title: Prediction of rock mass rating system based on continuous functions using Chaos–ANFIS model

abstract: Abstract Survey properties of soil and rock mass have always been associated with uncertainty. Hence, the behavior of the soil or rock cannot be investigated specifically by choosing a value specified for these properties. One of the most common systems for studying properties of rock mass is the rock mass classification system (RMR) which was developed by Bieniawski. In this system the input parameters are divided into several classes, and each class has particular rating. In this system, because of uncertainties of the input parameters, determining the definite boundary between the classes and assigning a specified value to a particular class is difficult, so when the input parameters are close to the boundary between the classes, the class rating with certainity is not decided. The aim of this paper is to propose a hybrid nonlinear Chaotic and Neuro-Fuzzy system modeling for the basic RMR system uncertainty based on continuous functions. This model also proves the theory of Bieniawski that is based on nonlinear systems by using chaos theory and mathematical relations. The main advantage of proposed model is to directly predict output of RMR system classification system without considering the input parameters so that it leads to better results and a higher level of prediction rock quality.


title: Organic solar cells: An overview

abstract: Organic solar cell research has developed during the past 30 years, but especially in the last decade it has attracted scientific and economic interest triggered by a rapid increase in power conversion efficiencies. This was achieved by the introduction of new materials, improved materials engineering, and more sophisticated device structures. Today, solar power conversion efficiencies in excess of 3% have been accomplished with several device concepts. Though efficiencies of these thin-film organicdevices have not yet reached those of their inorganic counterparts (η ≈ 10–20%); the perspective of cheap production (employing, e.g., roll-to-roll processes) drives the development of organic photovoltaic devices further in a dynamic way. The two competitive production techniques used today are either wet solution processing or dry thermal evaporation of the organic constituents. The field of organic solar cells profited well from the development of light-emitting diodes based on similar technologies, which have entered the market recently. We review here the current status of the field of organic solar cells and discuss different production technologies as well as study the important parameters to improve their performance.


title: NPCs Vote! Changing Voter Reactions Over Time Using the Extreme AI Personality Engine

abstract: Can non-player characters have human-realistic personalities, changing over time depending on input from those around them? And can they have different reactions and thoughts about different people? Using Extreme AI, a psychology-based personality engine using the Five Factor model of personality, I answer these questions by creating personalities for 100 voters and allowing them to react to two politicians to see if the NPC voters' choice of candidate develops in a realistic-seeming way, based on initial and changing personality facets and on their differing feelings toward the politicians (in this case, across liking, trusting, and feeling affiliated with the candidates). After 16 test runs, the voters did indeed change their attitudes and feelings toward the candidates in different and yet generally realistic ways, and even changed their attitudes about other issues based on what a candidate extolled.


title: Reactive programming with reactive variables

abstract: Reactive Programming enables declarative definitions of time-varying values (signals) and their dependencies in a way that changes are automatically propagated. In order to use reactive programming in an imperative object-oriented language, signals are usually modelled as objects. However, computations on primitive values then have to lifted to signals which usually involves a verbose notation. Moreover, it is important to avoid cycles in the dependency graph and glitches, both of which can result from changes to mutable global state during change propagation. This paper introduces reactive variables as extension to imperative languages. Changes to reactive variables are automatically propagated to other reactive variables but, in contrast to signals, reactive variables cannot be reified and used as values. Instead, references to reactive variables always denote their latest values. This enables computation without explicit lifting and limits the dependents of a reactive variable to the lexical scope of its declaration. The dependency graph is therefore topologically ordered and acyclic. Additionally, reactive updates are prevented from mutating global state to ensure consistency. We present a working prototype implementation in JavaScript based on the sweet.js macro system and a formalism for integration with general imperative languages.


title: Applying Computational Intelligence for enhancing the dependability of multi-cloud systems using Docker Swarm

abstract: Multi-cloud systems have been gaining popularity due to the several benefits of the multi-cloud infrastructure such as lower level of vendor lock-in and minimize the risk of widespread data loss or downtime. Thus, the multi-cloud infrastructure enhances the dependability of the cloud-based system. However, it also poses many challenges such as nonstandard and inherent complexity due to different technologies, interfaces, and services. Consequently, it is a challenging task to design multi-cloud dependable systems. Virtualization is the key technology employed in the development of cloud-based systems. Docker has recently introduced its container-based virtualization technology for the development of software systems. It has newly launched a distributed system development tool called Swarm, which allows the development of a cluster of multiple Swarm nodes on multiple clouds. Docker Swarm has also incorporated several dependability attributes to support the development of a multi-cloud dependable system. However, making Swarm cluster always available requires minimum three active manager nodes which can safeguard one failure. This essential condition for the dependability is one of the main limitations because if two manager nodes fail suddenly due to the failure of their hosts, then Swarm cluster cannot be made available for routine operations. Therefore, this paper proposes an intuitive approach based on Computational Intelligence (CI) for enhancing its dependability. The proposed CI-based approach predicts the possible failure of the host of a manager node by observing its abnormal behaviour. Thus, this indication can automatically trigger the process of creating a new manager node or promoting an existing node as a manager for enhancing the dependability of Docker Swarm.


title: NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity

abstract: We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a - English Semantic Textual Similarity, STS, and 2c - Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combining a wide array of features including semantic similarity scores obtained from various methods. One of our runs achieved weighted mean correlation score of 0.784 for sentence similarity subtask (i.e., English STS) and was ranked tenth among 74 runs submitted by 29 teams. For the interpretable similarity pilot task, we employed a rule-based approach blended with chunk alignment labeling and scoring based on semantic similarity features. Our system for interpretable text similarity was among the top three best performing systems.


title: Physical performance characteristics of high-level female soccer players 12-21 years of age

abstract: Performance assessment has become an invaluable component of monitoring player development and within talent identification programs in soccer, yet limited performance data are available for female soccer players across a wide age range. The aim of this study was to describe the physical performance characteristics of female soccer players ranging in age from 12 to 21 years. High-level female soccer players (n=414) were evaluated on linear sprinting (36.6 m with 9.1 m splits), countermovement jump (CMJ), and two agility tests. Separate one-way ANOVAs were used to compare performance characteristics between (1) each year of chronological age and (2) three age groups: 12-13 years, n=78, 14-17 years, n=223, and 18-21 years, n=113. Mean linear sprint speed over 9.1 m was similar across all chronological ages, however sprint speed over the final 9.1 m, CMJ height and agility scores improved until approximately 15-16 years. Outcomes from the group data indicated better performance on all tests for the 14-17-year-old group compared with the 12-13-year-old group. Additionally, sprint speed on the second and fourth 9.1 m splits and 36.6 m sprint speed as well as performance on the Illinois agility test was better in the 18-21-year-old group compared with the 14-17-year-old group. The findings from this study indicate that marked improvements of high intensity short duration work occur up until 15-16 years. Smaller gains in performance were observed beyond 16 years of age as evidenced by better performance on 36.6 m sprint speed, several sprint splits and the Illinois agility test in the college aged players (i.e., 18-21-year-old group).


title: DeepType: Multilingual Entity Linking by Neural Type System Evolution

abstract: The wealth of structured (e.g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence. So far, integration of these two different modalities is a difficult process, involving many decisions concerning how best to represent the information so that it will be captured or useful, and hand-labeling large amounts of data. DeepType overcomes this challenge by explicitly integrating symbolic information into the reasoning process of a neural network with a type system. First we construct a type system, and second, we use it to constrain the outputs of a neural network to respect the symbolic structure. We achieve this by reformulating the design problem into a mixed integer problem: create a type system and subsequently train a neural network with it. In this reformulation discrete variables select which parent-child relations from an ontology are types within the type system, while continuous variables control a classifier fit to the type system. The original problem cannot be solved exactly, so we propose a 2-step algorithm: 1) heuristic search or stochastic optimization over discrete variables that define a type system informed by an Oracle and a Learnability heuristic, 2) gradient descent to fit classifier parameters. We apply DeepType to the problem of Entity Linking on three standard datasets (i.e. WikiDisamb30, CoNLL (YAGO), TAC KBP 2010) and find that it outperforms all existing solutions by a wide margin, including approaches that rely on a human-designed type system or recent deep learning-based entity embeddings, while explicitly using symbolic information lets it integrate new entities without retraining.


title: Design and optimization of a novel six-axis force/torque sensor with good isotropy and high sensitivity

abstract: Six-axis force/torque sensor plays an important role in many fields. A novel six-axis force/torque sensor based on strain gauge was designed and optimized with design of experiments (DOE) approach and response surface methodology. In order to reduce the errors bonding strain gauges, we adopted strain distribution analysis to acquire the accurately positions. The strain compliance matrix was attained by the signals from strain gauges bonded on the elastic body of the sensor. According to strain compliance matrix of the sensor we can obtain a sensor with rather good measurement isotropy, high sensitivity and natural frequency which is nearly as bandwidth of commercial six-axis sensor.


