a bidirectional power flow. MATLAB/Simulink modeling and simulation results are presented and discussed to illustrate the proposed system's dependability and security.


Teachers' ICT Literacy and Utilization in English Language Teaching


title: Synthesis and Characterization of Novel Alcohol-Soluble Ladderlike Poly(silsesquioxane)s Containing Side-Chain Hydroxy Groups

abstract: Two novel kinds of alcohol-soluble ladder-like poly(silsesquioxane)s containing side-chain hydroxy groups (OH-T 1 and OH-T 2 ) were prepared successfully for the first time by hydrolysis of ladderlike poly(silses-quioxane)s with side-chain ester groups (Ester-T 1 and Ester-T 2 ), which were synthesized by stepwise coupling polymerization. A variety of characterization methods including FT-IR, 1 H NMR, 13 C NMR, 29 Si NMR, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), vapor pressure osmometry (VPO), gel permeation chromatography (GPC), and X-ray diffraction (XRD) were combined to demonstrate that the obtained alcohol-soluble polymers have ladderlike structures, although some structural defects still existed.


Measurement of Industrial Alcohol Concentration Capacitance Method and AIA-GA Nonlinear Correction


title: Evaluation of reaction rate enhancement by reduction in dimensionality.

abstract: The paths followed by ligands as they react with or dissociate from cell surface receptors may include weak association with nonreceptor portions of the surface followed by lateral diffusion in the plane of the membrane to a receptor. The change in dimensionality of the diffusion process by utilization of these nonspecific paths has been invoked by a number of investigators as a mechanism for enhancing reaction rate in biological systems. This paper extends our previous work on the calculation of diffusive rate constants for ligand-receptor paths. We find that they have little effect on rate constants unless the number of free receptors per cell has been reduced to less than or equal to 10(2). This number represents better than 90% occupancy for most eukaryotes, suggesting that the dimensional change mechanism is of limited consequence. We show further that when the free receptor number is low enough for rate enhancement, then the primary parameter of consequence is D'K*/D, where D' and D are the two- and three-dimensional diffusion coefficients, respectively, and K* the nonspecific affinity. A 10-fold rate enhancement with 100 free receptors requires that this parameter be of order 10(-3). This value is barely within the lower limit imposed by currently available experimental information, casting doubt on the relevance of nonspecific paths in cellular systems.


title: Machine Learning in Bioinformatics: A Novel Approach for DNA Sequencing

abstract: Machine learning is the adaptive process that makes computers improve from experience, by example, and by analogy. So It is a discipline of methodologies that provides, in one form or another, intelligent information processing capabilities for handling real life. Bioinformatics is one of the application of Machine Learning. Bioinformatics is the interdisciplinary science of interpreting biological data using information technology and computer science. Machine learning (ML) focuses on automatic learning from data set. Machine learning includes the learning speed, the guarantee of convergence, and how the data can be learned incrementally. We usually refer to methods like Artificial Neural Networks (ANNs), Genetic algorithms (GAs), and Fuzzy systems along with hybrid methods including a combination of some of these methods. One of the major problems is to classify the normal genes and the invalid genes which are infected by some kind of diseases. In genomic research, classifying DNA sequences into existing categories is used to learn the functions of a new protein. So, it is important to identify those genes and classify them. In order to identify the infected genes and the normal genes with the use of classification methods here we use the machine learning techniques. This paper gives a review on the mechanisms of gene sequence classification using Machine Learning techniques, which includes a brief detail on bioinformatics, literature survey and key issues in DNA Sequencing using Machine Learning.


title: Secure data transfer in IoT environment: Adopting both cryptography and steganography techniques

abstract: Owing to the unprecedented growth in computing power, electronics miniaturization and mobile and wireless network interconnections the internet has metamorphosed into Internet of Things (IoT) which refers to next stage of the information revolution whose context involves billions of individuals and devices interconnected to facilitate exchange of huge volume of data and information from diverse locations, demanding the consequent necessity for smart data aggregation followed by an increased obligation to index, hoard and process data with higher efficiency and effectiveness. But along with its myriad offered benefits and applications, emerges a novel complexity aspect in terms of many inherent hassles primarily security concerns during data transfer phases in IoT covering mostly data confidentiality and integrity features. Thus to enhance safe data transfer in smart IoT environment, a security scheme is proposed in this paper which addresses both the aforesaid issues, employing an integrated approach of lightweight cryptography and steganography (Simple LSB Substitution) technique during data transfer between IoT device and Home Server and adoption of combined approach of cryptography and steganography (Proposed MSB-LSB Substitution) technique during data transfer phases between Home Server and Cloud Servers.