title: Social Cognitive Theory, Entrepreneurial Self-Efficacy and Entrepreneurial Intentions: Tools to Maximize the Effectiveness of Formal Entrepreneurship Education and Address the Decline in Entrepreneurial Activity

abstract: The purpose of this paper is to examine existing literature on Social Cognitive Theory, entrepreneurial self-efficacy, and entrepreneurial competencies and intentions, to understand the current state of the field, design a new theoretical framework to help determine the best teaching strategies and identify career counselor competencies and possible contributions to the entrepreneurial education field. Entrepreneurial self-efficacy is proposed as a useful construct to increase the entrepreneurial intentions and competencies of students. Social cognitive theory, self-efficacy, and entrepreneurial self-efficacy are incorporated into an applied theoretical framework to improve the effectiveness of formal entrepreneurship education. Specifically, the model links the four sources of self-efficacy with entrepreneurial self-efficacy and entrepreneurial intentions; which lead to effective entrepreneurial education program outcomes.


title: Contextual feature based one-class classifier approach for detecting video response spam on YouTube

abstract: YouTube is one of the largest video sharing websites (with social networking features) on the Internet. The immense popularity of YouTube, anonymity and low publication barrier has resulted in several forms of misuse and video pollution such as uploading of malicious, copyright violated and spam video or content. YouTube has a popular and commonly used feature called as video response which allows users to post a video response to an uploaded or existing video. Some of the popular videos on YouTube receive thousands of video responses. We observe presence of opportunistic users posting unrelated, promotional, pornographic videos (spam videos posted manually or using automated scripts) as video responses to existing videos. We present a method of mining YouTube to automatically detect video response spam. We formulate the problem of video response spam detection as a one-class classification problem (a recognition task) and divide the problem into three sub-problems: promotional video recognition, pornographic or dirty video recognition and automated script or botnet uploader recognition. We create a sample dataset of target class videos for each of the three sub-problems and identify contextual features (meta-data based or non-content based features) characterizing the target class. Our empirical analysis reveals that certain linguistic features (presence of certain terms in the title or description of the YouTube video), temporal features, popularity based features, time based features can be used to predict the video type. We identify features with discriminatory powers and use it within a one-class classification framework to recognize video response spam. We conduct a series of experiments to validate the proposed approach and present evidences to demonstrate the effectiveness of the proposed solution with more than 80% accuracy.


title: Highly polarized electron donors, acceptors and donor–acceptor compounds for organic conductors

abstract: New types of π-electron donors and acceptors with high polarizability are reviewed. TTF derivatives with polarizable substituents, π-extended TTF analogues fused with nitrogen-containing heterocycles and non-TTF donors (i.e. electron donors) and chalcogen atom-containing quinones, TCNQ and DCNQI analogues (i.e. electron acceptors) are described. Donor–acceptor compounds showing electrochemically amphoteric properties are also discussed.


title: A technique for dynamic updating of Java software

abstract: During maintenance, systems are updated to correct faults, improve functionality, and adapt the software to changes in its execution environment. The typical software update process consists of stopping the system to be updated, performing the update of the code, and restarting the system. For systems such as banking and telecommunication software, however the cost of downtime can be prohibitive. The situation is even worse for systems such as air-traffic controllers and life-support software, for which a shut-down is in general not an option. In those cases, the use of some form of on-the-fly program modification is required. In this paper, we present a new technique for dynamic updating of Java software. Our technique is based oil the use of proxy classes and requires no support from the runtime system. The technique allows for updating a running Java program by substituting, adding, and deleting classes. We also present DUSC (dynamic updating through swapping of classes), a tool that we developed and that implements our technique. Finally, we describe an empirical study that we performed to validate the technique of a real Java subject. The results of the study show that our technique can be effectively applied to Java software with only little overhead in both execution time and program size.


title: Inferotemporal Cortex and Object Vision

abstract: Cells in area TE of the inferotemporal cortex of the monkey brain selectively respond to various moderately complex object features, and those that cluster in a columnar region that runs perpendicular to the cortical surface respond to similar features. Although cells within a column respond to similar features, their selectivity is not necessarily identical. The data of optical imaging in 'E have suggested that the borders between neighboring columns are not discrete; a continuous mapping of complex feature space within a larger region contains several partially overlapped columns. This continuous mapping may be used for various computations, such as production of the image of the object at different viewing angles, illumination conditions. and articulation poses.


Towards Semi-automatic Generation of R2R Mappings


title: Spy vs. Spy: Rumor Source Obfuscation

abstract: Anonymous messaging platforms, such as Secret, Yik Yak and Whisper, have emerged as important social media for sharing one's thoughts without the fear of being judged by friends, family, or the public. Further, such anonymous platforms are crucial in nations with authoritarian governments; the right to free expression and sometimes the personal safety of the author of the message depend on anonymity. Whether for fear of judgment or personal endangerment, it is crucial to keep anonymous the identity of the user who initially posted a sensitive message. In this paper, we consider an adversary who observes a snapshot of the spread of a message at a certain time. Recent advances in rumor source detection shows that the existing messaging protocols are vulnerable against such an adversary. We introduce a novel messaging protocol, which we call adaptive diffusion, and show that it spreads the messages fast and achieves a perfect obfuscation of the source when the underlying contact network is an infinite regular tree: all users with the message are nearly equally likely to have been the origin of the message. Experiments on a sampled Facebook network show that it effectively hides the location of the source even when the graph is finite, irregular and has cycles.


title: Learning Global Features for Coreference Resolution

abstract: There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters. Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative cluster-level features. We instead propose to use recurrent neural networks (RNNs) to learn latent, global representations of entity clusters directly from their mentions. We show that such representations are especially useful for the prediction of pronominal mentions, and can be incorporated into an end-to-end coreference system that outperforms the state of the art without requiring any additional search.


title: Observing algorithmic marketplaces in-the-wild

abstract: In this letter, we briefly summarize two recent works from our group that use observational data to study the mechanisms used by two large markets. First, we examine Uber's surge price algorithm, and observe that its incentive model may not be effective at changing driver behavior. Second, we study the adoption of dynamic pricing strategies by sellers on Amazon Marketplace, and investigate how these strategies interact with Amazon's "Buy Box" matching algorithm. We make our data available to the research community.


title: Calibrating Uncertainties in Object Localization Task

abstract: In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confidence interval for its bounding box. While recent Bayesian deep learning methods provide a principled way to estimate this uncertainty, the estimates for the bounding boxes obtained using these methods are uncalibrated. In this paper, we address this problem for the single-object localization task by adapting an existing technique for calibrating regression models. We show, experimentally, that the resulting calibrated model obtains more reliable uncertainty estimates.


title: A fully on-chip 25MHz PVT-compensation CMOS Relaxation Oscillator

abstract: A fully on-chip, low-power and small area CMOS Relaxation Oscillator (ROSC) with voltage integral feedback structure and a new Bandgap Reference voltage (BGR) for accurate oscillation frequency independent of the PVT and comparator's delay variations is presented. The designed circuit uses a new bandgap reference to generate the reference voltage required by relaxation oscillator which allow variations due to voltage and temperature to be compensated. Another merit of this oscillator is that the phase noise at low-offset frequency is suppressed by the voltage integral feedback circuit. The frequency of the relaxation oscillator is determined by the RC response time. Thus, the current and capacitance are controlled by temperature and process compensation circuits to compensate for the frequency variation. The ROSC is implemented in a 0.18µm CMOS technology and its active area is 0.14mm2. The target frequency is 25MHz and current consumption is 22µA, where V DD is 1.8V. The oscillation frequency variation for V DD ranges from 1.4 to 1.9V is 0.2% and for temperature ranges from −40 to 125°C is 0.18%.


title: Methods and techniques to help quality function deployment (QFD)

abstract: Quality function deployment (QFD) is a management tool that provides a visual connective process to help teams focus on the needs of the customers throughout the total development cycle of a product or process. It provides the means for translating customer needs into appropriate technical requirements for each stage of a product/process‐development life‐cycle. It helps to develop more customer‐oriented, higher‐quality products. While the structure provided by QFD can be significantly beneficial, it is not a simple tool to use. This article outlines how techniques such as fuzzy logic, artificial neural networks, and the Taguchi method can be combined with QFD to resolve some of its drawbacks, and proposes a synergy between QFD and the three methods and techniques reviewed.


title: Liver glycogen, enzyme activities, and pancreatic hormones in juvenile Atlantic salmon (Salmo salar) during their first summer in seawater

abstract: Our observation of very low liver glycogen concentration in 1-yr-old feeding Atlantic salmon (Salmo salar) in seawater net-pens in Puget Sound, Washington, led to studies of their metabolic status. We assessed liver glycogen concentration, activities of some hepatic enzymes involved in glycogen synthesis, depletion, and gluconeogenesis, and plasma profiles of glucose and pancreatic hormones (insulin and glucagon) in yearling Atlantic salmon before and after seawater transfer. Liver glycogen concentration in Atlantic salmon during the several months after seawater entry was much lower than in coho salmon (Oncorhynchus kisutch) of the same age in seawater. Other metabolic and hormonal indices throughout the study did not differ substantially between the two species. During their first summer in seawater net-pens, seemingly healthy, feeding 1-yr-old Atlantic salmon smolts are prone to high mortality. We hypothesize that the virtual absence of glycogen reserves in the liver makes seawater-adapted 1-yr-old Atl...


title: SUDOKUSAT—A Tool for Analyzing Difficult Sudoku Puzzles

abstract: Sudoku puzzles enjoy world-wide popularity, and a large community of puzzlers is hoping for ever more difficult puzzles. A crucial step for generating difficult Sudoku puzzles is the fast assessment of the difficulty of a puzzle. In a study in 2006, it has been shown that SAT solving provides a way to efficiently differentiate between Sudoku puzzles according to their difficulty, by analyzing which resolution technique solves a given puzzle. This paper shows that one of these techniques—unit resolution with failed literal propagation—does not solve a recently published Sudoku puzzle called AI Escargot, claimed to be the world’s most difficult. The technique is also unable to solve any of a list of difficult puzzles published after AI Escargot, whereas it solves all previously studied Sudoku puzzles. We show that the technique can serve as an efficient and reliable computational method for distinguishing the most difficult Sudoku puzzles. As a proof-of-concept for an efficient difficulty checker, we present the tool SudokuSat that categorizes Sudoku puzzles with respect to the resolution technique required for solving them.


title: High-Resolution Remote-Sensing Image Classification via an Approximate Earth Mover's Distance-Based Bag-of-Features Model

abstract: High-resolution remote-sensing image classification is a challenging task. In this letter, we first propose a bag-of-features (BOF) model-based classification framework for high-resolution remote-sensing images via Earth mover's distance (EMD) to perform histogram matching. Compared with conventional BOF, EMD-based BOF is insensitive to vector quantization and can explore the relations among visual codes. In addition, such relations can be utilized as a key discriminative feature for image classification task. However, EMD is not practically utilized because of expensive computational cost. Motivated by Pele and Werman, we propose a faster approximate EMD (AEMD), and our AEMD-based BOF can inherit the advantages of EMD. Experimental results on a multicategory remote-sensing image data set demonstrate the effectiveness of our classification framework.


Deep Learning and Computational Neuroscience


title: Altered gaze following during live interaction in infants at risk for autism: an eye tracking study

abstract: The ability to follow gaze is an important prerequisite for joint attention, which is often compromised in children with autism spectrum disorder (ASD). The direction of both the head and eyes provides cues to other people’s attention direction, but previous studies have not separated these factors and their relation to ASD susceptibility. Development of gaze following typically occurs before ASD diagnosis is possible, and studies of high-risk populations are therefore important. Eye tracking was used to assess gaze following during interaction in a group of 10-month-old infants at high familial risk for ASD (high-risk group) as well as a group of infants with no family history of ASD (low-risk group). The infants watched an experimenter gaze at objects in the periphery. Performance was compared across two conditions: one in which the experimenter moved both the eyes and head toward the objects (Eyes and Head condition) and one that involved movement of the eyes only (Eyes Only condition). A group by condition interaction effect was found. Specifically, whereas gaze following accuracy was comparable across the two conditions in the low-risk group, infants in the high-risk group were more likely to follow gaze in the Eyes and Head condition than in the Eyes Only condition. In an ecologically valid social situation, responses to basic non-verbal orienting cues were found to be altered in infants at risk for ASD. The results indicate that infants at risk for ASD may rely disproportionally on information from the head when following gaze and point to the importance of separating information from the eyes and the head when studying social perception in ASD.


title: The meaning in life questionnaire: Assessing the presence of and search for meaning in life.

abstract: Counseling psychologists often work with clients to increase their well-being as well as to decrease their distress. One important aspect of well-being, highlighted particularly in humanistic theories of the counseling process, is perceived meaning in life. However, poor measurement has hampered research on meaning in life. In 3 studies, evidence is provided for the internal consistency, temporal stability, factor structure, and validity of the Meaning in Life Questionnaire (MLQ), a new 10-item measure of the presence of, and the search for, meaning in life. A multitrait-multimethod matrix demonstrates the convergent and discriminant validity of the MLQ subscales across time and informants, in comparison with 2 other meaning scales. The MLQ offers several improvements over current meaning in life measures, including no item overlap with distress measures, a stable factor structure, better discriminant validity, a briefer format, and the ability to measure the search for meaning.


title: Splaying Log-Structured Merge-Trees

abstract: Modern persistent key-value stores typically use a log-structured merge-tree (LSM-tree) design, which allows for high write throughput. Our observation is that the LSM-tree, however, has suboptimal performance during read-intensive workload windows with non-uniform key access distributions. To address this shortcoming, we propose and analyze a simple decision scheme that can be added to any LSM-based key-value store and dramatically reduce the number of disk I/Os for these classes of workloads. The key insight is that copying a frequently accessed key to the top of an LSM-tree ("splaying'') allows cheaper reads on that key in the near future.


title: A multiple frame integration and mathematical morphology based technique for video text extraction

abstract: Text superimposed on the video frames provides supplement but important information about video content. Recognizing artificial text embedded in video frames is very important and meaning for video indexing and retrieval. In this paper, an efficient approach of text extraction based on temporal information and mathematical morphology is presented. In the first phase the blurred text is filtered, and then these clear text blocks are integrated in order to enhance the text. In the second phase, the edge map is got by using mathematical morphological edge detection. Lastly the missed text pixels in former phases are recalled using empirical rules. Experimental results show that our approach can effectively segment text from background and demonstrate the effectiveness of the proposed method in comparison with other related works.


Tuning PID Controller for Speed Control of DC Motor Using Soft Computing Techniques-A Review


title: STBC MIMO OFDM Systems with Implementation Impairments

abstract: Multi-input multi-output (MIMO) systems are often realized with low cost front-end architectures, e.g. the so called direct conversion architectures. However, such systems are very sensitive to imperfections in the analog front-end, resulting in radio frequency (RF) impairments such as in-phase/quadrature-phase (IQ) imbalance and carrier frequency offset (CFO). The RF impairments in such low cost front-end systems are unavoidable and can result in a severe performance degradation. In this paper we propose a generally applicable equalization technique for space-time block coded (STBC) MIMO orthogonal frequency division multiplexing (OFDM) communication systems. The Alamouti based STBC scheme is examined in detail. It is shown that the compensation scheme can be easily extended to other higher order STBC systems. We consider a digital compensation scheme for joint transmitter and receiver IQ imbalance along with front-end filter mismatch, CFO and frequency selective channel distortions.


title: Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data.

abstract: BACKGROUND AND PURPOSE ::: In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. ::: ::: ::: METHODS ::: In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). ::: ::: ::: RESULTS ::: For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. ::: ::: ::: CONCLUSION ::: Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.


A Model-based K-means Algorithm for Name Disambiguation


title: A visual and interactive automata theory course with JFLAP 4.0

abstract: We describe the instructional software JFLAP 4.0 and how it can be used to provide a hands-on formal languages and automata theory course. JFLAP 4.0 doubles the number of chapters worth of material from JFLAP 3.1, now covering topics from eleven of thirteen chapters for a semester course. JFLAP 4.0 has easier interactive approaches to previous topics and covers many new topics including three parsing algorithms, multi-tape Turing machines, L-systems, and grammar transformations.


title: Using similarity criteria to make negotiation trade-offs

abstract: Addresses the issues involved in software agents making trade-offs during automated negotiations in which they have information uncertainty and resource limitations. In particular the importance of being able to make tradeoffs in real-world applications is highlighted and an algorithm for performing trade-offs for multi-dimensional goods is developed. The algorithm uses the notion of fuzzy similarity in order to find negotiation solutions that are beneficial to both parties. Empirical results indicate the benefits and effectiveness of the trade-off algorithm in a range of negotiation situations.


title: Fostering User Engagement: Rhetorical Devices for Applause Generation Learnt from TED Talks

abstract: One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved. Therefore, many studies in conversation analysis work on this issue and suggest qualitatively con-structions that can effectively lead to audience's applause. To investigate these proposals quantitatively, in this study we an-alyze the transcripts of 2,135 TED Talks, with a particular fo-cus on the rhetorical devices that are used by the presenters for applause elicitation. Through conducting regression anal-ysis, we identify and interpret 24 rhetorical devices as triggers of audience applauding. We further build models that can rec-ognize applause-evoking sentences and conclude this work with potential implications.