title: New rate-2 STBC design for 2 TX with reduced-complexity maximum likelihood decoding

abstract: We propose a new full-rate space-time block code (STBC) for two transmit antennas which can be designed to achieve maximum diversity or maximum capacity while enjoying optimized coding gain and reduced-complexity maximum-likelihood (ML) decoding. The maximum transmit diversity (MTD) construction provides a diversity order of 2Nr for any number of receive antennas Nr at the cost of channel capacity loss. The maximum channel capacity (MCC) construction preserves the mutual information between the transmit and the received vectors while sacrificing diversity. The system designer can switch between the two constructions through a simple parameter change based on the operating signal-to-noise ratio (SNR), signal constellation size and number of receive antennas. Thanks to their special algebraic structure, both constructions enjoy low-complexity ML decoding proportional to the square of the signal constellation size making them attractive alternatives to existing full-diversity full-rate STBCs in [6], [3] which have high ML decoding complexity proportional to the fourth order of the signal constellation size. Furthermore, we design a differential transmission scheme for our proposed STBC, derive the exact ML differential decoding rule, and compare its performance with competitive schemes. Finally, we investigate transceiver design and performance of our proposed STBC in spatial multiple-access scenarios and over frequency-selective channels.


title: Recursive Unsolvability of Post's Problem of "Tag" and other Topics in Theory of Turing Machines

abstract: The equivalence of the notions of effective computability as based (1) on formal systems (e.g., those of Post), and (2) on computing machines (e.g., those of Turing) has been shown in a number of ways. The main results of this paper show that the same notions of computability can be realized within (1) the highly restricted monogenic formal systems called by Post the "Tag" systems, and (2) within a peculiarly restricted variant of Turing machine which has two tapes, but can neither write on nor erase these tapes. From these, or rather from the arithmetization device used in their construction, we obtain also an interesting basis for recursive function theory involving programs of only the simplest arithmetic operations. We show first how Turing machines can be regarded as programmed computers. Then by defining a hierarchy of programs which perform certain arithmetic transformations, we obtain the representation in terms of the restricted two-tape machines. These machines, in turn, can be represented in terms of Post normal canonical systems in such a way that each instruction for the machine corresponds to a set of productions in a system which has the monogenic property (for each string in the Post system just one production can operate). This settles the questions raised


title: Long-Term RNN: Predicting Hazard Function for Proactive Maintenance of Water Mains

abstract: Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures. Different from traditional survival analysis, this paper discovers how to apply recurrent neural network (RNN) to the long-term hazard function prediction. The proposed Long-Term RNN (LT-RNN) is able to leverage the precedent information shared by other entities, leading to more reliable long-term predictions. Specifically, our method allows a black-box treatment for modelling the hazard function which is often a pre-defined parametric form in typical survival analysis. The key idea of our approach is to model the hazard function as a nonparameteric function of the history. The same precedent information from other entities is embedded to a stitched vector for LT-RNN to automatically learn a representation of the long-term hazard function. We apply our model to the proactive maintenance problem using a large dataset from a water utility in Australia.


title: Self-Erasing Network for Integral Object Attention

abstract: Recently, adversarial erasing for weakly-supervised object attention has been deeply studied due to its capability in localizing integral object regions. However, such a strategy raises one key problem that attention regions will gradually expand to non-object regions as training iterations continue, which significantly decreases the quality of the produced attention maps. To tackle such an issue as well as promote the quality of object attention, we introduce a simple yet effective Self-Erasing Network (SeeNet) to prohibit attentions from spreading to unexpected background regions. In particular, SeeNet leverages two self-erasing strategies to encourage networks to use reliable object and background cues for learning to attention. In this way, integral object regions can be effectively highlighted without including much more background regions. To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models. Experiments on Pascal VOC well demonstrate the superiority of our SeeNet over other state-of-the-art methods.


title: Human Conversation Analysis Using Attentive Multimodal Networks with Hierarchical Encoder-Decoder

abstract: Human conversation analysis is challenging because the meaning can be expressed through words, intonation, or even body language and facial expression. We introduce a hierarchical encoder-decoder structure with attention mechanism for conversation analysis. The hierarchical encoder learns word-level features from video, audio, and text data that are then formulated into conversation-level features. The corresponding hierarchical decoder is able to predict different attributes at given time instances. To integrate multiple sensory inputs, we introduce a novel fusion strategy with modality attention. We evaluated our system on published emotion recognition, sentiment analysis, and speaker trait analysis datasets. Our system outperformed previous state-of-the-art approaches in both classification and regressions tasks on three datasets. We also outperformed previous approaches in generalization tests on two commonly used datasets. We achieved comparable performance in predicting co-existing labels using the proposed model instead of multiple individual models. In addition, the easily-visualized modality and temporal attention demonstrated that the proposed attention mechanism helps feature selection and improves model interpretability.


title: A robust shape model for multi-view car alignment

abstract: We present a robust shape model for localizing a set of feature points on a 2D image. Previous shape alignment models assume Gaussian observation noise and attempt to fit a regularized shape using all the observed data. However, such an assumption is vulnerable to gross feature detection errors resulted from partial occlusions or spurious background features. We address this problem by using a hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate object shape and pose hypotheses from randomly sampled partial shapes - subsets of feature points. The hypotheses are then evaluated to find the one that minimizes the shape prediction error. The proposed model can effectively handle outliers and recover the object shape. We evaluate our approach on a challenging dataset which contains over 2,000 multi-view car images and spans a wide variety of types, lightings, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.