Xavier : A Reinforcement-Learning Approach to TCP Congestion Control CS 221 Final Report


title: Scene Parsing with Object Instances and Occlusion Ordering

abstract: This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image, we select a subset of objects that explain the image well and have valid overlap relationships and occlusion ordering. This is done by minimizing an integer quadratic program either using a greedy method or a standard solver. Then we alternate between using the object predictions to refine the pixel labels and vice versa. The proposed system obtains promising results on two challenging subsets of the LabelMe and SUN datasets, the largest of which contains 45, 676 images and 232 classes.


title: An optimal method for stochastic composite optimization

abstract: This paper considers an important class of convex programming (CP) problems, namely, the stochastic composite optimization (SCO), whose objective function is given by the summation of general nonsmooth and smooth stochastic components. Since SCO covers non-smooth, smooth and stochastic CP as certain special cases, a valid lower bound on the rate of convergence for solving these problems is known from the classic complexity theory of convex programming. Note however that the optimization algorithms that can achieve this lower bound had never been developed. In this paper, we show that the simple mirror-descent stochastic approximation method exhibits the best-known rate of convergence for solving these problems. Our major contribution is to introduce the accelerated stochastic approximation (AC-SA) algorithm based on Nesterov’s optimal method for smooth CP (Nesterov in Doklady AN SSSR 269:543–547, 1983; Nesterov in Math Program 103:127–152, 2005), and show that the AC-SA algorithm can achieve the aforementioned lower bound on the rate of convergence for SCO. To the best of our knowledge, it is also the first universally optimal algorithm in the literature for solving non-smooth, smooth and stochastic CP problems. We illustrate the significant advantages of the AC-SA algorithm over existing methods in the context of solving a special but broad class of stochastic programming problems.


title: User characterization for online social networks

abstract: Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender and age; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots versus humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights into researchers and better services to online users.


Learning to Predict Readability using Diverse Linguistic Features


title: 3D building extraction with semi-global matching from stereo pair worldview-2 satellite imageries

abstract: Extraction of 3D building information from high resolution satellite imagery is generally carried out manually. Automatic algorithms to extract such information are highly desirable, but have so far met with limited success. Previously, we reported a highly automated method of extracting 3D building information from stereo pair high resolution satellite imagery by using normalized cross correlation (NCC) template matching technique. But the computation of depth is generally prone to error close to surface discontinuity with NCC template matching. In this paper, we present our results of using a semi-global matching (SGM) instead of NCC template matching technique to solve the problem.


title: A comparison of security requirements engineering methods

abstract: This paper presents a conceptual framework for security engineering, with a strong focus on security requirements elicitation and analysis. This conceptual framework establishes a clear-cut vocabulary and makes explicit the interrelations between the different concepts and notions used in security engineering. Further, we apply our conceptual framework to compare and evaluate current security requirements engineering approaches, such as the Common Criteria, Secure Tropos, SREP, MSRA, as well as methods based on UML and problem frames. We review these methods and assess them according to different criteria, such as the general approach and scope of the method, its validation, and quality assurance capabilities. Finally, we discuss how these methods are related to the conceptual framework and to one another.


title: Interpolyelectrolyte complexes of conjugated copolymers and DNA: platforms for multicolor biosensors.

abstract: Interchain interactions modulate the frequency of emission from a cationic water-soluble conjugated polymer. The polymer, PFPB, is obtained by a Suzuki copolymerization of p-phenylenebisboronic acid with a 95:5 mixture of 2,7-dibromo-9,9-bis(6'-bromohexyl)fluorene and 4,7-dibromo-2,1,3-benzothiadiazole, followed by quarternization of the pendant groups by addition of NMe3. The structure of PFPB contains 5% of the 2,1,3-benzothiadiazole (BT) chromophore within a cationic poly(fluorene-co-phenylene) polymer chain. The emission of PFPB is blue under dilute conditions (<1 x 10-6 M in repeat units) and green at higher concentrations. Energy transfer to dye-labeled ss-DNA is more efficient, relative to the parent polymer poly(9,9-bis(6'-N,N,N,-trimethylammonium)hexyl)fluorene-co-alt-1,4-phenylene) dibromide (PFP), as a result of improved spectral overlap. By using a peptide nucleic acid (PNA-C*) labeled with a red-emitting chromophore one can obtain three different emission colors, depending on the nature of the substrate under interrogation. If no ss-DNA is present, the solution emits blue. With a ss-DNA that is noncomplementary to PNA-C*, green emission is observed. Red emission occurs upon addition of ss-DNA complementary to the PNA sequence.


title: Original Contribution: Top 10 botanical ingredients in 2010 anti-aging creams

abstract: New developments in the realm of skin rejuvenation such as phytotherapy are at an astounding increasing pace in the cosmeceutical market. Yet, many of these products that are classified as cosmeceuticals are tested less vigorously and do not have to be approved by the Food and Drug Administration to establish efficacy and safety. Thus, as clinicians, we must ask the question, "Is there science-based evidence to validate the mechanism of these new treatments?" We assessed the top anti-aging creams currently on the market specifically evaluating their botanical ingredients. Some of the most common botanicals that are hot off the market are: Rosmarinus officinalis, Vitis vinifera (grape seed extract), Citronellol, Limonene, Oenothera biennis (evening primrose), Glycyrrhiza glabra (licorice extract), Aframomum angustifolium seed extract, Diosgenin (wild yam), N6 furfuryladenine (kinetin), and Ergothioneine. Through researching each of these botanical ingredients, we have concluded that randomized controlled trials are still needed in this area, but there is promise in some of these ingredients and science to validate them.


title: Landmark Classification With Hierarchical Multi-Modal Exemplar Feature

abstract: Landmark image classification attracts increasing research attention due to its great importance in real applications, ranging from travel guide recommendation to 3-D modelling and visualization of geolocation. While large amount of efforts have been invested, it still remains unsolved by academia and industry. One of the key reasons is the large intra-class variance rooted from the diverse visual appearance of landmark images. Distinguished from most existing methods based on scalable image search, we approach the problem from a new perspective and model landmark classification as multi-modal categorization , which enjoys advantages of low storage overhead and high classification efficiency. Toward this goal, a novel and effective feature representation, called hierarchical multi-modal exemplar (HMME) feature, is proposed to characterize landmark images. In order to compute HMME, training images are first partitioned into the regions with hierarchical grids to generate candidate images and regions. Then, at the stage of exemplar selection, hierarchical discriminative exemplars in multiple modalities are discovered automatically via iterative boosting and latent region label mining. Finally, HMME is generated via a region-based locality-constrained linear coding (RLLC), which effectively encodes semantics of the discovered exemplars into HMME. Meanwhile, dimension reduction is applied to reduce redundant information by projecting the raw HMME into lower-dimensional space. The final HMME enjoys advantages of discriminative and linearly separable. Experimental study has been carried out on real world landmark datasets, and the results demonstrate the superior performance of the proposed approach over several state-of-the-art techniques.


title: Literature survey on traffic-based server load balancing using SDN and open flow

abstract: At large data centers, server load balancing is used to balance the incoming requests among the servers to avoid excessive overloading of any individual server. However the present methods are static and suffer from many drawbacks. Static methods do not offer an even balance of data among servers and require dedicated hardware for load balancing. Hence, dynamic methods are required to enhance the load balance of server. Dynamic method provides load balancing of server inclusion of the dynamic state of traffic on the network. In this paper, the need for dynamic load balancing methods of SDN is discussed and provided with the literature survey of the existing static load balancing schemes.


Rethinking University Teaching in the Digital Age


title: Aiming and vaulting: Spider inspired leaping for jumping robots

abstract: Jumping spiders are capable of targeted jumps by using their front legs to guide the release of energy from their rear legs. In this paper, we present a simplified model of the jumping spider based on the anatomy of the real spider. The immediate goal of this model is to understand how the geometry of the legs affects the jumping motion, with the further goal of using this geometry in the future development of jumping robots. Through a set of simulations, dynamic analysis, and experiments with a physical realization of our model, we identify several features of the spiders' jumping mechanism, most notably that “vaulting” with the front legs allows the system to generate flatter take-off trajectories than could be achieved by simple aiming of a spring-release mechanism.


title: On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm

abstract: This paper considers the problem of simultaneous multiuser downlink beamforming. The idea is to employ a transmit antenna array to create multiple "beams" directed toward the individual users, and the aim is to increase throughput, measured by sum capacity. In particular, we are interested in the practically important case of more users than transmit antennas, which requires user selection. Optimal solutions to this problem can be prohibitively complex for online implementation at the base station and entail so-called Dirty Paper (DP) precoding for known interference. Suboptimal solutions capitalize on multiuser (selection) diversity to achieve a significant fraction of sum capacity at lower complexity cost. We analyze the throughput performance in Rayleigh fading of a suboptimal greedy DP-based scheme proposed by Tu and Blum. We also propose another user-selection method of the same computational complexity based on simple zero-forcing beamforming. Our results indicate that the proposed method attains a significant fraction of sum capacity and throughput of Tu and Blum's scheme and, thus, offers an attractive alternative to DP-based schemes.


title: Spam filtering by semantics-based text classification

abstract: Spam has been a serious and annoying problem for decades. Even though plenty of solutions have been put forward, there still remains a lot to be promoted in filtering spam emails more efficiently. Nowadays a major problem in spam filtering as well as text classification in natural language processing is the huge size of vector space due to the numerous feature terms, which is usually the cause of extensive calculation and slow classification. Extracting semantic meanings from the content of texts and using these as feature terms to build up the vector space, instead of using words as feature terms in tradition ways, could reduce the dimension of vectors effectively and promote the classification at the same time. In this paper, a novel Chinese spam filtering approach with semantics-based text classification technology was proposed and the related feature terms were selected from the semantic meanings of the text content. Both the extraction of semantic meanings and the selection of feature terms are implemented through attaching annotations on the texts layer-by-layer. This filter performed well when experimented on a public Chinese spam corpus.


title: General developmental health in the VPA-rat model of autism

abstract: Autism is a neurodevelopmental condition diagnosed by impaired social interaction, abnormal communication and, stereotyped behaviors. While post-mortem and imaging studies have provided good insights into the neurobiological symptomology of autism, animal models can be used to study the neuroanatomical, neurophysiological and molecular mediators in more detail and in a more controlled environment. The valproic acid (VPA) rat model is an environmentally triggered model with strong construct and clinical validity. It is based on VPA teratogenicity in humans, where mothers who are medicated with VPA during early pregnancy show an increased risk for giving birth to an autistic child. In rats, early embryonic exposure, around the time of neural tube closure, leads to autism-like anatomical and behavioral abnormalities in the offspring. Considering the increasing use of the VPA rat model, we present our observations of the general health of Wistar dams treated with a single intraperitoneal injection of 500 or, 600 mg/kg VPA on embryonic day E12.5, as well as their male and female offspring, in comparison to saline-exposed controls. We report increased rates of complete fetal reabsorption after both VPA doses. VPA 500 mg/kg showed no effect on dam body weight during pregnancy or, on litter size. Offspring exposed to VPA 500 mg/kg showed smaller brain mass on postnatal days 1 (P1) and 14 (P14), in addition to abnormal nest seeking behavior at P10 in the olfactory discrimination test, relative to controls. We also report increased rates of physical malformations in the offspring, rare occurrences of chromodacryorrhea and, developmentally similar body mass gain. Further documentation of developmental health may guide sub-grouping of individuals in a way to better predict core symptom severity.


title: Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters

abstract: We consider assortment optimization problems under the multinomial logit model, where the parameters of the choice model are random. The randomness in the choice model parameters is motivated by the fact that there are multiple customer segments, each with different preferences for the products, and the segment of each customer is unknown to the firm when the customer makes a purchase. This choice model is also called the mixture-of-logits model. The goal of the firm is to choose an assortment of products to offer that maximizes the expected revenue per customer, across all customer segments. We establish that the problem is NP complete even when there are just two customer segments. Motivated by this complexity result, we focus on assortments consisting of products with the highest revenues, which we refer to as revenue-ordered assortments. We identify specially structured cases of the problem where revenue-ordered assortments are optimal. When the randomness in the choice model parameters does not follow a special structure, we derive tight approximation guarantees for revenue-ordered assortments. We extend our model to the multi-period capacity allocation problem, and prove that, when restricted to the revenue-ordered assortments, the mixture-of-logits model possesses the nesting-by-fare-order property. This result implies that revenue-ordered assortments can be incorporated into existing revenue management systems through nested protection levels. Numerical experiments show that revenue-ordered assortments perform remarkably well, generally yielding profits that are within a fraction of a percent of the optimal.


title: The Development of a Rotational Wave Energy Conversion System: Design and Simulations

abstract: Electrical power generation by means of renewable energies is becoming a flourishing industry with many researchers focusing on investigating new and effective ways to harness this power. This paper presents a new proposed mechanism for harnessing the potential energy of open ocean waves and the procedures of efficiently converting this energy into electricity. Unlike many of the already proposed designs that utilize linear generators, this paper exposes some light on the idea of rotational conversion using ocean waves to make use of most-readily available generators. A simple buoy, piston, connecting rod and flywheel system fixed to a platform elevated from the sea level is designed and simulated to track the wave surface for converting the linear motion into rotational motion, which will eventually turn the generator. The results are validated with experimental data collected from a laboratory wave generator.


title: Single Image Shadow Removal via Neighbor-Based Region Relighting

abstract: In this paper we present a novel method for shadow removal in single images. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Then, we adjust the CIELAB \(a\) and \(b\) channels of the shadow region by adding constant offsets based on the difference of the median shadow and lit pixel values. We demonstrate that our approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.


MOTOR CURRENT SIGNATURE ANALYSIS BASED FAULT DIAGNOSIS OF INDUCTION MOTOR


title: MOTION CONTROLLER DESIGN FOR THE SPEED CONTROL OF DC SERVO MOTOR

abstract: This paper presents a study on speed control problem for Direct Current (DC) servo motor. In this study DC servo motor's mathematical model and equations were extracted and there were three different motion controller designed for controlling the angular velocity. It was created simulation model at the Matlab programme and implemented proportional-integral, fuzzy and adaptive neuro fuzzy inference system controllers respectively while it was working variable conditions. At the servo motor application it is expected that no overshoot, settling reference value quickly and minimum influence on the system stability.


title: Clinical biochemistry laboratory rejection rates due to various types of preanalytical errors

abstract: Introduction: Preanalytical errors, along the process from the beginning of test requests to the admissions of the specimens to the laboratory, cause the rejection of samples. The aim of this study was to better explain the reasons of rejected samples, regarding to their rates in certain test groups in our laboratory. Materials and methods: This preliminary study was designed on the rejected samples in one-year period, based on the rates and types of inappropriateness. Test requests and blood samples of clinical chemistry, immunoassay, hematology, glycated hemoglobin, coagulation and erythrocyte sedimentation rate test units were evaluated. Types of inappropriateness were evaluated as follows: improperly labelled samples, hemolysed, clotted specimen, insufficient volume of specimen and total request errors. Results: A total of 5,183,582 test requests from 1,035,743 blood collection tubes were considered. The total rejection rate was 0.65 %. The rejection rate of coagulation group was significantly higher (2.28%) than the other test groups (P < 0.001) including insufficient volume of specimen error rate as 1.38%. Rejection rates of hemolysis, clotted specimen and insufficient volume of sample error were found to be 8%, 24% and 34%, respecti vely. Total request errors, particularly, for unintelligible requests were 32% of the total for inpatients. Conclusions: The errors were especially attributable to unintelligible requests of inappropriate test requests, improperly labelled samples for inpatients and blood drawing errors especially due to insufficient volume of specimens in a coagulation test group. Further studies should be performed after corrective and preventive actions to detect a possible decrease in rejecting samples.


1 Information Retrieval on Turkish Texts


title: G.: SAF: Strategic Alignment Framework for Monitoring Organizations

abstract: Reaching a Strategic Alignment is a crucial aspect for any organization. The alignment can be achieved by controlling, through monitoring probes, the coherency of the Business Processes with the related Business Strategy. In this paper we present SAF, a powerful framework for those organizations that aim at a superior business performance and want to keep monitored the organization’s alignment. SAF has been applied to a real case study and it has also been compared with GQM + Strategy [2] and Process Performance Indicators Monitoring Model [16].


title: WLAN throughput management: A game theoretic TXOP scheduling approach

abstract: This paper investigates the dynamic selection of transmission opportunity (TXOP) which was originally proposed in IEEE 802.11e Enhanced Distributed Channel Access (EDCA). A game theoretic optimisation framework is proposed to schedule the optimal TXOP value for every station according to the current channel capacity and transmission requirements of the stations, in order to improve the efficiency of TXOP scheduling and channel resource utilisation. The advantages of the proposed decentralised mechanism include the increased flexibility in TXOP allocation and the reduced complexity. The proposed mechanism has potential applications in the future IEEE 802.11ax standard. Theoretical study shows the existence of Nash equilibrium where the stations achieve an optimal TXOP scheduling. Numerical simulations are presented to demonstrate the proposed framework.


title: Adaptive s-curve acceleration/deceleration control method

abstract: In high-speed machining process, in order to improve the machining accuracy, the velocity should be optimized flexibly in advance. The s-curve acceleration and deceleration control mode was more used in velocity control than others. Based on the essential principle of the s-curve acceleration and deceleration control mode, the pre-interpolation acceleration and deceleration control was adopted to work out the relevant motional parameters of different program blocks. Using the look-ahead method, the adaptive acceleration and deceleration was introduced to evaluate the data far in advance and adjust the feed rate. The adaptive velocity control method is simulated and the result shows that the velocity curve is smoother and the machining time is shorter than traditional method. The velocity control of NC machining is solved effectively and the machining accuracy and productivity will be improved greatly.


title: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030.

abstract: OBJECTIVE ::: The goal of this study was to estimate the prevalence of diabetes and the number of people of all ages with diabetes for years 2000 and 2030. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: Data on diabetes prevalence by age and sex from a limited number of countries were extrapolated to all 191 World Health Organization member states and applied to United Nations' population estimates for 2000 and 2030. Urban and rural populations were considered separately for developing countries. ::: ::: ::: RESULTS ::: The prevalence of diabetes for all age-groups worldwide was estimated to be 2.8% in 2000 and 4.4% in 2030. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. The prevalence of diabetes is higher in men than women, but there are more women with diabetes than men. The urban population in developing countries is projected to double between 2000 and 2030. The most important demographic change to diabetes prevalence across the world appears to be the increase in the proportion of people >65 years of age. ::: ::: ::: CONCLUSIONS ::: These findings indicate that the "diabetes epidemic" will continue even if levels of obesity remain constant. Given the increasing prevalence of obesity, it is likely that these figures provide an underestimate of future diabetes prevalence.


title: Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

abstract: The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Learning Important FeaTures), an efficient and effective method for computing importance scores in a neural network. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. We apply DeepLIFT to models trained on natural images and genomic data, and show significant advantages over gradient-based methods.


title: Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing

abstract: By moving service provisioning from the cloud to the edge, edge computing becomes a promising solution in the era of IoT to meet the delay requirements of IoT applications, enhance the scalability and energy efficiency of lightweight IoT devices, provide contextual information processing, and mitigate the traffic burdens of the backbone network. However, as an emerging field of study, edge computing is still in its infancy and faces many challenges in its implementation and standardization. In this article, we study an implementation of edge computing, which exploits transparent computing to build scalable IoT platforms. Specifically, we first propose a transparent computing based IoT architecture, and clearly identify its advantages and associated challenges. Then, we present a case study to clearly show how to build scalable lightweight wearables with the proposed architecture. Some future directions are finally pointed out to foster continued research efforts.


title: A 0.28 THz Power-Generation and Beam-Steering Array in CMOS Based on Distributed Active Radiators

abstract: In this paper, we present a scalable transmitter architecture for power generation and beam-steering at THz frequencies using a centralized frequency reference, sub-harmonic signal distribution, and local phase control. The power generation and radiator core is based on a novel method called distributed active radiation, which enables high conversion efficiency from DC to radiated terahertz power above fmax of a technology. The design evolution of the distributed active radiator (DAR) follows from an inverse design approach, where metal surface currents at different harmonics are formulated in the silicon chip for the desired electromagnetic field profiles. Circuits and passives are then designed conjointly to synthesize and control the surface currents. The DAR consists of a self-oscillating active electromagnetic structure, comprising of two loops which sustain out-of-phase currents at the fundamental frequency and in-phase currents at the second harmonic. The fundamental signal, thus gets, spatially filtered, while the second harmonic is radiated selectively, thereby consolidating signal generation, frequency multiplication, radiation of desired harmonic and filtration of undesired harmonics simultaneously in a small silicon footprint. A two-dimensional 4×4 radiating array implemented in 45 nm SOI CMOS (without high-resistivity substrate) radiates with an EIRP of +9.4 dBm at 0.28 THz and beam-steers in 2D over 80° in both azimuth and elevation. The chip occupies 2.7 mm × 2.7 mm and dissipates 820 mW of DC power. To the best of the authors' knowledge, this is the first reported integrated beam-scanning array at THz frequencies in silicon.


title: The NeOn Methodology framework: A scenario-based methodology for ontology development

abstract: This paper describes a scenario-based methodology called the NeOn Methodology framework. The aim of this framework is to speed up the construction of ontologies and ontology networks by reusing available knowledge resources (ontologies, non-ontological resources and ontology design patterns). The methodology is founded on four pillars: (1) a glossary of processes and activities; (2) a set of nine scenarios for building ontologies and ontology networks; (3) two ontology life-cycle models; and (4) a set of prescriptive methodological guidelines for performing specific processes and activities. The paper also shows how to apply this methodology framework to develop an ontology network within the multimedia domain. Finally, it presents an empirical evaluation of the NeOn Methodology framework.


title: Modelling Railway Interlocking Tables Using Coloured Petri Nets

abstract: Interlocking tables are the functional specification defining the routes, on which the passage of the train is allowed. Associated with the route, the states and actions of all related signalling equipment are also specified. This paper formally models the interlocking tables using Coloured Petri Nets (CPN). The CPN model comprises two parts: Signaling Layout and Interlocking Control. The Signaling Layout part is used to simulate the passage of the train. It stores geographic information of the signalling layout in tokens. The Interlocking Control part models actions of the controller according to the functions specified in the interlocking tables. The arc inscriptions in the model represent the content of the interlocking tables. Following our modelling approach we can reuse the same CPN net structure to model any new or modified interlocking system regardless of its size. Experimental results are presented to provide increased confidence in the model correctness.


title: Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames

abstract: Fire-flame detection using a video camera is difficult because a flame has irregular characteristics, i.e., vague shapes and color patterns. Therefore, in this paper, we propose a novel fire-flame detection method using fuzzy finite automata (FFA) with probability density functions based on visual features, thereby providing a systemic approach to handling irregularity in computational systems and the ability to handle continuous spaces by combining the capabilities of automata with fuzzy logic. First, moving regions are detected via background subtraction, and the candidate flame regions are then identified by applying flame color models. In general, flame regions have a continuous irregular pattern; therefore, probability density functions are generated for the variation in intensity, wavelet energy, and motion orientation and applied to the FFA. The proposed algorithm is successfully applied to various fire/non-fire videos, and its detection performance is better than that of other methods.


title: Packaging and cooling of an X-band digital array radar T/R module

abstract: This paper describes the packaging and cooling design of a low-cost and compact X-band transmit/receive (T/R) module for a digital array radar, made by using both packaged and bare dies. Due to the high power generated by the Gallium-Nitride (GaN) dies and the dense environment, a liquid cooling system is required. The design and manufacturing of this cooling system is described together with thermal analysis. The dies are attached to the heat sinks using sintered nano-silver. The attachment procedure is described together with thermal expansion calculations.


title: The power to reduce: pyridine nucleotides – small molecules with a multitude of functions

abstract: The pyridine nucleotides NAD and NADP play vital roles in metabolic conversions as signal transducers and in cellular defence systems. Both coenzymes participate as electron carriers in energy transduction and biosynthetic processes. Their oxidized forms, NAD+ and NADP+, have been identified as important elements of regulatory pathways. In particular, NAD+ serves as a substrate for ADP-ribosylation reactions and for the Sir2 family of NAD+-dependent protein deacetylases as well as a precursor of the calcium mobilizing molecule cADPr (cyclic ADP-ribose). The conversions of NADP+ into the 2'-phosphorylated form of cADPr or to its nicotinic acid derivative, NAADP, also result in the formation of potent intracellular calcium-signalling agents. Perhaps, the most critical function of NADP is in the maintenance of a pool of reducing equivalents which is essential to counteract oxidative damage and for other detoxifying reactions. It is well known that the NADPH/NADP+ ratio is usually kept high, in favour of the reduced form. Research within the past few years has revealed important insights into how the NADPH pool is generated and maintained in different subcellular compartments. Moreover, tremendous progress in the molecular characterization of NAD kinases has established these enzymes as vital factors for cell survival. In the present review, we summarize recent advances in the understanding of the biosynthesis and signalling functions of NAD(P) and highlight the new insights into the molecular mechanisms of NADPH generation and their roles in cell physiology.


title: ALGORITHMS FOR SIMULTANEOUS SPARSE APPROXIMATION PART II : CONVEX RELAXATION

abstract: A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherent structures in the input signals, and they are chosen from a large, linearly dependent collection.The first part of this paper presents theoretical and numerical results for a greedy pursuit algorithm, called simultaneous orthogonal matching pursuit.The second part of the paper develops another algorithmic approach called convex relaxation. This method replaces the combinatorial simultaneous sparse approximation problem with a closely related convex program that can be solved efficiently with standard mathematical programming software. The paper develops conditions under which convex relaxation computes good solutions to simultaneous sparse approximation problems.


title: Deep Retinex Decomposition for Low-Light Enhancement

abstract: Retinex model is an effective tool for low-light image enhancement. It assumes that observed images can be decomposed into the reflectance and illumination. Most existing Retinex-based methods have carefully designed hand-crafted constraints and parameters for this highly ill-posed decomposition, which may be limited by model capacity when applied in various scenes. In this paper, we collect a LOw-Light dataset (LOL) containing low/normal-light image pairs and propose a deep Retinex-Net learned on this dataset, including a Decom-Net for decomposition and an Enhance-Net for illumination adjustment. In the training process for Decom-Net, there is no ground truth of decomposed reflectance and illumination. The network is learned with only key constraints including the consistent reflectance shared by paired low/normal-light images, and the smoothness of illumination. Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance. The Retinex-Net is end-to-end trainable, so that the learned decomposition is by nature good for lightness adjustment. Extensive experiments demonstrate that our method not only achieves visually pleasing quality for low-light enhancement but also provides a good representation of image decomposition.


Neural Relation Classification with Text Descriptions


title: Application of Data Mining for Anti-money Laundering Detection: A Case Study

abstract: Recently, money laundering is becoming more and more sophisticated, it seems to have moved from the personal gain to the cliche of drug trafficking and financing terrorism. This criminal activity poses a serious threat not only to financial institutions but also to the nation. Today, most international financial institutions have been implementing anti-money laundering solutions but traditional investigative techniques consume numerous man-hours. Besides, most of the existing commercial solutions are based on statistics such as means and standard deviations and therefore are not efficient enough, especially for detecting suspicious cases in investment activities. In this paper, we present a case study of applying a knowledge-based solution that combines data mining and natural computing techniques to detect money laundering patterns. This solution is a part of a collaboration project between our research group and an international investment bank.


title: Rethinking ImageNet Pre-Training

abstract: We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \textbf{from random initialization}. The results are \textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision.


title: Multi-Robot Assembly Strategies and Metrics

abstract: We present a survey of multi-robot assembly applications and methods and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.


title: Estimation of transmitter I/Q imbalance using convolutional neural networks

abstract: Transmitter induced I/Q imbalance occurs when the modulator's in-phase and quadrature components are not orthogonal. This causes the real and imaginary components of the complex signal to interfere with each other. In addition to potentially degrading performance of the transmitter, I/Q imbalance can also be used as an identifying feature when performing Specific Emitter Identification (SEI) techniques. This work investigates Convolutional Neural Networks (CNNs) as a means to estimate transmitter I/Q imbalance using only raw I/Q data as input instead of predefined features, with no need for synchronization, demodulation, or test signals, for I/Q imbalance correction, SEI, or other applications. The selection of the CNN architecture and network design and training are discussed. Performance analysis shows the network's ability to estimate both gain and phase offset, with performance improving as the number of inputs to the network increases and/or as SNR increases.


title: Regularized Boost for Semi-Supervised Learning

abstract: Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes local smoothness constraints among data into account during ensemble learning. In this paper, we introduce a local smoothness regularizer to semi-supervised boosting algorithms based on the universal optimization framework of margin cost functionals. Our regularizer is applicable to existing semi-supervised boosting algorithms to improve their generalization and speed up their training. Comparative results on synthetic, benchmark and real world tasks demonstrate the effectiveness of our local smoothness regularizer. We discuss relevant issues and relate our regularizer to previous work.


title: Episodic Future Thinking Reduces Reward Delay Discounting through an Enhancement of Prefrontal-Mediotemporal Interactions

abstract: Summary Humans discount the value of future rewards over time. Here we show using functional magnetic resonance imaging (fMRI) and neural coupling analyses that episodic future thinking reduces the rate of delay discounting through a modulation of neural decision-making and episodic future thinking networks. In addition to a standard control condition, real subject-specific episodic event cues were presented during a delay discounting task. Spontaneous episodic imagery during cue processing predicted how much subjects changed their preferences toward more future-minded choice behavior. Neural valuation signals in the anterior cingulate cortex and functional coupling of this region with hippocampus and amygdala predicted the degree to which future thinking modulated individual preference functions. A second experiment replicated the behavioral effects and ruled out alternative explanations such as date-based processing and temporal focus. The present data reveal a mechanism through which neural decision-making and prospection networks can interact to generate future-minded choice behavior.


title: Detection of Intoxicated Drivers Using Online System Identification of Steering Behavior

abstract: Impaired driving is known to be among the leading causes of death and injury on roads; however, the existing measures to address this menace appear to be insufficient. This paper presents a novel method to detect intoxicated driving and lays a foundation that can be implemented in future cars to derive personalized models of drivers and to detect not only intoxicated driving but also other reckless driving styles. We employ system identification techniques to develop models for sober and impaired drivers. A total of 200 sets of data from various subject drivers were collected in a high-fidelity driving simulator. The lateral preview error and the steering wheel angle were considered the input and output of a driver, respectively. We will demonstrate that the autoregressive noise integration moving average with exogenous input (ARIMAX) model best fits the data to describe the steering behavior of drivers. The positions of model poles are shown to be a good indicator of intoxicated driving behavior. An aggressive driving style due to impaired driving leads to the migration of dominant poles toward the instability region. The Kalman filter and online identification techniques are used to update the driver model during driving. The poles of this updated model are used for the detection of impaired driving.


title: Value creation in E‐business

abstract: We explore the theoretical foundations of value creation in e-business by examining how 59 American and European e-businesses that have recently become publicly traded corporations create value. We observe that in e-business new value can be created by the ways in which transactions are enabled. Grounded in the rich data obtained from case study analyses and in the received theory in entrepreneurship and strategic management, we develop a model of the sources of value creation. The model suggests that the value creation potential of e-businesses hinges on four interdependent dimensions, namely: efficiency, complementarities, lock-in, and novelty. Our findings suggest that no single entrepreneurship or strategic management theory can fully explain the value creation potential of e-business. Rather, an integration of the received theoretical perspectives on value creation is needed. To enable such an integration, we offer the business model construct as a unit of analysis for future research on value creation in e-business. A business model depicts the design of transaction content, structure, and governance so as to create value through the exploitation of business opportunities. We propose that a firm's business model is an important locus of innovation and a crucial source of value creation for the firm and its suppliers, partners, and customers. Copyright © 2001 John Wiley & Sons, Ltd.


title: Superposition Coding Strategies: Design and Experimental Evaluation

abstract: We design and implement a software-radio system for Superposition Coding (SC), a multiuser transmission scheme that deliberately introduces interference among user signals at the transmitter, using a library of off-the-shelf point-to-point channel codes. We experimentally determine the set of rate-pairs achieved by this transmission scheme under a packet-error constraint. Our results suggest that SC can provide substantial gains in spectral efficiencies over those achieved by orthogonal schemes such as Time Division Multiplexing. Our findings also question the practical utility of the Gaussian approximation for the inter-user interference in Superposition-Coded systems.


title: Multichannel End-to-end Speech Recognition

abstract: The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend the end-to-end framework to encompass microphone array signal processing for noise suppression and speech enhancement within the acoustic encoding network. This allows the beamforming components to be optimized jointly within the recognition architecture to improve the end-to-end speech recognition objective. Experiments on the noisy speech benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system outperformed the attention-based baseline with input from a conventional adaptive beamformer.


title: Compile-time function memoization

abstract: Memoization is the technique of saving the results of computations so that future executions can be omitted when the same inputs repeat. Recent work showed that memoization can be applied to dynamically linked pure functions using a load-time technique and results were encouraging for the demonstrated transcendental functions. A restriction of the proposed framework was that memoization was restricted only to dynamically linked functions and the functions must be determined beforehand. In this work, we propose function memoization using a compile-time technique thus extending the scope of memoization to user defined functions as well as making it transparently applicable to any dynamically linked functions. Our compile-time technique allows static linking of memoization code and this increases the benefit due to memoization by leveraging the inlining capability for the memoization wrapper. Our compile-time analysis can also handle functions with pointer parameters, and we handle constants more efficiently. Instruction set support can also be considered, and we propose associated hardware leading to additional performance gain.


Comparative Study of Performance for 804.15.4 ZigBee and 6LoWPAN Protocols


Experiential learning theory: Previous research and new directions


Automatic Forgery of Cryptographically Consistent Messages to Identify Security Vulnerabilities in Mobile Services


title: Continuous delivery with Jenkins: Jenkins solutions to implement continuous delivery

abstract: This paper illustrates how Jenkins evolved from being a pure Continuous Integration Platform to a Continuous Delivery one, embracing the new design tendency where not only the build but also the release and the delivery process of the product is automated. In this scenario Jenkins becomes the orchestrator tool for all the teams/roles involved in the software lifecycle, thanks to which Development, Quality&Assurance and Operations teams can work closely together. Goal of this paper is not only to position Jenkins as hub for CD, but also introduce the challenges that still need to be solved in order to strengthen Jenkins' tracking capabilities.


title: A comparison of implicit and explicit channel feedback methods for MU-MIMO WLAN systems

abstract: In this paper we compare the implicit and explicit methods of providing channel state information (CSI) to the transmitter in a multi-user multiple-input-multiple-output (MU-MIMO) system as specified in the draft specification IEEE 802.11ac [1]. The comparison is made on the basis of both overhead requirements and packet-error-rate (PER) performance. We also propose a hybrid feedback scheme that combines the explicit and implicit feedback methods thus benefiting from more frequent calibration without incurring extra overhead.


title: Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology

abstract: Accurate detection of mitosis plays a critical role in breast cancer histopathology. Manual detection and counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Multispectral imaging is a recent medical imaging technology, proven successful in increasing the segmentation accuracy in other fields. This study aims at improving the accuracy of mitosis detection by developing a specific solution using multispectral and multifocal imaging of breast cancer histopathological data. We propose to enable clinical routine-compliant quality of mitosis discrimination from other objects. The proposed framework includes comprehensive analysis of spectral bands and z-stack focus planes, detection of expected mitotic regions (candidates) in selected focus planes and spectral bands, computation of multispectral spatial features for each candidate, selection of multispectral spatial features and a study of different state-of-the-art classification methods for candidates classification as mitotic or non mitotic figures. This framework has been evaluated on MITOS multispectral medical dataset and achieved 60% detection rate and 57% F-Measure. Our results indicate that multispectral spatial features have more information for mitosis classification in comparison with white spectral band features, being therefore a very promising exploration area to improve the quality of the diagnosis assistance in histopathology.


title: Information retrieval in big data using evolutionary computation: A survey

abstract: The basic aim of this paper is to present a broad picture of Big Data and to show how information can be retrieved using different methodologies and especially using evolutionary computation techniques that help in the information retrieval process in a better way compared to traditional retrieval techniques. Big data is considered as a collection of large data which are heterogeneous in nature and cannot be handled by traditional computing techniques and require more advanced statistical procedures to extract relevant information from a voluminous pool of data. This paper also covers some of the basic information retrieval models which are used and present a basic outline of evolutionary computation techniques.


title: Gender differences in kindergarteners' robotics and programming achievement

abstract: Early childhood is a critical period for introducing girls to traditionally masculine fields of science and technology before more extreme gender stereotypes surface in later years. This study looks at the TangibleK Robotics Program in order to determine whether kindergarten boys and girls were equally successful in a series of building and programming tasks. The TangibleK Program consisted of a six lesson robotics and programming curriculum that was implemented in three different kindergarten classrooms (N = 53 students). Although previous research has found that males outperform females in robotics and programming related fields, it was hypothesized that the young age of participants and their limited cultural indoctrination regarding gender stereotypes would allow boys and girls to have equal success in this program. Although boys had a higher mean score than girls on more than half of the tasks, very few of these differences were statistically significant. Boys scored significantly higher than girls only in two areas: properly attaching robotic materials, and programming using Ifs. Overall, both boys and girls were able to successfully complete the program.


title: Hierarchical Gaussian process latent variable models

abstract: The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.


title: Low-power dual-element memristor based memory design

abstract: Recently, the emerging memristor device technology has attracted significant research interests due to its distinctive hysteresis characteristic, which potentially can enable novel circuit designs for future VLSI circuits. In particular, characteristics such as non-volatility, non-linearity, low power consumption, and good scalability make memristor one of the most promising emerging memory technologies. Some important design parameter, however, such as speed, energy consumption, and distingushiablility, are mainly determined by the memristor's physical characteristics. In this paper, a key observation of memristor's asymmetric energy consumption is made by the detailed analysis of the transient power consumption. Based on this observation, we propose a dual-element memory structure in which each memory cell consists of two memristors. By constantly writing complement bits into the two elements within a cell, the dual-element is flexible to satisfy design constraints which are usually difficult to be satisfied with one-cell memory structure. Design space of the dual-element memory cell is studied and it shows that the trade-offs among the energy, speed, and distingushiablility should be explored for different design objectives. In particular, we show that under the energy-driven optimization, the proposed dual-element memory achieves the same programming speed and distinguishability as conventional single-element memory but the energy consumption can be reduced by up to 80%.


title: Detecting Reactive Oxygen Species by Immunohistochemistry

abstract: In cultured cells, an increase in cellular levels of reactive oxygen species (ROS) can be detected using multiple techniques including colorimetric assays, immunoblotting, and immunofluorescence. These methods can also be applied for ROS measurement in tissue samples, but often require tissue homogenization, and therefore do not distinguish between the different cell types within a tissue. Here, we describe a detailed protocol for determination of altered oxidative stress levels in different cell types in tissues, by detecting ROS-caused alteration of macromolecules using immunohistochemistry (IHC). This method is demonstrated by using 4HNE as a marker for lipid peroxidation in mouse pancreas tissue that contains precancerous lesions high in cellular oxidative stress.


Observations on the pineal gland, the Harderian glands, the retina, and the reproductive organs of adult female rats exposed to continuous light.


title: “Thanks for Sharing” – Identifying Users’ Roles Based on Knowledge Contribution in Enterprise Social Networks

abstract: Abstract While ever more companies use Enterprise Social Networks for knowledge management, there is still a lack of understanding of users’ knowledge exchanging behavior. In this context, it is important to be able to identify and characterize users who contribute and communicate their knowledge in the network and help others to get their work done. In this paper, we propose a new methodological approach consisting of three steps, namely “message classification”, “identification of users’ roles” as well as “characterization of users’ roles”. We apply the approach to a dataset from a multinational consulting company, which allows us to identify three user roles based on their knowledge contribution in messages: givers, takers, and matchers. Going beyond this categorization, our data shows that whereas the majority of messages aims to share knowledge, matchers, that means people that give and take, are a central element of the network. In conclusion, the development and application of a new methodological approach allows us to contribute to a more refined understanding of users’ knowledge exchanging behavior in Enterprise Social Networks which can ultimately help companies to take measures to improve their knowledge management.


title: Investigating the relationships among college students' satisfaction, addiction, needs, communication apprehension, motives, and uses & gratifications with Snapchat

abstract: Snapchat is a popular communication mode for college students. In this study, 475 college students reported their communication behaviors regarding their Snapchat use and other variables related to Snapchat, such as addiction and needs. Results indicated that college students are satisfied using Snapchat. Findings showed that there was a significant relationship between participant needs (entertainment and functional) with Snapchat addiction, intensity, and exhibitionism. Further, there is a relationship between participant needs (entertainment and functional) with communication apprehension (social media and Snapchat). Snapchat offers functional features that meet need of high apprehension individuals.Snapchat is used because it is fun and practical.As needs are met, users' satisfaction with Snapchat increases.


title: Scalable Maritime Traffic Map Inference and Real-time Prediction of Vessels' Future Locations on Apache Spark

abstract: In this paper, we propose scalable algorithms allowing primo to infer a map of vessels' trajectories and secundo to predict future locations of a vessel on sea. Our system is based on Apache Spark -a fast and scalable engine for large-scale data processing. The training dataset is event-based. Each event depicts the GPS position of the vessel at a timestamp. We propose and implement a workflow computing trips' patterns, with GPS locations of each trip summarized using GeoHashing. The latter is an efficient encoding of a geographic location into a short string of letters and digits. In order to perform prediction queries efficiently, we propose (i) a geohash positional index which maps each geohash to a list of pairs (trip-pattern-identifier, offset of the geohash in the geohash sequence of the trip-pattern), (ii) a departure-port index which maps each departure port to a list of trip-patterns' identifiers, as well as (iii) a pairwise geohash sequence alignment allowing to score the similarity of two geohash-sequences using queen-spatial neighborhood.